Crop Yield Forecasting

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equires information on the specific soil characteristics and their spatial distribution, on the basis of simulation units. Each soil type is identified by a geo-referenced cartographic unit (the geographic database) associated to two groups of factors (the analytical database): (i) the Rooting Depth; and (ii) the Soil Physical Group, which provides the soil’s hydrodynamic properties. The parameters characterizing the soil’s water retention and hydraulic conductivity do not exist in Belgium’s soil maps, or in the 1988 AARDEWERK database (Van Orshoven & Vandenbroucke 1993); however, they are necessary for the crop growth model. Therefore, the pedotransfer functions are used; these equations estimate the unknown parameters from the information that is available in Belgium’s soil database. 2.2.7. Remote sensing imagery data The B-CGMS uses the 1-km² spatial resolution and 10-day temporal resolution imagery of NOAA-AVHRR and SPOT-VEGETATION to improve its crop yield forecasts. The database includes historical satellite imagery for the last 15 years, and current imagery obtained every ten days. From the satellite imagery, the NDVI (Figure 1.3 below) is processed and the DMP is calculated. FIGURE 1.3. The Normalized Difference Vegetation Index Source: Tychon et al., 2000. An example of the NDVI processed from the satellite imagery in the B-CGMS database. Crop Yield Forecasting: Methodological and Institutional Aspects 37
2.2.8. The Crop Growth Simulation Model WOFOST is a semi-deterministic process-based model of physiological processes that simulates crop growth at a daily time interval. A simplified scheme of the WOFOST model’s input and output variables is shown in Figure 1.4. The following input variables (a complete list of which is available in Table B1.1., Annex B1.3) are used: • 46 crop parameters, including 34 single parameters and 12 multiple parameter tables (dynamic parameters that are a function of the DVS or of temperature); • Variety (regional cultivars, some parameters of which are modified); • Crop calendar; • Start of season (emergence or sowing); and • End of season (maturity or harvest). The simulation runs from sowing to maturity and is based on the crop’s response to weather and soil moisture conditions. The following variables are calculated: • Phenology (sowing-flowering-maturity); • Light interception; • Assimilation (Photosynthesis); • Respiration; • Assimilate partitioning; • Leaf area dynamics (growth and decay): • Evapotranspiration; and • Soil/water balance. 38 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: • CIPF; • CRA-W; • Centre de
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
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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
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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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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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