辽河流域玉米籽粒脱水特点及适宜收获期分析

辽河流域处于中国东北春玉米区南部,积温资源相对丰富,在该区域推广玉米机械粒收技术具有较好的热量资源基础,但区域内玉米收获时籽粒含水率偏高,机械粒收的破碎率、损失率偏高等质量问题突出。分析区域内主推品种的籽粒脱水特征、基于热量资源条件确定机械粒收的适宜时间,是解决上述问题的合理途径。2017年选择该区域主推的29个不同熟期玉米品种,在开鲁县和铁岭县开展了籽粒脱水动态观测试验。结合流域内常年春玉米播种日期、不同品种生长发育及籽粒脱水积温需求、历史气象数据等分析结果,建立不同品种在辽河流域适宜机械粒收时期的预测方法。结果显示, Logistic Power模型可以很好地模拟春玉米籽粒含水率变化过程。不同品种籽粒实收含水率与模拟含水率间存在极显著的线性关系,决定系数R2为0.916 (n=45),均方根误差RMSE为1.217。研究建立的不同品种籽粒含水率模型具有极佳的区域适用性,以2017年国审的4个宜机收品种及流域内2个主栽品种研究,明确了不同品种适宜机械粒收时期的分布规律。国审品种中,德育919和京农科728自播种至籽粒含水率降至25%活动积温需求低于3200°C d,在辽河流域大部地区可于9月中下旬达到高质量机械粒收的籽粒含水率要求。泽玉8911和吉单66积温需求低于3400°C d,可于10月上中旬在流域内实现机械粒收,较上述德育919和京农科728晚10～20d。而当地主栽的辽单575和京科968脱水至适宜籽粒含水率的积温需求较泽玉8911和吉单66多200°Cd,无法在当地常规收获期实现高质量的机械粒收。本研究检验了基于LogisticPower模型的籽粒含水率预测模型在区域分析应用中的精度。通过比较国审宜机收品种与当地主栽品种的籽粒含水率变化、成熟和脱水的积温需求以及适宜机械粒收日期的空间分布规律,更新现有品种有助于在辽河流域实现常规收获期内的高质量机械粒收。

Characteristics of maize grain dehydration and prediction of suitable harvest period in Liao River Basin

Liao River Basin (LRB) is located in the south of the northeast China, mainly planting spring maize. The technology of grain mechanical harvesting is promoted based on the better thermal resources in LRB, but it is impeded by the higher grain moisture at harvest, higher grain broken rate and higher yield loss rate. In order to solve problem, characteristics of grain dehydration of main cultivars need to be researched and the suitable harvesting time ought to be determined based on the thermal resources. The study was aimed at selecting suitable cultivars and evaluating the quality of grain mechanical harvesting. In 2017, the 29 maize cultivars with different maturity periods were weed to investigate grain dehydration dynamics in Kailu county and Tieling county. On the basis of the analysis results of sowing date of perennial spring maize in the basin, growth and development of different varieties, demand of grain dehydration accumulated temperature and historical meteorological data, the prediction method of suitable mechanical grain harvest period for different varieties was established in LRB. Logistic Power model was used to match the dynamic process of grain dehydration of spring maize. There was a significant linear relationship between the actual grain moisture at harvest and the simulated grain moisture (R ²= 0.916, RMSE = 1.217), showing an excellent regional adaptability of this model. Using four cultivars suitable for grain mechanical harvest in 2017 national trial and two main cultivars grown in LRB, distribution regulations of the suitable harvest period were clarified. Among the four cultivars, the actively accumulated temperature requirements of Deyu 919 and Jingnongke 728 were less than 3122°C d from sowing to the stage with grain moisture reducing to 25%, showing that the better period of grain mechanical harvest was in the middle and late September in most parts of LRB. While the other two cultivars of Zeyu 8911 and Jidan66 had accumulated temperature requirements 3400°C d, and suitable harvest period in early October which was roughly 10-20 days behind there of the two former cultivars. As less than for the two main cultivars—Liaodan 575 and Jingke 968, their requirements of accumulated temperature were 200°C d more than there of Zeyu 8911 and Jidan66, which is difficult to achieve a high-quality mechanical grain harvesting at normal time. The research verified the application accuracy of the predicting model of grain moisture established based on Logistic Power model. Comparing the changes of grain moisture content, the accumulated temperature requirements to maturity and dehydration, and spatial distribution regulations of suitable period of grain mechanical harvesting between national trial cultivars and local main cultivars, and updating the existing cultivars and helpful to achieve high-quality mechanical grain harvesting in the LRB.