plastic gardening pots – Nature Agriculture

October 11, 2023

Each of the four output measures has strengths and weaknesses

Many have predicted that such nations must bear much of the responsibility to produce the varieties that will feed the world in the coming decades . The large number of breeding centers in China, the decentralized nature of its research system, and the great heterogeneity among its centers offer a unique research opportunity to identify the relationship between varietal production, size of institute, and mix of crops in the breeding program. Finally, the results are of interest to China’s leaders who recently have announced a new round of research reforms in agriculture . To make our analysis tractable, and because of budgetary constraints, we limited the scope of our study to the breeding institutes of two crops within northern China. We chose crop-breeding institutes because crop breeding has been central to the growth of agricultural productivity in China as well as in the world. In China, crop breeding takes the largest proportion of resources in its agricultural research system . Crop-breeding institutes were chosen also because their research outputs and their consequences can be measured relatively easily—compared with, say, less applied research, research oriented towards natural resources management, or research leading to disembodied technological change. Wheat and maize are two of the most important staple crops in China, ranking second and third respectively after rice in terms of sown area and production. Wheat and maize production occupy somewhat overlapping areas and a large share of China’s wheat and maize breeding programs are located in the same institutes and similar regions,pot raspberries which allows us to measure economies of scope.The small and highly significant coefficients of economies of scale imply a significant cost saving associated with expanding the scale of breeding institutes. Such results are robust Most of the data used in this study were collected by the authors during 12 months of field work in China that began in the summer of 2001. Enumerators assembled panel data from 46 wheat and maize breeding institutes covering the years from 1981 to 2000.

The sample institutes include 40 prefectural-level institutes and 6 provincial-run institutes, selected at random from a comprehensive list of prefectural and provincial institutes in seven major wheat and maize provinces in northern China.Thirty-two of the sample institutes produce both wheat varieties and maize varieties . Four institutes specialize in producing wheat varieties . The other ten produce only maize varieties .To collect the data, teams of enumerators visited each institute for periods of up to one week and completed a set of questionnaires filled out by accountants and by enumerators. In general, the data cover four broad categories: income, costs, research output, and data on other characteristics of the institute. Since the data were not kept by a single department in any of the institutes, a great deal of cross-checking was needed to make the data consistent among the various departments. For example, the research coordination department typically kept information on income and expenditures. Personnel departments provided the data on salaries, educational accomplishments, and other information about current and past staff. Breeders kept the best information on the varieties they produced and the methods that they used in their breeding efforts. To examine cost efficiency, information is needed on two key variables, costs and output, especially since there is an a priori reason to believe that the small scale of many institutes may be an important source of inefficiency. In using our survey data to define measures of these key variables, we have had to deal with several methodological issues. As an economic activity, crop breeding has several characteristics that make it relatively hard to measure output and match measures of output to measures of costs associated with those outputs. These characteristics include the long lags between the time when costs are incurred and the resulting output is realized , uncertainty about what is an appropriate measure of output both conceptually and in practice , and the fact that output itself is uncertain when costs are incurred .Our measure of the total variable costs of each crop’s breeding activities includes the institute’s operating expenses, such as salaries, project administration, and other direct operating expenses. For cost categories that cannot be matched directly to a breeding project , we assigned a share of the costs of each category to breeding according to the number of full-time breeding staff . We deflated total variable costs by a provincial consumer price index, putting our cost figures into real 1985 terms . We assume that the products of China’s wheat and maize variety “factories” are the varieties that the breeders produce that are adopted by farmers. To measure output, we collected information on the number of varieties that were produced by the research institutes , the area sown to the varieties, and the trial yields of each variety . With these data, we constructed four measures of research output: the number of varieties released by the institute sown in the field during a given year, the number of varieties, weighted by the trial yields of the variety , the total area sown to all of the institute’s varieties during a given year ; and the number of varieties weighted by sown area and trial yields .

Although it is the most readily measured, the obvious flaw with number of varieties is that it does not take into account any quality characteristics of each variety, either yield or its other characteristics . Yield-weighted output accounts for the relative productivity of a variety in pure output terms. However, such a measure still leaves out all other quality characteristics, which an earlier study shows may be highly valued by farmers . For this reason, our third measure, area-weighted output, should be superior to the other two measures. If farmers value the characteristics in a variety—whether high yields or some other characteristic—they demonstrate their preference by adopting the variety . The last measure, yield-area weighted output combines the second and third measures. Since the variation in trial yields is small, the correlations between the third and fourth output measures are high . Hence, we would not expect much difference to result from using one versus the other. One special feature of crop variety production is the significant time lag between the time when costs are incurred in a breeding research program and the time when the resulting research output is realized. This issue is commonly discussed in studies of the returns to agricultural R&D , especially in relation to specification of econometric models relating agricultural productivity to research expenditures. In the present setting, the lag between investment and output has some further implications, akin to those that arise more generally in agricultural production economics, associated with biological lags. In microeconomic theory texts, the firm manager first chooses an output level , and then determines the cost-minimizing combination of inputs that will produce that output at minimum cost. The crop breeding institute’s director does not have that luxury, because the output from today’s investment is uncertain and will not be known for many years . As an approximation to this problem of decision-making under uncertainty, we might suppose that the director seeks to minimize the institute’s cost based on current expectations of the output that will be produced in the future as a result of the current research expenditures. Unfortunately,plastic gardening pots we cannot observe or measure, ex post, such expectations. One option is to use the output that was actually produced from the expenditures as a proxy of those expectations, but the problem remains of matching actual outputs to particular expenditures .

To deal with this problem empirically, we defined an average research lag to represent the number of years between the time when a breeding project officially begins and the time when a variety is released for commercial extension to the fields of farmers. Using this defined lag length, we modeled the cost of variety production as a function of the research output produced after a certain lag. To find the length of lag, we designed a section of the questionnaire to ask breeders in each of the 46 institutes specifically to estimate the average lag length for each crop. Based on the data we collected, the average lag length was 5.3 years for wheat and 4.5 years for maize. In our base model, we used a 5-year lag for both wheat and maize variety production. However, we also tried different lag lengths to check the robustness of our results. China’s agricultural research system has produced a steady flow of crop varieties in the past. On average, in each year during the period 1982-1995, China’s farmers grew 200 to 300 wheat varieties and 130 to 180 maize varieties in their fields . However, the number of new varieties being produced by research institutes varied significantly over time and across institutes. Based on our survey, 141 wheat varieties and 155 maize varieties were produced by our sample institutes during the period 1985- 2000 . Nineteen percent of the wheat varieties were developed by provincial institutes. The rate of production of new wheat and maize varieties increased over time. For example, prefectural maize institutes produced 34 maize varieties during 1985-1990, 47 varieties during 1990-1995, and 74 during 1995-2000. The number of wheat varieties created and commercialized by the sample institutes rose from 31 in 1985-1990 to 55 during each subsequent period . The number of varieties, however, varies sharply among institutes. For example, the Henan provincial wheat institute produced 12 wheat varieties from 1985 to 2000. The Mianyang prefectural crop breeding institute in Sichuan produced 14 wheat varieties. In contrast, 24 out of 36 of the sample wheat institutes produced fewer than 5 varieties. In fact, three wheat institutes did not produce a single variety during the entire 15-year sample period. Maize variety production also varies greatly among the sample institutes.In the same way that output varies across time and space, so does total cost. On average, the annual real total variable costs of the breeding program per institute for our sample of wheat institutes increased from 24,000 yuan to 38,000 yuan between 1981 and 2000. Similarly, the average annual total variable breeding cost for our sample of maize institutes rose from 38,000 to 53,000 yuan.The total cost of wheat and maize breeding, however, varies greatly among institutes. For example, the average provincial institute invested five times more in wheat breeding and about six times more in maize breeding than the average prefectural institute did. When comparing prefectural breeding stations, the total cost of wheat breeding in one institute could be more than three times that of the average prefectural institute. Dandong prefectural institute in Liaoning spent five times more than the average maize-breeding institute did. The average cost of variety production also varies from institute to institute and can be seen to vary systematically with research output. To compare costs and output, we have to account for the research lag. In the analysis that follows, research output is the annual mean of five years’ total research output from one of three five-year periods, 1985-1990, 1991-1995 and 1996-2000. The average annual cost associated with this output is the annual mean of five years’ total cost, lagged by five years. Therefore, the corresponding three five-year periods of cost are, respectively, 1980-1985, 1986-1990 and 1990-1995. Unlike total costs, average costs fall as the institutes produce more varieties . For wheat the cost per variety falls from 152,000 yuan for breeding institutes that produce only one variety to 60,000 yuan for those that produce more than four varieties. Similar patterns can be seen in the data when using area-weighted output. A plot of the data reveals a distinct L-shaped relationship between average cost and the size of research output .No matter what measure of output is used, or for what crop, as research output increases, the average cost of breeding research falls. The L-shaped relationship also is robust, holding over time and over institutes . The sharp fall in average costs of breeding as an institute’s output rises suggests that China’s wheat and maize research institutes are producing in an output range with strong economies of scale, such that efficiency might be increased by expanding the scale of production of China’s wheat and maize research institutes.

September 15, 2023

Impacts on agriculture from urbanization will then be disproportionate to the land area covered

The county lost about 6500 acres of agricultural land to urbanization between 1992 and 2008 . Most of this was prime farmland and farmland of local importance. Compared with other jurisdictions in California, the county has been relatively successful at protecting agricultural land from urbanization through land-preservation programs, incentives for farmers, and land use policies which make it difficult to develop land zoned for agriculture. Urbanization presents both opportunities and challenges for agriculture. In some regions, it enhances awareness about how food is produced and generates markets for agricultural products such that farmers produce crops more intensively . But it is more typically accompanied by challenges: the loss of agricultural land due to subdivision and development; vandalism at the urban edge ; and conflicts with new suburban residents about the noise, odor, and potential spray-drift associated with farming operations. Where development takes place in a dispersed pattern that fragments agricultural land, farming may become difficult on some remaining agricultural parcels due to such conflicts as well as to difficulties in moving farm machinery from field to field on more congested roads, creating a ripple effect whereby more agricultural land is then converted to urban uses. Also, fragmentation and loss of farmland cause farmers to lose benefits associated with being part of a large farming community, such as sourcing inputs, accessing information, sharing equipment, and supporting processing and shipping operations .Within previous research, we and our colleagues developed a set of storylines reflecting different climate change and urbanization policies for Yolo County in 2050 . As in IPCC Scenario A2 , our A2 storyline assumes that population growth will remain high,container grown raspberries with an approximate doubling of the current county population, to 394,000 .

This storyline assumes that economic growth and technological innovation remain high, that drive-alone motor vehicles remain the main transportation mode, and that current land use policies remain in place. Although much urbanization will be on previously unbuilt land, there will be some focus on infill development, higher densities, and greater land use mix, as indeed is evident within current development and in county and city planning documents. In terms of suburban sprawl, therefore, the A2 storyline is by no means a worst-case scenario. Rather, it is a continuation of practices in the 1990–2010 period. If this storyline had been based on prevailing development patterns from 1950–1990, suburban densities would have been in the range of 4–6 units per acre instead of 8 , less development would have occurred in medium- and high density forms, and a higher percentage of larger ranchettes would have been created . Suburban sprawl would then have covered a much larger percentage of the county, taking far more agricultural land out of production. In the IPCC’s B1 storyline, societies become more conscious of environmental problems and resource limits, and adapt policy accordingly. Under our Yolo County B1 storyline, population growth slows, reaching a mid-range population size of 335,000 by 2050 . Economic development is moderate, with a shift from the production of goods to a more service-based economy that is connected to the larger global economy. Technological innovation remains high, with an emphasis on small-scale, green technologies. More compact urbanization occurs through higher densities, increased infill, and a focus on small, locally owned retail stores rather than big box commercial developments. Current transportation and emission policies become more stringent, and the use of high-efficiency vehicles and alternative transport modes increases. In terms of agricultural landscapes, strategies such as conservation easements and tax incentives expand to help maintain land in farming. Farmers also place more emphasis on increasing carbon sequestration, reducing GHG emissions from fossil fuels and fertilizers, and relying on ecologically based practices that reduce dependence on non-renewable inputs .

To the two IPCC-based storylines, we added a third with more explicit GHG-emissions regulation and sustainability policies. Under our AB32-Plus storyline, Yolo County experiences slower population growth, reaching only 235,000 in 2050 through policies or voluntary actions that affect family planning and migration . Moderate economic growth focuses on value-added agricultural production enhancing the economic viability of the rural sector, and closer alignment between the rural and urban sectors supports both farmland preservation and protection of ecosystem services . A less resource-intensive lifestyle gains acceptance. Urbanization remains at the current extent through strict land use planning policies and development emphasizing efficient use of land, mixed use, intensive infill, increased densities, and growth in urban and neighborhood centers. Public policy emphasizes alternative modes of transportation and far cleaner vehicles. In order to both mitigate and adapt to the changing climate, agricultural producers make major changes in management practices, focusing on ecological intensification and diversification of cropping systems rather than non-renewable inputs and monocultures. Markets for agricultural products become more locally based, and thanks to both more compact physical form of communities and changing economics, travel distances decrease.In order to understand the type, extent, and likely locations of urbanization in the county, we modeled these three urbanization storylines using UPlan geographic information system–based software, a rule-based land use allocation model developed by the Information Center for the Environment at the University of California, Davis . UPlan is an open-source, relatively simple model that can be run on a sub-county area, a county, or a group of counties. It is suitable for fast, broad-brush urbanization modeling of large land areas using multiple development scenarios, and more than 20 counties in California have used it for urban-growth projections, including a group of rural counties in the San Joaquin Valley which employed it to develop an urban-growth blueprint . It has also been employed to assess the impacts of urbanization policies and growth on natural resources , to understand the risk of wildfires in rural woodlands from urban growth , and to evaluate the effect of land use policies on natural land conversion . UPlan relies on a number of demographic inputs to create scenarios reflecting possible locations and forms of new urban development . The software divides households into four residential land use types based on density parameters, while assigning employees to nonresidential land use types , also by density. Researchers designate “attractors” and “discouragements” , and assign weights to each within each scenario. Accessibility and neighborhood attractor parameters can be added in this way, which also allows for detailed local knowledge of development history and policy to be incorporated. For example, within our A2 scenario we assigned relatively strong attraction values to freeway interchanges for commercial development and somewhat less strong attraction values for residential development, because without specific land-protection policies, highly accessible freeway locations tend to attract such development. Within our B1 and AB32-Plus scenarios we assigned increasingly strong attraction values to town and neighborhood centers, as well as to existing commercial strips and rail station areas, because over a 40-year period these are likely to be a focus of infill development policy.

Finally, UPlan uses “masks” to prohibit growth in certain locations because of logistical or ownership considerations. For example, we masked existing parks and wetlands in this way. However, we assigned discouragement values to floodplains, because despite environmental concerns, development continues to occur in Sacramento Valley floodplains. For the purposes of this project,blueberry plant pot we modified UPlan in several ways when compared to previous uses. Because our time frame is longer, and given that land use politics and regulation can change greatly over 40 years, we no longer required that the model place new urban development in areas conforming to the current county General Plan . We also modified UPlan to allow development within existing urban areas; the tool had been used previously mainly to consider growth on unbuilt county lands outside of existing cities and towns. To predict infill development more accurately, we added additional attractors such as existing commercial strips, shopping centers, freeway retail zones, neighborhood centers, and rail transit station areas, all of which can potentially be redeveloped with higher densities. Partly as a result of these changes, our urbanization assumptions for both the B1 and AB32-Plus scenarios are considerably stronger than used by other researchers. For example, even the strongest growth-management scenario considered by Neimeier, Bai, and Handy in a study of urbanization scenarios for the nearby San Joaquin Valley still allows substantial suburban sprawl. This approach may accurately reflect current political realities for that region , but it involves very different assumptions from our back casting approach, which aimed at investigating potentially very-low-GHG scenarios in 2050. Table 2 shows the primary modeling assumptions we used in UPlan. The software divides new development by land use type , and then, drawing upon these inputs, allocates it across the landscape into the geographic cells with the highest combined attraction weights and the user-defined land use order. The model uses 50 m 50 m cells, roughly half an acre. This is a fine-grained grid conducive to handling small increments of development such as often occur at infill and urban-edge locations. The final output is a map displaying the location by land use type of future urbanization, as well as associated tables. Throughout the scenario-development process, we sought to keep our assumptions relatively simple and transparent. In their study of the New York metropolitan area, Solecki and Oliveri added a layer of new roads for their A2 scenario. For Yolo County, there is no political demand for new roads under current conditions, and the location ofsuch routes if added would be highly conjectural. Likewise, the concatenation of new residential clusters outside of existing urban areas was not modeled, because access to roads and proximity to past rural development are probably strong enough to approximate this relatively weak clustering tendency. Lastly, we did not need to consider land slope in our modeling, because the vast majority of the county is quite flat, and the western hills are far removed from existing population centers.

Among the specific assumptions within our three scenarios, the most controversial is that our AB32-Plus scenario assumes that all new development takes place within existing urban areas. We did this to develop the strongest possible back casting scenario for reducing GHG emissions. New development in Yolo County’s largest city, Davis, is in fact currently almost entirely infill, because voters passed ballot measures in 2000 and 2010 preventing any development on open space or agricultural lands without a majority vote of city residents. Statewide, infill development has greatly increased in recent years due to scarcities of unbuilt land in places like the central Bay Area and the Los Angeles Basin and is likely to increase further due to overbuilding of lower-density housing and strong needs for denser forms of housing . Redevelopment of existing urban lands is a main goal of the Sacramento region’s 2004 Preferred Blueprint Scenario and Sustainable Communities Strategy , as well as state legislation such as SB 375. While many practical constraints pertain to infill , within a strongly environmental vision of the future there is no physical or procedural reason why 100% infill could not be achieved if the political obstacles could be overcome, for example through an escalation in the urgency of the climate challenge. For all scenarios, we established density levels that are fairly close to the density levels of recent development in the more urban portions of the state. Our categories were Very Low Density Residential, with an average lot size of one acre; Low Density Residential, with an average density of 8 units per acre ; Medium Density Residential, with an average density of 20 units per acre ; and High Density Residential, with an average of 50 units per acre . In terms of building types, the Medium Density category might consist of two-to-three-story apartment or condominium buildings with significant green space around them, while the High Density category might include three-to-five-story multifamily buildings in a more urban format as well as some townhouses. It is important to emphasize that none of these categories requires high-rise apartment living, although this development type is not forbidden, and might in fact be desirable for limited locations within the county during the study period. We apportioned development differently between these residential types for each scenario.

August 7, 2023

Public investments in infrastructure support major drivers important to industry success

We have observed the gradual weakening of the position of grower cooperatives and have noted in our stylized history that several have disappeared while others have had to deal with declining market share and financial challenges. Some aspects of mandated marketing programs have been problematic. Some programs have been terminated by grower referendums and others have suffered adverse court decisions in regard to quantity control prorate programs or assessment of the benefits of generic advertising to individual private label firms. The weakened competitive position of grower cooperatives and problematic features of mandated marketing orders are a consequence of the existence of large producers and integrated grower-processors of sufficient size to have market power of their own. This is now more common than it was in the 1920s and 1930s when enabling legislation was initially crafted. We believe that erosion in the contribution of co-ops and marketing orders will likely carry forward into the 21st Century.Population numbers and per-capita incomes are the dominant determinants of ultimate demand for the produce of California farms and ranches. Table 14 reviews California, national, and worldwide prospects for population and economic growth. Demand within the state grew over the epochs with significant increases in population and per-capita incomes occurring in the recent past. The relative growth in California demands will likely exceed that of nationwide per-capita demands in the future, the result of continued immigration and rising incomes. Export demands, important in the early history of the state, have again become important, responding to rising incomes in important offshore markets in Europe, Asia, and elsewhere. It is obvious that California agriculture, being demand driven, must be sensitive to changes that effect state, national, and international demands for the products of its farms and ranches. Issues will relate not only to quantities in trade channels but also to quality and supply reliability. Future marketing opportunities will be defined in importance by trade to both local and distant markets as well as the location of competitive battles for market shares. High export dependency for many of its products, increased in-state population’s demand for food products,pot raspberries slower growth in national markets, and, above all, the possibility of both growing populations and incomes in developing economies will be important determinants for success.

These two drivers reflect the most negative of our outlooks.The SWP, which was funded differently than the CVP , may provide a financial model for future endeavors to serve particular sectors of the state, including agricultural, urban, and environmental water users. Highways are in a deteriorating state. Increased maintenance and traffic congestion add to transportation costs. Local roads are affected by inadequate local funding. Airports and harbors also face difficulties, including the need for health and security assurances. “User pay” may also be the coming mantra for covering the costs of research, development, and extension services. Private agricultural R&D investments now exceed public expenditures, a trend that is sure to continue, possibly to the detriment of discovery of basic scientific research necessary for applied research products. It may also skew products toward large-market products, curtailing development of applied research products focused on smaller markets, e.g., for smaller-volume horticultural crops of the sort common to California. We have postulated that superior management will continue to be a hallmark of a viable agricultural sector in the future. Higher tuition costs reduce public contributions to each student’s education at the state’s colleges and universities. Here, too, the shift ap-pears to be one of user pay, perhaps reducing educational opportunities and, along with that, less public support of the tenet that the benefits of a well-educated population serve society and the general welfare of the citizenry. Extension and public-education programs are also under budget scrutiny with the almost inevitable consequence of reduction if not elimination. Private extension and public-education programs may be developed for those willing to bear the cost. Programs without a core, definable economic market may cease to exist.The increasing regulation of agriculture is driven by environmental, worker, and consumer safety issues, among others. There has been a continuous increase in regulations, compliance challenges , and the like. The majority of regulatory pressures have been imposed since WWII during a period marked by rapid increases in the number of people living in California and a growing slate of concerns by the general public about the environment, labor, health, and consumer policies. A recent study of farmer responses to the effects of regulations reflects one attempt to categorize the broadening scope of regulatory activity: employee-related regulations—safety and health, employee rights, disclosure, transportation; community-related regulations—consumer health and safety, community public health and safety; natural resource-related regulations—air quality, water quality, water rights, threatened or endangered plants or animals, and wetlands; and regulations related to transportation of materials—transportation of hazardous wastes and of goods and materials .

Regulations had a perceived effect on management practices, including those of employee safety and training, paperwork, technology, management support and improvement, cultural practices, scale of operations, and efficiency . We in no way argue that regulatory activities are not in the public interest, but they do increasingly change the policy and regulatory environment within which economic activity exists, constraining options, increasing costs, and reducing the competitiveness of California agriculture. We can admit only to viewing the future as one in which regulations will have profound impacts on firm and industry productivity and competitive performance.The second set of new drivers is the flip side to the positive impact of population and income growth on demand: namely, competition for natural resources. Urban growth has already pushed agriculture virtually out of Los Angeles, Orange, San Diego, San Mateo, and Santa Clara Counties and is now spilling over the Tehachapis from the south and the Coast Range from the west into the Central Valley. Thirty-five million people demand more recreation space, more water, more land, and more public space . When we recognize that only a small part of California is hospitable to human habitation, which, in general, occurs in the same areas where agriculture thrives, the potential for increasing abrasion on the urban-rural interface is inevitable. In summary, both drivers are responsive to the demands of a growing non-farm population in the United States and in California. Both are external forces to which accommodation must inevitably be made. Litigation is only infrequently successful in preventing negative impacts. Agriculture has come to learn to work with other interest groups to make the best of possible outcomes. To the extent that they limit choices of producers and processors, they can add to the cost of production, reducing economic profitability and placing California producers at a competitive disadvantage to producers in other states and even in other countries that are not similarly affected. U.S. markets for some crops may not be affected unless there are alternate producers of the same or substitute products in other states or if there are offshore producers with lower costs of production. Shares of market in third-country markets may be affected if there are global competitors in those same markets with lesser constraints or non-regulated production options.Willard Cochrane in his history of U.S. agriculture argues that agriculture in the United States has basically been “supply driven.” That is, production was initiated for self-consumption , but marketable surpluses emerged as productivity increased.

Contrary to Malthus’ prediction that demand would outrun supply, agriculture in developed countries has been characterized by production expanding more rapidly than demand , leading to oversupply, low prices, and, ultimately,plastic gardening pots government intervention to support incomes. The individual farmer’s main defense to such situations was to improve efficiency by adopting new technology. But if new technology was rational for one, it was rational for all, so aggregate supply expanded further, thus pressing prices to lower levels. The argument thus arises that agriculture is on a perpetual “treadmill” of overproduction and low prices . But California agriculture was not settled by small homesteaders intent on feeding themselves first and then possibly producing small surpluses of basic commodities—grain, milk, eggs, and meat. California agriculture started with big farms and ranches producing much more than could be consumed by the farmers directly. California farmers produced to meet someone else’s demand—for hides and tallow on the East Coast and in Europe, meat for miners and those supplying miners, wheat for export, nuts and dried fruits for the East and Europe, and so on. This dominant focus on meeting changing product demands, coupled with the range of total products possible, meant that California agriculture could be opportunistic. But to be so, it had to constantly adapt to survive and, yes, thrive. Constantly adjusting to changing opportunities has meant that California agriculture has a perpetual thirst for new technology—better and cheaper is always a potential market advantage. Being a long distance from markets for both outputs and inputs placed an extra premium on efficiency and adaptiveness. This set of factors pulled California agriculture through a quick sequence of changes that, as incomes climbed and population grew, meant that California agriculture became more and more diversified—200 crops in 1970, 350 in 2000. A lesser focus on basic crops meant that California agriculture has been less influenced by, or dependent upon, U.S. farm programs. However, if programs offered opportunities, California agriculture made the best of them. After all, an agriculture that is more efficient or productive than that of the rest of the country should be able to perform better. California agriculture has done so in cotton, rice, and dairy. Being less focused on Washington, California agriculture sought favorable state policies on water, transportation, research, and development, as well as favorable tax treatment. Until 1961, rural areas dominated the state senate. California agriculture was able basically to get its own way pre-WWII and remained a powerful force thereafter, at least until it lost the Peripheral Canal battles in the 1970s.

A few other distinctions will round out our case that California agriculture is different. It has always been a capital-intensive but simultaneously very seasonally labor intensive agriculture. California agriculture has always had a strong dependence on distant markets but, as its own state market grew, it adjusted to meet growing “instate” demands. It has benefited greatly from being in the middle of a rapidly growing and rich “domestic” market. Having access to 35 million local customers is preferable to having only 0.75 million or even three million . The constant adjusting to meet changing demands of affluent consumers has had consequences for the nature of California agriculture. Since 1952, the share of output accounted for by annual field crops has fallen precipitously while production of higher-valued vegetable and perennial crops has increased substantially. Dairy production now dominates the livestock sector. The result is that a rising share of California agriculture is on longer, multiyear production cycles. This necessitates a longer planning framework if periodic price run-ups are not to be followed by rapid buildups in production capacity, which inevitably result in market gluts and falling prices. This is currently happening in the wine industry worldwide.It is now time to end this story. We have consulted history. We have argued that California agriculture has performed well compared to U.S. agriculture. Based on the total value of crops and livestock marketed, California became the highest-ranking agricultural state in 1948. It has maintained that ranking ever since while increasing the difference between it and the second most important agricultural state . In 1950 California accounted for 8 percent of the total value of U.S. agricultural production. Since then, the share has steadily risen. In 2000 California agricultural production was worth $25.5 billion, amounting to 13 percent of the U.S. total. The value of California agricultural production of crop and animal products is now more than the combined value of the next two states, Texas and Iowa. But California agriculture’s dependence on federal government farm payments has been significantly less than that of the rest of U.S. agriculture . In 2000 California’s payments amounted to $667 million out of total U.S. direct government payments of $22.9 billion—only about 3 percent of the total. In contrast, Iowa received about 10 percent of U.S. payments and Texas received about 7 percent. It is likely that payments to California producers will fall relative to grain-belt areas because field-crop production will continue to decline as growers shift to higher-gross-income crops as markets permit.