66 Emerging Precision Ranching Technology Is Enabling the Development of a “smart” Biome
Bibliographic record
Abstract
Abstract Precision ranching technology is revolutionizing remote data collection from livestock and the land base they depend on, greatly improving our ability to assess the entire biome. Remotely Piloted Aircraft System (RPAS or drone) based remote sensing to date has been used primarily to assess intensive cropping; but RPAS systems equipped with interchangeable cameras: high-resolution visual, thermal, LiDAR, and multi/hyperspectral imagers are now being deployed on rangelands, to monitor subtle changes in the visible, near infrared and infrared spectrums (radiation) that both plants and animals reflect. A single RPAS system can now be used simultaneously for high-quality vegetation mapping and behavioral analysis of animals, providing a suite of comprehensive data-collection tools for studying livestock in a variety of landscapes and rugged terrain. For example, we have used RPAS systems to measure behavioral and physiological indicators of cattle heat stress in feedlots and on pasture (Mufford et al., 2021). These same drones can be utilized for RPAS collection of high-resolution imagery (using multi/hyperspectral imagers and LiDAR) for vegetative and topographical mapping, greatly improving our ability to visualize and characterize habitat attributes such as forage availability, quality (stage of growth) and accessibility. Equipping cattle with new tracking technology, including "smart" GPS ear tags, rumen boluses, and wireless fencing collars (based on low-earth orbit satellites and cellular networks) enable animals to be readily identified and tracked on the landscape, while providing invaluable physiological data, such as internal body temperature to monitor estrous, heat stress, or the onset of disease (Figure 1). Additionally, rapid advances in wireless fencing systems have the potential to improve livestock management through autonomous mustering or the implementation of advanced rotational grazing for use in regenerative agriculture efforts. Wireless fencing may decrease production (infrastructure) costs, while also providing a strategy to better align forage demands with use, and thereby enhance pasture condition at the habitat and landscape level. Finally, non-invasive pasture weighing systems, enabled with or without RPAS technologies, are capable of monitoring key livestock production metrics year-round. Together, these technological advances enabled by this new "smart biome" will improve animal health and welfare while ultimately enhancing the sustainability of extensive livestock production systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".