A stochastic recursive interactive programming model for farm firm policy analysis

Baum, Kenneth; Richardson, James E.; Schertz, Lyle P.

doi:10.1016/0305-0548(69)90010-0

Cited by 4 publications

(2 citation statements)

References 12 publications

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“… Authors Title Source Country Territorial scope Methodology Typology policy Economic variables Environmental variables Contributing variables Socio-economic variables References Byerlee D., Halter A.N. (1974) A Macro-Economic model for agricultural sector analysis Scopus Nigeria Economic sector Logistic Regression Pricing policy x [ 27 ] Parikh KS (1977) An agricultural policy model for India* Science Direct India Economic sector Other analysis Access policy x x x [ 28 ] Baum K, Richardson J,Schertz L (1984) A stochastic recursive interactive programming model for farm firm policy analysis Science Direct United States Farms Lineal programming model Pricing policy x [ 29 ] Anderson R., Wilkinson M. (1985) Consumer demand for meat and the evaluation of agricultural policy Scopus EUnited States Economic sector Other analysis Pricing policy x x [ 30 ] Monke E.A., Pearson S.R. (1989) The policy analysis matrix for agricultural development …”

Section: Resultsmentioning

confidence: 99%

A systematic review of the evaluation of agricultural policies: Using prisma

Bastidas-Orrego,

Jaramillo,

Castillo-Grisales

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

A systematic review of the evaluation of agricultural policies: Using prisma

Bastidas-Orrego,

Jaramillo,

Castillo-Grisales

et al. 2023

Heliyon

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“…Their results support decision-makers to form national sediment control plans. Focusing on pricing and related policies, Baum et al (1984) introduce a joint application of optimization and simulation methods to develop a recursive programming model that considers the uncertainty of market prices and government policies in managing production strategies and decisionmaking in farms. They assess their model's quality by running two simulations with stochastic commodity prices and yields for a Texas farm and provide credible and different results for the various economic environments.…”

Section: Production and Operations Managementmentioning

confidence: 99%

Opportunities in farming research from an operations management perspective

Gupta

Berenji

Shukla

et al. 2023

Production and Operations Management

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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.

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