AgYields – a national database for collation of past, present and future pasture and crop yield data

The New Zealand agricultural sector has a rich heritage of measuring yield and growth rates for pastures and crops. These data are expensive to collect, spatially and temporally patchy, and stored in a range of electronic and physical platforms. A challenge for data collection and storage is the different priorities and skill sets of those undertaking the task. Thus, there is a need to provide guidelines for the collection, collation and publication of such data to standardize best practice and maximize the value gained from increasingly scarce resources available for pasture and crop research to support the primary industries. In addition, declining funding for field research, means there is an urgent need to draw together existing and future data into a publicly accessible industry good resource. This paper outlines the development of the AgYields web-based repository for pasture and crop growth rate and yield data. It describes the rationale for the database and the need for standardization of data collection to maximize the value of stored data in common formats. The intent is to provide a resource to enhance livestock and crop production systems throughout New Zealand and provide guidelines for future data collection

Using agricultural metadata : a novel investigation of trends in sowing date in on-farm research trials using the online farm trials database

Background: A growing ability to collect data, together with the development and adoption of the FAIR guiding principles, has increased the amount of data available in many disciplines. This has given rise to an urgent need for robust metadata. Within the Australian grains industry, data from thousands of on-farm research trials (Trial Projects) have been made available via the Online Farm Trials (OFT) website. OFT Trial Project metadata were developed as filters to refine front-end database searches, but could also be used as a dataset to investigate trends in metadata elements. Australian grains crops are being sown earlier, but whether on-farm research trials reflect this change is currently unknown. Methods: We investigated whether OFT Trial Project metadata could be used to detect trends in sowing dates of on-farm crop research trials across Australia, testing the hypothesis that research trials are being sown earlier in line with local farming practices. The investigation included 15 autumn-sown, winter crop species listed in the database, with trial records from 1993 to 2019. Results: Our analyses showed that (i) OFT Trial Project metadata can be used as a dataset to detect trends in sowing date; and (ii) cropping research trials are being sown earlier in Victoria and Western Australia, but no trend exists within the other states. Discussion/Conclusion: Our findings show that OFT Trial Project metadata can be used to detect trends in crop sowing date, suggesting that metadata could also be used to detect trends in other elements such as harvest date. Because OFT is a national database of research trials, further assessment of metadata may uncover important agronomic, cultural or economic trends within or across the Australian cropping regions. New information could then be used to lead practice change and increase productivity within the Australian grains industry. © 2021 Walters J et al

Enhancing Capacities of Digital Extension and Advisory Services in Odisha, India

While several digital platforms and applications developed for farmers collect data and information, more is needed to know about their use by the Extension and Advisory Services (EAS) to provide more relevant advice or design a data-informed extension. This report discusses what needs to be done to enhance the capacities of EAS based on in-depth reviews of farmers' use of three digital farmer services available in Odisha and interactions with select stakeholders who are familiar with and are part of these services. We found that EAS stakeholders needed to be fully aware of the types of data and information available or how best they could be used. We identified that four specific types of capacities need to be strengthened coherently and systematically