Assessment of parasitic load in goat through the examination of faecal matter

Parasitic infection do not show heavy rate of mortality, however there occurrence being chronic, most of the time leads to serious production losses, this led to study about severity of parasitic load and type of parasitic infection in goats. Parasitic infection most of the time leads to serious production losses. Gastrointestinal nematodes are ubiquitous parasites of grazing ruminants and cause decreases in survival, live weight gain, wool and milk production and reproduction performance. Parasitic problems are a serious problem in goat. Total 60 goat faecal samples were analyzed. These results would serve as a baseline for future studies. The majority of the faecal samples (70%) of both zone I and zone II had heavy parasitic load (>3000 epg) followed by 60 per cent samples of zone III. This indicates that majority of the goats of the study area had severe parasitic infection. Chi-square analysis revealed non-significant relation between parasitic load and categories of zones. Majority of samples (48.33%) were infected with the combination of Strongyles, Strongyloides and Coccidiosis. It can be concluded that faecal egg count level was severe in majority of the samples examined

EVALUATION OF MICRO-IRRIGATION, FERTIGATION AND WEED MANAGEMENT IN SUMMER GROUNDNUT

A field investigation was carried out at Junagadh during the summer seasons of 2011 and 2012 to evaluate the comparative efficacy of irrigation methods, fertigation and weed management in groundnut. The results revealed that irrigation-fertigation through sub-surface drip system was proved to be superior in respect of pod yield, weed biomass, WUE and economics. Among weed management practices, two hand weedings at 30 and 60 DAS was found to be the most effective in controlling weeds along with higher pod yield, WUE and net returns

A discrete-pulse optimal control algorithm with an application to spin systems

This article is aimed at extending the framework of optimal control techniques to the situation where the control field values are restricted to a finite set. We propose a generalization of the standard GRAPE algorithm suited to this constraint. We test the validity and the efficiency of this approach for the inversion of an inhomogeneous ensemble of spin systems with different offset frequencies. It is shown that a remarkable efficiency can be achieved even for a very limited number of discrete values. Some applications in Nuclear Magnetic Resonance are discussed

Optimally Convergent Quantum Jump Expansion

A method for deriving accurate analytic approximations for Markovian open quantum systems was recently introduced in [F. Lucas and K. Hornberger, Phys. Rev. Lett. 110, 240401 (2013)]. Here, we present a detailed derivation of the underlying non-perturbative jump expansion, which involves an adaptive resummation to ensure optimal convergence. Applying this to a set of exemplary master equations, we find that the resummation typically leads to convergence within the lowest two to five orders. Besides facilitating analytic approximations, the optimal jump expansion thus provides a numerical scheme for the efficient simulation of open quantum systems.Comment: 13 pages, 3 figure

Examples of Risk Tools for Pests in Peanut (Arachis hypogaea) Developed for Five Countries Using Microsoft Excel

Suppressing pest populations below economically-damaging levels is an important element of sustainable peanut (Arachis hypogaea L.) production. Peanut farmers and their advisors often approach pest management with similar goals regardless of where they are located. Anticipating pest outbreaks using field history and monitoring pest populations are fundamental to protecting yield and financial investment. Microsoft Excel was used to develop individual risk indices for pests, a composite assessment of risk, and costs of risk mitigation practices for peanut in Argentina, Ghana, India, Malawi, and North Carolina (NC) in the United States (US). Depending on pests and resources available to manage pests, risk tools vary considerably, especially in the context of other crops that are grown in sequence with peanut, cultivars, and chemical inputs. In Argentina, India, and the US where more tools (e.g., mechanization and pesticides) are available, risk indices for a wide array of economically important pests were developed with the assumption that reducing risk to those pests likely will impact peanut yield in a positive manner. In Ghana and Malawi where fewer management tools are available, risks to yield and aflatoxin contamination are presented without risk indices for individual pests. The Microsoft Excel platform can be updated as new and additional information on effectiveness of management practices becomes apparent. Tools can be developed using this platform that are appropriate for their geography, environment, cropping systems, and pest complexes and management inputs that are available. In this article we present examples for the risk tool for each country.Instituto de Patología VegetalFil: Jordan, David L. North Carolina State University. Department of Crop and Soil Sciences; Estados UnidosFil: Buol, Greg S. North Carolina State University. Department of Crop and Soil Sciences; Estados UnidosFil: Brandenburg, Rick L. North Carolina State University. Department of Entomology and Plant Pathology; Estados UnidosFil: Reisig, Dominic. North Carolina State University. Department of Entomology and Plant Pathology; Estados UnidosFil: Nboyine, Jerry. Council for Scientific and Industrial Research. Savanna Agricultural Research Institute; GhanaFil: Abudulai, Mumuni. Council for Scientific and Industrial Research. Savanna Agricultural Research Institute; GhanaFil: Oteng-Frimpong, Richard.Council for Scientific and Industrial Research. Savanna Agricultural Research Institute; GhanaFil: Brandford Mochiah, Moses.Council for Scientific and Industrial Research. Crops Research Institute; GhanaFil: Asibuo, James Y. Council for Scientific and Industrial Research. Crops Research Institute; GhanaFil: Arthur, Stephen. Council for Scientific and Industrial Research. Crops Research Institute; GhanaFil: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Monguillot, Joaquín Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Monguillot, Joaquín Humberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Rhoads, James. University of Georgia. Feed the Future Innovation Lab for Peanut; Estados Unido