Hill country futures - Resilient farmers and forages for the future

A thriving hill country farming sector is crucial for New Zealand’s economy and its regions. However, it faces numerous challenges, such as increased regulations, and changing societal expectations. To ensure the long-term success and well-being of farmers, farm systems, the environment, and rural communities, support is essential for building lasting resilience. To address some of these challenges, the Hill Country Futures Partnership programme was initiated, receiving $8.1 million funding over five years. This programme, co-funded by Beef + Lamb New Zealand (B+LNZ), the Ministry of Business, Innovation and Employment, PGG Wrightson Seeds, and RAGT New Zealand, concluded in 2023. During the programme, the farming community were actively engaged, and a collaborative research approach was employed involving B+LNZ, farmers, universities, Crown Research Institutes, and consulting agencies. The programme consisted of interconnected workstreams with a focus on resilient farmers and future-oriented forages. It generated a wide range of resources, including easily accessible extension materials, tools, and scientific publications, covering social, environmental, and technical aspects to support New Zealand’s hill country farming systems. And it showed how a collaborative approach, inclusive of researchers and farmers with diverse backgrounds and expertise, can help create a more resilient hill country future

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance