Prediction and assessment model of the impact of different grazing strategies on Xilingole grassland

Abstract

Grassland grazing strategies are crucial to the balance of grassland ecosystems and are closely related to the livelihoods of herders. The purpose of this paper is to study the effects, future trends of different grazing strategies on soil physics properties and vegetation biomass in Xilingole grassland. Based on the 19th graduate student mathematical modeling dataset, the raw data were preprocessed using Python, including missing value interpolation, normality test, Laeta criterion (also known as 3σ criterion) to eliminate outliers, and then combined with random forest classification algorithm and logistic regression algorithm to establish a mathematical model of the effects of different grazing strategies on soil physical properties and vegetation biomass, and analyzed in detail by their relationships The results showed that (1) the grazing strategy and the changes of soil and vegetation in the area were not significant. The results show that (1) the grazing strategy contributes more to vegetation biomass than soil physical properties, and the accuracy of the training set for random forest classification is 0.98, and the accuracy of the test set is 0.74. (2) The linear function of grazing intensity on soil physical properties and vegetation biomass is established by combining with logistic regression algorithm. For different grazing methods, the final grazing strategy results can be obtained by multiplying the grazing intensity by a weighting factor, and subject to the influence of multiple factors, the soil physical properties and vegetation biomass do not always maintain a positive or negative correlation with the change of grazing strategy, which is consistent with the actual grazing strategy influence.