Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework

This research provides an analytical and predictive framework, based on state-of-the-art machine-learning (ML) algorithms (random forest (RF) and generalized additive models (GAM)), that can be used to assess and improve the Common Agricultural Policy (CAP) impact/performance over the agricultural a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Dragos Sebastian Cristea, Sarina Rosenberg, Adriana Pustianu Mocanu, Ira Adeline Simionov, Alina Antache Mogodan, Stefan Mihai Petrea, Liliana Mihaela Moga
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	common agricultural policy random forest machine learning generalized additive model agriculture rural development Agriculture S
Acceso en línea:	https://doaj.org/article/561dd661057d446eb59b651eeda44522
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:561dd661057d446eb59b651eeda44522
record_format	dspace
spelling	oai:doaj.org-article:561dd661057d446eb59b651eeda445222021-11-25T16:02:44ZModelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework10.3390/agronomy111121052073-4395https://doaj.org/article/561dd661057d446eb59b651eeda445222021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2105https://doaj.org/toc/2073-4395This research provides an analytical and predictive framework, based on state-of-the-art machine-learning (ML) algorithms (random forest (RF) and generalized additive models (GAM)), that can be used to assess and improve the Common Agricultural Policy (CAP) impact/performance over the agricultural and rural environments, easing the identification of proper instruments that can be used by EU policy makers in CAP’s financial management. The applied methodology consists of elaborating a custom-developed analytical framework based on a dataset containing 22 relevant indicators, considering four main dimensions that describe the intricacies of the EU agricultural and rural environment, in the CAP context: rural, emissions, macroeconomic, and financial. The results highlight that an increase of the agricultural research and development funding, as well as the agriculture employment rate, negatively influence the degree of rural poverty. The rural GDP per capita is influenced by the size of the employment rate in agriculture. It seems that environmental sustainability, identified by both fertilizers used and emissions from agriculture parameters, significantly influences the GDP per capita. In predicting emissions in agriculture, the direct payment, degree of rural poverty, fertilizer use, employment in agriculture, and agriculture labor productivity are the main independent parameters with the highest future importance. It was found that when predicting direct payments, the rural employment rate, employment in agriculture, and gross value added must be considered the most. The agricultural, entrepreneurial income prediction is mainly influenced by the total factor productivity, while agricultural research and development investments depend on gross value added, direct payments, and gross value added in the agricultural sector. Future research, related to prediction models based on CAP indicators, should also consider the marketing dimension. It is recommended for direct payments to be used to invest in upgrading the fertilizers technologies, since environmental sustainability will influence economic growth.Dragos Sebastian CristeaSarina RosenbergAdriana Pustianu MocanuIra Adeline SimionovAlina Antache MogodanStefan Mihai PetreaLiliana Mihaela MogaMDPI AGarticlecommon agricultural policyrandom forestmachine learninggeneralized additive modelagriculturerural developmentAgricultureSENAgronomy, Vol 11, Iss 2105, p 2105 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	common agricultural policy random forest machine learning generalized additive model agriculture rural development Agriculture S
spellingShingle	common agricultural policy random forest machine learning generalized additive model agriculture rural development Agriculture S Dragos Sebastian Cristea Sarina Rosenberg Adriana Pustianu Mocanu Ira Adeline Simionov Alina Antache Mogodan Stefan Mihai Petrea Liliana Mihaela Moga Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework
description	This research provides an analytical and predictive framework, based on state-of-the-art machine-learning (ML) algorithms (random forest (RF) and generalized additive models (GAM)), that can be used to assess and improve the Common Agricultural Policy (CAP) impact/performance over the agricultural and rural environments, easing the identification of proper instruments that can be used by EU policy makers in CAP’s financial management. The applied methodology consists of elaborating a custom-developed analytical framework based on a dataset containing 22 relevant indicators, considering four main dimensions that describe the intricacies of the EU agricultural and rural environment, in the CAP context: rural, emissions, macroeconomic, and financial. The results highlight that an increase of the agricultural research and development funding, as well as the agriculture employment rate, negatively influence the degree of rural poverty. The rural GDP per capita is influenced by the size of the employment rate in agriculture. It seems that environmental sustainability, identified by both fertilizers used and emissions from agriculture parameters, significantly influences the GDP per capita. In predicting emissions in agriculture, the direct payment, degree of rural poverty, fertilizer use, employment in agriculture, and agriculture labor productivity are the main independent parameters with the highest future importance. It was found that when predicting direct payments, the rural employment rate, employment in agriculture, and gross value added must be considered the most. The agricultural, entrepreneurial income prediction is mainly influenced by the total factor productivity, while agricultural research and development investments depend on gross value added, direct payments, and gross value added in the agricultural sector. Future research, related to prediction models based on CAP indicators, should also consider the marketing dimension. It is recommended for direct payments to be used to invest in upgrading the fertilizers technologies, since environmental sustainability will influence economic growth.
format	article
author	Dragos Sebastian Cristea Sarina Rosenberg Adriana Pustianu Mocanu Ira Adeline Simionov Alina Antache Mogodan Stefan Mihai Petrea Liliana Mihaela Moga
author_facet	Dragos Sebastian Cristea Sarina Rosenberg Adriana Pustianu Mocanu Ira Adeline Simionov Alina Antache Mogodan Stefan Mihai Petrea Liliana Mihaela Moga
author_sort	Dragos Sebastian Cristea
title	Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework
title_short	Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework
title_full	Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework
title_fullStr	Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework
title_full_unstemmed	Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework
title_sort	modelling the common agricultural policy impact over the eu agricultural and rural environment through a machine learning predictive framework
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/561dd661057d446eb59b651eeda44522
work_keys_str_mv	AT dragossebastiancristea modellingthecommonagriculturalpolicyimpactovertheeuagriculturalandruralenvironmentthroughamachinelearningpredictiveframework AT sarinarosenberg modellingthecommonagriculturalpolicyimpactovertheeuagriculturalandruralenvironmentthroughamachinelearningpredictiveframework AT adrianapustianumocanu modellingthecommonagriculturalpolicyimpactovertheeuagriculturalandruralenvironmentthroughamachinelearningpredictiveframework AT iraadelinesimionov modellingthecommonagriculturalpolicyimpactovertheeuagriculturalandruralenvironmentthroughamachinelearningpredictiveframework AT alinaantachemogodan modellingthecommonagriculturalpolicyimpactovertheeuagriculturalandruralenvironmentthroughamachinelearningpredictiveframework AT stefanmihaipetrea modellingthecommonagriculturalpolicyimpactovertheeuagriculturalandruralenvironmentthroughamachinelearningpredictiveframework AT lilianamihaelamoga modellingthecommonagriculturalpolicyimpactovertheeuagriculturalandruralenvironmentthroughamachinelearningpredictiveframework
_version_	1718413317516558336

Modelling the Common Agricultural Policy Impact over the EU Agricultural and Rural Environment through a Machine Learning Predictive Framework

Ejemplares similares