###### Abstract  
Global warming is influenced by an increase in greenhouse gas (GHG) concentration in the atmosphere. Consequently, Net Ecosystem Exchange (NEE) is the main factor that influences the exchange of carbon (C) between the atmosphere and the soil. As a result, agricultural ecosystems are a potential carbon dioxide (CO\({}_{2}\)) sink, particularly rice paddies (_Oryza sativa_). Therefore, a static chamber with a portable CO\({}_{2}\) analyzer was designed and implemented for three rice plots to monitor CO\({}_{2}\) emissions. Furthermore, a weather station was installed to record meteorological variables. The vegetative, reproductive, and maturation phases of the crop lasted 95, 35, and 42 days post-sowing (DPS), respectively. In total, the crop lasted 172 DPS. Diurnal NEE had the highest CO\({}_{2}\) absorption capacity at 10:00 a.m. for the tillering stage (82 and 89 DPS), floral primordium (102 DPS), panicle initiation (111 DPS), and flowering (126 DPS). On the other hand, the maximum CO\({}_{2}\) emission at 82, 111, and 126 DPS occurred at 6:00 p.m. At 89 and 102 DPS, it occurred at 4:00 and 6:00 a.m., respectively. NEE in the vegetative stage was \(-25\) umolCO2 m\({}^{2}\) s\({}^{-1}\), and in the reproductive stage, it was \(-35\) umolCO2 m\({}^{2}\) s\({}^{-1}\), indicating the highest absorption capacity of the plife. The seasonal dynamics of NEE were mainly controlled by the air temperature inside the chamber (Tc) (R = \(-0.69\)), the relative humidity inside the chamber (RHG) (R = \(-0.66\)), and net radiation (R\({}_{n}\)) (R = \(-0.75\)). These results are similar to previous studies obtained via chromatographic analysis and eddy covariance (EC), which suggests that the portable analyzer could be an alternative for CO\({}_{2}\) monitoring.  
s 2024244022 Net Ecosystem Exchange (NEE) is one of the main processes that influence CO2 concentration in the atmosphere. Agricultural ecosystems, particularly rice paddles, play a crucial role in carbon absorption. Therefore, it is important to understand their function in carbon (C) flux [10]. For example, [PERSON] et al. [11] monitored lowland paddy fields for one year (dry and wet seasons) using eddy covariance (EC) to evaluate variations in NEE and find a suitable model for the better partitioning of NEE with respect to its components, such as gross primary production (GPP) and ecosystem respiration (Reco). [PERSON] et al. [12] calculated NEE in rice and wheat systems in the northwest Indo-Gangetic plains. This was the first estimation in a rice-wheat spring sequence using EC. [PERSON] et al. [13] investigated the characterization of CO2 fluxes in tropical lowland rice paddy ecosystems using EC to better understand the environmental impact in terms of C budget in submerged soil.  
The land-atmosphere exchange of matter and energy is recorded using EC [14], widely used given its solid theoretical basis. However, it is expensive, difficult to manipulate [15], and susceptible to information gaps [16]. On the other hand, static chambers are used to complement the deficiencies of EC. Nevertheless, they require long monitoring periods [17]. Additionally, the cost of chromatographic analysis for collected gases is high. In this regard, infrared sensors represent an opportunity to solve these challenges. They utilize the non-dispersive infrared (NDIR) principle to measure the concentration of CO2 instantly [18]. In addition, they are easy to acquire, manipulate, and program. An automatic estimation and sampling method based on sensors that can replace the conventional methods mentioned and simultaneously increase the efficiency in estimating greenhouse gas (GHG) fluxes is necessary [19].  
In this research, a CO2 analyzer was designed together with a static chamber to monitor diurnal and nocturnal NEE in rice fields. The objective was to establish a novel, efficient, and dependable method of making resource management decisions for sustainable agricultural practices in Peru.