Central Agricultural University

About: Central Agricultural University is a education organization based out in Imphal, Manipur, India. It is known for research contribution in the topics: Population & Agriculture. The organization has 1116 authors who have published 1157 publications receiving 9217 citations.

Resequencing of 429 chickpea accessions from 45 countries provides insights into genome diversity, domestication and agronomic traits.

Abstract: We report a map of 4.97 million single-nucleotide polymorphisms of the chickpea from whole-genome resequencing of 429 lines sampled from 45 countries. We identified 122 candidate regions with 204 genes under selection during chickpea breeding. Our data suggest the Eastern Mediterranean as the primary center of origin and migration route of chickpea from the Mediterranean/Fertile Crescent to Central Asia, and probably in parallel from Central Asia to East Africa (Ethiopia) and South Asia (India). Genome-wide association studies identified 262 markers and several candidate genes for 13 traits. Our study establishes a foundation for large-scale characterization of germplasm and population genomics, and a resource for trait dissection, accelerating genetic gains in future chickpea breeding.

Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.

Abstract: Background: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. Objective: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. Methods: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. Results: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. Conclusions: The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

Effect of salt stress on cucumber: Na+–K+ ratio, osmolyte concentration, phenols and chlorophyll content

Abstract: A pot experiment with 17 diverse genotypes of cucumber with four levels of salt stress viz., 0, 2, 4 and 6 dS m−1 was carried out during 2006. ANOVA revealed significant differences amongst genotypes and genotype × salt stress interaction indicating the genetic variability and differential response of the genotypes to different salt stress levels. The salt stress adversely affected the biochemical parameters; effects were severe under 4 dS m−1. No genotype could survive at 6 dS m−1. Sodium content, Na+–K+ ratio, proline, reducing sugars, phenol and yield reduction (%) increased significantly as the salt stress increased. Potassium, chlorophyll, membrane stability index and fruit yield decreased significantly under salt stress in all genotypes. However, the genotypes CRC-8, CHC-2 and G-338 showed lower accumulation of sodium, lesser depletion of potassium, lower Na+–K+ ratio and higher accumulation of proline, reducing sugars, phenols, better membrane stability and lower yield reduction (%) under salt stress, while CH-20 and DC-1 were sensitive to salt stress. Thus, a combination of traits such as higher membrane stability, lower Na+–K+ ratio, higher osmotic concentration and selective uptake of useful ions and prevention of over accumulation of toxic ions contribute to salt stress tolerance in cucumber. These traits would be useful selection criteria during salt stress breeding in cucumber.