Optimized nitrogen fertilizer management to balance lodging resistance and stem physicochemical properties of oats

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Although the physiological basis of lodging is established, the precise quantitative contribution of specific stem characteristics under varying N regimes remains underexplored. To bridge this gap and optimize N strategies, we conducted a two-year field trial (2018–2019) in the rain-fed Qinghai-Tibet Plateau using two cultivars with contrasting lodging resistance: LENA (resistant) and QY2 (susceptible). Under six N gradients (0, 60, 120, 180, 240, and 300 kg·ha⁻¹), we quantified structural polymers (lignin, cellulose, starch), non-structural carbohydrates (soluble sugars), and mineral elements (Ca, K, Si, Mg) in the second basal internode at the milk stage. Data were analyzed using hierarchical partitioning, Structural Equation Modeling (SEM), and the TOPSIS method to identify key determinants of stem strength. Results Lodging severity and stem physio-chemical profiles were significantly modulated by N dosage, genotype, and their interaction. Escalating N input marked elevated soluble protein levels while concurrently depressing the concentrations of lignin, cellulose, and key minerals (Si, Ca, K). Hierarchical partitioning analysis pinpointed Ca, K, Si, soluble sugars, and lignin as the predominant drivers, collectively accounting for 87% of the variation in the lodging index. Furthermore, SEM elucidated the regulatory pathways: genotype and N fertilization (including their interaction) indirectly exacerbated lodging risk by suppressing the accumulation of these structural and mineral components. The total standardized effects on the lodging index were recorded as -0.209 (genotype), 0.31 (N rate), and 0.156 (interaction). Conclusions Based on the comprehensive production and lodging resistance performance, it is recommended that in the rain-fed area of the Qinghai-Tibet Plateau, the suitable nitrogen application rate of the lodging-resistant variety LENA should be 180 kg·hm − 2 , while that of the lodging-prone variety QY2 should be controlled below 60 kg·hm − 2 . These findings provide a physiological framework for rational nitrogen management and offer novel mechanistic insights into oat lodging resistance. oats nitrogen management lodging carbon and nitrogen balance SEM TOPSIS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Nitrogen (N) is an essential macronutrient that governs crop physiology and productivity. While optimized N application is critical for enhancing photosynthesis, biomass accumulation, and grain protein content [ 1 , 2 ], it exerts a dual effect on crop performance. In intensive agricultural systems, excessive N input is prevalent, which not only intensifies environmental footprints but also disrupts the balance between vegetative growth and mechanical support, thereby predisposing crops to lodging [ 3 ]. In China, where oats are key for both food and feed, high-yield cultivation strategies often involve substantial water and fertilizer inputs. Consequently, stem overgrowth and compromised physical strength are frequent, leading to severe lodging events [ 4 ]. Lodging severely constrains oat production by destroying the canopy structure and inhibiting photosynthate translocation, which results in yield reduction and quality deterioration, while also complicating mechanized harvesting [ 5 ]. Given that unreasonable N rates are a primary determinant of lodging, a critical challenge in modern oat cultivation is to decouple yield gain from lodging risk through precise nitrogen management. The lodging resistance of stems is essentially determined by their physical and mechanical strength, which is the result of the synergistic accumulation of cell wall structural components, mineral nutrients, and non-structural carbohydrates. Existing studies have shown that lignin and cellulose are the main skeleton polymers of the cell wall, and their content directly determines the flexural strength of stems [ 6 ]. Mineral nutrients such as silicon (Si) and potassium (K) enhance stem rigidity by promoting epidermal silicification and regulating cell osmotic potential [ 7 ]. Non-structural carbohydrates such as starch and soluble sugar not only increase stem plumpness but are also the key substrates of cell wall biosynthesis[ 8 ]. However, excessive nitrogen application significantly disturbs the above process through the carbon and nitrogen metabolism balance mechanism: high nitrogen input forces photosynthetic products to preferentially flow to nitrogen metabolic sinks (such as protein synthesis), resulting in a lack of carbon skeleton required for cell wall construction [ 9 ]. Simultaneously, the rapid accumulation of biomass induced by high nitrogen levels produces a growth dilution effect, resulting in a significant decrease in the concentration of mineral elements that play a supporting role in the unit dry weight [ 10 ]. Although previous studies have explored the response of agronomic traits, physiology, and biochemistry to nitrogen in a variety of crops [ 11 , 12 ], a systematic analysis of the regulation mechanism of oat lodging is still insufficient. Lodging is a complex process involving the coordination of morphological, anatomical, and physiological factors, and there is a strong collinearity between the physical and chemical indicators. Conventional linear regression analysis cannot accurately quantify the independent contribution of each factor to the lodging index, and it is impossible to qualitatively analyze the specific path by which the nitrogen application rate affects lodging by regulating key traits. An in-depth analysis of this mechanism is crucial for developing precise lodging resistance strategies based on physiological indicators. In view of this, based on a field experiment conducted in the typical alpine rain-fed area of the Qinghai-Tibet Plateau from 2018 to 2019, two oat varieties with significant differences in lodging resistance (lodging-resistant LENA vs. susceptible QY2) were selected, and six nitrogen application gradients were set. The study focused on the use of hierarchical partitioning (HP) to screen the key driving factors affecting lodging, and constructed a structural equation model (SEM) to analyze the physiological regulation path of "nitrogen application rate-stem physical and chemical changes-lodging", and combined with TOPSIS method to determine the appropriate nitrogen application threshold. The purpose of this study was to reveal the physiological nature of oat lodging induced by nitrogen fertilizer and to provide a theoretical basis and quantitative recommendations for the precise management of nitrogen fertilizer in different genotypes of oats in this region. 2 Result 2.1 Difference analysis between two different lodging-resistant oat varieties The two-years (2018–2019) experimental data showed that (Fig. 2 ), the lodging-resistant cultivar LENA and the lodging-susceptible cultivar QY2 had extremely significant differences in all measured traits ( P < 0.01). In comparison to LENA, the susceptible variety QY2 exhibited markedly lower levels of structural (LC, CC) and non-structural components (SS, ST). Simultaneously, the accumulation of key mineral elements (Si, Ca, and K) that enhance mechanical strength was maintained at a high level. In contrast, the magnesium and soluble protein content in the stems of QY2 was significantly higher than that of LENA. 2.2 Impact of nitrogen fertilization on the lodging incidence and index in oats Nitrogen fertilizer treatment significantly affected the lodging phenotype of oat plants. In general, with an increase in the nitrogen application rate from N0 to N5, the field lodging rate and lodging index of the two varieties in 2018 (Fig. 3 a) and 2019 (Fig. 3 b) showed a significant upward trend and reached a peak under high nitrogen treatment (N4-N5). There were significant differences in the response patterns of different varieties to nitrogen fertilizer. The lodging-prone variety QY2 was sensitive to the nitrogen fertilizer. In 2018, the lodging rate in the field was as high as 63.27% even without nitrogen application (N0). When the fertilization amount was N1, the lodging rate increased to 95.97%, and the lodging index under this treatment was approximately 1.74 times that of LENA under the same nitrogen application level (35.03 vs. 20.13). In contrast, the lodging-resistant variety LENA had a high tolerance to nitrogen fertilizer, and no obvious lodging occurred in LENA under no nitrogen application in the two growing seasons and N1 treatment in 2019. Even with N5 treatment in 2019, the field lodging rate was significantly lower than that of QY2 under the same treatment. Although the lodging rate generally increased with the increase of lodging index, the lodging index of the lodging-resistant variety LENA increased to 50.19 under N1 treatment in 2019, but its actual field lodging rate remained at 0. 2.3 Effect of nitrogen application on physical and chemical indexes of oat stalk The content of soluble protein (SP) increased with increasing nitrogen application rate (Fig. 4 a). In 2018, when the nitrogen application rate increased from N0 to N5, the soluble protein content of QY2 increased from 34.17 mg/g to 81.58 mg/g, an increase of 138.7%; LENA also showed a similar growth trend, but the increase was small. When the nitrogen application rate increased from N0 to N5, its content increased from 23.33 mg/g to 49.29 mg/g, an increase of 111.27%. The change rule for 2019 was similar to that of 2018. The starch and soluble sugar contents initially increased and then decreased with increasing nitrogen application rate (Fig. 4 b-c). The two cultivars differed significantly in terms of soluble sugar accumulation ( P < 0.01) (Table 3 ). QY2 was more sensitive to nitrogen fertilizer, reaching the maximum value under the N1 treatment, which was significantly higher than that of the other treatments, approximately 2.2 times that of the N5 treatment. For LENA, the values peaked under the N3 regime, exhibiting a significant increase over all other treatments. In 2018 and 2019, LENA was significantly increased by 56.27% and 86.97%, respectively, compared with N5, which had the lowest content. The variation in starch content (ST) was similar to that of soluble sugar. QY2 and LENA reached their maximum values in the N1 and N3 treatments, respectively, and were significantly higher than those in the other treatments. QY2 was more sensitive to nitrogen fertilizer, and its soluble sugar content decreased from 119.34 mg/g in the N1 treatment to 66.21 mg/g in the N5 treatment in 2018. In contrast, LENA was more tolerant to nitrogen fertilizer. In 2019, the SS content of LENA under the N5 treatment (88.18 mg/g) was significantly higher than that of QY2 (79.93 mg/g). The effect of the nitrogen application rate on the mineral element content in stems was significant. During the two years, with the increase in the nitrogen application rate, the Si content of the two varieties showed a downward trend, but the decline in QY2 was greater than that in LENA (Fig. 4 d). In 2018, the Si content of QY2 decreased significantly from 4.05 mg/g in the N0 treatment to 1.72 mg/g in the N5 treatment. In contrast, the Si content of LENA remained at a high level under each treatment, which decreased significantly from 6.52 mg/g in the N0 treatment to 4.16 mg/g in the N5 treatment, and the content in the N5 treatment was significantly higher than that in QY2. Similar to the change rule of silicon content, the contents of potassium (K) and calcium (Ca) also decreased with the increase in nitrogen application (Fig. 4 e-f). In 2018, the potassium and calcium contents in QY2 under N5 treatment were 17.88% and 74.94% lower than those in N0 treatment, respectively. The potassium content of LENA decreased by 6.73%, and the decrease in calcium content was similar to that of QY2. Nitrogen fertilization exerted a significant influence on magnesium (Mg) accumulation ( P < 0.01), with levels generally rising in parallel with increasing N gradients (Fig. 4 g). The nitrogen application rate significantly affected the lignin (LC) and cellulose (CC) contents ( P < 0.01) (Table 3 ). With an increase in the nitrogen application level, the cellulose and lignin contents of the two varieties showed a downward trend (Fig. 4 h-i). During the 2018 season, cellulose levels in LENA and QY2 under the N5 regime were reduced by 15.78% and 13.73%, respectively, relative to the N0 control. Significant varietal differences were observed in lignin accumulation; for instance, under the N5 regime in 2019, LENA reached 9.66%, markedly surpassing the 8.05% recorded for QY2. 2.4 Response of oat culm physio-chemical characteristics and lodging to nitrogen dosage and cultivar differences Table 3 Analysis of variance Characteristics Variety Nitrogen Fertilizer Variety×Nitrogen Fertilizer SP 1131.3** 305.91** 51.85** ST 1503.01** 288.3** 239.34** SS 4022.31** 900.93** 620.78** Si 748.77** 43.52** 4.47** K 54.36** 158.25** 5.68** Ca 413.17** 1032.21** 11.98** Mg 101.56** 21.58** 7.75** CC 792.6** 453.31** 247.7** LC 861.74** 6.95** 26.15** FLR 2785.36** 306.11** 59.87** LI 535.27** 181.41** 20.43** The values indicate the F-statistics. *, **Denote significant differences at P < 0.05 and P < 0.01, respectively. Table 3 shows the effects of variety, nitrogen application rate, and their interaction on mineral nutrients, structural components, non-structural components, and lodging of oat stems. The results showed that oat varieties had extremely significant effects on all indicators ( P < 0.01), with the most significant effect on soluble sugar content (F = 4022.31, P < 0.001), followed by the lodging rate (F = 2785.36, P < 0.001). There was a significant main effect of nitrogen application rate on all indicators ( P < 0.01), among which the effect on calcium content was the most significant (F = 1032.21, P < 0.001), followed by soluble sugar (F = 900.93, P < 0.001). Further analysis of the effect of interaction showed that there was a significant interaction between variety (V) and nitrogen fertilizer (N) on lodging characteristics (FLR, LI) and stem physical and chemical indexes of oats ( P < 0.01), among which the effect on soluble sugar content was the most significant (F = 620.78, P < 0.001). 2.5 Screening of key physical and chemical factors affecting lodging index To address the issue of multicollinearity among physiological and biochemical indicators, we performed a hierarchical partitioning analysis based on linear mixed-effects models (Fig. 5 ). The model exhibited high explanatory power for the lodging index (Marginal R 2 = 0.870), indicating that the selected traits accounted for the vast majority of the variation, with a negligible influence from random effects. The analysis revealed distinct differences in the independent contributions of the three trait categories, ranked as follows: mineral nutrients > non-structural components > structural components. Specifically, mineral nutrients (Si, K, Ca, and Mg) were the dominant drivers, with an independent contribution of 57.36%, which was substantially higher than that of the other two categories. Non-structural components (SS, ST, and SP) followed, explaining 30.18% of the variation, whereas structural components (LC and CC) contributed the least (12.47% variation).Regarding specific variables, Calcium (Ca), Potassium (K), and Soluble Sugar (SS) showed highly significant effects on the lodging index ( P < 0.01), while Silicon (Si) and lignin (LC) were significant at the P < 0.05 level, identifying them as key regulators of lodging. 2.6 Physiological pathway analysis of nitrogen regulation of oat lodging Based on significant factors such as lignin, calcium, potassium, silicon, and soluble sugar screened by the hierarchical segmentation method, a segmented structural equation model (SEM) was constructed to analyze the specific path of variety, nitrogen application rate, and their interaction to regulate lodging index through the physiological and biochemical indices (Fig. 6 a). The model fitness index showed that the model fitted well with the data, Fisher 's C = 1.053, P = 0.591 > 0.05, and the Akaike information criterion (AIC) value was 55.053, which could be further analyzed. The results showed that the model explained 85% of the variation in lodging index (R 2 = 0.85). Mineral nutrients (MN) and non-structural components (NSC) exerted robust, direct negative regulation on the lodging index, evidenced by path coefficients of -0.732 and − 0.213, respectively ( P < 0.001). Lignin (SC) was predominantly modulated by soluble sugars (NSC), thereby exerting a significant indirect influence on lodging (coefficient: -0.0842; P < 0.001). Furthermore, the impact of genotype, nitrogen dosage, and their interplay on oat lodging was mediated via alterations in lignin, soluble sugars, and mineral profiles (Ca, K, and Si), with total effect values of -0.209, 0.31, and 0.156, respectively (Fig. 6 b). 2.7 Comprehensive evaluation Through a comprehensive evaluation of the lodging index and important factors, our results indicate that the optimal nitrogen dosage was genotype-dependent (Fig. 7 ). The fit degree of LENA under N1 treatment was the highest (0.74), but there was no significant difference between N0, N2, and N3 treatments. The degree of fit of LENA under N5 treatment was the lowest (0.427). The fit degree of QY2 under N0 treatment was the highest (0.556), but there was no significant difference with N1 treatment (0.501). The degree of fit of QY2 under N5 treatment was the lowest (0.39). 3 Discussion 3.1 Nitrogen-driven 'carbon-nitrogen metabolism trade-off' and variety-specific response In this study, the distinct stem traits observed among oat varieties confirmed that, alongside agronomic measures, the genetic background is a critical intrinsic determinant of lodging resistance [ 21 ]. Previous studies have established that lodging-resistant germplasm is typically characterized by high levels of Ca, K, Mg, Si, lignin, and hemicellulose [ 22 ]. Our results largely align with these findings regarding the accumulation of Ca, K, Si, crude fiber, and lignin in leaves. However, a divergence was observed for Mg. Unexpectedly, the lodging-prone variety QY2 exhibited a significantly higher Mg content than the lodging-resistant LENA. This anomaly may be attributed to differences in Mg utilization efficiency. It has been reported that lodging-prone varieties possess lower Mg efficiency; in high-N environments, they may preferentially utilize nitrogen and K, which inhibits the physiological functions of Mg (e.g., photosynthetic efficiency), leading to its ineffective retention within plant tissues rather than functional assimilation [ 23 ]. Notably, the lodging-resistant cultivar LENA accumulated significantly greater reserves of non-structural carbohydrates (specifically starch and soluble sugars), markedly surpassing the susceptible genotype QY2. Although this trend diverges from some studies conducted without nitrogen application [ 7 , 24 ], when considered alongside the significantly higher soluble protein content in QY2, it is likely attributable to the differential carbon-nitrogen metabolic responses induced by exogenous nitrogen. According to the "Carbon-Nitrogen Balance Hypothesis," high nitrogen input drives the preferential allocation of photosynthates toward nitrogen sinks (e.g., protein synthesis), thereby competitively inhibiting the conversion of carbon skeletons into cell wall structural components (lignin and cellulose) [ 25 – 27 ]. In this study, the susceptible cultivar QY2 exhibited high nitrogen sensitivity, with nitrogen application significantly stimulating soluble protein synthesis (N pool) but depleting non-structural components (C pool). This intense "source-sink competition" left QY2 with insufficient carbon reserves for cell wall construction during the rapid stem elongation. Furthermore, the "growth dilution effect" caused by rapid biomass accumulation [ 10 ] led to a significant reduction in the concentration of physically supportive elements (Ca, Si, and K) and lignin per unit dry weight. In contrast, the resistant variety LENA demonstrated superior homeostatic regulation. Nitrogen uptake in LENA appears to enhance photosynthetic source activity rather than merely driving vegetative growth. Consequently, LENA maintained a surplus of non-structural components and sustained the accumulation of structural materials, even under high nitrogen stress. 3.2 Mechanical threshold and carbon-nitrogen balance of stem lodging There is no simple linear correlation between the LI and FLR; rather, a "mechanical safety threshold" exists [ 28 , 29 ]. In this study, although FLR generally tracked with LI, the lodging-resistant variety LENA exhibited an LI of 50.19 under N1 treatment in 2019, yet its actual FLR remained at zero. This suggests that as long as the comprehensive physical strength of the stem—derived from lignin, cellulose, and silicification—is sufficient to resist external bending moments, the plant remains in a "mechanically safe zone" even if the risk index increases [ 30 , 31 ]. However, this safety margin is dynamic and environment dependent. Lodging is fundamentally the result of the interaction between "intrinsic factors" (stem mechanical weakening) and "extrinsic factors" (environmental stress). In 2019, the absence of extreme weather meant that the potential risk indicated by a high LI did not translate into actual lodging. In contrast, heavy rainfall during the early grain-filling stage in 2018 (July 23, 30.4 mm; data from era5: https://cds.climate.copernicus.eu ) acted as a trigger. The increased ear weight due to water absorption shifted the center of gravity upward; this, compounded by the impact of rain, forced plants in a critical state to exceed their safety threshold. Further analysis reveals that the magnitude of this safety threshold reflects genotypic differences in adaptability to nitrogen-induced "carbon-nitrogen imbalance." The lodging-prone variety QY2 is highly sensitive to nitrogen; the accumulation of its non-structural components peaks prematurely at low nitrogen levels (N1) and is rapidly depleted to fuel vigorous nitrogen metabolism, leaving insufficient carbon skeletons for constructing cell walls. Consequently, lodging occurs because the physical support falls below the safety limit. In contrast, the lodging-resistant variety LENA exhibits superior carbon-nitrogen homeostasis. Its accumulation peak for non-structural components shifted to the medium nitrogen (N3) level, maintaining a higher silicon and lignin content even under high nitrogen stress. This physiological advantage confers a higher safety threshold and greater buffering capacity, allowing LENA to maintain upright growth despite nitrogen-induced declines in mechanical properties. 3.3 Correlations of lodging index with genotype, nitrogen dosage, and culm physio-chemical traits 3.3.1 Mineral nutrient elements are the primary factors determining the lodging resistance of stems To address the issue of multicollinearity among physicochemical traits, we employed hierarchical partitioning to effectively eliminate interference between indicators and quantify their independent contributions to the lodging index. We found that mineral nutrients (Si, K, and Ca) explained 57.36% of the variation in the lodging index, identifying them as the primary determinants of stem strength. Previous reports have established the mechanisms underlying these effects. Silicon, acting as a "skeletal" material in the cell wall, significantly enhances stem mechanical hardness and toughness through deposition, thereby reducing the lodging index [ 32 ]. Calcium, a key component of pectin in the middle lamella, participates in cell wall construction and enhances intercellular adhesion and tissue compactness [ 33 ]. Potassium plays a critical role in promoting the development of thick-walled mechanical tissues by maintaining cell turgor [ 34 ]. Our results further confirmed that higher concentrations of these elements in the stem enhanced lodging resistance, thereby negatively regulating the lodging index. 3.3.2 The synergistic regulation mechanism of non-structural components and structural components Previous studies have suggested that structural components such as lignin are the primary determinants of stem strength [ 35 – 37 ]. However, our findings diverge from this perspective. Hierarchical partitioning analysis revealed that the independent contribution of non-structural components (soluble sugars, starch, soluble protein) to the lodging index (30.18%) exceeded that of structural components (lignin, cellulose) (12.47%). Structural Equation Modeling (SEM) further elucidated the underlying mechanism: soluble sugar exerted a significant direct negative effect on the lodging index (path coefficient = -0.213). While lignin also inhibited lodging, its influence was more complex. The model indicated that lignin primarily affected the lodging index indirectly, mediated by soluble sugar (path coefficient = -0.0842). These results suggest that soluble carbohydrates not only regulate cell osmotic potential to maintain stem turgor but also serve as a core substrate pool, indirectly regulating the synthesis of structural components (e.g., lignin) via the distribution of metabolic flux [ 38 , 39 ]. Therefore, evaluations of oat lodging resistance should emphasize not only lignin content but also the accumulation of nonstructural substances, particularly soluble sugars. 3.3.3 Physiological regulation of nitrogen application rate and varieties on lodging resistance through physiological and biochemical pathways This study elucidated a comprehensive pathway linking agronomic measures to physiological traits and, ultimately, to lodging resistance. Genotype, nitrogen application rate, and their interaction acted as exogenous drivers, determining the lodging index by regulating soluble sugar accumulation, lignin synthesis, and stem mineral uptake (Ca, K, and Si). Analysis of the total effects revealed that mineral nutrients had the strongest influence (-0.732), followed by non-structural components (-0.213) and structural components (-0.0842). We propose that the core objective of nitrogen management should be to maximize mineral absorption and utilization while coordinating carbon metabolite accumulation. The divergent performance of lodging-prone and lodging-resistant varieties under varying nitrogen levels is fundamentally attributable to their differential sensitivity to key physicochemical factors [ 11 ]. Therefore, rational nitrogen application mitigates lodging risk by optimizing the enrichment of Si, K, and Ca and balancing soluble sugar and lignin contents, thereby constructing a physically robust stem structure. In summary, oat stem lodging resistance results from the synergistic interplay of mineral nutrients and carbon metabolites, which is consistent with the classical research of Mulder et al. [ 40 ]. 3.4 Best quantity of applying nitrogenous fertilizer Rational nitrogen management is crucial for synergistically improving oat forage quality, enhancing lodging resistance, and optimizing yield [ 41 , 42 ]. Previous studies have indicated that appropriate nitrogen rates optimize population structure and strengthen stem mechanics, thereby reducing lodging risk [ 43 ]. Our results demonstrated significant genotypic differences in the nitrogen response. The lodging-prone variety QY2 proved highly sensitive to nitrogen fertilizer; its comprehensive evaluation scores under N0 and N1 were significantly higher than those under high nitrogen treatments, with no significant difference between N0 and N1. In contrast, the lodging-resistant variety LENA exhibited a higher nitrogen tolerance. Although the evaluation score was highest under N1 treatment (0.74), it did not differ statistically from the N0, N2, or N3 treatments. While low nitrogen levels help maintain traits associated with lodging resistance, nitrogen remains the primary driver of biomass accumulation, and insufficient application often results in significant yield penalties [ 44 ]. Therefore, provided that lodging risk is not significantly exacerbated and forage quality is preserved, higher nitrogen rates should be prioritized to fully exploit the yield potential. Therefore, strategies for yield enhancement are only viable if they sustain nutritional value without aggravating lodging risks. Oat production should adhere to the principle of "variety-specific fertilization" to achieve an optimal balance among yield, quality, and resistance. Specifically, for the lodging-prone variety QY2, N1 served as a reasonable threshold to ensure both yield stability and lodging resistance. Conversely, for the lodging-resistant variety LENA, N3 was the ideal application rate, maximizing forage yield while maintaining quality and resistance. Future research should further investigate optimal nitrogen management across diverse varieties and environmental conditions to simultaneously optimize yield, quality, and lodging resistance. 4 Conclusion Nitrogen fertilizer significantly influenced the physicochemical traits and lodging of oats ( P < 0.05). Optimizing nitrogen application regulates lodging resistance by modulating the accumulation of mineral nutrients, soluble sugars and lignin. Specifically, the contents of Ca, K, Si, soluble sugars, and lignin were closely correlated with lodging resistance, suggesting that they can serve as reliable indicators for evaluation. In conclusion, the varieties LENA and QY2 achieved an optimal balance among yield, quality, and resistance under the N3 (180 kg·hm⁻²) and N1 (60 kg·hm⁻²) treatments, respectively. Therefore, these nitrogen application rates are recommended for the study area and similar ecological regions. 5 Materials and Methods 5.1 Study area Field experiments were conducted in Ganhetan Town (Huangzhong District, Xining City), situated in the hinterland of the Qinghai-Tibet Plateau (36°30′57″N, 101°33′20″E; elevation 2628 m). This region features a typical plateau continental climate characterized by a short frost-free season (90–120 days) and intense solar radiation (2550–2600 annual sunshine hours). The area is cool and semi-arid, with a mean annual temperature of 3.5–4.5°C and significant diurnal fluctuations (12–16°C). Precipitation averages 450–500 mm annually—concentrated between June and August—while evaporation rates are 3–4 times higher. The dominant soil type is chestnut soil, with specific physicochemical properties detailed in Table 1 . Meteorological conditions during the 2018–2019 growing seasons (sourced from ERA5, https://cds.climate.copernicus.eu ) are illustrated in Fig. 1 . Table 1 Physicochemical properties of the soil at the experimental site prior to sowing. Index Organic matter (g·kg⁻¹) Total nitrogen (g·kg⁻¹) Alkaline hydrolysis nitrogen (mg·kg⁻¹) Available phosphorus (mg·kg⁻¹) Rapidly available potassium (mg·kg⁻¹) pH value 58.24 3.12 54.28 4.83 112.45 7.8–8.3 5.2 Test materials The oat varieties used in this study were LENA (Avena sativa L. cv. LENA) and Qingyin No.2 (Avena sativa L. cv. QY2), provided by Qinghai Province Academy of Animal Husbandry and Veterinary Sciences. Material characteristics are listed in Table 2 . The fertilizer tested was urea (46% N). Table 2 Characteristics of the test materials. Variety Characteristics LENA Plant height 90-110cm, middle-late maturity, scattered panicle type, yellow grain color, grain type spindle, tiller number 2–5, lodging resistance. QY2 Plant height 120-140cm, early maturity, ear type scattered, grain color yellow, grain type lanceolate, tiller number 2–3, easy lodging. 5.3 Experimental Design A field experiment was conducted from 2018 to 2019 using a randomized block design. Nitrogen was applied at six distinct rates: 0, 60, 120, 180, 240, and 300 kg N hm⁻² (designated as N0–N5, respectively). The experiment comprised 12 treatment combinations with three replicates, resulting in a total of 36 plots. Each plot covered an area of 15 m² (3 m × 5 m). Each treatment was applied once before sowing as a base fertilizer. The sowing rate was calculated according to the 1000-grain weight of seeds according to the number of seedlings of 300,000 plants per acre. Plots were established on a field previously cropped with highland barley, utilizing a 20 cm row spacing and 1 m alleyways. A uniform background fertility was established using 150 kg·hm⁻² diammonium phosphate prior to seeding. Hand-weeding was strictly enforced during the seedling, tillering, and jointing stages to minimize biotic interference. 5.4 Indicators and methods of determination 5.4.1 Main agronomic characteristics At the milk stage, six plants exhibiting uniform growth were randomly selected from each plot of each fertilizer gradient to measure the following indicators. Height of the center of gravity (HCG): The distance from the base of the stem (including leaves, sheaths, and spikes) to the equilibrium fulcrum after placing the main stem on the fulcrum for balance. Aboveground fresh weight (AFW): The fresh weight of a plant's aboveground parts, including stems, leaves, sheaths, and spikes. 5.4.2 Field lodging rate During the milk stage, the lodging area within each nitrogen treatment plot was assessed to determine the Field Lodging Rate (FLR)[ 13 ]. The calculation formula is as follows. $$\:FLR\left(\%\right)=\frac{\text{A}\text{r}\text{e}\text{a}\:\text{o}\text{f}\:\text{l}\text{o}\text{d}\text{g}\text{i}\text{n}\text{g}}{\text{A}\text{r}\text{e}\text{a}\:\text{o}\text{f}\:\text{s}\text{m}\text{a}\text{l}\text{l}\:\text{c}\text{o}\text{m}\text{m}\text{u}\text{n}\text{i}\text{t}\text{y}}\times\:100$$ 5.4.3 Lodging index Six plants exhibiting uniform vigor were selected for measurement in each plot. The breaking strength of the second internode (BS2) was measured using a YYD-1 stem strength tester (Zhejiang Tuopu Technology Co., Ltd., Hangzhou, China). Prior to measurement, the leaf sheaths were removed, and the stem was positioned on the instrument supports (set to a 2 cm span). The probe applied vertical pressure at a constant rate until fracture occurred. The peak force recorded at the breaking point was defined as the strength at breakage. Finally, Lodging Index (LI) was calculated using the measured BS2, center of gravity height (HCG), and fresh weight (AFW) according to the following formula[ 14 ]: $$\:LI=\frac{HCG\times\:\text{A}\text{F}\text{W}}{{BS}_{2}}$$ 5.4.4 Determination of stem physical and chemical indexes Harvested at the milky stage, the second internodes were subjected to thermal treatment (105°C for 30 min; 65°C to constant weight) and ground into fine powder (< 60 mesh). This prepared material was used to determine physicochemical characteristics, including starch (ST), soluble sugar (SS) [ 15 ], soluble protein (SP) [ 16 ], calcium (Ca), potassium (K), magnesium (Mg), silicon (Si) [ 17 , 18 ], cellulose content (CC) [ 19 ], lignin content (LC)[ 20 ]. 5.5 Data analysis Data integration and analysis were performed using Microsoft Excel 2019 and R software (version 4.0.2). Differences between means were assessed using independent sample t-tests, Duncan’s multiple range test ( P < 0.05), and Analysis of Variance (ANOVA). To elucidate how variety, nitrogen (N) dosage, and their interactions influence the oat lodging index, a segmented structural equation model (SEM) was developed to calculate the associated path coefficients. Furthermore, the TOPSIS algorithm was applied to comprehensively evaluate stem physicochemical traits and lodging risks, thereby determining the optimal N strategy for this region. All graphical representations were produced using R software and OriginPro 2024. Declarations Acknowledgements We thank the laboratory members for their support throughout the experimental process. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data availability The data and images that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests No conflict of interest declared. Funding This research was funded by Grass Seed Innovation and Its Role in Grassland Agricultural Systems (2023-NK-147), the China Agriculture Research System (CARS-34) and Qinghai innovation platform construction project (2025). Author’s contributions RF Z, GL L, R W, and WH L conceived and designed the experiments. RF Z performed the experiments and wrote the manuscript. RF Z, ZL J and W L analyzed the data. GL L contributed to the manuscript preparation and provided editorial advice. All authors read and approved the final manuscript. References Spiertz J. Nitrogen, sustainable agriculture and food security: a review. Sustainable Agric 2009:635–51. Herrera JM, Rubio G, Häner LL, Delgado JA, Lucho-Constantino CA, Islas-Valdez S, Pellet D. Emerging and established technologies to increase nitrogen use efficiency of cereals. Agronomy. 2016;6(2):25. Zhang W, Wu L, Wu X, Ding Y, Li G, Li J, Weng F, Liu Z, Tang S, Ding C. 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Cereal Res Commun. 2019;47(3):566–79. Dorairaj D, Ismail MR. Distribution of silicified microstructures, regulation of cinnamyl alcohol dehydrogenase and lodging resistance in silicon and paclobutrazol mediated Oryza sativa. Front Physiol. 2017;8:491. Kashiwagi T, Ishimaru K. Identification and functional analysis of a locus for improvement of lodging resistance in rice. Plant Physiol. 2004;134(2):676–83. Yamaji N, Ma JF. Spatial distribution and temporal variation of the rice silicon transporter Lsi1. Plant Physiol. 2007;143(3):1306–13. Jarvis MC. Structure and properties of pectin gels in plant cell walls. Plant Cell Environ. 1984;7(3):153–64. Allison EM, Walsby A. The role of potassium in the control of turgor pressure in a gas-vacuolate blue-green alga. J Exp Bot. 1981;32(1):241–9. Xu C, Gao Y, Tian B, Ren J, Meng Q, Wang P. Effects of EDAH, a novel plant growth regulator, on mechanical strength, stalk vascular bundles and grain yield of summer maize at high densities. Field Crops Res. 2017;200:71–9. Raza A, Asghar MA, Ahmad B, Bin C, Iftikhar Hussain M, Li W, Iqbal T, Yaseen M, Shafiq I, Yi Z. Agro-techniques for lodging stress management in maize-soybean intercropping system—a review. Plants. 2020;9(11):1592. Shah L, Yahya M, Shah SMA, Nadeem M, Ali A, Ali A, Wang J, Riaz MW, Rehman S, Wu W. Improving lodging resistance: using wheat and rice as classical examples. Int J Mol Sci. 2019;20(17):4211. Ma Q, Xu X, Wang W, Zhao L, Ma D, Xie Y. Comparative analysis of alfalfa (Medicago sativa L.) seedling transcriptomes reveals genotype-specific drought tolerance mechanisms. Plant Physiol Biochem. 2021;166:203–14. Xie M, Zhang J, Tschaplinski TJ, Tuskan GA, Chen J-G, Muchero W. Regulation of lignin biosynthesis and its role in growth-defense tradeoffs. Front Plant Sci. 2018;9:1427. Mulder E. Effect of mineral nutrition on lodging of cereals. Plant Soil 1954:246–306. Duan L, Ju Z, Ma X, Pan J, Mustafa AE-ZM, Jia Z. Research on enhancing the yield and quality of oat forage: Optimization of nitrogen and organic fertilizer management strategies. Agronomy. 2024;14(7):1406. Shah J, Wang X, Khan SU, Khan S, Gurmani ZA, Fiaz S, Qayyum A. Optical-sensor-based nitrogen management in oat for yield enhancement. Sustainability. 2021;13(12):6955. Pan J, Zhao J, Liu Y, Huang N, Tian K, Shah F, Liang K, Zhong X, Liu B. Optimized nitrogen management enhances lodging resistance of rice and its morpho-anatomical, mechanical, and molecular mechanisms. Sci Rep. 2019;9(1):20274. Kasemsap P, Bloom AJ. Breeding for higher yields of wheat and rice through modifying nitrogen metabolism. Plants. 2022;12(1):85. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 26 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers invited by journal 23 Jan, 2026 Editor invited by journal 23 Jan, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 20 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8646764","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579491840,"identity":"eafc4abf-9266-40da-b19d-16471eb96131","order_by":0,"name":"Ruifang Zhang","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Ruifang","middleName":"","lastName":"Zhang","suffix":""},{"id":579491841,"identity":"c40f2ba2-faf1-45ba-bcce-1fcbf1417d4d","order_by":1,"name":"Rui Wu","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wu","suffix":""},{"id":579491842,"identity":"ed36d1a3-1df1-4334-befe-7d4ffc7cccc8","order_by":2,"name":"Wenhui Liu","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Liu","suffix":""},{"id":579491843,"identity":"a271b530-2a82-4447-bf8e-1c1da54952fd","order_by":3,"name":"Guoling Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACCeaGAxAWmGHDw8/fQEgLI0wLmJEmIznjAGEtDDAtQOKwjUFDAn4dfLcbGw/83FFrt+F4Y+Phgl/neQwYDjB++JiDW4vknYMNB3vPHE/ecOZgw+GZfbd5zJkbmCVnbsOtxeBGYsMB3rZjySDGYd6e2zyWDQfYmHkJaDn4F6HlHI/BgQTCWg7zttXYgRk8Pw4Q1iIJUinbdiBBEuQX3oZkHskZB5vx+oXvRvLhj2/b6uz5jjcf/szzx86en7/54IePeLQwHACThxMbQBRjG5hswKMerqXOHsL7g1/xKBgFo2AUjEwAAD+BZm40NKbTAAAAAElFTkSuQmCC","orcid":"","institution":"Qinghai University","correspondingAuthor":true,"prefix":"","firstName":"Guoling","middleName":"","lastName":"Liang","suffix":""},{"id":579491844,"identity":"8ef838ef-7fb8-46eb-a3f1-0d51007235d8","order_by":4,"name":"Zeliang Ju","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Zeliang","middleName":"","lastName":"Ju","suffix":""},{"id":579491845,"identity":"5f910c93-9ce7-4b88-8707-2841c6823674","order_by":5,"name":"Wen Li","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-20 08:32:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8646764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8646764/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-026-08840-z","type":"published","date":"2026-05-01T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101314301,"identity":"400892d0-7dff-4dac-8e97-3434e04967bf","added_by":"auto","created_at":"2026-01-28 11:37:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84764,"visible":true,"origin":"","legend":"\u003cp\u003ePrecipitation and temperature in 2018 and 2019\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/bc749d874a6230ebece8de60.png"},{"id":101398051,"identity":"00205ebf-5d35-4fb8-9d23-531367ab9c91","added_by":"auto","created_at":"2026-01-29 09:39:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":445434,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in physical and chemical properties, lodging rate, and lodging index between two oat varieties with different lodging resistance. (a) and (b) show the differences in physical and chemical properties, lodging rate, and lodging index of two different lodging-resistant oat varieties during the growing seasons of 2018 and 2019, respectively. * * indicates that there is a significant difference at the \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/914860f4200e49a5b67fd181.png"},{"id":101398180,"identity":"5fb00139-b19d-455a-a1c4-de8fbb5a1a54","added_by":"auto","created_at":"2026-01-29 09:39:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":227245,"visible":true,"origin":"","legend":"\u003cp\u003eResponse of field lodging rate and lodging index to varying nitrogen dosages in two oat cultivars. (a) indicates 2018 data; (b) represents 2019 data. The dark green column represents the field lodging rate (left axis), and the light green column represents the lodging index (right axis). N0-N5 represents different amounts of nitrogen fertilizer. Different lowercase letters in the column indicate significant differences between treatments at the \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, the same below.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/33c80b59d12c738645580343.png"},{"id":101398040,"identity":"a69c1f48-9e3e-4589-a023-ee9b7ef9886d","added_by":"auto","created_at":"2026-01-29 09:39:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":542503,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of nitrogen fertilizer on stem physicochemical properties of two oat varieties with different lodging resistance\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/87bda155ac77385798c08b15.png"},{"id":101398048,"identity":"8dca6e9b-d52d-435d-9023-cadd4d3ed0dc","added_by":"auto","created_at":"2026-01-29 09:39:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118620,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical partitioning of lodging-related traits based on mixed-effects models\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/677691dd470a2f7b35a5fc8c.png"},{"id":101397999,"identity":"561ee162-439d-4725-a800-5f48460262e2","added_by":"auto","created_at":"2026-01-29 09:38:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":224732,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model and path analysis of the effects of nitrogen fertilizer and variety on the lodging index. (a) Structural equation model (SEM) showing the direct and indirect effects of variety (V), nitrogen application rate (N), and their interaction (V × N) on LI through structural components (SC), non-structural components (NSC), and mineral nutrients (MN). Blue and red arrows indicate significant negative and positive effects, respectively. The number next to the arrow is the standardized path coefficient. The value (R\u003csup\u003e2\u003c/sup\u003e) above the box represents the explanatory rate of the model to the variance of the variables. Significant level: * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. (b) Represents the standardized direct, indirect, and total effects of each factor on LI.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/225dba04daf2bca025ffc85c.png"},{"id":101397868,"identity":"bd785fb8-4cbe-41f4-93d5-be12e0ef994f","added_by":"auto","created_at":"2026-01-29 09:37:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":218975,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive evaluation of important physical and chemical indices and lodging index of the two varieties. Significant differences among treatments are denoted by distinct lowercase letters (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/afcb989d7d73ecc4c7da1510.png"},{"id":108437597,"identity":"938dae1a-0bc3-45a4-9e01-5668ee016a47","added_by":"auto","created_at":"2026-05-04 15:59:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2167798,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8646764/v1/3d28983c-b165-42ac-89cd-9f3f7037c17a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimized nitrogen fertilizer management to balance lodging resistance and stem physicochemical properties of oats","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNitrogen (N) is an essential macronutrient that governs crop physiology and productivity. While optimized N application is critical for enhancing photosynthesis, biomass accumulation, and grain protein content [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], it exerts a dual effect on crop performance. In intensive agricultural systems, excessive N input is prevalent, which not only intensifies environmental footprints but also disrupts the balance between vegetative growth and mechanical support, thereby predisposing crops to lodging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, where oats are key for both food and feed, high-yield cultivation strategies often involve substantial water and fertilizer inputs. Consequently, stem overgrowth and compromised physical strength are frequent, leading to severe lodging events [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Lodging severely constrains oat production by destroying the canopy structure and inhibiting photosynthate translocation, which results in yield reduction and quality deterioration, while also complicating mechanized harvesting [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given that unreasonable N rates are a primary determinant of lodging, a critical challenge in modern oat cultivation is to decouple yield gain from lodging risk through precise nitrogen management.\u003c/p\u003e \u003cp\u003eThe lodging resistance of stems is essentially determined by their physical and mechanical strength, which is the result of the synergistic accumulation of cell wall structural components, mineral nutrients, and non-structural carbohydrates. Existing studies have shown that lignin and cellulose are the main skeleton polymers of the cell wall, and their content directly determines the flexural strength of stems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mineral nutrients such as silicon (Si) and potassium (K) enhance stem rigidity by promoting epidermal silicification and regulating cell osmotic potential [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Non-structural carbohydrates such as starch and soluble sugar not only increase stem plumpness but are also the key substrates of cell wall biosynthesis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, excessive nitrogen application significantly disturbs the above process through the carbon and nitrogen metabolism balance mechanism: high nitrogen input forces photosynthetic products to preferentially flow to nitrogen metabolic sinks (such as protein synthesis), resulting in a lack of carbon skeleton required for cell wall construction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Simultaneously, the rapid accumulation of biomass induced by high nitrogen levels produces a growth dilution effect, resulting in a significant decrease in the concentration of mineral elements that play a supporting role in the unit dry weight [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although previous studies have explored the response of agronomic traits, physiology, and biochemistry to nitrogen in a variety of crops [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], a systematic analysis of the regulation mechanism of oat lodging is still insufficient. Lodging is a complex process involving the coordination of morphological, anatomical, and physiological factors, and there is a strong collinearity between the physical and chemical indicators. Conventional linear regression analysis cannot accurately quantify the independent contribution of each factor to the lodging index, and it is impossible to qualitatively analyze the specific path by which the nitrogen application rate affects lodging by regulating key traits. An in-depth analysis of this mechanism is crucial for developing precise lodging resistance strategies based on physiological indicators.\u003c/p\u003e \u003cp\u003eIn view of this, based on a field experiment conducted in the typical alpine rain-fed area of the Qinghai-Tibet Plateau from 2018 to 2019, two oat varieties with significant differences in lodging resistance (lodging-resistant LENA vs. susceptible QY2) were selected, and six nitrogen application gradients were set. The study focused on the use of hierarchical partitioning (HP) to screen the key driving factors affecting lodging, and constructed a structural equation model (SEM) to analyze the physiological regulation path of \"nitrogen application rate-stem physical and chemical changes-lodging\", and combined with TOPSIS method to determine the appropriate nitrogen application threshold. The purpose of this study was to reveal the physiological nature of oat lodging induced by nitrogen fertilizer and to provide a theoretical basis and quantitative recommendations for the precise management of nitrogen fertilizer in different genotypes of oats in this region.\u003c/p\u003e"},{"header":"2 Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Difference analysis between two different lodging-resistant oat varieties\u003c/h2\u003e \u003cp\u003eThe two-years (2018\u0026ndash;2019) experimental data showed that (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the lodging-resistant cultivar LENA and the lodging-susceptible cultivar QY2 had extremely significant differences in all measured traits (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In comparison to LENA, the susceptible variety QY2 exhibited markedly lower levels of structural (LC, CC) and non-structural components (SS, ST). Simultaneously, the accumulation of key mineral elements (Si, Ca, and K) that enhance mechanical strength was maintained at a high level. In contrast, the magnesium and soluble protein content in the stems of QY2 was significantly higher than that of LENA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Impact of nitrogen fertilization on the lodging incidence and index in oats\u003c/h2\u003e \u003cp\u003eNitrogen fertilizer treatment significantly affected the lodging phenotype of oat plants. In general, with an increase in the nitrogen application rate from N0 to N5, the field lodging rate and lodging index of the two varieties in 2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) showed a significant upward trend and reached a peak under high nitrogen treatment (N4-N5). There were significant differences in the response patterns of different varieties to nitrogen fertilizer. The lodging-prone variety QY2 was sensitive to the nitrogen fertilizer. In 2018, the lodging rate in the field was as high as 63.27% even without nitrogen application (N0). When the fertilization amount was N1, the lodging rate increased to 95.97%, and the lodging index under this treatment was approximately 1.74 times that of LENA under the same nitrogen application level (35.03 vs. 20.13). In contrast, the lodging-resistant variety LENA had a high tolerance to nitrogen fertilizer, and no obvious lodging occurred in LENA under no nitrogen application in the two growing seasons and N1 treatment in 2019. Even with N5 treatment in 2019, the field lodging rate was significantly lower than that of QY2 under the same treatment. Although the lodging rate generally increased with the increase of lodging index, the lodging index of the lodging-resistant variety LENA increased to 50.19 under N1 treatment in 2019, but its actual field lodging rate remained at 0.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Effect of nitrogen application on physical and chemical indexes of oat stalk\u003c/h2\u003e \u003cp\u003eThe content of soluble protein (SP) increased with increasing nitrogen application rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In 2018, when the nitrogen application rate increased from N0 to N5, the soluble protein content of QY2 increased from 34.17 mg/g to 81.58 mg/g, an increase of 138.7%; LENA also showed a similar growth trend, but the increase was small. When the nitrogen application rate increased from N0 to N5, its content increased from 23.33 mg/g to 49.29 mg/g, an increase of 111.27%. The change rule for 2019 was similar to that of 2018.\u003c/p\u003e \u003cp\u003eThe starch and soluble sugar contents initially increased and then decreased with increasing nitrogen application rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c). The two cultivars differed significantly in terms of soluble sugar accumulation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). QY2 was more sensitive to nitrogen fertilizer, reaching the maximum value under the N1 treatment, which was significantly higher than that of the other treatments, approximately 2.2 times that of the N5 treatment. For LENA, the values peaked under the N3 regime, exhibiting a significant increase over all other treatments. In 2018 and 2019, LENA was significantly increased by 56.27% and 86.97%, respectively, compared with N5, which had the lowest content. The variation in starch content (ST) was similar to that of soluble sugar. QY2 and LENA reached their maximum values in the N1 and N3 treatments, respectively, and were significantly higher than those in the other treatments. QY2 was more sensitive to nitrogen fertilizer, and its soluble sugar content decreased from 119.34 mg/g in the N1 treatment to 66.21 mg/g in the N5 treatment in 2018. In contrast, LENA was more tolerant to nitrogen fertilizer. In 2019, the SS content of LENA under the N5 treatment (88.18 mg/g) was significantly higher than that of QY2 (79.93 mg/g).\u003c/p\u003e \u003cp\u003eThe effect of the nitrogen application rate on the mineral element content in stems was significant. During the two years, with the increase in the nitrogen application rate, the Si content of the two varieties showed a downward trend, but the decline in QY2 was greater than that in LENA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In 2018, the Si content of QY2 decreased significantly from 4.05 mg/g in the N0 treatment to 1.72 mg/g in the N5 treatment. In contrast, the Si content of LENA remained at a high level under each treatment, which decreased significantly from 6.52 mg/g in the N0 treatment to 4.16 mg/g in the N5 treatment, and the content in the N5 treatment was significantly higher than that in QY2. Similar to the change rule of silicon content, the contents of potassium (K) and calcium (Ca) also decreased with the increase in nitrogen application (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-f). In 2018, the potassium and calcium contents in QY2 under N5 treatment were 17.88% and 74.94% lower than those in N0 treatment, respectively. The potassium content of LENA decreased by 6.73%, and the decrease in calcium content was similar to that of QY2. Nitrogen fertilization exerted a significant influence on magnesium (Mg) accumulation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with levels generally rising in parallel with increasing N gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eThe nitrogen application rate significantly affected the lignin (LC) and cellulose (CC) contents (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). With an increase in the nitrogen application level, the cellulose and lignin contents of the two varieties showed a downward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eh-i). During the 2018 season, cellulose levels in LENA and QY2 under the N5 regime were reduced by 15.78% and 13.73%, respectively, relative to the N0 control. Significant varietal differences were observed in lignin accumulation; for instance, under the N5 regime in 2019, LENA reached 9.66%, markedly surpassing the 8.05% recorded for QY2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Response of oat culm physio-chemical characteristics and lodging to nitrogen dosage and cultivar differences\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNitrogen Fertilizer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariety\u0026times;Nitrogen Fertilizer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1131.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305.91**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.85**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1503.01**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e288.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239.34**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4022.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e900.93**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e620.78**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e748.77**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.52**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.47**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e413.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1032.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.98**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.75**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e792.6**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e453.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e247.7**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e861.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.15**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2785.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.87**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e535.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.43**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe values indicate the F-statistics. *, **Denote significant differences at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the effects of variety, nitrogen application rate, and their interaction on mineral nutrients, structural components, non-structural components, and lodging of oat stems. The results showed that oat varieties had extremely significant effects on all indicators (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the most significant effect on soluble sugar content (F\u0026thinsp;=\u0026thinsp;4022.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by the lodging rate (F\u0026thinsp;=\u0026thinsp;2785.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a significant main effect of nitrogen application rate on all indicators (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), among which the effect on calcium content was the most significant (F\u0026thinsp;=\u0026thinsp;1032.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by soluble sugar (F\u0026thinsp;=\u0026thinsp;900.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further analysis of the effect of interaction showed that there was a significant interaction between variety (V) and nitrogen fertilizer (N) on lodging characteristics (FLR, LI) and stem physical and chemical indexes of oats (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), among which the effect on soluble sugar content was the most significant (F\u0026thinsp;=\u0026thinsp;620.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Screening of key physical and chemical factors affecting lodging index\u003c/h2\u003e \u003cp\u003eTo address the issue of multicollinearity among physiological and biochemical indicators, we performed a hierarchical partitioning analysis based on linear mixed-effects models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The model exhibited high explanatory power for the lodging index (Marginal R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.870), indicating that the selected traits accounted for the vast majority of the variation, with a negligible influence from random effects. The analysis revealed distinct differences in the independent contributions of the three trait categories, ranked as follows: mineral nutrients\u0026thinsp;\u0026gt;\u0026thinsp;non-structural components\u0026thinsp;\u0026gt;\u0026thinsp;structural components. Specifically, mineral nutrients (Si, K, Ca, and Mg) were the dominant drivers, with an independent contribution of 57.36%, which was substantially higher than that of the other two categories. Non-structural components (SS, ST, and SP) followed, explaining 30.18% of the variation, whereas structural components (LC and CC) contributed the least (12.47% variation).Regarding specific variables, Calcium (Ca), Potassium (K), and Soluble Sugar (SS) showed highly significant effects on the lodging index (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while Silicon (Si) and lignin (LC) were significant at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level, identifying them as key regulators of lodging.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.6\u003c/b\u003e Physiological pathway analysis of nitrogen regulation of oat lodging\u003c/h2\u003e \u003cp\u003eBased on significant factors such as lignin, calcium, potassium, silicon, and soluble sugar screened by the hierarchical segmentation method, a segmented structural equation model (SEM) was constructed to analyze the specific path of variety, nitrogen application rate, and their interaction to regulate lodging index through the physiological and biochemical indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The model fitness index showed that the model fitted well with the data, Fisher 's C\u0026thinsp;=\u0026thinsp;1.053, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.591\u0026thinsp;\u0026gt;\u0026thinsp;0.05, and the Akaike information criterion (AIC) value was 55.053, which could be further analyzed. The results showed that the model explained 85% of the variation in lodging index (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85).\u003c/p\u003e \u003cp\u003eMineral nutrients (MN) and non-structural components (NSC) exerted robust, direct negative regulation on the lodging index, evidenced by path coefficients of -0.732 and \u0026minus;\u0026thinsp;0.213, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lignin (SC) was predominantly modulated by soluble sugars (NSC), thereby exerting a significant indirect influence on lodging (coefficient: -0.0842; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the impact of genotype, nitrogen dosage, and their interplay on oat lodging was mediated via alterations in lignin, soluble sugars, and mineral profiles (Ca, K, and Si), with total effect values of -0.209, 0.31, and 0.156, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Comprehensive evaluation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough a comprehensive evaluation of the lodging index and important factors, our results indicate that the optimal nitrogen dosage was genotype-dependent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The fit degree of LENA under N1 treatment was the highest (0.74), but there was no significant difference between N0, N2, and N3 treatments. The degree of fit of LENA under N5 treatment was the lowest (0.427). The fit degree of QY2 under N0 treatment was the highest (0.556), but there was no significant difference with N1 treatment (0.501). The degree of fit of QY2 under N5 treatment was the lowest (0.39).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Nitrogen-driven 'carbon-nitrogen metabolism trade-off' and variety-specific response\u003c/h2\u003e \u003cp\u003eIn this study, the distinct stem traits observed among oat varieties confirmed that, alongside agronomic measures, the genetic background is a critical intrinsic determinant of lodging resistance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous studies have established that lodging-resistant germplasm is typically characterized by high levels of Ca, K, Mg, Si, lignin, and hemicellulose [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our results largely align with these findings regarding the accumulation of Ca, K, Si, crude fiber, and lignin in leaves. However, a divergence was observed for Mg. Unexpectedly, the lodging-prone variety QY2 exhibited a significantly higher Mg content than the lodging-resistant LENA. This anomaly may be attributed to differences in Mg utilization efficiency. It has been reported that lodging-prone varieties possess lower Mg efficiency; in high-N environments, they may preferentially utilize nitrogen and K, which inhibits the physiological functions of Mg (e.g., photosynthetic efficiency), leading to its ineffective retention within plant tissues rather than functional assimilation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the lodging-resistant cultivar LENA accumulated significantly greater reserves of non-structural carbohydrates (specifically starch and soluble sugars), markedly surpassing the susceptible genotype QY2. Although this trend diverges from some studies conducted without nitrogen application [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], when considered alongside the significantly higher soluble protein content in QY2, it is likely attributable to the differential carbon-nitrogen metabolic responses induced by exogenous nitrogen. According to the \"Carbon-Nitrogen Balance Hypothesis,\" high nitrogen input drives the preferential allocation of photosynthates toward nitrogen sinks (e.g., protein synthesis), thereby competitively inhibiting the conversion of carbon skeletons into cell wall structural components (lignin and cellulose) [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, the susceptible cultivar QY2 exhibited high nitrogen sensitivity, with nitrogen application significantly stimulating soluble protein synthesis (N pool) but depleting non-structural components (C pool). This intense \"source-sink competition\" left QY2 with insufficient carbon reserves for cell wall construction during the rapid stem elongation. Furthermore, the \"growth dilution effect\" caused by rapid biomass accumulation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] led to a significant reduction in the concentration of physically supportive elements (Ca, Si, and K) and lignin per unit dry weight. In contrast, the resistant variety LENA demonstrated superior homeostatic regulation. Nitrogen uptake in LENA appears to enhance photosynthetic source activity rather than merely driving vegetative growth. Consequently, LENA maintained a surplus of non-structural components and sustained the accumulation of structural materials, even under high nitrogen stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mechanical threshold and carbon-nitrogen balance of stem lodging\u003c/h2\u003e \u003cp\u003eThere is no simple linear correlation between the LI and FLR; rather, a \"mechanical safety threshold\" exists [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, although FLR generally tracked with LI, the lodging-resistant variety LENA exhibited an LI of 50.19 under N1 treatment in 2019, yet its actual FLR remained at zero. This suggests that as long as the comprehensive physical strength of the stem\u0026mdash;derived from lignin, cellulose, and silicification\u0026mdash;is sufficient to resist external bending moments, the plant remains in a \"mechanically safe zone\" even if the risk index increases [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, this safety margin is dynamic and environment dependent. Lodging is fundamentally the result of the interaction between \"intrinsic factors\" (stem mechanical weakening) and \"extrinsic factors\" (environmental stress). In 2019, the absence of extreme weather meant that the potential risk indicated by a high LI did not translate into actual lodging. In contrast, heavy rainfall during the early grain-filling stage in 2018 (July 23, 30.4 mm; data from era5: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) acted as a trigger. The increased ear weight due to water absorption shifted the center of gravity upward; this, compounded by the impact of rain, forced plants in a critical state to exceed their safety threshold. Further analysis reveals that the magnitude of this safety threshold reflects genotypic differences in adaptability to nitrogen-induced \"carbon-nitrogen imbalance.\" The lodging-prone variety QY2 is highly sensitive to nitrogen; the accumulation of its non-structural components peaks prematurely at low nitrogen levels (N1) and is rapidly depleted to fuel vigorous nitrogen metabolism, leaving insufficient carbon skeletons for constructing cell walls. Consequently, lodging occurs because the physical support falls below the safety limit. In contrast, the lodging-resistant variety LENA exhibits superior carbon-nitrogen homeostasis. Its accumulation peak for non-structural components shifted to the medium nitrogen (N3) level, maintaining a higher silicon and lignin content even under high nitrogen stress. This physiological advantage confers a higher safety threshold and greater buffering capacity, allowing LENA to maintain upright growth despite nitrogen-induced declines in mechanical properties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlations of lodging index with genotype, nitrogen dosage, and culm physio-chemical traits\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Mineral nutrient elements are the primary factors determining the lodging resistance of stems\u003c/h2\u003e \u003cp\u003eTo address the issue of multicollinearity among physicochemical traits, we employed hierarchical partitioning to effectively eliminate interference between indicators and quantify their independent contributions to the lodging index. We found that mineral nutrients (Si, K, and Ca) explained 57.36% of the variation in the lodging index, identifying them as the primary determinants of stem strength. Previous reports have established the mechanisms underlying these effects. Silicon, acting as a \"skeletal\" material in the cell wall, significantly enhances stem mechanical hardness and toughness through deposition, thereby reducing the lodging index [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Calcium, a key component of pectin in the middle lamella, participates in cell wall construction and enhances intercellular adhesion and tissue compactness [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Potassium plays a critical role in promoting the development of thick-walled mechanical tissues by maintaining cell turgor [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our results further confirmed that higher concentrations of these elements in the stem enhanced lodging resistance, thereby negatively regulating the lodging index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 The synergistic regulation mechanism of non-structural components and structural components\u003c/h2\u003e \u003cp\u003ePrevious studies have suggested that structural components such as lignin are the primary determinants of stem strength [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, our findings diverge from this perspective. Hierarchical partitioning analysis revealed that the independent contribution of non-structural components (soluble sugars, starch, soluble protein) to the lodging index (30.18%) exceeded that of structural components (lignin, cellulose) (12.47%). Structural Equation Modeling (SEM) further elucidated the underlying mechanism: soluble sugar exerted a significant direct negative effect on the lodging index (path coefficient = -0.213). While lignin also inhibited lodging, its influence was more complex. The model indicated that lignin primarily affected the lodging index indirectly, mediated by soluble sugar (path coefficient = -0.0842). These results suggest that soluble carbohydrates not only regulate cell osmotic potential to maintain stem turgor but also serve as a core substrate pool, indirectly regulating the synthesis of structural components (e.g., lignin) via the distribution of metabolic flux [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, evaluations of oat lodging resistance should emphasize not only lignin content but also the accumulation of nonstructural substances, particularly soluble sugars.\u003c/p\u003e \u003cp\u003e3.3.3 Physiological regulation of nitrogen application rate and varieties on lodging resistance through physiological and biochemical pathways\u003c/p\u003e \u003cp\u003eThis study elucidated a comprehensive pathway linking agronomic measures to physiological traits and, ultimately, to lodging resistance. Genotype, nitrogen application rate, and their interaction acted as exogenous drivers, determining the lodging index by regulating soluble sugar accumulation, lignin synthesis, and stem mineral uptake (Ca, K, and Si). Analysis of the total effects revealed that mineral nutrients had the strongest influence (-0.732), followed by non-structural components (-0.213) and structural components (-0.0842).\u003c/p\u003e \u003cp\u003eWe propose that the core objective of nitrogen management should be to maximize mineral absorption and utilization while coordinating carbon metabolite accumulation. The divergent performance of lodging-prone and lodging-resistant varieties under varying nitrogen levels is fundamentally attributable to their differential sensitivity to key physicochemical factors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, rational nitrogen application mitigates lodging risk by optimizing the enrichment of Si, K, and Ca and balancing soluble sugar and lignin contents, thereby constructing a physically robust stem structure. In summary, oat stem lodging resistance results from the synergistic interplay of mineral nutrients and carbon metabolites, which is consistent with the classical research of Mulder et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Best quantity of applying nitrogenous fertilizer\u003c/h2\u003e \u003cp\u003eRational nitrogen management is crucial for synergistically improving oat forage quality, enhancing lodging resistance, and optimizing yield [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Previous studies have indicated that appropriate nitrogen rates optimize population structure and strengthen stem mechanics, thereby reducing lodging risk [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our results demonstrated significant genotypic differences in the nitrogen response. The lodging-prone variety QY2 proved highly sensitive to nitrogen fertilizer; its comprehensive evaluation scores under N0 and N1 were significantly higher than those under high nitrogen treatments, with no significant difference between N0 and N1. In contrast, the lodging-resistant variety LENA exhibited a higher nitrogen tolerance. Although the evaluation score was highest under N1 treatment (0.74), it did not differ statistically from the N0, N2, or N3 treatments. While low nitrogen levels help maintain traits associated with lodging resistance, nitrogen remains the primary driver of biomass accumulation, and insufficient application often results in significant yield penalties [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therefore, provided that lodging risk is not significantly exacerbated and forage quality is preserved, higher nitrogen rates should be prioritized to fully exploit the yield potential.\u003c/p\u003e \u003cp\u003eTherefore, strategies for yield enhancement are only viable if they sustain nutritional value without aggravating lodging risks. Oat production should adhere to the principle of \"variety-specific fertilization\" to achieve an optimal balance among yield, quality, and resistance. Specifically, for the lodging-prone variety QY2, N1 served as a reasonable threshold to ensure both yield stability and lodging resistance. Conversely, for the lodging-resistant variety LENA, N3 was the ideal application rate, maximizing forage yield while maintaining quality and resistance. Future research should further investigate optimal nitrogen management across diverse varieties and environmental conditions to simultaneously optimize yield, quality, and lodging resistance.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eNitrogen fertilizer significantly influenced the physicochemical traits and lodging of oats (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Optimizing nitrogen application regulates lodging resistance by modulating the accumulation of mineral nutrients, soluble sugars and lignin. Specifically, the contents of Ca, K, Si, soluble sugars, and lignin were closely correlated with lodging resistance, suggesting that they can serve as reliable indicators for evaluation. In conclusion, the varieties LENA and QY2 achieved an optimal balance among yield, quality, and resistance under the N3 (180 kg\u0026middot;hm⁻\u0026sup2;) and N1 (60 kg\u0026middot;hm⁻\u0026sup2;) treatments, respectively. Therefore, these nitrogen application rates are recommended for the study area and similar ecological regions.\u003c/p\u003e"},{"header":"5 Materials and Methods","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Study area\u003c/h2\u003e \u003cp\u003eField experiments were conducted in Ganhetan Town (Huangzhong District, Xining City), situated in the hinterland of the Qinghai-Tibet Plateau (36\u0026deg;30\u0026prime;57\u0026Prime;N, 101\u0026deg;33\u0026prime;20\u0026Prime;E; elevation 2628 m). This region features a typical plateau continental climate characterized by a short frost-free season (90\u0026ndash;120 days) and intense solar radiation (2550\u0026ndash;2600 annual sunshine hours). The area is cool and semi-arid, with a mean annual temperature of 3.5\u0026ndash;4.5\u0026deg;C and significant diurnal fluctuations (12\u0026ndash;16\u0026deg;C). Precipitation averages 450\u0026ndash;500 mm annually\u0026mdash;concentrated between June and August\u0026mdash;while evaporation rates are 3\u0026ndash;4 times higher. The dominant soil type is chestnut soil, with specific physicochemical properties detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Meteorological conditions during the 2018\u0026ndash;2019 growing seasons (sourced from ERA5, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical properties of the soil at the experimental site prior to sowing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOrganic matter (g\u0026middot;kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal nitrogen (g\u0026middot;kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlkaline hydrolysis nitrogen (mg\u0026middot;kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAvailable phosphorus (mg\u0026middot;kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRapidly available potassium (mg\u0026middot;kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u0026ndash;8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Test materials\u003c/h2\u003e \u003cp\u003eThe oat varieties used in this study were LENA (Avena sativa L. cv. LENA) and Qingyin No.2 (Avena sativa L. cv. QY2), provided by Qinghai Province Academy of Animal Husbandry and Veterinary Sciences. Material characteristics are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The fertilizer tested was urea (46% N).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the test materials.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLENA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height 90-110cm, middle-late maturity, scattered panicle type, yellow grain color, grain type spindle, tiller number 2\u0026ndash;5, lodging resistance.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQY2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height 120-140cm, early maturity, ear type scattered, grain color yellow, grain type lanceolate, tiller number 2\u0026ndash;3, easy lodging.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Experimental Design\u003c/h2\u003e \u003cp\u003eA field experiment was conducted from 2018 to 2019 using a randomized block design. Nitrogen was applied at six distinct rates: 0, 60, 120, 180, 240, and 300 kg N hm⁻\u0026sup2; (designated as N0\u0026ndash;N5, respectively). The experiment comprised 12 treatment combinations with three replicates, resulting in a total of 36 plots. Each plot covered an area of 15 m\u0026sup2; (3 m \u0026times; 5 m). Each treatment was applied once before sowing as a base fertilizer. The sowing rate was calculated according to the 1000-grain weight of seeds according to the number of seedlings of 300,000 plants per acre. Plots were established on a field previously cropped with highland barley, utilizing a 20 cm row spacing and 1 m alleyways. A uniform background fertility was established using 150 kg\u0026middot;hm⁻\u0026sup2; diammonium phosphate prior to seeding. Hand-weeding was strictly enforced during the seedling, tillering, and jointing stages to minimize biotic interference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Indicators and methods of determination\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Main agronomic characteristics\u003c/h2\u003e \u003cp\u003eAt the milk stage, six plants exhibiting uniform growth were randomly selected from each plot of each fertilizer gradient to measure the following indicators.\u003c/p\u003e \u003cp\u003eHeight of the center of gravity (HCG): The distance from the base of the stem (including leaves, sheaths, and spikes) to the equilibrium fulcrum after placing the main stem on the fulcrum for balance.\u003c/p\u003e \u003cp\u003eAboveground fresh weight (AFW): The fresh weight of a plant's aboveground parts, including stems, leaves, sheaths, and spikes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Field lodging rate\u003c/h2\u003e \u003cp\u003eDuring the milk stage, the lodging area within each nitrogen treatment plot was assessed to determine the Field Lodging Rate (FLR)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The calculation formula is as follows.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:FLR\\left(\\%\\right)=\\frac{\\text{A}\\text{r}\\text{e}\\text{a}\\:\\text{o}\\text{f}\\:\\text{l}\\text{o}\\text{d}\\text{g}\\text{i}\\text{n}\\text{g}}{\\text{A}\\text{r}\\text{e}\\text{a}\\:\\text{o}\\text{f}\\:\\text{s}\\text{m}\\text{a}\\text{l}\\text{l}\\:\\text{c}\\text{o}\\text{m}\\text{m}\\text{u}\\text{n}\\text{i}\\text{t}\\text{y}}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3 Lodging index\u003c/h2\u003e \u003cp\u003eSix plants exhibiting uniform vigor were selected for measurement in each plot. The breaking strength of the second internode (BS2) was measured using a YYD-1 stem strength tester (Zhejiang Tuopu Technology Co., Ltd., Hangzhou, China). Prior to measurement, the leaf sheaths were removed, and the stem was positioned on the instrument supports (set to a 2 cm span). The probe applied vertical pressure at a constant rate until fracture occurred. The peak force recorded at the breaking point was defined as the strength at breakage. Finally, Lodging Index (LI) was calculated using the measured BS2, center of gravity height (HCG), and fresh weight (AFW) according to the following formula[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:LI=\\frac{HCG\\times\\:\\text{A}\\text{F}\\text{W}}{{BS}_{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.4.4 Determination of stem physical and chemical indexes\u003c/h2\u003e \u003cp\u003eHarvested at the milky stage, the second internodes were subjected to thermal treatment (105\u0026deg;C for 30 min; 65\u0026deg;C to constant weight) and ground into fine powder (\u0026lt;\u0026thinsp;60 mesh). This prepared material was used to determine physicochemical characteristics, including starch (ST), soluble sugar (SS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], soluble protein (SP) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], calcium (Ca), potassium (K), magnesium (Mg), silicon (Si) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], cellulose content (CC) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], lignin content (LC)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Data analysis\u003c/h2\u003e \u003cp\u003eData integration and analysis were performed using Microsoft Excel 2019 and R software (version 4.0.2). Differences between means were assessed using independent sample t-tests, Duncan\u0026rsquo;s multiple range test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Analysis of Variance (ANOVA). To elucidate how variety, nitrogen (N) dosage, and their interactions influence the oat lodging index, a segmented structural equation model (SEM) was developed to calculate the associated path coefficients. Furthermore, the TOPSIS algorithm was applied to comprehensively evaluate stem physicochemical traits and lodging risks, thereby determining the optimal N strategy for this region. All graphical representations were produced using R software and OriginPro 2024.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the laboratory members for their support throughout the experimental process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and images that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Grass Seed Innovation and Its Role in Grassland Agricultural Systems (2023-NK-147), the China Agriculture Research System (CARS-34) and Qinghai innovation platform construction project (2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRF Z, GL L, R W, and WH L conceived and designed the experiments. RF Z performed the experiments and wrote the manuscript. RF Z, ZL J and W L analyzed the data. GL L contributed to the manuscript preparation and provided editorial advice. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSpiertz J. Nitrogen, sustainable agriculture and food security: a review. Sustainable Agric 2009:635\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrera JM, Rubio G, H\u0026auml;ner LL, Delgado JA, Lucho-Constantino CA, Islas-Valdez S, Pellet D. Emerging and established technologies to increase nitrogen use efficiency of cereals. Agronomy. 2016;6(2):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Wu L, Wu X, Ding Y, Li G, Li J, Weng F, Liu Z, Tang S, Ding C. Lodging resistance of japonica rice (Oryza Sativa L.): morphological and anatomical traits due to top-dressing nitrogen application rates. 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Optical-sensor-based nitrogen management in oat for yield enhancement. Sustainability. 2021;13(12):6955.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan J, Zhao J, Liu Y, Huang N, Tian K, Shah F, Liang K, Zhong X, Liu B. Optimized nitrogen management enhances lodging resistance of rice and its morpho-anatomical, mechanical, and molecular mechanisms. Sci Rep. 2019;9(1):20274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasemsap P, Bloom AJ. Breeding for higher yields of wheat and rice through modifying nitrogen metabolism. Plants. 2022;12(1):85.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"oats, nitrogen management, lodging, carbon and nitrogen balance, SEM, TOPSIS","lastPublishedDoi":"10.21203/rs.3.rs-8646764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8646764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOat (\u003cem\u003eAvena sativa\u003c/em\u003e L.) serves as a vital dual-purpose crop for grain and forage; however, its production potential is frequently compromised by stem lodging resulting from improper nitrogen (N) management. Although the physiological basis of lodging is established, the precise quantitative contribution of specific stem characteristics under varying N regimes remains underexplored. To bridge this gap and optimize N strategies, we conducted a two-year field trial (2018\u0026ndash;2019) in the rain-fed Qinghai-Tibet Plateau using two cultivars with contrasting lodging resistance: LENA (resistant) and QY2 (susceptible). Under six N gradients (0, 60, 120, 180, 240, and 300 kg\u0026middot;ha⁻\u0026sup1;), we quantified structural polymers (lignin, cellulose, starch), non-structural carbohydrates (soluble sugars), and mineral elements (Ca, K, Si, Mg) in the second basal internode at the milk stage. Data were analyzed using hierarchical partitioning, Structural Equation Modeling (SEM), and the TOPSIS method to identify key determinants of stem strength.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLodging severity and stem physio-chemical profiles were significantly modulated by N dosage, genotype, and their interaction. Escalating N input marked elevated soluble protein levels while concurrently depressing the concentrations of lignin, cellulose, and key minerals (Si, Ca, K). Hierarchical partitioning analysis pinpointed Ca, K, Si, soluble sugars, and lignin as the predominant drivers, collectively accounting for 87% of the variation in the lodging index. Furthermore, SEM elucidated the regulatory pathways: genotype and N fertilization (including their interaction) indirectly exacerbated lodging risk by suppressing the accumulation of these structural and mineral components. The total standardized effects on the lodging index were recorded as -0.209 (genotype), 0.31 (N rate), and 0.156 (interaction).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBased on the comprehensive production and lodging resistance performance, it is recommended that in the rain-fed area of the Qinghai-Tibet Plateau, the suitable nitrogen application rate of the lodging-resistant variety LENA should be 180 kg\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, while that of the lodging-prone variety QY2 should be controlled below 60 kg\u0026middot;hm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. These findings provide a physiological framework for rational nitrogen management and offer novel mechanistic insights into oat lodging resistance.\u003c/p\u003e","manuscriptTitle":"Optimized nitrogen fertilizer management to balance lodging resistance and stem physicochemical properties of oats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 11:37:35","doi":"10.21203/rs.3.rs-8646764/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T16:53:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T18:02:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212473952737504879591365773840633725225","date":"2026-02-25T03:01:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T02:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197561671418024869331046855377799262677","date":"2026-01-28T15:48:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T15:33:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-23T15:18:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T17:26:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T17:24:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-01-20T08:08:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"193a913a-49dc-439e-9efa-65c1107ab8a1","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:59:23+00:00","versionOfRecord":{"articleIdentity":"rs-8646764","link":"https://doi.org/10.1186/s12870-026-08840-z","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2026-05-01 15:57:09","publishedOnDateReadable":"May 1st, 2026"},"versionCreatedAt":"2026-01-28 11:37:35","video":"","vorDoi":"10.1186/s12870-026-08840-z","vorDoiUrl":"https://doi.org/10.1186/s12870-026-08840-z","workflowStages":[]},"version":"v1","identity":"rs-8646764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8646764","identity":"rs-8646764","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}