Crop Yield Forecasting

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FIGURE B3.8 Example of WOFOST outputs aggregated to NUTS Level 3 for soft wheat Source: Balaghi et al., 2013. FIGURE B3.9 CGMS-MAROC’s web server Source: http://www.cgms-maroc.ma/cgms-map/ Crop Yield Forecasting: Methodological and Institutional Aspects 197
FIGURE B3.10 Crop production forecast by means of remote sensing Source: http://www.crts.gov.ma/thematiques/agriculture/prevision-de-la-production-agricole. Annex B3.2 - The JRC-MARS Crop Yield Forecasting System (MCYFS) As mentioned above (see Annex B.1.2), the JRC has developed the MCYFS, a crop yield forecasting system that provides timely forecasts of crop production, including biofuel crops, for Europe and other strategic areas (EU Countries, Maghreb, European part of Russia, Ukraine, Belarus) (Figure B.3.11 below). The MCYFS monitors crop vegetation growth (cereals, oil seed crops, protein crops, sugar beet, potatoes, pastures, rice), including the short-term effects of meteorological events on crop production. It also provides seasonal yield forecasts of key European crops (wheat, maize, etc.). 198 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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Page 151 and 152: 150 Crop Yield Forecasting: Methodo
Page 153 and 154: the National Agricultural Statistic
Page 155 and 156: 1.2.3. Other regional and global so
Page 157 and 158: 1.4. How do these different forecas
Page 159 and 160: commodity of interest within the op
Page 161 and 162: Potato and winter wheat acres are c
Page 163 and 164: During the growing season, the NASS
Page 165 and 166: 3. Linking up with crop production
Page 169 and 170: drying have been taken into account
Page 173 and 174: Meteorological data • USA-NOAA ht
Page 175 and 176: 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: FIGURE B3.6 CGMS-MAROC’s web-mapp
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 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 227 and 228: Navalgund, R.R., Parihar J.S., Ajai
Page 229 and 230: Royer, A. & Genovese, G. (eds). 200
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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Crop Yield Forecasting

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