Indian Agricultural Statistics Research Institute

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

Genome-wide identification and functional prediction of salt- stress related long non-coding RNAs (lncRNAs) in chickpea (Cicer arietinum L.)

Abstract: LncRNAs (long noncoding RNAs) are 200 bp length crucial RNA molecules, lacking coding potential and having important roles in regulating gene expression, particularly in response to abiotic stresses. In this study, we identified salt stress-induced lncRNAs in chickpea roots and predicted their intricate regulatory roles. A total of 3452 novel lncRNAs were identified to be distributed across all 08 chickpea chromosomes. On comparing salt-tolerant (ICCV 10, JG 11) and salt-sensitive cultivars (DCP 92–3, Pusa 256), 4446 differentially expressed lncRNAs were detected under various salt treatments. We predicted 3373 lncRNAs to be regulating their target genes in cis regulating manner and 80 unique lncRNAs were observed as interacting with 136 different miRNAs, as eTMs (endogenous target mimic) targets of miRNAs and implicated them in the regulatory network of salt stress response. Functional analysis of these lncRNA revealed their association in targeting salt stress response-related genes like potassium transporter, transporter family genes, serine/threonine-protein kinase, aquaporins like TIP1-2, PIP2-5 and transcription factors like, AP2, NAC, bZIP, ERF, MYB and WRKY. Furthermore, about 614 lncRNA-SSRs (simple sequence repeats) were identified as a new generation of molecular markers with higher efficiency and specificity in chickpea. Overall, these findings will pave the understanding of comprehensive functional role of potential lncRNAs, which can help in providing insight into the molecular mechanism of salt tolerance in chickpea.

Performance of Subsurface Drip Irrigation System with Line Source of Water Application in Okra Field

Abstract: Subsurface drip irrigation (SDI) is application of water below soil surface through the emitters, with discharge rates generally in the same range as surface drip irrigation. Performance of SDI with respect to uniformity of water and chemical application in a crop is an essential component for efficient management of SDI. A study was conducted to evaluate performance of SDI used for irrigating okra crop during 2003 and 2004. The parameters evaluated were pressure -discharge relationship of emitters, and uniformity of water application including discharge variation, coefficient of variation, uniformity coefficient, statistical uniformity, and distribution uniformity. The emitter discharge exponent was found to be 0.56 for inbuilt labyrinth type emitters used in SDI The maximum values of standard deviation, variation and coefficient of variation of emitter flow rates were found to be 0.057, 0.08 and 0.025, respectively. Analysis revealed that statistical uniformity, distribution uniformity and uniformity coefficient were more than 90% during two years of study. Based on these parameters, performance of SDI was found excellent during study period.

Statistical Approach for Biologically Relevant Gene Selection from High-Throughput Gene Expression Data.

Abstract: Selection of biologically relevant genes from high-dimensional expression data is a key research problem in gene expression genomics. Most of the available gene selection methods are either based on relevancy or redundancy measure, which are usually adjudged through post selection classification accuracy. Through these methods the ranking of genes was conducted on a single high-dimensional expression data, which led to the selection of spuriously associated and redundant genes. Hence, we developed a statistical approach through combining a support vector machine with Maximum Relevance and Minimum Redundancy under a sound statistical setup for the selection of biologically relevant genes. Here, the genes were selected through statistical significance values and computed using a nonparametric test statistic under a bootstrap-based subject sampling model. Further, a systematic and rigorous evaluation of the proposed approach with nine existing competitive methods was carried on six different real crop gene expression datasets. This performance analysis was carried out under three comparison settings, i.e., subject classification, biological relevant criteria based on quantitative trait loci and gene ontology. Our analytical results showed that the proposed approach selects genes which are more biologically relevant as compared to the existing methods. Moreover, the proposed approach was also found to be better with respect to the competitive existing methods. The proposed statistical approach provides a framework for combining filter and wrapper methods of gene selection.

District-Level Estimates of Poverty Incidence for the State of West Bengal in India: Application of Small Area Estimation Technique Combining NSSO Survey and Census Data

Abstract: Despite having long term efforts, poverty is an important and persistent social issue in India. Existing data based on socio-economic surveys produce state and nationally representative poverty estimates but cannot be used directly to generate reliable disaggregate or local level estimates. The state and national level estimates often mask the variations at the local level which in turn restricts the effective implementation of policies related to poverty alleviation locally within and between administrative units. This paper uses the Household Consumer Expenditure Survey data of NSSO and link with the Population Census data to produce the reliable district-level estimates of poverty incidence in the rural areas of West Bengal in India. In particular, small area estimation (SAE) method is explored to generate reliable district-level poverty estimates. The results clearly indicate that the district-level estimates generated by model-based SAE method are precise and representative. A map showing how poverty incidence varies by district across the State of West Bengal is also produced. The estimates generated from this research are useful for meeting the data requirements for policy research and strategic planning by different international organizations and by Departments and Ministries in the Government of India.

Crop-Suitability Analysis Using the Analytic Hierarchy Process and Geospatial Techniques for Cereal Production in North India

Abstract: Identification of cropland suitability is obligatory to adapting to the increased food needs driven by population expansion, environmental contamination, and climate change. Given this, the present study was conducted to assess cereal crops’ suitability in India’s Haryana state by integrating Analytic Hierarchy Process (AHP) and geographic information system (GIS) technique. Multiple factors were considered for this study, such as rainfall, temperature, soil texture, drainage density, pH, organic carbon, electrical conductivity, and slope. The AHP technique was utilized to decide the weights of each individual parameter using experts’ opinions. The crop-suitability model was developed using the model builder module in ArcGIS 10.8, and each input parameter was reclassified as per the optimum crop-growth requirement and overlaid utilizing the reclassify tool and weighted overlay analysis. The crop suitability classes were estimated as highly suitable, S1 (6%); moderately suitable, S2 (71%); and marginally suitable, S3 (23%) for the calculated arable land for the wheat crop. Similarly, the crop suitability class of rice S2 (28%); S3 (72%), for sorghum S1 (28%); S2 (71%); S3 (1%), for maize S2 (85%); S3 (15%) and for pearl millet S1 (60%); S2 (40%) were also estimated. The study has observed that, as per the soil physico-chemical characteristics and climate, the area is moderately fertile. Therefore, agricultural production can be improved by cultivating the crop in highly and moderately suitable zones. Diversification of marginally suitable regions for crops other than that for which it is not suitable can be taken up, which will also ensure the income security of marginal farmers.