Crop Yield Forecasting

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The stratification classes used are the following: Agriculture classes: 1. Cultivated greater than 80 percent 2. Cultivated between 30 and 80 percent 3. Cultivated 1 to 30 percent 4. Irrigation schemes 5. Non-commercial/communal area 6. Sugarcane 7. Horticulture/viticulture 8. Plantations 9. Smallholdings/peri-urban 10. Rangeland 11. Rangeland Non-agriculture classes: 12. Urban (residential/commercial – no cultivation) 13. Conservation, mines & rock (national parks, mines, bare rock) 14. Water bodies Classes 6 to 9 are collapsed into a single class to reduce the number of sample points required for classes in which the probability of finding cultivation of grain crops of interest is low. The collapsed stratum is then sampled. The non-agricultural classes (12 to 14) are not sampled because the probability of finding the crops of interest therein is very low or negligible. FIGURE 4.4 Stratification for summer crops The summer crops are stratified in high, medium and low strata based on field densities in a 5 km x 5 km grid. The stratification is used to guide the distribution and location of the field sample points, and thus to improve the overall statistical representation. Crop Yield Forecasting: Methodological and Institutional Aspects 125
Sample frame To set up the sample selection, a regular point grid of 225 m x 225 m is used and overlaid on the stratified map of South Africa. The number of points required per stratum and per province is calculated so as to provide a CV of approximately 10 percent per province and around 5 percent nationally. The sample of points for each estimate is then selected from the grid using systematic random sampling (Cochran 1977). This is done by stratum and province using GIS technology. These randomly selected points are then used by field or telephone interviewers to collect the basic data required for the statistical calculation of the crops. To relieve respondent burden, approximately 20 percent of the points are replaced on an annual basis. 2.2.1.2. Yield surveys using the telephone interview or field point frame (TAFSS) The data collection process Field data can be collected either by visiting the site where the point is located or by means of a telephone interview, depending on the type of survey procedure followed as per the relevant contract for the season. Telephone or field interview area and yield survey (TAFSS) questionnaires are designed for the survey, to collect information for the farming enterprise in which the point is located. If the point is located on a farming operation, information for the field (if the point is located on a field with a crop of interest) and the whole farming operation is collected. All enumerators undergo prior extensive training. The trained interviewers, the majority of whom are farmers themselves, visit the points to collect the data. The interviewers are contractually bound to refrain from disclosing any information concerning individual farms to any third party. The interviewers navigate to the points by means of a handheld Global Positioning System (GPS) and standard 1:250,000 map sheets. Overlaid on the standard map sheets are the locations of the points to be visited, as well as an indication of the optimum (shortest) road transect to follow to visit a number of allocated points. The interviewers should attempt to abide by the prescribed farm access control protocol and obtain permission to visit the point. The point and the field should then surveyed, the farmer interviewed and the data captured on the field survey questionnaire. The data capturing process and quality control A computer-based data capture and storage system has been developed and is used to easily enter field data into the system, as well as to perform essential quality control on the interviewer’s data input. This ensures that time is not wasted on correcting basic survey errors but is rather focused on data analysis and quick reporting. To make sure that the survey data is as relevant as possible, the survey and the reporting of the results must be completed in as short a time frame as possible. By using the Internet, the data is collected remotely (in the field or from other offices) and sent to a centralized server. This ensures data integrity and relevance at all times. 126 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
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1.4. How do these different forecas
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2. China national official sources:
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The NSRCP’s key technology includ
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2.1.2. Methodology and practices of
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FIGURE 2.2 Flow chart of NDVI-based
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2.1.3 Methodology and practices of
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various periods. By integrating thr
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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: a point sample frame. Three types o
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 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 169 and 170: drying have been taken into account
Page 173 and 174: Meteorological data • USA-NOAA ht
Page 175 and 176: 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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216 Crop Yield Forecasting: Methodo
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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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224 Crop Yield Forecasting: Methodo
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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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