Drivers and Barriers Influencing the Willingness to Adopt Technologies for Variable Rate Application of Fertiliser in Lower Austria

Even though a broad range of technologies for variable rate application of nitrogen fertiliser is available, there are hardly any documented cases of their use in Austria. In this study, the drivers and barriers of adoption have been investigated. A survey of 242 farmers in Lower Austria was conducted. The survey covered the farmers’ economic situation, concerns, and expectations regarding the future of their farms and their interest in precision farming technologies. A choice experiment was included in the survey to elicit farmers’ preferences for different features of variable rate application technologies. A series of multinomial logit, mixed logit and latent class logit models were run to analyze the choice experiment. Most farmers were interested in variable rate application, whereas technology costs, yield and environmental improvements were found to be important drivers of adoption. Also, farm size, farming system, technological level and network activities seem to play an important role in the uptake of variable rate application technologies.

Farmer preferences for adopting precision farming technologies: a case study from Italy

Precision farming (PF) technologies can help to mitigate the environmental impact of agriculture by reducing fertiliser use and irrigation while saving cost for the farmer. However, these technologies are not widely adopted in Europe. We study farmers’ willingness to adopt PF technologies based on a choice experiment. Among other determinants, we explore the role of social influence for the valuation of PF technology features. The data are analysed using mixed and latent class logit models. Our results show that knowledge of fellow farmers who adopted the technology positively influences the valuation of PF technology features, stressing the importance of networks.

Consumer Preferences for Sustainable Product Attributes and Farm Program Features

Previous literature primarily focused on consumers’ preference for specific sustainable attributes, such as a product being organic, eco-friendly, locally grown, and fair trade. Little is known about consumers’ preference for sustainable program features. We conduct two online choice experiments with U.S. consumers and find that consumers consistently care about farmers’ engagements in sustainable programs, and they are willing to pay a price premium for products from such programs. Consumers also value promoting science in sustainability, establishing concrete measurements of sustainability, and communicating sustainable practices with consumers and downstream industries. We apply the latent class logit model to investigate the potential segmentation of consumers. Three consumer segments are identified based on participants’ heterogeneity in preferences. Our research provides useful information for designing new sustainability programs.

Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections

Drivers’ decisions to either slow and stop or go at the onset of yellow signal impact on intersection safety. This novel study contributes to the new classification scheme for drivers. Two driving style indexes (i.e., the driving reliability index and dangerous driving index) are adopted, along with other known factors to analyze stop/go decision-making. Initially, the driving reliability index is extracted using a Hidden Markov Model (HMM). The dangerous driving index is calculated based on statistics extracted from dangerous driving records. A latent class logit model is then proposed to explore the factors which influence drivers’ decisions. Drivers are classified for analytical purposes into “low-risk” and “high-risk” categories according to driving styles and age. Results indicate that those considering “low-risk” tend to stop, while drivers considering “high-risk” are inclined to pass intersections. Furthermore, distractions from cell phones have different influences on each group of drivers. These findings help to determine driver preferences and may be used to formulate strategies to reduce unsafe driving occurring at signalized intersections.

Using Market Segmentation Analysis to Identify Consumer Preferences for Fresh Peach Attributes

Consumer preferences for attributes of fresh peach fruit in the United States are largely unknown on a national basis. We used a choice experiment to explore market segmentation based on consumer heterogeneous preference for fruit attributes including external color, blemish, firmness, sweetness, flavor, and price. We collected the data using an online survey with 800 U.S. consumers. Using a latent class logit model, we identified three segments of consumers differing by different sets of preferred quality attributes: experience attribute-oriented consumers, who valued fruit quality (48.8% of the sample); search attribute-oriented consumers, who valued fruit appearance (33.7% of the sample); and balanced consumers, who considered search attributes and experience attributes but who valued each in a balanced way (17.5% of the sample). Each group demonstrated differentiated demographics and purchasing habits. The results have important marketing implications for peach breeders and suppliers.