Spatial data management for livestock distribution in Ethiopia

Ethiopia has the largest livestock population in Africa. It is estimated at 105 million tropical livestock units, which includes 49.3 million heads of cattle, 47 million heads of sheep and goat, 8.3 million equines, 760 thousand camels and a poultry population of 38.13 million. The sector contributes to the livelihood of 60-70% of the Ethiopian population - this translates into approximately 44-52 million people whose subsidiary needs and economic activities rely on livestock production. Understanding the spatial distribution of livestock species is crucial in order to devise a feasible and geographically targeted livestock development policy. The aim of this study is to identify the research gap on the spatial distribution of livestock in LIVES project and to fill the gap by reviewing different literatures and related works. Besides, we proposed and developed a web-based spatial data management application for the LIVES project that makes use of researchers and academicians to have a ground reference to make further research and improve the internal operations of the organization, raises community satisfaction, and simplifies spatial data management and exchange. In order to conduct and justify this project we reviewed different journal articles and related works. In addition we have discussed with the GIS expert of LIVES project. In the mean time we have also collected livestock shape files from the LIVES project so as to use as an input to the proposed prototype web based application. Through the course of this project we were able to observer and identify that the LIVES project handles and manages their spatial data using ArcGIS Desktop application that lacks to handle and render up-to-date and quality livestock information via the web. We believed that having a web based application is necessary to permit timely livestock information dissemination, easy access, and render up-to-date livestock information to the ix public. Furthermore, it improves the efficiency of spatial data management and provides quality of services through the web

AEZWIN An Interactive Multiple-Criteria Analysis Tool for Land Resources Appraisal

Since the early 1980's, the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) have been collaborating on expanding FAO's Agro-Ecological Zones (AEZ) methodology of land resources appraisal by incorporating decision support tools for optimizing the use of land resources. Agro-ecological zoning involves the inventory, characterization and classification of the land resources for assessments of the potential of agricultural production systems. The characterization of land resources includes components of climate, soils and land form, basic for the supply of water, energy, nutrients and physical support to plants. When evaluating the performance of alternative land utilization types, often the specification of a single objective function does not adequately reflect the preferences of decision-makers, which are of multi-objective nature in many practical problems dealing with resources. Therefore interactive multicriteria model analysis (MCMA) has been applied to the analysis of AEZ models. A user friendly interface has been developed and documented in order to permit use of the software by persons with only very basic computing experience. The methodology of MCMA is illustrated in the companion paper by a detailed tutorial example

Land as a Renewable Resource: Integrating Climate, Energy, and Profitability Goals using an Agent-Based NetLogo Model

The objectives of this study center over the course of the beef production life cycle as a management strategy to optimize the financial and natural resource endowment on farms at the county level based on the data available. Although the application is to West Virginia, implications can be derived for other areas with similar resource endowments. The beef farms located in adjacent locations within a county are identified as suppliers of inputs to the farm of interest (or contracting farm) in order to provide the basic foundation for agglomeration economies.;Both an intertemporal component and a spatial component are involved since clustering systems are enhanced when key players are interconnected over space. This is accomplished by using an optimal control framework as the basis of a NetLogo agent-based model (ABM) that explicitly includes a spatial component. This model is intended to provide a foundation for developing agglomeration economies in which other locations are able to supply resources to given locations - or to serve as input markets - by taking advantage of the spatially integrated nature of the agriculture industry. The spatial component provides the basis for regional economic development through clustering among the agricultural and other sectors since they might share locally produced inputs/outputs in the supply chain, thereby enhancing both scope economies and agglomeration economies. Thus, the integration of environmentally friendly technologies that enhance diversified products for the area such as renewable energy as well as digested manure along with high quality beef products and carbon offsets would create new markets which expand market channels and spur economic development, of interest to policy makers at all levels. As a result, farmers would be able not only to produce essential inputs for their own farms but, given appropriate incentives, would also supply them to adjacent farms boosting the local economy. Furthermore, a comparison with conventional, confined animal feeding operations (CAFOs), is briefly provided for perspective as well as the basis for environmental improvement through PBB techniques. Our intention is to replicate a diversified PBB industry and its interaction with surrounding communities in order to identify the optimized paths of the farmer and society in an intertemporal setting. The design of policy instruments is based on the results from the ABM wherein maximizing farm-level profitability that is able to bring benefits to society in which clustering among locations contributes in intensifying the benefits from the adoption of sustainable best management practices (BMPs). Thus, the explicit recognition and use of multifunctional land attributes enables us to address bio-fuel production and climate-related issues such as carbon offsets as well as to expand adoption of sustainable BMPs across space and time. In order to determine policy instruments, we ran our ABM with the absence of carbon prices and cost-share programs as well as different carbon prices and cost-share percentages under different clustering systems along a planning horizon of 15 years. We also compared the profitability between a diversified entrepreneur with a specialized business as an approach to identify the financial motivation to establish our proposed business concept. Results indicate that in order to observe environmental and social benefits as well as economic development in Appalachia through the introduction of a diversified PBB industry, a combination of cost-share policies and carbon prices must be considered. Our results imply that for an average grass-fed beef enterprise with 93 acres of pastureland (as is typical of Appalachia) as the primary resource surrounded by nearby cow/calf farms within an approximate 20 mile radius, will need to rely on a minimum of {dollar}13 per ton CO2e reduced along with a cost share program willing to share the risk of no less than half of the capital investment associated with an anaerobic digester within a clustering system of up to two participants to successfully diversify its business bringing environmental and economic development to the region. Alternatively, a policy combination of 50 percent cost share with a {dollar}26 carbon price not only will enhance environmental improvement but also profitability under unexpected as well as certain weather conditions. We also found that more renewable energy can be generated when more farms join a regional cluster, implying a synergistic effect through clustering. We estimate results under both deterministic and stochastic situations. The latter relate primarily to weather uncertainty and animal death loss, since those are the variables for which data is available. (Abstract shortened by UMI.)

Showcasing Twenty Years of Venice Project Center Results Using Interactive Online Infographics

Amongst the wealth of data accumulated in Venice over the student project center\u27s twenty year existence, few strides have been made to publish the information in a graphically stimulating and user engaging manner. This project contributed to the ongoing initiative of the Venice Project Center with the launch of a visualization and data resource website. In support of the continued advancement of information graphics and data manipulation in Venice, the website aims to showcase the work of the project center and its students. Techniques and software for large scale data representation both linear and geospatial were researched and several recommendations were made towards the progression of interactive information dissemination regarding the problems facing the city

RELATIONSHIPS BETWEEN BEHAVIORAL MEASURES AND PRODUCTIVITY IN GROWING BEEF CATTLE

The relationships between behavioral measures, growth performance, and immunocompetence in receiving beef steers were explored in three experiments. Specifically, exit velocity (EV) and objective chute score (OCS) were examined as temperament measures. In experiment 1, no main effects or interactions with degradable intake protein (DIP) or interactions between temperament measures were observed (P ≥ 0.11); however, high OCS steers had greater intake and gain:feed (P \u3c 0.10) and slow EV steers had higher intake and gain (P \u3c 0.10) than their counterparts. In experiment 2, during week two of social observations, dominance hierarchy rankings were dependent on OCS and EV (P \u3c 0.05); slow EV steers also had increased antibody responses and gain (P \u3c 0.10) and high OCS steers had increased gain (P \u3c 0.10). In experiment 3, subjective chute scores and OCS were positively correlated (P \u3c 0.01) and both EV and OCS treatments changed over time (P \u3c 0.10); intake, vaccine titer response, and gain:feed responses to monensin were dependent on OCS treatment (P \u3c 0.10). The studies suggest that temperament, measured by EV and OCS, affects growth performance and health related measures and is related to social dominance behavior in receiving beef steers