Therefore, by specifying the means and covariance matrix of predicted chill in our five counties for 2025- 2050, we simulate the natural chill realization from a 5D multivariate normal distribution.To determine the potential growth in pistachio output by the year 2030, we consider bearing acreage growth in the past. Since the year 2000, harvested acreage grew by an average 10% yearly. Since 2010, the average rate was 13.5% . To assess the gains from MCE in the year 2030, we need to stipulate the total acreage at that year, and its distribution among our counties. We create acreage growth scenarios to get the edges of a potential acreage range in 2030. Two factors influence scenarios: growth rate and geographic distribution. For a high growth rate scenario, we let the total acreage grow by 13.5% yearly for six years , and then by then 10% yearly until 2030 . For a low growth scenario, we let acreage grow by 10% yearly for six years, then by 5% yearly until 2030 . Some counties are more prone to low chill years than others, and future growth in acreage might take this into account. If the counties’ acreage shares stay the same, i.e. all grow at the same rate, each county’s acreage in 2015 is thus multiplied by the growth factor to calculate county acreages in 2030. This represents a world where growers might not be aware of the perils of climate change, or trust MCE in solving future problems even in the risky counties. However, growers could also divert growth to the less affected counties, Madera and Tulare. To model this, we increase each county’s 2015 acreage by the appropriate growth factor for the first 6 years ,garden plastic pots to account for acres already planted. The difference between the sum of predicted acreage in 2021 and the total predicted acreage in 2030 is then allocated evenly between Tulare and Madera. First, we want to get a sense of the magnitude of loss, brought by insufficient chill without MCE.

Figure 4 shows the empirical cumulative distribution of total potential loss rate by scenario. For each simulation, the chill realization is used to construct a vector of county specific loss rate. This is multiplied by the share of that county in total acreage in 2030, which varies by scenario. Summing these, we get the weighted average loss rate for all our counties. The figure shows, as expected, that scenarios in which acreage growth is shifted north have lower probabilities of large loss events. About 30% of chill portion vector draws result in virtually no loss. The CDFs seem step-like, indicating the sharp decline in yield at the chill threshold for each affected county. The average expected loss by scenario is specified in Table 1. In our simulations, MCE seems to revert the market outcomes of insufficient chill almost completely. When simulated without MCE, the outcome market price ranges between $5,625 / ton and $36,019. However, simulation with MCE result in a price range between $5,625 and $5,704 – a minimal increase. This is probably the result of a relatively low price of MCE, compared to the output price. To get a better sense of the effect, Figure 5 shows the distributions of MCE effects in percent change for price and quantity. That is, the percent increase in quantity and decrease in price when using MCE, compared with the non MCE simulation. The mean price decrease is 13-31%, and the mean quantity increase is 32-88% . These averages include years, where the climate damages are nearly zero for all counties, about a third of simulations. Thus, the actual effects in insufficient chill years are actually higher. We now turn to look at the gains from MCE on aggregate profits, consumer surplus, and total welfare. Figure 6 presents the distribution of these gains, and they are almost exclusively positive. That is not very surprising for consumer surplus, as MCE lowers the price on a good with modeled elastic demand. Average consumer surplus gains from MCE range between $0.68 – 2.60 billion , scenario depending. Grower profits gains are almost always positive as well, with an average ranging between $0.49 – 1.22 billion .

This result was less obvious a priori, as there is some measure of oligopolistic power in each simulation. These gains in grower profits are not distributed evenly between counties. As the baseline climate is not homogeneous among counties, and climate change might be different as well, not all counties would be affected the same. Sharing the market with Kern and Kings counties, which are more susceptible to insufficient chill, the cooler counties of Madera and Tulare are predicted to be less affected by climate change. Yet, MCE lowers the price and their share of the market on years with insufficient chill, compared to the non-MCE baseline. That is, while the industry gains in total are positive, these counties’ profits are mostly negative. Figure 7 shows the profit gains distribution by county. Kern and Kings counties have mostly positive profit gains. Madera and Tulare counties have mostly negative gains, and positive ones only in the worst chill years, when they lack chill portions as well. Fresno county is somewhere in the middle. This result reflects a broader aspect of climate change and adaptation. Areas who are affected very little by climate change can still feel its effects. As there are winners and losers from climate change, there will be winners and losers from MCE.Note how the gain distributions vary between scenarios. The two “Same” scenarios, keeping the distribution of future acreage the same as 2014’s, but varying in total acreage by roughly a twofold, generate gain distributions that are closer to each other than to the “North” scenarios with respective growth rates. To see the effect of other parameters on welfare outcomes, we plot them separately. In Figure 8, the gains are plotted against each parameter, using the “High Same” acreage growth scenario. Recall that, since we did not include parameter realizations resulting in P C > 6, there are slight correlation between the parameters. This is therefore an approximation of an “all else equal” plot, which we still consider useful to see the effects of each parameter on gains. To start, we note that the gains from MCE increase with the potential loss rate, as expected. The profit gains seem to increase linearly with the potential loss rate, but consumer surplus seems to grow exponentially. This is probably due to the choice of our demand function: with elastic demand, the inverse demand function is an exponential function with negative exponent that is greater than .

Therefore, the integral below the inverse demand function, from zero to any quantity, is infinite. As the potential loss rate increases, this integral should grow in a non-linear fashion. When demand is more elastic, gains from MCE increase for growers and decrease for consumers, as expected. When supply is more elastic,raspberry plant pot gains from MCE decrease for everyone, as expected. Looking at the effects of market power, we notice that monopolistic power seems rather uncorrelated with consumer surplus. This is somewhat surprising, as we expected monopolistic power to decrease consumer’s benefits from MCE. However, note that we assumed monopolistic power does not change with loss rate. Had we modeled them with a correlation, the trend line would probably slope up. Monopsony power increases consumer surplus, prob-ably through the monopsony rents: as demand is elastic, restricting quantities lowers surplus. An increase must be the result of the rents. In a more realistic situation, where these rents are not necessarily included in consumer’s surplus, the result might be different. However, this does not change the total welfare outcomes. With respect to the entire market power measure, P C, it seems to increase both consumer surplus and profits from MCE. This also means that the potential losses from insufficient chill increase with market power, a point worthy of consideration in other settings as well. Crop breeding centers in agricultural research institutes around the world played a major role in feeding the world’s population during the 20th century . In the immediate aftermath of World War II and through the 1960s, scientists and politicians forecast serious food shortages and starvation across large parts of the world. Between 1960 and 2000, the world’s population doubled, but over the same period, grain production more than doubled, an increase almost entirely attributable to unprecedented increases in yields. The Malthusian nightmare never materialized, mainly because scientific innovations produced new technological packages that raised productivity and expanded output beyond anyone’s expectations . New crop varieties made up the heart of these packages, although they were supplemented by improved water control, greater use of chemical fertilizers, and increased know-how. Despite the enormous successes in the second half of the 20th century, science has not eliminated the possibility of serious global food shortages, and agricultural research establishments must meet even greater challenges in the 21st century . Growth rates of yields slowed during the 1980s and 1990s and the yield gap—the difference between yields on experimental plots and farmers’ fields—has shrunk .

When the slower growth rate of yield is coupled with rising demographic pressures and water and environmental concerns, new varieties that produce more food under increasingly challenging environments will be essential to meeting world demand, which is predicted to rise by 40 percent between now and 2025 . The task of those responsible for breeding new varieties, however, will have to be executed at a time when support for agricultural research in both developed and developing countries is waning. During the 1950s, 1960s and 1970s, agricultural scientists enjoyed rapidly expanding budgets, but during the past two decades the growth has slowed. Pardey and Beintema reported a real growth rate of global agricultural research spending during 1976-1981 of 4.5 percent per annum , but by 1991-96 this growth rate had fallen to 2.0 percent per annum . It has continued to decline since then. China is no exception. China’s real annual growth rate of agricultural research expenditure fell from 7.8 percent in 1976-81 and 8.9 percent in 1971-86 to 5.5 percent in 1991-96 . Similar patterns but in more exaggerated terms can be seen in the expenditures of research institutes in developed or developing countries, and in the international agricultural research system that are dedicated to crop varietal improvement . Hence, in an era of slower growth in agricultural research expenditures and increased demands for output, there will be rising pressure on the research system to come up with ways to produce more for less. In the parlance of production economics, this means that it will be necessary to become increasingly efficient at producing new varieties. Although several authors have recognized the importance of economies of scale and economies of scope in agricultural research , few studies have attempted to measure the nature of the processes used by the agricultural research “industry” to create new varieties—the technology used to produce varietal technology, sometimes called the research production function. Since the seminal work of Baumol et al. , economies of scale andeconomies of scope have been studied in a wide range of industries . However, only two studies—Branson and Foster and Byerlee and Traxler —have produced empirical evidence on economies of size in agricultural research, and there have not been any empirical studies on economies of scope. Moreover, the limited evidence on economies of size in agricultural research is mixed. Based on a unique set of data, collected specifically to examine the production economics of crop breeding centers, we use a cost function approach to estimate economies of scale, economies of scope and other aspects of the technology of crop varietal production in China.1 Although we are interested in the production economics of crop breeding, in general, our focus on China is appropriate for several reasons. First, China has a long and successful history of crop breeding and, although it is a developing country, its breeders have made breakthroughs that rival those of most developed countries . Hence, in some sense, our findings are relevant for the breeding programs of all nations. In addition, China is important in its own right as the largest country in the world, and as an example of a large developing country.