## 4 Results
### Analysis of the Selected Studies  
Based on the analysis of the 11 contributions on the recent applications of DTs in livestock animal production systems, the majority of them (eight contributions) focused mainly, but not exclusively, on animal welfare and management, while the more particular animal species of interest appeared to be dairy cattle and swine. Some of the contributions also included the application domains of precision livestock farming (PLF), production management, and intelligent livestock monitoring, as well as real-time environmental and energy control--these are strongly related to the animal welfare and management objective. In this respect, [PERSON] et al. [60] proposed a novel architecture for DTs of cows that covers their entire lifecycle, aiming to manage dairy cattle production and improve animal welfare. For this, a solution was implemented so as to construct the cows' digital shadow by utilizing an innovative custom-made intelligent collar incorporating ultra-wideband (UWB) chips and inertial measurement units (IMUs) to collect real-time location and neck movement data from cows categorized in the calf stage, the heifer stage, and the adult stage. According to this solution, the architecture is divided based on the reliability of the data into three levels: barn integration, the pasture agent, and the cloud, while implementation consists of five layers: the integration layer, the data management and information layer, the model and simulation layer, the partial decision-making layer, and the visualization layer. The testing of the DTs solution indicated the robustness and technical feasibility of its architecture, holding considerable reference value for the development of the DTs in the livestock production industry. Additionally, [PERSON] et al. [61] developed a smart DT to model the state of cattle by incorporating a long short-term memory (LSTM) neural network so as to detect and predict a range of the cattle's behavioral state, such as impending physiological cycles. According to this solution, a DT model of cattle based on deep learning (DL) is generated, utilizing the sensor data acquired from an IoT farm system so as to monitor the physiological cycle of cattle in real time and foresee the state of the next physiological cycle of cattle. Based on the evaluation results, although the accuracy of the DTs model is high in terms of behavioral state for a considerable amount of sample data, it appears to be unsatisfactory when the amount of sampled data is inadequate. A feasibility study regarding the preliminary design of a smart pig farm incorporating the DTs concept for improving animal welfare was presented by [PERSON] et al. in [62]. In this sense, the framework of smart livestock farms consisting of two layers, namely the digital farm engine and the digital farm framework, is overviewed, while a service scenario of autonomous control in livestock farms, which can be easily deployed, is briefly described so as to showcase the way in which physical livestock farm works with digital livestock farms. As mentioned previously, the design of the smart pig farm was preliminary and was not evaluated by obtaining real results. As an extension of this work, in [63], [PERSON] et al. proposed energy planning for a pigsty so as to provide comfortable feeding conditions. This solution incorporated the DTs concept for establishing an interaction between a physical and a virtual pigsty. Energy consumption comparisons were performed by combining a number of fans with different capacities in the pigsty, resulting in findings that indicated the expected energy usage and the installation guidelines for new fans. [PERSON]. [64], as well as [PERSON] and [PERSON] [52], introduced the reference architecture of a DT system for a smart animal welfare platform so as to predict the behavior of farm animals.