A specific gravity calculator for potatoes

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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“…Table 3 summarizes the variables used for modeling. Tuber SG was determined by the weight-in-air to weight-in-water method [ 76 ] as in Eq 5 : …”

Section: Methodsmentioning

confidence: 99%

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

2020

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“…Tubers with external defects such as secondary growth and soft rot were discarded. A representative sample of 20 medium-size tubers from each plot was used to determine tuber SG by the weight-in-air to weight-in-water method [54] as in equation 1: …”

Section: Methodsmentioning

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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RESEARCHT he specific gravity (SpGr) of potato (Solanum tuberosum L.) tubers is highly correlated with tuber dry matter and starch content (Vanasse et al, 1951), and since SpGr is easier to measure than these traits, it is widely used to estimate the culinary and processing quality of potatoes (Greenwood et al, 1952;Young et al, 1964). High-SpGr potatoes are better suited for frying, chipping, dehydration, baking, and mashing, and low-SpGr potatoes are usually used for boiling, canning, roasting, and potato salad.Specific gravity is typically measured on a sample of many tubers to estimate the mean value for a single source.

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confidence: 99%

Reliability of Measurement and Genotype × Environment Interaction for Potato Specific Gravity

et al. 2017

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Specific gravity (SpGr) is often used to measure the processing quality of potato (Solanum tuberosum L.) tubers for French fries or potato chips because of its strong correlation with dry matter content and ease of measurement. For French fry processing genotypes, the desirable range for mean SpGr is typically 1.080 to 1.095, and a small variance around the mean is essential for product uniformity. Two multi‐year, multi‐location trials were conducted to investigate the genetics of SpGr in elite russet germplasm. Consistent with earlier studies, the mean SpGr was measured with high repeatability within each environment: the median plot‐basis value was 0.83 for a national trial with six locations and 3 yr. In contrast, the median repeatability of the SD between tubers was only 0.21. Thus, multi‐environment trials are needed to identify genotypes with a narrow SpGr distribution. Finlay–Wilkinson stability analysis of the mean SpGr established one genotype as an outlier: when best linear unbiased predictions were regressed on the environment means, this genotype had a regression coefficient of 2.1, compared with 0.4 to 1.4 for the others. The genetic correlation between environments showed a consistent regional pattern in mean SpGr over the years. There was a higher mean correlation between environments within the Pacific Northwest (0.97), Upper Midwest (0.91), and Northeast (0.85) than between environments from the different regions (0.35–0.78). Although breeding for national adaptation is an attractive idea, our results suggest that genetic gain may be easier to achieve at the regional level.

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A specific gravity calculator for potatoes

Cited by 15 publications

References 5 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

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

Reliability of Measurement and Genotype × Environment Interaction for Potato Specific Gravity

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