Appropriate planting and nitrogen application patterns to support high-quality production of cultivated forage in light of issues of water scarcity, extensive field husbandry, and low productivity in cultivated grassland planting areas were investigated in this study. Using Medicago sativa L. (alfalfa) as the research object, this study analyzed the effects of planting patterns (conventional flat planting (FP) and ridge culture with film mulching (RM)) and nitrogen level (N0: 0 kg·ha⁻¹, N1: 80 kg·ha⁻¹, N2: 160 kg·ha⁻¹, N3: 240 kg·ha⁻¹) on the growth, yield, quality (crude protein content (CP), acid detergent fiber content (ADF), neutral detergent fiber content (NDF), and relative feeding value (RFV)), the water–nitrogen use efficiency, and economic benefits (EB) of alfalfa in the year of establishment. Results demonstrated that (1) RM might greatly increase the growth of alfalfa when compared to FP. The plant height, stem diameter, and leaf:stem ratio of alfalfa all increased under the same planting patterns before decreasing as the nitrogen application rate (NAR) increased. (2) Appropriate NAR combined with RM could improve the yield and quality of alfalfa. Compared with other treatments, the yield, CP, and RFV under RMN2 treatment increased by 5.9~84.9%, 4.9~28.6%, and 19.6~49.3%, respectively, and the ADF and NDF decreased by 14.0~27.6% and 13.0~26.1%, respectively. (3) Under the same nitrogen level, RM showed better performance than FP in terms of water use efficiency (WUE), irrigation water use efficiency (IWUE), precipitation use efficiency (PUE), partial factor productivity of nitrogen (PFPN), agronomic nitrogen use efficiency (ANUE), and EB of alfalfa. Under the same planting pattern, PFPN decreased as the NAR increased, while WUE, IWUE, PUE, ANUE, and EB first increased and then decreased as the NAR increased and reached a maximum value under the N2 condition. In conclusion, the RM planting pattern combined with a nitrogen level of 160 kg·ha⁻¹ can significantly promote alfalfa growth as well as the yield, quality, water–nitrogen use efficiency, and EB of alfalfa, making it a suitable planting management mode for alfalfa production in the Yellow River irrigation region in Gansu Province, China and areas with similar climate.

Measuring the carrying capacity of water resources and identifying obstacle factors are critical prerequisites for the rational allocation of regional water resources and the high-quality development of economic society. This study took Longnan City, a typical city in northwest China with abundant water resources but an underdeveloped economy, as the research object. Based on the DPSR (Driving–Pressure–State–Response), an evaluation indicator system was constructed. TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and an obstacle model were used to calculate the water resource carrying capacity and main obstacle factors of Longnan City from 2009 to 2019. The results showed that the carrying capacity of water resources in Longnan City had steadily improved, and the comprehensive closeness had increased from 0.44 (2009) to 0.60 (2019). From the perspective of the ruler layer, the carrying capacity of the driving force system increased from 0.05 in 2009 to 1.00 in 2019. The capacity of the state system increased during the change, with the highest value (0.85) appearing in 2013 and the lowest value appearing in 2016. All subsystems, except the pressure system, had a load-carrying capacity of 0.60 or more in 2019. The utilization of unconventional water resources, the proportion of eco-environmental water use, the volume of wastewater discharge, and the per capita urban daily water consumption are the primary factors affecting the water resource carrying capacity of Longnan City. Developing unconventional water sources, improving sewage treatment technology, promoting efficient water-saving technology, and strengthening environmental protection is the key to improving the water resources carrying capacity of Longnan City. This research provides the basis for enhancing the carrying capacity of water resources and sustainable urban development in Longnan City and other cities in China and water scarcity cities in other countries.

Scientific selection of appropriate herbage planting management mode is an important guarantee to promote artificial grassland development and grassland productivity. In this study, three-year-old alfalfa (Medicago sativa L.) and bromus inermis were applied to analyze the effects of planting patterns (bromus inermis and alfalfa mixed-sowing D1, bromus inermis mono-sowing D2), nitrogen application (pure nitrogen) level (N1: 60 kg·ha⁻¹, N2: 120 kg·ha⁻¹), and water regulation (upper and lower limits of irrigation are calculated as a percentage of field capacity θ_f, W1: slight water deficit 65~85% θ_f, W2: moderate water deficit 55~85% θ_f, W3: serious water deficit 45~85% θ_f) on herbage growth and water-nitrogen use efficiency. This research applied the principal component analysis, the TOPSIS model, and the combination evaluation to evaluate each treatment. Results demonstrated that (1) the plant height, leaf area index, and yield of mixed-sowing herbage were 81.63%, 119.52%, and 111.51%, higher than the mono-sowing herbage. Increasing the amount of irrigation and nitrogen application could enhance herbage yield. The herbage yield with the W1N2 treatment was the highest. In this treatment, the mixed-sowing herbage yield was 26,050.73 kg·ha⁻¹, and the mono-sowing herbage yield was 12,186.10 kg·ha⁻¹. (2) The crude protein content of mixed-sowing herbage increased by 41.44%, higher than mono-sowing herbage, and the relative feeding value decreased by 16.34%. Increasing irrigation and nitrogen application could improve the quality of herbage. Meanwhile, the quality of herbage treated with W1N2 was the best. (3) The water use efficiency (WUE), irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFP_N), and crude protein water use efficiency (CP_WUE) of mixed-sowing herbage were significantly higher than mono-sowing herbage. The PFP_N and the CP_WUE of herbage improved with increasing irrigation amount. Meanwhile, the WUE, the IWUE, and the CP_WUE of herbage also improved with increasing nitrogen application amount. The results showed that mixed-sowing of alfalfa and bromus inermis with slight water deficit (upper and lower limit of irrigation was 65~85% θ_f) and nitrogen application (120 kg·ha⁻¹) could have the best comprehensive production effect. At the same time, it was a planting and management mode of high yield, high quality, and high efficiency of artificial herbage in the oasis-desert interlacing area of Hexi, Gansu Province, China, and areas with similar climates.

Water scarcity and poor soil fertility are major limiting factors constraining agricultural production in the arid and semi-arid regions of Northwest China. Water–nitrogen synergistic regulation is an important approach to improving crop growth and enhancing agricultural productivity. In this study, four irrigation levels—severe water deficit (W1: 45–65% θ_f), moderate water deficit (W2: 55–70% θ_f), mild water deficit (W3: 65–80% θ_f), and full irrigation (W4: 75–90% θ_f)—and four nitrogen application rates—no nitrogen (N0, 0 kg·ha⁻¹), low nitrogen (N1, 80 kg·ha⁻¹), medium nitrogen (N2, 160 kg·ha⁻¹), and high nitrogen (N3, 240 kg·ha⁻¹)—were established to systematically analyze the effects of water–nitrogen coupling on osmotic adjustment substances, yield, and forage quality of alfalfa (Medicago sativa L.) leaves. The results showed that: (1) Proline (Pro) content increased significantly with intensified water deficit, with W1 being 82.29% higher than W4 on average. Soluble protein (SP) and soluble sugar (SS) contents increased with increasing water availability, with their average values under W4 being 26.50% and 36.92% higher than those under W1, respectively. Increasing nitrogen application significantly improved the accumulation of osmotic adjustment substances, with Pro reaching the lowest value at N2, SP peaking at N2, and SS peaking at N3. (2) Yield increased significantly with higher irrigation, and increased first and then decreased with nitrogen application. Yield under W4 was 94.20% higher than under W1, and N2 increased yield by 12.45–50.65% compared with other nitrogen levels. (3) Under the W4N2 treatment, crude protein (CP) content and relative feed value (RFV) increased by 34.54% and 51.10%, respectively, compared with W1N0, while acid detergent fiber (ADF) and neutral detergent fiber (NDF) decreased by 28.74% and 24.44%, respectively. (4) Correlation analysis indicated that Pro content was significantly positively correlated with ADF and NDF but negatively correlated with yield, CP, and RFV. In contrast, SP and SS contents were significantly negatively correlated with ADF and NDF and positively correlated with yield, CP, and RFV. (5) Principal component analysis identified that the combination of full irrigation (W4: 75–90% θ_f) and medium nitrogen application (N2, 160 kg·ha⁻¹) optimizes both yield and forage quality by balancing osmotic adjustment substances.

Plant nitrogen content (PNC) is a core physiological parameter characterizing crop nitrogen nutrition status. Its precise and dynamic monitoring is crucial for crop growth diagnosis, optimizing nitrogen fertilizer management, enhancing fertilizer use efficiency, and reducing agricultural nonpoint source pollution. This study utilized multispectral imagery from unmanned aerial vehicles (UAVs) to extract vegetation indices (VIs) and texture feature values (TFVs) during critical growth stages of alfalfa. By combining TFVs to construct texture indices (TIs), variables exhibiting extremely significant correlations with alfalfa PNC (p < 0.001) were identified. We used VIs, TIs, and their combined features as model inputs. The performance of four machine learning models—random forest regression (RFR), Support Vector Regression (SVR), Backpropagation Neural Network (BPNN), and gradient boosting (XG-Boost)—was comprehensively assessed for estimating alfalfa PNC. Our results indicate the following: (1) The correlation coefficients |r| between VIs and alfalfa PNC ranged from 0.56 to 0.68; TIs constructed from TFVs significantly enhanced PNC correlation compared to raw texture values, with |r| exceeding 0.6. (2) Integrating VIs and TIs substantially improved the accuracy of PNC estimation models across growth stages. Compared to using VIs or TIs alone, the validation set R² increased by 5.4–19.7%, 1.7–16.4%, and 5.2–17.2% for the branching, budding, and initial flowering stages, respectively. (3) The XG-Boost model demonstrated optimal performance across all growth stages and input variables. Particularly during the budding stage, the VIs + TIs model achieved the highest fitting accuracy: training set R² = 0.81, RMSE = 0.15%; validation set R² = 0.80, RMSE = 0.12%. In summary, integrating multispectral vegetation indices and texture indices effectively enhances the accuracy of PNC estimation in alfalfa, providing scientific support for precision field management and fertilization decisions in alfalfa cultivation.

Timely access to soil moisture conditions in farmland crops is the foundation and key to achieving precise irrigation. Due to their high spatiotemporal resolution, unmanned aerial vehicle (UAV) remote sensing has become an important method for monitoring soil moisture. This study addresses soil moisture retrieval in alfalfa fields across different growth stages. Based on UAV multispectral images, a multi-source feature set was constructed by integrating spectral and texture features. The performance of three machine learning models—random forest regression (RFR), K-nearest neighbors regression (KNN), and XG-Boost—as well as two ensemble learning models, Voting and Stacking, was systematically compared. The results indicate the following: (1) The integrated learning models generally outperform individual machine learning models, with the Voting model performing best across all growth stages, achieving a maximum R² of 0.874 and an RMSE of 0.005; among the machine learning models, the optimal model varies with growth stage, with XG-Boost being the best during the branching and early flowering stages (maximum R² of 0.836), while RFR performs better during the budding stage (R² of 0.790). (2) The fusion of multi-source features significantly improved inversion accuracy. Taking the Voting model as an example, the accuracy of the fused features (R² = 0.874) increased by 0.065 compared to using single-texture features (R² = 0.809), and the RMSE decreased from 0.012 to 0.005. (3) In terms of inversion depth, the optimal inversion depth for the branching stage and budding stage is 40–60 cm, while the optimal depth for the early flowering stage is 20–40 cm. In summary, the method that integrates multi-source feature fusion and ensemble learning significantly improves the accuracy and stability of alfalfa soil moisture inversion, providing an effective technical approach for precise water management of artificial grasslands in arid regions.