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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

(A) Discriminant analysis of the ionomes of four varieties and (B) their soil properties (right) in mango orchards in the state of São Paulo, Brazil: “Palmer” (93 obs.), “Tommy” (63 obs.), “Espada” (14 obs.) and Haden (5 obs.). Large semitransparent ellipses that enclose swarms of data points represent regions that include 95% of the theoretical distribution of canonical scores for each species. Smaller plain white ellipses represent confidence regions about means of canonical scores at 95% confidence level.
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Figure 1: (A) Discriminant analysis of the ionomes of four varieties and (B) their soil properties (right) in mango orchards in the state of São Paulo, Brazil: “Palmer” (93 obs.), “Tommy” (63 obs.), “Espada” (14 obs.) and Haden (5 obs.). Large semitransparent ellipses that enclose swarms of data points represent regions that include 95% of the theoretical distribution of canonical scores for each species. Smaller plain white ellipses represent confidence regions about means of canonical scores at 95% confidence level.

Mentions: Bartlett test showed that the variance of 3 of the 11 balances differed among varieties, i.e., [Fv / nutrients], [B / S,N,P,K,Ca,Mg] and [N / S]. Analysis of variance showed that 8 of the 11 balance means differed among varieties, with the exception of [B / S,N,P,K,Ca,Mg] (barely interpretable due to heterogeneous variance), [Mn / Cu,Zn] and [Zn / Cu]. The discriminant scores mapped the differences between ionomes of “Palmer,” “Tommy,” “Espada,” and “Haden” (Figure 1). The plant and soil DA maps showed that ionomes differed significantly between varieties. However, the swarms of foliar ionomes did not overlap among varieties while the swarms of soil nutrients in “Palmer” and “Haden” orchards, on the one hand, and “Tommy” and “Espada,” on the other, overlapped, therefore indicating genotypic dominance over soil nutrient supply. Nevertheless, because the amount of data was limited, the ROC partitioning was performed across varieties to provide a numerical example.


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

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

(A) Discriminant analysis of the ionomes of four varieties and (B) their soil properties (right) in mango orchards in the state of São Paulo, Brazil: “Palmer” (93 obs.), “Tommy” (63 obs.), “Espada” (14 obs.) and Haden (5 obs.). Large semitransparent ellipses that enclose swarms of data points represent regions that include 95% of the theoretical distribution of canonical scores for each species. Smaller plain white ellipses represent confidence regions about means of canonical scores at 95% confidence level.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: (A) Discriminant analysis of the ionomes of four varieties and (B) their soil properties (right) in mango orchards in the state of São Paulo, Brazil: “Palmer” (93 obs.), “Tommy” (63 obs.), “Espada” (14 obs.) and Haden (5 obs.). Large semitransparent ellipses that enclose swarms of data points represent regions that include 95% of the theoretical distribution of canonical scores for each species. Smaller plain white ellipses represent confidence regions about means of canonical scores at 95% confidence level.
Mentions: Bartlett test showed that the variance of 3 of the 11 balances differed among varieties, i.e., [Fv / nutrients], [B / S,N,P,K,Ca,Mg] and [N / S]. Analysis of variance showed that 8 of the 11 balance means differed among varieties, with the exception of [B / S,N,P,K,Ca,Mg] (barely interpretable due to heterogeneous variance), [Mn / Cu,Zn] and [Zn / Cu]. The discriminant scores mapped the differences between ionomes of “Palmer,” “Tommy,” “Espada,” and “Haden” (Figure 1). The plant and soil DA maps showed that ionomes differed significantly between varieties. However, the swarms of foliar ionomes did not overlap among varieties while the swarms of soil nutrients in “Palmer” and “Haden” orchards, on the one hand, and “Tommy” and “Espada,” on the other, overlapped, therefore indicating genotypic dominance over soil nutrient supply. Nevertheless, because the amount of data was limited, the ROC partitioning was performed across varieties to provide a numerical example.

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