The objective of this study was to develop linear and nonlinear statistical models for predicting enteric methane emissions from beef and dairy cattle (EME, MJ/day). Ration nutrient composition (g/kg), nutrient (kg/day), energy (MJ/day), and energy and organic matter (OM) digestibility (g/kg) were used as predictors of CH₄ production. Three databases of beef cattle, dairy cattle, and their combinations were developed using 34 published experiments to model EME predictions. Linear and nonlinear regression models were developed using a mixed-model approach to predict CH₄ production (MJ/day) of individual animals based on feed composition. For the beef cattle database, Equation methane (MJ/d) = 1.6063 (±0.757) + 0.4256 (±0.0745) × DMI + 1.2213 (±0.1715) × NDFI + −0.475 (±0.446) × ADFI had the smallest RMSPE (21.99%), with 83.51% of this coming from random error and a regression bias was 16.49%. For the dairy cattle database, the RMSPE was minimized (15.99%) for methane (MJ/d) = 0.3989 (±1.1073) + 0.8685 (±0.1585) × DMI + 0.6675 (±0.4264) × NDFI, of which 85.11% was from random error and the regression deviation was 14.89%. When the beef and dairy cattle databases were combined, the RMSPE was minimized (24.4%) for methane(MJ/d) = −0.3496 (±0.723) + 0.5941 (±0.0851) × DMI + 1.388 (±0.2203) × NDFI + −0.027 (±0.4223) × ADFI, of which 85.62% was from the random error and the regression bias was 14.38%. Among the nonlinear equations for the three databases, the DMI-based exponential model outperformed the other nonlinear models, but the predictability and goodness of fit of the equations did not improve compared to the linear model. The existing equations overestimate CH₄ production with low accuracy and precision. Therefore, the equations developed in this study improve the preparation of methane inventories and thus improve the estimation of methane production in beef and dairy cattle.

Arid ecological transition zones are highly sensitive to climate change and human activities, but land use optimization strategies for them often lack policy-oriented quantitative analysis. This study uses the Hexi Corridor in China as a case study, integrating multi-level policy planning indicators with the PLUS model to construct four scenarios: natural changes, economic growth, ecological protection, and planning-constrained development. This approach enhances policy compatibility (Kappa = 0.86). The study analyzes land use changes from 2000 to 2020 and simulates changes for 2030, with a focus on their impact on ecosystem service value (ESV). Key findings include the following: (1) Between 2000 and 2020, unused land and grassland dominated the area, with construction land expanding by 164.73%. (2) The planning-constrained development scenario maximized ESV (CNY 220.46 billion, up 7.7% from 2020), while controlling construction land growth (+30.11%). (3) Hydrological and climate regulation are the primary contributors to ESV, with the expansion of water areas by 113,032.60 hectares under ecological protection showing the effectiveness of policy intervention. Innovations in this study include the proposal of a “policy–model” coupling framework, offering actionable guidance for ecological protection and economic development in arid regions.