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Soybean Yield Estimation and Its Components: A Linear Regression Approach

Author

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  • Marcelo Chan Fu Wei

    (College of Agriculture “Luiz de Queiroz”, University of Sao Paulo, 11 Padua Dias Avenue, Piracicaba 13418-900, Brazil)

  • José Paulo Molin

    (College of Agriculture “Luiz de Queiroz”, University of Sao Paulo, 11 Padua Dias Avenue, Piracicaba 13418-900, Brazil)

Abstract

Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R 2 ) of 0.70, mean absolute error (MAE) of 639.99 kg ha −1 and root mean squared error (RMSE) of 726.67 kg ha −1 .

Suggested Citation

  • Marcelo Chan Fu Wei & José Paulo Molin, 2020. "Soybean Yield Estimation and Its Components: A Linear Regression Approach," Agriculture, MDPI, vol. 10(8), pages 1-13, August.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:8:p:348-:d:397359
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    References listed on IDEAS

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    1. Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
    2. Coelho, Anderson Prates & Faria, Rogério Teixeira de & Leal, Fábio Tiraboschi & Barbosa, José de Arruda & Dalri, Alexandre Barcellos & Rosalen, David Luciano, 2019. "Estimation of irrigated oats yield using spectral indices," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
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    Cited by:

    1. Yan Guo & Xiaonan Hu & Zepeng Wang & Wei Tang & Deyu Liu & Yunzhong Luo & Hongxiang Xu, 2021. "The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(11), pages 457-467.
    2. Magdalena Borowska & Janusz Prusiński, 2021. "Effect of soybean cultivars sowing dates on seed yield and its correlation with yield parameters," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 67(6), pages 360-366.
    3. Cezary A. Kwiatkowski & Małgorzata Pawłowska & Elżbieta Harasim & Lucjan Pawłowski, 2023. "Strategies of Climate Change Mitigation in Agriculture Plant Production—A Critical Review," Energies, MDPI, vol. 16(10), pages 1-27, May.

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