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Predicting crop yields with little ground truth: A simple statistical model for in-season forecasting

Domain:

agriculturegeospatial

Record type:

papermodel
Creator:
Sem
Publisher:
arXiv
Host:avatar
We present a fully automated model for in-season crop yield prediction, designed to work where there is a dearth of sub-national "ground truth" information. Our approach relies primarily on satellite data and is characterized by careful feature engineering combined with a simple regression model. As such, it can work almost anywhere in the world. Applying it to 10 different crop-country pairs (5 cereals -- corn, wheat, sorghum, barley and millet, in 2 countries -- Ethiopia and Kenya), we achieve RMSEs of 5%-10% for predictions 9 months into the year, and 7%-14% for predictions 3 months into the year. The model outputs daily forecasts for the final yield of the current year. It is trained using approximately 4 million data points for each crop-country pair. These consist of: historical country-level annual yields, crop calendars, crop cover, NDVI, temperature, rainfall, and evapotransporation. This version has been removed by arXiv administrators due to a copyright claim by a 3rd party

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doi.orgarxiv.org

Tags

Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

Creative Commons Attribution Non Commercial No Derivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

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