紫色土丘陵地区农田土壤养分空间分布预测

为深入研究紫色土丘陵区农田土壤养分空间分布规律,在GIS技术的支持下,利用研究区450个土壤实测数据,结合地形因子和土地利用类型,运用多重线性回归构建了土壤养分预测模型,对养分的空间分布进行预测。结果表明,土壤有机质和碱解氮含量与地形因子之间的相关性较强,有效磷和速效钾含量与地形因子之间的相关性较弱。土壤水田和旱地中有机质、碱解氮和有效磷含量均值间的差异显著(P<0.01),速效钾之间不显著(P=0.34)。基于地形因子的土壤养分预测模型与基于地形因子和土地利用方式组合的土壤养分预测模型预测结果精度对比表明,在预测变量中增加土地利用类型对提高预测模型的拟合度和预测精度作用非常微小,且仅用地形因子预测土壤养分的空间分布更方便,因此选用该模型对验证集数据进行预测。以验证集数据进行预测结果与实测值进行比较,结果显示预测值与实测值之间的差异甚小,有机质、碱解氮、有效磷和速效钾的相对偏差分别为0.09、0.19、0.08和0.12,均方根误差分别为1.38、3.42、1.03和1.57,说明基于地形因子的土壤养分预测模型的精度较高,可以很好地预测土壤养分分布规律。该研究结果可为丘陵地区农田合理施肥提供理论依据。

Prediction of spatial distribution of hilly farmland with purple soil nutrient

Abstract: The accurate prediction of the distribution of hilly farmland with purple soil nutrient is of great importance for fertilizing crops reasonably. Also it is very important as a means of increasing farmer income, protecting the environment and source, and improving the sustainable development of agriculture. Many research studies have focused on the spatial variation of soil nutrient, and conclude that the land use type and topographic features of the land are the key factors for the spatial distribution of soil nutrient. However, we can not predict the distribution of hilly farmland with purple soil nutrient based on presently known results. From the knowledge gaps outlined above, the objectives of this study were to predict the regularity of purple soil nutrient for hill farmland. We collected a typical hilly farmland of purple soil (2 km2) in Jiangjin, Chongqing as the study area. One model was built based on terrain attributes, and the other one was built based on a combination of terrain attributes and land use types. The study compared the complexes and fitting for the two models to identify which one is better. The results showed that organic matter and alkali-hydrolyzable nitrogen were positively correlated with topographic factors. This result agrees with previous results that indicated that alkali-hydrolyzable nitrogen is often positively correlated with organic matter in soil because the mineralization of organic matter could generate alkali-hydrolyzable nitrogen. Available phosphorus and rapidly available potassium were weakly correlated with topographic factors, suggesting that terrain attributes have little effect on those two soil nutrients. There were significant differences (P<0.01) of the average of organic matter, alkali-hydrolyzable nitrogen and available phosphorus between crop land and paddy field. But we did not observe a significant difference (P=0.34) of rapidly available potassium in these two fields. This suggests that land use type has a significant effect on the spatial distribution of organic matter, alkali-hydrolyzable nitrogen, and available phosphorus, but except for rapidly available potassium. Thus, if we built the soil nutrient prediction model based only on the land use type, rapidly available potassium may not need to be considered. A comparison of the performance for the two models demonstrates that prediction models based on terrain attributes are convenient, accurate and applicative. This suggests that the predictor variable of land use type as calculated in the model did not positively help to improving the prediction. This study could provide basic scientific evidence for the reasonable fertilization of hilly farmland.