Existing trait-based models that predict abundances of relevant taxa could serve as a useful starting point

Farmers do not manage for traits directly, but rather manage agroecosystems by manipulating the abundances and location of species or through physical and chemical manipulation of the agroecosystem . Traits are used implicitly by selecting or promoting species that have certain functional properties . Yet, management targets based on functional traits offer an opportunity to create management strategies tailored to environmental conditions and biotic interactions when the relation between species, their traits, and the environment is well understood. Given that farmers manipulate species and their abiotic environment, effective management strategies require an understanding of how trait response to the environment can be translated to the relative abundance targets for species. Farmers could then manipulate the biological, physical, or chemical components of agroecosystems to achieve these abundance targets. Management targets could be generated through quantitative trait-based modeling that converts functional trait based objectives into targets for the relative abundances of species .In this way, data on functional traits of a local species pool could be used to determine the relative abundance of species needed to achieve a functional traitgoal. A management strategy could then be implemented to try to achieve this relative abundance and then to test whether the implemented community meets the established functional trait goals and the delivery of the desired ecosystem services. For planned diversity, establishing communities with certain relative abundances is relatively straightforward . Storkey et al. used a model of plant competition to identify the community of 12 different cultivated legume species that delivered the greatest value of multiple ecosystem services. Low to medium levels of species diversity that captured wide functional contrasts were identified as being optimal. For associated diversity, which depends on ecological processes embedded in an agricultural setting, establishing and maintaining communities requires understanding how species, and their traits,ebb and flow tray respond to the specific management practices used; for example, how response traits determine the response of pollinator abundances to the presence of certain types of planted vegetation.

Several approaches have been proposed, for example, to increase the abundance of pest enemies, including habitat modification and food supplementation. However, it has been difficult to empirically assess how these factors contribute to the balance of natural enemies and pests and, thus, the level of pest control and resulting differences in crop yields. Given the importance of space and trophic position in determining agroecosystem services, trait-model iterations of management targets ought to be applied to specific spatial and trophic scales. Given that the implementation of these targets is iterative , it will be important to also consider how the properties of species and ecosystems change over the course of implementation .The problem of sample selection arises frequently in agricultural economics, such as in studies of individuals’ wages or labor supply. With large data sets of “well-behaved” data, the traditional approaches perform well These models include the two-step and maximum likelihood sample-selection approaches as well as the semi-parametric class of estimators . However, when sample sizes are small, data are non-experimental and somewhat contaminated,’ perhaps due to multicollinearity, and the researcher is not sure what data-generation process underlies the data, the traditional models may have difficulties and may produce unstable results. Unfortunately, many if not most data sets have these limitations and therefore traditional methods may not be fully satisfactory. Our objective is to summarize a new, semi-parametric approach for estimating small data set sample-selection problems and use it to examine an important problem in agricultural labor economics. The approach we take grew out of information theory and is based on the classical maximum entropy approach and the generalized maximum entropy work of Galan, Judge and Miller. Our main goal is to estimate the set of unknown parameters, incorporating all the possible information in the estimation procedure without making apriori assumptions regarding the underlying distribution. We use our method to study how agricultural employees choose to work in piece-rate or time-rate sectors, how the wage equations differ across these sectors, and how the female male wage differential varies across regions. Because we are interested in regions, the sample sizes are relatively small and traditional approaches may not perform well. We compare our estimates to those of four other methods. The first section specifies the sample-selection model. Section 2 develops the background and discusses the GME estimation model. Section 3 lists the relevant inference and diagnostic measures. Section 4 discusses the data and the main empirical results.Section 5 contains conclusions.

We want to examine how individuals decide whether to work in the piece-rate or time rate sectors of the agricultural labor market, whether women are paid less then men in these sectors, and whether these earnings differentials vary geographically. Consequently, we estimate the same model for various regions of the country. In these models, wage depends on the X matrix which includes age and age squared; farm work experience and its square; and dummies for white , females, and legal status . The C matrix includes these variables and whether the individual can speak English. For the Western Plains region, we drop the amnesty dummy due to lack of variation and include a dummy for Texas. We do not estimate the model for the North West region due to the lack of variation in many variables. We estimated models of piece-rate and time-rate wage equations and selection equations for each region using the GME and four other models: ordinary least squares , Heckman’s two-step estimator, Heckman’s fuIl-information maximum likelihood estimator, and Ahn-Powell’s method. The consistency of both of Heckman’s estimators depend on the assumption of joint normality of the residuals, which may be violated in our samples. Neither Heckman model produces fully acceptable estimates for any region. In the following tables, we do not report estimates for Heckman’s maximum likelihood estimator because it either falls to converge or its estimated ‘correlation coefficient lies outside the [-I, I] range for every region. We do list estimates for the Heckman two-step procedure even though the correlation between residuals of the selection equation and the wage in at least one sector lies outside [-I, I], for each sector. Where such a violation occurs, we report a “constrained” correlation coefficient of -1.The AP model uses a two-step estimator where both the joint distribution of the error term and the functional form of the selection equation is unknown. Because the AP estimator is robust to mis specification of the distribution of residuals and the form of the selection equation, we expect the AP estimator to perform better than Heckman’s parametric two-step estimator for large samples. Whether the AP method has an advantage in small samples is not clear. 

Table I reports estimates of the wage coefficients for the Mid West. Though the general sign patterns are similar across the models, the GME coefficients tend to have much smaller asymptotic standard errors than the other estimates – especially in the piece-rate sector, which has few observations. The coefficient patterns are generally similar to those found in the literature , but less precisely rneasured by the Heckman estimators, presurnably because the earlier studies used larger samples than here. For all models that we can, we calculate the R2 goodness of fit measure for both wage equations using the same method as for ordinary least squares. The AP model does not have a goodness of fit statistic as it does not estimate constants.The Heckman does slightly better at predicting the time-rate sector, but the GME does better in predicting the piece-rate sector. The GME does better overall, correctly predicting 92.5 percent compared to 86.8 percent for the two-step method. Results are sirnilar in other regions. For example, in the Western Plains region , the Heckman model predicts 79.2 percent of the observations accurately, while GME predicts 98.7 percent correctly. The corresponding percentages are 69.5 percent and 93.4 percent for the South East and 93.5 percent and 100 percent for California . For ease in comparing the various models, the Heckman sample-selection prabit equation contains the same variables as in the C matrix, which we use in the GME model to estimate the relative cost of being in the time-rate sector in each of the inequality restrictions . However, one right argue that only the constant term and the “extra” variable – the ability to speak English – belongs in the C matrix. The entropy-ratio test that the other nine coefficients are zero is 0.02, which is smaller than the critical value of X~ using a 0.05 criterion. Thus, we conclude that these other nine variables do not contain statistically significant information.We also examined whether the female-male wage differential varies across the country. We expect these differentials to vary regionally because agricultural labor markets are regional ,4×8 flood tray cover different crops, have different lengths of employment, and employ workers with different demographie characteristics. Table 2 shows the estimates of the coefficient on the female dummy for each estimated region. Because the left-hand variable is the logarithm of hourly earnings, these values are approximately the percentage difference between women’s wages and men’s. We find large differentials that vary substantially across regions. The GME estimates are closer to zero in most cases and have much smaller asymptotic standard errors than do the two-step estimates. The sign patterns for the two estimators are the same except for piece-rate workers in the Western Plains. The GME estimates indicate that women are paid substantially less than men except in the piece-rate sector in the Western Plains and the time-rate sector in California and that these differentials are statistically significant using a 0.05 criterion.We are in an exciting time in Biology.

Genomic discovery on a large scale is cheaper, easier and faster than ever. Picture a world where every piece of biological data is available to researchers from easy-to-find and well-organized resources; the data are accurately described and available in an accessible and standard formats; the experimental procedures, samples and time points are all completely documented; and researchers can find answers to any question about the data that they have. Imagine that, with just a few mouse clicks, you could determine the expression level of any gene under every condition and developmental stage that has ever been tested. You could explore genetic diversity in any gene to find mutations with consequences. Imagine seamless and valid comparisons between experiments from different groups. Picture a research environment where complete documentation of every experimental process is available, and data are always submitted to permanent public repositories, where they can be easily found and examined. We ‘can’ imagine that world, and feel strongly that all outcomes of publicly funded research can and should contribute to such a system. It is simply too wasteful to ‘not’ achieve this goal. Proper data management is a critical aspect of research and publication. Scientists working on federally funded research projects are expected to make research findings publicly available. Data are the lifeblood of research, and their value often do not end with the original study, as they can be reused for further investigation if properly handled. Data become much more valuable when integrated with other data and information . For example, traits, images, seed/sample sources, sequencing data and high-throughput phenotyping results become much more informative when integrated with germplasm accessions and pedigree data. Access to low-cost, high-throughput sequencing, large-scale phenotyping and advanced computational algorithms, combined with significant funding by the National Science Foundation , the US Department of Agriculture and the US Department of Energy for cyber in frastructure and agricultural-related research have fueled the growth of databases to manage, store, integrate, analyse and serve these data and tools to scientists and other stakeholders. To describe agricultural-related databases, we use the term ‘GGB database’. GGB databases include any online resource that holds genomic, genetic, phenotypic and/or breeding-related information and that is organized via a database schema, and contained within a database management system , or non-relational storage systems. GGB databases play a central role in the communities they serve by curating and distributing published data, by facilitating collaborations between scientists and by promoting awareness of what research is being done and by whom in the community. GGB databases prevent duplicated research efforts and foster communication and collaboration between laboratories . As more and more organisms are sequenced, cross-species investigations become increasingly informative, requiring researchers to use multiple GGB databases and requiring that GGB databases share data and use compatible software tools. Use of common data standards, vocabularies, ontologies and tools will make curation more effective, promote data sharing and facilitate comparative studies .

Viticulture is one of many agricultural industries affected

No evidence was found that GI, respiratory, or muscular problems of the head of the household affected the probability that the worker’s family would receive welfare payments are defined as the sum of the worker’s daily piece rate earnings and wages. Since five individuals in the larger sample reported implausible earnings, a sample of 362 individuals was used in the tobit regression reported in in table 4. Thirty~one of these individuals had zero earnings. This figure is lower than the percentage on unemployment compensation or welfare . The explanatory variables include demographic characteristics ; how long the worker had lived in Tulare County ; and the three health variables. None of the three health measures had a statistically significant effect. Indeed, only the age variables had statistically significant effects. Earnings rise with age until one reaches 35.6, then they fall with age. The effects, however, are small. A 45 year old worker only earns $1.31 less per day than one who is a decade younger and a 55 year old worker earns $5.58 less a 35 year old worker. Thus, in our sample, agricultural earnings do not vary much with respect to personal characteristics. Other studies of agricultural workers find substantial effects of union status and personal characteristics on earnings. The difference across studies is probably due to the relative homogeneity of our sample, which was restricted to field wcrkers in crop agriculture. Many census-based surveys also include dairy, livestock, and non~field worker employees. The narrowed focus of this survey explains the lack of impact of gender, education, or other factors on earnings. Further, in the surveyed county, unions have relatively little market power. A measure of job sanitation could be included in the earnings equation to capture a compensating earnings differential for more sanitary employment. Including a dummy variable for sanitary conditions has virtually no effect on the other coefficients.

The coefficient on the sanitation dummy is 0.0844 with an asymptotic t-statistic of 0.68. Thus, there is no evidence of a compensating differential. Five major policy-oriented conclusions can be drawn from this study. First,outdoor vertical plant stands in spite of California law mandating field toilets, over a quarter of all Tulare County workers, and half of workers younger than 20, did not have access to toilets. Second, as was expected, unsanitary work conditions, as reflected by the lack of field toilets, led to SUb-stantially higher rates of gastrointestinal disorders. However, the lack of sanitary conditions on the job is not a proxy for other dangerous conditions that cause respiratory or muscular problems. Third, living conditions also greatly affect health. Although the lack of a home toilet did not have a comparable effect. the lack of a home refrigerator more than tripled the probability of gastrointestinal problems. Fourth. only respiratory problems. of these three health variables, lead to higher unemployment compensation rates. None of the three health variables was statistically significantly related to either receiving welfare or lower daily earnings. Fifth, Mexican-born agricultural workers and their families were relatively unlikely to use the welfare system. However, these workers were relatively more likely than others to receive unemployment compensation. These results indicate that the probability of gastrointestinal disorders can be substantially reduced by improving living conditions and job site sanitation. While these disorders apparently are not severe enough to reduce earnings or increase demands upon the welfare system, they lower workers’ standard of living. Similarly, the standards at public camps should be examined more closely. Although such camps are subject to routine health inspections, whereas private camps are not, only public camps are associated with health problems in this sample. Indeed, inhabitants of public camps had over 4.25 times as high a probability of gastrointestinal disorders, 1.8 times as high a probability of respiratory problems, and 1.6 times as high a probability of muscualar problems as those who lived elsewhere. The net welfare effect of improving work place sanitation depends on the value workers’ place on such amenities, the costs to employers of providing sanitation, the negative effects of disease on labor productivity, and the societal cost of treatment of disease symptoms. Dunn has shown that the value workers put on field toilets is greater than the cost of providing them and.These results indicate that the productivity losses from the three diseases studied are relatively minor, as wages are little affected .

Thus, although Dunn’s study shows that workers value field toilets six times as much as the cost of providing them, this study failed to find additional benefits due to the improvement in societal output. Given the nature of the data set, we are unable tc obtain precise measures of the social cost of providing medical care . The cost to workers at local public clinics ranged from $12 up per visit. These costs exclude medicine, physician time, and the externality cost on local facilities . Consideration of these factors can only strengthen the case for providing more sanitation on the job. This study shows additional social benefits of reduced respiratory illness due to lesser demands on the unemployment compensation system, an issue largely ignored to date. Since many adverse living conditions contribute to all three types of disease, the public policy debate should also consider the costs and benefits of ameliorating living conditions, particularly for seasonal workers.Of the estimated 3 million farm workers in the United States, approximately 1 million are employed in California, where a well-documented labor crisis has been driven by diminishing numbers of male migrant workers.In 2017, Napa County vineyards, which employ 10,000 farm workers, experienced an estimated 12% shortage of vineyard laborers . This labor shortage could have been considerably worse if not for an influx of female workers into the Napa County labor pool. Between 2013 and 2016, the proportion of female seasonal laborers in Napa vineyards increased from 10% to 25%, mirrored by a smaller increase in permanent laborers . There are indications that similar gender shifts are occurring in other California regions. The economic motivation is, therefore, stronger than ever for agricultural companies to reduce barriers to the employment and retention of female workers. One such barrier is workplace sexual harassment .Studies in the United States estimate that from 40% to 75% of all working women have experienced SH and that rates have not decreased since the 1980s . Furthermore, SH rates are higher in male-dominated and lower-income jobs , categories that include agricultural labor . California is no exception; in one study, 80% of female farm laborers reported experiencing SH and, in a recent survey of farm workers in northern California, 44% of women reported SH . Defined as “unwanted sex-related behavior at work that is appraised by the recipient as offensive . . . or threatening her well-being” , SH of women is one of the most prominent and detrimental barriers to women’s career development and satisfaction . Sexual harassment covers a range of behaviors usually placed on a continuum of severity. One common typology assigns behaviors into three categories: gender harassment, unwanted sexual attention and sexual coercion . All of these behaviors have negative consequences for both the victims and the organizations in which they work . Victims report debilitating effects on their physical and mental well-being . In addition, they are less productive, less satisfied with and committed to their jobs and have higher absenteeism and turnover rates . SH may also act as a stress or for entire work teams, with negative impacts on intrateam interactions, cohesion and performance . All these consequences incur economic costs. To tackle SH, a company needs to understand the antecedents. For example, it is important for a company to know which workers are at highest risk and in what work scenarios SH is most likely to occur.

Organizational studies in other industries have identified multiple antecedent variables of SH over the last 30 years . It was our objective to test these in an agricultural context , with the aims of improving our understanding of which antecedent conditions are present in agricultural work environments, specifically viticulture, and to assess how they are related to reported incidence of SH and work outcomes, that is, job satisfaction and job retention. In doing so, our goal was to provide practical guidance for the local industry and, by extension, other agricultural industries, as well as to learn which approaches may be effective for addressing SH, a significant barrier to women excelling in the workforce. Our study focused on the organizational level of the work team because agricultural workers spend most of their time working in small groups . We quantified three categories of antecedent variables based on organizational models : personal and situational characteristics of female workers, job gender context and organizational climate . Our primary criterion for selecting each antecedent measure was the likelihood a company could influence that variable if it were linked to SH. These antecedents were compared with a measure of SH, which was then compared to work outcomes as a demonstration of how SH can negatively impact productivity . The personal and situational characteristics we measured were age, employment status, vertical plan rack duration of employment in the company, crew size and the presence of relatives on a crew. Previous studies have found that women with temporary employment contracts are more vulnerable to SH than those with permanent fixed contracts , and that younger women are consistently identified as at greater risk than older women .Job gender context refers to the “balance of genders in the work environment” . We adopted a common measure: the ratio of male to female members in a crew. Women have consistently been shown to be more vulnerable to SH in male-dominated teams and organizations than they are in gender balanced or female-dominated contexts . Organizational climate is the extent to which an organization tolerates SH and the effectiveness of any remedies put in place to combat it. A permissive social climate for SH behaviors, as well as failures to properly address complaints by recipients, facilitate SH . Awareness training programs are widespread across industries to educate employees on what constitutes SH and appropriate workplace behaviors . In California, these training programs are mandatory for supervisors in companies with at least 50 employees, but they are not mandatory for crew members. We took an indirect measure of organizational climate, measuring how many crew members had completed SH awareness training, to assess the impact of training on reported incidence of SH. We also measured hostile sexist attitudes associated with perpetration of, and tolerance for, SH . Hostile sexist attitudes were measured using questionnaire items from the Ambivalent Sexism Inventory that reflect aggressive attitudes to women and opinions that women are inferior . We measured incidence and severity of SH using the Sexual Experiences Questionnaire , which quantifies the three types of SH mentioned previously: gender harassment , unwanted sexual attention and sexual coercion . We measured two work outcomes using questionnaires for turnover intentions , which is an established predictor of actual turnover , and job satisfaction, which is negatively linked to turnover . We collected data from male and female Hispanic vineyard workers from 21 distinct crews across nine companies operating in Napa County. The nine companies consisted of seven contract labor companies and two estate vineyard companies who employed their crews directly. Each participating company, except one estate vineyard, had more than 50 employees. Eighty five participants reported they were permanent employees, and 198 participants reported they were temporary seasonal employees. At the time of the survey , all workers were engaged in standard crop-production tasks , but not harvest. Questionnaires were presented to workers in groups during their work breaks. Study questions were displayed on a flip chart while a bilingual researcher read them aloud in Spanish. Crew members answered using electronic response pads , which allowed participants to respond anonymously. Each question also had a “do not wish to respond” option so that participants could opt out of responding to specific items. All questions except the SEQ were presented to all participants, both male and female, within their work crews. After they finished the questionnaires, the male employees returned to work, out of sight and hearing range, and the female workers were taken aside in small groups to conduct the SEQ. All female workers agreed to participate in the SEQ, but some participants chose not to answer all items.

The nation needs to continue its recent trends of investment into rural infrastructure

Originally, it was planned to extend this reform from Anhui to the rest of China within several years after the start of the experiment. The State Council hoped to spread Anhui’s rural tax reform in one third of all provinces in 2002. However, recent problems with the system have appeared in Anhui. Although fees and taxes have been reduced, the fall in local revenues have limited the ability of the local government to implement a number of basic mandated expenditures, including the support of schools, health systems, and basic infrastructure maintenance. Recent government pronouncements have actually put the Anhui experiment on hold. It is likely that successful implementation of such a policy will require substantial reforms in other areas and a basic change in the way that government fiscal resources are shifted to poor areas to support basic services.In one of its most fundamental concessions , China agreed to phase out its export subsidies in the first year of WTO accession. Such subsidies have played considerable roles in assisting with the export of maize, cotton, and other agricultural products into international markets and in this way indirectly supporting domestic prices. In fact, after phasing out export subsidies, several of China’s sectors will likely be subject to much intensive competition from imports. Besides the elimination of export subsidies—which are “Red Box” investments, WTO also puts strict controls on the types and amounts of certain investments. In particular, domestic support to agriculture is divided into “Green Box” and “Amber Box” ones. As is the case with other WTO members, China faces no limitations in the amount that the nation can invest into those activities classified as Green Box, but face carefully circumscribed rules regarding the amount that can be invested into those activities listed as Amber Box policy. Hence, WTO will most likely force China to shift the composition of their investment portfolio. In planning their Amber Box investments, China accession protocol allows a de minimis level of investment that is equal to 8.5 percent of agricultural gross value product.

After intense negations this level was set somewhat below that enjoyed by other developing countries but above that allowed to developed countries . Moreover, the list of items that are used to in the computation of China’s AMS is wider than that used by other countries. For example, certain investment subsidies are not counted in the computation of AMS in developing countries. Developing countries also frequently can classify input subsidies for poor farmers as Green Box investments. Hence, on paper, China’s hands appear to be quite firmly tied in the scope of the investments that they are able to make after their WTO accession. However,vertical tower planter when one begins to add up the amount of fiscal funds that China has historically invested in these areas, it may be that the de minimis limits will not be binding.The biggest impact could be sometime in the future after China grew and its budget constraint was somewhat relaxed. At that time, however, China’s agreement should be thought of as fairly limiting as it closes future options to support its rural areas in ways that its neighbors in East Asia have done . In a post-WTO environment, China’s leaders will give more thought to how it can best use its de minimis budget. Most recently, a study by Huang and Rozelle shows that although most labor intensive agricultural commodities, such as livestock and horticulture, had negative NPRs in late 2001, the time just prior to China’s WTO accession, many land intensive products, including maize, wheat, oil seed crops and sugar, had NPRs ranging from 5 to 40 percent. Moreover, the crops with the positive NPRs are almost all under TQR management, a finding that has important implications how China may want to use its scarce AMS funds. Instead of continuing to support or subsidize these products, China may want to promote these crop productions through productivity enhanced investment measures, such as more agricultural research or transportation and communication investments. Since many of such investments have long gestation periods, the sooner leaders make the investments, the smaller the shock will be after China’s TQR management regime is removed. Although there are no limits on Green Box investments, fiscal constraints will make it so leaders must carefully allocate its investment into non-distorting procuctivity-enhancing activities.

Recent increases in the government’s support to enhance agricultural productivity growth indicate that China already has begun to respond to the challenges posed to China under the WTO regime and believes that investment-enhancing investments will play an important role in making China’s farmers competitive. For example, total agricultural research expenditures in real terms grew annually at more than 10 percent. Growth of these expenditures has grown during the late 1990s . Moreover, China currently considers agricultural biotechnology as one of the primary measures to improve its national food security, raise agricultural productivity, and create its competitive position in international agricultural markets. Public agricultural research investment in plant biotechnology has increased at a rate even faster than the rest of the research sectors . However, despite the growth in spending on agricultural research, investment intensity was only 0.44 percent in 1999, one of the lowest levels in the world . Much more needs to be done. Complementary investments are also needed. For example, financing agricultural technology extension is even more problematic . During China’s reform period, the expansion of the output of agricultural production due to the increased incentives from decollectivization ranks as one of the nation’s great achievements, though a significant portion of that gain arose from the mobilization of inputs. China’s future agricultural production increases, however, may not be able to rely on inputs as much as in the past. Other correlates of development, such as rising wage rates, environmental awareness, resource limitations, and recent China’s WTO accession, mean that there will be pressure on farmers to reduce input use and their production costs. As the nation’s farmers near input plateaus, further growth in output must begin to rely more on technological change and systems must be in place to generate the technology and extend it to farmers.Over the past several decades, tremendous improvements have been made in areas such as transportation, irrigation, and flood control. These projects should be continued in the future. Recent decisions to improve marketing infrastructure, including attempts to set up market and price reporting information and the standardization of agricultural product, are moving the emphasis of officials in the right direction. In other words, it is exactly these types of investments that the government is supposed to and is capable of making.

These are all Green Box policies, meaning there is no limit to the support China can give its domestic agriculture through such productivity-enhancing investments. Such investments may have a number of indirect effects, also. A better environment for China’s producers mean that investors, both domestic and from abroad may be will to transfer in better technology. The government should also invest in the activities that will help promote the import of technology and investment. In some case, productivity-enhancing technology can be more easily obtained by importing new technologies and inputs. In the WTO environment, opportunities exist to reduce the barriers that have been keeping China’s farmers from having access to the lowest cost technology in the world. Restrictions on the imports of seed, pesticides and herbicides and barriers keeping out foreign direct investment in the agricultural input sector should be expected to be gradually removed.Agricultural structure adjustment was considered as the central policy goal of the government in 2000 and further emphasized in 2001. This adjustment includes structure changes among agricultural commodities, quality improvement of China’s major commodities, and the promotion of regional specialization. These new policy efforts, in part taken as part of China’s effort to prepare for WTO membership, is called the “Strategic Adjustment of Agricultural Structure” . Key policies and measures to support these adjustments include many of the actions discussed above. The nation’s leaders believe that if they re-initiate grain marketing reforms,lettuce vertical farming redirect part of the government’s resource allocation from grain and cotton stables toward commodities in which they have a comparative advantage, such as horticulture crops, and the promotion of regional specialization. To do so, the focus of leaders is also new. They plan on relying more on technology improvement, investments in infrastructure, and setting up an environment in which local agricultural enterprises and integrated agricultural production and marketing can occur. Although out of control of those who are directly in charge of agriculture, there are a number of policies that can complement the structural transformation of agriculture and which will serve to make China more competitive in its post-WTO environment. For example, ultimately, agricultural producer must dramatically increase the scale of their operation. But, this will not occur until massive amounts of labor shift into the off-farm sector, in general, and into urban areas, in particular. Hence, policies that promote labor movement will also be good for agricultural income and production. While a complete discussion is beyond the scope of this paper, national leaders need to promote employment policies that will help lead to more urbanization , promote rural township development , and labor market development and irrigation. In addition, efforts to increase agricultural production and productivity should be in tandem with improvements in farmer’s ability to store food.The importance of agriculture in Mozambique stems both from a high percentage of the population engaged in agricultural activities, and from its economic contribution to the gross national product.

Agricultural productivity, however, remains very low, even by African standards. Zavale, Mabaya, and Christy report that maize yields are estimated at 1.4 tons/ha, far below the potential yields of 5 – 6.5 tons/ha. They also found that with the current technology, scope exists for fostering cost efficiency by 70 percent without any loss of the output.Besides cost inefficiency, a number of equally important factors are associated with low agricultural productivity in Mozambique. First, the use of improved agricultural technologies is very limited and unequal. Most of the production is rainfed, with extremely low use of external inputs, particularly among the poorest households, who also depend more on agricultural income. Additionally, of the 2 percent of farmers that used tractor mechanization in 2005, 49 percent were located in Maputo province, a region of relatively lower agricultural potential, but of better infrastructure, including roads. Second, associated with a lower use of improved agricultural technologies are credit and insurance market failures. Asset ownership, particularly liquid assets , and access to non-farm income activities have been shown to play an important role in overcoming credit constraints [10,11,12]. Furthermore, agricultural productivity rises significantly with increases in household income in parallel with the diminishing reliance on agriculture of wealthier households. Third, in Mozambique the beginning of the rainy season coincides with the highest rates of malaria incidence. Delays in some agricultural operations due to malaria or any other reasons usually translate into lower production per unit area. Farmer’s health status has been systematically ignored in adoption or impact assessment studies, much less malaria. Notwithstanding its importance, HIV/AIDS pandemic is given far more attention, one of the arguments being its potential effect on labor availability.Fourth, farmer’s nutritional status also plays a crucial role in enhancing agricultural productivity levels. Post-harvest losses significantly reduce household access to food during the dry season. When faced with prospects of high food storage losses, farmers are compelled to forego opportunities for inter-temporal price arbitrage through storage and are observed to sell their produce right after the harvesting season at prices lower than observed prices for purchases in the subsequent lean season. This has been dubbed “sell low, buy high” puzzle. As a result, many farmers are unable to purchase food during the dry season, debilitating their nutritional statuses, which deteriorate their ability to undertake some agricultural operations. To make matters worse, agricultural productivity and land availability appear to be shrinking for many Sub-Saharan African countries , including the apparently land-abundant countries like Mozambique. Jayne et al. found that the average per capita cultivated area has been declining over the last 40 years in SSA.

Regulated agricultural sources will be provided a financial incentive to aid in compliance

According to the California League of Conservation Voters , “Because agriculture has gone unregulated for so long as a source of air pollution while other sectors have been subject to air quality rules, there exist many viable opportunities to reduce air pollution from agricultural sources.” Thus, ending the exemption not only helps avert national sanctions, but will help the state clean the air . This mirrors Florez’s stated intent in offering the bill in the first place. According to Pollard , “Florez said he introduced [SB 700] because agriculture is a major contributor to air pollution that is related to epidemic levels of asthma in children and other health problems in the Central Valley.” In an attempt to put a “face” on the victims, Florez had residents from across the valley testify on behalf of SB 700. As reported by Grossi in the Fresno Bee , “Caleb Schneider, 16, of Hanford, said he has asthma, and he wants to see every effort made to clean the air. ‘When you can’t breathe’, he said, ‘nothing else matters.’” This narrative depicts the agriculture industry as a villain deserving of public policy burdens. The numbers and comparisons define the level of the burden. Since the contribution of the agriculture industry is “significant,” their responsibility in the cleanup should be proportionate. The proposed solution will make agricultural sources a part of the regulatory process just like every other industry in the California. For supporters of SB 700, this creates a sense of equity in the treatment of all sources of pollution. While this harkens back to the complex cause narrative, there is no doubt that supporters have emphasized the role of agricultural sources in the air pollution, allowing others to fade into the background. There is a strategically constructed link between the agricultural sources and the exemption . This adds to the somewhat negative construction of the agriculture industry. There is also some concern about the arbitrary and capricious nature of the regulatory structure of SB 700.

Many in agriculture do not see themselves as being like other industries. According to Roger Isom,vertical hydroponic garden vice president of the California Cotton Ginners and Growers Association, “It’s not like ag is an industrial source that’s going day after day. It’s seasonal. The question is how can we do our share and not be put out of business” . An editorial in the San Francisco Chronicle makes the case for differential treatment, “The farmers have a decent case for special consideration. A range of 200 crops call for different farming methods, making rule-making tricky. In a struggling economy, new costs should be minimized. As always, water, land prices and import figure, too” . The last component of this narrative is the potential consequences of imposing an unfair and overly broad regulatory approach on agriculture. Opponents of SB 700 argue the agricultural community will not be able to “absorb the additional operation costs resulting from new regulatory fees imposed by LADs, given the international competition in the marketplace for most agricultural operations” . While increased costs and decreasing competitive advantage will plague the industry, there will also be impacts felt at the level of individual small farms. According to state Senator Chuck Poochigian , “They are not corporate magnates. They are ordinary people trying to make a living. They are losing their farms. They are making no money at all in some cases. . . . [The bill] punishingly exceeds federal regulations” . This narrative uses very different language than the previous one . Here the agriculture industry is more often referred to as farmers, growers, ranchers, and dairymen. This “puts a face” on the seemingly faceless, corporate agriculture industry. It is these individuals that face the unfair and overreaching regulations of SB 700.

There is also a different interpretation of the multi-causal narrative. Instead of emphasizing the contributions of agriculture, all of the other sources are placed front and center. This is especially true of passenger vehicles and sprawling development. It is simply inequitable to single-out agriculture for regulation when this will result in increased costs with little or no improvement in air quality. It is only by using a modest approach to address California’s air exemption that this pain can be avoided. Hence, it is the regulatory approach of SB 700 that is the problem in this narrative, not air pollution in the valley. The narratives, as captured by the NPF, have a strong link to the theories of policy design discussed by Schneider and Ingram.Both the portrayal of characters and proposed policy solutions fit with the social construction of target populations and their hypothesized links to elements of policy design. This allows the use of these narratives to hypothesize about what kind of policy tools, agents, and implementation structures will be contained in SB 700. Both the “complex-cause” and “agriculture as significant contributor” provide a characterization or social construction of agriculture as villain in the narrative of causing pollution harmful to the health of citizens. While the “complex cause” narrative has many more villains, “agriculture as significant contributor” has only one and tells a damning tale of intentional causation. Thus, one should expect to see policy design elements used on negatively constructed target populations. The “agriculture as victim” narrative portrays agricultural interests in a much different light. This narrative shows agriculture as the victim of punitive and overly broad attempts to regulate their activities. It provides a more positive construction of this target population. So, given this portrayal, we should expect to see policy designs reflective of a positively constructed target population. All of these narratives coexist with one another in the larger debate surrounding air quality policy and SB 700. Agriculture and its interests are characterized as both villain and victim in the policy discourse.

Different policy solutions are linked to these different characterizations. These varying constructions as agriculture result in seemingly contradictory elements of policy design that both benefit and burden the agriculture industry. The choice of policy tools reflects the social constructions policymakers have used to construct target populations. These policy tools direct the treatment of both targets and agents . Schneider and Ingram argue that different types of policy tools contain different behavioral assumptions about the group being targeted by the policy. Thus, just as the narratives suggest, we should expect to see a mix of policy tools in SB 700 that seek to force the agriculture industry to comply; and those that seek to aid them in achieving compliance. The structure of the regulatory framework itself is based on the premise that the agricultural industry is a significant contributor and will not voluntarily comply. All agricultural sources are required to meet the most stringent technology standards , as well as the best available control measures for mitigation purposes. The required standards reflect what Schneider and Ingram term an authority tool. The expectation is that industry will obey the requirements. A locally administered permit system is another part of the regulatory structure. Agricultural sources emitting 50% or more of major source emission levels for PM-10 and ozone are required to pay a fee to operate or construct facilities. According to Schneider and Ingram , “User fees, rates, and charges also are used as incentives, but these do not carry as much positive valence as inducements.Charges can also be distinguished from sanctions in that they do not intend to convey social disapproval of an activity.” Thus, the regulatory structure itself reflects a somewhat negative to ambivalent tone concerning the agriculture industry. There are a host of other policy tools that will aid agricultural sources in their attempts to comply with the new regulatory framework. These tools echo themes from the agriculture as victim narrative. The first of these is the information clearinghouse on mitigation strategies. This fits the description of a capacity-building tool . These kinds of tools are supposed to “enlighten, remove impediments, and empower action by the target group or agency itself” . The agriculture industry is portrayed as a group that simply needs to learn about the best mitigation strategies available. This suggests a more positive social construction of the agriculture industry. It is not a question of willful neglect, but one of needed education.Specifically, financial institutions that provide service to agricultural interests will be granted access to additional monies in order to make it easier to provide loans to fund air pollution control measures. This inducement implies “respect for the target population and portray[s] a positive valence of the behavior that is desired” . Agriculture will receive financial resources to aid compliance with the new rules developed under SB 700’s regulatory framework. This suggests a positive tool for a positively constructed target group. The nature of the relationship between agent and target reflects themes of the agriculture as victim narrative. The clearest illustration of this relationship lies in the rule-making process for SB 700 . The policy tools utilized here are learning tools. This approach coincides with the “consensus-building” or “support-building” implementation structure . This design is “intended to provide a forum for participation and discussion that will enable lower-level agents or target populations to determine what should be done. Statutes usually allocate discretion to lower-level agents or even target populations” . This implementation structure sets the stage for the negotiation of both PM-10 and ozone rules developed by the SJVAPCD.

Although a large literature describes how recessions affect non-agricultural labor markets, few studies examine the effects of recessions in the seasonal agricultural labor market.1 We examine how the last three recessions affected hourly earnings, the probability of receiving a bonus,vertical home farming and weekly hours in agricultural labor market. We compare those results to those in three non-agricultural labor markets that rely on immigrants. We empirically test five hypotheses. First, we expect seasonal agricultural workers’ earnings to rise during major recessions. Because the income elasticities of demand for seasonal agricultural products such as fruits and vegetables are relatively inelastic, recessions cause a small, possibly negligible leftward shift of the labor demand curve in seasonal agriculture. In contrast, a recession’s may cause a significant leftward shift of the labor supply curve. Roughly half of hired, seasonal agricultural workers are undocumented.2 The Great Recession significantly reduced the number of new, undocumented immigrants entering the United States , causing a substantial leftward shift of the agricultural labor supply curve.3 Given a substantial leftward shift of the supply curve and only a minimal shift of the demand curve, agricultural workers’ earnings rise. Second, while we hypothesize that hourly earnings and the probability of receiving a bonus rose during the Great Recession, 2008–2009, we expect these earnings measures to rise by less or possibly fall in the earlier, relatively minor 1990–1991 and 2001 recessions. The Great Recession caused much larger decreases in new immigrant labor supply than in these earlier recessions . Third, we expect recessions to affect undocumented workers differently than documented workers because their labor markets are partially segmented. Evidence that these markets are partially segmented comes from earlier studies that show that, compared to documented workers, undocumented workers are more likely to be employed by farm labor contractors as opposed to farmers, and because their pay differs . Fourth, we expect weekly hours of employed agricultural workers to increase to compensate for the reduced flow of new immigrants during major recessions. Fifth, we expect recessions to have larger earnings effects in agricultural labor markets than in construction, hotel, and restaurant labor markets. These non-agricultural labor markets are more likely to have sticky wages due to union and other contracts and minimum wage laws. The first section discusses how recessions affect the supply curve of agricultural labor. The next section describes our two data sets. The third section presents our empirical results. The final section discusses our results and draws conclusions.In contrast, during a major recession, fewer undocumented immigrants enter the United States from Mexico and other countries. Passel, Cohn and Gonzalez-Barrera reported a large drop in the number of undocumented immigrants during the Great Recession relative to the recovery years afterward and to preceding years, which include milder recessions. They estimated that the number of undocumented immigrants rose monotonically from only 3.5 million in 1990 until it peaked at 12.2 million in 2007. However, the number of immigrants fell to 11.3 million by 2009 during the Great Recession. In contrast, they found that the supply of immigrant labor rose during relatively mild 2001 recession.These results are consistent with U.S. border patrol reports from the Department of Homeland Security’s Office of Immigration Statistics.

Agriculture’s reciprocal relationship with the overall economy is clear

Many of these assumptions and priorities also influence sustainable agriculture programs. Such an examination is critical if we are to avoid reproducing the problems engendered by conventional decision-making processes in the re- search, education, policy, and business institutions which determine agriculture. KennethDahlberg 9 notes that assumptions and biases which may occlude the development of sustain- able agriculture concepts include: separating ourselves from nature and viewing it as something which must be dominated; measuring progress in increasing applications of science and technology; emphasizing technology and formal social institutions over natural systems and less formal aspects of society; and failing to see how human societies fit into and are dependent upon larger natural systems. We would add to Dahlberg’s list the tendency to overlook the needs of human beings who are separated from us, whether it be by distance, by socioeconomic status, or by time. These types of assumptions govern how we understand the world and have been institutionalized in educational and research pro- grams. MacRae et al. note that many characteristics of the research process responsible for conventional agriculture’s great productivity create obstacles to developing sustainable agriculture. Among these are over reliance on reductionism and quantification, scientists’ belief in objective “truth,” and the divorce of research from its potential social consequences . Along with Patricia Allen those authors also cite obstacles posed by a peer review system and publishing process which tend to reward individual “isolated” achievement while discouraging long-range interdisciplinary work and innovative ideas. This is aggravated by research funding from private sources, which encourages research on technology development rather than social analysis. The same assumptions and biases which govern research and education are also embedded in much of U.S. agricultural policy. They are expressed primarily as short-term economic considerations such as maximizing production, minimizing production costs and consumer prices, vertical aeroponic tower garden and maximizing the market share of certain agricultural commodities. These priorities have largely been those of the agricultural sector, and not necessarily those that are best for society at large.

To address these types of whole-system issues we believe that sustainable agriculture concepts must go beyond placing top priority on environment and production practices and give greater emphasis to social issues. Current definitions are often based on two assumptions that we believe to be problematic: 1) that the farm is the primary locus for achieving agricultural sustainability and 2) that short-term micro-economic profitability is paramount.Major institutions promulgating “sustainable” agriculture often focus on the farm level rather than on the whole system. This is clear from the priorities of the U.S. Department of Agriculture’s Low Input Sustainable Agriculture program. LISA focused on “low input technologies [which] provide opportunities to reduce the farmer’s dependence on certain kinds of purchased inputs in ways that increase profits, reduce environmental hazards, and ensure a more sustainable agriculture for generations to come.”As these priorities demonstrate, agriculture is often thought of almost purely in terms of farms and farmers, a perspective traceable to the period in which most Americans were involved in farm production but which no longer reflects agriculture’s true scope. Even though the on-farm transformation of resources into food and fiber is a core process of the food and agriculture system, it is but one of many components. The system includes not only generating agricultural products, but also distributing those products and the infrastructure which affects production and distribution at regional, national, and global levels. Interactions among the larger environmental, social, and economic systems in which agriculture is situated directly influence agricultural production and distribution. The following briefly describes how these larger systems affect agriculture yet remain unaccounted for in many sustainable agriculture programs.Agricultural practices ranging from the development of irrigation projects to the use of agrichemicals have often had negative environmental impacts such as wildlife kills, pesticide residues in drinking water, soil erosion, groundwater depletion, and salinization. Substituting environmentally sound inputs for those which are damaging is an important step in addressing these problems. But ecological sustainability re- quires intensive management and substantial knowledge of ecological processes which go far beyond substitution and cannot be achieved merely by substituting inputs.

Such substitutions need to account for their complex and long-term ecological consequences. Otherwise they may engender secondary and perhaps more serious problems in the same way that conventional solutions frequently have been shown to do. Viewing agricultural systems as true ecosystems can serve as a model for bringing the whole-systems perspective to bear on social and economic issues as well. Instead, however, sustainability programs often take conventional approaches to solving these problems by changing the production practices which are directly at fault without addressing the total ecosystem context of either the problems or the alternative production practices which show promise as solutions. An example is the current emphasis on input substitution. Most projects funded by the USDA Low- Input Sustainable Agriculture program in its first two years, for instance, explore how inputs which cause environmental damage or incur expensive costs for the farmer can be replaced with more environmentally or economically benign inputs . In most cases single components of farming systems are being analyzed and little attempt is made to place these analyses in the context of whole agroecosystems.Agriculture both affects and is affected by the larger society. Farmer production decisions, for example, determine the diversity and quality of foods available to consumers, and farm size and technologies have been associated with the economic and social vigor of rural communities.At the same time, society deter- mines what is possible at the farm level. Farmers lose valuable farmland when encroaching urbanization creates zoning problems, inflates land values, and generates urban pollution which lowers crop productivity. Production decisions are heavily influenced by consumer decisions. A recent example is farmers’ voluntary discontinuation of Alar on apples. Although farmers continued to endorse the safety of Alar, they realized that this position was untenable in the face of consumer concerns. The international scope of agriculture also plays an important role. Social and economic conditions in other countries and global food supplies can greatly affect the viability of farming in local regions, as evidenced when the world grain shortages of the 1970s led to enormous expansion in U.S. grain production. When foreign demand for U.S. grain subsequently declined, many American farmers’ incomes fell, often to the point where debts incurred to expand production could not be paid, and major social and economic dislocations in the grain belt occurred.

Efforts in sustainable agriculture are not unlike those of their conventional counterparts in that they tend to serve certain clientele selectively and generally do not evaluate the social consequences of the technologies that sustainable agriculture encourages. For example, organic farming strategies are often sup- ported because they are environmentally sound, and in terms of the prices organic foods command, are profitable for farmers. An unintended and unaddressed social consequence of this is that people with low incomes often cannot afford organic products and thus are denied access to food containing fewer pesticide residues.The agricultural industry is a significant portion of the nation’s economy: in 1984 about 20 percent of U.S. jobs were in some aspect of food and fiber production, distribution, or service and these workers and their industries contributed 18 percent of the gross national product. The importance and volatility of food prices have made most governments reluctant to let market forces alone set these prices. Thus, a host of institutional measures have been implemented to address agricultural prices in order to manage their effects on consumer welfare, public coffers, farmer income, foreign exchange, food security, nutrition, and food distribution. Such policies include commodity programs, water and reclamation programs, import/export policies, and research and extension programs. Larger economic factors indirectly affect the agricultural system, factors such as interest rates, trade policy and negotiations, the exchange value of the U.S. dollar, and environmental regulations. In the context of these economic policies, agriculture is subject to non-agricultural constraints and conditions, a fact acknowledged broadly in the literature of both conventional and sustainable agriculture. Yet most research and extension programs in both conventional and sustainable agriculture do not recognize or address thesmacrofactors. Sustainable agriculture efforts generally concentrate on environmentally sound farm-level technologies which are economically profitable for farmers to adopt. Less commonly do such efforts address how the technologies they generate will affect or be affected by larger economic concerns in the long run.A second assumption behind many sustainable agriculture definitions, that short-term profitability is of ultimate importance, is also common. This is a central tenet of LISA, forming the first of its ten Guiding Principles: “If a method of farming is not profitable, it cannot be sustainable.”This is problematic, particularly since there is little acknowledgement that profitability is determined by policies, fiscal procedures, vertical gardening in greenhouse and business structures that can obstruct sustainability. We recognize that short-term profit- ability is important in commercial agricultural systems; clearly, if growers are to adopt sustainable agricultural practices, these must be profitable in the short run as well as the long run. The problem lies in pursuit of short-run profitability at the expense of environmental and social goals. In conventional agriculture, the drive to maximize short-term profit has meant that many pressing problems have been ignored or exacerbated. Natural resources have often been treated as expendable commodities , and agriculture has functioned more for financial gain than for human need.

The social costs of production have generally been neglected: chronic hunger, inequitable economic returns and unsafe working conditions for farm labor, possible negative health effects related to nutrition and agrichemical use, and the decline of socioeconomic conditions in rural communities associated with large-scale industrial agriculture. Subsuming social goals to economic goals may easily be reproduced in sustainability programs unless sustainability concepts address the fact that profitability and social goals are often not compatible in current economic systems.A useful concept of agricultural sustainability needs not only to acknowledge social issues as priorities equivalent to those of production, environment, and economics, but to recognize the need for balance among those disparate but highly interactive elements which comprise agriculture. Toward this, we offer the following perspective: A sustainable food and agriculture system is one which is environmentally sound, economically viable, socially responsible, non-exploitative, and which serves as the foundation for future generations. It must be approached through an interdisciplinary focus which addresses the many interrelated parts of the entire food and agriculture system, at local, regional, national, and international levels. Essential to this perspective is recognition of the whole-systems nature of agriculture; the idea that sustainability must be extended not only through time, but throughout the globe as well, valuing the welfare of not only future generations, but of all people now living and of all species of the biosphere.This sustainability concept moves beyond emphasis of farm-level practices and microeconomic profitability to that of the entire agricultural system and its total clientele. Richard Lowrance, Paul Hendrix, and Eugene Odum16 describe a model which approximates a whole-systems approach. They see four different loci or subsystems of sustainability: 1) farm fields where agronomic factors are paramount; 2) the farm unit wherein micro-economic concerns are primary; 3) the regional physical environment where ecological factors are central; and 4) national and international economies where macroeconomic issues are most important. Their model demonstrates that focusing on only one level of the agricultural system neglects others that are equally essential. A whole-systems perspective fosters an understanding of complex interactions and their diverse ramifications through- out agriculture and the systems with which it articulates. This understanding is at the root of sustainability. Vernon Ruttan17 describes an ever-widening comprehension of “whole system” as he delineates three waves of social concerns which have arisen about natural resource availability, environmental change, and human well-being. In the late 1940s and early 1950s the first wave focused on whether resources such as land, water, and energy were sufficient to sustain economic growth. The second wave, in the late 1960s and early 1970s, focused on the effect of growth-generated pollution on the environment . The most recent concerns, manifest since the mid-1980s, also center on adverse environmental effects, but the key distinction is the transnational issues such as global warming, ozone depletion, and acid rain. As agriculture and its impacts become increasingly globalized, the need for a whole-systems perspective,particularly in terms of decision-making, become increasingly critical. Dahlberg 9 observes that although the impacts of modern industrial society are global, the data and analytical tools we use to assess those impacts are limited by national, disciplinary, or sectoral boundaries.