Tag: round pots

The data also show some evidence that average costs fall with increases in output in joint wheat and maize institutes. The average cost per wheat variety is consistently lower in joint institutes than in wheat-only institutes. Similarly, the average cost per maize variety is consistently lower in joint institutes compared with maize-only institutes. For wheat , the cost per variety falls from 187,000 yuan in wheat-only institutes to 145,600 yuan in joint wheat and maize institutes. The same patterns also appear in data when the area-weighted output measure rather than number of varieties is used. Moreover, the evidence of economies of scope becomes stronger as the scale of research effort increases. Hence, our descriptive data provide evidence that economies of scope may be a source of efficiency differences among institutes. The evidence of economies of scope suggests a potential cost saving associated with combining a wheat-only institute and a maize-only institute into a bigger, joint, wheat and maize institute. Further analysis of the data also points to other factors that potentially could affect costs,plastic pots for planting although in some cases the descriptive statistics do not show a particularly strong correlation. The relatively low education level of China’s agricultural researchers has long been claimed to be one of the key factors limiting agricultural research productivity . Based on our data, the human capital in China’s wheat and maize breeding institutes is low compared with other countries . Our data also show that increases in the educational level of breeders help to reduce the cost of variety production. The institutes that have the highest average cost of variety production also tend to have the lowest proportion of breeders with post-secondary education .

Byerlee and Traxler suggest that efficiency in crop breeding increases when agricultural scientists from other disciplines work in conjunction with breeders. Although the share of scientists working on other agricultural disciplines in wheat and maize breeding institutes is quite high , compared to 30 percent in an average wheat improvement research program in a developing country , there is little difference in this share between institutes with low and high average costs. Finally, it is also unclear from visual inspection of the data in Table 3 whether breeding efficiency is affected by the source of a breeding institute’s genetic materials or the presence of retirees. In this section, we specify the econometric model to be used to study the efficiency of China’s crop breeding institutes, and discuss our strategy for estimating the model. We begin by specifying the relationship between costs and the factors that affect them in institutes that produce either one or two types of varieties . We also define measures for economies of scale, ray economies of scale, and economies of scope. Here we treat a breeding institute as a typical “firm” which applies inputs to produce research output . The total variable cost of an individual institute is expressed as a function of its research output, the price of its inputs and other institutional characteristics affecting the cost structure of crop breeding research.10 A wide range of different types of cost functions have been applied in the literature.We estimate economies of scale and scope in two ways: from a base model, where we estimate the relationship between cost and output taking account of the effects of annual salaries , time, province and institute type without the Z variables; and from a full model, which also includes the four covariates . In the final section we discuss the implications for economic efficiency of crop breeding that can be drawn from the estimated relationship between cost and output after controlling for other variables . We do so for both equation , the single-output cost function, and equation , the multiple-output cost function. Hence in our analysis we have four fundamental units of analyses: the base model for the single-output cost function ; the full model for the single-output cost function , and the base and full models for the multiple-output cost function.

We estimate the base cost function model with ordinary least squares to get initial estimates of economies of scale and scope. However, the OLS estimates of the parameters may be underestimated if there is measurement error in the construction of the output variable . One source of measurement error arises from the special nature of crop breeding and the decision making of its directors. The implicit behavioral assumption that underlies the cost function is that the research manager minimizes costs given the output of the institute. Such an assumption, even for a quasi-productive entity like a research institute, often has been made in cost analyses . While it is not difficult to imagine that the typical research manager in a breeding station strives to minimize the institute’s costs of given output, one characteristic that makes the plant breeding industry special is the long time lag between expenditure and the realization of the output. We are assuming that research managers make their cost-minimizing expenditure decisions based on the expected output of the breeding station. But the econometrician does not observe expected output; only actual output is measured. We measure actual output from a crop-breeding institute as the number of new varieties from that research institute adopted by farmers in the five-year period, 6-10 years after the research expenditure. This measure might vary systematically from the output that the manager was anticipating when expenditure decisions were made. One solution to measurement error is the use of instrumental variables . In order to account for the measurement error, we identify a set of instrumental variables and reestimate our model using three-stage iterative least squares. Since the relationship between output and cost basically depends on factors associated with supply-side decisions of the research institute, we turn to a series of demand-side factors in our search for exogenous IVs: farm-gate prices of wheat and maize, the prices of fertilizer and pesticides in input markets, the land-labor ratio in a region, the share of irrigated land to total cultivated land, and the multiple cropping index. We are also concerned with several other assumptions. In order to test for the effect of our assumption about the length of the lag between costs and research output , we conducted sensitivity analysis using data generated by an array of different lag structures. Further, the presence of unobserved heterogeneity may bias the estimates of our parameters of interest.

To eliminate the unwanted covariance between the unobserved factors and the other regressors we took advantage of the panel nature of the data, using both fixed- and random-effect methods. Finally, it is also possible that the cost minimization assumptions that underlie cost function analyses may not all be valid. As noted above,plant pot drainage these assumptions are avoided— albeit, at the expense of some other disadvantages—when we use a production function approach rather than a cost function approach. As a check on this aspect, we also estimated a Cobb-Douglas production function model, and found that the main findings regarding returns to scale are quite similar between the two approaches . The base model produced remarkably robust estimates of many of the parameters . The quadratic specification fits the data well with R2 estimates ranging from 0.53 to 0.75 for wheat and 0.52 to 0.72 for maize . The goodness of fit measures, however, systematically demonstrate that, for both wheat and maize, the models that use the area-weighted and area-yield weighted outputs have a significantly better fit. In all of the models the effect of an increase in wages on costs is positive and significant, in keeping with expectations and theory. All of the variables were normalized by dividing at their sample mean such that we can interpret the regression coefficients as elasticities at the mean. Economies of Scale After controlling for wages, region and year effects, and the institute type, the measures of economies of scale calculated from the estimated parameters are all much less than one and significantly so . The estimates of SCE for wheat institutes range from 0.22 to 0.26; those for maize institutes range from 0.14 to 0.32. The results imply that at the mean levels of research output and other explanatory variables, strong economies of scale exist for both wheat and maize institutes. If output increases by 10 percent, costs would increase no more than 3.2 percent. Evidence of such strong economies of scale from the multivariate analysis is consistent with the descriptive evidence and reflects the patterns in Figure 1. The elasticities of cost with respect to output are relatively small compared with those found in studies of non-profit institutions . The strong economies of scale are largely unchanged when we control for other institutional factors. Comparing results in Tables 4 and 5, after controlling for the four Z factors and their interactions with output, the SCE elasticities still fall in a similar range . Although the coefficients on variables representing several of the institutional factors are significant and suggest that there are other ways to affect breeding efficiency , the remarkably low and highly significant measures of SCE indicate that significant cost savings could be attained if the scale of China’s breeding institutions were expanded. Accounting for a number of the potential econometric problems does not significantly alter the magnitude or significance of the measures of economies of scale, as can be seen in Table 6.

To address concerns of measurement error, exclusion restriction tests of the validity of our demand-side instrumental variables show that they meet the statistical criteria required for identification. Using these instrumental variables and the 3SLS estimator does not substantively change the estimates of the economies of scale parameters. The economies of scale parameters range from 0.12 to 0.26. The results hold for both wheat and maize in both the base model and the full model. Allowing for lags of different lengths, or controlling for the unobserved heterogeneity also does not materially affect the estimates of economies of scale.11 Similar to the results generated by the parameter estimates of the single-output cost function, results based on the multiple output cost function also imply high and statistically significant estimates of ray economies of scale. The estimates of SOEray, which range from 0.33 to 0.39, mean that if wheat and maize institutes double their output of both wheat and maize varieties, the total variable cost of wheat and maize breeding would increase by only 33 to 39 percent. The strong ray economies of scale are also not affected by alternative estimation strategies or model specifications. While not as strong or as robust as the evidence of economies of scale, our multioutput cost function models show the existence of economies of scope between wheat and maize variety production, as summarized in Table 7. The estimates of SOE based on the parameter estimates of the base model indicate that there would be cost saving of about 10 percent if a wheat-only and maize-only breeding institute were combined into a joint wheat-maize institute. Bootstrapped confidence intervals show that the measured elasticities are statistically significantly different from zero. Unlike economies of scale, however, economies of scope are affected when other institutional factors are added. For example, if we control for the educational level of breeders, the cost savings from merging wheat and maize institutes drops from 10 to 5 percent, and it drops to only 3.8 percent when both human capital and spill-in variables are added to the model. In addition to the cost efficiency associated with the scale and scope of wheat and maize variety production, the statistical analysis supports the early descriptive findings and shows that economic efficiency is also affected by other institutional variables, as can be seen in Table 5. For example, except for one case, the coefficients on the interaction between breeder’s education and output are negative and significant. The magnitudes of the coefficients show that if research managers can increase the share of breeders with college and more education by 10 percent , the marginal cost will fall by around 1.0 percent. An increase in the proportion of genetic material used in breeding that comes from outside the province also increases efficiency .

An additional way to indicate the relative independence of California agriculture from direct government payments is to look at the share of net farm income made up of direct government payments. Over the period 1990–2000, direct government payments to U.S. producers were 28.3 percent of net farm income.Figure 20 shows annual ratios over the period 1960–2000.15 Direct government payments constituted 49 percent of U.S. net farm income in 2000 and 12 percent of California net farm income. Direct government payments increase the fixed cost of agricultural production without any corresponding increases in productivity .16 In the U.S. heartland , direct government payments account for nearly a quarter of the value of farmland . A recent study of soybean production in Argentina and Brazil concluded that production costs were 20 to 25 percent lower than in the U.S. heartland even though variable input costs per acre were lower in the U.S. . Annual land costs were as much as $80 per acre higher in the U.S. Thus, higher capitalized asset values affect competitiveness. California agriculture is more flexible and more responsive to changes in market conditions with its managerial ability to meet market driven domestic and worldwide consumer demands. Part of that flexibility and responsiveness comes from less reliance on direct government payments. Bottom Line: California agriculture is growing more rapidly than U.S. agriculture, is more flexible in selecting production alternatives, is more responsive to market driven demand signals,plastic pots for planting and is significantly less vulnerable to federal budget cuts. Every one of these attributes is a plus.

In the 21st Century, the three most important markets for California agriculture will be California, the United States, and higher-income, developing countries. All will continue to experience significant population growth . While projected growth in California to 2040 will not be as rapid as in the last 40 years , it will still be substantial—an increase of more than 24 million customers compared to a smaller increase in the preceding 40-year period. For the U.S. market, projected growth is slightly higher in the next 40 years . Most important, U.S. growth represents an increase of an additional 105 million customers, a larger growth increment than for the preceding 40-year period. As noted earlier, global population will increase by around 2.8 billion people with the majority residing in developing countries. A further plus is that their incomes should also be growing rapidly. Bottom Line: California agriculture is well positioned to take advantage of continued growth in state, national, and global population with parallel growth in incomes.California agriculture has always been vulnerable to its external environment precisely because it is demand-driven. Given that it produces predominantly income-sensitive products, growth, recession, depression, and global economic events all potentially cause significant changes in prices. This fact, coupled with a rising share of California output being perennial crops and livestock, means that the potential for boom or bust cycles is probably rising. Thus, the operative question is whether the external environment is becoming more volatile with increased global interdependence along with the rising dependence of all nations on trade. Leaving aside war and massive natural disasters , lowered trade barriers and freely functioning financial markets should increase international market stability compared to a world of protection and controlled financial flows. On the other hand, it is less and less possible for nations to isolate themselves from international economic events.

Bottom Line: While there is no strong evidence that global markets are becoming less stable, it is possible that, as individual countries liberalize, domestic price instability could increase, presenting additional challenges to farmers, growers, and ranchers.California agriculture grew very rapidly over the past half-century. Real value of production increased 70-fold. Agricultural production is now widely diversified to more than 350 commercial plant and animal products, exhibiting a constantly shifting composition and changes in the location of production, all abetted by growing demands for its products and rapid science-based technological changes. California agriculture is strongly buffeted by growing urban pressures for availability of key natural resources—reliable water supplies and productive land. Relentless pressure from environmental and other non-agricultural interests remain with respect to water quality, chemical contamination, air pollution, wildlife and aquatic habitats, and worker safety in the forefront. Agricultural prices clearly became more volatile after the global instability of the early 1970s. As agriculture became more complex internally, both technically and economically, it also became more interdependent with the rest of the economy and the world. It now purchases virtually all of its variable inputs from the non-agricultural economy and has a massive need for credit—short-term, long-term, and, increasingly, intermediate credit. It has probably become more export dependent despite the enormous growth of the California consumer market. In sum, it is more dynamic, more complex, more unstable, and more diverse, thus making California agriculture more vulnerable to external events. At many critical points in California history, California agriculture has been written off, but these periods of difficulty have been interspersed with more numerous periods of explosive growth . The share of perennials, or multiyear-production-cycle products, increased as California agriculture moved away from production of annual field crops and canning vegetables and shifted toward tree nuts, fresh fruits, and wine grapes. The frequency and amplitude of product price cycles seemed to increase. For example, an overabundance of average-quality wine grapes is occurring as recent plantings have come to harvest maturity.

There have been cycles in other products, such as prunes, cling stone peaches, and raisin grapes. The first years of the 21st Century are only the second time in history that low prices occurred across the entire product spectrum. The first was during the long-lasting Great Depression. But already in 2003 and at the beginning of 2004 there are signs of improvement in some prices, promising an improved economy.The idea of creating a new generation of agricultural system data, models and knowledge products is motived by the convergence of several powerful forces. First, there is an emerging consensus that a sustainable and more productive agriculture is needed that can meet the local, regional and global food security challenges of the 21st century. This consensus implies there would be value in new and improved tools that can be used to assess the sustainability of current and prospective systems, design more sustainable systems, and manage systems sustainably. These distinct but inter-related challenges in turn create a demand for advances in analytical capabilities and data. Second, there is a large and growing foundation of knowledge about the processes driving agricultural systems on which to build a new generation of models . Third, rapid advances in data acquisition and management, modeling, computation power, and information technology provide the opportunity to harness this knowledge in new and powerful ways to achieve more productive and sustainable agricultural systems . Our vision for the new generation of agricultural systems models is to accelerate progress towards the goal of meeting global food security challenges sustainably. But to be a useful part of this process of agricultural innovation, our assessment is that the community of agricultural system modelers cannot continue with business as usual. In this paper and the companion paper on information technology and data systems by Janssen et al. , we employ the Use Cases presented in Antle et al. , and our collective experiences with agricultural systems, data, and modeling, to describe the features that we think the new generation of models, data and knowledge products need to fulfill this vision. A key innovation of the new generation of models that we foresee is their linkage to a suite of knowledge products – which could take the form of new, user-friendly analytical tools and mobile technology “apps” – that would enable the use of the models and their outputs by a much more diverse set of stakeholders than is now possible. Because this new generation of agricultural models would represent a major departure from the current generation of models,plant pot drainage we call these new models and knowledge products “second generation” or NextGen. We organize this paper as follows. First, we discuss new approaches that could be used to advance model development that go beyond the ways that first generation models were developed, and in particular, the idea of creating a more collaborative “pre-competitive space” for model development and improvement, as well as a “competitive space” for knowledge product development. Then we describe some of the potential advances that we envisage for the components of NextGen models and their integration. We also discuss possible advances in model evaluation and strategies for model improvement, an important part of the approach. Finally, we discuss how these ideas can be moved from concept to implementation.A first step towards realizing the potential for agricultural systems models is to recognize that most work has been carried out by scientists in research or academic institutions, and thus motivated by research and academic considerations more than user needs.

A major challenge for the development of a new generation of models that is designed to address user needs, therefore, is to turn the model development process “on its head” by starting with user needs and working back to the models and data needed to quantify relevant model outputs. The NextGen Use Cases presented in Antle et al. show that most users need whole-farm models, and particularly for smallholder farms in the developing world, models are needed that take into account interactions among multiple crops and often livestock. Yet, many agricultural systems models represent only single crops and have limited capability to simulate inter-cropping or crop-livestock interactions. Why? One explanation is that many models were developed in the more industrialized parts of the world where major commodity crops are produced. Another explanation is that models of single crops are easier to create, require less computational resources, and are driven by a smaller set of data than models of crop rotations, inter-crops or crop-livestock systems. Additionally, researchers are responding to the incentives of scientific institutions that reward advances in science, and funding sources that are more likely to support disciplinary science. Component processes within single crops, or single economic outcomes, are more easily studied in a laboratory or institutional setting, and may result in more publishable findings. Producing useful decision tools for farmers or policy decision-makers is at best a secondary consideration in many academic settings. The need for more integrated, farming-system models has been recognized by many researchers for several decades, for example, to carry out analysis of the trade offs encountered in attempts to improve the sustainability of agricultural systems . For example, Antle and Capalbo and Stoorvogel et al. proposed methods for linking econometrically estimated economic simulation models with biophysical crop simulation and environmental process models. Giller et al. describe a complex bio-physical farming system modeling approach, and van Wijk et al. review the large number of studies that have coupled bio-physical and economic models of various types for farm-level or landscape-scale analysis. More recent work by AgMIP has developed software tools to enable landscape-scale implementation of crop and livestock simulation models so that they can be linked to farm survey data and economic models . While these examples show that progress has been made in more comprehensive, integrative approaches to agricultural system modeling, these modeling approaches are more complex and have high data demands, thus raising further challenges to both model developers and potential users. As we discuss below, methods such as modularization may make it possible to increase model complexity while having models that are relatively easy to understand and use. Other methods, such as matching the degree of model complexity to temporal and spatial scales, also can be used. Section 3.8 further discusses issues of model complexity and scale. While it is clear that model development needs to be better linked to user needs, it is also important to recognize that science informs stakeholders about what may be important and possible. Who imagined even a few years ago that agricultural decision support tools would use data collected by unmanned aerial vehicles linked to agricultural systems simulation models? So while model and data development need to be driven by user-defined needs, they must also be forward-looking, using the best science and the imaginations of creative scientists.As Jones et al. describe in their paper on the historical development of agricultural systems models, existing models evolved from academic agronomic research.