Most research and monitoring activities involving low-cost atmospheric sensors (LCS) have traditionally focused on urban environments, where air quality is a pressing concern. However, an emerging area of research is their application in rural landscapes, where land use is a key determinant of climate change and net zero outcomes, directly influencing greenhouse gas emissions, carbon sequestration, and ecosystem functioning. In this study, we examine the use of LCS NDIR CO<sub>2</sub> sensors to monitor atmospheric concentrations in agricultural settings. We present a data analysis framework that addresses errors associated with factory auto-baseline correction and, crucially, accounts for the influence of environmental parameters such as temperature and pressure. This approach leads to improved sensor performance. Results from deployments in both rural and urban environments demonstrate strong agreement with reference-grade instruments, as shown by the root mean square error (RMSE) and coefficient of determination (R<sup>2</sup>). Improvements of RMSE from 10 ppm to 4 ppm in winter and 5 ppm to 4 ppm in summer, with corresponding improved R<sup>2</sup> from 0.85 to 0.98 in winter and 0.86 to 0.97 in summer were obtained when compared to a simple scaling correction of the out-of-box data. Our study shows that corrected data not only reproduced the seasonal profile associated with rural emissions, but it also captured the diurnal variability, sometimes characterised by CO<sub>2</sub> change of up to 50 ppm, which is more than three times (15 ppm) the typical change in the urban environment. Overall, this study demonstrates the viability of low-cost CO<sub>2</sub> sensors and sensor networks for reliable long-term monitoring in rural environments. The observations obtained provide valuable inputs for developing analytical methodologies and offer actionable insights into the influence of rural land-use practices on climate.