A novel method for optimizing regional-scale management zones based on a sustainable environmental index

Yue Li, Davide Cammarano, Fei Yuan, Raj Khosla, Dipankar Mandal, Mingsheng Fan, Syed Tahir Ata-UI-Karim, Xiaojun Liu, Yongchao Tian, Yan Zhu, Weixing Cao, Qiang Cao*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

Delineating management zones (MZs) is considered one of the most important steps towards precision nitrogen (N) management, as MZs are required to optimize N inputs and improve environmental health. However, no reports have fully explored the optimization of regional MZs related to policymaking to achieve long-term Sustainable Development Goals. This study developed a new sustainable environmental index (SEI) by integrating the Euclidean distance after feature normalization, spatial autocorrelation, and expert knowledge. The SEI was then used to delineate MZs in the main wheat-producing provinces of China using the fuzzy C-mean clustering. The results showed that compared to the two data-driven-based methods (Random Forest- and all variables-based methods), the SEI-based method performed the best and identified 9 MZs in terms of practical production and spatial distribution of zones. Further analysis indicated that the dominant drivers of MZ delineation showed strong spatial heterogeneity and high input uncertainty. Climatic factors explained 15.6% of the yield variability, while both soil factors and topographic factors individually accounted for 10.2% of the variability. The similar spatial characteristics of input uncertainty were found to be consistent across various MZs, with a high level of uncertainty ranging from 0.7 on a scale of 0 to 1. Finally, this study provided potentially valuable suggestions for policymakers and farmers, as well as possible directions for further N management. Overall, the proposed methodological framework on regional MZs has important implications for precision N management, providing a new perspective for intensive sustainable development.

Original languageEnglish
JournalPrecision Agriculture
Volume25
Issue1
Pages (from-to)257-282
Number of pages26
ISSN1385-2256
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Environmental drivers
  • Input uncertainty
  • Machine learning
  • Regional crop management
  • Sustainable agriculture development
  • Weighted spatial analysis

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