Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Ritvik Sahajpal, Lucas Fontana, Pedro Lafluf, Guillermo Leale, Estefania Puricelli, Dan O’Neill, Mehdi Hosseini, Mauricio Varela, Inbal Becker-Reshef

Abstract

Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance compa-nies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth pro-gress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and ma-chine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demon-strate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones.

DOI

https://doi.org/10.31223/X52595

Subjects

Environmental Studies, Geographic Information Sciences, Remote Sensing, Spatial Science

Keywords

Crop Yield Forecasting, Mixed Effect Models

Dates

Published: 2020-11-09 16:55

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Data corresponds to personal farmer data that cannot be released without their authorization

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