Crop Yield Forecasting

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1.3. Release calendars: punctuality and timeliness The NBS’s crop yield forecasts obtained through the NSRCP are used as internal references to the crop estimates generated by traditional statistics. The yield forecasts are internally reported in three reports: a preliminary report, a pre-production report and a final production report, while the planting area is reported annually (Zhang et al. 2010). The crop production estimates are released at the press board of the NBS’s website 8 in mid-July, at the end of August, and in early December for summer grains, early rice and national grain output respectively. A detailed timeline of NBS’s internal crop estimates (China 2014b) is provided in Table B2.1, Annex B2.1. As for the MoA’s CHARMS, the change in the crop sown area can be determined one month before harvest and the crop production can be forecast every month at late growing stages, with the final estimation being delivered by May, September and early October for winter wheat, maize and cotton respectively (Yang 2007). Each province regularly reports the crop estimates based on the traditional method (a complete reporting system supplemented by sampling surveys) to the MoA (see Table B2.2, Annex B2.1; China 2013), and the CHARMS crop forecasts are disseminated internally. Both sources are reported to the State Council and are used internally for policymaking purposes and to supplement the NBS’s official agricultural statistics. The CMA’s agricultural forecasts are released through multiple channels. Internally, two national consulting conferences are hosted by the CMA every year (mid-May and late August) to assess the national yield of summer crops and autumn crops. Representatives are invited from all provincial meteorological bureaus and other official department and institutions, including the MoA, the NBS, the State Administration of Grain (SAG), the Ministry of Civil Affairs (MCA) and the Chinese Academy of Sciences (CAS). Externally, the critical agrometeorological updates are directly reported to the State Council, the Rural Work Leading Group of the Communist Party of China’s (CPC) Central Committee, the National Development and Reform Commission (NDRC), the Ministry of Finance (MoF), the MoA and other relevant government departments; the crop forecasts may be updated in the periodical Agro-Meteorological Bulletins with ten-day, monthly, seasonal or annual time periods. Besides, during important growing periods for specific crops, special topic reports are issued to enable the dynamic monitoring of crop conditions and yield prospects. In addition, Agro-Meteorological Disasters Reports are released when disasters occur (Wang and He 2009). Currently, the CMA is capable of obtaining the variation of average, yield and production (increase or decrease) of domestic crops two months before the harvest, and the quantitative estimation of yield and production one month before harvest. For foreign countries such as United States, India and Brazil the yield predictions are available one month before harvest. 8 NBS press board: http://www.stats.gov.cn/english/PressRelease/ Crop Yield Forecasting: Methodological and Institutional Aspects 57
1.4. How do these different forecasts compare? Purpose, coverage, scale and harmonization issues, accuracy The NBS is the only official body that releases crop estimates for China, thus providing a unified crop data outlet. Although the official organizations (the NBS, the MoA, and the CMA) conduct crop forecasts separately, they all directly report to the State Council and the forecasting results are for internal use only. The crop information is well-referenced and shared among the official organizations. As mentioned in Section 1.2.1.3 above, the CMA organizes two consulting conferences on grain production each year, with expert participants from the MoA. In addition, as the ultimate coordinator of China’s statistical system, the NBS uses the agricultural information provided by the MoA and the CMA as the basis for the official Agricultural Statistics. Therefore, the official Chinese sources of crop estimates and forecasts are highly harmonized. The non-official sources may provide separate references, flexible forecasts and more openly accessible information compared to official releases. Unlike official national organizations, which enjoy significant funding to build top-to-down survey offices and teams, information centres, observing stations and networks throughout the country, the non-official institutes and organizations rely mainly on advanced technology, such as RS, to offer crop services in a more flexible, focused and fast manner. In addition, the prediction results are generally freely accessible from publications, reports and websites. Although there may be some deviation and differences between the official and non-official results due to the sampling, statistical methods and basic data used, the non-official sources do provide an independent reference and more timely and detailed information. A detailed comparison of the coverage, accuracy, release frequency and accessibility of the various sources is provided in Table 2.1. 58 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
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 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 production can be predicted one
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
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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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