We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%.

中文翻译：

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气候变化与美国农业：面板数据中多维坡度异质性的解释

我们使用1950年至2015年的县级产量和天气数据来研究未来气候变化对美国农业生产率的潜在影响。要考虑生产对不同天气条件的适应性，至关重要的是要考虑生产的时空变化将天气映射到作物产量的过程。我们提出了一种新的面板数据估计技术，称为均值观测OLS（MO-OLS），该技术可在所有回归参数（截距和斜率）中实现时空异质性。两种形式的异质性都很重要：我们发现有力的证据表明生产函数参数适应当地的气候，并且产量对高温的敏感性从1950-89年开始下降。我们使用19种气候模型和3种温室气体排放情景，通过估算将玉米单产预测为2100。我们预计，未缓解的气候变化将大大降低产量。我们的平均预测（针对气候模型）是，仅适应一项措施就可以减轻36％的破坏，而符合巴黎目标的减排量则可以减轻76％。