## 5 Conclusions  
1. Systematically measured eight key physical parameters of wheat straw forage (moisture content 11.59%, bulk density 45.82 kg/m\({}^{3}\), friction coefficient, restitution coefficient, etc. ), establishing the first discrete element basic characteristic database for long-fiber materials, filling the gap in this field. 2. Established a discrete element simulation model based on the Hertz-Mindlin with JKR cohesion contact model. Using PB experiments and steepest ascent tests, three significant parameters affecting the angle of repose were screened. Central composite response surface design in RSM was employed for experimental analysis, further optimizing the significant parameters. Optimized results: dynamic friction coefficient between wheat straw forage particles = 0.19, static friction coefficient between wheat straw forage and steel plate = 0.47, JKR surface energy = 0.04 J/m\({}^{2}\). The simulated wheat straw angle of repose was 40.95\({}^{\circ}\), with a relative error of 3.2% compared to the actual physical angle of repose. 3. Intelligent calibration innovation: We proposed a PSO-BP fusion optimization framework, utilizing swarm intelligence algorithms to globally search neural network weights. This solved the problem of traditional RSM easily falling into local optima during high-dimensional nonlinear optimization. We obtained optimal parameters: dynamic friction coefficient between wheat straw forage particles = 0.17; static friction coefficient between  
Figure 16: Predicted and measured values of RSM and PSO-BP methods. Figure 17: Comparison of physical and simulation tests. wheat straw forage and steel plate = 0.46; and JKR surface energy = 0.03 J/m\({}^{2}\). The simulated angle of repose was 41.67\({}^{\circ}\), with a relative error of 1.5% compared to the actual angle of repose. 4. Accuracy and efficiency breakthrough: The PSO-BP model reduced the angle of repose prediction error to 1.5% (versus 3.2% for RSM), representing a 53.13% error reduction. Optimized parameters can be directly imported into EDEM software to guide feeding machinery design. Expected to reduce blockage failures by approximately 20%. 5. Although this study achieved breakthroughs in static parameter calibration for wheat straw (error reduced by 53.13%), limitations remain, such as weak environmental adaptability and insufficient real-time performance. Future work will extend the research to materials like corn straw and rice straw through dynamic compensation model development, algorithm hardware acceleration, and multi-crop validation. A cloud platform for straw material calibration will be established to enhance the framework's engineering universality and promote field application. Ultimately, a full-chain solution of \"parameter calibration-simulation optimization-equipment control\" will be built to support intelligent upgrading in animal husbandry. Conceptualization, Z.H. and H.L. ; methodology, B.F. and J.M. ; software, Z.H. and L.K. ; validation, Z.H. and H.L. ; formal analysis, Z.H. and X.T. ; investigation, Z.H. ; resources, Z.H. and J.M. ; data curation, Z.H. and X.T. ; writing--original draft preparation, Z.H. ; writing--review and editing, Z.H., L.K., S.A. and X.S. ; project administration, J.M. and B.F.; funding acquisition, B.F., H.L. and X.T. All authors have read and agreed to the published version of the manuscript. This research was supported by the following grants: 1. \"Supported by the earmarked fund for XJARS\" (Project Leader: [PERSON], Grant No. XJARS-10-12); 2. \"Key Talent Cultivation Program for Agriculture, Rural Areas and Farmers in Xinjiang Uygur Autonomous Region\" (Project Leader: [PERSON], Grant No. 2023 SNGGGC031); 3. \"Research on Equipment Technology for Straw-type Forage Utilization\" (Project Leader: [PERSON], Grant No. 2022B02042-3). Not applicable. All data are presented in this article in the form of figures and tables.