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Purpose <p>Methane (CH<sub>4</sub>) from livestock farming is a significant environmental hotspot, accounting for a substantial share of global anthropogenic emissions. However, LCA studies often rely on generic or model-based emission data that may lack accuracy. This study aims to develop and validate an innovative UAV-based methodology for direct, on-site measurement of enteric and manure-related CH<sub>4</sub> emissions from dairy cattle, to improve LCA emission inventories.</p> Methods <p>Methane emissions from three Italian dairy farms were quantified using a mass balance approach with an open-path TDLAS sensor mounted on unmanned aerial vehicles (UAVs). Uncertainty analysis evaluated correlations with wind speed, animal number, wind direction variability, temperature, and time since last feeding. Data quality was assessed using a Data Quality Rating (DQR) following the ISO 14040 and ISO 14044 standards, considering technological, geographical, and temporal representativeness as well as methodological consistency.</p> Results and discussion <p>Daily enteric emissions per animal unit (AU) were measured, ranging from 0.18 to 0.24&#xa0;kg CH<sub>4</sub>/AU/day. Based on an average live weight of 650&#xa0;kg per cow (1 AU = 500&#xa0;kg), this corresponds to approximately 0.23 to 0.31&#xa0;kg CH<sub>4</sub> per head per day, or 84 to 113&#xa0;kg CH<sub>4</sub> per head per year. Results showed that uncertainty decreased with higher wind speeds and larger herds but increased with variability in wind direction and temperature. The UAV-based measurements showed good agreement with IPCC model estimates (93–97% across sites), demonstrating reliability. Direct UAV-based CH<sub>4</sub> measurements demonstrated significantly higher data quality (DQR = 1.2) compared to IPCC Tier 2 estimates (DQR = 1.6) and Ecoinvent data (DQR = 2.6), highlighting the added value of high-resolution, site-specific monitoring in agricultural emission assessments. A comparative LCA of a model dairy farm using both UAV-measured and IPCC emission factors demonstrated that direct measurements improve the accuracy and site-specificity of environmental assessments, underscoring the value of primary data for robust, context-specific life cycle inventories. UAV-based methane measurements resulted in a climate change impact 5.2% higher than IPCC Tier 2 estimates, and 11.2% higher than assessments using generic Ecoinvent emission factors, highlighting their greater sensitivity to real-world emission dynamics. This difference was primarily driven by CH<sub>4</sub>, highlighting its pivotal role in farm-level LCA precision.</p> Conclusions <p>The UAV-based methodology provides a low-cost, innovative tool for direct, site-specific CH<sub>4</sub> emission measurement from dairy cattle, improving the reliability of LCA inventories. Its integration supports more accurate environmental assessments and sustainable decision-making in livestock farming.</p> Graphical Abstract <p></p>

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Direct measurement of methane emissions in cattle breeds by using UAV monitoring systems to improve data quality in LCA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • U. G. Spizzirri,
  • B. Notarnicola,
  • M. De Molfetta,
  • P. A. Renzulli,
  • F. Astuto,
  • D. Lovarelli,
  • M. Zoli,
  • D. Fosco

摘要

Purpose

Methane (CH4) from livestock farming is a significant environmental hotspot, accounting for a substantial share of global anthropogenic emissions. However, LCA studies often rely on generic or model-based emission data that may lack accuracy. This study aims to develop and validate an innovative UAV-based methodology for direct, on-site measurement of enteric and manure-related CH4 emissions from dairy cattle, to improve LCA emission inventories.

Methods

Methane emissions from three Italian dairy farms were quantified using a mass balance approach with an open-path TDLAS sensor mounted on unmanned aerial vehicles (UAVs). Uncertainty analysis evaluated correlations with wind speed, animal number, wind direction variability, temperature, and time since last feeding. Data quality was assessed using a Data Quality Rating (DQR) following the ISO 14040 and ISO 14044 standards, considering technological, geographical, and temporal representativeness as well as methodological consistency.

Results and discussion

Daily enteric emissions per animal unit (AU) were measured, ranging from 0.18 to 0.24 kg CH4/AU/day. Based on an average live weight of 650 kg per cow (1 AU = 500 kg), this corresponds to approximately 0.23 to 0.31 kg CH4 per head per day, or 84 to 113 kg CH4 per head per year. Results showed that uncertainty decreased with higher wind speeds and larger herds but increased with variability in wind direction and temperature. The UAV-based measurements showed good agreement with IPCC model estimates (93–97% across sites), demonstrating reliability. Direct UAV-based CH4 measurements demonstrated significantly higher data quality (DQR = 1.2) compared to IPCC Tier 2 estimates (DQR = 1.6) and Ecoinvent data (DQR = 2.6), highlighting the added value of high-resolution, site-specific monitoring in agricultural emission assessments. A comparative LCA of a model dairy farm using both UAV-measured and IPCC emission factors demonstrated that direct measurements improve the accuracy and site-specificity of environmental assessments, underscoring the value of primary data for robust, context-specific life cycle inventories. UAV-based methane measurements resulted in a climate change impact 5.2% higher than IPCC Tier 2 estimates, and 11.2% higher than assessments using generic Ecoinvent emission factors, highlighting their greater sensitivity to real-world emission dynamics. This difference was primarily driven by CH4, highlighting its pivotal role in farm-level LCA precision.

Conclusions

The UAV-based methodology provides a low-cost, innovative tool for direct, site-specific CH4 emission measurement from dairy cattle, improving the reliability of LCA inventories. Its integration supports more accurate environmental assessments and sustainable decision-making in livestock farming.

Graphical Abstract