Crop Yield Forecasting

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de la production agricole à une échelle régionale”, Proceedings of the seminar on Estimation de la production agricole à une échelle régionale, 16-20 September 1996. Arlon, Belgium. European Commission publication, Un système d’information agronomique pour la communauté européenne Series, Luxembourg. El Jarroudi, M., Kouadio, L., Martin, B., Giraud, F., Delfosse, P., Hoffmann, L., Curnel, Y. & Tychon, B. 2010. Modelling plant diseases impact with the Belgian Crop Growth Monitoring System. Paper prepared for the European Society for Agronomy (ESA) Congress, 1 September 2010, Montpellier, France. Available at http://orbi.ulg.ac.be/bitstream/2268/81367/2/Montpellier-01-09-2010.pdf. Last accessed on 16 October 2015. Rijks, D., Terres, J.M. & Vossen, P. (eds). 1998. An Agricultural Information System for the European Union. Agrometeorological applications for regional crop monitoring and production assessment: Space Applications Institute Accounts of the EU Support Group on agrometeorology (SUGRAM) 1991-1996. EU-JRC Publication: Ispra, Italy. Available at http://bookshop.europa.eu/en/agrometeorological-applications-for-regional-crop-monitoring-and-production-assessment-pbCLNA17735/. Last accessed on 16 October 2015. EUROSTAT. 1997. Crop yield forecasting methods. Proceedings of the seminar. Villefranche-sur-Mer, 24 to 27 October 1994. European Commission publication: Luxembourg. Available at http://bookshop.europa.eu/it/ crop-yield-forecasting-methods-pbCA9996665/. Last accessed on 16 October 2015. Genovese, G. & Bettio, M. (eds). 2004. Methodology of the MARS Crop Yield Forecasting System. Volume 4. Statistical data collection, processing and analysis. European Commission Publication: Luxembourg. Genovese, G., Fritz, S. & Bettio, M. 2006. A Comparison and Evaluation of Performances among Crop Yield Forecasting Models Based on Remote Sensing: Results from the Geoland Observatory of Food Monitoring. Proceedings of the ISPRS WG VIII/10 Workshop 2006. Remote Sensing Support to Crop Yield Forecast and Area Estimates. Available at http://www.isprs.org/proceedings/XXXVI/8-W48/71_ XXXVI-8-W48.pdf. Last accessed on 16 October 2015. JECAM. 2014. Site Progress Report. Available at http://www.jecam.org/GEO_JECAM_Belgium_Site_ Progress_Report_2014.pdf. Last accessed on 16 October 2015. Lazar, C. & Genovese, G. (eds). 2004. Methodology of the MARS Crop Yield Forecasting System. Vol. 2. Agrometeorological modelling, processing and analysis. European Commission Publication. Micale, F. & Genovese, G. (eds). 2004. Methodology of the MARS Crop Yield Forecasting System. Vol. 1. Meteorological data collection, processing and analysis. European Commission Publication. Oger, R., Buffet, D., Tychon, B., Dehem, D. & Eerens, H. 2000. Estimation et prévision des productions agricoles à l’échelle de la Belgique à l’aide d’un système intégré “Modèle Agrométéorologique – Télédétection”: Mise en place du système intégré. Paper presented at the seminar on Applications Opérationnelles de la Télédétection à l’Agriculture en Belgique, Brussels. Available at http://orbi.ulg. ac.be/bitstream/2268/94791/1/bruxmai2000estimation.pdf. Last accessed on 16 October 2015. Piccard I., van Diepen C.A., Boogaard H.L., Supit I., Eerens H. & Kempeneers P. 2002. Report 2/3, Other yield forecasting systems: description and comparison with the MARS Crop Yield Forecasting System (MCYFS). Cited in METAMP, Methodology Assessment of MARS Predictions. Wageningen, The Netherlands. Available at https://www.wageningenur.nl/upload_mm/c/1/7/fc696543-3eee-453b-a26baecbd7f03efd_Boogaard_et_al_2002.pdf. Last accessed on 16 October 2015. Crop Yield Forecasting: Methodological and Institutional Aspects 227
Royer, A. & Genovese, G. (eds). 2004. Methodology of the MARS Crop Yield Forecasting System. Vol. 3. Remote sensing information, data processing and analysis. European Commission Publication. Supit, I. 1994. Global Radiation. European Union Publication: Luxembourg. Tychon, B., Buffet, D., Dehem, D., Eerens, H. & Oger, R. 2001. The Belgian Crop Growth Monitoring System. Paper presented at the Second International Symposium: Modelling Cropping Systems, 16-18 July 2001. Florence, Italy. Tychon, B., Dehem, D., Eerens, H., Verheijen, Y., Veroustraete, F., Wouters, K., Oger, R. & Buffet, D. 2000. Prévision des productions végétales à l’échelle nationale basée sur un système intégré “Modéle agrométéorologique-télédétection”: analyse de sensibilité et domaine de validité en tant qu’outil d’aide à la décision en politique agricole, Rapport final (B-CGMS Project). Publication of the Ministère des Classes moyennes et de l’Agriculture: Brussels. Van Orshoven, J. & Vandenbroucke, D. 1993. Guide de l’utilisateur de AARDEWERK – Base de données de profils pédologiques. Rapport 18B. Institutvoor Land- en Waterbeheer, Katholieke Universitet Leuven: Leuven, Belgium. Chapter 2 Becker-Reshef, I., Justice, C., Sullivan, M., Vermote, E., Tucker, C., Anyamba, A., Small, J., Pak E., Masuoka, E., Schmaltz, J., Hansen, M., Pittman, K., Birkett, C., Williams, D., Reynolds, C. & Doorn, B. 2010. Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) Project. Remote Sensing, 2: 1589-1609. The Federative Republic of Brazil, the Russian Federation, the Republic of India, the People’s Republic of China, the Republic of South Africa & IBGE. 2014. BRICS: joint statistical publication: 2014. IBGE Publication: Rio de Janeiro, Brazil. Chen, S., Zhao, Y., Shen, S. & Li, Z. 2013. Study on Maize Yield Estimation and Accuracy Assessment Based on PyWOFOST Crop Model in Northeast China. Scientia Agricultura Sinica, 46(14): 2880-2893 (In Chinese with English abstract). Chen, X. 1992. Forecast study on China Grain Yield. Bulletin of Chinese Academy of Sciences, 4: 330-333 (in Chinese) Chen, X., Guo, J. & Yang, C. 2008. Yearly Grain Output Predictions in China 1980-2004. Economic Systems Research, 20(2):139-150. Chen, X., Pan, X. & Yang, C. 2001. On the study of China’s grain output prediction. International Transactions in Operational Research, 8(4): 429-437 Chen, X. & Yang, C. 2002. Characteristic of Agricultural Complex Giant System and National Grain Output Prediction. Systems Engineering Theory & Practice, 22(6):108-112 (In Chinese with English abstract). Chen, Z., Liu, H, Zhou, Q., Yang, G. & Liu, J. 2000. Sampling and scaling scheme for monitoring the change of winter wheat acreage in China. Transactions of the Chinese Society of Agricultural Engineering, 16(5): 126-129 (In Chinese with English abstract) 228 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
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FIGURE 1.2. Synthetic flowchart of
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B1.8., Annex B1.2). The historical
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• CIPF; • CRA-W; • Centre de
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2.2.8. The Crop Growth Simulation M
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take place, and the maximum daily t
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sought to minimize the prediction e
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Therefore, to optimize and automate
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FIGURE 1.6. Architecture of the B-C
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2.5. Human, financial and technical
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in 2014 49 . Preliminary yield esti
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national level) for 2014. GIS syste
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1.2. Inventory of forecasts availab
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and production can be predicted one
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1.4. How do these different forecas
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2. China national official sources:
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The NSRCP’s key technology includ
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2.1.2. Methodology and practices of
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FIGURE 2.2 Flow chart of NDVI-based
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2.1.3 Methodology and practices of
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various periods. By integrating thr
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indices have been developed, and th
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FIGURE 2.3 Data sources and institu
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2.4. Human, financial and technical
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method is applied to choose three t
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accuracy of the RS indicator and th
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TABLE 2.4 Crop calendar and release
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Institute for Research and Technolo
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■■ Yield • Phenological Event
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2. Morocco’s national official so
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• Level 3: Forecast of crop yield
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TABLE 3.5 Meteorological variables
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two are pre-processing tasks (see F
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FIGURE 3.5 Output data from Levels
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• The agricultural mask, that wil
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FIGURE 3.8 The coefficient of deter
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involved in agriculture, and is sup
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3.1.1.1. Area and Methodology used
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variable and the total rainfall of
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TABLE 3.8 Crop calendars and releas
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110 Crop Yield Forecasting: Methodo
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In administrative terms, South Afri
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1.2.1.1. The role of the Crop Estim
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to submit information. The main aim
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SAGIS releases the following inform
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FIGURE 4.2 Percentage of over/under
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the results from a subjective telep
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a point sample frame. Three types o
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Sample frame To set up the sample s
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North West are allocated proportion
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FIGURE 4.7 Methodology for determin
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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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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
Page 177 and 178: Annex B1.2 - The JRC-MARS Crop Yiel
Page 179 and 180: FIGURE B1.8 MCYFS: Synthetic schema
Page 181 and 182: FIGURE B1.10 The CGMS Statistical T
Page 183 and 184: IDSL indicates whether pre-anthesis
Page 185 and 186: Annex B1.5 - Websites Country Natio
Page 187 and 188: N_A5 By August 25 Seasonal report o
Page 189 and 190: IV. Official releases of crop produ
Page 191 and 192: M P7 M P8 _ By October 25 _ By Nove
Page 193 and 194: Annex B2.2 - Websites Country China
Page 195 and 196: FIGURE B3.2 Agrometeorological bull
Page 197 and 198: FIGURE B3.6 CGMS-MAROC’s web-mapp
Page 199 and 200: FIGURE B3.10 Crop production foreca
Page 201 and 202: from the various models are inserte
Page 203 and 204: FIGURE B3.14 Descriptive schema of
Page 205 and 206: Annex B3.3 - Websites Country Natio
Page 207 and 208: FIGURE B4.2 Gross income (South Afr
Page 209 and 210: Annex B4.3 - Example of SAGIS publi
Page 211 and 212: 4.2 Definition Oilseeds are defined
Page 213 and 214: Other countries estimate production
Page 215 and 216: Annex B4.5 - Advanced Sample Framew
Page 217 and 218: 216 Crop Yield Forecasting: Methodo
Page 219 and 220: FIGURE B5.2 USDA-FAS-PECAD: Crop Ex
Page 221 and 222: FIGURE B5.3A CropScape: Cropland Da
Page 223 and 224: FIGURE B5.7 Global Agricultural Mon
Page 225 and 226: 224 Crop Yield Forecasting: Methodo
Page 227: Navalgund, R.R., Parihar J.S., Ajai
Page 231 and 232: Huang, J., Wang, X., Li, X., Tian,
Page 233 and 234: Wang, M. & Wei, G. 2014. Promoting
Page 235 and 236: Zhao, J., Shi, K. & Wei, F. 2007. R
Page 237 and 238: Hutson, J.L. 1984. Estimation of hy
Page 239 and 240: Huddleston, H.F. 1978. Sampling tec
Page 241: Layout: • Laura Monopoli Cover ph
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