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Temperature thresholds and the effect of warming on American farmland value

Abstract

Many studies suggest that warming is harmful to American crop yields because of a temperature threshold near 30 °C whereupon yields abruptly fall. This study uses a flexible daily temperature bin specification in a Ricardian model to measure the response of farmland value to daily temperature. The analysis does not find evidence that high temperatures are particularly harmful to farmland or cropland value in the Eastern United States. Instead, temperature has a smooth hill-shaped effect on farmland value with a peak temperature of about 18 °C. Nonetheless, the Ricardian model predicts that cropland values would fall linearly by 15%/°C with uniform warming, while farmland values of mixed farms would fall far less.

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Notes

  1. We do not use degree-days as in Schlenker et al. (2006) because a quadratic specification of degree-days between 8 and 32 °C is equivalent to a quadratic specification of the mean growing season temperature (Massetti et al. 2016). The correlation between the two temperature measurements is in excess of 0.99. The control of extreme temperature at 34ׄ °C is not robust and is set at an arbitrary value ibid.

  2. We use the program developed by Solomon Hsiang: www.solomonhsiang.com/computing/stata-code.

  3. Note that contrary to the crop yield literature, we use average daily temperatures, not hourly temperatures. An average daily temperature of 30 °C corresponds to a maximum daily temperature of about 36 °C. The next bin would collect extremely rare days with mean temperature greater or equal to 33 °C and a daily maximum of about 39 °C, the largest bin typically used by the crop studies. In the literature, hourly temperatures are often interpolated from daily minimum and maximum temperatures using the same formula for all days. This essentially means that hourly temperatures are rescaled daily temperatures.

  4. In particular, the predicted value of land per hectare in each county is \( {\hat{y}}_{\mathrm{i}}=\mathit{\exp}\left({x}_{\mathrm{i}}^{\prime}\hat{\boldsymbol{\beta}}\right){N}^{-1}{\sum}_{\mathrm{i}=1}^N\mathit{\exp}\left({\hat{u}}_i\right) \).

  5. Confidence intervals built using the percentile method are almost identical and are not reported.

  6. We use c × m = 2,400. After adding all the observations over time for each county, we have a panel with 14,400 observations, which is very close to 14,460, the size of the full panel. The panel block bootstrap corresponds to the special case of m = 1.

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Acknowledgments

We would like to thank seminar participants at the Ifo Seminar Series and at the ASSA 2014 Conference in Philadelphia for useful comments. We are also grateful to Wolfram Schlenker and Michael Roberts for sharing their weather data with us. NCEP Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. Yeon Hak Kim and Evan Mistur provided excellent research support.

Funding

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7-2007-2013) under grant agreement no. 272608 (project CLI-EMA).

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Correspondence to Emanuele Massetti.

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Massetti, E., Mendelsohn, R. Temperature thresholds and the effect of warming on American farmland value. Climatic Change 161, 601–615 (2020). https://doi.org/10.1007/s10584-020-02694-6

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Keywords

  • Agriculture
  • Climate change
  • Extreme temperature
  • Ricardian
  • Threshold