摘要
为使DSSAT模型在河北省麦区得到应用并制定相应的最佳农田管理措施,本研究利用DSSAT模型模拟了小麦生长中的叶面积指数、地上部干物质积累量和产量的变化。结果表明,早播和晚播均会减少成熟期最大干物质量,当播种日期在10月9日时小麦最大干物质量和产量均达最高;播种密度为450株/m^2时产量达到最高,低于450株/m^2时小麦产量下降明显,高于450株/m^2时小麦产量也出现轻微下降;施氮量大于240 kg/hm^2时冬小麦产量增长速率降低,施氮量超过360 kg/hm^2时冬小麦产量变化不明显;每次最佳灌溉量在60 mm。因此,通过模型拟合确定的试验区合理播种日期为10/5—10/21,播种密度为450株/m^2,合理施氮量为240 kg/hm^2,每次灌溉量为60 mm。
For the application of DSSAT model and developing appropriate farmland management measures in Hebei Province, the DSSAT model was used to simulate the change of leaf area index, above-ground dry matter accumulation and yield in wheat. The results showed that both early and late sowing reduced the maximum dry matter quality at maturity. When the sowing date was on October 9, the maximum dry matter quality and yield of wheat reached the highest;when the planting density was 450 plants/m^2, the yield reached the highest, and lower than 450 plants/m^2 the yield of wheat decreased significantly, while the yield of wheat also decreased slightly when the planting density was higher than 450 plants/m^2. The yield of winter wheat decreased when the nitrogen application rate was more than 240 kg/hm^2. The yield of winter wheat was not obvious when the nitrogen application rate exceeded 360 kg/hm^2. Each time the optimal irrigation amount was 60 mm. Therefore, the reasonble sowing date for the test area determined by model fitting was 10/5-10/21, the planting density was 450 plants/m^2, and the reasonable nitrogen application rate was 240 kg/hm^2, with 60 mm per irrigation.
作者
葛连兴
李迎春
彭正萍
贺勇
潘婕
韩雪
李鸿池
GE Lianxing;LI Yingchun;PENG Zhengping;HE Yong;PAN Jie;HAN Xue;LI Hongchi(College of Resources and Environmental Sciences / Key Laboratory for Farmland Eco-environment of Hebei, Hebei Agricultural University, Baoding 071001, China;.Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China;Ningjin County Agricultural Bureau,Ningjin 055550,China)
出处
《河北农业大学学报》
CAS
CSCD
北大核心
2019年第2期18-23,共6页
Journal of Hebei Agricultural University
基金
国家自然科学基金项目(D010106)
国家重点研发计划项目(2017YFD0300905)