Crop Yield Forecasting

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FIGURE B3.4 The difference between the DSS crop mask and the GlcropV2 dataset Source: El Hairech et al., FP7-E-Agri meeting 2014, fourth Powerpoint presentation (http://www.e-agri.info/ meetings/meeting_07_presentations.html). FIGURE B3.5 The NDVI from MODIS (resolution: 250 m) Source: Balaghi et al., 2013 (http://modis.gsfc.nasa.gov/data/dataprod/). Crop Yield Forecasting: Methodological and Institutional Aspects 195
FIGURE B3.6 CGMS-MAROC’s web-mapping tool for visualizing NDVI data Source: http://www.cgms-maroc.ma/cgms-map/. FIGURE B3.7 The EMUs used in CGMS-MAROC as inputs for Level 2 Source: Balaghi et al., 2013. 196 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
Page 145 and 146: georeferenced. In addition to the o
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Page 149 and 150: and along with the infra-red bands
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: FIGURE B3.2 Agrometeorological bull
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 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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