DOI: 10.5061/dryad.z612jm6rc
title: Fencing the flux: Seasonal trends, environmental drivers, and mitigation opportunities of methane emissions from farm dams

description: Farm dams are significant methane (CH4) sources in agricultural
 landscapes. Fencing them to limit livestock access reduces organic matter
 and nutrient inputs, thereby limiting CH4 production. However, existing
 studies on the benefits of fencing are constrained by short durations,
 omission of ebullitive fluxes, limited spatial and temporal coverage, and
 small sample sizes. Here, we report a large-scale, multi-season assessment
 of total CH4 (diffusive + ebullitive) and carbon dioxide (CO2) fluxes from
 fenced and unfenced farm dams, along key environmental drivers. We
 monitored 113 farm dams in temperate mainland south-eastern Australia over
 two years, amounting to 39,552 and 45,408 hourly observations of total CH4
 and CO2 fluxes, respectively. We integrated field-measured emissions with
 Sentinel-2 indices, topo-climate variables, and geostatistical models to
 identify flux drivers, quantify temperature sensitivity, and spatially
 extrapolate mitigation potential across Local Government Authorities
 (LGAs). We found that fencing reduced CH4 fluxes by 66–82% across seasons
 while also significantly lowering the temperature sensitivity of CH4
 fluxes, slowing the exponential rise in emissions under warming
 conditions. Specifically, CH4 fluxes in fenced dams increased by 71% per
 10°C warming (Q10 = 1.71, EM = 0.4 eV), compared to unfenced dams
 increasing by 275% (Q10 = 3.75, EM = 0.98 eV). CH4 fluxes were driven by
 temperature, rainfall, and hydrological proxies (Modified Normalized
 Difference Water Index, MNDWI; Floating Algae Index, FAI), while CO2
 fluxes responded to rainfall and Normalized Difference Water Index (NDWI).
 Extrapolating our findings across the study area (526,296 km²), fencing
 all farm dams could cut CH4~ fluxes by 1.16–1.35 kt yr⁻¹. By combining
 high-resolution emission data with scalable management strategies, this
 study offers a framework to improve greenhouse gas inventories and guide
 targeted climate mitigation in agriculture.
description: This research was conducted in temperate mainland south-eastern
 Australia (Fig. 1), covering the states of Victoria, New South Wales, and
 southern Queensland. The region spans approximately from –39.14° to
 –25.85° latitude and from 140.96° to 168.00° longitude, encompassing
 temperate zones across these states with diverse landscapes, including
 temperate grasslands, coastal plains, and montane environments, and has a
 temperate climate influenced by the Southern Ocean. Seasonal temperatures
 range from 25–30°C in summer (December–February), 20–25°C in autumn
 (March–May), 15–25°C in spring (September–November), and 6–10°C in winter
 (June–August). Annual rainfall, peaking in winter, typically varies from
 400 to 1200 mm. Livestock grazing, particularly beef cattle and sheep,
 dominates the region’s agriculture. The study area spans 526,296
 km<sup>2</sup> and contains an estimated 249,305 farm dams,
 with an average total water surface area of about 24,000 hectares (ha)
 across 168 Local Government Authorities (LGAs) (Malerba et al.,
 2021). Over a period of 24 months (October 2022–October
 2024), we monitored 113 farm dams to analyse seasonal patterns of
 CH<sub>4</sub> and CO<sub>2</sub> emissions, their
 driving factors, and the effects of fencing to limit livestock access.
 These dams were spread across ten farming properties arranged along an
 approximately 1,498 km transect in temperate southeast Australia. Each
 property contained 2–25 farm dams, with treatments nearly balanced between
 fenced dams (n = 52; 685 monitoring days) and unfenced dams (n = 61; 963
 monitoring days). Sampling spanned all four seasons, including summer,
 autumn, winter, and spring, with individual dams monitored for periods
 ranging from one week to four months in one or multiple seasons, yielding
 a total of 1,648 dam-day observations.
description: # Fencing the flux: Seasonal trends, environmental drivers, and mitigation
 opportunities of methane emissions from farm dams ### Running title:
 **Fencing Farm Dams to Cut Methane Fluxes** #### 1. Overview This dataset
 (Odebiri_et_al_2025_Global_Change_Biology.zip) accompanies the article
 *“Fencing the Flux: Seasonal Trends, Environmental Drivers, and Mitigation
 Opportunities of Methane Emissions from Farm Dams”* published in *Global
 Change Biology* (Odebiri et al., 2025). The dataset provides the empirical
 and processed data used to quantify methane (CH₄) and carbon dioxide (CO₂)
 emissions from farm dams across temperate Southeastern Australia
 (2022–2024). It includes raw flux data, Sentinel-2 and terrain predictors,
 climate data, shapefiles, and analysis scripts used to assess seasonal
 patterns, environmental drivers, and emission mitigation potential via
 fencing interventions. #### 2. Contents of the Dataset | File/Folder |
 Description | | -------------------------------------- |
 ---------------------------------------------------------------------------------------------------------------------------- | | **Data/** | Contains raw and processed datasets. | | **All\_DataWeather\_sum\_Merged2.csv** | Raw flux data including CH₄ and CO₂ concentrations and ancillary field measurements. | | **GCB\_PAPER\_DATA.csv** | Comprehensive dataset integrating raw flux data with Sentinel-2, terrain, and climate predictors. | | **Python\_extract\_code/** | Contains `Extract_pixel_script_GCB.py` for extracting Sentinel-2 pixel values and terrain metrics using Google Earth Engine. | | **R\_code/** | Contains `GCB_Paper_R_Sript.R`, the R script used for data cleaning, mixed-effects analysis, and mapping. | | **Updated\_ALL\_Farm\_dams.shp** | Shapefile representing farm dam polygons across study regions. | | **Updated\_LCA\_shapefile.shp** | Shapefile defining Local Government Areas used for spatial aggregation and emission mapping. | | **Final\_Metadata\_Dictionary.xlsx** | Metadata table describing all variables, units, transformations, and sources. | | **README.txt** | Documentation of data, scripts, and methods (this file). | | **Licence.txt** | CC0 1.0 Public Domain Dedication. | #### 3. Data Description **Key Variables** | Variable | Description | Units / Type | Source / Derivation | | ---------------------------------------------- | ------------------------------------------------- | ------------------------- | -------------------------- | | **Name** | Unique farm dam identifier | Text | Field metadata | | **Treatment** | Management condition (FD = Fenced, UD = Unfenced) | Categorical | Field observation | | **Season** | Sampling season (Summer, Autumn, Winter, Spring) | Categorical | Derived from Date\_R | | **Gas** | Measured gas type (CH₄, CO₂, N₂O) | Text | Field measurement | | **mgm2day\_mean** | Mean flux rate | mg m⁻² day⁻¹ | Field measurement | | **NDVI, NDWI, ARVI, MNDWI, FAI, GCI** | Spectral indices | Unitless | Sentinel-2 L2A | | **B1–B12** | Sentinel-2 reflectance bands | Unitless (scaled) | Sentinel-2 MSI | | **Slope, Aspect, TWI, DEM** | Terrain variables | Degrees / m / Unitless | DEM-S (GA) | | **Temp\_mean, Humidity\_mean, Rainfall\_mean** | Climate predictors | °C / % / mm | ERA5 / BOM | | **Boltzmann\_15C, EM, Q10** | Temperature sensitivity parameters | eV⁻¹ / eV / Dimensionless | Derived via Boltzmann fits | #### 4. Methods Summary 1. **Data Collection:** Methane and CO₂ fluxes were measured using the *Pondi* chamber system at 130+ farm dams across Southeastern Australia between 2022–2024. 2. **Predictor Extraction:** Sentinel-2 indices and DEM-S terrain variables were extracted using `Extract_pixel_script_GCB.py` in Google Earth Engine. 3. **Data Integration:** All extracted predictors were merged with raw gas fluxes into `GCB_PAPER_DATA.csv`. 4. **Statistical Analysis:** Mixed-effects models (`nlme` package) were fitted to examine treatment and seasonal effects, while emission reduction potential was spatially quantified using R (`sf`, `ggplot2`). 5. **Mapping:** Total and per-hectare CH₄ reduction potentials were mapped at the LGA level using the `Updated_ALL_Farm_dams.shp` and `Updated_LCA_shapefile.shp` layers. #### 5. File Dependencies * `GCB_PAPER_DATA.csv` is the main dataset used in all analyses. * `All_DataWeather_sum_Merged2.csv` provides unmerged raw flux data. * `Extract_pixel_script_GCB.py` generates satellite and terrain predictors used in the R analysis. * `GCB_Paper_R_Sript.R` performs the full statistical and mapping workflow. #### 6. Citation Odebiri, O., Scheele, B. C., Lindenmayer, D. B., Smith, D. G., & Malerba, M. E. (2025). *Fencing the Flux: Seasonal Trends, Environmental Drivers, and Mitigation Opportunities of Methane Emissions from Farm Dams.* *Global Change Biology.* Data available at Dryad: [DOI: 10.5061/dryad.z612jm6rc] #### 7. License and Collaboration Note **License:** CC0 1.0 (Creative Commons Zero Public Domain Dedication) This dataset is released under the CC0 1.0 Public Domain Dedication, which permits users to copy, modify, distribute, and use the data for any purpose without restriction and without requiring attribution. **Collaboration Note:** While no attribution is legally required under CC0, researchers are warmly encouraged to contact the corresponding author (Odebiri et al.) if considering substantial reuse or collaborative extensions. This helps ensure consistent interpretation of the dataset and may foster opportunities for collaboration on related projects.