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Precision agriculture management based on a surrogate model assisted multiobjective algorithmic framework.

Du Cheng1, Yifei Yao1, Renyun Liu1

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This study introduces a bi-level optimization framework for sustainable agriculture, reducing water and nitrogen use while boosting crop yields. The new strategy significantly cuts resource consumption and enhances economic returns.

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Area of Science:

  • Agricultural Science
  • Optimization Theory
  • Environmental Management

Background:

  • Sustainable intensification requires optimizing irrigation and fertilization for increased crop yield.
  • Precision agriculture demands effective management strategies for resource allocation.

Purpose of the Study:

  • To develop a bi-level screening and optimization framework for precise irrigation and fertilization.
  • To enhance agricultural management by balancing conflicting objectives like yield, water use, and economic benefits.

Main Methods:

  • A bi-level optimization framework integrating upper-level screening and optimization with lower-level data-driven evolutionary algorithms.
  • Utilized the Fast Non-dominated Sorting Genetic Algorithm II (NSGA-II), a surrogate-assisted radial basis function model, and the Decision Support System for Agrotechnology Transfer (DSSAT).
  • Employed lower-level screening to identify optimal strategies from a large set of trade-off solutions.

Main Results:

  • Achieved a 44% reduction in water consumption.
  • Reduced nitrogen application by 37%.
  • Increased economic benefits by 7-8% through optimized irrigation and fertilization strategies.

Conclusions:

  • The proposed bi-level framework effectively optimizes irrigation and fertilization for sustainable intensification.
  • The approach successfully balances resource reduction with increased economic gains in agriculture.
  • This method offers a data-driven solution for complex agricultural management challenges.