Slow-release nitrogen fertilizers enhance crop production and reduce environmental pollution, but their slow nitrogen release may cause insufficient nitrogen supply in the early stages of crop growth. Mixed nitrogen fertilization (MNF), combining slow-release nitrogen fertilizer with urea, is an effective way to increase yield and income and improve nitrogen fertilizer efficiency. This study used urea alone (Urea) and slow-release nitrogen fertilizer alone (C/SRF) as controls and employed meta-analysis and a random forest model to assess MNF effects on crop yield and nitrogen partial factor productivity (PFPN), and to identify key influencing factors. Results showed that compared with urea, MNF increased crop yield by 7.42% and PFPN by 8.20%, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 20 °C, and elevations of 750–1050 m; in soils with a pH of 5.5–6.5, where 150–240 kg·ha⁻¹ nitrogen with 25–35% content and an 80–100 day release period was applied, and the blending ratio was ≥0.3; and when planting rapeseed, maize, and cotton for 1–2 years. The top three influencing factors were crop type, nitrogen rate, and soil pH. Compared with C/SRF, MNF increased crop yield by 2.44% and had a non-significant increase in PFPN, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 5 °C, average annual precipitation ≤ 400 mm, and elevations of 300–900 m; in sandy soils with pH > 7.5, where 150–270 kg·ha⁻¹ nitrogen with 25–30% content and a 40–80 day release period was applied, and the blending ratio was 0.4–0.7; and when planting potatoes and rapeseed for 3 years. The top three influencing factors were nitrogen rate, crop type, and average annual precipitation. In conclusion, MNF should comprehensively consider crops, regions, soil, and management. This study provides a scientific basis for optimizing slow-release nitrogen fertilizers and promoting the large-scale application of MNF in farmland.

Mixed sowing of forage grass can reduce soil erosion, improving forage nutritional composition, enhancing grassland productivity, and increasing community stability. It addresses issues faced by sown pasture, including a lack of diversity in planting patterns, low resource utilization efficiency, and poor sustainability. However, the effects of mixed sowing on forage yield and water use efficiency (WUE) vary depending on regional environmental conditions, management practices, and temporal factors. Based on publicly available field experiment data, this study utilized meta-analysis to quantitatively examine the effects of mixed sowing on forage yield and WUE in China. Additionally, a random forest model was employed to analyze the main influencing factors. The results showed that, compared with monoculture, mixed sowing significantly improved forage yield and WUE, with average increases of 58.3% (confidence interval: 44.3–72.3%) and 32.0% (confidence interval: 19.2–44.8%), respectively. Regarding yield, the effect of mixed sowing was the most pronounced in Shaanxi. Optimal conditions included experiments conducted during 2006–2008, annual precipitation of 200–600 mm, soil pH of 4−5, average annual temperature of 10–15 °C, altitudes below 2000 m, alfalfa (Medicago sativa) and Bromus inermis as the forage combination, two species in the mixture, a legume-to-grass species ratio of 1:1, a total seeding rate of 40–50 kg·ha⁻¹, and mixed sowing in the same row. For WUE, significant effects were observed in Gansu under the following conditions: experiments conducted during 2018–2020, annual precipitation of 400–600 mm, an average annual temperature of 5–10 °C, a soil pH of 8–9, altitudes of 1000–2000 m, oats (Avena sativa) and peas (Pisum sativum) as the forage combination, two species in the mixture, a legume-to-grass species ratio of 1:1, a total seeding rate of <50 kg·ha⁻¹, and mixed sowing in alternate rows. The random forest model indicated that the effects of mixed sowing on forage yield were primarily influenced by annual precipitation, average annual temperature, and experimental region. In contrast, the effects on WUE were mainly determined by forage combination, species type, and the legume-to-grass species ratio. This study provides a reference for enhancing alfalfa productivity and achieving efficient water use.

Agricultural production in Northwest China is widely constrained by residual plastic film pollution, excessive greenhouse gas emissions, and low productivity. Integrating biodegradable film with controlled-release nitrogen fertilizer offers a promising approach to optimize crop management, enhance yield, and improve environmental outcomes. In this study, three planting patterns (conventional flat planting, FP; ridge mulching with biodegradable film, BM; and ridge mulching with conventional plastic film, PM), two nitrogen fertilizer types (urea, U, and controlled-release nitrogen fertilizer, C), and four nitrogen application rates (0, 80, 160, and 240 kg·hm⁻²) were applied to systematically investigate their effects on alfalfa yield and N₂O emissions from grasslands. The results showed that BM and PM increased alfalfa yield by 23.49% and 18.65%, respectively, compared to FP, while C increased yield by 8.46% compared to urea. The highest yield (24.84 t·hm⁻²) was recorded under the BMC2 treatment, which was 97.11% higher than that of FPN0. N₂O emission flux and cumulative emissions increased with nitrogen application rate. Compared with U, C reduced cumulative N₂O emissions and greenhouse gas emission intensity (GHGI) by 23.89% and 25.84%, respectively. Compared to PM, BM reduced cumulative N₂O emissions and GHGI by 11.58% and 20.15%, respectively. Principal component analysis indicated that the combination of ridge mulching with biodegradable film and 160 kg·hm⁻² of C was optimal for simultaneously increasing alfalfa yield and reducing N₂O emissions, making it a suitable planting–fertilization strategy for the Yellow River irrigation district in Gansu and similar ecological regions.

The critical nitrogen dilution curve (CNDC) model enables precise nitrogen management by quantifying the threshold of nitrogen deficiency in crops, thereby enhancing both crop productivity and nitrogen use efficiency. However, its applicability to perennial crops remains unclear. In this study, alfalfa (Medicago sativa L.), a perennial leguminous forage, was used as the model crop. Based on two years of field experiments, CNDC models of aboveground biomass were constructed under two nitrogen fertilizer regimes: urea (0, 80, 160, and 240 kg·ha⁻¹, applied in a 6:2:2 basal-to-topdressing ratio) and controlled-release urea (CRU; 0, 80, 160, and 240 kg·ha⁻¹, applied as a single basal dose). Using these models, the nitrogen nutrition index (NNI) and cumulative nitrogen deficit (N_and) models were developed to diagnose alfalfa nitrogen status, and the optimal nitrogen application rates were determined via regression analysis. The results showed that critical nitrogen concentration and aboveground biomass followed a power function relationship under both fertilizer types. For CRU treatments, parameters a and b were 3.41 and 0.20 (first cut), 3.15 and 0.12 (second cut), and 2.24 and 0.40 (third cut), respectively. For urea treatments, a and b were 3.13 and 0.35 (first cut), 2.21 and 0.16 (second cut), and 1.75 and 0.73 (third cut). The normalized root mean square error (n-RMSE) of the models ranged from 3.1% to 13%, indicating high model reliability. Based on the NNI, N_and, and yield response models, the optimal nitrogen application rates were 175.44~181.71 kg·ha⁻¹ for urea and 145.63~153.46 kg·ha⁻¹ for CRU, corresponding to theoretical maximum yields of 14.76~17.40 t·ha⁻¹ and 16.76~20.66 t·ha⁻¹, respectively. Compared to urea, CRU reduced nitrogen input by 18.41~20.47% while achieving equivalent or higher theoretical yields. This study provides a scientific basis for nitrogen status diagnosis and precision nitrogen application in alfalfa cultivation.

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.