Search results

The purpose of this paper is to analyze the heterogeneity of regional grain production distribution in China, by examining the regional heterogeneity of absolute and relative changes in grain planting acreage, and explain it in terms of increasing labor costs and difficulties in agricultural inputs adjustment.

The study uses data from official statistical yearbooks and the satellite remote sensing image data of Landsat TM 30 m. Multivariate analysis is conducted to examine the effect of labor cost, difficulty in replacing agricultural input factors and other factors underpinning changes in grain acreage and grain structure adjustment.

The heterogeneity of changes in grain acreage and proportion of arable area for grain production are mainly determined using the labor cost and difficulties in the replacement of agricultural input factors.

To the best of the authors’ knowledge, this study is the first to systematically analyze the heterogeneity in restructuring grain production at provincial level and its causes. The results not only provide evidence of grain production restructuring at regional level, but also contribute to the understanding of the law of structural change in agricultural production.

While the peer effects of technology adoption are well established, few studies have considered the variation in peer effects resulting from the mutual proximity between leaders and followers and the heterogeneity of farmers' learning technology. This study addresses the gap in the literature by analyzing the peer effects of technology adoption among Chinese farmers.

Drawing on a government-led soil testing and formulated fertilization program, this study uses survey data of farmers from three Chinese provinces to examine the peer effects of technology adoption. This study uses a probit model to examine how mutual proximity influences peer effects and their heterogeneity. Accordingly, farmers were divided into two groups, namely small- and large-scale farmers, and then into leaders or followers depending on whether they were selected by the government as model farmers.

Both small- and large-scale farmers are more likely to use formula fertilizer if their peers do so. However, a large-scale farmer is more likely to adopt formula fertilizer if the average adoption behavior of other large-scale model (leader) farmers is higher, while a small-scale farmer is more likely to adopt formula fertilizer if other small-scale non-model (follower) farmers have higher average adoption behavior. Moreover, the peer effect was weakened by geographic distance among small-scale farmers and by economic distance among large-scale farmers.

This study elucidates the means of optimizing social learning and technology adoption among farmers.