We use our estimates of the response of global crop productivity to temperature change as an input to the GTAP CGE model in order to determine how national economic welfare is affected by climate impacts on crop yields. Figure 5 gives the global damage functions within the impacted sectors. Among all the cases that include CO2 fertilization, welfare changes are negligible at 1–2 degrees of warming, becoming negative at 3 degrees. In contrast, the No-CO2 case shows substantial global welfare losses, even at 1–2 degrees. Uncertainty bounds are large meaning no cases are statistically different from the reference case at the 95% confidence level. But the uncertainty in potential yield losses is also highly asymmetric: the possibility of large welfare losses is substantial whereas welfare gains are both smaller and less likely, particularly for a warming of 2–3 degrees. These welfare changes depend on modeled changes in harvested crop areas, production intensity, and consumption. We believe there is a general perception that empirical studies give more pessimistic estimates of crop response to warming than do process-based models . However, there is a lack of systematic comparisons between the two methods. In particular, because empirical studies do not include CO2 fertilization whereas process-based studies generally do, it is important to account for this difference in comparing the temperature response from the two methods. Here we are able to do this statistically, showing that once CO2 are controlled for, differences between empirical and process-based responses may be smaller than generally believed. Though the point estimates do show some evidence of more negative impacts from statistical studies at higher temperatures ,growing blueberries in pots the effect is not precisely estimated and error bars are large.
The poor representation of empirical studies within the yield impacts database, particularly at higher levels of warming, is a major limitation of this analysis. Inclusion of more recent studies would help with this, but this is not always straightforward. Many recent papers report the marginal effect of growing degree days rather than average growing season temperature and converting from one to the other is not simple . Standardized reporting of the impacts of a 1 °C increase in average temperature in empirical papers would help with this and should be encouraged. In addition, as noted above, the number of points at which the continuous response function estimated in empirical papers should be sampled for inclusion in the database is inevitably arbitrary. Some standardization would be useful and would help with interpretation in the future. Another finding from this paper is that there is little evidence in the existing literature that farm-level adaptations will substantially reduce the negative impacts of climate change on yields. The results presented here suggest that many actions described as adaptation in yield modeling studies would raise yields both in the current and in the future climate, meaning they do not necessarily reduce the negative impacts of future warming. If actions would confer benefit in the current climate but are not being adopted, economic logic suggests that models may be either overestimating benefits or they may be missing important costs of implementation. In either case, the potential for within-crop, farm-level adaptations that improve yields in the future climate more than in the present climate appears limited, at least as currently represented within the studies included in the meta analysis. This paper confirms the importance of CO2 fertilization in determining the average global impacts of changing temperature over the 21st century. Our results show the question of whether or not CO2 effects are included is more important than either the inclusion of adaptation or the type of study used to estimate the temperature response.
For both maize, wheat, and rice, CO2 fertilization fully offsets negative impacts of warming up to 1–2° for the global average yield effect. This demonstrates the importance of future work to better constrain the magnitude of this benefit. While we find good agreement between our results and those derived from FACE experiments, at least for the C3 crops , there is evidence that the fertilization effect depends critically on water and nutrient availability . Capturing this heterogeneity in CO2 fertilization by crop and farming intensity could be important in improving estimates of the yield impacts of climate change at both global and regional scales. Because of the importance of the CO2 fertilization effect, it should be clearly communicated when climate change impacts are presented without CO2 fertilization, which is often the case with statistical papers and sometimes with process-based models . Finally, this paper makes the connection between models of crop productivity and economic welfare. This is an essential step for informing damage functions in the simple Integrated Assessment Models such as DICE, PAGE, and FUND used to calculate the SCC . The economic impact results further underscore the importance of the CO2 fertilization effect: global welfare effects at 1–2 degrees of warming are negative without the CO2 fertilization effect but slightly positive for cases that include it. These results also show the complex connection between yield and welfare change. Despite error bounds on yield impacts being more or less symmetric, these same yield impacts give rise to highly asymmetric distributions over welfare changes, with substantial probability of large welfare losses. This asymmetry arises, despite the fact that the GTAP modeling framework allows for a large number of economic adaptations to moderate the adverse consequences of productivity shocks including changing inputs, shifting crop areas, trade adjustments, and consumption switching.The agricultural industry is often cited as a classic example of a competitive market. The observed performance of such markets, however, is the result not only of competitive forces but also of governmental intervention. Such intervention is often motivated by equity or distributional concerns.
Typically, the impact of such governmental intervention is evaluated only in terms of output markets . Such investigations are grossly inadequate since governmental policies impinge directly on asset as well as flow markets for both inputs and outputs. In general, the distributional consequences depend upon the ownership, utilization, quality, and technology associated with the assets. This paper develops a framework for capturing the distributional implications of governmental intervention in the agricultural sector recognizing its most important features. These features include competitiveness, asset fixity, rapid technological change, and institutional limits to credit availability. The first three features are documented by Theodore Schultz, Willard Cochrane, and G. L. Johnson. Theodore Schultz has also called attention to the large differences in the rates of return to resources among regions as well as across producers. Much of this variation emanates from differences in production techniques, human capital, and wealth controlled by individual producers. The limitations of credit availability for producers of different size classes have been noted by recent empirical evidence. This evidence strongly suggests that larger farmers borrow more; they borrow more to invest in capital; and their ability to borrow more stems, in part, from their higher repayment capacity . The equity and efficiency impacts of selected government policies have been addressed by a number of different frameworks, most of which are based on aggregative relationships. For example, in the agricultural development literature, aggregative relationships are specified for an agricultural sector and a non-agricultural sector. The micro-economic foundations of these frameworks, however, are not generally specified. As a result,drainage gutter the thorny problems of aggregation are pushed aside. Also, the distributional content of results forthcoming from such models is not very rich. The purpose of this paper is to advance a framework for evaluating the impact of governmental policies on agricultural production systems that is internally consistent at both the micro-level and the aggregate level. Assuming the major source of economic growth is technological change, the framework focuses on the incentives and constraints for technological adoption. Both the efficiency and distributional consequences of various policies are shown to depend upon landownership, land utilization, and the technology associated with land assets. To accomplish these purposes, a stylized model involving two technologies, traditional and new, is specified. At both the micro-level and the aggregate level, the framework admits a number of important features including uncertainty, varying degrees of risk aversion, both fixed and variable costs of technological adoption, and credit as well as land constraints. The model design allows the evaluation of a wide array of various policies. This set of policies includes price support, credit-funding enhancement, credit subsidies, crop insurance, price stabilization, input subsidies, and extension promotion. The basic micro-economic foundations of the framework are developed in section 2. Section 3 focuses on the micro-economic behavior of various farmers under alternative policies. Aggregation operators are applied in section 4 to capture the relevant macro-level causal relationships. Finally, the concluding section examines the operational use of the framework. The focus of this paper is on the qualitative efficiency and equity effects of various policies. In the context of a simple theoretical model which incorporates a number of important features of the economic environment, propositions have been derived which reveal ~ny insights for policy analysis. However, to operationalize these propositions, a considerable amount of empirical estimation is required.
Empirical analysis must begin by decomposing the farming population into relevant classes. This decomposition can be accomplished endogenously by the specification of a discrete/continuous behavioral model. The district choice relates to tedmology while the continuous choice is the amount of explanatory variables appearing in this model include the vector of expected returns defined by technology, the variances and covariances of returns defined across technologies, the variable cost of new inputs, the opportunity cost of financial funds, the fixed setup costs of various technologies, and available credit. Estimated relationships between the above explanatory variables and discrete technology choices and continuous land allocation choices are one component of the required empirical structure. estimation of the distribution of landholdings. A second component is an One potential distribution is the Pareto distribution specified in section 4. A third empirical component must relate the distribution of farm size to risk preferences. Estimation of this relationship will most likely require the use of primary data from representative samples. The final empirical component requires a set of linking equations between the policy instruments and the specified explanatory variables. For example, the empirical relationship between price supports and the vector of mean returns and the covariance matrix of returns across technologies must be determined. Armed with these four empirical components, a number of operational uses of the proposed framework are possible. First, one can simply simulate the effects of various policies through the four empirical components to determine the most effective integration of the various policies. This potential use of an empirical version of the proposed framework can only capture the quantitative effect of the proposed policy mixes; no attempt would be made to identify the optimal set of policies. Various trade-off relationships or alternative weightings in a scalar criterion function including two principal performance measures, efficiency and equity, could be specified. Theory and’ intuitive reasoning can be utilized heavily in isolating those trade-offs which allow a set of scalar criterion functions to be examined by parametric analysis. When such critierion functions cannot be captured, again, parametric analysis can be utilized with some objectives expressed as constraints motivated, perhaps, by a lexicographic ordering and/or as satisficing arguments. Various solution algorithms that can be employed to enhance the determination of a global optimum are available . Another potential use of the four empirical components relates to the notion of political economic markets. In a positive analysis of government behavior, the four components can represent a constraint structure which, along with a specified criterion function, can be used to infer, via revealed preference methodology, the trade-off between efficiency and equity . Such a positive analysis would allow economic researchers to perform effectively a role of social critics; that is, if past policies imply a value scheme which in some sense deviates from the public interest, then the implicit choice of trade-offs between efficiency and equity should at least be debated. Along similar lines, various economic interest groups could also employ the four empirical components to determine which set of policies they are prepared to support or oppose. Cooperatives are corporations that are owned and governed by the firms or people who use them; they differ from other businesses because they operate for the benefit of their members, rather than to earn profit for investors. Cooperatives have played an important historical role in promoting the economic welfare of California’s agricultural producers.