Crop Yield Forecasting

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2 Crop Yield Forecasting in China Ning Zhang 1 1. Crop yield forecast data for China 1.1. Brief description China is the first world producer of wheat and rice and is a major producer of maize and soybean. It successfully feeds approximately 20 percent of the world’s population, with less than 10 percent of arable land. Crop production in China has a direct effect on its people’s livelihood, the nation’s economy and social development and the world food 2 demand and supply balance. Being the most important priority in sustaining human society, crop production and yield estimation have always received great attention in China. In recent years, the Chinese government and society have made substantial efforts to develop and adopt innovative methods to improve accuracy and timeliness in providing agricultural services, such as crop yield forecasts. This Chapter will begin with an overview of the current domestic and international resources employed to compute crop yield estimates and forecasts in China. 1 Institute of Remote Sensing and Digital Earth, Chinese Academy of Science. 2 Notes on terminology: in Chinese sources, “grain” includes cereals, beans and potatoes, while it may also be categorized as summer grain, early rice and autumn grain depending on the production season. “Total grain production” refers to the total of cereals, beans and potatoes produced in a given region in one production year. “Food” includes, in addition to “grain,” edible vegetable oils, meat, poultry, dairy and marine products. Crop Yield Forecasting: Methodological and Institutional Aspects 53
1.2. Inventory of forecasts available, by source 1.2.1. Official national sources 1.2.1.1. The National Bureau of Statistics The National Bureau of Statistics (NBS) of the People’s Republic of China is directly under the control of the State Council and is in charge of statistics and economic accounting. It is a main provider of national statistical data and a coordinator of government departmental statistics and local statistics for the entire national statistical system (China 2015b). The NBS releases the official statistics for grain production and farmers’ disposable income. The NBS serves as the official dissemination body for China’s national crop production estimates. To produce these estimates, essentially two approaches are available: (1) the traditional approach, based on sample surveys combined with the complete reporting system, and (2) the remote sensing (RS) operation system (the National Statistics and Remote Sensing System of Crop Production – NSRCP). The crop yield forecasts obtained through the NSRCP are used only as internal references to the crop estimates generated by traditional statistics and are not disseminated to the public. 1.2.1.2. The Ministry of Agriculture The Ministry of Agriculture (MoA) of the People’s Republic of China is a component of the State Council and is in charge of agriculture and rural economic development. It is responsible for forecasting and publicizing rural economic information on various agricultural products and means of agricultural production, and for formulating agricultural and rural economic development strategy plans (China 2015a). To supervise the results of policy implementation, the MoA also collects a range of agricultural and rural statistics, which complement China’s overall agricultural and rural statistical system (Zhao and Zhou 2010). Similarly to the NBS, the MoA may adopt either of two different approaches to produce agricultural crop estimates and forecasts: (1) the traditional method for computing crop estimations for each province are the complete reporting system and sampling survey; while (2) the China Agriculture Remote Sensing Monitoring System (CHARMS) is a new source of crop forecasting data in the main grain-producing provinces. CHARMS was developed by the MoA’s Remote Sensing Application Center (RSAC) to provide agricultural information on crop condition, crop area, yield and production, as well as agriculture disasters analysis for five major crops (wheat, rice, maize, soybean and cotton; Huang et al. 2009). 1.2.1.3. The China Meteorological Administration The China Meteorological Administration (CMA) is a public institution that is directly affiliated to the State Council. The CMA has prioritized the provision of agrometeorological services for several years now. Beginning in the 1990s, the CMA’s agrometeorological yield forecasting system was gradually 54 Crop Yield Forecasting: Methodological and Institutional Aspects
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Crop Yield Forecasting: Methodologi
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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 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: national level) for 2014. GIS syste
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
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
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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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