Global food production significantly impacts the environment, contributing to greenhouse gas emissions, water pollution, biodiversity loss, and high societal costs. Regenerative agriculture (RA) offers a path toward more sustainable agricultural environments by promoting practices like reduced tillage, increased soil cover, cover cropping, diverse crop rotations, livestock integration, organic nutrient inputs, and reduced synthetic agrichemical use. While the precise definition of RA remains debated, this study adopts a definition emphasizing soil conservation as a starting point to regenerate and contribute to multiple ecosystem services, enhancing environmental, social, and economic dimensions of sustainable food production. Various national programs and corporate initiatives promote RA practices, often offering subsidies or requiring supplier adherence. However, these programs lack a standardized prioritization of practices tailored to specific production environments (climate, soil, cropping system). This necessitates a structured approach to prioritize RA practices for efficient resource allocation and increased adoption rates. The Analytical Hierarchy Process (AHP), a multicriteria assessment (MCA) method, is proposed to quantitatively prioritize RA efforts based on cumulative scientific knowledge.

The existing literature highlights the significant environmental impact of conventional agriculture and the potential of regenerative practices to mitigate these impacts. Studies have explored the benefits of various RA practices such as reduced tillage, cover cropping, crop rotation, and optimized nutrient management. However, a gap exists in the standardized prioritization of these practices across diverse cropping systems and environmental conditions. Several sustainability frameworks exist for evaluating agricultural sustainability, often focusing on outcome indicators but lacking a structured approach to evaluating and prioritizing farmers' efforts. This research builds upon existing knowledge to develop a context-specific prioritization methodology.

This study employed the Analytical Hierarchy Process (AHP) to prioritize eight regenerative agricultural practices across three distinct cropping systems: vineyards in Maharashtra, India; soybean systems in Brazil's Cerrado region; and maize systems in the US Midwest. Eleven experts with diverse backgrounds in agriculture and sustainability provided input. The AHP utilized pairwise comparisons using a Saaty scale (Table 2) to determine the relative importance of each practice within two groups: field management practices (cover cropping, crop rotation, residue management, and tillage management) and input management practices (optimizing nutrient application timing and rate, integrated pest management (IPM), and optimizing irrigation). Experts also provided normalized scores reflecting the fulfillment of potential scores for different practice levels within each practice (e.g., no-till vs. conventional tillage). The consistency of expert judgments was evaluated using the consistency index (CI) and consistency ratio (CR). To assess the robustness of expert opinions, the study performed Monte Carlo simulations comparing individual expert scores to the group mean. The study also applied the AHP-derived weights to a real-world dataset from 30 vineyards in Maharashtra, India, using a sustainability framework (RegenIQ) that included both effort and outcome indicators (Table 3). The framework combined effort and outcome scores (weighted 40% and 60%, respectively) to generate a composite sustainability score. Field data included fertilizer prescriptions, irrigation data, agro-technical operations, yield, nutrient concentrations in harvested fruit, and soil analyses (organic carbon, active carbon, and soil texture). Outcome indicator scoring utilized cumulative distributions or literature-based thresholds.

The AHP analysis revealed substantial variation in the prioritization of regenerative practices across the three cropping systems. For example, cover cropping received the highest importance in semi-arid vineyards (41%), while tillage management was most important for maize (36%). Residue management showed a similar pattern to cover cropping, with higher importance in vineyards. Nutrient rate optimization was consistently more important than nutrient timing across all systems. IPM efforts received relatively consistent importance across systems. Precision irrigation was highly important in vineyards but less so in soybean and maize systems. The coefficient of variation for practice weights varied considerably, ranging from 16% (crop rotation in soybean) to 86% (nutrient timing in maize), indicating areas of both agreement and disagreement among experts. Monte Carlo simulations demonstrated that relying on single experts could lead to significant differences in sustainability evaluations compared to using group means (differences up to 111%). The application of the sustainability framework to the Indian vineyard case study indicated an overall average sustainability score of 61 out of 100, highlighting areas for improvement. Vineyards generally scored well on efforts but performed poorly on outcome indicators like nitrogen and phosphorus balance and yield-scaled CO2 losses.

The findings demonstrate the importance of considering production environment-specific contexts when prioritizing regenerative practices. The AHP methodology effectively integrated expert knowledge to capture these nuances, highlighting the limitations of assuming uniform importance across different cropping systems. The significant variation in practice weights underscores the need for tailored approaches to promoting RA. The case study in Indian vineyards highlighted that while farmers may be implementing various efforts, significant gaps exist in achieving desirable outcomes. This framework can identify specific areas for improvement. The study’s findings support the development of more targeted and effective strategies for promoting RA adoption and optimizing resource allocation. The use of a group of experts, as suggested by the Monte Carlo analysis, provides a more robust and reliable approach than relying on individual experts’ judgments.

This study presents a novel, structured methodology using AHP for prioritizing regenerative agricultural practices across diverse cropping systems. The approach successfully integrates expert knowledge to identify production-environment specific priorities, highlighting the inadequacy of uniform practice importance assumptions. The case study validates the framework’s applicability in monitoring sustainability levels and identifying improvement areas. Future research should focus on expanding the dataset across more cropping systems and geographical locations, incorporating social and economic indicators, and investigating the iterative validation and improvement of expert responses using empirical data.

The AHP approach relies on pre-defined practices based on expert knowledge and literature review; thus, locally relevant practices may be overlooked. The framework currently focuses primarily on environmental indicators, excluding social and economic aspects, which will need future attention. The selection of experts influences the results. Future refinement may improve the choice of experts and the process for eliciting expert knowledge. While the Monte Carlo simulation highlighted the importance of using group means, the number of experts included in the study could be increased to enhance the robustness of the results further.