Journal of Resources and Ecology >
Deforestation, Climate Change and the Sustainability of Agriculture: A Review
Received date: 2022-12-20
Accepted date: 2023-08-15
Online published: 2023-12-27
This study aims to survey the literature and factual evidence on the nexus between deforestation and agriculture through an assessment of the potential impacts of climate change in the context of the world, India, and the Western Ghats. The Western Ghats region was chosen for this study because of its deep ecological significance. A few underlying themes were created and findings were documented under each theme that ranged from the causes of deforestation, the transformation of forest land for agriculture, the nexus between agriculture, deforestation and climate change, climate-driven agricultural vulnerability and the reconciliation of forest protection with agriculture. These findings suggest that shifting agriculture has been a dominant source of deforestation. The primary climatic impacts on agriculture are seen through crop yield falls. India’s arid and semiarid tropical regions have witnessed high climate-driven agricultural sensitivity. This could be on account of the fact that India’s tropical forests have witnessed high deforestation. The presence of higher tree densities in areas under Joint Forest Planning and Management in the Western Ghats create the potential for sparing remaining land areas for non-forest uses such as agriculture.
Gayatri KUNTE , Varadurga BHAT . Deforestation, Climate Change and the Sustainability of Agriculture: A Review[J]. Journal of Resources and Ecology, 2024 , 15(1) : 140 -150 . DOI: 10.5814/j.issn.1674-764x.2024.01.012
Table 1 Region-wise analysis of the most important drivers of climate-driven vulnerability of India’s agricultural system (2021- 2050) |
Region (Number of states) | Predominant exposure factor | Predominant sensitivity factor | Predominant adaptive capacity factor | Type of vulnerability |
---|---|---|---|---|
North-East India (1) | Rise in the number of drought years | Low rainfall | Low net irrigated area | Very high |
North India (3) | Projected fall in July rainfall, projected rise in minimum temperature, rise in the number of drought years | Low rainfall, high net sown area, high drought proneness | Low density of livestock, low ground water availability, low net irrigated area, high poverty | |
Central India (1) | Projected rise in minimum temperature, rise in the number of drought years | Low rainfall, high net sown area, low available water capacity, high drought proneness | Low net irrigated area and ground water availability | |
West India (3) | Projected rise in the number of drought years, projected increase in minimum temperature, projected decrease in rainfall in July | Low rainfall, high drought proneness, high net sown area | Low ground water availability, low net irrigated area | |
East India (3) | Projected fall in July rainfall, rise in the number of drought years | High net sown area | Low net irrigated area, low ground water availability | |
South India (3) | Projected fall in July rainfall, projected increase in minimum and maximum temperature, projected fall in total rainfall | Low rainfall, High net sown area, high drought proneness | Low ground water availability, low net irrigated area | |
North-East India (1) | Projected fall in July rainfall | High net sown area | Low net irrigated area | High |
North India (6) | Rise in the number of drought years, projected rise in minimum temperature, projected fall in July rainfall, projected rise in maximum temperature | Low rainfall, high drought proneness, high net sown area, low available water capacity | Low groundwater availability, low net irrigated area, low livestock density, high poverty | |
Central India (2) | Projected rise in the number of drought years, projected rise in minimum temperature | High net sown area, high drought proneness, low rainfall | Low net irrigated area, low groundwater availability | |
West India (3) | Projected rise in minimum temperature, projected rise in the number of drought years, projected fall in July rainfall | High net sown area, low rainfall | Low ground water availability, low net irrigated area | |
East India (3) | Projected fall in July rainfall, projected increase in minimum and maximum temperature, projected rise in the number of drought years | High net sown area, flood proneness, low available water capacity, high percentage of area operated by small and marginal farmers, low rainfall | Low ground water availability, low net irrigated area | |
South India (3) | Projected fall in July rainfall, projected rise in the number of drought years | Low rainfall, high net sown area | Low net irrigated area, low ground water availability |
Note: This table was adapted from Atlas on Vulnerability of Indian Agriculture to Climate Change, Indian Council of Agricultural Research (ICAR, 2013). |
Fig. 1 Global annual tree cover loss by dominant driversNote: Source: Global Forest Watch. https://www.globalforestwatch.org/dashboards/global/?location=WyJnbG9iYWwiXQ%3D%3D. |
Table 2 Global findings and mechanisms that reconcile forest-protection with agriculture |
Country | Region | Mechanisms to reconcile forest-protection with agriculture |
Chile | South America | Co-funding of agricultural investment and agroforestry and provision of credit for native-forest management and irrigation by the National Institute for Agricultural Development (INDAP) Incentives for fertilizer-use and build-up of irrigation equipment have aimed at increasing agricultural productivity( The increase in agricultural productivity creates the potential for sparing remaining land-area for forest-protection.) |
---|---|---|
Costa Rica | North America | Development of tree-pasture systems, agroforestry, and forest and watershed protection prioritized for the allocation of payments under the Payment for Environmental Services Scheme. Tree-planting incentives provided for farmers and forest-conservation support in indigenous territories also established under the scheme The provision of shade for coffee crops and livestock under agroforestry systems |
The Gambia | Africa | The focus on increasing community-participation in the sustainable management of forests, development of agroforestry and provision of strength to the forestry department under a component of the Gambia National Agricultural Investment Plan |
Ghana | Africa | The allocation of land in degenerated areas of forest reserves for the intercropping of food crops in the initial phases of plantation establishment as per a modified taungya system The creation of agroforestry research farms and the inclusion of agroforestry in the Cocoa sector fostered by the drive towards sustainable agriculture |
The Republicof Korea | Asia | Recognition of support for food production and the prevention of agricultural disasters through a possible re-establishment of forests in mountain watersheds |
Tunisia | Africa | The requirement for special authorization for the harvest of forests designated for the protection of water sources and for the interception of erosion(② It is crucial to note that the protection of water sources and the prevention of erosion can aid in agriculture.) |
Note: Data source: FAO, 2016. |
Table 3 An analysis of time periods, methodologies and regions of focus |
Literature | Time/Period | Methodology | Country/Region |
---|---|---|---|
Faria and de Almeida (2013) | 2000-2007 | Fixed and random effects models, Spatial Autoregressive Model | The Brazilian Amazon |
Tsiantikoudis et al. (2019) | 1990-2015 | Auto-regressive Distributed Lag Model | Bulgaria |
Pendrill et al. (2019) | 2005-2013 | Land-Balance Model Crop-Attribution Model | Countries with a significant proportion of surviving Tropical forests |
Lim et al. (2017) | 1980s to 1990s, 1990s to 2000s | Environmental Policy Integrated Climate Model based on Geographic Information System (GIS) | North Korea |
Fonta et al. (2018) | 2014 | Probit Model | West Africa |
Simtowe et al. (2019) | 2015 | Probit Model | Uganda |
Tessema et al. (2013) | 2003-2013 | Multinomial Logit Model | Ethiopia |
Agarwal (2017) | 1993-2012 | Ordinary Least Squares (OLS) and Autoregressive Distributed Lag Model | India |
Birthal et al. (2014) | 1969-2004 | Panel Fixed Effects Model | India |
Malik and Dhanda (2003) | 1970-2000 | Ordinary Least Squares (OLS) Model | India |
Lodh (2017) | 1999-2005, 1982-2002, 1982-1990, 1996-1997 | Sensitivity experiments | India |
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