Precision Phytopathology - Frontiers of Science

PRECISION 

PHYTOPATHOLOGY 

FRONTIERS 

OF SCIENCE 

ORGANIZADORES: 

Edson Luiz Furtado 

Sérgio Florentino Pascholati 

Waldir Cintra de Jesus Junior 

Willian Bucker Moraes 

EDITORA FEPAF 2019

© Direitos Reservados 

Fundação de Estudos e Pesquisas Agrícolas e Florestais 

1ª edição 2019 

Capa, Projeto Gráfico e Diagramação 

Bruna Zanotto Alves (www.bzaartdirection.com) 

Fundação de Estudos e Pesquisas Agrícolas e Florestais 

Unesp - Campus de Botucatu 

Fazenda Experimental Lageado 

Avenida Universitária, 3.780 

Cep: 18.610-034 

E-mail: fepaf@fepaf.org.br 

Tel: (14) 3880 7127 | www.fepaf.org.br 

É proibida a repodução total ou parcial, por qualquer meio ou forma, 

sem a expressa autorização (lei nº 9.610). O conteúdo dos capítulos 

é de inteira responsabilidade dos seus respectivos autores. 

Cataloging-in-publication by Technical Directory of Library and 

Documentation – São Paulo State University (Unesp), Agronomy Faculty of 

Agronomic Sciences - Botucatu (SP) 

P923 

Precision phytopathology frontiers of science / Organized 

by: Edson Luiz Furtado ... [et al.] – Botucatu: 

FEPAF, 2019 

443 p. : ils. color., fots. color., grafs. color., 

tabs. 

Other organizers: Sérgio Florentino Pascholati, 

Waldir Cintra de Jesus Junior, Willian Bucker Moraes 

ISBN 978-85-98187-99-0 

Includes bibliographical references 

1. Remote sensing. 2. Fungicides resistance. 3. 

Effectors. 4. Resistance responses. 5. Taxonomy. 6. 

Postharvest disease control. 7. Soilborne pathogens. 

I. Furtado, Edson Luiz. II. Pascholati, Sérgio Florentino. 

III. Jesus Junior, Waldir Cintra de. IV. Moraes, 

Willian Bucker. V. Fundação de Estudos e Pesquisas 

Agrícolas e Florestais. 

CDD 23. ed. (632)

ACKNOWLEDGEMENTS 

To the Agronomic Sciences Faculty / UNESP, Agriculture 

School "Luiz de Queiroz" (ESALQ/USP), São Carlos Federal 

University, "Lagoa do Sino" Campus (UFSCar), Espírito Santo 

Federal University (CCAE / UFES), Biological Institute (CEIB- 

IB-APTA) and Campinas Agronomic Institute (IAC-APTA), 

thank you for the support. 

To the Organizing Committee and Scientific Committee 

of the 48 th BRAZILIAN PHYTOPATHOLOGY CONGRESS, 

for the dedication and fundamental assistance in organizing and 

accomplishment the important event. 

To the authors of the chapters, who trusted and shared 

with us the responsibility of writing this book. 

To the State of São Paulo Foundation for Research 

Support (FAPESP), Higher Education Personnel Improvement 

Coordination (CAPES) and Scientific and Technological 

Development National Council (CNPq), thank you for 

your financial support.

To the Phytopathology Brazilian Society (SBF), Phytopathology 

Paulista Association (APF) and Foundation for 

Agricultural and Forestry Studies and Research (FEPAF), for 

the support. 

To Bruna Zanotto, for the professionalism and attention 

she gave us during the preparation of the work, with the 

cover design and the book text layout, as well as for the help 

in translation. 

To Ana Lucia de Grava Kempinas, UNESP Central Library 

librarian, who prepared us the cataloguing card. 

The Editors

PREFACE 

The Brazilian Phytopathological Society (SBF) was established 

in 1966 and since 1977 it has promoted Meetings with 

the aim of gathering professionals from Universities, Research 

Institutions or Industry, as well as undergraduate and graduate 

students, to share ideas, problems and solutions that could result 

in benefits to the whole society. 

Thus, SBF promoted, on August 10 - 14, 2015, in São Pedro 

– SP, the 48th Brazilian Meeting on Phytopathology, which had 

as major theme “Precision Phytopathology: Frontiers of Science”. 

This event was jointly organized by the School of Agriculture/UNESP, 

which was celebrating its 50 years of existence, “Luiz 

de Queiroz” College of Agriculture (ESALQ/USP) and Federal 

University of São Carlos (UFSCar), Lagoa do Sino Campus, and 

was supported by FAPESP, CAPES, CNPq and private companies. 

In addition to the inaugural lecture, given by Dr. Sergio 

Augusto Morais Carbonel, Director of the Agronomic Institute 

of Campinas, about the theme “Future Agriculture: Innovation 

with environmental respect”, 671 studies were presented, orally

or as posters, about different areas of phytopathology (Molecular 

Biology, Alternative Control, Biological Control, Cultural 

Control, Physical Control, Resistance Induction, Plant-Pathogen 

Interaction, Integrated Management, Phytopathological 

Method, Seed Pathology, Resistance and Applied Genomics). 

Groups of interested people also got together to discuss “Remote 

Sensing”, “Epidemiology” and “Forest Pathology”. 

There were 10 Round Tables (1. Remote sensing and plant 

diseases; 2. Effectors on plant-pathogen interaction; 3. Frontiers 

of epidemiology on disease management decision-making; 4. 

Advances in plant disease global clinics; 5. Resistance to fungicides: 

detection, risk analysis and management; 6. Global pathogens: 

a case study; 7. Monitoring of residues and toxicology in 

foods; 8. Advances in Phytopathology; 9. Resistance induction 

in postharvest diseases, and 10. Alternative products to control 

postharvest diseases), and lectures were given by renowned international 

and national scientists, leading to wide discussion 

on relevant and current themes. This book contains 13 Chapters 

related to the main lectures delivered during the event. 

The presence of a large contingent of important scientists 

from Latin America, the United States, Europe, Africa, Asia and

Oceania confirms the international character of this Meeting 

and reveals the increasing importance of Phytopathology for the 

national and international agricultural scenario. 

Considering the high quality of lectures, group discussions 

and oral presentations, as well as the large number of participants, 

there is no doubt that the result of this 48th Brazilian 

Meeting on Phytopathology is highly positive. Certainly, this 

book will improve the knowledge of researchers, professors, 

students, technicians, extension agents and several other professionals 

interested in phytopathology, significantly contributing 

to the scientific and technological development of agriculture. 

Congratulations to the Organizing Committee, to the Phytopathological 

Association in São Paulo State and to everyone 

who has contributed to the success of this event. 

The Organizing Committee of the 48th Brazilian Meeting 

on Phytopathology thanks the lecturers who sent their texts, allowing 

this book to be published. Similarly, I express my gratitude 

to the editors of this book for inviting me to write this preface. 

Francisco Xavier Ribeiro do Vale 

Full Professor of the Phytopathology Depart. at Federal University of Viçosa 

Former President of the Brazilian Phytopathological Society

PRESENTATION 

This publication, entitled “PRECISION PHYTOPATHOL- 

OGY: FRONTIERS OF SCIENCE”, includes texts related to 

some lectures delivered during the 48th BRAZILIAN MEET- 

ING ON PHYTOPATHOLOGY (which had as central theme 

“PRECISION PHYTOPATHOLOGY: FRONTIERS OF SCI- 

ENCE") and II BRAZILIAN MEETING ON POSTHARVEST 

PATHOLOGY, which were held on August 10th-14th, 2015, 

in São Pedro – SP, Brazil). Such important events were a joint 

action of the School of Agriculture/UNESP; “Luiz de Queiroz” 

College of Agriculture (ESALQ/USP); Federal University of São 

Carlos (UFSCAR), Lagoa do Sino Campus; Biological Institute 

(CEIB-IB-APTA), and Agronomic Institute (IAC-APTA). 

State-of-the-art information on the field is gathered in this 

publication, allowing the news and results presented at those 

meetings to be reached not only by attendees, but also by the 

largest number possible of professionals acting in the fields of 

teaching, research and extension, in the public and private sector, 

as well as undergraduate and graduate students from Brazil 

and abroad, and professionals of different segments of the pro-

ductive chains approached here. Technical-scientific advances 

for exchange of knowledge are shown, as well as challenges 

and perspectives to be overcome by research, development and 

innovation (RD&I) in the field of Phytopathology. 

Readers will have access to the texts of the main conferences 

given by several experts from different institutions from Brazil and 

abroad, who will approach some of the subjects discussed during 

the 10 round tables (1. Remote sensing and plant diseases; 2. Effectors 

on plant-pathogen interaction; 3. Frontiers of epidemiology 

on disease management decision-making; 4. Advances in plant 

disease global clinics; 5. Resistance to fungicides: detection, risk 

analysis and management; 6. Global pathogens: a case study; 7. 

Monitoring of residues and toxicology in foods; 8. Advances in 

Phytopathology; 9. Resistance induction in postharvest diseases, 

and 10. Alternative products to control postharvest diseases). 

The chapters are based on the lectures delivered in the 

Round Tables and on the Discussion Groups in the 48th BRA- 

ZILIAN MEETING ON PHYTOPATHOLOGY and II BRAZIL- 

IAN MEETING ON POSTHARVEST PATHOLOGY. Responsibility 

for chapter elaboration was totally on the authors.

ORGANIZING COMMITTEE OF 

XLVIII BRAZILIAN MEETING ON 

PHYTOPATHOLOGY 

and II BRAZILIAN MEETING ON 

POSTHARVEST PATHOLOGY 

XLVIII BRAZILIAN MEETING ON PHYTOPATHOLOGY 

President: Prof. Dr. Edson Luiz Furtado – FCA/UNESP, Campus de Botucatu-SP; 

Vice-President: Prof. Dr. Willian Bucker Moraes – UFES, Campus de Alegre-ES; 

Treasurer: Prof. Dr. Waldir Cintra de Jesus Junior – UFSCAR, Campus Lagoa do 

Sino, Buri-SP; 

Secretary: Prof. Dr. Sergio Florentino Pascholatti – ESALQ/USP, Piracicaba-SP; 

II BRAZILIAN MEETING ON POSTHARVEST PATHOLOGY 

President: Prof. Dr. Sergio Florentino Pascholatti – ESALQ/USP, Piracicaba-SP; 

Secretary: Dr. Eliane Aparecida Benato – Instituto Biológico-Apta, Campinas-SP 

Treasurer: Dr. Patricia Cia – Instituto Agronômico de Campinas – Apta, Jundiaí-SP 

SCIENTIFIC COMMITTEE 

Edson Luiz Furtado, Professor, PhD (FCA/UNESP), Botucatu-SP 

Sérgio Florentino Pascholati, Professor, PhD (ESALQ/USP), Piracicaba-SP 

Waldir Cintra de Jesus Junior, Professor, PhD (UFSCAR), Buri-SP 

Eliane Aparecida Benato PhD (IB / APTA), Campinas-SP 

Patrícia Cia, PhD (IAC / APTA), Jundiaí-SP 

José Otavio Menten, Professor, PhD (ESALQ/USP), Piracicaba-SP 

Ivan Herman Fischer, PhD (APTA), Bauru-SP 

José Raimundo Passos, Professor, PhD (IB/UNESP), Botucatu-SP 

Ana Carolina Firmino, Professor, PhD (UNESP), Dracena-SP

Yelitza Coromoto Colmenarez, PhD (CABI-International), Botucatu-SP 

Martha Maria Passador, PhD (CABI-International), Botucatu-SP 

Paulo C. Ceresine, Professor, PhD (FEIS/UNESP), Ilha Solteira-SP 

Ronaldo Dálio, PhD (CCSM-Apta), Cordeirópolis-SP 

Renate Krauze Sakate, Professor, PhD (FCA/UNESP), Botucatu-SP 

Luis Eduardo Aranha de Camargo, Professor, PhD (ESALQ/USP), Piracicaba-SP 

Celso Garcia Auer, PhD (Embrapa-Forest), Colombo-PR 

José Renato Stangarlin, Professor, PhD (UNIOESTE), Mal. Cândido Rondom-PR 

Marcelo A. B. Morandi, PhD (Embrapa-Environmen), Jaguariuna-SP 

Antonio de Góes, Professor, PhD (FCAVJ/UNESP), Jaboticabal-SP 

Cesar Junior Bueno, PhD (Biological Institute-Apta), Campinas-SP 

Adriana Zanin Kronka, Professor, PhD (FCA/UNESP), Botucatu-SP 

Ana Paula de O. A. Mello, Professor, PhD (UFSCAR), Araras-SP 

Willian M. C. Nunes, Professor, PhD (UEM), Maringá-PR 

Willian Bucker Moraes, Professor, PhD (UFES), Alegre-ES 

Stela Dalva V. M. Silva, PhD (CEPLAC), Itabuna-BA 

SUPPORT STAFF 

Cristiane De Pieri, Professor, MSc (FCA/UNESP-Doctoral student) 

Leila Cristiane Delmandi, Professor, MSc (FCA/UNESP-Doctoral student) 

João Alberto Zago, Professor, MSc (FCA/UNESP-Doctoral student) 

Dalila Carvalho Resende, Professor, PhD (ESALQ/USP-Post-doctoral student) 

João Parisi, PhD (Agronomical Institute– Apta) 

Marise C. Parisi, PhD (Instituto Biológico – Apta) 

SUPPORT 

FAPESP CAPES CNPq 

Associação Paulista de Fitopatologia 

Sociedade Brasileira de Fitopatologia

ENDEREÇO DOS EDITORES 

Edson Luiz Furtado 

Universidade Estadual Paulista (UNESP), Faculdade de Ciências Agronômicas, 

CEP 18610-307, Botucatu, São Paulo, Brazil. E-mail: elfurtado@fca. 

unesp.br 

Sérgio Florentino Pascholati 

Universidade de São Paulo (USP), Escola Superior de Agricultura ´Luiz 

de Queiroz´, CEP 13428-900, Piracicaba, São Paulo, Brazil. E-mail: sfpascho@usp.br 

Waldir Cintra de Jesus Junior 

Federal University of São Carlos (UFSCar), Center for Natural Sciences, 

Campus Lagoa do Sino, CEP 18290-000, Buri, São Paulo, Brazil. E-mail: 

wcintra@ufscar.br 

Willian Bucker Moraes 

Federal University of Espírito Santo (UFES), Center for Agricultural Sciences 

and Engineering, Department of Plant Production, CEP 29500-000, 

Alegre, Espírito Santo, Brazil. E-mail: willian.moraes@ufes.br

ENDEREÇO DOS AUTORES 

Adimara Bentivoglio Colturato 

Instituto de Ciências Matemáticas e de Computação (ICMC), USP de São 

Carlos/SP, Laboratório de Sistemas Embarcados Críticos (LSEC). E-mail: 

adimara@gmail.com. 

Alessandra Alves de Souza 

Centro de Citricultura Sylvio Moreira/ IAC. Rod Anhanguera, Km 158 

13490-970, Cordeirópolis-SP. E-mail: alessandra@centrodecitricultura.br. 

Ana Carolina Firmino 

UNESP-Univ Estadual Paulista, Faculdade de Ciências Agrárias e Tecnológicas, 

Km. 651, 17900-000, Dracena, São Paulo, Brasil. 

Brenda D. Wingfield 

Department of Genetics, Forestry and Agricultural Biotechnology Institute 

(FABI), University of Pretoria, Pretoria, South Africa. E-mail: jolanda. 

roux@fabi.up.ac.za. 

Dalilla Carvalho Rezende 

Instituto Federal do Sul de Minas, Campus Machado, Rodovia Machado – 

Paraguaçu, Km 3, Machado, MG, Brasil, 37750-000. 

Danilo Augusto dos Santos Pereira 

ETH Swiss Federal Institute of Technology, Plant Pathology Group, Zurich, 

CH 8092, Switzerland. E-mail: danilo.dossantos@usys.ethz.ch.

Diogo Maciel Magalhães 

Centro de Citricultura Sylvio Moreira/ IAC. Rod Anhanguera Km 158 

13490-970, Cordeirópolis-SP. Departamento de Genética, Evolução e Bioagentes, 

Instituto de Biologia, Universidade Estadual de Campinas, Campinas, 

São Paulo, Brazil. 

Heros J. Maximo 

Laboratório de Biotecnologia, Centro de Citricultura Sylvio Moreira, Instituto 

Agronômico – SP. 

Hugo José Tozze Júnior 

Universidade de São Paulo, Escola Superior de Agricultura “Luiz de Queiroz”, 

Av. Pádua Dias, CP.09, 13418-900, Piracicaba, São Paulo, Brasil. 

Jolanda Roux 

Department of Microbiology and Plant Pathology, Forestry and Agricultural 

Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa. 

Jon S. West 

Rothamsted Research, Harpenden, Herts AL5 2JQ, UK. E-mail: jon.west@ 

rothamsted.ac.uk. 

Kalinka Regina Lucas Jaquie Castelo Branco 

Instituto de Ciências Matemáticas e de Computação (ICMC), USP de São 

Carlos/SP, Laboratório de Sistemas Embarcados Críticos (LSEC). E-mail: 

kalinka@icmc.usp.br.

Lav R. Khot 

Center for Precision and Automated Agricultural Systems, Irrigated Agriculture 

Research and Extension Center, Washington State University, 24106 N. 

Bunn Road, Prosser, WA 99350. 

Leonardo Leoni Belan 

Federal University of Espírito Santo, Center for Agricultural Sciences and Engineering, 

Department of Plant Production, 29500-000 – Alegre – ES, Brazil. 

Leônidas Leoni Belan 

Federal University of Espírito Santo, Center for Agricultural Sciences and Engineering, 

Department of Plant Production, 29500-000 – Alegre – ES, Brazil. 

Marcos A. Machado 



Michael J. Wingfield 

Department of Microbiology and Plant Pathology, Forestry and Agricultural 

Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa. 

Natália Corniani 

CABI South America, Rua José Barbosa de Barros 1780, 18610-307, Botucatu 

- SP, Brazil. 

Paul Vincelli 

Department of Plant Pathology, College of Agriculture, Food and Environment, 

University of Kentucky, Lexington, KY.

Paulo Ceresini 

Universidade Estadual Paulista (UNESP) - Campus de Ilha Solteira, Faculdade 

de Engenharia, Departamento de Fitossanidade, Engenharia Rural e 

Solos, Ilha Solteira, SP, 15385-000, Brasil. Fone: +55183743-1948. E-mail: 

paulo.ceresini@bio.feis.unesp.br. 

Rafaela Carolina Constantino Roma-Almeida 

Universidade de São Paulo, Escola Superior de Agricultura ´Luiz de Queiroz´, 

Av. Pádua Dias, 11, Piracicaba, SP, Brasil, 13428-900. 

Raquel Caserta 

Centro de Citricultura Sylvio Moreira/ IAC. Rod Anhanguera, Km 158, 

13490-970 Cordeirópolis-SP. E-mail: raquel_caserta@hotmail.com. 

Reinaldo Rodrigues de Souza Neto 

Centro de Citricultura Sylvio Moreira/ IAC. Rod Anhanguera Km 158, 

13490-970, Cordeirópolis-SP. Departamento de Genética, Evolução e Bioagentes, 


São Paulo, Brazil 

Reza Ehsani 

Citrus Research and Education Center/IFAS, University of Florida, 700 Experiment 

Station Road, Lake Alfred, FL 33850. 

Ronaldo J. D. Dalio 



Sarah A.M. Perryman 

Rothamsted Research, Harpenden, Herts AL5 2JQ, UK.

Sindhuja Sankaran 

Department of Biological Systems Engineering, Washington State University, 

LJ Smith 202, P.O. Box 64120, Pullman, WA 99164. 

Thiago F. Leite 



Tiago S. Oliveira 



Wade Jenner 

CABI Europe – Switzerland, Rue des Grillons 1, 2800 Delémont, Switzerland. 

Wanderson Bucker Moraes 

Department of Plant Pathology, The Ohio State University, OARDC, 

Wooster, OH 44691, United States of America. 

Willian Eduardo Lino Pereira 

Centro de Citricultura Sylvio Moreira/ IAC. Rod Anhanguera Km 158, 

13490-970 Cordeirópolis-SP. Departamento de Genética, Evolução e Bioagentes, 


São Paulo, Brazil. 

Yelitza Coromoto Colmenarez 

CABI South America, Rua José Barbosa de Barros 1780, 18610-307, Botucatu 

- SP, Brazil. E-mail: y.colmenarez@cabi.org

1Applied Remote 

Sensing Systems in 

Phytopathology 

23 

5Understanding 

How a Resistant 

Host Responds to 

Xylella fastidiosa 

Infection: Lessons 

from Model Plants 

Optical Sensing 

for Disease 

Detection and 2Phenotyping 

47 

3Q o 

I (Strobilurin) 

Fungicides: Benefits 

and Risks for 

Agroecosystems 

69 

6Plantwise: 

Improving Food 

Security Through 

Better Plant 

Health System 

145 

187 

4Effectors and 

R-Genes Driving 

Molecular 

Plant-Microbe 

Interactions 

97 

7Monitoring of 

Hemileia vastatrix 

in Conilon Cofee 

Clones to Improve 

Fungicide Use 

213

8Ceratocystis 

Species: Taxonomic 

Challenges in a 

Group of Pathogens 

of Increasing 

Global Importance 

9Characterization 

of Isolates of 

Ceratocystis spp. 

Collected from 

Different Hosts 

245 

255 

11 

The 

12 

Phosphites 

Accelerated 

Evolution of 

Resistance to 

High Risk 

Fungicides in the 

Agroecosystem 

and the Need 

for Anti- 

Emergence 

Strategies 

on Postharvest 

Disease Control 

361 

389 

of Aerial 

Images Obtained 

10Use 

by Unmanned 

Aerial Vehicles 

(UAVs) to Detect 

Eucalyptus 

Diseases 

339 

Dynamic of 

13Spatio-Temporal 

Soilborne 

Pathogens and 

Pathogens 

Transmitted by 

Vector: Defining 

Patterns and 

Managing 

Epidemics 

413

1Applied Remote 

Sensing Systems 

in Phytopathology

PRECISON PHYTOPATHOLOGY: FRONTIERS OF SCIENCE 

1Applied Remote Sensing 

Systems in Phytopathology 

Sindhuja Sankaran; Lav R. Khot; Reza Ehsani. 

1. Introduction 

Remote sensing to assess plant stress responses can be achieved 

on different scales ranging from ground-based proximal sensing to 

satellite-based multispectral imaging systems. Current advancements 

in unmanned aerial technologies have enabled researchers and growers 

to explore the vast potential of this particular technology for the 

acquisition of high spatial and temporal resolution data at a lower cost 

Applied Remote Sensing Systems in Phytopathology 

25


than other methods. Such data can efficiency and accurately represent 

the variation in plant health conditions needed for real-time decision 

making by production managers. The plant disease status with respect 

to pathogen type, plant type and variety, and other environment factors 

can be effectively and consistently evaluated using such remote 

sensing technologies. Integration of some of the potential sensing 

techniques such as thermal, fluorescence, and hyperspectral imaging 

with multiple platforms, along with advancements in associated data 

mining tools can be applied in phytopathology. These include monitoring 

plant diseases under field conditions, assessing disease ratings 

during plant breeding for disease resistance, and monitoring the effect 

of abiotic conditions during disease progression. 

In this chapter, three broad applications of sensing and technology 

will be discussed, which include: (1) remote sensing technologies 

for detecting pests and diseases, (2) remote sensing technologies for 

evaluating phytopathological responses to insects/pathogens in plant 

breeding programs, and (3) other engineering advancements associated 

with phytopathology. In general, remote sensing-based disease management 

has three major applications: (i) assessing the pest/disease development 

and symptoms (Yue et al., 2012; Garcia-Ruiz et al., 2013), 

(ii) monitoring the pathogen/spores and insect pests in the atmosphere 

26 Applied Remote Sensing Systems in Phytopathology


(Gonzalez et al., 2011; Aylor et al., 2011), and (iii) precision spraying 

(Cao et al., 2013; Faiçal et al., 2014). There are great benefits of incorporating 

remote sensing technologies in Integrated Pest Management to 

sustain agricultural production. 

2. Remote Sensing Technologies 

Remote sensing data can be achieved using different types of 

platforms such as satellites, aircrafts, or unmanned aerial vehicles 

(UAVs) (Campbell, 2002; Zhang et al., 2012). The platform selection 

would depend on factors such as image resolution, sensor payload, 

coverage area, spatial resolution, speed of data acquisition, and costs 

involved in acquiring the desired data, along with benefits and limitations 

of the techniques. For example, the satellite-based imaging 

can be limited by cloud cover, frequency of data acquisition, and 

image spatial resolution. There are many types of sensing techniques 

including: hyperspectral/multispectral imaging, thermal imaging, 

fluorescence sensing, Light Detection and Ranging (LiDAR) scanning, 

and Synthetic Aperture Radar (SAR) scanning among others. 

The sensor selection is primarily dictated by the desired agricultural 

application and sensor accuracy. 


27


In agriculture, remote sensing is commonly referred to as scanning 

or observing an agricultural field or orchard without contact. Remote 

sensing technologies can serve as a useful diagnostic tool for a number 

of agricultural applications such as irrigation management, weed 

management, yield monitoring, soil fertility monitoring, disease monitoring, 

and other site-specific precision applications (Jackson, 1986; 

Nilsson, 1995; Moran et al., 1997; Thorp et al., 2004; Bastiaanssen et 

al., 2009; Salmon et al., 2015). The following sections will discuss the 

remote sensing applications in phytopathology. 

3. Sensing Applications in Phytopathology 

3.1. Sensing for disease and pest management in precision 

agriculture 

Remote sensing offers automated non-destructive methods for 

plant disease and pest detection in agriculture. The spectroscopic (hyperspectral, 

thermal sensing) and time-of-flight (LiDAR, SAR) -based 

sensing techniques can provide a practical and rapid means for the development 

of large-scale real-time disease monitoring system under 

field conditions. The plant diseases can be detected under asymptomatic 

and symptomatic conditions (Sankaran et al., 2010a; Mahlein et al., 



2012) using fluorescence imaging (Chaerle et al., 2007; Belasque et al., 

2008), multispectral or hyperspectral imaging (Zhang et al., 2003; Apan 

et al., 2004; Qin et al., 2009; Rumpf et al., 2010; Bauriegel et al., 2011), 

and other sensing techniques (Skottrup et al., 2008; Li et al., 2010). 

In our previous work, we evaluated a number of sensing techniques 

such as visible-near infrared spectroscopy (Sankaran et al., 

2011a, b, 2013a), mid-infrared spectroscopy (Sankaran et al., 2010b), 

fluorescence spectroscopy and imaging (Sankaran et al., 2012, 2013b, 

Wetterich et al., 2013), hyperspectral/multispectral imaging (Garcia-Ruiz 

et al., 2013), and thermal imaging (Sankaran et al., 2013a) to 

detect a major citrus disease, Huanglongbing (HLB). Among these techniques, 

the visible-near infrared spectroscopy, multispectral imaging, 

and thermal imaging (Sankaran et al., 2011a, b, 2013a; Garcia-Ruiz et 

al., 2013) showed high potential for non-destructive sensing, although 

these techniques can be sensitive to changes in environmental conditions 

such as light and wind. The effect of light changes on the data 

can be minimized by radiometrically correcting the spectral reflectance 

with reference calibration (e.g. Spectralon ® Targets, traceable to the 

National Institute of Standards and Technology, NIST). The mid-infrared 

offers identification of the chemical profile of the sample material 

with destructive analysis and minimal sample preparation. Our research 

(Sankaran et al., 2010b) found that starch accumulation caused by phlo- 


29


em-limiting bacteria, Liberibacter candidatus (associated with HLB) 

could be identified using mid-infrared spectroscopy. The technique offered 

a pre-symptomatic disease detection, and could be integrated with 

agricultural equipment such as mechanical harvesters for automated detection. 

The fluorescence features such as yellow fluorescence with UV 

excitation and simple fluorescence ratio (Sankaran et al., 2012, 2013b) 

also exhibited potential in HLB detection; however, the technique cannot 

be used remotely although proximal sensing is possible. The remote 

sensing techniques for disease detection offers rapid data acquisition 

over a larger field area, which can provide useful information in a timely 

and more precise manner than manual visual scouting. In addition, 

the remote sensing technologies can also assist in studying the pattern 

of the disease spread (Fig. 1). 

Similar to disease symptoms, early pest detection is also possible 

using remote sensing technologies. In a study performed by Tailanián 

et al. (2015), caterpillar damage on soy plantations was evaluated using 

ground-based hyperspectral and aerial-based multispectral sensing. The 

results indicated that Support Vector Machine (SVM) could classify the 

healthy, biotic-stressed (pest damage) or abiotic-stressed conditions 

with an overall classification accuracy of 95% using a field spectrometer. 

One of the challenges specified was that the stress induced by cater- 



pillars was a function of the plant rather than the degree of defoliation. 

Another study involved oak splendor beetle (pest) infestation in a forest 

using UAV-based infrared imaging (Lehmann et al., 2015). The overall 

classification accuracy derived using a modified normalized difference 

vegetation index (NDVI) was higher than 80%. Such studies (Peñuelas 

et al., 2015) indicate the potential of remote sensing techniques for disease 

vector (insects) detection in phytopathology. 

Figure 1. Aerial image showing the pattern of disease spread of 

pear canker disease: red represents trees that are highly symptomatic 

and on the verge of dying; white represents trees that are dead. 


31


3.2. Sensing for evaluating disease susceptibility in plant 

breeding 

In recent years, one of the major goals of plant breeding programs 

has been to introduce tolerance to common crop diseases, in addition 

to enhancing the yield potential of the plant. One of the common procedure 

by which the tolerance is identified is to segregate populations 

that exhibit disease tolerance and transmit those genes to the progenies 

(Schafer, 1997). Assessing the susceptibility of different plant 

cultivars to diseases caused by nematodes, fungal, viral, and bacterial 

pathogens, is one of the potential remote sensing applications. Remote 

sensing technologies have been used to monitor diseases (Nilsson, 

1995; Kumar et al., 2012; Li et al., 2012; Usha and Singh, 2013), and 

UAV-based sensing has been used to evaluate the disease severity and 

susceptibility of different varieties. 

In a study by Sankaran et al. (2015), 20-30 potato selections/ 

lines were tested to identify variety susceptibility to potato virus Y 

and early die (Verticillium wilt) diseases. A significant correlation coefficient 

between the canopy area estimated using UAV-based multispectral 

sensing and potato yield was achieved. Another recent study 

on evaluating Cercospora resistance in sugar beet varieties indicated 



that simple vegetation indices such as NDVI, leaf water index (LWI), 

and Cercospora Leaf Spot Index (CLSI) could be correlated with disease 

severity (Jansen et al., 2014). One of the major benefits of assessing 

the disease susceptibility using remote sensing technologies 

in breeding program is that they offer unbiased evaluation of disease 

symptoms. The standard procedure involved visual ratings, which can 

be subjective, slow, and inaccurate. 

Several researchers have used UAV-based sensing for disease 

monitoring in tree fruit production (Calderón et al., 2013, 2014; Garcia-Ruiz 

et al., 2013). A multispectral imaging sensor with six different 

spectral bands (530, 610, 690, 740, 850, and 900 nm) was used to detect 

HLB-infected trees in a citrus orchard. High-resolution multispectral 

images (5.5 cm/pixel) with a suitable classification algorithm (support 

vector machine) could be used for identifying HLB-infected trees with 

up to 85% accuracy. Amongst the different spectral bands, 710 nm provided 

the most useful information. Similarly, verticillium wilt in olive 

orchards was detected using high-resolution airborne hyperspectral and 

thermal imaging techniques (Calderón et al., 2013). Thermal data and 

Photochemical Reflectance Index were significantly correlated with 

disease severity. Similar techniques can be applied to evaluate disease 

severity in breeding programs. 


33


3.3. Advanced engineering approaches to disease control 

Remote sensing technologies are commonly used for plant disease 

monitoring in phytopathology. The remote sensing platforms (aircraft, 

unmanned helicopters, etc.) can also be applied to other phytopathological 

applications such as aerobiological sampling (Techy et al., 

2008; Schmale III et al., 2008; Gonzalez et al., 2011; Aylor et al., 2011) 

and precision spraying (Spraying , 2002; Zhu et al., 2010). 

Remote controlled UAVs have been used to study the movement 

of insects and plant pathogens, which can be useful in phytopathology 

to study the long-distance movement of pathogens and insect vectors. 

Maldonado-Ramirez et al. (2005) utilized UAVs to assess the relative 

spore concentration of the plant pathogen Gibberella zeae above the 

ground surface (60 m) by sampling the air at 8 m3/min using a device 

with vertically mounted Petri plates containing a Fusarium-selective 

medium. A total of about 13,000 viable spores of G. zeae was collected 

from 158 sampling flights in four study years. Schmale III et al. 

(2008) developed a sampling system for aerobiological sampling with 

five sampling patterns at altitudes ranging from 60-300 m and sampling 

speed ranging from 4-10 km/h. Techy et al. (2010) sampled the potato 

late blight pathogen, Phytophthora infestans using UAV at 25-45 m 



above the ground surface. Similar, the remote sensing platforms such as 

UAV can also be applied to precision spraying (Ru et al., 2011; Faiçal 

et al., 2014) and can be integrated with the sensing systems for site-specific 

spraying. Site-specific spraying refers to application of chemicals 

(pesticides, fungicides, fertilizers, etc.) at specific field locations where 

the nutrient deficiency or disease/pest symptoms on the crop appear. 

Engineering technologies can also be developed and applied to treat 

diseases, such as the use of heat treatment (Hoffman et al., 2013). 


35


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45

2Optical Sensing 

for Disease 

Detection and 

Phenotyping


2Optical Sensing for Disease 

Detection and Phenotyping 

Jon S. West; Sarah A.M. Perryman. 


Plant disease epidemics occur at different times and locations and 

can be caused by fungal and oomycete pathogens, viruses and viroids, 

bacteria, phytoplasmas and nematodes. Infection processes are directly 

influenced by the complexities of inoculum availability, growth stage 

of susceptible crop plants, and weather patterns (1). Detection of disease 

is key information to drive decisions on crop protection measures. 

Optical Sensing for Disease Detection and Phenotyping 49


The identification of the many pathogens that may attack a crop during 

a growing season is a challenge for some growers, who often do not 

have the skills needed to diagnose a disease accurately and this may 

compromise their ability to provide accurate crop protection. Plant 

health inspectors may face delays due to lab-based testing of samples. 

Fortunately, new developments in remote sensing and diagnostics offer 

the potential for in-field phenotyping of disease symptoms, mapping of 

disease to drive applications of crop protection products, and rapid detection 

or identification of pathogens before or at early stages of disease 

development. As farms become larger and growers become more reliant 

on mechanised equipment, the need for automated and user friendly 

diagnosis tools will be greater. Grower-friendly methods of pathogen 

detection need to be practical, readily available and cost effective. 

Methods to detect pathogens are becoming increasingly feasible 

due to advances in remote sensing and imaging, automated environmental 

samplers, biosensors, wireless communications and global 

positioning system (GPS). Platforms used for remote sensing include 

satellite, aircraft (including unmanned airborne vehicles; UAVs), and 

tractor-mounted systems. These can be used for spectroscopy or imaging 

of reflected visible and near InfraRed or thermal wavebands, or for 

fluoresced light following excitation of chlorophyll by appropriate light 

sources. This chapter discusses a wealth of established and emerging 

50 Optical Sensing for Disease Detection and Phenotyping


optical methods for diagnosis of plant diseases. Optical sensing methods 

can give greater precision over visual assessment to quantify traits 

such as disease resistance or efficacy of crop protection products. 

Other diagnostic methods also include spectral, fluorescence 

and image analysis methods applied to identification of spores in air 

or water at the microscopic scale, and also DNA-based methods (PCR, 

qPCR, isothermal DNA amplification, and next generation sequencing 

particularly for bacteria, viruses and viroids), antibody-based 

methods (fluorescence microscopy and resonance imaging, ELISA, 

lateral flow devices, biosensors such as holographic or SRi sensors) 

and biomarker-based methods (such as detection of toxins or other 

metabolites by electrochemical biosensors) (2). Plant and environmental 

samples to which these diagnostic methods can be applied include 

plant tissue samples, soil, air particulates, rain and water. This 

chapter does not discuss these non-optical methods, nor optical methods 

applied to spores, only to symptoms on plants. Use of disease 

detection and diagnostics has potential to enhance the environmental 

and economic benefit of crop protection against plant pathogens in 

both broad-field and protected crops. 

For farm decisions and for plant health, biosecurity and quarantine 

inspection, there is a strong benefit to rapid disease detection to 



avoid time delays and logistics of taking samples to a lab, or where portable 

diagnostic devices are available, targeted sampling using optical 

sensing to identify likely diseased areas, will increase detection efficiency. 

Choice of method depends on cost, speed required (processing 

and response time), and sensitivity. As an additional complication, disease 

symptoms, or phenotype, is often affected by recent weather conditions, 

leaf age and specific host-pathogen interactions. For example, 

powdery mildew of brassicas (Erysiphe cruciferarum) is often more 

severe on early leaves than on later leaves and recent wetness or high 

humidity may increase surface sporulation of many fungal pathogens, 

heavy rain may wash surface spores away and bright sunlight increases 

sporulation of wheat yellow rust (Puccinia striiformis). Significant 

differences in appearance and spread of symptoms can occur due to 

wet or dry conditions during colonisation of plant tissues by pathogens. 

As a result, any field-based optical methods should be robustly tested 

over a wide range of conditions and consideration made to conditions 

if comparing results over different seasons. In cereal crops, leaves may 

change from mostly green to mostly senesced in only a few days. This 

tends to occur earlier on diseased plants than healthy plants. However, 

although it is easy to distinguish between a senesced canopy and a 

green canopy, at this late stage of development it is usually not possible 

to determine which disease caused the early senescence. 



2. Disease detection by reflected light quality (spectroscopy) 

and imaging 

Disease can cause changes in leaf size, shape and colour, disturbances 

to the plant’s photosynthetic system or transpiration rate, 

and alter crop canopy morphology and canopy density. These changes 

affect the optical and thermal properties of the canopy, allowing the 

prospect of diseases to be detected remotely as summarized in Table 

1 (3-6). An important process in the development of disease measurement 

or phenotyping systems is validation by recording (also known 

as ground-truthing) a wide range of symptom severity simultaneously 

with collecting optical sensor data in the same experimental plots under 

a diverse variety of conditions such as leaf wetness, types of cloud cover 

and solar angle or light quality. 



Table 1. Summary of spectral features of healthy and diseased 

crop canopies 

Waveband Healthy Canopy Diseased Canopy 

Visible 

(400-700 nm) 

Near Infra-Red 

(NIR 

700-1200nm) 

Short-wave 

Infra-Red 

(SWIR 1200- 

2400 nm) 

Low reflectance 

except in green 

wavebands 

(≈550nm). 

High reflectance 

particularly 

from 730nm 

(the ‘red edge’) 

Generally low 

reflectance 

Increased reflectance, 

especially in the chlorophyll 

absorption 

bands due to disruption 

of photosynthesis and 

presence of surface 

spores or mycelium 

‘Red edge’ high reflectance 

is shifted to begin 

at shorter wavelengths 

(e.g. from 670nm). 

Also biomass reduction 

linked to tissue 

senescence, reduced 

growth, and defoliation 

reduces reflectance 

in the NIR compared 

with healthy plants 

Minor changes associated 

with water 

content possible 

Thermal 

infrared band 

(TIR ≈ (8000- 

14000 nm) 

Radiation emitted depends on leaf temperature, 

which is affected by transpiration 

rate (often affected by root and stem 

base disease and some foliar diseases) 



Spectroscopy is a method to measure overall quality of light, 

without forming a focused image, by measuring the mean intensity 

of each wavelength of light within a field of view. Practical systems 

need not measure a wide range of high-resolution spectra to obtain an 

optical signature for a crop canopy as this creates an enormous dataset. 

Instead a few discriminatory wavebands identified by research 

for a particular disease or type of symptom can be measured, processed 

or summarized, for example by dividing the intensity of light 

at one wavelength with that at another wavelength. To improve spatial 

resolution, spectral line or spectrographic imaging can be used by 

measuring individual spectra along a target line in the canopy. Light 

reflected from the target line is split by a spectrograph into individual 

wavelengths that are focused onto a camera sensor to create an 

image with a spectral and a spatial axis. The spatial resolution along 

the line depends on the optics used, but can be as small as 0.5 mm 

(7). Alternatively arrays of sensors (e.g. along a tractor spray boom) 

can also be used to improve spatial resolution. Spectrographic methods 

have been reported to distinguish between different diseases on 

the same crop (8) but often this is only possible when symptoms are 

mature (e.g. sporulating pustules) and are more difficult when there 

is a mixed range of different infection stages or mixture of different 

disease present at different severities. 



Imaging contrasts with spectroscopy by collecting much more 

data using more complicated equipment to form a focused image, of 

which each image pixel comprises light intensity in one, a few or 

hundreds of wavebands. Where more than one waveband is used, each 

pixel can be processed to discriminate plant stress. Both spectroscopy 

and imaging methods can be used to provide a disease map of a 

field and can use tractor, UAV, aircraft or satellite platforms. These 

different platforms can produce pixel sizes ranging from


Figure 1. Diagrammatic representation of effects of disease on 

light quality in PAR (Photosynthetically Active Radiation) and 

NIR (Near Infra-Red) bands, either reflected or fluoresced from 

a plant canopy, which can be processed using indices and other 

formulae to map traits. In addition, fluorescence occurs at 440nm 

& 520nm (due to cell wall fluorescence) and 684-693nm & 740nm 

(due to chlorophyll) but some of these wavebands can be absorbed 

by secondary pigments present in different plant species. 



3. Disease detection by fluorescence 

Diseases and other stresses often alter the photochemical efficiency 

of a plant, which can be detected by measurement of fluorescence 

of cell walls at 450-550 nm and chlorophyll at 690-740 nm) often well 

before disease symptoms become visible (Fig. 1). This occurs since not 

all absorbed light is used in photosynthesis but some is dissipated both as 

fluorescence in the visible- Near Infrared (NIR) wavelengths (peaking at 

690 nm) and as heat in TIR wavebands (Table 1.1). Increased chlorophyll 

fluorescence can indicate very early stages of disease or other stresses as 

plants react by decreasing photosynthesis, thus increasing fluorescence 

and heat emissions (14-16). Chlorophyll fluorescence measurement requires 

a pulse of supplementary light e.g. from lasers, Ultra Violet (UV) 

lamps or a bank of LEDs, applied either at night or to a region of the canopy 

that is heavily shaded from daylight in order to measure the resulting 

fluorescence. Pulsed light sources can be coupled with synchronised 

gated detectors or, less complex differential systems can be used by subtracting 

an image acquired with an exciting source on, from a background 

image acquired without the excitation. Fluorescence imaging typically 

uses digital cameras either fitted with a single bandwidth filter (usually 

at 690 nm) or by multispectral cameras if many fluorescence wavebands 

are used for diagnosis. High fluorescence emissions on leaves often oc- 



cur as small spots e.g. spots of high emission < 1 mm in diameter were 

caused by Tobacco Mosaic Virus on tobacco (17), by bean rust on beans 

(Uromyces appendiculatus;18) and initially (2-5 days after inoculation) 

by brown rust of wheat (Puccinia triticina; 19). These small spots of fluorescence 

may also surround positions of low fluorescence (where more 

advanced disease has disrupted the photosynthetic mechanism completely 

or masked the area with pustules of spores). Spectroscopy would not 

allow a discrimination of small positions of low and high fluorescence as 

this would give an apparently normal average light intensity in the field 

of view, which is why imaging methods are essential. 

Fluorescence imaging does not provide an unambiguous indication 

of the cause of specific stress but does enable anticipation of disease 

at early stages and so could be of use to plant health inspectors, 

extension workers and farmers to apply diagnostic methods, discussed 

below, in a more targeted way. Due to large energy requirements and 

the need for shading or night time operations, the technique may have 

increased relevance in protected crops, for example greenhouses, where 

there is control over the environment and systems could be moved by 

wires or gantries above the crop canopy or between rows of tall crops. 



In controlled systems, particularly for phenotyping and relatively 

low throughput systems (although automation can allow testing 

of many plants at night), luminescence or biophoton emission can 

also be detected to indicate a plant’s susceptibility to disease. Typically, 

individual plants are placed in a light-proof box. Top standardize 

light exposure, a pulse of bright light may be applied and then 

fluorescence of the plants measured using very long-exposure images 

captured minutes, hours or even days after the plant was exposed to 

the dark. In some cases, plants infected with a pathogen will give off 

more biophotons of light (20). 

Changes in the rate of transpiration influence the TIR wavebands 

(8000-14000 nm; Table 1.1). Lili et al. (21) suggested that eyespot 

(Oculimacula yallundae) and cereal cyst nematode (Heterodera 

avenae) patches of disease in winter wheat, could be detected and 

mapped using aerial instant thermal imagery. Infrared thermometers 

or thermoradiometers can be used remotely to measure thermal radiation, 

allowing the temperature of a surface to be estimated in the field 

of view of the instrument. Thermal imaging has greater potential than 

thermal radiometry as spatial data is collected, however equipment is 

still expensive. It has been used to detect tree decay due to increased 

water content in infected wood. 



4. General Considerations 

For all spectrographic, imaging, fluorescence and thermal measurements, 

there can be complications caused by the angle of view of 

the sensor to the crop. For example, the quality of light is different 

whether sun is being transmitted through leaves or reflected from them 

so if the weather is sunny, it is important to take measurements from a 

single direction, avoiding taking a mixture of transmitted or reflected 

signals and shadows caused by the machinery. The angle of view also 

affects which leaf layers are sampled or observed. This is less of an issue 

for prostrate plants but for cereals in particular, the uppermost leaf 

is usually healthy because it has only just been produced. Therefore a 

shallow view-angle across a canopy will see mostly a continuum of upper 

leaves that are healthy, rather than a more downward angle, which 

will tend to view the second and third leaves of the canopy, which may 

have disease symptoms present. 

5. Conclusions 

The optical methods discussed in this chapter require further 

development for each pathogen-crop system they are applied to, par- 



ticularly due to effects of ambient lighting, and variability in symptom 

expression. To exploit optical sensing methods fully in practical 

farming, additional precision agriculture methods must be integrated, 

such as data-sharing between vehicles and control of individual spray 

nozzles on a spray-boom. 



References 

1. McCartney, H.A.; Fitt, B.D.L.; West, J.S. Dispersal of foliar fungal 

plant pathogens: mechanisms, gradients and spatial patterns. In: Cooke, 

B.M.; Jones, D.G.; Kaye, B. (eds) The Epidemiology of Plant Diseases, 

2nd Edition. Springer, Dordrecht, 2006; p.159-192. 

2. Heard, S.; West, J.S. New developments in identification and quantification 

of airborne inoculum. In: Gullino ML & Bonants PJM (Eds) 

Detection and Diagnosis of Plant Pathogens, Plant Pathology in the 

21st Century 5. Springer, Dordrecht, The Netherlands, 2014, p.3-19. 

3. Rouse, J.W.; Haas, R.H.; Scheel, J.A.; Deering, D.W. Monitoring 

Vegetation Systems in the Great Plains with ERTS. Proceedings, 3rd 

Earth Resource Technology Satellite (ERTS) Symposium, v.1, p.48- 

62, 1974. Available at: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa. 

gov/19740022592.pdf , accessed on 25 Nov 2015. 

4. Carter, G.A. Responses of leaf spectral reflectance to plant stress. 

American Journal of Botany v.80, p.239-43, 1993. 



5. West, J.S.; Bravo, C.; Oberti, R.; Lemaire, D.; Moshou, D.; McCartney, 

H.A. The potential of optical canopy measurement for targeted 

control of field crop diseases. Annual Review of Phytopathology. v.41, 

p.593-614, 2003. 

6. West, J.S.; Bravo, C.; Oberti, R.; Moshou, D; Ramon, H.; McCartney, 

H.A. Detection of fungal diseases optically and pathogen inoculum by 

air sampling. Chapter 9 In: E-C Oerke et al. (Eds), Precision Crop Protection 

– The Challenge and Use of Heterogeneity, Springer Science, 

Dordrecht; p.135-149. 2010. DOI 10.1007/978-90-481-9277-9_9. 

7. Jørgensen, R.N. Study on line imaging spectroscopy as a tool for nitrogen 

diagnostics in precision farming, PhD Thesis. 2002. p.94. Agricultural 

Sciences Plant Nutrition and Soil Fertility Laboratory, Denmark. 

8. Mahlein A.-K. Plant Disease Detection by Imaging Sensors – Parallels 

and Specific Demands for Precision Agriculture and Plant Phenotyping. 

Plant Disease 2015. Available at http://apsjournals.apsnet.org/doi/ 

pdf/10.1094/PDIS-03-15-0340-FE accessed on 30 Nov 2015. 

9. Bravo, C.; Moshou, D.; West, J.; McCartney, H.A.; Ramon, H. Early 

disease detection in wheat fields using spectral reflectance. Journal of 

Biosystems Engineering. v.84, p.137-145. 2003 



10. Moshou, D.; Bravo, C.; West, J.S.; Wahlen, S.; McCartney, H.A.; 

Ramon, H. Automatic detection of yellow rust in wheat using reflectance 

measurements and neural networks. Journal of Computers and 

Electronics in Agriculture v.44, p.173-188. 2004. 

11. Moshou, D.; Bravo, C.; Oberti, R.; West, J.; Bodria, L.; McCartney, 

H.A.; Ramon, H. Plant disease detection based on data fusion of 

hyper-spectral and multi-spectral fluorescence imaging using Kohonen 

maps. Real-Time Imaging v.11, n.2, p75-83. 2005. 

12. Moshou, D.; Bravo, C.; Wahlen, S.; West, J.S.; McCartney, H.A.; 

De Baerdemaeker, J.; Ramon, H. Simultaneous identification of plant 

stresses and diseases in arable crops using proximal optical sensing and 

self-organising maps Precision Agriculture, v.7, n.3, p.149-164, 2006. 

13. Moshou, D.; Bravo, C.; Oberti, R.; West, J.S.; Ramon, H.; Vougioukas, 

S.; Bochtis, D. Intelligent multi-sensor system for the detection and 

treatment of fungal diseases in arable crops. Biosystems Engineering 

v.108, p.311 – 321, 2011. 

14. Scholes, J.D. Photosynthesis: cellular and tissue aspects in diseased 

leaves. In Ayres, P.G. (Ed.) Pests and Pathogens: Plant Responses to 

Foliar Attack, 1992, p. 85-106. Oxford: Bios Sci. 



15. Wright, D.P.; Baldwin, B.C.; Shepard, M.C.; Scholes, J.D. Sourcesink 

relationship in wheat leaves infected with powdery mildew. 1. 

Alterations in carbohydrate metabolism. Physiological and Molecular 

Plant Pathology v.47, p.237-53, 1995. 

16. Cecchi, G.; Mazzinghi, P.; Pantani, L.; Valentini, R.; Tirelli, D.; 

Deangelis, P. Re-mote-sensing of chlorophyll-a fluorescence of vegetation 

canopies .1. Near- and far-field measurement techniques. Remote 

Sensing of Environment. v.47, p.8-28, 1994. 

17. Daley, P.F. Chlorophyll fluorescence analysis and imaging in 

plant stress and disease. Canadian Journal of Plant Pathology v.17, 

p.167-73, 1995. 

18. Peterson, R.B.; Aylor, D.E. Chlorophyll fluorescence induction in 

leaves of Phaseolus vulgaris infected with bean rust (Uromyces appendiculatus). 

Plant Physiology v.108, p.163-71, 1995. 

19. Bodria, L.; Fiala, M.; Oberti, R.; Naldi, E. Chlorophyll fluorescence 

sensing for early detection of crop's diseases symptoms. Proceedings of 

the. American Society of Agricultural Engineering-CIGR World Congress, 

Chicago. IL, USA, July 28-31, 2002. DOI: 10.13031/2013.10946. 



20. Floryszak-Wieczorek, J, Gorski, Z.; Arasimowicz-Jelonek, M. Functional 

imaging of biophoton responses of plants to fungal infection. European 

Journal of Plant Pathology v.130, n.2, p.249-258, 2011. 

21. Lili, Z.; Duchesne, J.; Nicolas, H.; Rivoal, R. Détection infrarouge 

thermique des maladies du blé d'hiver. Bulletin OEPP/EPPO Bulletin 

v.21; p.659-72, 1991. 


3Q 0 

I (Strobilurin) 

Fungicides: 

Benefits and 

Risks for 

Agroecosystems


3Q 0 

I (Strobilurin) Fungicides: 

Benefits and Risks for Agroecosystems 

Paul Vincelli. 


The Q 0 

I fungicides are analogues of natural compounds produced 

by wood-rotting basidiomycetes, including Strobilurus tenacellus, in 

which the first strobilurin compounds were detected (1, 39). For this reason, 

the Q 0 

I fungicides are also called strobilurin fungicides. However, 

it is also common practice to refer to this group as the Q 0 

I fungicides, 

because these fungicides exert fungitoxicity by binding in the Q 0 

pocket 

Q 0 

I (Strobilurin) Fungicides: Benefits and Risks for Agroecosystems 

71


of the cytochrome bc1 complex, thereby inhibiting its function (1, 3). 

With important exceptions, the Q 0 

I fungicides control an unusually 

wide array of fungal diseases, including diseases caused by water molds, 

downy mildews, powdery mildews, leaf spotting and blighting fungi, fruit 

rotters, and rusts. They are used on a wide variety of crops, including cereals 

and other field crops, fruits, tree nuts, vegetables, turfgrasses, and 

ornamentals (1). Depending on the particular product and disease, Q 0 

I 

fungicides may be applied to foliage, flowers, fruits, and seed, as well as 

directed to the soil, including via chemigation. Application rates of Q 0 

I 

fungicides are considerably lower than those of older fungicidal compounds, 

and this contributes to several being considered as “reduced-risk” 

fungicides by the U.S. Environmental Protection Agency. Because of their 

efficacy, broad spectrum of activity, and generally favorable environmental 

characteristics, the Q 0 

I fungicides have become one of the most important 

fungicide groups used for disease control on crops in the USA (http:// 

water.usgs.gov/nawqa/pnsp/usage/maps/) and worldwide. 

2. Mobility in planta 

Several Q 0 

I fungicides are acropetally systemic, whereas others 

are translaminar but move very little or not at all in vascular tissues (1, 

72 Q 0 

I (Strobilurin) Fungicides: Benefits and Risks for Agroecosystems


3). Of course, patterns of mobility in plants influence the optimal use 

of products containing these fungicides. Systemic movement (when it 

occurs) and translaminar movement help to compensate for incomplete 

coverage of infectible plant surfaces. Redistribution in the vapor phase 

by some Q 0 

I fungicides (1, 3) can also help compensate for poor crop 

coverage, but only to a limited extent. Redistribution via these processes 

may be especially important in crops with dense or difficult-to-spray 

canopies (cucurbits, for example). Be aware that several days may be 

required for adequate protection to be achieved via translaminar movement. 

There are two reasons for this: 

1. Time is needed for redistribution, translaminar movement, 

and systemic movement to occur; and, 

2. Q 0 

I fungicides are typically more active on spore germination 

than on mycelial colonization. 

Thus, growers may not achieve optimum disease control if a Q 0 

I 

fungicide is applied with incomplete coverage around the time of an 

infection period. 

Q 0 


73


3. Effects on plant health independent of disease control 

3.1. Enhancement of Plant Performance 

Although many pesticides can influence the physiology of treated 

plants, the Q 0 

I fungicides are notable in this regard. Crops treated with 

Q 0 

I fungicides often exhibit delayed senescence, and they sometimes 

exhibit enhanced yield under conditions of low disease pressure. Such 

yield increases have been observed in a variety of crop plants, including 

corn (33, 34). In addition to yield enhancement, other positive agronomic 

responses have been documented, including increased stalk strength 

in corn (34) and reduced frost damage to foliage in corn (23). Published 

studies provide some insights into the physiological reasons for beneficial 

plant responses (8, 21, 39). Although Q 0 

I fungicides are marketed 

principally as fungicides, these physiological benefits are one reason for 

product marketing for “plant health,” encompassing more than disease 

control. Although numerous marketing claims have been replicated in 

third-party research, to my knowledge, increased tolerance to simulated 

hail damage has not yet been reported in public trials (6, 20). 

Beyond the question of whether the physiological plant-health 

benefits can be replicated in one or more trials, a critical question is 

whether these effects occur in a consistent or predictable manner. In var- 

74 Q 0 



ied crops, yield responses from Q 0 

I fungicides have been highly variable 

(9, 16, 20, 22, 27, 28, 36, 37, 38). It appears that benefits beyond disease 

control are very dependent on the crop and cultivar, the fungicide used, 

and the environmental conditions, and as such, these benefits can be 

difficult to predict. Thus, some scientific uncertainty remains over the 

agronomic and environmental conditions under which the physiological 

benefits justify applications even if disease risk is minimal. 

One of the ongoing challenges has been the question of how 

to best assess the frequency and magnitude of positive benefits from 

Q 0 

I fungicides under field conditions. Public trials are often conducted 

in replicated, randomized small-plot trials. While statistical rigor 

is always desirable, it can be reasonably postulated that small-plot 

research underestimates the fungicidal responses observed in producer 

fields. Peer-reviewed research to date does not support this hypothesis 

(35). However, industry scientists have shared unpublished 

data from several field experiments which support the hypothesis that 

large-scale trials are better for assessing the benefits of fungicides 

than small-plot trials. Frankly, because of the complexities and interacting 

factors involved in this topic, I believe the industry position 

remains defensible. Further research to address the question of plot 

size and fungicide benefits would certainly be welcome, but this is 

likely a low priority for funding sources. 

Q 0 


75


3.2. Phytotoxicity 

If a physiological effect occurs as a result of Q 0 

I fungicide application, 

it is very commonly positive. However, several Q 0 

I fungicides 

are known to cause phytotoxicity in certain, limited circumstances; the 

product label is an important guide to the known phytotoxicity risks. 

For example, apple cultivars with a genetic background which includes 

Macintosh are extremely sensitive to azoxystrobin. Indeed, these varieties 

are so sensitive that they can be injured when a sprayer is used to 

apply azoxystrobin to another crop (grapes, for example), rinsed, and 

then used to apply another fungicide to the apple crop! Another example: 

while trifloxystrobin may be used safely on most grapes, it can 

cause injury to Concord grapes. Kresoxim methyl is phytotoxic to certain 

sweet cherry varieties but not others. Producers should be aware 

of phytotoxicity concerns both for the treated crop and because of the 

possibility of injury to adjacent crops via spray drift. 

Phytotoxicity is also possible when Q 0 

I fungicides are tankmixed 

with materials that solubilize the cuticle: oils, surfactants, certain 

liquid formulations of insecticides, etc. Solubilizing the cuticle 

increases the risk of phytotoxicity. With Q 0 

I fungicides, much of the 

active ingredient is found in the cuticle rather than within the apoplast 

76 Q 0 



or symplast. This is true even when the molecule has crossed the lamina; 

it often is bound in the cuticle of the opposing leaf surface. Thus, 

application of a Q 0 

I spray in a way that causes abnormally high levels 

of fungicide to penetrate into the host tissue may potentially lead to 

phytotoxicity on certain crops or varieties where none has been observed 

before. Obviously, before applying a previously unused tankmix 

on a particular crop variety, it is a good idea to test-apply to small 

areas before treating large acreages. 

4. Potential for environmental impact 

Q 0 

I fungicides widely used in the USA show K oc 

values ranging 

from 500 to 16,000 (3, 11), indicating medium to strong attachment to 

organic matter. Most strobilurins are considered to be readily degradable 

in water (1). However, Q 0 

I fungicides can exhibit substantial ranges 

in half-life in soils and in stability in planta (3). 

Q 0 

I fungicides commonly exhibit low levels of toxicity (LD 50 

values 

>2000 mg/kg) to birds and mammals (3). The Bartlett et al review 

(3) review reports relatively low levels of toxicity to bees, with LD 50 

values of >20 to 310 µg per bee. The salient weakness in environmental 

Q 0 


77


toxicology of Q 0 

I fungicides is in toxicity to freshwater organisms. Q 0 

I 

fungicides widely used in the USA exhibit high toxicity (LC 50 

values 

generally well below 1 mg/liter) to fish, Daphnia magna (a representative 

invertebrate), and green algae (3, 11). At least some formulated 

products containing Q 0 

I fungicides have shown significant toxicity to 

amphibians (4, 15). Thus, significant concentrations of Q 0 

I fungicides 

in runoff (11, 30) could present risks to aquatic ecosystems, especially 

given the widespread use of these fungicides. A study by Battaglin et al 

(2) reported that concentrations of Q 0 

I fungicides in streams were commonly 

well below LC 50 

values of sensitive aquatic species. However, 

pesticides at part-per-billion concentrations may still exert an impact on 

stream ecology (for example, see 19). Fungicides (including a Q 0 

I fungicide) 

have been found at very low concentrations in tissues of amphibians 

in locations remote from cropped lands, although at this time the biological 

and ecological relevance of these findings is not yet known (26). 

5. Fungicide resistance 

5.1. Occurrence of resistance 

Globally, at least 35 species of ascomycetes, imperfect fungi, 

basidiomycetes, and oomycetes have been reported as having isolates 

78 Q 0 



exhibiting field resistance to the Q 0 

I fungicides (http://www.frac.info/ 

docs/default-source/QoI-wg/QoI-quick-references/species-with-qo-resistance-%28updated-2012%29.pdf?sfvrsn=4). 

Q 0 

I fungicides share a common biochemical mode of action: they 

all target complex III of the mitochondrial electron transport chain. Such 

a high degree of biochemical specificity in a toxin often is associated 

with a risk for the development of microbial resistance. In addition, 

there is another factor that may contribute to the high risk of resistance 

in Q 0 

I fungicides. Fungitoxic strobilurin compounds occur naturally, 

seemingly produced for defense of the fungus against competing fungi 

present in rotting wood. Thus, fungi in natural ecosystems have been 

exposed to strobilurins over geological time scales, and this presumably 

has allowed for natural selection of mutant alleles with some degree of 

resistance to these fungitoxins, even in fungal populations never previously 

treated with commercial Q 0 

I fungicides (24). This would account 

for the rather rapid appearance of strains with Q 0 

Iresistance in some pathosystems 

(32). Indeed, it is well-established that genetic resistance to 

pesticides and antimicrobial compounds can predate their use (5, 7, 14). 

At least three mutations in the cytochrome b gene have been associated 

with Q 0 

I resistance. The most common and most destructive is the 

G143A mutation (glycine to alanine at position 143). Resistance factors 

Q 0 


79


of isolates with the G143A mutation are often well over 100 (meaning 

that they exhibit EC 50 

values well over 100X higher than that of baseline 

isolates). In some fungi (including, notably, the rusts), an intron of 1337- 

2157 bp length may be found immediately after the triplet coding for 

glycine at position 143 (12). An intron in this position appears to interfere 

with normal splicing of cytochrome b exons, resulting in a defective 

cytochrome b. Thus, conveniently, the presence of the intron immediately 

downstream of G143 confers lethality to the G143A mutation. 

The second most commonly reported mutation is the F129L mutation 

(phenylalanine to leucine, position 129). G137R (glycine to arginine, 

position 137) has also been reported. Both the F129L and G137R 

mutations confer partial resistance, and Q 0 

I fungicidal products still may 

provide acceptable control in the field, as long as the G143A mutation is 

not present (25). Fungi with resistance to a Q 0 

I fungicide typically exhibit 

significant cross-resistance to others in the group (for example, see 17). 

5.2. Detection and monitoring 

In-vitro testing in defined media amended with fungicide is commonly 

used to evaluate isolate sensitivity to Q 0 

I fungicides. This is often 

accomplished by measuring spore germination after exposure to the 

80 Q 0 



fungicide at various doses, permitting calculation of an LC 50 

. Such tests 

are commonly conducted in the presence of salicylhydroxamic acid 

(SHAM) (29, 32). This is because an alternative oxidase pathway can 

be active in vitro in some fungi, which can mask the true sensitivity of 

isolates to the fungicide under field conditions. However, researchers 

should be aware that SHAM itself may be toxic to certain fungi at doses 

used to suppress alternative oxidase (18). 

An alternative to visually assessing spore germination is to test 

for fungicide resistance using a colorimetric approach. Vega et al (29) 

assessed reduction of resazurin in microtiter plates as a surrogate measure 

of fungal growth over a range of fungicide concentrations. The authors 

found excellent correlations between EC 50 

values obtained using 

the colorimetric approach as compared to those obtained by visually 

assessing conidial germination. The resazurin assay offered the advantages 

of rapidity and relative simplicity while not sacrificing accuracy 

or reliability. 

Molecular methods are valuable tools for detecting and monitoring 

Q 0 

I-resistant biotypes (17). Such tests are commonly PCR-based, 

with primers or restriction enzyme digests that discriminate mutant 

alleles from wild-type. As such, these tests allow for relatively rapid 

detection of specific, known mutations. This is also a potential weak- 

Q 0 


81


ness: if a novel mutation for resistance is present, the particular molecular 

test employed may not be designed to detect it. Quantitative PCR 

(qPCR) techniques are valuable for determining frequencies of resistance 

alleles, but they do not provide a quantitative estimate of overall 

sensitivity to the fungicide in question (10). qPCR may not permit early 

detection of a mutant allele below a frequency of 0.5-5%, because of 

the limits of detection and quantification. However, qPCR can be effective 

for documenting a population shift that would be expected to be 

associated with a breakdown in resistance. 

In designing a monitoring program for resistance to Q 0 

I fungicides, 

several considerations come to mind: 

• Robust testing techniques must be developed for the pathosystem(s) 

of interest. Depending only on molecular tests could 

potentially pose a hazard of obtaining a false negatives, as explained 

above. Thus, it may be valuable to include periodic in 

vitro tests to allow for detection of novel resistance genotypes. 

• In order to test suspect isolates for resistance, data are needed 

on baseline sensitivity of isolates from representative populations 

unexposed to the fungicide(s) of interest. 

82 Q 0 



• Instituting a routine, large-scale sensitivity monitoring program 

is expensive, so it is worth considering whether this is 

best use of scarce funds. One option for a less costly approach 

might be to encourage field personnel from farms, agricultural 

industries, and government and universities to informally 

monitor the commercial performance of fungicides of concern, 

to offer a laboratory testing service for suspect samples at little 

to no cost. Pathosystems to be monitored could be prioritized 

based on their relative risk of fungicide resistance (13). 

5.3. Reducing the risk of resistance 

The only way to eliminate the risk of resistance to at-risk fungicides 

is to not use them. In principle, if the genetic potential for resistance 

exists, use of the fungicide promotes selection towards resistance. 

Of course, eliminating all use of commercial fungicides is impractical. 

However, it is vital that producers understand that every time they 

use an at-risk fungicide, they may be promoting the development of a 

pathogen population with resistance. This is illustrated graphically in 

Vincelli (31). It therefore follows that non-fungicidal practices for reducing 

disease pressure--cultural practices, variety selection, biological 

Q 0 


83


controls--all contribute to reducing the buildup of fungicide resistance. 

Minimizing the need for fungicide applications through non-chemical 

disease-control practices is, therefore, a fundamental component of a 

strategy to reduce the risk of fungicide resistance on commercial farms. 

The guidelines of the Fungicide Resistance Action Committee 

(FRAC, http://www.frac.info/) are widely regarded as a foundation for 

reducing the risk of fungicide resistance. Of course, this applies to the 

Q 0 

I fungicides. FRAC’s Q 0 

I guidelines can be found online at http:// 

www.frac.info/working-group/qol-fungicides/general-use-recommendations. 

Q 0 

I fungicides 1 are all in a single cross-resistance group (FRAC 

Group 11) and must be treated accordingly. Mixing or alternating Q 0 

I 

fungicides is, of course, not an effective anti-resistance tactic: fungicides 

chosen for mixtures or alternations must be selected from other 

FRAC cross-resistance groups. 

One of the most important practices for reducing resistance risk is 

to limit the number of applications of Q 0 

I fungicides per season, whether 

used alone or in combination with a mixing partner from another 

cross-resistance group. This is recommended in order to reduce the pe- 

1 

FRAC Group 11: azoxystrobin, coumoxystrobin, dimoxystrobin, enoxastrobin, famoxadone, 

fenamidone, fenaminostrobin, fluoxastrobin, flufenoxystrobin, kresoxim-methyl, 

mandestrobin, metominostrobin, orysastrobin, pyraoxystrobin picoxystrobin, 

pyraclostrobin, pyrametastrobin, pyribencarb, triclopyricarb trifloxystrobin 

84 Q 0 



riod of exposure of the pathogen to Q 0 

I fungicides. This is expected to 

reduce overall selection pressure towards resistance. 

In addition to limiting the total number of Q 0 

I applications, it is 

advisable to limit the number of consecutive applications of Q 0 

I fungicides 

that are allowed on each crop, before the user must switch to an 

equal number of applications of non-Q 0 

I fungicides. For most crops, 

the number of consecutive Q 0 

I applications is limited to two before 

the grower must switch to a fungicide with a different mode of action. 

FRAC guidelines on certain crops are even stricter, advising never to 

apply Q 0 

I fungicides consecutively. Like the seasonal limit described 

above, this guideline is designed to reduce the opportunity for selection 

pressure towards resistance. With respect to the use of Q 0 

I fungicides in 

mixtures with other non-Q 0 

I fungicides, FRAC guidelines indicate that 

fungicide partners should be those that provide adequate disease control 

when used alone against the target disease. 

Since the growth stage most affected by Q 0 

I fungicides is spore 

germination, preventive applications are recommended over curative 

applications. The latter is thought to increase the risk of resistance, because 

the producer is treating a much larger population of spores and 

mycelium than would be treated preventively. Allowing a buildup of a 

large population of spores before treatment increases the chances that 

Q 0 


85


a resistant mutant will be present when the chemical is applied. See 

FRAC crop-specific recommendations for details. 

The translaminar movement of Q 0 

I fungicides is clearly an advantage 

from the standpoint of disease control, but it may present a 

challenge when a Q 0 

I fungicide is tank-mixed with a contact (=protectant) 

fungicide for resistance-management purposes. If spray coverage 

is inadequate, biologically active levels of Q 0 

I fungicides may 

be found on untreated leaf surfaces due to translaminar movement. 

The contact, of course, would not move translaminarly, so the Q 0 

I 

fungicide could be present on a leaf surface without the presence of 

the mixing partner. On such leaf surfaces, a Q 0 

I-resistant strain could 

take hold and flourish, should it arise. Whenever applying a mixture 

of a Q 0 

I fungicide with a contact fungicide, always strive for complete 

coverage of all plant surfaces. 

As discussed above, plant growth-enhancing effects have sometimes 

been observed in Q 0 

I-treated plants of several crop species. Where 

this occurs, it may present an incentive to use a product even under low 

disease pressure. While optimizing plant health is always an important 

objective, overuse use of a Q 0 

I fungicide for its growth-promoting 

qualities may increase selection pressure towards fungicide resistance. 

Users should be very mindful not to overuse any at-risk fungicide. Once 

86 Q 0 



resistance to Q 0 

I fungicides develops on a farm, there is a very good 

chance that efficacy of these products against that disease will be compromised 

for quite some time. This is because strains exhibiting G143A 

or F129L mutations are commonly ecologically quite fit even in the 

absence of Q 0 

I fungicides. 

6. Conclusion 

Q 0 

I fungicides represent an important component of the arsenal of 

disease-control options available to modern crop producers. This is due 

to their efficacy, broad spectrum activity, and generally favorable profile 

with respect to human and environmental toxicology. Fungicides 

in this FRAC group clearly are at risk for pathogen resistance. While 

it is impractical to prevent the buildup of resistance if these fungicides 

are being used on commercial farms, they should be used in ways that 

minimize the buildup of resistance. 

Q 0 


87


References 

1. Balba, H. Review of strobilurin fungicide chemicals. Journal of Environmental 

Science and Health Part B, v.42, p.441–451, 2007. 

2. Battaglin, W. A., Sandstrom, M. W., Kuivila, K. M., Kolpin, D. W., 

and Meyer, M. T.. Occurrence of azoxystrobin, propiconazole, and selected 

other fungicides in US streams, 2005–2006. Water, Air, and Soil 

Pollution, v.218, p.307–322, 2011. 

3. Bartlett, D. W., Clough, J. M., Godwin, J. R., Hall, A. A., Hamer, M., 

and Parr-Dobrzanski, B. The strobilurin fungicides. Pest Management 

Science, v.58, p.649-662, 2002. 

4. Belden, J., McMurry, S., Smith, L. and Reilley, P. Acute toxicity 

of fungicide formulations to amphibians at environmentally relevant 

concentrations. Environmental Toxicology and Chemistry, v.29, 

p.2477-80, 2010. 

5. Bhullar K., Waglechner, N., Pawlowski, A., Koteva, K., Banks, E. 

D., Johnston, M. D., Barton, H. A., and Wright, G. D. Antibiotic re- 

88 Q 0 



sistance is prevalent in an isolated cave microbiome. PLoS ONE 7(4): 

e34953. doi:10.1371/journal.pone.0034953, 2012. 

6. Bradley, C. A., and Ames, K. A. Effect of foliar fungicides on corn 

with simulated hail damage. Plant Disease, v.94, p.83-86, 2010. 

7. Délye, C., Deulvot, C., and Chauvel, B. DNA analysis of herbarium 

specimens of the grass weed Alopecurus myosuroides reveals herbicide 

resistance pre-dated herbicides. PLoS ONE 8(10): e75117. doi:10.1371/ 

journal.pone.0075117, 2013. 

8. Diaz-Espejo, A., Cuevasa, M. V., Ribas-Carbo, M., Flexas, J., Martorell, 

S., and Fernández, J. E. The effect of strobilurins on leaf gas 

exchange, water use efficiency and ABA content in grapevine under 

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4Effectors and 

R-Genes Driving 

Molecular 

Plant-Microbe 

Interactions


4Effectors and R-Genes Driving 

Molecular Plant-Microbe Interactions 

Ronaldo J. D. Dalio; Tiago S. Oliveira; Heros J. Maximo; Thiago 

F. Leite; Marcos A. Machado. 


Millions of microorganisms are in contact with plant organs in soil 

and shoot. Some of these microorganisms are pathogenic while others 

are endophytic. In the majority of the cases, the development of diseases 

can depend or not on the recognition of a threatening organism (1). 

Effectors and R-Genes Driving Molecular Plant-Microbe Interactions 

99


Plants allow interactions with an endophyte but avoid pathogens 

upon their recognition and disease development is restrained. On 

the other hand, pathogens have evolved mechanisms that spoil their 

recognition by the plant. If a plant does not recognize the presence of 

a pathogen, the defense system is not activated and the pathogen can 

grow and spread in the plant tissues (2). 

The co-evolution between plants and pathogens is a force that 

drives the development of new strategies to recognize pathogen patterns 

in plants, while pathogens originate new molecules capable of 

escape recognition, or turning off the plants defense system (3). The 

main key-players molecules in this constant arms-race between plants 

and pathogens are, on the pathogen side: pathogen associated molecular-patterns 

(PAMPs) and effectors; and, on the plant side: pattern 

recognition receptors (PRRs) and resistance genes. Plants recognize 

PAMPs by their arsenal of PRRs (mainly in the apoplast), while R-proteins 

(coded by R-genes) can recognize one effector in both apoplast 

and symplast (4). Upon recognition, plants activate a strong defense 

system that involves many biochemical barriers and processes that ultimately 

can lead to a hypersensitive response (HR) that can restrict the 

further development of the pathogen. 

Once the recognition is crucial for the fate of every plant-patho- 

100 Effectors and R-Genes Driving Molecular Plant-Microbe Interactions


gen interaction, it opens new perspectives for controlling diseases. 

Enhancing the ability of plants to recognize pathogens and artificially 

blocking the action of a given pathogen effector can lead to the development 

of a more efficient and sustainable management of diseases in 

either crops or natural ecosystems. 

This work summarizes our current knowledge on effector biology 

and plant defenses, including the interplay between PAMPs, PRRs, 

effectors, WRKYs and R-genes. 

2. PAMPs and PRRs 

Plant defense responses can be activated by signals indicating 

the presence of pathogens. These signals were originally referred to as 

elicitors, and now are named PAMPs (Pathogen-Associated Molecular 

Pattern), MAMPs (Microbe Associated Molecular Patterns), DAMPs 

(Damage Associated Molecular Patterns) or WHIMPs (Wound/Herbivory-Induced 

Molecular Patterns, depending on their origin or localization) 

and are recognized by plant receptors (4,5). 

PAMPs are conserved molecules among microbes, including 


101


pathogenic and non-adapted microorganisms (6). The term PAMP 

was coined for mammalian innate immune system. Considering that 

nonpathogenic microorganisms or microorganisms nonpathogenic for 

many hosts also possess PAMPs, the term MAMP (Microbe-Associated 

Molecular Pattern) is frequently used (5). The common PAMPs 

found in pathogens include bacterial flagellins (the main peptide component 

of the motility organ) (7), lipopolysaccharides (LPS, which are 

the glycolipid component of Gram-negative bacteria external membranes), 

elongation factor-Tu (an abundant protein involved in translation), 

peptidoglycans (PGN, an essential component of the microbial 

cell envelope) and muropeptides released from PGN by the action of 

lysozyme [reviewed in Aslam et al. (8)] chitin and ergosterol, major 

constituents of the fungal cell wall (9) and glucans from oomycetes 

(10). Some doubt can arise about the difference between PAMPs and 

avr gene products. Although exceptions are nowadays recognized, according 

to the traditional definition, some strains of pathogens have 

Avr proteins which interact directly in a gene for gene manner with 

the R genes in compatible hosts allowing the host to recognize and 

defend itself against the pathogen. Certain strains of the same pathogen 

may have lost or have altered Avr genes allowing an incompatible 

reaction to occur manifesting in disease symptoms in the plant. On 

the other hand, PAMPs are evolutionarily stable, essential metabolic 



or structural components of the microbe that cannot be lost without 

losing microbial viability or vigor (5). 

The recognition of PAMPs is carried out by PAMP receptors 

(Pattern Recognition Receptors, PPR). Previous studies have shown 

that these receptors are transmembrane proteins with an extracellular 

LRR (leucine-rich repeat) and an intracellular serine/threonine kinase 

domain such as FLS2, a receptor from flagellin (7) and the EFR1 receptor 

from EF-Tu, both from Arabidopsis (11). The first documented 

LRR kinase with a role in plant defense was Xa21, the product of a 

rice R gene. According to the authors the Xa21 gene product is similar 

to animal receptor kinases and functions in diverse rice species and 

subspecies to reduce infection (12). 

Each unit of a typical LRR domain carries 21-25 copies of the 

typical amino acid sequence 1 xxLxLxxNxLt/sGxIPxxLxxLxxL 24 , 

where leucine and other hydrophobic residues are hidden within the 

protein in an aqueous environment (red letters). The “x” residues are 

more variable and are exposed on the protein surface. The variable 

residues “x” in blue are primary candidates for determining pathogen 

specificity. Furthermore, the whole domain is crescent shaped with 

the concave surface composed of a β-sheet (5). 


103


Experiments indicated that the extracellular portion of the 

receptor FLS2 binds directly to a peptide that matches the eliciting 

flagellin sequence, and induces defenses that restrict bacterial 

growth. Plants lacking FLS2 are more susceptible to infection 

by a bacterial pathogen (7, 13). A pre-treatment of an fls2 mutant 

plant with various bacterial extracts (containing PAMPs other than 

flagellin) demonstrated that after subsequent inoculation, the mutant 

plant was able of restricting bacterial growth even though these 

plants lack the flagellin receptor. These results show that additional 

PAMPs present in the extracts were recognized by other receptors 

than FLS2 (9, 14). Chinchilla et al. (15) demonstrated that the 

LRR-kinase receptor BAK1 (BRI1-Associated receptor Kinase 1), 

which is involved in the regulation of brassinosteroid receptor (BRI) 

– a receptor for brassinolide hormone from arabidopsis – is also 

involved in the regulation of FLS2. According to the authors, the 

activation of FLS2 by flagellin involves the formation of a complex 

with BAK1. This process brings the intracellular kinase domain of 

both receptors into close proximity leading to transphosphorylation, 

which initiates the signaling process (4). Although BAK1 is not directly 

involved in the recognition of the pathogen, this receptor may 

have an important role in the regulation of FLS2 and other receptors 

involved in the recognition of PAMPs (3). 



Plants are also able to detect herbivory or wounding by means 

of transmembrane LRR-kinase receptors like PEPR1 – a LRR-kinase 

from arabidopsis. The eliciting signals for these receptors are 

plant-derived compounds. Wounding, methyl jasmonate, or ethylene 

induces the PROPEP1 gene (16) and a peptide derived from this protein 

(AtPep1) binds to and activates PEPR1 LRR-kinase (17). The 

activation of this system increases resistance to Pythium irregulare, 

which is a broad host-range fungal pathogen. Thus, the presence of a 

pathogen in a host can release host-derived compounds (for instance, 

compounds derived from plant cell wall by the action of enzymes 

such as xylanases, pectate lyases, and polygalacturonases, secreted 

by many pathogens) that activate plant innate immunity. Considering 

that these elicitors are derived from host, it has been proposed to call 

these compounds MIMPs (Microbe-Induced Molecular Patterns). In 

the case of elicitors derived from wound/herbivory, it has been proposed 

to call these compounds WHIMPs (5). DAMPs are molecules 

that under normal circumstances are not found outside or on the cell 

surface, but when occurs cell disruption after injury or necrosis, their 

structure and localization are altered and they are perceived as danger 

signals and can initiate a defense response in plants (De Lorenzo et al. 

2011; Lotze et al. 2007; Seong; Matzinger 2004). 

There are several important intricate aspects about LRR-kinase 


105


receptors. Firstly, they are very similar in plant and animals, like the 

plant FLS2 and the human TLR5. Both of them activate innate immune 

responses after they perceive flagellin (21). On the other hand, PAMP 

receptors are multifunctional, such that the amino acid sequence of 

BRI1 is identical to the systemin receptor of tomato (5) but have different 

affinities in different species. The transmembrane LRR-kinase 

ERECTA from arabidopsis is responsible for mediating resistance 

against Ralstonia solanacearum and Plectosphaerella cucumerina, 

as well as in controlling normal plant developmental processes (22, 

23). Multifunctionality may indicate that the protein is a co-receptor, 

not the primary ligand receptor. Alternatively, the fact that more than 

one type of ligand is able to bind directly to the receptor with high 

specificity and this could reflect the nature and specificity of the other 

co-receptors within the complex (5). 

After PAMP recognition, MAPK cascades are activated, leading 

to the activation of transcriptions factors, continuing the defense 

process. A model of MAPK-signaling pathway involving fls2 (a component 

of bacterial flagella) recognition was proposed by Asai et al. 

(24). According to the authors, after the recognition of fls2 by the FLS2 

receptor, MEKK1 is activated and the signal is subsequently transferred 

to MKK4/5 following by MPK3 and MPK6, which activates WRKY22/ 

WRKY29 transcription factors. Nevertheless, how the MAPK cascade 



is exactly involved in the process remain uncertain (25). Furthermore, 

other cascades may also be activated in parallel (26, 27). 

3. WRKY transcription factors 

The WRKY domain is defined by the conserved amino acid sequence 

WRKYGQK at its N terminal end and a novel CX 4–5 

CX 22–23 

HXH 

zinc-finger-like motif (28, 29). Based on the number of WRKY domains, 

and on the characteristics of the zinc finger motif, these proteins 

can be classified into three groups. Group I represented by 

proteins with two WRKY domains, whereas group II is characterized 

by proteins with only one WRKY domain. Proteins belonging 

to both groups I and II generally have the same type of zinc finger. 

A minor portion of WRKY proteins have a different zinc finger 

pattern and belong to the group III (30), only 20% of the WRKY 

proteins in higher plants (31). 

WRKY factors have high binding affinity for the DNA sequence 

(C/T)TGAC(T/C) referred to as the W box. Structural analysis demonstrated 

that both the conserved cysteine and histidine residues in the 

C 2 

H 2 

zinc finger-like motif, and the conserved WRKYGQK sequence 

are essential for correct DNA-binding activity (32). 


107


More than 70 members belonging to WRKY family have already 

been identified in arabidopsis and more than 100 in rice (33, 

34). Among the group III type WRKY proteins found in arabidopsis, 

nearly all members respond to diverse biotic stresses (35-37). According 

to the same authors, the green alga Chlamydomonas reinhardtii 

has only one WRKY gene which encodes a protein that belongs to 

the group I (with two WRKY domains) and in the GenBank database 

there are WRKY group-I-like sequences found in two non-photosynthetic 

eukaryotes (Dictyostelium discoideum and Giardia lamblia). 

The authors conclude that the group I WRKY genes may represent 

the ancestral form and originated some 1.5-2 billion years ago in eukaryotes, 

before the divergence of the plant phyla (31). 

4. WKRY Function in plant defense 

Several studies have showed increased levels of WRKY mRNA, 

protein and DNA-binding activities in response to viruses (38), bacteria 

or oomycetes (39) fungal elicitors (28, 40) and signaling substances 

such as salicylic acid (41). Furthermore, pathogen-mimicking 

treatments show selective up regulation of similar genes in rice, potato, 

sugarcane, and chamomile [reviewed by Ulker & Somssich (41)]. 



These data present strong evidence for the role of WRKY proteins 

in the regulation of defense responses (42). According to Dong et al. 

(35), 49 out of 72 WRKY genes tested from arabidopsis were responsive 

to bacterial infection or SA treatment. Studies with WRKY70 

identified it as an important regulatory component in the cross-talk 

between SA and JA-signaling during plant defense (43). As WRKY70 

has no effect on SA or JA levels, the authors suggest that it is an activator 

of SA-responsive genes and a repressor of JA-responsive genes. 

A loss of function arabidopsis mutant for WRKY70 gene was more 

susceptible to the fungi Botrytis cinerea and Erysiphe cichoracearum. 

Interestingly, these plants also show susceptibility to the bacteria Erwinia 

carotovora and Pseudomonas syringae (44-46). AtWRKY33 

loss of function mutants were also more susceptible to infection by 

B. cinerea and Alternaria brassicicola (47). Alternatively, some studies 

have shown that WRKY subgroup IIa contains members can have 

both, positive and negative roles in plant defense. According to Xu et 

al. (48) Atwrky18/Atwrky40 and Atwrky18/Atwrky60 double mutants 

were shown to be more resistant to P. syringae DC3000 and at the 

same time more susceptible to B. cinerea infection. Another member 

of WRKY subgroup IIa was shown to suppress basal defense against 

Blumeria graminis in barley, in plants with knockdown by RNA interference, 

increased resistance to the fungus was observed (49). 


109


5. Effectors 

Gohre & Robatzek (6) reviewed a large number of microorganisms 

that grow in plant tissues and need to overcome the host defenses. 

To survive within the plant, microorganisms can secrete effector molecules 

into plant cells and interfere with individual defense responses. 

Thus, effectors are very important molecules for pathogen virulence. 

They are responsible for promoting penetration into host tissues, persistence 

inside the host tissue, suppression of immune responses, allowing 

access to nutrients, proliferation, and growth (6). 

Common features from well characterized effectors are used by 

plant pathologists to search for possible candidates secreted effectors of 

new and old pathogens. These candidates are normally small, secreted 

proteins, which are rich in cysteine and show no obvious homology to 

other known proteins (6). Secreted effectors reach their cellular target at 

the intercellular interface between host cells and the pathogen (apoplastic 

effectors) or inside the host cells (cytoplasmic effectors) (50, 51). 

Apoplastic Effectors: once plants recognize pathogens, there is 

up-regulation of several pathogen-related proteins, such as pathogenesis-related 

protein 1 (PR1), chitinases, glucanases and proteinases. 

As a counter defense, fungi, oomycetes and bacteria actively secrete 



several types of intercellular effectors; most of them are enzyme inhibitors 

that target PR proteins (52-54). 

Other types of apoplastic effectors are: Small cysteine rich proteins 

such as elicitins (55), Avr2, Avr4 and Avr9 (51), which contain 

disulfide bridges formed by pairs of cysteines, normally inducing defense 

responses. Necrosis and ethylene-inducing like proteins: NLP 

family proteins are 25 kDa proteins widely distributed among bacteria, 

fungi and oomycetes. They were originally described from Fusarium 

oxysporum and have the ability to induce cell death in many 

plant species (51). Oomycetes also possess GP-42 transglutaminases 

and cellulose binding elicitor like (CBEL) proteins that can trigger 

necrosis and defense gene expression (51). 

Cytoplasmic effectors: bacteria can delivery effectors into the 

host cytoplasm through specialized secretion systems (56-58). Most 

of bacterial intracellular effectors are part of a strategy of nonspecific 

kinase suppression (3). Knowledge on eukaryotic effectors is sparse 

in comparison to that available for bacterial effectors; oomycetes are 

known to deliver two types of intracellular effectors, the RXLR (59) 

and the Crinkler effectors (CRN like family) (60). 

The RxLR (arginine, any amino acid, leucine, arginine) motif 


111


was used to identify effectors in Phytophthora sojae, Phytophthora 

ramorum, and Hyaloperonospora parasitica based on draft genomes 

(61). This motif is also found in effectors from malaria, a human parasite 

(5). RXLR and CRN motifs are required for effector translocation 

across cell membranes. 

6. Action of Effectors 

Effectors are able to disturb specific targets in the host and disrupt 

specific processes in cells, mainly connected to host defenses, 

thus they have an important function in virulence (5). Numerous defense 

mechanisms in the host are induced by PAMPs. Studies have 

been carried out to understand the role of effectors and their molecular 

mechanisms in suppressing PAMP signaling. Transcriptome 

approaches were used to demonstrate the role of AvrPto in suppressing 

a wide variety of transcripts using a Type III Secretion System 

(TTSS)-deficient bacteria and transgenic expression of AvrPto (62). 

He et al. (63) demonstrated that AvrPto and AvrPtoB act upstream 

of mitogen-activates protein kinase (MAPK) signaling based on two 

pieces of evidence: Firstly, non-pathogenic Pseudomonas syringae 

strains but pathogenic P. syringae suppresses early PAMP marker-gene 

activation and secondly, transgenic expression of AvrPto in 



arabidopsis, blocks early PAMP signaling and enables nonhost P. syringae 

growth. AvrPtoB is a bipartite protein which the amino terminus 

contributes to virulence and the C-terminus may have a function 

in blocking host cell death (64, 65). Furthermore, Janjusevic et al. 

(66) suggests that AvrPtoB is involved in host protein degradation 

because the C-terminus domain folds into an active E3 ligase. Several 

other effectors are related to degradation of proteins that play a 

role in PAMP perception, signaling or defense reaction (6). The degradation 

can be achieved either via protease activity or by exploiting 

the plant proteasome degradation pathway (67). The effector YopJ 

from Yersinia, a member of the phytopathogenic bacteria effectors 

family called AvrRxv, is able to inhibit MAPK cascades by acetylation 

of phosphorylation-regulated residues on a MEK protein (68). 

Li et al. (69) observed that the effectors HopS1, HopAI1, HopAF1, 

HopT1-1, HopT1-2, HopAA1-1, HopF2, HopC1, and AvrPto (AvrPto1) 

were able to suppress flg22-induced expression of NHO1, a well 

characterized MAMP-induced gene. 

It is commonly reported that PAMPs induce cell wall–based responses 

that can be inhibited by numerous bacterial type III secretion 

system effectors [reviewed by Bent & Mackey (5)]. Cell wall appositions, 

also called papillae, are localized cell wall thickenings induced 

by bacteria and other pathogens at the site of infection. A useful marker 


113


is callose because it is deposited during PAMP induced papilla formation. 

During this process vesicle traffic becomes polarized and delivers 

cell wall reinforcing and antimicrobial components to the site of 

the pathogen infection (70). A variety of effectors are able to suppress 

PAMP-induced callose deposition. Small host G proteins positively 

regulate vesicle trafficking and the effector HopM1 induces proteasome-dependent 

degradation of multiple arabidopsis proteins, targeting 

the G protein activation of the guanine exchange factor (GEF) and 

thus affecting vesicle trafficking. As a result, there is an inhibition of 

pathogen-induced callose deposition (71). Vesicle trafficking and cell 

wall–based defenses are mechanisms of nonhost resistance used by 

plants against powdery mildew fungi (72, 73). 

Pathogens are able to mimic plant hormones such as phytotoxin 

coronatine, an effector that mimics jasmonate 12-oxophytodienoic acid 

(OPDA) – a precursor in the synthesis of jasmonic acid (JA), that effectively 

suppresses salicylic-acid-mediated defense against biotrophic 

pathogens (58, 74). Furthermore, stomatal opening is induced, which 

helps pathogenic bacteria to gain access to the leaf internal structures 

(75). On the other hand, gibberellin is produced by the fungal pathogen 

Gibberella fujikuroi, leading to ‘foolish seedling’ syndrome and many 

pathogens are able to produce cytokinin that can promote pathogen colonization 

through the retardation of senescence in infected leaves (76). 



7. R-Proteins 

R Proteins are products of R genes encoded by specific hosts that 

evolved to recognize effector molecules or their action. Most of these 

proteins have a leucine-rich repeat (LRR) domain (described previously) 

as part of intracellular NBS-LRR proteins that also carry a nucleotide 

binding site (NBS) and other conserved domains, as an extracellular 

LRR in transmembrane receptor-kinase proteins, or in “receptor-like 

proteins” that have an extracellular LRR and a transmembrane domain 

(4-6). The NBS domain contains sequences conserved in plant and animal 

proteins (77, 78). Studies with crystal structures of the NBS domains 

of C. elegans cell death protein (CED-4) and mammalian apoptotic 

protease-activating factor 1 (Apaf-1) show that an ATP or ADP is 

bound in a pocket that is generally buried in the NBS domain (79-81). 

Several studies show direct interaction between R proteins and 

Avr proteins, where the specificity is by physical interaction (82-84) 

and the LRR domains are responsible for determining this specificity 

(5, 6). Nonetheless, some examples show contributions affecting specificity 

in the N-terminal regions of NBS-LRR proteins (85-87). On the 

other hand, a number of examples show that the R proteins can monitor 

the integrity of the host proteins and are activated only in response to an 

alteration in these proteins, known as indirect detection (88-90). 


115


In general, the main domains of these proteins are highly conserved 

across the taxa most probably by natural selection for maintenance 

of their function. However, in some R proteins, the predicted 

solvent-exposed residues lie along a concave face and the LRR region 

shows a lower level of conservation. This probably happens as a 

consequence of selection pressure caused by changes in the pathogen 

forcing the plant to adopt new or alter existing interactions in this 

part of the R proteins, thus allowing the recognition of different or 

altered pathogen Avr proteins by direct detection (5). Consequently, 

these R proteins represent a flexible part of the plant immune system 

and good examples include the members of the R gene family 

L from flax. These genes evolve very fast by single mutations and 

small insertions or deletions. Furthermore, L genes can be altered 

through intragenic recombination generating variation in the length 

of the LRR region or by extragenic recombination to generate more 

or fewer R genes at a given locus (91, 92). According to Dodds et 

al. (93) the AvrL567 gene family from flax rust, a target for R gene 

mediated resistance, is very diverse, suggesting a gene-for-gene arms 

race where Avr alleles evolve to escape host detection and new plant 

R genes emerge to detect these new Avr proteins (5) resulting in a 

co-evolutionary conflict where evolutionary selection favors resistance 

in plants and virulence in their pathogens (83). 



A plethora of other R genes possess a highly conserved structures 

and their action is in response to alterations in host metabolism or proteins 

caused by the pathogen and not a direct gene to gene association 

as in the classic avr-R gene interaction. As these R genes act via the 

indirect or “guard” mechanism (5), they are involved in the detection 

of alterations in highly conserved host proteins and as such need to 

maintain their structure to maintain their function, limited diversity. 

Arabidopsis RPM1, RPS2, and RPS5 NBS-LRR-type R proteins are 

able to detect changes in host proteins caused by Avr proteins of Pseudomonas 

syringae [reviewed by Dodds et al. (83)]. Furthermore, arabidopsis 

proteins RPS5 and PBS1 are required to detect P. syringae 

effector AvrPphB where RPS5 is a NBS-LRR protein and RPS1 is a 

protein kinase with unknown target (94-96). PBS1 protein is able to 

interact with both AvrPphB and RPS5, and in consequence, there is 

formation of a multimeric protein complex. AvrPphB is a cysteine protease 

and is responsible for cleavage of PBS1 at a specific site. RPS5 

detects the pathogen effectors AvrPphb by monitoring PBS1 integrity 

(97). In tomato the Prf gene codes for an NBS-LRR protein which 

is responsible for conferring resistance to P. syringae in strains that 

expresses the effector AvrPto or AvrPtoB. These two effectors are responsible 

for targeting the tomato protein kinase Pto. However, Pto 

associates with the N-terminal domain of the Prf protein outside of the 


117


NBS-ARC-LRR domain and it is this complex that is the regulatory 

switch that controls immune signaling. Both Pto and Prf participate in 

the specific recognition of AvrPtoB and that the principal role for Pto 

in immune signaling is the regulation of Prf (Mucyn et al. 2006). 

8. From pathogen recognition to defense activation 

The most common type of R protein is the NBS-LRR where the 

nucleotide binding domain must be functional for these proteins to confer 

disease resistance (5). DeYoung & Innes (97) proposed a model 

to explain pathogen recognition that allows defense activation by both 

indirect and direct recognition (Fig. 1). Proteins that are targeted by 

pathogens are normally present in a complex with at least one plant 

NBS-LRR protein in which the amino-terminal domain of the R protein 

is responsible for this interaction. In a normal conformational configuration, 

with or without the pathogen host target protein, the amino-terminal 

domain interacts with NBS and the LRR domains. As a result 

of these interactions the protein is maintained in a compact structure 

which is probably responsible for maintaining the NBS-LRR protein 

without activity. In the presence of the effector a conformational change 

is induced in the structure of NBS-LRR protein (for the direct recogni- 



tion) or modification of the host target protein (for indirect recognition). 

As a result, there is an exchange of ADP for ATP altering the structure 

of the NBS domain effectively altering the structural arrangement of 

the NBS-LRR domains. As a consequence of the exchanges described 

above there is the creation of new binding sites for downstream signaling 

molecules resulting in the activation of the plant defense response 

(97) often culminating in the typical hypersensitive response (HR). 


119


Figure 1. Proposed model for the protein structure of a full-length 

NB-LRR. Electrostatic interactions hold the NB-ARC domain 

and the N-terminus of the LRR domain together in a closed conformation. 

The C-terminus of the LRR domain extends from the 

folded protein like an antenna and functions in pathogen sensing. 

The CC/TIR domain interacts with both the NB-ARC and LRR 

domains, resulting in a tightly folded, auto-inhibited protein able 

to respond to pathogen invasion. Upon direct or indirect pathogen 

perception, the LRR domain transduces a signal which lifts 

auto- inhibition from the NB-LRR protein. The inactive, ADPbound 

NB-ARC domain becomes ATP- bound, and the protein 

adopts an open, active conformation, leaving the NB-LRR able 

to establish defense signaling. 



9. Conclusion 

An overview of the main players at the molecular plant-microbe 

interaction is shown in figure 2. 

Understanding the effectors attack strategy of pathogens is fundamental 

for plant pathologists. Successful pathogens have the ability 

to manipulate host recognition and responses. Recognition of pathogens 

effectors may give plants advantage in the war against invading 

microbes. The search and breeding plants possessing R-genes can be 

a powerful and extremely useful tool to protect crops. A new field 

of study in plant pathology, called effectoromics, uses effector molecules 

as probes to identify R-genes. Normally, a pool of core effectors 

(i.e. effectors that are fundamental for pathogen fitness) is tested over 

several germplasms. If there are defense responses, such as HR, it 

means that the germplasm tested possess a specific R-gene that recognizes 

the core effector. This identified R-gene automatically becomes 

a target for traditional and biotech breeding strategies. Some other 

technologies, such as RNAi, Host Induced Gene Silencing (HIGS), 

co-immuniprecipitation, nanobiotechnology, and genome editing are 

also likely to target MAMPs, PRRs, Effectors and R-genes to weaken 

pathogens and strengthening plant immunity, ultimately favoring 

more sustainable crops. 


121


Figure 2. General overview of the molecular plant-microbe interaction: 

A: PAMPs/MAMPs are detected by the plant PRRs initiating 

PAMP-triggered immunity (PTI) signaled by the Kinase 

Cascade that leads to regulation of gene expression by WRKY 

transcription factors. B: Successful pathogens deliver effectors 

which block the immune response leading to ETS. C: One effector 

(red ball) is recognized by a plant R protein activating ETI, 

restoring plant resistance. D: An adapted pathogen, which has developed 

new effectors (yellow balls) is selected and suppresses 

ETI, leading to ETS. E: Plants are selected that have evolved R 

proteins which can recognize the new effectors, ETI is again established 

producing a resistant plant. 



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5Understanding 

How a Resistant 

Host Responds to 

Xylella fastidiosa 

Infection: Lessons 

from Model Plants


5Understanding How a Resistant Host Responds to 

Xylella fastidiosa Infection: Lessons from Model Plants 

Raquel Caserta; Willian Eduardo Lino Pereira; Reinaldo Rodrigues 

de Souza Neto; Diogo Maciel Magalhães; Alessandra 

Alves de Souza. 


Since Xylella fastidiosa became a problem for Brazilian citrus growers, 

the scientific community has been trying to develop new attempts for 

resistance against this pathogen. X. fastidiosa causes Citrus Variegated 

Chlorosis (CVC) in all sweet orange varieties. This is a xylem limited 

bacterium transmitted by sharpshooter vectors; its growth inside xylem 

Understanding How a Resistant Host Responds to Xylella fastidiosa Infection: Lessons from Model Plants 

147


vessels impairing regular water flow, causing several damages to plant development. 

Besides citrus, strains of X. fastidiosa are been related to cause 

disease in different cultures around the world such as grape, coffee, plum, 

almond and more recently olive. In Brazil the main worry is about citrus 

culture since the country is a leader in global exports of orange juice, being 

responsible for over 60% of world production (USDA, 2012). Nowadays, 

the citrus industry has been facing a shrinking due to different phytosanitary 

problems, including CVC, Citrus Canker and more recently HLB. 

Plants showing CVC display symptoms such as water stress and reduction 

in fruit size which directly affect the orange juice production. 

Programs of citrus breeding are urgent to try to raise more resistant 

varieties to different diseases, but this scenario is difficult regarding orange 

trees. First because there is a deep need in previous studies to help 

finding good target genes for plant breeding and second because citrus 

transformation is a long-time method, which impairs the fast choice of 

potential genes to be used as markers or to transgenic approach. 

The knowledge of bacteria/host interactions is a valuable starting 

point in the search of potential genes involved with resistance. To assess 

these genes we made use of mandarin (C. reticulata Blanco) that is known 

to be resistant to CVC and shares agronomical characteristics with sweet 

148 Understanding How a Resistant Host Responds to Xylella fastidiosa Infection: Lessons from Model Plants


orange. Recent research developed by our team has shown that the resistance 

could be related to active genetic defense responses. EST libraries 

and RNA-seq were used to identify the modulated genes during different 

stages of X. fastidiosa infection in sweet orange and mandarin. Based 

on this analysis and other biological experiments we made a hypothetical 

model to explain the defense response of X. fastidiosa in a resistant host. 

This model involves perception, signal transduction and activation of defense-related 

genes. Aiming the expansion of our knowledge about the biological 

role of these genes we started using the model plants Arabidopsis 

thaliana and Nicotiana tabacum overexpressing or silencing homologues 

genes found in our gene expression analysis.In this chapter, results of generation 

of model plants transformed with genes possibly involved in defense 

against X. fastidiosa will be shown and discussed. This tool is highly 

important for researches aiming generation of resistant plants. 

2. Global gene expression to understand the resistance of tangerines 

to X. fastidiosa and selection of candidate genes for genetic 

transformation of sweet orange 

Although X. fastidiosa is a problem to sweet orange plants (C. 

sinensis), tangerines (Citrus reticulata) and some of their hybrids with 


149


sweet oranges display resistance or tolerance to X. fastidiosa and it was 

demonstrated that this feature is related to genetic basis, and the expression 

of some genes may be associated with the resistance response 

(Coletta - Filho et al., 2007; De Souza et al., 2007b). Extensive works 

of expressed sequence tags generated a large EST database, the CitEST 

that contains 30,000 unigenes sequences and is extremely helpful to understand 

the resistant/susceptible plant molecular interactions involving 

X. fastidiosa (De Souza et al, 2007a, 2007b, De Souza et al., 2009). 

X. fastidiosa can briefly survive in tangerines after mechanical inoculation, 

but bacterial population strongly reduces after some weeks. This 

fact is followed by the expression of a set of genes related to resistance, 

from genes that encode receptor proteins to those involved in classical 

defense pathways like auxin (AUX), ethylene (ET) and jasmonate (JA) 

in initial stages of infection, and later on, genes involved in salicylic 

acid (SA) pathway (De Souza et al., 2007b; Rodrigues et al., 2013). 

In resistant host, it is suggested that X. fastidiosa is recognized in the 

primary xylem, and its recognition triggers a response able to increase 

lignification of such vessels trapping bacteria in this region and impairing 

their spread to secondary vessels. In susceptible hosts, on the other 

hand, is possible to verify bacteria colonization in both primary and 

secondary xylem vessels (Niza et al., 2015). 



Identification of up regulation of genes associated with plant 

pattern-recognition receptors (PRR) suggests that mandarin may 

be recognizing bacterial pathogen-associated molecular patterns 

(PAMPS) and/or damage-associated molecular patterns (DAMPs) resultant 

of degrading cell wall and as a consequence activate PAMP/ 

pattern-triggered immunity (PTI). These recognitions together with 

over expression of genes related to cell wall biosynthesis and activation 

of AUX, ET and JA response pathways suggests that, initially, X. 

fastidiosa may be recognized as a necrotrophic microorganism (Rodrigues 

et al., 2013). Not only AUX, ET and JA take part of defense 

in tangerines, SA pathway is also required in later stages of infection, 

inferred by the expression of SA related genes after 21 days of 

infection. Phenolic compounds and reactive oxygen species are also 

present in defense response against X. fastidiosa in tangerines. These 

results suggest that initially the defense response is modulated as a 

necrotrophic attack, indicated by the AUX, ET and JA activation, but 

switches to a biotrophic pathogen signaling with requirement of SA 

pathway to kill bacteria (Rodrigues et al., 2013). The finding of a set 

of genes from tangerines involved in resistance against X. fastidiosa 

could determine some targets for sweet orange transformation aiming 

the development of more resistant plants. 


151


Besides prospection of genes related to defense in resistant 

host, the knowledge of bacterial behavior in plant and its peculiarities 

can raise information about candidate genes from the pathogen 

that could also be used in sweet orange transformation. Pathogen-derived 

resistance (PDR), thus, is another approach to be evaluated in 

sweet orange, using as candidate genes that could alter bacterial behavior 

and as a consequence its pathogenicity. Studies have shown 

that a fatty acid produced by X. fastidiosa and used as a molecular 

signal in intercellular communication, called quorum sensing (QS), 

would be interesting to change the bacterial colonization in plants. 

Since the perception of the DSF, the QS molecule, leads to increased 

adhesion characteristics and less movement of X. fastidiosa cells the 

efficiency of host colonization and transmission by insect vectors 

reduces (Chaterjee et al., 2008), alter communication through QS 

would be a possibility to reduce the development of disease caused 

by X. fastidiosa (Lindow et al., 2014). Population growth can also be 

exploited as a possibility of disease control. Growth rates are altered 

under stress conditions due to different mechanisms such as biofilm 

formation, efflux pumps and induction of toxin-antitoxin (TA) systems. 

This last one was related to reduction of population growth to 

persist to high cooper concentrations (Muranaka et al., 2012). The 

activation of a gene encoding a toxin able to kill bacteria under stress 



condition revealed a possible target for plants transformation aiming 

control of bacterial population. 

All results about defense against X. fastidiosa in tangerines combined 

with features of bacterial communication and growth were enabled 

to make a hypothetical model to study the candidate genes that 

could increase defense in transgenic plants. As the response in tangerines 

seems to start by the recognition of cell wall degrading residues, 

that activate AUX, ET and JA pathways, genes that encode recognition 

proteins would be interesting options. Besides these, candidate 

genes encoding transmembrane receptor proteins possibly involved in 

bacteria perception of PAMPS are other ones to be included. The introduction 

of the gene that encodes a CC-NBS-LRR protein involved 

in recognition of an unknown effector, and possibly activating effector-triggered 

immunity (ETI), would be another strategy, as well as 

genes belonging to SA pathway, shown to be required at later stages of 

infection in tangerines. Resistant host could be the source of such candidate 

genes. On the other hand, genes that modulate bacterial behavior 

are also an interesting approach, like rpfF or the one involved in toxin 

production. Possibilities of enhancement of tolerance in sweet orange 

through the introduction of defense genes from resistant host or genes 

able to change bacterial behavior are shown in Figure 1. 


153


Figure 1. Hypothetical model of defense in tangerines and possible 

candidate genes for sweet orange transformation aiming increase 

in tolerance to X. fastidiosa. First line of defense is represented by 

recognition of cell wall degraded patterns that trigger immunity 

related to AUX and JA. After bacterial population growth, perception 

of PAMPs and a possibly effector trigger response related to 

SA pathway. Other genes involved with expression of Miraculin, 

P450, Pathogen-related proteins (PR) and phenolic compound 

could be candidates for transformation. Besides genes from plants, 

PDR would also be used as a strategy of tolerance enhancement. 

The over expression of rpfF would increase DSF amounts, leading 

to reduction in colonization efficiency or toxin genes. 



3. Model plants as a tool for evaluating candidate genes and 

their response against X. fastidiosa 

The main goal of transformation aims increase in tolerance regarding 

sweet orange plants; however, the real contribution of candidate 

genes in this process is unknown. Citrus transformation is not 

an ordinary technique and evaluation of transformants is a long time 

methodology. Obstacles like escapes, rooting, grafting, long crop cycle 

and response variations between varieties difficult a lot the transformation 

citrus process (Peña et al., 2001). Additionally, the analysis 

of all the candidate genes in citrus is impracticable. Model plants play 

a fundamental role in assisting the choice of candidate genes for citrus 

transformation, due to their short life cycle, easy transformation 

process, sequenced genome and small size, making the evaluation of 

bacterial challenge responses faster. 

Considering the different options, there are possibilities to work 

with model plants containing homologs of the defense candidate genes 

knocked down, which could help assessments of their role in resistance 

characteristics. Moreover, model plants could be developed overexpressing 

these defense candidate genes. In addition, overexpressing is the only 

alternative when there is no homologue of defense genes, such as the RDP 

approach, where genes of X. fastidiosa could be used for transformation. 


155


4. Lessons learnt using Arabidopsis thaliana as a model plant 

X. fastidiosa colonization in A. thaliana was already reported 

by Rogers (2012) using Temecula strain, that cause Pierce Disease in 

grapevines. According to this work, it was not possible to see symptoms 

in infected A. thaliana plants, but electron microscopy and bacterial 

quantification through quantitative isolation of viable cell and qPCR 

methods revealed colonization of xylem vessels. Furthermore, Tsu-1 

and Van-0 ecotypes were more tolerant than Col-0. 

Aiming to establish this model system with strains that cause CVC 

the three A. thaliana ecotypes first evaluated by Rogers were inoculated 

with 9a5c (that cause CVC) and symptoms and bacterial population 

were evaluated by qPCR and fluorescence microscopy. Different from 

Temecula, 9a5c colonized the ecotype Col-0 more efficiently, indicating 

some specificity degree or host preference between the ecotypes and 

bacterial strains. Considering the amount of bacteria, A. thaliana infected 

with the CVC strain showed approximately 10 times less bacteria in 

relation to plants infected by grape strains. Col-0 was thus selected as 

the preferential ecotype for CVC strain studies (Pereira, 2014). 

According to the model previously presented, there are numerous 

possibilities to assess the contribution of response pathways or defense 



genes against X. fastidiosa. Whereas A. thaliana was established as a 

good model and there are many available genetic resources to this plant, 

a screening through the challenge of mutant plants was carried out to 

evaluate the role of some specific genes and different defense pathways 

against X. fastidiosa. For this, mutants of erf73 and rap2.2 related to 

ethylene pathway (Singh et al., 2002), rd22 and moa2.2 related to detoxification 

of reactive oxygen species (ROS) (Abe et al., 1997; Gillet 

et al., 1998; Apel & Hirt, 2004), rps5 a homologue of NBS-LRR that is 

involved with PAMP recognition and pseudoNBS belonging to the class 

of CC-NBS-LRR phylogenetically close to RPS5 (Tan et al., 2007; 

Boller & Felix, 2009) and atj2 responsible for tolerance to salt stress in 

A. thaliana (Zichang et al., 2010) were evaluated. The mutants used in 

this screening are listed in the table below. 

Homozygous of the chosen mutants were infected with the CVC 

11399-GFP strain previously transformed with a green fluorescent protein 

(GFP) (Niza et al., 2015) and among all mutants rap2.2 and rps5 

presented more bacteria than WT control plants according to qPCR 

evaluations of population, suggesting a possible involvement with the 

tolerance to the pathogen. Other two mutants, moa2.2 and atj2, presented 

fewer bacteria than the wide type control suggesting that they are 

possibly involved with the susceptibility to the bacterium (Figure 2). 


157


Table 1. SALK mutants of A. thaliana for the homologue genes 

from C. reticulata candidates to confer resistance to X. fastidiosa. 

Mutant ID Gene ID Gene 

T-DNA 

insertion 

SALK_146066C 

At5g25610 

rd22 

90pb 

SALK_127201C 

At1g12220 

rps5 

73pb 

SALK_039484C 

At1g72360 

erf73 

84pb 

SALK_010265C 

At3g14230 

rap2.2 

84pb 

SALK_071563C 

At5g22060 

atj2 

88pb 

SALK_051930C 

At3g14420 

moa2.2 

84pb 

SALK_080562C 

At4g14610 

pseudoNBS 

94pb 

Source: The Arabidopsis Information Resource (TAIR, 2013). 



3.5x10 6 

3x10 6 

2.5x10 6 

* 

* 

Cells/g of tissue 

2x10 6 

1x10 6 

* 

5.5x10 5 

* 

3.5x10 4 

WT rps5 pseudonbs rap2.2 moa2.2 atj2 erf73 rd22 

Figure 2. X. fastidiosa quantification in A. thaliana mutants after 

two weeks of inoculation. Asterisks above the bars indicate statistical 

difference in relation to the wild type control using ANOVA 

one way followed by Tukey test (p


Besides quantification of bacterial population, X. fastidiosa colonization 

in xylem vessels was assessed by fluorescence microscopy, 

looking for the bacterium GFP emission on the petioles of the plants. 

The two mutant lines, rap2.2 and rps5, that showed higher colonization 

by X. fastidiosa also displayed petioles with higher emission of 

GFP, indicating that they were more colonized when compared to the 

wild type. The expression of rap2.2 and rps5 in both mutant lines was 

assessed by RT-qPCR. Results showed that both genes have a knockdown 

level of expression in their respective mutants (Figure 3A). The 

same rap2.2 SALK mutant used in this evaluation was already analyzed 

by Zhao et al., (2012), who also did not found knock out, but a 

knock down level of approximately 75% in relation to the expression 

of WT control plants. It is not known, however, if the resultant protein 

is functional or not after the insertion of the T-DNA in the nucleotide 

sequence, but even though, their respective genes had been less 

transcribed into RNA. Taken together, qPCR and microscopy results 

corroborate to assume that rap2.2 and rps5 genes really could play a 

role in the tolerance of the plant. 

The possible involvement of both genes rap2.2 and rps5 in defense 

against X. fastidiosa is possibly due to their participation in the 

same pathway of response, acting however, at different times. Attack 

recognition would trigger rps5 overexpression and rap2.2 would work 



in the defense signaling. This hypothesis is supported by the fact that 

these genes display cross- regulation, once A. thaliana rps5 mutants 

exhibited reduced expression of rap2.2 and rap2.2 A. thaliana mutants 

exhibited rps5 less expressed (Figure 3B). 

Results with A. thaliana model plants support the hypothesis that 

rap2.2 and rps5 are involved in defense pathway against X. fastidiosa 

infection. The possible role of recognition and ethylene response 

against this pathogen is supported by bacterial population increase in 

mutant lines and their cross- regulation in expression. 

Further tests with A. thaliana showed another possibility besides 

rps5 in recognition role. Once the synthetic elf18 epitope from X. fastidiosa 

EF-Tu N-terminal shows an elicitor activity in Arabidopsis cell 

cultures (Kunze et al., 2004) the efr-1 mutant, which is insensitive to 

EF-Tu, was also challenge with X. fastidiosa. Col-0 WT and the homozygous 

line efr-1 mutants (Alonzo et al., 2003), acquired from Arabidopsis 

Biological Resource Center (ABRC), were inoculated with the 

same 11399-GFP strain mentioned above. Population of X. fastidiosa 

was evaluated by qPCR and fluoresce microscopy and results showed 

an increase from 5 to 10 times more cells in efr-1 mutants, when compared 

to WT, evidenced by a higher GFP emission in petioles of such 

plants under microscopy analysis (Figure 4). 


161


A 1 

0,9 

0,8 

0,7 

0,6 

0,5 

0,4 

0,3 

rps5 

* 

rap2.2 

* 

B 1 

0,9 

0,8 

0,7 

0,6 

0,5 

0,4 

0,3 

rap2.2 rps5 

* 

* 

AtRps5 

AtRap2.2 

Figure 3. Evaluation of gene expression in A. thaliana mutants. A. 

Relative gene expression of rps5 and rap2.2 in the mutants compared 

to wild type. B. Cross gene expression in the mutant lines. 

Actin was the endogenous gene used to standardization. Wild type 

(WT) was established with value 1 for comparisons. Asterisks 

above the bars indicate statistical difference in relation to the wild 

type control. The statistical analysis was done by T test using at 

least five plants for each mean. 



Log Xf Cells /mg of fresh tissue 

6 

5 

4 

3 

2 

1 

0 

* 

* 

efr-1 WT efr-1 WT efr-1 WT 

7 DAY 14 DAY 21 DAY 

Figure 4. A. Population of X. fastidiosa in A. thaliana efr-1 mutant 

lines and WT plants after 7, 14 and 21 DAI in WT. 

Besides the higher difference in population increase was seen at 

14DAI, an evident phenotypic change was displayed by efr-1 mutants 

at 7 DAI that did not occurred in WT plants. Chlorotic leaves were 

more abundant in mutants in this initial stage of infection, suggesting 

that EFR is important in recognition of X. fastidiosa in the beginning of 

infection and its absence allowed the multiplication of the bacteria due 


163


to the absence of an efficient PTI triggering. This lack of PTI response 

was demonstrated after evaluation of reactive species of oxygen (ROS) 

production in efr-1 and WT leaves after 24 hours (HAI) and 7 days of 

inoculation. The histochemical staining detection of 3,3’ diaminobenzidine 

(DAB) relative to oxidative burst was stronger in WT than in 

efr-1 leaves at 24 HAI. At 7 DAI, ROS was still observed in WT but no 

reaction was observed in efr-1 infected plants (Figure 5). 

According to these results the absence of EFR in efr-1 mutant 

impaired the perception of X. fastidiosa and consequently the activation 

of the defense response. The efr gene is thus a key candidate gene for 

further citrus transformation. 

5. Results using Nicotiana tabacum as a model plant 

The use of N. tabacum as a host for X. fastidiosa is a widespread 

technique (Andreote et al., 2006; Chatelet et al., 2011; Dandekar et al., 

2012; De La Fuente et al., 2013; Francis et al., 2008), especially because 

this plant shows symptoms in a shorter time than perennial cultures 

affected by the bacterium. Tobacco has been used since it was 

reported as host to X. fastidiosa (Lopes et al., 2000), but the lack of a 

quantitative evaluation method of the symptomatology did not allowed 



evaluations of severity of disease after inoculation. 

A 

efr-1 

Wt 

Mock 

inoculated 

7 DAI 

B 

efr-1 

Wt 

Non inoculated 

Mock inoculated 

24 HAI 

7 HAI 

Figure 5. Comparative response of WT and efr-1 leaves after challenge 

with X. fastidiosa. A. Chlorosis displayed by infected efr-1 

mutants at 7DAI. B. Histological detection of hydrogen peroxide 

(DAB stainig) efr-1 and WT at 24 HAI and 7 DAI. 


165


Aiming to better evaluate symptomatology in tobacco and consequently 

improve genetic functional analysis, a diagrammatic scale 

was developed based on the affected area of the leaves (Figure 6) 

(Pereira, 2014). This scale has been validated following the evaluation 

of five observers. After validation, the scale showed increased 

precision, accuracy and reproducibility of symptom severity analysis. 

After the establishment of diagrammatic all symptoms severity assessments 

could be performed quantitatively. 

1 

2 3 4 5 6 

0,6% 

4,5% 11% 18% 28% 45% 

Figure 6. Levels of the diagrammatic scale to evaluate severity 

symptoms on tobacco. Each leaf represents one level of severity 

and percentages below the leaves represent the area affected by 

the symptom. Numbers 1 to 6 are the scores given according to 

symptoms. 



Since X. fastidiosa can effectively colonize tobacco enabling assessments 

of the population development and symptoms, it is considered 

a susceptible model to our studies. 

5.1. N. tabacum as model for overexpression defense 

candidate genes 

One of the candidate genes chosen in the resistance model is a chaperone 

involved in drought stress and pathogen response in many cultures. 

This gene was selected mainly because its role increasing drought stress, 

that is one of the most important consequences of X. fastidiosa infection in 

plants. Our hypothesis was that suppressing the effects of drought stress, the 

plant could tolerate bacteria. Tobacco plants overexpressing this chaperone 

were challenged against X. fastidiosa and symptomatology was evaluated 

by severity analysis and quantification of bacterial population by qPCR. 

Results showed no statistical differences between the treatments. 

Severity analyses corroborate with bacterial quantification and in both 

WT and transgenic plants displayed no difference in response to X. fastidiosa 

infection, indicating that this chaperone is not directly involved 

in the resistance to X. fastidiosa (Figure 7). After these tests, it was possible 

to conclude that this candidate gene does not fit to our resistance 

model and is not a promising gene for further citrus transformation. 


167


A 

5 

Level average 

4 

3 

2 

1 

0 

a a a 

WT AS CH 

B 9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

Log cells/g of tissue 

a a 

a 

WT AS CH 

Figure 7. Analysis of symptoms and bacterial population in transgenic 

tobacco. White bars indicate WT controls. Gray bars indicate 

antisense (AS) mutant plants for the chaperone. Black bars 

indicate transgenic plants overexpressing (CH) the chaperone. A. 

Severity analysis of plant leaves. B. Quantification of X. fastidiosa 

in tobacco petioles by qPCR. Equal letters above the bars indicate 

no statistical significant difference by ANOVA one way followed 

by Tukey test (p


5.2. N. tabacum as model for PDR approach 

Since studies revealed peculiarities of X. fastidiosa communication 

behavior and growth with its relation to pathogen colonization, 

genes involved in these processes would be interesting targets for model 

plant transformation. 

One interesting feature of these bacteria is its ability to communicate 

each other through a fatty acid named DSF. Intercellular communication 

does not occur only in X. fastidiosa; however the DSF molecule 

used in this process is exclusive and synthesized by a gene named rpfF. 

Overproduction of DSF by grape transgenic plants showed a reduction 

of X. fastidiosa virulence in grape, after mechanical inoculation (Lindow 

et al., 2014). This suggests that DSF would be considered and avirulence 

signal, once promoted reduction in colonization efficiency in that host. 

In this way, the production of tobacco plants overproducing DSF might 

help to evaluate the possible impairment of bacterial colonization in plant 

and reduction of disease symptoms caused by X. fastidiosa. To test this 

hypothesis, tobacco plants were transformed with rpfF (responsible for 

DFS synthesis) isolated from 9a5c strain and cloned under the strong 35S 

promoter. In independent experiments using three generations of transgenic 

plants it was observed that transgenic plants displayed significant 

symptoms reduction compared with WT plants. The severity of symp- 


169


toms showed by WT plants was higher than showed by transgenic plants, 

according to scores given after three months of infection. Another feature 

observed was the increased symptoms of water stress in WT plants when 

compared with transgenic ones, which showed healthier appearance in 

all experiments. Severity of symptoms is closely related to the ability 

of xylem colonization and assessing bacterial population along different 

points in WT and transgenic plants showed that bacterial movement and 

colonization was occurring more efficiently in WT ones (Figure 8). 

A 

C 

B 

T-rpfF 

WT 

T-rpfF 

WT 

Figure 8. Tobacco experiments for evaluation of rpfF contribution 

against X. fastidiosa attack. A. Necrotic spots characteristics of X. 

fastidiosa symptoms in tobacco. B. Comparison of transgenic (to 

the left) and WT (to the right) symptoms in leaves closer to the 

point of bacterial inoculation. C. Comparison between general aspects 

of transgenic (to the left) and WT (to the right) plants. 



D 

Severity notes 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

WT 

Severity of symptoms in tobacco plants 

* * 

1AB2 1AB3 1AB4 1AB10 2AB3 

Group of transgenic plants evaluated 

E 

N of Xf cels/mg of plant tissue (log) 

X. fastidiosa moviment along tobacco plants 

6 

5 

4 

3 

2 

1 

0 

15 30 45 65 

Distance from the point of inoculation (cm) 

45 cm 

15 cm 

65 cm 

30 cm 

WT 

Transg 

Figure 8 (continued). D. Scores of severity of symptoms given to 

WT and groups of transgenic plants evaluated. E. Bacterial population 

assessed by qPCR in WT (blue) and transgenic plants (orange) 

in different points of collection. F. Schematic representation 

of heights in which leaves were collected for assessment of bacterial 

movement. Asterisks above the bars indicate statistical difference 

in relation to the wild type control. The statistical analysis 

was done by T test using at least ten plants for each mean. 


171


Model plant transformation with rpfF showed that the overproduction 

of an intercellular communication molecule was able to 

alter X. fastidiosa behavior, interfering with its efficiency of colonization 

in this host. Tobacco transformation and successive challenge 

and evaluation of symptoms showed that the strategy of using rpfF 

was promising to transform citrus plants aiming CVC resistance 

(Caserta, R. 2014). 

Besides quorum sensing, control of bacterial population growth 

is also an interesting approach aiming plant resistance to pathogens. 

It is known that X. fastidiosa has toxin-antitoxin (TA) system able 

to exterminating part of the population under stress conditions (Muranaka 

et al., 2012). This is a feature used as an adaptation to adverse 

environmental conditions aiming survival and multiplication when 

conditions become favorable again. TA systems are constituted by 

two genes in operon: a stable toxin which presents a lethal function to 

cell and the labile cognate antitoxin that binding on the toxin avoiding 

its lethality. On regular growth conditions both toxin and antitoxin 

are produced, and with the binding of them the bacterial population 

grows normally. On stress conditions, the antitoxin is easily degraded 

and the toxin exerts its lethal function killing the cell. Thus, the development 

of plants in which the bacterial growth would be hindered by 



high amounts of toxin was another PDR approach tested by our team. 

For this, the candidate gene xf2490, previously shown to be induced 

under high concentrations of copper (Muranaka et al., 2012), was chosen 

for overexpression in tobacco transformed plants. Challenge with 

9a5c strain in three different generations of transgenic plants showed 

a reduction in the number of symptomatic leaves in transgenic plants, 

which ranged from 30-50%, when compared to WT that showed an incidence 

of 75% of leaves with symptoms. Also, severity scores given 

to transgenic leaves was lower than those attributed to WT and both 

results are, possibly, due to a reduction in bacterial viability caused by 

overproduction of toxin by the plants (Figure 9). Altered bacterial viability 

probably interfered with X. fastidiosa colonization in transgenic 

plants and consequently attenuated its virulence. 

Results regarding the overexpression of two genes from the 

pathogen showed that both were good candidates for citrus transformation, 

since X. fastidiosa infection in transgenic plants containing 

such genes seemed to be somehow impaired to efficiently colonize 

the plants and consequently decrease the symptoms severity. Tobacco 

was a good model for challenging and its fast life cycle allowed 

robustness of results, since experiments were carried out in different 

generations of transgenic plants yielding comparable results. 


173


A 

B 

2.5 

Score of Severity 

2 

1.5 

1 

0.5 

* 

** * * 

0 

WT 

8 12 15 26 32 

Transgenic Plants 

Figure 9. Results obtained after challenge of tobacco plants overexpressing 

xf2490, a toxin coding gene. A. Differences between 

symptoms in a WT (to the left) and a transgenic (to the right) plant. 

B. Severity scores given to symptoms of WT and transgenic plants. 

Asterisks above the bars indicate statistical difference in relation 

to the wild type control. The statistical analysis was done by T test 

using at least ten plants for each mean. 



6. Concluding remarks 

The advance in knowledge regarding plant – pathogen interactions 

revealed peculiarities of defense response in resistant hosts against X. 

fastidiosa. All the possibilities involving the participation of key genes 

in the defense process however would still be completely unknown without 

studies of their real contribution in an alternative host. Model plants 

showed to be essential tools for screening of candidate genes aiming future 

development of sweet orange plants more resistant to X. fastidiosa. 

After transformation and analysis of a range of A. thaliana plants 

to try to understand which genes were possibly involved in recognition 

of the pathogen and which defense pathway was rather triggered, 

some lines started to be clearer. First, A. thaliana receptor mutant lines 

showed that possibly resistant plants might recognize X. fastidiosa 

through PAMP and also an unknown effector. This is suggested by results 

regarding increasing in bacterial population in plants where receptor 

proteins homologues to EFR and NBS-LRR were absent. Moreover, 

lack of oxidative burst in EFR mutants reinforces this trend. Besides, it 

seems that the pathway preferably activated for the defense is the one 

involving ethylene, since the mutants of a transcription factor for this 

pathway in A. thaliana displayed the same phenotype of increase in 

bacterial population. The use of model plants was also helpful in the 


175


knowledge of genes that are not directly involved in defense against 

X. fastidiosa, as showed by A. thaliana mutant of a gene related to detoxification 

of reactive oxygen species, and a tobacco overexpressing 

a gene involved with tolerance to drought stress. In both cases, symptoms 

displayed by bacterial infection did not differ from WT plants. 

Regarding tobacco, it showed to be a very good model in cases where 

PDR was the focus. In both cases when tobacco was transformed with 

genes chosen from X. fastidiosa genome, it was possible to evaluate 

symptomatology and bacterial colonization in different generations of 

the transgenic plants obtained. 

All results obtained in experiments with model plants allowed us 

to make a more refined insight into the possibilities of use candidate 

genes to citrus breeding. Among the possibilities of candidate genes for 

citrus transformation would be preferably those involved with recognition 

of PAMPs and those able to quickly activate the ethylene response 

pathway. In accordance with previous results, genes involved in oxidative 

stress response would not be a priority. Furthermore, the possibility 

to change the behavior of X. fastidiosa through the use of genes from its 

own genome seems to be a promising alternative. With the knowledge 

of this new information, another model of tolerance in Tangerine that 

could be transferred to sweet orange through the introduction of new 



genes is proposed in figure 11. In this model, genes already tested and 

showed to be promising candidates, such as those involved with recognition 

of PAMPs and PTI activation pathway are checked in green. 

Also checked in green are genes related to ethylene response and those 

from X. fastidiosa that showed to alter bacterial behavior and its virulence. 

These options were revealed as good candidates after model 

plants evaluations. On the other hand, genes that did not show directly 

involvement in early X. fastidiosa defense according to model plants 

results are checked in red. They represent options that would not be 

initially explored for sweet orange transformation. 

Actually, regarding results obtained with some genes in model 

plants, transformation of citrus had already started. In a PDR approach 

rpfF was the first to be chosen for transformation of sweet orange cultivars. 

As showed for tobacco plants, in citrus transgenic plants severity 

of CVC symptoms were greatly reduced. Bacterial movement along 

transgenic plants was equally difficult in Pineapple and Hamlin sweet 

orange varieties overexpressing rpfF (Caserta, R. 2014), suggesting that 

results obtained in model plants are a good guide for citrus response. 

As recognition of PAMPs seems to be important for defense against X. 

fastidiosa in model plants, the gene encoding EFR receptor was also 

chosen for sweet orange transformation. 


177


Figure 10. Candidate genes eligible for citrus breeding after model 

plants analysis. Genes already tested in previous experiments using 

either A. thaliana or tobacco and showed involvement in resistance 

against X. fastidiosa are checked in green. Genes that did not 

show a promising response are checked in red. 

Finally, transformation of model plants as a source of tests candidate 

genes involvement in defense against X. fastidiosa proved to be 

very effective in assisting a deeper understanding of potential defense 

response against this bacterial attack. Furthermore, model plants represent 

a rapid and faithful alternative that assists in the choice of the most 

promising genes for citrus breeding. 



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6Plantwise: 

Improving Food 

Security Through 

Better Plant 

Health System


6Plantwise: Improving Food Security 

Through Better Plant Health System 

Yelitza Coromoto Colmenarez; Natália Corniani; Wade Jenner. 

1. Introduction to Plantwise: a global programme lead by 

CABI working together with key partners 

The Centre for Agriculture and Biosciences International (CABI 

– www.cabi.org) is uniquely placed to tackle the issue of food security 

via co-ordination of the Plantwise programme. CABI, originally 

established in 1910, is an international not-for-profit organization that 

provides information and applies scientific expertise to solve problems 

in agriculture and the environment. Operating under an UN-registered 

Plantwise: Improving Food Security Through Better Plant Health System 

189


international treaty its mission and direction is influenced by 48 member 

countries, which help guide its activities in scientific publishing and 

international development. It has a Headquarters Agreement with the 

Government of the United Kingdom and operates through a network 

of regional centres located around the world and has broad experience 

with in-field project and extension work and the development of information 

resources. It also has good relationships with international donors, 

and strong partnerships with national and international agriculture 

and information organizations (LEACH; HOBBS, 2013). 

Plantwise was the first cross-CABI programme which relied on 

all aspects of the organization’s expertise. The Plantwise programme 

was endorsed by member countries in 2011 as they recognised that 

CABI is well-placed, due to its network of centres in Africa (Kenya, 

Ghana), Asia (China, India, Malaysia, Pakistan), the Americas (Brazil, 

Trinidad & Tobago) and Europe (Switzerland, UK), to deliver such a 

programme. The organisation has expertise in disseminating agricultural 

knowledge and training farmers in plant health. Furthermore, CABI 

manages high quality data on issues relating to plant health, widely 

used by the global scientific community, as well as legislative and regulatory 

personnel in developing countries. CAB Abstracts is the world’s 

largest agricultural information resource (about 9.5 million abstracts). 

190 Plantwise: Improving Food Security Through Better Plant Health System


Subsets of these abstracts are supplemented by 30,000 pest information 

data sheets to create the high impact Crop Protection Compendium and 

by 10,000 invasive species data sheets to create the open access Invasive 

Species Compendium (PLANTWISE, 2015). 

2. The Plantwise challenge: lose less and feed more 

In today’s world, we may believe that information is more 

widely available than ever before, yet agricultural advisory services 

in developing countries are still weak and there is a fundamentally 

inconsistent dialogue between farmers and those who are tasked with 

helping them. Many farmers do not have reliable access to advisory 

services due to their own restricted mobility. Similarly, the extension 

workers are often too few in number and do not have the budget needed 

to travel to individual farm sites. Although there continues to be 

significant losses of food to plant pests and other disorders, there is a 

poor and irregular flow of information about the threats that farmers 

face. Farmers routinely have to make vital decisions in response to 

unpredictable conditions and unknown risks – and without the right 

information at the right time, this is very difficult. The recommendations 

given to both female and male farmers have to be effective, 


191


available, practical, safe and economical, as well as climate-smart and 

gender-sensitive. An inconsistent engagement with farmers has several 

important consequences for the entire plant health system: slow 

awareness of new and emerging plant health problems; delayed responses 

in identifying the nature of the problems and giving suitable 

recommendations; systematic failure to learn from experiences; and 

inefficient use of existing sources of technical expertise. The net result 

is a failure to provide timely solutions that enable farmers to grow 

more food and earn more money (PLANTWISE, 2015). 

These challenges are common in many countries, including 

those that are CABI members. Based on these observations CABI developed 

the Plantwise programme with the aim of enabling female 

and male farmers around the world to improve the production and 

quality of their crops. 

3. Plantwise: a global alliance 

Progress continues in the fight against hunger, yet an unacceptably 

large number of people still lack the food they need for an active 

and healthy life. The latest available estimates indicate that about 795 



million people in the world were undernourished in 2014–16 (FAO, 

2015). Over half are smallholder farmers who rely on their crops to feed 

their families. The impact of plant pests and diseases on these crops 

can be catastrophic, destroying farmers’ livelihoods and threatening the 

food security of their households and their communities. In some areas, 

up to 70% of food is lost before it can be consumed. This problem is 

exacerbated by international trade, intensified production and climate 

change altering and accelerating the spread of plant pests. Clearly there 

is an opportunity to lose less and feed more by improving control of 

such pest problems, particularly in the developing world (OERTKE, 

2006; FINEGOLD et al., 2014). 

Plantwise (www.plantwise.org), an innovative global program, 

led by CABI, aims to contribute to increased food security, alleviated 

poverty and improved livelihoods by enabling male and female farmers 

around the world to improve the production and quality of their crops. 

Working in close partnership with relevant actors, Plantwise strengthens 

national plant health systems from within, enabling countries to 

provide farmers with the knowledge they need to produce more and in 

a sustainable way (ROMNEY et al., 2013). 

The Plantwise approach is based on three inter-linked components: 


193


1. An ever-growing network of locally-run plant clinics, where 

farmers can find advice to manage and prevent crop problems. 

Agricultural advisory staff trained learns methods to identify any 

problem on any crop brought to the clinics, with the support of 

a national and international network of diagnostic laboratories, 

and provide appropriate recommendations guided by national and 

international best practice standards. 

2. Improved information flows between everyone whose work 

supports farmers (e.g. extension, research, input suppliers and 

regulators). Collaboration within national plant health systems 

enables these actors to be more effective in their work to improve 

plant health, with concrete benefits for farmers. 

3. The Plantwise knowledge bank, a database with online and offline 

resources for pest diagnostic and advisory services, provides 

both locally relevant, comprehensive plant health information for 

everyone and a platform for collaboration and information sharing 

between plant health stakeholders. 

Plantwise Knowledge Bank, Plant clinic records are collated and 

analysed to support the quality of advice given to farmers and inform 



decision-making. By putting knowledge into the hands of smallholder 

farmers, Plantwise is able not only to help them lose less and feed 

more, but also to gather data which can assist all stakeholders in the 

plant health system-from research, agro-input supply, extension and 

policy-making. Most importantly, Plantwise is a development program 

which cooperates with a number of international and national organizations 

working to remove constraints to agricultural productivity. Countries 

are now using plant clinics and Knowledge Bank resources to improve 

national vigilance against pest outbreaks (KUHLMANN, 2014). 

4. Delivering the Plantwise vision: improving food security 

through better plant health system 

To properly feed the global masses, we need to recognize the 

food security is not just about increasing agricultural production, as 

one can triple production and still be poor, with animals dying due to 

aflatoxins and food losses due to inadequate storage facilities. Food 

security implies that food is available, accessible and well utilized 

(properly stored, prepared, including a nutritious and balanced diet 

to promote health). Furthermore, we need to think in broader terms 

and think beyond just dealing with the current crisis at hand (AZMJ, 

2014). 


195


Plantwise presents a unique opportunity to contribute to food security 

and improve the livelihoods of smallholder farmers, and is already 

well underway in doing this. CABI helps countries develop an 

integrated plant health system which meets the needs of farmers, researchers 

and governments. Wider agreement is needed on the importance 

of plant health. Up to date and comprehensive data are however 

hard to obtain and difficult to assess (BOA et al., 2015). 

Browning, a leading US plant pathologist, proposed a national 

plant health system comprising research, training, education and extension 

(BROWNING, 1998). A plant health system consists of the organisations, 

people and actions concerned with promoting, restoring and 

maintaining plant health, in order to reduce crop losses and increase 

crop quality. A strong plant health system requires the organised efforts 

and informed decisions of research, extension, input supply and 

regulation, which all serve to benefit farmers (PLANTWISE, 2015). 

The interactions among stakeholders in this system are underpinned by 

knowledge, data and information exchange. 

Plant health system components already exist in all countries but 

often operate in disparate ways. CABI provides training, capacity building 

and technical backstopping to help countries strengthen their own 

system. This system, once fully established, is managed and funded 



fully by the country concerned, or through public-private partnerships, 

without reliance on continued donor funding (PLANTWISE, 2015). 

The Plantwise approach develops sustainable mechanisms to deliver 

better plant health services that address farmer needs and improve 

output, including: (1) improving advisory services based on plant clinics 

and complementary extension approaches and delivering effective 

responses to any plant health problem affecting any crop; (2) improving 

regulatory systems so that plant health problems are detected early and 

advisory staff on the ground are able to communicate appropriate mitigation 

measures to farmers before the problems become devastating; 

(3) stimulating research that supports farmers’ needs; and (4) improving 

input supply ensuring provision of appropriate, legitimate and effective 

goods (PLANTWISE, 2015). 


197


Figure 1. How a plant health system works and what it achieves. A 

plant health system is defined by the set of all national plant health 

stakeholders and their linkages. Source: Plantwise, 2015. 



5. Plant Clinics 

Good plant health management is essential for producing healthy 

crops for human and animal consumption as well as for non-food purposes. 

Although a lot is known about the technical aspects of plant 

disease epidemiology and how to grow healthy crops, the majority of 

small-scale farmers in developing countries do not have access to adequate 

and timely advice on how to handle existing or emerging plant 

health problems (BENTLEY, 2009). Diagnostic capacity is scarce and 

limited in scope and quality (SMITH et al., 2008; MILLER et al., 2009) 

yet diagnostic facilities are often under-utilized. Innovations in service 

delivery are needed to make knowledge, information and inputs 

available to the millions of smallholders who depend on their crops for 

household food security and income (DANIELSEN; KELLY, 2010). 

Since 2003, community-based plant clinics have been piloted in 

several developing countries as a new way of providing regular, low-cost 

plant health services to farmers. Plant health clinics began in Bolivia in 

2003 (BENTLEY et al., 2009) but made their biggest steps forward in 

Nicaragua from 2005 onwards (DANIELSEN; FERNANDEZ, 2008). 

Plant clinics respond to the immediate needs of farmers, offering advice 

on demand. Clinics, owned and run by national and local bodies, take 

place on a regular basis, at least once every two weeks, in public places 


199


that are best suited to meeting farmers, e.g., marketplaces, meeting places 

or bus stations. In some countries, mobile clinics operating from vehicles 

have been successful in reaching more remote areas. They are simple 

facilities (Figure 2) consisting of basic equipment such as tables, chairs, 

shade, photos, reference books and tools (knives, scissors, hand lens) to 

examine the ‘patients’ (sick plants) (DANIELSEN; KELLY, 2010). 

Farmers can present any crop health problem they encounter, so 

plant clinics accept ‘any problem on any crop’, and are encouraged to 

bring samples of their sick plants. Details of symptoms (both observed 

and from consultation with the farmer) are recorded, along with the 

diagnosis and a written prescription with advice on how to manage the 

problem. The advice can be curative and/or preventive. Data about each 

farmer’s visit are held within a central repository and can be analysed to 

assess suitability of the advice given and enhance the accountability of 

the advisory service in a country. For instance, aggregated observations 

from the plant clinic data can help to identify new and emerging plant 

health problems and act as an early warning system to the regulatory 

bodies responsible for surveillance and response (PLANTWISE, 2015). 

The clinic staff, called plant doctors, are usually trained extension 

workers employed by local agricultural organizations, and continue 

to perform their normal duties on the days when they are not running 



plant clinics. The plant clinics tap into existing social dynamics and 

institutional structures and are adapted to the specific local conditions 

(BOA, 2009). Plant doctors should be knowledgeable about farmers 

and farming conditions, speak the local language and know what inputs 

are available. This puts them in an excellent position to offer the farmers 

practical advice. They are thoroughly introduced to the plant clinic 

concept and process through custom-made training courses designed 

by CABI. These courses build on the plant doctors’ existing knowledge 

and show them how to use their skills to maximum effect when diagnosing 

problems and giving recommendations. 

The Plantwise programme encourages plant doctors to offer plant 

health management advice guided by the principles of integrated pest 

management (IPM). They are advised and given information tools to 

recommend only locally-registered and available pesticides, which are 

not restricted by international conventions (PLANTWISE, 2015). For 

difficult diagnoses the plant clinics receive technical support from national 

diagnostic laboratories or backed up as necessary by the Plantwise 

diagnostic and advisory service in the United Kingdom (UK) or 

other international services. 

Plant clinics are thus a cornerstone of Plantwise, they serve to 

forge and strengthen links between existing organizations and stake- 


201


holders in a number of ways. Plantwise is based on making the best use 

of existing organizations, resources and personnel, recognizing their 

advantage in reaching rural communities and intimate knowledge of 

local farming conditions (PLANTWISE, 2014). 

Figure 2. Plant clinic held in Concepción, Huancayo, Peru. Source: 

Yelitza Colmenarez. 



6. Knowledge Bank 

Reaching the rural extension worker, and ultimately the farmer, 

with important plant health information is extremely challenging. 

Providing access to a wide range of information, from international 

scientific literature to simple, actionable factsheets in local language 

for farmers, is crucial to success in controlling plant health problems. 

Plantwise is underpinned with a web-based knowledge bank 

(http://www.plantwise.org/KnowledgeBank), developed and hosted 

by CABI. As described elsewhere (LEACH; HOBBS, 2013), the 

Knowledge Bank provides expert information that has been validated 

and checked and that can then be accessed by all in the plant health 

system. It delivers country-specific webpages, pest distribution maps, 

pest alerts, simple diagnostic tools, factsheets and pest management 

decision guides (FINEGOLD, 2014). Plant doctors and extension 

workers use the knowledge bank to access comprehensive, locally 

relevant and actionable information to help farmers on the ground 

(PLANTWISE 2014). 

In addition to information from the plant clinics and CABI’s 

own scientific resources, the knowledge bank provides access to data 

and information from a wide range of partner organizations and initiatives. 

This gives researchers and extension workers instant access to 


203


a network of free and open-access plant health information. The free 

and open access nature of this information means that plant health 

stakeholders worldwide can simultaneously access it from one source 

and use it to understand key production constraints and design targeted 

and timely responses in any region (PLANTWISE, 2015). 

Collecting, collating, and analysing data on the pests seen at the 

clinics is a crucial development to allow national stakeholders in the 

plant health system to deploy their resources effectively. However, 

information on what plant pests are reported to exist within a country 

can be extremely sensitive. Trade can be severely impacted if a new 

quarantine pest is indicated as being present on a commodity crop. 

Similarly, prices can change if major pest outbreaks are predicted. 

Plantwise had to be able to understand, appreciate and work through 

these issues. CABI works closely with key organizations, such as 

ministries of agriculture and national plant protection organizations, 

to agree policies and mechanisms for collecting, analysing, and publishing 

information on pests. 

The content in the Knowledge Bank that provides help with diagnosis, 

treatment and distribution of pests is open access and freely 

available to all. However, the clinic data, and the associated tools for 

processing and analysis, neaeded to be access-controlled so that only 



those users specifically identified by contributing partners could be 

allowed viewing rights. Hence data collected from plant clinics can 

be acquired and analysed in-country but will only be published with 

the express permission of the appropriate authorities. This resulted in 

the creation of an entirely separate access-controlled section of the 

Knowledge Bank, as illustrated in Figure 3. 

The Plantwise Online Management System (POMS), an access-controlled 

section within the knowledge bank, is a central resource 

for managing plant clinic data as well as programme monitoring, 

detailing the core Plantwise outputs. Information on training, 

people, partners, clinics and activities is held here for easy retrieval. 

Through access to the POMS, authorised national stakeholders are 

able to examine the Plantwise activities and results for their countries. 

Training in the POMS is given and the system will continue to be developed 

to meet extended stakeholder needs, such as offline viewing. 


205


Figure 3. Schematic diagram of access controlled and open access 

sections of the Knowledge Bank. Source: Plantwise, 2014. 

7. What has been achieved so far and next steps 

Since Plantwise started in 2009 (prior to this pilot plant clinics 



were run as part of the Global Plant Clinic programme) it has grown 

to work in 33 countries by the end of 2014, and met with strong support 

from farmers, governments, advisory services, NGOs, other plant 

health stakeholders and programme donors. The increasing number of 

partnerships has led to significant progress both in-country and at the 

programme level. It is estimated that approximately 1,900,000 farmers 

around the world had been reached directly through plant clinics and 

other Plantwise activities, as well as indirectly through farmer-to-farmer 

exchange and other spill-over effects, by the end of 2014. More and 

more plant clinic data are being stored in the knowledge bank and used 

as the basis for informed decision-making by plant health stakeholders. 

In addition, the knowledge bank provides critical information such as 

pest distribution maps, an online diagnostic tool and crop management 

support (CABI, 2013; CABI, 2014). 

Plantwise will be scaled up and scaled out to achieve its target of 

reaching 30 million female and male farmers by 2020 through the implementation 

of the Plantwise approach in a total of 40 countries. CABI 

will maintain its close engagement with national and local partners to 

ensure a shared vision and commitment towards reaching sustainability 

of the Plantwise approach (CABI, 2015). 


207


Figure 4. Plantwise Advances. Source: CABI, 2014. 



References 

1. AZMJ. Cracking the Nut Africa: Improving Rural Livelihoods and 

Food Security. 2014. 46 pp. (Report) 

2. Bentley, J. W. Impact of IPM extension for smallholder farmers in 

the tropics. In: Peshin, R.; Dhawan, A. K. (eds), Integrated Pest Management: 

Dissemination and Impact. The Netherlands: Springer Science+Business 

Media B.V, 2009. v.2, p.333-346. 

3. Bentley, J. W. et al. Plant health clinics in Bolivia 2000–2009: operations 

and preliminary results. Food Security, v.1, p.371-386, 2009. 

4. Browning, J.A. One phytopathologist's growth through IPM to holistic 

plant health: the key to approaching genetic yield potential. Annual 

Review of Phytopathology, v.36, p.1-24, 1998. 

5. Boa, E. How the global plant clinic began. Outlooks on Pest Management, 

v.20, p.112-116, 2009. 

6. Boa, E. Better Together: identifying the benefits of a closer integration 

between plant health, one health and agriculture. In: Zinsstag, J. 


209


et al. (eds.) One Health: the theory and practice of integrated health 

approaches. Wallingford: CABI Publishing Company, 2015. v.1, 

cap.22, pp.258-271. 

7. CABI. CABI in review 2013. Available at: . Accessed on: 3 Nov. 2015. 

8. CABI. CABI in review 2014. Available at: . Accessed on: 3 Nov. 2015. 

9. Danielsen, S.; Fernandez, M. Public plant health services for all. FU- 

NICA, Managua. 2008. 

10. Danielsen, S.; Kelly, P. A novel approach to quality assessment of 

plant health clinics. International Journal of Agricultural Sustainability, 

v.8, p.257-269, 2010. 

11. FAO. 2015 The State of food Insecurity in the World. Available at: < 

http:// w w w.fao.org/publications/sofi/en/>. Accessed on: 5 Nov. 2015. 

12. Finegold, C. et al. Plantwise Knowledge Bank: Building Sustainable 

Data and Information Processes to Support Plant Clinics in Kenya. 

Agricultural Information Worldwide, v.6, p.96-101, 2014. 



13. Kuhlmann, U. Plantwise: A global alliance for plant health support. 

International Agriculture in a Changing World: Good News from the 

Field, Bern, p.23, 2014. (Abstract). 

14. Leach, M.C.; Hobbs, S.L.A. Plantwise knowledge bank: delivering 

plant health information to developing country users. Learned Publishing, 

v.26, n.3, p.180-185, 2013. 

15. Miller, S. A. et al. Plant disease diagnostic capabilities and networks. 

Annual Review of Phytopathology, v.47, p.15-38, 2009. 

16. Oerke, E.C. Crop Losses to Pests. Journal of Agricultural Science, 

v.144, p.31-43, 2006. 

17. Plantwise. Plantwise Strategy 2015-2020. Available at: . Accessed on: 2 Nov. 2015. 

18. Romney, D.R. et al. Plantwise: Putting Innovation Systems Principles 

Into Practice. Agriculture for Development, v.18, p. 27-31, 2013. 

19. Smith, J.J. et al. The challenge of providing plant pest diagnostic 

services for Africa. European Journal of Plant Pathology, v.121, p.365- 

375, 2008. 


211

7Monitoring of 

Hemileia vastatrix 

in Conilon Cofee 

Clones to Improve 

Fungicide Use


7Monitoring of Hemileia vastatrix in Conilon 

Cofee Clones to Improve Fungicide Use 

Leônidas Leoni Belan; Waldir Cintra de Jesus Junior; Willian 

Bucker Moraes; Leonardo Leoni Belan. 


Brazil is the second-largest producer of conilon coffee (Coffea 

canephora Pierre ex. Froehn) worldwide (19). However, numerous factors, 

especially diseases, can affect the productivity of C. canephora 

var. conilon. Conilon coffee leaf rust (Hemileia vastatrix Berk. et Br.) 

Monitoring of Hemileia vastatrix in Conilon Cofee Clones to Improve Fungicide Use 

215


is the most important disease, which can result in yield loss up to 50 

% (21, 23, 6, 7). The damages caused by coffee leaf rust results in the 

early fall of the leaves and dry branches, which in turn reduces the grain 

production in the next year. 

Commercial cultivation of conilon coffee is performed using 

clonal varieties, which are composed of at least eight clones to guarantee 

satisfactory pollination and compensate for the phenomenon of 

genetic self-incompatibility among clones (9). The “Conilon Vitória 

– Incaper 8142” variety, the subject of this study, is composed of 13 

clones (13). Due to the genetic diversity of the species C. canehora, 

researchers have demonstrated greaty variability in relation to the resistance 

of the main cultivated clones, reporting that more productive 

clones are susceptible to leaf rust (20, 23). Furthermore the resistant 

levels of the clones of the “Conilon Vitória – Incaper 8142” variety 

may vary according to the predominant races of Hemileia vastatrix and 

temperature of the each coffee growing area. 

The clones of the clonal variety should be planted in rows, using 

the same proportion of each clone. Row planting provides the following 

advantages: increased in yield; improvement of the final quality of the 

product as a function of all plants in the same row reaching maturation 

at the same time; the possibility of planting clones with different mat- 

216 Monitoring of Hemileia vastatrix in Conilon Cofee Clones to Improve Fungicide Use


uration periods in the same areas, thus staggering the harvest, and ease 

of harvesting and performing crop management (13). 

Because the main clonal varieties of conilon coffee do not 

have a satisfactory level of resistance to coffee leaf rust, the main 

management strategy adopted by the growers is the application of 

fungicides, which involves a fixed schedule of spraying (7), without 

considering the factors that determine the occurrence of the disease 

particularly climate conditions and disease intensity. Those management 

strategies use triazol systemic fungicides (cyproconazole, 

flutriafol or triadimenol) associated with insecticides to the soil and 

or foliar applications of triazol systemic fungicides (cyproconazole, 

flutriafol or triadimenol) for coffee leaf rust control (Hemileia vastatrix) 

and leaf miner (Leucoptera coffeella) (7). Those methods can 

lead to greater production costs, a greater likelihood of residues being 

present on the coffee cherries, a greater chance of environmental 

pollution and increased risk to the health of fungicide applicators 

and consumers. By this management system, ecological, economic 

and social aspects are not considered. 

Given the differences in the resistance level of leaf rust in coffee 

clones that comprise the variety “Conilon Vitória – Incaper 8142”, is 

necessary to study strategies to control conilon coffee leaf rust (2). 


217


2. Chemical strategies to control coffee leaf rust 

Therefore, in order to turn chemical control less subjective and 

more efficient, (2) performed experiments in two municipalities of the 

Espírito Santo State – Brazil: Nova Venécia, altitude 65 m, south latitude 

18° 37’, west longitude 40° 28′ and Castelo, altitude 140 m, south 

latitude 20° 34’, west longitude 42° 12’. Monthly evaluations of disease 

incidence were performed between September 2010 and August 2011. 

Each experimental field was composed of an area of 0.2 ha containing 

plants of the 13 clones, of the clonal variety “Conilon Vitória – Incaper 

8142”. In each conilon experimental field, two strategy systems, to control 

coffee leaf rust were tested for these authors: 

1. Application of systemic fungicides based on the disease incidence, 

of the conilon plants, of each plot (DI strategy system). 

Monthly evaluations of the incidence of coffee leaf rust were 

performed on one marked plant in each repetition. In each 

plant, four plagiotropic branches, distributed among the four 

cardinal directions were randomly evaluated. The incidence 

of coffee leaf rust (%) was calculated by dividing, the total 

number of leaves with at least one rust pustule, by the total 



number of leaves sampled in the four plagiotropic branches, 

multiplied by 100 (1). The threshold of 5 % disease incidence 

was adopted to spray the systemic fungicide. Thus, whenever 

the incidence of coffee leaf rust was equal to, or greater than 5 

%, systemic fungicide was applied to the leaves (Opus ® - epoxiconazole 

– 0.6 L c.p. ha -1 [commercial product per hectare] 

(12.5 % a.i. ) [active ingredient] + Fulland ® - 1 L c.p. ha -1 , 

14.0 % of Cu, as micronutrient). 

2. The producer’s conventional system (SFA) - Application 

of systemic fungicide (Simboll ® - flutriafol – 5 L c.p. ha -1 ; 

12.5 % a.i.) in January 2011 in Nova Venécia - ES, to the soil 

broadcasted around the coffee plants, and foliar application of 

systemic foliar fungicide (Opus ® - epoxiconazole – 0.6 L c.p. 

ha -1 sprayed in September 2010 and in July 2011. In Castelo, 

only one application of systemic fungicide (Trinity ® - flutriafol 

– 2 L p.c. ha -1 ; 25 % a.i.) to the soil broadcasted around the 

coffee plants in December 2010. 

Monthly evaluations of the incidence of coffee leaf rust were performed 

between September 2010 and August 2011, in each of the 13 

clones, of the two strategy systems (DI and SFA). Using the monthly 


219


data of incidence of disease in each clone, the disease-progress curves 

corresponding to the evaluation period were constructed for each clone 

and management system in each site. In addition, the area under the disease 

progress curves (AUDPC) were calculated using the trapezoidal 

integration method (15). 



3. Results 

A difference was observed between the clones that comprise the 

“Conilon Vitória - Incaper 8142” cultivar with respect to the progress 

of coffee leaf rust within and between coffee rust strategy systems 

(DI and SFA) (Table 1). In the municipality of Nova Venécia, coffee 

leaf rust occurred in all clones in both systems, at different intensities 

(Table 1; Figure 1). Conversely, in both strategy systems in the municipality 

of Castelo, coffee leaf rust did not attack the CV03 clone; 

in the remaining clones, the disease occurred only after May 2011 

harvest (Table 1; Figure 2). 


221


Table 1. Areas under the disease-progress curve (AUDPC) for coffee 

rust in the 13 clones of conilon coffee that comprise the cultivar 

“Conilon Vitória – Incaper 8142”, under two disease management 

systems, integrated (DI) and the producer’s conventional system 

(SFA), in the period between September 2010 and August 2011 in 

fields in Nova Venécia and Castelo, State of Espírito Santo, Brazil 

Clones 

CV01 

AUDPC* 

DI Nova Venécia SFA Nova Venécia 

1103.80 A c 

979.55 A c 

CV02 

CV03 

CV04 

CV05 

CV06 

CV07 

CV08 

CV09 

CV10 

CV11 

CV12 

CV13 

2401.12 A a 

43.16 A e 

1408.97 A b 

575.35 A d 

1130.12 A c 

2084.82 B a 

1601.41 A b 

1041.93 A c 

1879.83 A b 

510.42 A d 

2473.88 B a 

1490.52 A b 

2549.56 A b 

12.51 A d 

1000.44 A c 

124.55 B d 

1016.60 A c 

2655.68 A b 

1067.85 B c 

282.36 B d 

1221.03 B c 

533.31 A d 

3171.68 A a 

537.47 B d 



DI Castelo 

524.03 B a 

438.48 B a 

0.00 A b 

11.85 B b 

23.27 B b 

397.72 B a 

476.94 C a 

69.20 C b 

5.95 B b 

47.40 C b 

310.35 A a 

452.27 D a 

37.07 C b 

AUDPC* 

SFA Castelo 

378.08 B b 

402.40 B b 

0.00 A b 

9.81 B b 

18.96 B b 

262.24 B b 

341.29 C b 

34.17 C b 

5.68 B b 

146.71 C b 

226.56 A b 

1046.27 C a 

97.46 C b 

*Mean values followed by the same uppercase letter in the horizontal direction 

and lowercase letter in the vertical direction do not differ from each other by the 

Scott-Knott test at a 5 % probability level. Source: Belan et al. (2015). 


223


30 

25 

Clone 01 

30 

25 

Clone 02 

Incidence (%) 

20 

15 

10 

5 

+ 

CL 

Incidence (%) 

20 

15 

10 

5 

+ 

* * 

CL 

0 

0 

30 

25 

Clone 03 

30 

25 

Clone 04 

Incidence (%) 

20 

15 

10 

5 

+ 

CL 

Incidence (%) 

20 

15 

10 

5 

+ 

* * 

CL 

0 

0 

30 

25 

Clone 05 

30 

25 

Clone 06 

Incidence (%) 

20 

15 

10 

5 

+ 

CL 

Incidence (%) 

20 

15 

10 

5 

+ 

* 

CL 

0 

0 

Figure 1. Monthly Incidence of coffee rust (%) in the 13 clones of conilon 

coffee that comprise the cultivar “Conilon Vitória – Incaper 8142” under 

two disease management systems, integrated (DI) and the grower’s conventional 

system (SFA), in the period between September 2010 and August 

2011. Municipality of Nova Venécia, State of Espírito Santo, Brazil. 

*Spraying not performed because of bad weather or harvest period. 

CL = Control level (5 % incidence). Source: Belan et al. (2015). 



30 

25 

Clone 07 

30 

25 

Clone 08 

Incidence (%) 

20 

15 

10 

5 

0 

+ 

* 

* 

CL 

Incidence (%) 

20 

15 

10 

5 

0 

+ 

CL 

30 

25 

Clone 09 

25 

20 

Clone 10 

Incidence (%) 

20 

15 

10 

5 

+ 

* 

CL 

Incidence (%) 

15 

10 

5 

+ 

* 

* 

CL 

Incidence (%) 

0 

25 

20 

15 

10 

5 

+ 

Clone 11 

CL 

Incidence (%) 

0 

25 

20 

15 

10 

5 

Sep Out Nov Dec Jan Fev Mar Apr May Jun Jul Aug 

Months 

+ 

Clone 12 

* 

* 

CL 

Incidence (%) 

0 

25 

20 

15 

10 

5 


Months 

+ 

Clone 13 

* 

CL 

0 


Months 

+ 

* 

Intergrated (DI) 

Conventional (SFA) 

Sprayings (DI) 

Sprayings (SFA) 

Soil application (SFA) 

Spraying not performed 

0 


Months 


225


30 

25 

Clone 01 

30 

25 

Clone 02 

Incidence (%) 

20 

15 

10 

5 

Incidence (%) 

20 

15 

10 

+ 

CL 

5 

+ 

CL 

0 

0 

30 

25 

Clone 03 

30 

25 

Clone 04 

Incidence (%) 

20 

15 

10 

5 

Incidence (%) 

20 

+ 

15 

10 

CL 

5 

+ 

CL 

0 

0 

30 

25 

Clone 05 

30 

25 

Clone 06 

Incidence (%) 

20 

15 

10 

5 

Incidence (%) 

20 

+ 

15 

10 

CL 

5 

+ 

CL 

0 

0 

Figure 2. Monthly Incidence of coffee rust (%) in the 13 clones of 

conilon coffee that comprise the cultivar “Conilon Vitória – Incaper 

8142” under two disease management systems, integrated (DI) and 

the grower’s conventional system (SFA), in the period between September 

2010 and August 2011. Municipality of Castelo, State of Espírito 

Santo, Brazil. CL = Control level (5 % incidence). Source: 

Belan et al. (2015). 



30 

25 

Clone 07 

30 

25 

Clone 08 

Incidence (%) 

20 

15 

10 

5 

0 

+ 

CL 

Incidence (%) 

20 

15 

10 

5 

0 

+ 

CL 

30 

25 

Clone 09 

30 

25 

Clone 10 

Incidence (%) 

20 

15 

10 

5 

Incidence (%) 

20 

15 

10 

+ 

CL 

5 

+ 

CL 

0 

0 

30 

25 

Clone 11 

30 

25 

Clone 12 

Incidence (%) 

20 

15 

10 

5 

Incidence (%) 

20 

15 

10 

+ CL 

5 

+ 

CL 

0 

0 

30 

25 

Clone 13 

Intergrated (DI) 

Incidence (%) 

20 

15 

10 

5 

+ 

CL 

+ 


Sprayings (DI) 

Sprayings (SFA) 

Soil application (SFA) 

0 


227


The different reaction of the conilon clones to coffee leaf rust is 

due to the genetic heterogeneity of the species, which is allogamous 

and self-incompatible (10, 4). Such genetic variability explains the 

difference observed between the clones with respect to the intensity 

of coffee leaf rust. 

There is no available published disease progress curve for the 

conilon leaf rust (18, 7). The results obtained in this study also revealed 

the importance of monitoring the intensity of the disease to 

determine the appropriate moment to apply fungicides. 

Analysis of the disease-progress curves for coffee leaf rust in Nova 

Venécia show that the maximum incidence occurred in the post-harvest 

of the coffee berries, between the months of May and June 2011, in 

both strategy systems (Figure 3). In addition, another increase in disease 

intensity was observed in January 2011 (Figure 3), which occurred 

as a function of the increase of disease incidence in the clones CV02, 

CV04, CV08 and CV12 (Figure 2). This second disease peak was also 

observed by Capucho et al. (2013), which was a favourable period to 

the disease and atypical for the region. 



Nova Venécia 

Incidence (%) 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

Integrated (DI) 



Months 

Figure 3. Monthly Incidence of coffee rust (%) in the cultivar 

“Conilon Vitória – Incaper 8142” under two disease management 

systems, integrated (DI) and the grower’s conventional system 

(SFA), in the period between September 2010 and August 2011. 

Municipalities of Nova Venécia and Castelo, State of Espírito Santo, 

Brazil. Vertical bars indicate the standard error of the mean of 

the 13 clones of conilon coffee that comprise the cultivar. Source: 

Belan et al. (2015). 


229


Castelo 

Incidence (%) 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 

Integrated (DI) 



Months 

Figure 3 (continued). Monthly Incidence of coffee rust (%) in the 

cultivar “Conilon Vitória – Incaper 8142” under two disease management 

systems, integrated (DI) and the grower’s conventional 

system (SFA), in the period between September 2010 and August 

2011. Municipalities of Nova Venécia and Castelo, State of Espírito 

Santo, Brazil. Vertical bars indicate the standard error of the 

mean of the 13 clones of conilon coffee that comprise the cultivar. 

Source: Belan et al. (2015). 



In a slightly different manner, the intensity of coffee rust in Castelo 

began to increase from the time of harvest (May 2011); however, the 

maximum incidence was also observed in the post-harvest period (in 

August 2011) (Figures 2 and 3), demonstrating that the peak of coffee 

leaf rust in this site occurred at least a month later (Figure 3). 

These differences might have occurred as a function of the climate 

differences (7) and the predominant races of H. vastatrix (5, 26) 

between the two growing areas. 

According to Ventura et al. (2007), the seasonal periodicity of 

coffee leaf rust in conilon coffee, differ markedly between regions, 

mainly as a function of climate conditions and crop management practices. 

These facts, together with the genetic diversity of the clones that 

constitute the clonal variety with respect to resistance to coffee leaf 

rust, justify disease management in a differentiated manner, considering 

a given incidence value as the reference value for decision-making. 

In Nova Venécia, regardless of the strategy system, clones 

CV02, CV07 and CV12 showed the highest disease intensities (Table 

1; Figure 1), and clones CV03, CV05 and CV11 showed the lowest 

values (Table 1), justified by the constant incidence values below the 

control level of 5 % (Figure 1). In contrast, in Castelo, the disease 


231


incidence did not show variations between months, remaining below 

the control level until harvesting time (in May 2011) in both systems 

(Figure 2). Based on these results, it is evident that in some cases 

(depending on the clone, intensity of disease and time of year), the 

application of fungicides is unnecessary. 

Thus, it is not possible to define a single management plan for 

coffee leaf rust for all conditions under which conilon coffee is grown. 

The intrinsic genetic variability of this species, the heterogeneity 

among the clones with respect to progression of the disease and the 

differences between regions in which the clones are cultivated (Table 

1) hinder the establishment of a management plan. The results of this 

study provide evidence for these facts; therefore, a management system 

based on monitoring the intensity of disease in each planting row 

would be the most appropriate. 

According with Belan et al. (2015), the coffee rust strategy system 

that used a differentiated manner for each planting row (DI) did not 

reduce the AUDPC in all cases. Clones CV05, CV08, CV09, CV10 and 

CV13 managed according to this system in the municipality of Nova 

Venécia showed greater disease intensity compared with the SFA system 

(Table 1). This fact can be justified by the application of systemic 

fungicide through the soil in the SFA system, for which the efficiency 



was reported by Capucho et al. (2013). Several advances in plant health 

management have occurred since 1984 due to the use of formulations 

containing fungicides and/or insecticides for soil application. Only one 

application of such products at the beginning of the rainy season is required 

to reduce disease intensity (7). Generally, these agrochemicals 

confer protection to the crop for a period greater than systemic fungicides 

applied to the leaves (8, 18, 11). In this study, the growers adopted 

this technique, performing the application in December in the 

SFA-Castelo assay and in January in the SFA-Nova Venécia assay. 

The residual effect of the fungicide applied through the soil in 

the SFA assay in Nova Venécia reduced 15 % in the intensity of coffee 

leaf rust compared with the DI area (Table 1). However, such reduction 

was not enough to mantain the disease incidence below the control 

level (5 %), in all clones (Figure 1). 

In the SFA strategy system in the municipality of Castelo, the 

systemic fungicide was applied to the soil in December 2012, when 

the maximum incidence of coffee leaf rust was 0.2 % (clone CV07) 

(Figure 2). These findings confirmed that as in the SFA strategy system, 

the disease incidence remained close to 0 % in the DI strategy 

system in which fungicide was not applied, with a maximum value of 

0.9 % (clone CV07) between December 2010 and May 2011. There- 


233


fore, the application of systemic fungicides was not necessary, and the 

application performed by the grower could have been avoided if the 

plants were monitored previously. Consequently, in the municipality 

of Castelo, the intensity of coffee leaf rust disease was the same in 

both strategy systems except for the clone CV12, which had lower 

disease intensity in the DI area (Table 1). 

Because of the variability in the duration of the latent and incubation 

periods of Hemileia vastatrix among the clones of C. canephora 

(20), there were differences among the clones with respect to the 

progression of the epidemic and among the cultivation sites; this fact 

is evident in both strategy systems (Figures 1 and 2, Table 1). Thus, 

the strategy systems considering fixed schedules for fungicide application 

is not effective, because the epidemic of the disease can start 

when the fungicide is no longer active, in the plant tissue. This fact 

justifies the implementation of a strategy system based on the monitoring 

of the disease in each clone, indicating the correct moment for 

fungicide application. 

Souza et al. (2011) also observed that the systemic fungicide 

cyproconazole + thiamethoxam GR applied in the second fortnight of 

Nov. conferred protection against coffee leaf rust until the end of February/March. 

However, in some harvests after this period, the coffee leaf 



rust disease-progress curves equalled or even surpassed the evolution 

of the disease in unsprayed plants (control). 

With respect to the effects of systemic fungicides applied through 

the soil and the practicability of their use for the grower, the wide adoption 

of systemic fungicides is justifiable in coffee cultivation. However, 

taking into consideration the sustainable management of conilon coffee 

plantations, the results of this study showed that it is possible to manage 

coffee leaf rust, rationalising the application of fungicides. 

Regardless of the strategy system, for the clones that exhibited 

higher AUDPC in Nova Venécia (CV02, CV07 and CV12) and in Castelo 

(CV01, CV02, CV06, CV07, CV11 and CV12), the DI system did 

not differ from the SFA system and was even more efficient in reducing 

the progression of the disease (Table 1). Some of these clones are 

among the most productive of the clonal variety “Conilon Vitória – Incaper 

8142” (13, 12). For these reasons a coffee leaf rust management 

system should be done in a differentiated manner for each clone; by 

doing that conilon yield will increase specially on the more productive 

and susceptible to the leaf rust disease. 

Among the challenges and goals for consolidating the DI strategy 

system is the rationalisation of the use of agricultural chemicals through 


235


a monitoring system of pest insects and diseases, thus reducing the risk 

of contamination to humans and the environment (22). As observed, it is 

possible to rationalise the use of fungicides in C. canephora cv. conilon 

plantations by using the DI strategy system of disease management because 

of the variable occurrence of coffee leaf rust of the clones. 

In the DI system, the number of fungicide applications varied 

between zero and seven and between zero and two in Nova Venécia 

and Castelo, respectively (Figures 1 and 2, respectively). It is noteworthy 

that clones CV02 and CV12 required six and seven applications 

of systemic fungicide, respectively, throughout the experimental 

period in the DI-Nova Venécia assay (Figure 1). This high number of 

applications can have negative consequences to the coffee agrosystem. 

According to Bergamin Filho and Amorim (2001), the higher 

frequency of fungicide application is one of the main factors that lead 

to greater selection pressure. This fact could lead to the selection of 

pathogen populations resistant to the applied fungicide because of the 

indiscriminate use of agrochemicals (25). 

Thus, certain precautions must be taken against resistance to establish 

coffee leaf rust management in a differentiated manner for each 

clone of C. canephora, such as the following: 1) alternating applications 

of fungicides with different active ingredients; 2) limiting the number 



of applications of a single active ingredient; 3) using active ingredients 

with different action modes; 4) using protector fungicides; 5) using 

only the recommended dosage; and 6) integrating chemical treatment 

with non-chemical management practices (14, 3, 25). 

Therefore, disease monitoring in planting rows makes it possible 

to waive the chemical control of coffee leaf rust in resistant clones or 

those in which environmental factors are limiting so that the disease 

reaches the control level. 

It is worth noting that this study was a pioneering study in using 

control levels for coffee rust in C. canephora (2). Because the action 

threshold that aids in decision-making regarding the need to apply agrochemicals 

is not known (20, 18), the control level of coffee leaf rust 

recommended for C. arabica (16, 24) was used, which allowed for differentiation 

between the evaluated management systems. 


237


References 

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Gonçalves AO, Lima AF, Alves FR. Metodologia de amostragem de folhas 

para quantificação da incidência da ferrugem em cafeeiro conilon. 

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Café. Salvador – BA, 2013, p.1-5, CD-ROM. 

2. Belan, LL, Jesus Junior, WC, Souza, AF, Zambolim, L, Tomaz, MA, 

Alves, FR, Ferrão, MAG, Amaral, JFT (2015) Monitoring of leaf rust 

in conilon coffee clones to improve fungicide use. Australasian Plant 

Pathology, 44:5-12. 

3. Bergamin Filho A, Amorim L (2001) Comparative epidemiology 

among pathosystems temperate and tropical: implications for fungicide 

resistance. Fitopatologia Brasileira 26: 119-127. 

4. Berthaud J (1980) L’incompatibilité chez Coffea canephora méthode 

de test et déterminisme génétique. Café, Cacao, Thé, Nogent-sur-Marne. 



5. Capucho AS (2011) Epidemiologia e resistência do cafeeiro Conilon 

à ferrugem. Federal University of Viçosa, Viçosa, p. 97. 

6. Capucho AS, Zambolim L, Cabral PGC, Maciel-Zambolim E, Caixeta 

ET (2012) Climate favourability to leaf rust in Conilon coffee. Australas 

Plant Pathol (on line). doi:10.1007/s13313-012-0187-6 

7. Capucho AS, Zambolim L, Lopes UM, Milagres NS (2013) Chemical 

control of coffee leaf rust in Coffea canephora cv. conilon. Australasian 

Plant Pathol (on line). 42:667–673. doi: 10.1007/s13313-013-0242-y 

8. Chalfoun SM, Carvalho VL (1999) Controle químico da ferrugem 

(Hemileia vastatrix Berk & Br.) do cafeeiro através de diferentes esquemas 

de aplicação. Pesquisa Agropecuária Brasileira 34:363– 367. 

doi:10.1590/S0100-204X1999000300006 

9. Charrier A, Berthaud J (1988) Principles and methods in coffea plant 

breeding: Coffea canephora Pierre. In: Clarke RJ, Macrae E (eds) Coffee: 

Agronomy. Elsevier Applied Science, p. 167-197. 

10. Conagin CHTM, Mendes ATJ (1961) Cytological and genetic research 

in three Coffea species: self-incompatibility in Coffea canephora 

Pierre ex Froehner. Bragantia 34: 787-804. 


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11. Dutra MR (2012) Advances in technology application for perennial 

crops, the case of coffee. In: Machado AKFM, Ogoshi C, Perina FJ, 

Silva GM, Santos Neto H, Costa LSAS, Alencar NE, Martins S.J, Earth 

WC, Zancan WLA (eds) Advances in optimization of the use of pesticides 

in pest management. Supreme Graphics and Publishing, Lavras, 

MG, Brazil, p. 37-47. 

12. Ferrão RG, Fonseca AFA,; Ferrão MAG, De Muner LH, Verdin Filho 

AC, Volpi OS, Marques EMG, Zucateli F (2007) Conilon coffee: production 

techniques with improved varieties. Incaper, Vitória, ES, Brazil. 

13. Fonseca AFA, Ferrão MAG, Ferrão RG, Verdin Filho AC, Volpi OS, 

Zucateli F (2004) ‘Conilon Vitória - Incaper 8142’: improved Coffea 

canephora var. kouillou clone cultivar for the state of Espírito Santo. 

Crop Breeding and Applied Biotechnology 4: 503-505. 

14. Heaney S, Slawson D, Hollomon DW, Smith M, Russel PE, Parry 

DW (1994) Fungicide Resistance. British Crop Protection Council 

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15. Shaner G, Finney R (1977) The effect of nitrogen fertilization on 

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16. Silva-Acuña R, Zambolim L, Ribeiro do Vale FX, Chaves GM, 

Pereira, A.A. (1992) Time of the first fungicide application based on 

the initial level of incidence for the control of coffee rust. Fitopatologia 

Brasileira 17: 36-41. 

17. Souza AF, Zambolim L, Jesus Junior VC, Cecon PR (2011) 

Chemical approaches to manage coffee leaf rust in drip irrigated 

trees. Australasian Plant Pathology (on line) 40: 293-300. Doi: 

10.1007/s13313-011-0046-x 

18. Souza AF, Zambolim L, Jesus Junior WC, Costa H (2009) Phytosanitary 

management of rust and leaf miner within the principles of integrated 

production conilon coffee.. In: Zambolim, L. ed. Technologies for the 

production of Coffee Conilon. UFV, Viçosa, MG, Brazil, pp. 47-64. 

19. United States Department Of Agriculture (2016) Production, Supply 

and Distribution Online. Available at: . [Accessed 13 Jul. 2016]. 

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and management of diseases conilon coffee. In: Stinger RG, Fonseca 

AFA, Bragança SM, Hand MAG, De Muner LH (eds) Conilon coffee. 

Incaper, Vitoria, ES, Brazil, p. 451-497. 


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and their Management. Natural Resources Institute: University of 

Greenwich, Medway Campus, Chatham, UK. 

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Souza Neto PN, Rios JA, Costa RD, Supplies LFP, Mantovani EC, 

Caixeta ET, Queizoz ME (2009a) Integrated Production of Coffee. 

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production in Brazil: Sustainable Farming Food Insurance. Brasilia, 

DF, Brazil: Map / ACS, pp. 341-444. 

23. Zambolim L, Sobreira DG, Souza AF, Costa H (2009b) Integrated 

management of diseases of conilon (Coffea canephora). In: Zambolim 

L (ed.) Technologies for the production of Coffee Conilon. UFV, 

Viçosa. MG, Brazil, pp. 1-46. 

24. Zambolim L, Vale FXR, Costa H, Pereira AA, Chaves GM (2002) 

Epidemiology and integrated control of coffee rust. In: Zambolim L 

(ed.) The state of the art technologies in coffee production. Federal University 

of Viçosa, Viçosa, MG, Brazil, pp. 369-450. 

25. Zambolim L, Venancio WS, Oliveira SHF (2007) Managing resistance 

of fungi to fungicides. Federal University of Viçosa, Viçosa, 

MG, Brazil. 



26. Zambolim L, Maciel-Zambolim E, Vale FXR, Pereira AA, Sakiyama 

NS, Caixeta ET (2005) Physiological races of Hemileia vastatrix 

Berk. et Br. in Brazil: Physiological variability, current situation and 

future prospects. In: Zambolim L, Zambolim EM, Várzea VMP (eds) 

Durable resistance to coffee leaf rust, 1st ed. Federal University of 

Viçosa, Viçosa, pp 75–98 


243

8Ceratocystis 

Species: 

Taxonomic 

Challenges 

in a Group of 

Pathogens 

of Increasing 

Global 

Importance


8Ceratocystis Species: Taxonomic Challenges in a 

Group of Pathogens of Increasing Global Importance 

Jolanda Roux; Brenda D. Wingfield; Michael J. Wingfield. 


The genus Ceratocystis includes numerous pathogens of plants, 

important to both agriculture and forestry. During the course of the 

past two decades, the number of reports of tree diseases caused by 

species in the C. fimbriata complex, as defined by phylogenetic inference, 

has increased more than three-fold (4). Ceratocystis acaciivora 

in Asia, C. albifundus in Africa, C. fimbriata sensu lato and C. cacao- 

Ceratocystis Species: Taxonomic Challenges in a Group of Pathogens of Increasing Global Importance 

247


fuenesta in South America, C. manginecans in the Middle East and 

C. platani in Europe, for example, all result in devastating diseases 

of trees. These species are easily moved in soil, on plant material and 

with the insects with which they are associated, and together with 

their often wide host range and ability to undergo host shifts, they 

represent a very significant global quarantine threat. Yet, the study of 

this group of pathogens is encumbered by a lack of ideal markers to 

clearly define species boundaries. 

2. Ceratocystis species 

The name Ceratocystis was first applied to a pathogen of sweet 

potato in the USA, C. fimbriata, in the late 1800s. Since its first 

description, the taxonomy of the genus and its type species has undergone 

several revisions. For many years the taxonomy of especially 

Ceratocystis and Ophiostoma species was confused, due to 

their morphological similarities. This ultimately resulted in the term 

ophiostomatoid fungi being applied to species of Ceratocystis and 

Ophiostoma, arguably resulting in increased taxonomic confusion; 

ironically, despite the application of DNA sequence data showing 

248 Ceratocystis Species: Taxonomic Challenges in a Group of Pathogens of Increasing Global Importance


that they reside in different orders of fungi, the Microascales and 

Ophiostomatales (1). 

Ceratocystis resides in the Ceratocystidaceae as part of the Microascales 

(3). However, both generic and species boundaries for fungi 

in this family are being subjected to ongoing revision. As part of 

this extensive revision, Ceratocystis will in future be applied only to 

species in the phylogenetic clade that accommodates C. fimbriata (5). 

At the species level, host range, morphology, mating studies, 

DNA sequence comparions and population genetic markers have been 

used to resolve boundaries for taxa in the C. fimbriata complex. While 

the boundaries remain unresolved at the present time, there is substantial 

evidence to show that there are many different species in the C. fimbriata 

complex. Some of our recent genomics studies have shown that 

there are multiple forms of the ITS gene region in some species of C. 

fimbriata s.l (2). There is also emerging evidence for hybridisation between 

species, which is not surprising given that many species occur in 

comnon niches and have been moved widely around the world. For this 

reason, it is likely that some species names currently being used will be 

reduced to synomymy while others will be shown to represent species 

complexes. The genomes of the many species in the C. fimbriata com- 


249


plex, now available to us, are already providing new and useful markers, 

for both taxonomic and biological studies. These will ultimately 

bring an increased understanding of this important group of pathogens 

and hopefully also facilitate steps to reduce their global spread. 



References 

1. De Beer ZW, Seifert KA, Wingfield MJ (2013). The ophiostomatoid 

fungi: their dual position in the Sordariomycetes. In: The Ophiostomatoid 

Fungi: Expanding Frontiers (Seifert KA, ZW De Beer and MJ Wingfield, 

eds). CBS Biodiversity Series 12. CBS, Utrecht, The Netherlands: 1-19. 

2. Naidoo K, Steenkamp ET, Coetzee MPA, Wingfield MJ, Wingfield 

BD (2013). Concerted evolution in the ribosomal RNA cistron. PLOS 

One 8, e59355.doi:10.1371/journal.pone.0059355 

3. Réblová M, Gams W, Seifert KA (2011). Monilochaetes and allied genera 

of the Glomerellales, and a reconsideration of families in the Microascales. 

Studies in Mycology, 68, 163–191. doi:10.3114/sim.2011.68.07 

4. Roux J, Wingfield MJ (2009). Ceratocystis species: Emerging pathogens 

of non-native plantation Eucalyptus and Acacia species. Southern 

Forests 71, 115-120. 


251


5. Wingfield BD, Van Wyk M, Roos H, Wingfield MJ (2013). Ceratocystis: 

emerging evidence for discrete generic boundaries. In: The 

Ophiostomatoid Fungi: Expanding Frontiers (Seifert KA, ZW De Beer 

and MJ Wingfield, eds). 12. CBS, Utrecht, The Netherlands: 57–64. 


9Characterization 

of Isolates of 

Ceratocystis spp. 

Collected from 

Different Hosts


9Characterization of Isolates of Ceratocystis spp. 

Collected from Different Hosts 

Ana Carolina Firmino; Hugo José Tozze Júnior; Edson Luiz 

Furtado. 


The genus Ceratocystis includes fungal species that lead to 

the collapse of xylem vessels, causing wilt symptom, followed by 

drought and radial dark striae in the inner part of the plant (9). Water 

and mineral flow is interrupted due to the pathogen development inside 

the vessels, which ends up obstructing the existent perforations 

Characterization of Isolates of Ceratocystis spp. Collected from Different Hosts 

257


on the tracheary elements (32). 

The species of this genus can be allocated to four distinct clades: 

1) Latin America; 2) North America; 3) Asia; and 4) Africa (23). Ceratocystis 

genus has wide geographic distribution and genetic diversity, 

and the greatest part of such diversity is seen in the Americas (1, 2, 3). 

These fungi have long been reported to cause problems to several 

economically important species, such as mango (Mangifera indica), 

Eucalyptus (Eucalyptus sp.) and cacao (Theobroma cacao). Currently, 

there are reports of new hosts to these fungi, e.g., teak (Tectona grandis) 

(11), kiwi (33) and passion fruit (Passiflora edulis) (12). 

In Brazil, three Ceratocystis species were reported, including 

C. paradoxa, C. cacaofunesta and C. fimbriata, which attack monocotyledon 

plants, cacao and several plant species, respectively. With 

the introduction of molecular studies, other two species were reported, 

C. mangicola and C. mangivora, which occur in mango trees in Brazil (41). 

Ceratocystis wilt in Eucalyptus was first noted in the southeast of 

Bahia State (BA) in 1997, and C. fimbriata was the only species related 

to this disease (8). However, in other countries, Eucalyptus has been 

affected by different species of this genus, such as C. Eucalyptusi (20), 

258 Characterization of Isolates of Ceratocystis spp. Collected from Different Hosts


C. moniliformopsis (45) and C. pirilliformis (4). 

Studies conducted with Ceratocystis isolates from Eucalyptus 

collected at different Brazilian regions have indicated that this fungus 

has rich genetic diversity (15, 16). There is evidence of greater diversity 

values for this fungus in crop areas formerly occupied by native vegetation, 

such as the Brazilian Cerrado, in Minas Gerais (10). 

Possible plant attack by species other than C. fimbriata in Latin 

America was already proven by Bodas et al (5). Those authors, using 

morphological and molecular analyses, reported that C. neglecta 

caused disease in E. grandis plants, while Van Wyk et al. (42) detected 

the presence of C. moniliformis in Eucalyptus in Ecuador. 

Considering the Brazilian cacao culture, Ceratocystis wilt has 

caused economic loss since 1978. C. fimbriata had long been regarded 

as the fungal species causing this disease in cacao; however, in 2005, 

based on molecular biology techniques, this fungus was reclassified and 

named C. cacaofunesta since it is specific to cacao (6). This Ceratocystis 

species had also been considered the major causal agent of drought 

in cacao in Latin America until 2015, when C. adelpha was found causing 

death to cacao trees in Ecuador (17) 


259


Studies involving morphological and pathogenicity analyses, 

mating types, DNA sequencing and molecular labeling have helped determine 

new Ceratocystis species (2, 21, 36). Thus, based on these data 

and on the importance of this pathogen to different crops, the present 

study aimed to evaluate the pathogenic, morphological, cultural and 

genetic variability of Ceratocystis in Brazil. 

2. Material collection and fungal isolation 

Material was requested and collected from Eucalyptus, mango 

and cacao production areas. Other isolates were obtained from the Forest 

Pathology Clinics at the Phytosanitary Defense Sector of the School 

of Agronomical Sciences, UNESP-Univ Estadual Paulista, Botucatu, 

São Paulo State, Brazil. 

Fragments of the collected materials were superficially disinfested 

and deposited on carrot disks, which were then used as baits for 

fungal isolation; this technique is considered selective by Moller & Dê 

Vay (27). Once perithecium was completely formed, the monosporic 

colony was obtained as described by Firmino et al. (13). 



A total of 71 isolates were obtained: 39 from Eucalyptus, 2 from 

teak, 2 from atemoya (Annona cherimola and A. squamosa hybrid), 4 

from mango, 18 from cacao, 1 from rubber tree (Hevea brasiliensis), 1 

from passion fruit, 2 from cedar (Acrocarpus fraxinifolius) and 2 from 

"juçara" (Euterpe edulis) (Table 1). 

3. Pathogenic characterization of isolates 

The adopted inoculation method was based on that used by Silveira 

et. al. (38) and described by Firmino et al. (13). Six seedlings 

were inoculated for each tested isolate. The study was standardized 

to include six-month-old seedlings, regardless of the species. Control 

was only inoculated with a culture medium disk (MEA - malt, yeast 

extract and agar) without the fungus. 

For the cross-pathogenicity test, the scarce availability of some 

species led us to choose some isolates that represented one group collected 

from a certain region. 

The inoculated plants underwent a destructive-type evaluation 


261


since the seedlings were transversally sectioned at the stem to measure 

the lesion caused by the fungus in the xylem. After evaluation, 

inoculated stem fragments were included in carrot baits to reisolate 

the fungus (27) and confirm that the induced symptoms were caused 

by the inoculated isolate. 



Table 1. Isolates used in the study. (To be continued) 

Isolate 

ACF1 

ACF2 

ACF3 

ACF4 

ACF5 

ACF6 

ACF7 

ACF8 

ACF9 

ACF10 

ACF11 

ACF12 

ACF13 

ACF14 

ACF15 

ACF16 

ACF17 

ACF18 

ACF19 

City/State 

Votuporanga/SP 

Votuporanga/SP 

Santa Bárbara d’Oeste/SP 

Tupi/SP 

Camacan/BA 

Canavieiras/BA 

Barra da Rocha/BA 

Camacan/BA 

Ilhéus/BA 

Camacan/BA 

Ilhéus/BA 

Camacan/BA 

Camacan/BA 

Ilhéus/BA 

Itacaré/BA 

Belém/PA 

CEPEC/CEPLAC/BA 

Itamari/BA 

Itajuípe/BA 

Host 

Mango 

Mango 

Mango 

Mango 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 

Cacao 


263


Table 1. Isolates used in the study. (To be continued) 

Isolate 

ACF20 

ACF21 

ACF22 

ACF23 

ACF24 

ACF25 

ACF26 

ACF27 

ACF28 

ACF29 

ACF30 

ACF32 

ACF33 

ACF34 

ACF35 

ACF36 

ACF37 

ACF38 

ACF39 

City/State 

Nazaré/BA 

Camacan/BA 

Ferradas/BA 

Botucatu/SP 

Botucatu/SP 

Montes Claros/MG 

Montes Claros/MG 

Araraquara/SP 

Itararé/SP 

Itararé/SP 

Itararé/SP 

Avaré/SP 

Avaré/SP 

Avaré/SP 

Avaré/SP 

Agudos/SP 

João Pinheiro/MG 

João Pinheiro/MG 

Manduri/SP 

Host 

Cacao 

Cacao 

Cacao 

Atemoya 

Atemoya 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 



Isolate 

ACF40 

ACF41 

ACF42 

ACF43 

ACF44 

ACF45 

ACF46 

ACF47 

ACF48 

ACF49 

ACF50 

ACF51 

ACF52 

AFC53 

ACF54 

ACF55 

ACF56 

ACF57 

ACF58 

City/State 

Olhos D’agua/MG 

Paraopeba/MG 

Paraopeba/MG 

Paraopeba/MG 

Itatinga/SP 

Itatinga/SP 

Itatinga/SP 

Itatinga/SP 

Itatinga/SP 

Itatinga/SP 

Cáceres/MT 

Cáceres/MT 

Bocaiuva/MG 

Bocaiuva/MG 

Bocaiuva/MG 

Sete Lagoas/MG 

Três Lagoas/MS 

Três Lagoas/MS 

Botucatu/SP 

Host 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Teak 

Teak 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 


265


Table 1. Isolates used in the study. (Conclusion) 

Isolate 

ACF59 

ACF60 

ACF61 

ACF63 

ACF64 

ACF65 

ACF66 

ACF67 

ACF68 

ACF69 

ACF70 

ACF71 

City/State 

Botucatu/SP 

Agudos/SP 

Agudos/SP 

Bauru/SP 

Ilha Solteira 

Tanhaçu/BA 

Avaré 

Avaré 

Parapuã/SP 

Parapuã/SP 

Nova Adamantina/MS 

Cruz das Almas/BA 

Host 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Eucalyptus 

Rubber 

Passion fruit 

Cedar 

Cedar 

Juçara 

Juçara 

Eucalyptus 

Eucalyptus 

The inoculated plants underwent a destructive-type evaluation 

since the seedlings were transversally sectioned at the stem to measure 

the lesion caused by the fungus in the xylem. After evaluation, 

inoculated stem fragments were included in carrot baits to reisolate 



the fungus (27) and confirm that the induced symptoms were caused 

by the inoculated isolate. 

In the tests conducted with Eucalyptus plants (Clone 219), the 

size of lesions greatly varied (Table 2). Of the isolates collected in São 

Paulo State (SP), the largest lesions were found in those obtained from 

the regions of Itararé, Avaré and Itatinga, varying from 5.38 to 7.00 cm. 

Although isolates from Araraquara, Bauru, Agudos, Botucatu and Manduri 

caused smaller lesions, they were capable of causing wilt in the 

plant; the same was observed for isolates from Três Lagoas and Nova 

Adamantina, collected in Mato Grosso do Sul State (MS). 

Isolates from Minas Gerais State (MG), which induced larger 

lesions, from 5.25 to 6.75 cm, belong to the regions João Pinheiro, Olhos 

d'Agua, Bocaiúva and Sete Lagoas. In Bocaiúva region, the obtained 

isolates showed variable lesion size (Table 2). 

The isolates from the region of Montes Claros (MG) and Cruz 

das Almas (BA) manifested small lesions, not causing apparent wilt 

in the inoculated plant. 

Pathogenicity studies of Ceratocystis isolates collected from different 

hosts indicated varied lesion sizes and aggressiveness among isolates 

obtained from the same host species, as observed in the present study (1). 


267


Eucalyptus plants demonstrated xylem darkening when inoculated 

with Ceratocystis isolates from cacao, mango, atemoya, teak, 

"juçara" and rubber plants, although these isolates caused smaller lesions, 

compared to Eucalyptus isolates. In this case, wilt was not observed 

in the inoculated plants. Most isolates were capable of growing 

in the carrot bait when reisolated from Eucalyptus xylem samples. 

Rubber plants (RRIM 600) were inoculated with rubber, mango, 

cacao, Eucalyptus, teak and atemoya isolates. At 90 days after inoculation, 

shoot blight was observed in plants inoculated with rubber 

and mango isolates; however, xylem darkening evolved only in rubber 

plants inoculated with their specific isolate. In none of the inoculations, 

latex flow was interrupted. 

In pathogenicity tests carried out with cacao plants (Cv. Teobahia), 

all tested cacao isolates were highly aggressive, causing wilt in 

the plants at 15 days after inoculation and death at 30 days after inoculation. 

In the cross-pathogenicity tests conducted with cacao plants, 

only isolates from atemoya, mango and teak were capable of causing 

darkening of xylem vessels, leading plants to death at approximately 30 

days after inoculation. Isolates from Eucalyptus, rubber and "juçara" 

neither caused significant lesions nor induced wilt in cacao plants over 

the experiment, indicating that such isolates are not capable of causing 



disease in plants of this species. Similar results were obtained by Baker 

et al. (1), who also noted that isolates from Eucalyptus were not capable 

of infecting cacao plants. Those same authors stated that Ceratocystis 

isolates from cacao, sweet potato, Platanus sp., coffee, mango and Eucalyptus 

are more aggressive in their hosts of origin and that, similarly 

to the present study, there is cross-pathogenicity in some cases. 

Ceratocystis isolate from cedar, when inoculated in Indian cedar 

plants, led them to death at three months after inoculation. This same 

isolate was capable of causing lesions in Eucalyptus seedlings, but wilt 

was not observed in the inoculated plants during the evaluation period. 

The isolate from passion fruit (Cv. Sul Brasil) did not cause symptom 

in any inoculated passion fruit or Eucalyptus plant. Slight xylem 

darkening was noted at the inoculation site, but it did not result in the 

death of plants. For this isolate, obtained from passion fruits, inoculations 

were also conducted in fruits, according to the methodology described 

by Firmino et al. (12). Symptoms of wilt caused by C. fimbriata 

were observed at approximately six days after inoculation. The fungus 

was reisolated from the infected fruit, confirming its pathogenicity. 

The isolates from "juçara" were not pathogenic to Eucalyptus, 

originating lesions slightly larger than in the control. According to the 


269


pathogenicity tests in "juçara", this isolate caused shoot blight at 90 

days after inoculation. 

Mango plants (Bourbon), at 20 days after inoculation, had wilt 

symptoms when inoculated with isolates from mango tree. At approximately 

30 days after inoculation, all plants had blight symptoms. In the 

cross-pathogenicity tests, one isolate from atemoya (ACF 23), one from 

cacao (ACF 9), one from teak (ACF 51) and four from Eucalyptus (ACF 

27, ACF 37, ACF 54 and ACF 55) were capable of invading the vessels of 

the tested mango plants, generating lesions and causing wilt symptoms. 

In the inoculated teak and atemoya plants, there was no variation 

in the size of lesions generated by their isolates, both of which caused 

wilt in the plant at 40 days after inoculation and shoot death at 90 days 

after inoculation (Table 2). 

4. Cultural characterization of isolates 

Cultural characterization was based on the mycelial growth 

rate and on certain morphological characteristics (colony coloration 

and presence of perithecium) of the colonies grown on culture medium. 

For each isolate, 6mm-diameter mycelial disks were obtained 



from the edges of a colony grown during 14 days on the culture 

medium MEA and transferred to the center of new Petri plates containing 

the same type of medium. 

After recultivation, the isolates were incubated at 25±1 ºC and 12- 

hour photoperiod. The experiment was daily evaluated by measuring the 

perpendicular diameters of the colony with the aid of a millimeter ruler. 

The average mycelial growth rate, expressed as cm/day, was used 

for statistical analyses. The experiment was conducted as completely 

randomized design, with five replicates per treatment, where each 

experimental plot was composed of one plate. Data corresponding to 

the mycelial growth rate of isolates underwent analysis of variance and 

means were compared according to Scott-Knott statistical tests. 

Colony growth rate ranged between 0.22 and 0.93 cm/day for isolates 

from Eucalyptus; 0.52 and 0.70 cm/day for isolates from mango; 

0.45 and 0.58 cm/day for isolates from cacao; 0.51 and 0.60 cm/day 

for isolates from atemoya; and 0.68 to 0.33 cm/day for isolates from 

teak (Table 5). The growth of isolates from passion fruit and rubber 

tree varied from 0.2 to 0.55 cm/day, respectively. Isolates from cedar 

had growth rate of 0.45 cm/day. Isolates from "juçara" had the greatest 

mycelial growth (2.95 and 3.02 cm/day). 


271


Table 2. Lesion size (cm) in plants inoculated with isolates of Ceratocystis 

sp. (To be continued) 

Inoculated plants 

Isolates 

Eucalyptus 

Cacao 

Mango 

Atemoya 

ACF1 

Mango 

2.75 

d 

3.00 

b 

12.37 

d 

- 

ACF2 

Mango 

3.50 

d 

5.00 

d 

13.00 

d 

- 

ACF3 

Mango 

2.13 

c 

- 

11.75 

d 

- 

ACF4 

Mango 

2.25 

c 

- 

11.50 

d 

- 

ACF5 

Cacao 

1.25 

b 

9.38 

f 

0.47 

b 

- 

ACF6 

Cacao 

2.50 

c 

7.50 

e 

- 

- 

ACF7 

Cacao 

2.20 

c 

6.25 

d 

- 

- 

ACF8 

Cacao 

1.25 

b 

5.25 

d 

- 

- 

ACF9 

Cacao 

1.25 

b 

5.50 

d 

10.25 

d 

- 

ACF10 

Cacao 

2.63 

c 

4.75 

c 

- 

- 

ACF11 

Cacao 

1.00 

b 

6.00 

d 

- 

- 

ACF12 

Cacao 

2.88 

c 

6.25 

d 

- 

- 

ACF13 

Cacao 

3.38 

d 

6.75 

d 

- 

- 

ACF14 

Cacao 

1.63 

c 

6.25 

d 

- 

- 

ACF15 

Cacao 

2.75 

c 

6.75 

d 

0.47 

b 

- 

ACF16 

Cacao 

1.95 

c 

6.25 

d 

- 

- 

ACF17 

Cacao 

2.25 

c 

7.00 

e 

- 

- 

ACF18 

Cacao 

2.25 

c 

4.93 

c 

- 

- 




Teak 

Cedar 

Juçara 

Rubber tree 

Passion fruit 

- 

- 

- 

1.5c 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

0.3b 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 


273





Isolates 

Eucalyptus 

Cacao 

Mango 

Atemoya 

ACF19 

Cacao 

2.50 

c 

6.25 

d 

- 

- 

ACF20 

Cacao 

0.75 

b 

5.50 

d 

- 

- 

ACF21 

Cacao 

1.25 

b 

6.00 

d 

- 

- 

ACF22 

Cacao 

2.25 

c 

5.50 

d 

- 

- 

ACF23 

Atemoya 

1.75 

c 

5.38 

d 

2.62 

c 

12.00 

b 

ACF24 

Atemoya 

1.86 

c 

5.00 

d 

- 

13.50 

b 

ACF25 

Eucalyptus 

1.88 

c 

- 

- 

- 

ACF26 

Eucalyptus 

3.75 

d 

- 

- 

- 

ACF27 

Eucalyptus 

2.50 

c 

0.65 

a 

4.87 

c 

- 

ACF28 

Eucalyptus 

5.38 

e 

- 

- 

- 

ACF29 

Eucalyptus 

5.75 

e 

- 

- 

- 

ACF30 

Eucalyptus 

6.38 

e 

0.62 

a 

0.42 

b 

- 

ACF31 

Eucalyptus 

6.38 

e 

0.56 

a 

- 

- 

ACF32 

Eucalyptus 

5.88 

e 

- 

- 

- 

ACF33 

Eucalyptus 

4.63 

d 

- 

- 

- 

ACF34 

Eucalyptus 

5.63 

e 

- 

- 

- 

ACF35 

Eucalyptus 

6.63 

e 

- 

- 

- 

ACF36 

Eucalyptus 

2.38 

c 

- 

- 

- 




Teak 

Cedar 

Juçara 

Rubber tree 

Passion fruit 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

0.4b 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

0.55b 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 


275





Isolates 

Eucalyptus 

Cacao 

Mango 

Atemoya 

ACF37 

Eucalyptus 

5.75 

e 

0.48 

a 

3.75 

c 

- 

ACF38 

Eucalyptus 

6.25 

e 

- 

- 

- 

ACF39 

Eucalyptus 

4.00 

d 

- 

- 

- 

ACF40 

Eucalyptus 

5.50 

e 

- 

- 

- 

ACF41 

Eucalyptus 

4.38 

d 

- 

- 

- 

ACF42 

Eucalyptus 

3.75 

d 

0.51 

a 

- 

- 

ACF43 

Eucalyptus 

4.38 

d 

- 

- 

- 

ACF44 

Eucalyptus 

7.00 

e 

- 

- 

- 

ACF45 

Eucalyptus 

6.00 

e 

0.53 

a 

0.87 

- 

ACF46 

Eucalyptus 

3.75 

d 

- 

- 

- 

ACF47 

Eucalyptus 

4.00 

d 

- 

- 

- 

ACF48 

Eucalyptus 

3.13 

c 

- 

- 

- 

ACF49 

Eucalyptus 

2.00 

c 

0.48 

a 

- 

- 

ACF50 

Teak 

5.00 

e 

0.90 

a 

- 

- 

ACF51 

Teak 

0.75 

b 

2.63 

b 

5.62 

c 

- 

ACF52 

Eucalyptus 

3.75 

d 

- 

- 

- 

ACF53 

Eucalyptus 

6.75 

e 

- 

- 

- 

ACF54 

Eucalyptus 

3.88 

d 

0.70 

a 

6.75 

c 

- 




Teak 

Cedar 

Juçara 

Rubber tree 

Passion fruit 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

0.4b 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

10.25 

b 

- 

- 

0.45b 

- 

12.20 

b 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 


277



sp. (Conclusion) 


Isolates 

Eucalyptus 

Cacao 

Mango 

Atemoya 

ACF55 

Eucalyptus 

5.25 

e 

0.60 

a 

5.75 

c 

- 

ACF56 

Eucalyptus 

2.55 

c 

- 

- 

- 

ACF57 

Eucalyptus 

2.50 

c 

- 

- 

- 

ACF58 

Eucalyptus 

3.05 

c 

- 

- 

- 

ACF59 

Eucalyptus 

2.65 

c 

- 

- 

- 

ACF60 

Eucalyptus 

2.38 

c 

- 

- 

- 

ACF61 

Eucalyptus 

2.35 

c 

- 

- 

- 

ACF62 

Eucalyptus 

3.25 

c 

- 

- 

- 

ACF63 

Eucalyptus 

3.00 

c 

- 

- 

- 

ACF64 

Rubber tree 

1.25 

c 

- 

- 

- 

ACF65 

Passion fruit 

0.40 

a 

- 

- 

- 

ACF66 

Cedar 

2.6 

c 

- 

- 

- 

ACF67 

Cedar 

2.4 

c- 

- 

- 

- 

ACF68 

Juçara 

0.68 

b 

- 

- 

- 

ACF69 

Juçara 

0.75 

b 

- 

- 

- 

ACF70 

Eucalyptus 

3.30 

d 

- 

- 

- 

ACF71 

Eucalyptus 

1.05 

b 

- 

- 

- 

Control 0.43* a 0.51 a 0.25 a 0.55* a 

Means followed by the same lowercase letter in the column do not differ significantly, 

according to Scott-Knott test at 5% (data transformed into 0.5√x; Coefficient 

of variation for Eucalyptus (35.10%), cacao (17.55%), mango (34.55%), 




Teak 

Cedar 

Juçara 

Rubber tree 

Passion fruit 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

1.8c 

- 

- 

- 

- 

- 

0a 

- 

4.5 

- 

- 

- 

- 

4.7 

- 

- 

- 

- 

- 

5.00 

- 

- 

- 

- 

6.75 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

0.56* a 0 a 0.65 0a 0a 

teak (6.10%), atemoya (7.22%), juçara (3.25%, rubber tree (2.10%).). (-) 

Not tested isolates. *Lesions resultant of the stem injury; IN: Inoculated but 

still not assessed plants. 


279


Isolates from cacao produced brown colonies, while the remaining 

studied isolates produced grayish colony. The brown coloration 

of isolates from cacao tree reflects the small quantity of mycelium 

and the large quantity of perithecia, which are dark brown. 

Some isolates did not produce perithecia under laboratory conditions 

using the MEA medium, including one isolate from mango 

(ACF2), three from Eucalyptus (ACF26, ACF55 and ACF 70) and 

two from "juçara"(ACF68 and ACF69). 

Compared to the remaining studied isolates, the one from 

"juçara" produced differentiated colonies which were cottony and 

grayish, evolving to black. Together with the growth rate and the 

absence of perithecium on the culture medium, such characteristics 

indicate that these isolates probably belong to C. paradoxa complex, 

normally reported to attack palm tree and sugarcane crops 

(25, 26, 28). The sexual form of the colony from "juçara", induced 

on medium based on tomato juice, had as different features only the 

colony coloration, which changed from dark to white and cottony. 

The sexual colony obtained from isolates from "juçara", when recultured 

on MEA medium, returned to its original coloration, without 

forming perithecia. 

Van Wyk et al (41), reclassifying the Brazilian isolates from 



mango tree, conducted their cultural characterization. They indicated 

that such isolates obtained a mycelial growth rate similar to those 

obtained in the present study for isolates from mango tree. Those 

authors cited a growth rate of 4.4 cm in seven days, i.e., 0.62 cm/ 

day. Ceratocystis isolates from mango tree from Oman and Pakistan 

also grew 0.62 mm/day (40). Low mycelial growth rate, which 

occurred for some isolates from Eucalyptus and teak, has been reported 

for species like C. piriniformis, growing approximately 0.18 

cm/day on MEA culture medium (4). Isolates of C. variospora, C. 

populicola and C. smalleyi grow on MEA medium, on average, 0.36 

cm/day, 0.34cm/day and 0.42 cm/day, respectively (24), proving 

that the low mycelial growth rates found in the present study are 

frequent for the genus Ceratocystis. 


281


Table 5. Mycelial growth of Ceratocystis isolates. (To be continued) 

Isolates/Host 

Growth Rate 

(cm/day) 

Colony 

Coloration 

Presence of 

perithecium 

ACF1 

Mango 

0.61 

c 

light gray 

+ 

ACF2 

Mango 

0.53 

b 

greenish gray 

- 

ACF3 

Mango 

0.52 

b 

greenish gray 

+ 

ACF4 

Mango 

0.70 

c 

light gray 

+ 

ACF5 

Cacao 

0.51 

b 

brown 

+ 

ACF6 

Cacao 

0.48 

b 

brown 

+ 

ACF7 

Cacao 

0.45 

b 

brown 

+ 

ACF8 

Cacao 

0.47 

b 

brown 

+ 

ACF9 

Cacao 

0.46 

b 

brown 

+ 

ACF10 

Cacao 

0.45 

b 

brown 

+ 

ACF11 

Cacao 

0.46 

b 

brown 

+ 

ACF12 

Cacao 

0.58 

b 

brown 

+ 

ACF13 

Cacao 

0.47 

b 

brown 

+ 

ACF14 

Cacao 

0.56 

b 

brown 

+ 

ACF15 

Cacao 

0.47 

b 

brown 

+ 

ACF16 

Cacao 

0.57 

b 

brown 

+ 

ACF17 

Cacao 

0.49 

b 

brown 

+ 

ACF18 

Cacao 

0.47 

b 

brown 

+ 

ACF19 

Cacao 

0.52 

b 

brown 

+ 



Isolates/Host 

Growth Rate 

(cm/day) 

Colony 

Coloration 


perithecium 

ACF20 

Cacao 

0.56 

b 

brown 

+ 

ACF21 

Cacao 

0.45 

b 

brown 

+ 

ACF22 

Cacao 

0.46 

b 

brown 

+ 

ACF23 

Atemoya 

0.51 

b 

whitish gray 

+ 

ACF24 

Atemoya 

0.60 

c 

whitish gray 

+ 

ACF25 

Eucalyptus 

0.22 

a 

whitish gray 

+ 

ACF26 

Eucalyptus 

0.68 

c 

greenish gray 

- 

ACF27 

Eucalyptus 

0.47 

b 

whitish gray 

+ 

ACF28 

Eucalyptus 

0.76 

c 

greenish gray 

+ 

ACF29 

Eucalyptus 

0.87 

c 

light gray 

+ 

ACF30 

Eucalyptus 

0.66 

c 

greenish gray 

+ 

ACF31 

Eucalyptus 

0.72 

c 

greenish gray 

+ 

ACF32 

Eucalyptus 

0.68 

c 

greenish gray 

+ 

ACF33 

Eucalyptus 

0.74 

c 

greenish gray 

+ 

ACF34 

Eucalyptus 

0.72 

c 

greenish gray 

+ 

ACF35 

Eucalyptus 

0.68 

c 

whitish gray 

+ 

ACF36 

Eucalyptus 

0.50 

b 

light gray 

+ 

ACF37 

Eucalyptus 

0.65 

c 

dark gray 

+ 

ACF38 

Eucalyptus 

0.72 

c 

dark gray 

+ 


283


Table 5. Mycelial growth of Ceratocystis isolates. (Conclusion) 

Isolates/Host 

Growth Rate 

(cm/day) 

Colony 

Coloration 


perithecium 

ACF39 

Eucalyptus 

0.63 

c 

light gray 

+ 

ACF40 

Eucalyptus 

0.53 

b 

dark gray 

+ 

ACF41 

Eucalyptus 

0.93 

c 

light gray 

+ 

ACF42 

Eucalyptus 

0.75 

c 

light gray 

+ 

ACF43 

Eucalyptus 

0.75 

c 

light gray 

+ 

ACF44 

Eucalyptus 

0.56 

b 

whitish gray 

+ 

ACF45 

Eucalyptus 

0.43 

b 

whitish gray 

+ 

ACF46 

Eucalyptus 

0.50 

b 

light gray 

+ 

ACF47 

Eucalyptus 

0.36 

a 

whitish gray 

+ 

ACF48 

Eucalyptus 

0.57 

b 

whitish gray 

+ 

ACF49 

Eucalyptus 

0.45 

b 

whitish gray 

+ 

ACF50 

Teak 

0.30 

a 

greenish gray 

+ 

ACF51 

Teak 

0.68 

c 

greenish gray 

+ 

ACF52 

Eucalyptus 

0.53 

b 

greenish gray 

+ 

ACF53 

Eucalyptus 

0.68 

c 

greenish gray 

+ 

ACF54 

Eucalyptus 

0.36 

a 

greenish gray 

+ 

ACF55 

Eucalyptus 

0.67 

c 

greenish gray 

- 



Isolates/Host 

Growth Rate 

(cm/day) 

Colony 

Coloration 


perithecium 

ACF56 

Eucalyptus 

0.45 

b 

gray 

+ 

ACF57 

Eucalyptus 

0.50 

b 

gray 

+ 

ACF58 

Eucalyptus 

0.55 

b 

gray 

+ 

ACF59 

Eucalyptus 

0.48 

b 

gray 

+ 

ACF60 

Eucalyptus 

0.35 

a 

gray 

+ 

ACF61 

Eucalyptus 

0.50 

b 

gray 

+ 

ACF62 

Eucalyptus 

0.36 

a 

gray 

+ 

ACF63 

Eucalyptus 

0.35 

a 

gray 

+ 

ACF64 

Rubber tree 

0.48 

bb 

gray 

+ 

ACF65 

Passion fruit 

0.20 

a 

brown 

+ 

ACF66 

Cedar 

0.45 

b 

whitish gray 

+ 

ACF67 

Cedar 

0.45 

b 

whitish gray 

+ 

ACF68 

Juçara 

2.95 

d 

black 

- 

ACF69 

Juçara 

3.02 

d 

black 

- 

ACF70 

Eucalyptus 

0.58 

b 

whitish gray 

- 

ACF71 

Eucalyptus 

0.45 

b 

whitish gray 

+ 


according to Scott-Knott test at 5% (data transformed into 0.5√X; 

Coefficient of variation equal to 5.98%) 


285


5. Morphological characterization of isolates 

In this experiment, we determined the size and the format of all 

types of spores (cylindrical endoconidia, doliform endoconidia, chlamydospores 

and ascospores) produced by the genus Ceratocystis. Perithecium 

measurement was also carried out. Thus, spores and perithecia 

were obtained directly from the cultures grown under the same conditions 

described in the previous item. Conidia were measured by using the 

video-camera system Opton, model TA-0124XS, installed in an optical 

microscope. The image was transmitted to a computer and analyzed by 

using the software EDN-2. For the equipment calibration, a micrographed 

blade was used (Carl Zeiss ® ). To study dimensions and formats, 30 spores 

and perithecia were randomly chosen from each studied isolate. The average 

values of length and width of spores and perithecia from each isolate 

were used for statistical analysis according to Scott-Knott test. 

There was great variation in the size of perithecia and spores 

for all isolates 

Based on the rostrum of perithecia, grouping of most isolates 

from cacao tree could be noted. Thus, this feature of the perithecium 

may help distinguish between isolates from cacao and those from other 

hosts (Table 8). Engelbrecht & Harrington (6) indicated a significant 



difference in the length of the rostrum of perithecia among isolates from 

cacao from Brazil, Colombia, Costa Rica and Ecuador. Isolates from 

Ecuador have longer rostrum, compared to the remaining ones. 

Isolates ACF2, ACF26, ACF55 and ACF 70 did not produce 

perithecia. At first, the lack of sexual structures was thought to be related 

to characteristics linked to the mating-type system of the fungus 

(22). However, according to studies of this reproduction system, all 

investigated isolates, except the isolate from "juçara", amplified the 

DNA region specific to the MAT-2 group (14). Therefore, the absence 

of perithecia in these isolates can be linked to the growing conditions. 

Isolate ACF46, obtained from Eucalyptus collected in the region 

of Itatinga/SP, and all other isolates collected in the same region had 

the length of the perithecium rostrum within the parameters of isolates 

from mango tree. 

Isolates ACF9 and ACF13 from cacao were noted to present double 

rostrum in the perithecium. Isolates ACF68 and ACF69 from "juçara" 

showed perithecium only when grown on medium Rashid & Trujillo 

(34) composed of: 200 ml tomato juice, 800 ml distilled water, 1g calcium 

carbonate, 20g Agar, 0.8g Oxgall powder, 0.66g PCNB (Pentachloronitrobenzene), 

0.2g streptomycin sulfate and 0.05g tetracycline. 

This medium is normally used to isolate C. paradoxa from the soil (28). 


287


The perithecium produced by isolates from "juçara" was different 

from those produced by Ceratocystis isolates from other 

hosts. The isolate from "juçara" had perithecium with appendices 

around its body, a feature cited as typical of species belonging to the 

C. paradoxa complex (26). 

Some authors have shown that certain morphological characteristics 

may collaborate to differentiate among species, such as presence 

or absence of spores of the doliform type, presence of a “collar” at the 

point where the rostrum of perithecium emerges, and short and conical 

spikes at the base of the asco (6, 24, 45). 

Based on statistical grouping, cylindrical conidia showed high 

variability in their size; however, this variation is scarcely noted visually. 

Absence of chlamydospore was observed for some isolates in this 

study (isolates ACF 8, ACF34 and ACF 46). 

Doliform conidia were observed for all isolates from the cities 

Paraopeba, Olhos d'Água, João Pinheiro, one isolate from Bocaiúva, 

two isolates from Itararé, one isolate from Avaré and one isolate from 

passion fruit (Table 6). The presence of this type of spore has been reported 

for other C. fimbriata isolates of the Latin America clade (T. C. 

HARRINGTON apud 7). 



Van Wyk et al. (40) studied Ceratocystis isolates from mango 

trees from the region of Oman and Brazil based on their genetic characteristics 

and on the presence of these conidia of the doliform type. They 

proposed that the isolates from mango collected in the region of Oman 

and Pakistan could be defined as a species different from C. fimbriata 

since the latter does not produce doliform conidia. Those authors suggested 

that this new species could be identified as C. manginecans. 

6. Molecular characterization of isolates 

DNA extraction from the collected isolates was conducted according 

to the method developed by Murray & Thompson (29). Amplification 

of the ITS-5.8S rDNA region was done by using the pairs 

of primers ITS 1/ ITS 4, cited by White et al. (44). For the amplification 

of part of the α-elongase gene, the pairs of oligonucleotide primers 

described by O’Donnell et al. (31) were used, and for the amplification 

of the histone region, the oligonucleotides described by Glass 

& Donaldson (19) were employed. Polymerase chain reaction (PCR), 

for all studied regions, was carried out according to the procedures 

described by Firmino et al. (13). 


289


Table 3. Perithecium measurements (µm) for isolates. (To be continued) 

Perithecia 

Body 

Isolates/Host 

Width 

Length 

ACF1 

Mango 

222.30 

e 

260.75 

e 

ACF3 

Mango 

235.70 

f 

243.93 

d 

ACF4 

Mango 

143.90 

b 

156.55 

b 

ACF5 

Cacao 

186.11 

d 

189.20 

c 

ACF6 

Cacao 

204.10 

e 

217.15 

d 

ACF7 

Cacao 

206.50 

e 

237.94 

d 

ACF8 

Cacao 

204.00 

e 

234.36 

d 

ACF9 

Cacao 

153.80 

c 

178.36 

c 

ACF10 

Cacao 

257.00 

f 

249.35 

e 

ACF11 

Cacao 

233.50 

f 

249.39 

e 

ACF12 

Cacao 

206.00 

e 

195.06 

c 

ACF13 

Cacao 

225.10 

e 

235.61 

d 

ACF14 

Cacao 

231.30 

f 

266.03 

e 

ACF15 

Cacao 

218.64 

e 

241.02 

d 

ACF16 

Cacao 

195.70 

e 

215.66 

d 

ACF17 

Cacao 

177.70 

d 

205.56 

d 

ACF18 

Cacao 

190.00 

e 

245.33 

d 

ACF19 

Cacao 

254.30 

f 

245.53 

e 



Perithecia 

Rostrum 

Rostrum base 

Superior part 

Length 

32.50 

c 

22.06 

c 

619.68 

f 

24.78 

b 

20.69 

c 

625.34 

f 

21.92 

a 

17.49 

a 

336.43 

c 

25.18 

b 

19.06 

b 

591.73 

e 

29.80 

c 

21.21 

c 

502.39 

d 

30.99 

c 

22.44 

c 

411.98 

c 

23.83 

b 

21.88 

c 

518.26 

d 

26.77 

b 

21.31 

c 

580.42 

e 

27.82 

c 

22.04 

c 

540.36 

e 

31.08 

c 

24.13 

d 

479.76 

d 

28.99 

c 

21.02 

c 

781.22 

g 

41.88 

e 

29.55 

e 

505.08 

d 

26.35 

b 

20.72 

c 

537.67 

e 

26.13 

b 

20.27 

b 

509.00 

d 

27.18 

b 

22.90 

c 

454.00 

d 

33.47 

d 

27.04 

e 

573.91 

e 

32.89 

d 

21.80 

c 

570.75 

e 

28.12 

c 

19.53 

b 

562.66 

e 


291



Perithecia 

Body 

Isolates/Host 

Width 

Length 

ACF20 

Cacao 

215.60 

e 

223.83 

d 

ACF21 

Cacao 

199.70 

e 

220.32 

d 

ACF22 

Cacao 

218.82 

e 

251.68 

e 

ACF23 

Atemoya 

210.05 

e 

233.02 

d 

ACF24 

Atemoya 

208.40 

e 

230.15 

d 

ACF25 

Eucalyptus 

223.60 

e 

196.88 

c 

ACF27 

Eucalyptus 

133.33 

b 

153.05 

b 

ACF28 

Eucalyptus 

202.40 

e 

208.14 

d 

ACF29 

Eucalyptus 

147.60 

b 

150.10 

b 

ACF30 

Eucalyptus 

272.40 

g 

273.50 

e 

ACF31 

Eucalyptus 

172.90 

d 

167.87 

c 

ACF32 

Eucalyptus 

161.50 

c 

167.72 

c 

ACF33 

Eucalyptus 

155.50 

c 

171.62 

c 

ACF34 

Eucalyptus 

196.70 

e 

198.73 

c 

ACF35 

Eucalyptus 

151.90 

c 

155.87 

b 

ACF36 

Eucalyptus 

295.70 

g 

307.95 

f 

ACF37 

Eucalyptus 

167.40 

c 

194.07 

c 

ACF38 

Eucalyptus 

175.00 

d 

188.14 

c 



Perithecia 

Rostrum 

Rostrum base 

Superior part 

Length 

30.69 

c 

24.12 

d 

546.58 

e 

28.80 

c 

20.79 

c 

492.05 

d 

26.44 

b 

21.61 

c 

534.34 

e 

35.00 

d 

20.56 

c 

703.23 

g 

35.49 

d 

20.47 

c 

951.65 

h 

31.90 

c 

22.88 

c 

632.97 

f 

22.35 

a 

18.70 

b 

671.93 

f 

29.80 

c 

21.10 

c 

357.28 

c 

22.11 

a 

18.09 

a 

359.09 

c 

29.65 

c 

18.45 

b 

388.31 

c 

24.56 

b 

18.60 

b 

357.80 

c 

21.61 

a 

16.60 

a 

399.65 

c 

22.34 

a 

17.58 

a 

388.88 

c 

26.92 

b 

18.70 

b 

733.56 

g 

21.04 

a 

17.35 

a 

216.54 

a 

30.72 

c 

23.77 

d 

603.88 

f 

30.97 

c 

19.10 

b 

772.52 

g 

25.07 

b 

20.20 

b 

427.08 

c 


293



Perithecia 

Body 

Isolates/Host 

Width 

Length 

ACF39 

Eucalyptus 

202.60 

e 

224.57 

d 

ACF40 

Eucalyptus 

184.50 

d 

192.55 

c 

ACF41 

Eucalyptus 

161.30 

c 

153.00 

b 

ACF42 

Eucalyptus 

188.60 

d 

208.65 

d 

ACF43 

Eucalyptus 

176.30 

d 

174.26 

c 

ACF44 

Eucalyptus 

251.50 

f 

286.80 

f 

ACF45 

Eucalyptus 

214.60 

e 

216.67 

d 

ACF46 

Eucalyptus 

210.50 

e 

206.53 

d 

ACF47 

Eucalyptus 

237.90 

f 

263.05 

e 

ACF48 

Eucalyptus 

190.70 

e 

194.68 

c 

ACF49 

Eucalyptus 

171.60 

d 

189.70 

c 

ACF50 

Teak 

240.89 

f 

282.45 

f 

ACF51 

Teak 

102.70 

a 

117.80 

a 

ACF52 

Eucalyptus 

184.20 

d 

197.63 

c 

ACF53 

Eucalyptus 

198.40 

e 

225.76 

d 

ACF54 

Eucalyptus 

138.90 

b 

146.00 

b 

ACF55 

Eucalyptus 

147.60 

b 

153.00 

b 

ACF56 

Eucalyptus 

236.00 

f 

261.05 

e 



Perithecia 

Rostrum 

Rostrum base 

Superior part 

Length 

26.71 

b 

19.12 

b 

667.88 

f 

22.56 

a 

17.06 

a 

377.80 

c 

23.68 

b 

17.00 

a 

451.62 

d 

25.59 

b 

19.64 

b 

352.59 

c 

23.72 

b 

18.46 

b 

411.53 

c 

29.11 

c 

23.26 

c 

625.76 

f 

34.16 

d 

23.61 

d 

648.96 

f 

34.92 

d 

25.16 

d 

646.71 

f 

28.83 

c 

23.98 

d 

621.84 

f 

31.15 

c 

20.98 

c 

648.58 

f 

24.92 

b 

19.49 

b 

650.61 

f 

32.42 

c 

21.39 

c 

737.51 

g 

21.03 

a 

16.86 

a 

300.70 

b 

22.63 

a 

18.05 

a 

464.44 

d 

25.45 

b 

20.10 

b 

411.72 

c 

21.15 

a 

17.93 

a 

411.10 

c 

23.68 

b 

18.70 

b 

671.93 

f 

25.50 

b 

21.25 

c 

387.20 

c 


295


Table 3. Perithecium measurements (µm) for isolates. (Conclusion) 

Perithecia 

Body 

Isolates/Host 

Width 

Length 

ACF57 

Eucalyptus 

153.70 

d 

174.26 

c 

ACF58 

Eucalyptus 

165.50 

c 

168.05 

c 

ACF59 

Eucalyptus 

155.00 

c 

168.00 

c 

ACF60 

Eucalyptus 

205.00 

e 

206.50 

d 

ACF61 

Eucalyptus 

149.20 

c 

155.05 

b 

ACF62 

Eucalyptus 

175.50 

d 

164.60 

c 

ACF63 

Eucalyptus 

187.30 

d 

189.05 

c 

ACF64 

Rubber tree 

175.50 

d 

170.60 

c 

ACF65 

Passion fruit 

205.20 

e 

220.70 

a 

ACF66 

Cedar 

162.00 

c 

164.20 

c 

ACF67 

Cedar 

155.64 

c 

142.16 

b 

ACF68 

Juçara 

198.30 

e 

197.55 

c 

ACF69 

Juçara 

187.10 

d 

189.23 

c 

ACF71 

Eucalyptus 

174.30 

d 

186.10 

c 



Perithecia 

Rostrum 

Rostrum base 

Superior part 

Length 

23.69 

b 

20.20 

C 

358.00 

c 

22.05 

a 

18.65 

a 

408.56 

c 

24.05 

b 

19.02 

b 

355.70 

c 

32.25 

d 

20.60 

C 

399.65 

c 

28.60 

c 

17.60 

a 

389.05 

c 

25.00 

b 

20.05 

B 

358.63 

c 

24.95 

b 

19.80 

b 

357.65 

c 

24.55 

b 

20.88 

c 

356.00 

c 

21.45 

c 

22.95 

c 

495.55 

d 

21.55 

a 

17.98 

a 

369.7 

c 

23.00 

b 

18.00 

a 

423.3 

c 

21.00 

a 

18.35 

a 

435.05 

d 

23.00 

a 

23.75 

a 

542.76 

d 

23.05 

b 

19.98 

b 

387.00 

c 

Means followed by the same lowercase letter in the column do not differ 

significantly, according to Scott-Knott test at 5% (data transformed into 

X √0.5; Coefficient of variation of the body width (9.98%), body length 

(8.79%), rostrum base (8.60%), superior part of the rostrum (9.01%) and 

rostrum length (14.05%). 


297


Table 4. Spore measurements (µm) for isolates. (To be continued) 

Chlamydospore 

Spores 

Cylindrical 

Isolates/Host 

Length 1 

Width 2 

Length 3 

Width 4 

ACF1 

Mango 

12.98 

b 

17.93 

c 

23.36 

e 

3.35 

d 

ACF2 

Mango 

14.34 

d 

11.40 

b 

15.90 

b 

3.45 

d 

ACF3 

Mango 

14.07 

c 

11.41 

b 

20.60 

d 

3.93 

f 

ACF4 

Mango 

13.07 

b 

10.62 

b 

21.02 

d 

2.45 

a 

ACF5 

Cacao 

13.25 

b 

9.03 

a 

24.91 

f 

2.82 

b 

ACF6 

Cacao 

12.96 

b 

10.14 

a 

16.01 

b 

3.02 

c 

ACF7 

Cacao 

12.48 

b 

11.06 

b 

18.61 

c 

3.21 

c 

ACF8 

Cacao 

- 

- 

- 

- 

22.18 

e 

3.16 

c 

ACF9 

Cacao 

14.65 

d 

8.88 

a 

24.50 

f 

3.37 

d 

ACF10 

Cacao 

12.84 

b 

11.30 

b 

20.37 

d 

2.75 

b 

ACF11 

Cacao 

12.96 

b 

10.82 

b 

16.68 

b 

2.92 

c 

ACF12 

Cacao 

14.00 

c 

10.41 

b 

25.26 

f 

3.50 

e 

ACF13 

Cacao 

12.60 

b 

8.57 

a 

19.80 

d 

3.65 

e 

ACF14 

Cacao 

13.90 

c 

10.42 

b 

24.10 

e 

3.19 

c 

ACF15 

Cacao 

12.07 

a 

9.21 

a 

22.46 

e 

3.19 

c 

ACF16 

Cacao 

13.88 

c 

10.83 

b 

21.12 

d 

3.37 

d 

ACF17 

Cacao 

14.58 

d 

9.23 

a 

19.85 

d 

3.04 

b 



Doliform 

Perithecia 

Ascospore 

Length 5 

Width 6 

Length 7 

Width 8 

- 

- 

- 

- 

4.86 

b 

4.14 

b 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

5.20 

c 

3.91 

a 

- 

- 

- 

- 

5.07 

c 

4.22 

b 

- 

- 

- 

- 

5.42 

d 

4.15 

b 

- 

- 

- 

- 

4.69 

b 

3.73 

a 

- 

- 

- 

- 

5.05 

c 

3.74 

a 

- 

- 

- 

- 

5.30 

d 

4.39 

b 

- 

- 

- 

- 

4.80 

b 

3.96 

a 

- 

- 

- 

- 

5.26 

d 

4.32 

b 

- 

- 

- 

- 

5.58 

d 

4.40 

b 

- 

- 

- 

- 

5.39 

d 

3.93 

a 

- 

- 

- 

- 

5.34 

d 

3.95 

a 

- 

- 

- 

- 

4.66 

b 

4.04 

b 

- 

- 

- 

- 

5.05 

c 

4.05 

b 

- 

- 

- 

- 

5.00 

c 

3.79 

a 

- 

- 

- 

- 

5.24 

d 

4.29 

b 


299



Chlamydospore 

Spores 

Cylindrical 

Isolates/Host 

Length 1 

Width 2 

Length 3 

Width 4 

ACF18 

Cacao 

13.49 

c 

9.85 

a 

18.70 

c 

3.65 

e 

ACF19 

Cacao 

13.57 

c 

9.70 

a 

18.67 

c 

3.51 

e 

ACF20 

Cacao 

12.88 

b 

9.82 

a 

22.28 

e 

3.36 

d 

ACF21 

Cacao 

14.51 

d 

9.09 

a 

21.92 

d 

3.39 

d 

ACF22 

Cacao 

13.00 

b 

9.95 

a 

19.02 

c 

3.04 

c 

ACF23 

Atemoya 

13.50 

c 

10.15 

b 

21.90 

e 

3.40 

d 

ACF24 

Atemoya 

13.54 

c 

10.21 

b 

22.59 

e 

3.44 

d 

ACF25 

Eucalyptus 

12.73 

b 

10.35 

b 

20.06 

d 

2.95 

c 

ACF26 

Eucalyptus 

13.64 

c 

11.24 

b 

13.54 

a 

4.56 

g 

ACF27 

Eucalyptus 

11.47 

a 

8.59 

a 

21.75 

d 

2.69 

b 

ACF28 

Eucalyptus 

13.07 

b 

10.09 

a 

18.34 

c 

3.35 

d 

ACF29 

Eucalyptus 

13.17 

b 

10.31 

b 

20.02 

d 

3.01 

c 

ACF30 

Eucalyptus 

12.40 

b 

9.91 

a 

13.50 

a 

3.12 

c 

ACF31 

Eucalyptus 

11.77 

a 

10.15 

a 

23.92 

e 

2.94 

c 

ACF32 

Eucalyptus 

13.28 

b 

9.87 

a 

14.53 

a 

3.96 

f 

ACF33 

Eucalyptus 

12.80 

b 

10.07 

a 

17.36 

b 

3.00 

c 

ACF34 

Eucalyptus 

- 

- 

- 

- 

15.68 

b 

3.33 

d 



Doliform 

Perithecia 

Ascospore 

Length 5 

Width 6 

Length 7 

Width 8 

- 

- 

- 

- 

5.10 

c 

3.85 

a 

- 

- 

- 

- 

5.00 

c 

3.99 

bb 

- 

- 

- 

- 

5.50 

d 

4.36 

b 

- 

- 

- 

- 

5.42 

d 

4.03 

b 

- 

- 

- 

- 

4.94 

b 

3.92 

a 

- 

- 

- 

- 

5.40 

d 

4.20 

b 

- 

- 

- 

- 

5.43 

d 

4.28 

b 

- 

- 

- 

- 

510 

c 

3.97 

a 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

4.71 

b 

3.68 

a 

- 

- 

- 

- 

5.03 

c 

3.94 

a 

6.70 

b 

5.76 

b 

4.19 

a 

4.09 

b 

7.96 

d 

4.78 

a 

5.48 

d 

3.74 

a 

7.21 

c 

5.80 

b 

4.57 

a 

3.72 

a 

- 

- 

- 

- 

4.66 

b 

3.31 

a 

- 

- 

- 

- 

4.64 

b 

3.58 

a 

6.44 

b 

5.92 

b1 

4.67 

b 

4.30 

b 


301



Chlamydospore 

Spores 

Cylindrical 

Isolates/Host 

Length 1 

Width 2 

Length 3 

Width 4 

ACF35 

Eucalyptus 

12.90 

b 

9.28 

a 

17.25 

b 

3.37 

d 

ACF36 

Eucalyptus 

11.54 

a 

8.46 

a 

28.27 

g 

3.11 

c 

ACF37 

Eucalyptus 

13.43 

b 

10.10 

a 

16.46 

b 

3.90 

f 

ACF38 

Eucalyptus 

13.26 

b 

10.92 

b 

19.02 

c 

3.05 

c 

ACF39 

Eucalyptus 

12.93 

b 

10.25 

b 

14.22 

a 

3.34 

d 

ACF40 

Eucalyptus 

12.46 

b 

10.79 

b 

20.27 

d 

3.22 

c 

ACF41 

Eucalyptus 

13.65 

c 

10.46 

b 

21.01 

d 

2.81 

b 

ACF42 

Eucalyptus 

13.42 

b 

11.04 

b 

17.74 

c 

3.35 

d 

ACF43 

Eucalyptus 

12.68 

b 

10.73 

b 

20.36 

d 

3.13 

c 

ACF44 

Eucalyptus 

14.88 

d 

10.98 

b 

16.43 

b 

3.98 

f 

ACF45 

Eucalyptus 

1350 

c 

11.48 

b 

22.73 

e 

3.25 

d 

ACF46 

Eucalyptus 

- 

- 

- 

- 

22.46 

e 

3.61 

e 

ACF47 

Eucalyptus 

13.91 

c 

11.22 

b 

18.85 

c 

3.21 

c 

ACF48 

Eucalyptus 

12.51 

b 

10.48 

b 

22.71 

e 

2.40 

a 

ACF49 

Eucalyptus 

15.69 

d 

10.38 

b 

18.23 

c 

3.64 

e 

ACF50 

Teak 

12.36 

b 

10.29 

b 

17.80 

c 

3.37 

d 

ACF51 

Teak 

14.45 

d 

10.76 

b 

20.22 

d 

3.10 

c 



Doliform 

Perithecia 

Ascospore 

Length 5 

Width 6 

Length 7 

Width 8 

- 

- 

- 

- 

5.07 

c 

4.06 

b 

- 

- 

- 

- 

4.92 

b 

4.05 

b 

6.75 

b 

5.45 

b1 

5.03 

c 

4.59 

b 

7.64 

c 

6.39 

c 

4.97 

b 

3.66 

a 

- 

- 

- 

- 

5.29 

d 

4.35 

b 

6.91 

b 

5.91 

c 

4.55 

a 

3.51 

a 

7.69 

c 

6.60 

c 

4.84 

b 

3.56 

a 

6.92 

b 

5.57 

b 

5.24 

d 

4.07 

b 

5.68 

a 

5.52 

b 

5.04 

c 

4.16 

b 

- 

- 

- 

- 

5.42 

d 

4.19 

b 

- 

- 

- 

- 

4.86 

b 

4.02 

b 

- 

- 

- 

- 

5.55 

d 

4.40 

b 

- 

- 

- 

- 

5.08 

c 

3.73 

a 

- 

- 

- 

- 

4.95 

b 

3.82 

a 

- 

- 

- 

- 

4.80 

b 

3.64 

a 

- 

- 

- 

- 

4.39 

a 

3.63 

a 

- 

- 

- 

- 

4.74 

b 

3.73 

a 


303



Chlamydospore 

Spores 

Cylindrical 

Isolates/Host 

Length 1 

Width 2 

Length 3 

Width 4 

ACF52 

Eucalyptus 

13.15 

b 

10.53 

b 

21.07 

d 

3.15 

c 

ACF53 

Eucalyptus 

13.58 

c 

10.79 

b 

16.29 

b 

2.94 

c 

ACF54 

Eucalyptus 

13.64 

c 

10.58 

b 

18.76 

c 

2.74 

b 

ACF55 

Eucalyptus 

14.23 

d 

9.85 

a 

12.61 

a 

4.26 

g 

ACF56 

Eucalyptus 

13.92 

c 

10.58 

b 

21.02 

d 

3.12 

c 

ACF57 

Eucalyptus 

13.01 

c 

10.56 

b 

18.85 

c 

3.26 

c 

ACF58 

Eucalyptus 

13.75 

c 

10.39 

b 

18.20 

c 

3.20 

c 

ACF59 

Eucalyptus 

13.50 

c 

10.55 

b 

20.63 

d 

3.07 

c 

ACF60 

Eucalyptus 

12.98 

b 

10.26 

b 

20.02 

d 

3.10 

c 

ACF61 

Eucalyptus 

12.88 

b 

10.99 

b 

19.96 

d 

3.16 

c 

ACF62 

Eucalyptus 

12.96 

b 

10.78 

b 

19.50 

d 

3.58 

d 

ACF63 

Eucalyptus 

12.90 

b 

10.63 

b 

20.06 

d 

2.98 

c 

ACF64 

Rubber tree 

13.56 

c 

10.59 

b 

18.95 

c 

3.35 

d 

ACF65 

Passion fruit 

12.43 

a 

9.52 

a 

17.80 

c 

3.93 

d 

ACF66 

Cedar 

12.08 

a 

11.03 

b 

18.97 

c 

2.78 

b 

ACF67 

Cedar 

15.10 

d 

12.13 

c 

18.03 

c 

3.49 

d 

ACF68 

Juçara 

14.05 

c 

10.15 

b 

22.89 

e 

3.85 

f 



Doliform 

Perithecia 

Ascospore 

Length 5 

Width 6 

Length 7 

Width 8 

- 

- 

- 

- 

4.92 

b 

3.89 

a 

- 

- 

- 

- 

4.80 

b 

4.02 

b 

7.37 

c 

5.56 

b 

4.44 

a 

3.88 

a 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

4.90 

b 

4.03 

b 

- 

- 

- 

- 

4.98 

b 

4.15 

b 

- 

- 

- 

- 

4.56 

aq 

3.98 

a 

- 

- 

- 

- 

4.55 

a 

3.69 

a 

- 

- 

- 

- 

4.38 

a 

3.72 

a 

- 

- 

- 

- 

4.74 

b 

3.66 

a 

- 

- 

- 

- 

4.37 

a 

3.67 

a 

- 

- 

- 

- 

5.02 

c 

4.16 

b 

- 

- 

- 

- 

4.88 

b 

3.97 

a 

7.95 

d 

6.98 

e 

5.98 

d 

5.26 

c 

- 

- 

- 

- 

4.65 

b 

3.60 

a 

- 

- 

- 

- 

5.43 

a 

4.14 

b 

- 

- 

- 

- 

- 

- 

- 

- 


305


Table 4. Spore measurements (µm) for isolates. (Conclusion) 

Chlamydospore 

Spores 

Cylindrical 

Isolates/Host 

Length 1 

Width 2 

Length 3 

Width 4 

ACF69 

Juçara 

14.16 

c 

10.12 

b 

17.06 

c 

3.92 

f 

ACF70 

Eucalyptus 

13.15 

c 

10.78 

b 

19.88 

d 

3.32 

d 

ACF71 

Eucalyptus 

12.12 

a 

11.05 

b 

20.23 

d 

3.55 

d 

For the sequencing of amplified fragments, 100 μl of the PCR 

product was purified with the Kit SV Gel and PCR Clean UP system 

(Promega ® ). The DNA from isolates was sequenced at the Center for 

Human Genome Studies of the University of São Paulo (USP). The 

sequences were edited by means of the software BioEdit Sequence 

Alignment Editor (1997-2005). After edition, these sequences were 

used to search for similar sequences by using the software Blastn of 

the NCBI. Then, the obtained sequences were aligned and processed 

with the program Mega 5.05 so that the phylogenetic tree of Cerato- 



Doliform 

Perithecia 

Ascospore 

Length 5 

Width 6 

Length 7 

Width 8 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 


according to Scott-Knott test at 5% (data transformed into X√0,5; 

Coefficient of variation of 1 (5.94%), 2 (8.89%), 3 (7.97%), 4 (6.05%) 5 

(6.97%), 6 (5.23%), 7(5.11%) and 8 (6.92%). 

cystis isolates could be built, using the method “Tamura-3-parameter” 

(39) for the ITS region and the method “p-distance” (30) for the 

histone and α-elongase regions. For all analyzed regions, the Neighbor 

Joining method was used to build distance matrix. A bootstrap 

was conducted with 10,000 replicates, for each analyzed region. 

In Figures 1, 2 and 3, the phylogenetic trees are shown with 

most isolates representing each collected region. 

The DNA sequences obtained from the ITS-5.8S rDNA region 


307


and from part of the α-elongase gene of the studied Ceratocystis 

isolates indicate that isolates from Eucalyptus, atemoya, teak, cedar, 

passion fruit and rubber belong to the species C. fimbriata. The sequenced 

Eucalyptus isolates from Itatinga region were identified as 

C. manginecans. One isolate obtained from Eucalyptus from Montes 

Claros region (ACF 25) and one isolate obtained from mango from 

Votuporanga region were identified as C. mangicola. The isolates 

from cacao were identified as C. cacaofunesta. Although the isolate 

from "juçara" grouped with isolates from Eucalyptus, there was not 

homology with any sample deposited at the GenBank; thus, this isolate 

will be further studied. 

The sequences of the histone region did not allow the identification 

of isolates since there were no deposits of DNA sequences of 

this region for Ceratocystis species at the GenBank. 

The genetic proximity of Eucalyptus isolates to a Ceratocystis 

species isolated from mango corroborates the data obtained in 

the morphological characterization. Thus, isolates from Eucalyptus 

from Itatinga and one isolate from Montes Claros are possibly derived 

of isolates from mango tree. Such similarity between isolates 

from mango and those from Eucalyptus suggests a common ances- 



tral for both pathogens and probable specialization of isolates from 

Eucalyptus to the host. Similar results were obtained by Ferreira et 

al. (7), who stated that most isolates from Eucalyptus and mango 

studied by them were close, according to the genetic analyses. 

In the phylogenetic analysis based on the ITS region, isolates 

from Brazilian cacao trees did not have high similarity to isolates from 

C. cacaofunesta from other countries. Studies have indicated a proximity 

of Brazilian Ceratocystis isolates from cacao tree to some isolates of 

this fungus collected in other Latin American countries (1). 

The isolate from passion fruit grouped with six isolates from Eucalyptus 

from different Brazilian states; however, in this clade, there 

was a clear division among the isolates from mango, Eucalyptus and 

passion fruit, and each isolate formed a different subgroup. 

Isolates from Avaré were closer to isolates from Eucalyptus from 

Bahia (C1988 and C1442) and from the cities of Manduri, Araraquara 

and Bocaiúva, in the state of São Paulo; the isolate from teak and the 

isolate from rubber tree were close, in another group. The isolates from 

atemoya and "juçara" were more distant from the remaining studied 

isolates, requiring further genetic studies to define their position. 


309


Figure 1. Phylogenetic tree built based on the DNA sequences obtained 

from the ITS-5.8S rDNA region of Ceratocystis isolates. 



The DNA sequence from the α-elongase region corroborates the 

results obtained for the ITS region, isolates from cacao continued separated 

from the remaining ones, while isolates from Eucalyptus that 

were close to the isolates from mango continued demonstrating high 

homology to the isolate from C. mangicola (Figure 2). Combination of 

DNA sequences of this fungus to distinguish between species has been 

applied in recent studies (18, 26, 43). 

As regards the DNA sequences from the histone region, they were 

only useful for distinguishing between isolates from cacao (C. cacaofunesta) 

and isolates from the remaining hosts (Figure 3). 


311



from the α-elongase region of Ceratocystis isolates. 




from the histone region of Ceratocystis isolates. 


313


In general, although it is highly criticized, the use of ITS region 

is useful to identify Ceratocystis isolates; therefore, it still has been employed 

in several studies (18, 23, 26, 37). In the present study, among 

all investigated regions, the ITS region showed to be promising to distinguish 

the collected isolates. According to Fourie et al. (18), the ITS 

region is the only region that provides significant support to the separation 

of most Ceratocystis species. This is well illustrated in the case of 

the present study, since this DNA region allowed the separation between 

C. cacaofunesta, C. mangicola and C. manginecans and isolates that 

had similarities to them. In addition, separation was made according to 

the collection region, since most isolates from Eucalyptus collected in 

São Paulo were distant from isolates from Eucalyptus collected in other 

regions of Brazil. This piece of information is highly important for the 

genetic breeding of this fungus, since the reaction of plant genotypes is 

known to vary according to the inoculated isolate (5, 13, 35, 46). 

7. Conclusion 

The obtained results led to the following conclusions: 

• All tested isolates were pathogenic to their hosts of origin. 



• Cross-pathogenicity was observed 

• Pathogenic, cultural and molecular features, as well as 

some morphological characteristics, allowed the separation 

between isolates from cacao and "juçara" and isolates from 

the remaining hosts. 

• DNA sequences obtained from the regions ITS-5.8S rDNA, 

α-elongase and histone from Ceratocystis isolates indicated that 

most identified isolates belong to the C. fimbriata complex. 

• More than one Ceratocystis species infect Eucalyptus in Brazil. 


315


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337

10 

Use 

of Aerial 

Images Obtained 

by Unmanned 

Aerial Vehicles 

(UAVs) to Detect 

Eucalyptus 

Diseases


10 

Use 

of Aerial Images Obtained by Unmanned Aerial 

Vehicles (UAVs) to Detect Eucalyptus Diseases 

Adimara Bentivoglio Colturato; Edson Luiz Furtado; Kalinka 

Regina Lucas Jaquie Castelo Branco. 


Eucalyptus and pine forests planted in Brazil generate 100% 

cellulose and paper production (18). 

Use of Aerial Images Obtained by Unmanned Aerial Vehicles (UAVs) to Detect Eucalyptus Diseases 

341


The importance of cultivating eucalyptus lies in its rapid growth, 

great adaptive capacity, high yield and countless applications in different 

sectors such as paper and cellulose industries, industrial wood panels, 

mechanically processed wood, coal-fired steelworks and biomass (1). 

Eucalyptus is subject to the attack of several pathologies such as 

tip drought, rust and Ceratocystis wilt. 

Tip drought is caused by the fungus Dothyorella sp., the perfect 

form of which corresponds to Botryosphaeria ribis. Its symptoms appear 

in the lower thirds of the canopy. They are lesions on the main 

stem tips, at the points of insertion of branches and twigs; in branches, 

at the points of insertion of twigs and petioles; in twigs, at the points 

of insertion of petioles. Botryosphaeria ribis has been reported as a 

pathogen of greater aggressiveness to plants predisposed to infection 

due to factors like defoliation, water deficit and boron deficiency (23). 

Boron deficiency has been common among eucalyptus plantations, 

especially for Corymbia citriodora. Deficiency symptoms are: new 

leaves of chlorotic, shrinking and coriaceous aspect that become brittle; 

apical bud death; bark and trunk splitting with rubber exudation 

and tissue necrosis (12). Besides affecting the growth and development 

of trees, such deficiency seems to be a predisposing factor for 

occurrence of fungi like B. ribis and Lasiodiplodia theobromae. 

342 Use of Aerial Images Obtained by Unmanned Aerial Vehicles (UAVs) to Detect Eucalyptus Diseases


Ceratocystis wilt caused by Ceratocystis fimbriata is a systemic 

disease in woody plants, due to the pathogen progress, causing dark 

radial stripes in transversal sections of woody organs, from the medulla 

to the outer wood or from the wood periphery to the medulla, or discoloration 

(dark spot) of wedge shape, generally from the periphery to 

the medulla (6). This pathogen can lead to more than 40% mortality in 

monoclonal plots; it affects 4-month to 5-year-old clonal plantations, 

including stump sprouts, root cuttings and clonal seedlings in a nursery, 

currently damaging 10 clones in four Brazilian states (8). 

It is the cause of diseases in a large number of woody plants and 

some herbs of economic importance such as black acacia, sweet potato, 

cacao, fig, gmelina, mango tree, rubber tree and eucalyptus (7). 

The causal agent of rust in eucalyptus, Puccinia psidii Winter, 

has a wide geographic distribution (9). It attacks species of the family 

Myrtaceae, including eucalyptus, "jambeiro", guava tree, "jabuticabeira", 

strawberry guava tree, "pitangueira" and "jamelãozeiro", 

responding differently to aggressiveness according to the host (2, 10). 

Nowadays, rust represents one of the major agents responsible 

for losses and injuries in eucalyptus reforestation in São Paulo 

State; it started being considered important in the mid 1990's among 


343


young eucalyptus plantations in the region of Itapetininga and Vale 

do Paraíba (4). 

In nursery seedlings and plants in the fields, rust attacks are 

restricted to soft organs such as leaf primordia, branch tips and main 

stem (9). Symptoms are small punctuations on the lower part of 

leaves, which are slightly protuberant and have light green or yellowish 

red coloration (20). 

Disease occurrence and management are linked to the quality 

and yield of eucalyptus plantations. Plant disease quantification methods 

are required, such as direct methods like evaluation of signs and 

symptoms, incidence and severity, or indirect methods like determination 

of the pathogen population, spatial distribution, effects on production 

(damage and/or loss) and the caused defoliation (13). 

Remote sensing based on aerial images is another method that 

can be used for both detection and quantification and has the advantage 

of not requiring contact with diseased plants. The disease can be 

quantified at diverse wavelengths. 

"The joint use of sensors, equipment, aircrafts, spacecrafts and 

others, with the aim of studying the terrestrial environment by means 

of records and analysis of the interactions between electromagnetic 



radiation and substances that compose the Earth planet in their most 

diverse manifestations” is the definition of Remote Sensing (16). It 

is based on the radiation reflected from the foliage. Reflectance differences 

can be obtained and used efficiently to map the incidence 

of a large number of plant pathogens. Frequently employed sensors 

are multispectral (normally RGB and NIR) and, most recently, hyperspectral, 

revealing as a tool to rapidly and efficiently detect diseased 

plants in small and large areas (15). 

Electromagnetic energy is found at larger quantities on the Earth 

and has its origin on the Sun, from where it propagates through the 

space as electromagnetic waves in the form of radiation. The intensity 

of the electromagnetic radiation that reaches a sensor is the parameter 

used to obtain data from the terrestrial surface targets, which are 

subsequently transformed into an interpretable measurement (15). The 

spectral behavior of vegetation is associated with the concepts of reflectance, 

absorptance and transmittance, and reflectance is most frequently 

used in vegetation studies involving remote sensing techniques. Spectral 

reflectance measurements constitute the main example of application 

of these techniques in phytopathology, once there are differences in 

spectral response between a healthy leaf and a diseased leaf. 

The largest part of the electromagnetic radiation of blue (0.35 to 


345


0.50μm) and red (0.50 to 0.62μm) intervals is absorbed by the chlorophyll, 

while in the green interval (0.62 to 0.70 μm), a great part is 

reflected. Near-infrared radiation (0.74 to 1.10 μm) is little affected by 

chlorophyll but highly influenced by the foliar structure of the target 

vegetation. In the visible spectrum and infrared regions, the reflected 

energy depends on properties like pigmentation, humidity and cell 

structure of the vegetation, mineral constitution and humidity of soils, 

and sediment quantity in water reservoirs (3). 

Learning the spectral behavior of the vegetation is fundamental 

to choose the region of the spectrum from which the study data will be 

obtained. In studies related to diseases in plants, the most frequently 

used regions are those of visible and near-infrared (21) since they best 

detect plant disease symptoms, considering that protoplasm degeneration 

occurs in most diseases and is followed by the death of cells, 

tissues and organs (15). 

One of the great advantages of remote sensing in agriculture is 

the capacity to map large areas within a short time. Another advantage 

is the possibility of an aerial view of the plantation, which can reveal 

details not visually observable at the soil level. 

The use of Unmanned Aerial Vehicles - UAVs has been shown a 



highly important tool in remote sensing. 

According to the report by DOD (United States Department 

of Defense) called Unmanned Aerial Vehicle Roadmap 2002 - 2027, 

which is one of the major and most complete documents on the state 

of the art technology, UAVs are: “aerial vehicles that do not carry a 

human operator, use aerodynamic forces to provide vehicle lift, can 

fly autonomously or be piloted remotely, can be expendable or recoverable, 

and can carry a lethal or nonlethal payload. Ballistic or 

semiballistic vehicles, cruise missiles, and artillery projectiles are not 

considered UAVs by the DOD definition” (5). 

Using images obtained by UAVs is advantageous not only because 

the resolution of the images obtained by satellites is not so high 

as that necessary for pathology observation, but also because the images 

obtained by UAVs are more accurate and can be obtained at any 

time and more than once on the same day, differently from those obtained 

by satellites. 

UAVs can and must provide pest monitoring and detection in increasingly 

larger areas. The use of this tool may improve the detection 

process and reduce both the work hours and the workload needed for a 

field survey, since a person walking at 2km/h during 8 hours a day can 


347


only sample 1000 hectares. Using machines is many times difficult 

because “carriers” are small and there is the risk of damaging species. 

Image processing a posteriori allows extracting a larger quantity 

of data, compared to those obtained by terrestrial sampling, normally 

restricted to a small percentage of the total area under analysis. 

Georeferencing of the aerial images and, consequently, plantto-plant 

georeferencing will allow field surveys that can establish correlations 

between the data obtained by images and the reality in the 

field, so that a more accurate database can be created. 

The low-cost expedited generation of thematic maps will allow 

the adoption of cultural practices recommended by accuracy agriculture, 

leading not only to additional economic gain but also to lower 

impact on the environment. 

The aim of this study was to compare direct methods (count of 

disease plants and quantification of the percentage of tissue lesioned 

by pathogens) and indirect methods (reflectance analysis) in different 

wavelengths (multispectral analysis) using Unmanned Aerial Vehicles 

and image processing to create a database that will allow the generation 

of spectral signatures to detect pathologies in eucalyptus plantations. 



2. Material and Methods 

Experiments were conducted in a laboratory and in the field. Each 

pathology was detected in a different locality of São Paulo State. From 

each planting, an area was selected and divided into plots of 10 streets 

containing 50 plants each; thus, each plot was constituted of 500 plants. 

For Ceratocystis wilt and tip drought, incidence data were collected; for 

rust, severity data were collected according to a diagrammatic scale (24). 

For reflectance evaluation in the laboratory, 30 leaves from 

healthy plants and 30 leaves from diseased plants were collected in 

the case of Ceratocystis wilt and tip drought. For rust, as there are 4 

severity levels -- healthy plant and levels I, II and III, 30 leaves were 

collected from each level. Sampling was done from several plants 

which were mixed for the experiment. Leaves were detached from the 

plants and placed inside plastic bags which were stored in a Styrofoam 

box containing ice to prevent water loss. On return, leaves were 

stored in the refrigerator until reflectance reading. 

The leaves collected in the field were analyzed in the laboratory. 

For reflectance measurement, a counting system was established 

with an Ocean Optics USB2000 spectrometer, an optical fiber (for the 


349


visible), led light, led supply source, a lens to prevent light from dissipating, 

a bulkhead and a table. The adopted focal distance was 12 cm. 

One measurement was performed for each leaf, totaling 30 

measurements of leaves from healthy plants and 30 measurements 

of leaves from plants with symptoms of tip drought and Ceratocystis 

wilt. For rust, as there are 4 severity levels - healthy plant and levels I, 

II and III, 30 measurements were performed for each level. This spectrometer 

provided data approximately between 450 nm and 720 nm. 

Flights were done with eBee UAV produced by Sensefly Ltd. 

The eBee is an unmanned aircraft, which is electric and of small size. 

It has an embedded system (hardware and software integrated to the 

aircraft), which is responsible for controlling all functions performed 

on board. These functions include navigation, control (or piloting), 

sensor control, actuator control and management of critical situations. 

From each area, the region where the disease was most significant 

was chosen and delimited for the mission. 

During each flight, images were obtained with cameras Canon 

in RGB and infrared (NIR) filter; subsequently, mosaics of each flown 

area were produced with the software Postflight Terra 3D. 



Reflectance data of the images were obtained by using the 

images in RGB. For the measurements, the software ColourWorker 

– image-based colour measurement was employed (17). Measurements 

accounted for 30 from healthy plants and 30 from plants with 

symptoms of the studied diseases. In this case, for rust, only data 

of healthy and diseased plants could be collected but not separated 

into severity levels. 

For the statistical modeling of the spectral signatures of the 

considered diseases obtained by the spectrometer and by the digital 

images, Multivariate Analysis techniques were employed (11, 14). 

First, the dimensionality of data was reduced based on the Principal 

Component methodology, considering only the two first components 

in all situations. Then, the technique Canonical Linear Discriminant 

Function was adopted (11, 14); it has as basis the classification of k 

groups according to linear functions in the multidimensional space using 

metrics as Euclidean distance and Mahalanobis distance – which 

considers the variance and covariance matrix of observations. This 

technique was subsequently applied to the principal component technique. 

Thus, it was possible to obtain the cross validation – probability 

of poor classification, which informs about the percentage of exit 

and error of the model obtained via principal components. 


351


Both techniques were carried out with the statistical software 

SAS (Statistical Analysis System) (22). 

3. Results 

The purpose of terrestrial evaluations was to identify and locate 

the plants affected by diseases and subsequently perform a manual evaluation, 

using the aerial image of the same place, to compare whether a 

plant identified as diseased in the terrestrial evaluation was also visibly 

diseased based on the image. 

For both Ceratocystis wilt and tip drought, accuracy was not 

100% based only on the aerial image since it only detects plants showing 

advanced-stage symptoms, while the field evaluation detected several 

plants with initial symptoms or symptoms on the lower part of the 

canopy, which could not be visually identified in the aerial image. 

For the area affected by rust, such manual evaluation, only visual, 

was inefficient since the symptoms of the disease are small punctuations 

on the leaves, which could not be observed based on the aerial image. 

Terrestrial evaluations proved that obtaining spectral signatures 



for each disease is necessary and important. These signatures can detect 

even plants with initial-stage symptoms. 

Data obtained in the laboratory with the spectrometer showed that 

separation between healthy and diseased plants can be done with minor 

error for the three evaluated diseases. For rust, data could be classified 

into four levels; however, some data that should be classified as level 

1 were classified as healthy or level 2, and some data related to level 2 

were erroneously classified as level 1 or level 3. This can be explained 

because evaluation based on a diagrammatic scale is very subjective, 

since the evaluator adopts an image as standard and tries to quantify the 

leaves into scores according to the scale. 

Healthy and diseased plants could be separated according to the 

model used to classify the image data. For rust, however, diseased 

plants could not be classified into severity levels based on the aerial images, 

since rust symptoms are small punctuations on the leaves, making 

difficult the visual classification based on the image. 

Laboratory analyses using a spectrometer collaborated to the reflectance 

data obtained by the aerial images, showing that in the case of 

the studied diseases spectral behavior was similar in both cases. 


353


The use of principal components in data analysis is a highly used 

tool in several studies involving reflectance analysis and allowed a considerable 

reduction in the dimensionality of data. Sankaran et al. (19) 

also employed principal component techniques followed by discriminant 

analysis to classify avocado plants attacked by Raffaelea lauricola, 

obtaining 94% accuracy in the classification of asymptomatic leaves 

from infected plants. Zhang et al. (25) also adopted linear discriminant 

function to detect mildew in wheat based on reflectance measurements. 

Application of the linear discriminant function technique generated 

eigenvectors, which helped obtaining spectral signatures and provided 

accuracy data by the used model. 

Spectral signatures were generated for each studied disease, but 

meanwhile these signatures can only be used to identify and classify 

diseases if the disease is previously known and if the data are collected 

with the same equipment or with equipment that presents the same 

spectral ranges. However, a study has already been developing to obtain 

spectral signatures from the linear discriminant function; such signatures 

will be part of a databank which will be used to identify these 

diseases and, in the case of rust, the severity level. 



References 

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Plantadas. Disponível em: . Access on 03/05/2013. 

2. ALFENAS, A.C.; ZAUZA, E.A.V.; MAFIA, R.G.; ASSIS, T.F.de. 

Clonagem e doenças do eucalyptus. Viçosa: Editora UFV, 2004. 442p. 

3. BAUERMANN, G.C. Uso de images de sensores remotos na estimativa 

de características dendrométricas de povoamentos de eucalyptus 

(Dissertação de mestrado). Universidade Federal de Santa 

Maria, 2008. 78p. 

4. CAMARGO, F.R.A.; TAKAHASHI, S.S.; FURTADO, E.L.; VAL- 

LE, C.F.; BONINE, C.A.V. Ocorrência e evolução da rust do eucalyptus 

em duas regiões do Estado de São Paulo. Fitopatologia Brasileira, 

v.22, p.254, 1997. 


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5. DOD - SECRETARY OF DEFENSE. Unmanned Aerial Vehicles(UAV) 

Roadmap. Washington, 2003. 195p. 

6. FERREIRA, F.A. & MILANI, D. Diagnose visual e controle de 

doenças abióticas e bióticas do eucalyptus no Brasil. International Paper, 

Mogi-Guaçu, 2002. 

7. FERREIRA, F.A.; MAFFIA, L.A.; FERREIRA, E.A. Detecção rápida 

de Ceratocystis fimbriata em lenho infetado de eucalyptus, mangueira 

e outros hospedeiros lenhosos. Fitopatologia Brasileira, Brasília, 

v.30, p. 543-545, 2005. 

8. FERREIRA, F.A.; MAFFIA, L.A.; BARRETO, R.W.; DEMUNER, 

N.L.; PIGATTO, S. Sintomatologia da murcha de Ceratocystis fimbriata 

em eucalyptus. Revista Árvore, Viçosa, MG, v.30, p. 155-162, 2006. 

9. FIGUEIREDO, M.B. Doenças fúngicas emergentes em grandes culturas. 

O Biológico. São Paulo, v.63, n. 01/02, p. 29-32, Jan-Dez, 2001. 

10. JOFFILY, J. Rust do eucalyptus. Bragantia, Campinas, v.4, n.8, p. 

475-487, 1944. 

11. JOHNSON, R.; WICHERN, D. Applied Multivariate Statistical Analysis. 

6th Edition. Upper Side River, NJ. Pearson Prentice Hall, 2007. 



12. MALAVOLTA, E.; TRANI, P.E; ATHAYDE, M.F; BRAGA, N.R; 

NOGUEIRA, S.S; MORAES, S.A. Nota sobre deficiência e toxidez de 

boro em espécies cultivadas do gênero Eucalyptus. Revista da Agricultura, 

v. 53, n. 4, p. 243-246, 1978. 

13. MORAES, S.A. de. Quantificação de doenças de plantas. 2007. Artigo 

em Hypertexto. Disponível em: . 

Access on: 03/05/2011. 

14. MORRISON, D.F. Multivariate Statistical Methods. 4th edition. 

New York, McGrow-HillBook Company 2004. 

15. NAUE, C.R.; MARQUES, M.W.; LIMA, N.B.; GALVINCIO, J.D. 

Sensoriamento remoto como ferramenta aos estudos de doenças de 

plantas agrícolas: uma revisão. Revista Brasileira de Geografia Física, 

n.03, 190-195. 2010. 

16. NOVO, E.M. de M. Sensoriamento remoto: principios e aplicações. 

São Paulo. Edgard Blucher. 1989. 308p. 

17. OSORIO, D.; ANDERSON, J.C. Measuring skin colour and spectral 

reflectance using an ordinary digital camera. 2007. Available at: 

 


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18. REZENDE, G. Eucalyptus. Bracelpa – Associação Brasileira 

de celulose e papel. Disponível em: Access on December 2014. 

19. SANKARAN, S.; EHSANI, R. Evaluation of visible-near infrared 

reflectance spectra of avocado leaves as a non-destructive tool for detection 

of laurel wilt. Plant disease, vol. 96, n. 11, p. 1683- 1689, 2012. 

20. SANTOS, A.F.; AUER, C.G.; GRIGOLETTI JÚNIOR, A. Doenças 

do eucalyptus no sul do Brasil: identificação e controle. Embrapa: Circular 

Técnica. Colombo, junho, 2001. 

21. SANTOS JUNIOR, R.F.; SANTOS, J.M.; RUDORFF, B.F.T.; 

MARCHIORATO, I.A. Detecção de Heterodera glycines em plantios 

de soja mediante espectrorradiometria no visível e infravermelho próximo. 

Fitopatologia Brasileira, Brasília, v. 27, p. 355-360, 2002. 

22. SAS. Sas software. Version 9.1. Cary, North Carolina: SAS Institute 

Inc., 1999. 

23. SILVEIRA, R.L.V.A.; KRUGNER, T.L; SILVEIRA, R.I; 

GONÇALVES, A.N. Efeito do boro na suscetibilidade de Eucalyptus 

citriodora a Botryosphaeria ribis e Lasiodiplodia theobromae. Fitopatologia 

Brasileira, v.21, n.4, p. 482-485, 1996. 



24. ZAMPROGNO, K.C.; FURTADO, E.L.; MARINO, C.L.; BONINE, 

C.A.; DIAS, D.C. Utilização de análise de segregantes agrupados na 

identificação de marcadores ligados a genes que controlam a resistência 

à rust (Puccinia psidii Winter) em Eucalyptus sp. Summa Phytopathologica, 

Botucatu, v.34, n.3, p. 253-255, 2008. 

25. ZHANG, J.C.; PU, R.; WANG, J.; HUANG, W.; YUAN, L.; LUO, 

J. Detecting powdery mildew of winter wheat using leaf level hyperspectral 

measurements. Computers and Electronics in Agriculture, 85, 

p. 13-23. 2012. 


359

11The Accelerated 

Evolution of 

Resistance to 

High Risk 

Fungicides in the 

Agroecosystem 

and the Need for 

Anti-Emergence 

Strategies


Accelerated Evolution of Resistance to High 

Risk Fungicides in the Agroecosystem and the 

11The 

Need for Anti-Emergence Strategies 

Paulo Cezar Ceresini; Danilo Augusto dos Santos Pereira. 


The emergence of resistance to fungicides is considered among 

the most serious threats to food security. Since the 70s the emergency 

of resistance to some of the most important classes of modern 

The Accelerated Evolution of Resistance to High Risk Fungicides 

in the Agroecosystem and the Need for Anti-Emergence Strategies 

363


site-specific selective fungicides [e.g. methylbenzimidazole (MBI); 

demethylation inhibitors (DMI); for instance, the triazoles; quinone 

outside inhibitors (Q o 

I); for example, the strobilurins, the succinate 

dehydrogenase inhibitors (SDHIs)] in various phytopathogenic fungi 

has compromised the disease management worldwide by limiting 

the fungicide options in the agroecosystem (25). With consistent increase 

in reports of fungicide resistance in phytopathogens in the 

last 50 years, this fact became highly relevant for the crop protection 

management. Although the emergence of resistance in phytopathogens 

is considered a global menace to food production, surprisingly 

little is known about the evolutionary process associated to development 

and dissemination of resistance to fungicides worldwide 

(11). Specially in Brazil, the current research contributions about 

the impact the emergence of resistance has on a wide range of local 

pathosystems is scarce, merely represented by literature reviews or 

punctual studies (13, 10, 7). 

2. Mechanisms associated to the development of acquired resistance 

to fungicides 

In order to better understand the resistance to fungicides in 

364 The Accelerated Evolution of Resistance to High Risk Fungicides 

in the Agroecosystem and the Need for Anti-Emergence Strategies


field populations of phytopathogens, many studies have characterized 

the mechanisms behind the reduction of sensibility to different 

molecules and the genetic bases of resistance. Four main mechanisms 

are associated to the development of acquired resistance to 

fungicides: a) Alterations in the targeted protein due to mutations 

in the coding gene, specially to many site-specific fungicides as the 

MBI, the DMI, the Q o 

I and the SDHI (25); b) Improved fungicide 

efflux associated with the action of ABC membrane-bound transporters 

and others transporters (30); c) Overexpression of the target 

protein, which requires increased fungicide dose for inhibition (8); 

d) Fungicide detoxification by metabolic enzymes, mostly reported 

for herbicides on grass (9) and not common for fungicides. 

It is widely accepted that different fungicides present different 

risk associated to the development of resistance in phytopathogens (6, 

5, 35). The site-specific fungicides interrupt particular cellular process 

and bind to specific target proteins (5, 35), therefore considered among 

the high risk for resistance development (6, 17). The so called high 

risk is associated to the strong selective pressure imposed by the fungicide 

on the phytopathogen populations (18). On the other hand, are 

the multi-site inhibitors acting in numerous cellular process that are 

considered of lower risk for resistance emergence (6, 35, 17). Using the 



365


same concept, different pathogens have different risk for developing 

resistance (17, 18). For instance, reports of many phytopathogen populations 

rapidly evolving resistance to high risk site-specific fungicides 

(e.g. Q o 

I) in only two years of usage (34). The vulnerability of site-specific 

fungicides to accelerated evolution of resistance relies on its mode 

of action and curative usage, high efficiency (e.g. high activity in low 

doses) and the biologic and epidemiology peculiarities of the targeted 

phytopathogen (e.g. population size, high reproductive rate, possibility 

of long distances dispersion) (25). 

Firstly, so resistance can occur, it is necessary inheritable variation 

to fungicide sensitivity in the phytopathogen population (15). 

Moreover, under the action of inhibitor single-site fungicides, where 

one point mutation in the targeted protein might confer high resistance 

level, the change is qualitative, resulting in two populations showing 

bimodal distribution for sensitivity (15, 25). However, for multi-site 

fungicides or some inhibitor single-site fungicides which possessed 

multiple alleles contributing to the resistance, it is observed a unimodal 

distribution with quantitative changes (15; 25). In both cases it is 

observed directional selection for lower sensibility acting in the discrete 

variation, for qualitative resistance, contrasting with the continue 

distribution for quantitative resistance, observed for gradual changes 

trending to resistance over time (15, 25). 




3. Distinction between acquired resistance and intrinsic resistance 

It is necessary to distinguish between the acquired resistance that 

develops in response to selection against a specific fungicide (quantitative 

or qualitative) from the intrinsic resistance. Some fungi species are natural 

insensitive to determined specific chemical groups, characterizing intrinsic 

resistance that defines the spectrum of action of fungicides. While little 

is known regarding the genetic bases of intrinsic resistance, associations 

have been made with genetic variation in the fungicide targeted site or 

target protein redundancy due to additional copies of the coding gene (25). 

Next, we present the general evolutionary phases of resistance development 

to fungicides. The resistance evolution to fungicides can be 

divided in emergency phase and selection phase (20). In the emergency 

phase, the resistant line emerges by mutation and subsequently must 

invade the pathogen population. In this phase, the number of fungicide 

resistant lesions is small and the resistant lines can become extinct because 

of simple stochastic variations, despite fungicide application providing 

higher adaptability to resistant lines than to sensitive lines. In the 

selection phase of resistance to fungicides, one resistant line is already 

present in one population of the pathogen, differentially selected by the 

application of specific fungicides. 



367


Attempts have been carried to predict the evolution of phytopathogens 

to resistance against fungicides. One approach to predict 

resistance to fungicides in field conditions employs the usage of induced 

mutagenesis by UV radiation or chemicals to increase the frequency 

of mutations, following strong selection with fungicides in 

vitro to assure the emergence of resistant genotypes. Several studies 

using this approach describe mutations in the ß-tubulin gene associated 

with resistance to benzimidazoles (27), mutations in the cytochrome b 

conferring resistance to strobilurins (31, 26, 1), and mutations in the 

succinate dehydrogenase gene conferring resistance to SDHI fungicides 

(2, 14, 33). Apparently, mutations generated in vitro using mutagenesis 

produces a variety of possible mutants, from which a small 

portion has emerged by selection in field conditions. The pleiotropic 

adaptive cost associated to the non-emerging mutations is raised as a 

plausible explanation for these observations (3). 

Determining the adaptive cost of mutations is considered the 

most practical approach in order to foresee the result of resistance 

evolution to fungicides in pathogen populations on field. To persist on 

field, resistant mutants must be pathogenic in planta and competitive 

against different isolates. Related to the Q o 

I fungicides, from the 22 

mutations detected in Q o 

I-resistant mutants, nine resulted in respira- 




tion deficiency or in detectable effects on the protein activities, while 

the G144A, F129L and G137R mutations caused no detectable cost 

related to adaptability (4). Therefore, it was proposed that mutations 

that confer high resistance levels, with less adaptive cost, are positively 

selected in field (20, 25). 

Besides in vitro mutagenesis experiments, if intrinsic resistance 

is found in some fungi species, it is expected that similar acquired 

resistance mechanism could be acting in different species, as 

example of parallel evolution. In the case of the Q o 

I fungicides, fungi 

that synthetize natural strobilurin are intrinsic resistant, e.g. Mycena 

galopoda possess the G143A mutation, subsequently related as 

acquired resistance in many different fungi species (22). Once the 

acquired resistance emerges in a species, also by parallel evolution, 

it is expected that similar mechanism will also occour in another 

phytopathogenic fungi species. Resistance to Q o 

I fungicides related 

to the G143A substitution was reported by the first time in 1997 in 

Mychosphaerella fijiensis (32) and in 1998 in Erysiphe graminis f. 

sp. tritici (34), and parallel evolution of this substitution has been 

reported since then for more than 20 phytopathogenic fungi species 

(12). Additional evidence for the repeatability in this mutation to resistance 

is found when detected multiple independent origins of the 



369


same mutation in populations of one same phytopathogen species 

(36). The detection of epistasis and simple functional limitations associated 

to a specific resistance mutation to fungicides in a range of 

phytopathogenic fungi species turn the evolutionary scenario more 

predictable. For example, in most of the fungi species, the G134A 

mutation results in higher adaptability under Q o 

I fungicides selective 

pressure. However, some cytochrome b genes have an intron after 

codon 143, and in these fungi, the mutation G143A has a pleiotropic 

effect in preventing the intron splicing and is lethal (16). This is a 

simple example of epistasis, throughout the mutation effect differs 

depending on the fungus genetic background and the need to preserve 

the intron splicing site is an example of functional limitation. 

The predictability relies in the fact that the species or fungal lineages 

that carry the intron are not evolving to G143A, and probably will 

evolve to alternative mutations in the cytochrome b as F129L (16). 

Functional restrictions are more subtle, susceptible to epistatic interaction 

and variation between species or fungal lineages are more 

difficult to predict as in the case of resistance to DMI (25). 

We intend, therefore, to discuss about how the evolutionary analysis 

can improve our predictive capability, as well as guide risk assessments 

about the emergence of resistance. The model of risk evaluation 




proposed by the Fungicide Resistance Action Committee (FRAC) 

considers a matrix to calculate the risk of resistance to fungicide, 

initially based on three criteria derived from practical experience 

with different fungicides and phytopathogens: the risk associated to 

the chemical mode of action of the fungicide, the risk assigned to 

the phytopathogen and the agronomic risk, one indicative of the favorability 

in the agroecosystem (23, 6). However, FRAC’s proposed 

matrix model did not correlate the observed number of years before 

resistance emergence (r s 

= -0,06, P = 0,6474) (28). Additionally, detailed 

study performed by GRIMMER et al. (2014) indicated that the 

use of the risk matrix model has limited prediction value within the 

dominant category of high risk fungicide, corroborating observations 

of MCDONALD & LINDE (2002). On the other hand, the model of 

evolutionary risk for phytopathogens based on population-genetic 

parameters was a better predictor for fungicide resistance evolution 

(r s 

to phytopathogen migration parameter = -0.72; P = 0.0001) than 

the one based on the risk matrix (28). Thus, the models based on 

risk matrix offer a general guidance of risk, but cannot predict when 

nor where the fungicide resistance will occur, or how fast it will be 

spread and compromise the crop management (24). Furthermore, it 

requires precise measures of mutation rate and population size, proportion 

of sensitive and resistant individuals in the phytopathogen 



371


populations (37), the selection coefficient (determined by the difference 

in adaptability between sensitive and resistant lineages to any 

fungicide) (38), as well as other factors influencing the survival and 

invasion of resistant lineages (19). 

A new model of risk evaluation proposed by GRIMMER et al. 

(2015) includes the identification of key traits as essential resistance 

risk determinants, including the latent period of pathogens in the year 

(a measure of the disease epidemic duration divided by the time taken 

between infection and pathogen reproduction), the number of cultivated 

species infected by the pathogen (narrow host range versus broad, with 

intensive fungicide action on the last), production in protected versus 

open field cropping system (with higher selection to resistant lineages 

in protected cropping system), and the fungicide molecule complexity 

(where molecules of high complexity exhibiting higher linkage specificity 

to the targeted site show higher probability of compromised efficiency 

due to small changes). The combined model of these key traits 

explained 61% of the temporal variation in years to the emergence of 

resistance to high risk site specific fungicides. The risk evaluation based 

on these key traits could be used to determine the resistance risk to 

fungicides with novel mode of action, for which there is no previous 

knowledge about resistance behavior. 




4. Strategies for fungicide resistance management 

Finally, we are going to discuss the implications of such knowledge 

to the practical management of fungicide resistance aiming to 

answer the following questions: Is it possible to prevent resistance to 

fungicide? Is it possible to manage the resistance to fungicide once it 

has emerged? In general, the global goal of the strategies to manage 

fungicide resistance is double. First, to prevent the emergence of variants 

in the phytopathogens that could resist to a fungicide treatment (at 

the “emergency phase” of resistance development), and subsequently 

reduce the selection of such variants (at the “selection phase”) (38). 

The emergency phase in the evolution towards fungicide resistance 

has been poorly studied. One of the few models currently evaluable 

(and maybe the only), parameterized to a scenario where a single 

mutation in Mycosphaerella graminicola on winter wheat, affecting the 

fungicide biding to its targeted protein (e.g., qualitative resistance or 

single step) suggests that it is unlikely that a resistant linage could have 

emerged when a fungicide with a novel mode of action is introduced 

(20). Thereby, the authors recommend that “anti-emergence” strategies 

should be identified and implemented in advance to avoid the emergence 

of lineages resistant to fungicides. For example, mixing fungi- 



373


cides of high risk, with one single site of specific action, with a fungicide 

of low risk, with multiple action sites, could delay the emergency 

of resistance to the single site component. 

In the selection phase, the addition of a different fungicide in mixture 

with the high risk fungicide (without reducing the dose of the high 

risk fungicide), would reduce the selection rate to resistance, resulting 

in increased effective lifetime for the high risk fungicide, enough for 

practical relevance. The mixed fungicide could be either a compost of 

multiple sites of action or simple sites. The addition of a mixed fungicide 

associated to reduced dose of the high risk fungicide would considerable 

reduce the selection to resistance (39). 

Nonetheless, it is not possible to generalize that all mixture containing 

fungicides of low risk and high risk of emergence would provide 

appropriate disease management while minimizing the risk of 

resistance development. A model proposed by MIKABERIDZE et al. 

(2013) indicated that, in the absence of adaptive cost to pathogen resistant 

lineages, the application of a mixture of high risk and low risk 

fungicide would still select for resistance. Consequently, the resistant 

lineage would eventually overcome the pathogen population and the 

sensitive lineage would be eliminated. For this reason, the high risk 

fungicide would not control the disease and only the low risk fungi- 




cide in the mixture would be acting against the disease. Accordingly, 

the high risk fungicide would become nonfunctional in the mixture 

and the usage of only the low risk fungicide would have the same 

effect, but with lower financial and environmental costs. However, if 

detected higher adaptive cost to the pathogen, then the mixture with 

sufficient fungicide dose for the high risk fungicide could be used efficiently 

by a longer period of time, as selection for the resistant lineage 

would be prevented (29). 

Another strategy to prevent the emergency of fungicide resistance 

is to avoid the use of high doses of fungicides with high risk. 

Most experimental studies and models published relating resistance 

to fungicide dose support the hypothesis that high doses select for 

resistance (39). 

As a modern fungicide anti-resistance strategy, the development 

and application of specific molecular diagnoses to mutations or other 

genetic changes conferring resistance is one important advance that favored 

early detection and faster quantification of the degree of fungicide 

resistance in field conditions 

Shortly, the strategies for resistance management have focused 

exclusively in the selection phase, aiming to reduce the resistance in- 



375


crease rate or selection time, once it is postulated that the circumstances 

that lead to the low frequencies of resistance initial occurrence in 

generally large phytopathogen populations, are considered difficult to 

measure (24). However, adopting anti-emergency strategies could, in 

fact, limit the resistance initial occurrence, and is of special importance 

when the scenario of resistance evolution is irreversible. In this scenario, 

the resistant mutants show no adaptive cost, tending to replace 

the sensitive lineages to the specific fungicide and predominate in the 

phytopathogen populations (21, 7). 

The theoretical scenarios here presented can be contrasted with 

data that evidence generalized distribution of resistance to Q o 

I strobilurin 

fungicides in contemporaneous local populations of P. oryzae, 

the causal agent of the wheat blast in Brazil. This is an important 

local example about where the resistance to Q o 

I fungicides increased 

quickly from 36% in 2005 to 90% of incidence in 2012. This scenario 

of accelerated evolution of Q o 

I resistance suggests: i) the orientation 

to adopt anti-resistance measures was not followed (as the restrict 

usage of maximum two pulverizations of strobilurin by cropping season, 

and always mixed with a fungicide of different mode of action 

(frequently triazoles); ii) or the measures were, in fact, inefficient to 

prevent the pathogen population to change towards a condition of 




complete resistance. As it seems a completely irreversible scenario, 

there are evidences that the resistance to strobilurins has no adaptive 

cost for the pathogen population (21), therefore the most appropriated 

action would be the complete suspension of Q o 

I fungicide usage to 

manage wheat diseases in Brazil (7). 



377


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12 

Phosphites 

on 

Postharvest 

Disease Control


12 

Phosphites on Postharvest Disease Control 

Rafaela Carolina Constantino Roma-Almeida; Dalilla Carvalho 

Rezende; Sérgio Florentino Pascholati. 


Postharvest durability of fruits and vegetables faces several problems, 

the first of which are the plant material characteristics. Sensitive 

soft tissues, associated with inappropriate conditions for the supply 

chain steps, may cause high rot incidence, which directly affects the 

quality and the shelf life of the product. Such factors have collaborat- 


391


ed to food losses worldwide. According to FAO (Food and Agriculture 

Organization of the United Nations), every year one-third of the world 

food production is lost or wasted. Of which, 45% are fruits, green vegetables, 

tubers and roots. Losses are greater in developing countries 

since they are mostly located in tropical climate zones, which favor 

such losses where infrastructure is inadequate (15). 

High levels of pesticide residues routinely found in foods (3), together 

with problems caused by these products to the environment and 

to the man, have shown new alternatives for plant disease management, 

especially in fruits and vegetables, most of which are directly consumed 

by the population. Furthermore, pathogen resistance, as found 

in the pathosystem Botrytis cinerea x strawberry (16), strengthens such 

sustainable view of postharvest disease management. Use of essential 

oils (34), chitosan (30), irradiation (10), biological control agents (35), 

resistance-inducing agents (9, 29), volatile compounds (18, 31, 38), 

thermal treatment (7, 8) and inorganic salts (12) has led to promising 

results considering alternative control. 

Phosphites are inorganic salts and have been currently recommended 

as foliar fertilizers in Brazil. The multifaceted potential of 

phosphites is known due to their effect on plant nutrition and activation 

392 Phosphites on Postharvest Disease Control


of host defense mechanisms, as well as their fungitoxic property (11). 

These characteristics make the use of phosphites an important tool in 

plant disease management both in the field and in the postharvest. Thus, 

this review aims to contextualize phosphites in Phytopathology, as well 

as to present their potential in postharvest management, based on examples 

available in the literature. 

2. Phosphites 

Phosphites are salts of the phosphorous acid (H 3 

PO 3 

), which are 

obtained in the industry by a neutralization reaction with a base, especially 

potassium hydroxide, originating potassium phosphite (22). Besides 

this salt, several other formulations can be found in the market, 

such as cupper, calcium and magnesium phosphite. 

In the 40's, phosphites were investigated to be used as source of 

phosphor, indicating efforts towards their use as fertilizers. Nevertheless, 

phosphite-treated plants had phosphor deficiency, evidencing that 

this salt does not behave as an efficient source of such macro-element 

(19). Already in the 70's, phosphites were found to have fungitoxic activity 

against microorganisms of the class Oomycetes, including Phy- 


393


tophthora, Pythium and Plasmopara (11). Subsequently, in 1977, fosetyl-aluminum 

was launched, under the trade name Aliette ® , (26). Once 

absorbed by the plant, this molecule undergoes hydrolysis and releases 

the ion phosphite, which translocates basipetally and acropetally, controlling 

infectious diseases in crops like citrus, apple and grapevine (22, 

28, 2). The broad action spectrum of phosphites is currently renowned, 

providing fungal and bacterial pathogen control (20). 

With the expiration of fosetyl patent, several companies started 

to commercialize different phosphite forms, such as fertilizers, 

resistance inducers / plant activators / biostimulants or fungicides 

(11) (Table 1). In general, the leaflets or labels of these products 

indicate that those destined to be used as fungicides have higher 

3- 

PO 4 

or phosphite content than fertilizers. 

The effects of phosphite salts on postharvest disease control have 

been increasingly reported in the last years. Thus, results available in 

the literature regarding the effect of phosphite application in the postharvest 

stage will be presented, as well as the implications of their use 

in the field for postharvest disease management. 



3. Use of phosphite in the postharvest stage 

In general, depending on the morphological characteristics of 

the fruit, immersion is the most indicated method for using phosphite 

in the postharvest stage, since it allows homogeneity in treatments of 

large volumes of plant material, besides possible combination with 

other control methods. 

Alexandre (1) studied different phosphite formulations on anthracnose 

control in Scarlet eggplant and noted that immersion of fruits 

in a solution containing potassium phosphite provided 36.3% disease 

control, showing maximal incidence at 20 days of storage, which was 

inferior to that of the fungicide copper oxychloride. In apples, phosphite 

application led to a reduction in the diameter of lesion caused by 

Penicillium (33). Furthermore, Blum et al. (4) found that the efficiency 

of treatment with this inorganic salt was similar to that of the fungicide 

benomyl, which is no longer available in the market. 

Nevertheless, as already cited (Table 1), phosphite concentration 

varies among the commercially available sources, which may result in 

reduced efficiency of a treatment based solely on this inorganic salt. 

Cerioni et al. (7) observed that immersion of lemons in potassium phosphite 

provided 80% and 90% green and blue mold control, respectively. 


395


Table 1. Examples of phosphorous acid- or potassium phosphite-based 

products commercialized worldwide (Adapted from New Ag International, 

2007) 

Product 

Aliette ® 

Nutri-Phite ® P+K 

ProPhyt ® 

Nutrol 

Phostrol ® 

Agrifos ® 

Hortifos-PK ® 

Fosphite ® 

Trafos line ® 

Phosfik PK ® 

Gerfos-K 

Lebosol ® -Kalium Plus 

Quimifol ® Fosfito Combat 

Phytogard ® 

K-Phos 

Company 

Bayer Cropscience 

Biagro Western Sales 

Luxembourg-pamol 

Lidochem 

NuFarm America 

Agrichem 

Agrichem 

Jh Biotech 

Tradecorp 

Biolchim 

L-Gobbi 

Lebosol 

Fênix Agro 

Stoller 

Pace International 

Country 

Germany 

USA 

USA 

USA 

USA 

Australia 

Brazil 

USA 

Spain 

Italy 

Italy 

Germany 

Brazil 

Brazil 

USA 



Active Ingredient 

Fosetyl-Aluminum* 

Phosphites and 

organic acids 

Monobasic 

potassium phosphite 

Potassium phosphite 

Phosphorous acid 



Salts of phosphorous acid 








Concentration 

45% ethylphosphonate 

28% P 2 

O 5 

54,4% 

potassium phosphite 

50% available P 2 

O 5 

53,6% salts of 

phosphorous acid 

600 g/L phosphorous acid 

27% P 2 

O 5 

53% salts of 

phosphorous acid 

42% P 2 

O 5 

30% P 2 

O 5 

30% P 2 

O 5 

27% P 2 

O 5 

30% P 2 

O 5 

28% P 2 

O 5 

54,5% Potassium phosphite 

Commercialized as 

Fungicide 

Fertilizer 

Fungicide 

Fungicide and fertilizer 

Fungicide 

Systemic fungicide 

Plant activator 

Fungicide 

Fertilizer 

Foliar Fertilizer 

Fertilizer 

Fertilizer 

Fertilizer 

Fertilizer/ 

biostimulant 

Systemic fungicide 

* Fosetyl-Al: ethylphosphonate 


397


However, when fruits were immersed in a solution containing hydrogen 

peroxide and 6 mmol L -1 copper sulfate and, subsequently, in a potassium 

phosphite solution, around 100% green and blue mold control was 

obtained after seven days of storage. Immersion of lemons in a mixture 

of potassium phosphite and sodium bicarbonate as treatment led 

to around 80% green mold control (8). In both studies, phosphite was 

used in a healing manner for rot management. Moreover, phosphite efficiency 

in fruit immersion treatment can increase with the addition of 

calcium chloride, leading to reduced diameter of anthracnose lesions in 

guavas (17) and blue mold in apples (5). 

Concomitant use of phosphite and thermal treatment has also 

provided excellent results. Dutra (13) observed that zinc phosphite 

application, together with thermal treatment at 47ºC during 4 or 5 

minutes, was efficient to control anthracnose in yellow passion fruit, 

reducing lesion diameter by approximately 50%. Cerioni et al. (7, 8) 

also noted greater rot control in citric fruits treated by immersion in 

potassium phosphite solution at 40 or 50ºC. 

Although potassium phosphite has a more extensive use, other 

formulations were also tested in postharvest disease management. 

Calcium phosphite application significantly reduced anthracnose lesion 

diameter in papaya (21). 



4. Pre-harvest phosphite use with effects on the postharvest 

Considering the promising results obtained by a large number 

of authors for postharvest phosphite application to control diseases, 

studies can be found in the literature evaluating the effect of some 

phosphite formulations applied still in the field (pre-harvest) to control 

postharvest diseases. 

Focusing on inoculum reduction of the pathogen Penicillium 

digitatum and behavior of the saprophytic fungi Cladosporium spp. 

and Epicocum purpuracens in mandarins, Rheiländer & Fullerton 

(32) evaluated the efficacy of fungicides and potassium phosphite, 

applied in the pre-harvest, on the number of microorganism colonies 

formed on the surface of fruits in the postharvest. Potassium phosphite 

did not show any effect on P. digitatum colony growth but significantly 

reduced the number of colonies of both saprophytic fungi. 

A similar study was conducted with the aim of evaluating the effect of 

phosphites; Trichothecium roseum and the combination of both were 

compared with lime sulfur treatment in the pre-harvest to control 

postharvest brown rot caused by Monilinia fructicola in peaches (25). 

Those authors found that only T. roseum treatment was capable of reducing 

postharvest pathogen infection in fruits by 63%, compared to 

non-treated fruits. Therefore, once again, phosphite application in the 


399


pre-harvest had no effect on postharvest disease control. Moreira & 

May-de-Mio (24), with the aim of evaluating the efficiency of fungicides 

and phosphites applied in the pre-harvest to control postharvest 

brown rot in peaches, concluded that boron and calcium phosphite did 

not control the disease, whereas potassium phosphite decreased the 

disease incidence by 28 to 60%, compared to control. 

Evaluating the effect of bioflavonoids and potassium phosphite 

on the control of postharvest rot caused by Penicillium spp. in ‘Fuji’ 

apples, Stella and collaborators (37) verified that potassium phosphite 

applied at 3 mL L -1 , five days before harvest, showed good efficiency in 

reducing disease severity. In addition, potassium phosphite was capable 

of delaying the development of lesions by Penicillium spp. in the fruits. 

However, Brackmann and collaborators (6) noted that potassium phosphite 

application one day before harvest was not efficient to control rots 

in ‘Fuji’ apples after six months of storage at 0.5º C. 

Experiments in integrated management system, including 

pre-harvest application of biological control agents and calcium and 

potassium phosphites plus the fungicide captan, were carried out in a 

peach orchard to verify postharvest brown rot control (24). When the biological 

control agent (T. roseum), applied in the pre-harvest, was com- 



bined with captan and phosphites applied during flowering and fruiting, 

postharvest brown rot control reached levels equivalent to those by 

chemical control; thus, it can be recommended for integrated management 

systems. A similar study was conducted to evaluate the control of 

bull's eye rot caused by Cryptosporiopsis perennans in apples; pre-harvest 

application of potassium phosphite + captan reduced postharvest 

disease severity by 60% and inoculum quantity on the surface of apples 

by 66% (36). Postharvest brown rot in peaches was also reduced by the 

joint application of lime sulfur + iodine + calcium phosphite and boron 

and potassium phosphite (27). These studies revealed that phosphites, 

applied in the pre-harvest to control postharvest diseases, are efficient if 

applied together with fungicides and/or biological control agents. Considering 

that this phosphite application mode is efficient, the fungicide 

quantity can be reduced in integrated management system. 

5. Prospects 

Since the use of phosphites does not cause changes in the physico-chemical 

qualities of fruits (8) and is capable of controlling rots, it 

becomes an important tool in the postharvest management. 


401


However, there is a current conflict regarding the allowance of 

using phosphites or phosphorous acid byproducts to control diseases 

in different countries due to residues in foods. As a consequence of 

the molecule non-differentiation by means of analytical techniques 

for phosphite from different sources such as the fungicide fosetyl, the 

phosphorous acid and its byproducts, the European Commission has 

recently established a maximum residue limit (MRL) for salts of phosphorous 

acid from foliar fertilization. A provisional MRL of 2 mg Kg -1 

fosetyl-Al was established, which corresponds to the sum of residues 

of fosetyl, phosphorous acid and their salts, expressed as fosetyl. Provisionally, 

the European Commission concluded that this value was not 

harmful to the human health (14). 

Thus, studies still need to be conducted, especially considering 

food safety, to determine a maximum residue limit and thus allow rational 

applications of this product both in the field and in the postharvest. 



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13Spatio-Temporal 

Dynamic of 

Soilborne 

Pathogens 

and Pathogens 

Transmitted by 

Vector: Defining 

Patterns and 

Managing 

Epidemics


13 

Spatio-Temporal 

Dynamic of Soilborne Pathogens 

and Pathogens Transmitted by Vector: 

Defining Patterns and Managing Epidemics 

Wanderson Bucker Moraes; Waldir Cintra de Jesus Junior; 

Willian Bucker Moraes; Edson Luiz Furtado. 


The spatio-temporal dynamic of plant disease describes the interaction 

that occur between pathogen and host on influence of the 

Spatio-Temporal Dynamic of Soilborne Pathogens and Pathogens 

Transmitted by Vector: Defining Patterns and Managing Epidemics 

415


environment (7, 36). The main way to characterize this interaction is 

by using mathematical models. These models are able to summarize 

the relation between disease and space-time through of mathematical 

function parameters (31). The biologic interpretation of these parameters 

assists in the choice of management strategies to control plant 

disease (7, 29). Thus, characterizations of the spatial-temporal dynamic 

of plant disease help understanding its behavior in the field and 

develop specific management strategies to control epidemics. 

The characterization of spatial pattern of epidemics is of fundamental 

importance to understand how plant disease spread in the field, 

which helps to develop hypothesis about the physical and biologic 

mechanisms that influence the disease dynamic (33). The quantification 

of spatial patterns has been used to identify possible inoculum sources 

of epidemics in the field (11, 42). Moreover, by knowing the spatial pattern 

has been possible understand the interaction mechanisms between 

vector-pathogen-host (11, 25). 

The analysis of spatial patterns of epidemics can be done by 

several statistical methods. Different statistical methods have been 

used to describe the spatial pattern of plant disease in multiple scales 

(32, 37, 47, 45, 44), which provides the necessary information to full 

416 Spatio-Temporal Dynamic of Soilborne Pathogens and Pathogens 

Transmitted by Vector: Defining Patterns and Managing Epidemics


understand the spatial dynamic of plant disease. The spatial analysis 

methods can be classified in point pattern, and correlation. The 

analyses of point pattern give information about the heterogeneity or 

data variance between unit samples not mapped in small scale (32). 

However, this method does not measure the spatial dependence of a 

diseased individual and yours neighbor. In the other hand, the correlations 

methods allow quantify the spatial dependence between diseased 

individual in large scale (33). The spatio-temporal dynamic also 

can be done by stochastic integration models, although this method 

has not been widely used yet (48). 

The study of the temporal dynamic of epidemics also is of fundamental 

importance to create hypotheses to explain the factors that 

influence the plant disease progress in the field. The growth models 

have been the main way used to describe the temporal dynamic of plant 

disease (29, 31, 46), which summarize the relationship between disease 

and time. The fitting of specific growth models to disease progress 

curves allow infer about the inoculum source of epidemics. Generally, 

polycyclic diseases are described by the Logistic and Gompertz models, 

while monocyclic diseases are characterized by the monomolecular 

model (8, 29, 31, 36). Moreover, the biologic interpretation of the 



417


growth model parameters also assists in the choice and creation of management 

strategies, given that polycyclic and monocyclic diseases have 

been controlled by methods that reduce the slope and intercept parameters, 

respectively (29, 31, 46). 

This chapter explores recent articles that characterize the spatio-temporal 

dynamic of epidemics. Soilborne pathogens and disease 

transmitted by vector have different epidemiologic behavior in the 

field. Therefore, we will explore the difference of the spatio-temporal 

dynamic of these epidemics, and how this information can help to develop 

specific control strategies. This chapter is divided in spatio-temporal 

dynamic of soilborne pathogens (Section II), and spatio-temporal 

pattern of pathogens transmitted by vector (Section III). 

2. Spatio-temporal pattern of soilborne pathogens 

The spatio-temporal dynamics of diseases caused by soilborne 

pathogens can be useful in determining the inoculum source 

of pathogens, the importance of the inoculum density and how the 

inoculum is dispersed in the soil. 




2.1. Spatial pattern of soilborne pathogens 

The knowledge of spatial pattern of plant disease is the first step 

to understand its behavior in the field. In most of the cases, the inoculum 

sources of soilborne pathogens have an aggregated distribution 

in the soil. Physical barriers existing in the soil limit the movement of 

soilborne pathogens. Therefore, the plant disease caused by soilborne 

pathogens tends to have an aggregated spatial pattern in the field. 

However, secondary infections may occur in some soilborne 

disease caused by external factors that disperse the inoculum from 

the primary inoculum source in the soil. Roumagnac et al. (2004) 

described the potential of seedborne as inoculum source of bacterial 

blight of onion on the soil, and the occurrence of secondary infections 

of this disease. They stated that the spatial pattern of bacterial blight 

was aggregated around the seedborne inoculum. However, the authors 

pointed out the occurrence of secondary focus dispersed from the origin 

inoculum source. They suggested that wind-driven rains spread 

the inoculum from the initial inoculum source, which resulted from 

longer-distance dispersal in the soil. 

Additionally, the transmission between plants might occur due 



419


the soil movement and infected plants parts in the field. The use of 

machines to management the soil compaction can disperse infested soil 

between crop rows. Thus, the disease is dispersed to locations near the 

inoculum source. The spatial distribution of verticillium wilt of olive 

showed that this disease spread around the initial inoculum source in 

the soil (35). The authors observed initial clustering of diseased plants, 

which increased in size and number over time. They reported that the 

increase of the clustering size occurred by the movement of infested 

soil from short distance, and the clustering number by the windblown 

of infected leaves fallen from long distances. 

Although an aggregated spatial pattern is likely to happy in soilborne 

epidemics in the field, a uniform spatial pattern of soilborne 

pathogens also may occur. The uniform spatial distribution of soilborne 

epidemics happen in soil highly infested, and the environment is favorable 

for the disease development and inoculum production. Beale 

et al. (2002) reported that Aphanomyces cochlioides was aggregated 

distributed in sugar beet fields at midseason, and uniform distributed at 

harvest. According to the authors, the root growth in direction to inoculum 

source, and the redistribution of inoculum source in the field might 

explain these results. Furthermore, they suggested that the inoculum 




density increased after midseason due the wet soil, which was favorable 

for A. cochlioides development and spread in the soil. 

2.2. Temporal dynamic of soilborne pathogens 

For epidemics caused by soil-borne plant pathogens, factors 

associated with the initial inoculum are usually more important than 

those associated with the progress rate. Generally, the initial disease 

intensity has no effect on the progress rate, but it might affects 

the maximum intensity. Often, high initial intensity values indicate 

high intensity of inoculum in the soil. For instance, the farmers can 

plant in place where root systems of previous diseased plants were 

not removed, and the new seedlings are planted in the same rows. 

Therefore, the initial inoculum is expected to be high in the soil of 

these rows. Ferreira et al. (2013) observed that the inoculum density 

affects the temporal dynamics of soil-borne pathogens. They reported 

that Ceratocystis wilt was severe in eucalyptus planted in forest 

sites with a likely soil-borne inoculum prior to planting, and disease 

symptoms were not evident before 20 months. 



421


2.3. Use of spatio-temporal information to management 

soilborne pathogens 

The fact of that the majority of soilborne pathogens have aggregated 

pattern in the field allow the adoption of site-specific management 

methods to control these epidemics. Thus, the farmers do not 

need to spray pesticide in the entire field, which reduce costs and increase 

the crop profits. However, a sampling strategy has to be developed 

to increase the chance of detect the disease clusters in the field, 

and on the same time reduce sampling costs. Holguin et al. (2015) 

reported that the spatial distribution of reniform nematode (RN) in 

cotton had an aggregated pattern on the soil. The authors pointed 

out that planting cotton after harvest host crops caused a significant 

neighborhood structure of the nematode population (Figure 1), with 

higher RN density at 15 to 30 cm depth after cotton (host) harvest and 

30 to 60 cm depth after the peanut (nonhost) harvest. Moreover, they 

stated that the soil particle size and nematode density were correlated, 

which could be used as predictor of reniform nematode variability in 

the field, and sample strategies to define areas for site-specific management. 

Thus, they suggested that a site-specific management method 

can be applied to control reniform nematode. 




Cotton 2012 Peanut 2013 

At plant 

2400 

2200 

2000 

1800 

1600 

3200 

3000 

2800 

2600 

2400 

2200 

2000 

1800 

individuals/100 cc soil 

Harvest 

4500 

4000 

3500 

3000 

3000 

2500 

2000 

1500 

1000 

500 

0 

individuals/100 cc soil 

Figure 1. Effect of crop rotation (cotton-host or peanut-nonhost) 

on reniform nematode distribution on the soil at 15 cm depth. 

Source: Adapted from Holguin et al. (2015). 

3. Spatio-temporal pattern of pathogens transmitted by vector 

The study of the spatio-temporal dynamic of epidemics spread by 

vectors can identify the importance of bugs and diseased plants as inocu- 



423


lum sources and, when vectors are important, the control of the vector and 

the removal of the diseased plants decrease the progress of the diseases. 

3.1. Spatial pattern of pathogens transmitted by vector 

Plant disease epidemics transmitted by vectors are dispersed from 

the primary foci of the pathogen in the field. The pathogen introduction 

general occurs by infected seedlings. Then, the vector spread the 

pathogen around the inoculum source, forming secondary focus of the 

disease. The Grapevine leafroll associated virus-3 was introduced by 

infected vegetative materials (12). The authors observed that the disease 

was spread around the inoculum source by the vector over time. 

The spatial pattern of peanut plants infected by Tomato spotted wilt 

virus was aggregated, but diseased plants were randomly distributed 

around the inoculum source (13). For the authors, this pattern is the result 

of the dispersion of the disease by the vector from the primary foci. 

The spread of leprosy by the citrus mites occurred not only for healthy 

plants near diseased plants, and the pathogen was dispersed to distant 

plants from the inoculum source, which explain the random patterns 

observed in this epidemic (3). 




The spatial distribution of disease transmitted by vector might 

be either aggregated or random. Some vectors cannot fly long distance 

from the inoculum source, which generates an aggregated pattern of 

the disease. Thus, the removal of diseased plant from inoculum source 

and the vector control can reduce the pathogen transmition for health 

plants. Tubajika et al. (2004) reported that Xylella fastidiosa was 

transmitted by vector insect (Homalodisca coagulate) in vineyards 

based on the spatial pattern of this disease. They found an aggregated 

pattern of this disease in the vineyards, with high spatial dependence 

within-row and across-row, suggesting the occurrence of vine to vine 

transmission. According to the authors, the spatial distribution of X. 

fastidiosa followed the vector feed pattern. Therefore, they concluded 

that the reduction of insect vector population in the field, and removal 

of infected plants should be adapted to management this disease. In 

the same way, the spatial distribution of coconut lethal yellowing was 

aggregated in the field (10). The authors pointed out that new infected 

tree occurred closer to previously diseased trees, suggesting the 

occurrence of secondary disease spread by insects according to the 

authors. Thus, they suggest that the removal of asymptomatic trees 

close to symptomatic trees can be an efficient way to control coconut 

lethal yellowing epidemics. 



425


However, the removal of asymptomatic plants close to diseased 

plants does not work to control some plant diseases transmitted by vectors. 

Some vectors can flight long distances from the inoculum source 

and transmitted the disease. Therefore, it is hard to know if the asymptomatic 

plants around the diseased plants are infected. Batista et al. 

(2008) observed that Citrus tristeza virus can be dispersed by vectors 

over longer distance in Cuba. The authors pointed out that the range of 

the spatial dependence of this disease goes up to 60 m from the inoculum 

source. Thus, they suggest that the removal of asymptomatic plants 

would not be efficient to control this disease. 

While the biological behavior of vectors is the most important 

characteristic that defines the spatial pattern of some diseases, the initial 

inoculum source defines the potential of dispersal of other diseases 

by vectors in the field. When the disease intensity is low in the region, 

some epidemics transmitted by vectors can be controlled using health 

seeds. Given that the pathogen are not present in the region, and that the 

infested seeds are the main inoculum source of the epidemic, the use of 

health seeds prevents the introduction of the pathogen in the field. Thus, 

even disease widespread by vectors, can be managed in the presence of 

high density of vectors. The sugarcane yellow leaf was spatially related 

with the aphid movement and the virus infection across the fields in 

Louisiana (34). The authors observed that the increase of sugarcane 




yellow leaf occurred during late spring and early summer, which also 

coincided with the increase of the virus vector. Although sugarcane yellow 

leaf was widespread by the vector, the authors pointed out that the 

disease incidence was low in Louisiana. Thus, they suggested that the 

inoculum pressure still low in this state and planting virus-free seedcane 

can control the sugarcane yellow leaf. 

Alteration in the spatial pattern of pathogens transmitted by 

vector could be caused by the species of vectors. The case of Citrus 

tristeza virus (CTV) is very didactical. The pathosystem is complex. 

Accordingly to Gottwald et al. (1997; 1998) isolates of CTV vary 

greatly in symptom expression, and a multitude of interactions can 

occur due to various combinations of the virus, host tree, aphid vector 

species, and environment. 

Four aphid species (Aphis gossypii, A. spiraecola, Toxoptera aurantii 

and Toxoptera citricida) have been associated with the natural 

movement of the pathogen (1, 14, 15, 26, 27, 38, 41, 49, 50). Based on 

published papers (17, 19, 38, 49) it could be concluded that T. citricida 

is the most efficient vector of CTV worldwide. This vector is up to 25 

times more efficient at transmitting CTV isolates than the next most 

efficient vector species, A. gossypii (50). Citrus is not a primary host 

for A. gossypii, which does not heavily colonize citrus, and vectoring 



427


of CTV may be due to migrants through the orchards from surrounding 

areas or crops. In contrast, citrus is the primary host for T. citricida, and 

large colonies of this aphid are found under favorable conditions (2, 41, 

49). The spatial and temporal dynamics of CTV appear to change as T. 

citricida becomes part of the pathosystem. 

Gottwald and others authors (18, 19, 20, 21, 22, 23) analyzed 

data from mapped multi-year studies of CTV incidence in eastern 

Spain and Florida, and they concluded that, for CTV epidemics in 

areas where A. gossypii was the predominant pathogen vector, CTV 

incidence progressed from low levels (~0.05) to high levels (~0.95) in 

8 to 15 years (Figure 2). In contrast, for orange plots in which CTV 

incidence was low (~0.05) at the beginning of the study, using the 

Gompertz model, incidence was estimated to increase to asymptotic 

levels in 3.5 to 6.0 years when T. citricida was the vector (Figure 3) 

(18, 19, 20, 21, 22, 23). Therefore, the more rapid temporal increase 

of CTV measured in Costa Rica and the Dominican Republic can be 

attributed primarily to the large populations of T. citricida that developed, 

combined with the greater transmission efficiency associated 

with T. citricida compared with pathosystems in which this vector is 

absent, such as those pathosystems characterized by A. gossypii as the 

primary vector (18, 19, 20, 21, 22, 23). 




1987 

1990 

Figure 2. Spatial patterns of Citrus tristeza virus infection in one 

plot in the Florida (USA), over time (1987 and 1990), in the presence 

of the vector Aphis gossypii. 

Source: Adapted from Gottwald et al. (1997; 1998; 1999). 



429


1992 1995 

Figure 3. Spatial patterns of Citrus tristeza virus infection in one 

plot in the Dominican Republic, over time (1992 and 1995), in the 

presence of the vector Toxoptera citricida. 

Source: Adapted from Gottwald et al. (1997; 1998; 1999). 

3.2. Temporal dynamic of pathogens transmitted by vector 

For epidemics spread by vectors, diseased plants as inoculum 

sources usually influence the progress rate. The largest number of initial 

diseased plants most likely influenced the progress rate of the epidemics, 

given that beetles are the major pathogen vectors. Thus, the greatest 




inoculum availability to disease transmission by beetles can be related 

to the higher rate of plant disease. The removal of the diseased mango 

trees decreased the progress of Ceratocystis wilt (40). They reported 

that the reduction of diseased plants reduced the inoculum availability 

in the field. Thus, they suggested that the secondary cycles of this epidemic 

reduced over time, which caused the decrease of the rate progress 

of this disease. The removal of inoculums sources and the control 

of the vector also were suggested to reduce the disease progress of bean 

pod mottle virus in soybean transmitted by bean leaf beetle (11). 

3.3. Use of spatio-temporal information to management 

pathogens transmitted by vector 

Spatial pattern of citrus canker (CC) (Xanthomonas axonopodis 

pv. citri) in São Paulo state, Brazil, changed after the introduction 

of the Asian leafminer (Phyllocnistis citrella) in 1996 (24). 

Insect is not vector (9) but the presence of the leafminer facilitates 

bacteria infection process and subsequent inoculum production, 

which exacerbated CC (30). 

Before the detection of the insect in 1996 spatial pattern of canker 



431


was substantially aggregate (Figure 4A), which makes it easy “prey” 

for eradication teams. After 1997 pattern became slightly aggregate 

(Figure 4B) and random (Figure 4C). 

Figure 4. Spatial pattern of citrus canker (Xanthomonas axonopodis 

pv. citri) at São Paulo state, Brazil, before and after the detection 

of citrus leafminer (Phyllocnistis citrella) in 1996 (A. Substantially 

aggregate; B. Slightly aggregate and, C. Random). 

Source: Adapted from Gottwald et al. (2007). 

The change caused by the introduction of citrus leafminer in the 

bacteria infection process had important consequences in the campaigns 




used for citrus canker eradication in Brazil at the past. The presence of 

the citrus leafminer changed the spatial pattern of citrus canker in São 

Paulo (6). Previously, citrus canker presented more aggregated distribution 

patterns. Afterwards, less aggregate patterns were observed, together 

with the more common presence of satellite foci, more distant 

from the initial foci of the disease in the plot. The experimental results 

presented by Jesus Junior et al. (2006) refer to changes that occurred in 

the monocyclic components of the disease. 

Based on alterations aforementioned it is possible to conclude: 

1) In the absence of Phyllocnistis citrella (before 1996) citrus canker 

had short-distance dispersion (easy prey for eradication teams); 2) In 

the presence of Phyllocnistis citrella (before 1996) citrus canker had 

long-distance dispersal (aerosols), occupying areas distant from the 

primary sources of inoculum. 

4. Conclusion 

The epidemiology of plant disease is complex, given the several 

interactions of multiple factors in the field. Therefore, a better understanding 

of the inoculum sources and the spatio-temporal dynamics 

of plant disease epidemics may lead to the adoption of more effective 



433


management measures. Characterizing the spatio-temporal dynamic 

of the soilborne pathogens and disease transmitted by vector can help 

in developing specific control strategies for each kind of disease. 

It is clear that the spatio-temporal dynamics of soilborne pathogens 

and disease transmitted by vector are different, most likely due 

the occurrence of root infection and aerial infection, respectively. 

Such information contributes to the development of management 

strategies for each type of plant disease epidemic. The aerial infection 

by vectors can be managed by removing the diseased plant 

parts, when the vector moves slowly and to short distance in the 

field. To manage root infections, it was recommended the use of 

resistance cultivars and the removal of the diseased and dead plants 

to prevent secondary infections. 

The removal of asymptomatic trees close to symptomatic trees 

was widely pointed out by the authors for some diseases, but they did 

not evaluate the economic aspect of this control method. Moreover, 

the number of asymptomatic trees that should be removed was not 

mentioned by the authors. Some crops have low profit and removal 

of plants is a drastic method of control; therefore, extra costs have to 

justified by the increase of production. 




The use of seed free of disease could be used to control some diseases, 

and avoid that they spread across the fields. Thus, the inspection 

and regulation of seed free of disease will help to prevent the disease 

dissemination. Moreover, the control of the virus vector is an alternative 

approach to control some diseases in fields with high intensity. 

The aggregated spatial distribution of soilborne disease can allow 

the use of site-specific management method. However, a sampling 

strategy for these diseases have to be developed to increase the 

chance of detects the nematode clusters in the field. 

With continued research, an effective, integrated disease management 

program could be developed to more effectively reduce the 

losses of both types of plant disease epidemics. Authors of this chapter 

believe that the field of plant pathology could benefit with further 

explorations of the use of the new statistics tools available to characterize 

plant disease epidemics. 



435


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2. BAR-JOSEPH, M.; ROCCAH, B.; LOEBENSTEIN, G. Evaluation 

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