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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Plant Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Plant Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1664-462X</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fpls.2026.1762962</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Nitrogen threshold under straw returning: optimizing nitrogen application to synergize the yield and nutritional stoichiometry of maize</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Zhang</surname><given-names>Xiaolong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Meng</surname><given-names>Xiangzeng</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Zhang</surname><given-names>Shan</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Liu</surname><given-names>Kaichang</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Lv</surname><given-names>Yanjie</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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<aff id="aff1"><label>1</label><institution>State Key Laboratory of Nutrient Use and Management, Shandong Academy of Agricultural Sciences</institution>, <city>Jinan</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Maize Research Institute, Taian Academy of Agricultural Sciences</institution>, <city>Taian</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Institute of Agricultural Resource and Environment, Jilin Academy of Agricultural Sciences</institution>, <city>Changchun</city>, <state>Jilin</state>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Kaichang Liu, <email xlink:href="mailto:liukc1971@126.com">liukc1971@126.com</email>; Yanjie Lv, <email xlink:href="mailto:lvyanjie_1977@163.com">lvyanjie_1977@163.com</email></corresp>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-25">
<day>25</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>17</volume>
<elocation-id>1762962</elocation-id>
<history>
<date date-type="received">
<day>08</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>10</day>
<month>02</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>23</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Zhang, Meng, Zhang, Liu and Lv.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Zhang, Meng, Zhang, Liu and Lv</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-25">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Straw returning has the potential to reduce nitrogen (N) input by enhancing soil fertility; however, the optimal N application rate may vary under different crop residue management practices.</p>
</sec>
<sec>
<title>Methods</title>
<p>Based on a long-term field experiment initiated in 2011, this study investigated the effects of two residue management methods, namely, no tillage with straw mulch (SM, full straw return) and plow tillage incorporating straw mulch to a depth of approximately 15 cm (SP, full straw return), in combination with five nitrogen application rates (0, 90, 150, 210, and 270 kg ha<sup>&#x2212;1</sup>) on maize grain yield, nutritional quality, and the partial factor productivity of nitrogen fertilizer (PFPN).</p>
</sec>
<sec>
<title>Results</title>
<p>The results indicated that the 1000-kernel weight and kernel number under SP were 7.09% and 6.26% higher than those under SM, respectively, resulting in a 9.24% higher yield in SP. Furthermore, PFPN was significantly greater under SP, by 13.88% compared with SM. This difference was more evident when the N application rate was below 150 kg ha<sup>&#x2212;1</sup>. Additionally, the comprehensive nutritional quality index (Q value), which integrates crude protein, crude fat, starch, and amino acid contents via the entropy weight method to avoid limitations of single-trait analysis, reached a higher linear plateau under SP than under SM, as SP enhanced the crude fat (by 6.68%) and starch (by 1.90%) contents in the grains. </p>
</sec>
<sec>
<title>Discussion</title>
<p>In conclusion, SP demonstrated greater potential for N fertilizer savings while achieving high grain yield and superior nutritional quality. The optimal N application rates were 113.82&#x2013;129.53 kg ha<sup>&#x2212;1</sup> for SP and 129.90&#x2013;135.81 kg ha<sup>&#x2212;1</sup> for SM, ensuring the coordinated improvement of yield, nutritional quality, and PFPN.</p>
</sec>
</abstract>
<kwd-group>
<kwd>no tillage with straw mulch (SM)</kwd>
<kwd>nutritional stoichiometry</kwd>
<kwd>partial factor productivity of nitrogen (PFPN)</kwd>
<kwd>plow tillage with straw mulch (SP)</kwd>
<kwd>sustainable intensification</kwd>
</kwd-group>
<funding-group>
<award-group id="gs1">
<funding-source id="sp1">
<institution-wrap>
<institution>Shandong Agriculture Research System</institution>
<institution-id institution-id-type="doi" vocab="open-funder-registry" vocab-identifier="10.13039/open_funder_registry">10.13039/501100018921</institution-id>
</institution-wrap>
</funding-source>
<award-id rid="sp1">SDARS-31-01</award-id>
</award-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Jilin Province Key Technology R&amp;D Program, China (20230302004NC), and the Shandong Agriculture Research System (SDARS-31-01).</funding-statement>
</funding-group>
<counts>
<fig-count count="9"/>
<table-count count="0"/>
<equation-count count="7"/>
<ref-count count="55"/>
<page-count count="12"/>
<word-count count="6156"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Crop and Product Physiology</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Maize (<italic>Zea mays</italic> L.), one of the world&#x2019;s most important crops, provides approximately two-thirds of the global energy source for humans and animals (<xref ref-type="bibr" rid="B31">Simi&#x107; et&#xa0;al., 2020</xref>). From 2000 to 2021, maize grain yield in Northeast China increased markedly by about 270%. However, achieving sustainable production that integrates both high yield and high quality remains a critical challenge in modern agricultural systems (<xref ref-type="bibr" rid="B19">Li et&#xa0;al., 2024</xref>). Nitrogen (N) is a critical factor for achieving high maize productivity; however, conventional N recommendations that prioritize yield maximization may compromise grain quality, as evidenced by diluted grain protein concentration and the insufficient enhancement of starch content relative to yield gains, while also reducing nitrogen use efficiency (NUE) and increasing production costs (<xref ref-type="bibr" rid="B5">Gao et&#xa0;al., 2023b</xref>; <xref ref-type="bibr" rid="B9">Guo et&#xa0;al., 2025</xref>; <xref ref-type="bibr" rid="B17">Li et&#xa0;al., 2025b</xref>). The widespread adoption of straw returning, a conservation tillage practice that sequesters carbon and mitigates emissions, further complicates N management (<xref ref-type="bibr" rid="B23">Liu et&#xa0;al., 2014</xref>). A central challenge for straw returning is the development of fertilization strategies that optimize nitrogen input to achieve synergistic improvements in yield and nutritional stoichiometry.</p>
<p>Straw returning is currently the predominant method for managing maize residues (<xref ref-type="bibr" rid="B36">Wang et&#xa0;al., 2021</xref>). Long-term straw returning enhances soil fertility and allows for reduced N input (<xref ref-type="bibr" rid="B13">Jiang et&#xa0;al., 2019</xref>). However, the decomposition rate of straw is strongly influenced by climatic factors such as temperature and soil moisture under various management practices, thereby affecting soil nutrient balance (<xref ref-type="bibr" rid="B37">Wang et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B34">Tian et&#xa0;al., 2019</xref>). For instance, no tillage systems with straw returning increase soil organic carbon but may cause nutrient imbalances in the C/N ratio, intensifying N competition between microorganisms and crops (<xref ref-type="bibr" rid="B7">Guan et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B14">Kan et&#xa0;al., 2020</xref>) and consequently decreasing grain NUE relative to plow tillage (<xref ref-type="bibr" rid="B27">Meng et&#xa0;al., 2023</xref>). In contrast, plow tillage disrupts the plow pan, facilitating improved grain yield (<xref ref-type="bibr" rid="B14">Kan et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B47">Yu et&#xa0;al., 2023</xref>). Appropriate N application enhances maize yield by extending the grain-filling period and increasing the mean filling rate (<xref ref-type="bibr" rid="B41">Wei et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B42">Wu et&#xa0;al., 2022</xref>). Some studies have reported that a 20% reduction in N application under straw returning can still maintain stable yields (<xref ref-type="bibr" rid="B32">Song et&#xa0;al., 2024</xref>). Nevertheless, factors such as latitude, climate, and cultivar variation necessitate the localization of residue management and N application strategies (<xref ref-type="bibr" rid="B44">Yang et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B4">Gao et&#xa0;al., 2023a</xref>; <xref ref-type="bibr" rid="B53">Zhang et&#xa0;al., 2023a</xref>). Therefore, optimizing residue management in conjunction with N fertilization is crucial for achieving high-yield, efficient maize production in Northeast China.</p>
<p>Grain quality is a key determinant of the economic value of maize and is markedly influenced by residue management. For example, plow tillage has been shown to increase both crude protein and starch contents in grains (<xref ref-type="bibr" rid="B50">Zhang et&#xa0;al., 2023b</xref>; <xref ref-type="bibr" rid="B19">Li et&#xa0;al., 2024</xref>). N application rate is another major determinant of grain protein concentration and starch characteristics (<xref ref-type="bibr" rid="B35">Wang et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B25">Lyu et&#xa0;al., 2022</xref>), although excessive fertilization can negatively affect starch properties (<xref ref-type="bibr" rid="B1">Chen et&#xa0;al., 2014</xref>). From a physiological perspective, N plays a dual regulatory role in the synthesis of grain storage protein and starch, with its effects strongly mediated by soil physical conditions altered by tillage. Plow tillage decreases soil bulk density and increases total porosity and aeration, thereby promoting root elongation (<xref ref-type="bibr" rid="B27">Meng et&#xa0;al., 2023</xref>). Enhanced root N uptake capacity ensures a continuous supply of ammonium (NH<sub>4</sub><sup>+</sup>) and nitrate (NO<sub>3</sub><sup>&#x2212;</sup>) to the shoots, where NH<sub>4</sub><sup>+</sup> is preferentially assimilated into glutamine via glutamine synthetase in the leaves and subsequently transported to developing grains as a precursor for storage protein synthesis (<xref ref-type="bibr" rid="B16">Lee et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B45">Yang et&#xa0;al., 2025</xref>). For starch synthesis, sufficient N availability upregulates the expression of key enzymes in the grain endosperm, while improved soil aeration under plow tillage further enhances these enzymatic activities by optimizing mitochondrial respiration (<xref ref-type="bibr" rid="B38">Wang et&#xa0;al., 2022</xref>). In contrast, no-tillage increases bulk density in the topsoil, restricts root penetration, and reduces the rate of N mineralization due to the development of anaerobic microenvironments. This not only limits root N uptake but also disrupts the balance between N and carbon metabolism, resulting in reduced carbon allocation to starch synthesis (<xref ref-type="bibr" rid="B29">Qin et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B3">Fiorini et&#xa0;al., 2018</xref>). In addition to protein and starch, maize grains contain considerable amounts of crude fat, essential and non-essential amino acids, and other nutritive components that collectively define their overall nutritional quality. It is hypothesized that plow tillage enhances grain nutritional quality by promoting root elongation and N uptake, and by preferentially allocating the additional N to developing kernels, thereby upregulating protein and starch biosynthesis under full straw return. However, the integrated effects of residue management and varying N application rates on these multifaceted nutritional traits remain insufficiently understood. A major challenge in systems involving full straw return is the absence of a well-defined quantitative N threshold. Without this benchmark, it is difficult to determine an N rate that simultaneously optimizes yield, grain quality, and nutrient use efficiency while avoiding excessive fertilization.</p>
<p>Optimized fertilization under straw returning can balance ecological and economic benefits without compromising yield (<xref ref-type="bibr" rid="B54">Zhang et&#xa0;al., 2025b</xref>). Nonetheless, the trade-offs between grain yield, nutritional quality, and NUE under combined residue management and N input remain inadequately characterized. Multi-criteria analytical approaches are increasingly applied to evaluate such complex interactions. Based on a long-term field experiment initiated in 2011, this study was conducted over the 2022 and 2023 growing seasons to assess how residue management and N rates influence maize grain yield, comprehensive nutritional quality index, and NUE. The objectives were to (i) elucidate the effects of these practices on yield, nutritional quality, and NUE, and (ii) determine the optimal N input range that ensures high yield, quality, and efficiency under each crop residue management practice.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Experimental site description</title>
<p>This long-term field experiment was established in 2011 and conducted at the Halahai Comprehensive Experimental Station of the Jilin Academy of Agricultural Sciences, located in Nong&#x2019;an County, Jilin Province (44&#xb0;40&#x2032;N, 125&#xb0;07&#x2032;E). The site experiences a temperate continental monsoon climate, and the soil is classified as Chernozem. The cropping system consisted of continuous maize (<italic>Zea mays</italic> L.) monoculture with full straw return. Prior to sowing in 2012, the physicochemical properties of the 0&#x2013;20 cm soil layer were as follows: total organic carbon, 1.72 g/kg; total N, 1.20 g kg<sup>&#x2212;1</sup>; available N, 110.09 mg kg<sup>&#x2212;1</sup>; available phosphorus, 26.03 mg kg<sup>&#x2212;1</sup>; and available potassium, 173.98 mg kg<sup>&#x2212;1</sup>. Meteorological data during the growing season (April&#x2013;October) are presented in <xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>. The mean temperature was 17.45 &#xb0;C in 2022 and 12.69 &#xb0;C in 2023, while total precipitation was 300.35 mm and 304.38 mm, respectively. The soil characteristics under different treatments have been described in detail in previous studies (<xref ref-type="bibr" rid="B27">Meng et&#xa0;al., 2023</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Detailed meteorological information during the maize growth period in 2022 and 2023. Tmean: average temperature (&#xb0;C); Tmax: maximum temperature (&#xb0;C); Tmin: minimum temperature (&#xb0;C).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g001.tif">
<alt-text content-type="machine-generated">Two side-by-side line and bar graphs display daily temperature and precipitation data, with mean, maximum, and minimum temperatures shown as lines, and precipitation as blue bars for the periods April to October 2022 and April to October 2023, respectively.</alt-text>
</graphic></fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Experimental design</title>
<p>The experiment followed a two-factor factorial design. The first factor involved two crop residue management practices: SM (no tillage with straw mulch, full straw return) and SP (plow tillage incorporating straw mulch to a depth of approximately 15 cm, full straw return). The second factor consisted of five) application rates: N<sub>0</sub> (0 kg N ha<sup>&#x2212;1</sup>), N<sub>90</sub> (90 kg N ha<sup>&#x2212;1</sup>), N<sub>150</sub> (150 kg N ha<sup>&#x2212;1</sup>), N<sub>210</sub> (210 kg N ha<sup>&#x2212;1</sup>), and N<sub>270</sub> (270 kg N ha<sup>&#x2212;1</sup>). The maize hybrid <italic>Fumin 985</italic> was sown on May 5, 2022, and May 4, 2023. Each treatment plot covered 149.5 m<sup>2</sup> and was planted at a density of 65,000 plants/ha, with a row spacing of 65 cm and a row length of 23 m. N fertilizer was applied in two stages: 40% as a basal application and 60% as a topdressing. Phosphorus (P<sub>2</sub>O<sub>5</sub>) and potassium (K<sub>2</sub>O) were applied before planting at a rate of 105 kg ha<sup>&#x2212;1</sup> each.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Sampling and measurements</title>
<sec id="s2_3_1">
<label>2.3.1</label>
<title>Grain dry weight</title>
<p>Thirty uniformly growing plants were tagged at the tasseling stage. Sampling was performed at 10, 20, 30, 40, 50, and 60 days after pollination (DAP). Five ears were collected per treatment at each sampling time. From the central portion of each ear, 1000 kernels were removed and oven-dried at 85 &#xb0;C until a constant weight was achieved (<xref ref-type="bibr" rid="B52">Zhang et&#xa0;al., 2025a</xref>).</p>
</sec>
<sec id="s2_3_2">
<label>2.3.2</label>
<title>Grain yield</title>
<p>At physiological maturity, maize ears were harvested from a 10.4 m<sup>2</sup> area in each plot to determine ear number per unit area. Twenty ears were selected to count the kernel number per ear. After oven-drying at 85 &#xb0;C to a constant weight, the 1000-kernel weight was recorded. Grain yield was then calculated on a dry weight basis and standardized to a 14% moisture content, following the method described by <xref ref-type="bibr" rid="B2">Duan et&#xa0;al. (2023)</xref>.</p>
<disp-formula>
<mml:math display="block" id="M1"><mml:mrow><mml:mi>Y</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>W</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mfenced><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#x2212;</mml:mo><mml:mn>0.14</mml:mn></mml:mrow></mml:mfenced><mml:mo>&#xd7;</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>&#x2212;</mml:mo><mml:mn>6</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>
</disp-formula>
<p>where <italic>Y</italic> is the grain yield (Mg/ha), <italic>N<sub>1</sub></italic> is the number of ears harvested per unit area (ears/ha), <italic>N<sub>2</sub></italic> is the number of kernels number, and <italic>W</italic> is the 1000-kernel weight (g).</p>
</sec>
<sec id="s2_3_3">
<label>2.3.3</label>
<title>Partial factor productivity of N fertilizer</title>
<p>The partial factor productivity of N fertilizer (<italic>PFPN</italic>), which was the specific proxy used for N use efficiency in this study, was calculated according to the method described by Good et&#xa0;al (<xref ref-type="bibr" rid="B6">Good et&#xa0;al., 2004</xref>). The specific calculation formula was as follows:</p>
<disp-formula>
<mml:math display="block" id="M2"><mml:mrow><mml:mi>P</mml:mi><mml:mi>F</mml:mi><mml:mi>P</mml:mi><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mi>G</mml:mi><mml:mi>W</mml:mi><mml:mo stretchy="false">/</mml:mo><mml:mi>N</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math>
</disp-formula>
<p>where <italic>GW</italic> and <italic>Ns</italic> are the grain weight and nutrition supply, respectively.</p>
</sec>
<sec id="s2_3_4">
<label>2.3.4</label>
<title>Grain nutritional quality</title>
<p>The nutritional quality of maize grain, an essential indicator of its nutritive value, was evaluated following the method described by <xref ref-type="bibr" rid="B55">Zhu et&#xa0;al. (2025)</xref>. Naturally air-dried kernels were analyzed using a near-infrared (NIR) spectrometer (NIRS&#x2122; DS3, FOSS, Hiller&#xf8;d, Denmark) within a wavelength range of 780&#x2013;2526 nm. Based on the distinct absorption and scattering characteristics of major grain components (e.g., crude protein, crude fat, and starch) under near-infrared (NIR) light, spectral data were converted into nutrient concentrations using a maize-specific calibration model. This approach enables rapid and non-destructive quantification of nutritional components. For each grain sample, three replicate NIR spectral measurements were conducted, and the mean value of these three spectra was used as the raw dataset for subsequent analysis.</p>
</sec>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Statistical analyses</title>
<sec id="s2_4_1">
<label>2.4.1</label>
<title>Calculation of coefficient of variation</title>
<p>The coefficient of variation (<italic>CV</italic>) was used to assess the stability of different treatments across varying climatic conditions. The specific calculation formula was as follows:</p>
<disp-formula>
<mml:math display="block" id="M3"><mml:mrow><mml:mi>C</mml:mi><mml:mi>V</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>&#x3c3;</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo stretchy="false">/</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>&#xd7;</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math>
</disp-formula>
<p>where &#x3c3;<italic><sub>i</sub></italic> and <italic>X<sub>I</sub></italic> are the standard deviation and mean value of different indicators.</p>
</sec>
<sec id="s2_4_2">
<label>2.4.2</label>
<title>Calculation of comprehensive nutritional quality value</title>
<p>To demonstrate the comprehensive nutritional value of crude protein, starch, crude fat, essential amino acids, and non-essential amino acids, the comprehensive nutritional quality index (Q value) for each treatment was calculated using the entropy weight method, as proposed by <xref ref-type="bibr" rid="B20">Liang et&#xa0;al. (2025)</xref>, to assess the relative contribution of individual nutritional indicators to overall grain quality. To ensure comparability among indicators with differing magnitudes, all measured grain quality parameters were standardized (<italic>P<sub>ij</sub></italic>):</p>
<disp-formula>
<mml:math display="block" id="M4"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>&#x2212;</mml:mo><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mfenced><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi><mml:mfenced><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo>&#x2212;</mml:mo><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mfenced><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:math>
</disp-formula>
<p>The formula for calculating information entropy (<italic>E <sub>j</sub></italic>) was as follows:</p>
<disp-formula>
<mml:math display="block" id="M5"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>&#xa0;</mml:mo><mml:mo>=</mml:mo><mml:mo>&#xa0;</mml:mo><mml:mo>&#x2212;</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>ln</mml:mi><mml:mn>30</mml:mn></mml:mrow></mml:mfrac><mml:mo>&#xd7;</mml:mo><mml:munderover><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>30</mml:mn></mml:mrow></mml:munderover><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>&#xd7;</mml:mo><mml:mi>ln</mml:mi><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>
</disp-formula>
<p>The formula for calculating the entropy weight (<italic>W <sub>j</sub></italic>) of each index was as follows:</p>
<disp-formula>
<mml:math display="block" id="M6"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#x2212;</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mn>5</mml:mn></mml:msubsup><mml:mfenced><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#x2212;</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:math>
</disp-formula>
<p>The formula for calculating the Q value (<italic>Y <sub>j</sub></italic>) of each treatment was as follows:</p>
<disp-formula>
<mml:math display="block" id="M7"><mml:mrow><mml:msub><mml:mi>Y</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mtext>&#xa0;</mml:mtext><mml:mo>=</mml:mo><mml:mtext>&#xa0;</mml:mtext><mml:munderover><mml:mo>&#x2211;</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>30</mml:mn></mml:mrow></mml:munderover><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>&#xd7;</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math>
</disp-formula>
</sec>
<sec id="s2_4_3">
<label>2.4.3</label>
<title>Variance analysis and data visualization</title>
<p>All&#xb7; statistical analyses&#xb7; were executed in R software (v4.3.2). One-way analysis of&#xb7; variance (ANOVA) was performed using the R package &#x201c;genstab&#x201d; (v4.3.2) to evaluate the effects of year, crop residue management, and N application rate on the measured variables. Multi-factor ANOVA was applied to determine the interaction effects among two or more factors. Significant differences between treatments were identified using Fisher&#x2019;s least significant difference (LSD) test, with statistical significance set at <italic>P</italic> &lt; 0.05. Data visualization and processing were performed using Origin 2021 (OriginLab, Northampton, USA) and Excel 2021 (Microsoft Corporation, Washington, USA).</p>
</sec>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Grain growth dynamics</title>
<p>N application rate exerted a significant influence on 1000-kernel weight DAP. Crop residue management (S) and the interaction between S and N significantly affected 1000-kernel weight at 40, 50, and 60 DAP (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;1</bold></xref>). At 60 DAP, the 1000-kernel weight increased from 202.00 g under N<sub>0</sub> to 298.69 g under N<sub>270</sub> across two years. Moreover, at 40, 50, and 60 DAP, the 1000-kernel weight under SP was 4.96%, 1.21%, and 3.18% greater than under SM, respectively. The most pronounced difference was observed at the N<sub>150</sub> treatment, where SP exceeded SM by 5.66% (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Effects of nitrogen application rate and crop residue management on the dynamic changes in 1000-kernel dry weight after pollination. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. DAP: days after pollination. Different lowercase letters indicate significant differences among nitrogen application rates within the same crop residue management, based on Fisher&#x2019;s least significant difference (LSD) test (<italic>P</italic> &lt; 0.05). * indicates 0.01 &#x2264; <italic>P</italic> &lt; 0.05; ** indicates <italic>P</italic> &lt; 0.01; ns indicates <italic>P</italic> &#x2265; 0.05. The numbers in the figure denote the experimental years. Error bars represent the standard error (SE) based on five biological replicates.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g002.tif">
<alt-text content-type="machine-generated">Twelve grouped vertical bar charts compare one-thousand-kernel dry weight for two treatments, SM (orange) and SP (blue), across various nitrogen application rates and days after planting (DAP) for years 2022 and 2023. Error bars, significance letters, and statistical notations are displayed above bars.</alt-text>
</graphic></fig>
<p>The coefficient of variation (CV) in the N0 treatment was the highest among all nitrogen application rates. On average, at 10, 20, and 30 days after pollination (DAP), the CV of 1000-kernel weight under SP was 8.84%, 25.26%, and 9.14% lower than that under SM, respectively. However, the CV of grain yield under SP (9.76%) was slightly higher than that under SM (9.30%) (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;2</bold></xref>).</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Grain yield</title>
<p>S, N, and their interaction significantly influenced grain yield (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;3</bold></xref>). On average, yield increased from 4.75 Mg ha<sup>&#x2212;1</sup> at N<sub>0</sub> to 12.36 Mg ha<sup>&#x2212;1</sup> at N<sub>270</sub>. The mean yield under SP was 9.24% higher than that under SM, and this difference was more pronounced at N rates below N<sub>150</sub>, where yield under SP was 14.07% greater (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Effects of nitrogen application rate and crop residue management on maize grain yield. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. Different lowercase letters indicate significant differences among nitrogen application rates within the same crop residue management based on Fisher&#x2019;s least significant difference (LSD) test (<italic>P</italic> &lt; 0.05). * indicates 0.01 &#x2264; <italic>P</italic> &lt; 0.05; ** indicates <italic>P</italic> &lt; 0.01; ns indicates <italic>P</italic> &#x2265; 0.05. The numbers in the figure denote the experimental years. Error bars represent the standard error (SE) of five biological replicates.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g003.tif">
<alt-text content-type="machine-generated">Grouped bar graphs and box plots compare grain yield (Mg per ha) by nitrogen application rates and crop residue management strategies, SM and SP, for years 2022 and 2023. Statistical significance and data distribution are displayed.</alt-text>
</graphic></fig>
<p>S and N also had significant effects on 1000-kernel weight and kernel number per ear, while their interaction significantly affected only 1000-kernel weight (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;3</bold></xref>). Over two years, 1000-kernel weight and kernel number increased from 156.56 g and 372.20 kernels per ear at N<sub>0</sub> to 302.37 g and 577.08 kernels per ear at N<sub>270</sub>, respectively. Furthermore, under SP, 1000-kernel weight and kernel number were 7.09% and 6.26% greater than under SM, respectively. At N rates below N<sub>150</sub>, the advantages of SP became more distinct, with increases of 11.21% in 1000-kernel weight and 7.14% in kernel number compared to SM (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4</bold></xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Effects of nitrogen application rate and crop residue management on maize grain yield components. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. Different lowercase letters indicate significant differences among nitrogen application rates within the same crop residue management, based on Fisher&#x2019;s least significant difference (LSD) test (<italic>P</italic> &lt; 0.05). * indicates 0.01 &#x2264; <italic>P</italic> &lt; 0.05; ** indicates <italic>P</italic> &lt; 0.01; ns indicates <italic>P</italic> &#x2265; 0.05. The numbers in the figure denote the experimental years. Error bars represent the standard error (SE) of five biological replicates.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g004.tif">
<alt-text content-type="machine-generated">Grouped bar graphs comparing the effects of nitrogen application rates on 1000-kernel weight and kernels number for SM and SP treatments in 2022 and 2023, with statistical significance indicated.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Partial factor productivity of N fertilizer</title>
<p>S and N had significant effects on the PFPN, while their interaction showed no significant effect (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5A</bold></xref>). On average, PFPN declined from 107.23 kg kg<sup>&#x2212;1</sup> at N<sub>90</sub> to 42.42 kg kg<sup>&#x2212;1</sup> at N<sub>270</sub>. The PFPN under SP was 13.88% higher than under SM (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5B</bold></xref>), with the greatest difference observed at N<sub>210</sub>, where PFPN was 15.85% higher in SP than in SM.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Effects of nitrogen application rate and crop residue management on the partial factor productivity of nitrogen fertilizer (PFPN). <bold>(A)</bold> Variance analysis results; <bold>(B)</bold> PFPN values. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. Y: year; N: nitrogen application rate; S: crop residue management. Different lowercase letters indicate significant differences among nitrogen application rates within the same crop residue management, as determined by Fisher&#x2019;s least significant difference (LSD) test. (<italic>P</italic> &lt; 0.05). The numbers in the figure denote the experimental years. * indicates 0.01 &#x2264; <italic>P</italic> &lt; 0.05; ** indicates <italic>P</italic> &lt; 0.01; ns indicates <italic>P</italic> &#x2265; 0.05. Error bars represent the standard error (SE) of five biological replicates.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g005.tif">
<alt-text content-type="machine-generated">Panel A presents an ANOVA summary table for nitrogen use efficiency (NUE), indicating year, site, and nitrogen application are significant factors. Panel B shows two bar graphs comparing NUE for 2022 and 2023, with SM and SP treatments across four nitrogen application rates. NUE decreases as nitrogen rates increase; SM generally shows lower NUE than SP. Error bars and different letters denote statistical differences.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Grain nutritional quality</title>
<p>N significantly affected crude protein and starch contents, while S significantly influenced crude fat and starch contents. The interaction between N and S was not significant for any of the three components (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;4</bold></xref>). Crude protein content increased from 4.93 mg g<sup>&#x2212;1</sup> at N<sub>0</sub> to 8.31 mg g<sup>&#x2212;1</sup> at N<sub>270</sub>, and starch content rose from 65.88% to 75.34% over the same range. Additionally, the crude fat and starch contents under SP were 4.65% and 1.17% higher than those under SM, respectively. At N levels below N<sub>150</sub>, SP enhanced crude fat and starch contents by 6.68% and 1.90%, respectively (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Effects of nitrogen application rate and crop residue management on crude protein, crude fat, and starch contents in maize grains. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. Different lowercase letters indicate significant differences among nitrogen application rates within the same crop residue management, as determined by Fisher&#x2019;s least significant difference (LSD) test (<italic>P</italic> &lt; 0.05). The numbers in the figure denote the experimental years. Error bars represent the standard error (SE) of three biological replicates.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g006.tif">
<alt-text content-type="machine-generated">Six grouped bar charts display crude protein, crude fat, and starch contents for two groups (SM and SP) across different nitrogen application rates in 2022 and 2023. Higher nitrogen increases protein and starch, but crude fat remains relatively unchanged. Error bars and statistical groupings are shown.</alt-text>
</graphic></fig>
<p>N also significantly affected total amino acids, essential amino acids, and non-essential amino acids, whereas S and the S&#xd7;N interaction had no significant effects (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;5</bold></xref>). Total amino acid content increased from 6.49% at N<sub>0</sub> to 9.31% at N<sub>270</sub>. Essential and non-essential amino acid contents rose from 1.80% and 4.69% at N<sub>0</sub> to 2.82% and 6.49% at N<sub>270</sub>, respectively. The relative increase in essential amino acids (12.19%) was greater than that of non-essential amino acids (8.63%) with increasing N application. Amino acids exhibiting increases exceeding 10% included TYR, LEU, ILE, ALA, PHE, and GLU (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7</bold></xref>).</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Effects of nitrogen application rate and crop residue management on amino acid contents in maize grains. <bold>(A)</bold> 2022; <bold>(B)</bold> 2023. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. N<sub>0</sub>: 0 kg N/ha; N<sub>90</sub>: 90 kg N/ha; N<sub>150</sub>: 150 kg N/ha; N<sub>210</sub>: 210 kg N/ha; N<sub>270</sub>: 270 kg N/ha. Amino acids include: PHE (phenylalanine), MET (methionine), LYS (lysine), LEU (leucine), ILE (isoleucine), THR (threonine), VAL (valine), ASP (aspartic acid), ALA (alanine), PRO (proline), GLY (glycine), GLU (glutamic acid), CYS (cysteine), ARG (arginine), TYR (tyrosine), SER (serine), and HIS (histidine). The numbers in the figure represent amino acid contents (%).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g007.tif">
<alt-text content-type="machine-generated">Heat map comparing concentrations of essential and non-essential amino acids in two sample groups (A and B) across different conditions; values are color-coded from blue (low) to orange (high), with a legend for essential and non-essential amino acids on the side.</alt-text>
</graphic></fig>
<p>Regression analysis between N application rate and the comprehensive nutritional quality index (Q value) showed that Q value initially increased and then plateaued with rising N levels (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8</bold></xref>). The N application rates corresponding to the Q value inflection point were lower for SP (206.67 and 188.33 kg N/ha) than for SM (229.00 and 192.33 kg N/ha) in 2022 and 2023, respectively. Furthermore, the plateau Q value under SP (0.778) was higher than that under SM (0.768).</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>Effects of nitrogen application rate and crop residue management on the comprehensive nutritional quality index (Q value) of maize grains. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. The numbers in the figure denote the experimental years. * indicates <italic>P</italic> &lt; 0.05. R<sup>2</sup> represents the coefficient of determination.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g008.tif">
<alt-text content-type="machine-generated">Four side-by-side scatter plots with trend lines compare Q value versus nitrogen application rate for SM and SP in 2022 and 2023. Each chart features segmented linear regression equations, R squared values, and orange data points.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>N application rate balancing yield, PFPN, and Q value</title>
<p>SP required a lower N application rate than SM to reach both yield and Q value plateaus. The plateau N application rates for yield were 192.50 kg N ha<sup>&#x2212;1</sup> under SM and 156.10 kg N ha<sup>&#x2212;1</sup> under SP. For Q value, the corresponding rates were 210.50 kg N ha<sup>&#x2212;1</sup> and 197.00 kg N ha<sup>&#x2212;1</sup>, respectively, while those for PFPN were 208.26 kg N ha<sup>&#x2212;1</sup> and 214.50 kg N ha<sup>&#x2212;1</sup> (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9</bold></xref>). Prior to the plateau stage of the fitted curves, the PFPN curve intersected both the yield and Q value curves. When PFPN was not used as a constraint, an additional N input of 9.35% for SM and 26.20% for SP beyond their respective yield thresholds was required to attain corresponding Q value thresholds. Synergistic improvements in yield, PFPN, and Q value were achieved within N application ranges of 129.90&#x2013;135.81 kg N ha<sup>&#x2212;1</sup> for SM and 113.82&#x2013;129.53 kg N ha<sup>&#x2212;1</sup> for SP. Within these ranges, the maximum grain yield potential reached 67.48%&#x2013;70.55% for SM and 72.91%&#x2013;82.97% for SP, while the optimal nutritional potential reached 61.71%&#x2013;64.50% for SM and 57.78%&#x2013;65.00% for SP.</p>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>Fitted curves showing the relationships among maize yield, partial factor productivity of nitrogen fertilizer (PFPN), comprehensive nutritional quality index (Q value), and nitrogen application rate. SM: no tillage with straw mulch; SP: plow tillage with straw mulch. The numbers in the figure denote the experimental years. * indicates <italic>P</italic> &lt; 0.05. R<sup>2</sup> represents the coefficient of determination.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-17-1762962-g009.tif">
<alt-text content-type="machine-generated">Four line graphs display normalized data on grain yield, Q value, and PFPN as functions of nitrogen application rates for SM and SP in 2022 and 2023. Each panel contains blue, green, and orange lines representing grain yield, Q value, and PFPN, respectively, along with scatter plot points. Correlation coefficients (R&#xb2;) for each variable are annotated in each panel, with all values above 0.87. The legend associates colors with variables and lines. Nitrogen application increases both yield and Q value curves, while PFPN declines at higher levels.</alt-text>
</graphic></fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<sec id="s4_1">
<label>4.1</label>
<title>Effects of crop residue management and N rate on grain dynamics and nutritional quality of maize</title>
<p>Consumer demand for grain production has progressively shifted from &#x201c;high yield&#x201d; to &#x201c;high yield with superior quality&#x201d; (<xref ref-type="bibr" rid="B12">Huang et&#xa0;al., 2025</xref>). Therefore, achieving both objectives through optimized cultivation techniques represents a fundamental goal of modern agriculture. Insufficient N application suppresses the grain filling rate, active filling duration, and ear differentiation process (<xref ref-type="bibr" rid="B41">Wei et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B48">Yu et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B42">Wu et&#xa0;al., 2022</xref>), thereby reducing grain dry weight and kernel number and ultimately constraining yield improvement (<xref ref-type="fig" rid="f2"><bold>Figures&#xa0;2</bold></xref>-<xref ref-type="fig" rid="f4"><bold>4</bold></xref>). Moreover, N application enhances total starch content in grains by stimulating amylose synthesis (<xref ref-type="bibr" rid="B19">Li et&#xa0;al., 2024</xref>) (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>). Since starch constitutes approximately 70% of total grain dry matter (<xref ref-type="bibr" rid="B11">He et&#xa0;al., 2021</xref>), this increase directly contributes to higher grain dry weight. The observed increase in crude protein content with N application was primarily driven by elevated levels of essential (up by 12.19%) and non-essential amino acids (up by 8.13%) (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7</bold></xref>), which serve as the structural precursors of proteins (<xref ref-type="bibr" rid="B22">Liu et&#xa0;al., 2024</xref>). From a metabolic perspective, higher soil nitrogen availability under SP enhances the activity of nitrogen metabolism enzymes (<xref ref-type="bibr" rid="B48">Yu et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B27">Meng et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B40">Wang et&#xa0;al., 2024</xref>). Glutamine synthetase catalyzes the conversion of ammonium into glutamine in the leaves, which is then transported to the grain as a precursor for storage protein synthesis, thereby contributing to improved grain quality (<xref ref-type="bibr" rid="B16">Lee et&#xa0;al., 2023</xref>). Additionally, elevated nitrogen availability under SP upregulates the activity of starch synthase in the endosperm (<xref ref-type="bibr" rid="B5">Gao et&#xa0;al., 2023b</xref>), resulting in a 1.90% higher starch content in SP compared to SM (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>). Previous studies have shown that excessive N input (e.g., 337 kg N/ha) can reduce starch content by promoting the formation of starch&#x2013;protein&#x2013;lipid complexes (<xref ref-type="bibr" rid="B19">Li et&#xa0;al., 2024</xref>). In contrast, no decline in starch content was observed in this study at 270 kg N/ha (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>), suggesting that this level was below the threshold required to induce substantial complex formation or disrupt starch biosynthesis.</p>
<p>Straw returning enhances crop yield by supplying nutrients and improving soil properties (<xref ref-type="bibr" rid="B36">Wang et&#xa0;al., 2021</xref>). Compared with no tillage with straw mulch (SM), plow tillage with straw mulch (SP) increases the straw&#x2013;soil contact area, stimulates microbial activity, and enhances soil N availability (<xref ref-type="bibr" rid="B49">Zhang et&#xa0;al., 2016</xref>). This in turn promotes plant nutrient uptake and biomass accumulation (<xref ref-type="bibr" rid="B19">Li et&#xa0;al., 2024</xref>), leading to higher yield potential. The yield advantage of SP was most evident under low N input (&lt; 150 kg ha<sup>&#x2212;1</sup>), where yield was 14.07% higher than that under SM (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>). Furthermore, there were no significant differences in amino acid or protein contents between SM and SP (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;4</bold></xref> and <xref ref-type="supplementary-material" rid="SM1"><bold>S5</bold></xref>). Thus, residue management appears to mainly influence the grain-filling process, resulting in higher starch accumulation under SP than under SM (<xref ref-type="fig" rid="f2"><bold>Figures&#xa0;2</bold></xref>, <xref ref-type="fig" rid="f6"><bold>6</bold></xref>). This difference can be attributed to environmental and soil factors, including physicochemical characteristics, temperature, and precipitation (<xref ref-type="bibr" rid="B5">Gao et&#xa0;al., 2023b</xref>).</p>
</sec>
<sec id="s4_2">
<label>4.2</label>
<title>Influence of crop residue management on N response regulation</title>
<p>As an integral component of nutrient management, straw returning can partially substitute for synthetic N inputs, thereby reducing crop dependence on chemical fertilizers (<xref ref-type="bibr" rid="B46">Yin et&#xa0;al., 2018</xref>). However, during straw decomposition, microbial immobilization of N can lead to competition between soil microorganisms and crops, producing variable effects on growth that depend on the residue management strategy (<xref ref-type="bibr" rid="B21">Lin et&#xa0;al., 2024</xref>). In this study, the peak yield and grain quality of maize were achieved at lower N application rates under SP. In contrast, SM required an additional 23.32% and 6.58% N input to reach comparable peak yield and quality, respectively (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9</bold></xref>). This outcome is likely due to SP enhancing available N levels in deeper soil layers (<xref ref-type="bibr" rid="B39">Wang et&#xa0;al., 2025</xref>), which improved PFPN by reducing the amount of fertilizer required to achieve equivalent yield (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5</bold></xref>). Previous research indicates that no tillage systems can substantially increase soil organic carbon content (<xref ref-type="bibr" rid="B14">Kan et&#xa0;al., 2020</xref>), but they often maintain lower soil available N compared with plow tillage (<xref ref-type="bibr" rid="B27">Meng et&#xa0;al., 2023</xref>). This imbalance in the C/N ratio may intensify competition for N between microorganisms and crops. Moreover, the decomposition of maize straw proceeds more slowly under SM due to elevated soil moisture and lower temperatures prior to planting (<xref ref-type="bibr" rid="B27">Meng et&#xa0;al., 2023</xref>). Moreover, differences existed between the two crop residue management practices in synchronizing straw N mineralization with the critical N demand stages of maize. SP improves soil aeration and increases temperature (<xref ref-type="bibr" rid="B27">Meng et&#xa0;al., 2023</xref>), thereby accelerating straw decomposition and N mineralization, which aligns nutrient release with crop demand and reduces N deficiency. In contrast, higher soil moisture and bulk density under SM create conditions that slow straw decomposition and delay N mineralization (<xref ref-type="bibr" rid="B51">Zhang et&#xa0;al., 2023</xref>). This asynchrony increases the plant&#x2019;s reliance on nitrogen inputs, explaining why SM requires higher N application rates to achieve comparable yield and quality. The delayed decomposition under SM therefore necessitates greater N input to offset microbial immobilization and sustain optimal yield and quality. Accordingly, N management strategies in systems employing straw return must be carefully adjusted based on the specific residue management practice adopted.</p>
</sec>
<sec id="s4_3">
<label>4.3</label>
<title>Balanced optimization of yield, nutritional quality, and PFPN</title>
<p>Integrating straw return with N fertilization can effectively enhance both crop yield and PFPN compared with either practice applied independently (<xref ref-type="bibr" rid="B15">Khan et&#xa0;al., 2015</xref>). However, improvements in yield and nutritional quality exhibit an optimal response range to N application (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9</bold></xref>). Beyond this range, excessive N accumulates in the soil due to limited maize uptake (<xref ref-type="bibr" rid="B8">Guo et&#xa0;al., 2024</xref>), subsequently decreasing grain PFPN (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5</bold></xref>). Spatial heterogeneity in optimal N levels arises from variations in residue management, straw incorporation rates, and regional fertilization practices. For instance, in subtropical regions, SP with 200 kg N ha<sup>&#x2212;1</sup>enhances yield while simultaneously mitigating greenhouse gas emissions (<xref ref-type="bibr" rid="B44">Yang et&#xa0;al., 2022</xref>). In Northwest China, straw return combined with 160&#x2013;180 kg N ha<sup>&#x2212;1</sup> increases yield and reduces nitrate leaching risk (<xref ref-type="bibr" rid="B26">Meng et&#xa0;al., 2021</xref>). Given the pivotal role of Northeast China in national grain production, optimizing the integration of N fertilization and straw return is crucial. Previous research has shown that SP with 172&#x2013;254 kg N ha<sup>&#x2212;1</sup> and SM with 147&#x2013;200 kg N ha<sup>&#x2212;1</sup> improved soil N retention and yield in the spring maize system of Northeast China (<xref ref-type="bibr" rid="B39">Wang et&#xa0;al., 2025</xref>). Additionally, SP with 262 kg N ha<sup>&#x2212;1</sup> was identified as optimal for maize starch quality (<xref ref-type="bibr" rid="B19">Li et&#xa0;al., 2024</xref>). However, assessing grain nutritional quality solely based on starch is inadequate. Therefore, in this study, a comprehensive nutritional quality index (Q value) incorporating starch, crude fat, crude protein, and amino acid contents was calculated using the entropy weight method to provide an integrated evaluation of maize grain nutritional quality. Our findings revealed that the N application rate required for SP to achieve the synergistic optimization of yield, PFPN, and Q value was 4.62%&#x2013;12.38% lower than that required for SM (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9</bold></xref>), demonstrating the superior efficiency of SP in residue management. Nevertheless, although SP saved 42.28&#x2013;84.97 kg N ha<sup>&#x2212;1</sup>, it achieved only 72.91%&#x2013;82.97% of maximum yield potential and 57.78%&#x2013;65.00% of optimal nutritional quality potential (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9</bold></xref>). Notably, the identified optimal N thresholds effectively mitigate the typical &#x201c;dilution effect,&#x201d; a common trade-off in which increased yield is accompanied by reduced protein or lysine concentrations (<xref ref-type="bibr" rid="B33">Sun et&#xa0;al., 2025</xref>). Within the optimal nitrogen range, yield and nutritional quality exhibited a synergistic upward trend (<xref ref-type="fig" rid="f9"><bold>Figure&#xa0;9</bold></xref>), with no evidence of declining protein or lysine concentrations (<xref ref-type="fig" rid="f6"><bold>Figures&#xa0;6</bold></xref>, <xref ref-type="fig" rid="f7"><bold>7</bold></xref>). These findings suggest that optimized nitrogen input balances the carbon and nitrogen metabolic demands of maize, thereby avoiding nutrient dilution resulting from excessive yield expansion or inadequate N supply (<xref ref-type="bibr" rid="B24">Lu et&#xa0;al., 2023</xref>). This limitation was primarily attributed to PFPN constraints. Future studies could explore increasing planting density as a potential strategy to overcome production limitations imposed by N use efficiency (<xref ref-type="bibr" rid="B9">Guo et&#xa0;al., 2025</xref>). Moreover, continuous excessive straw incorporation has been reported to negatively affect crop production (<xref ref-type="bibr" rid="B36">Wang et&#xa0;al., 2021</xref>). Therefore, optimizing straw return strategies remains necessary to balance soil fertility improvement with sustainable productivity.</p>
</sec>
<sec id="s4_4">
<label>4.4</label>
<title>The prospect of crop residue management</title>
<p>While no tillage is widely recognized for its soil health benefits, such as enhanced carbon sequestration and reduced erosion, potential risks associated with long-term no tillage include soil compaction and decreased nitrogen mineralization (<xref ref-type="bibr" rid="B51">Zhang et&#xa0;al., 2023</xref>). SP effectively addresses these limitations by disrupting the plow pan, improving soil aeration, and promoting microbial activity (<xref ref-type="bibr" rid="B24">Lu et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B51">Zhang et&#xa0;al., 2023</xref>). In this study, despite the higher energy and fuel requirements for crop production under SP, the PFPN and grain yield under SP exceeded those under SM by 15.85% and 9.24%, respectively. Moreover, the N application rate required for SP to reach the yield threshold was 29.29% lower than that for SM (<xref ref-type="bibr" rid="B28">Meng et&#xa0;al., 2024</xref>). When considering trade-offs among profitability, greenhouse gas emissions, and energy use efficiency, optimizing tillage may be more advantageous than continuous no tillage (<xref ref-type="bibr" rid="B10">Han et&#xa0;al., 2025</xref>). Therefore, the sustainability of tillage practices is inherently site-specific. Future research should focus on monitoring the long-term carbon footprint and energy efficiency under SP and SM in Northeast China to further optimize strategies for crop residue and nutrient management. Additionally, only one locally promoted maize cultivar was used in this study, although grain nutritional quality is highly heritable (<xref ref-type="bibr" rid="B43">Yan et&#xa0;al., 2025</xref>). Thus, future experiments will include both N-efficient and N-inefficient maize hybrids to verify and refine the identified nitrogen thresholds, thereby enhancing their generalizability. In Northeast China, the recommended N fertilization application rate for maize of 150&#x2013;240 kg ha<sup>&#x2212;1</sup> (<xref ref-type="bibr" rid="B30">Ren et&#xa0;al., 2025</xref>). Without considering PFPN, SM and SP could achieve maximum yield while saving 47.55 and 83.90 kg N/ha, respectively, and reach the highest nutritional index while saving 29.50 and 43.00 kg N/ha, respectively. These results indicate that SP enables greater N reduction than SM while maintaining high yield and nutritional quality. Although this study did not assess economic feasibility, developing innovative N management strategies to maximize PFPN remains a key direction for future research. For example, foliar application of nano carbon dots has been reported to enhance grain yield under N-deficient conditions at low cost and with minimal environmental impact (<xref ref-type="bibr" rid="B18">Li et&#xa0;al., 2025a</xref>). Overall, this study provides an effective technical reference for achieving high yield, superior nutritional quality, and efficient N utilization in spring maize production systems in Northeast China.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>This study demonstrated the crucial role of crop residue management in optimizing N utilization for spring maize production in Northeast China. SP enhanced dry matter accumulation in kernels during the late grain-filling stage, which increased kernel number and yield while significantly improving starch and fat contents, thereby elevating the overall nutritional quality of maize grain. Based on a multi-objective optimization approach integrating yield, the partial factor productivity of N fertilizer, and nutritional quality, the optimal N application ranges were identified as 113.82&#x2013;129.53 kg ha<sup>&#x2212;1</sup> for SP and 129.90&#x2013;135.81 kg ha<sup>&#x2212;1</sup> for SM under a planting density of 65,000 plants ha<sup>&#x2212;1</sup>. These optimized ranges provide an important reference for defining N input thresholds that simultaneously ensure high yield, superior grain quality, and efficient nutrient utilization in Northeast China&#x2019;s maize production systems.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>XZ: Methodology, Visualization, Writing &#x2013; original draft. XM: Conceptualization, Formal analysis, Writing &#x2013; review &amp; editing. SZ: Data curation, Investigation, Writing &#x2013; review &amp; editing. KL: Resources, Supervision, Writing &#x2013; review &amp; editing. YL: Funding acquisition, Supervision, Writing &#x2013; review &amp; editing.</p></sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec id="s10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p></sec>
<sec id="s12" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fpls.2026.1762962/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fpls.2026.1762962/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="DataSheet1.xlsx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/></sec>
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<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3004751">Guangxin Zhang</ext-link>, Shanxi Agricultural University, China</p></fn>
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