Ionomic profiling of plant tissues aims to understand the role of genetic factors and external conditions in mineral nutrient composition. However, little is known about the time-course changes occurring in these profiles during plant growth. The influences of genotype, environment, and management factors are not well understood. To clarify the variation in time-course data and to identify factors influencing these changes, we analyzed the ionomic leaf profiles of nine rice (Oryza sativa L.) cultivars, from transplantation to harvest, under different environmental conditions. An ANOVA was conducted separately for each element to elucidate the main effects of cultivar, fertilization, and growth stage, which were highly significant for all the elements observed except fertilization. The growth stage was the most significant for all elements except B. Conversely, the fertilization effect was not significant in half of the elements studied (Li, B, Na, Mg, P, S, K, Ca, and Cd). The elements during the growth stage were relatively stable across the environments and cultivars studied. To investigate the relationship between the changing pattern and the growth stage, we predicted the growth stage of rice based on the ionomic profile leaves using a machine learning model. Over 80% of the plants in this study were correctly classified into their growth stages with 10-fold cross-validation using the random forest model, with a highly significant contribution of the essential macronutrients P, Ca, S, Mg, and K as explanatory variables, indicating that they could be important indicators of the growth stage of rice plants.

中文翻译：

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水稻叶片离子谱的时程变化及其在生育期预测中的应用

植物组织的离子组分析旨在了解遗传因素和外部条件在矿物营养成分中的作用。然而，人们对植物生长过程中这些剖面中发生的时间过程变化知之甚少。基因型、环境和管理因素的影响尚不清楚。为了阐明时间过程数据的变化并确定影响这些变化的因素，我们分析了九个水稻（Oryza sativaL.) 栽培品种，从移植到收获，在不同的环境条件下。对每个元素分别进行方差分析，以阐明品种、施肥和生长阶段的主要影响，这对于除施肥外的所有观察到的元素都非常显着。对于除 B 之外的所有元素，生长阶段最为显着。相反，在研究的一半元素（Li、B、Na、Mg、P、S、K、Ca 和 Cd）中，施肥效果不显着。生长阶段的元素在所研究的环境和品种中相对稳定。为了研究变化模式与生长阶段之间的关系，我们使用机器学习模型根据离子组分布叶片预测了水稻的生长阶段。