A decline in some commodity markets and a shift in federal crop subsidy programs in the mid 1980s affected different growing regions in different ways. Under these circumstances it would not be surprising if the coefficients on the climate variables varied somewhat over time. In fact, however, they are very robust. Pair-wise Chow tests between the pooled model and the four individual census years in Table 3 reveal that the five climatic variables are not significantly different at the 10 percent level in any of the ten tests. Although we have excluded western counties because their agriculture is dependent on irrigation, what about irrigated areas east of the 100th meridian? To test whether these are affecting our results, we repeat the estimation excluding counties where more than 5% of farmland area is irrigated, and where more than 15% of the harvested cropland is irrigated.We also examine further the influence of population, excluding counties with a population density above 400 people per square mile or a population total above 200,000. The exclusion of the three sets of counties leaves the coefficient estimates virtually unchanged, and the lowest of the three p-values for the test of whether the five climate variables have the same coefficients is 0.85. It is not surprising that excluding irrigated counties east of the 100th meridian has little effect on our regression results,30l plant pot since very few are highly irrigated, and all receive a substantial amount of natural rainfall. Under these circumstances, irrigation is a much smaller supplement to local precipitation, small enough to have little effect on regression results.
By contrast, the p-value for the test of whether the five climate coefficients are the same in counties west of the 100th meridian is 10−11. Including western counties that depend crucially on large-scale irrigation significantly alters the equation. To test whether the time period over which the climate variables are calculated makes any difference, we replicate the analysis using as alternatives to the 30-year histories on which the estimates reported in Table 3 are based, 10- and 50-year averages. Neither of the alternatives yields climate coefficients significantly different from the pooled regression results based on the 30-year histories. These tests suggest that our model is stable for various census years, data subsets, and climate histories. Nevertheless, one might wonder whether there could be problems without liers or an incorrect parametrization. We briefly address these concerns. In a test of the robustness of our results to outliers, the analysis is replicated using median regression, where the sum of absolute errors is minimized both in the first-stage derivation of the parameter of spatial correlation and in the second stage estimation of the coefficients. Again, the climatic variables remain robust and are not significantly different. To test the influence of our covariates on the results we follow the idea of Leamer’s extreme bound analysis and take permutations of our model by including or excluding each of 14 variables for a total of 16,384 regressions.No sign switches are observed in any of the five climatic variables, again suggesting that our results are very stable. Further, the peak-level of degree days is limited to a relatively narrow range. We check sensitivity to the assumed length of the growing season by allowing the season to begin in either March, April, or May and end in either August, September, or October.Finally, in order to examine whether the quadratic specification for degree days in our model is unduly restrictive, we estimate a penalized regression spline for degree days 8◦C −32◦C and find that the quadratic approximation is consistent with the data.
Before turning to the determination of the potential impacts of global warming on the agricultural sector of the U.S. economy as measured by predicted changes in farmland values we briefly consider whether farmers’ expectations have changed over the period covered by our study, and whether this may affect our estimates. In the previous section we regressed farmland values on past climate averages, even though farmland values are determined using forward-looking expectations about future climate. The weather in the U.S. over the past century was viewed as a random drawing from what until recently was thought to be a stationary climate distribution. Our own data are consistent with this: the correlation coefficients between the 30-year average in 1968-1997 and the two previous 30-year averages of the century, i.e., for 1908-1937, and 1938-1967, are 0.998 and 0.996 for degree days , 0.91 and 0.88 for degree days , and 0.93 and 0.93 for precipitation variable. Accordingly, when we use the error terms from our regression and regress them on past values of the three climate averages, none of the coefficients is statistically significant. The same result holds if we move to the shorter 10-year climate averages. This suggests that past climate variables are not a predictor of farmland values once we condition on current climate. As pointed out above, consecutive census years give comparable estimates of the climate coefficients in our hedonic equation and none of them are significantly different.18 Similarly, we check whether the aggregate climate impacts for the four emission scenarios in Table 5 change if we use the 1982 census instead of the pooled model. Even though the standard deviations are fairly narrow, t-tests reveal that none of the eight mean impact estimates are significantly different . We conclude that our results are not affected by any significant change in expectations over the study period.
In the calculations which follow we use the regression coefficients from the semi-log model, which we have shown to be both plausible and robust, along with predictions from a general circulation model to evaluate the impacts of climate change. The climate model we use for this analysis is the most recent version of the UK Met Office Hadley Model, HadCM3, recently prepared for use in the next IPCC Assessment Report. Specifically, we use the model’s predicted changes in minimum and maximum average monthly temperatures and precipitation for four standard emissions scenarios identified in the IPCC Special Report on Emissions Scenarios . The chosen scenarios span the range from the slowest increase in greenhouse gas concentrations , which would imply a little less than a doubling of the pre-industrial level by the end of the century, to the fastest , associated with between a tripling and a quadrupling, and include two intermediate scenarios . We use the 1960-1989 climate history as the baseline and calculate average predicted degree days and precipitation for the years 2020-2049 and 2070-2099. The former captures impacts in the near to medium term, while the latter predicts impacts over the longer term, all the way to the end of the century, the usual benchmark in recent analyses of the nature and impacts of climate change.Predicted changes in the climatic variables are given in Table 4. Impacts of these changes on farmland values are presented in Table 5 for both the 2020- 2049 and 2070-2099 climate averages under all four emissions scenarios. Not surprisingly,pots with drainage holes results for the near-term 2020-2049 climate averages are similar under all four scenarios. The relative impact ranges from a 10% to a 25% decline in farmland value, which translates into an area-weighted aggregate impact of -$3.1 billion to -$7.2 billion on an annual basis.Although the aggregate impact is perhaps not dramatic, there are large regional differences. Northern counties, that currently experience cold climates, benefit by as much as 34% from the predicted warming, while others in the hotter southern states face declines in farmland value as high as 69%. Similarly, average relative impacts are comparable across scenarios for the individual variables degree days and degree days , but again there are large regional differences. The effect of an increase in the latter variable is always negative because increases in temperature above 34◦C are always harmful, while the effect of the former variable depends on whether a county currently experiences growing conditions above or below the optimal number of degree days in the 8 − 32◦C range.The impact estimates for the longer-term 2070-2099 climate average become much more uncertain as the range of predicted greenhouse gas emission scenarios widens. Predicted emissions over the course of the century are largely driven by assumptions about technological change, population growth, and economic development, and compounding over time leads to increasingly divergent predictions. The distribution of impacts now ranges from a average decline of 27% under the B1 scenario to 69% under the A1FI scenario. At the same time, the sharp regional differences observed already in the near to medium term persist, and indeed increase: northern counties generally benefit, while southern counties generally suffer.
Anexception is found in Appalachia, characterized by a colder climate than other counties at a similar latitude. Regional differences widen as counties with a very cold climate can benefit from continued warming: the maximum positive relative impact now ranges from 29% to 52%. However, the total number of counties with significant gains decreases in most scenarios. For the 2020-2049 time span, 446, 126, 269, and 167 counties, respectively, show statistically significant gains at the 95% level for the scenarios given in Table 4. These numbers change to 244, 202, 4, and 26 for the 2070-2099 time span. By the same token, the number of counties with statistically significant loses increases from 1291, 1748, 1762, and 1873 for the 2020-2049 time span to 1805, 1803, 2234, and 2236 for the 2070-2099 time span. The regional distribution of impacts is shown in Figure 1 for counties with significant gains and loses under the intermediate B2-scenario. The predicted changes are also closer to those in another general circulation model, the DOE/NCAR Parallel Climate Model , which we use as an alternative because it is considered a low-sensitivity model, as opposed to the mid-sensitivity HadCM3; for a given CO2 scenario the temperature changes are lower under the PCM than under the Hadley model. We replicated the impact analysis using the PCM climate forecasts in the appendix available on request. Not surprisingly, the predicted area-weighted aggregate damages are lower. However, the regional pattern remains the same: out of the 73% of counties that have statistically significant declines in farmland values under all four Hadley scenarios by the end of the century, 73% still have significant losses under the PCM A1FI model and 0.7% switch to having significant gains. The magnitude of temperature changes simply shifts the border between gainers and losers. Some of the predicted potential losses, in particular for the high emissions scenario in the later period toward the end of the century, are quite large. However, average temperature increases of 7◦Cwould lead to the desertification of large parts of the South. A way of interpreting the results that places them in the context of other studies and also highlights the role for policy, is that if emissions are fairly stringently controlled over the course of the coming century, as in B1, such that atmospheric concentrations of greenhouse gases remain alittle below double the pre-industrial level, predicted losses to agriculture, though not trivial, are within the range of the historically wide cyclical variations in this sector. If on the other hand concentrations climb beyond three times the pre-industrial level, as in A1F1, losses go well beyond this range. This suggests a meaningful role for policy involving energy sources and technologies, since choices among feasible options can make a major difference. A complete impact analysis of climate change on U.S. agriculture would require a separate analysis for counties west of the 100th meridian. Based on the information presently available, we do not believe the impact will be favorable. A recently published study down scales the HadCM3 and PCM predictions to California and finds that, by the end of the century, average winter temperatures in California are projected to rise statewide by about 2.2 ◦C under the B1 scenario and 3.0−4.0 ◦C under the A1FI scenario . Summer temperatures are projected to rise even more sharply, by about 2.2 − 4.6 ◦C under the B1 scenario and 4.1 − 8.3 ◦C under the A1FI scenario. Winter precipitation, which accounts for most of California’s water supply either stays about the same or decreases by 15-30% before the end of the century.