Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

ABSTRACT Wheat biomass yield focused on the production of quality silage is dependent on rainfall, temperature and nitrogen (N). The objective of the study was to validate the use of rainfall, thermal time and N as potential variables for the composition of the multiple linear regression model and simulation of wheat biomass yield for silage production under N supply conditions during the cycle, in the systems of succession. The study was conducted in 2012, 2013 and 2014, in randomized blocks with four replicates in 4 x 3 factorial, for N-fertilizer doses (0, 30, 60, 120 kg ha-1) and forms of N supply [single application (100%) in the stage V3 (third expanded leaf); split application (70%/30%) in the stages V3/V6 (third and sixth expanded leaves); split application (70%/30%) in the stages V3/E (third expanded leaf and beginning of grain filling)], respectively, in the systems soybean/wheat and maize/wheat. Rainfall and N are potential variables in the composition of the multiple linear regression model. Multiple linear regression models are efficient in the simulation of wheat biomass yield for silage under the N supply conditions during the cycle in the succession systems.

摘要 以优质青贮生产为目标的小麦生物量产量，受降雨、温度与氮素（N）调控。本研究旨在验证以降雨、热时间与氮素作为变量构建多元线性回归模型的可行性，并模拟轮作系统中全生育期供氮条件下青贮用小麦的生物量产量。试验于2012、2013及2014年开展，采用4×3因子随机区组设计，设置4次重复，试验因子包含氮肥施用量（0、30、60、120 kg ha⁻¹）与施氮方式：分别为V3期（第三片展开叶）一次性施入（100%）、V3/V6期（第三、第六片展开叶）分施（70%/30%）、V3/始粒期（E）分施（70%/30%）；试验覆盖大豆/小麦与玉米/小麦两种轮作系统。研究表明，降雨与氮素可作为多元线性回归模型的有效变量，该模型能够高效模拟轮作系统中全生育期供氮条件下青贮用小麦的生物量产量。