Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector

The agricultural sector needs to increase agricultural production to guarantee food security worldwide, however, to achieve these objectives agriculture must improve the sustainability of its activities and processes, specifically improving the efficiency of the sector. In these frameworks, adjusted agricultural planning and management is crucial, where the availability of information plays a determinant role, as well as the consideration of new technologies and methodologies. In the context of the new approaches of analysis, digital methodologies may bring relevant added value, namely those associated with predictive machine learning technologies. From this perspective, this study intends to identify the most adjusted models to predict the European Union farming output, taking into account machine learning approaches and statistical information from the Farm Accountancy Data Network. The results obtained highlight the most important farming variables that must be taken into account to predict the total output in the European Union farms.

The agricultural output has several parts, and depending on the characteristics of the farms, one of these parcels is related to crop production. Including in the crop output, the sources of these incomes are diverse. In any case, crop production has a fundamental role in the sustainability of the farms and society, as a source of income for the farmers and food for the population. In this context, it is important to understand the main factors that may support the stakeholders in predicting the crop output in the European Union farms. The main objective of this research is to identify the most adjusted models and the most important variables to predict crop income in the European Union context. For that, data from the Farm Accountancy Data Network were considered, as well as approaches associated with artificial intelligence. The main findings provide relevant insights and knowledge, namely for farmers and policymakers that may be considered in the processes of agricultural planning, management and policy design.

Agricultural planning always had an important role in the performance of agriculture, but in our days this component of agricultural management seems to have an increased responsibility, because of the challenges imposed by the current contexts, specifically those related to the sustainability of the associated activities and processes. In fact, currently, it is important to reduce the environmental impacts of the farming dynamics and raise production to deal with the increased demand for food worldwide. The livestock activities are particularly complex and call for adjusted plans and management decisions. The new technologies associated with the digital transition may bring relevant added value, namely to predict outputs. This chapter aims to suggest models and predictors to support the farmers and other stakeholders to better design policies and farm plans. Statistical information from the European Union databases was considered. The results found are useful tools to improve the performance of the European Union farms, particularly those specialised in livestock production.

The dynamics of the agricultural sector depend on the performance of the farms and their respective profitability. The cost control in the farms is particularly important, considering the reduced profit margins in agriculture. In fact, in some contexts, the level of farm costs is very similar to the amounts of income, calling, in many cases, for financial support for the farmers, justified by the need to guarantee food security and social and environmental sustainability. In this framework, contributions that support policymakers and farmers to make decisions that promote farm cost reduction are fundamental. Considering this scenario, this study intends to consider machine learning approaches and data from the European databases to identify the most adjusted approaches to predict the total costs in the farms. This study brought relevant outputs for the design of adjusted measures, plans and instruments for the European Union agriculture and respective processes and activities.

The control of the fertiliser costs in the agricultural sector is fundamental for the profitability of the farms and to mitigate environmental impacts. Indeed, the fertiliser costs have, at least, two components, one related to the fertiliser prices and the other associated with the amount of fertiliser applied in the farming processes. The fertiliser application in agricultural activities has a relevant impact on soil health and water quality. The efficiency of the processes linked with the fertiliser application in the farms is crucial to avoid disruptions in the sustainable development required for agriculture worldwide. In these frameworks, it is important to bring more insights about the predictors of the fertiliser costs in the European Union farms. Taking into account these motivations, this chapter considered artificial intelligence approaches and data from the European Union databases to identify the most adjusted models. The findings of this research contribute to the understanding of the most important variables to promote more sustainability in the European Union farming sector.

The crop protection costs have economic impacts on the profitability of the farms and environmental consequences due to, in some circumstances, the residues that remain in the soils after the application. The crop protection application may have also direct impacts on human health, because of the residues which remain in the agricultural products, particularly when applied in a non-efficient way. The Common Agricultural Policy in the European Union has already a set of measures to encourage farmers to reduce the level of crop protection application in farming activities. In any case, it is important to bring more insights into these contexts, specifically identifying the most important predictors of crop protection costs in the European Union farms. To achieve these objectives, this study takes into account approaches from the new technologies associated with the digital transition and data from the European Union Farm Accountancy Data Network. The insights obtained allowed us to highlight the most adjusted models and the most important variables to predict crop protection costs in European agriculture.

Energy is one of the most important production factors in farms, considering its impact on the profitability of the agricultural sector, its relationship with sustainability and the need for a green transition in agriculture to deal with the challenges created by climate change and the consequent global warming. In the green transition, it is important to replace fossil fuel sources with renewable energies and, in these contexts, the agricultural sector may make a double contribution, producing renewable energy and using more sustainable sources for the different processes and activities in the farms. Taking into account these motivations, this chapter proposes to select the models with better accuracy and the most relevant variables to predict the energy costs in the European Union farming sector. For that, machine learning models were considered, as well as statistical information from European Union databases. This chapter presents useful contributions to better understand the contexts associated with energy cost prediction in European farms.

The agricultural sector worldwide has an economic dimension related to the remuneration of the production factors applied in the sector, an environmental contribution associated with the sustainability of rural places and a social dimension related to the employment creation and the consequent level of remuneration of the labour. The question here is about the level of wages paid in the agricultural sector across the European Union countries and about the main factors that may be taken into account to predict the level of these wages paid to agricultural workers. This research intends to select the models with better precision to predict the wages paid in the European Union agriculture and to suggest important predictors from the enormous number of indicators that may be identified in the farms. The findings obtained may be considered relevant support for the design of social and agricultural policies in the European framework.

In general, the interest paid does not assume a relevant dimension in the overall costs present in the European Union farms. In fact, considering the agricultural sector characteristics, the Common Agricultural Policy measures and the dynamics of the banking sector in the European Union, the interest paid is a small part of the costs supported by the farmers. In any case, banking loans are fundamental for farming investments and in this way, it is important to understand their respective context. Considering these motivations, this research proposes to consider artificial intelligence approaches and data from the Farm Accountancy Data Network to identify the models with higher accuracy and the most important indicators to predict the interest paid by the farms of the European Union. The contributions of this research bring relevant insights into the dynamics of the bank loans for the European Union agricultural sector and the respective measures inside the Common Agricultural Policy framework.

It is not expected that the agricultural sector absorbs a great part of the employment in developed economies with a dynamic industry and services sector. When the percentage of employment in agriculture is high, this may be a sign of the weak performance of the farms. Every country wants to have a developed farming sector to not compromise the dynamics and performance of the economy. In any case, agricultural employment plays a fundamental role, particularly in rural spaces and in contexts of temporary crises in the remaining economy. Taking into account these motivations, this chapter aims to highlight the main approaches and variables that may be considered to predict labour use in the European Union farms. To achieve these aims, European Union agricultural statistics were considered, as well as models based on the new technologies associated with the digital transition worldwide. The results found may provide pertinent suggestions for a more sustainable farming sector, where the social contributions may be improved.