Global assessment of grassland carrying capacities and relative stocking densities of livestock

Piipponen, Johannes; Jalava, Mika; Leeuw, Jan De; Rizayeva, Afag; Godde, Cécile; Herrero, Mario; Kummu, Matti

doi:10.1002/essoar.10505875.1

Cited by 2 publications

(2 citation statements)

References 48 publications

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“…Keeping more livestock through transhumance to meet the high meat and milk demand leads to overgrazing which has been considered as contributing factor to the decline of wooded, shrub and grass savannah by the majority of local communities [7,47,48]. Overgrazing, combined with trampling by livestock hooves and subsequent soil compaction, leaves bare land which is subsequently exposed to the hot sun during the dry period and, ultimately, leads to soil [49] and wind erosion [50].…”

Section: Discussionmentioning

confidence: 99%

From Wooded Savannah to Farmland and Settlement: Population Growth, Drought, Energy Needs and Cotton Price Incentives Driving Changes in Wacoro, Mali

Sanogo

Dayamba

Renaud

et al. 2022

Land

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Land includes vegetation and water bodies and provides the basis for human livelihoods through primary production, food and freshwater supply, and multiple other ecosystem services. The last three decades have recorded frequent drought events as well as rapid population growth, which has often resulted in adverse land use and land cover change (LULCC) in the Sahel of Sub-Saharan Africa. In order to propose sustainable land management strategies, it is a prerequisite to investigate the rate of LULCC and its driving factors in specific locations. This study investigated the case of Wacoro municipality in Mali using a combined approach of remote sensing, Geographic Information Systems, and focus group discussions. Satellite images and local people’s perceptions on LULCC and drivers were collected and analyzed for the years 1990, 2000, 2010, and 2020. We found that the study area faced a rapid decrease in wooded savannah that was degraded and converted to shrub savannah and later to farmland and settlement. Changes were directly or indirectly related to the rapid population growth, high cotton price (which encouraged cropland expansion), drought, firewood extraction, and charcoal production, which was exacerbated by poverty. We suggest promoting integrated land management strategies that consider current and future livelihood needs and preserve the environment for the benefits of future generations. New agricultural policies, such as cotton price incentives, should always be accompanied by an assessment of their potential environmental impacts and design of adequate mitigation measures.

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Section: Discussionmentioning

confidence: 99%

From Wooded Savannah to Farmland and Settlement: Population Growth, Drought, Energy Needs and Cotton Price Incentives Driving Changes in Wacoro, Mali

Sanogo

Dayamba

Renaud

et al. 2022

Land

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“…In recent years, livestock grid data have been applied to many aspects as primary data. 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]. There are also specific applications in environmental impact assessment research, such as quantifying methane emissions based on livestock grid data [47].…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning

2021

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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.

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Global assessment of grassland carrying capacities and relative stocking densities of livestock

Cited by 2 publications

References 48 publications

From Wooded Savannah to Farmland and Settlement: Population Growth, Drought, Energy Needs and Cotton Price Incentives Driving Changes in Wacoro, Mali

From Wooded Savannah to Farmland and Settlement: Population Growth, Drought, Energy Needs and Cotton Price Incentives Driving Changes in Wacoro, Mali

High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning

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