Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a(n) facility organization based out in New Delhi, India. It is known for research contribution in the topic(s): Population & Small area estimation. The organization has 454 authors who have published 870 publication(s) receiving 7987 citation(s).

Effect of mulching on soil and plant water status, and the growth and yield of wheat (Triticum aestivum L.) in a semi-arid environment

Abstract: Mulching is one of the important agronomic practices in conserving the soil moisture and modifying the soil physical environment. Wheat, the second most important cereal crop in India, is sensitive to soil moisture stress. Field experiments were conducted during winter seasons of 2004–2005 and 2005–2006 in a sandy loam soil to evaluate the soil and plant water status in wheat under synthetic (transparent and black polyethylene) and organic (rice husk) mulches with limited irrigation and compared with adequate irrigation with no mulch (conventional practices by the farmers). Though all the mulch treatments improved the soil moisture status, rice husk was found to be superior in maintaining optimum soil moisture condition for crop use. The residual soil moisture was also minimum, indicating effective utilization of moisture by the crop under RH. The plant water status, as evaluated by relative water content and leaf water potential were favourable under RH. Specific leaf weight, root length density and dry biomass were also greater in this treatment. Optimum soil and canopy thermal environment of wheat with limited fluctuations were observed under RH, even during dry periods. This produced comparable yield with less water use, enhancing the water use efficiency. Therefore, it may be concluded that under limited irrigation condition, RH mulching will be beneficial for wheat as it is able to maintain better soil and plant water status, leading to higher grain yield and enhanced water use efficiency.

Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.

Abstract: Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.

Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

Abstract: Salinity tolerance in rice is highly desirable to sustain production in areas rendered saline due tovarious reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS). Here, we implemented GWAS to identify loci controlling salinity tolerance in rice. A custom-designed array based on 6,000 single nucleotide polymorphisms (SNPs) in as many stress-responsive genes, distributed at an average physical interval of <100 kb on 12 rice chromosomes, was used to genotype 220 rice accessions using Infinium highthroughput assay. Genetic association was analysed with 12 different traits recorded on these accessions under field conditions at reproductive stage. We identified 20 SNPs (loci) significantly associated with Na + /K + ratio, and 44 SNPs with other traits observed under stress condition. The loci identified for various salinity indices through GWAS explained 5–18% of the phenotypic variance. The region harbouring Saltol, a major quantitative trait loci (QTLs) on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na + / K + ratio measured at reproductive stage in our study. In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7. The current association mapping panel contained mostly indica accessions that can serve as source of novel salt tolerance genes and alleles. The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.

Performance evaluation of AquaCrop model for maize crop in a semi-arid environment

Abstract: Crop growth simulation models of varying complexity have been developed for predicting the effects of soil, water and nutrients on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for a given region using the data generated from field experiments. In this study, a water-driven crop model AquaCrop, developed by FAO was calibrated and validated for maize crop under varying irrigation and nitrogen regimes. The experiment was conducted at the research farm of the Water Technology Centre, IARI, New Delhi during kharif 2009 and 2010. Calibration was done using the data of 2009 and validation with the data of 2010. Irrigation applications comprised rainfed, i.e. no irrigation (W 1 ) irrigation at 50% of field capacity (FC) (W 2 ) at 75% FC (W 3 ) and full irrigation (W 4 ). Nitrogen application levels were no nitrogen (N 1 ), 75 kg ha −1 (N 2 ) and 150 kg ha −1 (N 3 ). Model efficiency ( E ), coefficient of determination ( R 2 ), Root Mean Square error (RMSE) and Mean Absolute Error (MAE) were used to test the model performance. The model was calibrated for simulating maize grain and biomass yield for all treatment levels with the prediction error statistics 0.95 E R 2 −1 . Upon validation, the E was 0.95 and 0.98; MAE was 0.11 and 1.08 and RMSE was 0.1 and 0.75 for grain and biomass yield, respectively. The prediciton error in simulation of grain yield and biomass under all irrigation and nitrogen levels ranged from a minimum of 0.47% to 5.91% and maximum of 4.36% to 11.05%, respectively. The highest and the lowest accuracy to predict yield and biomass was obtained at W 4 N 3 and W 1 N 1 treatments, respectively. The model prediciton error in simulating the water productivity (WP) varied from 2.35% to 27.5% for different irrigation and nitrogen levels. Over all, the FAO AquaCrop model predicted maize yield with acceptable accuracy under variable irrigation and nitrogen levels.

Extensive in silico analysis of NF1 splicing defects uncovers determinants for splicing outcome upon 5′ splice‐site disruption

Abstract: We describe 94 pathogenic NF1 gene alterations in a cohort of 97 Austrian neurofibromatosis type 1 patients meeting the NIH criteria. All mutations were fully characterized at the genomic and mRNA levels. Over half of the patients carried novel mutations, and only a quarter carried recurrent minor-lesion mutations at 16 mutational warm spots. The remaining patients carried NF1 microdeletions (7%) and rare recurring mutations. Thirty-six of the mutations (38%) altered pre-mRNA splicing, and fall into five groups: exon skipping resulting from mutations at authentic splice sites (type I), cryptic exon inclusion caused by deep intronic mutations (type II), creation of de novo splice sites causing loss of exonic sequences (type III), activation of cryptic splice sites upon authentic splice-site disruption (type IV), and exonic sequence alterations causing exon skipping (type V). Extensive in silico analyses of 37 NF1 exons and surrounding intronic sequences suggested that the availability of a cryptic splice site combined with a strong natural upstream 3' splice site (3'ss)is the main determinant of cryptic splice-site activation upon 5' splice-site disruption. Furthermore, the exonic sequences downstream of exonic cryptic 5' splice sites (5'ss) resemble intronic more than exonic sequences with respect to exonic splicing enhancer and silencer density, helping to distinguish between exonic cryptic and pseudo 5'ss. This study provides valuable predictors for the splicing pathway used upon 5'ss mutation, and underscores the importance of using RNA-based techniques, together with methods to identify microdeletions and intragenic copy-number changes, for effective and reliable NF1 mutation detection.