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

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.

Variation in the angiosperm ionome

“…In general, when growth is limited by a particular nutrient, plants accumulate elements supplied in excess of requirements (Sterner and Elser , Hawkesford et al , Broadley et al , ). The effects of nutrient deficiencies on the ionomes of many plant species have been documented (Sorreano , Baxter et al , Parent et al , Tomasi et al , Pii et al , Watanabe et al , Maillard et al , Campos et al ). It has been proposed that alterations in the ionome might be used to diagnose particular physiological conditions, such as mineral deficiencies, much more accurately than assays of single elements (Baxter et al , Parent et al , , Pii et al , Campos et al ), but it has yet to be proven that the ionomes of all angiosperms respond similarly to nutrient deficiencies.…”

“…; Broadley et al , , Osaki et al , Willey and Wilkins , Watanabe et al , White et al , , , He et al , Verboom et al ). The most informative elements for discrimination between plant families or orders are generally Ca and Mg and the least informative N and P (White et al , Parent et al , ). Similarly, when the genetic variation in element concentrations in leaves of angiosperm species is partitioned into a taxonomic hierarchy, it is often observed that the proportion of variation in leaf mineral concentration assigned to the order level and above is greater for Al, Ca, Mg and Si than for N and P (Thompson et al , Broadley et al , Hodson et al , Watanabe et al , Fyllas et al ), which suggests that leaf concentrations of structural elements, such as Ca, Mg and Si, were influenced greatly by ancient evolutionary processes, whereas the concentrations of elements involved in metabolism, such as N and P, were subject to more recent evolutionary pressures.…”

Evolution of the predictive criteria for the tropical fruit tree nutritional status

“…In Brazil, some studies were carried out with CND in soybean (Urano et al 2006;Urano et al, 2007), eucalyptus (Silva et al, 2004), guava and mango (Parent et al, 2013b). The original CND method used D log-centered ratios (clr) or contrasts as recommended to avoid biases in compositional data analysis (Aitchison, 1986).…”

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

“…In the curve, optimal predictor delimiter corresponds to the maximum Youden index (J = sensitivity + specificity -1), (Youden, 1950). The area under the sensitivity vs. specificity curve (AUC) can be used as accuracy index for classification where the set is as the response delimiter (Parent et al, 2013b).…”