Suchergebnisse

The Agriculture Resource Management Survey (ARMS) is an annual survey of U.S. farm and ranch operators and it is a primary data resource for a huge array of economic analyses. The Economic Research Service (ERS), collaborating with the National Agricultural and Statistics Service, two Agencies of the United States Federal Government, has attempted to respond to increasing demand for access to this data. However, the survey is conducted under a pledge of confidentiality that allows use of the data only for the purpose of statistical analysis. This raises significant barriers to allowing remote access to the ARMS data. Presented with this conflict, ERS sought to use new technologies to enable the Agency to provide easier access to the data while ensuring its confidentiality. The restricted access extranet application that the ARMS team developed was deployed in September, 2004, and a public version of the tool was deployed less than a month later. This paper will describe both the tool and the process by which it was successfully developed.

The goal of the study is to get insight into agriculture production and market constraints in the AJK region. To achieve the objectives of the study, in the first stage, production function is estimated to obtain technical efficiency scores that are explicitly dependent on the farm and farm-specific variables. For this, single-step stochastic production estimation was applied. Tobit regression was employed in the second phase, with the market participation index as the dependent variable and market accessibility factors and efficiency as explanatory variables. The findings show that all inputs contributed favourably and considerably to farm production, with a mean technical efficiency of 58 percent, indicating that sample farmers might achieve the maximum production frontier by raising their efficiency to 42 percentage points. Among the determinants of technical efficiency farm size, land fragmentation and traction power negatively contributed to efficiency. Market participation was low as approximately 47 percent of the sampled farmers had less than 50 percent market participation and 20 percent did not participate in the market. The remaining 33 percent had market participation greater than 50 percent. The major factors that affect market participation were production efficiency, distance from roads and the market, credit facility, training, experiences, and internet and refrigerator facilities. All these variables were positive and significantly contributed to market participation.

The environment affects agriculture, via soils, weather, etc. and agriculture affects the environment locally at farm level and via its impact on climate change. Locating agriculture within its spatial environment is thus important for farmers and policy makers. Within the EU countries collect detailed farm data to understand the technical and financial performance of farms ; the Farm Accountancy Data Network. However, knowledge of the spatial-environmental context of these farms is reported at gross scale. In this paper, Irish farm accounting data is geo-referenced using address matching to a national address database. An analysis of the geographic distribution of the survey farms, illustrated through a novel 2D ranked pair plot of the coordinates, compared to the national distribution of farms shows a trend in the location of survey farms that leads to a statistical difference in the climatic variables associated with the farm. The farms in the survey have significantly higher accumulated solar radiation values than the national average. As a result, the survey may not be representative spatially of the pattern of environment x farm system. This could have important considerations when using FADN data in modelling climate change impacts on agri-economic performance.

Introduction -- Background: USDA's National Agricultural Statistics Service and Economic Research Service -- Dimensions of farm complexity -- Conceptual issues : defining farming, farms, farmers, and agriculture -- The growing complexity of farm business structure : implications for data collection -- A broader data infrastructure : administrative and other nonsurvey data sources -- Bibliography -- Appendix.

Metadata only record ; Integrated Pest Management (IPM) programs based on the Farmer Field School approach are being implemented in many countries. Their benefits have been recognized by a broad range of stakeholders, including farming communities, local and national governments, NGOs and donors, who are now supporting such programs. Substantial investment in this approach calls for comprehensive documentation of its impact and sustainability. This study reviews twenty-five impact studies and discusses the overall results. Most of the ongoing programs have conducted impact evaluations of a kind. These varied in focus, approach, methodology and robustness. Results, however, commonly remained buried in project reports. Impact evaluation of the IPM Farmer Field School has proven to be complex because of methodological obstacles, because of the range of immediate and developmental impacts, and because of different perspectives of stakeholders. Consequently, there is no agreed conceptual framework for measuring impact. ; Available in SANREM office, FS

International audience ; This paper presents a multi-agent model for simulating attitude formation and change based on perception and communication in the context of stabilization operations. The originality of our model comes from (1) attitude computation that evaluates information as part of a history relative to the individual (2) a notion of co-responsibility for attitude attribution. We present a military scenario of French operations in Afghanistan along with polls results about the opinion of citizen toward present Forces. Based on these field data, we calibrate the model and show the resulting attitude dynamics. We study the sensibility of the model to the co-responsibility factor.

International audience ; This paper presents a multi-agent model for simulating attitude formation and change based on perception and communication in the context of stabilization operations. The originality of our model comes from (1) attitude computation that evaluates information as part of a history relative to the individual (2) a notion of co-responsibility for attitude attribution. We present a military scenario of French operations in Afghanistan along with polls results about the opinion of citizen toward present Forces. Based on these field data, we calibrate the model and show the resulting attitude dynamics. We study the sensibility of the model to the co-responsibility factor.

International audience ; This paper presents a multi-agent model for simulating attitude formation and change based on perception and communication in the context of stabilization operations. The originality of our model comes from (1) attitude computation that evaluates information as part of a history relative to the individual (2) a notion of co-responsibility for attitude attribution. We present a military scenario of French operations in Afghanistan along with polls results about the opinion of citizen toward present Forces. Based on these field data, we calibrate the model and show the resulting attitude dynamics. We study the sensibility of the model to the co-responsibility factor.