Date of Original Version
To effectively manage water resources in agricultural production, it is necessary to understand the spatiotemporal variation of the water footprint (WF) and the influences of agricultural inputs. Employing spatial autocorrelation analysis and a geographically weighted regression (GWR) model, we explored the spatial variations of the WF and their relationships with agricultural inputs from 1998 to 2012 in Northeast China. The results indicated that: (1) the spatial distribution of WFs for the 36 major maize production prefectures was heterogeneous in Northeast China; (2) a cluster of high WFs was found in southeast Liaoning Province, while a cluster of low WFs was found in central Jilin Province, and (3) spatial and temporal differentiation in the correlations between the WF of maize production and agricultural inputs existed according to the GWR model. These correlations increased over time. Our results suggested that localized strategies for reducing the WF should be formulated based on specific relationships between the WF and agricultural inputs.
Duan, P., Qin, L., Wang, Y., & He, H. (2015). Spatiotemporal Correlations between Water Footprint and Agricultural Inputs: A Case Study of Maize Production in Northeast China. Water, 7(8), 4026-4040. doi: 10.3390/w7084026
Available at: http://dx.doi.org/10.3390/w7084026
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