Front. Plant Sci. Frontiers in Plant Science Front. Plant Sci. 1664-462X Frontiers Media S.A. 10.3389/fpls.2025.1598110 Plant Science Original Research Effects of subsurface drip irrigation and nitrogen fertilizer management on N2O emissions and forage yield in alfalfa production Ma Hongxiu Sun Quan * Zhang Xiaojuan Jiang Peng College of Forestry and Prataculture, Ningxia University, Yinchuan, China

Edited by: Junying Chen, Northwest A & F University, China

Reviewed by: Kaihua Liu, Hohai University, China

Yunlong Zhai, Tarim University, China

*Correspondence: Quan Sun, sqnxu@sina.com

03 06 2025 2025 16 1598110 22 03 2025 19 05 2025 Copyright © 2025 Ma, Sun, Zhang and Jiang 2025 Ma, Sun, Zhang and Jiang

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Reducing emissions of the greenhouse gas nitrous oxide (N2O) while improving forage yield and quality is essential for sustainable agriculture in the context of global warming. However, how to reduce N2O emissions through water and nitrogen management in alfalfa planting is still unclear. In this two-year field experiment, the effects of three irrigation rates (W1, 375 mm; W2, 525 mm; W3, 675 mm) and five nitrogen (N) fertilizer application rates (N0, 0 kg N ha−1; N1, 75 kg N ha−1; N2, 150 kg N ha−1; N3, 225 kg N ha−1; N4, 300 kg N ha−1) on alfalfa yield, quality, resource use efficiency, and N2O emissions were explored. The results showed that irrigation combined with N application resulted in greater N2O emissions than irrigation alone. The cumulative N2O emissions increased with the increase of irrigation rate, and the average maximum cumulative N2O emissions of the W3 treatment (0.58 kg ha−1) increased by 94.14% and 57.38% compared with that of the W2 and W1 treatment, respectively. The cumulative N2O emissions also increased with the increase of the N application rate, and the average cumulative N2O emissions of the N4 treatment (0.69 kg ha−1) increased by 31.99%, 62.87%, 108%, and 173% compared with that of the N3, N2, N1, and N0 treatments, respectively. The variation of the average N2O emission coefficient was similar to that of the cumulative N2O emissions, and the W3 treatment (5.46) and N4 treatment (4.84) had the largest coefficients. Yield, crude protein, crop water productivity (WPc), and N2O emissions increased with the increase of N application rate, regardless of irrigation rate, with maxima occurring at N2 or N3 levels. These results suggest that the low NUE may be caused by the high cumulative N2O emissions. Besides, the combination of the irrigation rate 525 mm and the N application rate 150–225 kg N ha-1 could significantly increase alfalfa yield and crude protein content compared to other irrigation and nitrogen application treatments. However, further increasing irrigation and N rates failed to obtain further yield and crude protein increases, but led to N2O emission increase and WPc and NUE reductions. This may cause serious resource waste and environmental pollution.

 nitrogen utilization environmental pollution resource utilization efficiency crude protein yield section-in-acceptance Crop and Product Physiology Introduction

Nitrous oxide (N2O) is a potent greenhouse gas in the atmosphere that can retain up to 121 years (Jordan et al., 2020; Wang et al., 2024). It has a global warming potential 265–298 times that of carbon dioxide (IPCC, 2014). In addition, N2O is a stratospheric ozone-depleting substance (Ravishankara et al., 2009). Nitrous oxide emissions from agricultural soils account for more than 60% of the global anthropogenic N2O emissions, and this proportion is as high as 70% in China (Tian et al., 2020). Nitrogen fertilizer is an important source of N2O emissions. Currently, China consumes about 30% of the world’s N fertilizer, being the world’s largest consumer, so N2O emissions from China’s agricultural soils cannot be ignored (Yu et al., 2019). In recent years, N fertilizer topdressing by drip fertigation has been recommended in alfalfa (Medicago sativa) planting. The subsurface drip fertigation can accurately supply the water and nutrients to the root zone, increase the absorption and utilization efficiency of water and nitrogen by the roots, reducing nitrogen loss (Zheng et al., 2018; Yahaya et al., 2023). However, alfalfa has a low NUE due to the great N losses caused by N2O emissions, ammonia volatilization, and nitrate leaching (Sehy et al., 2003; Ni et al., 2020). The large amount of N2O emissions not only leads to a waste of resources, but also seriously threatens the environmental security. Recent study (Tian et al., 2019) has shown that from 1860 to 2016, the global annual N2O emissions from chemical N fertilizers increase from 0.3 Tg N2O-N to 3.3 Tg N2O-N. Therefore, it is necessary to optimize water and N fertilizer management to reduce N2O emissions in alfalfa planting (Benckiser et al., 2015; Zhao et al., 2021).

Alfalfa is a legume forage widely cultivated in the arid and semi-arid regions of northwest China. Although alfalfa has a strong drought adaptability, water deficit in these regions still greatly affects its growth, dry matter yield, and quality (Lamm et al., 2012; Liu et al., 2021). Local farmers always increase alfalfa yields through over irrigation by traditional irrigation ways such as flood irrigation, resulting in high water consumption and low WPc (Liu et al., 2021). This further exacerbates the water scarcity. Therefore, irrigation regime optimization is very necessary. Subsurface drip irrigation is a water-saving irrigation method. Under the premise of equal yield, subsurface drip irrigation saves 50%-60% and 20%-30% water compared with furrow irrigation and surface drip irrigation, respectively (Hassanli et al., 2008; Wang et al., 2020). This is due to that subsurface drip irrigation can directly deliver water and nutrients to plant roots and avoid surface water evaporation, thus improving the irrigation water productivity and avoiding waste of water resources (Dukes and Scholberg, 2005; Du et al., 2017). Exogenous N is a necessary for efficient and high-quality production of crops (Gao et al., 2020). In recent years, with the increase in forage demand for livestock production in China, over application of N has become a common practice for increasing alfalfa yield (Hou et al., 2021). However, over application of N reduces the positive effect, and causes greater nutrient growth than reproductive growth, thereby delaying plant maturation and reducing crop NUE and yields (Kunelius, 1974; Sun et al., 2023). This may further negatively impact the environment, ecosystem function, and biodiversity (Ren et al., 2019a; Gilles et al., 2021). Besides, soil anaerobic environment caused by over irrigation and over fertilization can accelerate the N loss by N2O emissions due to denitrification (Snyder et al., 2009; Li et al., 2020). Due to soil water greatly impacts crop NUE (Ren et al., 2019b), it is necessary to optimize the irrigation and N fertilization regimes to minimize the negative impacts of N loss on the environment while increasing WPc, NUE, and yields (Ju and Gu, 2014; Li et al., 2022).

Irrigation and N fertilization are vital for alfalfa production (Li et al., 2019). The anaerobic soil environment caused by over irrigation and the over application of N can accelerate N2O emissions, causing large N losses (Snyder et al., 2009; Li et al., 2020). How to optimize water and N supply to reduce N2O emissions while increasing alfalfa NUE, WPc, yield, and quality under subsurface drip irrigation remains unclear. This study hypothesized that reducing irrigation and N application rates may maximize alfalfa WPc, NUE, and yields while reducing the N2O emissions. To verify the hypothesis, this study investigated the effects of three irrigation rates and five N rates on alfalfa yield, quality, resource use efficiency, and N2O emissions from alfalfa fields (plants and soil) under subsurface drip irrigation. Besides, this study also clarified the optimal water and N fertilizer management in alfalfa planting. The aim was to achieve the coordination of alfalfa production and environmental protection in the arid regions of northwest China.

 Materials and methods Experimental site

Field experiments were conducted in 2022 and 2023 in Botanical Garden 2 Village, Liangtian Town, Yinchuan, Ningxia Hui Autonomous Region, China (106°18’ E, 38°40’ N, 1100 m a.s.l.). The experimental site has a temperate continental climate. According to the report of the Ningxia Meteorological Bureau (http://nx.cma.gov.cn/index.html), the annual sunshine duration in the experimental site was about 3032 hours, the frost-free period was 185 days, the annual average temperature was 8.7°C, the annual average precipitation was 200 mm, and the annual average evapotranspiration was 1694 mm. The physicochemical properties of surface soil (0–30 cm) sampled before the field experiment in April 2022 were determined according to the methods of Bao (2000): The soil type was aeolian sandy soil (91.76% sand, 7.04% silt, and 1.20% clay) according to the USDA soil classification. The soil pH was 8.62, the organic matter content was 4.67 g/kg, the available nitrogen content was 11.20 mg/kg, the available potassium content was 81.42 mg/kg, and the available phosphorus content was 2.44 mg/kg.

Air temperature and precipitation data during both crop growing seasons were obtained from the local meteorological station ( Figure 1 ). The rainfall in the growing seasons in 2022 and 2023 were 54.5 and 56.0 mm, accounting for 82.7% and 88.9% of the annual rainfall, respectively. Besides, about 50% of the rainfalls was less than 5 mm and could not be used by crops. There was no significant difference in the monthly average temperature between the two alfalfa growing seasons, with the lowest average temperature in October and the highest in July.

Precipitation, daily mean temperature, and reference evapotranspiration (ETr) during the growing seasons of alfalfa in 2022 and 2023 in the experimental site. ETr is calculated according to the methods of Allen et al. (1998) and Yan et al. (2021).

 Experimental design

Alfalfa seeds (cultivar Magna Graze 401, Canada) were sown in spring in 2022, with a sowing rate of 15 kg ha-1 and a row spacing of 20 cm. A split-plot design was adopted, with three irrigation levels as the main plots and five N application rates as the sub-plots. The irrigation rates included 375 (W1), 525 (W2), and 675 mm (W3), and the N application rates included 0 (N0), 75 (N1), 150 (N2), 225 (N3), and 300 (N4) kg ha-1. There were a total of fifteen treatments, and each treatment had three replicates. The area of each plot was 12.5 m2 (2.5 m × 5 m). The plots were separated by vertically embedded plastic films (0–60 cm) to prevent mutual influence. The subsurface drip irrigation system used in this study was composed of a water pump, a filter, a fertilizer tank, and water pipes (inner diameter: 13 mm, wall thickness: 1.5 mm). The pipe spacing was 80 cm, the buried depth was 20 cm, the flow rate was 3.6–6 L/(m·h), and the pressure was 0.06 MPa (Xiang, 2015; Zhuge et al., 2003). Irrigation was conducted every 7 days (in case of rain or extreme heat, it was delayed or advanced by 1–2 days). A water flow meter was used to control the amount of irrigation. Alfalfa stems and leaves were harvested two times in 2022. The irrigation amount from sowing to the first harvest accounted for 60% of the total irrigation amount, and that from the first harvest to the second accounted for 40%. In the second year, alfalfa stems and leaves were harvested four times, and 25% of the total irrigation amount was irrigated before each harvest ( Supplementary Table S1 ). Urea (N: 46%) was applied through the subsurface drip irrigation system after dissolving in water. The timing of N fertilization was consistent with that in local fields. Seventy percent and thirty percent of urea were applied at 2 and 67 days after emergence (Days), respectively in 2022. In the 2023, 40%, 30%, and 30% of urea were applied at 2, 45, and 73 days after leaves turning green (days), respectively. The details for irrigation and N fertilization were shown in Supplementary Table S2 . Other agricultural managements such as weeding were the same in all plots.

 Sampling and measurements

At the beginning of flowering (about 10% of flowering), three sampling plots (1 m × 1 m for each) were selected in the center of each plot to harvest alfalfa stems and leaves, with a stubble height of 5 cm. After that, the fresh weight was measured. The dry matter yield was measured after drying at 75°C. Hay yield was calculated on a dry matter basis (Fan et al., 2016). The dried plant samples were crushed by a pulverizer, passed through a 0.25 mm sieve, and stored in a ziplock bag at room temperature for the determination of alfalfa quality. Alfalfa N content was determined using the Kjeldahl 8400 automatic analyzer (FOSS, Hilleroed, Denmark), and the contents of neutral detergent fiber (NDF, %) and acid detergent fiber (ADF, %) were determined by the method of Raffrenato et al. (2017). Alfalfa crude protein content (CP, %) and relative feeding value (RFV) were calculated using Equations 1, 2 (Ferreira et al., 2015).

CP = 6.25 × N RFV = ( 88.9 − 0.779 × ADF ) × ( 120 / NDF ) / 1.29

where N is the nitrogen content of alfalfa samples (%).

 Crop water productivity and nitrogen use efficiency

The actual crop evapotranspiration (ETc ) during the growing seasons was calculated using the method of Allen et al. (1998). Due to the arid climate, flat terrain, and deep groundwater table in the experimental site, groundwater recharge, surface runoff, and deep seepage were ignored. Then, ET c was calculated using Equation 3:

ET c = P + I − Δ WS

Where P is the precipitation (mm) during the alfalfa growing season, I is the irrigation amount (mm) during the alfalfa growing season, ΔWS is the change of soil water content (soil water content at the beginning of the experiment minus that at the end of the experiment (mm)).

The WPC (kg m3) was calculated using Equation 4:

WP C = HY / ET C

The irrigation water productivity (WPI, kg m3) was calculated using Equation 5:

WP I = HY / I

Where HY is annual hay yield (kg ha-1), and I is the total irrigation amount (mm).

Kjeldahl method (Jung et al., 2003) was used to determine the N content in alfalfa root, stems, and leaves. Plant N accumulation was calculated as the sum of N content in each organ. The agronomic efficiency of N (AEN, kg kg-1), N use efficiency (NUE, %), physiological efficiency of N (PEN, kg kg-1 N), and partial factor productivity of N (PFPN, kg kg-1) were calculated using Equations 6–9 (Tan et al., 2017):

AEN = Annual hay yield in N application plot − Annual hay yield in zero N plot N rate NUE = Annual hay yield N uptake PEN = Annual hay yield in N application plot − Annual hay yield in zero N plot N uptake in N application plot − N uptake in zero N plot PFPN = Annual hay yield N rate N<sub>2</sub>O collection and determination

The N2O fluxes from plants and soil were measured by static chamber-gas chromatography (GC) (Ning et al., 2020). The static chamber consisted of a chamber (50 cm × 50 cm × 100 cm) and a stainless steel base. Sponge and aluminum foil layers were covered on the walls of the chamber to reduce internal air temperature variations during sampling. The top of the stainless steel base was provided with a groove (2 cm in width and 5 cm in depth), which was sealed with water during gas collection. Inside the chamber was a fan and an electronic thermometer to measure the temperature of the air inside. Under normal circumstances, the soil greenhouse gas flux was measured every 7–10 days. If there was an abnormal temperature (extremely high temperature or extremely low temperature) during the alfalfa growing season, sampling frequency was increased. Besides, the timing of gas collection was postponed in case of heavy rainfall. The gas sampling was performed at 10: 00 - 14: 00 every day. Four gas samples were collected in 30 minutes (at 0, 10, 20, and 30 min) using a polypropylene syringe (50 mL) equipped with a nylon stopcock, and the samples were immediately transported to the laboratory for analysis using the Agilent gas chromatograph (7890A, USA). The N2O flux was calculated using Equation 10 (Kamran et al., 2022):

F =   M V 0 · H · d c d t · 273 273 + T · P P 0

where F is the N2O flux (μg N m−2 h−1), M is the molar mass of the measured gas (g mol−1), H is the height of the chamber (cm), dc/dt is the linear regression slope of gas concentration at the time approaching zero, T is the average temperature in the sampling chamber (°C), P is the pressure in the sampling chamber (Pa), and V0 and P0 are the volume (mL) and pressure (Pa) at standard conditions.

The cumulative N2O emissions (kg ha−1) was calculated using Equation 11 (Afreh et al., 2018):

E c = ∑ i = 1 n F i + 1 + F i 2 × t i + 1 − t i × 24

where EC is the cumulative N2O emissions during each growing season, F is the daily N2O flux, i is the ith measurement, (ti+1-ti ) is the time interval between two adjacent samplings (days), and n is the number of observations during the growing season.

The N2O emission coefficient (EF, %) was calculated using Equation 12:

EF = N 2 O emissions in the N application plot − N 2 O emissions in the zero N plot N rate Soil moisture and inorganic nitrogen content

To determine soil moisture and inorganic N content, three soil samples (0–20 cm) were taken with an auger near the static chamber for gas collection in each plot on the same day of gas sampling. The three soil samples were mixed and used as the sample of the plot (Wang et al., 2016). The soil moisture content was measured by weighing after drying the soil samples in an oven. The water-filled pore space (WFPS) was calculated using Equation 13 (Zhang et al., 2020):

 WFPS ( % ) = soil moisture content  %   × soil bulk density 1   −   Soil bulk density 2.65 × 100  

The NH4 +-N and NO3 –N in the soil were extracted with 2 mol L−1 KCl (soil: KCl solution = 1: 5), and their contents were measured by colorimetry using a spectrophotometer (UV-2102 PCS, Shanghai Spectrometer Co., Ltd., Shanghai, China) (Wang et al., 2015).

 Data analysis

SPSS 18.0 (IBM Corp, USA) was used for ANOVA. Tukey’s test was used to test the significance of differences in the means between treatments at p< 0.05 and p< 0.01. The direct and indirect effects of N rates and irrigation rates on N2O emissions, alfalfa NUE, WPc, yield, and quality were evaluated using a structural equation model (SEM) using the “lavaan” package in R software version 4.0.0 (R Core Team, 2020; Rosseel, 2012). The SEM was constructed based on the following assumptions: (1) Increasing the N rate might increase the N2O emission coefficient and the N2O emissions, and reduce the NUE. (2) Optimal irrigation rate and N rate could significantly improve alfalfa WPC, yield, and quality. The relative chi-square (χ2/df), comparative fit index (CFI), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to assess the degree of fit (Kline, 2005). Figures were drawn using Excel 2016 (Microsoft Corp, USA) and Origin 8.0 (Origin Lab Corp, USA).

 Results Alfalfa yield and quality

Alfalfa yield increased with the increase of irrigation rate in the two years, but there was no significant difference between W2 and W3 levels. Increasing the N rate from 0 to 225 kg N ha-1 resulted in a significant increase in alfalfa yield, but further increasing the N rate did not increase yield. The W2N2 treatment had the highest yield in 2022. In 2023, the W3N2 treatment had the highest yield, but there was no difference between W2N2, W2N3, W3N3, and W3N2 treatments ( Tables 1 , 2 ).

Effects of irrigation (W) and nitrogen (N) interaction on yield, crude protein content (CP), relative feeding value (RFV), neutral detergent fiber (NDF) content, and acid detergent fiber (ADF) content of alfalfa in 2022 and 2023.

Year Treatment Hay yield (t/ha) Crude protein (%) Relative feed value (%) Neutral detergent fiber (%) Acid detergent fiber (%)
2022 W1 N0 4.16 ± 0.11h 17.28 ± 0.43e 172.77 ± 8.64a 37.61 ± 1.88g 24.65 ± 1.23g
N1 5.43 ± 0.27g 19.39 ± 0.89cd 160.67 ± 8.03b 38.68 ± 1.93fg 28.54 ± 1.43ef
N2 6.22 ± 0.31ef 21.60 ± 1.15ab 148.98 ± 7.45bcde 40.83 ± 2.04defg 30.41 ± 1.52cde
N3 6.66 ± 0.05de 21.15 ± 0.95ab 143.71 ± 7.19cdef 42.23 ± 2.11def 30.71 ± 1.54cde
N4 5.23 ± 0.26g 21.68 ± 1.05ab 136.95 ± 6.85efgh 43.66 ± 2.18bcd 31.77 ± 1.59bcd
W2 N0 5.72 ± 0.28fg 17.89 ± 0.24de 160.10 ± 8.00b 39.39 ± 1.97efg 27.17 ± 1.36fg
N1 7.70 ± 0.27bc 21.36 ± 0.96ab 155.21 ± 7.76bc 40.15 ± 2.01defg 28.42 ± 1.42ef
N2 8.79 ± 0.44a 22.45 ± 1.01a 145.57 ± 7.28cde 41.14 ± 2.06defg 31.59 ± 1.58bcd
N3 7.63 ± 0.38bc 21.58 ± 0.99ab 137.37 ± 6.87efgh 42.84 ± 2.14cde 33.26 ± 1.66abc
N4 6.95 ± 0.35d 20.60 ± 0.99bc 131.19 ± 6.56fghi 44.00 ± 2.20bcd 34.67 ± 1.73a
W3 N0 7.02 ± 0.35d 17.81 ± 0.76de 152.25 ± 7.61bcd 41.11 ± 2.06defg 27.83 ± 1.39ef
N1 7.77 ± 0.39b 20.81 ± 0.94abc 140.03 ± 7.00defg 43.89 ± 2.19bcd 29.42 ± 1.47def
N2 8.08 ± 0.40b 21.47 ± 1.33ab 128.46 ± 6.42ghi 46.26 ± 2.31abc 32.40 ± 1.62abc
N3 8.18 ± 0.42b 21.24 ± 0.63ab 124.21 ± 6.21hi 46.91 ± 2.35ab 33.83 ± 1.69ab
N4 7.14 ± 0.27cd 17.03 ± 1.05e 119.05 ± 5.95i 48.42 ± 2.42a 34.64 ± 1.73a
Variation source
W ** ** ** ** **
N ** ** ** ** **
W×N ** ** ns ns ns
2023 W1 N0 8.71 ± 0.62f 17.16 ± 0.53e 174.17 ± 8.71a 36.39 ± 1.82f 26.88 ± 1.34g
N1 9.18 ± 0.55ef 18.95 ± 0.95d 164.98 ± 8.25a 37.77 ± 1.89ef 28.39 ± 1.42fg
N2 9.80 ± 0.29bcd 20.21 ± 0.52bc 151.90 ± 7.60bc 40.03 ± 2.00cdef 30.63 ± 1.53def
N3 9.65 ± 0.33bcde 19.68 ± 0.63bcd 146.30 ± 7.31cde 41.13 ± 2.06bcde 31.51 ± 1.58de
N4 9.17 ± 0.21ef 19.43 ± 0.54cd 133.95 ± 6.70efg 43.75 ± 2.19abc 33.47 ± 1.67bcd
W2 N0 9.17 ± 0.16ef 19.53 ± 0.44bcd 161.88 ± 8.09ab 38.54 ± 1.93def 28.29 ± 1.41fg
N1 9.95 ± 0.15bc 20.62 ± 0.13bc 151.89 ± 7.59bc 40.30 ± 2.01cde 29.87 ± 1.49ef
N2 10.87 ± 0.41a 23.30 ± 0.45a 140.95 ± 7.05cdef 42.59 ± 2.13bc 31.86 ± 1.59de
N3 10.96 ± 0.06a 22.46 ± 0.67a 139.36 ± 6.97cdefg 42.37 ± 2.12bcd 33.01 ± 1.65cd
N4 9.39 ± 0.32cde 20.20 ± 0.83b 129.08 ± 6.45fgh 44.67 ± 2.23ab 35.18 ± 1.76abc
W3 N0 9.25 ± 0.19def 19.51 ± 0.32cd 147.78 ± 7.39cd 41.20 ± 2.06bcde 30.49 ± 1.52def
N1 10.15 ± 0.32b 20.72 ± 0.83b 137.51 ± 6.88defg 43.29 ± 2.16bc 32.49 ± 1.62cde
N2 11.00 ± 0.24a 22.76 ± 0.03a 127.89 ± 6.39fgh 45.11 ± 2.26ab 35.13 ± 1.76abc
N3 10.88 ± 0.34a 22.66 ± 1.11a 126.21 ± 6.31gh 44.91 ± 2.25ab 36.34 ± 1.82ab
N4 9.91 ± 0.21bc 18.52 ± 0.55d 117.73 ± 5.89h 47.57 ± 2.38a 37.27 ± 1.86a
Variation source
W ** ** ** ** **
N ** ** ** ** **
W×N ns ** ns ns ns

W1, W2, and W3 represent irrigation rates of 375, 525, and 675 mm, respectively, while N0, N1, N2, N3, and N4 represent nitrogen application rates of 0, 75, 150, 225, and 300 kg ha-1, respectively. Data are presented as mean ± SD (n = 3). Based on Tukey’s test, different lowercase letters in each column represent significant differences in means between treatments (p< 0.05).

Effects of irrigation (W) and nitrogen (N) treatments on yield, crude protein content (CP), relative feeding value (RFV), neutral detergent fiber (NDF) content, and acid detergent fiber (ADF) content of alfalfa in 2022 and 2023.

Year Treatment Hay yield (t/ha) Crude protein (%) Relative feed value (%) Neutral detergent fiber (%) Acid detergent fiber (%)
2022 W1 5.52 ± 0.41b 20.22 ± 0.71a 152.62 ± 5.20a 40.60 ± 1.45b 29.22 ± 1.05b
W2 7.47 ± 0.11a 20.77 ± 0.42a 145.89 ± 2.92a 41.50 ± 0.83b 31.02 ± 0.62ab
W3 7.41 ± 0.34a 19.67 ± 0.68a 132.80 ± 4.74b 45.32 ± 1.57a 31.62 ± 1.09a
N0 5.70 ± 0.24b 17.66 ± 0.35d 161.71 ± 3.23a 39.37 ± 0.79d 26.55 ± 0.53d
N1 7.06 ± 0.13a 20.52 ± 0.41bc 151.97 ± 3.04b 40.91 ± 0.82cd 28.80 ± 0.58c
N2 7.65 ± 0.18a 21.84 ± 0.44a 141.00 ± 2.82c 42.74 ± 0.85bc 31.46 ± 0.63b
N3 7.50 ± 0.07a 21.32 ± 0.43ab 135.10 ± 2.70cd 43.99 ± 0.88ab 32.60 ± 0.65ab
N4 6.03 ± 0.63b 19.77 ± 0.69c 129.06 ± 4.62d 45.36 ± 1.57a 33.69 ± 1.15a
2023 W1 9.39 ± 0.33b 19.14 ± 0.71b 154.69 ± 5.70a 39.90 ± 1.43b 30.24 ± 1.08b
W2 10.08 ± 0.29a 21.28 ± 0.69a 145.07 ± 4.76b 41.84 ± 1.35b 31.76 ± 1.03b
W3 10.26 ± 0.23a 20.91 ± 0.54a 131.93 ± 3.41c 44.55 ± 1.09a 34.45 ± 0.85a
N0 9.09 ± 0.31c 18.73 ± 0.65c 161.27 ± 5.59a 38.71 ± 1.36c 28.56 ± 1.01c
N1 9.84 ± 0.37b 20.18 ± 0.75b 152.16 ± 5.68b 40.64 ± 1.49bc 30.39 ± 1.11c
N2 10.55 ± 0.21a 22.05 ± 0.57a 139.99 ± 3.66c 42.48 ± 1.17b 32.46 ± 0.90b
N3 10.53 ± 0.27a 21.76 ± 0.82a 138.27 ± 5.21c 43.07 ± 1.62ab 33.83 ± 1.28ab
N4 9.59 ± 0.35bc 19.45 ± 0.62bc 127.36 ± 4.06d 45.48 ± 1.50a 35.42 ± 1.16a

Data are presented as mean ± SD (n = 3). Based on Tukey’s test (p< 0.05), different lowercase letters indicate significant differences in the means between treatments. The treatment abbreviations are the same as those in Table 1 .

The CP content increased with the increase of irrigation rate in the two years, but there was no significant difference between W2 and W3 levels. The CP content of alfalfa increased significantly from N0 to N3, but decreased significantly from N3 to N4. The average CP content of the N2 treatment was the highest, which was 24% and 18% higher than that of N0 treatment in 2022 and 2023, respectively. The W2N2 treatment had the highest CP content in 2022. In 2023, the W2N3 and W3N2 treatment had the highest CP content. The W1N0 and W3N4 treatment had the lowest CP content in both years ( Tables 1 , 2 ).

The RFV value gradually decreased with the increase of irrigation rate in the two years, and the average annual RFV value was the highest at W1 level, which was 15%-17% higher than the lowest value at W3 level. The RFV value decreased with the increase of N rate. The N0 treatment had the highest RFV value, which was 25%-27% higher than the lowest value of the N4 treatment. The W1N0 treatment had the highest RFV value, and the W3N4 treatment had the lowest RFV value ( Tables 1 , 2 ).

The average NDF and ADF contents were the highest at W3 level in the two years, which increased by 12% and 8%-14%, respectively compared with those at W1 treatment. With the increase of N rate, the NDF and ADF increased linearly, and the NDF and ADF contents of the N4 treatment increased by 15% - 17% and 24% - 27%, respectively compared with those of the N0 treatment in the two years. The W3N4 treatment had the highest NDF and ADF contents, and the W1N0 treatment had the lowest NDF and ADF contents ( Tables 1 , 2 ).

 Crop water productivity and nitrogen use efficiency

The ETC increased with the increase of irrigation rate in the two years, and the ETC at W3 level significantly increased by 73% and 84% in 2022 and 2023, respectively compared with that at W1 level. The WPC and WPI at W1 and W2 level significantly increased compared with those at W3 level. The WPI and WPC of the N2 and N3 treatments were the highest, and there was no difference between N2 and N3 treatments. Besides, the WPC and WPI of the N2 treatment increased by 13% – 33% and 15% – 38%, respectively compared with those of the N0 treatment. The W1N3 treatment had the highest WPC and WPI ( Tables 3 , 4 ).

Effects of irrigation (W) and nitrogen (N) interaction on evapotranspiration (ETC ), crop water productivity (WPC), irrigation water productivity (WPI), nitrogen agronomic efficiency (AEN), nitrogen use efficiency (NUE), nitrogen physiological efficiency (PEN) and partial factor productivity of nitrogen (PFPN) of alfalfa in 2022 and 2023.

Year Treatments ET C (mm) Crop Water Productivity (kg m3) Irrigation water productivity (kg m3) Agronomic efficiency of N (kg kg-1) N use efficiency (%) Physiological efficiency of N (kg kg-1) Partial factor productivity of N (kg kg-1)
2022 W1 N0 201.34 ± 7.76d 20.69 ± 1.28ef 22.19 ± 0.58efg NA NA NA NA
N1 209.44 ± 4.19cd 25.94 ± 1.81c 28.96 ± 1.45c 33.82 ± 0.68b 36.22 ± 0.72a 36.20 ± 0.72a 144.72 ± 2.89b
N2 211.99 ± 4.24cd 29.36 ± 2.05ab 33.18 ± 1.66b 27.45 ± 0.55c 33.92 ± 0.68bc 30.05 ± 0.60b 82.91 ± 1.66e
N3 215.23 ± 9.75c 31.00 ± 1.16a 35.56 ± 0.28a 22.56 ± 0.93d 32.66 ± 1.34cd 27.61 ± 1.13c 60.02 ± 2.47g
N4 219.01 ± 8.81c 23.94 ± 2.15cd 27.91 ± 1.40cd 7.23 ± 0.30i 25.85 ± 1.06h 11.87 ± 0.24h 34.86 ± 0.70i
W2 N0 282.19 ± 5.64b 21.95 ± 1.41def 23.59 ± 1.08e NA NA NA NA
N1 289.69 ± 5.79b 26.61 ± 1.46bc 29.36 ± 1.03c 40.40 ± 0.81a 35.08 ± 0.70ab 24.66 ± 0.49d 205.42 ± 4.11a
N2 293.27 ± 5.87b 29.99 ± 2.10a 33.49 ± 1.67b 34.64 ± 0.69b 31.27 ± 0.63def 21.15 ± 0.42e 117.15 ± 2.34c
N3 291.19 ± 6.97b 26.22 ± 1.93c 29.07 ± 1.45c 12.95 ± 0.53g 29.82 ± 1.24ef 14.74 ± 0.61f 67.81 ± 2.82f
N4 292.88 ± 8.01b 23.77 ± 1.83cde 26.50 ± 1.32d 5.16 ± 0.21j 30.11 ± 1.25ef 10.48 ± 0.44ij 46.42 ± 1.93h
W3 N0 364.35 ± 8.39a 19.28 ± 1.41f 20.80 ± 1.04g NA NA NA NA
N1 367.59 ± 7.35a 21.17 ± 1.48def 23.04 ± 1.15ef 20.22 ± 0.40g 35.23 ± 0.70ab 13.38 ± 0.27g 207.30 ± 4.15a
N2 371.72 ± 7.43a 21.76 ± 1.52def 23.95 ± 1.20e 14.19 ± 0.28f 31.54 ± 0.63de 11.54 ± 0.23hi 107.73 ± 2.15d
N3 374.34 ± 7.49a 21.86 ± 1.55def 24.24 ± 1.24e 10.32 ± 0.21h 29.73 ± 0.59f 10.45 ± 0.21i 72.67 ± 1.45f
N4 375.54 ± 7.51a 19.03 ± 1.11f 21.17 ± 0.81fg 0.84 ± 0.02k 27.83 ± 0.56g 1.36 ± 0.03k 47.61 ± 0.95h
Variation source
W ** ** ** ** ** ** **
N ** ** ** ** ** ** **
W×N ns ** ** ** ** ** **
2023 W1 N0 373.73 ± 9.37c 23.29 ± 1.33c 46.47 ± 3.29c NA NA NA NA
N1 375.19 ± 7.50c 24.49 ± 1.86abc 48.98 ± 2.91bc 12.55 ± 0.25f 60.41 ± 1.12a 12.67 ± 0.25d 122.39 ± 2.45b
N2 380.14 ± 7.60c 25.79 ± 1.24a 52.28 ± 1.56a 14.51 ± 0.29e 52.83 ± 1.06b 15.41 ± 0.31b 65.31 ± 1.31d
N3 384.18 ± 7.68c 25.13 ± 1.16ab 51.49 ± 1.76ab 8.47 ± 0.35g 46.82 ± 1.92c 10.14 ± 0.42e 43.46 ± 1.79f
N4 385.92 ± 12.95c 23.78 ± 0.59bc 48.94 ± 1.12bc 3.13 ± 0.13i 45.12 ± 1.85cd 5.08 ± 0.10f 30.57 ± 0.61g
W2 N0 533.89 ± 10.68b 17.17 ± 0.37e 34.94 ± 0.61fg NA NA NA NA
N1 541.39 ± 10.83b 18.37 ± 0.33e 37.91 ± 0.59e 20.78 ± 0.42c 44.84 ± 0.90cd 12.68 ± 0.25d 132.61 ± 2.65a
N2 539.89 ± 10.80b 20.13 ± 0.73d 41.42 ± 1.55d 22.68 ± 0.45b 38.37 ± 0.77fg 13.85 ± 0.28c 72.45 ± 1.45c
N3 543.58 ± 10.77b 20.16 ± 0.29d 41.76 ± 0.23d 16.12 ± 0.66d 42.58 ± 1.75de 18.07 ± 0.74a 49.35 ± 2.03e
N4 546.63 ± 13.19b 17.18 ± 0.53e 35.80 ± 1.22ef 1.52 ± 0.06j 40.12 ± 1.65efg 2.97 ± 0.12g 31.72 ± 1.30g
W3 N0 700.53 ± 12.32a 13.20 ± 0.05h 27.41 ± 0.57j NA NA NA NA
N1 710.69 ± 14.21a 14.28 ± 0.35fgh 30.08 ± 0.96hi 24.07 ± 0.48a 45.55 ± 0.91cd 14.05 ± 0.28c 135.31 ± 2.71a
N2 710.39 ± 14.21a 15.48 ± 0.23f 32.61 ± 0.71gh 23.38 ± 0.47ab 42.58 ± 0.85de 17.99 ± 0.36a 73.33 ± 1.47c
N3 712.94 ± 14.26a 15.26 ± 0.33fg 32.25 ± 1.00h 14.52 ± 0.29e 40.90 ± 0.82ef 15.33 ± 0.31b 48.35 ± 0.97ef
N4 713.69 ± 14.27a 13.89 ± 0.56gh 29.38 ± 0.62ij 4.43 ± 0.09h 36.97 ± 0.74g 5.23 ± 0.10f 33.04 ± 0.66g
Variation source
W ** ** ** ** ** ** **
N ns ** ** ** ** ** **
W×N ns ns ns ** ** ** *

Data are presented as mean ± SD (n = 3). Based on Tukey’s test (p< 0.05), different lowercase letters indicate significant differences in the means between treatments. The treatment abbreviations are the same as those in Table 1 .

Effects of irrigation (W) and nitrogen (N) treatments on evapotranspiration (ET C ), crop water productivity (WPC), irrigation water productivity (WPI), nitrogen agronomic efficiency (AEN), nitrogen use efficiency (NUE), nitrogen physiological efficiency (PEN) and partial factor productivity of nitrogen (PFPN) of alfalfa in 2022 and 2023.

Year Treatment ETC (mm) Crop water productivity (kg m3) Irrigation water productivity (kg m3) Agronomic efficiency of N (kg kg-1) N use efficiency (%) Physiological efficiency of N (kg kg-1) Partial factor productivity of N (kg kg-1)
2022 W1 214.22 ± 8.80c 26.53 ± 1.09a 29.95 ± 1.23a 23.07 ± 0.95a 32.59 ± 1.34a 26.79 ± 1.10a 81.70 ± 3.36b
W2 289.85 ± 5.80b 25.71 ± 0.51a 28.40 ± 0.57a 23.29 ± 0.47a 31.76 ± 0.64a 17.83 ± 0.36b 109.56 ± 2.19a
W3 370.71 ± 7.41a 20.62 ± 0.41b 22.64 ± 0.45b 11.39 ± 0.23c 31.08 ± 0.62a 9.18 ± 0.18c 108.83 ± 2.18a
N0 286.40 ± 11.77a 20.91 ± 0.86d 22.49 ± 0.92d NA NA NA NA
N1 292.76 ± 12.03a 24.90 ± 1.02b 27.48 ± 1.13b 31.90 ± 1.31a 35.98 ± 1.48a 25.07 ± 1.03a 188.29 ± 7.74a
N2 292.32 ± 5.85a 27.03 ± 0.54a 30.21 ± 0.60a 25.43 ± 0.51b 32.24 ± 0.64b 20.91 ± 0.42b 102.59 ± 2.05b
N3 293.59 ± 5.87a 26.36 ± 0.53a 29.62 ± 0.59a 15.28 ± 0.31c 30.85 ± 0.62b 17.64 ± 0.35c 67.13 ± 1.34c
N4 299.75 ± 12.32a 22.54 ± 0.93c 25.53 ± 1.05c 4.47 ± 0.18d 28.45 ± 1.17c 8.08 ± 0.33d 43.72 ± 1.80d
2023 W1 384.90 ± 15.81c 24.82 ± 1.02a 50.29 ± 2.07a 9.79 ± 0.40c 51.98 ± 2.14a 10.97 ± 0.45c 66.31 ± 2.72b
W2 541.08 ± 10.82b 18.60 ± 0.37b 38.37 ± 0.77b 15.28 ± 0.31b 41.48 ± 0.83b 11.89 ± 0.24b 71.53 ± 1.43a
W3 709.65 ± 14.19a 14.42 ± 0.29c 30.35 ± 0.61c 16.60 ± 0.33a 41.50 ± 0.83b 13.15 ± 0.26a 72.50 ± 1.45a
N0 543.20 ± 22.32a 18.13 ± 0.74c 36.76 ± 1.51b NA NA NA NA
N1 549.66 ± 22.58a 19.30 ± 0.79abc 39.51 ± 1.62ab 19.39 ± 0.80a 50.94 ± 2.09a 13.31 ± 0.55c 131.84 ± 5.42a
N2 543.47 ± 10.87a 20.47 ± 0.41a 42.10 ± 0.84a 20.19 ± 0.40a 44.60 ± 0.89b 15.75 ± 0.31a 70.36 ± 1.41b
N3 546.90 ± 10.94a 20.18 ± 0.40ab 41.83 ± 0.84a 13.04 ± 0.26b 43.43 ± 0.87bc 14.51 ± 0.29b 47.05 ± 0.94c
N4 556.06 ± 22.85a 18.53 ± 0.76bc 38.55 ± 1.58ab 3.07 ± 0.13c 40.46 ± 0.47c 4.49 ± 0.18d 32.20 ± 1.32d

Data are presented as mean ± SD (n = 3). Based on Tukey’s test (p< 0.05), different lowercase letters indicate significant differences in the means between treatments. The treatment abbreviations are the same as those in Table 1 .

In both years, the PFPN at W2 level significantly increased by 8%-34% compared with that at W1 level, but there was no difference between W3 and W1 levels. In both years, the AEN, NUE, PEN, and PFPN significantly reduced with the increase of N rate. The average AEN, NUE, PEN, and PFPN of the N4 treatment decreased by 85%, 21%, 64%, and 76%, respectively compared with those of the N0 treatment. The W3N1 treatment had the highest PFPN, and there was no difference between W3N1 and W2N1 treatment. The W1N1 treatment had the highest NUE ( Tables 3 , 4 ).

 N<sub>2</sub>O emissions, soil moisture content, and inorganic nitrogen content

In 2022, N2O flux peaks were observed at 14 and 74 days (12 and 7 days after N topdressing, respectively). In 2023, N2O flux peaks were observed at 14, 52, and 80 days (12, 7, and 7 days after the first, second, and third N topdressing). In the later stages of crop growth, irrigation and N treatments had little effect on N2O flux ( Figure 2 ).

Effects of irrigation (W) and nitrogen (N) treatments on N2O fluxes during the alfalfa growing seasons in 2022 and 2023. Error bars represent standard deviation (SD). The red arrows indicate fertilization events. W1, W2, and W3 represent irrigation rates of 375, 525, and 675 mm, respectively, and N0, N1, N2, N3, and N4 represent nitrogen application rates of 0, 75, 150, 225, and 300 kg ha-1, respectively.

Irrigation (W), N fertilization (N), and their interaction (W × N) had significant effects on the cumulative N2O emissions. With the increase of irrigation and N rates, the cumulative N2O emissions showed an increasing trend. In 2022 and 2023, the cumulative N2O emissions at W3 level increased by 82% and 106%, respectively compared with that at W1 level, and the cumulative N2O emissions of the N4 treatment increased by 192% and 153%, respectively compared with that of the N0 treatment ( Figure 3 ). The W3N4 treatment had the highest cumulative N2O emissions, and the W1N0 treatment had the lowest cumulative N2O emissions ( Figure 3 ). The change of N2O emission coefficient was similar to that of the cumulative N2O emissions in the two years. The W3N4 treatment had the highest N2O emission coefficient.

Effects of irrigation (W) and nitrogen (N) treatments on the cumulative N2O emissions during the alfalfa growing seasons in 2022 and 2023. Different lowercase letters indicate significant differences between treatments at p< 0.05 (Tukey’s test). The treatment abbreviations are the same as those in Figure 2 .

In the two years, irrigation and N fertilization had no significant effect on WFPS values due to the short irrigation interval. However, the WFPS value increased with the increase of irrigation rate, and the WFPS value of the N1 treatment decreased compared with that of the N0 treatment at each irrigation level ( Supplementary Figure S1 ).

Soil inorganic N content showed similar dynamics in the two years. In 2022 and 2023, the content of NH4 +-N ranged from 2.2 to 4.0 mg kg-1 and the content of NO3 –N ranged from 2.5 to 7.5 mg kg-1 in the surface soil ( Supplementary Figure S2 , S3 ). Nitrogen application increased soil inorganic N contents compared with N0 treatment. Peaks in NO3 –N content was observed during 10–17 and 71–77 days for all treatments in the first year, and during 10-17, 49-55, and 77–83 days in the second year ( Supplementary Figure S3 ). The NO3 –N content was maintained at a high level under W3N4 treatment, and the irrigation treatment alone had no significant effect on the contents of NH4 +-N and NO3 –N. soil N2O flux was significantly positively correlated with WFPS, NH4 +-N content, and NO3 –N content during the two growing seasons ( Supplementary Figure S4 ).

 Pearson correlation analysis and structural equation modeling results

Most of the NUE indicators including AEN, NUE, PEN, and PFPN were positively correlated with RFV, WPC, and WPI, and negatively correlated with ADF, RDF, N2O flux, and N2O emission coefficient ( Figure 4 ). Alfalfa yield was significantly positively correlated with CP, ETC , and N2O emission coefficient.

Correlation analysis of alfalfa yield, crude protein (CP), relative feeding value (RFV), acid detergent fiber (ADF), neutral detergent fiber (NDF), evapotranspiration (ETC ), crop water productivity (WPC), irrigation water productivity (WPI), nitrogen agronomic efficiency (AEN), nitrogen use efficiency (NUE), nitrogen physiological efficiency (PEN), partial factor productivity of nitrogen (PFPN), N2O flux, and N2O emission coefficient. Red and blue represent negative and positive correlations, respectively. *p< 0.05; **p< 0.01; ***p< 0.001.

The SEM model showed that increasing N rate resulted in an increase in N2O emissions and a decrease in NUE, with factor loading of 0.66 and -0.92, respectively (p< 0.01). In addition, irrigation and N fertilization significantly increased CP content, yield, and WPC, with factor loading of 0.71, 0.54, and 0.92, respectively (p< 0.01). Overall, the results supported the hypothesis of the model, that is moderately reducing irrigation and N application rates may maximize water and nutrient use efficiency and alfalfa yields while reducing the N2O emissions. ( Figure 5 ).

Structural equation modeling (SEM) for the effects of irrigation rate and nitrogen application rate on N2O emissions, N2O emission coefficients, nitrogen use efficiency, crop water productivity, yield, and crude protein content. The numbers adjacent to the arrows are the factor loading, which explains the variance of the observed variable, and the width of the line is proportional to the factor loading. The red and blue lines indicate negative and positive effects, respectively. Critical paths are marked with *.

 Discussion

Nitrous oxide emissions from farmland are affected by multiple factors, such as climatic factors, soil properties, and agricultural managements (Akiyama et al., 2009; Cai and Akiyama, 2017). The results of this study showed that there were different N2O flux peaks in each growing season. This is directly related to the increase of soil NO3 − (2.5-7.5 mg N kg-1) after N fertilization. Interestingly, the first peak N2O flux occurred 12 days after the first N topdressing in the two years, and another peaks occurred 7 days after the second and third N topdressing. This may be related to the temperature change during the growing season. The increase in temperature during the second and third N topdressing can enhance the respiration of microorganisms, causing soil oxygen deficit. Denitrifying microorganisms utilize nitrate in soil as an electron acceptor and reduce it to nitrogen through a series of enzymatic reactions, accelerating the denitrification (Braker et al., 2010; ChengHsien et al., 2020). Therefore, the peaking time of N2O flux is significantly earlier than that after N application in spring. In addition, after N topdressing, the sufficient soil inorganic N, particularly nitrate nitrogen, provides more substrates for denitrifying microorganisms, accelerating the denitrification. This may also be an important reason for the early appearance of peak N2O flux (Millar et al., 2018; Schellenberg et al., 2012). It was also found that after the fourth harvest at 106 days in the second year, no significant N2O fluxes were observed after irrigation alone. This may be due to the fact that the substrates such as organic carbon and nitrogen in the soil are diluted or lost, resulting in insufficient substrates for microorganisms. This limits the growth and metabolism of microorganisms, and reduces their activities (Li et al., 2020). In this study, irrigation combined with N fertilization caused higher N2O emissions than irrigation alone. This may be due to the fact that high soil moisture content hinders gas diffusion, and causes an anaerobic soil environment. This makes the metabolic activities of denitrifying microorganisms more active, and increases soil denitrification potential and rate, i.e., reducing nitrate nitrogen to gaseous nitrogen more quickly, thus increasing N2O emissions (Sainju et al., 2012). In this study, compared with high irrigation rate W3 (675 mm), the irrigation rate 375–525 mm was more conducive to improving soil permeability and microbial environment, thereby inhibiting denitrification and reducing N2O emissions (Abalos et al., 2014). High N application rates resulted in higher accumulative N2O emissions and higher N2O emission coefficients than other nitrogen application rate treatments in this study. This may be due to the fact that most of the applied N could not be absorbed and utilized by alfalfa, and the N residues in soil are used by soil microorganisms for nitrification and denitrification (Liu et al., 2017; Lyu et al., 2019), thereby increasing N2O emissions. It was found that when the N application rate was increased to 300 kg N ha-1, the two-year average N2O emission coefficient increased to 5% compared with that of the N1 treatment. Therefore, reducing the N application rate is an effective way to reduce the N2O emission in alfalfa planting, and N2 may be the optimal N rate because the two-year average N2O emissions of the N2 treatment could be significantly reduced by 63% compared with that of the N4 treatment.

Soil inorganic N is the main source of microbial N2O production (Millar et al., 2018; Xiao et al., 2018). This study results showed that the N2O flux peak was significantly enhanced after irrigation combined with N fertilization, and the soil NO3 –N content was high during N2O flux peaks, and the high soil NO3 –N content lasted for about two weeks after N topdressing. This result was validated by the correlation analysis results, that is, there was a significant positive correlation between N2O fluxes and NO3 –N (R2 = 0.82)/NH4 +-N (R2 = 0.21) content.

Optimizing WPC is one of the focus of this study. It can be achieved by reducing ETC and increasing alfalfa yield (Li et al., 2019). In this study, the WPC at W3 level was lower than that at W1 and W2 levels. This may be due to the increased soil ETC and percolation ( Table 4 ) (Li et al., 2019; Cai et al., 2020). Besides, it was found that the effect of N application rates on ETC was not significant, but the N application rate of 150–225 kg N ha-1 significantly increased alfalfa yield, so both WPC and WPI can be maximized. In 2023, at different irrigation levels, NAE increased significantly with the increase of irrigation rate, while NUE showed a downward trend. This may be due to the fact that under drought conditions, irrigation promotes alfalfa growth, and NAE continues to rise due to the release of yield potential. However, after exceeding the optimal irrigation rate, NUE decreases due to nitrogen losses through leaching and denitrification. This contradiction highlights the importance of water-nitrogen coupling optimization in alfalfa planting. The PFPN, AEN, NUE, and NP of the N4 treatment decreased compared with those of the N1 treatment. This is mainly due to that the imbalance between alfalfa N requirement and N supply (Liu et al., 2015) inhibits the growth and development of alfalfa roots, reduces the uptake of nutrients and water, and ultimately affects alfalfa yield (Islam et al., 2012). Therefore, the N application rate of 75–225 kg ha-1 is more conducive to promoting root development and root activity, regulate the distribution of photo assimilates in plant shoots, and effectively improve alfalfa resource use efficiency and yield, compared with other N application rates (Vasileva and Pachev, 2015). Mumford et al. (2019) reported that N2O emissions from dry farmlands are an important pathway for N loss and the main cause of low NUE. This is confirmed by the negative correlation between N2O emissions and NUE in this study ( Figure 4 ). In conclusion, both over irrigation and over N application could affect alfalfa WPC and NUE, and the optimal irrigation(W2, 525 mm) and N application rates(N2/N3, 150–225 kg ha-1) could achieve high resource use efficiency.

In arid and semi-arid regions, irrigation and fertilization are the main determinants of forage yield and quality (Djaman et al., 2020). The results of this study showed that the rainfalls during the growing season of alfalfa in 2022 (54.5 mm) and 2023 (56.0 mm) were low, and increasing the irrigation rate significantly increased alfalfa yield. This is due to that sufficient water and nutrient supply improves alfalfa leaf photosynthesis, thus increasing alfalfa biomass (Ferreira et al., 2015; Li and Su, 2017). However, the subsurface drip irrigation can reduce water evaporation, so the irrigation rate W2 is sufficient to meet the water needs of crop growth, and further increasing the irrigation rate has no significant effect on alfalfa yield.

The nutritional quality of forage determines the value in use and value in exchange, because it affects the digestion of forage, the energy and nutrient absorption by livestock, and ultimately the yield and quality of livestock products (Richman et al., 2015). Crude protein content (CP), relative feed value (RFV), neutral detergent fiber (NDF), and acidic detergent fiber (ADF) are important indicators to measure the nutritional quality of forage (McDonald et al., 2021). In this study, the change trend of CP content with irrigation rate was similar to that of yield, while the contents of NDF and ADF increased significantly at W3 level compared with those at W1 and W2 levels. This may be due to that over irrigation accelerates crop maturation, reduces CP content, and increases cell wall contents and fiber count (Liu et al., 2021). Compared with the N0 treatment, applying 150–225 kg ha-1 of N fertilizer significantly improved alfalfa yield, CP content, and RFV. However, further increasing N application rate led to a decrease in alfalfa yield, CP content, and RFV. This may be due to that the soil available N content is low (11.2 mg kg−1) at the experimental site. N application can increase the chlorophyll content and photosynthetic capacity of leaves, which increases the dry matter yield and the synthesis of amino acids, thus improving the protein content of alfalfa (Gao et al., 2020). However, excessive N inputs can affect nodulation and N fixation, but can also be counterproductive to crop growth (Xie et al., 2015; Reinprecht et al., 2020).

According to recent survey, most farmers in the experimental site applied 450 kg ha-1 of N to pursue high yield. This adversely affects alfalfa quality, resource utilization, and environmental health (Fan et al., 2016; Sha et al., 2021). When assessing the feasibility of agricultural managements such as irrigation and N fertilization, it is important to consider not only their impacts on crop yields, but also their impacts on the environment (Tan et al., 2017; Zhang et al., 2022). In general, the irrigation rate of 525 mm combined with the N application rate of 150–225 kg N ha-1 could increase alfalfa yield, quality, and resource use efficiency, while reducing N2O emissions. Thus, it is the optimal combination for local alfalfa planting under subsurface drip irrigation.

 Conclusion

The cumulative N2O emissions showed an increasing trend with the increase of irrigation and N application rates. High cumulative N2O emissions are an important reason for the low NUE. The irrigation rate of 525 mm and the N application rate of 150–225 kg ha-1 could significantly improve the yield and quality of alfalfa compared with the over irrigation(W3, 675 mm) and over N fertilization(N4, 300 kg ha-1) by local farmers. However, further increasing the irrigation and N application rates could not further increase the yield and quality of alfalfa, but caused an increase in N2O emissions and a decrease in WPC and NUE. This may cause serious resource waste and environmental pollution. However, rainfall and soil texture are different in different arid regions. This may significantly affect the relationship between resource use and greenhouse gas emissions during the growing season of alfalfa. Therefore, it is necessary to clarify the response of resource use efficiency to climate change under different precipitations and soil types in the future, to further optimize irrigation and fertilization strategies.

 Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

 Author contributions

HM: Validation, Writing – review & editing, Formal analysis, Software, Writing – original draft, Conceptualization. QS: Writing – review & editing, Funding acquisition, Conceptualization, Supervision. XZ: Data curation, Writing – review & editing. PJ: Data curation, Writing – review & editing.

 Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by the Ningxia Hui Autonomous Region Key R&D Program (2022BEG02004).

 Acknowledgments

Thanks to Mr. Li Yulong for his guidance and help in the paper.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1598110/full#supplementary-material

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