Relevant bibliographies by topics / Whole-farm

Academic literature on the topic 'Whole-farm'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Whole-farm"

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Turvey, Calum G. "Whole Farm Income Insurance." Journal of Risk and Insurance 79, no. 2 (July 8, 2011): 515–40. http://dx.doi.org/10.1111/j.1539-6975.2011.01426.x.

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Turvey, Calum Greig, and J. Lowenberg-DeBoer. "Farm-to-Farm Productivity Differences and Whole-Farm Production Functions." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 36, no. 2 (July 1988): 295–312. http://dx.doi.org/10.1111/j.1744-7976.1988.tb03277.x.

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Jagger, Craig. "Whole-Farm vs. Part-Farm Voluntary Land Retirement Programs." North Central Journal of Agricultural Economics 8, no. 1 (January 1986): 41. http://dx.doi.org/10.2307/1349080.

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Schils, R. L. M., M. H. A. de Haan, J. G. A. Hemmer, A. van den Pol-van Dasselaar, J. A. de Boer, A. G. Evers, G. Holshof, J. C. van Middelkoop, and R. L. G. Zom. "DairyWise, A Whole-Farm Dairy Model." Journal of Dairy Science 90, no. 11 (November 2007): 5334–46. http://dx.doi.org/10.3168/jds.2006-842.

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S. Watkins, R. ""Payneham Vale": integrated whole farm Planning." Pacific Conservation Biology 9, no. 1 (2003): 65. http://dx.doi.org/10.1071/pc030065.

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IN 1908, Ron's grandfather, Issac Gray, took up an uncleared block of land 15 km north of Frankland in the south-west of Western Australia (see Fig. 1, Hobbs 2003). During that time he ran a few cattle in the bush and clearing of the native woodlands of Wandoo (white gum) Eucalyptus wandoo, J arrah E. marginata and Marri (Redgum) E. calophylla was slow and tedious. Ron's parents took over the farm in 1947, and with the advent of the bulldozer, clearing of Watkin's property and surrounding district began in earnest during the 1950s. Clearing continued as fast "as money permitted", until almost the last natural vegetation was knocked down in 1978 (Fig. 1). Annual pastures with some cropping (for supplementary feed) were the main source of fodder for sheep and cattle.
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Anderson, Kim B., and John E. Ikerd. "Whole Farm Risk-Rating Microcomputer Model." Journal of Agricultural and Applied Economics 17, no. 1 (July 1985): 183–87. http://dx.doi.org/10.1017/s0081305200017209.

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AbstractThe Risk-Rating Model is designed to give extension specialists, teachers, and producers a method to analyze production, marketing, and financial risks. These risks may be analyzed either individually or simultaneously. The risk associated with each enterprise, for all combinations of enterprises, and for any combination of marketing strategies is estimated. Optimistic, expected, and pessimistic returns above variable cost and/or total cost are presented in the results. The probability that total return will be equal to or greater than variable cost and/or total cost is also estimated.
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Fraser, D. "Site mapping for whole farm planning." Cartography 21, no. 2 (December 1992): 1–8. http://dx.doi.org/10.1080/00690805.1992.9713943.

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Zulauf, Carl. "Whole farm safety net programs: an emerging US farm policy evolution?" Renewable Agriculture and Food Systems 35, no. 4 (August 20, 2019): 435–38. http://dx.doi.org/10.1017/s1742170519000279.

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AbstractThe 2018 farm bill is the latest in a history that dates to 1933. Commodity assistance is the only program in all farm bills, but with evolutionary changes. Current farm commodity programs largely make payments to farms, a stark contrast to the 1930s when they limited supply, put a floor under market price, and dampened price increases via public stocks. Crop insurance, which began as an experimental pilot program in 1938, now has its own farm bill title. Almost all commodity and insurance programs have provided assistance based on a calculation specific to an individual commodity's price and/or yield. However, an evolutionary change to whole farm commodity programs may be in its infant stages. They provide assistance for variation in a farm's aggregate revenue across multiple crops. Whole farm experiments currently exist in both the commodity and crop insurance titles. Analysis of a whole farm commodity program finds that its payments differ by year from actual payments made by current commodity programs and are smaller in total.
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Christie, K. M., C. J. P. Gourley, R. P. Rawnsley, R. J. Eckard, and I. M. Awty. "Whole-farm systems analysis of Australian dairy farm greenhouse gas emissions." Animal Production Science 52, no. 11 (2012): 998. http://dx.doi.org/10.1071/an12061.

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The Australian dairy industry contributes ~1.6% of the nation’s greenhouse gas (GHG) emissions, emitting an estimated 9.3 million tonnes of carbon dioxide equivalents (CO2e) per annum. This study examined 41 contrasting Australian dairy farms for their GHG emissions using the Dairy Greenhouse Gas Abatement Strategies calculator, which incorporates Intergovernmental Panel on Climate Change and Australian inventory methodologies, algorithms and emission factors. Sources of GHG emissions included were pre-farm embedded emissions associated with key farm inputs (i.e. grains and concentrates, forages and fertilisers), CO2 emissions from electricity and fuel consumption, methane emissions from enteric fermentation and animal waste management, and nitrous oxide emissions from animal waste management and nitrogen fertilisers. The estimated mean (±s.d.) GHG emissions intensity was 1.04 ± 0.17 kg CO2 equivalents/kg of fat and protein-corrected milk (kg CO2e/kg FPCM). Enteric methane emissions were found to be approximately half of total farm emissions. Linear regression analysis showed that 95% of the variation in total farm GHG emissions could be explained by annual milk production. While the results of this study suggest that milk production alone could be a suitable surrogate for estimating GHG emissions for national inventory purposes, the GHG emissions intensity of milk production, on an individual farm basis, was shown to vary by over 100% (0.76–1.68 kg CO2e/kg FPCM). It is clear that using a single emissions factor, such as milk production alone, to estimate any given individual farm’s GHG emissions, has the potential to either substantially under- or overestimate individual farms’ GHG emissions.
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Hardaker, J. Brian, Louise H. Patten, and David J. Pannell. "UTILITY-EFFICIENT PROGRAMMING FOR WHOLE-FARM PLANNING*." Australian Journal of Agricultural Economics 32, no. 2-3 (August 12, 1988): 88–97. http://dx.doi.org/10.1111/j.1467-8489.1988.tb00677.x.

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Dissertations / Theses on the topic "Whole-farm"

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Shockley, Jordan Murphy. "WHOLE FARM MODELING OF PRECISION AGRICULTURE TECHNOLOGIES." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/105.

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This dissertation investigated farm management concerns faced by grain producers due to the acquisition of various precision agriculture technologies. The technologies evaluated in the three manuscripts included 1) auto-steer navigation, 2) automatic section control, and 3) autonomous machinery. Each manuscript utilized a multifaceted economic model in a whole-farm decision-making framework to determine the impact of precision agriculture technology on machinery management, production management, and risk management. This approach allowed for a thorough investigation into various precision agriculture technologies which helped address the relative dearth of economic studies of precision agriculture and farm management. Moreover, the research conducted on the above technologies provided a wide array of economic insight and information for researchers and developers to aid in the advancement of precision agriculture technologies. Such information included the risk management potential of auto-steer navigation and automatic section control, and the impact the technologies had on optimal production strategies. This dissertation was also able to provided information to guide engineers in the development of autonomous machinery by identifying critical characteristics and isolating the most influential operating machine. The inferences from this dissertation intend to be employed in an extension setting with the purpose of educating grain producers on the impacts of implementing such technologies.
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Alemu, Aklilu W. "Modelling greenhouse gas emissions in cattle: From rumen to the whole-farm." Elsevier B.V. (Animal Feed Science and Technology), 2011. http://hdl.handle.net/1993/14668.

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Mathematical modeling in animal agriculture can be applied at various levels including at the tissue, organ, animal, farm, regional and global levels. The purposes of this research were i) to evaluate models used to estimate volatile fatty acid (VFA) and methane (CH4) production and assess their impact on regional enteric CH4 inventory, and ii) to develop a process-based, whole-farm model to estimate net farm GHG emissions. In the first study, four VFA stoichiometric models were evaluated for their prediction accuracy of rumen VFA and enteric CH4 production. Comparison of measured and model predicted values demonstrated that predictive capacity of the VFA models varied with respect to the type of VFA in rumen fluid which impacted estimated enteric CH4 production. Moving to a larger scale assessment, we examined the enteric CH4 inventory from Manitoba beef cattle (from 1990 to 2008) using two mechanistic rumen models that incorporate VFA stoichiometric models: COWPOLL and MOLLY, and two empirical models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis). The estimated absolute enteric CH4 production varied among models (7 to 63%) indicating that estimates of GHG inventory depend on model selection. This is an important consideration if the values are to be used for management and/or policy-related decisions. Development of models at the individual farm component level (animal, soil, crop) does not accurately reflect net GHG emissions generated from the whole production system. We developed a process-based, whole-farm model (Integrated Components Model, ICM), using the existing farm component models COWPOLL, manure-DNDC and some aspects of IPCC to integrate farm components and their associated GHG emissions. Estimates of total farm GHG emissions and their relative contribution using the ICM were comparable to estimates using two other whole-farm models (Integrated Farm System Model and Holos model). Variation was observed among models both in estimating whole-farm GHG emissions and the relative contribution of the different sources in the production system. Overall, whole-farm models are required to explore management options that will mitigate GHG emissions and promote best management practices. However, for full assessment of the production system, other benefits of the system (e.g., carbon sequestration, ecosystem services), which are not part of current whole-farm models, must be considered.
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Bhogaraju, Prabhakar V. "A Case-Based Reasoner for Evaluating Crop Rotations in Whole-Farm Planning." Thesis, Virginia Tech, 1996. http://hdl.handle.net/10919/36817.

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I have worked on a Case-Based Reasoning (CBR) system that evaluates crop rotations for their soil erosion and risk of insect pest problems. The purpose of this system is to provide decision support for an automated whole-farm planner (CROPS). CROPS (Buick et al., 1992) generates crop rotation plans that can address some of the environmental, economic and legislative pressures facing natural resource managers. To generate and recommend a crop rotation plan CROPS requires estimates on the soil erosion risks and pesticide pollution potential of the crop rotation. In this research I have designed and prototyped a system that can assist CROPS in the process of whole-farm planning by providing information required for determining the soil erosion risks and the pesticide pollution potential of crop rotations. Inputs for the system include: a crop rotation, its tillage and residue management practices, and field conditions. Soil erosion risk is quantified using the C-value. Pest risks are likelihood of pest outbreaks that require control in a crop rotation. CBR was the chosen methodology for system implementation. In CBR, solutions to new problem situations are derived from retrieving and adapting solutions to similar problem situations experienced in the past. The system was prototyped using Esteemâ ¢, a CBR development shell, and runs on a PC under the MS. Windowsâ ¢, operating system.
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Martin, Benjamin A. "TWO ESSAYS ON WHOLE FARM MODELING AND CROP MARKETING IN WESTERN KENTUCKY." UKnowledge, 2018. https://uknowledge.uky.edu/agecon_etds/65.

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This thesis is composed of two essays that investigate whole farm planning and crop marketing in western Kentucky. In the first essay, contracting decisions between food corn producers and a mill are analyzed to observe factors affecting the bushel amount farmers contract. Unbalanced panel data containing seven years’ worth of pricing and contract information are used with a fixed-effects model to generate parameter estimates and quantify their effect on bushels contracted. It was found that contract attributes, market condition, and relationship-specific assets had a significant effect on producers’ food corn contracting decisions. The second essay utilizes mixed-integer programming to optimize resource allocation and marketing strategy for a hypothetical farm. Post-optimal analysis is performed to determine non-binding capacities for drying and storage equipment. The model is re-run with these non-binding capacities to observe changes in net returns as well as planting, harvesting, and marketing strategies. New equipment and associated costs are identified, and the change in net returns from the base case is used as net cash flow in a net present value investment analysis. Results of the investment analysis indicate increasing drying and storage capacity is a wise investment given the scenario modeled.
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Bailey, Alastair S. "The estimation of input-output coefficients for agriculture from whole farm accounting data." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320135.

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Sorensen, Emily Allyson. "Modeling Whole Farm Systems to Enhance Beginning Small Farmer Success in Southwest Virginia." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72282.

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The number of very small farms (<10 acres) is increasing and beginning farmers (in practice for <10 years) are more likely to run them. Very small farms are typically complex systems in which the farmer manages both production of a diverse array of crops and marketing of crops directly to consumers and their failure rate in early years is high. This work seeks to increase the likelihood of success for beginning farmers by understanding these complex systems better. We collected qualitative and quantitative data from interviews with three successful beginning farm operations in Southwest Virginia covering practical and philosophical aspects of farm production, sales and management. We mapped social, environmental and economic aspects of farming systems and studied how farmers use resources (Community Capitals) and management to enhance their system's success, developing a broader definition of success that encompasses what farmers gain from farming beyond profitability. Using these maps, we created a system dynamics model of a small farm system in STELLA including unique components such as customer attraction and retention. Through model development, we learned that these successful farmers began their operations with experience and financial resources, and employed their skills, resourcefulness and cultural and social capital to charge prices for their products that could sustain their operations financially. Using our model, current and aspiring farmers, service providers, and small farm advocates will be able to simulate real or hypothetical farm systems to better understand what establishing a successful small farm might require and how to confront potential challenges.
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Cox, Beverly Gwen. "Impact of Precision Feeding Strategies on Whole Farm Nutrient Balance and Feeding Management." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/32444.

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Impact of precision feeding with feed management software was assessed for whole farm nutrient balance (WFNB) and feeding management from January through December 2006. Nine treatment and six control farms were selected in four regions of the Chesapeake Bay Watershed of Virginia. Herd sizes averaged 271 and 390 lactating cows for treatment and control farms while milk yield averaged 30 and 27 kg/d per lactating cow, respectively. Crop hectares grown averaged 309 and 310 ha for treatment and control farms, respectively. Treatment farms purchased and installed feed management software (TMR Tracker, Digi-Star LLC, Fort Atkinson WI) between May and October 2006 and received more frequent feed analysis and feedback. Data were collected for calendar year 2005 and 2006 to compute WFNB using software from the University of Nebraska. On treatment farms, up to five feed samples were obtained monthly from individual feedstuffs and each total mixed ration (TMR) fed to lactating cows. Control farms submitted TMR samples every 2 mo. Standard wet chemistry analysis of samples was performed. Data stored in the software were collected monthly from each treatment farm concurrent with feed sampling. Producers from each treatment farm participated in a 24-question personal interview in December 2006 addressing installation, operation, and satisfaction with the software. Daily feeding deviation of all ingredients across treatment farms averaged 173 ± 163 kg/d. This corresponded to average daily overfeeding of CP and P of 17.6 ± 17 and 0.4 ± 0.3 kg/d, respectively. Feeding deviation did not differ between feeders. Milk production was negatively associated with kg total deviation and kg CP deviation, but positively related to P deviation. Whole farm nutrient balance did not differ between treatment and control farms. All producers indicated TMR Tracker met expectations. Change made to the feeding program due to TMR Tracker was correlated (r=0.80) with perceived improvement in ration consistency. In conclusion, producers perceived feed management software as beneficial, but WFNB was not reduced after 3 to 6 mo of using feed management software; however, the large variation in daily over or under feeding indicates potential for future reductions in WFNB through reduced feeding variability.
Master of Science
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Abreu, Daniel Carneiro de. "Whole-farm modeling approach to evaluate different crop rotations in organic dairy systems." Universidade Federal de Viçosa, 2014. http://www.locus.ufv.br/handle/123456789/6596.

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Fundação de Amparo à Pesquisa do Estado de Minas Gerais
O mercado de trigo (Triticum aestivum L.) orgânico para produção de pão cresceu em grande magnitude na região da Nova Inglaterra, nos Estados Unidos. Este nicho de mercado representa uma alternativa de renda para os produtores de leite orgânico abastecerem este mercado através do cultivo e colheita de grãos em rotação de cultura na própria fazenda. Objetivou-se com este estudo determinar a sustentabilidade de oito sequências de rotação de cultura (três anos de rotação) durante o período de 25 anos em uma propriedade produtora de leite orgânica bem manejada. Uma fazenda média foi simulada utilizando o modelo computacional Integrated Farm System Model (versão 3.6) para avaliar o efeito da rotação de cultura no desempenho da cultura, impacto ambiental e rentabilidade. As estratégias de rotação incluíram pasto contínuo (azevém e trigo), milho (Zea mays L.) colhido cedo seguido de trigo de inverno (milho-trigo de inverno- pasto), milho seguido de trigo de primavera (milho-trigo de primavera-pasto), pasto em rotação com trigo de inverno (azevém/trigo - trigo de inverno - azevém/trigo), pasto em rotação com trigo de primavera (azevém/trigo - trigo de primavera - azevém/trigo), soja [Glycine max (L.) Merr.] em rotação por trigo de inverno (soja - trigo de inverno - azevém/trigo) e primavera (soja - trigo de primavera - azevém/trigo), milho em cultivo consecutivo (milho - milho - azevém/trigo) e soja seguida de milho (soja - milho - azevém/trigo). O trigo foi colhido em grão e comercializado a preço premium em todos os anos simulados. Em todas as simulações foram cultivados azevém e trigo (Lolium perenne / Trifolium pratense) consorciados no terceiro ano. Em geral, não houve benefício econômico e ambiental na rotação de cultura em comparação o pasto contínuo (monocultivo). Entretanto, entre as rotações de cultura, o cultivo de trigo de inverno deve ser incentivado, particularmente em rotação com a soja, para reduzir o impacto ambiental e aumentar rentabilidade da fazenda.
The market for high-quality organic bread wheat (Triticum aestivum L.) is increasing in New England. This economic niche represents one alternative income for organic dairy producers (if they include wheat in their crop rotation) to supply this market by raising wheat as a cash crop. Our objective was to determine the sustainability to eight crop rotation sequences of 3-yr rotations in a long-term (25-yr) well-managed organic dairy farm. A medium-sized organic dairy farm was simulated with the Integrated Farm System Model (IFSM, version 3.6) to evaluate crop rotation (management) effects on crop performance, environmental impacts and profitability. The cropping strategies included continuous ryegrass/red clover (continuous grass), corn (Zea mays L.) harvested early followed by winter wheat (corn-wwheat-grass), corn followed by spring wheat (corn-swheat-grass), ryegrass/red clover rotated with winter wheat (grass-wwheat-grass), ryegrass/red clover in rotation with spring wheat (grass-swheat-grass), soybean [Glycine max (L.) Merr.] rotated by both winter wheat (soybean-wwheat-grass) and spring wheat (soybean- swheat-grass), corn double cropped (corn-corn-grass) and soybean followed by corn (soybean- corn-grass). Wheat was harvested as a cash crop in all simulated years and sold at a premium price. All rotations were in long rotation with perennial ryegrass/red clover (Lolium perenne / Trifolium pratense) over the 3-yr. In general, there was no economic and environmental benefit to shifting land from continues grass-based production to specified cropping rotations. However, under crop rotation, use of winter wheat should be encouraged, particularly soybean replaced with cash crop wheat, to reduce environmental impact and improve farm profitability.
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White, April Frye. "A Goal Programming Approach to Simultaneously Minimize Whole Farm Ration Cost and Phosphorus Balance." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1593622413223357.

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Stewart, Brittany Allison. "The Impact of Feed Management Software on Whole-Farm Nutrient Balance on Virginia Dairy Farms." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/42718.

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Agricultural runoff is the largest source of nitrogen and phosphorus pollution entering the Chesapeake Bay, contributing 38% of nitrogen and 45% of phosphorus (USEPA, 2010). Since agricultural runoff is the number one contributing source of nitrogen and phosphorus entering the Chesapeake Bay, action needs to be taken to reduce nitrogen and phosphorus on agriculture production facilities, such as dairy farms. The impact of feed management software on whole-farm nutrient balance was studied on 18 dairy farms located in Virginia from 2006 to 2010. Nine farms began using the TMR Tracker feed management software in 2006 and were compared to 9 control farms not using feed management software. Each of the treatment farms were visited on a monthly basis to collect ration and feed ingredient samples and feed management data. Whole-farm nutrient balance was calculated using University of Nebraska software. Herd sizes and crop hectares averaged 314 and 366 for treatment and 298 and 261 for control farms. Milk production averaged 3,226 and 2,650 tonnes per year respectively. Measures of surplus (input-output) and use efficiency (input/output) for nitrogen and phosphorus were analyzed over a four year time span and did not differ between treatment and control farms whether expressed on a per farms, cow or hectare basis. Due to the large variation in feeding accuracy within farms, the use of feed management software did not influence whole-farm nutrient balance. Sources of variation that contributed to loading errors were investigated within the feed management data. Percent load deviation increased over time from 2007 to 2009 from 0.94 ± 0.53 to 2.37 ± 0.50 percent of the actual load weight. Effects of month, day of the week and time of day on percent load deviation were not significant. There was no effect of percent load deviation on milk production. No relationship was observed between percent load deviation and whole-farm nutrient balance.
Master of Science
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Books on the topic "Whole-farm"

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Higgins, Elizabeth Mansager. Whole farm planning: A survey of North American experiments. Greenbelt, Md: Henry A. Wallace Institute for Alternative Agriculture, 1998.

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Karl, North, and Northeast Organic Farming Association, eds. Whole-farm planning: Ecological imperatives, personal values, and economics. White River Junction, Vt: Chelsea Green Pub., 2011.

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Crespo, Silvio. Cacao beans today: With sliced and whole beans and farm illustrations. Lititz, PA: Wilbur Chocolate Co., 1986.

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The resilient farm and homestead: An innovative permaculture and whole systems design approach. White River Junction, VT: Chelsea Green Pub., 2013.

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Nordblom, Thomas L. A whole-farm model based on experimental flocks and crop rotations in northwest Syria. Aleppo, Syria: International Center for Agricultural Research in the Dry Areas, 1987.

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Kaiser, Harry Mason. Impact of generic fluid milk advertising on whole, lowfat, and skim milk demand. Ithaca, N.Y: Dept. of Agricultural, Resource, and Managerial Economics, College of Agriculture and Life Sciences, Cornell University, 1995.

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Birch, Ashley, and Sam Birch. Magic Farm: A Whole New World. Farshore, 2011.

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Stephenson, Garry. Whole Farm Management: Growing a Farm from Start-Up to Sustainability. Storey Publishing, LLC, 2019.

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Stephenson, Garry. Whole Farm Management: Growing a Farm from Start-Up to Sustainability. Storey Publishing, LLC, 2019.

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Whole farm case studies: A how-to guide. [Corvallis, Or.]: Oregon State University Extension Service, 1994.

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Book chapters on the topic "Whole-farm"

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Olson, Kent, and John Westra. "Whole-Farm Planning." In The Economics of Farm Management, 315–43. 2nd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003280712-17.

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Janke, Rhonda R. "Whole-Farm Planning and Analysis." In Agronomy Monographs, 63–73. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2016. http://dx.doi.org/10.2134/agronmonogr43.c5.

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Edwards-Jones, G., J. B. Dent, O. Morgan, and M. J. McGregor. "Incorporating farm household decision-making within whole farm models." In Understanding Options for Agricultural Production, 347–65. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-3624-4_17.

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Ma, Hui. "Ontology-Based Agri-Environmental Planning for Whole Farm Plans." In Lecture Notes in Computer Science, 65–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16385-2_9.

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Williams, Gerald G. "Highlights From 45 Years of Experience with Whole-farm Demonstrations." In Transferring Technology for Small-Scale Farming, 115–27. Madison, WI, USA: American Society of Agronomy, 2015. http://dx.doi.org/10.2134/asaspecpub41.c8.

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Colantoni, Andrea, Massimo Cecchini, Danilo Monarca, Guido Alfaro Degan, and Gianluca Coltrinari. "Comparison of Whole-Body-Vibration Exposure Between Quarry and Farm Activities." In Lecture Notes in Civil Engineering, 242–51. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98092-4_26.

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Chardon, X., C. Rigolot, C. Baratte, R. Martin-Clouaire, J. P. Rellier, C. Raison, A. Le Gall, et al. "A whole farm-model to simulate the environmental impacts of animal farming systems: MELODIE." In Modelling nutrient digestion and utilisation in farm animals, 403–11. Wageningen: Wageningen Academic Publishers, 2011. http://dx.doi.org/10.3920/978-90-8686-712-7_44.

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Nuthall, Peter L. "Not perfect." In Farm business management: the decisive farmer, 62–72. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781800620124.0006.

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Abstract This chapter narrates the next meeting held by the farmers. In this case the whole question of accurate budgeting was traversed. Farmers should make sure you are a realistic budgeter of farm systems and possible actions. Accurate forecasts are the basis of good decision-making. As all farmers largely operate in an uncertain world it is not possible to be accurate at all times, but you can at least look at the possible highs and lows together with their chances of occurring, i.e. the probabilities. All this means a farmer should constantly budget, and even work with consultants and extension people, practising the skills to be a good budgeter.
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Anderson, Colin Ray, Janneke Bruil, M. Jahi Chappell, Csilla Kiss, and Michel Patrick Pimbert. "Conceptualizing Processes of Agroecological Transformations: From Scaling to Transition to Transformation." In Agroecology Now!, 29–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61315-0_3.

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AbstractIn this chapter, we survey the recent literature that speaks directly to the issue of bringing agroecology to scale. We discuss the shift towards analytical frameworks that consider not only the farm level but rather whole food system transformations. We then introduce the multi-level perspective on sustainability transitions which we adopt for the purpose of this book. Moving beyond the technical analysis often found in research on sustainability ‘transitions’, our approach thus adopts agency-centric approach to food systems ‘transformation’. To do this, we introduce the notion of domains of transformation, which represent discrete areas where the conflict between agroecology and the dominant food regime manifests and where the potential for collective and transformation is transformation is most potent.
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Sintori, Alexandra, Konstantinos Tsiboukas, and George Zervas. "Evaluating Socio-economic and Environmental Sustainability of the Sheep Farming Activity in Greece: A Whole-Farm Mathematical Programming Approach." In Methods and Procedures for Building Sustainable Farming Systems, 219–35. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5003-6_15.

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Conference papers on the topic "Whole-farm"

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Maheshwari, Aditi, Curtis Dyreson, Jennifer Reeve, Vishal Sharma, and Anthony Whaley. "Automating and Analyzing Whole-Farm Carbon Models." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00057.

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Terry W Griffin. "Whole-Farm Benefits of GPS-Enabled Navigation Technologies." In 2009 Reno, Nevada, June 21 - June 24, 2009. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2009. http://dx.doi.org/10.13031/2013.27020.

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Crozier, Paul, Christiane Adcock, Shreyas Ananthan, Luc Berger-Vergiat, Michael Brazell, Nicholas Brunhart-Lupo, Marc Henry de Frahan, et al. "Harnessing exascale for whole wind farm high-fidelity simulations to improve wind farm efficiency." In Proposed for presentation at the SC21 held November 15-19, 2021 in St. Louis, MO U.S.A. US DOE, 2021. http://dx.doi.org/10.2172/1897371.

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C Alan Rotz and Sasha D Hafner. "Whole Farm Impact of Anaerobic Digestion and Biogas Use on a New York Dairy Farm." In 2011 Louisville, Kentucky, August 7 - August 10, 2011. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2011. http://dx.doi.org/10.13031/2013.37768.

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C. Alan Rotz, Jouke Oenema, and Herman van Keulen. "Whole-Farm Management to Reduce Nitrogen Losses from Dairy Farms." In 2003, Las Vegas, NV July 27-30, 2003. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2003. http://dx.doi.org/10.13031/2013.13787.

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C. Alan Rotz and Timothy M. Harrigan. "Predicting Suitable Days for Field Operations in a Whole Farm Simulation." In 2004, Ottawa, Canada August 1 - 4, 2004. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2004. http://dx.doi.org/10.13031/2013.16706.

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"Does increasing ewe fecundity reduce whole-farm greenhouse gas emissions intensities?" In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.b2.harrison.

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"Integrating biophysical and whole-farm economic modelling of agricultural climate change mitigation." In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.b3.dumbrell.

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DuPont, Bryony L., and Jonathan Cagan. "Multi-Stage Optimization of Wind Farms With Limiting Factors." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12503.

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Larger onshore wind farms are often installed in phases, with discrete smaller sub-farms being installed and becoming operational in succession until the farm as a whole is completed. An extended pattern search (EPS) algorithm that selects both local turbine position and geometry is presented that enables the installation of a complete farm in discrete stages, exploring optimality of both incremental sub-farm solutions and the completed project as a whole. The objective evaluation is the maximization of profit over the life of the farm, and the EPS uses modeling of cost based on an extensive cost analysis by the National Renewable Energy Laboratory (NREL). The EPS uses established wake modeling to calculate the power development of the farm, and allows for the consideration of multiple or overlapping wakes. A limiting factor is used to determine the size of wind farm stages: optimization stages based on the number of turbines currently available for development (representative of limitations in initial capital, which is commonly encountered in wind farm stage development). Two wind test cases are considered: a unidirectional test case with constant wind speed and a single wind direction, and a multidirectional test case, with three wind speeds and a defined probability of occurrence for each. The test case shown in the current work is employed on a 4000 km by 4000 km solution space. In addition, two different methods are performed: the first uses the optimal layout of a complete farm and then systematically “removes” turbines to create smaller sub-farms; the second uses a weighted multi-objective optimization over sequential, adjacent land that concurrently optimizes each sub-farm and the complete farm. The exploration of these resulting layouts indicates the value of full-farm optimization (in addition to optimization of the individual stages) and gives insight into how to approach optimality in sub-farm stages. The behavior exhibited in these tests cases suggests a heuristic that can be employed by wind farm developers to ensure that multi-stage wind farms perform at their peak throughout their completion.
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Li, Lin, Zhiyu Jiang, Jungao Wang, and Muk Chen Ong. "Predicting the Heading Misalignment of a Vessel-Shaped Offshore Fish Farm Under Waves and Currents." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77476.

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A vessel-shaped fish farm concept for open sea applications has been proposed recently. The whole system consists of a vessel-shaped floater, fish cages positioned longitudinally along the floater, and a single-point mooring system. The whole system weathervanes; this feature increases the spread area for the fish waste. However, the downstream cages may experience reduced water exchange when the vessel is parallel to the currents. This situation may jeopardize the fish health. A dynamic positioning (DP) system may be necessary to improve the flow conditions. This paper investigates the misalignment angle between the heading of the vessel-shaped fish farm and the currents under combined wave and current conditions. The misalignment angle is critical for the estimation of the DP system consumption. A numerical model of the fish farm system with flexible nets is developed. Current reduction factors are included to account for the flow velocity reductions between the net panels. The heading of the system is obtained by finding the equilibrium condition of the whole system under each combined wave and current condition. An integrated method using metamodels is proposed and applied for the prediction of the misalignment angle for a reference site. The probability distribution of the misalignment angle between the vessel heading and the currents is calculated using the Kriging metamodel for the reference site. Based on the prediction, the requirement for the DP system to improve the flow condition in the fish cages is discussed.
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Reports on the topic "Whole-farm"

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Powers, Wendy, James R. Russell, and Mathew M. Haan. Whole Farm Nutrient Balance Under Different Grazing Systems: Project Overview. Ames: Iowa State University, Digital Repository, 2002. http://dx.doi.org/10.31274/farmprogressreports-180814-1275.

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Schattman, Rachel, Vern Grubinger, Lisa McKaeg, and Katie Nelson. Whole Farm Water Use: A Survey of Vegetable Producers in New England States | 2018. USDA Northeast Climate Hub, June 2018. http://dx.doi.org/10.32747/2018.6938606.ch.

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Produce safety and climate change are two overlapping risks that face vegetable producers in the northeastern United States. Because of recent public health outbreaks (and subsequent litigation) traced back to fresh produce, food safety hazard identification and risk mitigation has become the focus of significant regulatory changes in the United States (FDA 2015)
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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Micrometeorological methods for inferring whole canopy evapotranspiration in large agricultural structures: measurements and modeling. United States Department of Agriculture, October 2015. http://dx.doi.org/10.32747/2015.7594402.bard.

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Original objectives and revisions The original objectives as stated in the approved proposal were: (1) To establish guidelines for the use of micrometeorological techniques as accurate, reliable and low-cost tools for continuous monitoring of whole canopy ET of common crops grown in large agricultural structures. (2) To adapt existing methods for protected cultivation environments. (3) To combine previously derived theoretical models of air flow and scalar fluxes in large agricultural structures (an outcome of our previous BARD project) with ET data derived from application of turbulent transport techniques for different crops and structure types. All the objectives have been successfully addressed. The study was focused on both screenhouses and naturally ventilated greenhouses, and all proposed methods were examined. Background to the topic Our previous BARD project established that the eddy covariance (EC) technique is suitable for whole canopy evapotranspiration measurements in large agricultural screenhouses. Nevertheless, the eddy covariance technique remains difficult to apply in the farm due to costs, operational complexity, and post-processing of data – thereby inviting alternative techniques to be developed. The subject of this project was: 1) the evaluation of four turbulent transport (TT) techniques, namely, Surface Renewal (SR), Flux-Variance (FV), Half-order Time Derivative (HTD) and Bowen Ratio (BR), whose instrumentation needs and operational demands are not as elaborate as the EC, to estimate evapotranspiration within large agricultural structures; and 2) the development of mathematical models able to predict water savings and account for the external environmental conditions, physiological properties of the plant, and structure properties as well as to evaluate the necessary micrometeorological conditions for utilizing the above turbulent transfer methods in such protected environments. Major conclusions and achievements The major conclusions are: (i) the SR and FV techniques were suitable for reliable estimates of ET in shading and insect-proof screenhouses; (ii) The BR technique was reliable in shading screenhouses; (iii) HTD provided reasonable results in the shading and insect proof screenhouses; (iv) Quality control analysis of the EC method showed that conditions in the shading and insect proof screenhouses were reasonable for flux measurements. However, in the plastic covered greenhouse energy balance closure was poor. Therefore, the alternative methods could not be analyzed in the greenhouse; (v) A multi-layered flux footprint model was developed for a ‘generic’ crop canopy situated within a protected environment such as a large screenhouse. The new model accounts for the vertically distributed sources and sinks within the canopy volume as well as for modifications introduced by the screen on the flow field and microenvironment. The effect of the screen on fetch as a function of its relative height above the canopy is then studied for the first time and compared to the case where the screen is absent. The model calculations agreed with field experiments based on EC measurements from two screenhouse experiments. Implications, both scientific and agricultural The study established for the first time, both experimentally and theoretically, the use of four simple TT techniques for ET estimates within large agricultural screenhouses. Such measurements, along with reliable theoretical models, will enable the future development of lowcost ET monitoring system which will be attainable for day-to-day use by growers in improving irrigation management.
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Shpigel, Nahum Y., Ynte Schukken, and Ilan Rosenshine. Identification of genes involved in virulence of Escherichia coli mastitis by signature tagged mutagenesis. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7699853.bard.

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Mastitis, an inflammatory response of the mammary tissue to invading pathogenic bacteria, is the largest health problem in the dairy industry and is responsible for multibillion dollar economic losses. E. coli are a leading cause of acute mastitis in dairy animals worldwide and certainly in Israel and North America. The species E. coli comprises a highly heterogeneous group of pathogens, some of which are commensal residents of the gut, infecting the mammary gland after contamination of the teat skin from the environment. As compared to other gut microflora, mammary pathogenic E. coli (MPEC) may have undergone evolutionary adaptations that improve their fitness for colonization of the unique and varied environmental niches found within the mammary gland. These niches include competing microbes already present or accompanying the new colonizer, soluble and cellular antimicrobials in milk, and the innate immune response elicited by mammary cells and recruited immune cells. However, to date, no specific virulence factors have been identified in E. coli isolates associated with mastitis. The original overall research objective of this application was to develop a genome-wide, transposon-tagged mutant collection of MPEC strain P4 and to use this technology to identify E. coli genes that are specifically involved in mammary virulence and pathogenicity. In the course of the project we decided to take an alternative genome-wide approach and to use whole genomes bioinformatics analysis. Using genome sequencing and analysis of six MPEC strains, our studies have shown that type VI secretion system (T6SS) gene clusters were present in all these strains. Furthermore, using unbiased screening of MPEC strains for reduced colonization, fitness and virulence in the murine mastitis model, we have identified in MPEC P4-NR a new pathogenicity island (PAI-1) encoding the core components of T6SS and its hallmark effectors Hcp, VgrG and Rhs. Next, we have shown that specific deletions of T6SS genes reduced colonization, fitness and virulence in lactating mouse mammary glands. Our long-term goal is to understand the molecular mechanisms of host-pathogen interactions in the mammary gland and to relate these mechanisms to disease processes and pathogenesis. We have been able to achieve our research objectives to identify E. coli genes that are specifically involved in mammary virulence and pathogenicity. The project elucidated a new basic concept in host pathogen interaction of MPEC, which for the best of our knowledge was never described or investigated before. This research will help us to shed new light on principles behind the infection strategy of MPEC. The new targets now enable prevalence and epidemiology studies of T6SS in field strains of MPEC which might unveil new geographic, management and ecological risk factors. These will contribute to development of new approaches to treat and prevent mastitis by MPEC and perhaps other mammary pathogens. The use of antibiotics in farm animals and specifically to treat mastitis is gradually precluded and thus new treatment and prevention strategies are needed. Effective mastitis vaccines are currently not available, structural components and effectors of T6SS might be new targets for the development of novel vaccines and therapeutics.
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