## 1 Introduction  
Since the initiation of China's 14 th Five-Year Plan period, the livestock industry has emerged as a pivotal pillar of the agricultural economy, with sustainable development playing a critical role in advancing modern animal husbandry [1]. China possesses abundant wheat straw resources, with an annual output of approximately 700-800 million tons, accounting for 30% of the global total. Rice straw, corn straw, and wheat straw collectively constitute 80% of the national straw yield, with wheat straw alone contributingnearly one-fourth (1.9 billion tons) [2]. Wheat straw feedstock is nutritionally rich, containing substantial trace elements, and serves as a versatile, renewable bioresource, making it a vital raw material for ruminant feeding systems such as cattle and sheep [3]. Currently, China's livestock sector is undergoing transformative development, prioritizing intensification and scale efficiency while advancing mechanization, digitalization, and standardization [4]. Wheat straw forage, as an important roughage resource, involves complex granular mechanics behaviors during processes like forage utilization, silage production, and compaction forming, requiring precise simulation to support equipment design optimization. Existing calibration methods lack sufficient applicability for long-fiber materials like wheat straw. The core objective of this study is to establish a set of high-precision, high-efficiency parameter calibration methods for wheat straw forage. The discrete element method (DEM), which tracks the microscopic motion of individual particles to reveal the macroscopic mechanical behavior of granular systems, has been widely applied in agricultural engineering due to its time- and labor-efficient nature, cost-effectiveness, and visualization capabilities. Researchers globally have conducted discrete element modeling and parameter calibration for materials such as soils and grains; [PERSON] et al. calibrated the contact parameters between substrate particles based on the Hertz-Mindlin with the JKR cohesion contact model through integrated repose angle simulation tests, optimized the dominant parameters using the response surface methodology, and obtained the simulated repose angle at the optimal solution; the validity of the calibrated parameters was verified via rotary tillage tests [5]. [PERSON] et al. calibrated the contact parameters between soil particles on loess plateau slopes using the same method as [PERSON] et al., and also obtained the simulated repose angle at the optimal solution; the validity of the calibrated parameters was verified through slope rotary tillage tests on the loess plateau [6]. [PERSON] et al. established a discrete element model of _Capsicum annuum_ based on reverse engineering, calibrated and optimized its contact parameters using the response surface methodology, and determined the repose angle via the cylinder-lifting method; the validity of the calibrated parameters was demonstrated [7]. [PERSON] et al. took corncobs as the research object, calibrated their contact parameters using Plackett-Burman and Box-Behnken experiments, determined the optimal parameter combination through the response surface methodology, and verified the accuracy of the calibrated parameters via corncob bending simulation tests [8]. The results indicate that all aforementioned studies employed response surface methodology (RSM) to optimize significant parameters. RSM typically uses second-order polynomials (e.g., quadratic regression models) to fit response surfaces, which tends to trap in local optima and exhibits limited capability for high-dimensional nonlinearity. Consequently, it struggles to address complex nonlinear optimization problems in DEM parameter calibration. Moreover, parameter optimization for wheat straw forage presents specific challenges: the contact mechanics of long-fiber materials inherently involve strong nonlinearity, with notable rolling friction and bonding effects, making them far more complex than spherical particles.