Crop Yield Forecasting

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List of Figures BELGIUM FIGURE 1.1 FIGURE 1.2 FIGURE 1.3 FIGURE 1.4 FIGURE 1.5 FIGURE 1.6 FIGURE 1.7 B-CGMS descriptive flowchart Synthetic flowchart of the B-CGMS The Normalized Difference Vegetation Index Crop Growth Model simplified scheme Flowchart of the data for yield forecasting in the B-CGMS, with the StatCaT Statistical Toolbox Architecture of the B-CGMS database management system Architecture of the B-CGMS GIS server CHINA FIGURE 2.1 FIGURE 2.2 FIGURE 2.3 The National Bureau of Statistics’ general methodology for yield estimation Flow chart of NDVI-based statistical model in CHARMS Data sources and institutional structure of official yield forecasting organizations MOROCCO FIGURE 3.1 FIGURE 3.2 FIGURE 3.3 FIGURE 3.4 FIGURE 3.5 FIGURE 3.6 FIGURE 3.7 FIGURE 3.8 FIGURE 3.9 The CGMS-MAROC’s institutional structure The design of the CGMS-MAROC’s system Input data for Levels 1, 2, and 3 of the CGMS-MAROC Overview of the CGMS-MAROC crop monitoring components Output data from Levels 1, 2 and 3 of the CGMS-MAROC The linear regression and similarity analysis tool Linear regression between observed and predicted yields The coefficient of determination (R2) of regression models between yields of the three main cereals (soft wheat, durum wheat, and barley) at national level from February to April (data from 1999 to 2011) Information systems coordinated by the Ministry of Agriculture SOUTH AFRICA FIGURE 4.1 FIGURE 4.2 FIGURE 4.3 FIGURE 4.4 FIGURE 4.5 FIGURE 4.6 FIGURE 4.7 FIGURE 4.8 FIGURE 4.9 Overview of the members of the CELC, CEC, National Crop Estimates Consortium (NCSC) and of the data input providers Percentage of over/under estimation of the final maize forecast (seventh forecast) by the CEC since 2000, compared to the final estimate (reconciled figures) at the end of the season Model used to classify the data inputs to the CEC Stratification for summer crops Objective Yield Survey for maize Kernel determination Methodology for determining number of ears in a wheat field Graphic description of the trend analysis Digitized crop field boundaries Crop Yield Forecasting: Methodological and Institutional Aspects 21
FIGURE 4.10 FIGURE 4.11 FIGURE 4.12 FIGURE 4.13 FIGURE 4.14 FIGURE 4.15 FIGURE 4.16 Coverage of digitized field crop boundaries Random selection of a points sample The VLA used for aerial surveying Customized interface in ArcPad for data capturing Demonstration of crop type classification by aerial surveying fields in the Free State province Crop type classification development calendar for summer crops Free State crop type classification UNITED STATES FIGURE 5.1 FIGURE 5.2 FIGURE 5.3 FIGURE 5.4 Synthetic flowchart of crop acreage, yield and production estimates The USDA economic information system Reliability of US Crop Production Forecasts Objective Yield Sample Sizes 22 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 and 20: DAFF DBMS DEM DG-AGRI DMN DMP DPO D
Page 21: RMSE RS RSAC RSS RVI SACOTA SAFEX S
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
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accuracy of the RS indicator and th
Page 83 and 84:
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
Page 93 and 94:
TABLE 3.5 Meteorological variables
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two are pre-processing tasks (see F
Page 97 and 98:
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
Page 107 and 108:
variable and the total rainfall of
Page 109 and 110:
TABLE 3.8 Crop calendars and releas
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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
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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
Page 123 and 124:
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
Page 147 and 148:
of generating signature classes for
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and along with the infra-red bands
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Page 153 and 154:
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
Page 159 and 160:
commodity of interest within the op
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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
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FIGURE B1.2 Grid covering the area
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Annex B1.2 - The JRC-MARS Crop Yiel
Page 179 and 180:
FIGURE B1.8 MCYFS: Synthetic schema
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FIGURE B1.10 The CGMS Statistical T
Page 183 and 184:
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
Page 191 and 192:
M P7 M P8 _ By October 25 _ By Nove
Page 193 and 194:
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
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
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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
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
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Hutson, J.L. 1984. Estimation of hy
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Huddleston, H.F. 1978. Sampling tec
Page 241:
Layout: • Laura Monopoli Cover ph
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Crop Yield Forecasting

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