In arid and semi-arid regions, the cultivation of artificial grasslands commonly suffers from low productivity due to insufficient water supply. The rational application of water-retaining agents is an important approach to alleviating production constraints in artificial grasslands facing resource-based water scarcity. This study investigated two types of water-retaining agents [starch-grafted acrylate water-retaining agent (B1) and polyacrylamide water-retaining agent (B2)] and four application rates [0 kg·hm⁻² (CK), 30 kg·hm⁻² (T1), 60 kg·hm⁻² (T2), 90 kg·hm⁻² (T3)], systematically analyzing their effects on the growth, osmotic adjustment substances, antioxidant enzyme activities, and yield of alfalfa. The results showed that alfalfa plant height, stem diameter, leaf area, branch number, soluble sugar (SS), soluble protein (SP), and proline (Pro) all exhibited a decreasing trend with increasing cutting times. The activities of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) in alfalfa leaves initially increased and then decreased with increasing application rates of water-retaining agents, while malondialdehyde (MDA) content showed a decreasing trend. Under the B2T2 treatment, both alfalfa yield and water-use efficiency (WUE) reached their highest values, recorded as 4931.97 kg·hm⁻² (2022), 6021.44 kg·hm⁻² (2023) and 2.19 kg·m⁻³ (2022), 2.39 kg·m⁻³ (2023), respectively. Based on the principal component analysis for comprehensive evaluation, the B2T2 treatment (polyacrylamide water-retaining agent applied at 60 kg·hm⁻²) achieved the highest comprehensive score in both years and could synergistically improve alfalfa yield and water-use efficiency. However, its applicability in the Yellow River irrigation region of Gansu Province and similar ecological areas still requires further verification through field trials.

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.

Background: Amidst the pressing need to balance global food security and climate governance, achieving synergistic optimisation between crop yield enhancement and agricultural greenhouse gas reduction has become the central imperative for advancing the transition to green agriculture. Purpose: To investigate the effects of cropping systems and nitrogen fertiliser application on goji berry production systems in arid regions. Method: This study employed two cropping systems (goji berry–alfalfa intercropping (I), goji berry monocropping (M)), and four nitrogen application rates (N0 (0 kg ha⁻¹), N1 (150 kg ha⁻¹), N2 (300 kg ha⁻¹), N3 (450 kg ha⁻¹)). The effects of planting patterns and nitrogen fertiliser regulation on the physicochemical properties of goji berry farmland soil, greenhouse gas emissions, and yield were analysed. Result: (1) Soil temperatures under I were significantly lower than under M, and nitrogen application levels, cropping systems, and the interaction between nitrogen application and cropping systems significantly influenced soil nutrients; (2) Cultivation patterns and nitrogen application levels exerted a highly significant influence on soil greenhouse gas emission fluxes in goji berry fields. CO₂ emission flux peaked under IN3 treatment (annual average: 342.45 mg m⁻² h⁻¹), while N₂O emissions peaked under MN3 (annual average 0.23 mg m⁻² h⁻¹). CH₄ absorption was highest under MN0 (annual average −0.25 mg m⁻² h⁻¹); (3) Cropping systems and nitrogen application rates significantly influence greenhouse gas indicators including cumulative CO₂ emissions, cumulative N₂O emissions, and GWP. At the same nitrogen application level, GWP decreased by 5.63% on average in M compared to I, while under the same cropping system, N3 increased by 62.45% on average in N3 compared to N0; (4) Cropping systems and nitrogen application levels significantly influenced goji berry yield and economic returns. Under the same cropping system, N2 yielded the highest goji berry production and return on investment, with I and M yielding 2768.99 kg ha⁻¹ and 4.06 and 3067.78 kg ha⁻¹ and 3.15, respectively. Conclusions: The IN2 reduced soil greenhouse gas emission fluxes, cumulative emissions, and global warming potential while simultaneously increasing goji berry yield, net revenue, and return on investment. This approach minimises land resource wastage and represents a management model for achieving high yields with reduced emissions in goji berry fields within the Yellow River diversion irrigation districts of Gansu Province and similar ecological zones.

Rapid and accurate monitoring of soil total nitrogen (STN) is fundamental to optimising nitrogen fertiliser management, improving nitrogen use efficiency, and reducing agricultural non-point source pollution; however, traditional methods struggle to provide timely, large-scale, and continuous monitoring. Based on the relationship between maize canopy spectral characteristics and soil nitrogen distribution, this study acquired multi-spectral drone imagery during the jointing, tasselling, and grain filling stages, whilst simultaneously collecting soil samples from the 0–20, 20–40, and 40–60 cm soil layers. Using canopy reflectance (Band), vegetation indices (VIs), textural features (TF), and a fusion of these three features (Band+VIs+TF) as input variables, machine learning models, including Random Forest Regression (RFR), Gradient Boosted Decision Trees (GBDT), K-Nearest Neighbours (KNN), and XGBoost, were constructed to estimate STN. The results indicate that (1) STN is significantly correlated with spectral reflectance, vegetation indices, and textural features, with absolute correlation coefficients ranging from 0.60 to 0.89; (2) the accuracy of STN estimation by the four models showed a decreasing trend with increasing soil depth, with the highest accuracy observed in the 0–20 cm soil layer; (3) the validation accuracy of STN estimation followed the order: grain filling stage (R² = 0.833) > tasselling stage (R² = 0.794) > jointing stage (R² = 0.761); (4) under feature fusion conditions, the STN estimation accuracy of all four models was superior to that of using a single feature, and the prediction accuracy of the XGBoost model (0.833) was higher than that of the other three models (0.701–0.734). In summary, during the grain filling stage of maize, using feature fusion as input variables and the XGBoost model can significantly improve the accuracy of STN estimation in maize fields, with the 0–20 cm soil layer yielding the best results.

Water and nitrogen application rates directly affect crop yield formation and protein accumulation. Nitrogen metabolism, as a key physiological process linking water and nitrogen supply with crop growth, plays a critical role in regulating nitrogen uptake, transformation, and accumulation. However, the functional relationships among different nitrogen metabolism indicators in mediating the formation of high-quality and high-yield alfalfa under water–nitrogen regulation remain unclear. In this study, alfalfa (Medicago sativa L.) was subjected to four nitrogen application levels [N0 (0 kg·hm⁻²), N1 (80 kg·hm⁻²), N2 (160 kg·hm⁻²), N3 (240 kg·hm⁻²)] and four irrigation gradients [severe deficit (W0, 45–60% θ_f), moderate deficit (W1, 55–70% θ_f), mild deficit (W2, 65–80% θ_f), and full irrigation (W3, 75–90% θ_f), where θ_f represents field capacity]. The relationships between water–nitrogen regulation and alfalfa nitrogen metabolism and productive performance were analyzed. The results showed that (1) both irrigation amount and nitrogen application rate significantly affected the activities of leaf nitrate reductase (NR), glutamine synthetase (GS), glutamate synthase (GOGAT), and soluble protein (SP) content (p < 0.05). Aboveground nitrogen content (TN) initially increased and then decreased with increasing irrigation and nitrogen application, reaching its maximum under W2N2, with leaves being the primary site of aboveground nitrogen accumulation. (2) Under W2N2, alfalfa yield and crude protein accumulation (CP-a) both reached their maximum values, averaging 10.22 t·ha⁻¹ and 1187.10 kg·ha⁻¹, respectively. (3) Structural equation modeling (SEM) indicated that water–nitrogen regulation primarily influenced yield and CP-a formation through its effects on GS activity and TN. Based on these findings, a comprehensive evaluation model (GS–TN–Yield–CP-a) was constructed, and the preliminary suitable water–nitrogen regulation ranges for high-quality and high-yield alfalfa were identified as an irrigation amount of 69.4–85.9% θ_f and a nitrogen application rate of 102.6–222.3 kg·hm⁻². These results can provide a theoretical basis for high-quality and high-yield alfalfa management in arid and semi-arid regions, but further verification through field experiments is still required.