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Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica).

Parent SÉ, Parent LE, Rozane DE, Natale W - Front Plant Sci (2013)

Bottom Line: Traditional multivariate methods were found to be biased.A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalanced and high/low-yielders.The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree(-1) proved to be a fairly informative test (area under curve = 0.84-0.92).

View Article: PubMed Central - PubMed

Affiliation: Department of Soils and Agrifood Engineering, ERSAM, Université Laval Québec, QC, Canada.

ABSTRACT
Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflicting results and wrong inferences. Our objective was to present an unbiased statistical approach of plant nutrient diagnosis using a balance concept and mango (Mangifera indica) as test crop. We collected foliar samples at flowering stage in 175 mango orchards. The ionomes comprised 11 nutrients (S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Traditional multivariate methods were found to be biased. Ionomes were thus represented by unbiased balances computed as isometric log ratios (ilr). Soil fertility attributes (pH and bioavailable nutrients) were transformed into balances to conduct discriminant analysis. The orchards differed more from genotype than soil nutrient signatures. A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalanced and high/low-yielders. The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree(-1) proved to be a fairly informative test (area under curve = 0.84-0.92). The [P / N,S] and [Mn / Cu,Zn] balances were found to be potential sources of misbalance in the less productive orchards, and should thus be further investigated in field experiments. We propose using a coherent pan balance diagnostic method with median ilr values of top yielders centered at fulcrums of a mobile and the critical Mahalanobis distance as a guide for global nutrient balance. Nutrient concentrations in weighing pans assisted appreciating nutrients as relative shortage, adequacy or excess in balances.

No MeSH data available.


Related in: MedlinePlus

Bias measured by discrepancy between the Mahalanobis distance from the TN population across the isometric log ratios (x-axis) and (top) the Mahalanobis distance from the TN population across the natural log of concentrations and (bottom) the DRIS nutrient imbalance index.
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Figure 6: Bias measured by discrepancy between the Mahalanobis distance from the TN population across the isometric log ratios (x-axis) and (top) the Mahalanobis distance from the TN population across the natural log of concentrations and (bottom) the DRIS nutrient imbalance index.

Mentions: The Mahalanobis distance from TN specimens based on the natural log of concentrations, as well as the DRIS nutrient imbalance indexes using TN specimens as standards, were compared to the Mahalanobis distance from TN specimens based on unbiased ilr balances in Figure 6. The bias of the approach can be appreciated quantitatively by the importance of the departure from the 1:1 line on the plot where concentrations are log-transformed (R2 = 0.017) and by the importance of the residuals where concentrations are computed as DRIS NII (R2 = 0.48). Both approaches (log of concentrations and DRIS) produced noisy diagnoses, possibly leading to conflicting interpretations.


Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica).

Parent SÉ, Parent LE, Rozane DE, Natale W - Front Plant Sci (2013)

Bias measured by discrepancy between the Mahalanobis distance from the TN population across the isometric log ratios (x-axis) and (top) the Mahalanobis distance from the TN population across the natural log of concentrations and (bottom) the DRIS nutrient imbalance index.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3824108&req=5

Figure 6: Bias measured by discrepancy between the Mahalanobis distance from the TN population across the isometric log ratios (x-axis) and (top) the Mahalanobis distance from the TN population across the natural log of concentrations and (bottom) the DRIS nutrient imbalance index.
Mentions: The Mahalanobis distance from TN specimens based on the natural log of concentrations, as well as the DRIS nutrient imbalance indexes using TN specimens as standards, were compared to the Mahalanobis distance from TN specimens based on unbiased ilr balances in Figure 6. The bias of the approach can be appreciated quantitatively by the importance of the departure from the 1:1 line on the plot where concentrations are log-transformed (R2 = 0.017) and by the importance of the residuals where concentrations are computed as DRIS NII (R2 = 0.48). Both approaches (log of concentrations and DRIS) produced noisy diagnoses, possibly leading to conflicting interpretations.

Bottom Line: Traditional multivariate methods were found to be biased.A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalanced and high/low-yielders.The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree(-1) proved to be a fairly informative test (area under curve = 0.84-0.92).

View Article: PubMed Central - PubMed

Affiliation: Department of Soils and Agrifood Engineering, ERSAM, Université Laval Québec, QC, Canada.

ABSTRACT
Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflicting results and wrong inferences. Our objective was to present an unbiased statistical approach of plant nutrient diagnosis using a balance concept and mango (Mangifera indica) as test crop. We collected foliar samples at flowering stage in 175 mango orchards. The ionomes comprised 11 nutrients (S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Traditional multivariate methods were found to be biased. Ionomes were thus represented by unbiased balances computed as isometric log ratios (ilr). Soil fertility attributes (pH and bioavailable nutrients) were transformed into balances to conduct discriminant analysis. The orchards differed more from genotype than soil nutrient signatures. A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalanced and high/low-yielders. The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree(-1) proved to be a fairly informative test (area under curve = 0.84-0.92). The [P / N,S] and [Mn / Cu,Zn] balances were found to be potential sources of misbalance in the less productive orchards, and should thus be further investigated in field experiments. We propose using a coherent pan balance diagnostic method with median ilr values of top yielders centered at fulcrums of a mobile and the critical Mahalanobis distance as a guide for global nutrient balance. Nutrient concentrations in weighing pans assisted appreciating nutrients as relative shortage, adequacy or excess in balances.

No MeSH data available.


Related in: MedlinePlus