# Development of Predictive Equations for Thermal Conductivity of Compost Bedding
This system, coupled with its relatively low operating costs, will provide dairy farmers with a tool to reduce the emission of greenhouse gases to the atmosphere once correctly managed. This may, in turn, reduce the impact of this animal production system on climate change. Compared to window composting, CBP barns have a larger surface area to heat generating volume, thus more heat losses. Maintaining a high pack temperature is important in CBP barns [14]. This requires an understanding of the thermal properties and heat balance in composing pack systems, which can potentially improve CBP management and design [4; 15]. In recent years, considerable effort has gone into developing techniques to determine the thermal properties of composing materials as they are relevant in many areas of agriculture engineering, agronomy, and animal science [16; 17]. Thermal properties of composing materials affect the temperature and biodegradation rate during the composing process. Well-determined thermal properties of compost feedstock will therefore contribute to practical thermodynamic approaches and mathematical models involving heat and mass transfer [18]. However, it can be very expensive and physically impractical to obtain many of the parameters. Therefore, feasible simplifications are to be sought, and various compromises are to be adopted in computational simulation case studies that emphasize either the model complexity or material data accuracy [19]. Thermal conductivity, specific heat capacity, and thermal diffusivity are the three most important thermal properties regarding heat transfer analysis [15], and these are used in engineering design calculations involving the thermal processing of agricultural products. In agricultural materials, temperature and moisture content greatly influence the thermal properties due to the relatively high specific heat, thermal conductivity, and thermal diffusivity of water [20]. These three thermal properties can be measured by several methods. Although thermal properties are very important in composing, information of their values for various compost materials is lacking. In general, it is difficult to determine the thermal properties of moist materials because forced heating during the measurements causes internal liquid and/or gas convection. This often results in overestimating the thermal properties. The thermal probe method is the most attractive method because it uses relatively simple equipment to determine the thermal properties for moist materials [21]. Thermal conductivity (\(k\)) refers to the intrinsic ability of a material to transfer or conduct heat [22]. Therefore, the \(k\) of composing materials affects temperature through conduction and the rate of biodegradation during the composing process [15]. Studies have shown that \(k\) can assist in the monitoring of the moisture content of compost material in different layers with the fundamental requirements for understanding the composting process [23]. The \(k\) can assist in the monitoring of the moisture content of compost bedding material in different layers with the fundamental requirements in both animal welfare and pack management. In this context, the determination of \(k\) is extremely important for the calculation of heat and mass transfer between the bedding material and the animals. Accurate transfer models will enable: (1) the selection of the most suitable materials for the composting process, (2) with the dimensioning of ventilation systems that promote surface drying of the bedded pack, and (3) the ability to infer bed management requirements by predicting, for example, the amount of heat and moisture produced during the composting process with the depth of the pack. In the short term, these mathematical models could provide the research community with an approach to design systems with cleaner exhaust air and lower impacts on animals as well as those individuals that live or work in the surroundings of these agricultural facilities.