Abstract
In the arid grasslands of northern China, unreasonable grazing methods can reduce the water content and species numbers of grassland vegetation. This project uses solar-powered GPS collars to obtain track data for sheep grazing. In order to eliminate the trajectory data of the rest area and the drinking area, the kernel density analysis method was used to cluster the trajectory point data. At the same time, the vegetation index of the experimental area, including elevation, slope and aspect data, was obtained through satellite remote sensing images. Therefore, using trajectory data and remote sensing image data to establish a neural network model of grazing intensity of sheep, the accuracy of the model could be high. The results showed that the best input parameters of the model were the combination of vegetation index, sheep weight, duration, moving distance and ambient temperature, where the coefficient of determination , and the mean square error MSE = 0.73. The error of grazing intensity obtained by the model is the smallest, and the spatial-temporal distribution of grazing intensity can reflect the actual situation of grazing intensity in different locations. Monitoring the grazing behavior of sheep in real time and obtaining the spatial-temporal distribution of their grazing intensity can provide a basis for scientific grazing.
Highlights
IntroductionGrassland resources are extremely rich with various natural grasslands of about
Academic Editor: Dimitrios MoshouGrassland resources are extremely rich with various natural grasslands of about400 million hectares in China, which account for about 41% of the land area [1]
The daily feeding intake and trajectory data were used to calculate the spatial-temporal distribution of sheep grazing intensity; the accuracy of the grazing intensity calculated by this method was low
Summary
Grassland resources are extremely rich with various natural grasslands of about. 400 million hectares in China, which account for about 41% of the land area [1]. Inner Mongolia grassland belongs to the northern temperate zone, and the total area ranks first among the five grasslands in China. Due to the advantages of the natural conditions, Inner. Mongolia has become an important livestock production base in China [2]. By monitoring the grazing behavior of sheep with modern technology, the spatial-temporal distribution of the grazing intensity of sheep can be obtained, which will provide basic data support for the study of forage–livestock balance and rotation grazing. The traditional artificial observation method of sheep’s grazing behavior is timeconsuming, laborious and subjective. Wang et al [7] studied the grazing behavior of
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