Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India

Nandy, Anirban; Singh, P.

doi:10.1016/j.jclepro.2020.122106

Cited by 42 publications

(46 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al, [49] 2019 ✗ ✓ ✗ ✓ ✗ ✗ ✗ Nandy et. al, [50] 2020 ✗ ✓ ✗ ✗ ✗ ✓ ✗ Tayala et. al, [51] 2020 ✗ ✓ ✗ ✓ ✗ ✗ ✗ This Study ✔ ✔ ✔ ✔ ✔ ✔ ✘ Study Year Scope of Paper Method COVID-19 DEA based modeling Machine Learning ANN MLP Fuzzy Based A.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Aydın

Yurdakul

2020

Applied Soft Computing

View full text Add to dashboard Cite

The COVID-19 pandemic, which first spread to the People of Republic of China and then to other countries in a short time, affected the whole world by infecting millions of people and have been increasing its impact day by day. Hundreds of researchers in many countries are in search of a solution to end up this pandemic. This study aims to contribute to the literature by performing detailed analyses via a new three-staged framework constructed based on data envelopment analysis and machine learning algorithms to assess the performances of 142 countries against the COVID-19 outbreak. Particularly, clustering analyses were made using k-means and hierarchic clustering methods. Subsequently, efficiency analysis of countries were performed by a novel model, the weighted stochastic imprecise data envelopment analysis. Finally, parameters were analyzed with decision tree and random forest algorithms. Results have been analyzed in detail, and the classification of countries are determined by providing the most influential parameters. The analysis showed that the optimum number of clusters for 142 countries is three. In addition, while 20 countries out of 142 countries were fully effective, 36% of them were found to be effective at a rate of 90%. Finally, it has been observed that the data such as GDP, smoking rates, and the rate of diabetes patients do not affect the effectiveness level of the countries.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Aydın

Yurdakul

2020

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…Sustainable agricultural practices aim to reduce these environmental costs while ensuring food safety for the present and future generations [7][8][9]. Therefore, effective resource use is necessary to minimize the depletion of resources that contribute to agricultural sustainability and eco-efficiency [10].…”

Section: Introductionmentioning

confidence: 99%

“…Data Envelopment Analysis (DEA) is a nonparametric efficiency estimation technique developed by [28], which is considered to be the most important method for allocating resources and measuring the relative efficiency of DMUs [10,29,30]. This technique has the potential to predict resource usage and relative efficiency of DMUs based on their performance.…”

Section: Introductionmentioning

confidence: 99%

Eco-Efficiency in Farm Management for Sustainable Agriculture: a Two-Stage Data Envelopment Analysis in Wheat Production

Güney¹

2021

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Program (MAP) introduced the "miracle wheat" in 1954 and spread throughout Asia and Africa in the 1950s and 1960s, and the reform continued with other crops like rice and maize [1,2].The reform has increased agricultural production with the use of new technologies, water management practices, mechanization and inorganic chemicals, and made it possible for farmers to produce more agricultural products at lower costs. However, agrochemical input and natural resource intensification generate the pressure of agriculture on natural resources such as soil

show abstract

“…We cannot be exhaustive given the importance of this work. Examples of agriculture include Mosbah et al [9], Nandy and Singh [10], Toma et al [12], Kuo et al [7], Mardani and Salapour [8],or environment protection such as, Korhonen and Luptacik [6], Fare et al [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Agricultural Performance in Burkina Faso Using Data Envelopment Analysis

Yougbaré

2021

Eur. J. Pure Appl. Math.

View full text Add to dashboard Cite

In this paper, we used the DEA method, to apply to the agriculture sector in BurkinaFaso. We applied the method with forty-one (41) agricultural farmers with data taking into account cultivated area, manual (human) labour, the liquidity fund of the working household, the number of crops (products) and the expected farm incomes. The results highlight the seven (7) technical efficiencies. Taking into account the variable scale yields, fourteen (14) efficient farms are obtained. These results show how non-efficient farms in the DEA sense are indexed to efficient farms that can serve as benchmarks or benchmarks in terms of performance improvement. This study shows that efficiency does not depend on the category of operator but on overall performance (efficiency and productivity).

show abstract

Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India

Cited by 42 publications

References 72 publications

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Eco-Efficiency in Farm Management for Sustainable Agriculture: a Two-Stage Data Envelopment Analysis in Wheat Production

Analysis of Agricultural Performance in Burkina Faso Using Data Envelopment Analysis

Contact Info

Product

Resources

About