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A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification

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  • Kropp, Ian
  • Nejadhashemi, A. Pouyan
  • Deb, Kalyanmoy
  • Abouali, Mohammad
  • Roy, Proteek C.
  • Adhikari, Umesh
  • Hoogenboom, Gerrit

Abstract

Sustainable intensification entails increasing the yield of existing agricultural lands while reducing the impact on the environment. Therefore, we sought to optimize irrigation and fertilizer scheduling on the farm level with respects to crop yield and environmental impact. Unlike traditional optimization, multi-objective optimization techniques provide a set of optimal solutions that collectively represent the tradeoffs between the conflicting objectives. As a result, decision makers can then prioritize and select their optimal trade-off from the global set of optimal solutions. To implement such an optimization platform, this study integrates the Unified Non-dominated Sorting Genetic Algorithm-III (U-NSGA-III) based multi-objective optimization platform with the Decision Support System for Agrotechnology Transfer crop model. The U-NSGA-III algorithm optimizes a farm level agricultural production system against a myriad of soil, crop, and climate objectives. With this platform, we were able to find irrigation and nitrogen schemes that reduced water usage by 48%, nitrogen usage by 26%, and nitrogen leaching by 51%.

Suggested Citation

  • Kropp, Ian & Nejadhashemi, A. Pouyan & Deb, Kalyanmoy & Abouali, Mohammad & Roy, Proteek C. & Adhikari, Umesh & Hoogenboom, Gerrit, 2019. "A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification," Agricultural Systems, Elsevier, vol. 173(C), pages 289-302.
    • Handle: RePEc:eee:agisys:v:173:y:2019:i:c:p:289-302
    DOI: 10.1016/j.agsy.2019.03.014
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    11. Wang, Yongqiang & Sun, Kexin & Gao, Yunhe & Liu, Ruizhe & Shen, Hongzheng & Xing, Xuguang & Ma, Xiaoyi, 2024. "Improving crop model accuracy in the development of regional irrigation and nitrogen schedules by using data assimilation and spatial clustering algorithms," Agricultural Water Management, Elsevier, vol. 291(C).
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