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Identifying crop yield gaps with site- and season-specific data-driven models of yield potential

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A Correction to this article was published on 02 December 2021

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Abstract

There is considerable interest and value in identifying the gap between crop yields that have actually been achieved, and yields that could have potentially been achieved. A suite of methods currently exist to estimate the yield potential of a crop, but there are no approaches that predict the site- and season-specific yield potential using datasets that are readily available and easily accessible for farmers. The aim of this study was to fill this need and develop a novel approach to identify crop yield gaps through site- and season-specific models of crop yield potential. The study focused on cotton lint yield, with data from 14 different seasons and 68 different fields from a collection of large, irrigated cotton farms in eastern Australia. This abundance of yield data was then joined with other spatial and temporal datasets that describe yield, such as rainfall, temperature, soil, and management. A quantile random forest machine learning model was then used to model yield at 30 m resolution, where the 95th percentile predictions were treated as the yield potential. The yield gaps at a 30 m resolution were then estimated for all seasons and sites. The results were compared to a more traditional ‘historical maximum yield’ approach, where no data modelling and only empirical yield data was used to estimate the yield potential. This revealed that there was a general agreement between the two approaches, although the quantile machine learning approach is both site- and season-specific, not just site-specific. Overall, there is a great need for alternative approaches to estimate yield potential and yield gaps, as the approaches currently available possess many limitations. The approach developed in this study has the potential for wide-spread adoption in broadacre cropping systems, and if the causes of yield gaps are identified, could lead to the implementation of management strategies to close them.

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Data availability

Much of the data used in this study is private (crop yield data) and not available for public release.

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The code developed in this study is not available.

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Acknowledgements

The authors would like to show gratitude to the Cotton Research and Development Corporation (CRDC) for funding the research presented here. Precision Cropping Technologies (PCT) were instrumental in providing expert knowledge and access to the large yield mapping datasets. The authors are also grateful to the farm managers, agronomists, and companies for their time and assistance, and for allowing this research to take place on their properties. The authors would also like to thank the Editor and anonymous reviewers for their helpful comments.

Funding

This research was funded by the Cotton Research and Development Corporation (CRDC), and the University of Sydney.

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  1. Faculty of Science, School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW, Australia

    Patrick Filippi, Brett M. Whelan, R. Willem Vervoort & Thomas F. A. Bishop

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  1. Patrick Filippi
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Correspondence to Patrick Filippi.

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Filippi, P., Whelan, B.M., Vervoort, R.W. et al. Identifying crop yield gaps with site- and season-specific data-driven models of yield potential. Precision Agric 23, 578–601 (2022). https://doi.org/10.1007/s11119-021-09850-7

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