de Borja Reis André Froes, Moro Rosso Luiz, Purcell Larry C, Naeve Seth, Casteel Shaun N, Kovács Péter, Archontoulis Sotirios, Davidson Dan, Ciampitti Ignacio A
Department of Agronomy, Kansas State University, Manhattan, KS, United States.
Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States.
Front Plant Sci. 2021 Jun 15;12:675410. doi: 10.3389/fpls.2021.675410. eCollection 2021.
Biological nitrogen (N)-fixation is the most important source of N for soybean [ (L.) Merr.], with considerable implications for sustainable intensification. Therefore, this study aimed to investigate the relevance of environmental factors driving N-fixation and to develop predictive models defining the role of N-fixation for improved productivity and increased seed protein concentration. Using the elastic net regularization of multiple linear regression, we analyzed 40 environmental factors related to weather, soil, and crop management. We selected the most important factors associated with the relative abundance of ureides (RAU) as an indicator of the fraction of N derived from N-fixation. The most relevant RAU predictors were N fertilization, atmospheric vapor pressure deficit (VPD) and precipitation during early reproductive growth (R1-R4 stages), sowing date, drought stress during seed filling (R5-R6), soil cation exchange capacity (CEC), and soil sulfate concentration before sowing. Soybean N-fixation ranged from 60 to 98% across locations and years ( = 95). The predictive model for RAU showed relative mean square error (RRMSE) of 4.5% and an R value of 0.69, estimated cross-validation. In addition, we built similar predictive models of yield and seed protein to assess the association of RAU and these plant traits. The variable RAU was selected as a covariable for the models predicting yield and seed protein, but with a small magnitude relative to the sowing date for yield or soil sulfate for protein. The early-reproductive period VPD affected all independent variables, namely RAU, yield, and seed protein. The elastic net algorithm successfully depicted some otherwise challenging empirical relationships to assess with bivariate associations in observational data. This approach provides inference about environmental variables while predicting N-fixation. The outcomes of this study will provide a foundation for improving the understanding of N-fixation within the context of sustainable intensification of soybean production.
生物固氮是大豆[（L.）Merr.]最重要的氮源，对可持续集约化生产具有重要意义。因此，本研究旨在调查驱动固氮的环境因素的相关性，并建立预测模型，以确定固氮对提高产量和增加种子蛋白浓度的作用。利用多元线性回归的弹性网络正则化方法，我们分析了40个与天气、土壤和作物管理相关的环境因素。我们选择了与脲类相对丰度（RAU）相关的最重要因素，作为来自固氮的氮素比例的指标。与RAU最相关的预测因子是氮肥施用、生殖生长早期（R1 - R4阶段）的大气水汽压亏缺（VPD）和降水量、播种日期、鼓粒期（R5 - R6）的干旱胁迫、土壤阳离子交换量（CEC）以及播种前土壤硫酸盐浓度。不同地点和年份的大豆固氮率在60%至98%之间（= 95）。RAU的预测模型在交叉验证估计中显示相对均方误差（RRMSE）为4.5%，R值为0.69。此外，我们建立了类似的产量和种子蛋白预测模型，以评估RAU与这些植物性状的关联。变量RAU被选为预测产量和种子蛋白模型的协变量，但相对于产量的播种日期或蛋白的土壤硫酸盐而言，其影响程度较小。生殖生长早期的VPD影响了所有自变量，即RAU、产量和种子蛋白。弹性网络算法成功地描绘了一些用观测数据中的双变量关联难以评估的经验关系。这种方法在预测固氮的同时提供了关于环境变量的推断。本研究结果将为在大豆生产可持续集约化背景下增进对固氮的理解奠定基础。
