Evaluation of the models indicated that APSIM-maize and DSSAT CERES-maize accurately simulated days to flowering and maturity with root mean square error (RMSE) values ranging from 1.73–4.09 and 1.66–5.36 days, respectively. However, the DSSAT CERES-maize model over-estimated the maturity period...
本文以中国关中地区为研究区域,首先利用杨凌站点2009-2012年夏玉米(品种为“郑单958”)和长武站点2017-2018年春玉米(品种为“先玉335”)试验数据对APSIM- Maize模型进行参数率定和验证;然后基于两个站点1971-2010年的历史气象数据,利用36种全球气候模式(GCMs)和NWAI-WG统计降尺度方法预测该地区在RCP4.5和 RCP8.5两...
Using seasonal rainfall with APSIM to improve maize production in the Modder River catchment 来自 Semantic Scholar 喜欢 0 阅读量: 4 作者: KM Nape 摘要: In order to meet the food requirements of an ever-growing population, agricultural production needs to increase. This is especially true for ...
Days after planting to anthesis (APSIM-Maize, anthesis (DAP) RMSE = 1.91 days; DSSAT-CERES-Maize, anthesis (DAP) RMSE = 2.89 days) and maturity (APSIM-Maize, maturity (DAP) RMSE = 3.35 days; DSSAT-CERES-Maize, maturity (DAP) RMSE = 3.13 days) were adequately simulated, with RMSEn ...
We explored whether APSIM performs well in the Midwest, so that the associated model capabilities would be available for application in this region. Our approach included calibration and testing of several APSIM models (maize [Zea mays L.], soil water, soil N, surface organic matter, manure,...
The grain yield performance were higher under first sowing date. The results led to the conclusion that APSIM model is efficient in simulating maize growth and development in arid environment of Samaru.doi:10.54386/jam.v20i3.545A.M. YAMUSAF.M.AKINSEYEJournal of Agrometeorology...
research, two most popular Bayesian methods, namely generalized likelihood uncertainty estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM), were used for the first time to estimate parameters of the maize module of the Agricultural Productions Systems sIMulator model (APSIM-maize). ...
Assessing the contribution of weather and management to the annual yield variation of summer maize using APSIM in the North China Plain. Field Crops Res. 194, 94-102.Sun, H.; Zhang, X.; Wang, E.; Chen, S.; Shao, L.; Qin, W. Assessing the contribution of weather and management to...
research, two most popular Bayesian methods, namely generalized likelihood uncertainty estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM), were used for the first time to estimate parameters of the maize module of the Agricultural Productions Systems sIMulator model (APSIM-maize). ...
Similarly,APSIM effectively predicted Leaf Area Indexattained at the flowering (r2 = 0.90) and maturity(r2 = 0.94) stages. However, APSIM under-estimatedmaize biomass and yield at 75% shading. In conclusion,the model can be reliably employed to simulatemaize productivity in agroforestry systems ...