Soybean (Glycine max L. Merr) is one of the world's most important cultivated crops grown for use as food and feed.  It   was  known  to  be  grown  on  over  120  million hectares around the world in 2017 (FAOSTAT, 2019). The world soybean production increased by 3.79% annually   from  2000  to  2020  and  reached  an average  annual production of 361 million tons in 2020. Soybean is a good source of protein for human and animal nutrition and as a biofuel feedstock (Delele, 2021). It consists of more than 36% proteins, 30% carbohydrates, and excellent amounts of dietary fiber, vitamins, and minerals (Chuwang et al., 2018). The soybean grain accounts for 57% of the world's oilseed production, which makes it the most important crop for producing edible oil (USDA, 2001; SOYSTATS, 2011). Soybean oil has many uses including its use in the industry as fuel additive (Schori and Charles, 2003). The introduction of soybean in West Africa was an initiative of Agricultural Research but its cultivation has remained underdeveloped despite the efforts and the progress made by the research teams (Picasso et al., 1984). In Burkina Faso, soybeans were chosen in 2006 by the government as a strategic crop with an estimated annual production of 51.708 tons (DGPER, 2020). Soybean is used essentially in food and feed and contributes to strengthening the resilience of poor households to food insecurity (Pimentel et al., 2005). Indeed, soybean cultivation in Burkina Faso is less rigorous than corn and cotton; however, it requires well-drained soils, optimal production temperatures between 25 and 33°C and seasonal rainfall ranging from 500 to 800 mm (Sia, 2016). Several regions of the country with high potential for soybean production have been identified: the regions of Hauts Bassins, Boucle de Mouhoun, Cascades, Centre East, East and Southeast (Bila et al., 2009). These regions constitute a real potentiality for the promotion of soybeans for achieving food security. Despite the strengths of this crop as a cultural precedent, the soybean sector is still being structured in Burkina Faso. The first seed imports were American varieties and those developed by IITA. Since the inception of soybean improvement in 1974, IITA has played a leading role in developing tropical high-yielding soybean varieties and improved agronomic technologies (Khojely et al., 2018). This has contributed to the promotion of high-yielding soybean varieties, suitable for processing and marketing needs and suitable for smallholder farmers in sub-Saharan Africa (Siamabele, 2021). Despite its great potential, soybean production is influenced by climatic and agronomic factors, pests and nutrient availability in the soil (Aduloju et al., 2009). Abiotic stresses including drought, flooding, lodging, frost and nutrient deficiency in the soil make up the major abiotic constraints (Mannan, 2014). Other constraints (biotic in nature) including stresses caused by diverse pathogens (bacteria, fungi and virus), pests (nematodes) and weeds form the second major production constrains of soybean. All these multiple constraints have detrimental effects on soybean growth and development and can result in significant yield losses in soybean (Kim et al., 2016). Although, new soybean accessions tolerant to pod dehiscence and resistant to diseases such as soybean rust, a bacterial pustule, have been developed by the International Institute of Tropical Agriculture (IITA), Burkina Faso have very few improved varieties. Currently, the most used and widespread are G196 and G197. Increasing soybean production to meet the required quantities can best be achieved through an increase in yield per unit area, which can partly be achieved by the cultivation of high-yielding improved varieties. Consequently, the introduction and careful selection of newly improved soybean genotypes in breeding programs may provide the necessary genetic variability for the development and the adaptation of cultivars to biotic and abiotic stresses (Boni, 2015). This study aims to assess yield performance and adaptability of 21 newly developed soybean genotypes in Burkina Faso.

Plant and experimental site

The planting material consisted of 21 newly developed soybean genotypes obtained from IITA (Abuja, Nigeria) and three soybean varieties (G175, G196 and G197) already used in Burkina Faso breeding program (Table 1). The field study was conducted during the 2020 raining season at the Farako-Bâ Research Station in Burkina Faso. The genotypes were planted on 20 June 2020 at a rate of 1 seed per 5 cm per planting (20 plants/m) with a sowing density of 50×5 cm flat. Weed control was done manually on the 15 and 45^th day after sowing.

Experimental design

The experimental design is an alpha lattice of 3 replicates separated from each other by 2 m. Each replication consists of 24 entries distributed in 4 blocks, each consisting of 6 varieties. The blocks are separated from each other by 80 cm. The elementary plot is represented by 4 rows of 4 m with 50 cm of row spacing, that is, a gross area of 0.5 m×4 m×4 (8 m²). The useful plot consists of the two central lines, that is, an area of 4 m². The plant harvest was done when 95% pods reach maturity.

Data collection

The agronomical and morphological growth parameters (Table 2); 50% flowering, 50 and 95% pod maturity, number of nodes, plant height, pod clearance, number of branches, number of pods per plant, nodulation (number of nodules per plant, nodule diameter, nodule distribution on root system), shattering, hundred seed weight, plant yield, potential yield and grain yield were recorded. The soybean rust, one of the major diseases and bacteria pustule, frogeye disease and soybean mosaic virus have been evaluated at R3 and R6 stages.

Data analysis

Analysis of variance (ANOVA) was performed using XLSTAT version 2016 on all quantitative variables. All treatment means were compared using the Least Significant Difference (LSD) at 5% level of significance. A correlation analysis between agromorphological parameters was also carried out. In order to structure the soybean lines  from IITA and Burkina Faso, the principal component analysis and Cluster analysis was performed with R software using average linkage.

Variability for days to flowering and days to maturity

Analysis of variance between soybean genotypes was highly significant (? < 0.001) for days to flowering (50% flowering) and days to maturity (Table 3).

Flowering days ranged from 45 (G175) to 58 days (TGX1993-4FN, TGX2011-6F, TGX2022-4E and TGX2023-3E). From the 24 genotypes, 15 showed values below the average for the days to flowering (51 days), including two controls (G175 and G197). These findings suggested different genetic backgrounds among the 24 soybean genotypes studied for days to flowering. Baig et al. (2018) also claimed that different plant genotypes have significant impact on days to flowering.

Maturity days ranged from 78 to 106 days and 99 to 119 days for 50 and 95% maturity, respectively. For the 50% maturity, 11 genotypes presented values below the average (98 days). The control genotype G175 with 78 days for  days  to  50%  maturity  confirmed  its  precocity (group 00). It is followed by the genotypes TGX2008-4F (89 days) and TGX2025-14E (91 days). Concerning days to 95% maturity the genotypes TGX1989-19F and TGX2008-4F were the first to reach their physiological maturity around 99 days. They were followed by genotypes TGX2010-11F and TGX2025-14F (101 days). These results agreed with Zorome (2017) and Singh and Shrestha (2019) who found similar variations.

Variability in soybean plant nodulation

Analysis of variance conducted for the soybean genotypes showed significant difference for the number of nodules (? < 0.001). However, no significant differences were observed for nodule diameter and score. The nodules number ranged from 1 to 33 with an average of 11 nodules per plant.

Among the 24 soybean genotypes, 8 nodulated more efficiently (above average) and the best were genotypes TGX2023-3E (with 33 nodules), TGX1987-14F (26 nodules) and TGX2011-6F (18 nodules). The numbers of nodules per plant in this study are lower than the nodulation norm for soybean, which is in the order of 30 to   50   nodules/plant   (IRAD/CNSPG,   2006).  The   low number of nodules could be due to the soil texture, nutrient content and low photosynthesis which are important factors in nodule mass or both. The nodules of all the genotypes had interesting diameters that varied from 4 to 7 mm. These nodules belong to the large nodule category (3.5 to 5.0 mm) (Djekoun and Planchon, 1991). The large nodules would indicate high nitrogenase activity in the lines and constitute a real water reserve and would be less affected by drought (Thu et al., 1986).

Variability in plant morphological traits

The results of the analysis of variance showed highly significant differences (p<0.001) among the genotypes for pod clearance and the number of nodes. Genotypes also exhibited significant difference in the mean plant height. Plant height ranged from 60 (G196) to 109 cm (TGX2017-5E) and pod clearance ranged from 9 (G196) to 24 cm (TGX2011-6F). Of the introduced lines, 12 had a mean plant height above the trial average, reflecting their good adaptability to the environment. The mean plant height range observed in the current study agrees satisfactorily with those observed by Njoroge et al. (2015). The genotypes also presented very interesting pod clearance with an average of 15 cm. Among the 24 genotypes evaluated, 19 including control G175 and G197, showed pod clearance greater than those recommended (11 to 12 cm). These genotypes have the advantage of better resistance to Sclerotinia rot and facilitate good, mechanized harvesting (TERRE-INOVIA, 2019). Low pod clearance stages may expose them to soil splash from rainfall and thus make them more susceptible to white mold (IRAD/CNSPG, 2006). Greater pod clearance reduces harvest losses especially since the lowest pods usually have the most and largest seeds (Thai et al., 2019).

Yields and yield components performance

Analysis of variance showed significant difference for the number of pods per plant and 100 seed weight. These variations agreed with Zorome (2017). They might be attributed to genetic variability that characterizes soybean genotypes. The number of pods per plant ranged from 29 (TGX2008-4F) to 88 plants (TGX2019-1E) and hundred seed weight ranged from 10 (TGX1989-19F, TGX2023-3E) to 16 g (TGX2009-16F). Hundred seed weight is essential character to optimize the yield and also play important role in adaptation of a cultivar and influence to seed vigor (Morrison and Xue, 2007). There was no statistically significant difference among the genotypes for number of branches and yields. However, grain yield was found very interesting and ranged from 666.67 to 2083.33 kg/ha with an average of 1248 kg/ha. Genotype TGX2017-5E (2083.33 kg/ha) had the highest mean grain yield   followed   by   genotypes  TGX1993-4FN  (1666.67  kg/ha), TGX2017-6E (1666.67 kg/ha) and TGX2016-4E (1583.33 kg/ha). The early control genotype G175 gave a yield of 1416.67 kg/ha with the potential of producing a yield of 6 t/ha. Among the introduced genotypes, 11 presented mean grain yield higher than the average of the trial (1250 kg/ha).

Pod shattering

Significant difference (p<0.001) was recorded for pod shattering among the 24 soybean genotypes. Pod shattering ranged from 0 to 50% with an average of 12% (Table 3). In this study, 15 genotypes were resistant to shattering (0-10% shattered pod) and 9 genotypes were intermediate (11-70%) according to Bailey et al. (1997) and Mohammed et al. (2014). From the 21 introduced genotypes, 71.43% were resistant to pod shattering. Pod shattering is a serious constraint that causes 34 to 99% seed losses (Katembo, 2018). In tropical areas such as Burkina Faso, delayed harvesting contributes to considerable yield loss, especially on varieties which are susceptible to pod shattering (Krisnawati and Adie, 2017). Pod shattering is affected by different environ-mental factors such as dry climate, low humidity, high temperature, and rapid temperature changes (Agrawal et al., 2002). Zhang and Boahen (2010) reported that the rate of shattering was faster on non-irrigated soybean than irrigated soybean. Pods position (lower, middle and upper) in soybean plants influences pod-shattering (Krisnawati and Adie, 2017). Pods position at the lower part was more susceptible to pod shattering than those at middle and upper part.

Reaction of soybean genotypes to diseases

Observations were carried out on soybean rust, SMV, bacteria pustule and frogeye leaf spot diseases. Symptoms of rust and soybean mosaic virus were observed in the trial. Almost all the plants of the trial were completely free of bacteria pustule and frogeye leaf spot disease symptoms. Significant difference (p<0.001) was observed for rust (R3 and R6 stages) diseases among the 24 soybean genotypes. However, no significant differences were observed for SMV. Figure 1 shows soybean diseases and scores distribution, the impact of rust disease (R6 stage) and SMV to grains yield. Most genotypes of the trial did not show symptoms of rust (R3 and R6 stage) with a score of 1 (0% disease infection). Only 3 genotypes had a score of 2 (25% of disease symptoms). The results of this research showed that rust disease and SMV with the observed scores did not affect grain yield. In previous researches, rust appears to be a damaging crop disease causing severe yield losses in tropical and subtropical soybean-producing countries (Kolmer et al., 2009). SMV have been reported and can cause  yield  losses  ranging  from  8  to  94%  (Gui et al., 2021).

Correlation test

The Pearson correlation matrix at the 5% threshold revealed several correlations between the variables studied (Table 4). Strong positive and significant (P≤0.0001) correlations were observed between grain yield and plant height (r = 0.612), grain yield and pod clearance (0.470). Grain yield was  positive  and  significantly   correlated  to  the number of pods (r= 0.255) and the number of nodules (r= 0.245). This means that a higher value of those agro-morphological characters will result in a higher grain yield. Hundred seed weight was positive and significantly correlated with number  of  pods (r = 0.245),  number of branches (r= 0.314) and 95% day to maturity(r= 0.269). This result contradicts that of Li et al. (2020) who showed that the hundred seed weight was negatively correlated with  numbers  of  pods  and branches. Pod number per plant was found to have a strong, positive and significant (P≤0.0001) correlation to number of branches (r= 0.609), number of nodes  (r= 0.538)  and  plant  height (r= 0.512). The present research demonstrated that it was beneficial to cultivate high yielding soybeans by choosing higher plant height, pod clearance, more pods, mores  nodules,  mores  nodes  of the main stem and more branches. The associations reveal that hundred seed weight was negatively correlated to pod shattering (r= -0.235). The results of this research followed previous studies conducted by Ngalamu et al. (2013) and Li et al. (2020).

Cluster analysis in soybean genotypes

The cluster analysis resulting from the hierarchical ascending classification (HAC) grouped the genotypes of soybean in three classes (Figure 2). Cluster 1 consisted of 9 genotypes, the most yielded, the highest plant height, the highest pod clearance, the highest pod number, the highest hundred seeds weight, the highest number of nodules,  the   highest   number    of    nodes   and branches (Table 5). Cluster 2 consisted of 5 genotypes and was characterized by the lower yields. Cluster 3 included 10 genotypes with the earliest days to flowering and days to maturity, and acceptable grain yield (1181 kg/ha). The genotypes from this group had the best shattering (9%).

PCA analysis

Principal component analysis (PCA) is usually used in plant breeding to reduce the variables and genotypes of groups to determine the best parameters which explain the variability. Figures 3 to 5 show the results of the principal component analysis performed with the different parameters, their   contribution     to    the    genetic   variability, variables and genotypes distribution in PCA Biplot. Analysis distinguished two axes that explain 51.6% of the total genetic variability within the soybean genotypes. Yield parameters, plant yield and potential yield are positively correlated to axis 1 (32.5% of the total variability) and contributed to 9% of the total variability (51.6%). Grain yield highly correlated to axis 1 and contributed between 7.5 and 9% to the variability. Yield components such as pod number per plant were highly correlated to axis 2 (19.1% of the total variability) with a contribution to variability of between 5 and 6%. Hundred seeds weight, shattering and nodules diameter contributed very little to the variability, that is, 2% each of the total variability (51.6%). Morphological parameters such as plant height and the number of nodes correlated  to  axis 2 contributed around 9 and 8% of the total variability, respectively.

Soybean genotypes evaluated in this study presented  an interesting variability and adaptability ability for most of the traits. The introduced genotypes TGX2017-5E, TGX1993-4FN, TGX2017-6E and TGX2016-4E were the most high-yielding (1500-2000 kg/ha). The association study revealed that soybean yield has a positive correlation  with  plant  height,  pod  clearance, number of pods, number of nodules, number of nodes and number of branches. Those  yield  components  could be used as selection criteria in the breeding program to obtain high-yielding    soybean    cultivars.   The    soybean    disease assessment in this study identified symptoms of rust disease and VMS. Most genotypes showed symptoms with a score of 1. All genotypes were asymptomless for bacterial pustule and frogeye leaf spot disease. Further research could confirm the adaptability of these soybean genotypes in different agro-ecological zones of Burkina Faso and facilitate their adoption by farmers.

The authors have not declared any conflict of interests.

The authors are very grateful to the International Institute of Tropical Agriculture (IITA) for providing the seeds used for this research. They thank Dr Abush Abebe for the collaboration.