What determines how green crop farming can get? Spatial factors or green awareness spillovers

This study intends to examine if traditional local factors (seasonal weather conditions) and/or green awareness spillovers contribute to the spatial dependency of farmland allocated to organic farming after its uptake in Taiwan. To investigate the push and pull factors to improve the policy targeting on environmentally-friendly farming practices, we assess spatial autocorrelation of the adoption intensity of organic farming with exploratory analysis, and expand that by exploring how explanatory factors affect the adoption intensity using a spatial Tobit regression analysis, taking into consideration that the adoption intensity is a typical example of censored data. Based on township-level data of 323 townships constructed from 213,534 rice farm households drawn from the 2015 Agriculture Census, we find high-high clusters (hot spots) are mostly in the northern and the eastern parts of Taiwan, whereas the majority of low-low clusters (cold spots) locate in central and southern Taiwan. Such spatial aspects of organic adoption intensity suggest that a spatially targeted program in promoting environmental awareness is pertinent to fostering the development of organic agriculture. The results from the spatial lag Tobit regression estimation provide empirical evidence supporting the role of local weather conditions and green awareness spillovers in explaining the spatial patterns of organic agriculture in Taiwan. In light of the stylized fact that the majority of the rice farm households in Taiwan are small with 84% having farmland areas less than 1 ha, the findings provide practical references to policy practitioners in tailoring farm programs or policies in line with the notion of inclusive and sustainable development.