A novel model to assess soil productivity in the dry-hot valleys of China

来源 :Journal of Mountain Science | 被引量 : 0次 | 上传用户:baihe0415
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Accurate evaluation of soil productivity has been a long-standing challenge. Although numerous models for productivity assessment exist, most are cumbersome to use and require substantial parameter inputs. We developed a new empirical soil productivity model based on field investigations of soil erosion, soil physicochemical properties, and crop yields in the dry-hot valleys(DHVs) in China. We found that soil p H, and organic matter and available potassium contents significantly affected crop yields under eroded conditions of the DHVs. Moreover, available potassium content was the key factor affecting soil productivity. We then modified an existing soil productivity model by adding the following parameters: contents of effective water, potassium, organic matter, and clay, soil p H, and root weighting factor. The modified soil productivity model explained 63.5% of the crop yield. We concluded that the new model was simple, realistic, and exhibited strong predictability. In addition to providing an accurate assessment of soil productivity, our model could potentially be applied as a soil module in comprehensive crop models. Accurate evaluation of soil productivity has been a long-standing challenge. Many developed for new productivity soil productivity model based on field investigations of soil erosion, soil physicochemical properties, and crop yields in the dry-hot valleys (DHVs) in China. We found that soil p H, and organic matter and available potassium contents significantly affected crop yields under eroded conditions of the DHVs. factor affecting soil productivity. We then modified an existing soil productivity model by adding the following parameters: contents of effective water, potassium, organic matter, and clay, soil p H, and root weighting factor. The modified soil productivity model explained 63.5% of the crop yield. We said that the new model was simple, realistic, and strong strong predictability. In addition to p roviding an accurate assessment of soil productivity, our model could be applied as a soil module in comprehensive crop models.
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