Journal articles: 'Farm management'

1

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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2

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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Iglesias, Gregorio, Rodrigo Carballo, Javier Abanades, and Deborah Greaves. "WAVE FARM LAYOUT AND COASTAL IMPACTS." Coastal Engineering Proceedings 1, no. 34 (October 30, 2014): 36. http://dx.doi.org/10.9753/icce.v34.management.36.

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4

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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5

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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6

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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7

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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8

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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9

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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10

Evans, M. R. "Agricultural Smells from Livestock Farms—Farm Waste Management." Agricultural Wastes 18, no. 2 (January 1986): 173–74. http://dx.doi.org/10.1016/0141-4607(86)90011-9.

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11

WARE, D. W. "USING FARM MANAGEMENT RESEARCH IN FARM PLANNING*." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 3, no. 2 (November 13, 2008): 44–48. http://dx.doi.org/10.1111/j.1744-7976.1955.tb01279.x.

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12

JEFFREY., A. G. "THE USE OF FARM ACCOUNTS IN FARM MANAGEMENT." Journal of proceedings of the Agricultural Economics Society 8, no. 1 (November 5, 2008): 48–68. http://dx.doi.org/10.1111/j.1477-9552.1948.tb01918.x.

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13

Sinclair, E. R. "A COMPUTER-BASED FARM-MANAGEMENT PACKAGE FOR PINEAPPLE FARMS." Acta Horticulturae, no. 334 (October 1993): 191–96. http://dx.doi.org/10.17660/actahortic.1993.334.19.

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14

Shichinohe, Chosei. "Taira Yori, Farm Management." Journal of Rural Problems 27, no. 3 (1991): 147–49. http://dx.doi.org/10.7310/arfe1965.27.147.

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15

PETTIT, G. H. N. "FARM MANAGEMENT ENTERPRISE STUDIES." Journal of proceedings of the Agricultural Economics Society 6, no. 1 (November 5, 2008): 56–73. http://dx.doi.org/10.1111/j.1477-9552.1940.tb01894.x.

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16

Замятин, Н. В., Г. В. Смирнов, and В. И. Маковкин. "Tank Farm Situational Management." Успехи кибернетики / Russian Journal of Cybernetics, no. 1(13) (March 31, 2023): 7–14. http://dx.doi.org/10.51790/2712-9942-2023-4-1-01.

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добыча сырья и его переработка в готовую продукцию связаны с использованием резервуарных парков. С ростом резервуарного парка возрастают проблемы, обусловленные количественным учетом хранимых веществ и продуктов их переработки, Решение этих проблем невозможно осуществить без разработки эффективных систем управления. Предложен подход к построению системы ситуационного управления резервуарным парком, позволяющий повысить эффективность функционирования технологических процессов за счет своевременного принятия управленческих решений по устранению нештатных ситуаций. Приведен пример перехода от нештатных ситуаций к штатным с использованием нечеткой ситуационной сети и правил перехода. the extraction and processing of raw materials requires the use of tank farms (TF). As the tank farm grows, there are issues with product metering, and an efficient management system is required. We proposed an approach to the development of a tank farm situational management system. The system increases the process efficiency with its prompt management decisions to rectify any emergencies. The paper contains an example of restoring the normal operation with an fuzzy situational network and transition rules.

17

S, Kaviarasu, Navaneesh K M, Abisheck G, and Rishikesh N. "Poultry Farm Management Application." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (October 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26068.

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The poultry farming industry plays a vital role in meeting the global demand for poultry products. However, traditional farm management practices often face challenges related to data management, resource optimization, and operational efficiency. In response to these challenges, this paper presents the Poultry Farm Management System (PFMS), a web-based application designed to modernize and streamline poultry farm management. PFMS utilizes the Model-View-Controller-Service (MVCS) architecture and a three-tier architectural framework, integrating technologies such as Go (Golang), MongoDB, and React. This paper outlines the methodology, system architecture, software modules, development process, database design, user interface design, implementation details, testing, deployment, security measures, and user training. It also discusses the results, cost- benefit analysis, potential enhancements, and the significant benefits PFMS brings to the poultry farming industry. Key Words: MVCS architecture, three tier architecture, schema design, efficiency, performance

18

Albrecht, Gregory L., Quirine M. Ketterings, Karl J. Czymmek, Michael E. Amburgh, and Danny G. Fox. "Whole Farm Nutrient Management: Capstone Course on Environmental Management of Dairy Farms." Journal of Natural Resources and Life Sciences Education 35, no. 1 (January 2006): 12–23. http://dx.doi.org/10.2134/jnrlse2006.0012.

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19

Gloy, Brent A., Jeffrey Hyde, and Eddy L. LaDue. "Dairy Farm Management and Long-Term Farm Financial Performance." Agricultural and Resource Economics Review 31, no. 2 (October 2002): 233–47. http://dx.doi.org/10.1017/s1068280500004032.

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The financial performance and relationships between several management factors and financial performance are examined in a panel of 107 New York dairy farms. A panel regression model with fixed effects is estimated in an effort to identify management factors that influence profitability. The model is estimated with two-stage least squares to account for endogenous farm size and debt use variables. Production management factors such as farm size, rate of milk production, and milking system had a positive impact on farm profitability. Financial management variables for the type of accounting system used and the debt use were also significantly related to profitability. Unlike the findings of many other studies, measures of human capital did not have a statistically significant impact on profitability.

20

Cooper, Julia. "Uniform or farm-specific management in on-farm research?" American Journal of Alternative Agriculture 9, no. 3 (September 1994): 126. http://dx.doi.org/10.1017/s0889189300005804.

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21

Ahiakwo, A. A., and C. B. Isirima. "FARM MACHINERY UTILIZATION AND MAINTENANCE EFFECTS ON SUSTAINABLE DEVELOPMENT OF COLLEGE FARMS." Engineering and Technology Journal 08, no. 01 (January 3, 2023): 1795–801. http://dx.doi.org/10.47191/etj/v8i1.02.

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In order to expose students in tertiary institution learning agricultural science to the concept of farm mechanization, which could aid self-reliance after graduation, the federal government of Nigeria has provided farm machinery to make teaching and learning effective in this regard. The focus of this study therefore is to assess farm machinery deployment, management and maintenance effect on mechanization for sustainable development of college farms in Nigerian. The study population comprised all federal and state colleges covered by tertiary institution trust fund (tetfund). Sample of 24 colleges offering agricultural education was randomly drawn for the study. A structured questionnaire was used for data collection. Data obtained was statistically analysed using mean from a 4 point likert rating scale. Results obtained revealed that out of the 252 farm machinery available in the sampled colleges, 25 percent are tractor, 20 percent each are plough and harrow while 18percent are ridger and planter/fertilizer distributor respectively. The mean response of 2.4, 2.2 and 2.3 in disagreement to the research questions shows that there is need to improve in farm machinery utilization and maintenance as well as for teaching and learning in the colleges.

22

Grilli, Annette Renee, Malcolm Spaulding, Christopher O'Reilly, and Gopu Potty. "OFFSHORE WIND FARM MACRO AND MICRO SITING PROTOCOL APPLICATION TO RHODE ISLAND." Coastal Engineering Proceedings 1, no. 33 (December 15, 2012): 73. http://dx.doi.org/10.9753/icce.v33.management.73.

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Since 2008, the Rhode Island (RI) Coastal Resources Management Council has been leading the development of an Ocean Special Area Management Plan (Ocean SAMP), in partnership with the University of Rhode Island, resulting in an extensive multidisciplinary analysis of the Rhode Island offshore environment and its suitability to site offshore wind farms. As part of SAMP, a comprehensive macro-siting optimization tool: the Wind Farm Siting Index (WIFSI), integrating technical, societal, and ecological constraints, was developed within the conceptual framework of ecosystem services. WIFSI uses multivariate statistical analyses (principal component and k-means cluster analyses) to define homogeneous regions, which integrate and balance ecological and societal constraints as part of a Cost/Benefit tool. More recently, a Wind Farm micro-Siting Optimization Tool was developed (WIFSO), which uses a genetic algorithm to derive the optimal layout of a wind farm sited within one of the macro-siting selected regions. In this work, we present an overview of the current state of development of the integrated macro- and micro- siting tools.

23

Zeuli, Kimberly A., and Robert P. King. "Gender Differences in Farm Management." Applied Economic Perspectives and Policy 20, no. 2 (October 1998): 513–29. http://dx.doi.org/10.2307/1350005.

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24

Kassahun, Ayalew, Robbin Bloo, Cagatay Catal, and Alok Mishra. "Dairy Farm Management Information Systems." Electronics 11, no. 2 (January 12, 2022): 239. http://dx.doi.org/10.3390/electronics11020239.

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Nowadays, different types of farm management information systems (FMIS) are being used in practice in several sectors of farming, such as dairy, arable, fruits, vegetables, and meat farming. The goal of this research is to identify, evaluate, and synthesize existing FMISs in the Dutch dairy sector and present the state–of–the–art. We performed a multivocal literature review (MLR) to find sources both in scientific and grey literature. A grey literature search was adopted because most of the FMISs were not reported in the scientific literature. To support and improve the effectiveness of the MLR process, an online survey was first sent to Dutch dairy farmers to identify the FMISs that are being used in practice. With the help of the MLR process, we identified 50 FMISs used by Dutch dairy farmers. We identified 33 features of these FMISs and listed the advantages and disadvantages of the FMISs.

25

Winter, F. "COMPUTER AIDED FRUIT FARM MANAGEMENT." Acta Horticulturae, no. 276 (July 1990): 371–72. http://dx.doi.org/10.17660/actahortic.1990.276.42.

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26

Collinson, M. P. "Economics of tropical farm management." Agricultural Economics 1, no. 2 (June 1987): 192–93. http://dx.doi.org/10.1111/j.1574-0862.1987.tb00018.x.

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27

Orde, Anne. "The Culleys and Farm Management." Northern History 41, no. 2 (September 2004): 327–38. http://dx.doi.org/10.1179/007817204790181069.

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28

Lines, Roger. "Book Review: Farm Woodland Management." Outlook on Agriculture 17, no. 2 (June 1988): 86. http://dx.doi.org/10.1177/003072708801700211.

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29

ABBOTT, W. S. "THE ART OF FARM MANAGEMENT." Journal of proceedings of the Agricultural Economics Society 8, no. 3 (November 5, 2008): 208–22. http://dx.doi.org/10.1111/j.1477-9552.1949.tb01932.x.

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30

THOMAS, W. J. "THE TEACHING OF FARM MANAGEMENT." Journal of proceedings of the Agricultural Economics Society 10, no. 2 (November 5, 2008): 158–68. http://dx.doi.org/10.1111/j.1477-9552.1953.tb01866.x.

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31

Mumey, G. A. "Improving On-Farm Financial Management." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 35, no. 4 (December 1987): 735–40. http://dx.doi.org/10.1111/j.1744-7976.1987.tb02264.x.

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32

Evans, M., R. Mondor, and D. Flaten. "Expert Systems and Farm Management." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 37, no. 4 (November 13, 2008): 639–66. http://dx.doi.org/10.1111/j.1744-7976.1989.tb00783.x.

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33

Lins, David A. "Future Directions in Farm Management." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 37, no. 4 (November 13, 2008): 765–74. http://dx.doi.org/10.1111/j.1744-7976.1989.tb00795.x.

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34

Loree, J. Wilson. "Technology in Farm Business Management." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 42, no. 4 (December 1994): 493–96. http://dx.doi.org/10.1111/j.1744-7976.1994.tb00042.x.

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35

Esslemont, R. J. "Computers in dairy farm management." British Veterinary Journal 144, no. 4 (July 1988): 351–62. http://dx.doi.org/10.1016/0007-1935(88)90064-4.

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36

Sonka, Steven T. "Information management in farm production." Computers and Electronics in Agriculture 1, no. 1 (October 1985): 75–85. http://dx.doi.org/10.1016/0168-1699(85)90007-9.

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37

Snellen, W. B. "Management of farm irrigation systems." Agricultural Water Management 28, no. 2 (September 1995): 183–84. http://dx.doi.org/10.1016/0378-3774(95)90010-1.

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38

Fountas, S., C. G. Sorensen, Z. Tsiropoulos, C. Cavalaris, V. Liakos, and T. Gemtos. "Farm machinery management information system." Computers and Electronics in Agriculture 110 (January 2015): 131–38. http://dx.doi.org/10.1016/j.compag.2014.11.011.

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39

Legg, Wilfrid, and Kevin Parris. "Farm management and the environment." Journal of Environmental Management 82, no. 3 (February 2007): 299–301. http://dx.doi.org/10.1016/j.jenvman.2006.08.013.

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40

Chindarkar, Namrata, Yvonne Jie Chen, and Shilpa Sathe. "Link between farm electricity supply management, farm investments, and farm incomes - Evidence from India." Energy Policy 141 (June 2020): 111407. http://dx.doi.org/10.1016/j.enpol.2020.111407.

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41

Menardo, Simona, Giacomo Lanza, and Werner Berg. "The Effect of Diet and Farm Management on N2O Emissions from Dairy Farms Estimated from Farm Data." Agriculture 11, no. 7 (July 11, 2021): 654. http://dx.doi.org/10.3390/agriculture11070654.

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The N2O emissions of 21 dairy farms in Germany were evaluated to determine the feasibility of an estimation of emissions from farm data and the effects of the farm management, along with possible mitigation strategies. Emissions due to the application of different fertilisers, manure storage and grazing were calculated based on equations from the IPCC (Intergovernmental Panel of Climate Change) and German emission inventory. The dependence of the N2O emissions on fertiliser type and quantity, cultivated crops and diet composition was assessed via correlation analysis and linear regression. The N2O emissions ranged between 0.11 and 0.29 kg CO2eq per kilogram energy-corrected milk, with on average 60% resulting from fertilisation and less than 30% from fertiliser storage and field applications. The total emissions had a high dependence on the diet composition; in particular, on the grass/maize ratio and the protein content of the animal diet, as well as from the manure management. A linear model for the prediction of the N2O emissions based on the diet composition and the fertilisation reached a predictive power of R2 = 0.89. As a possible mitigation strategy, the substitution of slurry for solid manure would reduce N2O emissions by 40%. Feeding cows maize-based diets instead of grass-based diets could reduce them by 14%.

42

Omobitan, Omobolaji, and Aditya R. Khanal. "Examining Farm Financial Management: How Do Small US Farms Meet Their Agricultural Expenses?" Journal of Risk and Financial Management 15, no. 3 (March 10, 2022): 133. http://dx.doi.org/10.3390/jrfm15030133.

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Small farms in the US have significant challenges in financial management. This study examines how small farmers undertake farm financial management to meet their agricultural and farm-related spending and expenses. Using primary survey data from Tennessee, the study investigates the factors influencing the extent of use of five financing sources to meet the spending and expenses: cash/fund directly generated from the sale of agricultural products, farmer’s past savings, farm household’s off-farm income, income/incentives from government payments, and external loans. Using negative binomial regression estimation of generalized linear models, findings suggest that the decision on the use of financing sources is significantly influenced in general by age, education, income and land acreage holdings, off-farm work, and risk factors related to farmer or farm household. However, the associated factors and their effects on the extent of use are different depending on the financing source.

43

Harling, Kenneth F., and Phoebe Quail. "Exploring a general management approach to farm management." Agribusiness 6, no. 5 (September 1990): 425–41. http://dx.doi.org/10.1002/1520-6297(199009)6:5<425::aid-agr2720060503>3.0.co;2-q.

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44

Lai, John, Nicole J. Olynk Widmar, Michael A. Gunderson, David A. Widmar, and David L. Ortega. "Prioritization of farm success factors by commercial farm managers." International Food and Agribusiness Management Review 21, no. 6 (July 14, 2018): 817–32. http://dx.doi.org/10.22434/ifamr2017.0035.

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This study elicits U.S. agricultural producer preferences for five key management success factors: managing output prices; managing production; controlling costs; managing land/equipment/facilities; and managing people. The objective of this analysis was to determine the relative importance of each of the five profit-centric functional areas of management among U.S. farm managers. Significant heterogeneity in preferences was observed over the management areas. Farm managers, on average, placed the highest importance in controlling costs (29% preference share). Differences emerged among groups of farmers in a latent class model where managing people became relatively important to the viability of the agribusiness.

45

Marineci, Cristina Daniela, Cornel Chiriţă, and Simona Negreş. "Comprehensive medication management – part I." Farmacist.ro 4, no. 201 (2021): 7. http://dx.doi.org/10.26416/farm.201.4.2021.5323.

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46

Öhlmér, Bo. "On‐farm computers for farm management in Sweden: potentials and problems." Agricultural Economics 5, no. 3 (July 1991): 279–86. http://dx.doi.org/10.1111/j.1574-0862.1991.tb00158.x.

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47

Öhlmér, B. "On-farm computers for farm management in Sweden: potentials and problems." Agricultural Economics 5, no. 3 (July 1991): 279–86. http://dx.doi.org/10.1016/0169-5150(91)90049-q.

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48

Marineci, Cristina Daniela, and Simona Negreş. "Comprehensive medication management – part II. How does a hypertensive patient benefit from comprehensive medication management?" Farmacist.ro 5, no. 202 (2021): 7. http://dx.doi.org/10.26416/farm.202.5.2021.5579.

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49

Ondersteijn, C. J. M., A. C. G. Beldman, C. H. G. Daatselaar, G. W. J. Giesen, and R. B. M. Huirne. "Farm structure or farm management: effective ways to reduce nutrient surpluses on dairy farms and their financial impacts." Livestock Production Science 84, no. 2 (December 2003): 171–81. http://dx.doi.org/10.1016/j.livprodsci.2003.09.013.

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50

Väre, Minna, Tiina E. A. Mattila, Pasi Rikkonen, Maria Hirvonen, and Risto H. Rautiainen. "Farmers’ perceptions of farm management practices and development plans on organic farms in Finland." Organic Agriculture 11, no. 3 (April 6, 2021): 457–67. http://dx.doi.org/10.1007/s13165-021-00352-4.

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AbstractOrganic farming is increasing in Finland, and organic farms have become larger than conventional farms, on average. As the structural change has been rapid, farmers need a broad range of new competencies to manage their farms, ranging from agronomic skills to advanced technology, labor management, and marketing skills. In this study, the characteristics of organic and conventional farms and farmers were assessed, with special focus on management practices and future development plans on organic farms. The study was based on telephone interviews of a sample of active farmers who applied for agricultural subsidies in 2014. The data consisted of 3045 farmers; 312 of them practiced organic farming and 2733 conventional farming. The data were analyzed using multivariable logistic regression. Having beef production as the main production line, having plans to develop farm production in the next 5 years, considering farm management as very important, and frequently experiencing mental strain because of farm management were significant predictors for being an organic farmer. Dairy production was less frequently organic compared to crop production. Nearly half (42%) of organic farmers planned to make changes in their farming, most commonly expanding their production. Thus, competence for managing the farm operation becomes more crucial, which increases the need for training and management consulting services. Organic farming may increase with both farm successions and new entrants joining the farming sector. Special attention should be paid to supporting these new entrants without farm-family background.

Journal articles on the topic 'Farm management'

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