GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data

Sorghum [Sorghum bicolor (L.) Moench] is a very important crop in the arid and semi-arid tropics of India and African subcontinent. In the process of release of new cultivars using multi-location data major emphasis is being given on the superiority of the new cultivars over the ruling cultivars, while very less importance is being given on the genotype?×?environment interaction (GEI). In the present study, performance of ten Indian hybrids over 12 locations across the rainy seasons of 2008 and 2009 was investigated using GGE biplot analysis. Location attributed higher proportion of the variation in the data (59.3–89.9%), while genotype contributed only 3.9–16.8% of total variation. Genotype?×?location interaction contributed 5.8–25.7% of total variation. We could identify superior hybrids for grain yield, fodder yield and for harvest index using biplot graphical approach effectively. Majority of the testing locations were highly correlated. ‘Which-won-where’ study partitioned the testing locations into three mega-environments: first with eight locations with SPH 1606/1609 as the winning genotypes; second mega-environment encompassed three locations with SPH 1596 as the winning genotype, and last mega-environment represented by only one location with SPH 1603 as the winning genotype. This clearly indicates that though the testing is being conducted in many locations, similar conclusions can be drawn from one or two representatives of each mega-environment. We did not observe any correlation of these mega-environments to their geographical locations. Existence of extensive crossover GEI clearly suggests that efforts are necessary to identify location-specific genotypes over multi-year and -location data for release of hybrids and varieties rather focusing on overall performance of the entries.     

Dissolution of Dominant Soil Phosphorus Fractions in Phosphorus-Responsive Soils of Bihar,India: Effects of Mycorrhiza and Fertilizer Levels

We investigated the effects of Arbuscular Mycorrhiza (AM) fungi and various phosphorus (P) levels on the distribution and availability of P in dominant soils of Bihar, India. Potassium chloride (KCl)-P (labile P), sodium hydroxide (NaOH)-P (Fe-Al-bound P), hydrochloric acid (HCl)-P (Ca-bound P), and residual P (Res-P) fractions were analyzed in the soils under maize plant. Ca-bound P was the most abundant P fraction in the alkaline soils (65% of the total P) followed by neutral soil (35% of the total P), whereas it was less abundant (<4%) in the acidic soil type. Fe-Al-bound P was found to be highest for acidic soil (65% of the total P). Soils under the inoculation with Glomus mossae and control gave the highest and lowest values (15.63 mg kg^?1 and 10.74 mg kg^?1 respectively) for the labile fraction which was similar to the organically bound residual fractions of P (200.17 mg kg^?1 and 193.66 mg kg^?1 respectively. Inoculation of the soils with AM fungi leads to the redistribution of P fractions in different soils which consequently helps in improvement of available P in soil conducive for plant uptake.     

Microbial growth and modification of corn distillers dried grains with solubles during fermentation

Corn distillers dried grains with solubles (DDGS) were fermented with bacteria or yeast. The microbial growth on three DDGS media-unmodified DDGS, enzyme-pretreated DDGS, and supplemented DDGS - was compared with standard media. Comparative growth profile indicated peaks in microbial growths at different times. The highest specific growth rates (SGR) of 0.6, 0.54 and 0.51 h⁻¹ were observed for Saccharomyces Boulardii on unmodified DDGS, yeast extract, and enzyme-modified DDGS, respectively. Pediococcus Acidilactici and Lactobacillus Plantarum had the SGR of 0.17 and 0.07 h⁻¹, respectively, on control DDGS. However, L. Plantarum on unmodified DDGS demonstrated the highest cell count (2.91 × 10¹¹ CFU/mL), followed by S. boulardii at 1.5 × 10⁹ CFU/mL on YEPD, and P. acidilactici at 1.16 × 10¹⁰ CFU/mL on MRS (de Man, Rogosa and Sharpe) media. Protein digestibility of DDGS improved slightly due to fermentation, and resulted in reduced phytic acid content for some treatments. Production of lactic acid was highest on unmodified DDGS.     

Molecular characterization and increased expression of the Nile tilapia, Oreochromis niloticus (L.), T-cell receptor beta chain in response to Streptococcus agalactiae infection

The complete cDNA sequence of the Nile tilapia T-cell receptor (TCR) β chain was cloned using 5' RACE. The full-length, 1263-bp cDNA contained a 942-bp open reading frame (ORF) encoding a 314-amino-acid protein. Sequence analyses revealed that the Nile tilapia TCR β chain contains four conserved cysteine residues involved in the formation of disulphide bridges and a conserved amino acid motif believed to be important for assembly and signalling of the TCR αβ/CD3 complex, both of which are normally found in the TCR β chain of other vertebrates. As detected using semi-quantitative and quantitative RT-PCR, the highest expression level of TCR β was detected in the thymus. Interestingly, Streptococcus agalactiae significantly induced the up-regulation of the TCR β chain, and the strongest up-regulation was detected in the brain and peripheral blood leucocytes (PBLs). In in vitro experiments, concanavalin A and Aeromonas hydrophila were found to significantly increase the expression of the TCR β chain in PBLs after 48 h (P < 0.01) and 72 h (P < 0.05), respectively. Furthermore, real-time PCR analysis showed that intraperitoneal injection (IP) of 10(7) cfu mL(-1) of S. agalactiae could induce TCR β expression that was greater than the expression observed following administration of 10(9) cfu mL(-1). The presence of the TCR β chain in fish detected in this study suggests the presence of T-cell populations that have been found in higher vertebrates, which may play a crucial functional role in the response to fish pathogens.     

Isolation, stability and bioactivity of Jatropha curcas phorbol esters

Jatropha curcas seed oil, which can be utilized for biodiesel production upon transesterification, is also rich in phorbol esters (PEs). In this study, PEs from J. curcas oil (Jatropha factors C₁ and C₂ (purified to homogeneity), Jatropha factors C₃ and (C₄ + C₅) (obtained as mixtures) and PE-rich extract (containing all the above stated Jatropha factors) were investigated. The concentrations of Jatropha PEs were expressed equivalent to Jatropha factor C₁. In the snail (Physa fontinalis) bioassay, the order of potency (EC₅₀, μg/L) was: PE-rich extract < factor C₃ mixture < factor C₂ < factor C₁ < factor (C₄ + C₅). In the Artemia salina bioassay, the order of potency (EC₅₀, mg/L) was: factor C₂ < factor C₃ mixture < factor C₁ < factor (C₄ + C₅) mixture. In addition, Jatropha PEs exhibited platelet aggregation (ED₅₀, μM, factor C₂ < factor C₃ mixture < factor C₁ < factor (C₄ + C₅) mixture. The stability of a PE-rich extract was evaluated and found to be low at room temperature but favourable in ethanol over a range of temperatures. By integrating the isolation methodology developed in this study in the Jatropha biodiesel industry, PEs could be obtained as value-added co-products.     

Genetics of resistance to Sesamia inferens infestation and its correlation with yield in maize

Sesamia inferens (Walker) causes 25.7–78.9% losses in maize production in South and South‐East Asia. The genetic basis of host plant resistance is the prerequisite for resistance breeding. Twenty‐four populations derived from S. inferens resistant × susceptible inbreds were used to study the genetic regulation of resistance to S. inferens in maize, to determine the importance of genetic effects through generation mean analysis (GMA) and to understand correlation between resistance and yield. Resistant and susceptible inbreds differed significantly in leaf and stem injury ratings (LSIR), a measure of host plant resistance. Mean LSIR range among resistant and susceptible parents was 2.15–2.55 and 7.83–8.22, respectively. The broad‐sense heritability ranged from 0.40 to 0.71, and the mean number of effective factors ranged from 1.9 to 2.6. The resistance against S. inferens was largely governed by additive × additive (i), followed by dominance (d) and additive (a) gene effects. Significant negative correlation (?0.27 to ?0.96) was observed between LSIR and yield. The findings suggest reciprocal recurrent selection for development of new inbreds with resistance followed by their involvement in hybrid development to exploit additive and non‐additive gene effects/variance.     

Modelling regional level spatial distribution of aphid (Lipaphis erysimi) growth in Indian mustard using satellite-based remote sensing data

Abstract

We developed a procedure for preparing a model for mapping spatially distributed zones of aphid pest (Lipaphis erysimi) outbreaks at a regional level. This study employed near-surface meteorological parameters derived from National Oceanic and Atmospheric Administration (NOAA) Television and Infra-Red Operational Satellites (TIROS) Operational Vertical Sounder (TOVS) data and field observations of disease infestation. The study area comprised three sites representing semi-arid and sub-humid regions of dominant Indian mustard (Brassica juncea L.)-growing regions of India. A model based on TOVS-derived cumulative surface air temperature and minimum specific humidity (SpH) was developed to estimate the date of ‘aphid onset’ (first appearance), date of peak infestation and location of severity with respect to aphid population density. Aphid population growth rate during the linear growth phase between aphid onset to peak was computed using SpH-weighted temperature and dates of sowing of the crop (crop age). Sowing dates of mustard crop, of northwest India, were obtained from spectral growth profiles derived from time series remote sensing (RS) products of the SPOT-4 VEGETATION sensor. Estimated dates of peak aphid infestation and peak population showed a strong match with the observed data. The location of peak aphid population density was depicted in each spatial grid of 25×25 km² for parts of northwest India. The simulated aphid population build-up and date of peak population density was validated with observed data for an unknown site in the Sriganganager district, Rajasthan state, India. Comparison of predicted dates of attaining peak aphid population with observations showed a deviation of ±7 days. After validation, the regional level model was applied over a large area of a mustard-growing region for varying dates of sowing, surface air temperature and specific humidity, to show the spatial distribution of aphid growing severity zones (population density) and to predict dates of severe aphid infestation (peak population) at each grid level in the region.