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EOSTAT Trusted methods for the use of EO data for crop type mapping and crop yield forecasting

Open Meeting of the UN-CEAG (54th Session of the UN Statistical Commission)​






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    Presentation
    Optimizing Surveys for Earth Observation Applications on Crop Type Mapping and Crop Yield Estimation
    Open Meeting of the UN-CEAG (54th Session of the UN Statistical Commission)​
    2023
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    Article
    FAO Crop Yield Forecasting Philosophy in National Food Early Warning Systems 1998
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    The paper describes the crop forecasting philosophy adopted by FAO in the ambit of national and regional food security projects in developing countries.
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    Book (stand-alone)
    Recent Practices and Advances for AMIS Crop Yield Forecasting at Farm and Parcel Level: A Review 2017
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    In most countries, although the proportion of national GDP constituted by the agricultural sector has been declining for decades, the forecasting of food production remains a major challenge for all the economic actors of modern societies. At all levels – government, industry, farm, household – decisions must be taken on the basis of advanced knowledge of the potential influence of economic, biotic and abiotic factors upon crop yields of the major food commodities, especially the four major crop s constituting the priorities of the Agricultural Market Information System (AMIS). The vital character of food commodities, coupled with the limited stocks, largely explains the price volatility observed and the erratic behavior of economic agents in light of the lack of information. In reaction to these phenomena, at the 2011 G20 summit, the ministers of agriculture decided to launch AMIS, an inter-agency platform tasked with enhancing food market transparency and encouraging the coordination of policy action in response to market uncertainty. At the same time, the private sector started to become active. The Bill & Melinda Gates Foundation invested in the monitoring of agricultural production, by introducing a change in data collection and analyses methods (Global Strategy for improving Agricultural and Rural Statistics) and by supporting technical advances in modelling (the NEXTGEN project) and the use of new technologies (the Spurring a Transformation for Agriculture through Remo te Sensing project, or STARS3). The food industry also began to model food production, aiming to make a joint academic, administration and industry effort for the purpose of Assessing Sustainable Nutrition Security.

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