Research Article | | Peer-Reviewed

The Marginal Effect of Investment in Machinery, Livestock, and Buildings on Irish Agricultural Output and Costs

Received: 3 December 2023     Accepted: 18 December 2023     Published: 28 December 2023
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Abstract

To achieve economically sustainable and profitable farms, farmers must manage various factors that impact farm output and costs. Numerous factors can influence farms' output, including soil quality, environmental conditions, farm size, system, and farmers' experience. This study investigates the impact of investment increases and decreases on farm gross output, direct costs, and overhead costs in Ireland, utilizing the Deep Neural Networks method. The data source for this study is a farm survey of pastoral-based livestock systems from 1996 to 2018. The findings reveal that, on average, Irish farmers ranging from the second gross output decile to the fifth decile will experience an increase in their gross output of 9% to 12.6% if they increase their investment in machinery, livestock, and buildings by 10%. Surprisingly, farmers in the first, ninth, and tenth deciles will experience a decrease in their gross output of 7.7%, 0.05%, and 3.77%, respectively, if investments are increased. This discrepancy may be attributed to the fact that the lowest and highest gross output farms primarily rely on subsidies and have already made substantial investments, respectively, resulting in a lack of positive response to investment increases. As expected, a 10% increase in investments leads to an increase in direct and overhead costs across most deciles, while a decrease in investments results in a decrease in overhead costs across all deciles. The findings of this paper emphasize the significance of farm investments in agricultural output and costs, providing valuable insights for agricultural policymakers and other stakeholders in making research-based decisions.

Published in International Journal of Agricultural Economics (Volume 8, Issue 6)
DOI 10.11648/j.ijae.20230806.21
Page(s) 305-314
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Agricultural Output, Agricultural Costs, Machine Learning, Modelling in Agricultural Economics

References
[1] Adesina, A. A., & Djato, K. K. (1997). Relative efficiency of women as farm managers: Profit function analysis in Côte d'Ivoire. Agricultural Economics: The Journal of the International Association of Agricultural Economists, 16 (968-2016-75259), 47-53.
[2] Hennessy, T., & Heanue, K. (2012). Quantifying the effect of discussion group membership on technology adoption and farm profit on dairy farms. The Journal of Agricultural Education and Extension, 18(1), 41-54.
[3] Mishra, A. K., El-Osta, H. S., & Sandretto, C. L. (2004). Factors affecting farm enterprise diversification.
[4] Tijani, A. A., Alimi, T., & Adesiyan, A. T. (2006). Profit efficiency among Nigerian poultry egg farmers: a case study of aiyedoto farm settlement, Nigeria. Research Journal of Agricultural Biological Sciences, 2(6), 256-261.
[5] Fayama, T., Poda, L. J., Traore, I., Ouedraogo, S., & Ouattara, B. (2022). Determinants of the Adoption of Forage Crops in the Rural Municipality of Koumbia in Burkina Faso. International Journal of Agricultural Economics, 7(3), 140-145.
[6] Junaidu, M., Abdullahi, B. S., Ibrahim, U. G., & Nekabari, B. D. (2021). Contribution of Sesame Production to the Livelihood of Farmers in Dutsin-Ma Local Government Area, Katsina State, Nigeria. International Journal of Agricultural Economics, 7(1), 29-35.
[7] Ouedraogo, S. A., Zahonogo, P., & Al-Hassan, R. M. (2021). Market Participation of Smallholder Farmers and Food Crop Productivity: Evidence from Burkina Faso. International Journal of Agricultural Economics, 6(1), 12-20.
[8] Prager, K., & Posthumus, H. (2010). Socio-economic factors influencing farmers’ adoption of soil conservation practices in Europe. Human dimensions of soil and water conservation, 12, 1-21.
[9] Bokusheva, R., Bezlepkina, I., & Lansink, A. O. (2009). Exploring farm investment behaviour in transition: The case of Russian agriculture. Journal of Agricultural Economics, 60(2), 436-464.
[10] Carey, J. M., & Zilberman, D. (2002). A model of investment under uncertainty: modern irrigation technology and emerging markets in water. American Journal of Agricultural Economics, 84(1), 171-183.
[11] Towne, M., & Rasmussen, W. (1960). Farm gross product and gross investment in the nineteenth century. Trends in the American economy in the nineteenth century, 255-316.
[12] Weersink, A. J., & Tauer, L. W. (1989). Comparative analysis of investment models for New York dairy farms. American Journal of Agricultural Economics, 71(1), 136-146.
[13] Skevas, T., Wu, F., & Guan, Z. (2018). Farm capital investment and deviations from the optimal path. Journal of Agricultural Economics, 69(2), 561-577.
[14] Hanrahan, L., McHugh, N., Hennessy, T., Moran, B., Kearney, R., Wallace, M., & Shalloo, L. (2018). Factors associated with profitability in pasture-based systems of milk production. Journal of Dairy Science, 101(6), 5474-5485.
[15] Parvin, M. T., & Akteruzzaman, M. (2012). Factors affecting farm and non-farm income of haor inhabitants of Bangladesh. Progressive Agriculture, 23(1-2), 143-150.
[16] Strappazzon, L., Knopke, P., & Mullen, J. D. (1995). Productivity growth: total factor productivity on Australian broadacre farms. Australian Commodities: Forecasts and Issues, 2(4), 486.
[17] Yee, J., Ahearn, M. C., & Huffman, W. (2004). Links among farm productivity, off-farm work, and farm size in the Southeast. Journal of Agricultural and Applied Economics, 36(3), 591-603.
[18] Clay, N., Garnett, T., & Lorimer, J. (2020). Dairy intensification: Drivers, impacts and alternatives. Ambio, 49(1), 35-48.
[19] Magdoff, F., Foster, J. B., & Buttel, F. H. (Eds.). (2000). Hungry for profit: The agribusiness threat to farmers, food, and the environment. NYU Press.
[20] Smith, L. E., & Siciliano, G. (2015). A comprehensive review of constraints to improved management of fertilizers in China and mitigation of diffuse water pollution from agriculture. Agriculture, Ecosystems & Environment, 209, 15-25.
[21] Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009). Exploring strategies for training deep neural networks. Journal of machine learning research, 10(1).
[22] Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, 1-15.
[23] Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., & Ashmore, R. (2018). Testing deep neural networks. arXiv preprint arXiv:1803.04792.
[24] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
[25] De’ath G, Fabricius KE, 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81(11):3178–3192.
[26] Grömping, U. (2009). Variable importance assessment in regression: linear regression versus random forest. The American Statistician, 63(4), 308-319.
[27] Lindner, C., & Cootes, T. F. (2015). Fully automatic cephalometric evaluation using random forest regression-voting. In IEEE International Symposium on Biomedical Imaging (ISBI) 2015–Grand Challenges in Dental X-ray Image Analysis–Automated Detection and Analysis for Diagnosis in Cephalometric X-ray Image.
[28] Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological methods, 14(4), 323.
[29] Fanelli, G., Gall, J., & Van Gool, L. (2011, June). Real time head pose estimation with random regression forests. In CVPR 2011 (pp. 617-624). IEEE.
[30] Qin, F. W., Bai, J., & Yuan, W. Q. (2017). Research on intelligent fault diagnosis of mechanical equipment based on sparse deep neural networks. Journal of Vibroengineering, 19(4), 2439-2455.
[31] Sambasivam, G. A. O. G. D., & Opiyo, G. D. (2021). A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian informatics journal, 22(1), 27-34.
[32] Kirchweger, S., Kantelhardt, J., & Leisch, F. (2015). Impacts of the government-supported investments on the economic farm performance in Austria. Agricultural Economics, 61(8), 343-355.
[33] Zdeněk, R., & Lososová, J. (2020). Investments of Czech farms located in less favoured areas after EU accession. Agricultural Economics, 66(2), 55-64.
[34] Czubak, W., Pawłowski, K. P., & Sadowski, A. (2021). Outcomes of farm investment in Central and Eastern Europe: The role of financial public support and investment scale. Land Use Policy, 108, 105655.
[35] Mogues, T., Fan, S., & Benin, S. (2015). Public investments in and for agriculture. The European Journal of Development Research, 27, 337-352.
Cite This Article
  • APA Style

    Haydarov, D., Zhang, C. (2023). The Marginal Effect of Investment in Machinery, Livestock, and Buildings on Irish Agricultural Output and Costs. International Journal of Agricultural Economics, 8(6), 305-314. https://doi.org/10.11648/j.ijae.20230806.21

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    ACS Style

    Haydarov, D.; Zhang, C. The Marginal Effect of Investment in Machinery, Livestock, and Buildings on Irish Agricultural Output and Costs. Int. J. Agric. Econ. 2023, 8(6), 305-314. doi: 10.11648/j.ijae.20230806.21

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    AMA Style

    Haydarov D, Zhang C. The Marginal Effect of Investment in Machinery, Livestock, and Buildings on Irish Agricultural Output and Costs. Int J Agric Econ. 2023;8(6):305-314. doi: 10.11648/j.ijae.20230806.21

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  • @article{10.11648/j.ijae.20230806.21,
      author = {Dilovar Haydarov and Chaosheng Zhang},
      title = {The Marginal Effect of Investment in Machinery, Livestock, and Buildings on Irish Agricultural Output and Costs},
      journal = {International Journal of Agricultural Economics},
      volume = {8},
      number = {6},
      pages = {305-314},
      doi = {10.11648/j.ijae.20230806.21},
      url = {https://doi.org/10.11648/j.ijae.20230806.21},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijae.20230806.21},
      abstract = {To achieve economically sustainable and profitable farms, farmers must manage various factors that impact farm output and costs. Numerous factors can influence farms' output, including soil quality, environmental conditions, farm size, system, and farmers' experience. This study investigates the impact of investment increases and decreases on farm gross output, direct costs, and overhead costs in Ireland, utilizing the Deep Neural Networks method. The data source for this study is a farm survey of pastoral-based livestock systems from 1996 to 2018. The findings reveal that, on average, Irish farmers ranging from the second gross output decile to the fifth decile will experience an increase in their gross output of 9% to 12.6% if they increase their investment in machinery, livestock, and buildings by 10%. Surprisingly, farmers in the first, ninth, and tenth deciles will experience a decrease in their gross output of 7.7%, 0.05%, and 3.77%, respectively, if investments are increased. This discrepancy may be attributed to the fact that the lowest and highest gross output farms primarily rely on subsidies and have already made substantial investments, respectively, resulting in a lack of positive response to investment increases. As expected, a 10% increase in investments leads to an increase in direct and overhead costs across most deciles, while a decrease in investments results in a decrease in overhead costs across all deciles. The findings of this paper emphasize the significance of farm investments in agricultural output and costs, providing valuable insights for agricultural policymakers and other stakeholders in making research-based decisions.
    },
     year = {2023}
    }
    

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    T1  - The Marginal Effect of Investment in Machinery, Livestock, and Buildings on Irish Agricultural Output and Costs
    AU  - Dilovar Haydarov
    AU  - Chaosheng Zhang
    Y1  - 2023/12/28
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    N1  - https://doi.org/10.11648/j.ijae.20230806.21
    DO  - 10.11648/j.ijae.20230806.21
    T2  - International Journal of Agricultural Economics
    JF  - International Journal of Agricultural Economics
    JO  - International Journal of Agricultural Economics
    SP  - 305
    EP  - 314
    PB  - Science Publishing Group
    SN  - 2575-3843
    UR  - https://doi.org/10.11648/j.ijae.20230806.21
    AB  - To achieve economically sustainable and profitable farms, farmers must manage various factors that impact farm output and costs. Numerous factors can influence farms' output, including soil quality, environmental conditions, farm size, system, and farmers' experience. This study investigates the impact of investment increases and decreases on farm gross output, direct costs, and overhead costs in Ireland, utilizing the Deep Neural Networks method. The data source for this study is a farm survey of pastoral-based livestock systems from 1996 to 2018. The findings reveal that, on average, Irish farmers ranging from the second gross output decile to the fifth decile will experience an increase in their gross output of 9% to 12.6% if they increase their investment in machinery, livestock, and buildings by 10%. Surprisingly, farmers in the first, ninth, and tenth deciles will experience a decrease in their gross output of 7.7%, 0.05%, and 3.77%, respectively, if investments are increased. This discrepancy may be attributed to the fact that the lowest and highest gross output farms primarily rely on subsidies and have already made substantial investments, respectively, resulting in a lack of positive response to investment increases. As expected, a 10% increase in investments leads to an increase in direct and overhead costs across most deciles, while a decrease in investments results in a decrease in overhead costs across all deciles. The findings of this paper emphasize the significance of farm investments in agricultural output and costs, providing valuable insights for agricultural policymakers and other stakeholders in making research-based decisions.
    
    VL  - 8
    IS  - 6
    ER  - 

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Author Information
  • School of Geography, Archaeology and Irish Studies, University of Galway, Galway City, Ireland; Rural Economy and Development Programme, Teagasc - the Agriculture and Food Development Authority, Galway, Ireland

  • School of Geography, Archaeology and Irish Studies, University of Galway, Galway City, Ireland

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