Indian Agricultural Statistics Research Institute

Industrially produced N-fertilizer is essential to the production of cereals that supports current and projected human populations. We constructed a top-down global N budget for maize, rice, and wheat for a 50-year period (1961 to 2010). Cereals harvested a total of 1551 Tg of N, of which 48% was supplied through fertilizer-N and 4% came from net soil depletion. An estimated 48% (737 Tg) of crop N, equal to 29, 38, and 25 kg ha(-1) yr(-1) for maize, rice, and wheat, respectively, is contributed by sources other than fertilizer- or soil-N. Non-symbiotic N2 fixation appears to be the major source of this N, which is 370 Tg or 24% of total N in the crop, corresponding to 13, 22, and 13 kg ha(-1) yr(-1) for maize, rice, and wheat, respectively. Manure (217 Tg or 14%) and atmospheric deposition (96 Tg or 6%) are the other sources of N. Crop residues and seed contribute marginally. Our scaling-down approach to estimate the contribution of non-symbiotic N2 fixation is robust because it focuses on global quantities of N in sources and sinks that are easier to estimate, in contrast to estimating N losses per se, because losses are highly soil-, climate-, and crop-specific.

Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.

Salinity tolerance in rice is highly desirable to sustain production in areas rendered saline due to various reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS). Here, we implemented GWAS to identify loci controlling salinity tolerance in rice. A custom-designed array based on 6,000 single nucleotide polymorphisms (SNPs) in as many stress-responsive genes, distributed at an average physical interval of <100 kb on 12 rice chromosomes, was used to genotype 220 rice accessions using Infinium high-throughput assay. Genetic association was analysed with 12 different traits recorded on these accessions under field conditions at reproductive stage. We identified 20 SNPs (loci) significantly associated with Na(+)/K(+) ratio, and 44 SNPs with other traits observed under stress condition. The loci identified for various salinity indices through GWAS explained 5-18% of the phenotypic variance. The region harbouring Saltol, a major quantitative trait loci (QTLs) on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na(+)/K(+) ratio measured at reproductive stage in our study. In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7. The current association mapping panel contained mostly indica accessions that can serve as source of novel salt tolerance genes and alleles. The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.

Rice ( Oryza sativa L.)–wheat ( Triticum aestivum L.) is the major cropping system occupying 13.5 million ha in the Indo‐Gangetic Plains of South Asia. Conventional‐tillage practices are resource and cost intensive. A 7‐yr study evaluated six treatments (T) involving three tillage methods and two rice establishment methods on crop yield, water productivity, and economic profitability in a rice–wheat rotation. Average rice yields in the conventional practice of puddling and transplanting without (T1) and with (T2) mid‐season alternate wetting‐drying were highest (7.81–8.10 Mg ha −1 ) and increased with time (0.26 Mg ha −1 yr −1 ) in T2. Compared to T1, rice yields in direct drill‐seeding with zero‐tillage averaged 16% lower on flat (T5) and 43% lower in raised beds (T3). Rice yield in raised beds (T3 and T4) decreased with time (0.14–0.45 Mg ha −1 yr −1 ). Conversely, wheat yielded 18% higher after zero compared to conventional‐tillage. Treatment 2, despite low soil matric potential during vegetative development, had higher water productivity with 25% less water use compared with T1 and 19% less compared with other treatments. Conventional‐tillage and crop establishment practices had higher net cash return in rice but in wheat it was higher with zero‐tillage. Overall, T2 and T5 had the highest net returns (∼1225US$) and T3 and T4 had the lowest (747–846 US$) in the rice–wheat system. Zero‐tillage on flat beds (T5), however, would conceivably be more sustainable than the conventional T2 in the long‐run. Yields of zero‐tillage with direct‐seeding of rice on flat beds (T5) must improve before adoption occurs.

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with ten drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

Drought stress is the major abiotic factor limiting crop production. Co-inoculating crops with nitrogen fixing bacteria and plant growth-promoting rhizobacteria (PGPR) improves plant growth and increases drought tolerance in arid or semiarid areas. Soybean is a major source of high-quality protein and oil for humans. It is susceptible to drought stress conditions. The co-inoculation of drought-stressed soybean with nodulating rhizobia and root-colonizing, PGPR improves the root and the shoot growth, formation of nodules, and nitrogen fixation capacity in soybean. The present study was aimed to observe if the co-inoculation of soybean (Glycine max L. (Merr.) nodulating with Bradyrhizobium japonicum USDA110 and PGPR Pseudomonas putida NUU8 can enhance drought tolerance, nodulation, plant growth, and nutrient uptake under drought conditions. The results of the study showed that co-inoculation with B. japonicum USDA110 and P. putida NUU8 gave more benefits in nodulation and growth of soybean compared to plants inoculated with B. japonicum USDA110 alone and uninoculated control. Under drought conditions, co-inoculation of B. japonicum USDA 110 and P. putida NUU8 significantly enhanced the root length by 56%, shoot length by 33%, root dry weight by 47%, shoot dry weight by 48%, and nodule number 17% compared to the control under drought-stressed. Co-inoculation with B. japonicum, USDA 110 and P. putida NUU8 significantly enhanced plant and soil nutrients and soil enzymes compared to control under normal and drought stress conditions. The synergistic use of B. japonicum USDA110 and P. putida NUU8 improves plant growth and nodulation of soybean under drought stress conditions. The results suggested that these strains could be used to formulate a consortium of biofertilizers for sustainable production of soybean under drought-stressed field conditions.

Soil salinity is a major constraint to rice production in large inland and coastal areas around the world. Modern high yielding rice varieties are particularly sensitive to high salt stress. There are salt tolerant landraces and traditional varieties of rice but with limited information on genomic regions (QTLs) and genes responsible for their tolerance. Here we describe a method for rapid identification of QTLs for reproductive stage salt tolerance in rice using bulked segregant analysis (BSA) of bi-parental recombinant inbred lines (RIL). The number of RILs required for the creation of two bulks with extreme phenotypes was optimized to be thirty each. The parents and bulks were genotyped using a 50K SNP chip to identify genomic regions showing homogeneity for contrasting alleles of polymorphic SNPs in the two bulks. The method was applied to 'CSR11/MI48' RILs segregating for reproductive stage salt tolerance. Genotyping of the parents and RIL bulks, made on the basis of salt sensitivity index for grain yield, revealed 6,068 polymorphic SNPs and 21 QTL regions showing homogeneity of contrasting alleles in the two bulks. The method was validated further with 'CSR27/MI48' RILs used earlier for mapping salt tolerance QTLs using low-density SSR markers. BSA with 50K SNP chip revealed 5,021 polymorphic loci and 34 QTL regions. This not only confirmed the location of previously mapped QTLs but also identified several new QTLs, and provided a rapid way to scan the whole genome for mapping QTLs for complex agronomic traits in rice.

mitigation can provide a strong incentive for climate policy through reductions in air pollutant emissions that occur when targeting shared sources. However, reducing air pollutant emissions may also have an important co-harm, as the aerosols they form produce net cooling overall. Nevertheless, aerosol impacts have not been fully incorporated into cost-benefit modeling that estimates how much the world should optimally mitigate. Here we find that when both co-benefits and co-harms are taken fully into account, optimal climate policy results in immediate net benefits globally, overturning previous findings from cost-benefit models that omit these effects. The global health benefits from climate policy could reach trillions of dollars annually, but will importantly depend on the air quality policies that nations adopt independently of climate change. Depending on how society values better health, economically optimal levels of mitigation may be consistent with a target of 2 °C or lower.

In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.

Abstract Sustainable goals for contemporary world seek viable solutions for interconnected challenges, particularly in the fields of food and energy security and climate change. We present bamboo, one of the versatile plant species on earth, as an ideal candidate for bioeconomy for meeting some of these challenges. With its potential realized, particularly in the industrial sector, countries such as China are going extensive with bamboo development and cultivation to support a myriad of industrial uses. These include timber, fiber, biofuel, paper, food, and medicinal industries. Bamboo is an ecologically viable choice, having better adaptation to wider environments than do other grasses, and can help to restore degraded lands and mitigate climate change. Bamboo, as a crop species, has not become amenable to genetic improvement, due to its long breeding cycle, perennial nature, and monocarpic behavior. One of the commonly used species, moso bamboo ( Phyllostachys edulis ) is a potential candidate that qualifies as industrial bamboo. With its whole‐genome information released, genetic manipulations of moso bamboo offer tremendous potential to meet the industrial expectations either in quality or in quantity. Further, bamboo cultivation can expect several natural hindrances through biotic and abiotic stresses, which needs viable solutions such as genetic resistance. Taking a pragmatic view of these future requirements, we have compiled the present status of bamboo physiology, genetics, genomics, and biotechnology, particularly of moso bamboo, to drive various implications in meeting industrial and cultivation requirements. We also discuss challenges underway, caveats, and contextual opportunities concerning sustainable development.

Abstract Food security involves the sustainable utilization of soil and land resources. Zero‐tillage (ZT) practice is a proponent of better resource utilization, to improve soil physical condition, and a potential sink to atmospheric carbon. However, the impact varies across climates, over the ZT history, cropping systems, and soil depths. A meta‐analysis was performed, based on 4,131 paired data from 522 studies spread globally, to evaluate the effect of ZT in comparison to conventional tillage, on soil physical condition (bulk density; mean weight diameter of aggregates; field capacity water content; and steady‐state infiltration rate), soil organic carbon (SOC) content, and the root response (root length density). Zero‐tillage significantly improved mean weight diameter of aggregates and field capacity water content at surface and subsurface layers by 19–58% and 6–16%, respectively, and resulted in no change in bulk density in either of the layers, but infiltration rate increased by 66%. Surface 0‐ to 5‐ and 5‐ to 10‐cm layers had significantly higher SOC content under ZT, whereas in other layers, the SOC content either reduced or did not change, resulting in a small and insignificant variation in the SOC stock (~1.1%) in favor of ZT. The root length density improved by ~35% in ZT only at 0‐ to 5‐cm soil depth. Effect of climate, soil type, or cropping system could not be broadly recognized, but the impact of ZT certainly increased over time. Improvements in soil aggregation and hydraulic properties are highly convincing with the adoption of ZT, and therefore, this practice leads to the better and sustainable use of soil resources.

Amphiphilic copolymers, synthesized from poly (ethylene glycols) and various aliphatic diacids, which self assemble into nano-micellar aggregates in aqueous media, were used to develop controlled release (CR) formulations of imidacloprid [1-(6 chloro-3-pyridinyl methyl)-N-nitro imidazolidin-2-ylideneamine] using encapsulation technique. High solubilisation power and low critical micelle concentration (CMC) of these amphiphilic polymers may increase the efficacy of formulations. Formulations were characterised by Infrared (IR) spectroscopy, Dynamic Light Scattering (DLS) and Transmission Electron Microscope (TEM). Encapsulation efficiency, loading capacity and stability after accelerated storage test of the developed formulations were checked. The kinetics of imidacloprid release in water from the different formulations was studied. Release from the commercial formulation was faster than the CR formulations. The diffusion exponent (n value) of imidacloprid, in water ranged from 0.22 to 0.37 in the tested formulations. While the time taken for release of 50 % of imidacloprid ranged from 2.32 to 9.31 days for the CR formulations. The developed CR formulations can be used for efficient pest management in different crops.

Agricultural experiments demand a wide range of statistical tools for analysis, which includes exploratory analysis, design of experiments, and statistical genetics. It is a challenge for scientists and students to find a suitable platform for data analysis and publish the research outputs in quality journals. Most of the software available for data analysis are proprietary or lack a simple user interface, for example SAS is available in ICAR (Indian Council of Agricultural Research) for data analysis, though it is a highly advanced statistical analysis platform, and its complexity holds back students and researchers from using it. Some web applications like WASP (https://ccari.res.in/waspnew.html) and OPSTAT (http://14.139. 232.166/opstat/) used by the agricultural research community are user friendly but these applications don't provide options to generate plots and graphs.

KEY MESSAGE: Integration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts. The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.

Abstract Seeds of tomato were magnetoprimed at 100 mT for 30 min followed by imbibition for 12 and 24 h, respectively, at 20 °C, to examine the biochemical and molecular changes involved in homeostasis of hydrogen peroxide (H 2 O 2 ) and its signaling associated with hormone interactions for promoting vigor. The relative transcript profiles of genes involved in the synthesis of H 2 O 2 like Cu-amine oxidase (AO) , receptor for activated C kinase 1 (RACK1) homologue ( ArcA2 ) and superoxide dismutase (SOD1 and SOD9) increased in magnetoprimed tomato seeds as compared to unprimed ones with a major contribution (21.7-fold) from Cu-amine oxidase. Amongst the genes involved in the scavenging of H 2 O 2 i.e, metallothionein ( MT1 , MT3 and MT4 ), catalase ( CAT1 ) and ascorbate peroxidase ( APX1 and APX2 ), MT1 and MT4 exhibited 14.4- and 15.4-fold increase respectively, in the transcript abundance, in primed seeds compared to the control. We report in our study that metallothionein and RACK1 play a vital role in the reactive oxygen species mediated signal transduction pathway to enhance the speed of germination in magnetoprimed tomato seeds. Increased enzymatic activities of catalase and ascorbate peroxidase were observed at 12 h of imbibition in the magnetoprimed seeds indicating their roles in maintaining H 2 O 2 levels in the primed seeds. The upregulation of ABA 8 ′ -hydroxylase and GA3 oxidase1 genes eventually, lead to the decreased abscisic acid/gibberellic acid (ABA/GA 3 ) ratio in the primed seeds, suggesting the key role of H 2 O 2 in enhancing the germination capacity of magnetoprimed tomato seeds.

Indian jujube (Zizyphus mauritiana Lamk), an indigenous fruit crop of India has been widely used in traditional medicine for treating various kinds of diseases. Chinese jujube has been studied; however systematic study on Indian jujube is lacking. In this work, 12 commercial cultivars of Z. mauritiana were evaluated for their ascorbic acid (AA), total phenolics (TPH), flavonoids (TF), and total antioxidant activity (AOX). Results indicate that Indian jujube is a good source of ascorbic acid and total phenolics ranging from 19.54 to 99.49 mg/100 g and 172 to 328.6 mg GAE/100 g, respectively. Total AOX ranged from 7.41 to 13.93 and 8.01 to 15.13 μmol Trolox/g in FRAP and CUPRAC, respectively. Principal component analysis was performed to find a linear combination of the functional attributes which would account for most of the variance in the observed attributes. GGE biplots revealed that ZG-3, Elaichi and Gola, are promising genotypes in terms of total phenolics and flavonoids.

South Asian countries will have to double their food production by 2050 while using resources more efficiently and minimizing environmental problems. Transformative management approaches and technology solutions will be required in the major grain-producing areas that provide the basis for future food and nutrition security. This study was conducted in four locations representing major food production systems of densely populated regions of South Asia. Novel production-scale research platforms were established to assess and optimize three futuristic cropping systems and management scenarios (S2, S3, S4) in comparison with current management (S1). With best agronomic management practices (BMPs), including conservation agriculture (CA) and cropping system diversification, the productivity of rice- and wheat-based cropping systems of South Asia increased substantially, whereas the global warming potential intensity (GWPi) decreased. Positive economic returns and less use of water, labor, nitrogen, and fossil fuel energy per unit food produced were achieved. In comparison with S1, S4, in which BMPs, CA and crop diversification were implemented in the most integrated manner, achieved 54% higher grain energy yield with a 104% increase in economic returns, 35% lower total water input, and a 43% lower GWPi. Conservation agriculture practices were most suitable for intensifying as well as diversifying wheat-rice rotations, but less so for rice-rice systems. This finding also highlights the need for characterizing areas suitable for CA and subsequent technology targeting. A comprehensive baseline dataset generated in this study will allow the prediction of extending benefits to a larger scale.

Abstract A study was under taken for identifying the trends in pre and post-monsoon groundwater levels using Mann-Kendall test and Sen’s slope estimator, and for time series modelling of groundwater levels for forecasting the pre and post-monsoon water levels in Karnal district of Haryana. Results showed that the groundwater levels had significantly declined during 1974 to 2010. Average rates of water level decline were 0.228 and 0.267 m/yr during pre and post-monsoon seasons, respectively. There was rapid decline in water level between 2001 and 2010. The ARIMA (0, 1, 2) was identified as the appropriate model for time series modelling and forecasting. Results showed that the pre and post-monsoon groundwater level in 2050 would decline by 12.97 m and 12.00 m over the observed water level in 2010, and reach to a level of 29.95 m and 28.14 m below ground surface. The average rate of decline of pre and post-monsoon groundwater level in the district during this period would be 0.32 and 0.30 m/yr, respectively.

Glucosinolates are anti-nutritional factors present abundantly in the seed meal fraction of oilseed Brassica species. They are found in varying levels among different genotypes. Those genotypes containing less than 30 µmol/g are considered low/zero glucosinolate type and are preferred for edible purposes due to low pungency. Twenty two different genotypes were taken for the analysis of glucosinolates by spectrophotometry. A regression model was obtained using Ordinary Least Square technique which predicted a formula. Total glucosinolates (µmol/g) = 1.40 + 118.86 × A425, where A425 is the absorbance at 425 nm. The total glucosinolate content obtained by the prediction formula when compared with HPLC data showed a correlation coefficient of 0.942. This high correlation between the two data sets validated the developed methodology. This method also simplifies the estimation of total glucosinolates by excluding the use of HPLC or other sophisticated instruments.