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The world's rangelands and drylands are undergoing rapid change, and consequently are becoming more difficult to manage. Big data and digital technologies (digital tools) provide land managers with a means to understand and adaptively manage change. An assortment of tools—including standardized field ecosystem monitoring databases; web‐accessible maps of vegetation change, production forecasts, and climate risk; sensor networks and virtual fencing; mobile applications to collect and access a variety of data; and new models, interpretive tools, and tool libraries—together provide unprecedented opportunities to detect and direct rangeland change. Accessibility to and manager trust in and knowledge of these tools, however, have failed to keep pace with technological advances. Collaborative adaptive management that involves multiple stakeholders and scientists who learn from management actions is ideally suited to capitalize on an integrated suite of digital tools. Embedding science professionals and experienced technology users in social networks can enhance peer‐to‐peer learning about digital tools and fulfill their considerable promise. 

Abstract Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming.