Two-part fractional regression model with conditional FDH responses: an application to Brazilian agriculture

Souza, Geraldo da Silva e; Gomes, Eliane Gonçalves; Alves, Eliseu

doi:10.1007/s10479-020-03752-z

Cited by 4 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…da Silva e Souza et al. (2020) develop a two‐part fractional regression model with conditional free disposal hull efficiency (i.e., a nonparametric method to measure the efficiency of production) responses to support two‐stage regression analysis. Output is gross income, and inputs are land and labor expenses and other technological inputs.…”

Section: Stakeholder Engagement For Farming In a Digital Eramentioning

confidence: 99%

Opportunities in farming research from an operations management perspective

Gupta

Berenji

Shukla

et al. 2023

Production and Operations Management

View full text Add to dashboard Cite

We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers’ welfare and the interests of other stakeholders have drawn OM researchers’ attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field‐based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming.

show abstract

Section: Stakeholder Engagement For Farming In a Digital Eramentioning

confidence: 99%

Opportunities in farming research from an operations management perspective

Gupta

Berenji

Shukla

et al. 2023

Production and Operations Management

View full text Add to dashboard Cite

show abstract

“…The search process revealed a few different studies in the agri-food context that we cite here for the sake of completeness. In such studies, the adjective "fractional" is related to types of machine learning algorithms [10,11], regression models [12] or empirical models [13,14].…”

Section: Fractional Modelsmentioning

confidence: 99%

A Mini-Review on Recent Fractional Models for Agri-Food Problems

Tomasiello

Macías‐Díaz

2023

Mathematics

View full text Add to dashboard Cite

This work aims at providing a concise review of various agri-food models that employ fractional differential operators. In this context, various mathematical models based on fractional differential equations have been used to describe a wide range of problems in agri-food. As a result of this review, we found out that this new area of research is finding increased acceptance in recent years and that some reports have employed fractional operators successfully in order to model real-world data. Our results also show that the most commonly used differential operators in these problems are the Caputo, the Caputo–Fabrizio, the Atangana–Baleanu, and the Riemann–Liouville derivatives. Most of the authors in this field are predominantly from China and India.

show abstract