Determination of the Appropriate Application Rate of Inoculant for Enhanced Soybean Production

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This study evaluated the effects of different rhizobial inoculant rates (0, 5, 7.5, 10, and 12.5 g/kg seed) on growth, yield, and Biological Nitrogen Fixation (BNF) of three soybean varieties, Afayak, Favour, and Jenguma, using a split-plot design with variety as the main plot and inoculant rate as the subplot. Significant effects of variety and inoculant rate were observed for key agronomic traits. Compared to the uninoculated control, grain yield increased (p < 0.001) in 2023 by 27.3%, 78.8%, 45.9%, and 146.5%, and in 2024 by 47.6%, 54.2%, 87.9%, and 180.6% at 5, 7.5, 10, and 12.5 g/kg seed, respectively. The 10 g/kg rate resulted in the highest nitrogen fixation: 51.5 kg/ha in 2023 and 56.4 kg/ha in 2024. Jenguma responded best to inoculation, followed by Afayak and Favour. Economic analysis using partial budgeting and marginal rate of return (MRR) identified the most profitable rates as those exceeding a 100% MRR threshold. Results underscore the importance of variety-specific inoculation strategies to enhance nitrogen use efficiency and sustainable soybean production in the region. Biological sciences/Plant sciences Earth and environmental sciences/Environmental sciences Rhizobial inoculant Biological nitrogen fixation (BNF) Soybean varieties Economic analysis Sub-Saharan Africa agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Soybean (Glycine max L. Merrill) has emerged as a crucial cash crop in Ghana, particularly across the northern savannah regions, where it significantly contributes to food security, rural income, and soil fertility enhancement through its symbiotic nitrogen fixation capacity. Beyond its agronomic role, soybean serves as a vital source of affordable plant-based protein for low-income households and as a key input in livestock feed production. In recent years, increasing interest in sustainable intensification has driven the adoption of rhizobial inoculants to enhance biological nitrogen fixation (BNF) and maximize yield potential. Rhizobia inoculation has consistently demonstrated promise in improving nodulation, biomass production, and grain yield under controlled environments and on-farm trials. However, the efficacy of inoculation is not universal, as reports of variable responsiveness and non-responsiveness continue to emerge from both national and international studies (Thilakarathna & Raizada, 2017; Yadav & Chandra, 2014). Several factors influence inoculation response, including soybean genotype, soil fertility, rhizobia strain compatibility, and inoculation rate. Of particular concern is the growing competition between introduced strains and indigenous rhizobia, which are often less effective in nitrogen fixation but may outcompete inoculant strains under field conditions (Mathenge et al., 2019). In Ghana, widespread promotion of soybean cultivation and inoculant use began over a decade ago, with initial recommendations largely guided by the manufacturer's application rates tailored for other agroecologies. Early trials reported positive yield responses; however, the continued increase in soybean production has likely altered the native rhizobial ecology, increasing populations of ineffective strains and potentially diminishing inoculant efficacy. Recent evidence suggests that inoculation response may decline when inoculant rates are suboptimal relative to indigenous rhizobia pressure or when varietal differences in nodulation potential are not accounted for (Argaw & Tsigie, 2015). This calls for a reassessment of recommended inoculation rates and the interaction between inoculant dose and varietal traits under current field conditions. Furthermore, studies on the economic feasibility of varying inoculation rates under farmer-managed conditions remain limited, despite their importance for informed decision-making. Addressing this knowledge gap requires evaluating both agronomic performance and cost-effectiveness across a range of varieties and inoculation levels. This study hypothesizes that the optimal inoculation rate varies with soybean variety and that standard manufacturer-recommended rates may no longer be adequate due to increased competition from indigenous rhizobia. The objectives were to: (i) determine the appropriate rhizobia inoculant rate for soybean varieties in farmers’ fields, and (ii) assess the economic viability of varying inoculation rates for sustainable soybean production in northern Ghana. 2. Materials and methods 2.1. Description of experimental sites The experiment was conducted at the Savannah Agriculture Research Institute (SARI) field in the Tolon District of Northern Ghana. This site is located in the Guinea Savannah Agroecological Zone, between 9° 25′ N and 00° 58′ W. The rainfall pattern is unimodal, occurring from May to October, with peaks in August and September. From Fig. 1 , rainfall totals varied significantly between years. In 2023, peak rainfall was observed in August (254.3 mm) and October (238.1 mm), whereas in 2024, September recorded the highest rainfall (330.3 mm), followed by August (225.1 mm). Several months in 2024, including March, May, November, and December, recorded no rainfall, contrasting sharply with the 2023 values of 5 mm, 126 mm, 140.1 mm, and 13.2 mm, respectively. June and July in 2024 received 149.1 mm and 85.2 mm, respectively, both exceeding the amounts in 2023 (97.3 mm and 107.1 mm), indicating a shift in rainfall onset and intensity. Average temperatures across both years were relatively stable, with minor fluctuations (Fig. 2 ). May was the warmest month in both years, registering 30.2°C in 2023 and 29.1°C in 2024. August was the coolest month, averaging 26.4°C in 2023 and 26.8°C in 2024. Overall, temperatures in 2024 were slightly lower than in 2023 for most months, particularly in May, October, and November, suggesting a modest cooling trend during the growing season. These climatic differences between 2023 and 2024 may have directly affected crop growth stages, water availability, and the efficiency of soil amendments such as inoculants. 2.2. Characterization of soil at experimental sites Prior to the application of treatments, composite soil samples were collected from the top 0–20 cm layer across the experimental field to characterize baseline soil fertility. The samples were air-dried, gently crushed, and passed through a 2-mm sieve for the analysis of physicochemical properties. Soil pH was measured in a 1:2.5 soil-to-water suspension using a digital pH meter, following the procedure described by McLean (1982). Organic carbon content was determined using the Walkley-Black wet oxidation method, as outlined by Nelson and Sommers (1982). Total nitrogen was quantified using the micro-Kjeldahl digestion method, according to Bremner and Mulvaney (1982). Available phosphorus was extracted using the Bray-1 method (Bray and Kurtz, 1945), which is suitable for acidic soils, and quantified colorimetrically with the molybdenum blue method. Exchangeable potassium was extracted with 1 M ammonium acetate (NH₄OAc) at pH 7.0 and measured using a flame photometer, as described by Thomas (1982). The initial soil characteristics presented in Table 1 offer critical insights into the baseline fertility status of the experimental site before treatment application. The soil pH (5.3) indicates moderately acidic conditions, which can affect nutrient availability and rhizobial activity. According to Brady and Weil (2016), acidic soils may limit the proliferation and effectiveness of rhizobia, especially if pH falls below 5.5. The low organic carbon (0.62%) and total nitrogen (0.04%) signify poor soil fertility typical of degraded tropical soils (FAO, 2006), underscoring the importance of biological inputs like inoculants. The available phosphorus (5.81 mg/kg) is also below the critical threshold for legumes, which often require ≥ 15 mg/kg for optimal nodulation and growth (Sanginga et al., 2002). The exchangeable potassium (0.23 cmol/kg) is relatively moderate but may still limit yield potential when coupled with other nutrient constraints. These baseline conditions justify the use of rhizobial inoculants as a strategy to enhance biological nitrogen fixation and support soybean productivity. They also help contextualize the crop's response to the treatments applied. Table 1 Physico-chemical characteristics of the soil at 0–20 cm depth at Nyankpala Soil properties Values Texture Sandy loam pH (1:2.5 H 2 O) 6.50 EC (µS/cm) 1.9 Available phosphorus (P) (mg kg − 1 ) 6.98 Organic carbon (g kg − 1 ) 1.58 Total nitrogen (N) (g kg − 1 ) 0.65 Exchangeable potassium (cmol (+)/kg) 0.65 2.3. Experimental design and treatments The experiment was designed as a split-plot with three replications, and the treatments tested included soybean varieties {Afayak (TGx1834-5E), Favour (TGx 1844-22E), and Jenguma (TGx-1448-2E)} as the main plot, along with inoculation rates of 0, 5, 7.5, 10, and 12.5g on the sub-plot. The control plants (0g) were not inoculated with Rhizobium. No blanket chemical fertilizer was applied to either the inoculated or uninoculated plots or plants. A reference crop (Maize) plot was included for Biological Nitrogen Fixation (BNF) assessment. A plot size of 4×4 m was utilised, with a planting distance of 50×10 cm. The inoculants (brand name NoduMax), produced by IITA, were a commercial peat-based formulation of Bradyrhizobium japonicum, consisting of a balanced blend of 50% culture (rhizobia) and 50% carrier material (peat). The test soybeans are long-maturing (110–115 days), with 45 days to 50% flowering, and have a potential grain yield of 2.0-3.5 t/ha. To assess BNF, a non-fixing reference crop (maize) was included in the treatment combinations. 2.4. Seed rhizobium inoculation The peat-based inoculants were added to the soybean seeds in a container after moistening the seeds. The inoculants and the seeds were mixed thoroughly until the seeds were adequately coated with the inoculants and allowed to air-dry on a sheet of polythene in the shade for a few minutes, after which they were planted on the plots. The treatments with inoculation received 5, 7.5, 10 and 12.5g of inoculants per 1 kg of seed. 2.5. Data Collection Shoot dry weight, root dry weight, nodule dry weight, and nodule number per plant were recorded from ten representative plants at eight weeks after planting (8 WAP). After the root systems of the ten plants were cut and gently washed on a 2-mm mesh sieve under a jet of tap water, the nodules were detached, counted, and oven-dried at 65ºC for 48 hours. The pods from these ten plants were removed and counted to determine the pod load (i.e., pod number per plant). The shoots of five plants, sub-sampled from the ten harvested, were also oven-dried at 65ºC for 48 hours, and their weights were recorded. After threshing the pods harvested from the designated area of each treatment plot, the grains were adequately sun-dried on a concrete platform and weighed on an electronic balance. One hundred seeds from each treatment were randomly selected and weighed. This procedure was replicated three times, and the average weight of 100 seeds was determined. Data collection involved assessing the growth and yield parameters, including nodulation, root and shoot biomass, pod yield, grain yield, 100-seed weight, and harvest index. Biomass was sampled at the full pod stage (R4 stage). 2.6. Estimation of N 2 -fixed The technique used to estimate N fixation was the Total Nitrogen Difference (TND) method. This was done by comparing the total nitrogen of the legume with that of a non-legume [14]. The amount of N fixed was calculated by subtracting the total nitrogen of the reference crop (maize) from that of the legume (soybean), and the difference value is assumed as N derived by BNF (N2 fixed). Thus, N 2 fixed = Total N in legume -Total N in reference crop, $$\:\text{W}\text{h}\text{e}\text{r}\text{e}\:total\:N\:ın\:legume=\:\frac{\left(Dry\:matter\:weight\:kg\:ha\:X\:\%\:N\:in\:plants\right)\:}{100}$$ Then, shoot nitrogen content was analysed using the Kjeldahl procedure [FAO, 2008]. 2.7. Economic analysis of treatments The economic benefits associated with inoculation rates for soybean varieties were assessed using partial budgeting, as outlined in Soha (2014) and Biratu et al. (2022). All variable input costs were considered, along with the seasonal average operational costs applicable to all treatments during the cropping season in the research area. The amounts farmers paid for clearing land, planting, purchasing supplies such as seed, and hiring labour for weeding, harvesting, and transporting farm products to their homes were all considered variable costs. Subsequently, the difference between each treatment's gross income and total production costs was calculated to determine its value or net return per hectare. The average annual net returns over the study period were used to compute the mean net returns. There were no levies on capital expenses, including land, capital interest, farm equipment depreciation, or other overhead costs. After dividing the net benefit by the operational cost, the benefit ratio for each treatment was established. This is the difference between the total income obtained from selling the crop and the total cost of producing it. $$\:Marginal\:Net\:Benefits\:(GHS/ha)\:=\:Change\:in\:Net\:Benefits\:/\:Change\:in\:Quantity$$ This measures the change in net benefits when one more unit of output is produced (e.g., one more hectare of crop). It shows the profitability of adding a unit $$\:Marginal\:Rate\:of\:Return\:\left(100\%\right)\:=\:(Marginal\:Net\:Benefits\:/\:Marginal\:Cost)\:*\:100\%$$ This expresses the profitability of an additional unit of output as a percentage. It's a way to compare the benefits of adding more inputs (e.g., fertilizers) against the additional cost. 2.8. Statistical analysis All data collected were subjected to statistical analysis using Genstat Discovery Edition 10. Nodule count was transformed before the analysis. Analysis of variance (ANOVA) was done to determine differences in means among treatments. All treatment means were compared using the Least Significant Difference (LSD) at a 5% significance level. 3. Results and discussions 3.1. Growth parameters 3.1.1. Nodule number The results demonstrated significant effects of both soybean variety and Bradyrhizobium inoculation rate on nodule formation across the two cropping seasons. In the first season, nodule number varied significantly among the tested varieties (P < 0.001), with Afayak and Favour producing the highest counts (Table 2 ). This varietal variation in nodulation may be attributed to differences in the genetic potential of the cultivars to associate with Rhizobium, as reported by Herridge et al. (2008) and Thilakarathna and Raizada (2017). These findings align with earlier studies that emphasize the importance of host genotype in determining symbiotic performance and nodulation efficiency. Bradyrhizobium inoculation significantly enhanced nodule number relative to the control in both seasons. In the first season, the application of 5, 7.5, 10, and 12.5 g of inoculant increased (p < 0.001) nodulation by 66%, 160%, 147%, and 160%, respectively, over the uninoculated control (Table 2 ). Notably, the 12.5 g inoculant rate produced the highest nodule count, although it was statistically similar to the 7.5 g and 10 g rates. This suggests a possible plateau in inoculation effectiveness beyond 7.5 g, indicating that further increases in inoculant dose may not significantly enhance nodulation. These results corroborate findings by Dakora and Keya (1997) and Tamiru and Girma, (2019), who reported that beyond optimal levels, increasing Rhizobium dose does not linearly enhance nodule formation. In the second season, varietal differences in nodulation were again significant (p < 0.001), with Afayak maintaining superior nodule production, followed by Favour and Jenguma, which did not differ significantly. The stability of Afayak’s performance across seasons implies its robust nodulation potential and adaptability under varying environmental conditions, consistent with observations by Savala et al. (2023). Inoculation effects were again significant (p < 0.001) in the second season. The inoculant rates of 5, 7.5, 10, and 12.5 g increased nodule number by 45%, 127%, 143%, and 174%, respectively, over the control. While the 12.5 g application resulted in the highest nodule numbers, rates of 7.5 g and 10 g also performed comparably well. The 5 g rate, however, produced results statistically similar to the control, highlighting the need for adequate inoculant dosage to trigger effective nodulation. These outcomes affirm the dose-responsive nature of Bradyrhizobium inoculation, as previously noted by Bala and Giller (2001) and Hungria et al. (2005). Seasonal differences in nodulation performance may also reflect variations in environmental conditions such as soil moisture, temperature, and background native Rhizobium populations, which are known to influence inoculant efficacy and symbiotic development (Zengeni et al., 2003; Giller, 2001). Overall, the interaction between soybean genotype and inoculation rate plays a crucial role in maximizing nodule number. Afayak appears to be a superior variety in terms of nodulation, while inoculant rates of 7.5 to 12.5 g offer optimal benefits without significant differences among them, suggesting a threshold level of inoculant efficiency under the tested conditions. 3.1.2. Nodule dry weight Nodule dry weight, a critical indicator of nitrogen-fixing efficiency and symbiotic activity, was significantly influenced by both soybean variety and Bradyrhizobium inoculation rate across seasons. In the first season, varietal differences were statistically significant (p < 0.001), with Afayak and Favour recording the highest nodule dry weights, though Favour’s performance was statistically similar to Afayak (Table 2 ). This suggests that both varieties have strong potential for effective symbiotic association under favourable inoculation conditions. The observed varietal differences in nodule biomass may stem from differences in root architecture, rhizosphere interactions, and genetic predisposition for rhizobial compatibility, as previously reported by Thilakarathna and Raizada (2017) and Herridge et al. (2008). Bradyrhizobium inoculation significantly increased (p < 0.001) nodule dry weight in the first season relative to the uninoculated control. The applications of 5, 7.5, 10, and 12.5 g of inoculant enhanced nodule biomass by 75%, 90%, 208%, and 176%, respectively. The 12.5 g inoculant rate led to the highest nodule dry weight; however, it was statistically equivalent to the 7.5 g and 10 g applications. These results imply a diminishing return in nodule biomass accumulation beyond the 10 g rate, indicating that nodule saturation or resource competition may occur at higher inoculant doses. Such nonlinear responses to inoculation rates have also been reported by Hungria et al. (2005) and Tamiru and Girma (2019), who noted that while increasing inoculant doses can boost nodulation and biomass, optimal rates must be identified to avoid resource inefficiency. In the second season, a shift (p < 0.05) in varietal performance was observed. Favour emerged as the top performer in terms of nodule dry weight, while Afayak and Jenguma recorded similar, but lower, values. This change may be attributed to environmental variations such as rainfall, soil moisture, and microbial dynamics between the two seasons, which are known to influence symbiotic performance ( Zengeni et al., 2003; Giller, 2001 ). The consistent performance of Favour across both seasons indicates its adaptability and robustness in supporting nodule development under varying agroecological conditions. The effect of Bradyrhizobium inoculation on nodule dry weight in the second season was again statistically significant (p < 0.001). Compared to the control, the 5, 7.5, 10, and 12.5 g inoculation rates improved nodule dry weight by 85%, 95%, 206%, and 181%, respectively. While the 10 g and 12.5 g applications produced the highest dry weights, they were statistically similar, suggesting that 10 g may be the most efficient dose for maximizing nodulation benefits. Lower rates, especially 5 g and 7.5 g, resulted in reduced biomass and did not differ significantly from one another, reinforcing the idea that suboptimal inoculant doses may limit rhizobial colonization and symbiotic nitrogen fixation, as supported by Bala and Giller (2001) and Savala et al (2023). . Taken together, these findings highlight the importance of selecting suitable soybean varieties and optimizing inoculation rates for enhancing symbiotic performance. Favour consistently showed high nodulation capacity in terms of nodule dry weight, particularly under high inoculant application. Meanwhile, the 10 g rate appeared optimal across seasons, offering a balance between efficiency and performance. These outcomes are crucial for guiding farmer decisions on variety and input selection for sustainable legume production systems in sub-Saharan Africa. 3.1.3. Shoot dry weight Shoot dry weight is a key indicator of vegetative growth and overall plant vigor, often reflecting the efficiency of symbiotic nitrogen fixation. Across both seasons, the shoot biomass of soybean was significantly influenced by variety and Bradyrhizobium inoculation rate, with notable seasonal variability. In the first season, Afayak and Jenguma exhibited the highest (p < 0.001) shoot dry weights, while Favour followed closely behind (Table 2 ). This suggests that Afayak and Jenguma had greater vegetative growth potential, likely due to their superior symbiotic interactions or resource acquisition abilities. Similar varietal influences on shoot biomass have been documented by Savala et al (2023) and Thilakarathna and Raizada (2017), who reported that differences in soybean cultivar biomass can be linked to differences in nitrogen fixation capacity and root morphology. Inoculation with Bradyrhizobium significantly (p < 0.001) increased shoot dry weight compared to the control. The 5, 7.5, 10, and 12.5 g inoculant applications enhanced shoot biomass by 98%, 75%, 98%, and 121%, respectively, over the uninoculated treatment. The 12.5 g inoculation rate produced the highest shoot biomass, although it was statistically similar to the 10 g treatment. These results suggest a threshold beyond which additional inoculants may not further enhance shoot biomass significantly, aligning with the findings of Hungria et al. (2005) and. Tamiru and Girma (2019), who observed diminishing returns beyond optimal inoculant rates. Notably, the control treatment had the lowest shoot dry weight, underscoring the importance of effective Rhizobium inoculation for soybean productivity. Interestingly, the interaction between variety and inoculant rate significantly affected shoot dry weight, although no such interaction was noted for root dry weight (P > 0.05). This indicates that the shoot biomass response was more sensitive to combined genotype and inoculation effects, possibly due to genotype-specific efficiency in translocating fixed nitrogen from nodules to aerial plant parts, as also observed by Herridge et al. (2008). In the second season, similar trends were observed with some seasonal variation (p < 0.05). Afayak again emerged as the superior variety in terms of shoot dry weight, which corroborates its performance in the first season and further supports its consistent adaptability and biomass accumulation potential. Favour and Jenguma followed, showing comparable performance. Seasonal consistency in Afayak’s shoot biomass supports its selection for environments with fluctuating climatic conditions, a trend supported by Dakora and Keya (1997). Bradyrhizobium inoculation again significantly (p < 0.001) improved shoot dry weight across treatments in the second season. The 5, 7.5, 10, and 12.5 g inoculant rates increased shoot biomass by 106%, 145%, 172%, and 218%, respectively, over the control. The 12.5 g treatment showed the highest improvement, although it was statistically similar to the 10 g application. These findings demonstrate the robust, positive response of soybeans to higher inoculant rates under favourable field conditions. Additionally, the comparable effects of 10 g and 12.5 g treatments suggest that 10 g may be a more economically viable dose without compromising yield, in agreement with reports by Bala and Giller (2001). The seasonal differences in shoot biomass responses may be explained by variations in environmental factors such as rainfall, temperature, and soil microbial activity, which influence both rhizobial survival and host plant physiology (Giller, 2001; Zengeni et al., 2003). Overall, the data confirm that effective Bradyrhizobium inoculation substantially enhances shoot biomass and that Afayak remains a promising variety for maximising biomass accumulation and, by extension, potential grain yield. 3.1.4. Root dry weight Root dry weight is an essential indicator of plant root development, contributing to nutrient and water uptake as well as nodule formation and function. The current study revealed significant effects of both soybean variety and Bradyrhizobium inoculation rate on root dry weight, with clear seasonal variations. In the first season, Afayak and Jenguma produced significantly higher (p < 0.001) root dry weights than Favour (Table 2 ). This trend suggests that these two varieties may possess more vigorous root systems, potentially providing greater surface area for nodulation and nutrient absorption. Previous studies, such as those by Savala et al (2023) and Thilakarathna and Raizada (2017), also demonstrated that varietal differences can influence root biomass and, consequently, symbiotic efficiency with rhizobia. Bradyrhizobium inoculation significantly enhanced root dry weight across all application rates compared to the control. Increases of 4.75%, 20.33%, 37.09%, and 41.69% were recorded for the 5g, 7.5g, 10g, and 12.5g treatments, respectively. The control treatment yielded the lowest root dry weight, highlighting the positive impact of rhizobial inoculation on root development. However, the increases were relatively moderate compared to shoot and nodule dry weight, suggesting that root biomass is less sensitive to inoculant rate than aboveground growth or nodulation. These findings align with those of Herridge et al. (2008) and Hungria et al. (2005), who noted that while inoculation promotes biomass production, the degree of response varies by plant organ and environmental condition. There was no significant interaction between variety and inoculation rate for root dry weight (P > 0.05), indicating that the effect of inoculation on root growth was consistent across the varieties tested. This suggests a generalised benefit of Bradyrhizobium inoculation on root development regardless of genotype under the given conditions. In contrast to the first season, the second season showed a shift (p < 0.001) in varietal performance, with Favour producing the highest root dry weight. Afayak and Jenguma had statistically similar but lower root dry weights. This reversal highlights the influence of seasonal environmental factors—such as rainfall, temperature, and microbial dynamics—on varietal expression and growth performance, as emphasised by Giller (2001 ) and Zengeni et al. (2003). Bradyrhizobium inoculation again significantly (p < 0.001) improved root dry weight in the second season. Increases of 17.84%, 27.75%, 47.75%, and 62.52% over the control were observed for the 5g, 7.5g, 10g, and 12.5g inoculant treatments, respectively. These increases were more pronounced than in the first season, possibly due to more favourable soil moisture or microbial conditions enhancing rhizobial colonisation and root growth. As in the first season, the 10g and 12.5g rates showed comparable effects, suggesting a saturation point in the benefit curve, similar to findings reported by Bala and Giller (2001 ) and Tamiru and Girma (2019)). The consistent enhancement of root dry weight with increasing inoculant rate across seasons reinforces the value of Bradyrhizobium inoculation in promoting soybean root development. However, the lack of interaction between variety and inoculation rate in both seasons further underscores that while certain varieties may inherently develop larger root systems, the benefits of inoculation apply broadly. Table 2 Effects of variety and inoculant rate on nodule count, nodule dry weight, shoot dry weight, and root dry weight of soybean in 2023 and 2024 cropping seasons Treatment Nodule Count (No/plant) Nodule Dry weight (mg/plant) Shoot dry weight (kg/ha) Root dry weight (kg/ha) 2023 2024 2023 2024 2023 2024 2023 2024 Variety (V) Afayak 36a 34.27a 1136a 991b 3467a 2793a 951a 827b Favor 29b 25.63b 1193a 1108a 2784b 2084b 610b 552a Jenguma 27b 24.47b 988b 861b 3477a 2777a 879a 804b Lsd (5%) 4.1 5.05 68.4 139.4 353.9 411.8 121.9 65.7 Inoculation rate (I) 0 15c 14.22b 527d 462c 1818d 1118d 674b 555c 5 25b 20.56b 922c 857b 3600b 2304c 706b 654b 7.5 39a 32.28a 1001c 900b 3178c 2736bc 811ab 709b 10 37a 34.56a 1624b 1415a 3600b 3044ab 924a 820a 12.5 39a 39.00a 1454a 1300a 4018a 3556a 955a 902a LSD (5%) 5.4 6.52 88.3 179.9 327.8 531.6 157.4 84.9 Pr(Vx I) 0.002 0.435 < .001 0.015 < .001 0.330 0.104 0.176 CV 17.3 23.20 8.0 18.20 10.1 20.8 19.4 11.70 Pr(Vx I): Interaction CV: Coefficient of variation and LSD: Least significant difference. Means with the same lower letters on the same horizontal column is not significant 3.2.1. Pod number Pod number is a critical yield component in soybean, directly influenced by the plant’s genetic potential and its interaction with microbial symbionts such as Bradyrhizobium . The findings from this study show that both variety and inoculation rate significantly affected pod number across seasons, although their interaction was not statistically significant. In the first season, varietal differences were significant (p < 0.001), with Afayak and Jenguma exhibiting the highest pod numbers (Table 3 ). This suggests superior reproductive performance of these varieties under the prevailing environmental conditions. The Favour variety, while trailing in pod number, had values comparable to Jenguma. Similar varietal effects have been reported by Thilakarathna and Raizada (2017), who found that differences in soybean genotypes contributed significantly to pod formation due to variations in nodulation capacity and nitrogen use efficiency. The application of Bradyrhizobium inoculant at different rates significantly influenced (p < 0.001) pod numbers. Specifically, inoculation at 5g, 7.5g, 10g, and 12.5g increased pod numbers by 12%, 36%, 84%, and 84%, respectively, over the uninoculated control. These results demonstrate a clear positive trend with increasing inoculant rates, up to 10g, beyond which the response plateaued. The 10g and 12.5g rates produced statistically similar pod numbers, both outperforming the lower inoculant rates and the control. This is consistent with findings by Hungria et al. (2005) and Savala et al (2023), who noted that higher rhizobial inoculation levels enhance nitrogen fixation, thereby improving reproductive performance and pod formation. No significant interaction between variety and inoculant rate was observed for pod number (P > 0.05), suggesting that the response to Bradyrhizobium inoculation was relatively uniform across the tested varieties in the first season. In the second season, varietal differences again significantly (p < 0.05) influenced pod number. Afayak consistently recorded the highest pod count, confirming its superior yield potential across seasons. The Favour and Jenguma varieties exhibited similar pod numbers, both lower than Afayak. This seasonal consistency in varietal performance echoes previous findings by Bala and Giller (2001), who emphasized genotype stability across variable field conditions. Inoculation effects on pod number were hıghly significant (p < 0.001). Applications of 5g, 7.5g, 10g, and 12.5g Bradyrhizobium inoculant resulted in 104%, 106%, 101%, and 105% increases in pod number, respectively, over the control. Unlike in the first season, differences among the inoculant rates were less pronounced, indicating a ceiling effect in pod number response under potentially more favorable environmental conditions in the second season. This saturation effect has been observed in similar studies (e.g., Herridge et al., 2008), where increased soil moisture and microbial activity in the second season likely supported better inoculant performance even at moderate doses. Again, no significant interaction was observed between variety and inoculation rate, further supporting the notion that while varietal differences exist, Bradyrhizobium inoculation consistently enhances pod number regardless of genotype. 3.2.2. Pod dry weight Pod dry weight is a critical yield determinant in soybean, reflecting both reproductive success and effective nutrient utilization, especially nitrogen derived from biological nitrogen fixation (BNF). The results of this study demonstrated that both variety and Bradyrhizobium inoculation rate significantly influenced pod dry weight, with clear seasonal trends. In the first season, the varietal effect on pod dry weight was significant (p < 0.001), with Jenguma recording the highest dry weight among the tested varieties (Table 3 ). The Afayak and Favour varieties showed statistically similar pod dry weights. These differences highlight the inherent genotypic variation in reproductive allocation and sink strength among soybean varieties, consistent with findings by Savala et al (2023), who reported variability in pod biomass accumulation across soybean cultivars under inoculated conditions. Bradyrhizobium inoculation significantly (p < 0.001) increased pod dry weight. Inoculant rates of 5g, 7.5g, 10g, and 12.5g resulted in pod dry weight increases of 31.73%, 64.13%, 68.31%, and 91.68%, respectively, over the control. The highest value was observed at 12.5g, which was statistically comparable to the 10g treatment, suggesting a possible plateau effect at higher inoculant levels. This pattern aligns with the work of Hungria et al. (2005) and Bala and Giller (2001), who observed that increased inoculant rates enhance nitrogen fixation and, consequently, pod biomass up to an optimal threshold. Importantly, although pod number and dry weight both increased with higher inoculant application, there was no significant interaction between variety and inoculant rate (P > 0.05), indicating that the benefits of inoculation were consistent across genotypes. This observation agrees with Thilakarathna and Raizada (2017), who noted that while host genotype influences nodulation efficiency, inoculant benefits are generally genotype-independent under effective strains. In the second season, varietal differences in pod dry weight were not statistically significant, suggesting environmental or seasonal factors may have masked genotypic distinctions. This seasonal variation in varietal response could be due to differences in rainfall distribution, temperature, or soil microbial dynamics, as also noted by Herridge et al. (2008) in studies of inoculation across agroecological zones. In contrast, Bradyrhizobium inoculation remained highly significant (p < 0.001), with inoculant treatments at 5g, 7.5g, 10g, and 12.5g increasing pod dry weight by 40.77%, 77.25%, 108.56%, and 171.75%, respectively, over the control. These results show a stronger inoculation response in the second season, with the 12.5g rate outperforming all other treatments. Significant differences were observed between the 5g and 7.5g rates, as well as between the 10g and 12.5g rates, suggesting a dose-dependent response consistent with improved nitrogen nutrition and biomass partitioning to reproductive structures. These findings corroborate the observations by Dakora and Keya (1997) and Hungria and Vargas (2000), who emphasised that the synergistic effect of effective inoculation and favourable environmental conditions significantly enhances soybean pod development and dry matter accumulation. 3.2.3. Grain yield Grain yield is the ultimate indicator of agronomic performance and economic viability of soybean production. In this study, grain yield was significantly influenced by both variety and Bradyrhizobium inoculation rate, with discernible variations across the two seasons. In the first season, significant differences (p < 0.001) in grain yield were observed among the soybean varieties (Table 3 ). Jenguma recorded the highest grain yield, outperforming Afayak, while Favour and Afayak had statistically similar yields. The superior performance of Jenguma may be attributed to its higher pod number and pod dry weight, which are crucial determinants of yield (Savala et al., 2023). These results corroborate earlier findings by Abaidoo et al. (2007), who reported that varietal differences in grain yield are often linked to differential nodulation efficiency and nutrient partitioning. Bradyrhizobium inoculation had a marked effect on grain yield. The 5g, 7.5g, 10g, and 12.5g inoculation rates increased yield by 27.33%, 78.75%, 45.93%, and 146.52%, respectively, over the control. The 12.5g treatment resulted in the most substantial yield improvement, significantly surpassing all other treatments. This indicates a strong positive response to increased inoculation rates, likely due to enhanced nitrogen fixation and better root development (Hungria et al., 2005). Interestingly, while the 7.5g treatment produced a higher yield than the 10g rate, this could be attributed to environmental or physiological factors affecting the efficiency of nitrogen use. These results are consistent with findings by Bala and Giller (2001), who observed that optimal inoculant rates may vary by location and host genotype. During the second season, no significant varietal differences were detected in grain yield. This contrasts with the first season and may reflect environmental variability such as rainfall distribution or temperature stress, which can influence crop performance irrespective of genotype (Herridge et al., 2008). Nonetheless, Bradyrhizobium inoculation remained a significant (p < 0.001) determinant of grain yield. The application of 5g, 7.5g, 10g, and 12.5g inoculant increased grain yield by 47.56%, 54.15%, 87.88%, and 180.63%, respectively, over the control. The 12.5g rate again produced the highest yield, affirming its consistent performance across seasons. Unlike the first season, the grain yields of the 5g, 7.5g, and 10g inoculation rates were statistically similar but still significantly greater than the uninoculated control. This suggests that even moderate inoculant applications can provide a substantial yield benefit under field conditions, supporting the findings of Thilakarathna and Raizada (2017), who reported that rhizobial inoculation can improve soybean yields by up to 90%, depending on soil conditions and strain effectiveness. Overall, the consistent positive response of grain yield to increasing Bradyrhizobium inoculation, particularly at the 12.5g rate, underscores the potential for optimizing biological nitrogen fixation to enhance soybean productivity in low-input systems. 3.2.4. Hundred seed weight The 100-seed weight is an important parameter that reflects the overall vigor and quality of the soybean seeds. In this study, significant variations in 100-seed weight were observed between varieties and inoculation rates, but the influence of these factors differed between the two seasons. In the first season, a significant varietal effect was observed on 100-seed weight (Table 3 ). Afayak exhibited the largest seeds, followed by Jenguma, while Favour and Jenguma produced grains of similar size. The larger seed size of Afayak can be attributed to its genotype, which likely has superior traits for seed size determination (Yang et al., 2021). These findings are consistent with other studies that report varietal differences in seed size, which are often governed by genetic factors and environmental interactions (Anyoni et al., 2023). The application of Bradyrhizobium inoculant also significantly affected seed weight. Inoculation at 5g, 7.5g, 10g, and 12.5g increased 100-seed weight by 8.33%, 9.26%, 11.11%, and 16.67%, respectively, over the control. While all inoculant rates improved seed weight, the 12.5g rate led to the highest seed weight, significantly outperforming the control. These results suggest that increased nitrogen fixation from higher inoculation rates contributed to better seed development, as rhizobia enhance nutrient availability, particularly nitrogen, which is critical for seed size (Giller et al., 2009). However, the interactions between varieties and inoculants were not significant, indicating that inoculation did not markedly alter the seed size response depending on the variety. This suggests that inoculation's impact on seed weight may be largely independent of the specific variety grown, supporting findings by Zimmer et al., (2016), who noted that seed size is more influenced by genetic factors and external nutrient availability than by rhizobial strain interaction. In contrast to the first season, the second season showed no significant varietal differences in 100-seed weight. This lack of significant variation could be attributed to environmental factors such as changes in temperature, water availability, or soil nutrient levels, which can influence seed size independent of genetic factors (Pedersen & Sawyer, (2009). Additionally, there was no response to inoculation concerning seed weight in the second season. This suggests that the inoculant's impact on seed weight may have been more pronounced in the first season, potentially due to better inoculant establishment and nitrogen fixation under more favourable conditions. The absence of a seasonal effect on inoculation also highlights the complex interplay between inoculant effectiveness and environmental conditions, as well as the possibility that certain seasons may offer better conditions for inoculant efficiency (Hungria et al., 2000). 3.2.5. Harvest index The harvest index (HI) is a key indicator of the efficiency with which a plant converts its biomass into economically valuable yield components, such as seeds. The effect of variety and inoculation rate on the harvest index of soybean was evaluated in both growing seasons, revealing a mixture of varietal and inoculation effects, as well as seasonal patterns. In the first season, the analysis of harvest index indicated that the varietal effect was not significant (Table 3 ). This suggests that the soybean varieties tested (Afayak, Favour, and Jenguma) exhibited similar efficiencies in converting biomass to seed yield, possibly due to similar growth patterns or environmental conditions that allowed for comparable allocation of resources to reproductive and vegetative growth. Such findings are consistent with previous studies, where varietal differences in harvest index were often minor in certain environmental conditions (Anyoni et al., (2023). However, inoculation significantly influenced the harvest index. The 7.5g and 12.5g inoculant treatments exhibited higher harvest index values compared to the control, suggesting that these inoculation levels led to more efficient biomass partitioning, possibly through increased nitrogen fixation or improved nutrient availability for seed production. This is in line with findings by Giller et al. (2009), who reported that rhizobial inoculation could improve resource allocation towards seed development by enhancing nitrogen availability. Conversely, the 5g and 10g inoculant treatments showed similar harvest index values to the control, indicating that these rates may have been less effective in enhancing soybean productivity and biomass allocation. Notably, significant interactions between varieties and inoculants were observed concerning the harvest index (P < 0.05). This suggests that the effect of inoculation on harvest index may depend on the specific variety used, with certain varieties potentially benefiting more from higher inoculant doses. These interactions may be attributed to genetic factors influencing the responsiveness of plants to external inputs like inoculants, as highlighted by Zimmer et al., (2016), who discussed how different soybean varieties have variable responses to inoculation, depending on factors such as symbiotic compatibility and nutrient uptake efficiency. In the second season, no significant differences were observed in the varietal effect on harvest index. This lack of varietal influence could be attributed to either uniform environmental conditions during the second season or the possibility that the varieties tested responded similarly under the given management practices. Such findings reflect the complex nature of the harvest index, which can be influenced by both genetic and environmental factors, as well as the interaction between them (Pedersen & Sawyer, 2009). Additionally, no response to inoculation was observed concerning the harvest index. This suggests that in the second season, the inoculant treatments did not have a noticeable impact on the allocation of biomass to seeds, possibly due to less favourable conditions for nitrogen fixation or insufficient inoculant effectiveness under the seasonal conditions. This is consistent with previous studies that report varying responses of soybeans to inoculation depending on seasonal factors such as soil temperature, moisture, and nutrient availability (Hungria et al., 2000). The harvest index of soybean was influenced by inoculation in the first season, but this effect was less pronounced in the second season. Varietal differences in harvest index were not significant in either season, suggesting that the inoculation rates and environmental conditions played a more dominant role in determining the efficiency of biomass conversion into seeds. Future studies should further explore the potential interactions between inoculant rates, variety selection, and environmental factors to optimise soybean productivity and harvest index under varying conditions. Table 3 Effects of variety and inoculant rate on pod number, pod dry weight, grain yield, grain quality, and harvest index of Soybean in 2023 and 2024 cropping seasons Treatment Pod number (No/plant) Pod dry weight (mg/plant) Grain yield (kg/ha) 100-Seed weight (g) Harvest Index (%) 2023 2024 2023 2024 2023 2024 Variety (V) Afayak 44a 13.1b 3589b 2801 2567b 1267 12.4a 14.27 0.7 0.54 Favor 30b 14.5ab 3511b 2795 2306b 1381 11.0b 14.80 0.7 0.44 Jenguma 34b 15.5a 4950a 2839 3244a 1316 11.9b 14.40 0.7 0.46 LSD (5%) 4.8 1.74 509.1 208.20 331.3 243.90 0.62 0.916 NS 0.150 Inoculation rate (I) 0 25c 7.8b 2657d 1565e 1694d 759c 10.8b 14.67 0.7b 0.47 5 28bc 15.9a 3500c 2203d 2157c 1120b 11.7a 14.33 0.6b 0.43 7.5 34b 16.1a 4361b 2774c 3028c 1170b 11.8a 14.56 0.7ab 0.48 10 46a 15.7a 4472ab 3264b 2472b 1426b 12.0a 14.33 0.6b 0.41 12.5 46a 16.0a 5093a 4253a 4176a 2130a 12.6a 14.56 0.8a 0.60 LSD (5%) 6.2 2.25a 657.3 268.80 427.7 314.90 0.80 1.182 0.12 0.194 Pr(Vx I) 0.652 0.533 0.299 0.464 < .001 0.786 0.34 0.277 0.013 0.978 CV 17.2 15.7 16.4 9.60 15.8 23.90 6.80 8.20 17.5 40.4 Pr(Vx I): Interaction CV: Coefficient of variation and LSD: Least significant difference. Means with the same lower letters on the same horizontal column is not significant The interaction between soybean variety and inoculant application rate significantly influenced grain yield during the 2023 cropping season, whereas no significant interaction was observed in 2024 (Table 3 ). In 2023, notable varietal differences in grain yield emerged across varying inoculant rates, underscoring the importance of genotype-specific responses to inoculant application. Specifically, the variety Jenguma demonstrated the highest yield efficiency at an inoculant rate of 12.5 g, outperforming both Favour and Afayak . Similarly, at an inoculant rate of 7.5 g, Jenguma again exhibited superior performance, followed by Afayak and Favour . These results suggest a potential synergistic effect between Jenguma and higher inoculant rates, possibly due to enhanced nitrogen fixation or root colonization efficiencies, as reported in earlier studies (e.g., Maluk et al., (2023); Thilakarathna & Raizada, 2017). Conversely, in 2024, the absence of significant interactive effects may be attributed to environmental variability such as soil nutrient status, rainfall distribution, or other agroecological factors, which may have masked the response to inoculant rates. Moreover, no significant differences in grain yield were observed among varieties at the lowest (5 g) and highest (12.5 g) inoculant application rates, indicating that moderate levels of inoculant (e.g., 7.5 g) may be more critical for distinguishing varietal responses under certain conditions. These findings highlight the necessity of considering both genotype and inoculant management in optimizing soybean productivity, particularly in the context of variable seasonal and environmental conditions (Peoples et al., 2009; Salvagiotti et al., 2008). 3.2.6. Amount of N 2 -fıxed There was no significant interaction (p > 0.05) between variety and inoculant rate in either season (Pr = 0.975 in 2023 and 0.908 in 2024), suggesting that the response to inoculation was consistent across the varieties tested. This indicates that while both variety and inoculant rate significantly influenced N₂ fixation independently, their combined effect was not synergistic. Similar observations were made by Abaidoo et al. (2007) in West African soils, where varietal and inoculant responses were independent in most cases. The amount of nitrogen (N₂) fixed by soybeans was significantly influenced by variety during both the 2023 and 2024 growing seasons (Table 1 ). Among the three varieties evaluated, Jenguma consistently recorded the highest levels of N₂ fixation, with values of 51.22 kg/ha in 2023 and 55.97 kg/ha in 2024, significantly (p < 0.05) higher than those observed for Afayak and Favour, which exhibited comparable and lower fixation values. The superiority of Jenguma could be attributed to its better nodulation ability and compatibility with the indigenous or introduced Bradyrhizobium strains, consistent with earlier findings by Kermah et al. (2018) and Okogun and Sanginga (2003), who reported that varietal differences in symbiotic effectiveness influence the quantity of nitrogen fixed by soybean. The inoculant application rate significantly affected (p < 0.05) the amount of N₂ fixed across both seasons. In both 2023 and 2024, the inoculant rate (10 g/kg seed) led to the greatest N₂ fixation (51.52 kg/ha and 56.41 kg/ha, respectively), significantly outperforming lower rates and the control (no inoculation). The 12.5 g/kg rate, while not significantly different from 10 g/kg, did not provide further benefits, indicating a potential plateau or even marginal inefficiency at high inoculation rates. These results support the hypothesis that effective rhizobial inoculation enhances biological nitrogen fixation (BNF), especially in soils with low native rhizobia populations or suboptimal strains (Herridge et al., 2008; Thilakarathna & Raizada, 2017). However, the diminishing returns beyond 10 g/kg may reflect competition among introduced and indigenous strains or limitations in nodule occupancy at higher inoculant concentrations. Although the general trends were consistent across years, slightly higher N₂ fixation was observed in 2024 across all treatments, possibly due to more favourable climatic conditions, improved soil health, or cumulative effects of inoculation. This aligns with findings by Peoples et al. (2009), who noted that environmental conditions and seasonal variability play a crucial role in determining the success of BNF in legumes. 3.3. Economic analysis Table 4 presents a partial budget analysis of the inoculant application rates for the three soybean varieties. The cost of inoculation was the only variable cost among the treatments. In other words, all other expenses for soybean cultivation were consistent across all the treatments. Initially, all the treatments yielded positive net benefits, indicating that the additional benefits of any treatment exceed the extra costs incurred. For Afayak, the 7.5g/1kg seed inoculation rate produced the highest net benefits of GHS 81,875. The 10g/1kg and 12.5g/1kg seed inoculation rates were outperformed by the 7.5g/1kg seed inoculation rate, signifying that those two treatments resulted in lower net benefits alongside higher costs. The 12.5g/1kg seed inoculation rate generated the highest net benefits of GHS 96400 for the Favour variety. Similarly, the 12.5g/1kg seed inoculation rate yielded the highest net benefits of GHS 142,925 for the Jenguma variety. However, in the case of Jenguma, the 10g/1kg seed inoculation rate was outperformed by the 7.5g/1kg seed inoculation rate. Table 4 A partial budget analysis of different inoculation rates on soybean varieties Treatment Total variable cost (GHS/ha) Yield (kg/ha) Price (GHS/kg) Gross benefits (GHS/ha) Net benefit (GHS/ha) Afayak 0 0 1722 25 43050 43050 5 50 2389 25 59725 59675 7.5 75 3278 25 81950 81875 10 100 2500 25 62500 62400 12.5 125 2944 25 73600 73475 Favour 0 0 1278 25 31950 31950 5 50 1778 25 44450 44400 7.5 75 2083 25 52075 52000 10 100 2528 25 63200 63100 12.5 125 3861 25 96525 96400 Jenguma 0 0 2083 25 52075 52075 5 50 2306 25 57650 57600 7.5 75 3722 25 93050 92975 10 100 2389 25 59725 59625 12.5 125 5722 25 143050 142925 Since all the treatments produced positive net benefits, further analysis is required to determine which treatments have the best economic value. Table 2 presents the marginal analysis of the treatments. All dominant treatments were dropped before performing the marginal analysis. For Afayak, both treatments (5g and 7.5g inoculant rates) had MRR above the minimum acceptable rate of return (100%), the 7.5g/1kg seed inoculation rate gave the best economic value and is therefore recommended. In the case of Favour, the 12.5g/1kg seed inoculation rate produced the highest MRR and also the largest net benefits and is therefore recommended. For Jenguma, the most economically valuable rate is the 12.5g/1kg rate because it generated the highest net benefits and also produced an MRR that is significantly higher than the minimum acceptable rate of return. This recommendation criterion is consistent with that of Soha (2014) and Biratu et al. (2022). Table 5 Marginal analysis of inoculation rates on soyean varieties. Treatment Total variable cost (GHS/ha) Marginal cost (GHS/ha) Net benefits (GHS/ha) Marginal net benefits (GHS/ha) Marginal rate of return (100%) Afayak 0 0 43050 5 50 50 59675 16625 33,250 7.5 75 25 81875 22200 88,800 Favour 0 0 31950 5 50 50 44400 12450 2,490 7.5 75 25 52000 7600 30,400 10 100 25 63100 11,100 44,400 12.5 125 25 96400 33,300 133,200 Jenguma 0 0 52075 5 50 50 57600 5,525 11,050 7.5 75 25 92975 35,375 141,500 12.5 125 50 142925 49,950 99,900 4. Conclusion The study demonstrated that Rhizobium inoculation significantly improves soybean growth, nodulation, and yield, with responses varying by variety and inoculant rate. Among the tested rates, 10 g/kg of seed generally resulted in the best performance across the three soybean varieties, particularly in Jenguma, which exhibited the highest yield response. Afayak responded moderately, while Favour showed a relatively lower response to inoculation. These results highlight the importance of optimising inoculant rates based on varietal performance to enhance biological nitrogen fixation and soybean productivity. The economic analysis confirmed that profitability varied by variety and inoculation rate, with the most favourable rates yielding both high net benefits and acceptable MRR values. These findings underscore the significance of integrating agronomic performance with economic efficiency when selecting optimal inoculant application rates for soybean cultivation. Adoption of appropriate inoculation practices can contribute to more sustainable and efficient soybean cultivation, particularly in regions with low native Rhizobium populations and limited soil fertility. Further research is recommended to validate these findings under different agro ecological conditions and with a broader range of soybean genotypes. Declarations Acknowledgement We gratefully acknowledge the Council for Scientific and Industrial Research–Savanna Agricultural Research Institute (CSIR-SARI) for providing the field space and institutional support that made this research possible. Author’s contributions All authors reviewed the manuscript. ALAA, BH and JU: conceptualization, and design of the experiment. ALAA, JU, BH, IAA, AH, and RA: investigation, statistical analysis and interpretation, writing review, supervision, and editing. ALAA, JU, BH, IAA, AH, and RA: read and approved of the final manuscript. Conflict of Interest: The authors declare that no conflict of interest exists concerning the published work. Consent for Publication: All authors have granted their consent for publication. Consent to participate: Not applicable Funding: No funding is available. Data Availability: The datasets generated and/or analysed during the current study are available on paper. Code Availability: Not applicable. 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Effects of Rhizobium Inoculation and Phosphorus Fertilizer rates on Nitrogen Fixation and Nutrient Uptake of Chickpea (Cicer arietinum L.) at Goro, Bale Zone, Oromia Regional State.Greener Journal of Agricultural Sciences Vol. 9(4), pp. 436-446(2019). https://doi.org/10.15580/GJAS.2019.4.101419186. Thilakarathna, M. S., & Raizada, M. N. A meta-analysis of the effectiveness of diverse rhizobia inoculants on soybean traits under field conditions. Soil Biology and Biochemistry , 105 , 177-196(2017). https://doi.org/10.1016/j.soilbio.2016.11.022 Thilakarathna, M. S., & Raizada, M. N. A meta-analysis of the effectiveness of diverse rhizobia inoculants on soybean traits under field conditions. Soil Biology and Biochemistry , 105 , 177-196(2017). https://doi.org/10.1016/j.soilbio.2016.11.022 Thomas, G.W. Exchangeable cations. In A.L. Page (Ed.) (1982), Methods of Soil Analysis. Part 2 (pp. 159–165). ASA and SSSA. https://doi.org/10.2134/agronmonogr9.2.2ed.c9 Yadav, A. & Chandra, K. Mass Production and Quality Control of Microbial Inoculants. Proceedings of the Indian National Science Academy. 80. 483. (2014). DOI:10.16943/ptinsa/2014/v80i2/5. Yang, Qing & Lin, Gaoming & Lv, Huiyong & Wang, Cunhu & Yang, Yongqing & Liao, Hong. Environmental and genetic regulation of plant height in soybean. BMC Plant Biology. 21(2021). 10.1186/s12870-021-02836-7. Yeboah, E., Asamoah, G., Boafo, K., & Abunyewa, A. Effect of Biochar Type and Rate of Application on Maize Yield Indices and Water Use Efficiency on an Ultisol in Ghana. Energy Procedia , 93, 1016–1023 (2016) . DOI: 10.1016/j.egypro.2016.07.143 Zengeni, R., Mpepereki, S., & Giller, K.E. Adaptation of rhizobia to acid soils in Southern Africa. Symbiosis , 35(2), 159–170 (2003). DOI: 10.4314/sajest.v5i2.39826 Zimmer, Stephanie & Messmer, Monika & Haase, Thorsten & Piepho, Hans-Peter & Mindermann, Anke & Schulz, Hannes & Habekuß, Antje & Ordon, Frank & Wilbois, Klaus & Heß, Jürgen. Effects of soybean variety and Bradyrhizobium strains on yield, protein content and biological nitrogen fixation under cool growing conditions in Germany. European Journal of Agronomy. 72. 38-46(2016). 10.1016/j.eja.2015.09.008. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6802922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":481124364,"identity":"22ae6ce2-3213-4e7f-bdf7-9649e254bb75","order_by":0,"name":"Abdul-Latif Abdul-Aziz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie2PsYrCQBCGZxkYm5C0Cbk7X2EkYKmvIqS1EGzuCJwrgq1vs/WGBdMItoKN0SdIcRC726S6Ksl1B7dfsezAfPzzAzgcfxIhNawACEV5s6PnD1MYQkJMuFFoWJJVAHAaNv9eJTiYbV7z7NMfIX18LWcvBFjeLx1KqHOpQ06bw+j6qlJ7GCXJsism30rNjFYJjtdIoVU8iruUsbH1F7xpU9aR2vQrfLSKZtMqolKmX5mchMwlF9EeEWOhCo+wp8vbuXhU9XsWjA9GVE+VzYPRrnx01v8Jeu07dL1B1L/Zdjgcjn/DN+2cPpEZH1ZWAAAAAElFTkSuQmCC","orcid":"","institution":"CSIR-Savanna Agricultural Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Abdul-Latif","middleName":"","lastName":"Abdul-Aziz","suffix":""},{"id":481124365,"identity":"b0e0516e-ff05-4e90-8c1c-62c6a8baea04","order_by":1,"name":"Bashiru Haruna","email":"","orcid":"","institution":"CSIR-Savanna Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Bashiru","middleName":"","lastName":"Haruna","suffix":""},{"id":481124366,"identity":"be1e4af7-15a2-42cc-8bfc-c6201f7ef558","order_by":2,"name":"Ulzen Jacob","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Ulzen","middleName":"","lastName":"Jacob","suffix":""},{"id":481124367,"identity":"d5c87d68-347e-43df-b43b-9b451d5e4e31","order_by":3,"name":"Issah Alidu Abukari","email":"","orcid":"","institution":"CSIR-Savanna Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Issah","middleName":"Alidu","lastName":"Abukari","suffix":""},{"id":481124368,"identity":"cf1cd72a-b4e3-40dc-b57b-026d82c78678","order_by":4,"name":"Abdulai Haruna","email":"","orcid":"","institution":"CSIR-Savanna Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Abdulai","middleName":"","lastName":"Haruna","suffix":""}],"badges":[],"createdAt":"2025-06-02 14:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6802922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6802922/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23244-z","type":"published","date":"2025-11-11T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86149483,"identity":"aaaeee98-db9f-4068-9c89-57d41d5691e8","added_by":"auto","created_at":"2025-07-07 09:52:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31772,"visible":true,"origin":"","legend":"\u003cp\u003eTotal rainfall distribution in the 2022 and 2023 seasons.\u003c/p\u003e\n\u003cp\u003eSource: SARI Agrometeorological station reports, 2023.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6802922/v1/de578740d8af47d37d8cc4b0.png"},{"id":86149264,"identity":"659b2c2d-946b-4a0d-94bf-d68125e0a0a6","added_by":"auto","created_at":"2025-07-07 09:44:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24680,"visible":true,"origin":"","legend":"\u003cp\u003eTemperature distribution in the 2023 and 2024 seasons\u003c/p\u003e\n\u003cp\u003eSource: SARI Agrometeorological Station Reports, 2023.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6802922/v1/d93547f31b780b05895f6874.png"},{"id":86149268,"identity":"efbba381-200b-4967-8aac-f001a4b4173e","added_by":"auto","created_at":"2025-07-07 09:44:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18842,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect of variety and inoculant rate on grain yield of soybean during the 2023 cropping season.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6802922/v1/aea0f80a3875e4fc57bee72e.png"},{"id":86150572,"identity":"4a7eba42-278f-4c08-8415-9b907b15353f","added_by":"auto","created_at":"2025-07-07 10:00:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11338,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of variety on the amount of N\u003csub\u003e2\u003c/sub\u003e-fıxed of soybean during the 2023 and 2024 growing seasons.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6802922/v1/4e846e4a77a83aa39004e229.png"},{"id":86149266,"identity":"0920122d-f824-434a-93ff-4240f77ed4b9","added_by":"auto","created_at":"2025-07-07 09:44:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12553,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of inoculant rate on the amount of N\u003csub\u003e2\u003c/sub\u003e-fıxed of soybean during the 2023 and 2024 growing seasons.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6802922/v1/2c966738ae2ea72ea008bffb.png"},{"id":96105275,"identity":"95ed18ca-925a-4ed5-9670-2748540b54f8","added_by":"auto","created_at":"2025-11-17 16:10:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1485953,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6802922/v1/d77a3271-db60-4d24-825e-4e625bde6fda.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determination of the Appropriate Application Rate of Inoculant for Enhanced Soybean Production","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoybean (Glycine max L. Merrill) has emerged as a crucial cash crop in Ghana, particularly across the northern savannah regions, where it significantly contributes to food security, rural income, and soil fertility enhancement through its symbiotic nitrogen fixation capacity. Beyond its agronomic role, soybean serves as a vital source of affordable plant-based protein for low-income households and as a key input in livestock feed production. In recent years, increasing interest in sustainable intensification has driven the adoption of rhizobial inoculants to enhance biological nitrogen fixation (BNF) and maximize yield potential. Rhizobia inoculation has consistently demonstrated promise in improving nodulation, biomass production, and grain yield under controlled environments and on-farm trials. However, the efficacy of inoculation is not universal, as reports of variable responsiveness and non-responsiveness continue to emerge from both national and international studies (Thilakarathna \u0026amp; Raizada, 2017; Yadav \u0026amp; Chandra, 2014). Several factors influence inoculation response, including soybean genotype, soil fertility, rhizobia strain compatibility, and inoculation rate. Of particular concern is the growing competition between introduced strains and indigenous rhizobia, which are often less effective in nitrogen fixation but may outcompete inoculant strains under field conditions (Mathenge et al., 2019).\u003c/p\u003e \u003cp\u003eIn Ghana, widespread promotion of soybean cultivation and inoculant use began over a decade ago, with initial recommendations largely guided by the manufacturer's application rates tailored for other agroecologies. Early trials reported positive yield responses; however, the continued increase in soybean production has likely altered the native rhizobial ecology, increasing populations of ineffective strains and potentially diminishing inoculant efficacy. Recent evidence suggests that inoculation response may decline when inoculant rates are suboptimal relative to indigenous rhizobia pressure or when varietal differences in nodulation potential are not accounted for (Argaw \u0026amp; Tsigie, 2015). This calls for a reassessment of recommended inoculation rates and the interaction between inoculant dose and varietal traits under current field conditions. Furthermore, studies on the economic feasibility of varying inoculation rates under farmer-managed conditions remain limited, despite their importance for informed decision-making. Addressing this knowledge gap requires evaluating both agronomic performance and cost-effectiveness across a range of varieties and inoculation levels. This study hypothesizes that the optimal inoculation rate varies with soybean variety and that standard manufacturer-recommended rates may no longer be adequate due to increased competition from indigenous rhizobia. The objectives were to: (i) determine the appropriate rhizobia inoculant rate for soybean varieties in farmers\u0026rsquo; fields, and (ii) assess the economic viability of varying inoculation rates for sustainable soybean production in northern Ghana.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Description of experimental sites\u003c/h2\u003e\n \u003cp\u003eThe experiment was conducted at the Savannah Agriculture Research Institute (SARI) field in the Tolon District of Northern Ghana. This site is located in the Guinea Savannah Agroecological Zone, between 9\u0026deg; 25\u0026prime; N and 00\u0026deg; 58\u0026prime; W. The rainfall pattern is unimodal, occurring from May to October, with peaks in August and September. From Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, rainfall totals varied significantly between years. In 2023, peak rainfall was observed in August (254.3 mm) and October (238.1 mm), whereas in 2024, September recorded the highest rainfall (330.3 mm), followed by August (225.1 mm). Several months in 2024, including March, May, November, and December, recorded no rainfall, contrasting sharply with the 2023 values of 5 mm, 126 mm, 140.1 mm, and 13.2 mm, respectively. June and July in 2024 received 149.1 mm and 85.2 mm, respectively, both exceeding the amounts in 2023 (97.3 mm and 107.1 mm), indicating a shift in rainfall onset and intensity. Average temperatures across both years were relatively stable, with minor fluctuations (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). May was the warmest month in both years, registering 30.2\u0026deg;C in 2023 and 29.1\u0026deg;C in 2024. August was the coolest month, averaging 26.4\u0026deg;C in 2023 and 26.8\u0026deg;C in 2024. Overall, temperatures in 2024 were slightly lower than in 2023 for most months, particularly in May, October, and November, suggesting a modest cooling trend during the growing season. These climatic differences between 2023 and 2024 may have directly affected crop growth stages, water availability, and the efficiency of soil amendments such as inoculants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Characterization of soil at experimental sites\u003c/h2\u003e\n \u003cp\u003ePrior to the application of treatments, composite soil samples were collected from the top 0\u0026ndash;20 cm layer across the experimental field to characterize baseline soil fertility. The samples were air-dried, gently crushed, and passed through a 2-mm sieve for the analysis of physicochemical properties. Soil pH was measured in a 1:2.5 soil-to-water suspension using a digital pH meter, following the procedure described by McLean (1982). Organic carbon content was determined using the Walkley-Black wet oxidation method, as outlined by Nelson and Sommers (1982). Total nitrogen was quantified using the micro-Kjeldahl digestion method, according to Bremner and Mulvaney (1982). Available phosphorus was extracted using the Bray-1 method (Bray and Kurtz, 1945), which is suitable for acidic soils, and quantified colorimetrically with the molybdenum blue method. Exchangeable potassium was extracted with 1 M ammonium acetate (NH₄OAc) at pH 7.0 and measured using a flame photometer, as described by Thomas (1982). The initial soil characteristics presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e offer critical insights into the baseline fertility status of the experimental site before treatment application. The soil pH (5.3) indicates moderately acidic conditions, which can affect nutrient availability and rhizobial activity. According to Brady and Weil (2016), acidic soils may limit the proliferation and effectiveness of rhizobia, especially if pH falls below 5.5. The low organic carbon (0.62%) and total nitrogen (0.04%) signify poor soil fertility typical of degraded tropical soils (FAO, 2006), underscoring the importance of biological inputs like inoculants. The available phosphorus (5.81 mg/kg) is also below the critical threshold for legumes, which often require\u0026thinsp;\u0026ge;\u0026thinsp;15 mg/kg for optimal nodulation and growth (Sanginga et al., 2002). The exchangeable potassium (0.23 cmol/kg) is relatively moderate but may still limit yield potential when coupled with other nutrient constraints. These baseline conditions justify the use of rhizobial inoculants as a strategy to enhance biological nitrogen fixation and support soybean productivity. They also help contextualize the crop\u0026apos;s response to the treatments applied.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhysico-chemical characteristics of the soil at 0\u0026ndash;20 cm depth at Nyankpala\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil properties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTexture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSandy loam\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH (1:2.5 H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvailable phosphorus (P) (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganic carbon (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal nitrogen (N) (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExchangeable potassium (cmol (+)/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Experimental design and treatments\u003c/h2\u003e\n \u003cp\u003eThe experiment was designed as a split-plot with three replications, and the treatments tested included soybean varieties {Afayak (TGx1834-5E), Favour (TGx 1844-22E), and Jenguma (TGx-1448-2E)} as the main plot, along with inoculation rates of 0, 5, 7.5, 10, and 12.5g on the sub-plot. The control plants (0g) were not inoculated with Rhizobium. No blanket chemical fertilizer was applied to either the inoculated or uninoculated plots or plants. A reference crop (Maize) plot was included for Biological Nitrogen Fixation (BNF) assessment. A plot size of 4\u0026times;4 m was utilised, with a planting distance of 50\u0026times;10 cm. The inoculants (brand name NoduMax), produced by IITA, were a commercial peat-based formulation of Bradyrhizobium japonicum, consisting of a balanced blend of 50% culture (rhizobia) and 50% carrier material (peat). The test soybeans are long-maturing (110\u0026ndash;115 days), with 45 days to 50% flowering, and have a potential grain yield of 2.0-3.5 t/ha. To assess BNF, a non-fixing reference crop (maize) was included in the treatment combinations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Seed \u003cem\u003erhizobium\u003c/em\u003e inoculation\u003c/h2\u003e\n \u003cp\u003eThe peat-based inoculants were added to the soybean seeds in a container after moistening the seeds. The inoculants and the seeds were mixed thoroughly until the seeds were adequately coated with the inoculants and allowed to air-dry on a sheet of polythene in the shade for a few minutes, after which they were planted on the plots. The treatments with inoculation received 5, 7.5, 10 and 12.5g of inoculants per 1 kg of seed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Data Collection\u003c/h2\u003e\n \u003cp\u003eShoot dry weight, root dry weight, nodule dry weight, and nodule number per plant were recorded from ten representative plants at eight weeks after planting (8 WAP). After the root systems of the ten plants were cut and gently washed on a 2-mm mesh sieve under a jet of tap water, the nodules were detached, counted, and oven-dried at 65\u0026ordm;C for 48 hours. The pods from these ten plants were removed and counted to determine the pod load (i.e., pod number per plant). The shoots of five plants, sub-sampled from the ten harvested, were also oven-dried at 65\u0026ordm;C for 48 hours, and their weights were recorded. After threshing the pods harvested from the designated area of each treatment plot, the grains were adequately sun-dried on a concrete platform and weighed on an electronic balance. One hundred seeds from each treatment were randomly selected and weighed. This procedure was replicated three times, and the average weight of 100 seeds was determined. Data collection involved assessing the growth and yield parameters, including nodulation, root and shoot biomass, pod yield, grain yield, 100-seed weight, and harvest index. Biomass was sampled at the full pod stage (R4 stage).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Estimation of N\u003csub\u003e2\u003c/sub\u003e-fixed\u003c/h2\u003e\n \u003cp\u003eThe technique used to estimate N fixation was the Total Nitrogen Difference (TND) method. This was done by comparing the total nitrogen of the legume with that of a non-legume [14]. The amount of N fixed was calculated by subtracting the total nitrogen of the reference crop (maize) from that of the legume (soybean), and the difference value is assumed as N derived by BNF (N2 fixed).\u003c/p\u003e\n \u003cp\u003eThus, N\u003csub\u003e2\u003c/sub\u003e fixed\u0026thinsp;=\u0026thinsp;Total N in legume -Total N in reference crop,\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{W}\\text{h}\\text{e}\\text{r}\\text{e}\\:total\\:N\\:ın\\:legume=\\:\\frac{\\left(Dry\\:matter\\:weight\\:kg\\:ha\\:X\\:\\%\\:N\\:in\\:plants\\right)\\:}{100}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThen, shoot nitrogen content was analysed using the Kjeldahl procedure [FAO, 2008].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. Economic analysis of treatments\u003c/h2\u003e\n \u003cp\u003eThe economic benefits associated with inoculation rates for soybean varieties were assessed using partial budgeting, as outlined in Soha (2014) and Biratu et al. (2022). All variable input costs were considered, along with the seasonal average operational costs applicable to all treatments during the cropping season in the research area. The amounts farmers paid for clearing land, planting, purchasing supplies such as seed, and hiring labour for weeding, harvesting, and transporting farm products to their homes were all considered variable costs. Subsequently, the difference between each treatment\u0026apos;s gross income and total production costs was calculated to determine its value or net return per hectare. The average annual net returns over the study period were used to compute the mean net returns. There were no levies on capital expenses, including land, capital interest, farm equipment depreciation, or other overhead costs. After dividing the net benefit by the operational cost, the benefit ratio for each treatment was established. This is the difference between the total income obtained from selling the crop and the total cost of producing it.\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:Marginal\\:Net\\:Benefits\\:(GHS/ha)\\:=\\:Change\\:in\\:Net\\:Benefits\\:/\\:Change\\:in\\:Quantity$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThis measures the change in net benefits when one more unit of output is produced (e.g., one more hectare of crop). It shows the profitability of adding a unit\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:Marginal\\:Rate\\:of\\:Return\\:\\left(100\\%\\right)\\:=\\:(Marginal\\:Net\\:Benefits\\:/\\:Marginal\\:Cost)\\:*\\:100\\%$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThis expresses the profitability of an additional unit of output as a percentage. It\u0026apos;s a way to compare the benefits of adding more inputs (e.g., fertilizers) against the additional cost.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eAll data collected were subjected to statistical analysis using Genstat Discovery Edition 10. Nodule count was transformed before the analysis. Analysis of variance (ANOVA) was done to determine differences in means among treatments. All treatment means were compared using the Least Significant Difference (LSD) at a 5% significance level.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Growth parameters\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Nodule number\u003c/h2\u003e \u003cp\u003eThe results demonstrated significant effects of both soybean variety and \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation rate on nodule formation across the two cropping seasons. In the first season, nodule number varied significantly among the tested varieties (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Afayak and Favour producing the highest counts (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This varietal variation in nodulation may be attributed to differences in the genetic potential of the cultivars to associate with Rhizobium, as reported by Herridge et al. (2008) and Thilakarathna and Raizada (2017). These findings align with earlier studies that emphasize the importance of host genotype in determining symbiotic performance and nodulation efficiency. \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation significantly enhanced nodule number relative to the control in both seasons. In the first season, the application of 5, 7.5, 10, and 12.5 g of inoculant increased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) nodulation by 66%, 160%, 147%, and 160%, respectively, over the uninoculated control (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the 12.5 g inoculant rate produced the highest nodule count, although it was statistically similar to the 7.5 g and 10 g rates. This suggests a possible plateau in inoculation effectiveness beyond 7.5 g, indicating that further increases in inoculant dose may not significantly enhance nodulation. These results corroborate findings by Dakora and Keya (1997) and Tamiru and Girma, (2019), who reported that beyond optimal levels, increasing Rhizobium dose does not linearly enhance nodule formation.\u003c/p\u003e \u003cp\u003eIn the second season, varietal differences in nodulation were again significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Afayak maintaining superior nodule production, followed by Favour and Jenguma, which did not differ significantly. The stability of Afayak\u0026rsquo;s performance across seasons implies its robust nodulation potential and adaptability under varying environmental conditions, consistent with observations by Savala et al. (2023). Inoculation effects were again significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the second season. The inoculant rates of 5, 7.5, 10, and 12.5 g increased nodule number by 45%, 127%, 143%, and 174%, respectively, over the control. While the 12.5 g application resulted in the highest nodule numbers, rates of 7.5 g and 10 g also performed comparably well. The 5 g rate, however, produced results statistically similar to the control, highlighting the need for adequate inoculant dosage to trigger effective nodulation. These outcomes affirm the dose-responsive nature of \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation, as previously noted by Bala and Giller (2001) and Hungria et al. (2005).\u003c/p\u003e \u003cp\u003eSeasonal differences in nodulation performance may also reflect variations in environmental conditions such as soil moisture, temperature, and background native Rhizobium populations, which are known to influence inoculant efficacy and symbiotic development (Zengeni et al., 2003; Giller, 2001). Overall, the interaction between soybean genotype and inoculation rate plays a crucial role in maximizing nodule number. Afayak appears to be a superior variety in terms of nodulation, while inoculant rates of 7.5 to 12.5 g offer optimal benefits without significant differences among them, suggesting a threshold level of inoculant efficiency under the tested conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Nodule dry weight\u003c/h2\u003e \u003cp\u003eNodule dry weight, a critical indicator of nitrogen-fixing efficiency and symbiotic activity, was significantly influenced by both soybean variety and \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation rate across seasons. In the first season, varietal differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Afayak and Favour recording the highest nodule dry weights, though Favour\u0026rsquo;s performance was statistically similar to Afayak (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests that both varieties have strong potential for effective symbiotic association under favourable inoculation conditions. The observed varietal differences in nodule biomass may stem from differences in root architecture, rhizosphere interactions, and genetic predisposition for rhizobial compatibility, as previously reported by Thilakarathna and Raizada (2017) and Herridge et al. (2008). \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation significantly increased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) nodule dry weight in the first season relative to the uninoculated control. The applications of 5, 7.5, 10, and 12.5 g of inoculant enhanced nodule biomass by 75%, 90%, 208%, and 176%, respectively. The 12.5 g inoculant rate led to the highest nodule dry weight; however, it was statistically equivalent to the 7.5 g and 10 g applications. These results imply a diminishing return in nodule biomass accumulation beyond the 10 g rate, indicating that nodule saturation or resource competition may occur at higher inoculant doses. Such nonlinear responses to inoculation rates have also been reported by Hungria et al. (2005) and Tamiru and Girma (2019), who noted that while increasing inoculant doses can boost nodulation and biomass, optimal rates must be identified to avoid resource inefficiency.\u003c/p\u003e \u003cp\u003eIn the second season, a shift (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in varietal performance was observed. Favour emerged as the top performer in terms of nodule dry weight, while Afayak and Jenguma recorded similar, but lower, values. This change may be attributed to environmental variations such as rainfall, soil moisture, and microbial dynamics between the two seasons, which are known to influence symbiotic performance \u003cb\u003e(\u003c/b\u003eZengeni et al., 2003; Giller, 2001\u003cb\u003e).\u003c/b\u003e The consistent performance of Favour across both seasons indicates its adaptability and robustness in supporting nodule development under varying agroecological conditions. The effect of \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation on nodule dry weight in the second season was again statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to the control, the 5, 7.5, 10, and 12.5 g inoculation rates improved nodule dry weight by 85%, 95%, 206%, and 181%, respectively. While the 10 g and 12.5 g applications produced the highest dry weights, they were statistically similar, suggesting that 10 g may be the most efficient dose for maximizing nodulation benefits. Lower rates, especially 5 g and 7.5 g, resulted in reduced biomass and did not differ significantly from one another, reinforcing the idea that suboptimal inoculant doses may limit rhizobial colonization and symbiotic nitrogen fixation, as supported by Bala and Giller (2001) and Savala et al (2023). .\u003c/p\u003e \u003cp\u003eTaken together, these findings highlight the importance of selecting suitable soybean varieties and optimizing inoculation rates for enhancing symbiotic performance. Favour consistently showed high nodulation capacity in terms of nodule dry weight, particularly under high inoculant application. Meanwhile, the 10 g rate appeared optimal across seasons, offering a balance between efficiency and performance. These outcomes are crucial for guiding farmer decisions on variety and input selection for sustainable legume production systems in sub-Saharan Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Shoot dry weight\u003c/h2\u003e \u003cp\u003eShoot dry weight is a key indicator of vegetative growth and overall plant vigor, often reflecting the efficiency of symbiotic nitrogen fixation. Across both seasons, the shoot biomass of soybean was significantly influenced by variety and \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation rate, with notable seasonal variability. In the first season, Afayak and Jenguma exhibited the highest (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) shoot dry weights, while Favour followed closely behind (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests that Afayak and Jenguma had greater vegetative growth potential, likely due to their superior symbiotic interactions or resource acquisition abilities. Similar varietal influences on shoot biomass have been documented by Savala et al (2023) and Thilakarathna and Raizada (2017), who reported that differences in soybean cultivar biomass can be linked to differences in nitrogen fixation capacity and root morphology. Inoculation with \u003cem\u003eBradyrhizobium\u003c/em\u003e significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increased shoot dry weight compared to the control. The 5, 7.5, 10, and 12.5 g inoculant applications enhanced shoot biomass by 98%, 75%, 98%, and 121%, respectively, over the uninoculated treatment. The 12.5 g inoculation rate produced the highest shoot biomass, although it was statistically similar to the 10 g treatment. These results suggest a threshold beyond which additional inoculants may not further enhance shoot biomass significantly, aligning with the findings of Hungria et al. (2005) and. Tamiru and Girma (2019), who observed diminishing returns beyond optimal inoculant rates. Notably, the control treatment had the lowest shoot dry weight, underscoring the importance of effective Rhizobium inoculation for soybean productivity. Interestingly, the interaction between variety and inoculant rate significantly affected shoot dry weight, although no such interaction was noted for root dry weight (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This indicates that the shoot biomass response was more sensitive to combined genotype and inoculation effects, possibly due to genotype-specific efficiency in translocating fixed nitrogen from nodules to aerial plant parts, as also observed by Herridge et al. (2008).\u003c/p\u003e \u003cp\u003eIn the second season, similar trends were observed with some seasonal variation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Afayak again emerged as the superior variety in terms of shoot dry weight, which corroborates its performance in the first season and further supports its consistent adaptability and biomass accumulation potential. Favour and Jenguma followed, showing comparable performance. Seasonal consistency in Afayak\u0026rsquo;s shoot biomass supports its selection for environments with fluctuating climatic conditions, a trend supported by Dakora and Keya (1997). \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation again significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) improved shoot dry weight across treatments in the second season. The 5, 7.5, 10, and 12.5 g inoculant rates increased shoot biomass by 106%, 145%, 172%, and 218%, respectively, over the control. The 12.5 g treatment showed the highest improvement, although it was statistically similar to the 10 g application. These findings demonstrate the robust, positive response of soybeans to higher inoculant rates under favourable field conditions. Additionally, the comparable effects of 10 g and 12.5 g treatments suggest that 10 g may be a more economically viable dose without compromising yield, in agreement with reports by Bala and Giller (2001).\u003c/p\u003e \u003cp\u003eThe seasonal differences in shoot biomass responses may be explained by variations in environmental factors such as rainfall, temperature, and soil microbial activity, which influence both rhizobial survival and host plant physiology (Giller, 2001; Zengeni et al., 2003). Overall, the data confirm that effective \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation substantially enhances shoot biomass and that Afayak remains a promising variety for maximising biomass accumulation and, by extension, potential grain yield.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. Root dry weight\u003c/h2\u003e \u003cp\u003eRoot dry weight is an essential indicator of plant root development, contributing to nutrient and water uptake as well as nodule formation and function. The current study revealed significant effects of both soybean variety and \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation rate on root dry weight, with clear seasonal variations. In the first season, Afayak and Jenguma produced significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) root dry weights than Favour (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This trend suggests that these two varieties may possess more vigorous root systems, potentially providing greater surface area for nodulation and nutrient absorption. Previous studies, such as those by Savala et al (2023) and Thilakarathna and Raizada (2017), also demonstrated that varietal differences can influence root biomass and, consequently, symbiotic efficiency with rhizobia. \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation significantly enhanced root dry weight across all application rates compared to the control. Increases of 4.75%, 20.33%, 37.09%, and 41.69% were recorded for the 5g, 7.5g, 10g, and 12.5g treatments, respectively. The control treatment yielded the lowest root dry weight, highlighting the positive impact of rhizobial inoculation on root development. However, the increases were relatively moderate compared to shoot and nodule dry weight, suggesting that root biomass is less sensitive to inoculant rate than aboveground growth or nodulation. These findings align with those of Herridge et al. (2008) and Hungria et al. (2005), who noted that while inoculation promotes biomass production, the degree of response varies by plant organ and environmental condition. There was no significant interaction between variety and inoculation rate for root dry weight (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the effect of inoculation on root growth was consistent across the varieties tested. This suggests a generalised benefit of \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation on root development regardless of genotype under the given conditions.\u003c/p\u003e \u003cp\u003eIn contrast to the first season, the second season showed a shift (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in varietal performance, with Favour producing the highest root dry weight. Afayak and Jenguma had statistically similar but lower root dry weights. This reversal highlights the influence of seasonal environmental factors\u0026mdash;such as rainfall, temperature, and microbial dynamics\u0026mdash;on varietal expression and growth performance, as emphasised by Giller (2001\u003cb\u003e)\u003c/b\u003e and Zengeni et al. (2003). \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation again significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) improved root dry weight in the second season. Increases of 17.84%, 27.75%, 47.75%, and 62.52% over the control were observed for the 5g, 7.5g, 10g, and 12.5g inoculant treatments, respectively. These increases were more pronounced than in the first season, possibly due to more favourable soil moisture or microbial conditions enhancing rhizobial colonisation and root growth. As in the first season, the 10g and 12.5g rates showed comparable effects, suggesting a saturation point in the benefit curve, similar to findings reported by Bala and Giller (2001\u003cb\u003e)\u003c/b\u003e and Tamiru and Girma (2019)).\u003c/p\u003e \u003cp\u003eThe consistent enhancement of root dry weight with increasing inoculant rate across seasons reinforces the value of \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation in promoting soybean root development. However, the lack of interaction between variety and inoculation rate in both seasons further underscores that while certain varieties may inherently develop larger root systems, the benefits of inoculation apply broadly.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of variety and inoculant rate on nodule count, nodule dry weight, shoot dry weight, and root dry weight of soybean in 2023 and 2024 cropping seasons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNodule Count (No/plant)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNodule Dry weight (mg/plant)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eShoot dry weight (kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRoot dry weight (kg/ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfayak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.27a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1136a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e991b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3467a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2793a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e951a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e827b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.63b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1193a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1108a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2784b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2084b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e610b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e552a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJenguma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.47b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e988b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e861b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3477a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2777a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e879a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e804b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLsd (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e353.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e411.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e121.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInoculation rate (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.22b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e527d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e462c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1818d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1118d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e674b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e555c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.56b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e922c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e857b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3600b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2304c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e706b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e654b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.28a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1001c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e900b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3178c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2736bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e811ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e709b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.56a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1624b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1415a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3600b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3044ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e924a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e820a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.00a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1454a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1300a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4018a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3556a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e955a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e902a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e327.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e531.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e157.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePr(Vx I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePr(Vx I): Interaction CV: Coefficient of variation and LSD: Least significant difference. Means with the same lower letters on the same horizontal column is not significant\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Pod number\u003c/h2\u003e \u003cp\u003ePod number is a critical yield component in soybean, directly influenced by the plant\u0026rsquo;s genetic potential and its interaction with microbial symbionts such as \u003cem\u003eBradyrhizobium\u003c/em\u003e. The findings from this study show that both variety and inoculation rate significantly affected pod number across seasons, although their interaction was not statistically significant. In the first season, varietal differences were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Afayak and Jenguma exhibiting the highest pod numbers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests superior reproductive performance of these varieties under the prevailing environmental conditions. The Favour variety, while trailing in pod number, had values comparable to Jenguma. Similar varietal effects have been reported by Thilakarathna and Raizada (2017), who found that differences in soybean genotypes contributed significantly to pod formation due to variations in nodulation capacity and nitrogen use efficiency. The application of \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculant at different rates significantly influenced (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) pod numbers. Specifically, inoculation at 5g, 7.5g, 10g, and 12.5g increased pod numbers by 12%, 36%, 84%, and 84%, respectively, over the uninoculated control. These results demonstrate a clear positive trend with increasing inoculant rates, up to 10g, beyond which the response plateaued. The 10g and 12.5g rates produced statistically similar pod numbers, both outperforming the lower inoculant rates and the control. This is consistent with findings by Hungria et al. (2005) and Savala et al (2023), who noted that higher rhizobial inoculation levels enhance nitrogen fixation, thereby improving reproductive performance and pod formation. No significant interaction between variety and inoculant rate was observed for pod number (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the response to \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation was relatively uniform across the tested varieties in the first season.\u003c/p\u003e \u003cp\u003eIn the second season, varietal differences again significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) influenced pod number. Afayak consistently recorded the highest pod count, confirming its superior yield potential across seasons. The Favour and Jenguma varieties exhibited similar pod numbers, both lower than Afayak. This seasonal consistency in varietal performance echoes previous findings by Bala and Giller (2001), who emphasized genotype stability across variable field conditions. Inoculation effects on pod number were hıghly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Applications of 5g, 7.5g, 10g, and 12.5g \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculant resulted in 104%, 106%, 101%, and 105% increases in pod number, respectively, over the control. Unlike in the first season, differences among the inoculant rates were less pronounced, indicating a ceiling effect in pod number response under potentially more favorable environmental conditions in the second season. This saturation effect has been observed in similar studies (e.g., Herridge et al., 2008), where increased soil moisture and microbial activity in the second season likely supported better inoculant performance even at moderate doses. Again, no significant interaction was observed between variety and inoculation rate, further supporting the notion that while varietal differences exist, \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation consistently enhances pod number regardless of genotype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Pod dry weight\u003c/h2\u003e \u003cp\u003ePod dry weight is a critical yield determinant in soybean, reflecting both reproductive success and effective nutrient utilization, especially nitrogen derived from biological nitrogen fixation (BNF). The results of this study demonstrated that both variety and \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation rate significantly influenced pod dry weight, with clear seasonal trends. In the first season, the varietal effect on pod dry weight was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Jenguma recording the highest dry weight among the tested varieties (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Afayak and Favour varieties showed statistically similar pod dry weights. These differences highlight the inherent genotypic variation in reproductive allocation and sink strength among soybean varieties, consistent with findings by Savala et al (2023), who reported variability in pod biomass accumulation across soybean cultivars under inoculated conditions. \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increased pod dry weight. Inoculant rates of 5g, 7.5g, 10g, and 12.5g resulted in pod dry weight increases of 31.73%, 64.13%, 68.31%, and 91.68%, respectively, over the control. The highest value was observed at 12.5g, which was statistically comparable to the 10g treatment, suggesting a possible plateau effect at higher inoculant levels. This pattern aligns with the work of Hungria et al. (2005) and Bala and Giller (2001), who observed that increased inoculant rates enhance nitrogen fixation and, consequently, pod biomass up to an optimal threshold. Importantly, although pod number and dry weight both increased with higher inoculant application, there was no significant interaction between variety and inoculant rate (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the benefits of inoculation were consistent across genotypes. This observation agrees with Thilakarathna and Raizada (2017), who noted that while host genotype influences nodulation efficiency, inoculant benefits are generally genotype-independent under effective strains.\u003c/p\u003e \u003cp\u003eIn the second season, varietal differences in pod dry weight were not statistically significant, suggesting environmental or seasonal factors may have masked genotypic distinctions. This seasonal variation in varietal response could be due to differences in rainfall distribution, temperature, or soil microbial dynamics, as also noted by Herridge et al. (2008) in studies of inoculation across agroecological zones. In contrast, \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation remained highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with inoculant treatments at 5g, 7.5g, 10g, and 12.5g increasing pod dry weight by 40.77%, 77.25%, 108.56%, and 171.75%, respectively, over the control. These results show a stronger inoculation response in the second season, with the 12.5g rate outperforming all other treatments. Significant differences were observed between the 5g and 7.5g rates, as well as between the 10g and 12.5g rates, suggesting a dose-dependent response consistent with improved nitrogen nutrition and biomass partitioning to reproductive structures. These findings corroborate the observations by Dakora and Keya (1997) and Hungria and Vargas (2000), who emphasised that the synergistic effect of effective inoculation and favourable environmental conditions significantly enhances soybean pod development and dry matter accumulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Grain yield\u003c/h2\u003e \u003cp\u003eGrain yield is the ultimate indicator of agronomic performance and economic viability of soybean production. In this study, grain yield was significantly influenced by both variety and \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation rate, with discernible variations across the two seasons. In the first season, significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in grain yield were observed among the soybean varieties (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Jenguma recorded the highest grain yield, outperforming Afayak, while Favour and Afayak had statistically similar yields. The superior performance of Jenguma may be attributed to its higher pod number and pod dry weight, which are crucial determinants of yield (Savala et al., 2023). These results corroborate earlier findings by Abaidoo et al. (2007), who reported that varietal differences in grain yield are often linked to differential nodulation efficiency and nutrient partitioning. \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation had a marked effect on grain yield. The 5g, 7.5g, 10g, and 12.5g inoculation rates increased yield by 27.33%, 78.75%, 45.93%, and 146.52%, respectively, over the control. The 12.5g treatment resulted in the most substantial yield improvement, significantly surpassing all other treatments. This indicates a strong positive response to increased inoculation rates, likely due to enhanced nitrogen fixation and better root development (Hungria et al., 2005). Interestingly, while the 7.5g treatment produced a higher yield than the 10g rate, this could be attributed to environmental or physiological factors affecting the efficiency of nitrogen use. These results are consistent with findings by Bala and Giller (2001), who observed that optimal inoculant rates may vary by location and host genotype.\u003c/p\u003e \u003cp\u003eDuring the second season, no significant varietal differences were detected in grain yield. This contrasts with the first season and may reflect environmental variability such as rainfall distribution or temperature stress, which can influence crop performance irrespective of genotype (Herridge et al., 2008). Nonetheless, \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation remained a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) determinant of grain yield. The application of 5g, 7.5g, 10g, and 12.5g inoculant increased grain yield by 47.56%, 54.15%, 87.88%, and 180.63%, respectively, over the control. The 12.5g rate again produced the highest yield, affirming its consistent performance across seasons. Unlike the first season, the grain yields of the 5g, 7.5g, and 10g inoculation rates were statistically similar but still significantly greater than the uninoculated control. This suggests that even moderate inoculant applications can provide a substantial yield benefit under field conditions, supporting the findings of Thilakarathna and Raizada (2017), who reported that rhizobial inoculation can improve soybean yields by up to 90%, depending on soil conditions and strain effectiveness. Overall, the consistent positive response of grain yield to increasing \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculation, particularly at the 12.5g rate, underscores the potential for optimizing biological nitrogen fixation to enhance soybean productivity in low-input systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Hundred seed weight\u003c/h2\u003e \u003cp\u003eThe 100-seed weight is an important parameter that reflects the overall vigor and quality of the soybean seeds. In this study, significant variations in 100-seed weight were observed between varieties and inoculation rates, but the influence of these factors differed between the two seasons. In the first season, a significant varietal effect was observed on 100-seed weight (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Afayak exhibited the largest seeds, followed by Jenguma, while Favour and Jenguma produced grains of similar size. The larger seed size of Afayak can be attributed to its genotype, which likely has superior traits for seed size determination (Yang et al., 2021). These findings are consistent with other studies that report varietal differences in seed size, which are often governed by genetic factors and environmental interactions (Anyoni et al., 2023). The application of \u003cem\u003eBradyrhizobium\u003c/em\u003e inoculant also significantly affected seed weight. Inoculation at 5g, 7.5g, 10g, and 12.5g increased 100-seed weight by 8.33%, 9.26%, 11.11%, and 16.67%, respectively, over the control. While all inoculant rates improved seed weight, the 12.5g rate led to the highest seed weight, significantly outperforming the control. These results suggest that increased nitrogen fixation from higher inoculation rates contributed to better seed development, as rhizobia enhance nutrient availability, particularly nitrogen, which is critical for seed size (Giller et al., 2009). However, the interactions between varieties and inoculants were not significant, indicating that inoculation did not markedly alter the seed size response depending on the variety. This suggests that inoculation's impact on seed weight may be largely independent of the specific variety grown, supporting findings by Zimmer et al., (2016), who noted that seed size is more influenced by genetic factors and external nutrient availability than by rhizobial strain interaction.\u003c/p\u003e \u003cp\u003eIn contrast to the first season, the second season showed no significant varietal differences in 100-seed weight. This lack of significant variation could be attributed to environmental factors such as changes in temperature, water availability, or soil nutrient levels, which can influence seed size independent of genetic factors (Pedersen \u0026amp; Sawyer, (2009). Additionally, there was no response to inoculation concerning seed weight in the second season. This suggests that the inoculant's impact on seed weight may have been more pronounced in the first season, potentially due to better inoculant establishment and nitrogen fixation under more favourable conditions. The absence of a seasonal effect on inoculation also highlights the complex interplay between inoculant effectiveness and environmental conditions, as well as the possibility that certain seasons may offer better conditions for inoculant efficiency (Hungria et al., 2000).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5. Harvest index\u003c/h2\u003e \u003cp\u003eThe harvest index (HI) is a key indicator of the efficiency with which a plant converts its biomass into economically valuable yield components, such as seeds. The effect of variety and inoculation rate on the harvest index of soybean was evaluated in both growing seasons, revealing a mixture of varietal and inoculation effects, as well as seasonal patterns. In the first season, the analysis of harvest index indicated that the varietal effect was not significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that the soybean varieties tested (Afayak, Favour, and Jenguma) exhibited similar efficiencies in converting biomass to seed yield, possibly due to similar growth patterns or environmental conditions that allowed for comparable allocation of resources to reproductive and vegetative growth. Such findings are consistent with previous studies, where varietal differences in harvest index were often minor in certain environmental conditions (Anyoni et al., (2023). However, inoculation significantly influenced the harvest index. The 7.5g and 12.5g inoculant treatments exhibited higher harvest index values compared to the control, suggesting that these inoculation levels led to more efficient biomass partitioning, possibly through increased nitrogen fixation or improved nutrient availability for seed production. This is in line with findings by Giller et al. (2009), who reported that rhizobial inoculation could improve resource allocation towards seed development by enhancing nitrogen availability. Conversely, the 5g and 10g inoculant treatments showed similar harvest index values to the control, indicating that these rates may have been less effective in enhancing soybean productivity and biomass allocation. Notably, significant interactions between varieties and inoculants were observed concerning the harvest index (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This suggests that the effect of inoculation on harvest index may depend on the specific variety used, with certain varieties potentially benefiting more from higher inoculant doses. These interactions may be attributed to genetic factors influencing the responsiveness of plants to external inputs like inoculants, as highlighted by Zimmer et al., (2016), who discussed how different soybean varieties have variable responses to inoculation, depending on factors such as symbiotic compatibility and nutrient uptake efficiency. In the second season, no significant differences were observed in the varietal effect on harvest index. This lack of varietal influence could be attributed to either uniform environmental conditions during the second season or the possibility that the varieties tested responded similarly under the given management practices. Such findings reflect the complex nature of the harvest index, which can be influenced by both genetic and environmental factors, as well as the interaction between them (Pedersen \u0026amp; Sawyer, 2009). Additionally, no response to inoculation was observed concerning the harvest index. This suggests that in the second season, the inoculant treatments did not have a noticeable impact on the allocation of biomass to seeds, possibly due to less favourable conditions for nitrogen fixation or insufficient inoculant effectiveness under the seasonal conditions. This is consistent with previous studies that report varying responses of soybeans to inoculation depending on seasonal factors such as soil temperature, moisture, and nutrient availability (Hungria et al., 2000). The harvest index of soybean was influenced by inoculation in the first season, but this effect was less pronounced in the second season. Varietal differences in harvest index were not significant in either season, suggesting that the inoculation rates and environmental conditions played a more dominant role in determining the efficiency of biomass conversion into seeds. Future studies should further explore the potential interactions between inoculant rates, variety selection, and environmental factors to optimise soybean productivity and harvest index under varying conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of variety and inoculant rate on pod number, pod dry weight, grain yield, grain quality, and harvest index of Soybean in 2023 and 2024 cropping seasons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePod number (No/plant)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePod dry weight\u003c/p\u003e \u003cp\u003e(mg/plant)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGrain yield\u003c/p\u003e \u003cp\u003e(kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e100-Seed weight\u003c/p\u003e \u003cp\u003e(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eHarvest Index (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfayak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3589b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2567b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3511b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2306b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.0b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJenguma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4950a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3244a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.9b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e509.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e331.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e243.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInoculation rate (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2657d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1565e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1694d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e759c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.8b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3500c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2203d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2157c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1120b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4361b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2774c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3028c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1170b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4472ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3264b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2472b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1426b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.0a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5093a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4253a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4176a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2130a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.6a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e268.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e427.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e314.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePr(Vx I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePr(Vx I): Interaction CV: Coefficient of variation and LSD: Least significant difference. Means with the same lower letters on the same horizontal column is not significant\u003c/p\u003e \u003cp\u003eThe interaction between soybean variety and inoculant application rate significantly influenced grain yield during the 2023 cropping season, whereas no significant interaction was observed in 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In 2023, notable varietal differences in grain yield emerged across varying inoculant rates, underscoring the importance of genotype-specific responses to inoculant application. Specifically, the variety \u003cem\u003eJenguma\u003c/em\u003e demonstrated the highest yield efficiency at an inoculant rate of 12.5 g, outperforming both \u003cem\u003eFavour\u003c/em\u003e and \u003cem\u003eAfayak\u003c/em\u003e. Similarly, at an inoculant rate of 7.5 g, \u003cem\u003eJenguma\u003c/em\u003e again exhibited superior performance, followed by \u003cem\u003eAfayak\u003c/em\u003e and \u003cem\u003eFavour\u003c/em\u003e. These results suggest a potential synergistic effect between \u003cem\u003eJenguma\u003c/em\u003e and higher inoculant rates, possibly due to enhanced nitrogen fixation or root colonization efficiencies, as reported in earlier studies (e.g., Maluk et al., (2023); Thilakarathna \u0026amp; Raizada, 2017). Conversely, in 2024, the absence of significant interactive effects may be attributed to environmental variability such as soil nutrient status, rainfall distribution, or other agroecological factors, which may have masked the response to inoculant rates. Moreover, no significant differences in grain yield were observed among varieties at the lowest (5 g) and highest (12.5 g) inoculant application rates, indicating that moderate levels of inoculant (e.g., 7.5 g) may be more critical for distinguishing varietal responses under certain conditions. These findings highlight the necessity of considering both genotype and inoculant management in optimizing soybean productivity, particularly in the context of variable seasonal and environmental conditions (Peoples et al., 2009; Salvagiotti et al., 2008).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.6. Amount of N\u003csub\u003e2\u003c/sub\u003e-fıxed\u003c/h2\u003e \u003cp\u003eThere was no significant interaction (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between variety and inoculant rate in either season (Pr\u0026thinsp;=\u0026thinsp;0.975 in 2023 and 0.908 in 2024), suggesting that the response to inoculation was consistent across the varieties tested. This indicates that while both variety and inoculant rate significantly influenced N₂ fixation independently, their combined effect was not synergistic. Similar observations were made by Abaidoo et al. (2007) in West African soils, where varietal and inoculant responses were independent in most cases. The amount of nitrogen (N₂) fixed by soybeans was significantly influenced by variety during both the 2023 and 2024 growing seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the three varieties evaluated, Jenguma consistently recorded the highest levels of N₂ fixation, with values of 51.22 kg/ha in 2023 and 55.97 kg/ha in 2024, significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher than those observed for Afayak and Favour, which exhibited comparable and lower fixation values. The superiority of Jenguma could be attributed to its better nodulation ability and compatibility with the indigenous or introduced \u003cem\u003eBradyrhizobium\u003c/em\u003e strains, consistent with earlier findings by Kermah et al. (2018) and Okogun and Sanginga (2003), who reported that varietal differences in symbiotic effectiveness influence the quantity of nitrogen fixed by soybean.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe inoculant application rate significantly affected (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) the amount of N₂ fixed across both seasons. In both 2023 and 2024, the inoculant rate (10 g/kg seed) led to the greatest N₂ fixation (51.52 kg/ha and 56.41 kg/ha, respectively), significantly outperforming lower rates and the control (no inoculation). The 12.5 g/kg rate, while not significantly different from 10 g/kg, did not provide further benefits, indicating a potential plateau or even marginal inefficiency at high inoculation rates. These results support the hypothesis that effective rhizobial inoculation enhances biological nitrogen fixation (BNF), especially in soils with low native rhizobia populations or suboptimal strains (Herridge et al., 2008; Thilakarathna \u0026amp; Raizada, 2017). However, the diminishing returns beyond 10 g/kg may reflect competition among introduced and indigenous strains or limitations in nodule occupancy at higher inoculant concentrations. Although the general trends were consistent across years, slightly higher N₂ fixation was observed in 2024 across all treatments, possibly due to more favourable climatic conditions, improved soil health, or cumulative effects of inoculation. This aligns with findings by Peoples et al. (2009), who noted that environmental conditions and seasonal variability play a crucial role in determining the success of BNF in legumes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Economic analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a partial budget analysis of the inoculant application rates for the three soybean varieties. The cost of inoculation was the only variable cost among the treatments. In other words, all other expenses for soybean cultivation were consistent across all the treatments. Initially, all the treatments yielded positive net benefits, indicating that the additional benefits of any treatment exceed the extra costs incurred. For Afayak, the 7.5g/1kg seed inoculation rate produced the highest net benefits of GHS 81,875. The 10g/1kg and 12.5g/1kg seed inoculation rates were outperformed by the 7.5g/1kg seed inoculation rate, signifying that those two treatments resulted in lower net benefits alongside higher costs. The 12.5g/1kg seed inoculation rate generated the highest net benefits of GHS 96400 for the Favour variety. Similarly, the 12.5g/1kg seed inoculation rate yielded the highest net benefits of GHS 142,925 for the Jenguma variety. However, in the case of Jenguma, the 10g/1kg seed inoculation rate was outperformed by the 7.5g/1kg seed inoculation rate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA partial budget analysis of different inoculation rates on soybean varieties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal variable cost (GHS/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYield (kg/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrice (GHS/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGross benefits (GHS/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNet benefit (GHS/ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAfayak\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eFavour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eJenguma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSince all the treatments produced positive net benefits, further analysis is required to determine which treatments have the best economic value. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the marginal analysis of the treatments. All dominant treatments were dropped before performing the marginal analysis. For Afayak, both treatments (5g and 7.5g inoculant rates) had MRR above the minimum acceptable rate of return (100%), the 7.5g/1kg seed inoculation rate gave the best economic value and is therefore recommended. In the case of Favour, the 12.5g/1kg seed inoculation rate produced the highest MRR and also the largest net benefits and is therefore recommended. For Jenguma, the most economically valuable rate is the 12.5g/1kg rate because it generated the highest net benefits and also produced an MRR that is significantly higher than the minimum acceptable rate of return. This recommendation criterion is consistent with that of Soha (2014) and Biratu et al. (2022).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMarginal analysis of inoculation rates on soyean varieties.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal variable cost (GHS/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal cost (GHS/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet benefits (GHS/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal net benefits (GHS/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarginal rate of return (100%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfayak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33,250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30,400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44,400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e133,200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJenguma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35,375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e141,500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49,950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99,900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe study demonstrated that Rhizobium inoculation significantly improves soybean growth, nodulation, and yield, with responses varying by variety and inoculant rate. Among the tested rates, 10 g/kg of seed generally resulted in the best performance across the three soybean varieties, particularly in Jenguma, which exhibited the highest yield response. Afayak responded moderately, while Favour showed a relatively lower response to inoculation. These results highlight the importance of optimising inoculant rates based on varietal performance to enhance biological nitrogen fixation and soybean productivity. The economic analysis confirmed that profitability varied by variety and inoculation rate, with the most favourable rates yielding both high net benefits and acceptable MRR values. These findings underscore the significance of integrating agronomic performance with economic efficiency when selecting optimal inoculant application rates for soybean cultivation. Adoption of appropriate inoculation practices can contribute to more sustainable and efficient soybean cultivation, particularly in regions with low native Rhizobium populations and limited soil fertility. Further research is recommended to validate these findings under different agro ecological conditions and with a broader range of soybean genotypes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the Council for Scientific and Industrial Research\u0026ndash;Savanna Agricultural Research Institute (CSIR-SARI) for providing the field space and institutional support that made this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript. ALAA, BH and JU: conceptualization, and design of the experiment. ALAA, JU, BH, IAA, AH, and RA: investigation, statistical analysis and interpretation, writing review, supervision, and editing. ALAA, JU, BH, IAA, AH, and RA: read and approved of the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare that no conflict of interest exists concerning the published work. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e All authors have granted their consent for publication. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding is available. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The datasets generated and/or analysed during the current study are available on paper. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbaidoo, R. 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Part 2: Chemical and Microbiological Properties\u003c/em\u003e (pp. 595\u0026ndash;624). ASA and SSSA.\u003cstrong\u003eDOI:\u003c/strong\u003e10.2134/agronmonogr9.2. 2ed.c31\u003c/li\u003e\n \u003cli\u003eCIMMYT. \u003cem\u003eFrom Agronomic Data to Farmer Recommendations: An Economics Training Manual.\u0026nbsp;\u003c/em\u003eCompletely revised edition. Mexico D.F.(1988). . https://iaes.cgiar.org/sites/default/files/pdf/120.pdf\u003c/li\u003e\n \u003cli\u003eDakora, F., \u0026amp; Keya, S. Contribution of legume nitrogen fixation to sustainable agriculture in Sub-Saharan Africa. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5-6), 809-817(1997). https://doi.org/10.1016/S0038-0717(96)00225-8\u003c/li\u003e\n \u003cli\u003eFood and Agriculture Organization [FAO]. \u003cem\u003eGuidelines for soil description\u003c/em\u003e (4th ed.) (2006). 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T., Murdoch, A., Barros, M., Beukes, C., Vascon\u0026ccedil;elos, M., Harrison, E., Daniell, T. J., Quilliam, R. S., Iannetta, P. P., \u0026amp; James, E. K. Biological nitrogen fixation by soybean (Glycine max [L.] Merr.), a novel, high protein crop in Scotland, requires inoculation with non-native bradyrhizobia. \u003cem\u003eFrontiers in Agronomy\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 1196873(2023). https://doi.org/10.3389/fagro.2023.1196873\u003c/li\u003e\n \u003cli\u003eMathenge, C., Thuita, M., Masso, C., Gweyi-Onyango, J., \u0026amp; Vanlauwe, B. Variability of soybean response to rhizobia inoculant, vermicompost, and a legume-specific fertilizer blend in Siaya County of Kenya. \u003cem\u003eSoil and Tillage Research\u003c/em\u003e, \u003cem\u003e194\u003c/em\u003e, 104290(2019). https://doi.org/10.1016/j.still.2019.06.007\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMcLean, E.O.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSoil pH and lime requirement.\u003c/em\u003e In A.L. Page (Ed.), \u003cem\u003eMethods of Soil Analysis. Part 2: Chemical and Microbiological Properties\u003c/em\u003e (pp. 199\u0026ndash;224)\u003cstrong\u003e\u0026nbsp;(1982)\u003c/strong\u003e. ASA and SSSA. \u003cstrong\u003eDOI:\u003c/strong\u003e10.2134/agronmonogr9.2.2ed.c12\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNelson, D.W., \u0026amp; Sommers, L.E.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eTotal carbon, organic carbon, and organic matter.\u003c/em\u003e In A.L. Page (Ed.), \u003cem\u003eMethods of Soil Analysis. Part 2: Chemical and Microbiological Properties\u003c/em\u003e (pp. 539\u0026ndash;579)\u003cstrong\u003e\u0026nbsp;(1982)\u003c/strong\u003e. ASA and SSSA.\u003cstrong\u003eDOI:\u003c/strong\u003e10.2134/agronmonogr9.2.2ed.c29\u003c/li\u003e\n \u003cli\u003eOkogun, J.A. \u0026amp; Sanginga, N. Can introduced and indigenous rhizobial strains compete for nodule formation by promiscuous soybean in the moist savanna agroecological zone of Nigeria? Biology and Fertility of Soils, 38(1), 26-31(2003). https://doi.org/10.1007/s00374-003-0611-8\u003c/li\u003e\n \u003cli\u003ePedersen, Palle \u0026amp; Sawyer, John. Soybean Response to Inoculation and Nitrogen Application Following Long-Term Grass Pasture. Crop Science. 49(2009). DOI: 10.2135/cropsci2008.08.0510.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePeoples, M. B., Herridge, D. F., \u0026amp; Ladha, J. K.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eBiological nitrogen fixation: An efficient source of nitrogen for sustainable agricultural production?\u003c/em\u003e\u003cem\u003ePlant and Soil\u003c/em\u003e, 174(1\u0026ndash;2)\u003cstrong\u003e\u0026nbsp;(1995)\u003c/strong\u003e, 3\u0026ndash;28.\u003cstrong\u003eDOI:\u003c/strong\u003e10.1007/BF00032239\u003c/li\u003e\n \u003cli\u003ePeoples, M.B., Brockwell, J., Herridge, D.F. \u003cem\u003eet al.\u003c/em\u003e The contributions of nitrogen-fixing crop legumes to the productivity of agricultural systems. \u003cem\u003eSymbiosis\u003c/em\u003e\u003cstrong\u003e48\u003c/strong\u003e, 1\u0026ndash;17 (2009). https://doi.org/10.1007/BF03179980\u003c/li\u003e\n \u003cli\u003eSalvagiotti, F., Cassman, K., Specht, J., Walters, D., Weiss, A., \u0026amp; Dobermann, A. Nitrogen uptake, fixation and response to fertilizer N in soybeans: A review. \u003cem\u003eField Crops Research\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(1), 1-13(2008). https://doi.org/10.1016/j.fcr.2008.03.001\u003c/li\u003e\n \u003cli\u003eSanginga, N. \u0026amp; Okogun, J. \u0026amp; Vanlauwe, Bernard \u0026amp; Dashiell, K. The contribution of nitrogen by promiscuous soybeans to maize based cropping the moist savanna of Nigeria. Plant and Soil. 241. 223-231(2002). 10.1023/A:1016192514568.\u003c/li\u003e\n \u003cli\u003eSavala, C. E., Muananamuale, C. P., Malita, C., Wiredu, A. N., Chibeba, A. M., Elia, P., \u0026amp; Chikoye, D. Symbiotic effectiveness of Bradyrhizobium strains on soybean growth and productivity in Northern Mozambique. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1084745(2023). https://doi.org/10.3389/fsufs.2022.1084745\u003c/li\u003e\n \u003cli\u003eShah H, Sharif M, Majid A, Hayat U and Munawar A. From experimental data to farmer recommendation: an economic analysis of on-farm trial of UMMB feed for milking animals in rain-fed Pothwar, Pakistan. \u003cem\u003eLivestock Research for Rural Development. Volume 21(\u003c/em\u003e2009)\u003cem\u003e, Article #117.\u0026nbsp;\u003c/em\u003eRetrieved May 23, 2025, from http://www.lrrd.org/lrrd21/8/shah21117.htm\u003c/li\u003e\n \u003cli\u003eSoha, M. E. D. The partial budget analysis for sorghum farm in Sinai Peninsula, Egypt. \u003cem\u003eAnnals of Agricultural Sciences\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(1), 77-81(2014). https://doi.org/10.1016/j.aoas.2014.06.011\u003c/li\u003e\n \u003cli\u003eTamiru M. and Girma A. Effects of Rhizobium Inoculation and Phosphorus Fertilizer rates on Nitrogen Fixation and Nutrient Uptake of Chickpea (Cicer arietinum L.) at Goro, Bale Zone, Oromia Regional State.Greener Journal of Agricultural Sciences Vol. 9(4), pp. 436-446(2019). https://doi.org/10.15580/GJAS.2019.4.101419186.\u003c/li\u003e\n \u003cli\u003eThilakarathna, M. S., \u0026amp; Raizada, M. N. A meta-analysis of the effectiveness of diverse rhizobia inoculants on soybean traits under field conditions. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e, 177-196(2017). https://doi.org/10.1016/j.soilbio.2016.11.022\u003c/li\u003e\n \u003cli\u003eThilakarathna, M. S., \u0026amp; Raizada, M. N. A meta-analysis of the effectiveness of diverse rhizobia inoculants on soybean traits under field conditions. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e, 177-196(2017). https://doi.org/10.1016/j.soilbio.2016.11.022\u003c/li\u003e\n \u003cli\u003eThomas, G.W. Exchangeable cations. In A.L. Page (Ed.) (1982), \u003cem\u003eMethods of Soil Analysis. Part 2\u003c/em\u003e (pp. 159\u0026ndash;165). ASA and SSSA. https://doi.org/10.2134/agronmonogr9.2.2ed.c9\u003c/li\u003e\n \u003cli\u003eYadav, A. \u0026amp; Chandra, K. Mass Production and Quality Control of Microbial Inoculants. Proceedings of the Indian National Science Academy. 80. 483. (2014). DOI:10.16943/ptinsa/2014/v80i2/5.\u003c/li\u003e\n \u003cli\u003eYang, Qing \u0026amp; Lin, Gaoming \u0026amp; Lv, Huiyong \u0026amp; Wang, Cunhu \u0026amp; Yang, Yongqing \u0026amp; Liao, Hong. Environmental and genetic regulation of plant height in soybean. BMC Plant Biology. 21(2021). 10.1186/s12870-021-02836-7.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eYeboah, E., Asamoah, G., Boafo, K., \u0026amp; Abunyewa, A.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eEffect of Biochar Type and Rate of Application on Maize Yield Indices and Water Use Efficiency on an Ultisol in Ghana.\u003c/em\u003e\u003cem\u003eEnergy Procedia\u003c/em\u003e, 93, 1016\u0026ndash;1023\u003cstrong\u003e(2016)\u003c/strong\u003e. \u003cstrong\u003eDOI:\u003c/strong\u003e10.1016/j.egypro.2016.07.143\u003c/li\u003e\n \u003cli\u003eZengeni, R., Mpepereki, S., \u0026amp; Giller, K.E. Adaptation of rhizobia to acid soils in Southern Africa. \u003cem\u003eSymbiosis\u003c/em\u003e, 35(2), 159\u0026ndash;170 (2003). \u003cstrong\u003eDOI:\u003c/strong\u003e10.4314/sajest.v5i2.39826\u003c/li\u003e\n \u003cli\u003eZimmer, Stephanie \u0026amp; Messmer, Monika \u0026amp; Haase, Thorsten \u0026amp; Piepho, Hans-Peter \u0026amp; Mindermann, Anke \u0026amp; Schulz, Hannes \u0026amp; Habeku\u0026szlig;, Antje \u0026amp; Ordon, Frank \u0026amp; Wilbois, Klaus \u0026amp; He\u0026szlig;, J\u0026uuml;rgen. Effects of soybean variety and Bradyrhizobium strains on yield, protein content and biological nitrogen fixation under cool growing conditions in Germany. European Journal of Agronomy. 72. 38-46(2016). 10.1016/j.eja.2015.09.008.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rhizobial inoculant, Biological nitrogen fixation (BNF), Soybean varieties, Economic analysis, Sub-Saharan Africa agriculture","lastPublishedDoi":"10.21203/rs.3.rs-6802922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6802922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoybean productivity in sub-Saharan Africa is often constrained by low soil fertility and limited biological nitrogen fixation (BNF). This study evaluated the effects of different rhizobial inoculant rates (0, 5, 7.5, 10, and 12.5 g/kg seed) on growth, yield, and Biological Nitrogen Fixation (BNF) of three soybean varieties, Afayak, Favour, and Jenguma, using a split-plot design with variety as the main plot and inoculant rate as the subplot. Significant effects of variety and inoculant rate were observed for key agronomic traits. Compared to the uninoculated control, grain yield increased (p \u0026lt; 0.001) in 2023 by 27.3%, 78.8%, 45.9%, and 146.5%, and in 2024 by 47.6%, 54.2%, 87.9%, and 180.6% at 5, 7.5, 10, and 12.5 g/kg seed, respectively. The 10 g/kg rate resulted in the highest nitrogen fixation: 51.5 kg/ha in 2023 and 56.4 kg/ha in 2024. Jenguma responded best to inoculation, followed by Afayak and Favour. Economic analysis using partial budgeting and marginal rate of return (MRR) identified the most profitable rates as those exceeding a 100% MRR threshold. Results underscore the importance of variety-specific inoculation strategies to enhance nitrogen use efficiency and sustainable soybean production in the region.\u003c/p\u003e","manuscriptTitle":"Determination of the Appropriate Application Rate of Inoculant for Enhanced Soybean Production","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 09:44:37","doi":"10.21203/rs.3.rs-6802922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-30T10:23:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T10:47:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T13:32:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T08:36:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-19T14:13:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T19:20:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8475986893902585327629157634024393158","date":"2025-07-05T10:57:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107470199725437393579858849007354239521","date":"2025-07-05T10:12:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268974979526916577219890260678944711342","date":"2025-07-05T05:36:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314144072058118109896474198543686229107","date":"2025-07-04T15:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291178714005127537517116031323011383908","date":"2025-07-03T11:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114541921176969804615515652602939320691","date":"2025-07-03T10:40:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-03T10:04:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-03T10:01:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-11T08:43:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T12:26:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-04T12:18:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d142d02-aeeb-4887-9e01-aa20a958f249","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51090118,"name":"Biological sciences/Plant sciences"},{"id":51090119,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-11-17T16:06:20+00:00","versionOfRecord":{"articleIdentity":"rs-6802922","link":"https://doi.org/10.1038/s41598-025-23244-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-11 15:57:25","publishedOnDateReadable":"November 11th, 2025"},"versionCreatedAt":"2025-07-07 09:44:37","video":"","vorDoi":"10.1038/s41598-025-23244-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23244-z","workflowStages":[]},"version":"v1","identity":"rs-6802922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6802922","identity":"rs-6802922","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}