Towards a unified approach to prioritization of regenerative agricultural practices across cropping systems

Global food production imposes significant environmental costs, including substantial greenhouse gas emissions, water pollution, and biodiversity loss. Regenerative agriculture (RA) aims to improve sustainability by promoting practices such as reduced tillage, increased soil cover, cover crops, diverse rotations, livestock integration, greater use of organic inputs, and reduced synthetic agrichemical use. While definitions of RA vary, this study adopts a definition emphasizing soil conservation as the entry point to regenerate multiple ecosystem services with environmental, social, and economic benefits. Many national programs and private initiatives encourage adoption of such practices, but they often present non-prioritized lists that overlook local production environments. Because agricultural systems vary by climate, soils, and crops, the relative importance of practices should be tailored to specific production environments. Without prioritization, adoption may be slower and resources less efficiently used. There is a need for a structured, standardized, production-specific evaluation of practice importance to facilitate scientific discussion, cross-system comparisons, and knowledge transfer. Among quantitative methods, the Analytical Hierarchy Process (AHP) is a widely used multicriteria assessment that integrates expert knowledge via pairwise comparisons. This study proposes using AHP to prioritize regenerative practices and sets objectives to: (i) test whether synthesizing expert knowledge can identify priorities and their variation across production environments; (ii) validate the reliability of the methodology and experts; and (iii) demonstrate credibility for identifying sustainability management gaps using a monitoring framework in Maharashtra, India.

The paper reviews overlapping concepts related to regenerative agriculture (e.g., conservation agriculture, climate-smart agriculture, sustainable intensification) and notes the ongoing debate over RA definitions. It references policy initiatives (EU soil monitoring law), national support programs (e.g., USDA NRCS EQIP; UK SFI and Countryside Stewardship; Brazil’s programs), and corporate sustainable sourcing driven by decarbonization and broader environmental goals. Existing sustainability frameworks (FAO SAFA, RISE, MASC) typically assess outcomes but do not prioritize farmer efforts by production context. The authors highlight AHP as a suitable multicriteria method previously applied in environmental planning, mining, irrigation optimization, and soil conservation prioritization. They also note emerging global databases for empirical benchmarking (e.g., SoilHealthDB, NAPESHM, FAOSTAT nutrient balances, Global Crop Nutrient Removal) that could complement expert-based approaches.

Study regions: Three production environments were analyzed: (a) vineyards in Maharashtra, India (semiarid, BSh; Vertisols; fine texture) and used for the case study; (b) soybean in Brazil’s Cerrado (Aw; Ferralsols; medium texture); (c) maize in the US Midwest (Dfb; Phaeozems; medium texture). AHP procedure: Using the Saaty scale for pairwise comparisons, eleven domain experts (from academia and industry; expertise in nutrient management, carbon cycling, crop protection, irrigation, soil health) independently compared the importance of eight practices. Field management practices included: cover cropping, crop rotations, residue management, and tillage management. Input management practices included: optimizing nutrient timing, optimizing nutrient rate, integrated pest management (IPM), and optimizing irrigation delivery and rate. Field and input practices were analyzed separately with distinct objectives: (i) field practices’ importance for supporting RA; (ii) input practices’ importance for mitigating environmental footprint. Pairwise comparison matrices were used to derive weights via the eigenvector method; consistency ratios (CR) were checked and found acceptable (≤0.1). Experts also scored practice levels (e.g., no-till vs. conventional till) as fulfillment of potential (best = 100%). Sustainability framework (RegenIQ): The framework combines eight effort indicators (the above practices) with seven outcome indicators measured or estimated at field level: SOC%/Clay% ratio; active carbon (POXC); available water capacity (AWC); system N balance; system P balance; yield-scaled CO2 losses; irrigation water use. Outcome scoring used production-environment-specific cumulative distributions (20th/40th/60th/80th percentiles) for SOC%/Clay%, AC, AWC, and yield-scaled CO2 losses; literature-based thresholds for N and P balances; and an efficiency ratio for irrigation water use based on ETc and rainfall. Composite sustainability score S is computed via weighted linear combination of normalized indicators, prioritizing outcomes (40% efforts, 60% outcomes). Case study data collection: Thirty table-grape vineyards in Maharashtra were surveyed (March–April 2023). Data included fertilizer prescriptions, irrigation, field operations (including residue, cover crop, tillage), yields, and N and P in harvested fruit. Soils were sampled for texture, organic carbon, and active carbon following standardized protocols. AHP-derived weights for vineyards were used to compute efforts scores; outcomes were scored from measurements; the composite sustainability score was then calculated.

- AHP-derived prioritization varied strongly by production environment. Cover cropping had highest importance in semiarid vineyards (41%), followed by soybean (30%) and maize (21%). Residue management showed a similar pattern with greater importance in vineyards (29%) than soybean (23%) and maize (14%). Crop rotation (evaluated in annual systems only) was slightly more important in maize (28%) than soybean (23%). Tillage management was most important in maize (36%), then vineyards (30%), then soybean (24%). - Nutrient rate was consistently rated more important than nutrient timing across systems: vineyards 27% vs 19%; soybean 26% vs 20%; maize 37% vs 21%—reflecting pollution risks from over-application (especially N) compared to suboptimal timing. - IPM importance was similar across systems: vineyards 23%, soybean 29%, maize 26%. Precision irrigation was most important in vineyards (31%), then soybean (25%), then maize (16%), consistent with irrigation’s role in drier climates. - Equality of weights (within ±10% of equal weight) among averaged experts varied: 13% (maize), 43% (vineyards), 63% (soybean), indicating differing degrees of differentiation among practices by system. - Expert robustness: Coefficients of variation for practice weights ranged widely; maize showed the highest disagreement, soybean the lowest. Notable disagreements included cover cropping, crop rotation, and nutrient timing in maize, and IPM in vineyards; high agreement included crop rotation in soybean. Monte Carlo simulations showed an average absolute difference of 20% between scores based on individual experts versus group mean, with maximum differences up to 111%, underscoring the value of aggregating multiple experts. - Case study (30 vineyards, Maharashtra): Composite sustainability scores ranged 53–73 (out of 100), mean 61, indicating room for improvement. Average efforts scores were relatively high, but outcome indicators underperformed, particularly N and P balances and yield-scaled CO2 losses.

The findings demonstrate that assuming uniform importance of regenerative practices is generally incorrect and highly dependent on production environment. The AHP approach, aggregating multiple expert judgments, captures production-specific priorities and nuances; for example, cover cropping is more critical in dry vineyard systems to build soil carbon and conserve moisture, while tillage management is prioritized in continental maize systems to mitigate soil carbon loss. The method identifies areas of expert consensus and disagreement, and simulations show reliance on a single expert can substantially skew evaluations, justifying group-based aggregation. Coupling AHP-derived effort weights with outcome indicators provides a more holistic sustainability assessment and can reveal management gaps, as seen in the vineyards where nutrient balances and yield-scaled CO2 losses lag despite good practice adoption. The approach can guide targeted interventions, inform sustainable sourcing programs, and support efficient allocation of public and private resources. Integrating this expert-based framework with empirical datasets can iteratively validate and refine priorities, improving guidance across diverse cropping systems.

This study introduces a standardized, expert-informed AHP methodology to prioritize regenerative agricultural practices tailored to specific production environments. Applied across vineyards (India), soybean (Brazil), and maize (USA), the approach revealed substantial variation in practice importance and validated the need for context-specific prioritization. Coupled with a multi-indicator sustainability framework, it successfully identified management gaps in 30 Indian vineyards. The methodology can underpin sustainable sourcing schemes, enhance cross-system comparability, and improve resource allocation. Future work should: (i) integrate expert-derived priorities with large empirical databases to iteratively validate and refine weights; (ii) expand to include social and economic indicators with appropriate expert input; (iii) tailor thresholds for nutrient balances regionally; and (iv) extend applications to additional crops, soils, and climates to benchmark sustainability across production environments.

- The AHP approach uses a predefined list of practices based on literature and expert discussions; locally relevant practices not captured may be overlooked. - Results depend on expert judgment; while consistency was checked and multiple experts reduce bias, single-expert reliance can lead to large deviations (up to 111% observed in simulations). - The framework in this study emphasizes environmental aspects; costs, logistical feasibility, and socio-economic factors were not included and may affect adoption. - Case study validation was limited to vineyards in a specific region and soil/climate context; generalizability requires application to broader systems. - Outcome thresholds (e.g., nutrient balances) may need regional tailoring for improved accuracy.