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Soil moisture is a major limiting factor in most dryland agricultural production systems around the globe. In dryland agriculture the amount of water available to grow a crop is determined primarily by the in-season rainfall and the amount of water stored in the soil profile prior to seeding of the crop. Soil water content and water storage capacity are key parameters. Soil moisture data measurements are a compromise between the spatial scale of the investigated site, the required spatial resolution, and the depth of investigation of the applied method.A bibliographic search of the measurement of soil moisture content at field-scale was done, giving an overview of current practices available to determine the spatial variability within a field, and its applicability to farm management practices.Articles published between April 2013 and March 2023 were searched, retaining only the articles with horizontal resolution less than or equal to 100 m, minimum vertical support at a depth greater than or equal to 30 cm from the soil surface, a minimum of two vertical layer depths, and the topic of the document was associated with the measurement of soil moisture at field-scale.The results of this review highlight progress in the past decade but currently there is no one method that can achieve absolute continuous spatial soil moisture in 3D at the field level. Some areas of research show promise but is still some distance away from a reliable, timely, and accurate soil moisture mapping required for many extensive dryland farming systems.

Feeding a growing global population requires improving agricultural production in the face of multidimensional challenges; and digital agriculture is increasingly seen as a strategy for better decision making. Agriculture and agricultural supply chains are increasingly reliant on data, including its access and provision from the farm to the consumer. Far-reaching data provision inevitably needs the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) that offer data originators and depository custodians with a set of guidelines to safeguard a progressive data availability and reusability. Through a systematic literature review it is apparent that although FAIR data principles can play a key role in achieving sustainable agricultural operational and business performance, there are few published studies on how they have been adopted and used. The investigation examines: (1) how FAIR data assimilate with the sustainability framework; and (2) whether the use of FAIR data by the agriculture industry, has an impact on agricultural performance. The work identifies a social science research gap and suggests a method to guide agriculture practitioners in identifying the specific barriers in making their data FAIR. By troubleshooting the barriers, the value propositions of adopting FAIR data in agriculture can be better understood and addressed.