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Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates

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

Abstract

Forecasts of crop yield are an important tool for a variety of stakeholders, but most studies produce large-scale, late-season yield forecasts that are not appropriate for farmers. Farmers require forecasts of crop yield mid-season and at fine spatial resolutions to guide site-specific adaptive crop management practices. This study created empirical random forest models to forecast cotton yield mid-season at three different spatial resolutions: 30 m, field, and farm aggregation. Large yield mapping datasets from 14 different seasons were utilised to build the model across 68 different fields from six large cotton farms in eastern Australia. A generic conceptual framework is proposed to empirically model crop yield. This topsaw framework represents the spatial and temporal factors that drive yield, including topography (t), organisms (o), plant measurements (p), soil (s), agronomy/management (a), and weather (w). In this study, the specific predictor variables to represent these driving factors of yield included elevation (t), Normalised Difference Vegetation Index, Enhanced Vegetation Index, MODIS Evapotranspiration (p), gamma radiometrics grid, clay content digital soil map (s), rainfall data, and growing day degrees (w). Forecasts of cotton yield increased in accuracy as the spatial resolution of predictions became coarser. Using a leave-one-year-out cross-validation, cotton yield could be forecasted to an accuracy of 0.42 Lin's Concordance Correlation Coefficient (LCCC) and Root Mean Square Error (RMSE) of 2.11 bales ha−1 at 30 m resolution. This improved to a 0.63 LCCC and 1.72 bales ha−1 RMSE at the field resolution, and a 0.65 LCCC and 0.77 bales ha−1 at the aggregation-scale. As the developed approach solely relied on publicly available predictor variables, there is an opportunity to apply this to any cotton field in Australia, and over a much larger area. Overall, this study is an important step in building an operational approach to forecast mid-season cotton yield at fine spatial resolutions, and has the potential to improve production, input use efficiency, and profitability for individual growers and the Australian Cotton Industry as a whole.

Suggested Citation

  • Filippi, Patrick & Whelan, Brett M. & Vervoort, R. Willem & Bishop, Thomas F.A., 2020. "Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates," Agricultural Systems, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:agisys:v:184:y:2020:i:c:s0308521x20307551
    DOI: 10.1016/j.agsy.2020.102894
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    References listed on IDEAS

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    2. Mavromatis, T., 2016. "Spatial resolution effects on crop yield forecasts: An application to rainfed wheat yield in north Greece with CERES-Wheat," Agricultural Systems, Elsevier, vol. 143(C), pages 38-48.
    3. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    4. Hearn, A. B., 1994. "OZCOT: A simulation model for cotton crop management," Agricultural Systems, Elsevier, vol. 44(3), pages 257-299.
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    Cited by:

    1. Zhang, Chen & Di, Liping & Lin, Li & Li, Hui & Guo, Liying & Yang, Zhengwei & Yu, Eugene G. & Di, Yahui & Yang, Anna, 2022. "Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data," Agricultural Systems, Elsevier, vol. 201(C).
    2. Ji, Zhonglin & Pan, Yaozhong & Li, Nan, 2021. "Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model," Ecological Modelling, Elsevier, vol. 455(C).

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