An Explainable Digital Twin Framework for Integrating Regenerative Agriculture, Climate-Resilient Food Systems, and Sustainable Nutrition

Sustainable agricultural management faces growing challenges from climate change, water scarcity, energy constraints, soil degradation, and the need to ensure both food production and nutritional security. This study proposes an integrated computational framework combining a Global Agricultural Foundation Representation Model (GAFRM), Self-Evolving Explainable Digital Twin (SEDT), Self-Evolving Evolutionary Foundation Optimizer (SEEFO), and an enhanced Green Regenerative Agriculture Sustainability Index (GRASI). The framework operationalizes the food–water–energy–carbon–nutrition (FWEC-N) nexus by explicitly incorporating crop micronutrient density into the agricultural decision architecture and modeling its relationship with regenerative practices such as cover cropping, biochar application, and zero tillage. Using multi-source global datasets, GAFRM learns transferable agricultural representations, SEDT enables adaptive prediction under climate uncertainty, and SEEFO performs five-objective optimization of agricultural productivity, irrigation water use, energy demand, net carbon balance, and overall sustainability, while nutritional quality is evaluated through the MODI outcome indicator. The enhanced GRASI further evaluates nutrient output, soil restoration, carbon storage, and climate resilience within a unified sustainability framework. The framework was evaluated using a global agricultural dataset covering approximately 60 representative countries across six continents and 15 climate zones over the 2000–2026 period. SEDT achieved an RMSE of 3.18, MAE of 2.29, R2 of 0.972, and NSE of 0.968, while SEEFO achieved the highest Hypervolume (0.956) and the lowest GD (0.028), IGD (0.039), and Spread (0.162) among the benchmark optimization algorithms. The observed performance differences were statistically significant according to the Wilcoxon signed-rank and Friedman tests (p < 0.05). The findings indicate that integrating nutritional quality with resource efficiency, carbon balance, soil regeneration, and climate resilience provides a more comprehensive basis for evaluating regenerative agricultural strategies. The architecture establishes a fully transparent, explainable decision-support environment through explainable AI (XAI) feature attributions, bridging the gap between digital precision farming, regenerative ecosystem restoration, and sustainable human nutrition under increasing environmental uncertainty.