Crop Yield Forecasting

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FIGURE 1.7. Architecture of the B-CGMS GIS server Source: Tychon et al., 2000. The architecture of the B-CGMS implementation, which may be described as a 3-tier architecture, has three levels: the GIS Browser, the GIS Server, and the Database. In addition, the B-CGMS system produces an agrometeorological bulletin 44 every three months, providing crop yield forecasts for all agricultural regions for the following crops: winter wheat, winter barley, fodder maize, potatoes, sugar beet and winter rape. 44 http://b-cgms.cra.wallonie.be/wp-content/uploads/2014/09/BCGMS_Bulletin_2014_09_Fr.pdf Crop Yield Forecasting: Methodological and Institutional Aspects 47
2.5. Human, financial and technical infrastructure The system established by the NIS falls under the Government of Belgium’s agricultural policy. Final estimates of agricultural statistics (yield, area and production) are available online 45 for the years from 2007 to 2013 (at national, regional, provincial and agricultural region levels), as well as provisional production estimates (at national level) for 2014. GIS systems are widely used to represent Wallonia’s agricultural features 46 . The system established under the B-CGMS is linked to universities and national institutions involved in agriculture, and is supported by national and international projects (e.g. BELSPO and JECAM 47 ). Qualified staff of the four main partners (IRM, CRA-W, ULg and VITO) manages the system remotely. The system also enjoys the strong support of VITO, which an essential partner of the system as it provides a regular supply of remotely sensed imagery and updates to the system with more advanced data, which are obtained with a new type of sensors. The know-how developed within the B-CGMS is then transferred to developing countries in the framework of a specific project (i.e. the Global Monitoring for Food Security project – GMFS 48 ). 2.6. Institutional structure and sustainability The system established by the NIS under the Ministry of Economics, and therefore the institutional structure, is well-established and sustainable. As mentioned above, the system established under the B-CGMS was created in 1998 to work on a two-year project, financed by BELSPO, to develop a “Belgian” CGMS, with a team composed of members of three institutes: ULg, CRA-W, and VITO. From 2001 to 2006, a bilateral collaboration with the Heilongjiang Province Institute for Meteorological Sciences was funded, with the objective of improving both the Belgian and the Chinese CGMSs. Since 2006, the B-CGMS continued on an operational basis, through voluntary funding from ULg, CRA-W and VITO. Starting in 2015, two new projects (iPOT and BELCAM) funded by BELSPO will support the B-CGMS for four more years, thus enabling technical improvements to be made. 2.7. Innovation and integration with regional- and global-level initiatives The B-CGMS is an adaptation of the broader CGMS system for EU countries and thus operates in strong partnership with the JRC-MARS Unit, although the two systems use different datasets. Because the system makes use of agrometeorological models and 45 Agricultural statistics: http://statbel.fgov.be/fr/statistiques/chiffres/economie/agriculture/exploitations/#.VKqr4Xte83Y 46 GeoPortal for Wallonia: http://geoportail.wallonie.be/en/home.html 47 JECAM Belgium/France: http://www.jecam.org/?/project-reports/belgium-france 48 GMFS: http://www.gmfs.info/ 48 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 and 22: RMSE RS RSAC RSS RVI SACOTA SAFEX S
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: FIGURE 1.6. Architecture of the B-C
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
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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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of generating signature classes for
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and along with the infra-red bands
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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
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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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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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Page 227 and 228:
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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