Crop Yield Forecasting

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Xu, Z. & Zhou, W. 2013. China’s Capacity Assessment to Produce Agricultural and Rural Statistics. Paper prepared for the Sixth International Conference on Agricultural Statistics (ICAS-VI). Available at http://eventos.ibge.gov.br/icas2013/index.php?option=com_eventos&task=download&tipo=arquivo&id=55&uri=eventos.ibge.gov.br/icas2013&auth=1. Accessed on [please insert date]. Yang, Q., Xu Y., Lin, E., Xiong, W. & Chen, X. 2009. Application of DSSAT crop model on prediction of potential yield of spring wheat in Ningxia. Agricultural Research in the Arid Areas. 27(2): 41-48 (in Chinese with English abstract). Yang, Q., Xu, Y., Lin, E., Xiong, W. & Chen, X. 2009a. Application of DSSAT crop model on prediction of potential yield of spring wheat in Ningxia. Agricultural Research in the Arid Areas. 27(2): 41-48 (in Chinese with English abstract). Yang, S. 2007. Remote Sensing Monitoring Operation System for Agriculture. Paper prepared for the Fourth International Conference on Agriculture Statistics (ICAS-IV), 22-24 October 2007. Beijing. Available at http://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/ICAS4/ Remote_Sensing_Monitoring_Operation_System_for_Agriculture.pdf. Accessed on 16 October 2015. Yang, S., Shen, S., Li, B., Tan, B. & Li, Z. 2009b. Mapping Rice Yield Based on Assimilation of ASAR Data with Rice Growth Model. Journal of Remote Sensing, 13(2): 282-289. Yi, X., Wang, J. & Song, Y. 2010. Application of Climate Suitability Index to Dynamical Prediction of Early Rice Yield. Meteorological Monthly. 36(6): 85-89 (in Chinese with English abstract). Yu, W., Feng, R., Ji, R., Zhang, Y., Chen, P., Zhao, L. & Wu, J. 2011. Advances in rice planting area extraction technology based on MODIS data. Journal of Meteorology and Environment, 27(2): 56-61 (in Chinese with English abstract). Yu, W., Feng, R., Ji, R., Zhao, X., Wu, J., Jiang, P. & Zhang, Y. 2013. Extracting Rice Planting Area and Precision Analysis by Using MODIS Data. Chinese Agricultural Science Bulletin, 29(33): 32-36 (in Chinese with English abstract). Zhang, F., Wu, B., Liu, C. & Luo, Z. 2004a. Methods of monitoring crop phonological stages using time series of vegetation indicator. Transactions of the CSAE, 20: 155-159 (in Chinese with English abstract). Zhang, F., Zhu, Z., Pan, Y., Hu, T. & Zhang, J. 2010. Application of Remote Sensing Technology in Crop Acreage and Yield Statistical Survey in China. Paper prepared for the Meeting on the Management of Statistical Information Systems (MSIS 2010), Working Paper 16, 26-29 April 2010. Daejeon, Republic of Korea. Available at http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.50/2010/ wp.16.e.pdf. Accessed on 16 October 2015. Zhang, S. & Wu, W. 2003. Operational System on Agrometeorological Yield Forecasting in Liaoning. Liaoning Meteorological Quarterly. 20(4): 9-11 (in Chinese). Zhang, X., Zhuang, L., Wang, L. & Zou, C. 2004b. Operational System on Agrometeorological Yield Forecasting in Henan. Meteorology Journal of Henan (1): 43-45 (in Chinese with English abstract). Zhao A., Zhu, X. & Li, T. 2014. Estimation of winter wheat yield in Beijing based on the HJ-1 images. Research of Agricultural Modernization, 35(5): 573-577 (in Chinese with English abstract). Crop Yield Forecasting: Methodological and Institutional Aspects 233
Zhao, J., Shi, K. & Wei, F. 2007. Research and Application of Remote Sensing Technology in Chinese Agricultural Statistics. Paper prepared for the Fourth International Conference on Agriculture Statistics (ICAS-IV), 22-24 October 2007. Beijing. http://www.fao.org/fileadmin/templates/ess/documents/ meetings_and_workshops/ICAS4/Research_and_Application_of_Remote_Sensing_Technology_in_ Chinese_Agricultural_Statistics.pdf. Zhao, J. & Zhou, W. 2010. The Integrated Survey Framework in the Redesign of Sample Surveys in China Agricultural and Rural Statistics. Paper prepared for the Fifth International Conference on Agricultural Statistics (ICAS-V), 12-15 October 2010, Kampala. Available at http://www.fao.org/fileadmin/templates/ ess/documents/meetings_and_workshops/ICAS5/PDF/ICASV_5.2_065_Paper_Zhao.pdf, accessed on 16 October 2015. Chapter 3 Balaghi, R., El Hairech, T., Tahri, M. & Lahlou, M. 2013. CGMS-MAROC: Manuel d’utilisation. DMN/ INRA/DSS Publication. Available at http://www.cgms-maroc.ma/docs/tutorialcgms.pdf. Last accessed on 16 October 2015. Balaghi, R., Jlibene, M., Tychon, B. & Eerens, H. 2013a. Agrometeorological Cereal Yield Forecasting in Morocco. INRA Publication, Rabat. Available at http://www.inra.org.ma/publications/ ouvrages/ prediction1113en.pdf. Last accessed on 16 October 2015. Bensaid, K. 2011. Élaboration d’outils de prévision de la production céréalière. Paper prepared for the E-AGRI Workshop on Crop yield forecasting based on remote sensing, 12-14 October 2011, Rabat. Available at http://www.inra.org.ma/docs.asp?codedocs=136&codelangue=23. Last accessed on 16 October 2015. Bouzaffour, S. & Hanuschak G. 1998. Crop Monitoring in Morocco Using Remote Sensing and Geographic Information Systems: From a Management Perspective. Paper prepared for the Agricultural Statistics 2000 Conference, Washington D.C., 18-20 March 1998. Available at http://www.nass.usda. gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/GIS_Reports/Crop%20 Monitoring%20in%20Morocco%20Using%20Remote%20Sensing.pdf. Last accessed on 16 October 2015. Craig, M. & Atkinson, D. 2013. A Literature Review of Crop Area Estimation. FAO Publication: Rome. Available at http://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/ GS_SAC_2013/Improving_methods_for_crops_estimates/Crop_Area_Estimation_Lit_review.pdf. Last accessed on 16 October 2015. Defourny, P., Blaes, X. & Bogart, P. 2006. Respective contribution of yield and area estimates to the error in crop production forecasting. Proceedings of the Workshop on Remote sensing support to crop yield forecast and area estimates, 30 November – 1 December 2006, Stresa, Italy. Available at http:// www.isprs.org/proceedings/XXXVI/8-W48/39_XXXVI-8-W48.pdf. Last accessed on 16 October 2015. DSS. 2014. Use of Remote Sensing and GIS for building and updating Area Frame Sampling. Proceedings of the Workshop on Crop Yield Forecasting based on Remote Sensing, Rabat, 12-14 October 2011. Available at http://www.inra.org.ma/docs/E-Agri_tahiri.pdf. Last accessed on 16 October 2015. 234 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
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FIGURE 2.3 Data sources and institu
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2.4. Human, financial and technical
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method is applied to choose three t
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accuracy of the RS indicator and th
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TABLE 2.4 Crop calendar and release
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Institute for Research and Technolo
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■■ Yield • Phenological Event
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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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Page 185 and 186: Annex B1.5 - Websites Country Natio
Page 187 and 188: N_A5 By August 25 Seasonal report o
Page 189 and 190: IV. Official releases of crop produ
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Page 193 and 194: Annex B2.2 - Websites Country China
Page 195 and 196: FIGURE B3.2 Agrometeorological bull
Page 197 and 198: FIGURE B3.6 CGMS-MAROC’s web-mapp
Page 199 and 200: FIGURE B3.10 Crop production foreca
Page 201 and 202: from the various models are inserte
Page 203 and 204: FIGURE B3.14 Descriptive schema of
Page 205 and 206: Annex B3.3 - Websites Country Natio
Page 207 and 208: FIGURE B4.2 Gross income (South Afr
Page 209 and 210: Annex B4.3 - Example of SAGIS publi
Page 211 and 212: 4.2 Definition Oilseeds are defined
Page 213 and 214: Other countries estimate production
Page 215 and 216: Annex B4.5 - Advanced Sample Framew
Page 217 and 218: 216 Crop Yield Forecasting: Methodo
Page 219 and 220: FIGURE B5.2 USDA-FAS-PECAD: Crop Ex
Page 221 and 222: FIGURE B5.3A CropScape: Cropland Da
Page 223 and 224: FIGURE B5.7 Global Agricultural Mon
Page 225 and 226: 224 Crop Yield Forecasting: Methodo
Page 227 and 228: Navalgund, R.R., Parihar J.S., Ajai
Page 229 and 230: Royer, A. & Genovese, G. (eds). 200
Page 231 and 232: Huang, J., Wang, X., Li, X., Tian,
Page 233: Wang, M. & Wei, G. 2014. Promoting
Page 237 and 238: Hutson, J.L. 1984. Estimation of hy
Page 239 and 240: Huddleston, H.F. 1978. Sampling tec
Page 241: Layout: • Laura Monopoli Cover ph
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