The Soil Nutrient Analyzer was used to analyze soil nutrients in 450 samples of a purple soil hilly area with an area of ​​2 km2. Supported by correlation analysis software, the terrain factor and land use type were used as predictors and multiple linear regression was used to construct the soil nutrient. The soil nutrient prediction model predicts the spatial distribution of nutrients.
The distribution laws of soil organic matter content and alkali-hydrolyzable nitrogen content are similar, and the distribution pattern is more obvious. The high value areas are mainly distributed in the valleys, the low value areas are mainly distributed on the ridges, and the slopes between the ridges and valleys are transitional areas. The determination of soil nutrient fastness tester found that the high value areas of soil available phosphorus content were mainly distributed on steep slopes and northward slopes, while the low value areas were located in the valley. The spatial distribution pattern of soil available K content is also similar to that of organic matter. The high value area is distributed in the valley, while the low value area is located on the ridge.
The combination prediction model of soil nutrients based on soil nutrient topography factor prediction model and land use method shows that prediction accuracy and land use type variables can not improve the prediction accuracy of the model, and only use topographic factors to predict the spatial distribution of soil nutrients more Convenient, so choose this model to predict validation data. Comparing the dataset prediction results measured by the soil nutrientometer with the measured values, the results show that the difference between the predicted value and the measured value is small.
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