Phylogeography and Population Structure Analysis Reveal Diversity by Gene Flow and Mutation in Ustilago segetum (Pers.) Roussel tritici Causing Loose Smut of Wheat

Ustilago segetum (Pers.) Roussel tritici (UST) causes loose smut of wheat account for considerable grain yield losses globally. For effective management, knowledge of its genetic variability and population structure is a prerequisite. In this study, UST isolates sampled from four different wheat growing zones of India were analyzed using the second largest subunit of the RNA polymerase II (RPB2) and a set of sixteen neutral simple sequence repeats (SSRs) markers. Among the 112 UST isolates genotyped, 98 haplotypes were identified. All the isolates were categorized into two groups (K = 2), each consisting of isolates from different sampling sites, on the basis of unweighted paired-grouping method with arithmetic averages (UPGMA) and the Bayesian analysis of population structure. The positive and significant index of association (IA = 1.169) and standardized index of association (rBarD = 0.075) indicate population is of non-random mating type. Analysis of molecular variance showed that the highest variance component is among isolates (91%), with significantly low genetic differentiation variation among regions (8%) (Fst = 0.012). Recombination (Rm = 0) was not detected. The results showed that UST isolates have a clonal genetic structure with limited genetic differentiation and human arbitrated gene flow and mutations are the prime evolutionary processes determining its genetic structure. These findings will be helpful in devising management strategy especially for selection and breeding of resistant wheat cultivars

Molecular diagnostic assay for pre-harvest detection of Tilletia indica infection in wheat plants

The current study describes a new diagnostic method for the rapid and accurate detection of Tilletia indica, the pathogen accountable for causing Karnal bunt (KB) disease in wheat. This method uses quantitative real-time polymerase chain reaction (qPCR) and a primer set derived from glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of T. indica to identify the presence of the pathogen. The qPCR assay using this primer set was found highly sensitive, with a limit of detection (LOD) value of 4 pg of T. indica DNA. This level of sensitivity allows for the detection of the pathogen even in cases of different growth stages of wheat, where no visible symptoms of infection on the wheat plants can be seen by naked eyes. The study also validated the qPCR assay on ten different wheat cultivars. Overall, this study presents a valuable molecular tool for rapid, specific and sensitive detection of KB fungus in wheat host. This method has practical applications in disease management, screening of wheat genotypes against KB and can aid in the development of strategies to mitigate the impact of Karnal bunt disease on wheat production

Conservation agriculture based crop management practices impact diversity and population dynamics of the insect-pests and their natural enemies in agroecosystems

Human efforts to grow abundant food through the persistent use of resource-intensive farming practices have resulted in declining soil health, and deterioration of ecosystem functions and services. Conservation agriculture (CA) has emerged as a practice to minimize the impacts of conventional resource-exhaustive and energy-intensive agriculture. Minimum soil disturbance, permanent soil cover, and diversification are the key components of CA. Tillage through conventional practices on the other hand has detrimental effects on the soil and environment as it requires deep inversion of soil with instruments such as mouldboard plow, disc plow etc. leaving very less organic matter in soil after establishment of crop. Even though, CA advocates many benefits over conventional agriculture in terms of soil and water conservation, the consequent changes in moisture and temperature regimes due to reduced tillage and surface cover would likely going to influence the biological activity, including insect pests and their natural enemies which dwell within these agroecosystems. The changed crop conditions under CA may favor particular insect communities and their ecological niches. The adoption of such practices may lead to decrease in insect pests with major activity on the crop canopy. However, the activity of the insect pests that spend their maximum life span at the soil surface or beneath the soil surface may increase. Recent insect-pest outbreaks in North-Western India and imbalances reported in Indo-Gangetic Plains point to the need for a better understanding of the inter-relationships between tillage intensity, residue retention, and insect pest population dynamics. The current review analyzes the existing state of knowledge of these dynamics and presents the scenarios that may emerge as CA get more acceptance. This review will help to develop countermeasures to improve performance and ecosystem services of Conservation agriculture (CA) based cropping systems

Genetic diversity and population structure analyses in barley (Hordeum vulgare) against corn-leaf aphid, Rhopalosiphum maidis (Fitch)

Corn-leaf aphid (CLA), Rhopalosiphum maidis (Fitch) (Hemiptera: Aphididae) is a serious economic pest of barley worldwide. Breeding for aphid resistance in plants is considered a cost-effective and environmentally safe approach for aphid control, compared to the use of chemical pesticides. One of the challenges in breeding for aphid resistance is the identification of resistant plant genotypes, which can be achieved through the use of molecular markers. In the present study, a set of aphid specific 10 simple-sequence repeats (SSR) markers were used to investigate genetic diversity and population structure analyses in 109 barley genotypes against R. maidis. Three statistical methods viz., multivariate hierarchical clustering based on Jaccard’s similarity coefficient, principal coordinate analysis (PCoA) and the Bayesian approach were utilized to classify the 109 barley genotypes. The analyses revealed four subpopulations i.e., SubPop1, SubPop2, SubPop3 and SubPop4 with 19, 46, 20 and 24 genotypes including admixtures, respectively and represented 17.43%, 42.2%, 18.34% and 22.01% genotypes of the total population size, respectively. The studied SSR markers produced 67 polymorphic bands, with an average of 6.7 and ranging from 3 to 12 bands. Heterozygosity (H) was found to be highest in SSR28 (0.64) and lowest in SSR27 (0.89). The observed genetic diversity index varied from 0.10 to 0.34 (with an average of 0.19). Major allele frequency varied from 74.08% to 94.80%. On an average, 87.52% of the 109 barley genotypes shared a common major allele at any locus. Based on the Aphid Infestation Index (AII), only 2 genotypes were found to be resistant against CLA. SubPop2 also had lowest mean aphid population (28.83), widest genetic similarity index (0.60-1.00) and highest genetic similarity coefficient (0.82), which highlighted its potential for inclusion in future CLA resistance breeding programs

Examples of risk tools for pests in Peanut (Arachis hypogaea) developed for five countries using Microsoft Excel

Suppressing pest populations below economically-damaging levels is an important element of sustainable peanut (Arachis hypogaea L.) production. Peanut farmers and their advisors often approach pest management with similar goals regardless of where they are located. Anticipating pest outbreaks using field history and monitoring pest populations are fundamental to protecting yield and financial investment. Microsoft Excel was used to develop individual risk indices for pests, a composite assessment of risk, and costs of risk mitigation practices for peanut in Argentina, Ghana, India, Malawi, and North Carolina (NC) in the United States (US). Depending on pests and resources available to manage pests, risk tools vary considerably, especially in the context of other crops that are grown in sequence with peanut, cultivars, and chemical inputs. In Argentina, India, and the US where more tools (e.g., mechanization and pesticides) are available, risk indices for a wide array of economically important pests were developed with the assumption that reducing risk to those pests likely will impact peanut yield in a positive manner. In Ghana and Malawi where fewer management tools are available, risks to yield and aflatoxin contamination are presented without risk indices for individual pests. The Microsoft Excel platform can be updated as new and additional information on effectiveness of management practices becomes apparent. Tools can be developed using this platform that are appropriate for their geography, environment, cropping systems, and pest complexes and management inputs that are available. In this article we present examples for the risk tool for each country.Fil: Jordan, David L.. University of Georgia; Estados Unidos. North Carolina State University; Estados UnidosFil: Buol, Greg S.. North Carolina State University; Estados UnidosFil: Brandenburg, Rick L.. North Carolina State University; Estados UnidosFil: Reisig, Dominic. North Carolina State University; Estados UnidosFil: Nboyine, Jerry. Council for Scientific and Industrial Research Savanna Agricultural Research Institute; GhanaFil: Abudulai, Mumuni. Council for Scientific and Industrial Research Savanna Agricultural Research Institute; GhanaFil: Oteng Frimpong, Richard. Council for Scientific and Industrial Research Savanna Agricultural Research Institute; GhanaFil: Mochiah, Moses Brandford. Council for Scientific and Industrial Research Crops Research Institute; GhanaFil: Asibuo, James Y.. Council for Scientific and Industrial Research Crops Research Institute; GhanaFil: Arthur, Stephen. Council for Scientific and Industrial Research Crops Research Institute; GhanaFil: Akromah, Richard. Kwame Nkrumah University Of Science And Technology; GhanaFil: Mhango, Wezi. Lilongwe University Of Agriculture And Natural Resources; MalauiFil: Chintu, Justus. Chitedze Agricultural Research Service, Lilongwe; MalauiFil: Morichetti, Sergio. Aceitera General Deheza; ArgentinaFil: Paredes, Juan Andres. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Instituto de Patología Vegetal; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; ArgentinaFil: Monguillot, Joaquín Humberto. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Instituto de Patología Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Singh Jadon, Kuldeep. Central Arid Zone Research Institute, Jodhpur; IndiaFil: Shew, Barbara B.. North Carolina State University; Estados UnidosFil: Jasrotia, Poonam. Indian Institute Of Wheat And Barley Research, Karnal; IndiaFil: Thirumalaisamy, P. P.. India Council of Agricultural Research, National Bureau of Plant Genetic Resources; IndiaFil: Harish, G.. Directorate Of Groundnut Research, Junagadh; IndiaFil: Holajjer, Prasanna. National Bureau Of Plant Genetic Resources, New Delhi; IndiaFil: Maheshala, Nataraja. Directorate Of Groundnut Research, Junagadh; IndiaFil: MacDonald, Greg. University of Florida; Estados UnidosFil: Hoisington, David. University of Georgia; Estados UnidosFil: Rhoads, James. University of Georgia; Estados Unido

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