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机构地区:[1]甘肃农业大学信息科学技术学院,甘肃兰州730070 [2]兰州交通大学现代信息技术与教育中心,甘肃兰州730070 [3]甘肃农业大学农学院,甘肃兰州730070
出 处:《草业学报》2018年第3期57-66,共10页Acta Prataculturae Sinica
基 金:国家自然科学基金项目(31360315);甘肃农业大学盛彤笙科技创新基金项目(GSAU-STS-1717);现代农业技术体系建设专项(CARS-17-GW-9)资助
摘 要:作物光合生产与干物质积累模拟模型是作物生长模型的核心,在已有作物光合作用和干物质积累模型基础上,采用辐射利用率,考虑环境因子对光合速率的影响、呼吸作用消耗同化量,构建基于生理生态过程的胡麻光合生产与干物质积累模拟模型。通过不同肥料、播种方式、种植密度及氮磷水平的初步检验,模型具有较好的模拟效果和较强的适用性。不同肥料设置,干物质积累模拟值的RMSE值为0.3486~1.9538 g·株^(-1),平均为1.0467g·株^(-1),决定系数R^2取值范围在0.6311~0.9832,平均为0.8532;3种播种方式的干物质积累模拟值的RMSE值为0.5685~1.1164 g·株^(-1),平均为0.8692 g·株^(-1),决定系数R^2取值范围在0.8745~0.9662,平均为0.9282;7种种植密度干物质积累模拟值的RMSE值为0.0807~0.2086 g·株^(-1),平均为0.1584 g·株^(-1),R^2取值范围在0.9677~0.9965,平均为0.9869;12种氮磷水平模型检验模拟值的RMSE值为0.0952~1.2375 g·株^(-1),平均为0.4000 g·株^(-1),R^2取值范围在0.8964~0.9959,平均为0.9700。最后对APSIM模型和Aqua Crop模型在现有研究基础上对不同氮磷水平胡麻生物量的模拟进行了简单对比。研究结果为进一步构建胡麻生长模型奠定基础,为应用模型分析胡麻生长及制定生产决策提供依据。Photosynthesis and dry matter accumulation simulation models are the core of crop growth models. Taking existing models and using radiation use efficiency, and considering the influence of environmental factors and respiration consumption on photosynthetic rate, a physiological and ecological processes model for photosynthesis and dry matter accumulation in oilseed flax was established. This model produced improved simulation and applicability during validation experiments with different sowing methods, planting density and nitrogen and phosphorus levels. For example, the root mean squared error (RMSE) value with different plant ing densities ranged from 0. 0807 to 0. 2086, with a mean of 0. 1584 while the R2 ranged from 0. 9677 to 0. 9965, mean 0. 9869. The RMSE value with different nitrogen and phosphorus levels ranged from 0. 0952 to 1. 2375 with a mean of 0. 4000 while the R2 range from 0. 8964 to 0. 9959, mean 0. 9700. The biomass simulations from this model AquaCrop (FAO Crop were Model compared with the APSIM (Agricultural Production System Simulator) and to Simulate Yield Response to Water) with different nitrogen and phosphorus levels. The results of this study lay the foundation for the further development of oilseed flax growth models, and provide the basis for the application of growth model and management decisions in flax.
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