Land degradation and desertification (LDD) has gained worldwide policy attention due to decline in land quality and the resultant economic burden accrued upon a vast population reliant on land-based natural capital. In India, the impacts are becoming apparent as 24 out of 29 states have been experiencing LDD since the early 2000s. Here, we adopt a mixed-method approach combining hierarchical cluster analysis (HCA) and multilinear regression analysis (MLRA) with contextual assessments to draw on, using state-level information from 2011–2013, an age-old question: do farming operations aggravate LDD? The HCA identified 11 states that currently lead the trajectory with the highest state-wise percentages of LDD areas under ‘water erosion’, ‘salinity’, and ‘water logging’, which are the three most widely reported impacts of farming practices on land systems’ sustainability as apparent in the relevant LDD literature. MLRA of the 11-state cluster revealed that state-wise NPK fertilizer-application rates (p < 0.01); number of moldboard 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. In recent years, the government has introduced several improved measures, such as nutrient-based subsidies (NBS) and integrated nutrient management (INM), to help farmers optimize agrochemical applications, thus moving from traditional practices (input intensive) to a more land-conservation-centric approach. However, at the cognitive level, farmers are unsure/unaware of the benefits. Moreover, due to various institutional weaknesses, lack of sensitization, extension services, capacity building, incentives, and certain inherent flaws in these newer strategies, their uptake remains low, while traditional practices continue at the grassroots. We attempt to broaden the regulatory purview by reflecting on the DPSIR framework (Driver–Pressure–State–Impact–Response), highlighting the role of indirect drivers of LDD that largely influence farmers’ preferences for certain practices. In the final section, we emphasize the need for a data revolution to support process-level research to be able to devise more informed and targeted LDD control–abatement strategies, which is currently largely absent.

The recent development of portable X-ray fluorescence spectrometers (PXRF) has created new avenues for rapid plant elemental concentration determination at reduced cost while avoiding hazardous chemicals. A few studies have indicated the potential use of PXRF for homogenous plant tissue analysis. However, there is a lack of information for analysis of heterogeneous plant samples like livestock forage, which consists of a mixture of several species and plant parts, each varying in elemental concentration. Our objective was to evaluate PXRF for forage analysis, specifically the effect of forage particle size and scan time on important elements including P, K, Ca, and Fe determination. Hay samples (n = 42) were oven dried (60°C for 3 days) and ground into three particle sizes (≤0.5 mm, 0.25–0.5 mm and 1–2 mm). Prepared samples were scanned by PXRF using a vacuum (<10 torr) without a filter. Samples were placed in cups over thin prolene X-ray film and scanned for 180 s. A subset (n = 29) were also scanned for 60 and 120 s. PXRF counts for P, K, Ca, and Fe were compared with laboratory Inductively Coupled Plasma Optical Emission Spectroscopy (ICP) determinations, using regression models. Results indicated that these elements could potentially be determined with PXRF (r² ≥ 0.70) in heterogeneous forage samples. Relationship strength increased with decreasing particle size, however, the relationship was still strong (r² ≥ 0.57) at the largest particle size. Scanning time did not affect the relationship with ICP concentration for any of the particle sizes evaluated. This work demonstrated that with the right sample preparation PXRF can obtain results comparable to acid digestion and ICP regardless of sample composition, and suggests the potential for in situ determinations.