While several studies have focused on the actual adoption of agricultural apps and the relevance of the apps’ content, very few studies have focused on drivers of the farmer’s intention and initial decision to adopt. Based on a survey of 394 smallholder farmers in 2019, this study investigated willingness to adopt an agricultural advice app in Guanajuato, Mexico. A structural equation modeling approach, based on the unified theory of acceptance and use of technology (UTAUT), was applied. To understand the farmers’ adoption decisions, extended constructs were studied (e.g., mastery-approach goals) along with the farmers’ age and participation in an innovation hub. Results showed that the intention to adopt the app is predicted by how farmers appraise the technical infrastructure and acquire new knowledge by using an app. The multi-group analysis revealed that performance expectancy is a relevant predictor of the intention to adopt, whereas the mastery-approach goal is relevant only for younger farmers and farmers not connected to the innovation hub. This study provides valuable insights about the innovation hubs’ role in the intention to adopt apps, offering precision agriculture advice in developing countries. The findings are useful for practitioners and app developers designing digital-decision support tools.

Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s built-in GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the application provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations, and commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the application’s characteristics. More enhancements are planned for inclusion in the future to increase the application’s function as a decision support tool.

Mobile phone apps can be a cost-effective way to provide decision support to farmers, and they can support the collection of agricultural data. The digitisation of agricultural systems, and the efforts to close the digital divide and to include smallholders, make data ownership and privacy issues more relevant than ever before. In Central and South American countries, smallholders’ preferences regarding data licenses and sharing have largely been ignored, and little attention has been paid to the potential of nonfinancial incentives to increase the uptake of digital solutions and participation by farmers. To investigate incentives for smallholder farmers to potentially use an agricultural advisory app in which they share their data, a Discrete Choice Experiment was designed. Based on a survey of 392 farmers in Mexico, preferences for attributes related to its usage were revealed using a conditional logit (CL) model. To explore heterogeneity, groups and profiles were explored through a latent class (LC) model. The CL model results revealed, for example, farmers’ positive preference to receive support at first use and access to training, while negative preference was found for sharing data with private actors. The LC identified three classes which differ in their preference for attributes such as the degree of data sharing. Furthermore, for example, a farmer’s connectedness to an innovation hub was found to be one of the significant variables in the class membership function. The main contribution of the study is that it shows the importance of nonfinancial incentives and the influence of data sharing on farmer preferences.

While current studies have focused on adoption and the relevant content of the app to become a decision support system, very few studies have focused on the farmer's intention and initial decision to adopt. Based on a survey of 394 smallholder farmers this study investigated Mexican farmers’ willingness to adopt an agricultural advice app. A Structural Equation Modelling approach, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was applied. To understand farmers’ adoption decision, extended constructs were studied (e.g. mastery-approach goals) along with farmers’ age and participation in an innovation hub. Results showed that the intention to adopt the app is predicted by how farmers believe that technical infrastructure exists and by the expectation of the farmers using the app to acquire new knowledge. The multi-group analysis revealed that performance expectancy is a relevant predictor of the intention to adopt, whereas the mastery-approach goal is relevant only for younger and farmers not connected to the innovation hub. The results may well be a baseline to research further suitable non-financial incentives for different farmers’ groups, then encourage initial adoption and enhance uptake. The findings are useful for practitioners and app developers designing digital decision support tools.

Achieving a circular and climate-neutral bioeconomy by 2050 requires not only high-quality recycling but also the large-scale integration of renewable carbon from biomass and atmospheric CO₂ into material systems. Plastics represent the world’s largest and most rapidly growing carbon sink, positioning them as a critical intervention point for replacing fossil-based feedstocks with renewable alternatives. Because plastic packaging is one of the most visible material streams encountered by consumers in daily life, a transition toward sustainable, recyclable bioplastics has the potential to deliver both meaningful environmental benefits and strong societal impact, accelerating public awareness and acceptance of renewable carbon solutions. Poly(ethylene furanoate) (PEF)—a fully bio-based polyester synthesized from plant-derived 2,5-furandicarboxylic acid (FDCA) and monoethylene glycol (MEG)—offers a promising pathway toward more sustainable packaging due to its superior mechanical strength and gas-barrier performance relative to polyethylene terephthalate (PET). This study presents a cradle to grave life cycle assessment (LCA) of PEF resin production and PEF bottle applications, using industrially relevant, at-scale process data covering biomass feedstock conversion, polymer synthesis, packaging manufacture, use phase, and end of life. Bottle applications were selected as a focal point due to their technical maturity, commercial relevance, and suitability for direct comparison with incumbent PET systems. The results indicate that PEF can reduce greenhouse gas emissions by up to 71% and fossil resource depletion by 26% compared to PET at the resin level when biogenic carbon uptake is included. Moreover, the material’s enhanced functional properties enable lightweight, recyclable bottle designs with carbon footprint reductions of up to 88% for 500 mL formats under a baseline recycling rate scenario of 72%, with the remaining share directed to municipal solid-waste incineration with energy recovery. Sensitivity analyses reveal that virgin PEF maintains environmental advantages over PET even when PET incorporates high levels of recycled content, highlighting the complementary roles of renewable carbon and circular material strategies. Prospective scenario modeling underscores the importance of sustainable feedstock selection and process electrification, with sucrose-based routes offering the largest potential for further decarbonization. Overall, the findings demonstrate that PEF is a scalable biopolymer capable of delivering substantial climate benefits while supporting circularity objectives. By targeting a highly visible consumer application—plastic packaging—this transition amplifies the societal impact of adopting renewable carbon materials. The study provides actionable insights for policymakers, industry stakeholders, and sustainability practitioners working to advance a more resilient, renewable, and consumer-recognizable plastics economy.