Agroforestry is an ecological agricultural model that promotes the coordinated development of agriculture and animal husbandry. Exploring appropriate water and nitrogen management strategies for forage grasses in agroforestry systems is of great significance for improving productivity. This study aims to investigate the effects of different water and nitrogen management practices on the growth, nitrogen uptake, and utilization efficiency of intercropped alfalfa in a goji berry-alfalfa system. It is assumed that moderate water deficiency combined with appropriate nitrogen fertilizer can optimize the growth of alfalfa in the intercropping of wolfberry and alfalfa. This study was based on a 2-year (2021 and 2022) field trial, focusing on alfalfa in a goji berry||alfalfa system. Four irrigation levels [full irrigation (W0, 75–85% θ_fc), mild water deficit (W1, 65–75% θ_fc), moderate water deficit (W2, 55–65% θ_fc), and severe water deficit (W3, 45–55% θ_fc)] and four nitrogen application levels [no nitrogen (N0, 0 kg·hm⁻²), low nitrogen (N1, 150 kg·hm⁻²), medium nitrogen (N2, 300 kg·hm⁻²), and high nitrogen (N3, 450 kg·hm⁻²)] were set up to systematically analyze the effects of water and nitrogen regulation on biomass allocation, nitrogen translocation, hay yield, and nitrogen use efficiency of alfalfa. The results showed that (1) irrigation and nitrogen application levels significantly affected the stem-to-leaf and root-to-shoot ratios of alfalfa (p < 0.01). The smallest stem-to-leaf ratio (0.758) was observed under W1N2, while the smallest root-to-shoot ratio (0.595) was observed under W0N2. (2) Irrigation and nitrogen application levels significantly affected nitrogen accumulation and nitrogen translocation in alfalfa (p < 0.05). The maximum nitrogen accumulation was observed under W0N2, which was 43.39% higher than that under W0N0. The maximum nitrogen translocation was observed under W1N2, which was 15.1% and 33.4% higher on average than that under W0N0 and W3N0, respectively. (3) Irrigation and nitrogen application had highly significant effects on alfalfa hay yield (p < 0.01). The highest hay yield (8325 kg·hm⁻² and 12,872 kg·hm⁻²) was achieved under W0N2. The nitrogen productivity of alfalfa increased with increasing water deficit and initially increased, then decreased with increasing nitrogen application. The nitrogen use efficiency of alfalfa followed the order N2 > N1 > N3 and W1 > W0 > W2 > W3, with the highest value of 9.26 under W1N2. Based on the comprehensive evaluation of alfalfa in agroforestry systems under water and nitrogen regulation using the entropy weight-TOPSIS method, mild water deficit combined with medium nitrogen application (W1N2) can optimize the stem-to-leaf ratio, root-to-shoot ratio, and nitrogen use efficiency of alfalfa without significantly reducing yield and nitrogen production efficiency. This water-nitrogen combination is suitable for use in goji berry||alfalfa systems in the Yellow River irrigation area of Gansu Province and similar ecological zones.

Soil moisture plays a critical role in the global water cycle, the exchange of matter and energy within ecosystems, and the movement of water in plants. Accurate monitoring of soil moisture is essential for drought early warning systems, irrigation decision-making, and crop growth assessment. The use of drone-based multispectral remote sensing technology for estimating the soil moisture content offers advantages such as wide coverage, high accuracy, and efficiency. However, the soil background can often interfere with the accuracy of these estimations. In specific environments, such as areas with strong winds, removing soil background noise may not necessarily enhance the precision of estimates. This study utilizes unmanned aerial vehicle (UAV) multispectral imagery and employs a vegetation index threshold method to remove soil background noise. It systematically analyzes the response relationship between spectral reflectance, spectral indices, and the soil moisture content in the top 0–10 cm layer of alfalfa; constructs K-Nearest Neighbors (KNN), Random Forest Regression (RFR), ridge regression (RR), and XG-Boost inversion models; and comprehensively evaluates model performance. The results indicate the following: (1) The XG-Boost model validation set had the highest R² value (0.812) when spectral reflectance was used as the input variable, which was significantly better than the other models (R² = 0.465 to 0.770), and the RFR model validation set had the highest R² value when the spectral index was used as the input variable (0.632), which was significantly better than the other models (R² = 0.366 to 0.535). (2) After removing soil background noise, the accuracy of the soil moisture estimates for each model did not show significant changes; specifically, the R² value for the XG-Boost model decreased to 0.803 when using spectral reflectance as the input, and the R² value for the RFR model dropped to 0.628 when using spectral indices. (3) Before and after removing the soil background noise, the spectral reflectance can provide more accurate data support for the inversion of the soil moisture content than the spectral index, and the XG-Boost model is the most effective in the inversion of the soil moisture content when using the spectral reflectance as the input variable. The research findings provide both theoretical and technical support for the retrieval of the surface soil moisture content in alfalfa using drone-based multispectral remote sensing. Additionally, they offer evidence that validates large-scale soil moisture remote sensing monitoring.

Water resources are fundamental to economic and social development. Improving agricultural water-use efficiency is essential for alleviating water scarcity, ensuring food security, and fostering sustainable growth. This study examines the effects of irrigation levels (severe water deficit, W0: 45–55% θ_FC; moderate water deficit, W1: 55–65% θ_FC; mild water deficit, W2: 65–75% θ_FC; full irrigation, W3: 75–85% θ_FC) and nitrogen application rates (N0: 0 kg·hm⁻², N1: 150 kg·hm⁻², N2: 300 kg·hm⁻², N3: 450 kg·hm⁻²) on soil environment, crop yield, and water–nitrogen use efficiencies in Lycium barbarum under integrated water–fertilizer drip irrigation. The coordinated application of water and nitrogen significantly influenced yield and efficiencies (p < 0.05) by modifying rhizosphere conditions such as soil moisture, temperature, salinity, and enzyme activities. Soil temperature increased with nitrogen application (N1 > N2 > N0 > N3), with N1 raising soil temperature by 4.98–8.02% compared to N0, N2, and N3. Electrical conductivity was lowest under N0, showing a 7.53–18.74% reduction compared to N1, N2, and N3. Urease activity peaked under N3 (31.84–96.78% higher than other treatments), while alkaline phosphatase and catalase activities varied across treatments. The yield was highest under N2, at 6.79–41.31% higher than other nitrogen treatments. Water use efficiency (WUE), growth use efficiency (GUE), and nitrogen agronomic efficiency (NAE) peaked under N2, while nitrogen use efficiency (NUE) decreased with higher nitrogen rates. Among irrigation levels, W0 showed the highest soil temperature, while W3 exhibited the lowest conductivity in the 0–40 cm layer. W2 had the highest soil enzyme activities, yielding 4.41–42.86% more than other levels, with maximum efficiencies for WUE, GUE, NUE, and NAE. The combination of mild water deficit (65–75% θ_FC) and 300 kg·hm⁻² nitrogen application (W2N2) resulted in the highest yield (2701.78 kg·hm⁻²). This study provides key insights for implementing integrated drip irrigation in northwest China’s arid regions.

Rational nitrogen applications can not only improve nutrient use efficiency, but also reduce environmental pollution caused by nitrogen leaching. To explore reasonable nitrogen application strategies for synergistically enhancing alfalfa production and ecological benefits, this study calibrated and validated the APSIM–Lucerne model based on field experiments conducted from 2021 to 2023. The effects of nitrogen application levels of 0, 80, 120, 140, 160, 180, 200, and 240 kg/ha on alfalfa yield, soil NO₃⁻–N and NH₄⁺–N residues, and nitrogen use efficiency under dry, normal, and wet years were simulated. The results indicate: (1) The calibrated APSIM–Lucerne model effectively simulates alfalfa yield and soil nitrogen residuals (R² ranging from 0.67 to 0.91, NRMSE between 6.55% and 24.03%). (2) Increased nitrogen application significantly elevates soil nitrogen residue, yet alfalfa yield follows a pattern of initial increase followed by decline, with nitrogen fertilizer use efficiency continuously decreasing. Under identical nitrogen application rates, the wet year type proves more advantageous for achieving high yields, low nitrogen residue, and high nitrogen fertilizer use efficiency. (3) The nitrogen application thresholds for achieving increased alfalfa yields and high efficiency during dry years, normal years, and wet years are 107–140 kg/ha, 135–160 kg/ha, and 150–183 kg/ha, respectively.

Scientific nitrogen management is essential for maximizing crop growth potential while minimizing resource waste and environmental impacts. Alfalfa (Medicago sativa L.) is the most widely cultivated high-quality leguminous forage crop globally, and is capable of providing nitrogen through nitrogen fixation. However, there remains some disagreement regarding its nitrogen management strategies. This study conducted a three-year field experiment and calibrated the APSIM-Lucerne model. Based on the calibrated model, three typical precipitation year types (dry, normal, and wet years) were selected. Combining field experiments, eight nitrogen application scenarios (0, 80, 120, 140, 160, 180, 200, and 240 kg·ha⁻¹) were set up. With the objectives of increasing alfalfa yield, nitrogen partial productivity, and nitrogen agronomic efficiency, this study investigates the appropriate nitrogen application thresholds for alfalfa under different precipitation year types. The results showed the following: (1) Alfalfa yield increased first and then decreased with the increase in nitrogen application level. The annual yield of the N160 treatment was the highest (13.39 t·ha⁻¹), which was 5.15% to 32.39% higher than that of the other treatments. (2) The APSIM-Lucerne model could well reflect the growth process and yield of alfalfa under different precipitation year types. The R² and NRMSE between the simulated and observed values of the former were 0.85–0.91 and 5.33–7.44%, respectively. The R² and NRMSE between the simulated and measured values of the latter were 0.74–0.96 and 2.73–5.25%, respectively. (3) Under typical dry, normal, and wet years, the optimal nitrogen application rates for alfalfa yield increases were 120 kg·ha⁻¹, 140 kg·ha⁻¹, and 160 kg·ha⁻¹, respectively. This study can provide a basis for precise nitrogen management of alfalfa under different precipitation year types.

Water and nitrogen management influences farmland productivity and greenhouse gas emissions by regulating the soil micro-environment. However, the synergistic optimization strategy among yield improvement, economic benefit, and emission reduction in intercropping systems in arid regions remains unclear. Based on a two-year field experiment using an intercropping system of wolfberry and alfalfa, this study established four irrigation levels [full irrigation (W0), mild water deficit (W1), moderate water deficit (W2), and severe water deficit (W3)] and four nitrogen application levels [0 (N0), 150 (N1), 300 (N2), and 450 kg·ha⁻¹ (N3)]. The effects of water and nitrogen regulation on soil hydrothermal conditions, greenhouse gas emissions, crop yield, and economic benefits were systematically analyzed. The results showed that soil water content increased with higher nitrogen application rates but decreased with a more severe water deficit. In contrast, soil temperature exhibited the opposite trend, with the W3 treatment increasing by 2.23–2.41 °C compared to W0 during the full fruiting period. The emission fluxes of CO₂ and N₂O increased with higher nitrogen application rates but decreased with a more severe water deficit. CH₄ acted as a sink, with its uptake decreasing as nitrogen application increased and the water deficit intensified. CO₂ was the dominant contributor to the global warming potential of the intercropping system of wolfberry and alfalfa, accounting for 85.3–94.6% of the total. The emission fluxes of CO₂ and N₂O were significantly positively correlated with the soil water content, while the CH₄ emission flux was significantly positively correlated with the soil temperature. The W0N2 treatment achieved the highest system yield and net profit, whereas the W1N2 treatment exhibited the highest return on investment. A comprehensive evaluation using the entropy weight–TOPSIS model identified W1N2 as the optimal treatment. An integrated water–nitrogen decision model determined that the optimal water and nitrogen combination for achieving a high yield, a high efficiency, and low emissions was an irrigation amount of 4245–4413 m³·ha⁻¹ and a nitrogen application rate of 290–323 kg·ha⁻¹. The findings of this study can provide a scientific basis for the sustainable water and nitrogen management of characteristic cash crop intercropping systems in arid regions.