Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks

Abstract

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.

Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece

“…One way to think about diffusion is as extensive, aggregate adoption. A considerable amount of time passes between new technology's creation and farmers' acceptance of it [16,17]. Many variables may influence how new technology is adopted.…”