Integrated decision support for promoting crop rotation based sustainable agricultural management using geoinformatics and stochastic optimization

Shubham Aggarwal, Rallapalli Srinivas, Harish Puppala, Joe Magner

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Sustainable agricultural management is essential for ensuring food security and economic development. Efficient agricultural land use based on crop rotation practices can deliver greater soil fertility and higher economic potential. We proposed a decision support tool (DST) for preserving land fertility, maximizing agricultural profit, minimizing agricultural pollution, and water usage. The proposed DST links geoinformatics, stochastic pairwise comparison (SPC), and constraint optimization to suggest the suitable crops for growing. To demonstrate the proposed DST, suitability of seven major crops in Muzaffarnagar district in Uttar Pradesh (India), where the footprint of sugarcane cultivable region is nearly 90% is analyzed and the findings are presented. The crops cultivated in the study region and the criteria suitable for their cultivation are identified using the hybrid system approach. The DST primarily encompasses qualitative and quantitative analysis coupled with geospatial analysis. Qualitative analysis guides the decision-maker in finalizing the crucial criteria to be assessed for cultivation, while quantitative analysis uses beta distribution for pairwise comparison to understand the significance of finalized criteria. We collected the data concerning parameters related to the finalized criteria by considering 2700 soil samples. Data required at the ungauged locations are estimated using the kriging interpolation technique. The findings of this study suggest that sugarcane can be allocated up to 20% of the land area. In addition to the principal crops (i.e., sugarcane, wheat, and rice), potato, mustard, maize, and sorghum also have good cultivation potential in Muzaffarnagar and can be grown on up to 20%, 22%, 18%, 21% of the land area respectively while just 1.5%, 1.8%, 0.1%, and 0% of land area, is used for their cultivation. With the prime focus on knowledge transfer from scientific studies to farmers, we used an open-source geospatial repository to develop an interactive dashboard that can fetch farmers' locations and present each crop's suitability based on optimized crop rotation practices.

Original languageEnglish (US)
Article number107213
JournalComputers and Electronics in Agriculture
Volume200
DOIs
StatePublished - Sep 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Agricultural management
  • Crop rotation
  • Decision support
  • Optimization
  • Water quality

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