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对吉林省桦甸地区春玉米群体NAR动态变化进行系统的试验研究,探讨不同玉米品种、不同地区以及不同栽培条件下净同化率(NAR)变化规律。利用归一化方法将最大NAR和播种至成熟天数设为1,以相对NAR(0-1)和相对时间(0-1)为变量建立数学模型。研究结果表明,净同化率在整个生育期呈“M”形双峰曲线,前期峰值出现在拔节期,后期出现在灌浆期,且前期峰值高于后期。对NAR模型进行筛选,变化规律符合4次方数学模型NARR=a+b(TR)+c(TR)2+d(TR)3+e(TR)4;建立春玉米群体相对NAR动态变化数学模型NARR=-0.01+10.54(TR)-39.72(TR)2+52.81(TR)3-23.37(TR)4,r=0.946 1**。利用模型对2006年吉林桦甸的密度试验以及河北廊坊的品密试验进行检验,相关系数分别在0.91～0.93和0.83～0.88之间,均达到极显著相关水平,说明该模型符合东北平原以及黄淮海流域变化规律。净同化率模型的确定可实现作物产量性能方程的数字化、模式化和指标化。
The dynamic changes of NAR in spring maize population in Huadian area, Jilin province, were systematically studied to investigate the changes of net assimilation rate (NAR) under different corn varieties, different regions and different cultivation conditions. Using the normalization method, the maximum NAR and the number of sowing to mature days are set as 1, and the mathematical model is established with relative NAR (0-1) and relative time (0-1) as variables. The results showed that the net assimilation rate showed a “M ” shape bimodal curve throughout the whole growth period, with the early peak appearing in the jointing stage and the late filling stage, and the earlier peak was higher than the late stage. The NAR model was screened according to the fourth-order mathematical model NARR = a + b (TR) + c (TR) 2 + d (TR) 3 + e Model NARR = -0.01 + 10.54 (TR) -39.72 (TR) 2 + 52.81 (TR) 3-23.37 (TR) 4, r = 0.946 1 **. The model was used to test the density test of Huadian, Jilin Province in 2006 and the product density test of Langfang, Hebei Province. The correlation coefficients were between 0.91 ~ 0.93 and 0.83 ~ 0.88, respectively, reaching extremely significant correlation levels, indicating that the model was in accordance with the Northeast Plain and Yellow Huaihai Basin changes. The determination of the net assimilation rate model can realize the digitization, patternization and indexization of crop yield performance equation.