Sudhir Kumar

Physiological traits for high temperature stress tolerance in Brassica juncea

Temperature stress is one of the major limitations to crop productivity worldwide. Identifying suitable screening indices and quantifiable traits would facilitate the crop improvement process for high temperature tolerance. The objective of the present study was to screen and to identify temperature tolerant Brassica genotypes on the basis of physiological parameters viz. relative water content (RWC), total chlorophyll content, membrane stability index (MSI), total carotenoid content and yield. Fifteen Brassica juncea genotypes subjected to temperature stress by growing the crops at three dates of sowing i.e. 15th October (D1), 1st November (D2) and 15th November (D3); showed decrease in RWC, MSI and chlorophyll content at D2 and D3 sowings compared to the D1. Genotypes like Proagro, NDR 8801 and CS-52 showed lower decline in MSI, RWC, chlorophyll and carotenoid content in leaves and seed yield/plant, while Pusa Agrani, EJ-15 and Pusa Tarak showed comparatively greater decline in the above parameters. The results suggest that physiological parameters like MSI, RWC and chlorophyll and carotenoid contents can be used as simple indices for screening and identifying temperature stress tolerant genotypes.

Exploitation of Heterosis and Combining ability for Yield and its Contributing traits in Crosses of Two-row and Six-row barley (Hordeum vulgare L.) under Rainfed Environment.

Combining ability for grain yield and its components in barley was studied in a set of four females (Testers 6 rowed), six males (lines 2 rowed) and their resultant F1’s with standard check k-603. Twenty two crosses, excluding BCU -4925 x K-603 and BCU-4922 x Karan-16, showed significant positive economic heterosis for grain yield per plant. Analysis for variance for combining ability analysis revealed that the variance due to General Combining Ability (GCA) and Specific Combing Ability (SCA) were highly significant for most of the traits studied. The estimated value of σ2A was higher than its σ2D for plant height and thousand seed weight which indicated the predominance of additive gene action as the ratio of σ2A/σ2D was more than unity, while rest of the traits showed preponderance of non-additive gene action. The value of average degree of dominance for plant height and thousand seed weight indicated partial dominance while rest of the traits viz., chlorophyll content, spike length, awn length, number of effective tillers, number of grains per spike, harvest index and grain yield per plant showed over-dominance. Two male parents (BCU-4925 and BCU-4927) and one male parent (Lakhan) had significant and positive effect for grain yield per plant. Five crosses were found to be good specific combiners for grain yield with maximum SCA effect in cross BCU-4922 x Lakhan.

Statistical Data Analysis Tools: Software Prospects for Crop Productivity

In agriculture research, the role of statistical analysis is quite predominant. Knowledge of statistics play a pivotal role for recommendation of specific farming system to be adopted, choice of tree/crop species, crop spacing, and other components of a farming system. This leads to the problem of choosing a tool for data analysis and subsequent activities that require statistical content. Spread sheet is one the oldest and command tool used to explain the statistical peculiarities in the research data. Yet, Excel is not a full-fledged statistical tool. Besides this basic tool, other tools are also available based on different languages to perform statistical functions. However, these language-based packages need extensive learning and training. The user must know about programming before they can be put into good use. Of course, several commercial statistics packages like SAS, SPSS, SYSTAT, STATA, and MINITAB, etc. with graphical user interface (GUI) allow command line input and programming for analysis. However, such softwares are expensive and in many cases beyond the means of the individual users. But still a number of programs have statistical functionality; however before deciding upon a statistical software, one should be clear about the requirements of statistical software and it applicability with the data available.

Studies on Pest Complex and Seasonal Incidence of Shoot Fly, Atherigona Soccata (Rondani) on Maize in Western U.P.

An investigation to study the pest complex and seasonal incidence of shoot fly, Atherigona soccata (Rondani) in maize was carried out for two consecutive seasons i.e. 2013 and 2014. During the experimentation, eleven insect species were found attacking on different growth stage of maize crop, out of these stem borer, Chilo partellus (Swinhoe) and shoot fly, A. soccata (Rondani) were recorded as major pests, two insect Rhopalosiphum maidis and Pyrilla perpusilla was recorded as stray pests while other seven insects were appeared as minor insects to this region. The peak number of egg laid by shoot fly 2.27 and 2.37 egg/plant were recorded at end week of March (13th Standard Week) during the years, 2013 and 2014, respectively. The peak dead heart caused by shoot fly 26.18 and 26.40 per cent was recorded at first week of April (14th Standard Week) during the both years of experimentation i.e. 2013 and 2014, respectively. In case of shoot fly incidence among the weather factors, only morning and evening relative humidity showed positive correlation while other had negative correlation.

Phenomics: unlocking the hidden genetic variation for breaking the barriers in yield and stress tolerance

The rate of genetic gain in yield, quality, input use efficiency and adaptability of crops to biotic and abiotic stresses must be improved significantly to achieve global food and nutritional security by 2050. To achieve this goal, deciphering the physiological genetic basis and assembly of component traits through analytical breeding is necessary. The two pillars of analytical breeding are genotyping and phenotyping. Advances in genotyping technologies such as single-nucleotide polymorphism genotyping and genotyping by sequencing have made deep genotyping cheaper and quicker, while phenotyping has lagged behind and thus remains a rate limiting step. Recently, phenomics has emerged as a new way of accurately phenotyping large set of genotypes. Phenomics employ non-invasive sensors and advanced computational platforms for non-destructive and high-throughput phenotyping. The depth of component phenotypic traits and the spatio-temporal dynamic phenotypic data generated in phenomics are enormous and unparallel to the conventional phenotyping. The utility of phenomics in QTL mapping and genome-wide association studies has already been demonstrated in important food crops. Phenomics has high potential for phenome-wide association studies, genomics selection models for enhancing selection efficiency, and genetic-ecophysiological crop simulation models for prediction of genotype-phenotypes relationship, in silico phenotyping and ideotype design. With the advancement in the depth of phenome data acquisition and analyses capabilities of phenomics, phenome assisted breeding and phenomic selection is anticipated to be a reality in near future. Complementary use of conventional phenotyping and advanced phenomics is suggested to assist in fundamental discoveries and analytical crop breeding.