Crop Yield Forecasting

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3.4. Institutional structure and sustainability Because the CGMS-MAROC is coordinated by the Ministry of Agriculture (of which the INRA and the DSS are part), and by the DMN by means of a formal agreement, its establishment is ensured and sustainable. FIGURE 3.9 Information systems coordinated by the Ministry of Agriculture Source: Workshop on Crop yield forecasting based on remote sensing, 2011. Rabat, Morocco . The CGMS-MAROC is one of the many information system projects coordinated by the Ministry of Agriculture, which is investing in the use of new technologies for data production and dissemination. Crop Yield Forecasting: Methodological and Institutional Aspects 109
110 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
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: TABLE 3.8 Crop calendars and releas
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
Page 129 and 130: 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: 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
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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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166 Crop Yield Forecasting: Methodo
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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
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Annex B1.2 - The JRC-MARS Crop Yiel
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FIGURE B1.8 MCYFS: Synthetic schema
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FIGURE B1.10 The CGMS Statistical T
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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
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M P7 M P8 _ By October 25 _ By Nove
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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
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FIGURE B3.14 Descriptive schema of
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Annex B3.3 - Websites Country Natio
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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
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FIGURE B5.2 USDA-FAS-PECAD: Crop Ex
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FIGURE B5.3A CropScape: Cropland Da
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FIGURE B5.7 Global Agricultural Mon
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Navalgund, R.R., Parihar J.S., Ajai
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Royer, A. & Genovese, G. (eds). 200
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Huang, J., Wang, X., Li, X., Tian,
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Wang, M. & Wei, G. 2014. Promoting
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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
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Layout: • Laura Monopoli Cover ph
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