Agricultural land-use dynamics: Assessing the relative importance of socioeconomic and biophysical drivers for more targeted policy |
| |
| Institution: | 1. Área de la Cadena Agroalimentaria, Grupo Agroecosost, IFAPA, Camino de Purchil s/n, Granada, Spain;2. Área de la Cadena Agroalimentaria, IFAPA, Carretera Bailén-Motril, Mengibar, Jaén, Spain;1. Water Systems and Global Change Group, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands;2. Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands;1. Department of Wildlife and Fisheries Sciences, Texas A&M University, 210 Nagle Hall, College Station, TX, 77843, USA;2. LiDAR Applications for the Study of Ecosystems with Remote Sensing (LASERS) Laboratory, Department of Ecosystem Science and Management, Texas A&M University,1500 Research Parkway Building B, Suite 217, College Station, TX, 77843, USA;3. Department of Ecosystem Science and Management, Texas A&M University,Horticulture/Forest Science Building, 495 Horticulture St, College Station, TX, 77843, USA;4. Departamento de Geografía, Universidad Nacional de Costa Rica, Naranjo, Costa Rica;5. Department of Wildlife and Fisheries Sciences, Texas A&M University, 210 Nagle Hall, College Station, TX, 77843, USA;1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China;2. Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079, China;3. Collaborative Innovation Center for Geospatial Information Science, Wuhan University, Wuhan, 430079, China;4. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong |
| |
| Abstract: | A detailed understanding of multiple human and environmental factors influencing land allocations among agricultural uses can facilitate more efficient and targeted land policy. To show this, we used a comprehensive dataset of socioeconomic, physiographic, and climatic indicators to investigate potential determinants of land-use in Australia’s intensive agricultural region during the period 1992–2010. We applied a seemingly unrelated regressions land-use shares spatial error model with random effects coupled with variance decomposition analysis to identify the statistical significance, direction and magnitude of observed associations between land-use and its drivers.Population: density, rainfall, equity ratio, and access to markets were the most influential policy-relevant land-use factors. Land allocations to cereals and livestock production were significantly influenced by spatiotemporal rainfall and temperature variability. Improved pastures, cereals, annual and perennial crops plantations were larger in regions with better access to markets. Increases in equity ratio (i.e., better financial position) were associated with larger land allocations to improved pastures and annual crops and smaller extensive grazing area. Marginal associations were detected between land-use and output prices, and higher population density was associated with lower shares for all high value agricultural land-uses. The results suggest that improved transportation infrastructure, zoning regulations, and mechanisms to reduce farm debt exposure and risks from climate variability could have significant impact on the configuration of the Australian agricultural landscape. |
| |
| Keywords: | Land-use Spatial econometrics Random effects Policy Australia Variance decomposition Climate Drivers |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
