Abstract
Farmers may adapt to climate change by substituting away from the crops most severely affected. In this paper we estimate the substitution caused by a moderate change in climate in the US Midwest. We pair a 10-year panel of satellite-based crop coverage with spatially explicit soil data and a fine-scale weather data set. Combining a proportion type model with local regressions, we simultaneously address the econometric issues of proportion dependent variables and spatial correlation of unobserved factors. We find the change in expected crop coverage and then we link those changes to the expected changes from an estimated climate dependent yield equation. Ceteris paribus, we find that climate induced changes in yield are offset by land coverage changes for rice and cotton but they are strongly amplified for corn and soy.
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Notes
There is currently insufficient land cover data to extend our analysis to other states.
The CDL is generated based on Resourcesat-1 AWiFS, Landsat 5 TM, and Landsat 7 ETM + satellites and has a ground resolution of 56 or 30 meters, depending on the year and sensors used (Mueller and Seffrin 2006).
LCC codes are based on physical measurements only. One reason why we don't reproduce it with our variables is that it is based on the soil series, which is categorical and we don't use. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/about/history/?cid=nrcs143_021436.
Planting season and growing season vary across crops and regions. In the six states along the Mississippi-Missouri river corridor, the planting season is from April to May for corn, rice, and cotton, and from May to June for soybean. The harvest season is October for rice and corn, and November for cotton and soybean. Growing season is defined as the period between planting season (included) and harvest season (included).
We have ten year’s land use, soil and weather information from 2001 to 2010. Growing season weather enters regressions as a lag variable. For 2001, there is no lagged land use data or lagged weather data, so 2001 is dropped from the regressions. Regressions for 2002 to 2010 use the growing season weather from 2001 to 2009.
Source: http://www.climatewizard.org/.
We also tried 130 km to define neighbors in the regressions. It turns out to have smaller maximum likelihood and large minimum Chi square. So we choose 70 km for the regressions.
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Acknowledgements
We are grateful to Michael Roberts and Wolfram Schlenker for sharing both their weather data and expertise. The remaining errors are those of the authors.
Funding
This project was funded by the Environmental Biosciences Institute at Berkeley and Illinois.
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Appendices
Appendix A: Crop Coverage Response to Unit Change in Weather
Table 5 and Fig. 8 summarize the crop coverage share changes for two scenarios in both short run and long run. In one scenario, daily temperature increases by one degree for all months in 2009 and 2010. In the other scenario, monthly precipitation decreases by 1 cm in all the months, and temperature increases as above. Table 5 shows that in the short run, 1-degree warming decreases corn and soy shares. With 1-degree warming, 1.6% less land (a 5% decrease) in the north and 4.3% less land (a 77% decrease) in the south is covered by corn; 1.2% less land (a 6% decrease) in the north and 0.3% less land (a 1% decrease) in the south is covered by soy. One degree warming increases rice share by 0.042 (a 40% increase in the south) and cotton share by 0.072 (a 61% increase in the south). It suggests that warming favors rice and cotton. Looking at corn and soy, dry warming decreases coverage more than warming alone in the north, but this pattern does not hold for corn in the south, which is wetter. Figure 8 shows that although the averages in share change are different in the scenarios with and without a change in precipitation, the difference is small and the share change patterns are similar. This indicates that a 1 cm change in precipitation is not large enough to have significant effects on crop adaptation.
Distribution of crop coverage changes with unit change in temperature and precipitation. x-axes are crop share changes. For example, 0.2 in the first panel means corn share increases from \( a \) to \( a + 0.2 \). SR stands for short run, which is the year when the weather change happens. LR stands for long run, which is 5 years after the weather change happens. For corn and soy, all six states are included. For rice and corn, only the three south states are included, because there is little rice and cotton in the north
Table 5 and Fig. 8 also show that the share changes of corn and soy in the long run are larger on average and the distributions have fatter tails. This indicates that it takes time for farmers to fully adjust crop coverage to weather shocks. The share changes of rice, cotton, and other land use in the long run are larger. We also check the crop share changes in 2020 and find that they are very similar to those in 2015. This suggests that 5 years is long enough for the farmers to complete the adaptation.
To illustrate how crop adaptation varies across landscapes, Fig. 9 maps out the long-run share changes for the 1-degree-warmer scenario and the 1-degree-warmer-and-1-centimeter-drier scenario, respectively. The findings are as follows. First, the two scenarios have similar land cover shifting patterns, which confirms the findings in Fig. 8. Second, rice and cotton in the south spread toward the north, which is expected, because the north becomes more suitable for rice and cotton. Third, the main crops, mainly rice and cotton, take land from minor crops and other uses in the south. This suggests that a 1-degree increase from current temperature is beneficial to rice and cotton. This is consistent with the fact that rice and cotton are heat-requiring crops. Last, but not at least, farmers make different crop adaptations, even when facing same level of climate change, and the reason is soil.
Crop coverage changes with unit change in weather. Notes A 20% change reported here means corn (for example) share increases from \( a \) to \( a + 0.2 \)
Appendix B: Full Regression Results of County Yield Equations by Crop
See Table 6.
Appendix C: Predicted Crop Coverage Changes Under the A2 Scenario
Figures 10 displays the spatial variations of crop adaptation under A2 model considering both temperature and precipitation changes. It is similar to the crop adaptation under A1B model, shown in Fig. 5 in the main text. The figures show that in the face of climate change, farmers, in general, will grow less corn and soy, more rice and cotton, and shift rice and cotton towards north.
Predicted crop coverage changes under the A2 scenario. Notes: a 20% change reported here means corn (for example) share increases from \( a \) to \( a + 0.2 \)
Figure 11 depicts the distributions of predicted crop share changes under climate change scenarios. Compared to Fig. 8, Fig. 11 has wider distributions, which is expected because the A1B and A2 scenarios have larger increases in temperature than a one-unit increase. It also shows that the four climate change scenarios lead to similar land use shifting patterns, which suggests that a drying climate within the predicted magnitude does not significantly worsen the growth condition for crops. Therefore, we conclude that for the Mississippi-Missouri river system, the major concern about climate change is warming, not drying.
Distribution of predicted crop coverage changes under climate change scenarios. Notes: x-axes are crop share changes. For example, − 0.5 in the first panel means corn share decreases from \( a \) to \( a - 0.5 \). For corn and soy, all six states are included. For rice and corn, only the changes in the three south states are included, because there is no rice and cotton in the north
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Xie, L., Lewis, S.M., Auffhammer, M. et al. Heat in the Heartland: Crop Yield and Coverage Response to Climate Change Along the Mississippi River. Environ Resource Econ 73, 485–513 (2019). https://doi.org/10.1007/s10640-018-0271-7
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DOI: https://doi.org/10.1007/s10640-018-0271-7