Crop Yield Forecasting

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IRM JAS JAWF JECAM JRC-MARS LAI LDA LL LSWI MARS Project MAS MCYFS MoA MODIS MoF MSG MPPS MPZ MRU NAFU NAMC NASS NBS NCSC NDRC NDVI NIS NMSs NOAA NOAA-AVHRR NPP NSRCP NUTS OYS PAMOS PASG PDA PDA PECAD PICES PPS PRF PSU RADI/CAS Institut Royal Météorologique de Belgique June Crops/Stocks Survey (United States) Joint Agricultural Weather Facility (United States) GEO Joint Experiment for Crop Assessment and Monitoring MARS Unit of the Joint Research Center Leaf Area Index Limpopo Department of Agriculture (South Africa) Lower Limit Land Surface Water Index Monitoring of Agriculture with Remote Sensing March Crops/Stocks Survey (United States) MARS Crop Yield Forecast System Ministry of Agriculture Moderate resolution Imaging Spectroradiometer Ministry of Finance Meteosat Seconde Generation (Belgium) Multivariate Probability Proportional to Size Major Production Zone Monitoring and Reporting Unit National African Farmers Union National Agricultural Marketing Council (South Africa) National Agricultural Statistics Service (United States) National Bureau of Statistics (China) National Crop Estimates Consortium (South Africa) National Development and Reform Commission Normalized Difference Vegetation Index National Institute of Statistics National Meteorological Services (Morocco) National Oceanic and Atmospheric Administration National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer Net Primary Production National Statistics and Remote Sensing System of Crop Production Nomenclature of territorial units for statistics (from the French Nomenclature des unités territoriales statistiques) Objective Yield Survey Provincial Agrometeorological Operation and Service System Percentage of Average Seasonal Greenness Personal Digital Assistant Provincial Department of Agriculture Production Estimates and Crop Assessment Division (United States) Producer Independent Crop Estimates Survey Probability Proportional to Size Protein Research Foundation Primary Sampling Unit Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences Crop Yield Forecasting: Methodological and Institutional Aspects 19
RMSE RS RSAC RSS RVI SACOTA SAFEX SAG SAGIS SAM SANSA SANSOR SAR SAT SAWS SCM SDVI SEVIRI SGI SMME SMU SPI SSM SST STRM TAFSS TU UAV UCL UKZN UNIMI USDA USDA/NASS VCI VITO WASDE WOFOST Root Mean Square Error Remote Sensing Remote Sensing Application Center (China) Remote Sensing Section (United States) Ratio Vegetation Index South African Cereals and Oilseeds Trade Association South African Future Exchange State Administration of Grain South African Grain Information System Southern Annular Mode South African National Space Agency South African National Seed Organisation Synthetic Aperture Radar Saturation South African Weather Service Systematic Clustering Means Standardized Difference Vegetation Index Spinning Enhanced Visible and Infrared Imager Small Grain Institute (South Africa) Small, Medium, and Micro-sized Enterprise Soil Mapping Unit Standardized Precipitation Index Stratified Sampling Method Sea Surface Temperature Shuttle Radar Topography Mission telephone or field interview area and yield survey Terrain Unit Unmanned Aerial Vehicle Université catholique de Louvain University of KwaZulu-Natal University of Milan US Department of Agriculture (United States) United States Department of Agriculture’s National Agricultural Statistics Service (Morocco) Vegetation Condition Index Flemish Institute for Research and Technology (from the Flemish Vlaamse Instelling voor Technologisch Onderzoek) World Agricultural Supply and Demand Estimates World Food Studies 20 Crop Yield Forecasting: Methodological and Institutional Aspects
Page 1 and 2: Crop Yield Forecasting: Methodologi
Page 3 and 4: This publication was prepared with
Page 5 and 6: Contents Acknowledgements 9 Preface
Page 7 and 8: 3. Linking up with crop production
Page 9 and 10: ANNEXES............................
Page 11 and 12: 10 Crop Yield Forecasting: Methodol
Page 13 and 14: org) monitors current year conditio
Page 15 and 16: eference dataset, whereas actors of
Page 17 and 18: Remaining challenges The main unkno
Page 19: DAFF DBMS DEM DG-AGRI DMN DMP DPO D
Page 23 and 24: FIGURE 4.10 FIGURE 4.11 FIGURE 4.12
Page 25 and 26: 24 Crop Yield Forecasting: Methodol
Page 27 and 28: FIGURE 1.1 B-CGMS descriptive flowc
Page 29 and 30: the CGMS. The CGMS uses daily meteo
Page 31 and 32: 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 and 84:
TABLE 2.4 Crop calendar and release
Page 85 and 86:
Institute for Research and Technolo
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
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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
Page 135 and 136:
• Southern Annular Mode (SAM) fro
Page 137 and 138:
To facilitate the running of such a
Page 139 and 140:
2.6. Innovation and integration wit
Page 141 and 142:
Digitization has been completed for
Page 143 and 144:
FIGURE 4.11 Random selection of a p
Page 145 and 146:
georeferenced. In addition to the o
Page 147 and 148:
of generating signature classes for
Page 149 and 150:
and along with the infra-red bands
Page 151 and 152:
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 167 and 168:
Page 169 and 170:
drying have been taken into account
Page 171 and 172:
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 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:
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:
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:
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Crop Yield Forecasting

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