Modelling Agriculture, Forestry and Other Land Use (AFOLU) in response to climate change scenarios for the SAARC nations

• Published in: Environmental monitoring and assessment Vol. 192; no. 4; p. 236
• Main Authors: Singh, Ram Kumar, Sinha, Vinay Shankar Prasad, Joshi, Pawan Kumar, Kumar, Manoj
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2020; Springer Nature B.V
• Subjects: Afghanistan; Agricultural land; Agriculture; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Bangladesh; Bhutan; Climate Change; Climate change influences; Climate change scenarios; Climate models; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring; Environmental science; Forestry; Geopolitics; Human populations; Humans; India; Indian Ocean Islands; Intergovernmental Panel on Climate Change; Land use; Land use classification; Models, Theoretical; Monitoring/Environmental Analysis; Multilayer perceptrons; Nations; Nepal; Neural networks; Pakistan; Sri Lanka; Tracing; Afghanistan; Bangladesh; Bhutan; Pakistan; Sri Lanka; Indian Ocean Islands; Nepal; India; Maldives; Land use land cover change; Multilayer perceptron; Geospatial modelling; Artificial neural network
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-020-8144-2

Agriculture and forestry are the two major land use classes providing sustenance to the human population. With the pace of development, these two land use classes continue to change over time. Land use change is a dynamic process under the influence of multiple drivers including climate change. Therefore, tracing the trajectory of the changes is challenging. The artificial neural network (ANN) has successfully been applied for tracing such a dynamic process to capture nonlinear responses. We test the application of the multilayer perceptron neural network (MLP-NN) to project the future Agriculture, Forestry and Other Land Use (AFOLU) for the year 2050 for the South Asian Association for Regional Cooperation (SAARC) nations which is a geopolitical union of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka. The Intergovernmental Panel on Climate Change (IPCC) and Food and Agriculture Organization (FAO) use much frequently the term ‘AFOLU’ in their policy documents. Hence, we restricted our land use classification scheme as AFOLU for assessing the influence of climate change scenarios of the IPCC fifth assessment report (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5). Agricultural land would increase in all the SAARC nations, with the highest increase in Pakistan and Maldives; moderate increase in Afghanistan, India and Nepal; and the least increase in Bangladesh, Bhutan and Sri Lanka. The forestry land use will witness a decreasing trend under all scenarios in all of the SAARC nations with varying levels of changes. The study is expected to assist planners and policymakers to develop nations’ specific strategy to proportionate land use classes to meet various needs on a sustainable basis.