![]() Machine learning models returned R 2 values of 0.49–0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R 2 (0.37). ![]() We developed, evaluated and compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, random forest, neural networks and Gaussian processes. We exploited a data set of 273 field experiments conducted from 1979 to 2017 in Quebec (Canada). 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. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. 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. ![]() Statistical modeling is commonly used to relate the performance of potato ( Solanum tuberosum L.) to fertilizer requirements.
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