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 .