941 research outputs found

    Simulation of site-specific irrigation control strategies with sparse input data

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    Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

    Air pollution and livestock production

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    The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

    Arkansas Bulletin of Water Research - Issue 2018

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    The Arkansas Bulletin of Water Research is a publication of the Arkansas Water Resources Center (AWRC). This bulletin is produced in an effort to share water research relevant to Arkansas water stakeholders in an easily searchable and aesthetically engaging way. This is the second publication of the bulletin and will be published annually. The submission of a paper to this bulletin is appropriate for topics at all related to water resources, by anyone conducting water research or investigations. This includes but is not limited to university researchers, consulting firms, watershed groups, and other agencies. Prospective authors should read the “Introduction to the Arkanasas Bulletin of Water Research” contained within this publication and should refer to the AWRC website for additional infromation. https://arkansas-water-center.uark.edu

    Precision Poultry Farming

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    This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg productio

    Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions

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    © 2020 Elsevier Ltd Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R2), Adjusted-R2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R2 of 0.9983 and 0.9991 respectively, compared with MLR models with R2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33

    Can money always talk? : implication for environmental compensation by international agribusiness

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    With the development of agricultural industrialization, the environmental issues of intensive animal farming are attracting increasing attention. Using survey data from 313 Chinese households living near the large-scale broiler farms of an international food company, this paper employs a contingent valuation method and discrete choice experiment to quantify the willingness to accept an air pollution compensation scheme. We find the following. (1) 42% of respondents have a nonmonetary preference for compensation; thus, the conventional contingent valuation method is unsuitable for application to them. (2) The results of a probit and tobit model show that in addition to income, “trust and perception” dominate decision-making based on willingness to accept; however, the effect of actual distance is weak. (3) Because of the positive externalities of roads, schools, and job opportunities, the combination of nonmonetary options is feasible and beneficial for both sides (the company and households) in the long term. Thus, from the perspective of the global value chain, it is worth studying nonmonetary compensation strategies in order to explore the sustainable development strategies of multinational corporations

    Proceedings of the European Conference on Agricultural Engineering AgEng2021

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    This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

    Mesophilic anaerobic co-digestion of poultry dropping and Carica papaya peels: Modelling and process parameter optimization study

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    The study evaluated anaerobic co-digestion of poultry dropping and pawpaw peels and the optimization of important process parameters. The physic-chemical analyses of the substrates were done using standard methods after application of mechanical, thermal and chemical pre-treatments methods. Gas chromatography analysis revealed the gas composition to be within the range of 66–68% methane and 18– 23% carbon dioxide. The study equally revealed that combination of the different pre-treatment methods enhanced enormous biogas yield from the digestion. Optimization of the generated biogas data were carried out using the Response Surface Methodology and the Artificial Neural Networks. The coefficient of determination (R2) for RSM (0.9181) was lower compare to that of ANN (0.9828). This shows that ANN model gives higher accuracy than RSM model for the current. Further usage of Carica papaya peels for biogas generation is advocated
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