Pollen‐Based Screening of Soybean Genotypes for High Temperatures

Soybean [Glycine max (L.) Merr.] reproduction is sensitive to temperatures > 35°C. Two studies were conducted to determine temperature effects on soybean pollen germination (PG) and to detect genotypic differences. Pollen collected from 44 genotypes (Maturity Groups III to VI) grown outdoors was subjected to in vitro temperatures from 15 to 50°C at 5°C intervals. Genotypes differed significantly for in vitro PG percentage (mean of 81%) and tube length (mean of 437 μm). Mean cardinal temperatures (Tmin, Topt, and Tmax) were 13.2, 30.2, and 47.2°C for PG and 12.1, 36.1, and 47.0°C for pollen tube growth. Genotypes differed for leaf cell membrane thermostability (CMTS), but CMTS did not correlate with pollen parameters. Cumulative temperature response index, CTRI (unitless), of each genotype calculated as the sum of eight individual stress responses (ISRs) derived from maximum PG, maximum pollen tube length (PTL), and the maximum (Tmax), minimum (Tmin), and optimum (Topt) temperatures for PG and for PTLs was used to group genotypes for temperature tolerance. Heat‐tolerant genotype (DG 5630RR) was less sensitive to high temperature (38/30°C) compared with heat‐intermediate (PI 471938) and heat‐sensitive (Stalwart III) genotypes that had deformed pollen, with reduced apertures and collumellae heads. Hence, pollen can be used as a screening tool for heat tolerance. Most sensitive to temperature was D88‐5320 with a CTRI of 6.8, while AG 4403RR was most tolerant with a CTRI of 7.5. Elevated [CO2] did not modify reproductive parameters or CTRI. The study also revealed that heat tolerance of vegetative tissue had little or no relationship with the heat tolerance of reproductive tissue. Maturity groups lacked a specific trend for tolerance to high temperature. The identified high temperature‐tolerant genotypes and temperature‐dependent pollen response functions might be useful in soybean breeding and modeling programs, respectively. [1]

Effect of Three Resistant Soybean Genotypes on the Fecundity, Mortality, and Maturation of Soybean Aphid (Homoptera: Aphididae)

The fecundity, longevity, mortality, and maturation of the soybean aphid, Aphis glycines Matsumura (Homoptera: Aphididae), were characterized using three resistant soybean, Glycine max (L.) Merrill, genotypes (‘Dowling’, ‘Jackson’, and PI200538 ‘Sugao Zarai’) and two susceptible genotypes (‘Pana’ and ‘Loda’). Antibiosis in the resistant genotypes was demonstrated by a significant decrease in fecundity and longevity and increased mortality of A. glycines. Aphid fecundity, measured as number of offspring produced in the first 10 d by each viviparous aptera, was higher on Pana than on the resistant genotypes. Aphid longevity, the mean number of days a 1-d-old adult lived, was 7 d longer on Pana than on Dowling and Jackson. The mortality of both viviparous apterae and nymphs on resistant genotypes was significantly higher than on susceptible genotypes. A greater number of first instars survived to maturation stage (date of first reproduction) on susceptible plants than on resistant plants. None of the first instars placed on Dowling and PI200538 leaves survived to maturation. Observations of aphid behavior on leaves indicated that aphids departed from the leaves of resistant plants 8–24 h after being placed on them, whereas they remained indefinitely on leaves of susceptible cultivars and developed colonies. Reduced feeding due to ingestion of potentially toxic compounds in soybean may explain the possible mechanism of resistance to the soybean aphid. [2]

Exploring Nitrogen Limitation for Historical and Modern Soybean Genotypes

The United States (USA) and Argentina (ARG) account for over 50% of the global soybean [Glycine max (L.) Merr.] production. Soybean N demand is partially met (50–60%) by the biological nitrogen fixation (BNF) process; however, an unanswered scientific knowledge gap exists on the ability of the BNF process to fulfill soybean N demand at varying yield levels. The overall objective of this study is to explore the potential N limitation using different N strategies for historical and modern soybean genotypes. Four field experiments were conducted during 2016 and 2017 growing seasons in Kansas (USA) and Santa Fe (ARG). Twenty‐one historical and modern soybean genotypes released from the 1980s to 2010s were tested under three N treatments: (i) control, without N application (Zero‐N); (ii) 56 kg N ha−1 applied at R3‐R4 growth stages (Late‐N); and (iii) 670 kg ha−1 equally split at planting, R1, and R3–R4 growth stages (Full‐N). Historical soybean yield gains, from the 1980s to 2010s, were 29% in the USA and 21% in ARG. Following the yield trend, seed N content increased for modern genotypes in parallel to the reduction on seed protein concentration. Regarding N treatments, Full‐N produced 12% yield increase in the USA and 4% in ARG. Yield improvement was mainly related to increases in aboveground biomass, seed number (genotype effect), and to a lesser extent, to seed weight (N effect). This study suggests a potential N limitation for soybean, although there are still questions about the way in which N must be provided to the plant. [3]

Genetic Diversity of Soybean Yield Based on Cluster and Principal Component Analyses

The objective of this study was to determine analysis of variance, descriptive statistics, cluster and principle components analysis to understand their genetic diversity for ten soybean genotypes on seed yield (ten/fed.) during 2014 and 2015 seasons. Results for analysis of variance indicated highly significant genotypes and years and significant genotypes x years interaction for seed yield. The soybean genotypes Giza 111, Giza 30 and Crawford for seed yield (ton/fed.) were produced the highest mean values. The 2014 season had greater than 2015 season for seed yield (ton/fed.) in most soybean genotypes.  Standard deviation, standard error, coefficient of variation and range for seed yield (ton/fed.) has noticed considerable genetic diversity in the ten genotypes. The ten soybean genotypes based on seed yield were grouped into four clusters using cluster analysis. The first, second and third clusters comprised of two genotypes i.e., (Giza 32 and Crawford), (Giza 30 and Giza 111) and (Hybrid 129 and Hybrid 132), respectively. While, the fourth cluster consisted of four genotypes viz., Giza 21, Giza 22, Giza 35 and Clark. The second cluster had recorded highest mean seed yield, followed by the first, fourth and third clusters. The principle components analysis showed that PC1 and PC2 having eigen values highest than unity explained 82.55% of total variability among soybean genotypes attributable to seed yield and accounted with values 67.77% and 14.78%, respectively. PC1 and PC2 noticed positive association with all and most genotypes, respectively. Biplot obtained from the PC1 and PC2 almost confirmed the cluster analysis grouped. The biplot displayed positive and strong relationships between most studied genotypes. Based on the cluster and principle components analysis, the wide diversity among the studied genotypes were found, their direct use as parents in hybridization programs to maximize the use of genetic diversity and expression of heterosis and develop high yielding soybean varieties. [4]

GGE Biplot Analysis for Identification of Ideal Soybean [Glycine max L. Merrill] Test and Production Locations in Zambia

Aim: The aim of the study was to Identify an ideal soybean testing environment in Zambia. The specific objectives were to determine the adaptation of new soybean lines (IITA) in different locations and also identify the existence of soybean mega-environments in Zambia.

Study Design: A Randomised Complete Block Design with four (4) replications at each location was used to carry out the experiment. Each plot had 4 rows of 6 m long each.

Place and Duration of Study: A multi- environment was carried out in the 2013/2014 agricultural season in four locations (Golden Valley Agricultural Research Trust (GART), Kabwe, Msekera and Masumba Research stations) in agro -ecological regions 1 and 2 of Zambia.

Materials and Methods: The experimental material consisted of 15 genotypes of soybeans viz., TGX 1740-2F (G1), TGX 1830-20E (G2), TGX 1835-10E (G3), TGX 1887-65F (G4), TGX 1904-6F (G5), TGX 1987-11F (G6), TGX 1987-23F (G7), TGX 1988-9F (G8), TGX 1988-18F (G9), 1988-22F (G10), TGX 1989-60F (G11), TGX 1990-129F (G12), Magoye (G13), Safari (G14) and Lukanga (G15). Planting was done in the last week of December (2013) to the first week of January (2014) across the locations and weed control was done by hand. Fertilisation with basal dressing at a rate of 200 kg/ha compound D was done with no inoculation for all the genotypes at planting across all locations. Data collection started when the crop had reached 50% flowering and the other parameters were recorded when the crop had reached maturity. Data analysis was done using Genstat version 16 and GGE biplot.

Results: The results showed that the best soybean location for Zambia was Kabwe; which was representative and discriminating. The genotypes yield mean score was 1239 Kg/ha and TGX 1988-22F was the highest yielding genotype with 1517 kg/ha while the lowest was TGX 1835-10E with 418 kg/ha. In terms of variability in accordance to GGE biplot, Safari was the most variable while the most stable was TGX 1988-22F. Therefore, the study concluded that the best genotype for general adaptability was the variety TGX 1988-22F which was ideal across all the locations as it was high yielding and stable. Six genotypes had a yield which was below the mean performance of the genotypes across all the locations; these were Lukanga, TGX 1835-10E, TGX 1830-20E, TGX 1988-18F, TGX 1987-23F and TGX 1987-11F. Also, three mega-environments were identified, Kabwe/Msekera which had TGX 1988-22F as the winning genotype, GART had safari and Masumba had Magoye.

Conclusion: The study was able to establish that Kabwe was the best test and production location for soybean in Zambia. [5]

Reference

[1] Salem, M.A., Kakani, V.G., Koti, S. and Reddy, K.R., 2007. Pollen‐based screening of soybean genotypes for high temperatures. Crop Science, 47(1), pp.219-231.

[2] Li, Y., Hill, C.B. and Hartman, G.L., 2004. Effect of three resistant soybean genotypes on the fecundity, mortality, and maturation of soybean aphid (Homoptera: Aphididae). Journal of Economic Entomology, 97(3), pp.1106-1111.

[3] Ortez, O.A., Salvagiotti, F., Enrico, J.M., Prasad, P.V., Armstrong, P. and Ciampitti, I.A., 2018. Exploring nitrogen limitation for historical and modern soybean genotypes. Agronomy Journal, 110(5), pp.2080-2090.

[4] F. El-Hashash, E. (2016) “Genetic Diversity of Soybean Yield Based on Cluster and Principal Component Analyses”, Journal of Advances in Biology & Biotechnology, 10(3), pp. 1-9. doi: 10.9734/JABB/2016/29127.

[5] Cheelo, P., Lungu, D. and Mwala, M. (2017) “GGE Biplot Analysis for Identification of Ideal Soybean [Glycine max L. Merrill] Test and Production Locations in Zambia”, Journal of Experimental Agriculture International, 15(3), pp. 1-15. doi: 10.9734/JEAI/2017/30154.