Relevant bibliographies by topics / Farm management

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Farm management'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Farm management"

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N, Naveen Raj. "Farm Fresh Web Management." International Journal of Research Publication and Reviews 6, no. 4 (April 2025): 13740–44. https://doi.org/10.55248/gengpi.6.0425.1641.

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Payel, Rani, Atikur Rahman Md, and Mahmud Hasan. "Farming Information Management System of Bangladesh." Research and Applications of Web Development and Design 4, no. 1 (February 18, 2021): 1–10. https://doi.org/10.5281/zenodo.4548426.

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<em>&ldquo;Farming Information Management System of Bangladesh&rdquo; is a web-based system that is developed to giving the information about building a (small or big) farm to World Wide Web so that anyone can get information from any place rather than going to a farm. The major goal of this system is to reduce time that people waste while going to a farm to collect the information needed, especially, </em><em>when we live in the village it becomes difficult to get information</em><em>. </em><em>User can buy and sell their farms products by using this system. They also can read recent news, make posts to interact with other users. Users also get notification about news updates, offers, successful buy and sell confirmation etc. The proposed system delivers a great user experience because it is both secure and easy to use, it saves a lot of time, money and hassle. It is also easy to use and very effective, allows the government to deliver farming information around the country easily. It is also very cost effective and time saving when compared to traditional system. </em>
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Barnard, C. S. "Farm management." Agricultural Administration 20, no. 2 (January 1985): 119–21. http://dx.doi.org/10.1016/0309-586x(85)90030-5.

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Errington, Andrew. "Farm management." Agricultural Systems 18, no. 1 (January 1985): 61–65. http://dx.doi.org/10.1016/0308-521x(85)90057-5.

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Middelmann, R. "FARM MANAGEMENT RECORDS." Acta Horticulturae, no. 716 (August 2006): 143–48. http://dx.doi.org/10.17660/actahortic.2006.716.19.

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GREEN, D. A. G. "African Farm Management." African Affairs 88, no. 353 (October 1989): 611–12. http://dx.doi.org/10.1093/oxfordjournals.afraf.a098242.

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Webster, J. P. G. "African farm management." Agricultural Administration and Extension 30, no. 2 (January 1988): 161–62. http://dx.doi.org/10.1016/0269-7475(88)90124-9.

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Fawcett, Roy H. "African farm management." Agricultural Systems 29, no. 1 (1989): 93. http://dx.doi.org/10.1016/0308-521x(89)90073-5.

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Zelenko, Оksana. "Features Farm Management." Economic journal of Lesia Ukrainka Eastern European National University 9, no. 1 (2017): 71–75. http://dx.doi.org/10.29038/2411-4014-2017-01-71-75.

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Leuchter, Miriam. "MANAGEMENT FARM TEAMS." Journal of Business Strategy 19, no. 3 (March 1998): 29–32. http://dx.doi.org/10.1108/eb039934.

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Dissertations / Theses on the topic "Farm management"

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Dunaway, Tarrah M. "Farm Financial Performance of Kentucky Farms." UKnowledge, 2013. http://uknowledge.uky.edu/agecon_etds/13.

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This study examines farm financial performance of Kentucky farms using Kentucky Farm Business Management data from 1998-2010. Logit models are used to estimate the likelihood of farm characteristics affecting whether financial ratios fall into critical zones or not. The results show that large farms in terms of total gross returns and total assets are less likely to experience repayment capacity problems. Total gross returns significantly affect all five financial measures. These findings will help farmers and lenders understand what factors influence farm financial performance. Profitability migration is tested to see if the migration probabilities differ across business cycles. Migration drift is also tested to determine if the Markov property of independence is violated. Results show substantial retention in return on equity (ROE) performance over time, and a tendency for trend-reversal if ROE changes occur. Results are compared to previous literature using ARMS data and Illinois FBFM.
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Vaddella, Venkata Kumar. "Ammonia emissions management and modeling from storages of dairy manure." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Dissertations/Spring2010/V_Vaddella_1030810.pdf.

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Elshami, Omar Mohd Eltom. "A management information system for farm machinery management." Thesis, University of Newcastle Upon Tyne, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240836.

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Holland, Cody. "Labor standards and efficiency estimation of farms in the Kansas Farm Management Association." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/13607.

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Master of Science<br>Department of Agricultural Economics<br>Michael Langemeier<br>The objectives of this thesis are to examine the labor requirements of Kansas crop and livestock enterprises and farms and the connection between labor efficiency and productivity, and other important farm characteristics including farm size and type. The derived labor requirements are compared to current KFMA labor requirements. Enterprise summary reports and a five year whole-farm panel data set from 1,016 Kansas Farm Management Association (KFMA) farms are used in the analysis. Whole-farm labor requirements are computed with and without an adjustment for managerial and overhead cost. Individual regressions will be estimated to determine the effects that farm size, type, region and profit margin have on labor requirements. The estimation results suggest that many of the current labor requirements still in use are accurate. However, there are enterprises with labor requirements that need updating. When the newly estimated requirements are compared to the previous KFMA requirements, 14 enterprises have lower labor requirements. Irrigated alfalfa showed the greatest decrease in labor required when compared to the previous standard, decreasing from 3.85 hrs/acre to 1.70 hrs/acre. Regression estimation results indicated that whole farm labor standards that were corrected for un-allocated overhead and managerial costs appear to be a more accurate representation of farm labor requirements.
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Osborne, William A. "IMPROVING FARM MANAGEMENT DECISIONS BY ANALYZING PRODUCTION EXPENDITURE ALLOCATIONS AND FARM PERFORMANCE STANDING." UKnowledge, 2013. http://uknowledge.uky.edu/agecon_etds/20.

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This study examines the potential effects of categorical increases in production expenditures on farm income performance according to farm standing. The objective of this study is to expose differences in anticipated net farm income return from production expenditure investments and the optimal expense allocation strategy for each performance level. Studying farm performance through segregation by utilizing a two-tier analysis and quantile regression acknowledges the possibility that managerial strategy can differ based on managerial ability. Study outcomes are useful to farm managers because they offer more prescription style results and interpretations than found in other farm performance studies. Study findings show that as managerial proficiency increases so does a manager’s ability to extract higher returns from additional expenditures in certain input categories. Additionally, better managers are able to produce higher returns from more investment sources than their lower performing peers. Overall, study results and interpretations point to the importance of farm management ability as the key input for improving farm performance.
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Jette-Nantel, Simon. "Implications of Off-Farm Income for Farm Income Stabilization Policies." UKnowledge, 2013. http://uknowledge.uky.edu/agecon_etds/15.

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This dissertation examines to what extent off-farm diversification may be an appropriate and accessible tool to mitigate the adverse effects from market failures and incompleteness in the crop and farm income insurance market. While the influence of the nonfarm sector has long been recognized as a primary force in shaping farm structure, off-farm income is rarely acknowledge as a risk management tool for operators and households of commercial farms. The dissertation develops a dynamic model that includes capital market imperfections, economies of scale in farm production, and the presence of adjustment costs in labor allocation decisions. The model provides a realistic characterization of the environment defining income and financial risks faced by farm operators, as well as the risk management alternatives available to them. It is found that introducing off-farm labor can substantially mitigate the adverse effects of farm income risk on farm operators' and households' welfare, even for larger commercial farms. However, the diversification of labor by the main operator seems to impose labor and managerial constraints that can reduce the intensity and technical efficiency of the farm production. Alternatively, diversification at the household level through the allocation of spousal labor off the farm provides benefits in mitigating the adverse effects of farm income risk on farm production and efficiency, and on operators and households welfare. It thus provides an efficient risk management alternative that is consistent with most rationales that are invoked to justify farm policies. Results suggest that the increasing incidence and importance of off-farm income within the farm population of most OECD countries is highly relevant in the design of effective farm policies This form of diversification can reduce the need and effectiveness of farm income stabilization polices. While it has been argued elsewhere that broader economic policies had a large influence in closing the income gap between farm and urban households, such policies may also have a role to play in addressing farm income risk issues and, in some cases, may represent more sustainable and efficient policy alternatives.
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Gedikoglu, Haluk McCann Laura. "Adoption of nutrient management practices." Diss., Columbia, Mo. : University of Missouri--Columbia, 2008. http://hdl.handle.net/10355/6614.

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Title from PDF of title page (University of Missouri--Columbia, viewed on March 17, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Laura McCann. Vita. Includes bibliographical references.
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Shaban, Mohammed Z. "On-Farm Water Management Game With Heuristic Capabilities." DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1255.

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A modern computer-based simulation tool (WaterMan) in the form of a game for on-farm water management was developed for application in training events for farmers, students, and irrigators. The WaterMan game utilizes an interactive framework, thereby allowing the user to develop scenarios and test alternatives in a convenient, risk-free environment. It includes a comprehensive soil water and salt balance calculation algorithm. It also employs heuristic capabilities for modeling all of the important aspects of on-farm water management, and to provide reasonable scores and advice to the trainees. Random events (both favorable and unfavorable) and different strategic decisions are included in the game for more realism and to provide an appropriate level of challenge according to player performance. Thus, the ability to anticipate the player skill level, and to reply with random events appropriate to the anticipated level, is provided by the heuristic capabilities used in the software. These heuristic features were developed based on a combination of two artificial intelligence approaches: (1) a pattern recognition approach; and (2) reinforcement learning based on a Markov Decision Processes approach, specifically, the Q-learning method. These two approaches were combined in a new way to account for the difference in the effect of actions taken by the player and action taken by the system on the game world. The reward function for the Q-learning method was modified to reflect the anticipated type of the WaterMan game as what is referred to as a partially competitive and partially cooperative game. Twenty-two different persons classified under three major categories (1) practicing farmers; (2) persons without an irrigation background; and (3) persons with an irrigation background, were observed while playing the game, and each of them filled out a questionnaire about the game. The technical module of the game was validated in two ways: through conducting mass balance calculations for soil water content and salt content over a period of simulation time, and through comparing the WaterMan technical module output data in calculating the irrigation requirements and the use of irrigation scheduling recommendations with those obtained from the same set of input data to the FAO CropWat 8 software. The testing results and the technical validation outcomes demonstrate the high performance of the WaterMan game as a heuristic training tool for on-farm water management.
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Gauthier, Laurent. "Development and use of a database and program package for farm production management." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74030.

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Petersen, Nicole Jade. "Estimating farm dam storage using SPOT imagery." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11341.

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Includes abstract.<br>Includes bibliographical references.<br>The objective of this study is to establish a methodology in which remote sensing can be used to support the monitoring of water resources. SPOT XS imagery and object-oriented classification was used to identify farm dams and their surface area. Two equations applied to determining the capacity of dams were used to convert surface area to volume. The results showed a similarity between fieldwork and object-oriented classification data for surface area. Overall, there appears to be a strong positive correlation between object-oriented classification and unsupervised classification. The correlation between object-oriented classification and supervised classification ranged from strong positive association to little or no association. This study concludes that remote sensing is a useful tool in identifying water bodies and generating an estimate of volume stored.
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Books on the topic "Farm management"

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M, Edwards William, ed. Farm management. 4th ed. Boston: WCB/McGraw Hill, 1999.

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M, Edwards William, ed. Farm management. 3rd ed. New York: McGraw-Hill, 1994.

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M, Edwards William, and Duffy Patricia Ann 1955-, eds. Farm management. 7th ed. New York, NY: McGraw-Hill, 2012.

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M, Edwards William, and Duffy Patricia Ann 1955-, eds. Farm management. 6th ed. Boston, Mass: McGraw-Hill, 2008.

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Nix, John. Farm management pocketbook. Ashford: Farm Business Unit, Dept. of Agricultural Economics, Wye College, University of London, 1987.

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Kim, Chong-mu. Farm management economics. Seoul: Sung Kyun Kwan University Press, 1987.

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Nix, John. Farm management pocketbook. 2nd ed. Ashford: Dept. of Agricultural Economics, Wye College, 1989.

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1945-, Hill Paul, and University of London. Wye College., eds. Farm management pocketbook. 3rd ed. London: Imperial College at Wye, 2001.

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Martin, Taylor, ed. Applied farm management. 2nd ed. Oxford, UK: Blackwell Science, 2003.

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Nix, John. Farm management pocketbook. Ashford, Kent: Department of Agricultural Economics, Wye College, 1985.

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Book chapters on the topic "Farm management"

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Berger, Janna. "Farm Management." In Encyclopedia of Food and Agricultural Ethics, 1–8. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-6167-4_463-1.

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Berger, Janna. "Farm Management." In Encyclopedia of Food and Agricultural Ethics, 703–9. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0929-4_463.

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Roche, Julian. "Farm management." In Agribusiness, 124–68. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9781351039741-5.

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Duffy, Patricia, and Sam Funk. "Farm management." In The Routledge Handbook of Agricultural Economics, 658–69. New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315623351-35.

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Berger, Janna. "Farm Management." In Encyclopedia of Food and Agricultural Ethics, 868–74. Dordrecht: Springer Netherlands, 2019. http://dx.doi.org/10.1007/978-94-024-1179-9_463.

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Peters, G. H. "Farm Income and Farm Management." In Agriculture, 86–101. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-3448-2_7.

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Wasniki, Sumit B., and Sneha Pandey. "Farm Management Principles." In Agribusiness Management, 85–105. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003490111-6.

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Olson, Kent, and John Westra. "Management." In The Economics of Farm Management, 10–29. 2nd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003280712-2.

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Cookson, Tim, Chad Andicochea, and Roberto Portela. "First Aid and Prehospital Management in Rural Areas." In Farm Toxicology, 9–17. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80441-0_2.

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Barkley, Andrew, and Paul W. Barkley. "Farm and agribusiness management." In Principles of Agricultural Economics, 445–62. 4th ed. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003367994-16.

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Conference papers on the topic "Farm management"

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Roy, Nirban, Shuvam Chakraborty, Katha Khan, and Biswadip Basu Mallik. "Decentralized Autonomous Farm Management." In 2025 National Conference on Communications (NCC), 1–6. IEEE, 2025. https://doi.org/10.1109/ncc63735.2025.10983047.

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Prabakaran, S., Dharun T, Gowsik H, Anu Prakash K, and Kirubakaran K. "Smart Farm Management Using Machine Learning." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT), 225–29. IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923013.

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Varsha, Lokavarapu, Suravarapu Ankith, Mucharla Deeksha, and J. Swaminathan. "AI-Driven Farm Management System using Streamlit." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724140.

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Krishnamurthy, K. T., Manjunath Managuli, Sushma Malipatil, K. Bagyalakshmi, Sudha V. Salake, Pankaja S. Kadalagi, and Sheetal B. Patil. "IoT Based Poultry Farm Smart Management System." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616803.

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Vatchova, Boriana, Yordanka Boneva, and Alexander Gegov. "Relation Based Knowledge Extraction for Animal Farm Management." In 2024 IEEE 12th International Conference on Intelligent Systems (IS), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705208.

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Graf, Jérôme, Pavel Chuprikov, Patrick Eugster, and Patrick Jahnke. "FARM: Comprehensive Data Center Network Monitoring and Management." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), 520–30. IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00055.

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Evsyukov, Dmitry, Svetlana Kukartseva, Kirill Kravtsov, Georgy Kuznetsov, and Danila Masich. "IoT-based farm management systems for modern agronomy." In 2025 24th International Symposium INFOTEH-JAHORINA (INFOTEH), 1–4. IEEE, 2025. https://doi.org/10.1109/infoteh64129.2025.10959282.

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Putranto, Muhammad Dzaki Dwi, Ahmad Ali Hakam Dani, Hastie Audytra, I. Gusti Bagus Baskara Nugraha, and Suhono Harso Supangkat. "Smart Monitoring and Watering System for Farm Management Assistance." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 525–30. IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913277.

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Ghazaei, Elman, Omid Feizi, Amir A. Ghavifekr, Mina Salim, and Armin Hassanzadeh. "Wind Farm Power Prediction with Transformer Encoder." In 2024 9th International Conference on Technology and Energy Management (ICTEM), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/ictem60690.2024.10631900.

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Zhang, Han, Zhigang Tian, and Mingjian Zuo. "Component-level Wind Farm Maintenance Considering Seasonal Uncertainties." In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 146–51. IEEE, 2024. http://dx.doi.org/10.1109/icphm61352.2024.10626855.

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Reports on the topic "Farm management"

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Hartzler, Robert G., and David Rueber. Weed Management Trials—Northern Research Farm. Ames: Iowa State University, Digital Repository, 2004. http://dx.doi.org/10.31274/farmprogressreports-180814-416.

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Gillespie, Jeffrey, Christine Whitt, and Christopher Davis. Structure, management practices, and production costs of U.S. beef cow-calf farms. Washington, D.C.: United States Department of Agriculture, Economic Research Service, July 2023. http://dx.doi.org/10.32747/2023.8134136.ers.

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The cow-calf segment of the U.S. beef industry is diverse in farm size, structure, and location, with farms located in every State and ranging from very small to very large. Modest structural change has occurred in this segment over the past two decades, resulting in moderately fewer farms that produce more animals and are more specialized in cow-calf production. This report compares cow-calf farms by region, farm size, phases of beef production that are present on the farm, and farm typology using the cow-calf version of the 2018 USDA Agricultural Resource Management Survey. Larger scale cow-calf farms were found in the Northern Plains and West regions, whereas smaller scale farms tended to be located in the Southeast and Southern Plains regions. Larger scale cow-calf farms had lower economic costs per cow and tended to adopt advanced technologies, management practices, and production systems at greater rates than smaller farms
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Fawcett, Jim, Josh Sievers, Chris Beedle, and Lance Miller. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/farmprogressreports-180814-1377.

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Fawcett, Jim, Josh Sievers, Chris Beedle, and Lance Miller. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/farmprogressreports-180814-1473.

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Fawcett, Jim, Josh Sievers, Chris Beedle, and Lance Miller. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/farmprogressreports-180814-1485.

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Fawcett, Jim, Josh Sievers, Joel DeJong, and Karl Nicolaus. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/farmprogressreports-180814-1629.

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Fawcett, Jim, Josh Sievers, Joel DeJong, and Karl Nicolaus. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/farmprogressreports-180814-1657.

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Fawcett, Jim, Josh Sievers, Joel DeJong, and Karl Nicolaus. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/farmprogressreports-180814-1677.

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Fawcett, Jim, Josh Sievers, and Wayne Roush. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2015. http://dx.doi.org/10.31274/farmprogressreports-180814-1766.

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Fawcett, Jim, Josh Sievers, and Wayne Roush. On-Farm Corn and Soybean Management Trials. Ames: Iowa State University, Digital Repository, 2015. http://dx.doi.org/10.31274/farmprogressreports-180814-2342.

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