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

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Abstract

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.

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Data availability and material

Data is available in public domain.

Abbreviations

DSS:

Decision support system

AHP:

Analytical hierarchy process

SWAT:

Soil and water assessment tool

Swg:

Switchgrass

Mxg:

Miscanthus

CC30:

Continuous corn with 30% stover removal

CC50:

Continuous corn with 50% stover removal

HRUs:

Hydrological Response Units

APV:

Aggregate pollutant value

Nopt :

Nitrate load

Popt :

Phosphorous load

Nbase :

Nitrate loads for the baseline scenario

Pbase :

Phosphorous loads for the baseline scenario

BPCopt :

Optimized biomass production cost

BPCmisc :

Biomass production cost for Miscanthus

CR:

Consistency ratio

CI:

Consistency index

RI:

Random index

NSE:

Nash sutcliffe efficiency

PBIAS:

Percentage bias

r2 :

Coefficient of determination

MLSOPT:

Multi-level spatial optimization technique

GWLQA:

Great lakes water quality agreement

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Acknowledgements

The authors would like to thank P.G. Senapathy Center for Computing Resource, IIT Madras for providing the access of VIRGO Super Cluster for doing all the simulations during experiment.

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None.

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Authors

Contributions

All the authors conceived the experiments, Ashish Kumar, Vamsi Krishna Vema and K.P. Sudheer designed the experiment. Ashish Kumar performed the experiment. The experiment data and results were analyzed by Ashish Kumar, Vamsi Krishna Vema, Cicily Kurian and Jobin Thomas. K.P. Sudheer supervised the research as a part of Ashish Kumar’s master’s thesis. Ashish Kumar and Vamsi Krishna Vema prepared the original draft and all the authors contributed to the manuscript revisions.

Corresponding author

Correspondence to Vamsi Krishna Vema.

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The authors declare that they have no conflict of interest.

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Kumar, A., Vema, V.K., Kurian, C. et al. A decision support system for the identification of critical zones in a watershed to implement land management practices. Stoch Environ Res Risk Assess 35, 1649–1664 (2021). https://doi.org/10.1007/s00477-021-01983-5

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  • DOI: https://doi.org/10.1007/s00477-021-01983-5

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