The degree of genetic variation of the 36 soybean genotypes was assessed under low (0 kg ha-1) and high P (100 kg ha-1) conditions. Some articles have already been published or accepted for publication and are listed in the dissertation.
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- Introduction
- Taxonomy and distribution
- Adaptation and production
- Genetic variability of soybean under low and high P conditions
- Interaction and response of genotypes to levels of soil P
- Combining ability for low P tolerance and Optimum P responsiveness
- Research gaps identified in the review of literatures
The author also reported that the plateau of soybean productivity curve has not been reached, indicating the potential for further improvement in the productivity of the crop. Determining the extent of genetic variability of soybean genotypes under low and high P condition is an important step in understanding the genetic potential of the.
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- Abstract
- Introduction
- Materials and Methods
- Description of the study site
- Data collection and statistical analysis techniques
- Socio-economic characteristics of respondent farmers
- Results
- Farmers’ preferences of the different uses of soybean
- Different kinds of meals prepared from soybean alone and in
- Drawbacks of consuming soybean
- Marketing of soybean
- Timing of soybean sale and farmers judgment on the timing of sale
- Demand of soybean as compared to other crops and farmers coping
- Discussion
- Conclusions and research implications
The proportion of farmers surveyed who prepared 'injera', the common traditional Ethiopian flat bread, by mixing soybeans with other grains was relatively very small. The highest percentage of farmers surveyed rated the demand for soybeans as poor compared to other crops (Table 2.4-3).
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- Abstract
- Introduction
- Materials and Methods
- Study sites
- Data collection and statistical analysis techniques
- Results
- Socio-economic characteristics of the respondent farmers
- Major types of soils in the survey area
- Change in soil fertility over time
- Indicators used by farmers to detect change in soil fertility
- Farmers’ perceptions on the major causes of soil fertility decline
- Farmers’ evaluation of farmer’s cooperatives service in supplying
- Discussions
- Conclusion and implications of research
Most respondents (66.1%) believed that long-term use of fertilizers reduces soil fertility. Some farmers, on the other hand, responded that the fertility of the soil on their farm is increasing. Most respondents believed that long-term use of fertilizers reduces soil fertility.
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Abstract
Introduction
The availability of genetic variability in a gene pool is a prerequisite for a breeding program (Aditya et al., 2011) and is an important factor in obtaining the expected genetic gain from selection. The existence of rich genetic variability in soybean that offers enormous potential to improve several economically important traits was reported by Verma et al. 2011) emphasized the importance of genetic parameters, such as Genotypic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation ( PCV), heritability (H2) and genetic gain (GA) in estimating genetic variability. They also reported the highest H2 (91%) for traits such as days to 50% flowering, number of primary branches per plant and weight of 100 seeds; while two parameters, namely pod number and dry matter weight per plant, combined high H2 and GA estimates (Aditya et al., 2011).
Materials and methods
- Germplasm
- Experimental sites
- Experimental design and management
- Laboratory analysis
- Statistical analysis
The traits studied include: days to 50% flowering, days to maturity, weight of fresh biomass ie. the average weight of aboveground biomass of five freshly harvested plants at the late pod filling stage, pod number i.e. the average of the total number of pods counted on each of the five randomly selected plants, pod length which was the average length of five randomly selected pods from five plants, number of seeds per pod ie. the average number of seeds from five randomly selected pods from five different plants, plant height, 100-seed weight, and grain yield. The combined analysis for genotype X location (GXL) interaction was analyzed using SAS software, as a two factor experiment for each of the P levels. Determination of the number of clusters was performed using pseudo F, cubic clustering criterion (CCC) and Pseudo T2 plots analyzed by SAS Version 9.2 software (SAS Institute, 2008) based on the procedure described by SAS.
Results
- Genotype X environment interaction
- Performance of genotypes
- Genotypic and phenotypic variances
- Genetic parameters
- Cluster analysis
This is reported in Table 4.4-2 for the traits days to maturity, aboveground biomass weight, root biomass weight, root length, number of pods, plant height and hundred seed weight. The variation explained by each PC is listed in Tables 4.4-4 and 4.4-5 for low and high P conditions, respectively. At 100 kg ha-1 P, group one contained 16 genotypes (Table 4.4-11) and was characterized by the highest group average for most important productivity.
Discussion
- Genetic differences and implications for selection
- Clusters
This finding is in line with. the finding of Waluyo et al. 2004) who reported that the nodulation character of soybean is dependent on P availability in the soil. The genotypes showed highly significant differences for most of the traits studied in all the locations. Genotype Hardee-1 was the best general combiner for most of the quantitative traits under both low and high P conditions.
Conclusion and implications
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Abstract
This study was conducted to evaluate the response of soybean genotypes to phosphorus levels for some important root and nodulation traits. None of the root and nodulation parameters showed a significant difference for GXP and XGXP site interactions in the between-site analysis, while genotypes showed a significant difference for all parameters except nodule number and total nodule weight. Both mean separation and cluster analysis showed that genotype PR-142 (26) was the best genotype for most of the traits, while AGS-3-1, SCS-1, AGS 234 and H3 performed well for most of the traits studied. .
Introduction
The nodulation properties of soybeans depend on the availability of soil nutrients, such as Ca and P fertilization (Waluyo et al., 2004) and the types of soybean genotypes (Moharram et al., 1994). Olufajo (1990) also reported that P fertilization enhanced nodulation of promiscuous soybeans for Bradyrhizobium japonicum inoculation. 1994) reported improved nodulation and nitrogen fixation of soybeans as a result of P application. Therefore, the objectives of this study were to assess the response of soybean genotypes to different levels of P, locations, and their interaction for the nodulation and root traits of soybeans in acidic soils of western Ethiopia.
Materials and methods
- Experimental sites
- Experimental design and treatments
- Laboratory analysis
- Data collection
- Statistical analysis
Nodulation and nitrogen fixation are also very important traits for improving soil fertility by providing plant-usable nitrogen to the soybean crop itself and crops that follow it. According to Waluyo et al. 2004), P is important in the initiation of nodule formation and the development and function of the nodules produced. Also, root characteristics, such as root fresh weight, which is the weight of the roots; root volume, which is the amount of water displaced from the measuring cylinder by the root, and taproot length, which is the length of the central taproot, were measured on randomly selected five plants from each treatment.
Result
- Response of genotypes and Genotype X P interactions in each
- Response of genotypes and Genotype X P interactions over locations
- Performance of genotypes in each location
- Performance of genotypes over P levels and locations
- Clustering of the genotypes
- Pearson correlation of root and nodulation traits with yield and related
The highest total nodule weight at Assossa was produced by genotype H3 at 200 kg ha-1 applied P. Total nodule weight produced was generally low for the control and 100 kg ha-1 P level. There were highly significant and positive associations between grain yield and plant fresh weight, root fresh weight, taproot length and total nodule weight at 100 kg ha-1 applied P (Table 5.4-10).
Discussions
Significant and positive correlations of grain yield and 100 seed weight with rooting traits, viz. root volume and root length and plant fresh weight at low P (Table 5.4-9) indicate the importance of root properties for low P tolerance. A highly significant and negative correlation between nodulation characteristics, i.e. number of nodules, total nodule weight and effective nodule weight with 100-seed weight at low P (Table 5.4-9) indicate the competitiveness of these traits. On the other hand, yield is insignificantly related to all these nodulation traits at low P (Table 5.4-9), indicating that nodulation has little effect on yield.
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- Abstract
- Introduction
- Materials and methods
- Experimental sites, designs, and management
- Laboratory analysis
- Statistical analysis
- Results
- Response of genotypes to different P regimes in each location
- Response of genotypes to different P regimes over locations
- Performance of genotypes in each location
- AMMI analysis
- Performance of genotypes over-locations
- Discussions
- Conclusions and implications
The IPCA1 versus P levels main effects accounting for 31% of the total treatment sum of squares (Table 6.4-5) showed that zero and 200 kg ha-1. The absence of a significant interaction between genotype and P level for all traits in Jimma, and most traits in Mettu and Assossa, indicates that there is very limited change in the relative performance of genotypes with changing P level. Thus, the relative performance of genotypes in each of the three P levels for grain yield was determined from Assossa data.
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Abstract
Introduction
Genetic analysis of soybean under different P regimes provides very useful information that helps design a sustainable breeding strategy. Literature on soybean combining ability under low and high P conditions was not found, indicating that this study may be the first of its kind. Therefore, this study was designed to evaluate the combining ability of soybean lines for quantitative traits under low and high P conditions in acidic soils.
Materials and methods
- Germplasm
- Experimental design and management
- Statistical analysis
Soil samples were collected from the top layer (0-20 cm) of the fields before the experiments were planted. A test of homogeneity of error variance of locations was performed before performing the combined analysis. Where = population mean, gi = effect of the general combining ability of the ith parent, gj = effect of the general combining ability of the jth parent, sij = effect of the specific combining ability of the cross between the ith and the jth parent, so that sij = sji, (gL)ijk.
Results
- Combining ability of selected agronomic traits under low P conditions
- General and specific combining abilities of parents and crosses under
- Combining ability variation under high P conditions
- General combining abilities of parents under high P conditions
- Mean performance of parents and crosses under low P conditions
The GCA effect of Hardee-1 was highly significant and positive for number of seeds per pods, pod density, plant height, pod number and significant for grain yield under low P condition (Table 7.4-2). There was a highly significant difference between the SCA effects of 100 seed weight and significant differences for the number of seeds per plant. pod and pod length. Under high P conditions, Hardee-1 showed positive and highly significant GCA effects for pod set, plant height, grain yield and pod number and significant for 100-seed weight (Table 7.4-2).
Discussion
In addition, the relative contribution of GCA was higher than SCA for some quantitative traits. Comparison of performance of semi-allelic methods in genetic analysis of bread wheat. The relative contribution of SCA was higher than GCA for most of the studied traits under both P conditions, indicating that non-additive gene actions were more important than additive.
Conclusion and implications for future research
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- Introduction and objectives of the study
- Brief summary of the research findings
- Farmers perception on the different kinds of soils, the fertility status of
- Evaluation of soybean for performance under varying P regimes
- Assessment of the genetic variability of soybean under low and high P
- Combining ability of the soybean genotypes under low and high P
- Implications of research findings for breeding soybean for low P tolerance
- Challenges encountered and recommendations
A significant GXP interaction was revealed for grain yield at only one location (Assosa); while genotypes showed a highly significant difference for most traits at all locations. The fact that farmers recognized the role of soybean in crop rotation and improving soil fertility as the main reason for soybean production could be considered as an important motive for increasing the scale of soybean, and it was learned that much needs to be done to improve the value chain of the crop both on local as well as the central market. A significant difference between the genotypes at each of the locations and in the combined analysis over locations and P levels indicates that the genotypes showed significant variation allowing the identification of genotypes with better performance.
