A remote sensing-based approach to management zone delineation in small scale farming systems

Davide Cammarano, Hainie Zha, Lucy Wilson, Yue Li, William D. Batchelor, Yuxin Miao

Research output: Contribution to journalArticlepeer-review

Abstract

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (305155” N; 1150206” E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

Original languageEnglish (US)
Article number1767
JournalAgronomy
Volume10
Issue number11
DOIs
StatePublished - Nov 2020

Bibliographical note

Funding Information:
Funding: This project was funded by the Agri Tech in China Newton Network+ (ATCNN) and Norwegian Ministry of Foreign Affairs (SINOGRAIN II, CHN-17/0019).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Crop yield
  • Satellite remote sensing
  • Site-specific nutrient management
  • Soil brightness
  • Soil fertility
  • Spatial variability

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