Examining Farmers’ Adoption Decisions towards Precision-Agricultural Practices in Greek Dairy Cattle Farms

Nowadays, the sustainability of Greek dairy cattle farms is questionable due to low competitiveness and high GHG emissions. In this context, the BIOCIRCULAR project, funded by the EYDE ETAK, developed a series of alternative practices focusing on precision agriculture principles. However, the adoption of any practice from farmers is not a given, and depends on several determinants. Hence, the objective of this study is to examine farmers’ adoption decisions regarding precision-agricultural practices in Greek dairy production systems, as well as the economic and environmental impacts of this adoption. In order to achieve this, a bio-economic model was developed based on mathematical programming methods. The proposed model simulates a large number of dairy cattle farms with or without crop production, including different management strategies and their relevant costs, and provides an environmental assessment of the adopted practices based on GHG emissions. Moreover, in order to analyze farmers’ adoption decisions, different policy measures, linked to various environmental outcomes, were examined. The results highlighted that the adoption of precision-agricultural practices led to significantly better economic and environmental outcomes. Furthermore, it was found that different levels of incentives can be efficiently targeted to encourage the adoption of new feeds and, more broadly, to secure the sustainability of the sector.

Factors Affecting New Agricultural Business Entities’ Adoption of Sustainable Intensification Practices in China: Evidence from the Main Apple-Producing Areas in the Loess Plateau

The unsustainability of China’s agricultural production requires an urgent shift from traditional to more sustainable practices; however, the acceleration thereof remains challenging. New agricultural business entities (NABEs) lead agricultural modernization and strongly guide the application of innovative agricultural technologies and models. Thus, an understanding of the factors that influence NABEs’ adoption of sustainable intensification practices will promote their widespread adoption. We developed a model based on innovation diffusion theory and the technology–organization–environment framework, which can both distinguish the influencing factors at different adoption stages and identify the influencing factors of technology adoption from a multidimensional perspective. The results indicate that differences in regional agroecological endowments emerge as the most important influencing factor. Relative advantage, perceived barriers, and agricultural extension services have a significant effect on adoption intention and decision, but a smaller effect on intention. Management and risk response capacities have a significant positive effect on adoption decisions, but no effect on intention. Meanwhile, organizational size has no effect on adoption intention or decision. Adoption intention significantly positively influences, but only partially explains, adoption decisions. Our findings provide a basis for technology promoters to categorize potential adopters by technology adoption stage and provide targeted strategies to stimulate technology demand.

Adoption of complementary climate-smart agricultural technologies: lessons from Lushoto in Tanzania

Background Agriculture is important for economic growth and development in many countries in Sub-Saharan Africa, including Tanzania. However, agricultural production and productivity remain relatively low, with significant yield gaps attributed to factors such as limited access to and low adoption of appropriate agricultural technologies, and climate-related risks resulting from climate variability and change. This paper explores the drivers of adoption of climate-smart agricultural (CSA) technologies and practices, taking into account the complementarity among agricultural technologies and heterogeneity of the farm households, using data from Lushoto in Tanzania. Methods We use a Multivariate Probit analysis of cross-sectional data collected from 264 smallholder farmers in Lushoto—a climate hotspot in Tanzania—to understand the drivers of household decisions to adopt CSA technologies and practices. The technologies included diversification of multiple stress (drought, floods, pests, diseases)-tolerant crop varieties, use of fertilizers, and application of herbicides and pesticides. The Multivariate Probit model was preferred as it takes into account the inter-relationships of the technologies as well as heterogeneity of the smallholder farmers for more robust estimates. The independent variables used in the analysis included household socio-economic factors such as the relative importance of crop and livestock enterprises, household land size, social capital, access to agricultural credit and weather information, previous experience with fertilizer use and household characteristics (age, education and gender of household head, and household size). Results About 63% of the households diversified their crop enterprises, shifting to improved resilient crops and crop varieties. Another 37% adopted fertilizers, while 38% applied pesticides and herbicides. Conditional on the unobservable heterogeneity effects, the results show that household adoption decisions on diversification of multiple stress-tolerant crops and crop varieties, fertilizer, and pesticides and herbicides are complementary. In addition, the results confirm existence of unobserved heterogeneity effects leading to varying impact of the explanatory variables on adoption decisions among farmers with similar observable characteristics. Conclusions The findings indicate that any effective CSA technology adoption and diffusion strategies and policies should take into account the complementarity of the technologies and heterogeneity of the smallholder farmers. Therefore, inter-related technologies should be promoted as a package or bundled while taking into consideration household and farm-level constraints to adoption.

Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions

We propose and analyze the first model for clinical trial design that integrates each of three important trends intending to improve the effectiveness of clinical trials that inform health-technology adoption decisions: adaptive design, which dynamically adjusts the sample size and allocation of interventions to different patients; multiarm trial design, which compares multiple interventions simultaneously; and value-based design, which focuses on cost-benefit improvements of health interventions over a current standard of care. Example applications are to seamless phase II/III dose-finding trials and to trials that test multiple combinations of therapies. Our objective is to maximize the expected population health-economic benefit of health-technology adoption decisions less clinical trial costs. We show that unifying the adaptive, multiarm, and value-based approaches to trial design can reduce the cost and duration of multiarm trials with efficient adaptive look ahead policies that focus on value to patients and account for correlated rewards across arms. Features that differentiate our approach from much other work on stochastic optimization include stopping times that balance sampling costs and the expected value of information of those samples, performance guarantees offered by new asymptotic convergence proofs, and the modeling of arms’ potentially different sampling costs. Our proposed solution can be computed feasibly and can randomize patients. The class of trials for the base model assumes that health-economic data are collected and observed quickly. Related work from Bayesian optimization can enable the further inclusion of trials with intermediate duration delays between the time of treatment initiation and observation of outcomes. This paper was accepted by Stefan Scholtes, healthcare management.

Efficiency or Innovation? The Long-Run Payoff of Cloud Computing

Considering the mixed arguments and uncertainty about the payoff of cloud computing, this paper empirically studies the long-term cloud computing impact on the financial performance, specifically from the perspective of efficiency and innovation. Taking 253 pairs of listed companies in China as the research sample, propensity score matching and difference in differences techniques combined with OLS regression are conducted to analyze a rolling 5-year panel data. The analysis results show that cloud computing adoption leads to years of financial performance decline followed by an upturn. The downward trend is more pronounced when it is adopted with innovation. This paper contributes to the existing literatures by leveraging archival performance data to verify the long-term business value and revealing the value realization difference between efficiency- and innovation-oriented cloud computing adoptions. The findings remind the managers to see the two sides of cloud computing and make rational adoption decisions, especially cloud-based innovation, according to their actual situations.

The Journey of Business Opportunity Evaluation: When and Why Does Opportunity Novelty Promote Vs. Inhibit Opportunity Adoption?

As a typical characteristic of entrepreneurial opportunities, novelty is essential for firms to establish and maintain a sustainable competitive advantage under the current complex and dynamic business environment. However, why is it that some entrepreneurs adopt novel opportunities but others do not. Little is known about the precise nature of cognitive evaluation for opportunity novelty. Drawing upon information processing theory and construal level theory (CLT), we propose that the effects of opportunity novelty on adoption decisions depend on entrepreneurs' construal level through which information is processed. We design an experiment and find partial support for our hypotheses. Results indicate that entrepreneurs using a low-level construal perceive more risk for opportunity novelty, which in turn decreases the possibility of opportunity adoption. Meanwhile, opportunity novelty also positively influences entrepreneurs' creativity perception, which in turn increases the possibility of opportunity adoption. But construal level does not play any role in this evaluation path. Taken together, the findings improve our understanding of “how entrepreneurs evaluate an opportunity based on its objective characteristics” by providing empirical insights into the cognitive information processing process from opportunity novelty to adoption. Additionally, we discuss implications for entrepreneurial practice and future research.

Assessing the joint adoption and complementarity between in-field conservation practices of Kansas farmers

Agricultural conservation systems consist of a myriad of conservation practices. The mix and intensity of conservation practices adopted can benefit farmers and affect the entire production system in addition to soil and water conservation. The purpose of this study is to examine and analyze farmer adoption of and complementarity between conservation practices from a joint and conditional probabilistic perspective using Kansas as a case study. We develop a modeling framework that can analyze and examine farmers’ joint and conditional adoption decisions using a multinomial logistic regression model. This framework is used to estimate conditional probabilities of adopting conservation practices given adoption of other practices to better capture the complementarity between different conservation practices. These estimates allow for an assessment of linkages between adoption of different conservation practices and the socioeconomic factors that affect the likelihood of adopting conservation practices given other conservation practices have already been adopted on-farm. The results can help guide policy and outreach efforts to promote further intensification of adoption by farmers.