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) (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.
Highlights
Small scale farms represent about 80% of the farming area of China
Relative variance (RV) approximates the amount of variability explained by maximum and minimum air temperatures to in mid-May from 2008 in to terms
organic carbon (OC) because they best characterise areas that would benefit from differential management. the lowest recorded during the period of study
Summary
Small scale farms represent about 80% of the farming area of China. Given the need to produce more food on the same amount (or less) of land while reducing environmental pollution, such areas are faced with tough challenges. Farmers manage their fields by experience and need science-based evidence to make the system more efficient. Precision nutrient management can be achieved either through a sensor-based or a map-based approach. The former uses sensors to guide site-specific N management based on the quantification of crop reflectance. Colaço & Bramley [2] demonstrated how the sensor-based approach could
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