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An agricultural production function is estimated using a Chinese county-level dataset along with associated geographic information. County-specific output-input elasticities are computed using the geographically weighted regression (GWR) and mapped with the geographical information system (GIS). A comparison of the ordinary least squares and GWR estimates confirms that allowing spatial variation in the parameters improves model fit of the agricultural production function, and provides valuable insights into the relative importance of inputs in different regions. Moran's indices reveal the spatial dependence of output per unit of land, and four inputs across regions. Mappings of GWR estimates help to detect a few clusters of high output-input elasticities: for labour in the Northeast, North, Southwest and Northwest China, for land in the Central and Southwest, for mechanical power in the North, Northwest and coastal area of the East and South and for fertiliser in the East. The county-specific scale elasticities suggest constant return to scale is likely to hold in the Central, East, Southwest and South. [ABSTRACT FROM AUTHOR]