Showing papers by "International Maize and Wheat Improvement Center published in 2018"

Construction of the third-generation Zea mays haplotype map.

Abstract: Author(s): Bukowski, Robert; Guo, Xiaosen; Lu, Yanli; Zou, Cheng; He, Bing; Rong, Zhengqin; Wang, Bo; Xu, Dawen; Yang, Bicheng; Xie, Chuanxiao; Fan, Longjiang; Gao, Shibin; Xu, Xun; Zhang, Gengyun; Li, Yingrui; Jiao, Yinping; Doebley, John F; Ross-Ibarra, Jeffrey; Lorant, Anne; Buffalo, Vince; Romay, M Cinta; Buckler, Edward S; Ware, Doreen; Lai, Jinsheng; Sun, Qi; Xu, Yunbi | Abstract: BackgroundCharacterization of genetic variations in maize has been challenging, mainly due to deterioration of collinearity between individual genomes in the species. An international consortium of maize research groups combined resources to develop the maize haplotype version 3 (HapMap 3), built from whole-genome sequencing data from 1218 maize lines, covering predomestication and domesticated Zea mays varieties across the world.ResultsA new computational pipeline was set up to process more than 12 trillion bp of sequencing data, and a set of population genetics filters was applied to identify more than 83 million variant sites.ConclusionsWe identified polymorphisms in regions where collinearity is largely preserved in the maize species. However, the fact that the B73 genome used as the reference only represents a fraction of all haplotypes is still an important limiting factor.

Burning issues of paddy residue management in north-west states of India

Abstract: Disposal of paddy residue has turn out to be a huge problem in north-west Indian states, resulting farmers prefer to burn the residues in-situ. Paddy residue management is of utmost important as it contains plant nutrients and improves the soil-plant-atmospheric continuum. Burning biomass not only pollutes environment and results in loss of appreciable amount of plant essential nutrients. The objectives of the review paper is to access the amount of residue generation, its utilization in-situ and ex-situ, emphasize harmful effects of residue burning on human health, soil health and environment of north-west states of India specially in Punjab and Haryana. This paper also discusses the possible strategies, financial and socio-economic evaluation of the paddy residue management technologies and accentuates the assessment of range of potential policy instruments which would offer avenues for sustainable agriculture and environment. Timely availability of conservation agriculture (CA) machinery is of utmost significance to manage the paddy residues in-situ. Collection and transportation of voluminous mass of paddy residue is cumbersome, therefore, ex-situ residue management is still not an economically viable option. The agricultural waste opens vivid options for its versatile usage and is possible if residue is collected and managed properly. It is a prerequisite for surplus residues to be used for CA. There is an urge to create awareness among farming communities to incline them to understand importance of crop residues in CA for sustainability and resilience of Indian agriculture.

Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Abstract: Improving photosynthesis to raise wheat yield potential has emerged as a major target for wheat physiologists. Photosynthesis-related traits, such as nitrogen per unit leaf area (Narea) and leaf dry mass per area (LMA), require laborious, destructive, laboratory-based methods, while physiological traits underpinning photosynthetic capacity, such as maximum Rubisco activity normalized to 25 °C (Vcmax25) and electron transport rate (J), require time-consuming gas exchange measurements. The aim of this study was to assess whether hyperspectral reflectance (350-2500 nm) can be used to rapidly estimate these traits on intact wheat leaves. Predictive models were constructed using gas exchange and hyperspectral reflectance data from 76 genotypes grown in glasshouses with different nitrogen levels and/or in the field under yield potential conditions. Models were developed using half of the observed data with the remainder used for validation, yielding correlation coefficients (R2 values) of 0.62 for Vcmax25, 0.7 for J, 0.81 for SPAD, 0.89 for LMA, and 0.93 for Narea, with bias <0.7%. The models were tested on elite lines and landraces that had not been used to create the models. The bias varied between -2.3% and -5.5% while relative error of prediction was similar for SPAD but slightly greater for LMA and Narea.

Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding.

Abstract: Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.

Assessing soil properties and nutrient availability under conservation agriculture practices in a reclaimed sodic soil in cereal-based systems of North-West India.

Abstract: Soil quality degradation associated with resources scarcity is the major concern for the sustainability of conventional rice-wheat system in South Asia. Replacement of conventional management practices with conservation agriculture (CA) is required to improve soil quality. A field experiment was conducted to assess the effect of CA on soil physical (bulk density, penetration resistance, infiltration) and chemical (N, P, K, S, micronutrients) properties after 4 years in North-West India. There were four scenarios (Sc) namely conventional rice-wheat cropping system (Sc1); partial CA-based rice-wheat-mungbean system (RWMS) (Sc2); CA-based RWMS (Sc3); and CA-based maize-wheat-mungbean (Sc4) system. Sc2 (1.52 Mg m−3) showed significantly lower soil bulk density (BD). In Sc3 and Sc4, soil penetration resistance (SPR) was reduced and infiltration was improved compared to Sc1. Soil organic C was significantly higher in Sc4 than Sc1. Available N was 33% and 68% higher at 0–15 cm depth in Sc3 and Sc4, respe...

Genome-Wide Association Analyses Identify QTL Hotspots for Yield and Component Traits in Durum Wheat Grown under Yield Potential, Drought, and Heat Stress Environments

Abstract: Understanding the genetic bases of economically important traits is fundamentally important in enhancing genetic gains in durum wheat. In this study, a durum panel of 208 lines (comprised of elite materials and exotics from the International Maize and Wheat Improvement Center gene bank) were subjected to genome wide association study (GWAS) using 6,211 DArTseq single nucleotide polymorphisms (SNPs). The panel was phenotyped under yield potential (YP), drought stress (DT), and heat stress (HT) conditions for 2 years. Mean yield of the panel was reduced by 72% (to 1.64 t/ha) under HT and by 60% (to 2.33 t/ha) under DT, compared to YP (5.79 t/ha). Whereas, the mean yield of the panel under HT was 30% less than under DT. GWAS identified the largest number of significant marker-trait associations on chromosomes 2A and 2B with p-values 10-06 to 10-03 and the markers from the whole study explained 7-25% variation in the traits. Common markers were identified for stress tolerance indices: stress susceptibility index, stress tolerance, and stress tolerance index estimated for the traits under DT (82 cM on 2B) and HT (68 and 83 cM on 3B; 25 cM on 7A). GWAS of irrigated (YP and HT combined), stressed (DT and HT combined), combined analysis of three environments (YP + DT + HT), and its comparison with trait per se and stress indices identified QTL hotspots on chromosomes 2A (54-70 cM) and 2B (75-82 cM). This study enhances our knowledge about the molecular markers associated with grain yield and its components under different stress conditions. It identifies several marker-trait associations for further exploration and validation for marker-assisted breeding.

Capturing farm diversity with hypothesis-based typologies : An innovative methodological framework for farming system typology development

Abstract: Creating typologies is a way to summarize the large heterogeneity of smallholder farming systems into a few farm types Various methods exist, commonly using statistical analysis, to create these typologies We demonstrate that the methodological decisions on data collection, variable selection, data-reduction and clustering techniques can bear a large impact on the typology results We illustrate the effects of analysing the diversity from different angles, using different typology objectives and different hypotheses, on typology creation by using an example from Zambia’s Eastern Province Five separate typologies were created with principal component analysis (PCA) and hierarchical clustering analysis (HCA), based on three different expert-informed hypotheses The greatest overlap between typologies was observed for the larger, wealthier farm types but for the remainder of the farms there were no clear overlaps between typologies Based on these results, we argue that the typology development should be guided by a hypothesis on the local agriculture features and the drivers and mechanisms of differentiation among farming systems, such as biophysical and socio-economic conditions That hypothesis is based both on the typology objective and on prior expert knowledge and theories of the farm diversity in the study area We present a methodological framework that aims to integrate participatory and statistical methods for hypothesis-based typology construction This is an iterative process whereby the results of the statistical analysis are compared with the reality of the target population as hypothesized by the local experts Using a well-defined hypothesis and the presented methodological framework, which consolidates the hypothesis through local expert knowledge for the creation of typologies, warrants development of less subjective and more contextualized quantitative farm typologies

Spot blotch disease of wheat: the current status of research on genetics and breeding

Abstract: The spot blotch disease of wheat is caused by Bipolaris sorokiniana, which is an anamorph (teleomorph Cochliobolus sativus). The disease mainly occurs in warm humid wheat growing regions, and the Eastern Gangetic Plains (EGP) of South Asia is a hotspot. Significant progress has been made in recent years in characterizing the host-pathogen interaction. The study of the pathogen's life cycle and diversity have been an active area of research. A number of resistance sources have also been identified, characterized and utilized for breeding. Although immunity has not been observed in any genotype, cultivars displaying a relatively high level of resistance have been developed and made available to farmers. Further progress will require a regular use of marker-assisted breeding, genomic selection gene editing and transgenic interventions. This review summarizes the current state of knowledge about genetic and breeding efforts on wheat-B. sorokiniana pathosystem and discusses ways in which emerging tools can be used for future research to understand the mechanism involved in infection and for developing cultivars exhibiting a high level of resistance. This article is protected by copyright. All rights reserved.

Multimodel ensembles improve predictions of crop-environment-management interactions.

Abstract: A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits

Abstract: Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of genomic selection (GS). Multi-trait models are more complex than univariate models and usually require more computational resources, but they are preferred because they can exploit the correlation between traits, which many times helps improve prediction accuracy. For this reason, in this paper we explore the power of multi-trait deep learning (MTDL) models in terms of prediction accuracy. The prediction performance of MTDL models was compared to the performance of the Bayesian multi-trait and multi-environment (BMTME) model proposed by Montesinos-Lopez et al. (2016), which is a multi-trait version of the genomic best linear unbiased prediction (GBLUP) univariate model. Both models were evaluated with predictors with and without the genotype×environment interaction term. The prediction performance of both models was evaluated in terms of Pearson’s correlation using cross-validation. We found that the best predictions in two of the three data sets were found under the BMTME model, but in general the predictions of both models, BTMTE and MTDL, were similar. Among models without the genotype×environment interaction, the MTDL model was the best, while among models with genotype×environment interaction, the BMTME model was superior. These results indicate that the MTDL model is very competitive for performing predictions in the context of GS, with the important practical advantage that it requires less computational resources than the BMTME model.

High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging.

Abstract: In the crop breeding process, the use of data collection methods that allow reliable assessment of crop adaptation traits, faster and cheaper than those currently in use, can significantly improve resource use efficiency by reducing selection cost and can contribute to increased genetic gain through improved selection efficiency. Current methods to estimate crop growth (ground canopy cover) and leaf senescence are essentially manual and/or by visual scoring, and are therefore often subjective, time consuming, and expensive. Aerial sensing technologies offer radically new perspectives for assessing these traits at low cost, faster, and in a more objective manner. We report the use of an unmanned aerial vehicle (UAV) equipped with an RGB camera for crop cover and canopy senescence assessment in maize field trials. Aerial-imaging-derived data showed a moderately high heritability for both traits with a significant genetic correlation with grain yield. In addition, in some cases, the correlation between the visual assessment (prone to subjectivity) of crop senescence and the senescence index, calculated from aerial imaging data, was significant. We concluded that the UAV-based aerial sensing platforms have great potential for monitoring the dynamics of crop canopy characteristics like crop vigor through ground canopy cover and canopy senescence in breeding trial plots. This is anticipated to assist in improving selection efficiency through higher accuracy and precision, as well as reduced time and cost of data collection.

Genetic dissection of grain zinc concentration in spring wheat for mainstreaming biofortification in CIMMYT wheat breeding

Abstract: Wheat is an important staple that acts as a primary source of dietary energy, protein, and essential micronutrients such as iron (Fe) and zinc (Zn) for the world’s population. Approximately two billion people suffer from micronutrient deficiency, thus breeders have crossed high Zn progenitors such as synthetic hexaploid wheat, T. dicoccum, T. spelta, and landraces to generate wheat varieties with competitive yield and enhanced grain Zn that are being adopted by farmers in South Asia. Here we report a genome-wide association study (GWAS) using the wheat Illumina iSelect 90 K Infinitum SNP array to characterize grain Zn concentrations in 330 bread wheat lines. Grain Zn phenotype of this HarvestPlus Association Mapping (HPAM) panel was evaluated across a range of environments in India and Mexico. GWAS analysis revealed 39 marker-trait associations for grain Zn. Two larger effect QTL regions were found on chromosomes 2 and 7. Candidate genes (among them zinc finger motif of transcription-factors and metal-ion binding genes) were associated with the QTL. The linked markers and associated candidate genes identified in this study are being validated in new biparental mapping populations for marker-assisted breeding.

Potential for re-emergence of wheat stem rust in the United Kingdom.

Abstract: Wheat stem rust, a devastating disease of wheat and barley caused by the fungal pathogen Puccinia graminis f. sp. tritici, was largely eradicated in Western Europe during the mid-to-late twentieth century. However, isolated outbreaks have occurred in recent years. Here we investigate whether a lack of resistance in modern European varieties, increased presence of its alternate host barberry and changes in climatic conditions could be facilitating its resurgence. We report the first wheat stem rust occurrence in the United Kingdom in nearly 60 years, with only 20% of UK wheat varieties resistant to this strain. Climate changes over the past 25 years also suggest increasingly conducive conditions for infection. Furthermore, we document the first occurrence in decades of P. graminis on barberry in the UK . Our data illustrate that wheat stem rust does occur in the UK and, when climatic conditions are conducive, could severely harm wheat and barley production. Clare Lewis et al. report the first identification in nearly 60 years of a cultivated wheat plant infected with the fungal pathogen Puccinia graminis f.sp. tritici (wheat stem rust) in the United Kingdom. They find that only 20% of UK wheat varieties are resistant to this strain and urge growers to resume resistance breeding programs.

Measuring the potential for sustainable intensification of aquaculture in Bangladesh using life cycle assessment

Abstract: Food production is a major driver of global environmental change and the overshoot of planetary sustainability boundaries. Greater affluence in developing nations and human population growth are also increasing demand for all foods, and for animal proteins in particular. Consequently, a growing body of literature calls for the sustainable intensification of food production, broadly defined as “producing more using less”. Most assessments of the potential for sustainable intensification rely on only one or two indicators, meaning that ecological trade-offs among impact categories that occur as production intensifies may remain unaccounted for. The present study addresses this limitation using life cycle assessment (LCA) to quantify six local and global environmental consequences of intensifying aquaculture production in Bangladesh. Production data are from a unique survey of 2,678 farms, and results show multidirectional associations between the intensification of aquaculture production and its environmental impacts. Intensification (measured in material and economic output per unit primary area farmed) is positively correlated with acidification, eutrophication, and ecotoxicological impacts in aquatic ecosystems; negatively correlated with freshwater consumption; and indifferent with regard to global warming and land occupation. As production intensifies, the geographical locations of greenhouse gas (GHG) emissions, acidifying emissions, freshwater consumption, and land occupation shift from the immediate vicinity of the farm to more geographically dispersed telecoupled locations across the globe. Simple changes in fish farming technology and management practices that could help make the global transition to more intensive forms of aquaculture be more sustainable are identified.

Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance

Abstract: New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1-5, 1-9, etc.). Machine learning (ML) is a field of computer science that uses algorithms and existing samples to capture characteristics of target patterns. In this paper we discuss several state-of-the-art ML methods that could be applied in GS. Many of them have already been used to predict rust resistance in wheat. Others are very appealing, given their performance for predicting other wheat traits with similar characteristics. We briefly describe the proposed methods in the Appendix.

Genome-Wide Association Study Reveals Novel Genomic Regions for Grain Yield and Yield-Related Traits in Drought-Stressed Synthetic Hexaploid Wheat.

Abstract: Synthetic hexaploid wheat (SHW; 2n = 6x = 42, AABBDD, Triticum aestivum L.) is produced from an interspecific cross between durum wheat (2n = 4x = 28, AABB, T. turgidum L.) and goat grass (2n = 2x = 14, DD, Aegilops tauschii Coss.) and is reported to have significant novel alleles-controlling biotic and abiotic stresses resistance. A genome-wide association study (GWAS) was conducted to unravel these loci [marker–trait associations (MTAs)] using 35,648 genotyping-by-sequencing-derived single nucleotide polymorphisms in 123 SHWs. We identified 90 novel MTAs (45, 11, and 34 on the A, B, and D genomes, respectively) and haplotype blocks associated with grain yield and yield-related traits including root traits under drought stress. The phenotypic variance explained by the MTAs ranged from 1.1% to 32.3%. Most of the MTAs (120 out of 194) identified were found in genes, and of these 45 MTAs were in genes annotated as having a potential role in drought stress. This result provides further evidence for the reliability of MTAs identified. The large number of MTAs (53) identified especially on the D-genome demonstrate the potential of SHWs for elucidating the genetic architecture of complex traits and provide an opportunity for further improvement of wheat under rapidly changing climatic conditions.

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture

Abstract: Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a "meta picture" of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype×environment interaction (G×E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.