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Low-temperature stress significantly affects physiological processes inducing plant growth hindrance,and reducing crop productivity. The present study aimed to understand the low-temperature stress inducedphysio-chemical behaviour of five papaya genotypes (Pusa Nanha, Red Lady, P-9-5, P-7-9 and one wild relativespecies Vasconcellea cundinamarcensis). Low temperature stress significantly decreased the canopy gasexchange parameters, relative water content (RWC), membrane stability index (MSI) and chlorophyll contentin all the papaya genotypes, while tending to increase the total soluble sugars, proline and total solubleproteins, although the alterations were genotype-specific. V. cundinamarcensis showed greater tolerance tolow-temperature stress followed by P-9-5 and P-7-9 than other genotypes by maintaining the highest RWC,MSI, total soluble proteins, chlorophyll and proline contents. Our results showed that the chlorophyll content,membrane lipid peroxidation and MSI could be used as the indices for the screening of potential papayagenotypes against cold tolerance.

qRT-PCR is a globally accepted technique for assaying gene expression in relative terms which compares the difference between critical threshold (C

The time of concentration (ToC) is an important parameter in rainfall−runoff simulation for designing and evaluating an urban drainage system (UDS). There are several lumped and distributed methods available in the literature for estimating the ToC. However, these methods lead to significantly varied values. Therefore, it is imperative to choose an appropriate and best-suited method for estimating the ToC. This study analyses eight lumped approach-based and two distributed approach-based methods for estimating the ToC in an urban area of Gurugram, a satellite city in the National Capital Region (NCR) of Delhi in India. Considering the ToC obtained by the Natural Resource Conservation Service (NRCS) method as the ‘true’ value, the Carter method among lumped methods and the SWDM method between the distributed methods results in ToC values in agreement with the NRCS method. Furthermore, to study the impact of the underestimation or overestimation of ToC on drainage, the system is evaluated in terms of variation in flood volume, duration, peak discharge, and the time to peak for different ToC values. The simulations were carried out by setting the model in SWMM, and it was found that flood volume increases by 4.25 times and the duration increases by 7.25 times if the ToC is increased from 0.1 to 6.14 h. The results infer that ToC estimation methods significantly impact the design and performance of an urban drainage infrastructure.

Due to the concerning effects of climate change, groundwater will be one of the significant sources of water for both primary and secondary use in the future. Therefore, identifying the spatial patterns of groundwater distribution might help implement practical water resources management projects. Springs are a potential source of groundwater in the Indian Himalayan Region. The main objective of the current study is to explore a novel methodological approach that utilizes the Variance Inflation factor (VIF) to perform a feature selection procedure and most used machine learning (ML) algorithms, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN) for generating a groundwater spring potential map of the Ravi Basin in Himachal Pradesh, India. Used, 1834 spring and non-spring locations were selected from the field and split into two groups. Of 1834 samples, 70% (1283) were used for model training, and 30% (551) were used for model validation. The model’s overall accuracy of 0.89, 0.87, and 0.88 for RF, GBM, and NN, respectively, around 10% area, has a very high potential for spring occurrence. The novel methodology can be employed to find the initial information for GW exploitation for inaccessible areas and the lack of data sources in this area.

High urbanization rate and climate change are the main drivers of urban floods in developing countries. The increase in urban flooding incidents has become a significant threat to cities, which also result into considerable losses of life and the economy. Adapting to the risks of a changing climate and ill-effects of urbanization is imperative for national and local governments. This calls for a functionally and structurally resilient urban drainage infrastructure. Functional resilience is the coping capacity of system against external threats such as urbanization and climate change, whereas structural resilience is the resilience against internal failures such as blockage of inlets or sewers, structural damage of a pipe, bed load sediment deposition, asset aging/decay, and sewer collapse. This work aims to understand the impact of nature-based solutions on urban drainage resilience. Various researchers have identified Low Impact development (LID) practices as a potential solution to enhance drainage systems' resilience. LID can be defined as a land development and retrofit strategy that emphasizes the protection and use of distributed interventions to reduce the volume and rate of stormwater runoff from a developed landscape. In the present study, the green roofs and rain gardens are simulated in a part of Gurugram city of India using the Storm Water Management Model (SWMM) 5.2. Sensitivity analysis is conducted to overcome the problem of a lack of in-depth data to perform model calibration and validation. The simulations were carried out by developing various scenarios for functional and structural resilience assessment. The results indicate that if 25% of potential subcatchments are deployed with LIDs, functional resiliency of the system enhances by 25%, and structural resilience of vulnerable nodes decreases by 17%. The study reveals that introduction of LIDs aids into enhancing the functional resilience of the system rather than structural resilience. This research provides evidence of LIDs' positive influence on the resilience performance of drainage systems. Overall, the study can help urban planners and drainage management engineers to develop understanding on LIDs role vis-à-vis city's resilience to urban flood problems.

This study presents a resilience-based evaluation of an urban drainage system (UDS) under the impact of functional and structural failure modes. Resilience based analysis is carried out using SWMM for a part of Gurugram City of Haryana, India. For simulating the drainage system response, half-hourly rainfall data available from GPM-IMERG was utilized to estimate the design storm of suitable duration. Sensitivity analysis is carried out to overcome the problem of lack of in-depth data to perform model calibration and validation. Besides, a comparison of three different estimation approaches of subcatchment width, an important SWMM parameter, is also presented. Different rainfall and urban growth scenarios were considered to analyse the drainage system's functional and structural resilience. For the studied UDS, a total of 22 vulnerable nodes were identified through the structural resilience approach, and the functional resilience approach revealed that urbanization has more pronounced effects on UDS than climate change.

Rice is a food crop of global importance, cultivated in diverse agro-climatic zones of the world. However, in the process of domestication many beneficial alleles have been eroded from the gene pool of the rice cultivated globally and eventually has made it vulnerable to a plethora of stresses. In contrast, the wild relatives of rice, despite being agronomically inferior, have inherited a potential of surviving in a range of geographical habitats. These adaptations enrich them with novel traits that upon introgression to modern cultivated varieties offer tremendous potential of increasing yield and adaptability. But, due to the unavailability of their genetic as well as genomic resources, identification and characterisation of these novel beneficial alleles has been a challenging task. Nevertheless, with the unprecedented surge in the area of conservation genomics, researchers have now shifted their focus towards these natural repositories of beneficial traits. Presently, there are several generic and specialized databases harboring genome-wide information on wild species of rice, and are acting as a useful resource for identification of novel genes and alleles, designing of molecular markers, comparative analysis and evolutionary biology studies. In this review, we introduce the key features of these databases focusing on their utility in rice breeding programs.

Flood hazard is assessed for the data scarce lower Bharathapuzha basin in Kerala, India, using a hydrologic-hydraulic approach. L-moment-based regional flood frequency analysis along with a non-dimensional analysis of hydrographs is used to generate scenarios for flood hazard assessment. A fully hydrodynamic 1D river flow model is calibrated for the rivers of lower Bharathapuzha basin for the year 1992 and validated for the year 1994. The widely available SRTM DEM is used to extract river cross-sections and the limited available discharge and water level data are used to carefully calibrate the 1D river flow model. Subsequently, a coupled 1D-2D flood inundation model is used to simulate the flood inundation extent for the year 2002. In the absence of LIDAR data, a cartographic DEM derived from readily available topo maps is used as an input in the coupled 1D-2D model. Further, in the absence of microwave Synthetic Aperture Radar (SAR) data, the flood inundation extent is validated using the readily available optical IRS-1D WiFS sensor data which is mainly intended for vegetation and drought monitoring. A suitable methodology is used to delineate flood inundation extent from the partial cloud-covered WiFS image. The regional flood frequency estimates and the calibrated and validated flood inundation model are then used to assess the flood hazard.

Traditional drought monitoring is based on observed data from both meteorological and hydrological stations. Due to the scarcity of station observation data, it is difficult to obtain accurate drought distribution characteristics, and also tedious to replicate the large-scale information of drought. Thus, Gravity Recovery and Climate Experiment (GRACE) data are utilized in monitoring and characterizing regional droughts where ground station data is limited. In this study, we analyzed and assessed the drought characteristics utilizing the GRACE Groundwater Drought Index (GGDI) over four major river basins in India during the period of 2003–2016. The spatial distribution, temporal evolution of drought, and trend characteristics were analyzed using GGDI. Then, the relationship between GGDI and climate factors were evaluated by the method of wavelet coherence. The results indicate the following points: GRACE’s quantitative results were consistent and robust for drought assessment, out of the four basins, severe drought was noticed in the Cauvery river basin between 2012 and 2015, with severity of −27 and duration of 42 months, other than Godavari river basin, the remaining three basins displayed significant negative trends at monthly and seasonal scales, the wavelet coherence method revealed that climate factors had a substantial effect on GGDI, and the impact of Southern Oscillation Index (SOI) on drought was significantly high, followed by Sea Surface Temperature (SST) Index (namely, NINO3.4) and Multivariate El Niño–Southern Oscillation Index (MEI) in all the basins. This study provides reliable and robust quantitative result of GRACE water storage variations that shares new insights for further drought investigation.

Regional-scale precise soil moisture measurements are required for remote sensing-based soil moisture product validation besides, complimenting in several hydrological and agricultural appl...

The structural basis of effector protein disease promotion will open new insights of how microbes modulate hosts for their own advantage providing new tools for further research. Structural and functional studies of host resistance genes mediate activation of HR and SAR host defence and provide knowledge that can be used to engineer future crops which will be resistant to a broad range of pathogens. These studies will focus on how effectors are recognized by plant immune system. Research applied on model species demonstrates that the interactions between plant defence and development under abiotic and biotic stresses are mostly mediated by hormone cross-signalling. In this chapter, we discuss briefly the signalling molecules including transcription factors, volatile compounds and their role in plant defence response.

Rice is a staple food for more than half of the global population. With the increasing population, the yield of rice must correspondingly increase to fulfill the requirement. Rice is cultivated worldwide in four different types of ecosystems, which are limited by the availability of irrigation water. However, water-limiting conditions negatively affect rice production; therefore, to enhance productivity under changing climatic conditions, improved cultivation practices and drought-tolerant cultivars/varieties are required. There are two basic approaches to cultivation: (1) plant based and (2) soil and irrigation based, which can be targeted for improving rice production. Crop plants primarily follow three mechanisms: drought escape, avoidance, and tolerance. Based on these mechanisms, different strategies are followed, which include cultivar selection based on yield stability under drought. Similarly, soil- and irrigation-based strategies consist of decreasing non-beneficial water depletions and water outflows, aerobic rice development, alternate wetting and drying, saturated soil culture, system of rice intensification, and sprinkler irrigation. Further strategies involve developing drought-tolerant cultivars through marker-assisted selection/pyramiding, genomic selection, QTL mapping, and other breeding and cultivation practices such as early planting to follow escape strategies and decreasing stand density to minimize competition with weeds. Similarly, the identification of drought-responsive genes and their manipulation will provide a technological solution to overcome drought stress. However, it was the Green Revolution that increased crop production. To maintain the balance, there is a need for another revolution to cope with the increasing demand.

Salinity adversely affects the yield and growth of rice (Oryza sativa L.) plants severely, particularly at reproductive stage. Long non-coding RNAs (lncRNAs) are key regulators of diverse molecular and cellular processes in plants. Till now, no systematic study has been reported for regulatory roles of lncRNAs in rice under salinity at reproductive stage. In this study, total 80 RNA-seq data of Horkuch (salt-tolerant) and IR-29 (salt-sensitive) genotypes of rice were used and found 1626 and 2208 transcripts as putative high confidence lncRNAs, among which 1529 and 2103 were found to be novel putative lncRNAs in root and leaf tissue respectively. In Horkuch and IR-29, 14 and 16 lncRNAs were differentially expressed in root tissue while 18 and 63 lncRNAs were differentially expressed in leaf tissue. Interaction analysis among the lncRNAs, miRNAs and corresponding mRNAs indicated that these modules are involved in different biochemical pathways e.g. phenyl propanoid pathway during salinity stress in rice. Interestingly, two differentially expressed lncRNAs such as TCONS_00008914 and TCONS_00008749 were found as putative target mimics of known rice miRNAs. This study indicates that lncRNAs are involved in salinity adaptation of rice at reproductive stage through certain biochemical pathways.

Present study assesses the effect of finer land-use classification in simulating the rainfall-runoff response of Kangsabati reservoir catchment (3,627 km2) and command (7,112 km2) by considering cropland heterogeneity in variable infiltration capacity (VIC) model. High resolution LISS-IV satellite imageries were used for the land-use classification. Global sensitivity analysis was performed using VIC-ASSIST to identify the most and least influential parameters based on the sensitivity index of elementary effects. A fully distributed calibration approach was employed using 16 (detailed) and 8 (lumped) vegetation classes. Low flows during lean periods were over-estimated and peak flows were under-estimated by both the model setups at Kangsabati reservoir site. Detailed land-use classification resulted in the reduction in streamflow over-estimation (Percent Bias (PBIAS) from −20.99 to −14.41 during calibration and from –22.83 to –7.17 during validation) at daily time step. It further demonstrates the improvement in simulating the peak flows; hence, highlighting the importance of detailed land-use classification for vegetation parameterization in VIC model setup. River discharge regulation at Kangsabati reservoir resulted in poor model performance at Mohanpur, downstream site of Kangsabati reservoir. Therefore, calibration for Mohanpur was performed after updating the VIC simulated streamflow with routed reservoir spillage using Hydrologic Engineering Center-River Analysis System (HEC-RAS) model. Streamflow updation employing HEC-RAS at Mohanpur improved the modelling efficiency (Nash–Sutcliffe efficiency (NSE) from 0.50 to 0.65 during calibration and from 0.55 to 0.67 during validation) and reduced bias (PBIAS from 6.25 to –2.23 during calibration and from 15.06 to 7.40 during validation) considerably for daily flows. Model performance with reasonable accuracy was achieved at both the calibration locations which demonstrates the potential applicability of VIC model to predict streamflow in the monsoon dominated Kangsabati reservoir catchment and command.

Salinity stress is the most prominent stress impacting rice productivity worldwide. In the past, several quantitative trait loci (QTLs) for salinity tolerance had been identified in rice, however their utilization in rice breeding programs is largely confounded due to the unwanted linkage drag associated with the QTL region. Thus, it is strongly desirable to delimit the QTL region to a least possible chromosomal interval minimising any unwanted association. Addressing this, we have evaluated 68 recombinant inbred lines (RILs) derived from a cross between a salinity tolerant parent ‘Kolajoha’ and a salinity sensitive parent ‘Ranjit’ for identification of QTL(s) involved in imparting salinity tolerance at seedling stage. Genotyping by sequencing approach (GBS) was followed for SNP identification at genome wide scale. Around 3649 SNPs were identified by GBS method initially at 20% minor allele frequency. After filtering of SNPs with polymorphism with less than 10–15% of missing data, a total of 1248 SNPs were mapped to 1247 recombination points and the genetic map was constructed with a total map length of 1201.21 cM and resolution of 0.95 cM between markers. For 10 traits, a total of 23 additive QTLs were identified of which only 1 was a major QTL and 22 were minor QTLs. The average QTL interval size is about 2945 kb. Epistatic QTL mapping had identified one pair of QTLs that contribute significantly in the phenotypic variation of traits among the RILs. Total 1895 genes were identified in the QTL intervals, majority of them are located in Chr1 of rice genome between 22.09 and 38.29 Mb region. Although, this region is not very narrow, some of the genes falling in this region can be utilized for validation of QTLs in future. One differentially methylated region was found to be colocalized within the QTL intervals determined in Chr2 which indicates their potential role in epigenetic modifications in improving stress tolerance in rice.

Wild relatives of Brassica are a rich reservoir of genes that are invaluable for the improvement of cultivated species. Sinapis alba is a close relative of crop Brassicas that possesses several desirable traits such as tolerance to Alternaria black spot disease, heat stress, insect pests and nematodes. This study is aimed at developing and characterizing hybrids between Brassica juncea and S. alba with the ultimate goal of transferring genes for tolerance to Alternaria brassicae and heat stress, the traits that are lacking in cultivated Brassica. We generated three hybrids between B. juncea and S. alba through protoplast fusion. The hybridity was confirmed through cytology and molecular markers. While two of the hybrids were symmetric, the third one was asymmetric and had greater resemblance to B. juncea. Hybrids showed some characteristic features of the parents and were fully male and female fertile and also set seeds upon back crossing with the parent species. In vitro leaf assay and field inoculation studies revealed that the hybrids are highly resistant to A. brassicae. Besides, hybrids set seeds at temperature of > 38 °C when parents failed to produce seeds indicating that hybrids possess heat tolerance. These stable hybrids provide a reliable genetic resource for transfer of genes from S. alba into cultivated Brassica species.

Last couple of decades have seen the outburst of a number of satellite based precipitation products with Tropical Rainfall Measuring Mission (TRMM) as the most widely used for hydrologic applications. Transition of TRMM into Global Precipitation Mission (GPM) promises enhanced spatio-temporal resolution along with upgrades in sensors and rainfall estimation techniques. Dependence of systematic error components in rainfall estimates of Integrated Multi-satellitE Retrievals for GPM (IMERG), and their variation with climatology and topography, was evaluated over 86 basins in India for year 2014 and compared with the corresponding (2014) and retrospective (1998–2013) TRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a majority of Indian basins, with significant improvement in low rainfall estimates showing smaller negative biases in 75 out of 86 basins. IMERG increased the inter-basin variability in bias for medium and high rainfall estimates. Low rainfall estimates in TRMM showed a systematic dependence on basin climatology, with significant overprediction in semi-arid basins which gradually improved in the higher rainfall basins. Medium and high rainfall estimates of TRMM exhibited a strong dependence on basin topography, with declining skill in the higher elevation basins. Systematic dependence of error components on basin climatology and topography was reduced in IMERG, especially in terms of topography. Rainfall-runoff modeling using Variable Infiltration Capacity (VIC) model over a flood prone basin (Mahanadi) revealed that improvement in rainfall estimates in IMERG didn’t translate into improvement in runoff simulations. More studies are required over basins in different hydro-climatic zones to evaluate the hydrologic significance of IMERG.

Improving oil yield of Indian mustard (Brassica juncea) is exigent as it is a major oilseed crop of Indian subcontinent, which has severe shortage of vegetable oil production in the world. Some of the regulators of seed development have been shown to improve oil yield in Arabidopsis. Arabidopsis KLUH (AtKLUH), a maternal regulator of seed size, has been shown to control seed oil content. In this study, we identified three homologs of AtKLUH in B. juncea, BjKLUH1, BjKLUH2-1 and BjKLUH2-2. We observed that BjKLUH1 differentially expresses in developing seeds in B. juncea accessions with varying seed size and oil content. Further, analyses for seed oil content in B. juncea transgenics carrying AtKLUH demonstrated an increase in seed oil up to 8.3% compared to wild-type plants. The results of this study suggest that KLUH may have a role in seed development and is a good candidate for engineering seed oil accumulation in B. juncea.

While urban areas in India are rapidly expanding, the analysis of how the precipitation regimes are changing is very limited. In the present study, an attempt has been made to explore the trends in rainfall pattern over 20 most populated urban agglomeration (cities) of India using high resolution gridded, daily rainfall product (0.25° × 0.25°) obtained from India Meteorological Department (IMD). The analysis comprised of annual and monsoonal rainfall and rainy days; 1- and 10-day annual maximum rainfall; 95 and 99 percentile extreme threshold rainfall magnitude in 30 years moving window; and number of events above 95 and 99 percentile threshold in each year during 1901–2015. The Mann-Kendall (Modified Mann-Kendall) test at 0.1 significance level to detect trends, and the Thiel-Sen’s approach to estimate % change over mean have been used. Annual and monsoonal rainfall are found to be increasing with shrinking rainy days during the same period that infers rise in heavy rainfall events. Besides, the majority of the cities show increasing trends in 1- and 10-day annual maximum rainfall, number of extreme threshold events, and magnitude of extreme threshold rainfall in 30-year moving window. The Pettitt-Mann-Whitney (PMW) test was used to detect change point year. Cities with significant change point at probability level ≥90% were selected to analyze pre- and post-change point year trend in time series. In addition, the trend in precipitation variables for multiple climatic windows of 30 years with an advancement of 5 years in each window (1901–1930, 1906–1935 …, 1986–2015) show that unlike a unique long-term trend, different climatic windows have varying trend direction over the period of time. Overall, the analysis reveals the rising trend in extreme rainfall events over majority of the cities in long term (1901–2015); however, mixed pattern of trends are observed in moving climatic windows.

Among the varieties of pathogens infecting plants, fungal pathogens are infamous for causing extensive damage to the plants. The establishment of fungal pathogen on plant tissues is largely determined by the cross-reactivity of the pathogen-secreted biomolecules with host immune response. Plants alleviate the stressful environment by employing different stress-adaptive responses. Particularly, to fend off pathogen and to keep them healthy, plants have evolved a highly complex defence response. The robustness of the plant immunity against invading fungus depends on how the components of the shared signalling network are manoeuvred by the plant for its own defence. However, due to the rapid rate of evolution of fungus pathogenesis genes coupled with global climate changes, the conditions are becoming more favourable for growth and progression of the fungal pathogens. Consequently, the incidences of fungus attack on otherwise resistant host species have now escalated. Thus, the important challenge facing global agriculture is to minimise the crop losses incurred due to plant diseases. In this context, a deeper insight into the mechanism of plant-fungi interaction is necessary to combat the invading pathogens. In this chapter, we have touched upon the mechanistic aspect of plant-fungi interaction and how this information can be utilised strategically for designing climate smart crops.

The advantages of high input agriculture are fading away due to degenerating soil health and adverse effects of climate change. Safeguarding crop yields in the changing environment and dynamics of pest and pathogens, has posed new challenges to global agriculture. Thus, integration of new technologies in crop improvement has been imperative for achieving the breeding objectives in faster ways. Recently, enormous potential of genome editing through engineered nucleases has been demonstrated in plants. Continuous refinements of the genome editing tools have increased depth and breadth of their applications. So far, genome editing has been demonstrated in more than fifty plant species. These include model species like Arabidopsis, as well as important crops like rice, wheat, maize etc. Particularly, CRISPR/Cas9 based two component genome editing system has been facile with wider applicability. Potential of genome editing has unfurled enormous possibilities for engineering diverse agronomic traits including durable resistance against insect-pests and pathogens. Novel propositions of developing insect and pathogen resistant crops by genome editing include altering the effector-target interaction, knocking out of host-susceptibility genes, engineering synthetic immune receptor eliciting broad spectrum resistance, uncoupling of antagonistic action of defense hormones etc. Alternatively, modification of insect genomes has been used either to create gene drive or to counteract resistance to various insecticides. The distinct advantage of genome editing system is that it can knock out specific target region in the genome without leaving the unwanted vector backbone. In this article, we have reviewed the novel opportunities offered by the genome editing technologies for developing insect and pathogen resistant crop-types, their future prospects and anticipated challenges.

Identification of the seed varieties is essential in the quality control and high yield crop growth. The existing methods of varietal identification rely primarily on visual examination and DNA fingerprinting. Although the pattern of DNA fingerprinting allows precise classification of seed varieties but fraught with challenges such as low rate of polymorphism amongst closely related species, destructive method of analysis and a huge cost involved in identification of robust markers such as simple sequence repeat (SSR) and single nucleotide polymorphisms. Here, we propose a fast, non-contact and non-invasive technique, deep learning assisted optical coherence tomography (OCT) for subsurface imaging in order to distinguish different seed varieties. The volumetric dataset of, (a) four rice varieties (PUSA Basmati 1, PUSA 1509, PUSA 44 and IR 64) and, (b) seven morphologically similar seeds of rice landrace Pokkali was acquired using OCT technique. A feedforward deep neural network is implemented for deep feature extraction and to classify the OCT images into their relevant classes. The proposed method provides the classification accuracy of 89.6% for the dataset of total 158,421 OCT images and 82.5% in classifying the dataset of total 56,301 OCT images collected from Pokkali seeds. The current technique can accurately classify seed varieties irrespective of the morphological similarities and can be adopted for the removal of varietal duplication and assessment of the purity of the seeds.

Oilseed crops play an important role in the agricultural economy. Apart from being an integral component of human diet and industrial applications, they are also gaining importance as replacement to fossil fuels for meeting the energy needs. The last two decades have been marked by several important events in genetic engineering and identification of gene targets for enhancing seed oil content in oilseed crops, and will aid the successful development of new generation high yielding oil crops. Specifically, genetic engineering has shown real breakthrough in enhancing oil content in oilseed rape, camelina, soybean and maize. Moreover, ongoing research efforts to decipher the possibilities of genetic modifications of key regulators of oil accumulation along with physiological and biochemical studies to understand lipid biosynthesis will set a platform to produce transgenic oilseed crops with enhanced oil content. In this review, we briefly describe different genetic engineering approaches explored by different researchers for enhancing oil content. Further, we discuss a few promising and potential approaches and challenges for engineering oil content in oilseed crops.

To avoid the nuisance of frequent flooding during rainy season, designing an efficient stormwater drainage system has become the need of the hour for present world engineers and urban planners. The present case study deals with providing a solution to stormwater management problem in an urbanized area. Mann–Kendall and Sen’s slope tests are used to perform the trend analysis of rainfall events using daily rainfall data (1956–2012), while the L-moments-based frequency analysis method is employed to estimate the design storm for a small urbanized area in West Bengal, India, using daily annual maximum rainfall (1975–2013). SWMM (Storm Water Management Model) and MIKE URBAN models are used to design an efficient drainage system for the study area. Two-dimensional (2D) MIKE URBAN model is primarily used to overcome the limitation of one-dimensional (1D) SWMM in simulating flood extent and flood inundation. Model simulation results from MIKE URBAN are shown for an extreme rainfall event of July 29, 2013. A multi-purpose detention pond is also designed for groundwater recharge and attenuating the peak of outflow hydrograph at the downstream end during high-intensity rainfall. This study provides an insight into the importance of 2D model to deal with location-specific flooding problems.

The study analyses the impact of climate change on streamflow regime of the Mahanadi River basin (MRB) in projected climate scenarios obtained from CMIP5 models. Projections from a total of 9 Global Climate Models (GCMs) were utilized. Integrated MIKE 11 NAM-HD, a conceptual hydrological model was employed to simulate the streamflow at Hirakud and Mundali gauging sites of upper Mahanadi River basin (UMRB) and middle Mahanadi River basin (MMRB), respectively. Prior to generating the streamflow regimes for the projected scenarios, GCM simulated precipitation and temperature were bias corrected using India Meteorological Department (IMD) observed gridded precipitation and temperature datasets. Precipitation was corrected using quantile mapping technique employing four different approaches, i.e., seasonal, hybrid, monthly non-hybrid, and monthly hybrid approaches. Hybrid approaches explicitly correct the extreme rainfall using Generalized Extreme Value (GEV) distribution whereas Gamma distribution is used for normal rainfall. Temperature data was corrected using Gaussian distribution on daily basis employing monthwise time series. L-moments based frequency analysis method was used to estimate the 50-year return period rainfall for observed and bias corrected projected time series to assess the efficacy of bias correction methods in correcting the extreme events. Monthly hybrid approach was found to resolve rainfall climatology over both UMRB and MMRB with improved skill in extreme rainfall correction. Though, monthly non-hybrid approach produced the seasonal cycle of rainfall with highest accuracy, it performed poorly in correcting the extremes. Under projected climate scenarios mean annual rainfall is found to be increasing for BCC-CSM1.1(m), HadGEM2-AO, GFDL-CM3, and IPSL-CM5A-LR whereas other GCMs show mixed signal compared to their mean state of historic period (1976–2005). Streamflow regime in projected climate was analyzed using ensemble mean of simulated streamflow from identified GCMs. Mean monthly streamflow were overall found to be increasing towards the end of the century i.e., in far-future defined as 2070–2099. Daily high flows (defined at 5% and 10% exceedance probability) were found to be increasing in their magnitude and frequency in far-future whereas occurrences of low flows (defined at 90% and 95% exceedance probability) were found to be predominantly decreasing under projected climate.

The wild species of rice are expected to harbour novel beneficial alleles which have been lost from cultivated rice during the process of domestication. This unchecked loss of alleles has made cultivated rice varieties more susceptible to the changing climatic conditions. Therefore, to develop climate-resilient rice crops, it is desirable to transfer the beneficial alleles from the wild rice species to the cultivated ones. In this direction, efforts are underway to completely unlock the genetic blueprint of the wild relatives of rice, which should facilitate the identification of novel alleles and their subsequent use in various rice-breeding programs. Although the sequence information is now generated with ease, it is equally imperative to develop user-friendly informatics resources harbouring the information that could be made easily accessible to users with diverse research interest. Despite the availability of a plethora of informatics resources for cultivated rice varieties, very few are available for the wild relatives of rice. Nevertheless, with the unprecedented surge in exploratory genomics efforts of wild relatives of rice and concurrent advancement in the frontier areas of bioinformatics, it is foreseeable that the informatics resources for wild relatives of rice will rise too. These resources will definitely spur new avenues of research in rice biology and thus should ensure a stable global food supply.

The history of DNA sequencing dates back to 1970s. During this period the two first generation nucleotide sequencing techniques were developed. Subsequently the Sanger's dideoxy method of sequencing gained popularity over Maxam and Gilbert's chemical method of sequencing. However, in the last decade, we have observed revolutionary changes in DNA sequencing technologies leading to the emergence of next-generation sequencing (NGS) techniques. NGS technologies have enhanced the throughput and speed of sequencing combined with bringing down the overall cost of the process over a time. The major applications of NGS technologies being genome sequencing and resequencing, transcriptomics, metagenomics in relation to plant-microbe interactions, exon and genome capturing, development of molecular markers and evolutionary studies. In this review, we present a broader picture of evolution of NGS tools, its various applications in crop plants, and future prospects of the technology for crop improvement.

Rice blast disease caused by a fungus Magnaporthae oryzae is one of the most important biotic factors that severely damage the rice crop. Several molecular approaches are now being applied to tackle this issue in rice. It is of interest to study long non-coding RNA (lncRNA) in rice to control the disease. lncRNA, a non-coding transcript that does not encode protein, is known to play an important role in gene regulation of various biological processes. Here we describe a computational pipeline to identify lncRNA from a resistant rice line. The number of lncRNA found in resistant line was 1429, 1927 and 1981 in mock and M. oryzae (ZB13 and Zhong) inoculated samples, respectively. Functional classification of these lncRNA reveals a higher number of long intergenic non-coding RNA compared to antisense lncRNA in both mock and M. oryzae inoculated resistant rice lines. Many intergenic lncRNA candidates were identified from resistant rice line and their role to regulate the resistance mechanism in rice during M. oryzae invasion is implied.

Cytoplasmic male sterility (CMS) system based on Moricandia arvensis cytoplasm is being used to breed hybrid Brassica juncea varieties. The male fertility restorer gene for this CMS was introgressed into B. juncea from M. arvensis and has been tagged with a dominant SCAR marker. The present study was undertaken to fine map the Rf locus and to assign it to specific B. juncea linkage group. We used information obtained from genome walking, anther transcriptome of the male sterile and the fertility restorer lines and synteny relationship with B. rapa for designing markers for targeted fine mapping. Two BC1F1 populations derived from the cross between CMS and fertility restorer lines were used for mapping. Sequence information obtained through genome walking from the SCAR3 indicated that the Rf gene is located in a genomic region having synteny with B. rapa chromosome A09. Screening of more than 550 SSR markers designed based on B. rapa A09 sequence information identified 12 markers flanking the Rf gene and confirmed its location on B. juncea A09 linkage group. Two (one EST-SSR and one STS) out of 67 markers designed based on differential gene expression between anther transcriptomes were found to be informative and were linked to the Rf gene. The markers were validated in a second BC1F1 population. The closest flanking markers BjESSR01 and BjEST01 were at 0.6 and 1.4 cM from the Rf locus.