Compositional analysis of plant nutrients and soil P pools

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Present models using compositional data constrained to a simplex such as plant and soil analyses disregard the fact that components must interact within a closed information space. Since any change in one constituent must be accompanied by a change in other constituents by resonance in a closed system, system patterns of organization may be spoiled by spurious correlations. Compositional analysis takes into account all possible interactions among components within a simplex. Compositional data analysis has been successfully applied in many scientific fields and, more recently, for nutrient diagnosis of many crop species and interpreting phosphorus transformations in soils. As a measure of deviation from targeted composition, the Mahalanobis distance was found to detect nutritionally imbalanced corn plants as 'outliers'. On the other hand, the phosphorus fractionation procedures for soils, crop residues, and organic products allow distinguishing between labile and non labile P pools. The P in labile and non labile pools in response to a driving variable (time, crop rotation, soil conservation practice, added P) can be modelled by compositional analysis using formalisms such as the perturbation vector, the simplicial metric, and the Mahalanobis distance. Soil P buffering capacity that protects the soil against soluble P loss and controls the P supply to roots is an important soil function that can be related to either the simplicial metric or the Mahalanobis distance. The Mehlich-III P saturation ratio proved to be a reliable indicator of soil P buffering capacity. The relationships between Mehlich-III indexes and labile and non labile pools of P could support the present environmental thresholds for both mineral and organic soils. Our main objective is to develop compositional models for crop nutrients and soil P in soil-plant systems. In this project, we will apply the compositional-Mahalanobis concept to ten crops in Quebec and Brazil. In addition, we will fractionate the P in soils, organic inputs, and crop residues and analyze P fractions using compositional analysis as well as indexes of soil P buffering capacity. On the long run, we will classify soils and plants according to their compositional patterns in response to driving variables.