A recent review article explored methods that are used in agriculture to detect ammonia in pig housing. In the second part of our discussion of this topic, we focus on the protocols and frameworks and their importance in measuring ammonia in pig production.
Five methods for ammonia monitoring, including electrochemical (EC) gas sensors, photoacoustic spectroscopy (PAS) gas sensors, laser absorption spectroscopy (LAS) gas sensors, Fourier transform infrared (FT-IR) spectroscopy gas sensors, and colorimetric gas detector tubes, were investigated by a team of researchers from several Irish institutions. This review article, which was published in the journal Computers and Electronics in Agriculture (1), described the benefits and drawbacks of each method, which we recapped in the first part of this article (2).
In part two, the measurement protocols and frameworks used in measuring ammonia in pig production are discussed.
Because pigs often spend 5–6 months in barns during the production process, constantly monitoring their air quality is important (3). To ensure optimal swine production, pig houses need to ensure ammonia levels, which often is secreted from urine and feces, are reduced and maintained at either very low or nonexistent levels (3). Doing so also eliminates the concern of other external environmental issues arising, as ammonia has larger implications for the ecosystem, such as damaging the soil (4).
As mentioned in the first part of this article (2), gas sensors are often used to detect ammonia levels. However, another key aspect to monitoring ammonia levels are the measurement protocols.
“Measurement protocols also play a decisive role, as monitoring duration, sampling locations, housing design, and ventilation systems critically shape reported emission values and determine the applicability of different technologies in practice,” the authors wrote in their study (1).
In mechanically ventilated pig houses, researchers can conduct straightforward exhaust-based measurements, as airflow rates can be measured or estimated with reasonable accuracy (1). However, this is not the case with naturally ventilated barns. They pose a greater challenge because they are susceptible to variable wind conditions and complex airflow patterns. As a result, it requires them to use multipoint sampling and alternative approaches to airflow estimation, which means they are less certain about the results achieved with a more complicated method (1).
The authors emphasize that standardized frameworks, such as those outlined in Best Available Techniques (BAT) reference documents, are essential for regulatory reporting (1). At the same time, experimental research often demands more flexible and intensive monitoring designs. Without careful alignment between technology and protocol, even high-end instruments risk generating biased or incomplete datasets.
To bridge the gap between technical performance and practical decision-making, the researchers introduced a decision-support flowchart designed to guide users toward appropriate monitoring solutions. This visual tool, accessible through the review article, considers factors such as monitoring objectives (research versus compliance), housing type, budget constraints, and desired data resolution (1). This chart is designed to help farmers select the best option for monitoring ammonia, since there are a lot of options and not one that is significantly superior to the rest.
The flowchart is particularly relevant for stakeholders navigating new regulatory requirements or exploring mitigation strategies such as slurry acidification, dietary manipulation, or ventilation optimization. By matching sensor capabilities to specific use cases, the tool aims to improve both data quality and cost-effectiveness (1).
Although the technology has made enormous strides over the past couple of decades, the researchers acknowledge that a few key issues remain. For example, a review of the literature shows that studies of sensor performance across seasons and housing types is limited (1). Another main issue is cross-interference, which directly impacts measurement reliability, particularly at higher ammonia concentrations (1). The authors also highlight the need for more extensive application of FT-IR analyzers in pig production research, as well as systematic evaluations of sensor performance in both full and empty barns to establish reliable baselines (1). Naturally ventilated systems, in particular, remain a major methodological challenge, with no universally accepted approach to emission quantification.
Looking forward, the review points to emerging opportunities at the intersection of sensing, data analytics, and modeling. For example, machine learning (ML) techniques could help correct sensor drift and compensate for environmental interferences, while computational fluid dynamics models may improve understanding of airflow and emission dynamics within pig houses. Integrated monitoring systems that combine affordable sensors with automated sampling and ventilation controls could transform ammonia measurement from a retrospective exercise into a real-time management tool.
Ammonia monitoring is expected to become even more critical with the forthcoming revision of the European Union’s Industrial Emissions Directive (2010/75/EU), which is likely to impose stricter reporting and mitigation requirements on intensive livestock operations (1). Against this regulatory backdrop, the need for reliable, practical, and cost-effective measurement approaches has never been greater.
