摘要
Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.
Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.
作者
Luwaga Denis
Mavuto Denis Tembo
Mtafu Manda
Alimasi Wilondja
Ngagne Ndong
Joshua Koskei Kimeli
Nansamba Phionah
Luwaga Denis;Mavuto Denis Tembo;Mtafu Manda;Alimasi Wilondja;Ngagne Ndong;Joshua Koskei Kimeli;Nansamba Phionah(African Centre of Excellence in Neglected and Underutilized Biodiversity (ACENUB), Mzuzu, Malawi;Department of the Built Environment, Faculty of Environmental Sciences, Mzuzu University, Mzuzu, Malawi;Department of Environmental Sciences, Faculty of Agriculture, Uganda Martyrs University, Kampala, Uganda;Department of Biological Sciences, Faculty of Science Technology and Innovation, Mzuzu University, Mzuzu, Malawi;Center for Research in Biodiversity, Ecology, Evolution and Conservation, Bukavu, Democratic Republic of the Congo;Unit dEnseignement et de Recherche en Hydrobiologie Applique (UERHA), Biology and Chemistry Department, Institut Suprieur Pdagogique de Bukavu (ISP), Bukavu, Democratic Republic of the Congo;Department of Forestry, Faculty of Environmental Sciences, Mzuzu University, Mzuzu, Malawi;Department of Plant Biology, Faculty of Science and Techniques, University of Cheikh Anta Diop, Dakar, Senegal;Department of Fisheries and Aquatic Sciences, Faculty of Environmental Sciences, Mzuzu University, Mzuzu, Malawi)