Learning from the past in the transition to open-pollinated varieties

In Nepal, hybrid seed introduction caused major yield gains in agricultural production, but at high environmental costs. The development of high-yielding open-pollinated varieties has spurred hope for more sustainable production systems. Nepal’s government is interested in boosting their use. This research aimed to identify farmer perceptions on the factors behind the past adoption of hybrid seeds in order to propose guidelines to support the diffusion of open-pollinated varieties. Using in-depth interviews, a focus group and participant observation we explored how the process of hybrid seed diffusion has taken place in Panchkhal valley, a representative case study. Social influencers such as change agents, peers, neighbours and seed sellers, as well as economic gains emerged as major reasons for hybrid seed adoption. We learnt that the role of external agents, on which most of the governmental strategies rely, changed over time as peer-based strategies became essential after the diffusion process started. To boost the adoption of open-pollinated seeds, efforts should concentrate in developing high-yielding varieties, engaging early-adopters among influential caste members and seed sellers, distributing seeds to both disadvantaged and wealthy farmers, and using different instruments, from institutional agencies to NGOs, to deliver training on sustainable farming techniques and their economic and environmental advantages

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping