A decision support system for the identification of critical zones in a watershed to implement land management practices

Authors : Ashish Kumar, Vamsi Krishna Vema, Cicily Kurian, Jobin Thomas & K. P. Sudheer

Publication : Stochastic Environmental Research and Risk Assessment (2021)

Paper

The increasing demand for food and clean energy, such as biofuel calls for a sustainable food-energy nexus in the agriculture sector. Mixed cropping pattern of food and biofuel crops is a viable strategy to meet the escalating demands of the biofuel production at the cost of food production. The implementation of the proposed solutions of simulation–optimization frameworks, at larger spatial scales, is a challenging task. One of the commonly adopted approaches is to implement the solution initially in critical zones that are sensitive to the land management practices and are critical for achieving the objectives. Despite the different techniques to identify the critical zones, this study proposes a new approach to identify the critical zones within a watershed, where the land use changes are essential to improve the social and physical environment while meeting the concurring demands for food and biofuel production. A decision support system (DSS), utilizing the concept of analytical hierarchy process (AHP) is developed to choose the number of optimal solutions from the Pareto-optimal Front to reduce the uncertainty involved in solution adaption by the decision-maker and identification of the critical zone. The results from the study indicate how solution strategies can influence the objective of optimal balance between crop demand and nutrient minimization using different cases. The proposed land use using the developed framework reduced the Total Nitrogen and Total Phosphorous loads by 29% and 38%, respectively from the watershed by converting about 44% of the baseline land use to different cropping patterns with the restriction on minimal food grain and biomass production. The outcome of the framework indicates that the adaptation of more robust objective function for spatial optimization through the developed DSS can reduce the nutrient load in the downstream water.