Agricultural firm survival: The case of farmer cooperatives in the United States

Grashuis, Jasper

doi:10.1002/agr.21612

Cited by 22 publications

(8 citation statements)

References 48 publications

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“…In general, SA, which combines the time of occurrence and the variables affecting the event, is an analysis that deals with the time from the beginning of an event defined in the specified time to the occurrence of the event. The proportional hazards regression model, which is the most widely used in SA and explains the cause-effect relationship between lifespan and explanatory variables, without defining a specific distribution in lifespan, is a semi-parametric method developed by David R. Cox in 1972 and is known as the Cox regression model (Grashuis, 2020). It is necessary to provide the assumption of proportional hazards of the variable defined as the dependent variable in Cox regression.…”

Section: Econometric Model Of Accelerating Factors On Both Innovation...mentioning

confidence: 99%

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Aydoğan

Demiryürek

Özer

et al. 2021

Integr Envir Assess & Manag

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The primary purpose of this study is to reveal the factors accelerating both the innovativeness and sustainability levels of paddy farmers. The data used in the study were obtained from the questionnaires conducted with 70 paddy farmers in the Bafra district of Samsun province, Turkey, determined by a simple random sampling method. Paddy farmers were divided into three groups as low, medium, and high innovativeness groups with hierarchical cluster analysis. An ANOVA was used to compare the socioeconomic and farming characteristics of the paddy farmers, and accelerated failure time models were used to analyze the factors affecting the innovativeness and sustainability variables. The research results indicated that the number of years of formal education, the agricultural experience, the amount of labor per unit area, the animal assets, the share of agricultural income in the total income, the number of memberships to farmer organizations, paddy production area, sustainable agriculture area, and cooperation scores differed from the innovativeness groups. The farmers' collaboration in agricultural activities increased the likelihood that they are an innovator. The results concluded that the number of years of formal education, global good agriculture practice, agricultural experience, household size, animal assets, and women's employment influenced both the innovativeness and sustainability variables. Encouraging the participation of women in agricultural production in rural areas, including sustainability-related issues in addition to technical issues in farmer training programs, and ensuring the participation of women in such training are issues that should be considered in future.

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Section: Econometric Model Of Accelerating Factors On Both Innovation...mentioning

confidence: 99%

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Aydoğan

Demiryürek

Özer

et al. 2021

Integr Envir Assess & Manag

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“…In the next stage of analyses, we employ the widely used CPH model (e.g. Key and Roberts, 2007;Dressler and Stokes, 2010;Grashuis, 2019) to investigate factors influencing the duration of adoption. Following Cox (1972), the CPH model is specified in equation (5) as;…”

Section: Afr 805mentioning

confidence: 99%

Duration analyses of precision agriculture technology adoption: what's influencing farmers' time-to-adoption decisions?

Ofori

Griffin

Yeager

2020

AFR

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PurposePrecision technologies have been available at the farm level for decades. Some technologies have been readily adopted, while the adoption of other technologies has been slower. The purpose of this study is to examine the factors influencing farmers' time-to-adoption decisions as duration between year of commercialization of precision agriculture (PA) technologies and year of adoption, at the farm level.Design/methodology/approachTime-to-adoption, which is the difference in years between technologies becoming commercially available and the year of adoption was determined using non-parametric duration analysis, and the impact of specific farm/farmer characteristics on time-to-adoption were estimated using a semi-parametric Cox proportional-hazard (CPH) model, based on a panel dataset of 316 Kansas farms from 2002 to 2018.FindingsThe findings indicate that, time-to-adoption for embodied-knowledge technologies such as automated guidance and section control were statistically shorter than for information-intensive technologies such as yield monitors, precision soil sampling and variable rate fertility. Duration was indirectly (directly) proportional to commercialization date of embodied-knowledge (information-intensive) technology. More so, time-to-adoption statistically differed among technologies within these two broad categories. Time-to-adoption varies across farm location and between both types of technologies. Millennial farmers are more likely to adopt both types of technologies sooner compared to baby boomers. Net farm income, percentage changes in debt-to-asset ratio, corn to total crop acres and machinery investment had no significant impact on the time-to-adoption for both information-intensive and embodied-knowledge technologies. On the other hand, while variations exist, time-to-adoption of PA technologies is mainly driven by location of farm, generation of farmer, number of workers, years of farming experience, total acres cropped and the cost of crop insurance.Originality/valueThis study investigates how the financial position of farms, amongst other important factors might influence time-to-adoption of PA technologies. Results are useful to extension personnel and retailers for planning marketing or farm outreach programs taking into consideration that, time-to-adoption differs across regions and by specific characteristics, key amongst them: generation of farmer, number of workers, years of farming experience, total acres cropped and the cost of crop insurance.

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“…While the issue of firm survival is of central importance in the agri-food industry (Hough et al, 2003;Blanchard et al, 2012;Teratanavat et al, 2005;Cruz et al, 2010;Bontemps et al, 2012;Grashuis et al, 2020), there are relatively few empirical studies of the determinants of survival for SME wineries. An exception is Valette et al (2018), which examines the survival rates of cooperatives in the French wine industry.…”

Section: Theoretical Framework 21 Literature Review Of Networkmentioning

confidence: 99%

Do institutional networks affect winery survival?

Olmos

Castel

Malorgio

2021

BFJ

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PurposeThe present study aims to provide new evidence regarding the factors that determine the survival of firms in the Spanish wine industry and to improve the understanding of sector dynamics.Design/methodology/approachThe empirical analysis, conducted over a representative sample of wineries in the DOC Rioja wine industry, is based on non-parametric (Kaplan–Meier graph) and semi-parametric survival models (Cox proportional hazard model).FindingsThe empirical model finds that wineries with a higher number of networks with institutions enjoy better survival prospects. This study also shows that a winery’s previous performance affects the winery’s survival probability; therefore, successful wineries in the past encounter a smaller hazard of exit. Although spending on R&D and exporting are factors likely to improve wineries' efficiency and competitiveness, these factors did not contribute significantly to the survival of DOC Rioja wineries.Originality/valueThis paper makes a significant contribution to the understanding of the determinants of wineries' survival and has important policy implications. In order to raise the probability of survival, policy makers should promote the networks that link wineries and institutions. Moreover, this study is based on survival analysis which, although frequently used in medical and behavioural sciences, has rarely been applied to wine economics. Finally, it uses a unique data set obtained from primary data collection, which previous studies have not analysed in relation to the probability of winery survival.

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Agricultural firm survival: The case of farmer cooperatives in the United States

Cited by 22 publications

References 48 publications

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Duration analyses of precision agriculture technology adoption: what's influencing farmers' time-to-adoption decisions?

Do institutional networks affect winery survival?

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