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.

Genetic Correlation Between Fe and Zn Biofortification and Yield Components in a Common Bean (Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) is the most important legume for direct human consumption worldwide. It is a rich and relatively inexpensive source of proteins and micronutrients, especially iron and zinc. Bean is a target for biofortification to develop new cultivars with high Fe/Zn levels that help to ameliorate malnutrition mainly in developing countries. A strong negative phenotypic correlation between Fe/Zn concentration and yield is usually reported, posing a significant challenge for breeders. The objective of this study was to investigate the genetic relationship between Fe/Zn. We used Quantitative Trait Loci (QTLs) mapping and Genome-Wide Association Studies (GWAS) analysis in three bi-parental populations that included biofortified parents, identifying genomic regions associated with yield and micromineral accumulation. Significant negative correlations were observed between agronomic traits (pod harvest index, PHI; pod number, PdN; seed number, SdN; 100 seed weight, 100SdW; and seed per pod, Sd/Pd) and micronutrient concentration traits (SdFe and SdZn), especially between pod harvest index (PHI) and SdFe and SdZn. PHI presented a higher correlation with SdN than PdN. Seventy-nine QTLs were identified for the three populations: 14 for SdFe, 12 for SdZn, 13 for PHI, 11 for SdN, 14 for PdN, 6 for 100SdW, and 9 for Sd/Pd. Twenty-three hotspot regions were identified in which several QTLs were co-located, of which 13 hotpots displayed QTL of opposite effect for yield components and Fe/Zn accumulation. In contrast, eight QTLs for SdFe and six QTLs for SdZn were observed that segregated independently of QTL of yield components. The selection of these QTLs will enable enhanced levels of Fe/Zn and will not affect the yield performance of new cultivars focused on biofortification.

Character Association and Path Coefficient Analysis in Indian Mustard (Brassica juncea L.)

Background: Correlation analysis informs us about the relative importance of the breeding traits and quite useful for plant breeders to formulate their breeding and selection strategies. Path coefficient analysis splits the correlation coefficient into the measures of indirect and direct effect. It identifies the indirect and direct influence of different autonomous genotype on a dependent trait. Present study aimed to determine major seed contributing components affecting seed yield and also to know the relationship among these components. Methods: Present investigation was carried out on 147 advanced breeding lines to know the correlations and path coefficient for 12 yield and yield contributing traits. The material was evaluated in augmented block design with 07 blocks, each block having 21 lines. A set of three check varieties were repeated in each block. Observations were recorded on number of primary branches/plant, number of secondary branches/plant, plant height (cm), silique/plant, fruiting zone length (cm), main shoot length (cm), number of seeds/siliquae, siliquae length (cm), biological yield/plant (g), seed yield/plant (g), harvest index and test weight. Result: The seed yield per plant showed positive and significant correlation with primary branches per plant (0.273), secondary branches per plant (0.280), siliquae per plant (0.627), biological yield per plant (0.744), harvest index (0.188), test weight (0.212) and oil content (0.225). Biological yield per plant (0.5747) had maximum direct effect on seed yield per plant followed by siliquae per plant (0.2438), harvest index (0.127), oil content (0.118) and test weight. These characters have positive and significant association with seed yield per plant. The study thus indicated that biological yield per plant, siliquae per plant, harvest index and test weight are the important characters which should be considered in selection programme in Indian mustard.

Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan

One of the most important traits that plant breeders aim to improve is grain yield which is a highly quantitative trait controlled by various agro-morphological traits. Twelve morphological traits such as Germination Percentage, Days to Spike Emergence, Plant Height, Spike Length, Awn Length, Tillers/Plant, Leaf Angle, Seeds/Spike, Plant Thickness, 1000-Grain Weight, Harvest Index and Days to Maturity have been considered as independent factors. Correlation, regression, and principal component analysis (PCA) are used to identify the different durum wheat traits, which significantly contribute to the yield. The necessary assumptions required for applying regression modeling have been tested and all the assumptions are satisfied by the observed data. The outliers are detected in the observations of fixed traits and Grain Yield. Some observations are detected as outliers but the outlying observations did not show any influence on the regression fit. For selecting a parsimonious regression model for durum wheat, best subset regression, and stepwise regression techniques have been applied. The best subset regression analysis revealed that Germination Percentage, Tillers/Plant, and Seeds/Spike have a marked increasing effect whereas Plant thickness has a negative effect on durum wheat yield. While stepwise regression analysis identified that the traits, Germination Percentage, Tillers/Plant, and Seeds/Spike significantly contribute to increasing the durum wheat yield. The simple correlation coefficient specified the significant positive correlation of Grain Yield with Germination Percentage, Number of Tillers/Plant, Seeds/Spike, and Harvest Index. These results of correlation analysis directed the importance of morphological characters and their significant positive impact on Grain Yield. The results of PCA showed that most variation (70%) among data set can be explained by the first five components. It also identified that Seeds/Spike; 1000-Grain Weight and Harvest Index have a higher influence in contributing to the durum wheat yield. Based on the results it is recommended that these important parameters might be considered and focused in future durum wheat breeding programs to develop high yield varieties.

Role of beneficial microbes with nitrogen and phosphorous levels on canola productivity

A research was conducted to evaluate the impact of various nitrogen and phosphorus levels along with beneficial microbes to enhance canola productivity. The research was carried out at Agronomy Research Farm, The University of Agriculture Peshawar in winter 2016-2017. The experiment was conducted in randomized complete block factorial design. The study was comprised of three factors including nitrogen (60, 120 and 180 kg ha-1), phosphorous (70, 100 and 130 kg ha-1) and beneficial microbes (with and without BM). A control treatment with no N, P and BM was also kept for comparison. Application of beneficial microbes significantly increased pods plant, seed pod, seed filling duration, 1000 seed weight, biological yield and seed yield as compared to control plots. Nitrogen applied at the rate of 180 kg ha-1 increased pods plant-1, seed pod, seed filling duration, seed weight, biological yield and seed yield. Maximum pods plant-1, seed pod, early seed filling, heavier seed weight, biological yield, seed yield, and harvest index were observed in plots treated with 130 kg.ha-1 phosphorous. As comparison, the combine treated plots have more pods plant-1, seeds pod-1, seed filling duration, heaviest seeds, biological yield, seed yield and harvest index as compared to control plots. It is concluded that application of beneficial microbes with N and P at the rate of 180 kg ha-1 and 130 kg ha-1, respectively, increased yield and its attributes for canola.

Assessment of deficit irrigation impact on agronomic parameters and water use efficiency of six chickpea (Cicer Arietinum L.) cultivars under Mediterranean semi-arid climate

Six kabuli chickpea genotypes (Cicer Arietinum L.) were evaluated under three water levelss at the open field during February -June 2018. This study was conducted to evaluate the chickpea water stress, on soil water dynamic, agromorphological traits, and water use efficiency to estimate variability levels between varieties and to identify the varieties of chickpea adaptable on semi-arid bioclimatic stage. For this purpose, a trial was conducted at the Higher Agronomic Institute of Chott Mariem (Tunisia). There is no effect of the treatment on the height, biological yield, and branching number. The seeds weigh, PCG, seed yield, harvest index, and water use efficiency relative to seed have the highest value in T1 (100% of ETc) when water use efficiency relative to biological yield, number of pods and of seeds recorded the highest values in T3 (50% of ETc). Univariate analysis showed highly significant differences between genotypes for many traits. Principal Component Analysis was performed for all traits and allowed to define two axes. The first one explains 49.30% of the variability of the total trait and was formed by genotypes ‘Beja’, ‘Nayer’ and’ ‘Rebha’. Genotypes forming this axe are closely related to each other according to their common morphological characters like height (r=0.88), biological yield (r=0.93), bringing the number (r=0.53), seed yield (r=0.81), WUE relative to seed (r=0.75), harvest index (r=0.65) and WUE relative to biological yield (r=0.94). The second clustered genotypes ‘Bochra’ and ‘Nour’. This second axe (27.99%) is represented by pods number (r=0.87), seed number (r=0.87) and PCG (r=0.78).

Production and Profitability of Hybrid Rice Is Influenced by Different Nutrient Management Practices

The government of Nepal has recommended blanket fertilizer application for rice cultivation, which results in lower nutrient use efficiency (NUE) particularly under rainfed conditions. With the aim of finding an appropriate nutrient management practices concerning rice production and profitability, a field experiment was conducted during rainy season of 2017 and 2018 at Kavrepalanchowk and Dang district of Nepal. Altogether, five treatments comprising various nutrient management practices viz. Nutrient Expert Model (NE), use of Leaf Color Chart (LCC), Government Recommended Fertilizer Dose (GON), Farm Yard Manure (FYM), and Farmers’ Field Practice (FFP), were laid out in RCBD with four replications in farmers’ fields. The analysis of variance showed significant difference between treatments for test weight and grain yield in Kavrepalanchowk whereas all traits except number of effective tillers were significant in Dang. The significantly higher grain yield and harvest index were obtained in NE, followed by LCC; and the overall straw yield was highest in LCC, followed by NE in both the locations. Also, yield gap analysis suggested the NE had 44.44% and 23.97% increase in yield as compared to FPP in Kavrepalanchowk and Dang, respectively. The combined analysis with Best Linear Unbiased Estimator revealed the interaction of nutrient management and location significantly effects the straw yield and harvest index across both the locations. The estimated mean straw yield and harvest index were 10.93 t/ha and 34.98%, respectively. Both correlation study and biplot of principal component analysis signaled grain yield had positive correlation with all other traits. Furthermore, the net revenue was maximum for NE, followed by LCC in both the locations. The benefit: cost ratio was highest for NE which was 1.55 in Kavrepalanchowk and 2.61 in Dang. On the basis of these findings, NE and LCC can be effectively used as nutrient management practice by the farmers to obtain maximum production and profitability in Rice.

IMPACT OF DIFFERENT LEVELS OF BULK DENSITIES COMBINATION ON YIELD AND YIELD COMPONENT OF WHEAT (TRITICUM AESTIVUM L.)

An experiment was conducted on the impact of different levels of bulk densities combination on yield and yield components of wheat. Three bulk density levels 1.00-1.30 (A) g cm-3, 1.30-1.60 (B) g cm-3 and 1.60-1.90 (C) g cm-3 with three different depths (0-15, 16-30 and 31-45 cm) were used in the experiment using silt loam soil (Pedocals). The total treatments were 27 in the experiment with three replications. The experimental design was completely randomized design (CRD). The treatment means were checked at the confidence level of 95% of probability. The soil 0-45 cm deep was shifted from the field (no-till) to the net house and was act as control. The results showed that the number of tillers plant-1, number of leaves plant-1 and thousand grain yield were non-significantly affected by all the treatments while number of seed emerged, root length, dry root weight, plant height, grain yield , biological yield and harvest index were significantly influenced by various treatments. The highest number of seed germinated (8.00) was found in the treatment BBB (1.30-1.60 g cm-3 in 0-15 cm, 15-30 cm, 30-45 cm soil depth), BAB (1.30-1.60 g cm-3 in 0-15 cm, 1.00-1.30 g cm-3 in 15-30 cm and 1.30-1.60 g cm-3 in 30-45 cm soil depth) and BCB (1.30-1.60 g cm-3 in 0-15 cm, 1.60-1.90 g cm-3 in 15-30 cm and 1.30-1.60 g cm-3 in 30-45 cm) while the lowest number of seed emerged (3.6) was found in AAA (1.00-1.30 g cm-3 in 0-15 cm, 15-30 cm and 30-45 cm soil depth), ACA (1.00-1.30 g cm-3 in 0-15 cm, 1.60-1.90 g cm-3 in 15-30 cm and 1.00-1.30 g cm-3 in 30-45 cm soil depth) and CAC (1.60-1.90 g cm -3 in 0-15 cm, 1.00-1.30 g cm-3 in 15-30 cm and 1.60-1.90 g cm-3 in 30-45 cm soil depth). The highest root length (13.12 cm) was found in the treatment AAA while the lowest root length (10.05 cm) was found in CAC. The highest dry root weight (5.67g) was found in the treatment ABA while the lowest dry root weight (4.79g) was found in control treatment. The highest plant height (42.67 cm) was found in the treatment of BBA while the lowest plant height (29.67 cm) was found in control. The highest biological yield (12.1 metric ton ha-1) was found in the treatment ABA while the lowest biological yield (6.8 metric ton ha-1) was found in control. The highest grain yield (4.4 metric ton ha-1) was found by ABB while the lowest (2.7 metric ton ha-1) was found in the control. The highest harvest index (39.79%) was found in the control while the lowest harvest index (34.54%) was found in CCA. The experiment showed that increasing bulk density above 1.60 g cm-3 resulted in decreasing the yield and yield component of the winter wheat. The number of seed emerged, root length, dry root weight, plant height, grain yield, biological yield and harvest index of wheat were significantly affected by soil bulk density in various depths.