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

组成土壤和植物数据接近于某个整体，并且在组成部分之间具有层次关系，作为自组织系统的连接部分。传统的土壤和植物模型是基于19世纪初和20世纪上半叶在其他条件假设下发展起来的概念。这种方法严重威胁了当前植物营养、土壤保持和土壤肥力理论的一致性和进展。它否认了成分数据相互作用的事实。由于数据冗余（封闭系统的一个组成部分通过差分计算是冗余的），尺度依赖性（尺度变化产生子组成不相干）和非正态分布（组成空间约束在0和整个之间，而实际空间不受约束），数值偏差在统计分析中会产生噪声。最有希望克服偏差的技术是2003年阐述的等距对数比（ilr）变换，它消除了引起相互作用的噪声（一自由度）。我们用数值的概念来平衡，关系到人类看待自然现象的一种直观的方式。比率是两个组成部分之间的平衡，而ilr是组成部分子集的几何平均值之间的平衡。他们有欧几里得几何。形成系统子集的组分群，如阴离子和阳离子组分、微观和宏观聚集体或生化C或P组分，根据健全的理论排列成正交平衡。如果目标是推导多元距离，那么平衡的设计方式就不重要了。否则，它们必须对研究人员和最终用户有意义。利用ilr变量和受试者工作特征（ROC）曲线来诊断系统“健康”，我们评估了作物营养（“植物健康”）、土壤有机残留物分解模型（C固存）、土壤P转化模型和土壤团聚体指数（“土壤健康”）的诊断组织和土壤测试的性能。在植物营养中，平衡是根据植物生理、土壤生物地球化学和作物管理中的养分相互作用来设计的。在土壤肥力方面，平衡有助于确定肥料和石灰的需要量。吸收碳的微团聚体与宏观团聚体相互平衡，以确定土壤健康状况。磷形态平衡为地球化学库和生物库，每个库又细分为磷有效性降低的库。这些平衡模型将使用在加拿大、巴西和新西兰收集的数据集和文献进行测试。土壤和植物的测试性能将使用ROC进行测量，并与临床生物学中测量人类健康的测试进行比较。我们设想将平衡概念应用于土壤分析的光谱技术、水培法中的养分平衡以及土壤-植物系统中养分转移的建模。全世界都需要在保持作物生产力的同时尽量减少环境和经济风险。在解释元分析计量经济学（作物响应）和土壤-植物模型的结果时，与现有模型相比，成分数据的无偏模型的制定将减少噪音。该研究项目将维持和扩大在这些问题上的国际合作（例如Laval与upc -西班牙、联合国教科文组织-巴西和悉尼大学）。为了使平衡概念适应现实世界，迫切需要形成HQP。HQP将校准和验证植物营养和土壤质量方面的大型数据集，而农学、生物地球化学（包括生物指示植物）、营养和生态学方面的论文和理论将基于对自然界平衡的良好了解而重新审视。这个项目引领了农业科学平衡的范式转变。

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.