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.

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% θf), moderate water deficit (W2: 55–70% θf), mild water deficit (W3: 65–80% θf), and full irrigation (W4: 75–90% θf)—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% θf) and medium nitrogen application (N2, 160 kg·ha−1) optimizes both yield and forage quality by balancing osmotic adjustment substances.

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.

Soil salinization, exacerbated by water scarcity in arid and semi-arid regions, represents a major constraint on the sustainable intensification of commercial wolfberry production. Appropriate irrigation management offers an approach to reducing water demand, mitigating salt stress, and improving wolfberry production performance. To investigate this, a field experiment (2021–2023) was conducted to evaluate the effects of four irrigation levels—full irrigation (W0: 75–85% θf), mild deficit (W1: 65–75% θf), moderate deficit (W2: 55–65% θf), and severe deficit (W3: 45–55% θf)—on wolfberry growth, physiology, yield, quality, and water productivity. The results demonstrated that wolfberry plant height, stem diameter, leaf SPAD values, net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Cond) were significantly decreased as water deficit intensified. All growth indicators peaked during the autumn fruiting stage, while photosynthetic parameters reached their maximum during the fruiting stage. Water regulation significantly affected wolfberry quality (P < 0.01), with reduced irrigation leading to quality decline. Wolfberry yield, dry weight of fruits per plant, 100-fruit dry weight, dry-to-fresh ratio, and crop water productivity (WPc) all peaked under the W1 treatment; irrigation and wolfberry evapotranspiration (ETc act) were highest under the W0 treatment, increasing by 7.70%–91.36% and 5.22%–64.95% compared to other treatments. The ETc act showed a quadratic relationship with irrigation, as well as with wolfberry yield and quality. Application of partial least squares structural equation modeling (PLS-SEM) elucidated a direct and significant pathway from water regulation to both wolfberry growth and water productivity. Comprehensive evaluation using Entropy Weight-TOPSIS, RAR, and PCA methods revealed that the W1 treatment (65%–75% θf) synergistically enhanced wolfberry yield and quality while ensuring efficient water utilization. Therefore, mild deficit irrigation (W1) is proposed as a water-saving, yield-stabilizing, and quality-enhancing strategy for wolfberry production in arid and semi-arid regions.

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.

AbstractGoji berry planting in arid saline-alkali areas faces the challenges of soil micro-ecological imbalance and excessive application of nitrogen fertilizer. ObjectivesTo clarify the interactive effects of different planting patterns and nitrogen application levels on the structure, diversity and function of soil microbial communities in the root zone of goji berry in arid saline-alkali areas, which is of great significance for optimizing the planting system of goji berry in this area and reducing nitrogen application rates and improving nitrogen use efficiency. MethodsBased on field experiments, this study set up two planting patterns: goji berry monoculture and goji–alfalfa intercropping, four nitrogen application gradients: 0 kg·hm-² (N0), 150 kg·hm-² (N1), 300 kg·hm-² (N2), and 450 kg·hm-² (N3). The culturable microbial counts, community structure, diversity and functional genes of bacteria and fungi were analyzed by dilution coating plate method, high-throughput sequencing and functional prediction. Results: Compared with monoculture, the abundances of culturable soil bacteria and actinomycetes were significantly increased under the goji–alfalfa intercropping pattern, while the abundance of culturable fungi and the relative abundance of potential pathogens were inhibited, and the bacterial community structure was optimized. For example, the relative abundance of Proteobacteria decreased, and the proportion of Gemmatimonadota, Actinomycetota and Thermomicrobiota, increased. Diversity analysis showed that N1 treatment was beneficial to maintain the diversity and stability of bacterial and fungal communities in the goji-alfalfa pattern, while N3 treatment significantly inhibited microbial diversity in the goji berry monoculture pattern. The functional prediction showed that the function of bacteria was mainly amino acid metabolism and carbohydrate metabolism. The ILN1 treatment appeared to facilitate the transformation of fungi to mixed trophic strategies such as endophyte-plant pathogens, while the goji berry monoculture pattern tended to rely more heavily on saprophytic nutrition.ConclusionsIn the arid saline-alkali area, the nitrogen reduction management mode of goji-alfalfa intercropping with 150 kg·hm-² could effectively reconstruct the microbial community in the root zone of goji berry. It is a suitable cultivation and nitrogen application management mode for the green and sustainable development of goji berry industry in this area.

To address agricultural green development and carbon neutrality goals, saline-alkali land remediation urgently requires a shift toward ecologically intensive pathways, but field strategies that synergistically enhance soil quality and carbon sink function in saline-alkali soils via water-saving irrigation and intercropping remain limited, particularly from the soil microenvironment driver perspective. This 4-year field study, using barren land (BL) as control, examined the effects of water management (rainfed (R) and deficit irrigation (D)) and planting patterns (wolfberry monoculture (W), alfalfa monoculture (A), and wolfberry-alfalfa intercropping (WA)) on soil amelioration, carbon turnover, and carbon sink function in saline-alkali land. Results showed that deficit-irrigated wolfberry-alfalfa intercropping (DWA) significantly improved soil water content, reduced surface soil pH and salt accumulation, and increased enzyme activities (alkaline phosphatase (ALP), catalase (CAT), urease (UE), and (3-glucosidase ((3-GC)) as well as organic carbon fractions (SOC, MBC, ROOC, and DOC) and their proportions. Compared with BL, DWA increased the carbon pool management index (CPMI) by 179.46-241.57 and enhanced carbon pool quality in the 30-40 cm soil layer. Deficit irrigation, relative to rainfed conditions, increased ecosystem respiration (ER) and gross ecosystem productivity (GEP), with DWA showing net carbon uptake. Partial least squares path modeling indicated that water management and planting patterns mainly enhanced soil carbon pool quality and carbon sink function indirectly by regulating organic carbon fractions and enzyme activities. In summary, DWA created a favorable crop root-zone microenvironment, simultaneously achieving water saving, salt suppression, soil improvement, and carbon sink enhancement, providing scientific reference for low-carbon agriculture development in saline-alkali lands of arid and semi-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.

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.

Amid global climate change and water scarcity, enhancing water productivity while maintaining ecological sustainability in arid artificial grasslands is vital for regional water-forage-livestock system stability. However, how water management and biological interactions synergistically improve system resilience and resource efficiency remains unclear. This study aimed to evaluate whether legume-grass mixture combined with mild deficit irrigation could improve soil water and nutrient conditions, forage productivity, and water use efficiency. A three-year field experiment was conducted with three deficit irrigation levels (full irrigation (W0: 75%–85% θf), mild water deficit (W1: 65%–75% θf), moderate water deficit (W2: 55%–65% θf), where θf is field capacity) and three planting patterns (alfalfa monoculture (A), Bromus inermis monoculture (B), and alfalfa-Bromus inermis mixture (M)). Results showed that soil water content decreased with increasing deficit, but the mixture buffered moisture fluctuations and promoted soil organic matter accumulation under W1, while also alleviating photosynthetic inhibition. Compared with A and B monocultures, the mixture increased average yield by 5.13%–10.61% and 133.56%–137.39%, respectively, with land equivalent ratios exceeding 1.0 under all water regimes. Yield reduction under W1 was relatively small, while water use efficiency (WUE, FWUE, CPWUE) was significantly improved; the mixture increased average WUE by 12.86%–28.44% over A monoculture. W1 also enhanced crude protein content and relative feed value, with the mixture showing superior overall forage quality. Partial least squares path modeling indicated that water deficit and planting pattern primarily regulated yield and water productivity indirectly through soil water‑nutrient status and physiological processes. Comprehensive entropy weight‑TOPSIS evaluation showed that legume‑grass mixture under mild deficit (MW1) synergistically achieved water savings, soil fertility improvement, stable yield, quality enhancement, and increased efficiency, providing a theoretical basis for sustainable artificial grassland production in arid regions.

Incomplete spatio-temporal (ST) data from sensor networks in precision agriculture often limits environmental modeling and decision-making accuracy. To address this, we propose the Spatio-Temporal Conditional Diffusion Framework (ST-CDF), a generative approach for high-fidelity data reconstruction. The framework's core is a deep denoising network that integrates a Graph Attention Network (GAT) to explicitly model non-Euclidean spatial correlations, a Differential Attention Transformer to capture abrupt temporal dynamics, and an Inverse Discrete Wavelet Transform (IDWT) module to preserve multi-scale signal details. The generative process is constrained by a physics-informed training objective, which injects known physical laws (i.e., the Penman-Monteith equation for reference evapotranspiration, ET0) as an inductive bias, ensuring the imputed data maintains physical consistency. For privacy-preserving deployment on resource-constrained IoT devices, we extend the framework with a Federated Cluster-Guided Distillation (Fed-CGD) strategy. We conducted extensive experiments against established methods on two real-world agricultural datasets. ST-CDF demonstrated improved imputation accuracy across evaluated metrics. Its efficacy was most pronounced in the physically-demanding ET0 calculation task, where data imputed by ST-CDF at an 80% missing rate achieved a Root Mean Square Error (RMSE) of 0.3485 and a Coefficient of Determination (R2) of 0.7558, outperforming the baseline models. Furthermore, we explore ST-CDF as an explainable (XAI) framework for active agricultural decision support, demonstrating its utility in performing counterfactual simulations of "what-if" interventions, such as irrigation. The findings highlight ST-CDF as an effective, physically-grounded, and interpretable tool for data-driven scientific computation and precision agriculture.

Agricultural production frequently encounters challenges, including soil nitrogen pollution and imbalances resulting from improper irrigation and fertilization practices. This study focuses on wolfberry farmland, analyzing the effects of four irrigation levels [full irrigation (W0, 75%−85% θf), mild water deficit (W1, 65%−75% θf), moderate water deficit (W2, 55%−65% θf), and severe water deficit (W3, 45%−55% θf)] and four nitrogen application levels [no nitrogen application (N0, 0 kg·ha−1), low nitrogen application (N1, 150 kg·ha−1), medium nitrogen application (N2, 300 kg·ha−1), and high nitrogen application (N3, 450 kg·ha−1)] on nitrogen uptake by wolfberry plants, soil nitrogen loss, plant-soil nitrogen balance, and nitrogen use efficiency. The results indicate that: (1) Plant dry matter yield (1338.90−2893.52 kg·ha−1), fruit yield (1368.19−2623.09 kg·ha−1), plant nitrogen uptake (28.32−96.89 kg·ha−1) and fruit nitrogen uptake (23.53−63.56 kg·ha−1) all increased with higher irrigation and nitrogen application levels, following the trend W1 > W0 > W2 > W3 and N2 > N3 > N1 > N0. Compared with the other treatments, W1N2 treatment increased by 4.37%−116.11%, 6.36%−91.72%, 15.23%−242.16% and 10.86%−170.13%, respectively. (2) Soil NO3−–N content initially decreased, then increased, and ultimately decreased again with increasing soil depth, demonstrating inconsistent trends in response to changes in irrigation and nitrogen application. The highest residual soil NO3−–N at the end of the wolfberry growth period was recorded in the W0N3 treatment, measuring 186.17 kg·ha−1. In contrast, the lowest level was observed under the W3N0 treatment at 90.13 kg·ha−1, which was reduced by 12.25%−51.59% compared with other treatments. (3) The soil N2O flux (28.50–433.41 ug·m−2·h−1) and total emissions (0.40–1.67 kg·ha−1) increased with increased irrigation and nitrogen application. (4) The W1N1 treatment showed the highest nitrogen productivity (14.29 kg·kg−1), absorption efficiency (0.85 kg·kg−1), and recovery efficiency (27.14%), outperformed other treatments by 0.64–10.94 kg·kg−1, 0.10−0.65 kg·kg−1, and 2.52–18.80%, respectively. Overall, a combination of 392.40 mm of irrigation and 150 kg·ha−1 of nitrogen represented the optimal strategy for efficient and sustainable wolfberry production in the Yellow River irrigation districts of Gansu and similar regions.

An imbalance between the supply and demand of nutrients within the crop–soil system has resulted from the prevalent practice of excessive fertilization in agricultural agriculture. In order to increase crop growth, improve resource usage efficiency, and reduce agricultural nonpoint source pollution, appropriate cropping management techniques are essential. This study examined the effects of four nitrogen application rates (0 kg·ha−1 (C0), 80 kg·ha−1 (C1), 160 kg·ha−1 (C2), and 240 kg·ha−1 (C3)) and three alfalfa cropping systems (traditional flat planting, FP; ridge-covered biodegradable mulch, JM; and ridge-covered conventional mulch, PM) on soil inorganic nitrogen transport, nitrogen allocation within alfalfa plants, and soil N2O emissions. Throughout the alfalfa growth phase, the dynamics of nitrogen balance within the soil–plant–atmosphere system were quantitatively examined. The findings showed: (1) The concentrations of soil NO3−–N and NH4+–N rose with the rate of nitrogen application but decreased with soil depth. The PMC3 treatment had the largest inorganic nitrogen reserves at the end of the alfalfa growth period. (2) The pattern of PM > JM > FP for nitrogen uptake and nitrogen accumulation in biomass in alfalfa leaves and stems peaked at the C2 nitrogen treatment rate. (3) As nitrogen application rates increased, grass-land N2O emission flow and total emissions also followed PM > JM > FP. (4) The PMC2 treatment showed apparent nitrogen balances of 9.73 kg·ha−1 and 1.84 kg·ha−1 during the two-year growing season, with apparent nitrogen loss rates of 6.08% and 1.15%, respectively, both significantly lower than other treatments, according to nitrogen balance analysis. In summary, the nitrogen application pattern combining ridge-covering conventional plastic mulch with moderate nitrogen application levels can achieve nitrogen balance in alfalfa grassland systems within the Yellow River irrigation district of Gansu Province, China, and similar ecological zones.

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.

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.

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 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% θFC; moderate water deficit, W1: 55–65% θFC; mild water deficit, W2: 65–75% θFC; full irrigation, W3: 75–85% θFC) 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% θFC) 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.

Water scarcity and soil degradation are critical challenges confronting agricultural production in arid and semi-arid regions. Wolfberry is a key industrial crop in arid regions, contributing significantly to local specialty agriculture. Enhancing its growth environment and optimizing water utilization are essential for the sustainable use of agricultural resources and the advancement of modern agroforestry production systems. This study investigated the effects of water regulation and planting patterns on a wolfberry-alfalfa system through a three-year field experiment. Four irrigation regimes (full irrigation, W0; mild deficit, W1; moderate deficit, W2; severe deficit, W3) were applied under two planting patterns (monoculture, M; intercropping, I). Key indicators assessed included soil water dynamics, root-canopy characteristics, yield, and water-use efficiency. The results indicated that soil water content (SWC) decreased with horizontal distance and varied with depth, with intercropping systems generally exhibiting lower SWC than monoculture systems (W0 > W1 > W2 > W3), with reductions ranging from 3.09 % to 9.22 %. Root length density (RLD), leaf area index (LAI), cumulative intercepted photosynthetically active radiation (CIPAR), and radiation use efficiency (RUE) were maximized under MW1 and IW0 treatments. However, the yield advantage of intercropped wolfberry was not statistically significant compared to monoculture. Among all treatments, IW1 achieved the highest net income (78,235.20 CNYha(-)(1)) and the greatest economic water use efficiency (16.23 CNYm(-)(3)), exceeding other treatments by 6.38 %-224.49 % and 6.36 %-146.66 %, respectively. Stepwise multiple linear regression analysis identified the fraction of photosynthetically active radiation (FPAR) and RUE as the primary determinants of wolfberry yield. The comprehensive evaluation concluded that mild deficit irrigation (65 %-75 % theta(f)) emerged as an effective strategy for improving water productivity in northwestern China and comparable arid environments.

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 (Nand) 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, Nand, 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.