Department of Natural Resources, TERI School of Advanced Studies, 10, Institutional Area, Vasant Kunj, New Delhi, 110070, India.
Special Center for Disaster Research, Jawaharlal Nehru University, New Delhi, 110067, India.
Environ Monit Assess. 2020 Mar 14;192(4):236. 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.
农业和林业是为人类提供食物的两大主要土地利用类型。随着发展的步伐，这两类土地利用类型随着时间的推移不断发生变化。土地利用变化是一个受多种驱动因素影响的动态过程，包括气候变化。因此，追踪变化的轨迹具有挑战性。人工神经网络 (ANN) 已成功应用于追踪这一动态过程，以捕捉非线性响应。我们测试了多层感知器神经网络 (MLP-NN) 的应用，以预测 2050 年南亚区域合作联盟 (SAARC) 国家的农业、林业和其他土地利用 (AFOLU) 的未来情况，SAARC 是由阿富汗、孟加拉国、不丹、印度、尼泊尔、马尔代夫、巴基斯坦和斯里兰卡组成的一个地缘政治联盟。政府间气候变化专门委员会 (IPCC) 和联合国粮食及农业组织 (FAO) 在其政策文件中经常使用“AFOLU”一词。因此，我们将土地利用分类方案限制为 AFOLU，以评估 IPCC 第五次评估报告 (RCP 2.6、RCP 4.5、RCP 6.0 和 RCP 8.5) 中气候变化情景的影响。在所有的 SAARC 国家中，农业用地都会增加，其中巴基斯坦和马尔代夫的增长幅度最大；阿富汗、印度和尼泊尔的增长幅度适中；孟加拉国、不丹和斯里兰卡的增长幅度最小。在所有的 SAARC 国家中，所有情景下的林业用地都将呈下降趋势，变化程度不同。这项研究有望帮助规划者和决策者制定各国特定的战略，根据可持续性的要求，为各类土地的利用分配提供相应的策略。
