SVMLEACH – NK POTATO: A simple software tool to simulate nitrate and potassium co-leaching under potato crop

Fortin, J.G.; Morais, A.; Anctil, François; Parent, Léon‐Étienne

doi:10.1016/j.compag.2014.11.025

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…The optimal nutrient input is the one returning yield of high-quality tubers [32], where profitability is maximized and the environmental footprint minimized [94,95]. To compute the optimal economic N, P, K doses at a given site, all the predictive features, but not N, P and K doses, were held constant (fixed input data).…”

Section: Economic or Agronomic Optimal Dosesmentioning

confidence: 99%

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

2020

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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.

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Section: Economic or Agronomic Optimal Dosesmentioning

confidence: 99%

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

2020

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“…Furthermore, the MARS model, with its tree-structure, provides valuable insights into the relationships between input and output variables and identify important input subsets. Given these advantages, there is a growing interest in using the tree-based MARS in water quality and groundwater studies [34][35][36]. The utility of tree-based classifiers can be further enhanced using bagging and boosting techniques, used by ensemble-based classifiers such as the random forests (RF) [37] and the gradient boosting trees (GBT) algorithms [38,39].…”

Section: Introductionmentioning

confidence: 99%

Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

Uddameri

Silva

Singaraju

et al. 2020

Water

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The performance of four tree-based classification techniques-classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths-an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.Water 2020, 12, 1023 2 of 27 worldwide [5,6]. Intensification of agricultural activities for both food and energy will further increase the risks of nitrate contamination in aquifers across the world [7][8][9].Nitrate is mobile and fairly recalcitrant, especially in shallow groundwater systems that typically tend to be under oxidizing conditions. Nitrate exhibits the ability to spread over large areas and cannot be treated in-situ using conventional plume scale treatment technologies [10]. Therefore, individual homeowners are often required to install costly point-of-use treatment systems to mitigate nitrate risks arising from the ingestion of contaminated groundwater [11,12]. However, as nitrate is colorless and odorless, many people do not realize the risk of nitrate contamination and are unwittingly exposed to elevated levels of nitrate over long periods of time [13]. Therefore, nitrate contamination must be prevented through proper land management practices. Additionally, areas with a high susceptibility to nitrate pollution must be carefully delineated, with the goal of increasing public awareness regarding elevated health risks arising from nitrate exposures. Such an effort is also useful to prioritize monitoring activities and ensure that the limited fiscal and logistic resources are being used in a prudent manner.Mapping the susceptibility of aquifers to nitrate contamination is an essential step in mitigating and managing nitrate contamination. Multi-criteria decision making (MCDM) methods, such as DRASTIC [14], have been widely used to map aquifer vulnerabili...

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“…11. Fortin et al (2015) developed SVMLEACH-NK POTATO to simulate the daily dynamics of N and K leaching losses during the potato-growing season in Quebec, Canada. The model uses the least squares support vector machines (LS-SVMs) method, a machine learning technique that looks for patterns in data and performs regression in a high-dimension feature space (Fortin et al 2014).…”

Section: Nutmonmentioning

confidence: 99%

Outputs: Potassium Losses from Agricultural Systems

Goulding

Murrell

Mikkelsen

et al. 2020

Improving Potassium Recommendations for Agricultural Crops

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Potassium (K) outputs comprise removals in harvested crops and losses via a number of pathways. No specific environmental issues arise from K losses to the wider environment, and so they have received little attention. Nevertheless, K is very soluble and so can be leached to depth or to surface waters. Also, because K is bound to clays and organic materials, and adsorbed K is mostly associated with fine soil particles, it can be eroded with particulate material in runoff water and by strong winds. It can also be lost when crop residues are burned in the open. Losses represent a potential economic cost to farmers and reduce soil nutritional status for plant growth. The pathways of loss and their relative importance can be related to: (a) the general characteristics of the agricultural ecosystem (tropical or temperate regions, cropping or grazing, tillage management, interactions with other nutrients such as nitrogen); (b) the specific characteristics of the agricultural ecosystem such as soil mineralogy, texture, initial soil K status, sources of K applied (organic, inorganic), and rates and timing of fertilizer applications. This chapter provides an overview of the main factors affecting K removals in crops and losses through runoff, leaching, erosion, and open burning.

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SVMLEACH – NK POTATO: A simple software tool to simulate nitrate and potassium co-leaching under potato crop

Cited by 9 publications

References 19 publications

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

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

Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

Outputs: Potassium Losses from Agricultural Systems

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