Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
Machine-learning methodologies are part of the artificial intelligence approaches with several applications in different fields of science and dimensions of human life. These techniques appear in the frameworks of the digital transition, where smart technologies bring relevant contributions, such as...
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-04-01
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| Series: | Open Agriculture |
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| Online Access: | https://doi.org/10.1515/opag-2022-0191 |
| _version_ | 1797817261598179328 |
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| author | Martinho Vítor João Pereira Domingues |
| author_facet | Martinho Vítor João Pereira Domingues |
| author_sort | Martinho Vítor João Pereira Domingues |
| collection | DOAJ |
| description | Machine-learning methodologies are part of the artificial intelligence approaches with several applications in different fields of science and dimensions of human life. These techniques appear in the frameworks of the digital transition, where smart technologies bring relevant contributions, such as improving the efficiency of the economic sectors. This is particularly important for sectors such as agriculture to deal with the challenges created in the context of climate changes. On the other hand, machine-learning approaches are not easy to implement, considering the complexity of the algorithms associated. Taking this into account, the main objective of this research is to present a model to predict fertiliser costs in the European Union (EU) farms through artificial neural network analysis. This assessment may provide relevant information for farmers and policymakers in the current scenario where the concerns are to identify strategies to mitigate the environmental impacts, including those from the agricultural sector and the respective use of chemical resources. To achieve these objectives, statistical information for the EU agricultural regions from the Farm Accountancy Data Network was considered for the period 2018–2020. The findings obtained show relative errors between 0.040 and 0.074 (showing good accuracy) and the importance of the total utilised agricultural area and the total output to predict the fertiliser costs. |
| first_indexed | 2024-03-13T08:51:03Z |
| format | Article |
| id | doaj.art-051ca8b98d0341c5a3556111f1ca3644 |
| institution | Directory Open Access Journal |
| issn | 2391-9531 |
| language | English |
| last_indexed | 2024-03-13T08:51:03Z |
| publishDate | 2023-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Agriculture |
| spelling | doaj.art-051ca8b98d0341c5a3556111f1ca36442023-05-29T11:03:59ZengDe GruyterOpen Agriculture2391-95312023-04-0181118284510.1515/opag-2022-0191Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networksMartinho Vítor João Pereira Domingues0Agricultural School (ESAV) and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu (IPV), 3504-510 Viseu, PortugalMachine-learning methodologies are part of the artificial intelligence approaches with several applications in different fields of science and dimensions of human life. These techniques appear in the frameworks of the digital transition, where smart technologies bring relevant contributions, such as improving the efficiency of the economic sectors. This is particularly important for sectors such as agriculture to deal with the challenges created in the context of climate changes. On the other hand, machine-learning approaches are not easy to implement, considering the complexity of the algorithms associated. Taking this into account, the main objective of this research is to present a model to predict fertiliser costs in the European Union (EU) farms through artificial neural network analysis. This assessment may provide relevant information for farmers and policymakers in the current scenario where the concerns are to identify strategies to mitigate the environmental impacts, including those from the agricultural sector and the respective use of chemical resources. To achieve these objectives, statistical information for the EU agricultural regions from the Farm Accountancy Data Network was considered for the period 2018–2020. The findings obtained show relative errors between 0.040 and 0.074 (showing good accuracy) and the importance of the total utilised agricultural area and the total output to predict the fertiliser costs.https://doi.org/10.1515/opag-2022-0191farm accountancy data networkartificial intelligenceeuropean union agricultural regions |
| spellingShingle | Martinho Vítor João Pereira Domingues Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks Open Agriculture farm accountancy data network artificial intelligence european union agricultural regions |
| title | Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks |
| title_full | Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks |
| title_fullStr | Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks |
| title_full_unstemmed | Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks |
| title_short | Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks |
| title_sort | fertiliser cost prediction in european union farms machine learning approaches through artificial neural networks |
| topic | farm accountancy data network artificial intelligence european union agricultural regions |
| url | https://doi.org/10.1515/opag-2022-0191 |
| work_keys_str_mv | AT martinhovitorjoaopereiradomingues fertilisercostpredictionineuropeanunionfarmsmachinelearningapproachesthroughartificialneuralnetworks |