Journal
CROP SCIENCE
Volume 60, Issue 2, Pages 739-750Publisher
WILEY
DOI: 10.1002/csc2.20053
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Funding
- CAPES Foundation
- Kansas Corn Commission
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Yield estimations are of great interest to support interventions from governmental policies and to increase global food security. This study presents a novel model to perform in-season corn yield predictions at the US-county level, providing robust results under different weather and yield levels. The objectives of this study were to: (i) evaluate the performance of a random forest classification to identify corn fields using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and weather variables (temperature, precipitation, and vapor pressure deficit, VPD); (ii) evaluate the contribution of weather variables when forecasting corn yield via remote sensing data, and perform a sensitivity analysis to explore the model performance in different dates; and (iii) develop a model pipeline for performing in-season corn yield predictions at county-scale. Main outcomes from this study were: (i) high accuracy (87% on average) for corn field classification achieved in late August, (ii) corn yield forecasts with a mean absolute error (MAE) of 0.89 Mg ha(-1), (iii) weather variables (VPD and temperature) highly influenced the model performance, and (iv) model performance decreased when predictions were performed early in the season (mid-July), with MAE increasing from 0.87-1.36 Mg ha(-1) when forecast timing changed from day of year 232-192. This research portrays the benefits of integrating statistical techniques and remote sensing to field survey data in order to perform more reliable in-season corn yield forecasts.
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