Crop Yield Forecasting

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3 Crop Yield Forecasting in Morocco Michele Bernardi 1 1. Crop yield forecasts data for Morocco 1.1. Brief description Crop monitoring and yield forecasting is an essential component of climate risk management in agriculture in Morocco, as the agricultural sector is one of the economy’s main areas and presents a high annual variability. Crop yield forecasts are elaborated by the Department of Strategy and Statistics of the Ministry of Agriculture (DSS 2 ), on the basis of a qualitative survey and of “subjective yields”; this, in turn, is an assessment carried out in relation to a sample of plots. In a more complete approach, the Crop Growth Monitoring System – Morocco (CGMS-MAROC 3 ) is the national system for crop monitoring and for the agrometeorological yield forecasting of cereal crops. It was initiated by the National Institute for Agricultural Research (INRA 4 ) within the framework of the E-AGRI 5 project. As illustrated in Figure 1 below, CGMS-MAROC is coordinated by the INRA, through a formal consortium with the National Department of Meteorology (DMN 6 ), the DSS and the Institut Agronomique et Vétérinaire Hassan II (IAV 7 ). Its development was made possible with the support of a technological collaboration with international research institutions, namely the Flemish 1 Independent Consultant. 2 DSS: http://www.agriculture.gov.ma/pages/statistiques-agricoles 3 CGMS-MAROC: http://www.cgms-maroc.ma/ 4 INRA: http://www.inra.org.ma/accueil1.asp?codelangue=23&po=2 5 E-AGRI: http://www.e-agri.info/ 6 DMN: http://www.marocmeteo.ma/ 7 IAV: http://www.iav.ac.ma/ Crop Yield Forecasting: Methodological and Institutional Aspects 83
Institute for Research and Technology (VITO 8 ), the Research Centre of the European Union (EU-JRC 9 ), the Research Institute of the University of Wageningen (ALTERRA 10 ) and the University of Milan (UNIMI 11 ). CGMS-MAROC is the first operational system for the crop monitoring and the agro-meteorological yield forecasting of cereal crops in Morocco, as well as the first of these systems to be institutionalized through a strategic partnership to enable its development and sustainability. Similarly to the B-CGMS in Belgium, the concept behind the CGMS-MAROC is to provide an adapted and improved version of the CGMS 12 implemented by the MARS Unit of the Joint Research Center (JRC-MARS 13 ). As mentioned in Annex B1 relating to the Belgium case study, in 1992, the JRC developed a crop yield forecasting system, which it maintains to this day and which provides timely forecasts for crop production – including biofuel crops – for Europe and other strategic areas (EU Member States, the Maghreb region, the European part of Russia, Ukraine, and Belarus). The MCYFS 14 monitors crop vegetation growth (for cereals, oil seed crops, protein crops, sugar beet, potatoes, pastures, rice), including the short-term effects of meteorological events upon crop production. The MCYFS also provides seasonal yield forecasts for key European crops (wheat, maize, etc.). FIGURE 3.1 The CGMS-MAROC’s institutional structure Source: Balaghi et al. 2013 The CGSM-MAROC is coordinated by the INRA through a formal consortium with the DSS, the DMN and the IAV, each institution having its own functions and responsibilities. This partnership ensures the system’s efficiency and sustainability. 8 VITO: https://vito.be/en 9 EU-JRC: https://ec.europa.eu/jrc/ 10 ALTERRA: http://www.wageningenur.nl/en/Expertise-Services/Research-Institutes/Alterra.htm 11 UNIMI: http://www.unimi.it/ 12 The Crop Growth Monitoring System (CGMS) is the core of the MARS Crop Yield Forecast System (MCYFS) currently used in forecasting activities in Europe by AGRI4CAST action. http://mars.jrc.ec.europa.eu/mars/About-us/AGRI4CAST/ Models-Software-Tools/Crop-Growth-Monitoring-System-CGMS 13 JRC-MARS: http://mars.jrc.ec.europa.eu/mars/About-us/The-MARS-Unit 14 MCYFS; http://marswiki.jrc.ec.europa.eu/agri4castwiki/index.php/Main_Page 84 Crop Yield Forecasting: Methodological and Institutional Aspects
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Crop Yield Forecasting: Methodologi
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This publication was prepared with
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Contents Acknowledgements 9 Preface
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3. Linking up with crop production
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ANNEXES............................
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10 Crop Yield Forecasting: Methodol
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org) monitors current year conditio
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eference dataset, whereas actors of
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Remaining challenges The main unkno
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DAFF DBMS DEM DG-AGRI DMN DMP DPO D
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RMSE RS RSAC RSS RVI SACOTA SAFEX S
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FIGURE 4.10 FIGURE 4.11 FIGURE 4.12
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24 Crop Yield Forecasting: Methodol
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FIGURE 1.1 B-CGMS descriptive flowc
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the CGMS. The CGMS uses daily meteo
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estimate is computed by interviewin
Page 33 and 34: FIGURE 1.2. Synthetic flowchart of
Page 35 and 36: B1.8., Annex B1.2). The historical
Page 37 and 38: • CIPF; • CRA-W; • Centre de
Page 39 and 40: 2.2.8. The Crop Growth Simulation M
Page 41 and 42: take place, and the maximum daily t
Page 43 and 44: sought to minimize the prediction e
Page 45 and 46: Therefore, to optimize and automate
Page 47 and 48: FIGURE 1.6. Architecture of the B-C
Page 49 and 50: 2.5. Human, financial and technical
Page 51 and 52: in 2014 49 . Preliminary yield esti
Page 53 and 54: national level) for 2014. GIS syste
Page 55 and 56: 1.2. Inventory of forecasts availab
Page 57 and 58: and production can be predicted one
Page 59 and 60: 1.4. How do these different forecas
Page 61 and 62: 2. China national official sources:
Page 63 and 64: The NSRCP’s key technology includ
Page 65 and 66: 2.1.2. Methodology and practices of
Page 67 and 68: FIGURE 2.2 Flow chart of NDVI-based
Page 69 and 70: 2.1.3 Methodology and practices of
Page 71 and 72: various periods. By integrating thr
Page 73 and 74: indices have been developed, and th
Page 75 and 76: FIGURE 2.3 Data sources and institu
Page 77 and 78: 2.4. Human, financial and technical
Page 79 and 80: method is applied to choose three t
Page 81 and 82: accuracy of the RS indicator and th
Page 83: TABLE 2.4 Crop calendar and release
Page 87 and 88: ■■ Yield • Phenological Event
Page 89 and 90: 2. Morocco’s national official so
Page 91 and 92: • Level 3: Forecast of crop yield
Page 93 and 94: TABLE 3.5 Meteorological variables
Page 95 and 96: two are pre-processing tasks (see F
Page 97 and 98: FIGURE 3.5 Output data from Levels
Page 99 and 100: • The agricultural mask, that wil
Page 101 and 102: FIGURE 3.8 The coefficient of deter
Page 103 and 104: involved in agriculture, and is sup
Page 105 and 106: 3.1.1.1. Area and Methodology used
Page 107 and 108: variable and the total rainfall of
Page 109 and 110: TABLE 3.8 Crop calendars and releas
Page 111 and 112: 110 Crop Yield Forecasting: Methodo
Page 113 and 114: In administrative terms, South Afri
Page 115 and 116: 1.2.1.1. The role of the Crop Estim
Page 117 and 118: to submit information. The main aim
Page 119 and 120: SAGIS releases the following inform
Page 121 and 122: FIGURE 4.2 Percentage of over/under
Page 123 and 124: the results from a subjective telep
Page 125 and 126: a point sample frame. Three types o
Page 127 and 128: Sample frame To set up the sample s
Page 129 and 130: North West are allocated proportion
Page 131 and 132: FIGURE 4.7 Methodology for determin
Page 133 and 134: 2.2.3. Provincial Department of Agr
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• Southern Annular Mode (SAM) fro
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To facilitate the running of such a
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2.6. Innovation and integration wit
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Digitization has been completed for
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FIGURE 4.11 Random selection of a p
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georeferenced. In addition to the o
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of generating signature classes for
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and along with the infra-red bands
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150 Crop Yield Forecasting: Methodo
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the National Agricultural Statistic
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1.2.3. Other regional and global so
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1.4. How do these different forecas
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commodity of interest within the op
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Potato and winter wheat acres are c
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During the growing season, the NASS
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3. Linking up with crop production
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drying have been taken into account
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Meteorological data • USA-NOAA ht
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FIGURE B1.2 Grid covering the area
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Annex B1.2 - The JRC-MARS Crop Yiel
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FIGURE B1.8 MCYFS: Synthetic schema
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FIGURE B1.10 The CGMS Statistical T
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IDSL indicates whether pre-anthesis
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Annex B1.5 - Websites Country Natio
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N_A5 By August 25 Seasonal report o
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IV. Official releases of crop produ
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M P7 M P8 _ By October 25 _ By Nove
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Annex B2.2 - Websites Country China
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FIGURE B3.2 Agrometeorological bull
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FIGURE B3.6 CGMS-MAROC’s web-mapp
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FIGURE B3.10 Crop production foreca
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from the various models are inserte
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FIGURE B3.14 Descriptive schema of
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Annex B3.3 - Websites Country Natio
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FIGURE B4.2 Gross income (South Afr
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Annex B4.3 - Example of SAGIS publi
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4.2 Definition Oilseeds are defined
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Other countries estimate production
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Annex B4.5 - Advanced Sample Framew
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FIGURE B5.2 USDA-FAS-PECAD: Crop Ex
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FIGURE B5.3A CropScape: Cropland Da
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FIGURE B5.7 Global Agricultural Mon
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Navalgund, R.R., Parihar J.S., Ajai
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Royer, A. & Genovese, G. (eds). 200
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Huang, J., Wang, X., Li, X., Tian,
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Wang, M. & Wei, G. 2014. Promoting
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Zhao, J., Shi, K. & Wei, F. 2007. R
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Hutson, J.L. 1984. Estimation of hy
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Huddleston, H.F. 1978. Sampling tec
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Layout: • Laura Monopoli Cover ph
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Crop Yield Forecasting

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