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Article

A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems

1
James Hutton Institute, Errol Road, Invergowrie DD25BQ, UK
2
College of Resource and Environmental Sciences, China Agricultural University, 2 Yuanmingyuan W. Rd, Beijing 100193, China
3
RSK ADAS Ltd., 172 Chester Road, Helsby WA60AR, UK
4
Department of Biosystems Engineering, Auburn University, 200 Tom Corley Building, Auburn, AL 36849, USA
5
Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, 1991 Upper Buford Circle, St. Paul, MN 55108, USA
*
Authors to whom correspondence should be addressed.
Current address: Department of Agronomy, Purdue University, West Lafayette, IN 47906, USA.
Agronomy 2020, 10(11), 1767; https://doi.org/10.3390/agronomy10111767
Received: 18 September 2020 / Revised: 6 November 2020 / Accepted: 7 November 2020 / Published: 12 November 2020
(This article belongs to the Special Issue Site-Specific Nutrient Management)
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) (30°51′55″ N; 115°02′06″ 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. View Full-Text
Keywords: site-specific nutrient management; soil brightness; satellite remote sensing; crop yield; soil fertility; spatial variability site-specific nutrient management; soil brightness; satellite remote sensing; crop yield; soil fertility; spatial variability
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MDPI and ACS Style

Cammarano, D.; Zha, H.; Wilson, L.; Li, Y.; Batchelor, W.D.; Miao, Y. A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems. Agronomy 2020, 10, 1767. https://doi.org/10.3390/agronomy10111767

AMA Style

Cammarano D, Zha H, Wilson L, Li Y, Batchelor WD, Miao Y. A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems. Agronomy. 2020; 10(11):1767. https://doi.org/10.3390/agronomy10111767

Chicago/Turabian Style

Cammarano, Davide, Hainie Zha, Lucy Wilson, Yue Li, William D. Batchelor, and Yuxin Miao. 2020. "A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems" Agronomy 10, no. 11: 1767. https://doi.org/10.3390/agronomy10111767

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