Recent studies have shown that climate change will impose severe challenges on agriculture with profound implications. Although some hypothetical simulations have suggested that an optimal re‐arrangement of the growing season can substantially mitigate yield losses under future climate, no causal estimate has been provided on quantifying the extent to which farmers are adapting through growing‐season adjustments. Using a novel microlevel data with detailed crop progress information in China over 1993–2013, we show that both planting dates and growing season lengths significantly respond to contemporaneous temperature and precipitation. Our estimates suggest that, for a median site in our sample, the adaptive behavior in growing season adjustments can lead to a two to six days earlier planting date and another three to six days shorter growing season by the end of this century. These induced adjustments can avoid up to 9% of the crop damages caused by climate change. However, our empirical analysis does not find clear evidence of long‐run response or accompanied input adjustments, suggesting potential for developing policies and tools to further aid the adaptive process.

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The ultimate impact of climate change on human systems will depend on the natural resilience of ecosystems on which societies rely as well as on adaptation measures taken by agents, individually and collectively. No sector of the economy is more reliant on climate than agriculture. Evidence from the American settlement process suggests that societies can successfully adapt to new climatic environments. Whether and how much agriculture will manage to adapt to a changing climate remains an open question in the empirical economics literature, however. This article reviews the existing evidence on weather and/or climate impacts on agricultural outcomes from the economics literature, with a focus on methodological questions. Some key econometric issues associated with climate impact measurement are discussed. We also outline important questions that have not been adequately addressed and suggest directions for future research.

China is one of the most heavily polluted nations and is also the largest agricultural producer. There are relatively few studies measuring the effects of pollution on crop yields in China, and most are based on experiments or simulation methods. We use observational data to study the impact of increased air pollution (surface ozone) on rice yields in Southeast China. We examine nonlinearities in the relationship between rice yields and ozone concentrations and find that an additional day with a maximum ozone concentration greater than 120 ppb is associated with a yield loss of 1.12% ± 0.83% relative to a day with maximum ozone concentration less than 60 ppb. We find that increases in mean ozone concentrations, SUM60, and AOT40 during panicle formation are associated with statistically significant yield losses, whereas such increases before and after panicle formation are not. We conclude that heightened surface ozone levels will potentially lead to reductions in rice yields that are large enough to have implications for the global rice market.

The impacts of climatic factors on crop yields have been extensively examined in the literature on climate change impacts. However, how these factors affect decisions to abandon the crop is not well understood. This article uses a conceptual model to illustrate the mechanism of weather-induced crop abandonment, and empirically estimates the relationship between climatic factors and crop abandonment using US county-level data. A comparison of these estimates with the estimates of yield responses illustrates the rationale of the abandonment decisions. Further analyses show response heterogeneity in crop abandonment associated with different market conditions and certain institutional arrangement. The empirical results are consistent with the implications of the conceptual model.