The Plant Ionome Revisited by the Nutrient Balance Concept

Abstract

Tissue analysis is commonly used in ecology and agronomy to portray plant nutrient signatures. Nutrient concentration data, or ionomes, belong to the compositional data class, i.e., multivariate data that are proportions of some whole, hence carrying important numerical properties. Statistics computed across raw or ordinary log-transformed nutrient data are intrinsically biased, hence possibly leading to wrong inferences. Our objective was to present a sound and robust approach based on a novel nutrient balance concept to classify plant ionomes. We analyzed leaf N, P, K, Ca, and Mg of two wild and six domesticated fruit species from Canada, Brazil, and New Zealand sampled during reproductive stages. Nutrient concentrations were (1) analyzed without transformation, (2) ordinary log-transformed as commonly but incorrectly applied in practice, (3) additive log-ratio (alr) transformed as surrogate to stoichiometric rules, and (4) converted to isometric log-ratios (ilr) arranged as sound nutrient balance variables. Raw concentration and ordinary log transformation both led to biased multivariate analysis due to redundancy between interacting nutrients. The alr- and ilr-transformed data provided unbiased discriminant analyses of plant ionomes, where wild and domesticated species formed distinct groups and the ionomes of species and cultivars were differentiated without numerical bias. The ilr nutrient balance concept is preferable to alr, because the ilr technique projects the most important interactions between nutrients into a convenient Euclidean space. This novel numerical approach allows rectifying historical biases and supervising phenotypic plasticity in plant nutrition studies.

Foliar Nutrient Balance Standards for Maize (<i>Zea mays</i> L.) at High-Yield Level

“…The physiological age of the plant part is an important factor that affects nutrient concentrations [14]. This is why nutrient levels and ratios should be compared to standards at the same physiological age [9,48]. The N/P ratio close to 10 seemed to be appropriate for maize at silk stage.…”

“…However, the balances between plant nutrients should reflect the way the designer conceives the system, whether based on prior and expert knowledge [6,8,21,22], e.g. in terms of plant physiology, the agronomic practice or some statistical relationships [6,9] or by exploratory biplot analysis across clrs-where…”

“…Crop nutrient diagnosticians conduct CNCR and DRIS separately to identify the most limiting nutrients [7]. Because CNCR and DRIS are both methodologically biased [6,8,9], hence pathological, they may lead to conflicting interpretations [10][11][12][13].…”

Calagem, adubação e nutrição mineral de Anonáceas

“…Desse modo, o CND expande o conceito do DRIS de um espaço bidimensional para um multidimensional, além de indicar um padrão nutricional específico para cada frutífera (PARENT, et al, 2013). No Brasil, alguns estudos foram realizados, empregando-se o CND nas culturas da soja (URANO et al, 2006;URANO et al, 2007) Camacho et al (2012) para laranjeira Pera e Politi et al (2013) para a cultura da manga.…”

Site-Specific Multilevel Modeling of Potato Response to Nitrogen Fertilization

“…Because several soil data such as particle-size distributions are compositional, techniques of compositional data analysis (CoDa) (Aitchison, 1986;Egozcue et al, 2003;Parent et al, 2013) are required to avoid generating methodological biases in the multilevel models. Data related to soil texture are constrained to a constant sum, e.g., 100%.…”