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.

The objective of this study was to evaluate the difference of crop and livestock products regarding energy balances, greenhouse gas (GHG) emissions, carbon economic efficiency, and water use efficiency using a life cycle assessment (LCA) methodology on farms in three sub-oases within the Shihezi Oasis of China. The three sub-oases were selected within the Gobi Desert, at Shizongchang (SZC), Xiayedi (XYD), and Mosuowan (MSW), to represent the various local oasis types: i. Oasis; ii. overlapping oasis-desert; and iii. Gobi oasis. The results indicated that crop production in XYD Oasis had higher energy balances (221.47 GJ/ha), and a net energy ratio (5.39), than in the other two oases (p < 0.01). The production of 1 kg CW of sheep in XYD Oasis resulted in significantly higher energy balances (18.31 MJ/kg CW), and an energy ratio (2.21), than in the other two oases (p < 0.01). The water use efficiency of crop production in the SZC Oasis was lower than that of the XYD and MSW oases (p < 0.05). Alfalfa production generated the lowest CO₂-eq emissions (8.09 Mg CO₂-eq/ha. year) and had the highest water use efficiency (45.82 MJ/m³). Alfalfa (1.18 ¥/kg CO₂-eq) and maize (1.14 ¥/kg CO₂-eq) had a higher carbon economic efficiency than other crops (p < 0.01). The main sources of GHG emissions for crop production were fertilizer and irrigation. The structural equation modelling (SEM) of agricultural systems in the Shihezi Oasis showed that the livestock category significantly influenced the economic income, energy, and carbon balances.

The rapid advancement of unmanned aerial vehicle (UAV) technology has enabled the coordinated operation of multi-UAV systems, offering significant applications in agriculture, logistics, environmental monitoring, and disaster relief. In agriculture, UAVs are widely utilized for tasks such as ecological restoration, crop monitoring, and fertilization, providing efficient and cost-effective solutions for improved productivity and sustainability. This study addresses the collaborative task allocation problem for multi-UAV systems, using ecological grassland restoration as a case study. A multi-objective, multi-constraint collaborative task allocation problem (MOMCCTAP) model was developed, incorporating constraints such as UAV collaboration, task completion priorities, and maximum range restrictions. The optimization objectives include minimizing the maximum task completion time for any UAV and minimizing the total time for all UAVs. To solve this model, a deep reinforcement learning-based seagull optimization algorithm (DRL-SOA) is proposed, which integrates deep reinforcement learning with the seagull optimization algorithm (SOA) for adaptive optimization. The algorithm improves both global and local search capabilities by optimizing key phases of seagull migration, attack, and post-attack refinement. Evaluation against five advanced swarm intelligence algorithms demonstrates that the DRL-SOA outperforms the alternatives in convergence speed and solution diversity, validating its efficacy for solving the MOMCCTAP.

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.

The aim of this study is to explore the spatiotemporal variation and the transfer of ecosystem service value (ESV) in Jiuquan City from 2005 to 2020 to help ecological development. Based on the equivalent factor method and the grid analysis, employed to analyze the spatial and temporal changes in ecosystem service values in Jiuquan City, the fracture point model and the field strength model were applied to calculate the transfer of ecosystem service value in seven districts and counties of Jiuquan City. The ESV of Jiuquan City showed an overall increasing trend, and all individual ESVs showed increasing trends, except for water supply, with the ESV of regulating services showing the most significant growth from 2005 to 2020. The top three secondary ESVs were hydrological regulation, climate regulation, and environmental purification, with regulatory services accounting for the largest proportion, followed by support services, supply services, and cultural services. From 2005 to 2020, the distribution of high and low ESV zones in Jiuquan City did not change significantly, with the high-value zones being mainly located in Suzhou District, south of Subei County and Yumen City, and the low-value zones being concentrated in Dunhuang City, Guazhou County, north of Suebei County, and Jinta County. The ESVs transfers from each district in the study area, in descending order, were Guazhou County, Subei County, Yumen City, Dunhuang City, Aksai County, Jinta County, and Suzhou District. Guazhou and Subei were the main ESV exporters. Areas with high ESV exports tended to have high ESV values. Hydrologic regulation is the service type with the largest transfer volume, accounting for 19.00% of the total ESV transfer in Jiuquan. Jiuquan possesses the highest ecosystem service value in the Gansu Province, China, indicating a superior ecological status.

To explore the spatio-temporal variation and transfer of ecosystem service value (ESV) in Jiuquan City from 2005 to 2020 to help ecological development. Based on the equivalent factor method and grid analysis to analyze the spatial and temporal changes in the value of ecosystem services in Jiuquan City, the fracture point model and field strength model were applied to calculate the transfer of ecological service value in seven districts and counties of Jiuquan City.From 2005 to 2020, the ESV of Jiuquan City showed an overall increasing trend, and all individual ESVs showed an increasing trend, with the ESV of regulating services showing the most significant growth. The top three secondary ESVs are: hydrological regulation, climate regulation, and environmental purification. The value of regulating services accounts for the largest share, followed by support services, supply services and cultural services. From 2005 to 2020, the distribution of high and low ESV zones in Jiuquan City does not change significantly, with the high value zones mainly located in Sujhou District, south of Suebei County and Yumen City, and the low value zones are concentrated in Dunhuang City, Guazhou County, north of Suebei County and Jinta County. The ESVs shifted outward from each district in the study area were, in descending order, Guazhou County, Subei County, Yumen City, Dunhuang City, Akse County, Jinta County, and Suzhou District. Guazhou and Subei counties were the main ESV exporters. Areas with high ESV exports tended to have high ESV values. Hydrologic regulation is the service type with the largest transfer volume, accounting for 19.00% of the total ESV transfer in Jiuquan City. The ecological condition of Jiuquan City is good.