Technologies of precision agriculture, digital soil maps, and meteorological stations provide a minimum data set to guide precision farming operations. However, determining optimal nutrient requirements for potato (Solanum tuberosum L.) crops at subfield scale remains a challenge given specific climatic, edaphic, and managerial conditions. Multilevel modeling can generalize yield response to fertilizer additions using data easily accessible to growers. Our objective was to elaborate a multilevel N fertilizer response model for potato crops using the Mitscherlich equation and a core data set of 93 N fertilizer trials conducted in Québec, Canada. Daily climatic data were collected at 10 × 10 km resolution. Soils were characterized by organic matter content, pH, and texture in the arable layer, and by texture and tools of pedometrics across a gleization-podzolization continuum in subsoil layers. There were five categories of preceding crops and five cultivar maturity orders. The three Mitscherlich parameters (Asymptote, Rate, and Environment) were most often site-specific. Sensitivity analysis showed that optimum N dosage increased with non-leguminous high-residue preceding crops, coarser soils, podzolization, drier climatic condition, and late cultivar maturity. The inferential model could guide site-specific N fertilization using an accessible minimum data set to support fertilization decisions. As decision-support system, the model could also provide a range of optimum N doses across a large spectrum of site-specific conditions including climate change.

Gradients in the elemental composition of a potato leaf tissue (i.e. its ionome) can be linked to crop potential. Because the ionome is a function of genetics and environmental conditions, practitioners aim at fine-tuning fertilization to obtain an optimal ionome based on the needs of potato cultivars. Our objective was to assess the validity of cultivar grouping and predict potato tuber yields using foliar ionomes. The dataset comprised 3382 observations in Qué bec (Canada) from 1970 to 2017. The first mature leaves from top were sampled at the beginning of flowering for total N, P, K, Ca, and Mg analysis. We preprocessed nutrient concentrations (ionomes) by centering each nutrient to the geometric mean of all nutrients and to a filling value, a transformation known as row-centered log ratios (clr). A densitybased clustering algorithm (dbscan) on these preprocessed ionomes failed to delineate groups of high-yield cultivars. We also used the preprocessed ionomes to assess their effects on tuber yield classes (high-and low-yields) on a cultivar basis using k-nearest neighbors, random forest and support vector machines classification algorithms. Our machine learning models returned an average accuracy of 70%, a fair diagnostic potential to detect in-season nutrient imbalance of potato cultivars using clr variables considering potential confounding factors. Optimal ionomic regions of new cultivars could be assigned to the one of the closest documented cultivar.

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 R 2 values of 0.49-0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R 2 (0.37). The models were more likely to predict medium-size tubers (R 2 = 0.60-0.69) and tuber specific gravity (R 2 = 0.58-0.67) than large-size tubers (R 2 = 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 riskassessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks.

17Statistical modeling is commonly used to relate the performance of potato 18 (Solanum tuberosum L.) to fertilizer requirements. Prescribing optimal nutrient doses is 19 challenging because of the involvement of many variables including weather, soils, land 20 management, genotypes, and severity of pests and diseases. Where sufficient data are 21 available, machine learning algorithms can be used to predict crop performance. The 22 objective of this study was to predict tuber yield and quality (size and specific gravity) as 23 impacted by nitrogen, phosphorus and potassium fertilization as well as weather, soils 24 and land management variables. We exploited a data set of 273 field experiments 25 conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and 26 compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, 27 random forest, neuronal networks and Gaussian processes. Machine learning models 28 returned R 2 values of 0.49-0.59 for tuber marketable yield prediction, which were higher 29 than the Mitscherlich model R 2 (0.37). The models were more likely to predict medium-30 size tubers (R 2 = 0.60-0.69) and tuber specific gravity (R 2 = 0.58-0.67 ) than large-size 31 tubers (R 2 = 0.55-0.64) and marketable yield. Response surfaces from the Mitscherlich 32 model, neural networks and Gaussian processes returned smooth responses that agreed 33 more with actual evidence than discontinuous curves derived from k-nearest neighbors 34 and random forest models. When marginalized to obtain optimal dosages from dose-35 response surfaces given constant weather, soil and land management conditions, some 36 disagreements occurred between models. Due to their built-in ability to develop 37 recommendations within a probabilistic risk-assessment framework, Gaussian processes 38 stood out as the most promising algorithm to support decisions that minimize economic 39 or agronomic risks. 3 40 Keywords: 41 Precision fertilization, Mitscherlich model, k-nearest neighbors, random forest, neuronal 42 networks, Gaussian process, economic optimal dose, agronomic optimal dose, Solanum 43 tuberosum L. 4 44 2 Introduction 45 Modeling provides a quantitative understanding of how crop systems operate 46 [1]. Site-specific simulations of fertilizer requirements to obtain high local potato yield 47 and quality rely on models' ability to detect subtle variations in factors affecting plant 48 growth and environment and to learn from the past to make predictions [2]. Several crop 49 models have been developed with different degrees of sophistication, scale, and 50 representativeness [2]. Mechanistic models have been published for potato cropping 51 systems [3, 4]. Semi-mechanistic growth models could be used to downscale tuber yield 52 assessment from regional to field levels [5, 6]. Multilevel modeling can assist in 53 selecting a set of relevant parameters that impact tuber yield and fertilizer requirements, 54 but can hardly predict site-specific nutrient requirements [7]. 55 Several variables can im...