plastic pots for planting – Nature Ecrogrow

Economies of Scope and Other Determinants of Breeding Costs

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 .

It is evident that there is less year-to-year variation in the national mean of temperature than precipitation

By using a panel of county level data and including county and state by year fixed effects, we rely on across county variation in county-specific deviations in weather within states. This means that our estimates are identified from comparisons of counties that had positive weather shocks with ones that had negative weather shocks, within the same state. Put in another way, this approach non-parametrically adjusts for all factors that are common across counties within a state by year, such as crop price levels. If production in individual counties affects the overall price level, which would be the case if a few counties determine crop prices, or there are segmented local markets for agricultural outputs, then this identification strategy will not be able to hold prices constant. The assumption that our approach fully adjusts for price differences seems reasonable for most agricultural products for at least two reasons. First, production of the most important crops is spread out across the country and not concentrated in a small number of counties. For example, McLean County, Illinois and Whitman County, Washington are the largest producers of corn and wheat, respectively, but they only account for 0.58% and 1.39% of total production of these crops in the US. Second, our results are robust to adjusting for price changes in a number of different ways. In particular, the qualitative findings are similar whether we control for shocks with year or state by year fixed effects.6 Returning to equation , consider the second term, which is the change in profits due to the weather-induced change in quantities. We would like to obtain an estimate of this term based on long run variation in climate, since this is the essence of climate change. Instead, our approach exploits short run variation in weather. Since farmers have a more circumscribed set of available responses to weather shocks than to changes in climate, it seems reasonable to assume that Short Run > Long Run. 7 For example, farmers may be able to change a limited set of inputs in response to weather shocks. But in response to climate change,plastic pots for planting they can change their crop mix and even convert their land to non-agricultural uses .

Consequently, our method to measure the impact of climate change is likely to be downward biased relative to the preferred long run effect. In summary, the use of weather shocks to estimate the costs of climate change may provide an appealing alternative to the traditional production function and hedonic approaches. Its appeal is that it provides a means to control for time invariant confounders, while also allowing for farmers’ short run behavioral responses to climate change. Its weakness is that it is likely to produce downward biased estimates of the long run effect of climate change.Agricultural Production. The data on agricultural production come from the 1978, 1982, 1987, 1992, and 1997 Censuses of Agriculture. The Census has been conducted roughly every 5 years since 1925. The operators of all farms and ranches from which $1,000 or more of agricultural products are produced and sold, or normally would have been sold, during the census year, are required to respond to the census forms. For confidentiality reasons, counties are the finest geographic unit of observation in these data. In much of the subsequent regression analysis, county-level agricultural profits are the dependent variable. This is calculated as the sum of the Censuses’ “Net Cash Returns from Agricultural Sales for the Farm Unit” across all farms in a county. This variable is the difference between the market value of agricultural products sold and total production expenses. This variable was not collected in 1978 or 1982, so the 1987, 1992, and 1997 data are the basis for our analysis. The revenues component measures the gross market value before taxes of all agricultural products sold or removed from the farm, regardless of who received the payment. Importantly, it does not include income from participation in federal farm programs, labor earnings off the farm , or income from non-farm sources. Thus, it is a measure of the revenue produced with the land. Total production expenses are the measure of costs. It includes expenditures by landowners, contractors, and partners in the operation of the farm business.

Importantly, it covers all variable costs . It also includes measures of interest paid on debts and the amount spent on repair and maintenance of buildings, motor vehicles, and farm equipment used for farm business. The primary limitation of this measure of expenditures is that it does not account for the rental rate of the portion of the capital stock that is not secured by a loan so it is only a partial measure of farms’ cost of capital. Just as with the revenue variable, the measure of expenses is limited to those that are incurred in the operation of the farm so, for example, any expenses associated with contract work for other farms is excluded.Data on production expenses were not collected before 1987. The Census data also contain some other variables that are used for the subsequent analysis. In particular, there are variables for most of the sub-categories of expenditures . These variables are used to measure the extent of adaptation to annual changes in temperature and precipitation. The data also separately report the number of acres devoted to crops, pasture, and grazing. Finally, we utilize the variable on the value of land and buildings to replicate the hedonic approach. This variable is available in all five Censuses. Soil Quality Data. No study of agricultural land values would be complete without data on soil quality and we rely on the National Resource Inventory for our measures of these variables. The NRI is a massive survey of soil samples and land characteristics from roughly 800,000 sites that is conducted in Census years. We follow the convention in the literature and use the measures of susceptibility to floods, soil erosion , slope length, sand content, clay content, irrigation, and permeability as determinants of land prices and agricultural profits. We create county-level measures by taking weighted averages from the sites that are used for agriculture, where the weight is the amount of land the sample represents in the county. Since the composition of the land devoted to agriculture varies within counties across Censuses, we use these variables as covariates. Although these data provide a rich portrait of soil quality, we suspect that they are not comprehensive. It is this possibility of omitted measures of soil quality and other determinants of profits that motivate our approach.

Climate Data. The climate and weather data are derived from the Parameter-elevation Regressions on Independent Slopes Model .This model generates estimates of precipitation and temperature at 4 x 4 kilometers grid cells for the entire US. The data that are used to derive these estimates are from the more than 20,000 weather stations in the National Climatic Data Center’s Summary of the Month Cooperative Files. The PRISM model is used by NASA,drainage for plants in pots the Weather Channel, and almost all other professional weather services. It is regarded as one of the most reliable interpolation procedures for climatic data on a small scale. This model and data are used to develop month by year measures of precipitation and temperature for the agricultural land in each county for the 1970 – 1997 period. This was accomplished by overlaying a map of land uses on the PRISM predictions for each grid cell and then by taking the simple average across all agricultural land grid cells.To replicate the previous literature’s application of the hedonic approach, we calculated the climate normals as the simple average of each county’s annual monthly temperature and precipitation estimates between 1970 and two years before the relevant Census year. Furthermore, we follow the convention in the literature and include the January, April, July, and October estimates in our specifications so there is a single measure of weather from each season. Table 1 reports county-level summary statistics from the three data sources for 1978, 1982, 1987, 1992, and 1997. The sample is limited to the 2,860 counties in our primary sample.Over the period, the number of farms per county declined from approximately 765 to 625. The total number of acres devoted to farming declined by roughly 8%. At the same time, the acreage devoted to cropland was roughly constant implying that the decline was due to reduced land for livestock, dairy, and poultry farming. The mean average value of land and buildings per acre in the Census years ranged between $1,258 and $1,814 in this period, with the highest average occurring in 1978. The second panel details annual financial information about farms. We focus on 1987-97, since complete data is only available for these years. During this period the mean county-level sale of agricultural products increased from $60 to $67 million. The share of revenue from crop products increased from 43.5% to 50.2% in this period. Farm production expenses grew from $48 million to $51 million. Based on the “net cash returns from agricultural sales” variable, which is our measure of profits, the mean county profit from farming operations was $11.8 million, $11.5 million, and $14.6 million or $38, $38, and $50 per acre in 1987, 1992, and 1997, respectively. The third panel lists the means of the available measures of soil quality, which are key determinants of lands’ productivity in agriculture. These variables are essentially unchanged across years since soil and land types at a given site are generally time-invariant. The small time-series variation in these variables is due to changes in the composition of land that is used for farming.

Notably, the only measure of salinity is from 1982, so we use this measure for all years. The final panels report the mean of the 8 primary weather variables for each year across counties. The precipitation variables are measured in inches and the temperature variables are reported in Fahrenheit degrees. On average, July is the wettest month and October is the driest. The average precipitation in these months in the five census years is 3.9 inches and 2.0 inches, respectively.Table 2 explores the magnitude of the deviations between counties’ yearly weather realizations and their long run averages. We calculate the long run average variables as the simple average of all yearly county-level measurements from 1970 through two years before the examined year. Each row reports information on the deviation between the relevant year by month’s realization of temperature or precipitation and the corresponding long run average. The first column presents the yearly average deviation for the temperature and precipitation variables across the 2,860 counties in our balanced panel. The remaining columns report the proportion of counties with deviations at least as large as the one reported in the column heading. For example, consider the January 1987 row. The entries indicate that 73% of counties had a mean January 1987 temperature that was at least 1 degree above or below their long run average January temperature . Analogously in October 1997, precipitation was 10% above or below the long run average in 95% of all counties. Our baseline estimates of the effect of climate change follow the convention in the literature and assume a uniform five degree Fahrenheit increase in temperature and eight percent increase in precipitation associated with a doubling of atmospheric concentrations of greenhouse gases .It would be ideal if a meaningful fraction of the observations have deviations from long run averages as large as 5 degrees and 8% of mean precipitation. If this is the case, our predicted economic impacts will be identified from the data, rather than by extrapolation due to functional form assumptions. In both the temperature and precipitation panels, it is clear that deviations of the magnitudes predicted by the climate change models occur in the data. It is evident that for all four months there will be little difficulty identifying the 8% change in precipitation. However in the cases of temperature, deviations as large as +/- 5 degree occur less frequently, especially in July. Consequently, the effects of the predicted temperature changes in these months will be identified from a small number of observations and functional form assumptions will play a larger role than is ideal.

The comparable average for California was 7.4 percent of net farm income

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.

Spurs that bore fruit in a given year rarely flowered or bore fruit in a subsequent year

In spite of these efforts, relative fruit set is still variable and little improved since the early data reported in 1959 by Kester and Griggs. But, is fruit set the main limiting process for almond productivity? Another approach could be to increase the number of flowers per acre — but that approach demands more information on the eco-physiological basis that regulates flowering of almond spurs . Individual spurs tend to alternate bear with only a small percentage of spurs flowering the year after bearing . The authors have observed tagged spurs in outer canopy– exposed positions to live at least 15 years. To investigate this, an almond spur dynamics research project was initiated by Lampinen and colleagues in 2001. This study was designed to quantify the dynamics of spur renewal, fruitfulness and longevity and to determine how these dynamics are impacted by orchard management practices. Results from the study indicated that the number of flowers borne by individual spurs is a function of spur leaf area in the previous year and whether or not the spur bore a fruit in the previous year.Furthermore, spur mortality was much higher in spurs that had low previous year spur leaf area because fruit bearing competes with leaf growth and decreases the amount of source organ available on bearing spurs . Although there was a strong tendency for individual spurs to not bear fruit in successive years,pot raspberries whole trees or orchards are not strongly alternate bearing because fewer than 20% of the spurs on a tree bear fruit in a given year . In addition, the spur dynamics study documented that the key to ensure the largest flowering over an orchard’s life is to have the largest number of spurs possible with the optimal leaf area for flowering.

Proper irrigation during the previous year vegetative season and even after harvest can help to minimize spur death and has been reported to have a critical impact on subsequent bloom and fruit set . The almond spur dynamics study also provided information regarding the importance of PYSLA in determining subsequent spur flowering, fruit bearing and survival as well as the fact that spur fruit bearing in turn, reduces spur leaf in the same year . Thus, spur flowering and fruiting in two sequential years is relatively rare . However, the total number of flowering spurs on a tree may be of limited significance if greater relative fruit set of the flowers can compensate for decreased flower numbers in the orchard. Thus, understanding the relative impact of flower number and relative fruit set on almond tree yield in commercial orchards is essential for guiding efforts to improve orchard productivity and help growers determine the most profitable practices for almond crop management. To address this question we analyzed flowering and fruit set data recorded during the almond spur dynamics project. The study was conducted in a 145-acre orchard, planted in 1996, at 24 feet between and 21 feet within rows. The orchard planting consisted of rows of ‘Nonpareil’ alternating with pollinizer rows of ‘Monterey’ , and ‘Wood Colony’ . The orchard was located in Kern County on a sandy-loamy soil. Irrigation was carried out by micro-sprinklers and irrigation schedule was based on weekly measurement of midday stem water potential that was maintained between −0.7 and −1.2 MPa. Nitrogen was applied at 110 to 220 pounds per acre and leaf N content was between 1.95% and 2.45% over the period of the experiment. Bee hives were placed at a density of two to three hives per acre prior to bloom.

During the experiment, weather conditions during the pollination period were not limiting for bee activity. The orchard was divided into six equal-sized replicate blocks and 50 spurs were tagged in eight ‘Nonpareil’ trees within each of the six blocks. A total of 2,400 spurs were marked with aluminum tags in late March and early April 2001. Twelve spurs were selected on each of the northeast and northwest quadrants of individual trees and 13 spurs were selected on each of the southeast and southwest quadrants of the same trees. Tagged spurs were located at positions ranging from shaded to exposed portions of the canopy at a height of 3 to 12 feet. During the first 4 years of the study, lost tags or dead spurs were replaced with spurs in close proximity with similar light exposure to the original tagged spurs. The dynamics of annual growth, flowering, fruitfulness and spur mortality were quantified annually. For more detail see Lampinen et al. . The number of flowers produced on each tagged spur was counted in the spring of each year from 2002 through 2007. Multiple year records of PYSLA , previous year bearing, number of flowers in the current year and number of fruit in the current year were used to assess spur behavior in relation to PYSLA in spurs that bore no fruits in the previous year. These analyses involved data from 6,980 spurs spread over the 6 years. Kernel yield of the individual trees with tagged spurs and the kernel yield of the orchard containing those trees were also recorded for 6 years . Statistical analyses were carried out using ANOVA to test the significance at P < 0.01 of relationships between PYSLA and current year spur flower density , current year spur fruit density and current year spur relative fruit set. The same test was also used to test the significance of the relationship between tree yield and tree spur population relative fruit set and spur flower density . The number of flowers differentiated during the previous year is the first component of yield in fruit trees .

In almond spurs, flower formation was closely related to spur leaf area in the previous year . Thus, if the leaf area of each spur on a tree were known, the number of flowers that a tree would bear in the following year could be estimated, and, if spur relative fruit set were constant, spur fruit bearing and yield of that tree could be predicted. However, although the relationship between spur fruit density and PYSLA was significant, it was weaker than the relationship between spur flowering and PYSLA . This was because fruit set was highly variable in almond across years. Relative fruit set varied from 19% to 36% . These data apparently support the large effect of season, and particularly weather conditions, on the fruit set process. In almond, rainfall during the bloom period has been reported to affect pollinator activity and to wash pollen off stigmas . Anther dehiscence also can be affected by rain and high relative humidity . Temperature affects pollen germination, pollen tube growth , ovule degeneration and pollinator activity in the field . Wind can also affect pollinator activity. On the basis of this information, some have hypothesized that yield fluctuations can be explained mainly by variations in climatic factors . Actually, large relative fruit set variability also occurred among individual trees . This fluctuation could be a result of “on-trees” and “off-trees” occurring in the same orchard and season . On the other hand, fluctuations of relative fruit set of spur populations in different trees exposed to the same climatic conditions suggest that climatic conditions are not the major factor influencing tree spur population fruit set. In this experiment, at the spur level, there was no correlation between the PYSLA and relative fruit set in the current year . Thus, whereas previous year conditions are fundamental for flower formation on spurs , previous year leaf area did not appear to influence current year spur relative fruit set. Furthermore, spur fruiting was associated with reduced spur leaf area in the current season, suggesting that current year spur leaf area does not exert any influence on spur relative fruit set . In this experiment, the number of nuts borne by individual trees was significantly correlated with the number of nuts borne by the tagged spur populations in those trees . This suggests that our spur sample was relatively representative of the spur population of the trees. On a whole tree basis,plastic garden pots tree yield was not correlated with mean relative fruit set measured on tree spur populations. Instead, tree yields appeared to be more closely correlated with flower density on the tagged spur population. Thus, while relative fruit set is obviously important, it was not the primary yield limiting factor in this orchard situation, and increased relative fruit set when floral densities were low did not compensate for lower numbers of flowers . There were significant correlations between spur flower density and tree yield over years ; for individual years, the relationship was significant in 4 of the 6 years of our experiment . On the other hand, the relationship between tree relative fruit set and tree yield was not significant in any of the 6 years of the experiment.

However, it should be noted that the coefficients of determination were low due to the large number of points and the limited size of the spur sample compared with the total number of spurs borne by each tree; only 5.3% of the variability in tree yield can be explained by spur flower density. These results support the validity of flower density as an important parameter in the evaluation of almond cultivars . These data support the importance of total flower production for obtaining large crops. As a result of these spur dynamics studies, it is clear that the key to optimizing yields in commercial almond orchards is to focus on maximizing healthy populations of productive spurs. Some spur mortality is unavoidable and linked to insufficient spur leaf area associated with spur bearing and spur shading . Thus, continued productivity is dependent on spur renewal that is achieved by ensuring that there is annual growth of as many existing spurs as possible and new shoots that provide sites for new spurs . Health of spurs is also a function of total canopy light interception and good light distribution with the tree canopy . It is clearly important to select cultivars with the ability to produce large numbers of flowers and have crop management practices aimed at limiting abiotic stresses during the vegetative season . In an experiment not potentially biased by experimental manipulation , these results support the assertion of Kester and Griggs that reductions in total number of flowers due to adverse orchard conditions are not likely to be compensated for by increased relative fruit set when adequate pollinizers and pollinators are present and can result in some measure of crop reduction. Such was the case in this study since it was conducted in an orchard in which the ‘Nonpareil’ trees were flanked by two pollinizer cultivars selected for bloom overlap with ‘Nonpareil’ and relatively high populations of bee pollinators were placed in the orchard each year to facilitate pollination. Had such factors not been present in the orchard during bloom, it is likely that relative fruit set would have varied even more among years and measured tree yields would have been more dependent on variations in relative fruit set.Refrigeration can lead to post harvest loss and waste , although it is the most effective strategy to maintain the quality and prolong the shelf-life of horticultural products. The rates of metabolic reactions increase 2–3-fold for every 10°C rise in temperature, and low-storage temperature delays deterioration by slowing down respiration and ethylene production, and by reducing pathogen growth and water loss. Commodities such as apples, blackberries, blueberries, cherries, and grapes benefit from refrigeration, however, in produce originating from tropical and subtropical regions, such as tomato, banana, pineapple, potato, and basil, refrigeration may lead to injury. Postharvest chilling injury is initiated when the tissues of cold-sensitive species are stored between 0 and 15°C, but becomes apparent after transfer to warmer conditions. Because the affected species are taxonomically diverse and the organs affected vary, for example, fruit, tuber, root, leaf, and stem, PCI symptoms can be variable . However, some common phenotypes include tissue browning or blackening, pitted surfaces, shriveling, negative changes in texture, carbohydrates and aroma volatiles, and fungal infection. PCI severity is determined by many factors with temperature and storage time being the most important. If low temperatures are mild and exposure istransient, many metabolic functions will resume after rewarming, and visible symptoms may not develop.