Zoning and data fusion in precision horticulture: current and needed capabilities to assist decision-making
Management zoning has been one of the main ways that spatial agricultural data sets have been used in precision agriculture, particular as a means of data-fusion between multiple information layers. As with most precision agriculture technologies and methodologies, management zones began with arable cropping systems but have been adopted into perennial cropping systems. This review brief explores the evolution of management zones in agriculture (with an emphasis on horticulture), the basic concept, the key research areas in management zone delineation to date and the diverse ways that management zones have been applied into arable and perennial systems to support crop management. The future role of management zones, including their relevance in cropping systems with higher resolution information sources is discussed, along with the future need to have more spatio-temporally dynamic zones to respond to decision-specific management and to accommodate increasing availabilities of in-season information. To support this, a new concept of decision zones is proposed that is decision driven, more flexible in its data-fusion processing and simplifies the final zoning process. Management zones are expected to continue to be an important first step in understanding spatial relationships when entering into precision horticulture. They will also be important for site-specific crop management in the early stages of adoption when data layers are likely to be limited. However, as data and information layers accrue for a cropping system, and spatial understanding of production drivers and interactions develops, then 'decision zoning' should replace the current idea of management zones.
Taylor, J.A., Bates, T.R., Manfrini, L. and Guillaume, S. (2021). Zoning and data fusion in precision horticulture: current and needed capabilities to assist decision-making. Acta Hortic. 1314, 173-188
management zones, precision agriculture, k-means clustering, segmentation, decision support systems, decision zones