Digital agriculture has been developing rapidly over the past decade. However, studies have shown that the need for more ability to use these tools and the shortage of knowledge contribute to current farmer unease about digital technology. In response, this study investigated the influence of communication channels—mass media, social media, and interpersonal meetings—on farmers’ adoption, decision-making, and benefits obtained using technologies. The research uses data from 461 farmers in Brazil and 340 farmers in the United States, leaders in soybean production worldwide. The results show differences and similarities between these countries. LinkedIn has the highest positive association in Brazil between the communication channels and the digital agriculture technologies analyzed. In the United States, YouTube has the highest positive correlation. The overall influence of social media among Brazilian farmers is higher than among American farmers. The perceived benefits of using digital tools are more strongly associated with mass media communication in the United States than in Brazil. Regarding farm management decision-making, the study showed a higher relevance of interpersonal meetings in Brazil than in the United States. Findings can aid farmers, managers, academics and government decision makers to use communication channels more effectively in evaluating and adopting digital technologies.

Innovation in agricultural practices and food system performance is urgently needed to effectively respond to societal challenges of today and tomorrow. Expanding population, a changing climate, environmental distress, and the need for more nutritious and safe food supplies are only a few of those challenges. In response, new tools employing digital technology and big data are being developed and applied within agricultural production systems. This chapter explores the potential for the effective implementation of such tools and their prospective impact. While these tools provide the means to measure agricultural production activities as they happen, it is important to stress that adoption hinges on both technological and economic factors. The tools of digital technologies and big data are intriguing because their implementation is a key form of innovation and because the use of these technologies can foster additional innovation by making existing innovation systems even more effective.

Several studies have evaluated the effects of postharvest technologies on postharvest loss (PHL) incurred at a single stage of a food value chain. However, very few studies have assessed the effect of multiple technologies on PHL incurred at various stages of a food value chain. This study evaluated the effect of five technologies (harvesting tools, cold stores, plastic crates, fruit fly traps, and ground tarps) promoted by the Rockefeller Foundation Yieldwise Initiative (YWI) in Kenya on PHL incurred at three mango value chain stages (harvest, transportation, and point of sale). After extensive screening of the YWI data, the Kruskal–Wallis statistical test was used to compare each YWI promoted technology to smallholder farmers (SHF) traditional practices. Results indicated that plastic crates used to transport or store mangos and fruit fly traps used to attract and kill fruit flies were statistically significant (p < 0.05) in reducing PHL at the point of sale. Meanwhile, no statistical evidence of PHL reduction was observed from SHF using harvesting tools, cold stores, and ground tarps. Cold stores were the least adopted of the promoted technologies due to their high costs of implementation and utilization. While this study asserts that increased technology adoption is associated with PHL reduction, further research is needed to identify additional factors that favor technologies’ efficacy in reducing PHL in similar food value chains.

Several studies have identified a host of factors to be considered when attempting to reduce food postharvest loss (PHL). However, very few studies have ranked those factors by their importance in driving PHL. This study used the Random Forest model to rank the critical drivers of PHL in maize, mango, and tomato, cultivated in Tanzania, Kenya, and Nigeria, respectively. The study then predicted the maize, mango, and tomato PHLs by changing the levels of the most critical drivers of PHL and the number of farmers at each level. The results indicate that the most critical drivers of PHL consist of pre-harvest and harvest variables in the field, such as the quantity of maize harvested in the maize value chain, the method used to know when to begin mango harvest, and the type of pest that attacks plants in the tomato value chain. Furthermore, changes in the levels of a critical driver and changes in the number of smallholder farmers at a given level both have an impact on PHL, although the impact of the former is much higher than the latter. This study also revealed that the critical drivers of PHL can be categorized as either passive and difficult to manipulate, such as the geographic area within which a smallholder farmer lives, or active and easier to control, such as services provided by the Rockefeller Foundation YieldWise Initiative. Moreover, the affiliation of smallholder farmers to the YieldWise Initiative and a smallholder farmer’s geographic location are ubiquitous critical drivers across all three value chains. Finally, an online dashboard was created to allow users to explore further the relationship between several critical drivers, the PHL of each crop, and a desired number of smallholder farmers.