Cattle ranching has increased globally in the last decades, and although pasture expansion is well documented across different regions, there is little understanding of the intensity at which cattle operate in these areas. With freely available Sentinel-2 satellite imagery, we mapped for the first time polyethylene silage bags used for forage conservation in a year with the Random Forest algorithm, and proposed them as a spatial indicator of cattle intensity. For this, we combined monthly silage area with land cover and climatic variables in a regression framework to understand cattle intensity metrics at regional and farm scales throughout 20 million hectares in the Dry Chaco. In addition, we explored the impact of using maize silage supplementation on productive and environmental metrics at the farm scale in a precipitation gradient. We validated our models using a spatially explicit database of cattle distribution. Our results highlight that silage bags are accurate mappable objects with Sentinel-2, which can contribute to the understanding of cattle density, and heifer and steer density in pasture contexts at farm and regional scales. Finally, our whole-farm simulations support the idea that incorporating silage supplementation in cattle ranching regional analyses conducts to significant differences on environmental or productive estimations, which should be considered. The amount of stored forage that is used in supplementation has strong implications for the performance of cattle ranching, but remains difficult to quantify at the regional level with remote sensing. Silage bag mapping is thus an opportunity to improve the overall understanding of livestock intensification and its productive and environmental impacts, particularly in highly seasonal rangelands. Following this metric could be a valuable indicator of the cattle ranching performance in terms of it resilience, production increase and impacts over natural ecosystems (related to Sustainable Development Goal 2-zero hunger and also in the 15-life on land).
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http://dx.doi.org/10.1016/j.scitotenv.2022.158390 | DOI Listing |
Sci Rep
January 2025
Center for Animal Disease Control, University of Miyazaki, Miyazaki, 889-2192, Japan.
Accurately predicting the calving time in cattle is essential for optimizing livestock management and ensuring animal welfare. Our research focuses on developing a robust system for calving cattle classification and calving time prediction, utilizing 12-h trajectory data for 20 cattle. Our system classifies cattle as abnormal (requiring human assistance) or normal (not requiring assistance) and predicts calving times based on their individual behaviors.
View Article and Find Full Text PDFActa Biotheor
January 2025
Department of Veterinary Parasitology and Entomology, Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Nigeria.
Conflicts within the tsetse fly belt revealed a strong correlation between the dynamics of bovine trypanosomosis and the insurgency involving farmers and herders in Nigeria and parts of West Africa. This study examined the history, causes and influence of farmers-herdsmen conflicts on banditry, terrorism and food security as it relates to the epidemiology of African animal trypanosomosis (AAT). A combination of literature database searches, semi-structured questionnaires, and mathematical modeling was employed.
View Article and Find Full Text PDFHeliyon
November 2024
Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Effective management of brucellosis in human populations is closely tied to controlling the disease in domestic livestock. This study focused on identifying determinants of brucellosis prevalence in mixed industrial dairy and beef cattle farms within Isfahan Province, Iran. Employing a case control design, we compared 32 ranches with documented brucellosis within the previous year (12 months) to 38 farms with no brucellosis during the same timeframe.
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December 2024
Graduate School of Engineering, University of Miyazaki, Miyazaki, 889-2192, Japan.
Accurate calving time prediction plays a critical role in ensuring the well-being of both mother and calf during parturition. Challenges during the calving process, particularly in abnormal cases, often necessitate human intervention to prevent potentially fatal outcomes. This study proposes a novel system for automated prediction of normal and abnormal cattle calving cases based on posture analysis.
View Article and Find Full Text PDFAm J Ind Med
December 2024
Department of Environmental, Agricultural, and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Background: Farm operators are at a high risk of developing skin cancer due to their occupational sun exposure. With the growing incidence of skin cancer, it is also important to evaluate other occupational risk factors. Farm operators confront numerous physical, chemical, and biological hazards in their work environment.
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