A quantitative framework for characterizing the current and obtaining a future sustainable agricultural production mix meeting environmental, nutritional, and economic goals

Abstract

Abstract Transitioning toward sustainable agriculture production is key in achieving sustainable development goals. However, quantifying the sustainability status of current agricultural mix and figuring out a future sustainable crop and livestock production mix that is nutritionally adequate, low in environmental impact and profitable to farmers, is a formidable challenge for any country. Here through a case-study on Indian agriculture, we demonstrate the application of a framework to first characterize the current agricultural mix by comparing 31 sustainability indicators with respective benchmarks across nutrition (social), environment and economic dimensions in each of the 36 Indian states. Next, we demonstrate the application of mathematical optimization algorithms to calculate the sustainable production mix in each state with the objective of maximizing the farmers’ profit under the constraint that the total national agricultural production meets the nutritional requirements of population without exceeding the current environmental footprint levels. Characterization results show that India does not produce enough of certain micronutrients (vitamin-E, and choline) and the carbon, nitrogen and freshwater footprints exceed their planetary boundaries while the farmers’ income remains below national targets. Optimization algorithm generated the sustainable production amounts of 138 crop and livestock items in each state and showed that fruits, vegetables, legumes, and oilseeds production needs to increase by 50%–100% while the cereals production amounts need to reduce to 50% of current levels in India. This will result in an increase in farmer’s income by 25% on average, fulfil the nutritional requirements of population and reduce the environmental footprint by 10%. Our approach can act as a template for other countries in collecting necessary data on sustainability indicators and determining optimum crop and livestock mix.