Yuanbo Jiang

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.

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.

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.

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.

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.

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.

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.

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.

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.