Investigating Spatial Patterns of Methane Concentration in Feedlots Using Moran’s Index and GIS Tools
Bibliographic record
Abstract
Methane (CH4) emissions from beef feedlots contribute significantly to agricultural greenhouse gases. However, accurate, spatially resolved quantification of CH4under real-world conditions remains a challenge due to the variability of atmospheric transport and source distribution. This study integrates Uncrewed Aerial Vehicle (UAV) based CH4concentration measurements, spatial statistical analysis, and mass balance flux estimation to characterize feedlot CH4emissions and assess how atmospheric conditions influence measurement outcomes. High-resolution CH4concentration data were collected using a UAV-mounted CH4gas analyzer during two field days, with two flights conducted per day. Spatial patterns were evaluated using Moran’s Index (I) and kriging interpolation. A mass balance approach was used to estimate CH4flux (kg/h), incorporating CH4enhancement over background, wind speed, wind alignment (sin (θ)), and flight path geometry. A 30 m mixing height was applied as a conservative, uniform estimate across all flights. To assess the sensitivity of this assumption, emissions were recalculated using 100 m and 200 m heights. While total flux values increased with higher mixing heights, the relative ranking of emission magnitudes between flights remained consistent, demonstrating robustness of comparative results. Moran’s I values ranged from 0.343 to 0.483, indicating moderate spatial clustering of CH4concentrations. Kriging interpolation maps revealed localized CH4hotspots, influenced by wind speed and direction. Wind rose plots showed that plume visibility and dispersion were highly sensitive to prevailing wind conditions. Lower wind speeds (0.16-4 m/s) enabled clearer detection of near-source enhancements, while higher speeds resulted in more diluted plumes. Emission rates ranged from 184 to 596 g/day/head across the flight sessions. The highest value was observed when moderate CH4enhancement (0.21 ppm), combined with moderate wind (5.9 m/s) and favorable wind alignment (mean sin (θ) = 0.54), contributed to a well-defined flux boundary. Lower estimates were associated with weak CH4signals or limited plume transport. Flux estimates were strongest when CH4enhancements aligned with favorable crosswind geometry, allowing the UAV to effectively capture plume transport. This study demonstrates that UAV-based mass balance methods, combined with spatial and meteorological analysis, can produce reliable CH4emission estimates consistent with established inventory methods. The integrated approach offers a robust framework for characterizing real-time CH4emission behavior and supports improved feedlot emission monitoring strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".