Abstract
Accurate high-resolution gridded livestock distribution data are of great significance for the rational utilization of grassland resources, environmental impact assessment, and the sustainable development of animal husbandry. Traditional livestock distribution data are collected at the administrative unit level, which does not provide a sufficiently detailed geographical description of livestock distribution. In this study, we proposed a scheme by integrating high-resolution gridded geographic data and livestock statistics through machine learning regression models to spatially disaggregate the livestock statistics data into 1 km × 1 km spatial resolution. Three machine learning models, including support vector machine (SVM), random forest (RF), and deep neural network (DNN), were constructed to represent the complex nonlinear relationship between various environmental factors (e.g., land use practice, topography, climate, and socioeconomic factors) and livestock density. By applying the proposed method, we generated a set of 1 km × 1 km spatial distribution maps of cattle and sheep for western China from 2000 to 2015 at five-year intervals. Our projected cattle and sheep distribution maps reveal the spatial heterogeneity structures and change trend of livestock distribution at the grid level from 2000 to 2015. Compared with the traditional census livestock density, the gridded livestock distribution based on DNN has the highest accuracy, with the determinant coefficient (R2) of 0.75, root mean square error (RMSE) of 9.82 heads/km2 for cattle, and the R2 of 0.73, RMSE of 31.38 heads/km2 for sheep. The accuracy of the RF is slightly lower than the DNN but higher than the SVM. The projection accuracy of the three machine learning models is superior to those of the published Gridded Livestock of the World (GLW) datasets. Consequently, deep learning has the potential to be an effective tool for high-resolution gridded livestock projection by combining geographic and census data.
Highlights
China’s animal husbandry has unfolded rapidly in the past 40 years since the country’s reform and opening up
Understanding the spatial distribution of livestock is of great significance for the effective utilization of grassland resources, protection of the ecological environment, and sustainable development of animal husbandry [4]
Livestock grid data have been widely involved in the rational use of natural resources, such as assessing grass-livestock balance based on livestock grid data [44], estimating oxygen consumption of livestock [45], and quantifying water use for animal husbandry [46]
Summary
China’s animal husbandry has unfolded rapidly in the past 40 years since the country’s reform and opening up. It has become an unshakable leading economy in the agricultural and rural industries [1]. The development of animal husbandry requires a large number of grassland resources. CH4 and N2 O produced during livestock growth have become the main sources of agricultural greenhouse gas emissions [2,3]. Understanding the spatial distribution of livestock is of great significance for the effective utilization of grassland resources, protection of the ecological environment, and sustainable development of animal husbandry [4]. Traditional livestock statistics are collected at the administrative unit level, mainly extracted from the “China Statistical Yearbook”
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