Land degradation–desertification in relation to farming practices in India: an overview of current practices and agro-policy perspectives

Authors: CHAUDHURI, SRIROOP - Op Jindal Global University; ROY, MIMI - Op Jindal Global University; MCDONALD, LOUIS - West Virginia University; Emendack, Yves

Submitted to: Sustainability
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/6/2023
Publication Date: 4/7/2023
Citation: Chaudhuri, S., Roy, M., McDonald, L., Emendack, Y. 2023. Land degradation–desertification in relation to farming practices in India: An overview of current practices and agro-policy perspectives. Sustainability. 15(8). https://doi.org/10.3390/su15086383.
DOI: https://doi.org/10.3390/su15086383

Interpretive Summary: Land degradation-desertification (LDD) has emerged as a major policy concern at a global scale due to increased impacts of long-term agricultural sustainability of land-based natural capital and associated human development. Present research offers a bird's eye view of the LDD-farming connection in India to identify negative feedbacks between certain types of farm operations and land quality, at least at regional level. For the regulatory authorities, a point of concern should be that all these farming operations are on the rise, despite their impacts on land resources sustainability. In this regard, the authors envision the use of multiple linear regression analysis as a potential policy instrument as it has both the capacity to assess the current hazard situation, as well as predict future 'risks'. We envision that addressing the growing concerns around LDD will demand a paradigm shift in current farm policy outlook. This will require a conscious move-away from long-established farm practices, to embrace a more land conservation-centric vision. Marked reduction in tillage, widespread adoption of nutrient based subsidy and integrated nutrient management type schemes and bio-pesticides at grassroots will need to be adopted. This approach will encourage farmers, for their own long-term benefits. Farmers will adopt a more sustainable system's practice that will preserve land quality (and ecosystem services), without compromising the production targets (crop yield and income). This finding will urge policy makers to collect information from a wide variety of sources to construct robust statistical models for future risk prediction.

Technical Abstract: Land degradation and desertification (LDD) has gained worldwide policy attention due to detrimental impacts on the environment as well as economy. Here, we focus on India where LDD is on the rise in 24 out of 29 states since the early 2000s. We combine multivariate methods (hierarchical cluster analysis, HCA; and multiple linear regression analysis, MLRA) to draw on an age-old debate: Does farming operations aggravate LDD, using state-level information for 2011-2013. The HCA identified 11 states that currently lead the trajectory with highest state-wise percentages of LDD-areas under ‘water erosion’, ‘salinity’, and ‘water logging’, three most widely outcomes of farming, as reported in relevant literature. The MLRA in the 11-state cluster for the 2011-2013 period revealed that state-wise NPK fertilizer application rates (p<0.01); number of mouldboard ploughs (p<0.01); net irrigated area (p<0.01); groundwater-sourced irrigation (p<0.05); and multiple cropping practices (p<0.01) exacerbate LDD, while bio-pesticides (p<0.05) and zero-till drills (p<0.01) do the opposite. Interestingly, MLRA at national level did not yield any statistically significant relationships between LDD and farming. In the following sections we highlight pitfalls around current agro-policy out-look and recommend strategic measures to help the farmer at grassroots, move away from traditional practices to a more land conservation-centric approach. Concurrently, we attempt to broaden the regulatory vision by dis-cussing the DPSIR framework (Driver-Pressure-State-Impact-) framework and the need of a data 'revolution' to support high-end research and LDD modeling/prediction initiatives.