<p>Management of non-point-source emissions from pastured livestock is complicated by spatial and temporal distribution of emissions and how they interplay with equally complex landscape typological distributions. Wearable sensing of CO₂ concentrations near the tailhead may enable real-time, spatially-explicit monitoring of manure emissions, if concentrations correlate with defecation and urination events. The objective of this research was to explore the association between measured CO₂ concentrations from wearable sensors placed on the tailhead of horses and the occurrence of defecation and urination events. CO₂ sensors consisted of a TTGO-T-Beam microprocessor equipped with GPS and LoRa radio, soldered to a CJMCU-8128 environmental sensing board capable of measuring temperature, pressure, relative humidity, CO₂ and total volatile organic compounds (TVOC). Tail wraps were placed on 4 stalled horses for a total of 9&#xa0;days. Surveillance videos were collected over the same time frame and viewed to determine the time of defecation and urination occurrence. Data were analyzed visually for coherence, and quantitatively using analysis of variance, random forest regression, support vector machines, and extreme gradient boosting. Because defecation and urination events were in much lower quantity than non-events, random oversampling and undersampling were attempted on the classification approaches to improve accuracy and precision of signaling algorithms. Visual inspection revealed that although defecation and urination events corresponded to CO₂ peaks, there was considerable noise in CO₂ data suggesting that peaks in CO₂ also frequently occur in the absence of defecation and urination events. All classification algorithms showed poor accuracies (0.50 to 0.51), which were only marginally improved by over- (&lt; 0.51) and undersampling (&lt; 0.69). This preliminary assessment revealed considerable noise in sensing CO₂ emissions in production settings, which may preclude usefulness in manure sensing.</p>