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Evaluating yield response of paddy rice to irrigation and soil management with application of the AquaCrop model.

机译:使用AquaCrop模型评估水稻对灌溉和土壤管理的产量响应。

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Yield of lowland paddy rice (Oryza sativa L.) is sensitive to the water supply, which can be affected by irrigation management (i.e., irrigation method) and soil management (i.e., cropping history). The objective of this study is to analyze the responses of rice yield to the irrigation method and cropping history of a rice field by using the AquaCrop model. The field experiment was conducted in 2008 and 2009 with four treatments, i.e., two irrigation methods (continuous submergence vs. intermittent irrigation) and two different cropping history treatments (30 vs. 7 years of rice cropping). The model was calibrated in terms of soil water content, green canopy cover, aboveground dry biomass, and paddy rice yield. The validation of the model showed satisfactory simulation results for biomass and yield. The AquaCrop model proved to be robust and applicable for paddy rice under various irrigation methods and cropping histories, although it tended to underestimate the dry biomass at the earlier growth stage. The simulated rice yield was highly sensitive to the maximum canopy cover. The biomasses and yields in the seven-year (Y7) field were more sensitive to input data of climate and crop and soil parameters than in the 30-year (Y30) field, with an average sensitivity index of 9.5% vs. 2.9% for Y7 vs. Y30. In contrast, the average sensitivity index did not show an obvious difference for intermittent irrigation (II) and continuous submergence (CS), with an average sensitivity index of 6.6% vs. 5.9% for II vs. CS. Because the AquaCrop model was applied to the paddy rice crop for the first time and this research is site-specific, the model has to be further tested with different climate conditions, soil types, and crop varieties.
机译:低地水稻(Oryza sativa L.)的产量对供水很敏感,这可能会受到灌溉管理(即灌溉方法)和土壤管理(即种植历史)的影响。这项研究的目的是使用AquaCrop模型分析水稻产量对灌溉方法和稻田耕作历史的响应。田间试验于2008年和2009年进行了四种处理,即两种灌溉方法(连续淹没与间歇灌溉)和两种不同的种植历史处理(30年与7年的水稻种植)。根据土壤水分,绿冠层覆盖,地上干生物量和水稻产量对模型进行了校准。模型的验证显示出令人满意的模拟结果,生物量和产量。事实证明,AquaCrop模型是健壮的,适用于各种灌溉方法和耕作历史的水稻,尽管它往往低估了生育早期的干燥生物量。模拟的水稻产量对最大冠层覆盖高度敏感。与30年(Y 30 )相比,七年(Y 7 )领域的生物量和产量对气候,作物和土壤参数输入数据更敏感。 )字段,Y 7 与Y 30 的平均敏感度指数分别为9.5%和2.9%。相比之下,间歇灌溉(II)和连续淹没(CS)的平均灵敏度指数没有明显差异,II和CS的平均灵敏度指数分别为6.6%和5.9%。由于AquaCrop模型是首次应用于水稻作物,并且这项研究是针对特定地点的,因此必须在不同的气候条件,土壤类型和作物品种下对该模型进行进一步测试。

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