Terrestrial ecosystems depend heavily on their vegetation; it is possible to forecast future growth trends of regional vegetation by keeping an eye on changes in vegetation dynamics. To circumvent the potential reduction in prediction accuracy caused by the non-stationarity of meteorological changes, we analyzed the characteristics of NDVI (Normalized Difference Vegetation Index) spatial and temporal changes and the influencing factors over the past 20 years in the Maowusu Sandland of China via attribution analysis. We also constructed a comprehensive analysis system for vegetation pre-restoration. Moreover, we combined meteorological data from 2000 to 2018 and presented a deep-learning NDVI-Informer prediction model with a self-attentive mechanism. We also used distillation operation and fusion convolutional neural network for NDVI prediction. Incorporating a probsparse self-attention method successfully overcomes Transformer weaknesses by lowering the memory use and complexity of large time series. It significantly accelerates the inference speed of long time series prediction and works well with non-smooth data. The primary findings were: (1) the Maowusu Sandland’s 20-year average showed a consistent increasing trend in the NDVI at 0.0034 a⁻¹, which was mostly caused by climate change, with a relative contribution rate of 55.47%; (2) The Informer-based model accurately forecasted the NDVI in the research region based on meteorological elements and conducted a thorough analysis of the MAPE (mean absolute percentage error) (2.24%). This suggests that it can effectively lower the data’s volatility and increase prediction accuracy. The anticipated outcomes indicate that the trend will stabilize during the following ten years. To attain more sustainable and efficient agricultural production, the results of this study may be used to accurately estimate future crop yields and NDVI using previous data.

The agro-pastoral ecotone in northern China is the main production area of agriculture and animal husbandry, in which agricultural development relies entirely on groundwater. Due to the increasing water consumption of groundwater year by year, groundwater resources are becoming increasingly scarce. The substantial water demand and low germination rate in the first year are the main characteristics of alfalfa (Medicago sativa L.) yield in the agro-pastoral ecotone in northern China. Due to unscientific irrigation, water resources are seriously wasted, which restricts the development of local agriculture and animal husbandry. The study constructed the Dssat-Forages-Alfalfa model and used soil water content, leaf area index, and yield data collected with in situ observation experiments in 2022 and 2023 to calibrate and validate the parameters. The study found ARE < 10%, E_NRMS < 15%, and R² ≥ 0.85. The model simulation accuracy was acceptable. The study revealed that the water consumption at the surface soil layer (0–20 cm) was more than 6~12% and 13~31% than that at the 20–40 cm and 40–60 cm soil layers, respectively. The study showed when the irrigation quota was 30 mm, the annual yield of alfalfa (Medicago sativa L.) (7435 kg/ha) was consistent with that of the irrigation quota of 33 mm, and increased by 3.99% to 5.34% and 6.86% to 10.67% compared with that of irrigation quotas of 27 mm and 24 mm, respectively. To ensure the germination rate of alfalfa (Medicago sativa L.), it is recommended to control the initial soil water content at 0.8 θ_fc~1.0 θ_fc, with an irrigation quota of 30 mm, which was the best scheme for water-use efficiency and economic yield. The study aimed to provide technological support for the rational utilization of groundwater and the scientific improvement of alfalfa yield in the agro-pastoral ecotone in northern China.

Groundwater overexploitation in West Ordos necessitates sustainable irrigation practices. This study evaluated three irrigation levels—W1 (3300 m³ · ha⁻¹), W2 (2850 m³ · ha⁻¹), and W3 (2400 m³ · ha⁻¹)—by modifying the wide-width planting pattern of maize. Additionally, two levels of straw mulch were analyzed: F1 (9000 kg · ha⁻¹) and F2 (no mulch). The study aimed to investigate the effects of these treatments on corn growth dynamics, soil water temperature, soil enzyme activity, yield, grain quality, and water use efficiency. The results indicated a decline in growth indices, enzyme activities, grain quality, and yield under the limited irrigation levels W2 and W3 compared to W1. The highest corn yields were observed with W1F1 (6642.54 kg · ha⁻¹) and W2F1 (6602.38 kg · ha⁻¹), with the latter showing only a 0.6% decrease. Notably, water use efficiency in the W2F1 treatment improved by 4.69%, 12.08%, 10.27%, 12.59%, and 12.96% compared to W1F1, W3F1, W1F2, W2F2, and W3F2, respectively. Straw mulch (F1) significantly elevated the soil temperature, increasing the effective accumulated temperature during the growth period by 10.11~85.79 °C, and boosted the soil enzyme activity by 10–25%. Under limited irrigation, the W2 (2850 m³ · ha⁻¹) and F1 (9000 kg · ha⁻¹ straw) treatments achieved the highest water productivity of 2.48 kg·m⁻³, maintaining a high yield of 6602.38 kg · ha⁻¹ while preserving nutrients essential to the corn’s quality. This approach presents a viable strategy for wide-width corn planting in groundwater-depleted regions, offering a scientifically grounded and sustainable water management solution for efficient corn production in West Ordos.

Groundwater resources serve as the primary source of water in the agro-pastoral ecotone of northern China, where scarcity of water resources constrains the development of agriculture and animal husbandry. As a typical rainfed agricultural area, the agro-pastoral ecotone in Inner Mongolia is entirely dependent on groundwater for agricultural irrigation. Due to the substantial groundwater consumption of irrigated farmland, groundwater levels have been progressively declining. To obtain a sustainable irrigation pattern that significantly conserves water, this study faces the challenge of unclear water transport relationships among water, soil, and crops, undefined water cycle mechanism in typical irrigation units, and water use efficiency, which was not assessed. Therefore, this paper, based on in situ experimental observations and daily meteorological data in 2022–2023, utilized the DSSAT model to explore the growth processes of potato, oat, alfalfa, and sunflower, the soil water dynamics, the water balance, and water use efficiency, analyzed over a typical irrigation area. The results indicated that the simulation accuracy of the DSSAT model was ARE < 10%, nRMSE/% < 10%, and R² ≥ 0.85. The consumption of the soil moisture during the rapid growth stage for the potatoes, oats, alfalfa, and sunflower was 7–13% more than that during the other periods, and the yield was 67,170, 3345, 6529, and 4020 kg/ha, respectively. The soil evaporation of oat, potato, alfalfa, and sunflower accounted for 18–22%, 78–82%; 57–68%, and 32–43%, and transpiration accounted for 40–44%, 56–60%, 45–47%, and 53–55% of ETa (333.8 mm–369.2 mm, 375.2 mm–414.2 mm, 415.7 mm–453.7 mm, and 355.0 mm–385.6 mm), respectively. It was advised that irrigation water could be appropriately reduced to decrease ineffective water consumption. The water use efficiency and irrigation water use efficiency for potatoes was at the maximum amount, ranging from 16.22 to 16.62 kg/m³ and 8.61 to 10.81 kg/m³, respectively, followed by alfalfa, sunflowers, and oats. For the perspective of water productivity, it was recommended that potatoes could be extensively cultivated, alfalfa planted appropriately, and oats and sunflowers planted less. The findings of this study provided a theoretical basis for efficient water resource use in the agro-pastoral ecotone of Northern China.

(1) Background: Effective water management in agricultural systems poses a significant challenge, particularly in the Dengkouyangshui irrigation district. Inefficiencies and insufficient detail in water usage across crop growth stages have resulted in suboptimal water cycling. Recent infrastructure improvements and technological interventions necessitate a reevaluation of water usage, especially concerning changes in irrigation and seepage dynamics. (2) Methods: This study addresses these concerns by employing an integrated modeling approach that combines the DSSAT with the HYDRUS-1D soil hydrology model to simulate complex interactions among soil, crop growth, and irrigation practices within the district. Observational data were used to calibrate and validate the integrated model, including soil moisture, LAI, and crop yields from the 2022 and 2023 agricultural seasons. (3) Results: The simulation results strongly align with the empirical data, highlighting the ability of the model to capture the intricate dynamics of soil–water–atmosphere–plant interactions. (4) Conclusions: The soil’s retention and moisture-holding characteristics exhibited resilience during periods without water supplementation, with measurable declines in soil moisture at various depths, indicating the soil’s capacity to support crops in water-limited conditions. This study delineates water consumption by maize crops throughout their growth cycle, providing insights into evapotranspiration partitioning and quantifying seepage losses. An in-depth analysis of water balances at different growth stages informs irrigation strategies, suggesting optimal volumes to enhance efficiency during critical crop development phases. This integrative modeling approach is valuable for providing actionable data to optimize the water cycling process and improve agricultural sustainability in the Dengkouyangshui irrigation district.

Forage crop production globally faces challenges of low water and nitrogen use efficiency alongside increased environmental pressures, particularly in arid and semi-arid regions. The Hetao Irrigation District, a representative area of such regions, is characterized by limited rainfall, high evaporation, insufficient water, and severe soil salinization, which significantly hinder agricultural development. The findings from this study not only address local challenges but also provide insights applicable to other regions facing similar climatic and environmental constraints. This study aimed to optimize water and nitrogen management strategies for alfalfa production in the Hetao Irrigation District by calibrating and validating the DSSAT-FORAGES-Alfalfa model using field experimental data collected during 2022–2023. The calibration involved adjusting key parameters to minimize discrepancies between simulated and observed values, and validation was performed using an independent dataset, evaluated based on metrics such as MAE, RMSE, and R². The novelty of this research lies in its comprehensive calibration and validation process, which provides a robust framework for simulating alfalfa growth under diverse management scenarios. Various management scenarios were simulated, including six nitrogen application rates (0, 50, 100, 150, 200, and 250 kg ha⁻¹) and seven irrigation levels (45, 55, 65, 75, 85, 95, and 105 mm), to comprehensively evaluate their impacts on alfalfa yield and quality. These scenarios were designed to cover a wide range of practices, from deficit to excessive irrigation and fertilization, providing a robust assessment of optimal management strategies. Results indicated high predictive accuracy, with all performance metrics (e.g., MAE, RMSE, and R²) demonstrating the model’s reliability in simulating alfalfa growth under varying management conditions. Specifically, the normalized RMSE was below 10%, and the coefficient of determination (R²) exceeded 0.9, confirming the model’s robustness. Optimal management involving 260–340 mm of irrigation and 100–150 kg ha⁻¹ nitrogen application over the full growth cycle not only maximizes water use efficiency, but also achieves over 9% water saving and more than 25% reduction in nitrogen fertilizer application, compared to traditional local practices. These findings demonstrate the potential of optimized management strategies to significantly reduce ecological pressures while maintaining high crop productivity.