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Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland

Author

Listed:
  • Agnieszka Wawrzyniak

    (Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland)

  • Andrzej Przybylak

    (Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland)

  • Piotr Boniecki

    (Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland)

  • Agnieszka Sujak

    (Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland)

  • Maciej Zaborowicz

    (Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland)

Abstract

In the presented study, data regarding the size and structure of cattle herds in voivodeships in Poland in 2019 were analysed and modelled using artificial neural networks (ANNs). The neural modelling approach was employed to identify the relationship between herd structure, biogas production from manure and slurry, and the geographical location of herds by voivodeship. The voivodeships were categorised into four groups based on their location within Poland: central, southern, eastern, and western. In each of the analysed groups, a three-layer MLP (multilayer perceptron) with a single hidden layer was found to be the optimal network structure. A sensitivity analysis of the generated models for herd structure and location within the eastern group of voivodeships revealed significant contributions from dairy cows, heifers (both 6–12 and 12–18 months old), calves, and bulls aged 12–24 months. For the western voivodeships, the analysis indicated that only dairy cows and herd location made significant contributions. The optimal models exhibited similar values of RMS errors for the training, testing, and validation datasets. The model characterising biogas production from manure in southern voivodeships demonstrated the smallest RMS error, while the model for biogas from manure in the eastern region, as well as the model for slurry in central parts of Poland, yielded the highest RMS errors. The generated ANN models exhibited a high level of accuracy, with a fitting quality of approximately 99% for correctly predicting values. Comparable results were obtained for both manure and slurry in terms of biogas production across all location groups.

Suggested Citation

  • Agnieszka Wawrzyniak & Andrzej Przybylak & Piotr Boniecki & Agnieszka Sujak & Maciej Zaborowicz, 2023. "Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland," Agriculture, MDPI, vol. 13(7), pages 1-13, July.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1451-:d:1200401
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    1. Krzysztof Pilarski & Agnieszka A. Pilarska & Piotr Boniecki & Gniewko Niedbała & Kamil Witaszek & Magdalena Piekutowska & Małgorzata Idzior-Haufa & Agnieszka Wawrzyniak, 2021. "Degree of Biomass Conversion in the Integrated Production of Bioethanol and Biogas," Energies, MDPI, vol. 14(22), pages 1-16, November.
    2. Leszek Sieczko & Anna Justyna Parzonko & Anna Sieczko, 2021. "Trust in Collective Entrepreneurship in the Context of the Development of Rural Areas in Poland," Agriculture, MDPI, vol. 11(11), pages 1-26, November.
    3. Piotr Boniecki & Agnieszka Sujak & Gniewko Niedbała & Hanna Piekarska-Boniecka & Agnieszka Wawrzyniak & Andrzej Przybylak, 2023. "Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
    4. Şenol, Halil & Ali Dereli̇, Mehmet & Özbilgin, Ferdi, 2021. "Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Batzias, F.A. & Sidiras, D.K. & Spyrou, E.K., 2005. "Evaluating livestock manures for biogas production: a GIS based method," Renewable Energy, Elsevier, vol. 30(8), pages 1161-1176.
    6. Changgwon Dang & Taejeong Choi & Seungsoo Lee & Soohyun Lee & Mahboob Alam & Mina Park & Seungkyu Han & Jaegu Lee & Duytang Hoang, 2022. "Machine Learning-Based Live Weight Estimation for Hanwoo Cow," Sustainability, MDPI, vol. 14(19), pages 1-12, October.
    7. Monteiro, Eliseu & Mantha, Vishveshwar & Rouboa, Abel, 2011. "Prospective application of farm cattle manure for bioenergy production in Portugal," Renewable Energy, Elsevier, vol. 36(2), pages 627-631.
    8. Andrzej Hornowski & Andrzej Parzonko & Pavel Kotyza & Tomasz Kondraszuk & Piotr Bórawski & Luboš Smutka, 2020. "Factors Determining the Development of Small Farms in Central and Eastern Poland," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    9. Hyungah Lee & Dongju Kim & Jae-Hoi Gu, 2023. "Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms," Energies, MDPI, vol. 16(3), pages 1-21, February.
    10. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
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