{"paper_id":"1d98a4f2-06f4-450e-b8d2-444826aa7bed","body_text":"Varietal Reponses of rice (Oryza sativa L.)  to different Nitrogen levels under Conventional \nProduction Systems. \nBoadu Sober Ernest 1, 2, Esther Fobi Donkor 1, Charles Afriyie-Debrah 2, Maxwell Darko Asante \n2, Ralph K. Bam 2, Priscilla Francisco Ribeiro 2, Kirpal Agyemang Ofosu 2, Samuel Novo1, \nDaniel Dzorkpe Gamenyah 2, Vincent Opoku Agyemang 2\n1University of Energy and Natural Resources, Sunyani, Ghana. Department of Horticulture and \nCrop Production.\n2Council for Scientific and Industrial Research- Crop Research Institute, Fumesua, Ghana. Plant \nBreeding Department.\nBoadu Sober Ernest: revsoberboadu2@gmail.com; Esther Fobi Donkor: \nesther.donko@uenr.edu.gh; Charles Afriyie-Debrah: degreatdebrahgh@gmail.com; Maxwell \nDarko Asante: mdasante@gmail.com; Ralph K. Bam: ralphbam@gmail.com; Kirpal Ofosu \nAgyeman: kirpalofosu@gmail.com ; Novo Samuel: Samuel.novo@uen.edu.gh ; \nDaniel Gamenyah: gamenyahdaniel@gmail.com ; \nPriscilla Ribeiro: prisboat18@gmail.com; \nVincent Opoku Agyemang: vincent.opoku@stu.ucc.edu.gh \n*Corresponding Author\nEsther Fobi Donkor\nEmail: esther.donkor@uenr.edu.gh\nTel. Number: +233 549797672\nAuthor contributions: \nBoadu Sober Ernest was involved in the conceptualization, data curation, methodology, \ninvestigation and writing of the original draft of the manuscript.\nEsther Fobi Donkor, Charles Afriyie-Debrah and Maxwell Darko Asante were involved in \nthe conceptualization, supervision, formal analysis and reviewing and editing of the manuscript.\nRalph K. Bam, Priscilla Francisco Ribeiro, Kirpal Agyemang Ofosu, Daniel Dzorkpe \nGamenyah and Vincent Opoku Agyemang were involved in the data curation, investigation \nand methodology of the research.\nSamuel Novo was involved in the reviewing and editing of the manuscript and formal analysis\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nAbstract\nRice (Oryza sativa L.) is a vital staple crop globally, with its cultivation expanding to meet \nincreasing demand. In Sub-Saharan Africa, particularly Ghana, rice productivity is often limited \nby poor soil fertility. Farmers frequently apply high nitrogen (N) fertilizer rates to boost yields; \nhowever, excessive nitrogen use contributes to environmental problems such as nutrient leaching \nand pollution. While optimal nitrogen application rates have been extensively studied, limited \nresearch has focused on varietal responses among rice genotypes. This study evaluated the \nresponse of five rice varieties (CRI-Agra Rice, Togo Marshall, CRI-Amankwatia, CRI-Enapa, and \nJasmine 85) to different nitrogen rates (0, 30, 60, and 90 kg N/ha), focusing on \nmorphophysiological, biomass and yield-related traits.\nThe findings showed significant (p < 0.001) variations in these traits with increasing nitrogen \nlevels. Application of 90 kg N/ha led to substantial improvements: 40% increase in chlorophyll \ncontent, 34.3% in culm length, 71.4% in panicle number, 28.3% in straw dry weight and 42.9% in \ngrain yield over the control (0 kg N/ha). Nitrogen significantly promoted vegetative growth, \ndelayed flowering and enhanced biomass and grain production. Genotypic differences in nitrogen \nuse efficiency were also observed. Togo Marshall, CRI-Agra Rice and Jasmine 85 showed over \n30% increases in chlorophyll content, while CRI-Enapa exhibited higher plant height and panicle \nnumber at 90 kg N/ha. Togo Marshall and CRI-Enapa recorded the highest biomass and yield \nresponses, indicating superior nitrogen utilization.\nOverall, CRI-Enapa and Togo Marshall performed best at 60–90 kg N/ha. These findings highlight \nthe importance of genotype-specific nitrogen management strategies for improving rice \nproductivity and sustainability in Ghana and similar regions.\nKeywords: Rice, Genotype-specific nitrogen management, Sustainable rice production, Nitrogen \nUse Efficiency (NUE), Nitrogen fertilization.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\n1.0 Introduction\nRice is a major staple food crop, feeding more than half of the global population. Its production is \ninherently nutrient-intensive: nitrogen (N) is a critical macronutrient driving photosynthesis and \ngrain filling. In low-fertility soils, as often found in Sub-Saharan Africa (SSA), rice growth is \nstrongly limited by N deficiency (Zingore et al., 2022[1] and Tsujimoto, 2025[2]). \nGlobally, farmers have applied more N fertilizer to increase yields, but this has led to \nenvironmental pollution and in efficiencies (Srikanth et al., 2023[3]). On average, world fertilizer \nuse is ~146 kg/ha, whereas in Sub-Saharan Africa it is only ~22 kg/ha. The low input rates in \nAfrica contribute to persistent yield gaps: average rice yield in Africa (~2.4 t/ha) is far below that \nin Asia (~4.3 t/ha). This gap is attributed in part to insufficient nutrient supply and less efficient \nagronomy in African systems.\nIn Ghana, rice is widely cultivated under rain fed conditions, but average yields remain low (1–3 \nt/ha) compared to potential. National guidelines recommend N application rates of about 60–90 \nkg/ha for target yields of 3–4 t/ha (6, 3), yet farmers often apply less due to cost or knowledge \ngaps. Adequate N fertilization is known to enhance rice growth (tiller formation, leaf area, \nchlorophyll content) and yield components (panicle number, grain set) (Wang et al., 2022[4] and \nSrikanth et al., 2023[3]). However, excess N beyond the crop’s needs can lower nitrogen use \nefficiency (NUE) and even reduce yields due to lodging or delayed maturity (Wang et al., 2022[4] \nand Srikanth et al., 2023[3]). Therefore, it is crucial to identify the optimal N rates that maximize \nyield without waste.\nImportantly, rice genotypes vary in their response to N. High-yielding modern varieties often \nrequire high N inputs, while some local landraces or improved lines can maintain yield under lower \nN (Srikanth et al. (2023[3]). Studies in Asia have documented genotypic variability for traits such \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nas N uptake and utilization efficiency, with strong links between NUE and grain yield (Opuni et \nal., 2023[5] and Srikanth et al., 2023[3]). According to Srikanth et al. (2023)[3] rice yields \nincreased significantly with moderate N rates. However, there is a dearth of information on \ngenotype-specific N responses in West African rice germplasm, including Ghana (Opuni et al., \n2023[5]). This knowledge gap hampers precise fertilization recommendations.\nThis study thus evaluates the responses of five rice genotypes to different N fertilizer rates for \ngrowth and yield parameters under conventional farming system in Ghana. The study aimed to \ndetermine optimal N rates for each variety and assess genotype × N interactions. The results were \ninterpreted in light of comparable research on N response and NUE, to inform genotype-specific \nfertilization strategies that improve rice productivity and resource use efficiency in Ghana.\n2.0 Materials and Methods\n2.1 Study area\nTwo independent field experimentation was carried out in the major (March, 2023 - July, 2023) \nand minor (September - December, 2023) growing season at CSIR-Crops Research Institute, Rice \nBreeding Fields (6˚42'4.0728\"N, 1˚31'53.364\"W) in Fumesua, Ejisu municipality, which falls \nwithin the Forest agro-ecological zone in Ghana. Annual temperatures range from a minimum of \n21.1˚C to a maximum of 32.7˚C and a mean of 31.6˚C (Figure 3.1). The average annual rainfall is \n1550 mm, and the seasonal distribution of rainfall is uneven; there is less precipitation in the third \nquarter of the year (Agyemang et al., 2023[6]). Figure 1 shows the map of the research site. \n2.2 Plant Materials\nThe research used five (5) rice varieties (Togo Marshall, CRI-Amankwatia, CRI-Agra rice, \nJasmine 85 and CRI-Enapa) which are adapted to low-input conditions. These varieties consisted \n4 released varieties from the Crop Research Institute, Council for Scientific and Industrial \nResearch (CSIR-CRI, Fumesua-Kumasi). Table 1 shows the characteristics of the genetic \nmaterials used for the study.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nTable 1: Characteristics of genetics materials used for the study\nVarieties Year of \nrelease\nMaturity \nPeriod (days)\nAttribute Yield \nPotential \n(t/ha)\nCRI-Agra Rice 2013 125-130 Long, slender \nwhite\n7.5 - 8.0\nTogo Marshall - 120-125 Long, slender \nwhite\n7.0 - 7.5\nCRI-\nAmankwatia\n2010 115-120 Long, slender \nwhite\n7.5 - 8.0\nJasmine 85 2013 120-125 Long, slender \nwhite\n6.5 - 7.0\nCRI-Enapa 2017 130-135 Long, slender \nwhite and \naromatic\n8.0 - 8.5\n2.3 Experimental Design and Field Layout\nThe experiment in both seasons was laid out in randomized complete block design in a split-plot \narrangement with three replications. The trial consisted two experimental factors which were five \n(5) rice varieties (Togo Marshall, CRI-Amankwatia, CRI-Agra rice, Jasmine 85 and CRI-Enapa) \nand four (4) application rates of nitrogen fertilizer (0, 30, 60 and 90 kg N ha −1) The main plot \ntreatments were the five (5) rice varieties while the sub-plot treatments were the four levels of \nnitrogen fertilizer. \nPlot size was 4 m × 5 m, with 20 cm row spacing and seedlings planted at 20 cm × 20 cm spacing. \nThe N rates were chosen to encompass the recommended range for target yields up to 4–5 t/ha \n(APNI Rice Cropping Guide (2021[7]), plus a zero-N control. Prior to field demarcation, the field \nwas rotovated, levelled and then allowed to stay (lay fallow) for two weeks for volunteer crops to \ngrow together with weeds. The weeds and volunteer crops were then controlled by spraying with \nRoundup before being manually hoed to prepare the plots prior to transplanting. \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\n2.4 Soil characteristics\nBefore transplanting, soil samples were collected at different locations for compositional analysis \nat the CSIR-Soil Research Institute, Soil and Plant Chemistry laboratory, Kwadaso. The initial \nphysicochemical properties of experimental soil are presented in Table 2.\nTable 2: Initial Physio-chemical and mineralogical characteristics of soil samples from \nexperimental sites\nParameters Location Landon (1991) interpretation\nFumesua High Low\npH 1:2.5 5.42 >6.5 <5.8\n% 0. C 0.64 <99 > 0.6\n% N 0.06 >0.5 <0.2\n% O.M 1.1 >10.0 <2.0\nCa me/100g 2.13 >10.0 <4.0\nMg me/100g 1.85 >4.0 <0.5\nK me/100g 0.20 >0.6 <0.2\nNa me/100g 0.01 >1.0 <1.0\nT.E.B me/100g 4.19\nEx Acidity \nme/100g\n0.80\nE.C.E.C \nme/100g\n4.99\n%  B.S 83.98\nP mg/kg 11.52 >50.0 <15.0\n% Sand 74.00\n% Silt 12.00\n% Clay 14.00\nTexture Sandy loam\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\n2.5 Environmental condition during the study period\nDuring the major season of the experiment, average temperatures ranged from 24.2°C in July to \n27.2°C in March. Relative humidity was generally high, peaking in June at 86%, which coincided \nwith the highest rainfall of 157 mm. Rainfall during this period was consistently substantial, with \nMay (128 mm) and July (125 mm) also receiving notable amounts (Figure 2).\nIn the minor season of the experiment, temperatures were higher, ranging from 28.4°C in \nSeptember to 31.0°C in December. This period recorded lower humidity levels, ranging from 70% \nin November to 74% in September. Rainfall was more variable, with September still receiving a \nhigh amount (149 mm), but dropping significantly to 29 mm in December (Figure 2).\n2.6 Nursery, transplanting and cultural practices\nTo establish rice seedlings for the study, a nursery was set up. A nursery bowl (0.8 m × 0.5 m) \nfilled with topsoil dug at 0 – 15 cm depth from soil surface was used for establishing nursery for \nrice varieties. For each rice variety, healthy uniform seeds were pregerminated by soaking in water \nfor 24 hours. Subsequently, the water was removed and the seeds were placed in a shaded area for \ntwo days. The nursery bowl filled with soil was then sown with these pre-germinated rice seeds. \nAfter a period of 21 days, healthy and uniform growing rice seedlings of each variety were \ntransplanted to the demarcated experimental field using a planting distance of 20 cm × 20 cm. Both \nexperiments were rain-fed, however irrigation was augmented with irrigation systems as and when \nneeded. Weeds in the experimental fields were controlled by hand picking as well as application \nof selective weedicides.\nThe experimental fields were sprayed with a systemic insecticide K-Optimal (Lambda \nCyhalothrine 15 g/L +Acetamipride 20 g/L EC) to control common rice pests. All experimental \nplots were covered with a net at the flowering stage of the rice to help prevent birds from feeding \non the rice grains. The rice plant in the experiment area did not show any incidence of diseases \nand as such no disease control measure was employed.\n2.7 Fertiliser rates imposition\nImposition of fertiliser treatment was carried out 2-weeks after transplanting to ensure uniform \nestablishment of seedlings. Application of fertiliser was carried out using split application method \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nthus, applying two halves separately at different times as shown in Table 3. The amount of \nchemical nitrogen applied to achieved each application rate was estimated based on Equation 1 \nbelow;\nAmount of fertiliser per hectare (kg) = \n𝑡𝑎𝑟𝑔𝑒𝑡 𝑟𝑎𝑡𝑒 ( 𝑘𝑔\nℎ𝑎 )\n(% 𝑜𝑓 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑠𝑒𝑟 ×100) × 100𝑘𝑔 𝑜𝑓 𝑛𝑢𝑡𝑟𝑖𝑒𝑛𝑡\n   Equation 1\nTable 3: Application rate of various nitrogen fertilisation levels used in the present study\nNitrogen rate Basal application (NPK 15:15:15) Top dressing (Urea)\nControl (0 kg/ha) 0 kg 0 kg\n90 kg/ha 45 kg 45 kg\n60 kg/ha 30 kg 30 kg\n30 kg/ha 15 kg 15 kg\n2.8 Data Collection\nPlant height and tiller number (productive tillers/m2) were recorded at flowering for 10 randomly \nselected hills per plot. Leaf chlorophyll content (SPAD values) was measured at mid-tillering. At \nmaturity, each plot was harvested for yield and biomass data. Ten plants per plot were cut to \ndetermine number of panicles per plant, culm length and 1000-grain weight (adjusted to 14% \nmoisture). Grain yield/plot was determined by threshing all panicles in a subplot, weighing the \ngrain and converting to t/ha. Straw biomass (fresh straw weight and dry straw weight) was also \nmeasured.\n \n2.9 Statistical Analysis\nData collected were entered into excel sheets and subjected to analysis of variance (ANOVA) \nusing R statistical software package. Tukey’s test of significance (LSD; p = 95%) was used for \nmean separation among experimental variables, Statistical analysis was performed using model in \nEquation 2.\nYijk = µ + Vi + Rj + Sk + VRij + VSik + RSjk + VRSijk + ℇijk Equation 2\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nWhere:\nYijk represents the observation from ijkth rice variety, nitrogen rate and season, and \nμ is the overall mean; \nVi is the effect of the ith variety; \nRj is the effect of the jth nitrogen rate; \nSk is the effect of the kth season of experiment;  \nVRij  is the interactive effect of the ith variety with jth nitrogen rate, \nVSik is the interactive effect of the ith variety with kth season, \nRSjk is the interactive effect of the jth nitrogen rate with kth season, \nVRSijk is the interactive effect of the i th variety with jth nitrogen rate and  kth season of experiment \nand \n￿ijkl is the experimental error.\nPerson correlation analysis was carried using R (R Core Team, 2021[8]) to establish the \nrelationship and association between measured parameters among rice varieties. Corrplot in R \npackage version 0.84 (Wei et al, 2017[9]) was used to visualize the relationships among traits.\n3.0 Results \n3.1 Variability among the treatment combinations\nThe ANOVA analysis revealed significant differences (p< 0.001) among the five (5) rice varieties \nfor all the traits studied (Table 4). Nitrogen application was also significant (p < 0.001) for all the \ntraits measured (Table 4). The analysis also revealed significant season effect on number of \npanicles (p = 0.004), chlorophyll content (p = 0.001), straw fresh weight (p = 0.003) and number \nof tillers (p < 0.001). However, plant height, culm length, days to flowering, straw dry weight, \n1000-grain weight, and yield showed non-significant(p>0.05) season effect. (Table 4). \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nInteraction of variety and fertiliser rate was significant (p < 0.05) all the traits studied with plant \nheight, culm length and chlorophyll content recording high significant levels (p<0.001) (Table 4). \nThe results also showed that interaction of variety and season was non-significant (p > 0.05) for \nplant height, 1000-grain weight, number of tillers, number of panicles and yield. However, there \nwas significant interaction of variety and season for straw dry weight (p = 0.004) (Table 4). Except \nfor number of tillers (p = 0.01) and plant height (p = 0.025), interaction of fertiliser rate and season \nwas non-significant (p >0.05).  The analysis also revealed non-significant (p>0.05) effect of the \nthree-way interaction of variety, fertiliser rate and season except for chlorophyll content (p < \n0.001) (Table 4).\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nTable Error! No text of specified style in document.. Summary Analysis of Variance (ANOVA) of measured morphological, yield and biomass \nparameters of rice varieties cultivated under different N fertilization.\nDescriptives F.Prob of Source of Variance\nTraits Unit Mean Max Min CV Variety\nFert. \nrate Season\nVar\nX\nFert\nVar\nX\nSeason\nFert\nX\nSeason\nVar\nX\nFert\nX\nSeason\nPlant Height cm 92.20 136.60 72.80 14.9 0.004 <0.001 0.06ns <0.001 0.66 ns 0.03 0.26 ns\nCulm Length cm 84.70 110.00 54.40 13.00 <0.001 <0.001 0.08ns <0.001 0.50 ns 0.30 ns 0.07 ns\nNumber of tillers - 11.90 26.00 6.00 31.30 <0.001 <0.001 <0.001 0.003 0.74 ns 0.01 0.59 ns\nNumber of panicles - 10.40 18.00 6.00 24.40 <0.001 <0.001 0.004 0.018 0.76 ns 0.46 ns 0.32 ns\nDays to flowering - 75.10 93.00 69.00 4.40 0.01 <0.001 0.82 ns 0.035 0.51 ns 0.07 ns 0.84 ns\nFresh straw weight g 114.20 161.00 83.00 12.20 <0.001 <0.001 0.003 0.05 0.43 ns 0.47 ns 0.07ns\nChlorophyll content - 19.00 31.4.00 8.70 16.30 <0.001 <0.001 0.001 <0.001 0.09ns 0.21 ns <0.001\nDry Straw Weight g 61.40 121.00 56.00 29.00 <0.001 <0.001 0.18 ns 0.023 0.004 0.08 ns 0.38 ns\n1000-grain weight g 22.10 27.00 18.00 8.30 <0.001 <0.001 0.20 ns 0.025 0.47 ns 0.51 ns 0.52 ns\nYield/plot kg/ha 7252.20 10084.90 5058.10 14.70 <0.001 <0.001 0.16 ns 0.004 0.23 ns 0.66 ns 0.51 ns\nYield t/ha 7.30 10.10 5.00 14.80 <0.001 <0.001 0.16 ns 0.008 0.23 ns 0.84 ns 0.43ns\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\n3.2 Effect of fertiliser rate on growth parameters\nFertiliser application significantly affected measured morphophysiological traits (Figure 3 A-E). \nAbout 1.4-fold variation in chlorophyll content was observed between the upper and lower \nboundaries of fertiliser rates (Figure 3A). Chlorophyll content showed a direct association with \nincreasing fertiliser rate. When cultivated under 90 kg N/ha (22.1), 11.5%, 20% and 40.1% \nincrease in chlorophyll content was recorded compared to 60 kg N/ha (19.8), 30 kg N/ha (18.4) \nand 0 kg N/ha (15.8) respectively. Compare to the control (zero N application), 60 kg N/ha and 30 \nkg N/ha had 25.6% and 16.5% increase in chlorophyll content (Figure 3A). \nSimilarly, plant height varied 1.6-fold among various fertiliser rates (Figure 3B). Application of \nnitrogen at 90 kg N/ha resulted in 14.5% - 45.1% increase in plant height compared to 60 kg N/ha, \n30 kg N/ha and 0 kg N/ha. When rice was cultivated under nitrogen unamended soil (control), \n26.8% and 10.7% decrease in plant height was recorded compared to 60 kg N/ha and 30 kg N/ha \nrespectively (Figure 3B). Compared to 30 kg N/ha, plant height was 14.5% higher under 60 kg \nN/ha (Figure 3B). \nNumber of panicles was relatively higher for 90 kg N/ha (13 count) as compared to 60 kg N/ha \nand 30 kg N/ha which recorded 10.9 and 9.5 count of panicles respectively (Figure 3C). On the \nother hand, control recorded the lowest number of panicles of 7.8 count (Figure 3C). As illustrated \nin Figure 3D, number of tillers varied 1.9-fold among fertiliser rates. In general, a direct association \nwas observed between increasing fertiliser application and number of tillers. When grown under \n90 kg N/ha, 35.2 – 92.2% increase in number of tillers was recorded as compared to 60 kg N/ha, \n30 kg N/ha and 0 kg N/ha (Figure 3D). Compared to control, 60 kg N/ha and 30 kg N/ha had 42.2 \nand 18.9% increase in number of tillers respectively (Figure 3D). \nCulm length responded positively to fertiliser application. Fertiliser application resulted in 1.3-\nfold variation in culm length among the rice varieties (Figure 3E). It was clear that, soil amended \nwith nitrogen fertiliser recorded higher mean culm length compared to the control. Application \nrate at 90 kg N/ha recorded 34.3% increase in culm length compared to control whereas 60 kg \nN/ha and 30 kg N/ha recorded 21.4% and 13.3% increase in culm length respectively compared to \ncontrol which had the lowest culm length of 72.3 cm (Figure 3E).\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\n3.3 Interaction of variety and nitrogen application on growth parameters\nAcross all varieties, chlorophyll content increased positively in response to nitrogen application. \nHowever, the magnitude of increase varied significantly (p < 0.001) among varieties (Figure 4A - \nD). When cultivated at 90 kg N/ha, there was 26%, 30.4%, 36.1%, 38.9% and 81.7% increase in \nchlorophyll content for CRI-Enapa, CRI-Amankwatia, CRI-Agra, Togo Marshall and Jasmine 85 \nrespectively (Figure 4A). Despite the observed genotypic variation in response to N-fertilisation, \ngenotypes showed no significant variation in chlorophyll content when cultivated under 30 kg \nN/ha and 60 kg N/ha (Figure 4A).\nPlant height among varieties increased with increasing nitrogen application (Figure 4B).  Plant \nheight was relatively lower at 0 kg N/ha however, CR1-Amaankwatia recorded statistically similar \nplant height at 0 kg N/ha (76.98 cm) and 30 kg N/ha (81.7 cm) (Figure 4B). The percentage \nincrease in plant height of 61.7% was recorded for CRI-Enapa while 36.1 - 44.4% was recorded \nby Jasmine 85, Togo Marshall, CRI-Agra rice and CRI-Amankwatia under 90 kg N/ha application \nrate (Figure 4B). \nThe response of rice varieties to nitrogen application showed a consistent increase in number of \npanicles across all fertilizer rates (Figure 4C). Thus, number of panicles improved as nitrogen \nlevels increased from 0 kg/ha to 90 kg/ha, though the magnitude of response varied among the \ndifferent varieties (Figure 4C). Togo Marshall showed the highest number of panicles at all \nfertilizer rates, reaching 15.7 at 90 kg/ha, making it the most responsive variety to N application. \nCRI-Enapa also exhibited a strong response, increasing from 8.6 at 0 kg/ha to 14.6 at 90 kg/ha. \nJasmine85 showed a moderate response, with yield increasing from 7.53 to 12.7. CRI-Agra rice \nand CRI-Amankwatia followed a similar trend but had relatively lower yields compared to CRI-\nEnapa and Togo Marshall (Figure 4C). Despite variation in number of panicles among varieties, \npanicles were statistically similar at 30 kg N/ha and 60 kg N/ha for Jasmine, Togo Marshall, CRI-\nAgra rice and CRI-Amankwatia (Figure 4C).\nThe direct response of number of tillers to nitrogen application was observed among rice varieties \nin the study (Figure 4D). The highest tiller number at 90 kg/ha was observed in Togo Marshall \n(19.3 tillers), followed by CRI-Enapa (18.8 tillers) and CRI-Amankwatia (16.8 tillers). CRI-Agra \nrice had the lowest number of tillers (12.7) at 90 kg/ha, indicating a comparatively lower response \nto nitrogen application. The increase in tiller count was most pronounced between 60 kg/ha and \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\n90 kg/ha, particularly in CRI-Amankwatia and Togo Marshall (Figure 4D). At 0 kg N/ha, number \nof tillers was highest for Togo Marshall (11 tillers), followed by CRI-Enapa (8.83 tillers), while \nCRI-Agra Rice had the lowest (7.33 tillers). CRI-Amankwatia and Enapa displayed a sharp \nincrease of over 100% at 90 kg/ha, suggesting strong tillering response to nitrogen (Figure 4D).\nThe response of culm length to nitrogen (N) application showed an increasing trend across all \nvarieties (Figure 4E). Thus, culm length increased with increasing nitrogen application across all \nrice varieties, indicating a positive response to nitrogen fertilization. The highest culm length at 90 \nkg/ha was observed in CRI-Agra rice (105.8 cm), followed by CRI-Enapa (99.27 cm) and CRI-\nAmankwatia (96.97 cm). Togo Marshall, which had the shortest culm length at 0 kg/ha (62.87 cm), \nexhibited the greatest increase in length at 90 kg/ha (95.85 cm), suggesting a strong response to \nnitrogen application (Figure 4E). The lowest increase in culm length was observed in Jasmine 85, \nwhich reached only 87.33 cm at 90 kg/ha, indicating a relatively lower response to nitrogen \ncompared to other varieties (Figure 4E).\n3.4 Effect of nitrogen application on yield and yield contributing traits \nThe application of nitrogen (N) fertilizer influenced the number of days to flowering in rice, with \nan increasing trend observed as nitrogen levels increased (Figure 5A). Higher nitrogen rates \ndelayed flowering across all treatments, as indicated by the increasing number of days to flowering \nwith increasing nitrogen application. At 0 kg/ha, rice flowered the earliest at 71.63 days, while at \n90 kg/ha, flowering was delayed to 78.93 days (Figure 5A). A steady increase in days to flowering \nwas observed from 71.63 days (0 kg/ha) to 73.97 days (30 kg/ha), 75.90 days (60 kg/ha), and 78.93 \ndays (90 kg/ha) (Figure 5A).\nA direct relationship between nitrogen application and 1000-graain weight was observed as \nillustrated in Figure 5B. 1000-grain weight ranged from 20.2 g – 24.5 g for 0 kg N/ha and 90 kg \nN /ha respectively (Figure 5B). Compared to control, 5%, 112.4% and 211.3% increase in 1000-\ngrian weight was recorded when rice was grown under 30 kg N/ha, 60 kg N/ha and 90 kg N/ha \nrespectively (Figure 5B). 60 kg N/ha recorded 6.9% increase in 1000-grain weight compared to 30 \nkg N/ha (Figure 5B).\nYield increased progressively with increasing nitrogen rates, indicating a strong positive response \nof rice to nitrogen application. The lowest yield was observed at 0 kg/ha (5.97 t/ha), while the \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nhighest yield was recorded at 90 kg/ha (8.6 t/ha). A gradual yield increase was noted at \nintermediate nitrogen rates, with 6.9 t/ha at 30 kg/ha and 7.7 t/ha at 60 kg/ha (Figure 5C).\n3.5 Interaction of variety and nitrogen application on yield and yield contributing \nparameters\nThe interaction of rice variety and nitrogen fertilizer application influenced the days to flowering. \nGenerally, an increasing trend in the number of days to flowering was observed as nitrogen \napplication rates increased across all varieties (Figure 6A). At 0 kg/ha, the days to flowering ranged \nfrom 70.67 days (Jasmine85 and Togo Marshall) to 73 days (CRI-AgraRice). As nitrogen \napplication increased, all varieties exhibited delayed flowering, with the longest days to flowering \nrecorded at 90 kg/ha, ranging from 77.17 days (CRI-Enapa) to 80.2 days (Togo Marshall). CRI-\nAgra Rice and CRI-Amankwatia showed a moderate delay in flowering, with 79 and 78.67 days \nat 90 kg/ha, respectively. Jasmine85 and Togo Marshall showed the most pronounced delay in \nflowering with 79.67 and 80.17 days at 90 kg/ha, respectively (Figure 6A). \nThe 1000-grain weight was influenced by both nitrogen application rates and variety (Figure 6B). \nA direct relationship between grain weight was observed with higher nitrogen levels across all \nvarieties (Figure 6B). At 0 kg/ha, 1000-grain weight ranged from 19.5 g (CRI-Amankwatia, \nJasmine85) to 20.67 g (CRI-AgraRice, Togo Marshall). As nitrogen application increased to 90 \nkg/ha, grain weight increased across all varieties, with values ranging from 23.83 g (Jasmine85) \nto 25.67 g (CRI-Enapa) (Figure 6B). The most significant improvement in grain weight was \nobserved in CRI-Enapa, which increased from 20.5 g at 0 kg/ha to 25.67 g at 90 kg/ha. CRI-\nAmankwatia and Togo Marshall also showed considerable increases, reaching 24.33 g at 90 kg/ha. \nJasmine85 had the lowest 1000-grain weight at all fertilizer levels but still showed a positive \nresponse to nitrogen application (Figure 6B).\nThe rice yield was significantly influenced by nitrogen application across all varieties. An increase \nin nitrogen levels resulted in a progressive improvement in yield, with notable variations among \nthe different varieties (Figure 6C). At 0 kg/ha, the lowest yield was recorded in CRI-Enapa (5.3 \nt/ha), while the highest was observed in Togo Marshall (6.6 t/ha). Increasing nitrogen application \nto 90 kg/ha resulted in the highest yield for all varieties, with Togo Marshall achieving the highest \nyield (8.953 t/ha). CRI-Enapa showed the highest fold change (1.6) and the highest percentage \nincrease (61.6%), indicating the strongest response to nitrogen fertilization. Jasmine85 exhibited \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\na 44.98% increase (1.5-fold change), suggesting moderate nitrogen responsiveness. CRI-Agra rice, \nCRI-Amankwatia, and Togo Marshall had similar responses, with increases of approximately \n37%, highlighting their relatively stable yield gains under higher nitrogen rates (Figure 6C).\n3.6 Response of biomass production of rice varieties to nitrogen fertiliser application\nStraw fresh weight ranged from 100.4 g – 1132.6 g indicating 1.3-fold variation among fertiliser \napplication rates (Figure 7A). Compared to the control, 32%, 14% and 8.3% increase in straw fresh \nweight was recorded under 90 kg N/ha, 60 kg N/ha and 30 kg N/ha respectively (Figure 7A). \nSimilarly, 90 kg N/ha nitrogen recorded 21.9% and 15.2% increase in straw fresh weight compared \nto 30 kg N/ha and 60 kg N/ha respectively (Figure 7A). Application of 60 kg N/ha (115.1 g) \nresulted in 5.9% increase in straw fresh weight compared to 30 kg N/ha (108.7 g) (Figure 7A).\nDry straw weight exhibited 1.3-fold variation among fertiliser rates as illustrated in Figure 7B. Dry \nstraw weight increased with increasing nitrogen rate with 90 kg N/ha recording the highest dry \nstraw weight of 78.9 g (Figure 7B). Dry straw weight was 74 g and 75.9 g under 30 kg N/ha and \n60 kg N/ha respectively (Figure 7B).\n3.7 Interaction of variety and nitrogen application on biomass production\nRice varieties showed significant variation in their straw fresh weight in response to nitrogen \napplication. However, the degree of response varied among varieties (Figure 8A-B). CRI-Agra \nrice and Jasmine 85 exhibited statistically similar responses (around 30% increase), while CRI-\nAmankwatia had the lowest increase (28.2%). At 90 kg N/ha, CRI-Enapa had the highest response \nto nitrogen fertilization, with a 37.02% increase, suggesting it has the greatest potential for biomass \naccumulation under high nitrogen levels. Similarly, Togo Marshall also showed a strong response \n(34.34%) increase indicating a substantial improvement in fresh weight with nitrogen application \n(Figure 8A). Despite 0 kg N/ha recording the lowest fresh straw weight across all varieties, fresh \nstraw weight recorded at 0 kg N/ha, 30 kg N/ha and 60 kg N/ha were statistically similar for \nvarieties CRI-Agra rice and CRI-Amankwatia (Figure 8A).\nDry biomass of rice was significantly affected by nitrogen application rate (Figure 8B). In the \npresent study, 0.1, 1.2, 1.4, 1.4 and 1.6-folds change in dry biomass was recorded between the \nupper and lower application rate for Togo Marshall, CRI-Enapa, Jasmine 85, CRI-Amankwatia \nand CRI-Agra rice respectively (Figure 8B). Although, dry biomass increased with increasing \nnitrogen rates, for Togo Marshall, dry biomass at 0 Kg N/ha (96.7 g) was statistical similar to 60 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nkg N/ha (105 g) and 90 kg N/ha (92.5 g). The control, recorded statistically similar dry biomass at \n30 kg N/ha among varieties such Jasmine 85, CRI-Agra rice, CRI-Amankwatia and CRI-Enapa \n(Figure 8B).\n3.8 Association among the growth, yield and yield related parameters, and biomass\nDiverse association was observed in the measured parameters as illustrated in Figure 9. Yield \nexhibited a positive and strong significant association with dry straw weight (p < 0.01, r = 0.85) \nbut had a negative and strong significant association with fresh straw weight (p < 0.05, r = 0.92) \n(Figure 9). Similarly, growth parameters such as number of tillers (p < 0.01, r = 0.41), number of \npanicle (p < 0.01, r = 0.52) and plant height (p < 0.01, r = 0.52) had a strong positive and significant \nassociation with 1000-grain weight but a weak and significant positive association with culm \nlength (p < 0.05, r = 0.27) (Figure 9). Days to flowering had a strong negative but insignificant \nassociation (p>0.05, r = -0.42) with 1000-grain weight. Number of panicle (p < 0.05, r = -0.76), \nplant height (p < 0.05, r = -0.67) and number of tillers (p < 0.05, r = -0.71) had a strong negative \nand significant association with days to flowering (Figure 9). \n4.0 Discussion\n4.1Variability among the treatment combinations\nThe study showed that experimental factors including nitrogen application rate, rice variety and \nseason significantly influenced rice growth and yield traits, although the degree of response varied \nacross traits. For plant height and culm length, both variety and nitrogen rate had highly significant \neffects (p < 0.001), suggesting that genetic makeup and nitrogen availability strongly influence \nvegetative growth. Singh et al. (2017[10]) observed that varietal differences and nitrogen \napplication rates had a highly significant impact on plant height, attributing the variation to genetic \npotential and nutrient uptake efficiency. Ali et al. (2025[11]) reviewed that higher nitrogen rates \nincreased plant height, but the degree of response varied significantly among varieties, reinforcing \nthe role of genetic control over nitrogen responsiveness. Mishra et al. (2024[12]) also reported that \nboth genotype and nitrogen application significantly affected culm length, with taller varieties \nexhibiting greater nitrogen use efficiency under optimal fertilization conditions.\nThe number of tillers and number of panicles were also significantly influenced by both variety \nand fertilizer rate (p < 0.001), with a notable interaction between variety and season for tiller \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nnumber. Similar findings have been reported by Fageria (2007[13]), Reddy et al. (2022[14]) and \nPatel et al. (2018[15])   where both variety and fertilizer rate significantly influenced the number \nof tillers and panicles in rice. \nFresh and dry straw weight were significantly influenced by variety and nitrogen rate (p < 0.001), \nreflecting enhanced biomass accumulation with higher nitrogen levels. Fageria et al. (2003[16]), \nSingh and Verma (2013[17]) and Ju et al. (2021[18]) also reported significant fresh and dry straw \nweights were in their studies in rice.\nFor 1000-grain weight, grain yield (kg/ha) and yield (t/ha), nitrogen rate was the major driver (p < \n0.001), enhancing yield traits substantially. In line with the current findings, Singh et al. (2017[10]) \nand Mahajan et al. (2012[19]) have also reported that nitrogen application rate is a major \ndeterminant of 1000-grain weight, grain yield (kg/ha), and overall yield (t/ha) in rice highlighting \nthe critical role of nitrogen in maximizing rice production potential.\n4.2 Variation among traits in response to nitrogen application\nNitrogen is a vital nutrient for plant growth and development. Nitrogen plays a paramount role in \ncell growth, elongation, and division of crops (Fathi and Zeidali, 2021[20]). Its deficiency delays \nphenological development in vegetative and reproductive stages (Fathi and Zeidali, 2021[20]). The \nresults of the present study demonstrated a clear positive correlation between nitrogen application \nrate and morphological traits of rice. An increase in plant height was observed with increasing N \nrate particularly at 90 kg N/ha, highlighting the role of nitrogen in cell division, elongation, and \noverall vegetative growth (Figure 3). Conversely, the substantial reduction in plant height under \nnitrogen-deficient conditions (0 kg N/ha) further underscores the essential role of nitrogen in plant \ndevelopment (Figure 3). The results corroborate with the reports of Abdou et al. (2021[21]) and \nMboyerwa et al., 2021[22]). Similarly, chlorophyll content of rice increased positively in response \nto N application with 90 kg N/ha recording the highest chlorophyll content (Figure 3A). Thus, as \nthe nitrogen fertilizer rate increased from 0 kg N/ha to 90 kg N/ha, a consistent increase in \nchlorophyll content was observed. These findings suggest that increasing nitrogen availability \nsignificantly enhances chlorophyll production in the plants. Even so, at lower fertilizer rates, \nnotable improvements in chlorophyll content were observed. The application of 60 kg N/ha \nresulted in a 25.6% increase in chlorophyll content compared to the control, while 30 kg N/ha led \nto a 16.5% increase (Figure 3A). This indicates that even moderate levels of nitrogen fertilization \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\ncan have a meaningful impact on chlorophyll production. The observed relationship between \nnitrogen fertilization and chlorophyll content is consistent with the fundamental role of nitrogen \nin chlorophyll synthesis. Nitrogen have been reported as a crucial component of chlorophyll \nmolecules, and its increased availability promotes greater chlorophyll production, which can \nenhance the plant's photosynthetic capacity and overall growth potential (Zhou et al., 2022[23]). \nThese findings are in line with Iqbal et al. (2021[24]) who reported more greenness of leaves at \nhigher N application rate in rice. Swain and Sandip (2010[25]) also reported an increased in SPAD \nvalues with an increase in nitrogen levels from 0 to 150 kg N ha-1  in rice.\nIn addition to influencing plant height and chlorophyll content of rice, nitrogen application also \nsignificantly affected panicle and tiller production (Figure 3 C-D). The observed increase in the \nnumber of panicles and tillers with increasing nitrogen rates indicates nitrogen’s role in promoting \nreproductive development in rice. The significant reduction in panicle number under 0 N kg/ha, \nhighlights the importance of adequate nitrogen availability for optimal reproductive growth hence \nhighlighting the role of nitrogen in cell elongation and internode expansion. Increased panicle \nproduction at higher nitrogen rates is likely due to enhanced tillering and greater nutrient uptake, \ncontributing to improved yield potential. This finding aligns with reports of Zhou et al. (2022[23]), \nZhou et al (2017[26]) and Firouzi (2015[27]) which indicated that, optimized nitrogen fertilizer \napplication (OFA) increases rice yield by improving tiller quality, enhancing panicle development \nand increasing the number of filled spikelet. \nDays to flowering was also significantly affected by nitrogen application (Figure 4.A). Higher \nnitrogen levels resulted in a delayed flowering period, likely due to the prolonged vegetative \ngrowth phase before transitioning to the reproductive stage. Applying the right amount of N \nfertilizer can significantly increase biomass, and also high biomass is only possible under N \nfertilization conditions (Fathi et al., 2016[28]). The significant positive response of biomass to \nnitrogen application as demonstrated in this study could be attributed to the role of N to maintain \nleaf surface survival; as leaf surface durability increases, the duration and rate of leaf \nphotosynthesis also increase, allowing the plant to produce more fresh and dry matter (Fathi, \n2022[29]). Nitrogen deficiency stimulates competition for the transfer of this element in the plant, \nimpairs timely and complete formation of reproductive organs by decreasing crop growth rate \n(CGR), delays plant phenology, lowers harvest index and ultimately reduces grain yield and \nbiomass of plants (Fathi and Zeidali, 2021[20]). \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nThe application of N-fertiliser also showed a direct relationship with yield traits such as grain yield \n(Kg/ha and t/ha) and 1000-grain weight (Figure 4 A-C). The observed relationship between \nnitrogen fertilization and yield parameters is consistent with the fundamental role of nitrogen in \nimproving photosynthetic efficiency which in turn enhances overall yield. This trend further \nsuggests that higher nitrogen availability promotes not only vegetative growth but also \nreproductive development, which is essential for grain yield. Increased 1000-grain weight and \ngrain yield at 90 kg N/ha is likely due to enhanced tillering and panicle formation hence, \ncontributing to improved yield potential. The results align with the work of Shrestha et al. \n(2022[30]), Prasad and Mailapalli (2018[31]) and Jun-li (2014[32]). \n4.3 Variation among rice varieties in Response to Nitrogen Application \n4.3.1 Growth parameters\nThe photosynthetic apparatus of plants consists mainly of N, a widely used fertilizer in plants \n(Bassi et al., 2018[33]). Hence, N is essential for increasing leaf area, affects plant growth habits \nand leaf longevity, and ultimately affecting photosynthetic efficiency (Olszewski et al., 2014[34]). \nThe revealed significant variation among rice varieties in their response to nitrogen (N) fertilisation \n(Figure 4 A–E) indicates genotypic differences in nitrogen uptake efficiency, assimilation and \nutilization (Nguyen et al., 2016[35]). Varieties; Togo Marshall, CRI-Agra rice and Jasmine 85 \nexhibited over 30% increase in chlorophyll content at 90 kg N/ha indicating the ability of such \ngenotypes to absorb and assimilate nitrogen at highest rate hence leading to enhanced \nphotosynthetic performance (Figure 4.A). Some varieties also require lower fertilisation rate to \nreach chlorophyll saturation to improve photosynthetic performance. CRI-Enapa had lower \nchlorophyll content at 90 kg N/ha but produced higher plant height, number of panicles and tiller \nat 90 kg N/ha compared to other rice varieties in the present study. Responsive rice varieties exhibit \ngreater internode elongation and overall plant height increase due to auxin synthesis under higher \nfertilisation rates (Shafi et al., 2023[36]). Varieties; Togo Marshall and CRI-Agra rice exhibited \nhigher plant height in response to N fertilisation compared to varieties such as CRI-Amankwatia \n(Figure 4.B). Thus, low-N-responsive varieties may show stunted growth even under higher \nnitrogen levels, indicating genetic constraints on nitrogen use efficiency (NUE) (Lal et al ., \n2024[37]). Similar varietal differences in nitrogen use efficiency and chlorophyll response have \nbeen reported in rice by Peng et al. (2021[38]). They reported that, improved varieties recorded \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nhigher biomass production with lower chlorophyll content under reduced nitrogen inputs, \nindicating better nitrogen-use efficiency. Singh and Dwivedi (2016[39]) also observed that rice \nvarieties like 'Swarna' attained chlorophyll saturation and maximum tillering at moderate nitrogen \napplication. \nIncrease in number of tillers at highest N fertilisation rate recorded by CRI-Amankwatia, CRI-\nEnapa and Togo Marshall could have accounted for the increased number of panicles observed \namong these varieties. Additionally, the positive significant association observed between number \nof tillers and panicles further justifies the aforementioned observation (Figure 4.C - D). Thus, \nresponsive varieties prioritize converting most tillers into panicle-bearing stems (Mohapatra et al., \n2025[40]). Varieties with shorter days to flowering have been reported to have relatively smaller \nleaves and tillers as they transition quickly to the reproductive stage, limiting the time available \nfor tiller formation (Hussien et al., 2014[41]; Yan et al., 2024[42]). This could have accounted for \nreduction in panicle production among varieties Jasmine 85 and CR-Agra rice (Figure 4.C).\nThe observed increase in culm length across all rice varieties in response to nitrogen (N) \nfertilization suggests that nitrogen plays a crucial role in promoting stem elongation and overall \nplant height (Figure 4.E). The positive correlation between nitrogen application and culm length \ncan be attributed to several physiological and biochemical mechanisms. Nitrogen is a fundamental \ncomponent of amino acids, proteins and enzymes involved in cell division and elongation (Luo et \nal., 2020[43]). Higher nitrogen availability enhances the synthesis of structural proteins and \ngrowth regulators such as gibberellins, which promote internode elongation and contribute to \nincreased culm length (Zimmermann et al., 2021[44]). This explains why culm length increased \nprogressively with nitrogen application across all varieties (Figure 4.E). The differential responses \namong varieties highlight genetic variability in nitrogen use efficiency (NUE) and sensitivity to \nnitrogen-driven growth promotion. CRI-Agra rice, which exhibited the longest culm length at 90 \nkg/ha, may have a higher capacity to assimilate and utilize nitrogen for stem elongation. \nConversely, Jasmine 85, which recorded the least increase in culm length, may have either lower \nNUE or genetic constraints in internode elongation, limiting its responsiveness to nitrogen \napplication. Sun et al . (2020[45]) and Pan et al. (2019[46]) reported similar results in rice. \nFurthermore, the significant increase in culm length in Togo Marshall, despite having the shortest \ninitial culm length at 0 kg/ha, suggests that this variety exhibits a high nitrogen responsiveness \nunder fertilized conditions (Figure 4.E). This could be attributed to a greater plasticity in internode \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nelongation when nitrogen availability is improved. The relatively lower response in Jasmine 85 \ncould be due to a genetically determined shorter culm, where the variety prioritizes resource \nallocation towards reproductive rather than vegetative growth.\n4.3.2 Biomass traits\nThe significant variation in straw fresh weight and dry straw weight among rice varieties in \nresponse to nitrogen (N) fertilization highlights the differential nitrogen use efficiency (NUE) and \nbiomass accumulation potential of each variety (Figure 5 A - B). The observed trends suggest that \ngenetic factors, physiological traits and nitrogen uptake efficiency play critical roles in determining \nbiomass responses to nitrogen application. Rice varieties differ in their ability to uptake, assimilate \nand utilize nitrogen, which influences their biomass production. The higher fresh weight response \nin CRI-Enapa and Togo Marshall at 90 kg N/ha suggests that these varieties have a greater nitrogen \nuptake efficiency (NUpE) and higher nitrogen utilization efficiency (NUtE) under increased \nnitrogen availability. This aligns with studies indicating that high-N-responsive rice genotypes \noften exhibit superior root system architecture, enhanced nitrate reductase activity and efficient \nnitrogen remobilization to support biomass accumulation (Hoyt, 2022[47]; Bharati et al ., \n2020[48]; Bharati et al., 2019[49]). Conversely, CRI-Amankwatia, which exhibited the lowest \nincrease in fresh straw weight, may have a lower capacity for nitrogen uptake or a limited ability \nto convert absorbed nitrogen into biomass (Figure 5A). This could be due to differences in root \nmorphology, nitrogen transporters or internal nitrogen allocation patterns. The increase in biomass \nwith nitrogen application can also be linked to enhanced photosynthesis due to enhanced \nchlorophyll content. Nitrogen is a key component of chlorophyll and rubisco, the enzyme \nresponsible for carbon fixation. CRI-Enapa and Togo Marshall, which recorded higher biomass \naccumulation at 90 kg N/ha had higher leaf chlorophyll content, increased light interception and \ngreater photosynthetic efficiency, leading to improved carbon assimilation and dry matter \nproduction (Figure 4A). The differences in nitrogen response among varieties may also be \ninfluenced by how nitrogen is partitioned between vegetative and reproductive organs. Varieties, \nCRI-Amankwatia and CRI-Agra rice, which showed similar fresh straw weight at 0 kg N/ha, 30 \nkg N/ha, and 60 kg N/ha, might allocate nitrogen preferentially to reproductive structures rather \nthan vegetative biomass. This trade-off between biomass and reproductive growth has been \nreported in nitrogen-efficient rice varieties by Srikanth et al. (2023[3]); Wang et al. (2022[4]) and \nLiu et al. (2015[49])\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\n4.3.3 Yield traits\nThe observed delay in flowering across all rice varieties with increasing nitrogen application \nsuggests that nitrogen availability plays a crucial role in regulating the transition from the \nvegetative to the reproductive phase (Luo et al., 2020[43]). Nitrogen is known to promote \nvegetative growth by enhancing cell division, chlorophyll content, and photosynthetic efficiency \n(Pérez-Álvarez et al., 2024[50]). Consequently, excess nitrogen may prolong the vegetative phase, \ndelaying the expression of floral genes such as heading date 3a (Hd3a) and rice flowering locus \nT1 (RFT1), which are critical for floral induction (Tamaki et al., 2007[51]). The extent of this \ndelay varied among varieties, likely due to genetic differences in nitrogen use efficiency (NUE) \nand sensitivity to nitrogen-induced hormonal regulation. Togo Marshall and Jasmine85 exhibited \nthe most pronounced delay, possibly due to their higher responsiveness to nitrogen in terms of \nbiomass accumulation before flowering (Figure 5A).\nThe improvement in 1000-grain weight with increasing nitrogen levels indicates the role of \nnitrogen in grain filling and assimilate translocation. Nitrogen enhances the synthesis of proteins \nand starch, which are essential for grain development (Peng et al., 2014[52]). The variation in \ngrain weight response across varieties could be attributed to differences in sink strength, where \nsome varieties (CRI-Enapa) may have a greater ability to accumulate and translocate \nphotosynthates into grains compared to others. The significantly higher grain weight in CRI-Enapa \nsuggests superior nitrogen partitioning, which enhances grain filling duration and grain size \n(Figure 5C).\nRice yield improvement with nitrogen application can be explained by increased tillering, \nenhanced leaf area index and prolonged photosynthetic activity (Fageria and Baligar, 2005[53]). \nHowever, the magnitude of yield increase varied among varieties, likely due to differences in \nnitrogen uptake efficiency and source-sink dynamics. The highest yield recorded in Togo Marshall \nat 90 kg/ha suggests a strong balance between vegetative growth and reproductive output. In \ncontrast, CRI-Enapa exhibited the highest percentage increase in yield (61.63%), indicating a \nhigher responsiveness to nitrogen fertilization (Figure 5C). This could be linked to enhanced \nnitrogen uptake efficiency, improved biomass accumulation and efficient nitrogen remobilization \nto reproductive organs. Mingotte et al. (2013[54]) and Hawkesford (2017[55]) reported similar \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nresults in their studies.  Genetic variability plays a crucial role in N response, with certain cultivars \nconsistently outperforming others across N rates. These findings emphasize the importance of \nvariety-specific N management strategies to optimize yield while avoiding excessive fertilizer use \n(Jahan et al., 2022[56]).\nThe yield observed in CRI-Agra Rice, CRI-Amankwatia, and Togo Marshall under different \nnitrogen levels suggests that these varieties maintain a relatively efficient nitrogen use strategy. \nThe moderate increase in Jasmine85, despite its lower grain weight, implies that it may have \nlimitations in nitrogen assimilation and grain filling efficiency. Excessive nitrogen application can \nsometimes lead to luxury consumption, where the plant takes up more nitrogen than needed, \npotentially leading to lodging, excessive vegetative growth, and reduced harvest index (Wei et al., \n2023[57]). \nOverall, the interaction between nitrogen application and rice variety influences key agronomic \ntraits, primarily through nitrogen's role in modulating vegetative and reproductive growth. \nUnderstanding these mechanisms will be crucial for optimizing nitrogen management strategies \ntailored to specific rice varieties, ensuring both productivity gains and environmental sustainability\n4.4 Relationship between growth, biomass and yield and yield related parameters.\nThe observed associations among yield, biomass and morphophysiological traits suggest that key \nagronomic parameters play a crucial role in determining rice productivity (Figure 9). The strong \npositive correlation between yield and dry straw weight implies that higher biomass accumulation \ncontributes positively to grain production. This relationship may be attributed to enhanced \nphotosynthetic efficiency and resource allocation, where dry matter is effectively translocated \nfrom vegetative structures to reproductive organs. Conversely, the negative and strong correlation \nbetween yield and fresh straw weight suggests that excessive vegetative growth in terms of fresh \nbiomass may not necessarily translate to higher grain yield. This could be due to excessive water \nretention in fresh biomass, which may compete with grain filling for available assimilates.\nThe significant positive correlations of number of tillers, number of panicles and plant height with \n1000-grain weight suggest that these traits contribute to overall grain development and yield \nstability (Figure 9). Higher tillering capacity and panicle production likely enhance the grain sink \npotential, leading to increased grain weight (Parida et al., 2022[58]). Additionally, taller plants \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nmight have better light interception and photosynthetic activity, which could further promote grain \nfilling.\nThe negative but insignificant correlation between days to flowering and 1000-grain weight \n(Figure 9) suggests that late-flowering varieties might not necessarily produce heavier grains. The \nweak association might indicate that while early-flowering plants allocate more resources toward \ngrain filling, late-flowering varieties might experience environmental stressors (e.g., drought or \nhigh temperatures) that limit their grain-filling capacity (Wingler and Soualiou, 2025[59]; Chen et \nal., 2023[60]). Moreover, the strong negative and significant associations between days to \nflowering and number of panicles, plant height and number of tillers further emphasize the trade-\noffs between vegetative growth duration and reproductive output. Early-flowering plants may \nprioritize reproductive development over excessive vegetative growth which may explain why \nthey produce fewer tillers and panicles but potentially higher grain yield efficiency (Olliff-Yang \net al., 2021[61]; Hossain et al., 2024[62]). The trade-off between early flowering and vegetative \nbiomass has been reported as a drought escape mechanism in cereals such as rice maize, wheat \nand barley (Jagadish et al., 2012[63]; Xi et al., 2023[64]). \n. \n5.0 Conclusion\nThe findings of the study underscore the significant influence of nitrogen application on the \nmorpho-agronomic, biomass and yield parameters of rice genotypes. Nitrogen, being a critical \nnutrient for plant growth and development, positively correlated with key growth traits such as \nplant height, chlorophyll content, tiller production, panicle number, and overall grain yield. The \nobserved variations among rice genotypes in their response to nitrogen fertilization highlight the \nimportance of genotype-specific nitrogen management strategies to optimize productivity and \nresource use efficiency. Varieties; CRI-Enapa and Togo Marshall exhibited superior nitrogen \nuptake and utilization efficiency compared CRI-Amankwatia and Jasmine 85. The observed trends \nindicate that while higher nitrogen levels generally enhance biomass accumulation and yield, the \nefficiency of nitrogen use varies across genotypes. Also, the significant relationship between \nnitrogen application and yield, biomass and growth parameters reinforce that optimized nitrogen \nfertilization not only improves yield but also influences the physiological and biochemical \nprocesses essential for rice growth.\nGenotypes like Togo Marshall and CRI-Agra rice which demonstrated a strong response to \nnitrogen fertilization, are suitable candidates for high-input farming systems. Optimizing nitrogen \nuse will be key to increasing rice yields while minimizing environmental impacts such as nitrogen \nleaching and greenhouse gas emissions.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nAcknowledgements: We thank CSIR-Crops Research Institute for their assistance and Senior \nmembers and field staff of CRI rice Improvement program.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.10.681668doi: bioRxiv preprint \n\nREFRENCES\n1. Zingore S, Adolwa IS, Njoroge S, Johnson JM, Saito K, Phillips S, Sileshi GW. Novel \ninsights into factors associated with yield response and nutrient use efficiency of maize and \nrice in sub-Saharan Africa: a review. Agron Sustain Dev. 2022; 42:82. doi:10.1007/s13593-\n022-00821-4\n2. 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