Changing paradigm in soil and plant sciences: design of balances between components

点击翻译按钮获取中文摘要

Compositional soil and plant data are closed to some whole and have a hierarchy of relationships between components as connected parts of a self-organizing system. Traditional soil and plant models are based on concepts developed at the beginning of the 19th and the first half of the 20th century under the ceteris paribus assumption. This approach is a serious threat to the coherence and progress in current theories on plant nutrition, soil conservation and soil fertility. It denies the fact that compositional data interact. Numerical biases produce noise in statistical analysis due to data redundancy (one component of a closed system is redundant as computed by difference), scale dependency (change of scale produces sub-compositional incoherence) and non-normal distribution (compositional space is constrained between 0 and the whole while the real space is unconstrained). The most promising technique to overcome biases is the isometric log ratio (ilr) transformation elaborated in 2003 that removes noise (one dof) causing interaction. We use a numerical concept of balance, relates to an intuitive way for human beings to look at natural phenomena. A ratio is a balance between two components while an ilr is a balance between geometric means of subsets of components. The ilrs have Euclidean geometry. Groups of components that form system subsets such as anionic and cationic species, micro- and macro-aggregates or biochemical C or P fractions are arranged into orthogonal balances based on sound theory. If the objective is to derive a multivariate distance, the way balances are designed is not important. Otherwise, they must make sense to the researcher and the end-user. Using ilr variables and the receiver operating characteristic (ROC) curve to diagnose system ‘health’, we evaluate the performance of diagnostic tissue and soil tests for crop nutrition (‘plant health’), decomposition models of organic residues in soil (C sequestration), soil P transformation models and soil aggregate indices (‘soil health’). In plant nutrition, balances are designed according to nutrient interactions in plant physiology, soil biogeochemistry and crop management. In soil fertility, balances assist determining fertilizer and lime requirements. Micro-aggregate, that sequester C, are balanced with macro-aggregates to define soil health. The P forms are balanced into geochemical and biological pools, each subdivided into pools of decreasing P availability. Those balance models will be tested using data sets collected in Canada, Brazil and New-Zealand and from literature. Test performance for soils and plants will be measured using ROC and compared to tests in clinical biology to measure human health. We envisage applications of the balance concept to spectroscopic techniques for soil analysis, nutrient balance in hydroponics and the modeling of nutrient transfer in soil-plant systems. There is a need worldwide to sustain crop productivity while minimizing the environmental and economic risks. The formulation of unbiased models for compositional data will reduce noise compared to current models when interpreting results of meta-analysis econometric (crop response) and soil-plant models. This research program will maintain and expand international collaboration on these issues (e.g. Laval with UPC-Spain, UNESP-Brazil and University of Sydney). There is urgent need to form HQP for adapting balance concepts to the real world. The HQP will calibrate and validate large datasets in plant nutrition and soil quality while papers and theories in agronomy, biogeochemistry (including bio-indicator plants), nutrition and ecology will be revisited based on sound knowledge of balances in nature. This program leads a paradigm shift to balances in agricultural sciences.