Compaction of Coarse-Textured Soils: Balance Models across Mineral and Organic Compositions

Xu, Yan; Jiménez, Maikel Abreu; Parent, Léon‐Étienne; Leblanc, Michaël A.; Ziadi, Noura; Parent, Léon E.

doi:10.3389/fevo.2017.00083

Cited by 15 publications

(10 citation statements)

References 81 publications

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“…Soil compaction has a negative impact on root extension and water movement i.e., the reduction of nutrient uptake potential leading to a severe reduction of tuber yield [8]. Xu et al [110] developed pedotransfer functions for potato grown on light-textured soils that could be useful in future models.…”

Section: Plos Onementioning

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: Plos Onementioning

confidence: 99%

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

2020

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“…Light absorption is also influenced by soil features such as particle roundness [15][16][17][18], soil pH, and the Mehlich-3 soil test for Ca, Mg and Mn [19]. The IRS detects Al-OH (2200 nm) and Fe-OH (2290 nm) [20,21] that in turn have an impact on soil structure [22] and IR reflectance [23]. The VIS-NIR spectra are sensitive to soil moisture and C content [24].…”

Section: Introductionmentioning

confidence: 99%

Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling

Parent

2021

PLoS ONE

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Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84–0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81–0.93) or raw percentages (R2 = 0.76–0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49–0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69–0.92) than 4X (R2 = 0.87–0.95). The MIR (R2 = 0.45–0.80) performed better than NIR (R2 = 0.40–0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86–0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.

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“…, the reduction of nutrient uptake potential leading to a severe reduction of tuber yield [8]. Xu et al [104] developed pedotransfer functions for potato grown on light-textured soils that could be useful in future models.…”

Section: Discussionmentioning

confidence: 99%

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

Coulibali

Cambouris

Parent

2020

Preprint

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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...

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Compaction of Coarse-Textured Soils: Balance Models across Mineral and Organic Compositions

Cited by 15 publications

References 81 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

Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling

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

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