Schlenker et al. indicate that dryland and irrigated counties require two separate estimation equations, unlike the single estimation equation in Mendelsohn and Dinar . A single equation erroneously implies that dryland farms requiring irrigation in the future will have access to analogous large-scale water projects peculiar to the western US at a given point in history. In testing the null hypothesis that each of the 16 climate variables in the original analysis are individually the same in the dryland and irrigated farm sub-groups, they find that between 4 and 6 coefficients are significantly different from 0, depending on the weighting method. Even though Schlenker et al. did not have access to water data on irrigated counties at the time, their F-test was still able to provide sufficient proof of the bias in pooling dryland and irrigated counties into one model. After studying climate impacts to dryland agriculture in the US , Schlenker et al. study the impact of water availability and degree days on California farmland values. Their cross-sectionaldataset represents individual farms, rather than county aggregates.Including groundwater and surface water supply corrects for the omitted irrigation variable bias in Mendelsohn et al. . Importantly, Schlenker et al. include a nonlinear measure of temperature effects on crop growth known as degree days.Their results suggest a positive relationship between the long-run annual availability of surface water and farmland value . They find that the coefficient on surface water is sensitive to water price: as water price per acre-foot increases, this coefficient decreases. They also find that the coefficient on degree days is positive and statistically significant, while degree days squared is negative and statistically significant. They do not use these relationships to estimate impact to farmland value under future climate scenarios. A criticism of degree days, as used in Schlenker et al. , is that it is a measure of weather not climate . In contrast to cross-sectional analysis, Deschenes and Greenstone estimate the impact of yearly fluctuations in weather on annual farm profits using US countylevel panel data , under 3 climate scenarios: uniform, Hadley II , and Hadley II .

When they account for county and year effects,hydroponic vertical farm the results from all three models show a negative impact on profits. With the addition of state-by year fixed effects, all three models show a positive impact on annual profits. Fisher et al. find data and coding errors in the Deschenes and Greenstone model, biasing the original results in the positive direction. Specifically, the climate variable on the average number of degree days has a zero value for several counties, and climate projections varied by state while their historic climate data varied by county. Both of these errors tend to result in a regression toward the mean effect, with warm counties projected to get cooler, and vice versa. In response, Deschenes and Greenstone acknowledge the data and coding errors, and find that the $1.3 billion benefit in annual profits under Hadley II is actually a $4.5 billion loss.However, Deschenes and Greenstone disagree that state-by year fixed effects are misspecified. Like Fisher et al. , they find that state-by-year fixed effects tend to absorb most of the weather variability, resulting in a positive impact on profits. Their purpose in including state-by-year fixed effects is to control for state-level shocks in prices and productivity. To test for this, Deschenes and Greenstone include two additional specifications of year fixed effects: varying according to 9 USDA Farm Resource Regions, and varying according to 9 US Census Divisions. The results from an F-test reject the null hypothesis of zero local shocks. Just as excluding all year effects, as in Fisher et al. , may bias the results downward, including fixed effects at the state level may be too strict, biasing the results upwards. The two intermediate cases of region-by-year fixed effects may present a “happy medium” to this problem. Schlenker and Roberts use panel data to study yield impacts to cotton in the western US.Constructing a dataset that incorporates the entire distribution of temperatures within a day, and across all days of the growing season, they find that the level of yield decline is greater under nonlinear temperature effects than linear ones. Even under a moderate emissions scenario , cotton yields decline across the western US by approximately 30%. Their approach is analogous to statistical crop studies discussed in the Impacts of Climate Change to California section .

Massetti and Mendelsohn test the use of panel data on a Ricardian model using the same Agricultural Census data as Deschenes and Greenstone . They test the stability of climate variables using two panel data approaches against a repeated cross-section Ricardian model. Both panel models have relatively stable climate variables across the six Census years tested. There are $15 billion in welfare gains for a uniform 2.7 C warming and 8% precipitation increase for both panel models, although this ignores distributional welfare impacts. In contrast, the climate variables of a repeated cross-section Ricardian model vary through time. As a result, the welfare calculations also vary through time. Deschenes and Kolstad use panel data on aggregate county-level farm profits to study the differential effects of climate and yearly fluctuations in weather . Their climate variables include a 5-year moving-average of the annual degree days and precipitation, while weather variables are represented by annual degree days and precipitation. While none of the coefficients on annual degrees days are statistically significant with either the historical or CCSM models, their study is instructive in finding that the climate variable has a greater magnitude than the weather variable both in the baseline and climate change scenarios. This corroborates the theory that long-term changes in weather are more costly for some farmers than short-run fluctuations. They tease out which farmers may be most affected by changes in climate by analyzing 15 of the largest crops . They find that certain crop revenues respond positively to degree days , while others respond negatively . A few econometric approaches study specific adaptations.Using a hedonic property value approach, they find that the marginal value of average water supplies from the Central Valley Project or State Water Project decreases as access to other sources increases. Lobell and Field study the use of federal crop insurance and emergency payments/loans in California from 1993–2007. They find that the most common cause of insurance and disaster payments during this period is excess moisture. Cold spells and heat waves are also important causes.

We return to the question posed in the title. What have we been assessing with respect to the human and institutional responsiveness known as adaptation to climatic change in more recent studies on the topic? Several sub-questions are subsequently discussed. To what extent have study results identified economically efficient adaptations? To what extent have economically efficient adaptations reduced vulnerability to climatic changes and/or welfare losses? Have these studies identified limits to adaptive capacity in the agricultural sector, tempering the optimism of earlier studies? We have examined both normative and positive approaches to studying adaptation. Normative approaches have provided insight into which adaptations may be economically efficient equating this with the optimal solution to the farmer’s objective function. There are two such adaptations explicitly represented in the CALVIN/SWAP models: changes to crop mix and water transfers/markets.9 As water resources decline, the resulting crop mix will reflect a decline in field crop acreage, with relatively less change for specialty crops . CALVIN includes water markets as an institutional adaptation. Under climate-induced water reduction scenarios, water is transferred from low-value to high-value use. Implicit in this is the transfer of land from agricultural to urban uses, though this is not directly modeled in these studies. In the WEAP-CVPM framework, Joyce et al. implicitly model the potential for converting to drip irrigation, particularly for thirsty field crops. By contrast, economically efficient adaptation is assumed, rather than modeled, in positive approaches, such as Ricardian models. Ricardian approaches have thus studied how climatic change will impact agriculture in the presence of long-run economically efficient adaptations. Without knowing the actual adaptations undertaken,nft vertical farming this approach provides limited analysis on economic efficiency. Hanemann argues that Ricardian models may not even capture long-run efficiency because economic agents do not behave optimally even in the long run. Studies of both short-run and mid-to-long run suggest that farmers with access to groundwater will tend to increase pumping, increasing the likelihood of aquifer subsidence, to compensate for losses in surface water or increases in crop water demands . Based on definitions in the latest IPCC report, this is maladaptation more than it is efficient adaptation. Schlenker and Roberts suggest that there is minimal adaptation even in the long run when they find that the results of their isolated time series are similar to those of the isolated cross section. Suffice it to say, that Ricardian approaches are capturing some level of adaptation, but it is likely not economically efficient. Panel data studies on farm profits are not able to capture adaptation even implicitly. In both programming and econometric approaches, vulnerability is measured as loss in economic welfare , which is perhaps the greatest limitation of comparative static approaches. Unlike economic welfare, vulnerability is a dynamic concept. For example, the move from field to high-value crops dampens the economic welfare decline caused by a warm-dry climate mid-to-late century. That is, the percentage loss in farm revenue is less than the decline in farm acreage. Water markets are also likely to dampen the welfare loss associated with climate change . However, these high-value crops tend to have lower heat tolerance as temperature increases . Further, field crops are generally regarded as more secure assets with lower associated production costs, than vegetable or tree crops.

The concept of vulnerability is able to capture this insecurity. Vulnerability to overall profit loss may be reduced by the crop mix change, but the increased variability in farm income will also increase vulnerability to temperature increases. Medellin-Azuara et al. illustrate this with high-value orchard crops, where the gross revenue declines even as prices increase. Econometric approaches illustrate that California agricultural land value may be particularly vulnerable to changes in surface water supply and nonlinear temperature effects . Deschenes and Kolstad also illustrate that farm profits may be more responsive to climate than annual fluctuations in temperature and precipitation. Several analyses illuminate our understanding of adaptive capacity. The overarching focus for many CALVIN-SWAP studies is to start with a worst-case scenario approach and see how well we fare even with some of the best-case farmer and institutional responses . Joyce et al. also illustrate an example of adaptive capacity through time. Assuming drip irrigation is more widely adopted in the Central Valley by mid-century, they find that groundwater pumping declines. However, as the climate continues to warm towards the end of the century, the positive effects of drip irrigation are eliminated. Beyond this, a discussion of adaptive capacity is lacking. We have moved ahead in the past 15–20 years from the early agro-economic assessments of the early/mid-1990s, but it appears that we are also standing still. This review has illustrated the various ways comparative static approaches have incorporated adaptive actions to illuminate our understanding of climatic impacts to California agriculture. But, as critics suggest , questions of when and how much farmers and institutions will adapt are left unanswered. Responsiveness — the key characteristic of decision-making — is only vaguely addressed, and, important distributional consequences of climate impacts to agriculture while alluded to, are not identified. Lack of responsiveness and distributional consequences is mostly due to a dearth of individual farm-level data, rather than the incapacity of programming and econometric approaches to accommodate a more specific analysis. Using the same county-level data with more innovations in a comparative static framework could only take programming and econometric approaches so far. There is also a degree of comfort with identifying the primary barrier to moving forward as uncertainty in climate projections. While vulnerability arises out of biophysical processes, it is critical to understand that it is imposed on a pre-existing, dynamic socioeconomic structure . It is important that our economic models do more to capture this structure.