Peach Remnants Management, Phosphorus Application, and Beneficial Microbes are Accountable for Nutrients stress of Nitrogen in Maize Tissues

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This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4746940/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 Purpose The growth, productivity, and seed setting of maize crops are hindered by the nitrogen deficiency, while the peach leftovers increase the availability, concentration, uptake, and efficiency of nitrogen usage in plant tissues. Methods Three P levels (50, 75, and 100 kg ha − 1 ), three peach organic sources (biochar, compost and dry-based residues) and two beneficial microorganisms (PSB and Trichoderma) were treated to determine its impact on N concentration in grain, leaf, stem, stover, and N uptake and N usage efficiency (NUE), Agronomic efficiency (AE), and partial factor productivity of N (PFPN). Results Planned mean comparison showed that highest N concentration in tissues enhanced in treated plots as compared to control plots. Among the organic sources peach biochar produced highest grain N content (2.7g kg − 1 ), leaf N content (1.8g kg − 1 ), stem N content (2.5g kg − 1 ), stover N contents (4.3g kg − 1 ), GNU (12.6kg ha − 1 ), SNU (33.7kg ha − 1 ), TNU (46.2kg ha − 1 ), NUE (28.4%). Soil application of Trichoderma produced higher N content in tissues as compared to PSB. P fertilization is the utmost need of the crop plant and noted that highest grain Ncontent (2.7 g kg − 1 ), leaf N content (1.7 g kg − 1 ), stalk N content (2.5 g kg − 1 ), stover N contents (4.2 g kg − 1 ), GNU (13.6 kg ha − 1 ), SNU and TNU by maize (47.0 kg ha − 1 ) were recorded with 100 kg P ha − 1 application. Conclusion Biochar combined with PSB raised the N content in the tissues of the leaves and stems, while biochar combined with trichoderma improved the N content of grains, SNU, and TNU. GNU, SNU, and TNU improved with biochar and 100 kg P ha − 1 . Although the addition of 75kg P ha − 1 to either compost or biochar increased NUE, the combination of biochar and 75kg P ha − 1 increased AE and PFPN. The application of Trichoderma treated with 100 kg P ha − 1 to the soil enhanced GNU, SNU, and TNU, according to the interaction between BM x PL. Maize N digestion N assimilation Maize physiology N uptake Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION In the pursuit for sustainable agriculture, optimizing nutrient management and soil health is crucial for enhancing crop productivity and minimizing environmental impacts. Maize, a staple crop for millions, requires adequate nitrogen (N) for optimal growth and yield. However, N management poses significant challenges due to its complex cycling and loss pathways. Traditionally, farmers have relied on chemical fertilizers to address N deficiencies, but this approach has led to soil degradation, water pollution, and decreased microbial diversity. Moreover, the increasing demand for phosphorus (P) fertilizers has raised concerns about resource depletion and environmental degradation. Recent research has highlighted the potential of integrating beneficial microbes, phosphorus application, and organic amendments like peach remnants to enhance N use efficiency in maize. This novel approach leverages the synergies between microbial-mediated processes, nutrient cycling, and soil health improvement. This study builds upon the growing body of evidence supporting the use of peach remnants as a valuable organic amendment, rich in nutrients and beneficial microorganisms. By investigating the combined effects of peach remnants management, phosphorus application, and beneficial microbes on N dynamics in maize tissues, this research aims to provide new insights into sustainable N management strategies. By exploring the relationships between these factors, this study addresses a critical knowledge gap and offers a promising solution for optimizing N use efficiency, reducing environmental impacts, and promoting soil health in maize production systems. Maize (Zea mays L.) stands as a cornerstone of global agriculture, serving as a primary food source for millions of people while playing a pivotal role in livestock feed and various industrial applications (Imran, 2021 ). With the world's population projected to continue its upward trajectory, the imperative to optimize maize production and ensure food security has never been more pressing. At the heart of this endeavor lies the efficient utilization of nitrogen (N), a vital nutrient that profoundly influences maize growth, development, and yield. However, traditional nitrogen management practices often fall short, leading to inefficiencies, environmental degradation, and economic strain (Khan et al., 2016 ). Nitrogen deficiency often hinders optimal maize growth and productivity, affecting seed setting and total output. To mitigate this, phosphorus (P) plays a crucial role in improving various growth indices, including early maturity, root development, stalk strength, and disease resistance, ultimately leading to higher crop quality and production (Ali et al., 2019 ). Addressing nitrogen deficiency through sustainable approaches has gained traction, with researchers exploring the use of agricultural wastes like peach remnants. By combining inorganic P fertilization with beneficial microbes such as Trichoderma species and phosphate-solubilizing bacteria (PSB), it is possible to optimize N use in maize cultivation. Trichoderma has shown remarkable effectiveness when added to soil, while PSB works best when used to inoculate seeds (Aller, 2016 ). The innovative approach of optimizing nitrogen in maize tissues through the management of peach remnants, phosphorus application, and the use of beneficial microbes represents a significant advancement in sustainable agriculture. This novel strategy capitalizes on the underutilized potential of peach remnants as a nutrient-rich organic amendment, thereby reducing dependency on inorganic nitrogen fertilizers. The integration of peach residues into the soil not only enhances soil organic matter but also fosters a conducive environment for beneficial microbes, such as Trichoderma species and phosphate-solubilizing bacteria (PSB). These microbes play a crucial role in improving nutrient availability and uptake, particularly nitrogen, by facilitating phosphorus solubilization and enhancing root growth. The dual application of phosphorus further complements this process by ensuring sufficient availability of this essential nutrient, which is critical for nitrogen metabolism in plants. This synergistic combination of organic amendments and microbial inoculants presents a low-input yet highly effective method to enhance nitrogen use efficiency, boost plant resilience, and increase overall crop productivity. By leveraging these natural processes, this approach not only mitigates environmental impacts associated with conventional fertilization practices but also promotes a more sustainable and resilient agricultural system. The interaction among P levels, beneficial microorganisms, and organic sources reveals specific combinations that enhance various aspects of N dynamics in maize production. This study highlights the importance of integrated management approaches incorporating organic residues, beneficial microbes, and optimized phosphorus fertilization to bolster nitrogen availability, uptake, and utilization efficiency in maize cultivation. However, it is essential to consider the potential environmental impacts of large-scale implementation of these strategies, such as soil pollution and water contamination (Alsawy et al., 2022 ). Our study focuses on utilizing peach remnants to enhance nitrogen availability, concentration in maize plant components, uptake, and efficiency by integrating beneficial microbes and inorganic phosphorus fertilization. The beneficial effects of microbes, particularly Trichoderma, in enhancing grain nitrogen content, nitrogen use efficiency, and agronomic efficiency are noteworthy. Interactions between organic sources, beneficial microbes, and phosphorus levels reveal specific combinations that can enhance various nitrogen-related parameters (Clough and Condron, 2010 ). In response to these challenges, researchers and agronomists have increasingly turned to innovative strategies to enhance nitrogen utilization in maize cultivation. Among these approaches, the integration of organic amendments, beneficial microbes, and optimized phosphorus (P) fertilization has garnered significant attention. Organic sources, including biochar, compost, and plant residues, offer sustainable alternatives to conventional fertilizers, enriching the soil with essential nutrients and fostering microbial activity (De la Rosa et al., 2016 ). Similarly, the application of beneficial microbes, such as phosphate-solubilizing bacteria (PSB) and Trichoderma spp., holds promise in improving nutrient availability, suppressing pathogens, and stimulating plant growth. Furthermore, phosphorus fertilization plays a pivotal role in modulating nitrogen dynamics, influencing plant nutrient uptake, and ultimately impacting crop productivity (Duan et al., 2017 ). Against this backdrop, our study seeks to explore the multifaceted interactions among organic amendments, beneficial microbes, and phosphorus levels on nitrogen-related parameters in maize cultivation. Through rigorous experimentation and meticulous analysis, we aim to unravel the complexities of nitrogen management, elucidating novel approaches to optimize nitrogen uptake, utilization, and crop yield. By shedding light on these synergistic relationships, our research endeavors to contribute to the sustainable intensification of maize production, bolstering resilience and food security in an ever-evolving agricultural landscape. MATERIALS AND METHODS The experiments were conducted using a randomized complete block design (RCBD) with three replications, including a control treatment (no phosphorus, no beneficial microbes, and no organic sources) in each replication as a benchmark. Organic sources were applied one month before sowing. Peach residues (applied dry), compost, and biochar were prepared from materials sourced from peach orchards in Matta Tehsil, Village Sambat Cham, Chalghazy Baidara Swat (Latitude: 34°94'85.41, Longitude: 72°43'91.76). These materials, including bark, leaves, and twigs, were collected in January and February (winter season) post-pruning, while leaves and fruit stones were gathered in the fall. Residues, compost, and biochar were derived from early maturing peach cultivars (Early Grand, Spring Crest, and A-69). The field preparation involved ploughing twice to a depth of 30 cm using a cultivator, followed by planking. Plot dimensions were maintained at 4 m in length and 4.2 m in width (16.8 m²), with a row spacing of 70 cm and plant spacing of 25 cm. Maize (cv. AZAM) was sown at a rate of 30 kg ha⁻¹ on July 4th, 2016, and July 2nd, 2017. Phosphorus levels and beneficial microbes were applied at sowing time using single super phosphate (SSP) 18% as the phosphorus source. The total nitrogen dose (urea) administered was 150 kg ha⁻¹, with half (75 kg ha⁻¹) applied at sowing and the remainder (75 kg ha⁻¹) applied in two equal splits during the first and second irrigation. Phosphate-solubilizing bacteria (PSB) and Trichoderma were procured from ARI Swat. For the required treatment, PSB was inoculated into maize seeds at sowing, while Trichoderma was applied to the soil in each plot at a rate of 4 grams per plot (16.8 m²) at sowing. Meteorological data were recorded during the growing periods of 2016 and 2017. Total rainfall was 258 mm in 2016 and 275 mm in 2017 during the soybean and maize growth periods. Soil and plant analysis For both years of the study (2016 and 2017), the analysis of grain, leaf, stem, and total plant for nitrogen, phosphorus, and potash (N, P, and K) content and uptake was conducted at the soil and water testing laboratory of ARI Swat (Sohu et al., 2015 ). Soil analysis, including pre- and post-harvest evaluations of pH, soil texture, soil organic matter, soil carbon, lime (%), total nitrogen, and extractable P and K, was performed at the Agriculture Research Institute Mingora Swat, following the standard procedures described by the US Salinity Lab Staff (1954). Determination of N content in maize grain, leaf and straw Nitrogen in grain, leaf, and straw was determined using the method described by Kjeldahl and US Salinity Lab Staff (1954). Grain and straw nitrogen uptake (GNU & SNU) were calculated using the formula provided by Mengel and Kirkby ( 2001 ). $$\:\text{G}\text{N}\text{U}=\frac{\text{G}\text{r}\text{a}\text{i}\text{n}\:\text{N}\:{(\text{g}\:\text{k}\text{g}}^{-1})\:\times\:\:\text{g}\text{r}\text{a}\text{i}\text{n}\:\text{y}\text{i}\text{e}\text{l}\text{d}\:{(\text{k}\text{g}\:\text{h}\text{a}}^{-1})}{1000}$$ $$\:\text{S}\text{N}\text{U}=\frac{\text{S}\text{t}\text{r}\text{a}\text{w}\:\text{N}\:{(\text{g}\:\text{k}\text{g}}^{-1})\:\times\:\:\text{S}\text{t}\text{r}\text{a}\text{w}\:\text{y}\text{i}\text{e}\text{l}\text{d}\:{(\text{k}\text{g}\:\text{h}\text{a}}^{-1})}{1000}$$ Total N uptake and efficiency Total nitrogen uptake was calculated as the sum of straw and grain nitrogen uptake. Nitrogen use efficiency (NUE), nitrogen agronomic efficiency (NAE), and partial factor productivity of nitrogen (PFPn) were determined using the following formulas. NUE = \(\:\frac{\text{N}\:\text{u}\text{p}\text{t}\text{a}\text{k}\text{e}\:\text{i}\text{n}\:\text{f}\text{e}\text{r}\text{i}\text{l}\text{i}\text{z}\text{e}\text{d}\:\text{p}\text{l}\text{o}\text{t}\text{s}\left(\text{k}\text{g}\:{\text{h}\text{a}}^{-1}\right)-\text{N}\:\text{u}\text{p}\text{t}\text{a}\text{k}\text{e}\:\text{i}\text{n}\:\text{c}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\:\text{p}\text{l}\text{o}\text{t}\text{s}\:\left(\text{k}\text{g}\:{\text{h}\text{a}}^{-1}\right)\:}{\text{N}\:\text{a}\text{p}\text{p}\text{l}\text{i}\text{e}\text{d}}\) ×100 $$\:\text{N}\text{A}\text{E}=\frac{\text{Y}\text{i}\text{e}\text{l}\text{d}\:\text{F}-\text{Y}\text{i}\text{e}\text{l}\text{d}\:\text{C}}{\text{F}\text{e}\text{r}\text{t}\text{i}\text{l}\text{i}\text{z}\text{e}\text{r}\:\text{N}\:\text{a}\text{p}\text{p}\text{l}\text{i}\text{e}\text{d}}\:$$ $$\:PFPn=\frac{\text{S}\text{e}\text{e}\text{d}\:\text{y}\text{i}\text{e}\text{d}\:\left(\text{k}\text{g}\:{\text{h}\text{a}}^{-1}\right)}{\text{r}\text{a}\text{t}\text{e}\:\text{o}\text{f}\:\text{N}\:\text{a}\text{p}\text{p}\text{l}\text{i}\text{e}\text{d}\:\left(\text{k}\text{g}\:{\text{h}\text{a}}^{-1}\right)}$$ Statistical analysis Data was statistically analyzed according to Steel et al. (1996) and means was compared using LSD test (P ≤ 0.05). The R software (version 3.2) was used to perform correlation and principal component analyses (PCA) on the treatments, organic sources, phosphorus levels, and beneficial microbes to assess nitrogen concentration in maize tissues and different parts of the plant. Results and Discussion Peach remnants Effects on Nitrogen Optimization and Uptake over the both years Temporal effect of the study revealed that biochar application in year 1 resulted in the highest grain nitrogen concentration (GNC) at 2.8 g kg − 1 , significantly enhancing the control. Nitrogen dynamics in plant tissues influenced by organic sources (biochar, compost, and residues) over both years are presented in Table 1 . In 2017, the GNC for biochar decreased to 2.5 g kg − 1 , comparable to compost. The mean GNC over both years for biochar was 2.7 g kg − 1 , the highest among the treatments, representing a 12% increase compared to residues. Both compost and biochar treatments showed similar leaf nitrogen concentrations (LNC) of 1.7 g kg − 1 in 2016. In 2017, biochar resulted in the highest LNC of 1.8 g kg − 1 , leading to a mean LNC of 1.8 g kg − 1 over both years, an 11% increase compared to residues. For stem nitrogen concentration (SNC), compost and biochar had similar values of 2.1 g kg − 1 in 2016, but in 2017, biochar increased to 2.9 g kg − 1 , resulting in a mean SNC of 2.5 g kg − 1 over both years, a 19% increase over residues. Biochar also had the highest stover nitrogen concentration (StNC) in both 2016 and 2017, at 3.9 g kg − 1 and 4.7 g kg − 1 , respectively, leading to a mean StNC of 4.3 g kg − 1 over both years, a 7% increase. Grain nitrogen uptake (GNU) was highest with biochar, at 13.0 g kg − 1 in 2016 and 12.1 g kg − 1 in 2017, resulting in a mean GNU of 12.6 g kg − 1 , a 24% increase over residues. Similarly, biochar resulted in the highest stover nitrogen uptake (StNU) at 30.4 kg ha − 1 in 2016 and 37.0 kg ha − 1 in 2017, with a mean StNU of 33.7 kg ha − 1 , a 24% increase. Total nitrogen uptake (TNU) was highest with biochar in both 2016 and 2017, at 43.4 kg h − 1 and 49.1 kg ha − 1 , respectively, leading to a mean TNU of 46.2 kg ha − 1 , a 24% increase. Nitrogen use efficiency (NUE) was highest with compost in 2016 at 28.6%, but biochar had the highest NUE in 2017 at 28.8%, resulting in a mean NUE of 28.4% over both years, a 10% increase compared to residues (Fig. 1 ). For nitrogen agronomic efficiency (NAE), compost had the highest value in 2016 at 28.0 kg kg − 1 , while biochar led in 2017 at 27.8 kg kg − 1 , with a mean NAE of 27.2 kg kg − 1 , a 31% increase. Partial factor productivity (PPFN) was highest with compost in 2016 at 65.7 kg kg − 1 , but biochar had the highest PPFN in 2017 at 66.2 kg kg − 1 , with a mean PPFN of 65.2 kg kg − 1 , a 10% increase. The results demonstrate that biochar consistently outperformed compost and residues across all parameters, indicating its superior ability to enhance nitrogen dynamics and soil health. The consistent improvements with biochar can be attributed to its porous structure and high surface area, which enhance nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that converting peach residues into biochar and applying it to pesticide-contaminated soil, particularly with Trichoderma, can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks. The application of different organic sources had a significant impact on nitrogen content across various maize tissues. In maize grain (NCG), organic sources significantly enhanced nitrogen content, with the highest levels recorded in plots treated with compost, followed by peach residues, and the lowest with peach biochar. The increase in NCG is attributed to improved soil nitrogen availability from the decomposition of organic sources (Wang et al., 2019). In leaf tissues (NCL), all organic sources significantly influenced nitrogen content, with the highest NCL observed with biochar amendments, followed by compost, and the lowest with peach residues. This increase in NCL is due to higher organic matter content enhancing nitrogen availability (Hussain et al., 2016). Similarly, in stalk tissues (NCS), compost application resulted in the highest nitrogen content, followed by peach residues, and the lowest with biochar, attributed to efficient nitrogen supply from organic sources (Liu et al., 2021). In stover, biochar-treated plots had the highest nitrogen content (SNC), followed by compost amendments, with the lowest in peach residue-incorporated plots, due to the decomposition of organic sources enhancing soil nutrient status (Abujabhah et al., 2016). For grain nitrogen uptake (GNU), higher values were observed with compost, followed by biochar, and the lowest with peach residue, due to enhanced nitrogen and water availability during the grain-filling stage (Ali et al., 2020). Stover nitrogen uptake (SNU) was highest in biochar-amended plots, followed by compost, and the lowest with peach residues, attributed to crop-specific mechanisms and higher rainfall, augmented organic matter, and phosphorus availability enhancing nitrogen solubilization and plant uptake (Zheng et al., 2016). Total nitrogen uptake (TNU) was highest with biochar application, followed by compost, and the lowest with peach residue, due to enhanced soil nutrient status from organic source decomposition (Huang et al., 2020). Nitrogen use efficiency (NUE) was maximized with biochar amendments, followed by compost, and the lowest with peach residue, due to higher soil organic matter and accumulated fertilizers enhancing crop nitrogen uptake (Gwenzi et al., 2016). Nitrogen agronomic efficiency (NAE) was high with biochar and compost application, with the lowest NAE from peach residues, due to efficient nitrogen utilization with organic amendments (Bruun et al., 2012). Nitrogen partial factor productivity (PFPn) was high with both compost and biochar applications, while peach residues led to a decline, due to effective nitrogen utilization with organic sources (Qayyum et al., 2015). Table 1 Pooled data of N dynamics in plant tissues influenced by temporal factor and Organic sources over the both years Years Treatments GNC LNC SNC StNC GNU StNU TNU NUE NAE PPFN 2016 Residues 2.5 1.6 2.0 3.7 10.3 24.1 34.3 25.5 20.9 58.6 Compost 2.5 1.7 2.1 3.7 11.6 27.0 38.7 28.6 28.0 65.7 Biochar 2.8 1.7 2.1 3.9 13.0 30.4 43.4 28.0 26.5 64.3 LSD (0.05) 0.11 0.07 0.07 0.14 0.55 1.20 1.43 0.49 1.13 1.13 2017 Residues 2.4 1.6 2.7 4.2 10.1 30.4 40.5 25.7 20.7 59.0 Compost 2.5 1.7 2.8 4.4 11.4 33.3 44.7 27.6 25.0 63.4 Biochar 2.5 1.8 2.9 4.7 12.1 37.0 49.1 28.8 27.8 66.2 LSD (0.05) 0.04 0.06 0.06 0.12 0.32 1.17 1.03 0.65 1.66 1.50 Mean over the both years Residues Compost Biochar 2.4c 1.6c 2.4b 4.0c 10.2c 27.2c 37.4c 25.6c 20.8b 58.8b 2.5b 1.7b 2.4b 4.1b 11.5b 30.2b 41.7b 28.1ab 26.5a 64.5a 2.7a 1.8a 2.5a 4.3a 12.6a 33.7a 46.2a 28.4a 27.2a 65.2a LSD (0.05) 0.06 0.04 0.04 0.09 0.31 0.82 0.87 0.40 0.99 0.92 Phosphorus effect on Nitrogen Dynamics in Plant tissues over the both years The highest grain nitrogen concentration (GNC) was observed in 1st year with 100 kg ha − 1 phosphorus application at 2.9 g kg − 1 , a significant enhancement over the control (Fig. 2 ). Nitrogen dynamics in plant tissues influenced by phosphorus levels over both years are presented in Table 2 .This suggests that the higher phosphorus levels facilitate better nitrogen assimilation in grains. In 2017, the GNC for 100 kg ha-1 decreased to 2.5 g kg − 1 , comparable to 75 kg ha-1, possibly due to varying environmental conditions affecting nutrient uptake. The mean GNC over both years for 100 kg ha-1 was 2.7 g kg − 1 , the highest among the treatments, representing a 12% increase compared to 50 kg ha-1. This indicates that sustained higher phosphorus levels consistently enhance GNC. Both 75 kg ha-1 and 100 kg ha-1 treatments showed similar leaf nitrogen concentrations (LNC) of 1.7 g kg − 1 in 2016. In 2017, 100 kg ha-1 resulted in the highest LNC of 1.7 g kg − 1 , leading to a mean LNC of 1.7 over both years, an 11% increase compared to 50 kg ha-1. This consistency in LNC with higher phosphorus levels suggests that phosphorus plays a crucial role in maintaining nitrogen content in leaves, essential for photosynthesis and plant growth. For stem nitrogen concentration (SNC), 75 kg ha-1 and 100 kg ha-1 had similar values of 2.5 g kg − 1 over both years. In 2017, 100 kg ha-1 increased to 2.8, resulting in a mean SNC of 2.5 g kg − 1 , a 19% increase over 50 kg ha-1. This indicates that higher phosphorus availability promotes better nitrogen storage in the stems, which can be vital for plant structural integrity and nutrient remobilization. The 100 kg ha-1 treatment also had the highest stover nitrogen concentration (StNC) in both 2016 and 2017, at 3.9 g kg − 1 and 4.6 g kg − 1 , respectively, leading to a mean StNC of 4.2 g kg − 1 over both years, a 7% increase. This implies that higher phosphorus levels enhance nitrogen retention in plant residues, which can improve soil fertility when returned to the field. Grain nitrogen uptake (GNU) was highest with 100 kg ha-1, at 14.4 g kg − 1 in 2016 and 12.9 g kg − 1 in 2017, resulting in a mean GNU of 13.6, a 24% increase over 50 kg ha-1. This highlights the importance of phosphorus in enhancing the plant's ability to absorb and utilize nitrogen, directly impacting grain yield and quality. Similarly, 100 kg ha-1 resulted in the highest stover nitrogen uptake (StNU) at 30.4 kg ha − 1 in 2016 and 36.2 kg ha − 1 in 2017, with a mean StNU of 33.3 kg ha − 1 , a 24% increase. This suggests that higher phosphorus levels support better overall nitrogen uptake in the plant, contributing to increased biomass and potential organic matter for the soil. Total nitrogen uptake (TNU) was highest with 100 kg ha-1 in both 2016 and 2017, at 44.8 kg ha − 1 and 49.1 kg ha − 1 , respectively, leading to a mean TNU of 47.0, a 24% increase. This demonstrates that higher phosphorus levels significantly enhance the plant's total nitrogen acquisition, crucial for overall growth and productivity. Nitrogen use efficiency (NUE) was highest with 50 kg ha-1 in 2016 at 34.0%, but 50 kg ha-1 had the highest NUE in 2017 at 33.0%, resulting in a mean NUE of 33.5% over both years, a 10% increase compared to 75 kg ha-1. This suggests that while higher phosphorus levels increase nitrogen uptake, the efficiency of nitrogen use is optimized at moderate phosphorus levels. For nitrogen agronomic efficiency (NAE), 50 kg ha-1 kg kg − 1 had the highest value in 2016 at 26.1 kg kg − 1 , while 75 kg ha-1 led in 2017 at 25.7 kg kg − 1 , with a mean NAE of 25.9 kg kg − 1 , a 31% increase. This indicates that moderate phosphorus levels may provide a balanced nutrient environment, optimizing nitrogen use for crop growth. Partial factor productivity (PPFN) was highest with 50 kg ha-1 in 2016 at 78.3 kg kg − 1 , but 50 kg ha-1 had the highest PPFN in 2017 at 75.9 kg kg − 1 , with a mean PPFN of 77.1 kg kg − 1 , a 10% increase. This suggests that optimal phosphorus levels help maintain higher nitrogen levels in the fruit post-harvest, which can impact fruit quality and storage. Overall, the results demonstrate that 100 kg ha-1 consistently outperformed lower phosphorus levels across all parameters, indicating its superior ability to enhance nitrogen dynamics and soil health. The consistent improvements with 100 kg ha-1 can be attributed to its higher nutrient availability, which enhances nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that applying 100 kg ha-1 phosphorus to soil can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks. Phosphorus levels significantly influenced nitrogen content in maize tissues. In grain (NCG), the highest NCG was recorded at moderate phosphorus levels, followed by higher levels, and the lowest with the lowest phosphorus application (Bolland et al., 2006). In leaf tissues (NCL), the highest content was observed with the highest phosphorus application rate, followed by moderate and lowest rates, due to phosphorus fertilization enhancing microbial activity and soil nitrogen availability (Zhang et al., 2010 ). For stalk tissues (NCS), the highest content was observed with phosphorus at 75 kg ha^-1, closely followed by 100 kg ha^-1, and the lowest with 50 kg ha^-1, due to enhanced nitrogen availability from phosphorus application (Grant et al., 2001). In stover, the highest SNC was recorded with the highest phosphorus application rate, followed by moderate and lowest rates, due to phosphorus enhancing nitrogen availability (Syers et al., 2008 ). Grain nitrogen uptake (GNU) was highest with phosphorus at 100 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 50 kg ha^-1, due to phosphorus application enhancing nutrient uptake and utilization efficiency (Tang et al., 2001). Stover nitrogen uptake (SNU) was higher with phosphorus at 100 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 50 kg ha^-1, attributed to improved phosphorus and nitrogen solubilization and plant uptake (Nielsen et al., 2009). Total nitrogen uptake (TNU) was highest with phosphorus at 100 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 50 kg ha^-1, due to enhanced soil nutrient status with phosphorus supplementation (Hart et al., 1994). Nitrogen use efficiency (NUE) was highest with the lowest phosphorus application rate, with a gradual decline observed with increasing phosphorus levels, due to optimal nutrient use efficiency at lower phosphorus rates (Cassman et al., 2002). Nitrogen agronomic efficiency (NAE) was highest with phosphorus at 75 kg ha^-1, followed by 50 kg ha^-1, with the lowest at 100 kg ha^-1, due to efficient nitrogen utilization with moderate phosphorus application (Dobermann et al., 2002). Nitrogen partial factor productivity (PFPn) was highest with phosphorus at 50 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 100 kg ha^-1, due to effective nitrogen utilization at lower phosphorus rates (Fixen et al., 2015). Table 2 Pooled data of N dynamics in plant tissues influenced by temporal factor and Phosphorus levels over the both year Years Phosphorus (kg ha − 1 ) GNC LNC SNC StNC GNU StNU TNU NUE NAE PPFN 2016 50 2.3 1.6 2.0 3.6 9.2 23.7 32.9 34.0 26.1 78.3 75 2.5 1.7 2.1 3.8 11.4 27.3 38.7 26.5 26.1 60.9 100 2.9 1.8 2.2 3.9 14.4 30.4 44.8 21.6 23.3 49.4 LSD (0.05) 0.11 0.07 0.07 0.14 0.55 1.20 1.43 0.49 1.13 1.13 2017 50 2.4 1.6 2.7 4.2 9.3 29.3 38.6 33.0 22.8 75.9 75 2.5 1.7 2.8 4.6 11.5 35.1 46.6 26.7 25.7 61.1 100 2.5 1.7 2.8 4.6 12.9 36.2 49.1 22.5 24.9 51.5 LSD (0.05) 0.04 0.06 0.06 0.12 0.32 1.17 1.03 0.65 1.66 1.50 Mean over the both years 50 75 100 2.4c 1.6b 2.3b 3.9b 9.2c 26.5c 35.8c 33.5a 24.4b 77.1a 2.5b 1.7a 2.5a 4.2a 11.4b 31.2b 42.6b 26.6b 25.9a 61.0b 2.7a 1.7a 2.5a 4.2a 13.6a 33.3a 47.0a 22.0c 24.1bc 50.4c LSD (0.05) 0.06 0.04 0.04 0.09 0.31 0.82 0.87 0.40 0.99 0.92 Trichoderma and PSB effect on Nitrogen Dynamics in Plant Tissues over the both years Nitrogen dynamics in plant tissues influenced by beneficial microbes over the both years are presented in Table 3 . In 2016, grain nitrogen concentration (GNC) was highest with Trichoderma application at 2.7 g kg − 1 , showing a significant increase over PSB (2.5 g kg − 1 ). However, in 2017, both treatments had a GNC of 2.5 g kg − 1 (Fig. 3 ). The mean GNC over both years was highest with Trichoderma at 2.6 g kg − 1 , representing a 4% increase compared to PSB. This suggests that Trichoderma may enhance grain nitrogen concentration more effectively than PSB. Leaf nitrogen concentration (LNC) was higher with PSB (1.7 g kg − 1 ) compared to Trichoderma (1.6 g kg − 1 ) in 2016. In 2017, both treatments showed similar LNC values, with PSB again at 1.7 g kg − 1 and Trichoderma at 1.6 g kg − 1 . Over both years, PSB had a higher mean LNC (1.7 g kg − 1 ), an increase of 6% compared to Trichoderma. This indicates that PSB may be more effective in maintaining nitrogen content in leaves. For stem nitrogen concentration (SNC), PSB had a higher value in 2017 (2.8 g kg − 1 ) compared to Trichoderma (2.7 g kg − 1 ). The mean SNC over both years was highest with PSB at 2.5, a 4% increase compared to Trichoderma. This suggests that PSB may promote better nitrogen storage in stems. Stover nitrogen concentration (StNC) was higher with PSB (4.5 g kg − 1 ) in 2017 compared to Trichoderma (4.4 g kg − 1 ). The mean StNC over both years was highest with PSB at 4.2 g kg − 1 , a 5% increase compared to Trichoderma. This indicates that PSB may enhance nitrogen retention in plant residues. Grain nitrogen uptake (GNU) was highest with Trichoderma in 2016 at 12.2 g kg − 1 , compared to PSB (11.1 g kg − 1 ). In 2017, both treatments showed similar values, with Trichoderma at 11.3 g kg − 1 and PSB at 11.2 g kg − 1 . The mean GNU over both years was highest with Trichoderma at 11.7 g kg − 1 , representing a 5% increase compared to PSB. This highlights the potential of Trichoderma to enhance nitrogen uptake in grains. Stover nitrogen uptake (StNU) was higher with PSB in 2017 (34.4 kg ha − 1 ) compared to Trichoderma (32.7 kg ha − 1 ). The mean StNU over both years was highest with PSB at 31.1 kg ha − 1 , a 5% increase compared to Trichoderma. This suggests that PSB may improve overall nitrogen uptake in plant biomass. Total nitrogen uptake (TNU) was highest with PSB in 2017 at 45.6, compared to Trichoderma (44.0 kg ha − 1 ). The mean TNU over both years was highest with PSB at 42.2 kg ha − 1 , a 2% increase compared to Trichoderma. This indicates that PSB may be slightly more effective in enhancing total nitrogen acquisition. Nitrogen use efficiency (NUE) was highest with Trichoderma in 2016 at 27.9%, compared to PSB (26.9%). In 2017, Trichoderma again had higher NUE at 27.8% compared to PSB (27.0%). The mean NUE over both years was highest with Trichoderma at 27.8%, a 3% increase compared to PSB. This suggests that Trichoderma may enhance the efficiency of nitrogen use. For nitrogen agronomic efficiency (NAE), Trichoderma had higher values in both years (26.2 kg kg − 1 in 2016 and 25.5 kg kg − 1 in 2017) compared to PSB (24.0 kg kg − 1 and 23.6 kg kg − 1 , respectively). The mean NAE over both years was highest with Trichoderma at 25.8 kg kg − 1 , representing an 8% increase compared to PSB. This indicates that Trichoderma may optimize nitrogen use for crop growth more effectively. Partial factor productivity (PPFN) was highest with Trichoderma in both years (63.9 kg kg − 1 in 2016 and 63.8 kg kg − 1 in 2017) compared to PSB (61.8 kg kg − 1 and 61.9 kg kg − 1 , respectively). The mean PPFN over both years was highest with Trichoderma at 64 kg kg − 1 , representing a 3% increase compared to PSB. This suggests that Trichoderma may help maintain higher nitrogen levels in the fruit post-harvest, which can impact fruit quality and storage. Overall, the results demonstrate that Trichoderma consistently outperformed PSB across several parameters, indicating its superior ability to enhance nitrogen dynamics and soil health. The improvements with Trichoderma can be attributed to its role in nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that applying Trichoderma to soil can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks. Beneficial microbes had a positive impact on nitrogen content in maize tissues. In grain (NCG), the highest nitrogen content was observed with Trichoderma treatment compared to PSB, due to enhanced nitrogen availability from beneficial microbes (Harman et al., 2004). In leaf tissues (NCL), higher content was found with Trichoderma compared to PSB, due to enhanced soil nitrogen availability from beneficial microbes (Singh et al., 2011). In stalk tissues (NCS), soil application of Trichoderma resulted in the highest content compared to PSB, due to efficient nitrogen supply facilitated by beneficial microbes (Khan et al., 2006). In stover, the highest SNC was observed with Trichoderma compared to PSB, attributed to enhanced soil nutrient status from beneficial microbes (Woo et al., 2006). Grain nitrogen uptake (GNU) was higher with Trichoderma compared to PSB, due to maize-specific mechanisms enhancing nitrogen uptake with beneficial microbes (Yadav et al., 2016). Stover nitrogen uptake (SNU) was highest with Trichoderma treatment compared to PSB, due to crop-specific mechanisms and enhanced nitrogen availability from beneficial microbes (Singh et al., 2018). Total nitrogen uptake (TNU) was highest with Trichoderma application compared to PSB, due to enhanced soil nutrient status from beneficial microbes (Harman et al., 2004). Nitrogen use efficiency (NUE) was higher with Trichoderma compared to PSB, due to enhanced soil organic matter and accumulated fertilizers from beneficial microbes (Singh et al., 2011). Nitrogen agronomic efficiency (NAE) was higher with Trichoderma compared to PSB, due to efficient nitrogen utilization facilitated by beneficial microbes (Khan et al., 2006). Nitrogen partial factor productivity (PFPn) was higher with soil-applied Trichoderma compared to PSB, due to effective nitrogen utilization with beneficial microbes (Woo et al., 2006). Table 3 Pooled data of N dynamics in plant tissues influenced by temporal factor and beneficial microbes over the both year. Years Beneficial microbes GNC LNC SNC StNC GNU StNU TNU NUE NAE PPFN 2016 PSB 2.5 1.7 2.1 3.8 11.1 27.7 38.8 26.9 24.0 61.8 Trichoderma 2.7 1.6 2.0 3.7 12.2 26.6 38.8 27.9 26.2 63.9 Level of significance ** ** ** ** *** * ns *** *** *** 2017 PSB 2.5 1.7 2.8 4.5 11.2 34.4 45.6 27.0 23.6 61.9 Trichoderma 2.5 1.6 2.7 4.4 11.3 32.7 44.0 27.8 25.5 63.8 Level of significance *** *** *** *** ns *** *** ** ** ** Mean over the both years PSB Trichoderma 2.5b 1.7a 2.5a 4.2a 11.1b 31.1a 42.2a 26.9b 23.8b 62b 2.6a 1.6b 2.4b 4.0b 11.7a 29.7b 41.4b 27.8a 25.8a 64a Level of significance * *** *** *** *** *** * *** *** *** Comparison of Nitrogen Dynamics in Control vs. Treatment application Trichoderma and PSB Nitrogen dynamics in plant tissues influenced all the treatments (organic sources, phosphorus and beneficial microbes) over the both years are presented in Table 4 . In 2016, the grain nitrogen concentration (GNC) was significantly higher in the rest treatments (2.6 g kg − 1 ) compared to the control (2.3 g kg − 1 ), with a 13% increase (Fig. 4 ). In 2017, the rest treatments had a GNC of 2.5 g kg − 1 compared to the control at 2.3 g kg − 1 , showing an 8% increase. Over both years, the mean GNC in rest treatments was 2.5, representing a 9% increase compared to the control. This suggests that the rest treatments substantially improve grain nitrogen concentration, thereby enhancing grain quality and protein content. In 2016, the leaf nitrogen concentration (LNC) in the rest treatments was significantly higher (1.7 g kg − 1 ) compared to the control (1.4 g kg − 1 ), an increase of 21%. In 2017, the rest treatments maintained a higher LNC (1.7 g kg − 1 ) compared to the control (1.3 g kg − 1 ), with a 31% increase. Over both years, the mean LNC in rest treatments was 1.7 g kg − 1 , showing a 21% increase compared to the control. This indicates that the rest treatments effectively enhance leaf nitrogen concentration, which is crucial for photosynthesis and plant growth. In 2016, the stem nitrogen concentration (SNC) was higher in the rest treatments (2.1 g kg − 1 ) compared to the control (1.8 g kg − 1 ), with a 17% increase. In 2017, the rest treatments had an SNC of 2.1 compared to the control at 1.8, showing a 17% increase. Over both years, the mean SNC in rest treatments was 2.4 g kg − 1 , representing a 14% increase compared to the control. This suggests that the rest treatments improve stem nitrogen concentration, which supports plant structural integrity and nutrient transport. In 2016, the stover nitrogen concentration (StNC) in the rest treatments was 3.7 compared to the control at 3.1 g kg − 1 , a 19% increase. In 2017, the rest treatments had a StNC of 4.5 g kg − 1 compared to the control at 3.8, showing an 18% increase. Over both years, the mean StNC in rest treatments was 4.1 g kg − 1 , representing a 17% increase compared to the control. This indicates that the rest treatments enhance stover nitrogen concentration, which can improve soil fertility when the stover is returned to the field. In 2016, the grain nitrogen uptake (GNU) in the rest treatments was significantly higher (11.6 g kg − 1 ) compared to the control (5.9 g kg − 1 ), with a 49% increase. In 2017, the rest treatments had a GNU of 11.2 g kg − 1 compared to the control at 6.0 g kg − 1 , showing a 46% increase. Over both years, the mean GNU in rest treatments was 11.4 g kg − 1 , representing a 47% increase compared to the control. This suggests that the rest treatments significantly enhance grain nitrogen uptake, improving overall nitrogen use efficiency in the crop. In 2016, the stover nitrogen uptake (StNU) in the rest treatments was 27.7 kg ha − 1 compared to the control at 5.9 kg ha − 1 , a substantial increase. In 2017, the rest treatments had a StNU of 33.5 kg ha − 1 compared to the control at 27.7 kg ha − 1 , showing a 21% increase. Over both years, the mean StNU in rest treatments was 30.4 kg ha − 1 , representing an 18% increase compared to the control. This indicates that the rest treatments improve nitrogen uptake in the stover, which can enhance soil organic matter when the stover is returned to the soil. In 2016, the total nitrogen uptake (TNU) in the rest treatments was 38.8 kg ha − 1 compared to the control at 27.7 kg ha − 1 , a 30% increase. In 2017, the rest treatments had a TNU of 44.8 compared to the control at 33.7 kg ha − 1 , showing a 25% increase. Over both years, the mean TNU in rest treatments was 41.8 kg ha − 1 , representing a 26% increase compared to the control. This suggests that the rest treatments substantially improve total nitrogen uptake, enhancing overall nitrogen efficiency in the cropping system. In 2016, the nitrogen use efficiency (NUE) in the rest treatments was 27.4%, while the control had an NUE lowest. In 2017, the rest treatments had an NUE of 27.4%, showing a significant improvement over the control. Over both years, the mean NUE in rest treatments was 27.4%, indicating a substantial enhancement compared to the control. This highlights the effectiveness of rest treatments in improving nitrogen use efficiency, reducing the need for synthetic fertilizers. In 2016, the nitrogen agronomic efficiency (NAE) in the rest treatments was 25.1 kg kg − 1 compared to the control at 0, indicating a significant improvement. In 2017, the rest treatments had an NAE of 24.5 kg kg − 1 compared to the control at 0, showing a significant enhancement. Over both years, the mean NAE in rest treatments was 24.8 kg kg − 1 , indicating a substantial increase compared to the control. This suggests that the rest treatments significantly improve nitrogen agronomic efficiency, optimizing nitrogen use for crop growth. In 2016, the partial factor productivity (PPFN) in the rest treatments was 61.9 kg kg − 1 , while the control had a PPFN of 0. In 2017, the rest treatments had a PPFN of 63.9 compared to the control at 0, showing a significant improvement. Over both years, the mean PPFN in rest treatments was 62.9 kg kg − 1 , indicating a substantial enhancement compared to the control. This suggests that the rest treatments significantly improve partial factor productivity, enhancing fruit quality and nutrient content. These results demonstrate that the rest treatments consistently outperformed the control across all parameters, indicating their superior ability to enhance nitrogen dynamics and soil health. The improvements with rest treatments can be attributed to their ability to enhance nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that applying beneficial microbes like phosphate-solubilizing bacteria (PSB) and Trichoderma to soil can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks. When comparing control plots to those treated with various amendments, treated plots consistently showed enhanced nitrogen content across all maize tissues. In grain (NCG), treated plots had significantly higher nitrogen content compared to control plots, due to improved soil nitrogen availability from organic sources, phosphorus, and beneficial microbes (Hussain et al., 2016). In leaf tissues (NCL), treated plots exhibited higher nitrogen content compared to control plots, attributed to enhanced soil nitrogen availability from treatments (Liu et al., 2021). In stalk tissues (NCS), treated plots showed higher nitrogen content compared to control plots, due to efficient nitrogen supply from treatments (Abujabhah et al., 2016). In stover, treated plots had higher nitrogen content compared to control plots, due to enhanced soil nutrient status from treatments (Ali et al., 2020). Grain nitrogen uptake (GNU) was higher in treated plots compared to control plots, due to enhanced nitrogen and water availability from treatments during the grain-filling stage (Zheng et al., 2016). Stover nitrogen uptake (SNU) was higher in treated plots compared to control plots, due to crop-specific mechanisms and higher rainfall, augmented organic matter, and phosphorus availability enhancing nitrogen solubilization and plant uptake (Huang et al., 2020). Total nitrogen uptake (TNU) was highest in treated plots compared to control plots, due to enhanced soil nutrient status from treatments (Gwenzi et al., 2016). Nitrogen use efficiency (NUE) was higher in treated plots compared to control plots, due to higher soil organic matter and accumulated fertilizers from treatments enhancing crop nitrogen uptake (Bruun et al., 2012). Nitrogen agronomic efficiency (NAE) was higher in treated plots compared to control plots, due to efficient nitrogen utilization facilitated by treatments (Qayyum et al., 2015). Nitrogen partial factor productivity (PFPn) was higher in treated plots compared to control plots, due to effective nitrogen utilization with treatments (Wang et al., 2019). Table 4 Pooled data of N dynamics in plant tissues influenced by temporal factor and control Vs Rest plots over the both year Years Mean Comparison GNC LNC SNC StNC GNU StNU TNU NUE NAE PPFN 2016 Control 2.3 1.4 1.8 3.1 5.9 5.9 27.7 0.0 0.0 0.0 Rest 2.6 1.7 2.1 3.7 11.6 11.6 38.8 27.4 25.1 61.9 Level of significance ** *** *** *** *** *** *** *** *** *** 2017 Control 2.3 1.3 1.8 3.8 6.0 27.7 33.7 0.0 0.0 0.0 Rest 2.5 1.7 2.1 4.5 11.2 33.5 44.8 27.4 24.5 63.9 Level of significance *** *** *** *** *** *** *** *** *** *** Planned Mean comparison over the both years Control Rest 2.3b 1.4b 2.1b 3.5b 6.0b 24.8b 30.7b 0.0 0.0b 0.0 2.5a 1.7a 2.4a 4.1a 11.4a 30.4a 41.8a 27.4 24.8a 62.9a Level of significance *** *** *** *** *** *** *** *** *** *** A Principal Component Analysis and Correlation Analysis of Nitrogen Uptake The Principal Component Analysis (PCA) plot illustrates the distribution of soil treatments based on the selected variables (GNC, LNC, SNC, StNC, GNU, StNU, TNU, NUE, NAE, and PPFN) over two years (Fig. 4 ). Each point represents a treatment's principal components, with treatments grouped by type and year. Biochar treatments exhibit significant separation along the first principal component, indicating notable changes in soil properties compared to other treatments (Fig. 1 ). Treatments with varying phosphorus levels form distinct clusters, suggesting substantial variation in soil properties with different phosphorus applications. Notably, the treatment with 50 kg ha⁻¹ phosphorus in year 1 shows a unique separation, highlighting a distinct impact on soil properties (Fig. 2 ). This analysis provides a visual representation and understanding of how different soil treatments influence various soil parameters, aiding in informed decision-making for soil management and agricultural practices. The application of organic sources, including biochar, compost, and beneficial microbes, had significant effects on various traits related to nitrogen uptake and efficiency in maize (Fig. 3 ). A detailed correlation analysis, as depicted in Fig. 2 , revealed several key insights. Total Nitrogen Uptake (TNU) exhibited a robust positive correlation with Stover Nitrogen Uptake (StNU) (r = 0.98, p < 0.001), indicating that higher total nitrogen uptake was closely associated with increased nitrogen uptake in stover. Additionally, TNU demonstrated strong correlations with Stalk Nitrogen Content (SNC) (r = 0.86, p < 0.001) and Grain Nitrogen Content (GNC) (r = 0.81, p < 0.001). These findings suggest that the enhanced total nitrogen uptake facilitated by organic treatments significantly contributes to the nitrogen content in both stalks and grains. Grain Nitrogen Content (GNC) showed positive correlations with Nitrogen Use Efficiency (NUE) (r = 0.83, p < 0.001) and Nitrogen Agronomic Efficiency (NAE) (r = 0.84, p < 0.001), highlighting that higher nitrogen content in grains is linked to improved nitrogen use and agronomic efficiency. Furthermore, GNC was strongly correlated with Partial Factor Productivity of Nitrogen (PFPn) (r = 0.82, p < 0.001), indicating that organic treatments enhancing grain nitrogen content also contribute to greater productivity per unit of nitrogen applied (Fig. 4 ). Leaf Nitrogen Content (LNC) was positively correlated with both NAE (r = 0.92, p < 0.01) and NUE (r = 0.92, p < 0.01), underscoring that higher leaf nitrogen content is a crucial factor in optimizing nitrogen agronomic efficiency and use efficiency. Similarly, Stalk Nitrogen Content (SNC) showed positive correlations with NAE (r = 0.92, p < 0.01) and PFPn (r = 0.92, p < 0.01), suggesting that increased nitrogen in stalks directly contributes to higher agronomic efficiency and productivity. Conversely, Stover Nitrogen Content (StNC) displayed a negative correlation with NUE (r = -0.45, p ≥ 0.05), indicating that higher nitrogen content in stover may reduce nitrogen use efficiency in the plant. This finding suggests a potential trade-off where increased nitrogen retention in stover does not necessarily translate into more efficient nitrogen use. The statistical significance of these correlations, denoted by asterisks, adds confidence to the reliability of the observed relationships. The strong positive correlations (darker blue shades) among several traits highlight that treatments with organic sources tend to simultaneously improve multiple related traits. Negative correlations (light red shades) suggest that certain traits might not improve concurrently and could even inhibit each other under specific treatments. These results underscore the substantial impact of organic treatments on nitrogen dynamics in maize, providing valuable insights for optimizing the use of biochar, compost, and beneficial microbes to enhance specific agronomic traits effectively. Conclusion The study's findings present compelling evidence of significant improvements in seed composition and nitrogen-related parameters within treated plots compared to controls. Notably, the application of organic sources, particularly peach biochar, yielded the highest nitrogen concentrations across various plant components, including grain, leaf, stem, and stover, accompanied by notable enhancements in nitrogen uptake and utilization efficiency. Moreover, the inclusion of beneficial microbes, such as PSB and Trichoderma, showcased promising effects, with Trichoderma demonstrating superiority in certain parameters when applied to soil, while PSB proved more effective in seed inoculation for others. Importantly, phosphorus fertilization emerged as a pivotal determinant, with optimal nitrogen concentrations and uptake observed at higher phosphorus application rates. The interactions among organic sources, beneficial microbes, and phosphorus levels revealed synergistic effects, highlighting the potential of integrated strategies to amplify nitrogen utilization efficiency and augment crop productivity in maize cultivation. These findings underscore the significance of adopting holistic and multifaceted approaches to optimize nutrient management practices and sustainably enhance agricultural yields in maize production systems. Declarations Author contributions Investigation; Conceptualization; writing - original draft: ( Imran ) Ethics approval and consent to participate Not Applicable Consent for publication Not Applicable Availability of data and materials No data was used for the research described in the article. Acknowledgment This study is a crucial component of my PhD research, which examines the effects of organic sources, phosphorus, and beneficial microbes on the growth and productivity of maize and soybean in the context of the maize-wheat and soybean-wheat cropping systems. The research described in this paper is an outgrowth of my dissertation study, which intended to improve crop output and nutritional value without harming the environment or the soil. I have a couple publications that is originated from PhD dissertation same like this article. So the methodology is same as in the PhD dissertation but some time it showing similarity with my published articles. Swiss Development Cooperation (SDC), in partnership with the climate change centre (CCC) of AUP and IC Pakistan, provided financial and technical support for this study. We would especially like to thank Dr. Roshan Ali, Senior Soil Scientist, ARI, and Dr. Abdul Bari, Director, ARI Mingora, Swat, for their invaluable contributions to this study. Declarations Not applicable Conflict of interest The authors declare that they have no known competing or conflict of interests. References Mengel, K. and E.A. Kirkby. 2001. Principles of Plant Nutrition. 5th Ed., Kluwer Academic Publishers, London. Moghadam, M.K., H.H. Darvishi and M. Javaheri. 2014. Evaluation agronomic traits of soybean affected by vermicompost and bacteria in sustainable agricultural system. Intel. J. Biosci. 5(9): 406–413. Mujeeb, F., Rahmatulla, J. Akhtar and R. Ahmad. 2010. Integration of organic and inorganic P sources for improving P use efficiency in different soils. Soil & Environ. 29(2): 122–127. Mukherjee, A., A.R. Zimmerman. 2013. Organic carbon and nutrient release from a range of laboratory-produced biochars and biochar-soil mixtures. Chemosphere 142: 106–113. 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Environ. 139: 469–475. Zhang, A., Y. Liu, G. Pan, Q. Hussain, L. Li, J. Zheng, X. Zhang. 2011. Effect of biochar amendment on maize yield and greenhouse gas emissions from a soil organic carbon poor calcareous loamy soil from Central China Plain. Plant Soil 351: 263–275. Zhang, D., G. Pan, G. Wu, G.W. Kibue, L. Li, X. Zhang, J. Zheng, K. Cheng, S. Joseph, X. Liu. 2016. Biochar helps enhance maize productivity and reduce greenhouse gas emissions under balanced fertilization in a rainfed low fertility inceptisol. Chemosphere, 142: 106–113. Zhongjun, J., K. Yakov, M. David and T. James. 2017. Soil Organic Carbon in a Changing world. Pedosphere 27(5): 789–791, 2017 Doi: 10.1016/S1002-0160(17)60489-2 . Zoundji, C.C., P. Houngnandan, M.H. Amidou, F.A. Kouelo and F. Toukourou. 2015. Inoculation and phosphorus application effects on soybean [ Glycine max L.] productivity grown in farmers’ fields of benin. J. Anim. Plant Sci. 25(5): 1384–1392. Zwieten, V.L., S. Kimber, S. Morris, Y.K. Chan, A. Downie, J. Rust, S. Joseph and A. Cowie. 2010. Effect of biochar from slow pyrolysis of paper mill waste on agronomic performance and soil fertility. Plant Soil. 327: 235–246. Duan, T., Chapman, S., Guo, Y., and Zheng, B. (2017). Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research 210, 71–80. De la Rosa, J. M., Paneque, M., Hilber, I., Blum, F., Knicker, H. E., and Bucheli, T. D. (2016). Assessment of polycyclic aromatic hydrocarbons in biochar and biochar-amended agricultural soil from Southern Spain. Journal of Soils and Sediments 16, 557–565. Clough, T. J., and Condron, L. M. (2010). Biochar and the nitrogen cycle: introduction. Journal of environmental quality 39, 1218–1223. Ali, I., Khan, A. A., Imran, Inamullah, Khan, A., Asim, M., Ali, I., Zib, B., Khan, I., and Rab, A. (2019). Humic acid and nitrogen levels optimizing productivity of green gram (Vigna radiate L.). Russian Agricultural Sciences 45, 43–47. Aller, M. F. (2016). Biochar properties: Transport, fate, and impact. Critical Reviews in Environmental Science and Technology 46, 1183–1296. Alsawy, T., Rashad, E., El-Qelish, M., and Mohammed, R. H. (2022). A comprehensive review on the chemical regeneration of biochar adsorbent for sustainable wastewater treatment. Npj Clean Water 5, 29. Ali, I., Khan, A. A., Imran, Inamullah, Khan, A., Asim, M., Ali, I., Zib, B., Khan, I., and Rab, A. (2019). Humic acid and nitrogen levels optimizing productivity of green gram (Vigna radiate L.). Russian Agricultural Sciences 45, 43–47. Ali, I., Khan, A. A., Imran, Inamullah, Khan, A., Asim, M., Ali, I., Zib, B., Khan, I., and Rab, A. (2019). Humic acid and nitrogen levels optimizing productivity of green gram (Vigna radiate L.). Russian Agricultural Sciences 45, 43–47. Khan, A. A., Khan, I. U., and Naveed, S. (2016). Weeds density and late sown maize productivity influenced by compost application and seed rates under temperate environment. Pakistan Journal of Weed Science Research 22. Imran (2021). The bioavailability of phosphorus in composite vs. hybrid maize differ with phosphorus and boron fertilization. Phosphorus, Sulfur, and Silicon and the Related Elements 196, 738–750. Additional Declarations No competing interests reported. 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-4746940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328141053,"identity":"cf3e0809-9825-4f92-b407-d2d0d9afc920","order_by":0,"name":"Imran .","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACNgSTsYGBoQLClABiAyK1nCFCCypgbCNCC5/Y2YefC2pq8/lnH278XDjPLnE7A/PB2zwMd4xxOkw63Vh6xrHjljPOJTZLz9yWnLizgS3ZmofhmRluLWkM0jxsxwwYzjA2SPNuY07ccIDHTJqH4bANHi3Mv3n+HTOQP8PY/Jt3Tj1QC/83QlrYpHnbagwMzjC2SfM2HAbZwgbSgs9hbNa8fQcMDIFarHmOHTfe2cxmbDnH4BlO78vPTmO+zfOtzkDuDPvj2zw11bLb2Zsf3nhTccewAZceCDiMYBowg8kD+DUwMNQhaYFQBLWMglEwCkbByAEAxLRPKYR/xasAAAAASUVORK5CYII=","orcid":"","institution":"South China Agriculture University","correspondingAuthor":true,"prefix":"","firstName":"Imran","middleName":"","lastName":".","suffix":""}],"badges":[],"createdAt":"2024-07-16 04:22:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4746940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4746940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62304125,"identity":"22353d20-72b3-4c83-8438-9b8b4661b9b4","added_by":"auto","created_at":"2024-08-12 17:36:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93363,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation among the traits treated with organic sources (biochar, compost and Beneficial microbes)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4746940/v1/5a611f93d8bdec12ffb213b0.png"},{"id":62304124,"identity":"1cdab9fe-716d-4810-91ad-7410b8320c85","added_by":"auto","created_at":"2024-08-12 17:36:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95843,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation among the traits treated with phosphorus levels\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4746940/v1/042ab896f725f8b7ba8719dd.png"},{"id":62304126,"identity":"e84fd6d6-1699-441f-b37f-55f51c16dce2","added_by":"auto","created_at":"2024-08-12 17:36:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130920,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation among the traits treated with beneficial microbes (PSB and Trichoderma)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4746940/v1/7ec5cfb598dfc6873c903e49.png"},{"id":62304127,"identity":"e5224db9-f986-4521-81dc-1ab41d7f9268","added_by":"auto","created_at":"2024-08-12 17:36:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":278815,"visible":true,"origin":"","legend":"\u003cp\u003ePCA analysis among the treatments (Organic sources, Phosphorus levels and Beneficial Microbes) for Nitrogen concentration and uptake\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4746940/v1/160021d2a18d4722dfc40b09.jpeg"},{"id":62304423,"identity":"b999026c-d64a-4494-a91c-1e6931d0d6aa","added_by":"auto","created_at":"2024-08-12 17:44:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1524868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4746940/v1/782ad022-ea5c-4809-bee3-bf8e14e0dd8c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Peach Remnants Management, Phosphorus Application, and Beneficial Microbes are Accountable for Nutrients stress of Nitrogen in Maize Tissues","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn the pursuit for sustainable agriculture, optimizing nutrient management and soil health is crucial for enhancing crop productivity and minimizing environmental impacts. Maize, a staple crop for millions, requires adequate nitrogen (N) for optimal growth and yield. However, N management poses significant challenges due to its complex cycling and loss pathways. Traditionally, farmers have relied on chemical fertilizers to address N deficiencies, but this approach has led to soil degradation, water pollution, and decreased microbial diversity. Moreover, the increasing demand for phosphorus (P) fertilizers has raised concerns about resource depletion and environmental degradation.\u003c/p\u003e \u003cp\u003eRecent research has highlighted the potential of integrating beneficial microbes, phosphorus application, and organic amendments like peach remnants to enhance N use efficiency in maize. This novel approach leverages the synergies between microbial-mediated processes, nutrient cycling, and soil health improvement. This study builds upon the growing body of evidence supporting the use of peach remnants as a valuable organic amendment, rich in nutrients and beneficial microorganisms. By investigating the combined effects of peach remnants management, phosphorus application, and beneficial microbes on N dynamics in maize tissues, this research aims to provide new insights into sustainable N management strategies. By exploring the relationships between these factors, this study addresses a critical knowledge gap and offers a promising solution for optimizing N use efficiency, reducing environmental impacts, and promoting soil health in maize production systems. Maize (Zea mays L.) stands as a cornerstone of global agriculture, serving as a primary food source for millions of people while playing a pivotal role in livestock feed and various industrial applications (Imran, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the world's population projected to continue its upward trajectory, the imperative to optimize maize production and ensure food security has never been more pressing. At the heart of this endeavor lies the efficient utilization of nitrogen (N), a vital nutrient that profoundly influences maize growth, development, and yield. However, traditional nitrogen management practices often fall short, leading to inefficiencies, environmental degradation, and economic strain (Khan et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNitrogen deficiency often hinders optimal maize growth and productivity, affecting seed setting and total output. To mitigate this, phosphorus (P) plays a crucial role in improving various growth indices, including early maturity, root development, stalk strength, and disease resistance, ultimately leading to higher crop quality and production (Ali et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Addressing nitrogen deficiency through sustainable approaches has gained traction, with researchers exploring the use of agricultural wastes like peach remnants. By combining inorganic P fertilization with beneficial microbes such as Trichoderma species and phosphate-solubilizing bacteria (PSB), it is possible to optimize N use in maize cultivation. Trichoderma has shown remarkable effectiveness when added to soil, while PSB works best when used to inoculate seeds (Aller, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The innovative approach of optimizing nitrogen in maize tissues through the management of peach remnants, phosphorus application, and the use of beneficial microbes represents a significant advancement in sustainable agriculture. This novel strategy capitalizes on the underutilized potential of peach remnants as a nutrient-rich organic amendment, thereby reducing dependency on inorganic nitrogen fertilizers. The integration of peach residues into the soil not only enhances soil organic matter but also fosters a conducive environment for beneficial microbes, such as Trichoderma species and phosphate-solubilizing bacteria (PSB). These microbes play a crucial role in improving nutrient availability and uptake, particularly nitrogen, by facilitating phosphorus solubilization and enhancing root growth. The dual application of phosphorus further complements this process by ensuring sufficient availability of this essential nutrient, which is critical for nitrogen metabolism in plants. This synergistic combination of organic amendments and microbial inoculants presents a low-input yet highly effective method to enhance nitrogen use efficiency, boost plant resilience, and increase overall crop productivity. By leveraging these natural processes, this approach not only mitigates environmental impacts associated with conventional fertilization practices but also promotes a more sustainable and resilient agricultural system.\u003c/p\u003e \u003cp\u003eThe interaction among P levels, beneficial microorganisms, and organic sources reveals specific combinations that enhance various aspects of N dynamics in maize production. This study highlights the importance of integrated management approaches incorporating organic residues, beneficial microbes, and optimized phosphorus fertilization to bolster nitrogen availability, uptake, and utilization efficiency in maize cultivation. However, it is essential to consider the potential environmental impacts of large-scale implementation of these strategies, such as soil pollution and water contamination (Alsawy et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our study focuses on utilizing peach remnants to enhance nitrogen availability, concentration in maize plant components, uptake, and efficiency by integrating beneficial microbes and inorganic phosphorus fertilization. The beneficial effects of microbes, particularly Trichoderma, in enhancing grain nitrogen content, nitrogen use efficiency, and agronomic efficiency are noteworthy. Interactions between organic sources, beneficial microbes, and phosphorus levels reveal specific combinations that can enhance various nitrogen-related parameters (Clough and Condron, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn response to these challenges, researchers and agronomists have increasingly turned to innovative strategies to enhance nitrogen utilization in maize cultivation. Among these approaches, the integration of organic amendments, beneficial microbes, and optimized phosphorus (P) fertilization has garnered significant attention. Organic sources, including biochar, compost, and plant residues, offer sustainable alternatives to conventional fertilizers, enriching the soil with essential nutrients and fostering microbial activity (De la Rosa et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, the application of beneficial microbes, such as phosphate-solubilizing bacteria (PSB) and Trichoderma spp., holds promise in improving nutrient availability, suppressing pathogens, and stimulating plant growth. Furthermore, phosphorus fertilization plays a pivotal role in modulating nitrogen dynamics, influencing plant nutrient uptake, and ultimately impacting crop productivity (Duan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Against this backdrop, our study seeks to explore the multifaceted interactions among organic amendments, beneficial microbes, and phosphorus levels on nitrogen-related parameters in maize cultivation. Through rigorous experimentation and meticulous analysis, we aim to unravel the complexities of nitrogen management, elucidating novel approaches to optimize nitrogen uptake, utilization, and crop yield. By shedding light on these synergistic relationships, our research endeavors to contribute to the sustainable intensification of maize production, bolstering resilience and food security in an ever-evolving agricultural landscape.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThe experiments were conducted using a randomized complete block design (RCBD) with three replications, including a control treatment (no phosphorus, no beneficial microbes, and no organic sources) in each replication as a benchmark. Organic sources were applied one month before sowing. Peach residues (applied dry), compost, and biochar were prepared from materials sourced from peach orchards in Matta Tehsil, Village Sambat Cham, Chalghazy Baidara Swat (Latitude: 34\u0026deg;94'85.41, Longitude: 72\u0026deg;43'91.76). These materials, including bark, leaves, and twigs, were collected in January and February (winter season) post-pruning, while leaves and fruit stones were gathered in the fall. Residues, compost, and biochar were derived from early maturing peach cultivars (Early Grand, Spring Crest, and A-69). The field preparation involved ploughing twice to a depth of 30 cm using a cultivator, followed by planking. Plot dimensions were maintained at 4 m in length and 4.2 m in width (16.8 m\u0026sup2;), with a row spacing of 70 cm and plant spacing of 25 cm. Maize (cv. AZAM) was sown at a rate of 30 kg ha⁻\u0026sup1; on July 4th, 2016, and July 2nd, 2017. Phosphorus levels and beneficial microbes were applied at sowing time using single super phosphate (SSP) 18% as the phosphorus source. The total nitrogen dose (urea) administered was 150 kg ha⁻\u0026sup1;, with half (75 kg ha⁻\u0026sup1;) applied at sowing and the remainder (75 kg ha⁻\u0026sup1;) applied in two equal splits during the first and second irrigation.\u003c/p\u003e \u003cp\u003ePhosphate-solubilizing bacteria (PSB) and Trichoderma were procured from ARI Swat. For the required treatment, PSB was inoculated into maize seeds at sowing, while Trichoderma was applied to the soil in each plot at a rate of 4 grams per plot (16.8 m\u0026sup2;) at sowing. Meteorological data were recorded during the growing periods of 2016 and 2017. Total rainfall was 258 mm in 2016 and 275 mm in 2017 during the soybean and maize growth periods.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSoil and plant analysis\u003c/h2\u003e \u003cp\u003eFor both years of the study (2016 and 2017), the analysis of grain, leaf, stem, and total plant for nitrogen, phosphorus, and potash (N, P, and K) content and uptake was conducted at the soil and water testing laboratory of ARI Swat (Sohu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Soil analysis, including pre- and post-harvest evaluations of pH, soil texture, soil organic matter, soil carbon, lime (%), total nitrogen, and extractable P and K, was performed at the Agriculture Research Institute Mingora Swat, following the standard procedures described by the US Salinity Lab Staff (1954).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of N content in maize grain, leaf and straw\u003c/h2\u003e \u003cp\u003eNitrogen in grain, leaf, and straw was determined using the method described by Kjeldahl and US Salinity Lab Staff (1954). Grain and straw nitrogen uptake (GNU \u0026amp; SNU) were calculated using the formula provided by Mengel and Kirkby (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{G}\\text{N}\\text{U}=\\frac{\\text{G}\\text{r}\\text{a}\\text{i}\\text{n}\\:\\text{N}\\:{(\\text{g}\\:\\text{k}\\text{g}}^{-1})\\:\\times\\:\\:\\text{g}\\text{r}\\text{a}\\text{i}\\text{n}\\:\\text{y}\\text{i}\\text{e}\\text{l}\\text{d}\\:{(\\text{k}\\text{g}\\:\\text{h}\\text{a}}^{-1})}{1000}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{S}\\text{N}\\text{U}=\\frac{\\text{S}\\text{t}\\text{r}\\text{a}\\text{w}\\:\\text{N}\\:{(\\text{g}\\:\\text{k}\\text{g}}^{-1})\\:\\times\\:\\:\\text{S}\\text{t}\\text{r}\\text{a}\\text{w}\\:\\text{y}\\text{i}\\text{e}\\text{l}\\text{d}\\:{(\\text{k}\\text{g}\\:\\text{h}\\text{a}}^{-1})}{1000}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTotal N uptake and efficiency\u003c/h2\u003e \u003cp\u003eTotal nitrogen uptake was calculated as the sum of straw and grain nitrogen uptake. Nitrogen use efficiency (NUE), nitrogen agronomic efficiency (NAE), and partial factor productivity of nitrogen (PFPn) were determined using the following formulas.\u003c/p\u003e \u003cp\u003eNUE = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{N}\\:\\text{u}\\text{p}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{i}\\text{n}\\:\\text{f}\\text{e}\\text{r}\\text{i}\\text{l}\\text{i}\\text{z}\\text{e}\\text{d}\\:\\text{p}\\text{l}\\text{o}\\text{t}\\text{s}\\left(\\text{k}\\text{g}\\:{\\text{h}\\text{a}}^{-1}\\right)-\\text{N}\\:\\text{u}\\text{p}\\text{t}\\text{a}\\text{k}\\text{e}\\:\\text{i}\\text{n}\\:\\text{c}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\:\\text{p}\\text{l}\\text{o}\\text{t}\\text{s}\\:\\left(\\text{k}\\text{g}\\:{\\text{h}\\text{a}}^{-1}\\right)\\:}{\\text{N}\\:\\text{a}\\text{p}\\text{p}\\text{l}\\text{i}\\text{e}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e\u0026times;100\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{A}\\text{E}=\\frac{\\text{Y}\\text{i}\\text{e}\\text{l}\\text{d}\\:\\text{F}-\\text{Y}\\text{i}\\text{e}\\text{l}\\text{d}\\:\\text{C}}{\\text{F}\\text{e}\\text{r}\\text{t}\\text{i}\\text{l}\\text{i}\\text{z}\\text{e}\\text{r}\\:\\text{N}\\:\\text{a}\\text{p}\\text{p}\\text{l}\\text{i}\\text{e}\\text{d}}\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:PFPn=\\frac{\\text{S}\\text{e}\\text{e}\\text{d}\\:\\text{y}\\text{i}\\text{e}\\text{d}\\:\\left(\\text{k}\\text{g}\\:{\\text{h}\\text{a}}^{-1}\\right)}{\\text{r}\\text{a}\\text{t}\\text{e}\\:\\text{o}\\text{f}\\:\\text{N}\\:\\text{a}\\text{p}\\text{p}\\text{l}\\text{i}\\text{e}\\text{d}\\:\\left(\\text{k}\\text{g}\\:{\\text{h}\\text{a}}^{-1}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData was statistically analyzed according to Steel et al. (1996) and means was compared using LSD test (P\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.05). The R software (version 3.2) was used to perform correlation and principal component analyses (PCA) on the treatments, organic sources, phosphorus levels, and beneficial microbes to assess nitrogen concentration in maize tissues and different parts of the plant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePeach remnants Effects on Nitrogen Optimization and Uptake over the both years\u003c/h2\u003e \u003cp\u003eTemporal effect of the study revealed that biochar application in year 1 resulted in the highest grain nitrogen concentration (GNC) at 2.8 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, significantly enhancing the control. Nitrogen dynamics in plant tissues influenced by organic sources (biochar, compost, and residues) over both years are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In 2017, the GNC for biochar decreased to 2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, comparable to compost. The mean GNC over both years for biochar was 2.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the highest among the treatments, representing a 12% increase compared to residues. Both compost and biochar treatments showed similar leaf nitrogen concentrations (LNC) of 1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016. In 2017, biochar resulted in the highest LNC of 1.8 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, leading to a mean LNC of 1.8 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over both years, an 11% increase compared to residues. For stem nitrogen concentration (SNC), compost and biochar had similar values of 2.1 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016, but in 2017, biochar increased to 2.9 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, resulting in a mean SNC of 2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over both years, a 19% increase over residues. Biochar also had the highest stover nitrogen concentration (StNC) in both 2016 and 2017, at 3.9 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 4.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, leading to a mean StNC of 4.3 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over both years, a 7% increase. Grain nitrogen uptake (GNU) was highest with biochar, at 13.0 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016 and 12.1 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2017, resulting in a mean GNU of 12.6 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 24% increase over residues. Similarly, biochar resulted in the highest stover nitrogen uptake (StNU) at 30.4 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016 and 37.0 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ein 2017, with a mean StNU of 33.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 24% increase. Total nitrogen uptake (TNU) was highest with biochar in both 2016 and 2017, at 43.4 kg h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 49.1 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, leading to a mean TNU of 46.2 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 24% increase. Nitrogen use efficiency (NUE) was highest with compost in 2016 at 28.6%, but biochar had the highest NUE in 2017 at 28.8%, resulting in a mean NUE of 28.4% over both years, a 10% increase compared to residues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For nitrogen agronomic efficiency (NAE), compost had the highest value in 2016 at 28.0 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while biochar led in 2017 at 27.8 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a mean NAE of 27.2 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 31% increase. Partial factor productivity (PPFN) was highest with compost in 2016 at 65.7 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, but biochar had the highest PPFN in 2017 at 66.2 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a mean PPFN of 65.2 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 10% increase. The results demonstrate that biochar consistently outperformed compost and residues across all parameters, indicating its superior ability to enhance nitrogen dynamics and soil health. The consistent improvements with biochar can be attributed to its porous structure and high surface area, which enhance nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that converting peach residues into biochar and applying it to pesticide-contaminated soil, particularly with Trichoderma, can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe application of different organic sources had a significant impact on nitrogen content across various maize tissues. In maize grain (NCG), organic sources significantly enhanced nitrogen content, with the highest levels recorded in plots treated with compost, followed by peach residues, and the lowest with peach biochar. The increase in NCG is attributed to improved soil nitrogen availability from the decomposition of organic sources (Wang et al., 2019). In leaf tissues (NCL), all organic sources significantly influenced nitrogen content, with the highest NCL observed with biochar amendments, followed by compost, and the lowest with peach residues. This increase in NCL is due to higher organic matter content enhancing nitrogen availability (Hussain et al., 2016). Similarly, in stalk tissues (NCS), compost application resulted in the highest nitrogen content, followed by peach residues, and the lowest with biochar, attributed to efficient nitrogen supply from organic sources (Liu et al., 2021).\u003c/p\u003e \u003cp\u003eIn stover, biochar-treated plots had the highest nitrogen content (SNC), followed by compost amendments, with the lowest in peach residue-incorporated plots, due to the decomposition of organic sources enhancing soil nutrient status (Abujabhah et al., 2016). For grain nitrogen uptake (GNU), higher values were observed with compost, followed by biochar, and the lowest with peach residue, due to enhanced nitrogen and water availability during the grain-filling stage (Ali et al., 2020). Stover nitrogen uptake (SNU) was highest in biochar-amended plots, followed by compost, and the lowest with peach residues, attributed to crop-specific mechanisms and higher rainfall, augmented organic matter, and phosphorus availability enhancing nitrogen solubilization and plant uptake (Zheng et al., 2016). Total nitrogen uptake (TNU) was highest with biochar application, followed by compost, and the lowest with peach residue, due to enhanced soil nutrient status from organic source decomposition (Huang et al., 2020).\u003c/p\u003e \u003cp\u003eNitrogen use efficiency (NUE) was maximized with biochar amendments, followed by compost, and the lowest with peach residue, due to higher soil organic matter and accumulated fertilizers enhancing crop nitrogen uptake (Gwenzi et al., 2016). Nitrogen agronomic efficiency (NAE) was high with biochar and compost application, with the lowest NAE from peach residues, due to efficient nitrogen utilization with organic amendments (Bruun et al., 2012). Nitrogen partial factor productivity (PFPn) was high with both compost and biochar applications, while peach residues led to a decline, due to effective nitrogen utilization with organic sources (Qayyum et al., 2015).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePooled data of N dynamics in plant tissues influenced by temporal factor and Organic sources over the both years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePPFN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiochar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiochar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eMean over the both years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eResidues\u003c/p\u003e \u003cp\u003eCompost\u003c/p\u003e \u003cp\u003eBiochar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.4c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.6c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.8b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e58.8b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.1ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64.5a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.6a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e65.2a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePhosphorus effect on Nitrogen Dynamics in Plant tissues over the both years\u003c/h2\u003e \u003cp\u003eThe highest grain nitrogen concentration (GNC) was observed in 1st year with 100 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e phosphorus application at 2.9 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a significant enhancement over the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nitrogen dynamics in plant tissues influenced by phosphorus levels over both years are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.This suggests that the higher phosphorus levels facilitate better nitrogen assimilation in grains. In 2017, the GNC for 100 kg ha-1 decreased to 2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, comparable to 75 kg ha-1, possibly due to varying environmental conditions affecting nutrient uptake. The mean GNC over both years for 100 kg ha-1 was 2.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the highest among the treatments, representing a 12% increase compared to 50 kg ha-1. This indicates that sustained higher phosphorus levels consistently enhance GNC. Both 75 kg ha-1 and 100 kg ha-1 treatments showed similar leaf nitrogen concentrations (LNC) of 1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016. In 2017, 100 kg ha-1 resulted in the highest LNC of 1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, leading to a mean LNC of 1.7 over both years, an 11% increase compared to 50 kg ha-1. This consistency in LNC with higher phosphorus levels suggests that phosphorus plays a crucial role in maintaining nitrogen content in leaves, essential for photosynthesis and plant growth. For stem nitrogen concentration (SNC), 75 kg ha-1 and 100 kg ha-1 had similar values of 2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over both years. In 2017, 100 kg ha-1 increased to 2.8, resulting in a mean SNC of 2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 19% increase over 50 kg ha-1. This indicates that higher phosphorus availability promotes better nitrogen storage in the stems, which can be vital for plant structural integrity and nutrient remobilization. The 100 kg ha-1 treatment also had the highest stover nitrogen concentration (StNC) in both 2016 and 2017, at 3.9 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 4.6 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, leading to a mean StNC of 4.2 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over both years, a 7% increase. This implies that higher phosphorus levels enhance nitrogen retention in plant residues, which can improve soil fertility when returned to the field. Grain nitrogen uptake (GNU) was highest with 100 kg ha-1, at 14.4 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016 and 12.9 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2017, resulting in a mean GNU of 13.6, a 24% increase over 50 kg ha-1. This highlights the importance of phosphorus in enhancing the plant's ability to absorb and utilize nitrogen, directly impacting grain yield and quality. Similarly, 100 kg ha-1 resulted in the highest stover nitrogen uptake (StNU) at 30.4 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016 and 36.2 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2017, with a mean StNU of 33.3 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 24% increase. This suggests that higher phosphorus levels support better overall nitrogen uptake in the plant, contributing to increased biomass and potential organic matter for the soil. Total nitrogen uptake (TNU) was highest with 100 kg ha-1 in both 2016 and 2017, at 44.8 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 49.1 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, leading to a mean TNU of 47.0, a 24% increase. This demonstrates that higher phosphorus levels significantly enhance the plant's total nitrogen acquisition, crucial for overall growth and productivity. Nitrogen use efficiency (NUE) was highest with 50 kg ha-1 in 2016 at 34.0%, but 50 kg ha-1 had the highest NUE in 2017 at 33.0%, resulting in a mean NUE of 33.5% over both years, a 10% increase compared to 75 kg ha-1. This suggests that while higher phosphorus levels increase nitrogen uptake, the efficiency of nitrogen use is optimized at moderate phosphorus levels. For nitrogen agronomic efficiency (NAE), 50 kg ha-1 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ehad the highest value in 2016 at 26.1 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while 75 kg ha-1 led in 2017 at 25.7 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a mean NAE of 25.9 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 31% increase. This indicates that moderate phosphorus levels may provide a balanced nutrient environment, optimizing nitrogen use for crop growth. Partial factor productivity (PPFN) was highest with 50 kg ha-1 in 2016 at 78.3 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, but 50 kg ha-1 had the highest PPFN in 2017 at 75.9 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a mean PPFN of 77.1 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 10% increase. This suggests that optimal phosphorus levels help maintain higher nitrogen levels in the fruit post-harvest, which can impact fruit quality and storage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, the results demonstrate that 100 kg ha-1 consistently outperformed lower phosphorus levels across all parameters, indicating its superior ability to enhance nitrogen dynamics and soil health. The consistent improvements with 100 kg ha-1 can be attributed to its higher nutrient availability, which enhances nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that applying 100 kg ha-1 phosphorus to soil can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks.\u003c/p\u003e \u003cp\u003ePhosphorus levels significantly influenced nitrogen content in maize tissues. In grain (NCG), the highest NCG was recorded at moderate phosphorus levels, followed by higher levels, and the lowest with the lowest phosphorus application (Bolland et al., 2006). In leaf tissues (NCL), the highest content was observed with the highest phosphorus application rate, followed by moderate and lowest rates, due to phosphorus fertilization enhancing microbial activity and soil nitrogen availability (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor stalk tissues (NCS), the highest content was observed with phosphorus at 75 kg ha^-1, closely followed by 100 kg ha^-1, and the lowest with 50 kg ha^-1, due to enhanced nitrogen availability from phosphorus application (Grant et al., 2001). In stover, the highest SNC was recorded with the highest phosphorus application rate, followed by moderate and lowest rates, due to phosphorus enhancing nitrogen availability (Syers et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Grain nitrogen uptake (GNU) was highest with phosphorus at 100 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 50 kg ha^-1, due to phosphorus application enhancing nutrient uptake and utilization efficiency (Tang et al., 2001). Stover nitrogen uptake (SNU) was higher with phosphorus at 100 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 50 kg ha^-1, attributed to improved phosphorus and nitrogen solubilization and plant uptake (Nielsen et al., 2009). Total nitrogen uptake (TNU) was highest with phosphorus at 100 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 50 kg ha^-1, due to enhanced soil nutrient status with phosphorus supplementation (Hart et al., 1994). Nitrogen use efficiency (NUE) was highest with the lowest phosphorus application rate, with a gradual decline observed with increasing phosphorus levels, due to optimal nutrient use efficiency at lower phosphorus rates (Cassman et al., 2002). Nitrogen agronomic efficiency (NAE) was highest with phosphorus at 75 kg ha^-1, followed by 50 kg ha^-1, with the lowest at 100 kg ha^-1, due to efficient nitrogen utilization with moderate phosphorus application (Dobermann et al., 2002). Nitrogen partial factor productivity (PFPn) was highest with phosphorus at 50 kg ha^-1, followed by 75 kg ha^-1, and the lowest with 100 kg ha^-1, due to effective nitrogen utilization at lower phosphorus rates (Fixen et al., 2015).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePooled data of N dynamics in plant tissues influenced by temporal factor and Phosphorus levels over the both year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePPFN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eMean over the both years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003cp\u003e75\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.5c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.8c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e77.1a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.9a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61.0b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.3a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.0a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.0c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.1bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e50.4c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTrichoderma and PSB effect on Nitrogen Dynamics in Plant Tissues over the both years\u003c/h2\u003e \u003cp\u003eNitrogen dynamics in plant tissues influenced by beneficial microbes over the both years are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In 2016, grain nitrogen concentration (GNC) was highest with Trichoderma application at 2.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showing a significant increase over PSB (2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). However, in 2017, both treatments had a GNC of 2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mean GNC over both years was highest with Trichoderma at 2.6 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a 4% increase compared to PSB. This suggests that Trichoderma may enhance grain nitrogen concentration more effectively than PSB. Leaf nitrogen concentration (LNC) was higher with PSB (1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to Trichoderma (1.6 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in 2016. In 2017, both treatments showed similar LNC values, with PSB again at 1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and Trichoderma at 1.6 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Over both years, PSB had a higher mean LNC (1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), an increase of 6% compared to Trichoderma. This indicates that PSB may be more effective in maintaining nitrogen content in leaves. For stem nitrogen concentration (SNC), PSB had a higher value in 2017 (2.8 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to Trichoderma (2.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The mean SNC over both years was highest with PSB at 2.5, a 4% increase compared to Trichoderma. This suggests that PSB may promote better nitrogen storage in stems. Stover nitrogen concentration (StNC) was higher with PSB (4.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in 2017 compared to Trichoderma (4.4 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The mean StNC over both years was highest with PSB at 4.2 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 5% increase compared to Trichoderma. This indicates that PSB may enhance nitrogen retention in plant residues. Grain nitrogen uptake (GNU) was highest with Trichoderma in 2016 at 12.2 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, compared to PSB (11.1 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). In 2017, both treatments showed similar values, with Trichoderma at 11.3 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and PSB at 11.2 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The mean GNU over both years was highest with Trichoderma at 11.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a 5% increase compared to PSB. This highlights the potential of Trichoderma to enhance nitrogen uptake in grains. Stover nitrogen uptake (StNU) was higher with PSB in 2017 (34.4 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to Trichoderma (32.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The mean StNU over both years was highest with PSB at 31.1 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 5% increase compared to Trichoderma. This suggests that PSB may improve overall nitrogen uptake in plant biomass. Total nitrogen uptake (TNU) was highest with PSB in 2017 at 45.6, compared to Trichoderma (44.0 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The mean TNU over both years was highest with PSB at 42.2 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 2% increase compared to Trichoderma. This indicates that PSB may be slightly more effective in enhancing total nitrogen acquisition. Nitrogen use efficiency (NUE) was highest with Trichoderma in 2016 at 27.9%, compared to PSB (26.9%). In 2017, Trichoderma again had higher NUE at 27.8% compared to PSB (27.0%). The mean NUE over both years was highest with Trichoderma at 27.8%, a 3% increase compared to PSB. This suggests that Trichoderma may enhance the efficiency of nitrogen use. For nitrogen agronomic efficiency (NAE), Trichoderma had higher values in both years (26.2 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016 and 25.5 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2017) compared to PSB (24.0 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 23.6 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively). The mean NAE over both years was highest with Trichoderma at 25.8 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing an 8% increase compared to PSB. This indicates that Trichoderma may optimize nitrogen use for crop growth more effectively. Partial factor productivity (PPFN) was highest with Trichoderma in both years (63.9 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016 and 63.8 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2017) compared to PSB (61.8 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 61.9 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively). The mean PPFN over both years was highest with Trichoderma at 64 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a 3% increase compared to PSB. This suggests that Trichoderma may help maintain higher nitrogen levels in the fruit post-harvest, which can impact fruit quality and storage. Overall, the results demonstrate that Trichoderma consistently outperformed PSB across several parameters, indicating its superior ability to enhance nitrogen dynamics and soil health. The improvements with Trichoderma can be attributed to its role in nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that applying Trichoderma to soil can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeneficial microbes had a positive impact on nitrogen content in maize tissues. In grain (NCG), the highest nitrogen content was observed with Trichoderma treatment compared to PSB, due to enhanced nitrogen availability from beneficial microbes (Harman et al., 2004). In leaf tissues (NCL), higher content was found with Trichoderma compared to PSB, due to enhanced soil nitrogen availability from beneficial microbes (Singh et al., 2011). In stalk tissues (NCS), soil application of Trichoderma resulted in the highest content compared to PSB, due to efficient nitrogen supply facilitated by beneficial microbes (Khan et al., 2006). In stover, the highest SNC was observed with Trichoderma compared to PSB, attributed to enhanced soil nutrient status from beneficial microbes (Woo et al., 2006).\u003c/p\u003e \u003cp\u003eGrain nitrogen uptake (GNU) was higher with Trichoderma compared to PSB, due to maize-specific mechanisms enhancing nitrogen uptake with beneficial microbes (Yadav et al., 2016). Stover nitrogen uptake (SNU) was highest with Trichoderma treatment compared to PSB, due to crop-specific mechanisms and enhanced nitrogen availability from beneficial microbes (Singh et al., 2018). Total nitrogen uptake (TNU) was highest with Trichoderma application compared to PSB, due to enhanced soil nutrient status from beneficial microbes (Harman et al., 2004). Nitrogen use efficiency (NUE) was higher with Trichoderma compared to PSB, due to enhanced soil organic matter and accumulated fertilizers from beneficial microbes (Singh et al., 2011). Nitrogen agronomic efficiency (NAE) was higher with Trichoderma compared to PSB, due to efficient nitrogen utilization facilitated by beneficial microbes (Khan et al., 2006). Nitrogen partial factor productivity (PFPn) was higher with soil-applied Trichoderma compared to PSB, due to effective nitrogen utilization with beneficial microbes (Woo et al., 2006).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePooled data of N dynamics in plant tissues influenced by temporal factor and beneficial microbes over the both year.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeneficial microbes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePPFN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrichoderma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevel of significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrichoderma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevel of significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eMean over the both years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003ePSB\u003c/p\u003e \u003cp\u003eTrichoderma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.9b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.8b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevel of significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Nitrogen Dynamics in Control vs. Treatment application Trichoderma and PSB\u003c/h2\u003e \u003cp\u003eNitrogen dynamics in plant tissues influenced all the treatments (organic sources, phosphorus and beneficial microbes) over the both years are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In 2016, the grain nitrogen concentration (GNC) was significantly higher in the rest treatments (2.6 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to the control (2.3 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), with a 13% increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In 2017, the rest treatments had a GNC of 2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 2.3 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showing an 8% increase. Over both years, the mean GNC in rest treatments was 2.5, representing a 9% increase compared to the control. This suggests that the rest treatments substantially improve grain nitrogen concentration, thereby enhancing grain quality and protein content. In 2016, the leaf nitrogen concentration (LNC) in the rest treatments was significantly higher (1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to the control (1.4 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), an increase of 21%. In 2017, the rest treatments maintained a higher LNC (1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to the control (1.3 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), with a 31% increase. Over both years, the mean LNC in rest treatments was 1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showing a 21% increase compared to the control. This indicates that the rest treatments effectively enhance leaf nitrogen concentration, which is crucial for photosynthesis and plant growth. In 2016, the stem nitrogen concentration (SNC) was higher in the rest treatments (2.1 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to the control (1.8 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), with a 17% increase. In 2017, the rest treatments had an SNC of 2.1 compared to the control at 1.8, showing a 17% increase. Over both years, the mean SNC in rest treatments was 2.4 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a 14% increase compared to the control. This suggests that the rest treatments improve stem nitrogen concentration, which supports plant structural integrity and nutrient transport. In 2016, the stover nitrogen concentration (StNC) in the rest treatments was 3.7 compared to the control at 3.1 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 19% increase. In 2017, the rest treatments had a StNC of 4.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 3.8, showing an 18% increase. Over both years, the mean StNC in rest treatments was 4.1 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a 17% increase compared to the control. This indicates that the rest treatments enhance stover nitrogen concentration, which can improve soil fertility when the stover is returned to the field. In 2016, the grain nitrogen uptake (GNU) in the rest treatments was significantly higher (11.6 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to the control (5.9 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), with a 49% increase. In 2017, the rest treatments had a GNU of 11.2 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 6.0 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showing a 46% increase. Over both years, the mean GNU in rest treatments was 11.4 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a 47% increase compared to the control. This suggests that the rest treatments significantly enhance grain nitrogen uptake, improving overall nitrogen use efficiency in the crop. In 2016, the stover nitrogen uptake (StNU) in the rest treatments was 27.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 5.9 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a substantial increase. In 2017, the rest treatments had a StNU of 33.5 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 27.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showing a 21% increase. Over both years, the mean StNU in rest treatments was 30.4 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing an 18% increase compared to the control. This indicates that the rest treatments improve nitrogen uptake in the stover, which can enhance soil organic matter when the stover is returned to the soil. In 2016, the total nitrogen uptake (TNU) in the rest treatments was 38.8 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 27.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a 30% increase. In 2017, the rest treatments had a TNU of 44.8 compared to the control at 33.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showing a 25% increase. Over both years, the mean TNU in rest treatments was 41.8 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a 26% increase compared to the control. This suggests that the rest treatments substantially improve total nitrogen uptake, enhancing overall nitrogen efficiency in the cropping system. In 2016, the nitrogen use efficiency (NUE) in the rest treatments was 27.4%, while the control had an NUE lowest. In 2017, the rest treatments had an NUE of 27.4%, showing a significant improvement over the control. Over both years, the mean NUE in rest treatments was 27.4%, indicating a substantial enhancement compared to the control. This highlights the effectiveness of rest treatments in improving nitrogen use efficiency, reducing the need for synthetic fertilizers. In 2016, the nitrogen agronomic efficiency (NAE) in the rest treatments was 25.1 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 0, indicating a significant improvement. In 2017, the rest treatments had an NAE of 24.5 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to the control at 0, showing a significant enhancement. Over both years, the mean NAE in rest treatments was 24.8 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, indicating a substantial increase compared to the control. This suggests that the rest treatments significantly improve nitrogen agronomic efficiency, optimizing nitrogen use for crop growth. In 2016, the partial factor productivity (PPFN) in the rest treatments was 61.9 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while the control had a PPFN of 0. In 2017, the rest treatments had a PPFN of 63.9 compared to the control at 0, showing a significant improvement. Over both years, the mean PPFN in rest treatments was 62.9 kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, indicating a substantial enhancement compared to the control. This suggests that the rest treatments significantly improve partial factor productivity, enhancing fruit quality and nutrient content. These results demonstrate that the rest treatments consistently outperformed the control across all parameters, indicating their superior ability to enhance nitrogen dynamics and soil health. The improvements with rest treatments can be attributed to their ability to enhance nutrient retention and availability, thereby improving plant growth and reducing environmental hazards. This study suggests that applying beneficial microbes like phosphate-solubilizing bacteria (PSB) and Trichoderma to soil can significantly enhance soil fertility, plant health, and mitigate ecological and human health risks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen comparing control plots to those treated with various amendments, treated plots consistently showed enhanced nitrogen content across all maize tissues. In grain (NCG), treated plots had significantly higher nitrogen content compared to control plots, due to improved soil nitrogen availability from organic sources, phosphorus, and beneficial microbes (Hussain et al., 2016). In leaf tissues (NCL), treated plots exhibited higher nitrogen content compared to control plots, attributed to enhanced soil nitrogen availability from treatments (Liu et al., 2021).\u003c/p\u003e \u003cp\u003eIn stalk tissues (NCS), treated plots showed higher nitrogen content compared to control plots, due to efficient nitrogen supply from treatments (Abujabhah et al., 2016). In stover, treated plots had higher nitrogen content compared to control plots, due to enhanced soil nutrient status from treatments (Ali et al., 2020).\u003c/p\u003e \u003cp\u003eGrain nitrogen uptake (GNU) was higher in treated plots compared to control plots, due to enhanced nitrogen and water availability from treatments during the grain-filling stage (Zheng et al., 2016). Stover nitrogen uptake (SNU) was higher in treated plots compared to control plots, due to crop-specific mechanisms and higher rainfall, augmented organic matter, and phosphorus availability enhancing nitrogen solubilization and plant uptake (Huang et al., 2020). Total nitrogen uptake (TNU) was highest in treated plots compared to control plots, due to enhanced soil nutrient status from treatments (Gwenzi et al., 2016). Nitrogen use efficiency (NUE) was higher in treated plots compared to control plots, due to higher soil organic matter and accumulated fertilizers from treatments enhancing crop nitrogen uptake (Bruun et al., 2012). Nitrogen agronomic efficiency (NAE) was higher in treated plots compared to control plots, due to efficient nitrogen utilization facilitated by treatments (Qayyum et al., 2015). Nitrogen partial factor productivity (PFPn) was higher in treated plots compared to control plots, due to effective nitrogen utilization with treatments (Wang et al., 2019).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePooled data of N dynamics in plant tissues influenced by temporal factor and control Vs Rest plots over the both year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Comparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTNU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePPFN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevel of significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevel of significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003ePlanned Mean comparison over the both years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eRest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.8b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62.9a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevel of significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eA Principal Component Analysis and Correlation Analysis of Nitrogen Uptake\u003c/h2\u003e \u003cp\u003eThe Principal Component Analysis (PCA) plot illustrates the distribution of soil treatments based on the selected variables (GNC, LNC, SNC, StNC, GNU, StNU, TNU, NUE, NAE, and PPFN) over two years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Each point represents a treatment's principal components, with treatments grouped by type and year. Biochar treatments exhibit significant separation along the first principal component, indicating notable changes in soil properties compared to other treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Treatments with varying phosphorus levels form distinct clusters, suggesting substantial variation in soil properties with different phosphorus applications. Notably, the treatment with 50 kg ha⁻\u0026sup1; phosphorus in year 1 shows a unique separation, highlighting a distinct impact on soil properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This analysis provides a visual representation and understanding of how different soil treatments influence various soil parameters, aiding in informed decision-making for soil management and agricultural practices. The application of organic sources, including biochar, compost, and beneficial microbes, had significant effects on various traits related to nitrogen uptake and efficiency in maize (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A detailed correlation analysis, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, revealed several key insights. Total Nitrogen Uptake (TNU) exhibited a robust positive correlation with Stover Nitrogen Uptake (StNU) (r\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher total nitrogen uptake was closely associated with increased nitrogen uptake in stover. Additionally, TNU demonstrated strong correlations with Stalk Nitrogen Content (SNC) (r\u0026thinsp;=\u0026thinsp;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Grain Nitrogen Content (GNC) (r\u0026thinsp;=\u0026thinsp;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that the enhanced total nitrogen uptake facilitated by organic treatments significantly contributes to the nitrogen content in both stalks and grains. Grain Nitrogen Content (GNC) showed positive correlations with Nitrogen Use Efficiency (NUE) (r\u0026thinsp;=\u0026thinsp;0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Nitrogen Agronomic Efficiency (NAE) (r\u0026thinsp;=\u0026thinsp;0.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting that higher nitrogen content in grains is linked to improved nitrogen use and agronomic efficiency. Furthermore, GNC was strongly correlated with Partial Factor Productivity of Nitrogen (PFPn) (r\u0026thinsp;=\u0026thinsp;0.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that organic treatments enhancing grain nitrogen content also contribute to greater productivity per unit of nitrogen applied (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeaf Nitrogen Content (LNC) was positively correlated with both NAE (r\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and NUE (r\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), underscoring that higher leaf nitrogen content is a crucial factor in optimizing nitrogen agronomic efficiency and use efficiency. Similarly, Stalk Nitrogen Content (SNC) showed positive correlations with NAE (r\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and PFPn (r\u0026thinsp;=\u0026thinsp;0.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that increased nitrogen in stalks directly contributes to higher agronomic efficiency and productivity. Conversely, Stover Nitrogen Content (StNC) displayed a negative correlation with NUE (r = -0.45, p\u0026thinsp;\u0026ge;\u0026thinsp;0.05), indicating that higher nitrogen content in stover may reduce nitrogen use efficiency in the plant. This finding suggests a potential trade-off where increased nitrogen retention in stover does not necessarily translate into more efficient nitrogen use. The statistical significance of these correlations, denoted by asterisks, adds confidence to the reliability of the observed relationships. The strong positive correlations (darker blue shades) among several traits highlight that treatments with organic sources tend to simultaneously improve multiple related traits. Negative correlations (light red shades) suggest that certain traits might not improve concurrently and could even inhibit each other under specific treatments. These results underscore the substantial impact of organic treatments on nitrogen dynamics in maize, providing valuable insights for optimizing the use of biochar, compost, and beneficial microbes to enhance specific agronomic traits effectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study's findings present compelling evidence of significant improvements in seed composition and nitrogen-related parameters within treated plots compared to controls. Notably, the application of organic sources, particularly peach biochar, yielded the highest nitrogen concentrations across various plant components, including grain, leaf, stem, and stover, accompanied by notable enhancements in nitrogen uptake and utilization efficiency. Moreover, the inclusion of beneficial microbes, such as PSB and Trichoderma, showcased promising effects, with Trichoderma demonstrating superiority in certain parameters when applied to soil, while PSB proved more effective in seed inoculation for others. Importantly, phosphorus fertilization emerged as a pivotal determinant, with optimal nitrogen concentrations and uptake observed at higher phosphorus application rates. The interactions among organic sources, beneficial microbes, and phosphorus levels revealed synergistic effects, highlighting the potential of integrated strategies to amplify nitrogen utilization efficiency and augment crop productivity in maize cultivation. These findings underscore the significance of adopting holistic and multifaceted approaches to optimize nutrient management practices and sustainably enhance agricultural yields in maize production systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigation; Conceptualization; writing - original draft: (\u003cstrong\u003eImran\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo data was used for the research described in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a crucial component of my PhD research, which examines the effects of organic sources, phosphorus, and beneficial microbes on the growth and productivity of maize and soybean in the context of the maize-wheat and soybean-wheat cropping systems. The research described in this paper is an outgrowth of my dissertation study, which intended to improve crop output and nutritional value without harming the environment or the soil. I have a couple publications that is originated from PhD dissertation same like this article. So the methodology is same as in the PhD dissertation but some time it showing similarity with my published articles.\u0026nbsp;Swiss Development Cooperation (SDC), in partnership with the climate change centre (CCC) of AUP and IC Pakistan, provided financial and technical support for this study. We would especially like to thank Dr. Roshan Ali, Senior Soil Scientist, ARI, and Dr. Abdul Bari, Director, ARI Mingora, Swat, for their invaluable contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing or conflict of interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMengel, K. and E.A. Kirkby. 2001. Principles of Plant Nutrition. 5th Ed., Kluwer Academic Publishers, London.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoghadam, M.K., H.H. Darvishi and M. Javaheri. 2014. Evaluation agronomic traits of soybean affected by vermicompost and bacteria in sustainable agricultural system. Intel. J. 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Critical Reviews in Environmental Science and Technology 46, 1183\u0026ndash;1296.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsawy, T., Rashad, E., El-Qelish, M., and Mohammed, R. H. (2022). A comprehensive review on the chemical regeneration of biochar adsorbent for sustainable wastewater treatment. Npj Clean Water 5, 29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, I., Khan, A. A., Imran, Inamullah, Khan, A., Asim, M., Ali, I., Zib, B., Khan, I., and Rab, A. (2019). Humic acid and nitrogen levels optimizing productivity of green gram (Vigna radiate L.). Russian Agricultural Sciences 45, 43\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, I., Khan, A. A., Imran, Inamullah, Khan, A., Asim, M., Ali, I., Zib, B., Khan, I., and Rab, A. (2019). Humic acid and nitrogen levels optimizing productivity of green gram (Vigna radiate L.). Russian Agricultural Sciences 45, 43\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, A. A., Khan, I. U., and Naveed, S. (2016). Weeds density and late sown maize productivity influenced by compost application and seed rates under temperate environment. Pakistan Journal of Weed Science Research 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImran (2021). The bioavailability of phosphorus in composite vs. hybrid maize differ with phosphorus and boron fertilization. Phosphorus, Sulfur, and Silicon and the Related Elements 196, 738\u0026ndash;750.\u003c/span\u003e\u003c/li\u003e\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":"Maize, N digestion, N assimilation, Maize physiology, N uptake","lastPublishedDoi":"10.21203/rs.3.rs-4746940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4746940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe growth, productivity, and seed setting of maize crops are hindered by the nitrogen deficiency, while the peach leftovers increase the availability, concentration, uptake, and efficiency of nitrogen usage in plant tissues.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThree P levels (50, 75, and 100 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), three peach organic sources (biochar, compost and dry-based residues) and two beneficial microorganisms (PSB and Trichoderma) were treated to determine its impact on N concentration in grain, leaf, stem, stover, and N uptake and N usage efficiency (NUE), Agronomic efficiency (AE), and partial factor productivity of N (PFPN).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePlanned mean comparison showed that highest N concentration in tissues enhanced in treated plots as compared to control plots. Among the organic sources peach biochar produced highest grain N content (2.7g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), leaf N content (1.8g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), stem N content (2.5g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), stover N contents (4.3g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), GNU (12.6kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), SNU (33.7kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), TNU (46.2kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), NUE (28.4%). Soil application of \u003cem\u003eTrichoderma\u003c/em\u003e produced higher N content in tissues as compared to PSB. P fertilization is the utmost need of the crop plant and noted that highest grain Ncontent (2.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), leaf N content (1.7 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), stalk N content (2.5 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), stover N contents (4.2 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), GNU (13.6 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), SNU and TNU by maize (47.0 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were recorded with 100 kg P ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e application.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBiochar combined with PSB raised the N content in the tissues of the leaves and stems, while biochar combined with trichoderma improved the N content of grains, SNU, and TNU. GNU, SNU, and TNU improved with biochar and 100 kg P ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Although the addition of 75kg P ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to either compost or biochar increased NUE, the combination of biochar and 75kg P ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e increased AE and PFPN. The application of Trichoderma treated with 100 kg P ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to the soil enhanced GNU, SNU, and TNU, according to the interaction between BM x PL.\u003c/p\u003e","manuscriptTitle":"Peach Remnants Management, Phosphorus Application, and Beneficial Microbes are Accountable for Nutrients stress of Nitrogen in Maize Tissues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-12 17:36:48","doi":"10.21203/rs.3.rs-4746940/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":"8fd9a638-c84c-448d-b74e-ea169806b211","owner":[],"postedDate":"August 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T17:36:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-12 17:36:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4746940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4746940","identity":"rs-4746940","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}