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Journal articles on the topic 'Farm performance'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Kostlivý, Vladimír, Zuzana Fuksová, and Tamara Rudinskaya. "Drivers of farm performance in Czech crop farms." Agricultural Economics (Zemědělská ekonomika) 66, No. 7 (2020): 297–306. http://dx.doi.org/10.17221/231/2019-agricecon.

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When analysing drivers affecting the farm performance, the presence of different technologies should be taken into account. We assume that the technology used by crop farms is not the same for all producers and therefore we use latent class model to identify technological classes at first. Class definition is based on multidimensional classification and determination of indices given by the values of individual components. The principal components analysis is applied to estimate significant and robust weights for the index components. FADN (Farm Accountancy Data Network) database, Czech crop f

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2

Ma, Wanglin, Alan Renwick, and Quentin Grafton. "Farm machinery use, off-farm employment and farm performance in China." Australian Journal of Agricultural and Resource Economics 62, no. 2 (2018): 279–98. http://dx.doi.org/10.1111/1467-8489.12249.

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3

Kirby, Andrew, Takafumi Nishino, Luca Lanzilao, Thomas D. Dunstan, and Johan Meyers. "Turbine- and farm-scale power losses in wind farms: an alternative to wake and farm blockage losses." Wind Energy Science 10, no. 2 (2025): 435–50. https://doi.org/10.5194/wes-10-435-2025.

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Abstract. Turbine–wake and farm–atmosphere interactions can reduce wind farm power production. To model farm performance, it is important to understand the impact of different flow effects on the farm efficiency (i.e. farm power normalised by the power of the same number of isolated turbines). In this study we analyse the results of 43 large-eddy simulations (LESs) of wind farms in a range of conventionally neutral boundary layers (CNBLs). First, we show that the farm efficiency ηf is not well correlated with the wake efficiency ηw (i.e. farm power normalised by the power of front-row turbines

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4

Bojnec, Štefan, and Laure Latruffe. "Farm size, agricultural subsidies and farm performance in Slovenia." Land Use Policy 32 (May 2013): 207–17. http://dx.doi.org/10.1016/j.landusepol.2012.09.016.

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5

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 (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 sys

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Mulvaney, C. J., and D. J. Maccoll. "Monitoring and benchmarking farm performance." NZGA: Research and Practice Series 16 (January 1, 2016): 87–93. http://dx.doi.org/10.33584/rps.16.2016.3262.

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Summary StockCARE has become recognised as a programme that can add value to sheep and beef farm businesses. The recognition is most likely built on some of the key features and benefits: â€¢ A systems based programme which has been proven to add value â€¢ Monitoring and benchmarking are the platform, but the intensity of the collection, interpretation and utilisation of information related to key drivers of every production system, is unique â€¢ StockCARE has a proven framework for a disciplined approach to the collection and analysis of production information. â€¢ The initial emphasis is on

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Verhees, Frans J. H. M., Abele Kuipers, and Marija Klopcic. "Entrepreneurial Proclivity and Farm Performance." International Journal of Entrepreneurship and Innovation 12, no. 3 (2011): 169–77. http://dx.doi.org/10.5367/ijei.2011.0039.

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Farmers are advised to be entrepreneurial, but there is scant research showing that an entrepreneurial proclivity (EP) in farmers results in better performance. This study tests empirically whether an EP contributes to the performance of farms. A model with hypotheses about the relationship between EP and performance is provided and tested for a sample of Dutch and Slovenian farmers. EP has a universal positive influence on the performance and performance expectations of farmers in The Netherlands and in Slovenia. Proactiveness is the most influential element of EP for the performance of farms

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Roe, E., H. Buller, and J. Bull. "The performance of farm animal assessment." Animal Welfare 20, no. 1 (2011): 69–78. http://dx.doi.org/10.1017/s096272860000244x.

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AbstractThis paper argues that the current drive towards greater use of animal-based measures for welfare assessment raises important issues for how farm visits by welfare assessors are performed. As social scientists, we employ a number of contemporary social science ideas to offer a new approach to examining the practice and performance of farm animal assessment. We identify key findings from a recent study of contemporary farm assessment and speculate upon what some of the challenges of introducing animal-based measures may be. We conclude by arguing for a greater awareness of how sets of k

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9

Pederson, Glenn, Ananth Rao, and Michael Boehlje. "Determinants of Restructured Farm Loan Performance." Journal of Agricultural and Applied Economics 23, no. 2 (1991): 39–48. http://dx.doi.org/10.1017/s008130520001815x.

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AbstractA probabilistic model is applied to cross-sectional data to identify determinants of post-restructure performance of Federal Land Bank loans. The results indicate that restructured loans were sensitive to factors that determine the debt repayment burden and the repayment ability of the restructured farm operations. Loan performance is found to be relatively more sensitive to the levels of the post-restructure interest rate and cash farm income than to the financial structure and leverage position of the restructured farm. The relationships between the post-restructure interest rate, ca

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Veidal, Asbjørn, and Ola Flaten. "Entrepreneurial Orientation and Farm Business Performance." International Journal of Entrepreneurship and Innovation 15, no. 2 (2014): 101–12. http://dx.doi.org/10.5367/ijei.2014.0147.

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This study advances research on entrepreneurial orientation (EO) in the farming sector by examining how the configuration of on-farm diversification and location shapes the relationship between EO and farm business performance (archival financial performance and self-reported non-financial performance). Data were collected from 400 farms included in the Norwegian Farm Business Survey. The research hypotheses were tested using hierarchical moderated regression analysis. As hypothesized, a positive relationship was found between EO and non-financial performance. Other hypotheses were not confirm

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11

Isaacs, C., and T. A. White. "Driving farm performance using information systems." NZGA: Research and Practice Series 16 (January 1, 2016): 109–12. http://dx.doi.org/10.33584/rps.16.2016.3265.

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FarmIQ Systems Limited was established as a Primary Growth Partnership (PGP) between the Ministry for Primary Industries (MPI), the meat processor Silver Fern Farms and Landcorp Farming Limited. FarmIQ aims to create a truly integrated value and information chain for red meat from product back to farm. The major output of this partnership is the FarmIQ System â€’ farm management software developed in New Zealand for New Zealand farmers. Hill country farmers have historically been at a disadvantage compared to their lowland counterparts in terms of the ease of quantifying farm performance. The

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12

Furutsuka, Hideo, and Yoshifumi Genda. "The Financial Performance of Farm Management." Journal of Rural Problems 44, no. 3 (2008): 425–35. http://dx.doi.org/10.7310/arfe1965.44.425.

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13

Haque, M. A., M. D. Hussain, and M. A. Zaman. "On Farm Performance of Manual Pumps." Journal of Agricultural Machinery and Bioresources Engineering 3, no. 1 & 2 (1996): 19–26. http://dx.doi.org/10.61361/jambe.v.i12.58.

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This paper describes the on farm performances of three manually operated pumps widely used in Bangladesh namely, twin treadle pump, rower pump and No. 6 hand pump. Discharges from the treadle pump, rower pump and No. 6 hand pump at a head of 2.5 m were 0.66, 0.50 and 0.41 l/s respectively. Power input to the Rower pump and No. 6 Hand pump were 10% and 40% higher, respectively than that of the treadle pump at a head of 3.5 m. Treadle pump had the highest efficiency of 45% while the rower pump and the No. 6 hand pump had 38% and 25% efficiency, respectively. BCR values were 2.69, 1.83 and 1.17 r

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14

Charles R. Hurburgh, Jr., Lynn N. Paynter, Steven G. Schmitt, and Carl J. Bern. "Performance of Farm-Type Moisture Meters." Transactions of the ASAE 29, no. 4 (1986): 1118–23. http://dx.doi.org/10.13031/2013.30280.

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15

Manor, G., and D. Nir. "Accuracy of performance of farm machinery." Journal of Terramechanics 24, no. 1 (1987): 87–94. http://dx.doi.org/10.1016/0022-4898(87)90061-9.

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16

Pourzand, Farnaz, Ilan Noy, and Yiğit Sağlam. "Droughts and farms’ financial performance: a farm‐level study in New Zealand." Australian Journal of Agricultural and Resource Economics 64, no. 3 (2020): 818–44. http://dx.doi.org/10.1111/1467-8489.12367.

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17

Roskam, J. L., A. G. J. M. Oude Lansink, and H. W. Saatkamp. "The relation between technical farm performance and antimicrobial use of broiler farms." Poultry Science 99, no. 3 (2020): 1349–56. http://dx.doi.org/10.1016/j.psj.2019.10.054.

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18

Hadrich, Joleen C., and Frayne Olson. "Joint measurement of farm size and farm performance: a confirmatory factor analysis." Agricultural Finance Review 71, no. 3 (2011): 295–309. http://dx.doi.org/10.1108/00021461111177585.

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19

Hloušková, Zuzana, and Michaela Lekešová. "Farm outcomes based on cluster analysis of compound farm evaluation." Agricultural Economics (Zemědělská ekonomika) 66, No. 10 (2020): 435–43. http://dx.doi.org/10.17221/273/2020-agricecon.

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The purpose of this paper is to examine the internal structure of Czech agricultural holdings based on a multicriteria evaluation of the five dimensions representing the main functions of agriculture including production, economic<br />factors, financial stability, environmental, and social and other factors. A cluster analysis was performed to identify two clusters of farms. The first cluster consists of smaller holdings that specialize in livestock production and achieve poorer financial results compared to the second cluster, which includes a larger share of large holdings that focus

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20

Nimbalkar, Monali D., Y. B. Shambharkar Y.B.Shambharkar, and K. G. Gavade K.G. Gavade. "Role performance and Constraints faced by farm women Engaged in Agriculture." Indian Journal of Applied Research 3, no. 7 (2011): 7–9. http://dx.doi.org/10.15373/2249555x/july2013/3.

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21

Koketsu, Yuzo, Yosuke Sasaki, and Ryosuke Iida. "Sow Reproductive Performance, Lifetime Performance, Their Risk Factors, Predictors and Farm-Level Factors in Farm Data Analysis." Nihon Yoton Gakkaishi 59, no. 1 (2022): 1–17. http://dx.doi.org/10.5938/youton.59.1_1.

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22

Wähner, M., A. John, and C. Hoffmeyer. "Zum Einfluss des Wachstums und der Seitenspeckdicke auf die Fruchtbarkeits- und Aufzuchtleistung von Jungsauen – 1. Mitteilung: Vergleich der Merkmale Wachstum, Seitenspeckdicke, Fruchtbarkeit und Aufzuchtleistungen." Archives Animal Breeding 44, no. 2 (2001): 157–66. http://dx.doi.org/10.5194/aab-44-157-2001.

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Abstract. Title of the paper: Influence of growth and side fat thickness on reproduction and rearing performance of gilts. I. Comparison of characteristics growth, side fat thickness and reproduction performances Studies were executed in two breeding farms with gilts of Leicoma (farm B) and German Landrace (farm M). The influence of growth and side fat thickness on reproduction and breeding Performance was analysed. Date of birth, body weight and side fat thickness measured to selection (180. Living day) and insemination (236. day farm B; 242. day farm M) also pregnancy rate and appropriated b

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23

Kelvin Edem Bassey. "OPTIMIZING WIND FARM PERFORMANCE USING MACHINE LEARNING." Engineering Science & Technology Journal 3, no. 2 (2022): 32–44. http://dx.doi.org/10.51594/estj.v3i2.1284.

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The rapid expansion of wind energy as a sustainable power source has necessitated the adoption of advanced technologies to optimize wind farm performance. This study explores the application of machine learning (ML) algorithms to enhance the efficiency and maintenance scheduling of wind turbines. By leveraging predictive analytics, wind farm operators can significantly improve operational performance, reduce downtime, and extend the lifespan of turbines. Machine learning models can analyze vast amounts of data generated by wind turbines, including sensor readings, weather conditions, and histo

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24

Zulhanafiah, Zulhanafiah, and Ujang Paman. "Performance Evaluation of Farm Machinery Utilization Under Custom Hiring Services Managements." Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) 13, no. 3 (2024): 679. http://dx.doi.org/10.23960/jtep-l.v13i3.679-690.

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Farm machinery hire service is a custom hiring business model managed by farmers’ groups at the farm level. The model is developed to facilitate access to farm machinery by small farmers and derive income (profit) from hiring out machinery services. This research attempts to evaluate the performance of farm machinery hire services in the Indragiri Hulu Region, Riau Province. The field surveys were conducted from September to October 2022 in 11 districts in the province. A total of 11 managers and 22 operators from 11 farm machinery hire service groups were selected purposively and interviewed

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Repar, Nina, Pierrick Jan, Thomas Nemecek, Dunja Dux, and Reiner Doluschitz. "Factors Affecting Global versus Local Environmental and Economic Performance of Dairying: A Case Study of Swiss Mountain Farms." Sustainability 10, no. 8 (2018): 2940. http://dx.doi.org/10.3390/su10082940.

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Improving the sustainability of the dairy food chain requires a simultaneous improvement in global and local environmental performance, as well as in the economic performance of dairy farms. We investigated the effect of different structural, farm management, socio-demographic, technological and natural-environment-related factors on the economic and environmental performance of dairying. Our analysis relied on a case study of 56 Swiss alpine dairy farm observations, for which cradle-to-farm gate life cycle assessments and farm accountancy data were combined. The data refer to the years 2006 t

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Putri, Dewi Sartika, Muhammad Irfan Affandi, and Wuryaningsih Dwi Sayekti. "ANALISIS KINERJA USAHA DAN RISIKO PETAMBAK UDANG VANAME PADA SISTEM TRADISIONAL DAN SISTEM SEMI INTENSIFDI KECAMATAN LABUHAN MARINGGAI KABUPATEN LAMPUNG TIMUR." Jurnal Ilmu-Ilmu Agribisnis 8, no. 4 (2020): 625. http://dx.doi.org/10.23960/jiia.v8i4.4707.

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This study aims to analyse the farm performances and the risk of vaname shrimp farmers in conventional and semi-intensive technology in Labuhan Maringgai Subdistrict, East Lampung Regency with the consideration that the location was vaname shrimp production center. The sample in this study were 35 vaname shrimp farmers consisting of 24 conventional farmers and 11 semi-intensive farmers that were randomly selected disproportionately. The research data were collected in March 2019 and analysed using quantitative analysis of farm performance and production risk. The results showed that the farm p

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27

Duvivier, Predner, Gelin Doréus, Robers-Pierre Tescar, and Nicolas Ophny Carvil. "Agronomic characteristics and economic performance of cocoa farms in Haiti." Current Agronomy 1, no. 1 (2024): 44–53. http://dx.doi.org/10.2478/cag-2024-0005.

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Abstract This study aimed to describe the agronomic characteristics of the cocoa based farms, estimate their agronomic and economic performance and carry out a gender analysis. Data relating to crops economic value or food security importance, household size, farmers’ age, farm size, crop yield and farm family income were collected by focus groups or individual surveys. The results showed that, in average, the households’ size was 4.37 people and the producers’ age, 52 years. The female headed farm size was 1.56 ha against 2.20 ha for the male headed ones. Cocoa farm yield was 562.98 kg/ha and

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28

Schnitkey, Gary D., Peter J. Barry, and Paul N. Ellinger. "A Microcomputer Analysis of Farm Financial Performance." Journal of Agricultural and Applied Economics 19, no. 2 (1987): 203–9. http://dx.doi.org/10.1017/s0081305200025474.

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AbstractThis article describes the properties of the Farm Financial Simulation Model (FFSM). FFSM is a tool for analyzing the financial consequences of various managerial strategies and policy options that may be implemented in responding to farm financial stress. Various farm types from different geographical regions having differing enterprises, financial structures, tenure arrangements, and consumption patterns can be analyzed. The emphasis of FFSM is placed on modeling a farm's profitability, liquidity, solvency, and financial position and the model produces a coordinated set of financial

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Khanal, Aditya R., and Ashok K. Mishra. "Financial performance of small farm business households: the role of internet." China Agricultural Economic Review 8, no. 4 (2016): 553–71. http://dx.doi.org/10.1108/caer-12-2014-0147.

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Purpose The purpose of this paper is to investigate the impact of internet usage on financial performance of small farm business households in the USA. In particular, the authors want to assess the impact of internet usage on small farm businesses, where the owner’s main occupation is farming. Using a nationwide farm-level data in the USA and a non-parametric matching estimator, the study finds a significant positive impact of internet usage on gross cash income, total household income, off-farm income. The study further suggests that small farm businesses receive benefits from internet usage

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30

Nahar, T. N., M. R. Islam, M. S. Zaman, and S. S. Kibria. "Performance of native cows under farm conditions." Asian-Australasian Journal of Animal Sciences 8, no. 3 (1995): 219–21. http://dx.doi.org/10.5713/ajas.1995.219.

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31

Intong, Reynaldo L., Julnar E. Pepito, Jhonny M. Callejo, and Emilio M. Cruz. "Goat Farm Performance in Northern Mindanao, Philippines." Journal of Advanced Agricultural Technologies 5, no. 2 (2018): 123–28. http://dx.doi.org/10.18178/joaat.5.2.123-128.

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32

D'Hose, T., M. Cougnon, A. De Vliegher, K. Willekens, E. Van Bockstaele, and D. Reheul. "Farm Compost Application: Effects on Crop Performance." Compost Science & Utilization 20, no. 1 (2012): 49–56. http://dx.doi.org/10.1080/1065657x.2012.10737022.

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33

Gorton, Matthew, and Sophia Davidova. "Farm Performance, Direct Payments and EU Enlargement." EuroChoices 3, no. 1 (2004): 32–37. http://dx.doi.org/10.1111/j.1746-692x.2004.tb00008.x.

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34

Wagner, A. S., H. V. Sreekantaswamy, and S. T. Chanson. "Performance models for the processor farm paradigm." IEEE Transactions on Parallel and Distributed Systems 8, no. 5 (1997): 475–89. http://dx.doi.org/10.1109/71.598275.

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35

Naomasa, Emiko, Shawn Arita, Clyde Tamaru, and PingSun Leung. "ASSESSING HAWAII'S AQUACULTURE FARM AND INDUSTRY PERFORMANCE." Aquaculture Economics & Management 17, no. 2 (2013): 184–207. http://dx.doi.org/10.1080/13657305.2013.796235.

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36

Sheng, Yu, Alistair Davidson, Keith Fuglie, and Dandan Zhang. "Input Substitution, Productivity Performance and Farm Size." Australian Journal of Agricultural and Resource Economics 60, no. 3 (2016): 327–47. http://dx.doi.org/10.1111/1467-8489.12136.

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37

Chen, G., C. Baillie, and P. Kupke. "Evaluating On-Farm Energy Performance in Agriculture." Australian Journal of Multi-Disciplinary Engineering 7, no. 1 (2009): 55–61. http://dx.doi.org/10.1080/14488388.2009.11464799.

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38

Bjornson, B. "Farm equipment industry performance: past and future." International Food and Agribusiness Management Review 3, no. 1 (2000): 71–84. http://dx.doi.org/10.1016/s1096-7508(00)00028-8.

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39

Kambhampati, U., S. Morse, R. Bennett, and Y. Ismael. "Farm-Level Performance of Genetically Modified Cotton." Outlook on Agriculture 35, no. 4 (2006): 291–97. http://dx.doi.org/10.5367/000000006779398254.

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40

Feder, Gershon, Lawrence J. Lau, Justin Y. Lin, and Luo Xiaopeng. "Agricultural credit and farm performance in China." Journal of Comparative Economics 13, no. 4 (1989): 508–26. http://dx.doi.org/10.1016/0147-5967(89)90024-3.

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41

Ahmed, W. U., C. Moss, S. Roy, et al. "Wind Farm Wakes and Farm-to-Farm Interactions: Lidar and Wind Tunnel Tests." Journal of Physics: Conference Series 2767, no. 9 (2024): 092105. http://dx.doi.org/10.1088/1742-6596/2767/9/092105.

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Abstract Recent experimental and numerical evidence has shown that the cumulative wake generated from the overlapping of multiple wakes within a wind farm could reduce power performance and enhance fatigue loads of wind turbines installed in neighboring downstream wind farms and may also extend up to distances one order of magnitude larger than those typically considered for intra-farm wake interactions. Similar to individual wind turbine wakes, wind farm wakes have a velocity deficit and added turbulence intensity, both affected by the turbine rotor thrust forces and the incoming turbulence i

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42

Coderoni, Silvia, and Silvia Vanino. "The farm-by-farm relationship among carbon productivity and economic performance of agriculture." Science of The Total Environment 819 (May 2022): 153103. http://dx.doi.org/10.1016/j.scitotenv.2022.153103.

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Guesmi, Bouali, and Teresa Serra. "Can We Improve Farm Performance? The Determinants of Farm Technical and Environmental Efficiency." Applied Economic Perspectives and Policy 37, no. 4 (2015): 692–717. http://dx.doi.org/10.1093/aepp/ppv004.

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A. Wolf, Christopher, Mark W. Stephenson, Wayne A. Knoblauch, and Andrew M. Novakovic. "Dairy farm financial performance: firm, year, and size effects." Agricultural Finance Review 76, no. 4 (2016): 532–43. http://dx.doi.org/10.1108/afr-02-2016-0009.

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Purpose The purpose of this paper is to evaluate dairy farm financial performance over time utilizing farm financial ratios from three university business analysis programs. The evaluation includes measures of profitability, solvency, and liquidity by herd size. Design/methodology/approach Financial ratios to reflect profitability (rate of return on assets), solvency (debt to asset ratio), and liquidity (current ratio) were collected from Cornell University, Michigan State University, and the University of Wisconsin for dairy farms from 2000 to 2012. The distribution of farm financial performa

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45

Afolayan, M., I. I. Dafwang, and J. J. Omage. "Performance of Broilers fed On-Farm versus Commercial Feeds." Nigerian Journal of Animal Production 36, no. 1 (2021): 41–51. http://dx.doi.org/10.51791/njap.v36i1.1040.

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This study was conducted in the Animal Science Departmental farm to compare the growth performance of broiler chickens reared on three on-form feeds and three commercial feeds. Broilers were fed the six types of feed during the starter phase between 0-5 weeks of age and during the finisher phase between 5-9weeks. Results showed that although one of the commercial feeds outperformed all other feeds in terms of weight gain and feed efficiency; the overall growth performance on on-farm feeds were comparable to those on commercial feeds even where one of the on-farm feeds contained sub-optimal lev

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Manisha, Doot, Kumar Rohitash, and Lokendra. "Layout of poultry farm." Science World a monthly e magazine 2, no. 4 (2022): 420–24. https://doi.org/10.5281/zenodo.6470941.

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47

Riswandi, Dadang Iwan, and Syahrani Anisya. "Implementasi perancangan integrasi IE matrix balanced scorecard dalam penilaian kinerja bisnis salad sayur (Studi kasus: InaGreen Farm – Kabupaten Bandung Barat)." Jurnal Manajemen Bisnis dan Kewirausahaan 8, no. 5 (2024): 1250–63. http://dx.doi.org/10.24912/jmbk.v8i5.31108.

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InaGreen Farm is a company engaged in horticultural vegetables. InaGreen Farm is a producer and supplier of vegetables to retailers in Bandung City. In 2023 InaGreen Farm established a vegetable salad business unit by utilizing excess product stock to develop its business to increase company profits. The marketing carried out by the company in marketing vegetable salad products is Business to Consumer. InaGreen Farm in developing a business can certainly increase profits by paying attention to performance in terms of financial and non-financial aspects. Performance evaluation is an important c

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48

de Klein, Cecile A. M., Ross M. Monaghan, Marta Alfaro, Cameron J. P. Gourley, Oene Oenema, and J. Mark Powell. "Nitrogen performance indicators for dairy production systems." Soil Research 55, no. 6 (2017): 479. http://dx.doi.org/10.1071/sr16349.

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Nitrogen (N) is invaluable for maintaining agricultural production, but its use, and particularly inefficient use, can lead to environmental losses. This paper reviews N use efficiency (NUE) and N surplus indicators for dairy production systems to assess their utility for optimising N use outcomes and minimising environmental N losses. Using case-study examples, we also assess realistic goals for these indicators and discuss key issues associated with their use. Published whole-farm NUE and whole-farm N surplus values ranged within 10–65% and 40–700 kg N ha–1 year–1 respectively. In a study of

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Dobre, Iuliana, Marius Capra, Cristiana Costache, and Nicoleta Dorobantu. "Farm size and digitalization: Quantitative approach." Western Balkan Journal of Agricultural Economics and Rural Development 3, no. 1 (2021): 67–83. http://dx.doi.org/10.5937/wbjae2101067d.

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This paper takes into consideration the physical dimension impact of the quantitative description at the farm level over its economic performance. It is obvious that the dimension, if centred on different statistical intervals, generates different results, initiating the different types of potentiality that characterize the certain economic entity. The impact's capture was made through the registered data analysis, relevant for the enhancement of their quantitative characteristics. Dimensional phenomenon was studied considering the data from the National Institute of Statistics (INSSE) of Roma

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KANOULAS, V., G. A. PAPADOPOULOS, G. ARSENOS, E. D. TZIKA, and P. FORTOMARIS. "Effects of attapulgite dietary supplementation on sow performance in two commercial farms in Greece." Journal of the Hellenic Veterinary Medical Society 68, no. 2 (2018): 193. http://dx.doi.org/10.12681/jhvms.15605.

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Abstract:

The present study investigated the effects of attapulgite supplementation in sow diets during gestation and lactation on sow performance. The study comprised two reproductive phases (cycles) in two commercial farrow to finish farms: Farm A (capacity: 550 sows) and Farm B (capacity: 220 sows). The treatment groups were: a) control group (CN): the sows were fed a common gestation or lactation diet; b) attapulgite group (AT): the sows were fed the CN diet supplemented with attapulgite at 0,7% level; c) attapulgite plus group (AT+): the sows were fed the CN diet supplemented with attapulgite (0.7%

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