Genetic Variability and G×E Interactions in a Diverse Set of Groundnut Accessions

Background: Knowledge of the genetic diversity for various agronomic traits and their interaction with the environment and subsequent classification of genotypes will be beneficial for identification of divergent and stable sources of agronomic traits. Methods: A set of 96 groundnut germplasm accessions belonging to four botanical groups were evaluated for three years (2017 to 2019) for pod yield and component traits using AMMI analysis and subsequently accessions were classified based Euclidean cluster analysis. Result: Among different botanical groups, Virginia genotypes matured late and possessed high SPAD chlorophyll meter readings (SCMR) and pod yield compared to Spanish types. The component traits of pod maturity like days to flowering (first and 50%) showed low heritability and high genotype × environment interaction (GEI) and significant negatively affected sound mature kernel (SMK) and shelling per centage (SP). The cumulative contribution of environment and GEI component to the total variance was the highest in the expression of SP (67%) followed by days to maturity (54%) and days to 50% flowering (52%). Euclidean distance-based cluster analysis grouped the 96 accessions into five major clusters. Cluster I had accessions with higher pod yield, whereas cluster V contained accessions with low SLA, high SCMR and moderate pod yield. High yielding as well as stable accessions identified based on AMMI stability value (ASV) are NRCG 17332, 10076, 17268, 17197, 17108, 10106, 10089 and 17165. Trait specific as well as stable accessions identified in the present study can be useful donors for groundnut breeding programme.

Line × tester Study in Bread Wheat (Triticum aestivum L.) for Important Biochemical and Morphological Traits

: Using line × tester analysis, the current research analyses parental genotypes and their combinations in normal conditions and identifies the genes influencing yield characteristics. In the present study, 15 diverse genotypes, including 10 lines, 5 testers, and 50 F1s hybrids, were evaluated for 13 morphological and 2 biochemical traits. A suitable location was taken to study the effect of 15 characters. The results exposed that ability mean squares were significant for all studied additive and non-additive components. In this direction, the general combining ability of PBW-343, DBW-39, K-402, K-1317, KRL-210, and K-68 were higher than the remaining parents. For morphological traits like yield, the top five crosses were described based on SCA effects, namely, HD-3086 × HD-3171, K-402 × K-9107, K-1317 × K-9107, HD-2967 × K-0307 and K-402 × K-68 in F1 generation. In addition, the high value of heritability was estimated for plant height (77.32%), spike length (32.26%), biological yield/plant (59.52%), and grain yield/plant (68.76%). However, the moderate values of heritability were estimated for days to maturity (22.78%) and phenol color reaction (18.00%). The higher genetic advance was not found for recorded characters; however, a moderate genetic advance was recorded for grain yield per plant (13.15%) and harvest index (11.72%). High heritability coupled with moderate genetic advance was recorded for two characters grain yield per plant and harvest index in F1 and F2 generations.

Evaluation of radish genotypes with special study on sowing geometry at agricultural research institute, Tarnab, Peshawar

The research was carried out on the “Evaluation of Radish genotypes with special study on sowing geometry at Agricultural Research Institute, Tarnab Peshawar”. The experiment was laid out in randomized complete block design having three replications. The experiment comprised with two factors, one was sowing dates(19th Oct., 4th., Nov., and 19th Nov) and the other was different genotypes (Mino, Local Red and Local White). The data were recorded on Days to germination, Germination (%), Days to edible maturity, Number of leaves plant-1, Leaf length plant-1 (cm), Root length plant-1 (cm), Root diameter plant-1 (cm), root fresh weight (g), Plot weight (kg) and yield (t ha-1). Sowing dates significantly affected all yield parameters. Sowing of radish on 19th October has more number of Leaves (17.44 leaves) plant-1, Leaf length plant(22.611 cm), root length plant-1(24.058 cm), root diameter plant-1(3.12 cm) fresh root weight (503.95g), the maximum plot weight (50.90 kg plot-1) and higher yield (62.36t ha-1) was observed whereas the crop sown on 19th November showed the minimum days to germination (8.88 days), germination percentage (96.88%) and minimum days to maturity (53 days). Among genotypes Mino take minimum days to germinate(8.22 days), maximum germination percentage (99.22), minimum days to maturity (52.778), maximum number of leaves (19.44 leaves), leaf length (23.422 cm), greater root length (24.588 cm), maximum root diameter (3.53 cm), higher root fresh weight (536.62g), higher weight per plot (52.22 kg) and maximum yield (63.54t ha-1). The results emphasized that the suitable time of sowing for Mino genotype is 19th October for Peshawar.

Estimation of Gene Action and Interaction for Some Quantitative Characters in Rice (Oryza sativa L.) by Generation Mean Analysis

The present investigation was conducted to understand the genetic action for controlling the inheritance of some quantitative characters. The experimental materials consisted of three rice varieties, i.e., Mahsuri, Bhutmuri, IR36 and F1, F2, and F3 populations of Mahsuri×Bhutmuri (Cross I) and IR36×Bhutmuri (Cross II). To conduct the generation mean analysis, the parents and their F1, F2, and F3 populations were evaluated during June to October month of Kharif 2016 and Kharif 2017. Generation mean analysis was done for eighteen quantitative characters following the five parameter model. The Analysis of Variance revealed significant differences among the five generations for all the characters studied. The results of the scaling tests and joint scaling test revealed that the Simple additive-dominance model was inadequate for days to 50% flowering, days to maturity, number of panicles plant-1, number of primary branches panicle-1, number of secondary branches panicle-1 in Cross I, while it was for plant height, number of tillers plant-1, number of panicles plant-1, number of grains panicle-1, number of filled grains panicle–1 and fertility % in Cross II. Hence, the present studies have revealed that epistasis as a basic mechanism that cannot be ignored. Thus, formulating breeding policies on only main gene effects i.e. additive and dominance could be misleading.

Agro-morphological traits-based genetic diversity assessment in Ethiopian barley (Hordeum vulgare L.) landrace collections from Bale highlands, Southeast Ethiopia

Background Cultivated barley (Hordeum vulgare L.) is one of the world’s important cereal crops. Ethiopia is claimed to be the centre of origin due to its high phenotypic diversity and flavonoid patterns. It is widely cultivated on subsistence bases and important in supporting the livelihood of local poor. However, the local landraces are currently under threat of severing genetic erosion. Hence, assessing the extents of its genetic diversity is timely in improvement and conservation. Methodology 120 representative cultivated barley landraces have been collected from Bale highlands, Ethiopia, and tested at two locations using alpha lattice design. Data were collected on 21 agro-morphometric traits and analysed using MINITAB 19, SAS 9.4 and FigTree v1.4.3. Results Most morphotypes in each of the qualitative traits considered and mean performance values in most of the quantitative traits revealed wide range of variations suggesting existence of phenotypic diversity among the landraces. Analysis of variance also showed significant variations among the landraces. All the traits, except days to maturity and plant height showed a significant variation for location and treatment-location interactions revealing the high impact of environmental conditions on the variations. Estimates of the variance components also revealed a wider range of variations in most of the traits considered with eventual medium to low genotypic (GCV), phenotypic (PCV) and genotype–environment coefficients of variation (GECV). Estimates of heritability in broad sense (H2) is low (< 40%) in all the traits except in days to maturity. Grouping of the landraces showed poor geographic areas of collection-based pattern suggesting extensive gene flow among the areas. Conclusion The landraces evaluated in the present study showed high morphological diversity. However, the effect of environment factor is pronounced and thus, multiple locations and years with large number of samples must be considered to exploit the available genetic-based variations for breeding and conservation of the crop.

Effects of Sowing Dates on Yield and Yield Components of Different Varieties of Wheat (Triticum aestivum L.) in Western Uttar Pradesh

An experiment was conducted during Rabi season (November-December) of 2018–19 to study the effect of three dates of sowing (26th November, 11th December and 25th December) on growth, yield attributes and yield of fivewheat varieties (HD-2967, HD-3086, WH-1105, PBW-343 and PBW-226) at Agricultural Research Farm of IFTM University, Moradabad (UP) India. The experiment was laid out in Factorial Randomized Design with two replications. Sowing was done at spacing of 22.5 cm in sandy loam soil. The observations were recorded on growth, flowering, maturity, yield and yield components. Analysis of variance showed the significant variations were observed for the characters viz. plant height, days to flowering, spikes per plant, days to maturity, grain yield, test weight, straw yield, biological yield and harvest index due to changing dates of sowing. However, varietal variations were recorded for plant height, days to flowering, spikes per plant, days to maturity and test weight. Wheat sown on 26th November recorded significant increase in plant height, tillers plant-1, spikes-1 plant, grain yield and straw yield over late sowing on 11th December and 25th December. There was no significant variation among varieties for yield and yield contributing traits such as grains spike-1, grain and straw yield. Significant interaction effects between dates of sowing and varieties were found for days to flowering which showed that the variety HD-2967 is as best wheat variety for early sown and PBW-226 for late sown conditions in order to obtain better returns.

Sensitivity analysis of the CROPGRO-Canola model in China: A case study for rapeseed

Increasing domestic rapeseed production is an important national goal in China. Researchers often use tools such as crop models to determine optimum management practices for new varieties to increased production. The CROPGRO-Canola model has not been used to simulate rapeseed in China. The overall goal of this work was to identify key inputs to the CROPGRO-Canola model for calibration with limited datasets in the Yangtze River basin. First, we conducted a global sensitivity analysis to identify key genetic and soil inputs that have a large effect on simulated days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index. The extended Fourier amplitude test method (EFAST) sensitivity analysis was performed for a single year at 8 locations in the Yangtze River basin (spatial analysis) and for seven years at a location in Wuhan, China (temporal analysis). The EFAST software was run for 4520 combinations of input parameters for each site and year, resulting in a sensitivity index for each input parameter. Parameters were ranked using the top-down concordance method to determine relative sensitivity. Results indicated that the model outputs of days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index were most sensitive to parameters that affect the duration of critical growth periods, such as emergence to flowering, and temperature response to these stages, as well as parameters that affect total biomass at harvest. The five model outputs were also sensitive to several soil parameters, including drained upper and lower limit (SDUL and SLLL) and drainage rate (SLDR). The sensitivity of parameters was generally spatially and temporally stable. The results of the sensitivity analysis were used to calibrate and evaluate the model for a single rapeseed experiment in Wuhan, China. The model was calibrated using two seasons and evaluated using three seasons of data. Excellent nRMSE values were obtained for days to flowering (≤1.71%), days to maturity (≤ 1.48%), yield (≤ 9.96%), and above-ground biomass (≤ 9.63%). The results of this work can be used to guide researchers on model calibration and evaluation across the Yangtze River basin in China.

Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data

In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.

Genetic Variability, Heritability, and Genotypic Correlation of Soybean Agronomic Characters

<p>Genetic parameters are important in genetic improvement and variety development. This study aimed to determine the effective characters that can be applied as selection criterion in soybean breeding using genetic parameters. About 100 soybean genotypes were grown in the Muneng Agricultural Technology Research and Assessment Installation from April to July 2020. The trial was conducted using a randomized complete block design. The results showed that high genetic variability was found on days to maturity, number of branches per plant, number of productive nodes per plant, 100-seed weight, and seed yield. The high heritability was shown by days to maturity, plant height, number of branches per plant, and 100-seed weight. All phenotypic correlations were significant, except for the correlation between seed yield and days to maturity, plant height, number of branches, and number of productive nodes. The seed yield had no genotypic correlation with all agronomic characters observed. The genotypic correlation was only significant for plant height and number of productive nodes, number of branches and number of filled pods, as well as number of productive nodes and 100-seed weight. Therefore, the improvement of seed yield can be conducted through direct selection using the seed yield parameter or indirectly using the 100-seed weight.<br /><br /></p>

Correlation and Path Coefficient Analysis of Yield and Yield Components in Toria [Brassica rapa (L)] Genotypes

This experiment was conducted at SGCARS Jagdalpur (C.G.), to assess correlation and path coefficient for 11 quantitative characters on 60 toria genotypes including one check Indira toria. The Analysis of variance revealed highly significant differences among the genotypes for all the characters considered under study. Correlation coefficient analysis revealed significant positive correlation for the traits silique per plant and seed per siliqua both at phenotypic and genotypic level with seed yield per plant. Path analysis revealed that silique per plant had highest positive direct effect towards seed yield per plant followed by siliqua length, days to maturity, harvest index, primary branches per plant, plant height, seed per siliqua and days to 50% flowering.