Post Flowering Stalk Rot Complex of Maize - Present Status and Future Prospects

Post flowering stalk rot complex is one of the most serious, destructive and widespread group of diseases in maize and yield losses range from 10 to 42% and can be as high as 100% in some areas. PFSR nature is often complex as a number of fungi (like Fusarium verticillioides cause Fusarium stalk rot, Macrophomina phaseolina cause charcoal rot, Harpophora maydis cause late wilt) are involved in causation of the diseases. To combat this problem, identification of quantitative trait loci for resistance to PFSR would facilitate the development of disease resistant maize hybrids. Moreover, various chemical and biological control methods have been developed but ma¬jor emphasis is on development of maize cultivars with genetic resistance to for environment friendly control of the Post flowering stalk rot complex. The current paper reviews the information on distribution, impact of the disease, symptoms, epidemiology, disease cycle; genetics of resistance and integrated disease management approaches has been enumerated to understand the present status of knowledge about PFSR complex and will try to focus on the future perspectives available to improve PFSR management

IMAGE-BASED IDENTIFICATION OF MLB DISEASE OF MAIZE

Not AvailableIn recent years, deep learning techniques have become very popular in the field of image recognition and classification. Image-based diagnosis of diseases in crops using deep learning techniques has become trendy in the current scientific community. In this study, a deep convolutional neural network (CNN) model has been developed to identify the images of maydis leaf bight (MLB) (Cochliobolus heterostrophus) disease of maize (Zea mays L.) crop. A total of 1547 digital images of maize leaves (596 healthy and 951 infected with maydis leaf blight disease) have been collected from different agricultural farms using hand-held camera and smartphones. The images have been collected from the experimental plots of BCKV, West Bengal and ICAR-IARI, New Delhi during 2018–19. The architectural framework of popular state-of-the network ‘GoogleNet’ has been used to build the deep CNN model. The developed model has been successfully trained, validated and tested on the above-mentioned dataset. The trained model has achieved an overall accuracy of 99.14% on the separate test dataset.Not Availabl

Multi-environment field testing to identify stable sources of resistance to charcoal rot (Macrophomina phaseolina) disease in tropical maize germplasm

The charcoal rot caused by Macrophomina phaseolina is the devastating component of post flowering stalk rot (PFSR) complex which may cause 25 to 32 % yield loss in maize. Therefore for the first time, the study was carried out with multi-environments screening of 137 inbreds at three and 48 maize hybrids at six environments under artificially created epiphytotics at hot-spot locations to identify stable sources of charcoal rot resistance in Indian maize germplasm. Analysis of variance revealed strong effect of genotype by environment interaction on disease response and therefore indicated its complex nature. The mean disease score was ranging from 2.37 to 7.20 in inbreds, and 3.63 to 6.08 in hybrids. Additive main effects and multiplicative Interactions (AMMI) analysis could identifed, DQL1020, DML339, DML1, DQL1019, CM117-1-1 in inbreds and A-7501, CMH08-287, CMH08-292, BIO-562, and CMH08-350 in hybrids as stable sources of charcoal rot resistance. Each testing site viz., Ludhiana, Hyderabad and Delhi was identified as a separate test environment for screening against charcoal rot disease in India. In this study, AMMI model offers a good tool to assess the stability of genotypes and GGE biplot found an efficient tool to identify the mega environments in multi-environment testing. The identified sources of resistance in inbreds can be used in resistant breeding and hybrids can be recommended for cultivation in charcoal rot disease prone area

Relationships between soil water retention and soil composition

Relationships between soil water retention and physical composition have been examined in this study, using a database of two independent sets of soils from two regions in New Zealand: Canterbury in the South Island (Set 1) and Waikato in the North Island (Set 2). Five soils (Wakanui, Templeton, Temuka, Timpendean and Cookson) were selected from the Canterbury region, the first three being representative of the dominant texture types (i.e. silt and sandy loams) in the Canterbury Plains, and the Cookson and Timpendean soils being chosen to widen the range of textures to include soils higher in clay. Also, to investigate the possible effects of cultivation history, which affects both structure and organic matter level, four Wakanui soil sites representing four different management treatments, ranging from continuous arable to permanent grassland, were included. The second set, from the Waikato region, was selected from results of a sampling programme completed by staff of the NZ Soil Bureau (Joe and Watt, 1986). The soils from this set (Horotiu, Te Kowhai, Hamilton, Otorohanga, and Netherton) cover a wide range of textures, developed from different parent materials, and in different physiographic positions and climates. Water content measurements were made on undisturbed samples at suctions ranging from 0.98 kPa to 1500 kPa, using tension tables and pressure plate apparatus. For the Set 1 (Canterbury) soils, for which all measurements were completed by the author, particular attention was directed to the accurate measurements of composition data. Detailed particle-size analysis was carried out using a combination of sieving and sedimentation techniques. For the latter, both Sedigraph and pipette measurements were made and compared. The Sedigraph method was found to systematically overestimate the mass percentage at all equivalent diameters in the sedimentation range, compared to the standard pipette method. The results of this comparison were found to be in remarkably close agreement with results of a similar independent comparison undertaken previously by the New Zealand Soil Bureau. Measurements on soils in the Canterbury set showed, for all soils except one, a highly significant correlation between the total iron content of the sample and the difference between the Sedigraph and pipette methods. This strongly supported the hypothesis that the greater concentration of iron (a strong X-ray absorber) in the smaller size fractions (particularly clay) is the main factor causing the difference. Regression equations are also developed for converting Sedigraph data to their pipette equivalents. Organic carbon, a second key compositional parameter, was determined using three different methods: (i) Walkley and Black (1934); (ii) Dumas Combustion (the reference method); and (iii) Loss on ignition. The results of this study revealed a novel result for converting loss on ignition (L) to total organic carbon (T). In contrast to the traditional assumption a ≏ 0 and b ≏ 1.72 in the linear regression L = a + b(T), the results showed a significant intercept effect (i.e. a > 0), related to residual effects of adsorbed moisture and other components driven off on ignition. Inclusion of the clay term in the bivariate regression, i.e. L = a + b(T) + c(C), is shown to significantly improve the regression and accounts for the non-zero intercept effect. This study also confirmed that the Walkley and Black method can be used for reliable determination of organic carbon of these soils. Particular emphasis is placed in this thesis on the improved functional representation of particle-size distribution and the soil moisture characteristic. The modelling of particle-size distribution is a relatively neglected area of soil science research, so five different parametric models are evaluated. Three of these have not previously been introduced in the soil science literature: a simple one-parameter model borrowed from the geotechnics literature; and two modified lognormal models. One of the latter two models, the 'offset-renormalized lognormal' (ORL) model, is found to provide the optimum description of PSD for the majority of the soils. The general statistical approach to selection of an 'optimum' model is discussed. Both these topics, i.e. exploration of functional models for PSD, and a systematic approach to model selection, have been given little attention in the soil science literature. For representation of the soil moisture characteristic, the applicability of a simple two-parameter power-function model was tested. This model was found to provide a good fit to data over suction ranges important for key unsaturated processes in the field. A single-parameter version of this model, proposed by Gregson et al. (1987), based on an alleged correlation between the two parameters, is shown to be a result of a mathematical artefact (Buchan and Grewal, 1990). This study concludes that a minimum of two parameters are required to model the unsaturated portion of the soil moisture characteristic, while a third, θs, is required to define its saturation limit. When a proper scaling basis is selected for θv and ψ, the two parameters are found to be essentially independent. The real reason enabling an approximate one-parameter approach is also revealed. The relationship between power-function model parameters and composition parameters is then explored. The results of the study indicate that clay content of the soil is the most important composition parameter and is highly correlated with the exponent b (the 'pore-size distribution index') of the power-function model. This variable alone accounts for 66% of the variation in the b parameter. Correlation between the a-parameter and composition parameters is weak, presumably reflecting the relatively narrow range of values of the a parameter. Thus it is concluded that 'a' (=ln ψe, where ψe is a notional air-entry potential) cannot be reliably estimated from composition data. The best practical estimation for a is to assume that it is a constant, given by its mean value for a soil set (a = -1.0 in this study). However this approximation results in large (>±3%) differences between observed and estimated θv values for more than half of the soils. The possibility of rapid estimation of field capacity (FC) and wilting point (WP) water contents from the readily-measured saturation percentage (SP) of the soil, was also assessed in this study. A strong linear relationship between the mass percentage water contents at FC and WP, and the SP, indicates that SP can be used to provide a rapid estimate of these limits to plant-available water. However, the lack of correlation found between SP and available water capacity (AWC) indicates the inability of the SP method to provide good estimates of AWC in these soils

Identification and characterization of resistance to cowpea aphid (Aphis craccivora Koch) in Medicago truncatula

Background: Cowpea aphid (CPA; Aphis craccivora) is the most important insect pest of cowpea and also causes significant yield losses in other legume crops including alfalfa, beans, chickpea, lentils, lupins and peanuts. In many of these crops there is no natural genetic resistance to this sap-sucking insect or resistance genes have been overcome by newly emerged CPA biotypes. Results: In this study, we screened a subset of the Medicago truncatula core collection of the South Australian Research and Development Institute (SARDI) and identified strong resistance to CPA in a M. truncatula accession SA30199, compared to all other M. truncatula accessions tested. The biology of resistance to CPA in SA30199 plants was characterised compared to the highly susceptible accession Borung and showed that resistance occurred at the level of the phloem, required an intact plant and involved a combination of antixenosis and antibiosis. Quantitative trait loci (QTL) analysis using a F 2 population (n = 150) from a cross between SA30199 and Borung revealed that resistance to CPA is controlled in part by a major quantitative trait locus (QTL) on chromosome 2, explaining 39% of the antibiosis resistance. Conclusions: The identification of strong CPA resistance in M. truncatula allows for the identification of key regulators and genes important in this model legume to give effective CPA resistance that may have relevance for other legume crops. The identified locus will also facilitate marker assisted breeding of M. truncatula for increased resistance to CPA and potentially other closely related Medicago species such as alfalfa

An Overview of Databases and Tools for lncRNA Genomics Advancing Precision Medicine

The completion of the human genome project revealed many elusive questions about the “junk DNA” in the genome. One of the promising observations was the pervasive transcription and discovery of noncoding RNAs, which put aside the assumption of RNA as a mere messenger in the central dogma of molecular biology. It became evident that RNA plays more critical regulatory roles than simply being the genetic information carrier. The advancements in the next-generation sequencing methods in the last decade identified large numbers of long noncoding RNAs (lncRNAs) expressed during different conditions and performing diverse functions. Despite the rapidly growing advancements in various omics techniques to identify, annotate, and analyze the role of lncRNAs during different diseases, their molecular functions are still unexplored. To keep the pace of rapidly growing data and utilize them in novel lncRNA prediction, numerous computational repositories and predictive algorithms have been published in order to expand our horizons of the understanding and functions of lncRNAs. In this chapter, we described the current tools and computational repositories in the area of lncRNA biology covering numerous aspects like lncRNA identification, annotation, role in disease- or cell-type-specific expression, and detailed literature on their recent novel offbeat functions

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Not AvailablePost flowering stalk rot complex is one of the most serious, destructive and widespread group of diseases in maize and yield losses range from 10 to 42% and can be as high as 100% in some areas. PFSR nature is often complex as a number of fungi (like Fusarium verticillioides cause Fusarium stalk rot, Macrophomina phaseolina cause charcoal rot, Harpophora maydis cause late wilt) are involved in causation of the diseases. To combat this problem, identification of quantitative trait loci for resistance to PFSR would facilitate the development of disease resistant maize hybrids. Moreover, various chemical and biological control methods have been developed but major emphasis is on development of maize cultivars with genetic resistance to for environment friendly control of the Post flowering stalk rot complex. The current paper reviews the information on distribution, impact of the disease, symptoms, epidemiology, disease cycle; genetics of resistance and integrated disease management approaches has been enumerated to understand the present status of knowledge about PFSR complex and will try to focus on the future perspectives available to improve PFSR managementNot Availabl