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Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada

Author

Listed:
  • Zonlehoua Coulibali
  • Athyna Nancy Cambouris
  • Serge-Étienne Parent

Abstract

Statistical modeling is commonly used to relate the performance of potato (Solanum tuberosum L.) to fertilizer requirements. Prescribing optimal nutrient doses is challenging because of the involvement of many variables including weather, soils, land management, genotypes, and severity of pests and diseases. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. The objective of this study was to determine an optimal model predicting nitrogen, phosphorus and potassium requirements for high tuber yield and quality (size and specific gravity) as impacted by weather, soils and land management variables. We exploited a data set of 273 field experiments conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, random forest, neural networks and Gaussian processes. Machine learning models returned R2 values of 0.49–0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R2 (0.37). The models were more likely to predict medium-size tubers (R2 = 0.60–0.69) and tuber specific gravity (R2 = 0.58–0.67) than large-size tubers (R2 = 0.55–0.64) and marketable yield. Response surfaces from the Mitscherlich model, neural networks and Gaussian processes returned smooth responses that agreed more with actual evidence than discontinuous curves derived from k-nearest neighbors and random forest models. When conditioned to obtain optimal dosages from dose-response surfaces given constant weather, soil and land management conditions, some disagreements occurred between models. Due to their built-in ability to develop recommendations within a probabilistic risk-assessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks.

Suggested Citation

  • Zonlehoua Coulibali & Athyna Nancy Cambouris & Serge-Étienne Parent, 2020. "Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-32, August.
  • Handle: RePEc:plo:pone00:0230888
    DOI: 10.1371/journal.pone.0230888
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    References listed on IDEAS

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    1. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.
    2. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Data Mining in Agriculture," Springer Optimization and Its Applications, Springer, number 978-0-387-88615-2, July.
    3. Rajsic, Predrag & Weersink, Alfons, 2008. "Do farmers waste fertilizer? A comparison of ex post optimal nitrogen rates and ex ante recommendations by model, site and year," Agricultural Systems, Elsevier, vol. 97(1-2), pages 56-67, April.
    4. Zonlehoua Coulibali & Athyna Nancy Cambouris & Serge-Étienne Parent, 2020. "Cultivar-specific nutritional status of potato (Solanum tuberosum L.) crops," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-15, March.
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