Mechanical deep placement of slow/controlled-release fertilizer increases grain yield and nitrogen use efficiency by improving the carbon and nitrogen metabolism abilities of rice

Mechanical deep placement of slow/controlled-release fertilizer increases grain yield and nitrogen use efficiency by improving the carbon and nitrogen metabolism abilities of rice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mechanical deep placement of slow/controlled-release fertilizer increases grain yield and nitrogen use efficiency by improving the carbon and nitrogen metabolism abilities of rice shenggang pan, Xiaojuan Pu, Hanyue Guo, Yifei Wang, Longfei Xia, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6256990/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims Slow controlled release fertilizer has been paid more attention because of its slow release and long fertilizer cycle, the mechanized deep slow/controlled-release fertilizer (SRF) is desirable owing to its high nitrogen use efficiency. In this study, we elucidated the effects of mechanized deep SRF on the characteristics of carbon and nitrogen metabolism, grain yield and nitrogen use efficiency (NUE) of rice. Methods A two-year field experiment was conducted. Two rice cultivars, i.e., Meixiangzhan 2 (MX) and Y Liangyou 1378 (YL), were used and three kinds of fertilization modes, i.e., mechanized deep placement of conventional urea (CU), slow/controlled-release fertilizer (SRF) and compound fertilizer (CF), at 150 kg N ha − 1 , were designed, respectively. Results The results showed that the grain yields of MX and YL for SRF were 29.04% and 25.52% greater than those of CU, respectively, owing to the greater number of productive panicles, spikelets per panicle, and 1000-grain-weight. The nitrogen recovery efficiency of MX and YL under SRF were 42.31% and 33.65% higher than those under CU. The SRF treatment produced higher nitrogen agronomic efficiency (21.17 kg kg − 1 and 23.75 kg kg − 1 ) for MX and YL, which were 82.19% and 68.20% higher than those under CU, respectively. Moreover, the SRF treatment significantly improved the leaf area index and total aboveground biomass at the panicle initiation and heading stages, and nitrate reductase, glutamate synthase and glutamine oxoglutarate aminotransferase activities. The results of structural equation model (SEM) showed that yield components and nitrogen metabolism enzymes had significantly positive and direct regulatory effects on rice yield. Nitrogen use efficiency was significantly positively regulated by the activity of nitrogen-metabolizing enzymes and the accumulation of nitrogen. Conclusion Mechanical deep placement of slow/controlled-release fertilizer at a rate of 150 kg N per hectare increases grain yield and nitrogen use efficiency, which can be an efficient nitrogen fertilizer management practice in South China. Slow/controlled-release fertilizer Mechanical deep placement Yield Nitrogen use efficiency Rice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Rice is one of the most important staple foods that can feed nearly half of the world’s population (Li et al. 2022a ; Xu et al. 2023 ). China maintained its position as a major global producer of rice, with 7% of world’s cultivation area contributing significantly to 28% of global rice production (FAO, 2020 ; Liu et al. 2022 ). Nitrogen is an essential nutrient that affects rice growth and productivity (Wei et al. 2021 ; Deng et al. 2023 ). Since some ineffective fertilization methods have been adopted by the local farming community, the overall nitrogen use efficiency (NUE) of rice production systems in China is only 35% (almost half that of developed countries) (Islam et al. 2018; Li et al. 2023a ). Farmers usually apply excessive chemical N fertilizer, i.e., more than 300 kg N ha − 1 , to the paddy fields only in a single rice growing season to obtain higher yields (Yousef et al. 2022 ). Therefore, China is also the largest consumer of chemical N fertilizers in the world. Irrational N fertilization methods generally result in low NUE and environmental degradation (Jiang et al. 2016 ; Zhu et al. 2019 ). Multiple strategies have been employed to improve NUE by reducing the nitrogen fertilizer rate per unit area in recent years, such as breeding rice cultivars with high NUE (Chen et al. 2015 ; Zhong et al. 2024 ), increasing the fertilizer application time and split fertilizer application dose (He et al. 2023 ), using of slow/controlled-release fertilizer (Li et al. 2023b ), deep placement of N (Cheng et al. 2020 ; Li et al. 2021 ), and straw incorporation into the field (Chen et al. 2022 ). Previous research has shown that deep placement of fertilizer is an efficient method of fertilization (Mumtahina et al. 2023 ; Li et al. 2023a ). A 20% reduction in total urea under deep placement still led to an increase in rice yield compared with the traditional broadcasting fertilization method (Gu et al. 2022).Deep fertilization also reduces nitrogen loss through various mechanisms and increases the effectiveness of fertilizers (Chen et al. 2024 ; Su et al.2024). Pan et al. ( 2017 ) reported that, compared with manual surface fertilization, deep placement of different types of nitrogen fertilizers could improve the yield and economic benefits of rice. Slow/controlled-release fertilizers have attracted increasing attention because of their ability to control the rate and amount of nutrient release (Ransom et al. 2020 ; Jiang et al. 2024 ; Li et al. 2024). Previous studies have shown that SRF is also environmentally friendly owing to a decrease in N loss and improved NUE (Qiang et al. 2022 ). Compared with conventional fertilizers, SRF is advantageous because it reduces labor with a single basal application and has a higher NUE with periodic nutrient release (Zhu et al. 2021 ). Given this background, our study hypothesizes that (1) mechanized deep SRF can improve NUE of rice; (2) mechanized deep SRF can increase grain yield. The primary objective is to (a) access the effects of mechanized deep placement of SRF on grain yield and NUE in machine-transplanted rice, and (b) elucidate the underlying mechanisms of mechanical deep placement of SRF to increase grain yield and NUE in double-rice cropping systems in South China. Materials and methods Experimental site description A two-year field experiment was conducted in the early seasons (March-July) of 2021 and 2022 at the Experimental Farm of the College of Agriculture, South China Agricultural University (SCAU), in Guangzhou, Guangdong Province, China(23.13 °N, 113.18 °E, 18 m in elevation). The experimental site has a subtropical climate (Fig. 1 ). The properties of the experimental soils collected from the upper 20 cm are displayed in Table 1 . Table 1 Soil properties from the upper 20 cm soil in the early season of 2021 and 2022 Time pH Soil organic matter (g kg − 1 ) Total nitrogen (g kg − 1 ) Total phosphorus (g kg − 1 ) Total potassium (g kg − 1 ) 2021 5.69 20.63 3.26 1.16 20.63 2022 5.62 20.96 3.47 1.27 19.87 Fertilizer application An automated rice transplanter capable of transplanting rice seedlings synchronously with deep placement of fertilizer (developed by Changzhou YaMeiKe Mechanical Co., Ltd.) was used for transplanting the rice seedlings. The fertilizer application was accomplished according to Li et al. ( 2023a ). Briefly, when rice seedlings are transplanted, the automated rice transplanter can simultaneously dig a furrow approximately 6 cm deep between two adjacent rows. The fertilizers were then put into the furrow. Experimental treatments and design Slow/controlled-release fertilizer produced by Guangdong Tianhe Zhongjia Fertilizer Co., Ltd. (total nitrogen content (TN) = 25%, P 2 O 5 = 6%, K 2 O = 19%), written as SRF, was chosen. In addition, both conventional urea and compound fertilizer were selected as controls, referred to as CU and CF, respectively. Conventional urea was purchased from the market, and compound fertilizer was developed by Dongguan Fute Fertilizer Co., Ltd. (total nitrogen contents (TN) = 15%, P 2 O 5 = 4%, K 2 O = 6%). No N fertilizer was used to calculate N use efficiency, which was referred to as the CK. N fertilizer was applied at 150 kg N ha − 1 , whereas all fertilizer treatments also received the same amount of phosphorous and potassium, i.e., 75 kg P 2 O 5 ha − 1 and 150 kg K 2 O ha − 1 . Calcium superphosphate (16% P 2 O 5 ) was used as the phosphatic fertilizer, and potassium chloride (60% K 2 O) was used as the K fertilizer. Total N + P fertilizer was applied as basal fertilizer, 50% of the K fertilizer was applied as basal fertilizer, and the remaining K fertilizer was top-dressed 25 days after transplanting. Two rice cultivars, i.e., Meixiangzhan 2 (MX) and Y liangyou 1378 (YL),widely planted in South China, were used in the experiment.MX (the inbred rice)was developed by the Rice Research Institute, Guangdong Academy of Agricultural Science, China, and YL (two-line hybrid rice)was developed by the College of Agriculture, South China Agricultural University. The treatments were arranged in a randomized block design in triplicate. The size of each plot was 90 m 2 . Eighteen-day-old seedlings from wet bed nurseries were transplanted at four seedlings per hill at 30 cm×14 cm planting distance on 30th and 31th of March and harvested on the 11th and 12th of July in 2021 and 2022, respectively. The water and crop management practices were in accordance with the guidelines of the local agricultural department. Standard pesticides were spayed to avoid yield loss and quality damage. All the plots were flooded to a depth of 5 cm until the grain-filling stage. The water was subsequently drained for approximately 8 days before maturity. Leaf area index and total aboveground biomass Eight rice plants were selected at random from each plot at the mid-tillering stage, panicle initiation stage and heading stage and then separated into leaves, sheaths plus stems and panicles. All green leaf areas were measured with a LI-COR Model 3100 (Lincoln, NE), and the leaf area index (LAI) was calculated according to the method of Pan et al. ( 2017 ). The total rice plants were oven-dried at 70 ℃ until a constant weight to calculate the total aboveground biomass. Nitrogen metabolic enzymatic activity,including that of GS, NR and GOGAT The activities of nitrogen metabolic enzymes, i.e., GS, NR, and GOGAT were estimated according to Zhang et al. ( 2022 ). Nitrogen use efficiency At maturity, ten representative rice plants were taken from each plot and divided into leaves, sheaths, stems, and panicles, oven-dried at 70℃ and ground to determine the total nitrogen content according to the Kjedhal method. The NUE, including nitrogen recovery efficiency (NRE), nitrogen agronomic efficiency (NAE), nitrogen grain production efficiency (NGPE) and the nitrogen harvest index (NHI), was calculated according to Pan et al. ( 2017 ) and Zhang et al. ( 2022 ). Yield and yield components Fifteen rice plants from each plot were taken randomly and averaged to calculate productive panicles per hill, and eight representative rice plants were sampled to determine the spikelet number per panicle, grain filling percentage and 1000-grain weight. Finally, an area of 5 m 2 except for three adjacent border rows, was harvested to calculate the grain yield at 14% moisture content. Data analysis The experimental data were analyzed via Statistix 9.0 software. Differences between treatments were separated on the basis of the least significant difference test at a probability level of 0.05. The effects of year, rice cultivar, nitrogen type, and their interactions on the analysis of variance of grain yield and its components and NUE were determined via the general linear model procedure. The principal component analysis (PCA) biplot was generated via R studio with the lattice, permute, vegan, ggploSRF and scale packages. The figures were drawn with Sigplot 11.0. The structural equation model (SEM) was constructed and calculated via R studio with the Matrix, nlme, lme4, piecewise SEM v2.1.0, and readxl packages. Results Grain yield and its components Significant differences in the effects of mechanized deep placement of nitrogen fertilizer on grain yield and its components were detected between 2021 and 2022 (Table 2 ). The SRF treatment produced the highest grain yield among all treatments. The grain yields of MX and YL in the SRF treatment were 6.36 t ha − 1 and 7.50 t ha − 1 , which were 29.04% and 25.52% greater than those in the CU treatment, respectively. The number of productive panicles per hectare and spikelets per panicle were significantly greater in the SRF treatment than in the CU and CF treatments. A significant difference in grain yield was recorded between SRF and CF. The highest productive panicle, spikelets per panicle, and grain-filling rate were found in the SRF treatment for MX and YL, respectively. The productive panicle number per 10 4 ha was 267.45×10 4 and 264.15×10 4 , which were 11.65% and 10.17% greater in SRF than in CU for MX and YL, respectively. The spikelets per panicle of MX and YL in SRF were 149.51 and 159.80, which were 10.96% and 8.02% higher than those of CU. The SRF treatment had larger 1000-grain weight (21.64 g and 23.36 g) of MX and YL, which were 5.46% and 5.31% higher than those in CU. For MX and YL, spikelets per panicle and 1000-grain weight of YL were 6.88% and 7.95% greater than those of MX, respectively. The year and variety interaction, the year and nitrogen fertilizer type interaction, the variety and nitrogen fertilizer type interaction, and the year, variety, and nitrogen fertilizer type interaction were also significant with respect to spikelets per panicle. Moreover, the effects of variety and nitrogen fertilizer type on 1000-grain weight remained statistically significant. Table 2 Effects of deep placement of nitrogen fertilizer on grain yield and its components in machine-transplanted rice in early season of 2021 and 2022. Year Cultivar Treat- ments Productive panicles (10 4 ha −1 ) Spikelets per panicle Grain filling rate (%) 1000-grain weight(g) Grain yield (t ha − 1 ) 2021 MX CK 229.13c 127.54c 69.58 c 19.95c 3.48c CU 281.21b 139.20b 75.55b 20.65b 5.29b SRF 315.92a 152.56a 80.83a 21.64a 6.86a CF 305.51a 145.90ab 78.31ab 21.42a 6.43a mean 282.94 141.29 76.07 20.92 5.52 YL CK 194.41c 145.09c 72.42c 21.69c 4.10d CU 256.90b 152.15bc 75.15bc 22.71b 6.07c SRF 295.09a 162.33a 80.95a 23.46a 8.29a CF 284.68a 158.13ab 78.01ab 22.88ab 7.56b mean 257.77 154.43 76.63 22.69 6.51 2022 MX CK 187.47c 121.63c 76.64c 20.19c 2.88c CU 277.73b 130.28bc 81.52b 20.78b 4.56b SRF 308.98a 146.46a 86.81a 21.63a 5.85a CF 298.56ab 134.81ab 83.84ab 21.55a 5.03b mean 268.19 133.30 82.20 21.04 4.58 YL CK 177.06c 137.53 c 79.97b 20.88b 3.62c CU 232.60b 143.71b 83.19ab 22.79a 5.88b SRF 281.21a 157.26a 85.79a 23.25a 6.71a CF 260.38a 152.85a 84.46a 22.02 a 6.16b mean 237.81 147.84 83.35 22.24 5.59 Anova Y ns ns * ns ns C ** ns ns ** ** F ** ** ** ** ** Y×C ns ** ns ns ns Y×F ns ** ns ns ** C×F ns ** ns * ns Y×C×F ns ** ns ns ns Note : MX, Meixiangzhan 2 ; YL, Yliangyou 1378 . CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; Y: year; C: cultivar; F: fertilizer. Within a column, means followed by the same letter are not significantly different at the 0.05 probability level according to least significant different test (LSD 0.05). Nitrogen use efficiency (NUE) The nitrogen use efficiency (NUE) varied with the mechanical deep placement of nitrogen fertilizers in the early seasons of 2021 and 2022 (Table 3 ). Significant differences were noticed in NRE, NAE, NGPE, and NHI between CU and SRF. The SRF treatment had the highest NRE, NAE, NGPE, and NHI values, followed by the CF treatment, while the lowest values were found in the CU treatment. The NRE of MX and YL in SRF were 36.53% and 37.73%, which were 21.20% and 13.68% higher than those in CU. The SRF treatment had larger NAE (21.17 kg kg − 1 and 23.75 kg kg − 1 ) for MX and YL, which were 24.57% and 23.29% higher than those of CU. No significant difference in NRE or NAE was observed between SRF and CF for MX in the early season of 2021, however, a significant difference was found in the early season of 2022. Both SRF and CF had higher NGPE and NHI values than did CU for MX and YL, respectively. No significant differences in NGPE or NHI were detected between SRF and CF for MX. The year and nitrogen fertilizer type interaction and the year, variety, and nitrogen fertilizer type interaction were also significant with respect to the NAE and NHI, respectively. With respect to the rice cultivars, higher NRE, NAE and NGPE values were observed in YL, which were 7.40%, 14.73%, and 10.28% higher than those in MX, respectively. Table 3 Effects of deep placement of nitrogen fertilizer on nitrogen use efficiency in machine-transplanted rice in early season of 2021 and 2022. Year Cultivar Treat- ments NRE (%) NAE (kg·kg − 1 ) NGPE (kg·kg − 1 ) NHI (%) 2021 MX CK 30.91c 46.08c CU 26.54b 12.08b 34.63b 49.46b SRF 37.11a 22.53a 40.20a 52.00a CF 31.22ab 19.69a 39.77a 51.03ab mean 31.62 18.10 36.38 49.64 YL CK 33.29b 47.99d CU 29.54b 13.17c 36.30b 50.95c SRF 38.80a 27.93a 45.73a 54.12a CF 33.28ab 23.12b 43.75a 52.80 a mean 33.87 21.41 39.77 51.47 2022 MX CK 29.34b 44.53b CU 24.80b 11.16b 33.61ab 47.26a SRF 35.95a 19.81a 38.45a 48.96a CF 29.06b 14.30b 35.44a 48.86a mean 29.92 15.09 34.21 47.40 YL CK 33.93b 43.44b CU 26.91b 15.06b 39.93a 49.45a SRF 36.66a 19.56a 40.51a 51.12a CF 33.10a 15.40b 37.94a 49.40a mean 32.22 16.67 38.08 48.35 Anova Y ns ns ns ns C ns * * ns T ** ** ** ** Y×C ns ns ns ns Y×T ns * * ns C×T ns ns ns ns Y×C×T ns * * ns Note : MX, Meixiangzhan 2 ; YL, Yliangyou 1378 . CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; NRE:nitrogen recovery efficiency; NAE: nitrogen agronomic efficiency; NGPE: nitrogen grain production efficiency; NHI: nitrogen harvest index; Y: year; C: cultivar; F: fertilizer. Within a column, means followed by the same letter are not significantly different at the 0.05 probability level according to least significant different test (LSD 0.05). Leaf area index (LAI) The LAI at the mid-tillering, panicle initiation and heading stages are presented in Fig. 2 . The LAI of SRF at the panicle initiation and heading stages were 28.51% and 21.43% higher than those of CU for MX in the two years, respectively. No significant difference was found for MX between SRF and CF, ant the LAI of CF were 2.28, 6.04 and 5.33 at the mid-tillering, panicle initiation, and heading stages, respectively. The LAI of SRF at the mid-tillering and heading stages was 31.38% and 13.04% higher than that of CU for YL in the two years, respectively. For both rice cultivars, the LAI of YL was significantly greater than that of MX at the heading stage. Total aboveground biomass The total aboveground biomass at the mid-tillering, panicle initiation, heading stage, and maturity stages are shown in Fig. 3 . There was a significant difference in total aboveground biomass among all treatments for MX and YL during both years. The highest total aboveground biomass was recorded in SRF at the mid-tillering, panicle initiation, heading stage, and maturity stages, followed by CF and CU, and the lowest total aboveground biomass was observed in CF for both rice cultivars. The total aboveground biomass of MX in the SRF at the mid-tillering, panicle initiation, heading stage, and maturity stages are 70.92%, 40.75%, 16.74%, and 13.26% higher than those in the CU. Moreover, the total aboveground biomass of YL in SRF at the mid-tillering, panicle initiation, heading stage, and maturity stages were 81.25%, 29.84%, 18.61%, and 17.91% higher than those of CU. The total aboveground biomass was higher for YL than for MX at the heading stage. Nitrogen metabolic enzyme activities in the uppermost leaves of rice Nitrate reductase (NR) activity The NR activity in the uppermost leaves varied significantly in response to deep placement of different nitrogen fertilizers in both rice varieties in the early seasons of 2021 and 2022 (Fig. 4 ). Deep placement of SRF improved NR activities in both MX and YL during both years. The highest NR activity was found in SRF at the mid-tillering, panicle initiation, and heading stages for MX and YL, which were 10.46, 12.31, 7.12, 10.81, 12.84, and 9.01 µg g − 1 h − 1 , followed by CF and CU, whereas the lowest NR activity was found in CF. Regarding the rice cultivars, the NR activities of YL were 11.28 µg g − 1 h − 1 and 7.56 µg g − 1 h − 1 at the panicle initiation and heading stages, which were 7.84% and 17.76% higher than those of MX. GS activity The GS activities at the mid-tillering, panicle initiation, and heading stages are presented in Fig. 5 . The SRF treatment improved GS activities at the panicle initiation and heading stages. The highest GS activities of MX were observed in the SRF treatment at the panicle initiation and heading stages, with values of 19.71 ΔODg − 1 h − 1 FW and17.39 ΔODg − 1 h − 1 FW. The same trend occurred in YL, the SRF treatment led to higher GS activities (22.40 ΔODg − 1 h − 1 FW and 17.96 ΔODg − 1 h − 1 FW). The SRF treatment had larger GS activities of MX and YL at the heading stage, which were 18.24% and 15.09% higher than those of CU. With respect to the rice cultivars, the GS activities in YL was significantly greater than those in MX at the panicle initiation and heading stages. GOGAT activity The mechanical deep application of different nitrogen fertilizers significantly affected the GOGAT activity in the uppermost leaves of rice (Fig. 6 ). The SRF treatment significantly increased GOGAT activity. The SRF treatment led to the highest GOGAT activities of MX and YL at the heading stage, with values of 1.01µmolg − 1 min − 1 and 0.93 µmol g − 1 min − 1 . Moreover, the GOGAT activities of YL at the mid-tillering, panicle initiation, and heading stages were greater than those of MX. Correlation analysis and principal component analysis A structural equation model (SEM) was constructed to explain the possible direct or indirect pathways of grain yield and NUE under mechanized deep placement of nitrogen fertilizers (Fig. 7 ). The results of SEM fit were Fisher’s C = 40.372, p value = 0.002, df = 18, AIC = 92.372 and BIC = 104.98, indicating good model fit (P < 0.05, P < 0.01). According to SEM (R 2 M = 0.95, R 2 C = 0.95), nitrogen-metabolizing enzymes and yield components accounted for 95% (R 2 C = 0.95) of the effects of different treatments on grain yield when the random effect of ‘sampling points’ was considered. Among these pathways, yield components and nitrogen metabolism enzymes had significantly positive and direct regulatory effects on rice yield, and the pathway coefficients were 0.58 and 0.41, respectively. Furthermore, nitrogen use efficiency was significantly positively regulated by the activity of nitrogen-metabolizing enzymes and the accumulation of nitrogen, with pathway coefficients of 0.46 and 0.58, respectively. We also observed that the total aboveground biomass had significant positive and direct regulatory effects on nitrogen accumulation (R 2 C = 0.98), which indirectly affected NUE. The PCA biplot revealed 74.0% variance among all treatments (PC1 + PC2 = 0.74) (Fig. 8 ). The PCA results revealed that there were variations among all treatments and for both years, with the GOGAT at the mid-tillering, panicle initiation, and heading stages; the NR at the mid-tillering, panicle initiation, and heading stages; the grain filling rate; and 1000-grain-weight located in the first quadrant in the PCA, with nitrogen accumulation at the mid-tillering stage; productive panicle number per hill; the LAI at the mid-tillering and panicle initiation stages; and productive panicle number per hill located in the fourth quadrant in the PCA, indicating that significant differences between the SRF and CU treatments. Discussion Grain yield Slow/controlled-release fertilizers, urea and compound fertilizers are a concern because of their inconsistent nutrient release rates (Yan et al. 2020 ). Slow/controlled release-fertilizer is a type of fertilizer that releases nutrients at a slow rate and has a long release period. Urea is a type of water-soluble fast nitrogen fertilizer that releases nutrients quickly. Compound fertilizer has the advantages of high nutrient content and good physical properties (Qi et al. 2022 ). Previous studies have reported that different nitrogen fertilizer can significantly affect rice grain yield (Datta. 2017).Urea deep placement can markedly increase yield by nearly 12% compared with surface broadcast fertilizer (Yao et al. 2018 ). Broadcasting slow/controlled-release fertilizer could significantly affect rice grain yield by coordinating carbon-nitrogen metabolism (Jiang et al. 2024 ); Zhu et al. ( 2021 ) reported that mechanized deep placement of mixed urea (102 kg N ha − 1 as normal urea) plus controlled-release urea (48 kg N ha − 1 as controlled-release urea) could increase the grain yield by 4.0–11.0% compared with the local common fertilization method while reducing the N fertilization rate. The present study showed that mechanized deep placement of slow/controlled-release fertilizer had greater effects on grain yield than did urea or compound fertilizer because of an increase in the number of productive panicles per ha and the number of spikelets per panicle (Table 2 ). The reason was likely that mechanized deep placement of slow/controlled-release fertilizer promoted rice growth, such as a relatively high LAI (Fig. 2 ) and total aboveground biomass (Fig. 3 ). Correlation analysis revealed that the LAI at the heading stage and total aboveground biomass at maturity stage were positively associated with grain yield. However, there were no significant differences in grain yield among NPK briquette, NPK briquette with nitrification inhibitor, and controlled-release N fertilizer at the same application rate (Ming et al. 2021 ). The nutrient release characteristics of fertilizer are closely related to the soil pH, temperature, microbial species and activities in paddy fields (Ransom et al. 2020 ). Nitrogen use efficiency It is imperative to increase NUE by adopting suitable nitrogen fertilizer with suitable fertilization methods (Liu. 2019). Fertilizer deep placement has been shown to be an effective strategy to improve NUE by mitigating N loss in rice production systems (Pan et al. 2017 ; Li et al. 2022b ).Compared with surface broadcast fertilizer, deep placement of nitrogen fertilizer resulted in lower ammonia volatilization losses (Ke et al. 2018 ).Mi et al. ( 2017 ) reported that urea with nitrification inhibitors was more favorable than normal urea in double rice cropping systems. Our results revealed that mechanized deep placement of slow/controlled-release fertilizer could achieve a higher NUE than urea deep placement at the same rate because it lasts for a longer time due to the progressive release of slow/controlled-release fertilizer, eventually, the NUE improved. In addition, the present study revealed a higher NUE in YL than in MX, which was responsible for the larger LAI (Fig. 2 ) and higher N metabolic enzymes, i.e., GS, NR and GOGAT activities, in hybrid rice (Fig. 4 – 6 ). These results are in consistent with those of Yao et al. ( 2018 ). Moreover, Chen et al. ( 2015 ) reported that rice cultivars with high NUE could increase N uptake and ultimately increase nitrogen recovery and nitrogen agronomy efficiency. Physiological traits Different nitrogen fertilizer and fertilization method can affect rice growth and physiological traits (Kargbo et al. 2016 ; Ding et al. 2018 ). Zhang et al. ( 2022 ) found that basal fertilizer broadcast at a rate of 90 kg N ha − 1 and mechanical deep placement of tillering fertilizer at 45 kg N ha − 1 at a depth of 10 cm significantly increased the LAI and photosynthetic characteristics of rice leaves. Compared with broadcast solid fertilizer, deep placement of liquid fertilizer (with 10% less N) improved the anti-oxidant activities of the leaves at 150 kg ha − 1 NPK in two splits (90 kg ha − 1 NPK basal fertilizer plus 60 kg ha − 1 NPK tillering fertilizer) (Gui et al. 2022 ). This study showed that slow/controlled-release fertilizer with 150 kg N per ha under mechanical deep placement could significantly increase the LAI and total aboveground biomass at the panicle initiation and heading stages of MX and YL (Fig. 1 – 2 ), whereas the NR, GS, and GOGAT activities of rice leaves at the heading stage were also enhanced (Fig. 4 – 6 ). Conclusions Mechanical deep placement of slow/controlled-release fertilizer at a rate of 150 kg N per hectare can significantly increase grain yield and nitrogen use efficiency by nearly 30% and 20%, respectively, of Meixiangzhan 2 and Y liangyou 1378 , compared to urea deep placement at the same rate owing to an increase in productive panicles per hectare, spikelet number per panicle, and 1000-grain-weight. A larger LAI and total aboveground biomass were also found for deep placement of slow/controlled-release fertilizer. Moreover, the activities of nitrogen metabolism enzymes, including glutamate synthase (GS), nitrate reductase (NR), and glutamine oxoglutarate aminotransferase (GOGAT), were also improved. Therefore, the application of slow/controlled-release fertilizer with 150 kg N ha − 1 under mechanical deep placement can be an efficient N fertilizer management practice with the advantages of higher grain yield and NUE for rice production systems in South China. Abbreviations CU conventional urea CF compound fertilizer GS glutamate synthase GOGAT glutamine oxoglutarate amino transferase LAI leaf area index MX Meixiangzhan 2 NUE nitrogen use efficiency NR nitrate reductase NRE nitrogen recovery efficiency NAE nitrogen agronomic efficiency NGPE nitrogen grain production efficiency NHI nitrogen harvest index PCA principal component analysis SRF slow/controlled-release fertilizer SEM structural equation model TN total nitrogen YLY Y liangyou 1378 Declarations Author contributions SG and JY initiated and designed the research, XJ, HY, YF, LF and HD performed the experiments, XJ, HF and YF analyzed the data and wrote the manuscript, SG, HT and ZW revised the manuscript. All authors approved the final version of the manuscript. Declaration of competing interest The authors declare that they have no any competing interests that have affect the work reported in this paper. 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Agric Ecosyst Environ 247:236-245. https://doi.org/10.1016/j.agee.2017.07.001 Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6256990","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432578645,"identity":"bab12126-b116-4e5f-bc0e-a0aeccbba7e0","order_by":0,"name":"shenggang pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYJACZjjrg4GEHD8z88EHRGthnFFhYyzZzpZsQLQWZp4zaYkbzvOYCeBTbs5+9vDngoo7dvPdzx5+wdt22Nj4MIMZA0ONTTQuLZY9eWnSM848S954Ji/NQrLtsJzZYYa0BwzH0nIbcGgxOJBjxgw0PNmwIcfMwBBoC1DLcQPGhsO4tZx/Y/yZ9x9QS/8bM4PEtsOJm5sZ2yTwarmRYyDN23DYTl4ix/jBAZD3mZnZ8GqxnPHGTJrn2OEEA4k3ZowNwECWOMzGbJCAxy/m/DnGn3lqDtvL9wMZf0BR2X/+44MPNTa4HQalEzccYGCTgAsn4FCOrMVevoGB+QMehaNgFIyCUTCCAQDr/WAFWfpBrwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3784-4867","institution":"","correspondingAuthor":true,"prefix":"","firstName":"shenggang","middleName":"","lastName":"pan","suffix":""},{"id":432578646,"identity":"7f496cb5-bd54-4726-901d-d1d62a729dd1","order_by":1,"name":"Xiaojuan Pu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Pu","suffix":""},{"id":432578647,"identity":"2be33b1b-5a97-4068-9482-52af9ea5db2d","order_by":2,"name":"Hanyue Guo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hanyue","middleName":"","lastName":"Guo","suffix":""},{"id":432578648,"identity":"b0c99647-0e57-4cc0-ba49-28ebf28072e7","order_by":3,"name":"Yifei Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Wang","suffix":""},{"id":432578649,"identity":"26f12e1e-af8d-4efb-83e7-06291ff84c23","order_by":4,"name":"Longfei Xia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Longfei","middleName":"","lastName":"Xia","suffix":""},{"id":432578650,"identity":"29726d75-e595-4119-b567-548f0a6498cb","order_by":5,"name":"Hua Tian","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2025-03-19 01:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6256990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6256990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79667081,"identity":"d433a64c-f182-4be0-b519-880d8d225318","added_by":"auto","created_at":"2025-04-01 10:26:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61424,"visible":true,"origin":"","legend":"\u003cp\u003eAverage temperature and precipitation during the rice growth seasons in 2021 and 2022.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/824c78bf6ca16ec52f7637d5.png"},{"id":79667571,"identity":"f68f253d-0446-4116-8142-9e2b46cc2c7b","added_by":"auto","created_at":"2025-04-01 10:34:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136558,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of deep placement of nitrogen fertilizer on leaf area index in machine-transplanted rice in 2021 and 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e (A, C) MX, (B, D) YL; CK: no N application; CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; MT: mid-tillering stage; PI: panicle initiation stage; HS: heading stage. Different letters above bars are significantly different at the 0.05 probability level according to least significant different test (LSD 0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/4f4ecdf24a98c39b5f6ebe9b.png"},{"id":79667082,"identity":"4a4c403b-86b6-46f7-9bfe-6d54a86410ba","added_by":"auto","created_at":"2025-04-01 10:26:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152205,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of deep placement of nitrogen fertilizer on total aboveground biomass in machine-transplanted rice in 2021 and 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e (A, C) MX, (B, D) YL; CK: no N application; CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; MT: mid-tillering stage; PI: panicle initiation stage; HS: heading stage. Different letters above bars are significantly different at the 0.05 probability level according to least significant different test (LSD 0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/9a10cf2e27f46dc3bd5cc6cb.png"},{"id":79667083,"identity":"fa60e033-0cf9-4f3c-af48-035c2a6ef1d5","added_by":"auto","created_at":"2025-04-01 10:26:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139245,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of deep placement of nitrogen fertilizer on leaf NR activity in machine-transplanted rice in 2021 and 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e (A, C) MX, (B, D) YL; CK: no N application; CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; MT: mid-tillering stage; PI: panicle initiation stage; HS: heading stage. Different letters above bars are significantly different at the 0.05 probability level according to least significant different test (LSD 0.05).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/fbc1a5bf4375934ea5c5aea9.png"},{"id":79667575,"identity":"1d4346b6-3c40-465f-ae17-9d66898eeb08","added_by":"auto","created_at":"2025-04-01 10:34:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":130425,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of deep placement of nitrogen fertilizer on leaf GS activity in machine-transplanted rice in 2021 and 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e (A, C) MX, (B, D) YL; CK: no N application; CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; MT: mid-tillering stage; PI: panicle initiation stage; HS: heading stage. Different letters above bars are significantly different at the 0.05 probability level according to least significant different test (LSD 0.05).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/f00a5667931016890ecacf0f.png"},{"id":79669021,"identity":"6489c0ac-0941-4923-ae42-6e4e3d358900","added_by":"auto","created_at":"2025-04-01 10:50:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":161265,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of deep placement of nitrogen fertilizer on leaf GOGAT activity in machine-transplanted rice in 2021 and 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e (A, C) MX, (B, D) YL; CK: no N application; CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; MT: mid-tillering stage; PI: panicle initiation stage; HS: heading stage. Different letters above bars are significantly different at the 0.05 probability level according to least significant different test (LSD 0.05).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/28dcf4e0763adca8e7ab2c68.png"},{"id":79667576,"identity":"a01cfe11-b272-4d07-a8a0-b5be4aa07373","added_by":"auto","created_at":"2025-04-01 10:34:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":100214,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model (SEM) accounting the direct and indirect effects of nitrogen metabolism enzymes, nitrogen use efficiency, total above-ground biomass and yield components on the response of grain yield\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Numbers adjacent to arrows are path coefficients that indicate the directly standardized effect size of the relationship. The thickness of the arrow represents the strength of the relationship. Total standardized effects of the composite variables on ecosystem stability are shown in marginal and conditional R2 that represent the proportion of variance explained by all the predictors without and with accounting for random effects of the “sampling site”. Relationships between the residual variables of the measured predictors are not shown. NR, nitrate reductase activity; GS, glutamine synthetase activity; GOGAT, glutamate synthase; NUE, nitrogen use efficiency; NAE, nitrogen agronomic efficiency; NGPE, nitrogen grain production efficiency; NHI, nitrogen harvest index; PPN, productive panicle number per hill; GN, grain number per panicle; GFR, grain filling rate; TGW, 1000-grain-weight; LAI, leaf area index;\u003c/p\u003e\n\u003cp\u003eMT, mid-tillering stage; PI, panicle initiation stage; HS, heading stage; MS, maturity stage.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/8e2393a8c04f44f661d0e191.png"},{"id":79667086,"identity":"7c78697d-27f0-40c2-8fae-cd680bada592","added_by":"auto","created_at":"2025-04-01 10:26:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":81587,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) biplot between the samples of two rice cultivars under different treatments in 2021 and 2022\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e CK: no N application; CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer. PPN: productive panicle number per hill; GFR: grain filling rate; GN: grain number; TGW: 1000-grain-weight; NRE: nitrogen recovery efficiency; NAE: nitrogen agronomic efficiency; NGPE; nitrogen grain production efficiency; NHI: nitrogen harvest index; MTNA: nitrogen accumulation at mid-tillering stage; PINA: nitrogen accumulation at panicle initiation stage; HSNA: nitrogen accumulation at heading stage; MTBIOMASS: biomass at mid-tillering stage; PIBIOMASS: biomass at panicle initiation stage; MSBIOMASS:biomass at maturity stage; MTLAI: leaf area index at mid-tillering stage; PILAI: leaf area index at panicle initiation stage; HSLAI: leaf area index at heading stage; MTNR: nitrate reductase at mid-tillering stage; PINR: nitrate reductase at panicle initiation stage; HSNR: nitrate reductase at heading stage; MTGS: glutamate synthase at mid-tillering stage; PIGS: glutamate synthase at panicle initiation stage; HSGS: glutamate synthase at heading stage; MTGOGAT: glutamine oxoglutarate aminotransferase at mid-tillering stage; PIGOGAT: glutamine oxoglutarate aminotransferase at panicle initiation stage; HSGOGAT: glutamine oxoglutarate aminotransferase at heading stage.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/7ede37dadba7c34aa496f357.png"},{"id":82449949,"identity":"7f0beddf-61e0-4eb0-9ee3-7e120326484f","added_by":"auto","created_at":"2025-05-11 09:01:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1805068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6256990/v1/d95f0c9a-e656-40f4-a189-645df2d3a818.pdf"}],"financialInterests":"","formattedTitle":"Mechanical deep placement of slow/controlled-release fertilizer increases grain yield and nitrogen use efficiency by improving the carbon and nitrogen metabolism abilities of rice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice is one of the most important staple foods that can feed nearly half of the world\u0026rsquo;s population (Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). China maintained its position as a major global producer of rice, with 7% of world\u0026rsquo;s cultivation area contributing significantly to 28% of global rice production (FAO, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nitrogen is an essential nutrient that affects rice growth and productivity (Wei et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since some ineffective fertilization methods have been adopted by the local farming community, the overall nitrogen use efficiency (NUE) of rice production systems in China is only 35% (almost half that of developed countries) (Islam et al. 2018; Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Farmers usually apply excessive chemical N fertilizer, i.e., more than 300 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, to the paddy fields only in a single rice growing season to obtain higher yields (Yousef et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, China is also the largest consumer of chemical N fertilizers in the world.\u003c/p\u003e \u003cp\u003eIrrational N fertilization methods generally result in low NUE and environmental degradation (Jiang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Multiple strategies have been employed to improve NUE by reducing the nitrogen fertilizer rate per unit area in recent years, such as breeding rice cultivars with high NUE (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhong et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), increasing the fertilizer application time and split fertilizer application dose (He et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), using of slow/controlled-release fertilizer (Li et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), deep placement of N (Cheng et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and straw incorporation into the field (Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research has shown that deep placement of fertilizer is an efficient method of fertilization (Mumtahina et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). A 20% reduction in total urea under deep placement still led to an increase in rice yield compared with the traditional broadcasting fertilization method (Gu et al. 2022).Deep fertilization also reduces nitrogen loss through various mechanisms and increases the effectiveness of fertilizers (Chen et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Su et al.2024). Pan et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that, compared with manual surface fertilization, deep placement of different types of nitrogen fertilizers could improve the yield and economic benefits of rice.\u003c/p\u003e \u003cp\u003eSlow/controlled-release fertilizers have attracted increasing attention because of their ability to control the rate and amount of nutrient release (Ransom et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al. 2024). Previous studies have shown that SRF is also environmentally friendly owing to a decrease in N loss and improved NUE (Qiang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Compared with conventional fertilizers, SRF is advantageous because it reduces labor with a single basal application and has a higher NUE with periodic nutrient release (Zhu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given this background, our study hypothesizes that (1) mechanized deep SRF can improve NUE of rice; (2) mechanized deep SRF can increase grain yield. The primary objective is to (a) access the effects of mechanized deep placement of SRF on grain yield and NUE in machine-transplanted rice, and (b) elucidate the underlying mechanisms of mechanical deep placement of SRF to increase grain yield and NUE in double-rice cropping systems in South China.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eExperimental site description\u003c/p\u003e\n\u003cp\u003eA two-year field experiment was conducted in the early seasons (March-July) of 2021 and 2022 at the Experimental Farm of the College of Agriculture, South China Agricultural University (SCAU), in Guangzhou, Guangdong Province, China(23.13 \u0026deg;N, 113.18 \u0026deg;E, 18 m in elevation). The experimental site has a subtropical climate (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The properties of the experimental soils collected from the upper 20 cm are displayed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSoil properties from the upper 20 cm soil in the early season of 2021 and 2022\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil organic\u003c/p\u003e\n \u003cp\u003ematter (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal nitrogen\u003c/p\u003e\n \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal phosphorus\u003c/p\u003e\n \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal potassium\u003c/p\u003e\n \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFertilizer application\u003c/p\u003e\n\u003cp\u003eAn automated rice transplanter capable of transplanting rice seedlings synchronously with deep placement of fertilizer (developed by Changzhou YaMeiKe Mechanical Co., Ltd.) was used for transplanting the rice seedlings. The fertilizer application was accomplished according to Li et al. (\u003cspan class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Briefly, when rice seedlings are transplanted, the automated rice transplanter can simultaneously dig a furrow approximately 6 cm deep between two adjacent rows. The fertilizers were then put into the furrow.\u003c/p\u003e\n\u003cp\u003eExperimental treatments and design\u003c/p\u003e\n\u003cp\u003eSlow/controlled-release fertilizer produced by Guangdong Tianhe Zhongjia Fertilizer Co., Ltd. (total nitrogen content (TN)\u0026thinsp;=\u0026thinsp;25%, P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;6%, K\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;=\u0026thinsp;19%), written as SRF, was chosen. In addition, both conventional urea and compound fertilizer were selected as controls, referred to as CU and CF, respectively. Conventional urea was purchased from the market, and compound fertilizer was developed by Dongguan Fute Fertilizer Co., Ltd. (total nitrogen contents (TN)\u0026thinsp;=\u0026thinsp;15%, P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4%, K\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;=\u0026thinsp;6%). No N fertilizer was used to calculate N use efficiency, which was referred to as the CK. N fertilizer was applied at 150 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, whereas all fertilizer treatments also received the same amount of phosphorous and potassium, i.e., 75 kg P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 150 kg K\u003csub\u003e2\u003c/sub\u003eO ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Calcium superphosphate (16% P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) was used as the phosphatic fertilizer, and potassium chloride (60% K\u003csub\u003e2\u003c/sub\u003eO) was used as the K fertilizer. Total N\u0026thinsp;+\u0026thinsp;P fertilizer was applied as basal fertilizer, 50% of the K fertilizer was applied as basal fertilizer, and the remaining K fertilizer was top-dressed 25 days after transplanting.\u003c/p\u003e\n\u003cp\u003eTwo rice cultivars, i.e., \u003cem\u003eMeixiangzhan 2\u003c/em\u003e(MX) and \u003cem\u003eY liangyou 1378\u003c/em\u003e(YL),widely planted in South China, were used in the experiment.MX (the inbred rice)was developed by the Rice Research Institute, Guangdong Academy of Agricultural Science, China, and YL (two-line hybrid rice)was developed by the College of Agriculture, South China Agricultural University. The treatments were arranged in a randomized block design in triplicate. The size of each plot was 90 m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEighteen-day-old seedlings from wet bed nurseries were transplanted at four seedlings per hill at 30 cm\u0026times;14 cm planting distance on 30th and 31th of March and harvested on the 11th and 12th of July in 2021 and 2022, respectively. The water and crop management practices were in accordance with the guidelines of the local agricultural department. Standard pesticides were spayed to avoid yield loss and quality damage. All the plots were flooded to a depth of 5 cm until the grain-filling stage. The water was subsequently drained for approximately 8 days before maturity.\u003c/p\u003e\n\u003cp\u003eLeaf area index and total aboveground biomass\u003c/p\u003e\n\u003cp\u003eEight rice plants were selected at random from each plot at the mid-tillering stage, panicle initiation stage and heading stage and then separated into leaves, sheaths plus stems and panicles. All green leaf areas were measured with a LI-COR Model 3100 (Lincoln, NE), and the leaf area index (LAI) was calculated according to the method of Pan et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The total rice plants were oven-dried at 70 ℃ until a constant weight to calculate the total aboveground biomass.\u003c/p\u003e\n\u003cp\u003eNitrogen metabolic enzymatic activity,including that of GS, NR and GOGAT\u003c/p\u003e\n\u003cp\u003eThe activities of nitrogen metabolic enzymes, i.e., GS, NR, and GOGAT were estimated according to Zhang et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNitrogen use efficiency\u003c/p\u003e\n\u003cp\u003eAt maturity, ten representative rice plants were taken from each plot and divided into leaves, sheaths, stems, and panicles, oven-dried at 70℃ and ground to determine the total nitrogen content according to the Kjedhal method. The NUE, including nitrogen recovery efficiency (NRE), nitrogen agronomic efficiency (NAE), nitrogen grain production efficiency (NGPE) and the nitrogen harvest index (NHI), was calculated according to Pan et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Zhang et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eYield and yield components\u003c/p\u003e\n\u003cp\u003eFifteen rice plants from each plot were taken randomly and averaged to calculate productive panicles per hill, and eight representative rice plants were sampled to determine the spikelet number per panicle, grain filling percentage and 1000-grain weight. Finally, an area of 5 m\u003csup\u003e2\u003c/sup\u003eexcept for three adjacent border rows, was harvested to calculate the grain yield at 14% moisture content.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eThe experimental data were analyzed via Statistix 9.0 software. Differences between treatments were separated on the basis of the least significant difference test at a probability level of 0.05. The effects of year, rice cultivar, nitrogen type, and their interactions on the analysis of variance of grain yield and its components and NUE were determined via the general linear model procedure. The principal component analysis (PCA) biplot was generated via R studio with the lattice, permute, vegan, ggploSRF and scale packages. The figures were drawn with Sigplot 11.0. The structural equation model (SEM) was constructed and calculated via R studio with the Matrix, nlme, lme4, piecewise SEM v2.1.0, and readxl packages.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eGrain yield and its components\u003c/p\u003e\n\u003cp\u003eSignificant differences in the effects of mechanized deep placement of nitrogen fertilizer on grain yield and its components were detected between 2021 and 2022 (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The SRF treatment produced the highest grain yield among all treatments. The grain yields of MX and YL in the SRF treatment were 6.36 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 7.50 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which were 29.04% and 25.52% greater than those in the CU treatment, respectively. The number of productive panicles per hectare and spikelets per panicle were significantly greater in the SRF treatment than in the CU and CF treatments. A significant difference in grain yield was recorded between SRF and CF. The highest productive panicle, spikelets per panicle, and grain-filling rate were found in the SRF treatment for MX and YL, respectively. The productive panicle number per 10\u003csup\u003e4\u003c/sup\u003e ha was 267.45\u0026times;10\u003csup\u003e4\u003c/sup\u003e and 264.15\u0026times;10\u003csup\u003e4\u003c/sup\u003e, which were 11.65% and 10.17% greater in SRF than in CU for MX and YL, respectively. The spikelets per panicle of MX and YL in SRF were 149.51 and 159.80, which were 10.96% and 8.02% higher than those of CU. The SRF treatment had larger 1000-grain weight (21.64 g and 23.36 g) of MX and YL, which were 5.46% and 5.31% higher than those in CU. For MX and YL, spikelets per panicle and 1000-grain weight of YL were 6.88% and 7.95% greater than those of MX, respectively. The year and variety interaction, the year and nitrogen fertilizer type interaction, the variety and nitrogen fertilizer type interaction, and the year, variety, and nitrogen fertilizer type interaction were also significant with respect to spikelets per panicle. Moreover, the effects of variety and nitrogen fertilizer type on 1000-grain weight remained statistically significant.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of deep placement of nitrogen fertilizer on grain yield and its components in machine-transplanted rice in early season of 2021 and 2022.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreat-\u003c/p\u003e\n \u003cp\u003ements\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProductive panicles\u003c/p\u003e\n \u003cp\u003e(10\u003csup\u003e4\u003c/sup\u003eha\u003csup\u003e\u0026minus;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpikelets\u003c/p\u003e\n \u003cp\u003eper panicle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrain filling rate\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1000-grain weight(g)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrain yield\u003c/p\u003e\n \u003cp\u003e(t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.13c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.54c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.58 c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.95c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.48c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281.21b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.20b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.55b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.65b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.29b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315.92a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.83a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.64a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.86a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.90ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.31ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.42a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.43a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.41c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.09c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.42c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.69c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.10d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256.90b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.15bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.15bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.71b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.07c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.95a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.46a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.29a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284.68a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158.13ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.01ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.88ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.56b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.47c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.63c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.64c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.19c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e277.73b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.28bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.52b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.78b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308.98a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.46a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.81a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.63a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298.56ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.81ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.84ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.55a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177.06c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.53 c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.97b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.88b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.62c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232.60b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.71b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.19ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.88b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281.21a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157.26a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.25a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.71a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260.38a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.85a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.46a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.02 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.16b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026times;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026times;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u0026times;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026times;C\u0026times;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: MX, \u003cem\u003eMeixiangzhan 2\u003c/em\u003e; YL, \u003cem\u003eYliangyou 1378\u003c/em\u003e. CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; Y: year; C: cultivar; F: fertilizer. Within a column, means followed by the same letter are not significantly different at the 0.05 probability level according to least significant different test (LSD 0.05).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNitrogen use efficiency (NUE)\u003c/p\u003e\n\u003cp\u003eThe nitrogen use efficiency (NUE) varied with the mechanical deep placement of nitrogen fertilizers in the early seasons of 2021 and 2022 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant differences were noticed in NRE, NAE, NGPE, and NHI between CU and SRF. The SRF treatment had the highest NRE, NAE, NGPE, and NHI values, followed by the CF treatment, while the lowest values were found in the CU treatment. The NRE of MX and YL in SRF were 36.53% and 37.73%, which were 21.20% and 13.68% higher than those in CU. The SRF treatment had larger NAE (21.17 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 23.75 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for MX and YL, which were 24.57% and 23.29% higher than those of CU.\u003c/p\u003e\n\u003cp\u003eNo significant difference in NRE or NAE was observed between SRF and CF for MX in the early season of 2021, however, a significant difference was found in the early season of 2022. Both SRF and CF had higher NGPE and NHI values than did CU for MX and YL, respectively. No significant differences in NGPE or NHI were detected between SRF and CF for MX. The year and nitrogen fertilizer type interaction and the year, variety, and nitrogen fertilizer type interaction were also significant with respect to the NAE and NHI, respectively. With respect to the rice cultivars, higher NRE, NAE and NGPE values were observed in YL, which were 7.40%, 14.73%, and 10.28% higher than those in MX, respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of deep placement of nitrogen fertilizer on nitrogen use efficiency in machine-transplanted rice in early season of 2021 and 2022.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCultivar\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreat-\u003c/p\u003e\n \u003cp\u003ements\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNRE\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNAE\u003c/p\u003e\n \u003cp\u003e(kg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNGPE\u003c/p\u003e\n \u003cp\u003e(kg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNHI\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.91c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.08c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.54b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.63b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.46b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.53a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.22ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.69a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.77a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.03ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.29b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.99d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.54b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.17c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.30b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.95c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.93a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.73a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.12a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.28ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.12b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.75a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.80 a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.34b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.53b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.80b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.16b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.61ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.26a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.95a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.81a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.96a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.06b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.30b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.44a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.86a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.93b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.44b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.91b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.06b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.93a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.66a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.12a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.10a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.40b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.94a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnova\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026times;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026times;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u0026times;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY\u0026times;C\u0026times;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: MX, \u003cem\u003eMeixiangzhan 2\u003c/em\u003e; YL, \u003cem\u003eYliangyou 1378\u003c/em\u003e. CU: mechanized deep placement of conventional urea; SRF: mechanized deep placement of slow/controlled release fertilizer; CF: mechanized deep placement of compound fertilizer; NRE:nitrogen recovery efficiency; NAE: nitrogen agronomic efficiency; NGPE: nitrogen grain production efficiency; NHI: nitrogen harvest index; Y: year; C: cultivar; F: fertilizer. Within a column, means followed by the same letter are not significantly different at the 0.05 probability level according to least significant different test (LSD 0.05).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLeaf area index (LAI)\u003c/p\u003e\n\u003cp\u003eThe LAI at the mid-tillering, panicle initiation and heading stages are presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The LAI of SRF at the panicle initiation and heading stages were 28.51% and 21.43% higher than those of CU for MX in the two years, respectively. No significant difference was found for MX between SRF and CF, ant the LAI of CF were 2.28, 6.04 and 5.33 at the mid-tillering, panicle initiation, and heading stages, respectively. The LAI of SRF at the mid-tillering and heading stages was 31.38% and 13.04% higher than that of CU for YL in the two years, respectively. For both rice cultivars, the LAI of YL was significantly greater than that of MX at the heading stage.\u003c/p\u003e\n\u003cp\u003eTotal aboveground biomass\u003c/p\u003e\n\u003cp\u003eThe total aboveground biomass at the mid-tillering, panicle initiation, heading stage, and maturity stages are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. There was a significant difference in total aboveground biomass among all treatments for MX and YL during both years. The highest total aboveground biomass was recorded in SRF at the mid-tillering, panicle initiation, heading stage, and maturity stages, followed by CF and CU, and the lowest total aboveground biomass was observed in CF for both rice cultivars. The total aboveground biomass of MX in the SRF at the mid-tillering, panicle initiation, heading stage, and maturity stages are 70.92%, 40.75%, 16.74%, and 13.26% higher than those in the CU. Moreover, the total aboveground biomass of YL in SRF at the mid-tillering, panicle initiation, heading stage, and maturity stages were 81.25%, 29.84%, 18.61%, and 17.91% higher than those of CU. The total aboveground biomass was higher for YL than for MX at the heading stage.\u003c/p\u003e\n\u003cp\u003eNitrogen metabolic enzyme activities in the uppermost leaves of rice\u003c/p\u003e\n\u003cp\u003eNitrate reductase (NR) activity\u003c/p\u003e\n\u003cp\u003eThe NR activity in the uppermost leaves varied significantly in response to deep placement of different nitrogen fertilizers in both rice varieties in the early seasons of 2021 and 2022 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Deep placement of SRF improved NR activities in both MX and YL during both years. The highest NR activity was found in SRF at the mid-tillering, panicle initiation, and heading stages for MX and YL, which were 10.46, 12.31, 7.12, 10.81, 12.84, and 9.01 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eh\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, followed by CF and CU, whereas the lowest NR activity was found in CF. Regarding the rice cultivars, the NR activities of YL were 11.28 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eh\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 7.56 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eh\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at the panicle initiation and heading stages, which were 7.84% and 17.76% higher than those of MX.\u003c/p\u003e\n\u003cp\u003eGS activity\u003c/p\u003e\n\u003cp\u003eThe GS activities at the mid-tillering, panicle initiation, and heading stages are presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The SRF treatment improved GS activities at the panicle initiation and heading stages. The highest GS activities of MX were observed in the SRF treatment at the panicle initiation and heading stages, with values of 19.71 \u0026Delta;ODg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW and17.39 \u0026Delta;ODg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW. The same trend occurred in YL, the SRF treatment led to higher GS activities (22.40 \u0026Delta;ODg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW and 17.96 \u0026Delta;ODg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eFW). The SRF treatment had larger GS activities of MX and YL at the heading stage, which were 18.24% and 15.09% higher than those of CU. With respect to the rice cultivars, the GS activities in YL was significantly greater than those in MX at the panicle initiation and heading stages.\u003c/p\u003e\n\u003cp\u003eGOGAT activity\u003c/p\u003e\n\u003cp\u003eThe mechanical deep application of different nitrogen fertilizers significantly affected the GOGAT activity in the uppermost leaves of rice (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The SRF treatment significantly increased GOGAT activity. The SRF treatment led to the highest GOGAT activities of MX and YL at the heading stage, with values of 1.01\u0026micro;molg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emin\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eand 0.93 \u0026micro;mol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emin\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Moreover, the GOGAT activities of YL at the mid-tillering, panicle initiation, and heading stages were greater than those of MX.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis and principal component analysis\u003c/p\u003e\n\u003cp\u003eA structural equation model (SEM) was constructed to explain the possible direct or indirect pathways of grain yield and NUE under mechanized deep placement of nitrogen fertilizers (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The results of SEM fit were Fisher\u0026rsquo;s C\u0026thinsp;=\u0026thinsp;40.372, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.002, df\u0026thinsp;=\u0026thinsp;18, AIC\u0026thinsp;=\u0026thinsp;92.372 and BIC\u0026thinsp;=\u0026thinsp;104.98, indicating good model fit (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). According to SEM (R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eM\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.95, R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eC\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.95), nitrogen-metabolizing enzymes and yield components accounted for 95% (R\u003csup\u003e2\u003c/sup\u003eC\u0026thinsp;=\u0026thinsp;0.95) of the effects of different treatments on grain yield when the random effect of \u0026lsquo;sampling points\u0026rsquo; was considered. Among these pathways, yield components and nitrogen metabolism enzymes had significantly positive and direct regulatory effects on rice yield, and the pathway coefficients were 0.58 and 0.41, respectively. Furthermore, nitrogen use efficiency was significantly positively regulated by the activity of nitrogen-metabolizing enzymes and the accumulation of nitrogen, with pathway coefficients of 0.46 and 0.58, respectively. We also observed that the total aboveground biomass had significant positive and direct regulatory effects on nitrogen accumulation (R\u003csup\u003e2\u003c/sup\u003eC\u0026thinsp;=\u0026thinsp;0.98), which indirectly affected NUE.\u003c/p\u003e\n\u003cp\u003eThe PCA biplot revealed 74.0% variance among all treatments (PC1\u0026thinsp;+\u0026thinsp;PC2\u0026thinsp;=\u0026thinsp;0.74) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The PCA results revealed that there were variations among all treatments and for both years, with the GOGAT at the mid-tillering, panicle initiation, and heading stages; the NR at the mid-tillering, panicle initiation, and heading stages; the grain filling rate; and 1000-grain-weight located in the first quadrant in the PCA, with nitrogen accumulation at the mid-tillering stage; productive panicle number per hill; the LAI at the mid-tillering and panicle initiation stages; and productive panicle number per hill located in the fourth quadrant in the PCA, indicating that significant differences between the SRF and CU treatments.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGrain yield\u003c/p\u003e \u003cp\u003eSlow/controlled-release fertilizers, urea and compound fertilizers are a concern because of their inconsistent nutrient release rates (Yan et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Slow/controlled release-fertilizer is a type of fertilizer that releases nutrients at a slow rate and has a long release period. Urea is a type of water-soluble fast nitrogen fertilizer that releases nutrients quickly. Compound fertilizer has the advantages of high nutrient content and good physical properties (Qi et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies have reported that different nitrogen fertilizer can significantly affect rice grain yield (Datta. 2017).Urea deep placement can markedly increase yield by nearly 12% compared with surface broadcast fertilizer (Yao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Broadcasting slow/controlled-release fertilizer could significantly affect rice grain yield by coordinating carbon-nitrogen metabolism (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Zhu et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that mechanized deep placement of mixed urea (102 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as normal urea) plus controlled-release urea (48 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as controlled-release urea) could increase the grain yield by 4.0\u0026ndash;11.0% compared with the local common fertilization method while reducing the N fertilization rate. The present study showed that mechanized deep placement of slow/controlled-release fertilizer had greater effects on grain yield than did urea or compound fertilizer because of an increase in the number of productive panicles per ha and the number of spikelets per panicle (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The reason was likely that mechanized deep placement of slow/controlled-release fertilizer promoted rice growth, such as a relatively high LAI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and total aboveground biomass (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Correlation analysis revealed that the LAI at the heading stage and total aboveground biomass at maturity stage were positively associated with grain yield. However, there were no significant differences in grain yield among NPK briquette, NPK briquette with nitrification inhibitor, and controlled-release N fertilizer at the same application rate (Ming et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The nutrient release characteristics of fertilizer are closely related to the soil pH, temperature, microbial species and activities in paddy fields (Ransom et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNitrogen use efficiency\u003c/p\u003e \u003cp\u003eIt is imperative to increase NUE by adopting suitable nitrogen fertilizer with suitable fertilization methods (Liu. 2019). Fertilizer deep placement has been shown to be an effective strategy to improve NUE by mitigating N loss in rice production systems (Pan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).Compared with surface broadcast fertilizer, deep placement of nitrogen fertilizer resulted in lower ammonia volatilization losses (Ke et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Mi et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that urea with nitrification inhibitors was more favorable than normal urea in double rice cropping systems. Our results revealed that mechanized deep placement of slow/controlled-release fertilizer could achieve a higher NUE than urea deep placement at the same rate because it lasts for a longer time due to the progressive release of slow/controlled-release fertilizer, eventually, the NUE improved. In addition, the present study revealed a higher NUE in YL than in MX, which was responsible for the larger LAI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and higher N metabolic enzymes, i.e., GS, NR and GOGAT activities, in hybrid rice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These results are in consistent with those of Yao et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, Chen et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that rice cultivars with high NUE could increase N uptake and ultimately increase nitrogen recovery and nitrogen agronomy efficiency.\u003c/p\u003e \u003cp\u003ePhysiological traits\u003c/p\u003e \u003cp\u003eDifferent nitrogen fertilizer and fertilization method can affect rice growth and physiological traits (Kargbo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Zhang et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that basal fertilizer broadcast at a rate of 90 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and mechanical deep placement of tillering fertilizer at 45 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at a depth of 10 cm significantly increased the LAI and photosynthetic characteristics of rice leaves. Compared with broadcast solid fertilizer, deep placement of liquid fertilizer (with 10% less N) improved the anti-oxidant activities of the leaves at 150 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eNPK in two splits (90 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NPK basal fertilizer plus 60 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NPK tillering fertilizer) (Gui et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study showed that slow/controlled-release fertilizer with 150 kg N per ha under mechanical deep placement could significantly increase the LAI and total aboveground biomass at the panicle initiation and heading stages of MX and YL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas the NR, GS, and GOGAT activities of rice leaves at the heading stage were also enhanced (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMechanical deep placement of slow/controlled-release fertilizer at a rate of 150 kg N per hectare can significantly increase grain yield and nitrogen use efficiency by nearly 30% and 20%, respectively, of \u003cem\u003eMeixiangzhan 2\u003c/em\u003e and \u003cem\u003eY liangyou 1378\u003c/em\u003e, compared to urea deep placement at the same rate owing to an increase in productive panicles per hectare, spikelet number per panicle, and 1000-grain-weight. A larger LAI and total aboveground biomass were also found for deep placement of slow/controlled-release fertilizer. Moreover, the activities of nitrogen metabolism enzymes, including glutamate synthase (GS), nitrate reductase (NR), and glutamine oxoglutarate aminotransferase (GOGAT), were also improved. Therefore, the application of slow/controlled-release fertilizer with 150 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under mechanical deep placement can be an efficient N fertilizer management practice with the advantages of higher grain yield and NUE for rice production systems in South China.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCU \u0026nbsp; \u0026nbsp; \u0026nbsp;conventional urea\u003c/p\u003e\n\u003cp\u003eCF \u0026nbsp; \u0026nbsp; \u0026nbsp;compound fertilizer\u003c/p\u003e\n\u003cp\u003eGS \u0026nbsp; \u0026nbsp; \u0026nbsp;glutamate synthase\u003c/p\u003e\n\u003cp\u003eGOGAT \u0026nbsp;glutamine oxoglutarate amino transferase\u003c/p\u003e\n\u003cp\u003eLAI \u0026nbsp; \u0026nbsp; \u0026nbsp;leaf area index\u003c/p\u003e\n\u003cp\u003eMX\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eMeixiangzhan\u003c/em\u003e \u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNUE \u0026nbsp; \u0026nbsp; nitrogen use efficiency\u003c/p\u003e\n\u003cp\u003eNR \u0026nbsp; \u0026nbsp; \u0026nbsp;nitrate reductase\u003c/p\u003e\n\u003cp\u003eNRE \u0026nbsp; \u0026nbsp; nitrogen recovery efficiency\u003c/p\u003e\n\u003cp\u003eNAE \u0026nbsp; \u0026nbsp; nitrogen agronomic efficiency\u003c/p\u003e\n\u003cp\u003eNGPE \u0026nbsp; \u0026nbsp;nitrogen grain production efficiency\u003c/p\u003e\n\u003cp\u003eNHI \u0026nbsp; \u0026nbsp; \u0026nbsp;nitrogen harvest index\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp; \u0026nbsp; \u0026nbsp;principal component analysis\u003c/p\u003e\n\u003cp\u003eSRF \u0026nbsp; \u0026nbsp; \u0026nbsp;slow/controlled-release fertilizer\u003c/p\u003e\n\u003cp\u003eSEM \u0026nbsp; \u0026nbsp; \u0026nbsp;structural equation model\u003c/p\u003e\n\u003cp\u003eTN \u0026nbsp; \u0026nbsp; \u0026nbsp; total nitrogen\u003c/p\u003e\n\u003cp\u003eYLY \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cem\u003eY liangyou 1378\u003c/em\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSG and JY initiated and designed the research, XJ, HY, YF, LF and HD performed the experiments, XJ, HF and YF analyzed the data and wrote the manuscript, SG, HT and ZW revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no any competing interests that have affect the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by National Natural Science Foundation of China (31471442) and Guangdong Basic and Applied Basic Research Foundation (2021A1515011255).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen GZ, Ren L Q, Wang J Y, Liu F, Liu G X, Li H, Zhang P, Jia ZK (2024) Optimizing fertilization depth can promote sustainable development of dryland agriculture in the Loess Plateau region of China by improving crop production and reducing gas emissions. 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Agric Ecosyst Environ 247:236-245. https://doi.org/10.1016/j.agee.2017.07.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Slow/controlled-release fertilizer, Mechanical deep placement, Yield, Nitrogen use efficiency, Rice","lastPublishedDoi":"10.21203/rs.3.rs-6256990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6256990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e \u003cp\u003eSlow controlled release fertilizer has been paid more attention because of its slow release and long fertilizer cycle, the mechanized deep slow/controlled-release fertilizer (SRF) is desirable owing to its high nitrogen use efficiency. In this study, we elucidated the effects of mechanized deep SRF on the characteristics of carbon and nitrogen metabolism, grain yield and nitrogen use efficiency (NUE) of rice.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA two-year field experiment was conducted. Two rice cultivars, i.e., \u003cem\u003eMeixiangzhan\u003c/em\u003e 2 (MX) and \u003cem\u003eY Liangyou\u003c/em\u003e 1378 (YL), were used and three kinds of fertilization modes, i.e., mechanized deep placement of conventional urea (CU), slow/controlled-release fertilizer (SRF) and compound fertilizer (CF), at 150 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, were designed, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed that the grain yields of MX and YL for SRF were 29.04% and 25.52% greater than those of CU, respectively, owing to the greater number of productive panicles, spikelets per panicle, and 1000-grain-weight. The nitrogen recovery efficiency of MX and YL under SRF were 42.31% and 33.65% higher than those under CU. The SRF treatment produced higher nitrogen agronomic efficiency (21.17 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 23.75 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for MX and YL, which were 82.19% and 68.20% higher than those under CU, respectively. Moreover, the SRF treatment significantly improved the leaf area index and total aboveground biomass at the panicle initiation and heading stages, and nitrate reductase, glutamate synthase and glutamine oxoglutarate aminotransferase activities. The results of structural equation model (SEM) showed that yield components and nitrogen metabolism enzymes had significantly positive and direct regulatory effects on rice yield. Nitrogen use efficiency was significantly positively regulated by the activity of nitrogen-metabolizing enzymes and the accumulation of nitrogen.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMechanical deep placement of slow/controlled-release fertilizer at a rate of 150 kg N per hectare increases grain yield and nitrogen use efficiency, which can be an efficient nitrogen fertilizer management practice in South China.\u003c/p\u003e","manuscriptTitle":"Mechanical deep placement of slow/controlled-release fertilizer increases grain yield and nitrogen use efficiency by improving the carbon and nitrogen metabolism abilities of rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 10:26:07","doi":"10.21203/rs.3.rs-6256990/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e443144-c2f7-400d-9240-4d37b5d5201a","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-11T08:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 10:26:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6256990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6256990","identity":"rs-6256990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}