基于GIS的黄土丘陵沟壑区作物生产潜力模拟研究

A GIS-based Simulation of Crop Potential Productivity in Hilly Loess Region

从 YIELD模型的来源、输入文件及基本参数、模型中作物生产力计算各个子模型以及计算流程 4个方面作了简单的叙述 ,以黄土丘陵沟壑区典型小流域晋西狼窝沟为例 ,在地理信息系统 ( GIS)技术支持下 ,应用 YIELD模型对该流域的作物生产潜力进行了模拟 ,并从作物类型、地类、耕作措施及气候条件 4个方面对影响该流域作物产量的因素进行分析。结果表明 ,该模型对不同作物的模拟产量在总体上与实测产量基本相符合 ,表明模型可以应用于黄土丘陵沟壑区的作物产量模拟之中 ;对于不同地类来说 ,坝地的土壤水分和肥力条件明显高于梯田和坡耕地 ,因而坝地的模拟产量也高于梯田和坡地 ,但三者之间的差距没有实测产量显著 ;耕作措施是提高作物生产力的有效途径 ,对地膜覆盖、梯田以及施肥等耕作措施的模拟产量表明 ,这 3种耕作措施均能有效的提高作物生产力 ,其产量提高率均平均在 85 %以上 ,其中以施肥对作物的增产作用最大 ,增产率高达 95 % ,这与实测产量资料基本一致 ;气候条件是影响作物生产的直接因素 ,模拟结果表明模型对降水量和温度等气候条件十分敏感 ,不同年份降水量和温度的差异将直接导致作物生产力的显著不同。对 YIELD模型的模拟结果分析表明 。

We select the Langwogou catchment in hilly Loess region as the study area, and use a crop productivity model to investigate the spatial variability of crop productivity. Firstly, the YIELD model, including origin of the model, input files and basic parameters, sub model of calculating crop productivity and flow chart, is briefly described. Secondly, crop potential productivity of the catchment was modeled by integrating Geographic Information System with the YIELD model, and the effects of the type of crop, land type, cultivation practice and climatic condition on crop productivity of the catchment were analyzed. The results showed that the simulated yield is in agreement with observed yield for different crops. For different land types, soil water and nutrient levels is much better in bottom land than that in terrace and slope land, resulting in that the simulated yield in bottom land being higher than that in terrace and slope land, but the difference of simulated yield between different land type is not so obvious as that of observed yield. The cultivation practice is a means of enhancing crop yield, and the simulated yields for different cultivation practices, including plastic cover, terrace and fertilization, indicated that cultivation practices can effectively enhance crop productivity, and the average yield increase of three cultivation practices are more than 85%, especially for fertilization, whose improvement rate reaches 95%. This is in agreement with observed yield. The climatic condition is one of the most significant factors affecting crop growth, and the simulated result shows that the model is sensitive to climatic condition, such as precipitation and temperature. The annual variations of rainfall and temperature can result in different crop productivities. It can be concluded from this study that the YIELD model can be effectively used to model the crop potential productivity in Hilly Loess region.