Nutrient Balances of New Zealand Kiwifruit (<i>Actinidia deliciosa</i>cv. Hayward) at High Yield Level

Parent, Léon‐Étienne; Barlow, P. W.; Parent, Léon E.

doi:10.1080/00103624.2014.989031

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…The diagnostic accuracy of 72% reported here was lower than the generally obtained value of 80%, whereas the critical Mahalanobis distance of 3.9 was close to the range of 4.1–5.9 that has been reported for other crops 29 , 31 , 52 , 53 . Low accuracy was due to the high proportion of FP specimens (24%), indicating suboptimal fertilization, luxury consumption of nutrients, or leaf contamination.…”

Section: Discussionsupporting

confidence: 61%

Balance design for robust foliar nutrient diagnosis of “Prata” banana (Musa spp.)

Deus

Neves

Corrêa

et al. 2018

Sci Rep

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The “Cavendish” and “Prata” subgroups represent respectively 47% and 24% of the world banana production. Compared to world average progressing from 10.6 to 20.6 t ha−1 between 1961 and 2016, and despite sustained domestic demand and the introduction of new cultivars, banana yield in Brazil has stagnated around 14.5 t ha−1 mainly due to nutrient and water mismanagement. “Prata” is now the dominant subgroup in N-E Brazil and is fertigated at high costs. Nutrient balances computed as isometric log-ratios (ilr) provide a comprehensive understanding of nutrient relationships in the diagnostic leaf at high yield level by combining raw concentration data. Although the most appropriate method for multivariate analysis of compositional balances may be less efficient due to non-normal data distribution and limited nutrient mobility in the plant, robustness of the nutrient balance approach could be improved using Box-Cox exponents assigned to raw foliar concentrations. Our objective was to evaluate the accuracy of nutrient balances to diagnose fertigated “Prata” orchards. The dataset comprised 609 observations on fruit yields and leaf tissue compositions collected from 2010 to 2016 in Ceará state, N-E Brazil. Raw nutrient concentration ranges were ineffective as diagnostic tool due to considerable overlapping of concentration ranges for low- and high-yielding subpopulations at cutoff yield of 40 Mg ha−1. Nutrient concentrations were combined into isometric log-ratios (ilr) and normalized by Box-Cox corrections between 0 and 1 which may also account for restricted nutrient transfer from leaf to fruit. Despite reduced ilr skewness, Box-Cox coefficients did not improve model robustness measured as the accuracy of the Cate-Nelson partition between yield and the multivariate distance across ilr values. Sensitivity was 94%, indicating that low yields are attributable primarily to nutrient imbalance. There were 148 false-positive specimens (high yield despite nutrient imbalance) likely due to suboptimal nutrition, contamination, or luxury consumption. The profitability of “Prata” orchards could be enhanced by rebalancing nutrients using ilr standards with no need for Box-Cox correction.

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Section: Discussionsupporting

confidence: 61%

Balance design for robust foliar nutrient diagnosis of “Prata” banana (Musa spp.)

Deus

Neves

Corrêa

et al. 2018

Sci Rep

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“…Open ecosystems have several site-specific unexplained or uncontrollable sources of variation. While the R 2 of the Gaussian process regression was rather low (R 2 = 0.46, root-mean-square-error of 1447 kg ha −1 ) in testing, accuracy reached 83% in classification mode, a value comparable to other fruit crops [ 10 , 47 , 88 , 89 , 90 ]. The regression mode can compare current yields to modelled yields under optimized nutrient management.…”

Section: Discussionmentioning

confidence: 86%

Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem

Parent

Lafond

Paré

et al. 2020

Plants

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Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological conditions. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. The data set, that comprised soil, tissue, and meteorological data, was processed by Bayesian mixed models, machine learning, compositional data analysis, and Markov chains. Our investigative statistical models showed that meteorological indices had the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering stage showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (<−5 °C) before flower bud opening, showed negative effects. Soil and tissue tests, and N-P-K fertilization showed smaller effects. Gaussian processes predicted yields from historical weather data, soil test, fertilizer dosage, and tissue test with a root-mean-square-error of 1447 kg ha−1. An in-house Markov chain algorithm optimized yields modelled by Gaussian processes from tissue test, soil test, and fertilizer dosage as conditioned to specified historical meteorological features, potentially increasing yield by a median factor of 1.5. Machine learning, compositional data analysis, and Markov chains allowed customizing nutrient management of lowbush blueberry at local scale.

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“…The DRIS has incomplete geometry because its results are affected by how to choose the dual ratio expression, non‐additivity of functions and indices, and having extra degree of freedom (Modesto et al, 2014). The DRIS were shown to be methodologically biased from compositional model in several studies such as Parent et al (2013a, 2015) and Modesto et al (2014). The DRIS geometry reformed using the centered log ratio (CLR), but the CLR is influenced by abnormally high or low concentration in some components such as micronutrients used in fungicide (Filzmoser et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…In the dendrogram not only by shifting the ILR values at fulcrums toward the TN or TP sides were distinguished sound balances status but also by laying down the nutrient concentrations in buckets allowed interpreting the analytical results of nutrients in terms of deficiency, adequacy, or excess. (Parent et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…The DRIS has incomplete geometry because its results are affected by how to choose the dual ratio expression, non-additivity of functions and indices, and having extra degree of freedom (Modesto et al, 2014). The DRIS were shown to be methodologically biased from compositional model in several studies such as Parent et al (2013aParent et al ( , 2015 and Modesto Fig. 3.…”

Section: Pan Balance Diagrammentioning

confidence: 99%

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Investigating the Balance Status of Nutrients in Wheat Plant Using Isometric Log Ratio and Field Validation of Balances

2019

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Nutrient status of plants is determined by using critical nutrient range and nutrient ratios. The obtained data may not be generally error‐free due to the nature of compositional data is not taken into account Here, nutrient status of wheat (Triticum aestivum L.) in Entisols and Aridisols soils is investigated by isometric log ratio (ILR) technique. Leaf samples collected from 96 fields at booting stage. To validate the obtained ILR balances, two separate filed experiments were conducted employing a factorial experiment based on CRBD design with three replications. Concentrations of N, P, K, Ca, Mg, Zn, Fe, Mn, Cu, and B in the flag leaf tissue were determined at both fields. Nutrients were arranged into ad hoc balances and computed ILR and classified into four groups as balanced, misbalanced, high and low yielders by receiver operating characteristic (ROC) curve. The ROC classification applied to the data from survey fields and validation test respectively showed 4.67 and 4.02 as the critical Mahalanobis distance and 5034 and 6559 kg ha−1 as yield cut‐off for separating balanced and imbalanced samples at area under curve (AUC = 0.83 and 0.88). Comparing nutrient balance between reference (TN) and nonreference (TP) groups in the survey fields by Tukey's test showed that the balances of [Fe,Mn,Zn,Cu|B,Mg,Ca,K,P,N], [B|Mg,Ca,K,P,N], [Mg,Ca,K|P,N], [Mg,Ca|K], and [Mn|Zn,Cu] were negative. The [Fe|Mn,Zn,Cu] and [Zn|Cu] balances were positive. In the low yielding of survey fields, the balances of [Fe|Mn,Zn,Cu], [Mn|Zn,Cu], [Zn|Cu], [Fe,Mn,Zn,Cu|B,Mg,Ca,K,P,N], and [Ca,Mg|K] were imbalanced, but they become balanced through fertilizer treatments in the validation test. Core Ideas Nutrient balances can be interpreted simultaneously using pan balance approach. The values of isomeric log ratio represent the balances between two or more nutrients. Mahalanobis distance was set as a criterion for nutrient misbalance.

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Nutrient Balances of New Zealand Kiwifruit (Actinidia deliciosacv. Hayward) at High Yield Level

Cited by 11 publications

References 37 publications

Balance design for robust foliar nutrient diagnosis of “Prata” banana (Musa spp.)

Balance design for robust foliar nutrient diagnosis of “Prata” banana (Musa spp.)

Conditioning Machine Learning Models to Adjust Lowbush Blueberry Crop Management to the Local Agroecosystem

Investigating the Balance Status of Nutrients in Wheat Plant Using Isometric Log Ratio and Field Validation of Balances

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