Showing papers by "International Maize and Wheat Improvement Center published in 2019"

Climate change impact and adaptation for wheat protein

Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation

Abstract: The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder’s equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.

Resequencing of 429 chickpea accessions from 45 countries provides insights into genome diversity, domestication and agronomic traits.

Abstract: We report a map of 4.97 million single-nucleotide polymorphisms of the chickpea from whole-genome resequencing of 429 lines sampled from 45 countries. We identified 122 candidate regions with 204 genes under selection during chickpea breeding. Our data suggest the Eastern Mediterranean as the primary center of origin and migration route of chickpea from the Mediterranean/Fertile Crescent to Central Asia, and probably in parallel from Central Asia to East Africa (Ethiopia) and South Asia (India). Genome-wide association studies identified 262 markers and several candidate genes for 13 traits. Our study establishes a foundation for large-scale characterization of germplasm and population genomics, and a resource for trait dissection, accelerating genetic gains in future chickpea breeding.

Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics.

Abstract: Bread wheat improvement using genomic tools is essential for accelerating trait genetic gains. Here we report the genomic predictabilities of 35 key traits and demonstrate the potential of genomic selection for wheat end-use quality. We also performed a large genome-wide association study that identified several significant marker–trait associations for 50 traits evaluated in South Asia, Africa and the Americas. Furthermore, we built a reference wheat genotype–phenotype map, explored allele frequency dynamics over time and fingerprinted 44,624 wheat lines for trait-associated markers, generating over 7.6 million data points, which together will provide a valuable resource to the wheat community for enhancing productivity and stress resilience. Large-scale genomic analyses in wheat identify regions associated with 50 agronomic traits evaluated in South Asia, Africa and the Americas. This genotype–phenotype map can be used to enhance wheat productivity and stress resilience.

Genetic architecture of grain yield in bread wheat based on genome-wide association studies.

Abstract: Identification of loci for grain yield (GY) and related traits, and dissection of the genetic architecture are important for yield improvement through marker-assisted selection (MAS). Two genome-wide association study (GWAS) methods were used on a diverse panel of 166 elite wheat varieties from the Yellow and Huai River Valleys Wheat Zone (YHRVWD) of China to detect stable loci and analyze relationships among GY and related traits. A total of 326,570 single nucleotide polymorphism (SNP) markers from the wheat 90 K and 660 K SNP arrays were chosen for GWAS of GY and related traits, generating a physical distance of 14,064.8 Mb. One hundred and twenty common loci were detected using SNP-GWAS and Haplotype-GWAS, among which two were potentially functional genes underpinning kernel weight and plant height (PH), eight were at similar locations to the quantitative trait loci (QTL) identified in recombinant inbred line (RIL) populations in a previous study, and 78 were potentially new. Twelve pleiotropic loci were detected on eight chromosomes; among these the interval 714.4–725.8 Mb on chromosome 3A was significantly associated with GY, kernel number per spike (KNS), kernel width (KW), spike dry weight (SDW), PH, uppermost internode length (UIL), and flag leaf length (FLL). GY shared five loci with thousand kernel weight (TKW) and PH, indicating significantly affected by two traits. Compared with the total number of loci for each trait in the diverse panel, the average number of alleles for increasing phenotypic values of GY, TKW, kernel length (KL), KW, and flag leaf width (FLW) were higher, whereas the numbers for PH, UIL and FLL were lower. There were significant additive effects for each trait when favorable alleles were combined. UIL and FLL can be directly used for selecting high-yielding varieties, whereas FLW can be used to select spike number per unit area (SN) and KNS. The loci and significant SNP markers identified in the present study can be used for pyramiding favorable alleles in developing high-yielding varieties. Our study proved that both GWAS methods and high-density genetic markers are reliable means of identifying loci for GY and related traits, and provided new insight to the genetic architecture of GY.

Rethinking technological change in smallholder agriculture

Abstract: The concept of technology adoption (along with its companions, diffusion and scaling) is commonly used to design development interventions, to frame impact evaluations and to inform decision-making...

High Temporal-Resolution Transcriptome Landscape of Early Maize Seed Development.

Abstract: The early maize (Zea mays) seed undergoes several developmental stages after double fertilization to become fully differentiated within a short period of time, but the genetic control of this highly dynamic and complex developmental process remains largely unknown. Here, we report a high temporal-resolution investigation of transcriptomes using 31 samples collected at an interval of 4 or 6 h within the first six days of seed development. These time-course transcriptomes were clearly separated into four distinct groups corresponding to the stages of double fertilization, coenocyte formation, cellularization, and differentiation. A total of 22,790 expressed genes including 1415 transcription factors (TFs) were detected in early stages of maize seed development. In particular, 1093 genes including 110 TFs were specifically expressed in the seed and displayed high temporal specificity by expressing only in particular period of early seed development. There were 160, 22, 112, and 569 seed-specific genes predominantly expressed in the first 16 h after pollination, coenocyte formation, cellularization, and differentiation stage, respectively. In addition, network analysis predicted 31,256 interactions among 1317 TFs and 14,540 genes. The high temporal-resolution transcriptome atlas reported here provides an important resource for future functional study to unravel the genetic control of seed development.

The 4 per 1000 goal and soil carbon storage under agroforestry and conservation agriculture systems in sub-Saharan Africa

Abstract: The 4‰ initiative launched by the French government at COP21 in Paris in December 2015 aspires to increase global soil organic carbon (SOC) stocks at a rate of 0.4% per year. We conducted a systematic literature review on SOC storage under agroforestry and conservation agriculture systems in sub-Saharan Africa, where we reported 66 and 33 cases for both systems respectively. The results showed that SOC storage rates were significantly higher than 4‰ yr−1 in fallows and in multistrata agroforestry systems (P = 0.0001 and 0.0178, respectively), but not in alley cropping and parklands systems. For conservation agriculture, SOC storage rates were only significantly higher than 4‰ yr−1 (P = 0.0438) when all three principles were applied, i.e. no- or minimum tillage combined with crop residue retention and intercropping or rotation. The data showed very large variability in SOC storage rates as the result of various factors, including previous land-use history, experimental set up and approach used to determine SOC storage (diachronic versus synchronic approach), soil type, depth of soil sampling, type of crops and management, and duration of the experiment. SOC storage rates significantly decreased with time in the agroforestry systems (P = 0.0328). However, we were unable to find significant relationships with initial SOC stocks or tree density. Given the limited published data and the high variability in results, no significant relationships between SOC storage rates and site variables were found for conservation agriculture. We argue that there is a potential for SOC storage in agricultural soils of sub-Saharan Africa, as illustrated by SOC gaps observed on smallholder farms. Low SOC levels are, however, to a great extent the result of limited resources of most smallholder farmers. Practices such as agroforestry and conservation agriculture can restore SOC in these soils, but the 4‰ initiative has to be implemented on the grounds of the positive impact on crop productivity rather than on climate change mitigation. The efficiency in doing so will depend on the specific situations and will need economic support to smallholder farmers, including the promotion of good markets for sale of extra produce and for input supply, effective private support and policy, such as credit schemes and subsidies for inputs, and efficient extension services which incentivize farmers to invest in new technologies.

Doubled haploid technology for line development in maize: technical advances and prospects

Abstract: Increased efficiencies achieved in different steps of DH line production offer greater benefits to maize breeding programs. Doubled haploid (DH) technology has become an integral part of many commercial maize breeding programs as DH lines offer several economic, logistic and genetic benefits over conventional inbred lines. Further, new advances in DH technology continue to improve the efficiency of DH line development and fuel its increased adoption in breeding programs worldwide. The established method for maize DH production covered in this review involves in vivo induction of maternal haploids by a male haploid inducer genotype, identification of haploids from diploids at the seed or seedling stage, chromosome doubling of haploid (D0) seedlings and finally, selfing of fertile D0 plants. Development of haploid inducers with high haploid induction rates and adaptation to different target environments have facilitated increased adoption of DH technology in the tropics. New marker systems for haploid identification, such as the red root marker and high oil marker, are being increasingly integrated into new haploid inducers and have the potential to make DH technology accessible in germplasm such as some Flint, landrace, or tropical material, where the standard R1-nj marker is inhibited. Automation holds great promise to further reduce the cost and time in haploid identification. Increasing success rates in chromosome doubling protocols and/or reducing environmental and human toxicity of chromosome doubling protocols, including research on genetic improvement in spontaneous chromosome doubling, have the potential to greatly reduce the production costs per DH line.

Global wheat production with 1.5 and 2.0°C above pre-industrial warming

Abstract: Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield interannual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Quantifying carbon for agricultural soil management: from the current status toward a global soil information system

Abstract: The importance of building/maintaining soil carbon, for soil health and CO2 mitigation, is of increasing interest to a wide audience, including policymakers, NGOs and land managers. Integral to any...

Effects of tillage, crop establishment and diversification on soil organic carbon, aggregation, aggregate associated carbon and productivity in cereal systems of semi-arid Northwest India.

Abstract: Intensive tillage based management practices are threatening soil quality and systems sustainability in the rice-wheat belt of Northwest India. Furthermore, it is accentuated with puddling of soil, which disrupts soil aggregates. Conservation agriculture (CA) practices involving zero tillage, crop residue management and suitable crop rotation can serve as better alternative to conventional agriculture for maintaining soil quality. Soil organic carbon is an important determinant of soil quality, playing critical role in food production, mitigation and adaptation to climate change as well as performs many ecosystem functions. To understand the turnover of soil carbon in different forms (Total organic carbon-TOC; aggregate associated carbon-AAC; particulate organic carbon- POC), soil aggregation and crop productivity with different management practices, one conventional agriculture based scenario and three CA based crop management scenarios namely conventional rice-wheat system (Sc1), partial CA based rice-wheat-mungbean system (Sc2), full CA-based rice-wheat-mungbean system (Sc3) and maize-wheat-mungbean system (Sc4) were evaluated. TOC was increased by 71%, 68% and 25% after 4 years of the experiment and 75%, 80% and 38% after 6 years of the experiment in Sc4, Sc3 and Sc2, respectively, over Sc1 at 0–15 cm soil depth. After 4 years of the experiment, 38.5% and 5.0% and after 6 years 50.8% and 24.4% improvement in total water stable aggregates at 0–15 and 15–30 cm soil depth, respectively was observed in CA-based scenarios over Sc1. Higher aggregate indices were associated with Sc3 at 0–15 cm soil depth than others. Among the size classes of aggregates, highest aggregate associated C (8.94 g kg−1) was retained in the 1-0.5 mm size class under CA-based scenarios. After 6 years, higher POC was associated with Sc4 (116%). CA-based rice/maize system (Sc3 and Sc4) showed higher productivity than Sc1. Therefore, CA could be a potential management practice in rice-wheat cropping system of Northwest India to improve the soil carbon pools through maintaining soil aggregation and productivity.

Simultaneous Biofortification of Wheat with Zinc, Iodine, Selenium, and Iron through Foliar Treatment of a Micronutrient Cocktail in Six Countries.

Abstract: Field experiments were conducted on wheat to study the effects of foliar-applied iodine(I) alone, Zn (zinc) alone, and a micronutrient cocktail solution containing I, Zn, Se (selenium), and Fe (iro...

High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat.

Abstract: Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of two years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

Breeding wheat for resistance to Fusarium head blight in the Global North: China, USA, and Canada

Abstract: The objective of this paper is to review progress made in wheat breeding for Fusarium head blight (FHB) resistance in China, the United States of America (USA), and Canada. In China, numerous Chinese landraces possessing high levels of FHB resistance were grown before the 1950s. Later, pyramiding multiple sources of FHB resistance from introduced germplasm such as Mentana and Funo and locally adapted cultivars played a key role in combining satisfactory FHB resistance and high yield potential in commercial cultivars. Sumai 3, a Chinese spring wheat cultivar, became a major source of FHB resistance in the USA and Canada, and contributed to the release of more than 20 modern cultivars used for wheat production, including the leading hard spring wheat cultivars Alsen, Glenn, Barlow and SY Ingmar from North Dakota, Faller and Prosper from Minnesota, and AAC Brandon from Canada. Brazilian wheat cultivar Frontana, T. dicoccoides and other local germplasm provided additional sources of resistance. The FHB resistant cultivars mostly relied on stepwise accumulation of favorable alleles of both genes for FHB resistance and high yield, with marker-assisted selection being a valuable complement to phenotypic selection. With the Chinese Spring reference genome decoded and resistance gene Fhb1 now cloned, new genomic tools such as genomic selection and gene editing will be available to breeders, thus opening new possibilities for development of FHB resistant cultivars.

An Update of Recent Use of Aegilops Species in Wheat Breeding.

Abstract: Aegilops species have significantly contributed to wheat breeding despite the difficulties involved in the handling of wild species, such as crossability and incompatibility. A number of biotic resistance genes have been identified and incorporated into wheat varieties from Aegilops species, and this genus is also contributing toward improvement of complex traits such as yield and abiotic tolerance for drought and heat. The D genome diploid species of Aegilops tauschii has been utilized most often in wheat breeding programs. Other Aegilops species are more difficult to utilize in the breeding because of lower meiotic recombination frequencies; generally they can be utilized only after extensive and time-consuming procedures in the form of translocation/introgression lines. After the emergence of Ug99 stem rust and wheat blast threats, Aegilops species gathered more attention as a form of new resistance sources. This article aims to update recent progress on Aegilops species, as well as to cover new topics around their use in wheat breeding.

Genetic dissection of drought and heat-responsive agronomic traits in wheat.

Abstract: High yield and wide adaptation are principal targets of wheat breeding but are hindered by limited knowledge on genetic basis of agronomic traits and abiotic stress tolerances. In this study, 277 wheat accessions were phenotyped across 30 environments with non-stress, drought-stressed, heat-stressed, and drought-heat-stressed treatments and were subjected to genome-wide association study using 395 681 single nucleotide polymorphisms. We detected 295 associated loci including consistent loci for agronomic traits across different treatments and eurytopic loci for multiple abiotic stress tolerances. A total of 22 loci overlapped with quantitative trait loci identified by biparental quantitative trait loci mapping. Six loci were simultaneously associated with agronomic traits and abiotic stress tolerance, four of which fell within selective sweep regions. Selection in Chinese wheat has increased the frequency of superior marker alleles controlling yield-related traits in the four loci during past decades, which conversely diminished favourable genetic variation controlling abiotic stress tolerance in the same loci; two promising candidate paralogous genes colocalized with such loci, thereby providing potential targets for studying the molecular mechanism of stress tolerance-productivity trade-off. These results uncovering promising alleles controlling agronomic traits and/or multiple abiotic stress tolerances, providing insights into heritable covariation between yield and abiotic stress tolerance, will accelerate future efforts for wheat improvement.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Abstract: Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregon, Mexico over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.

BrAPI-an application programming interface for plant breeding applications.

Abstract: Motivation: Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge. Results: To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases.

A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.

Abstract: Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.

Gender differences in the adoption of agricultural technology: The case of improved maize varieties in southern Ethiopia

Abstract: This study explores the role of gender-based decision-making in the adoption of improved maize varieties. The primary data were collected in 2018 from 560 farm households in Dawuro Zone, Ethiopia, and were comparatively analyzed across gender categories of households: male decision-making, female decision-making and joint decision-making, using a double-hurdle model. The results show that the intensity of improved maize varieties adopted on plots managed by male, female, and joint decision-making households are significantly different. This effect diminishes in the model when we take other factors into account. Using the gender of the heads of households and agricultural decision-maker, the current study did not find significant evidence of gender difference in the rate and intensity of adoption of improved maize varieties. The intensity of adoption of improved maize varieties is lower for female-headed households where decisions are made jointly by men and women, compared to the male-headed households where decisions are made jointly. As the economic status is a key driver of adoption of improved maize varieties, it is recommended that the policies and programs that aim at developing and disseminating quality maize seeds in southern Ethiopia should emphatically support economically less endowed but more gender egalitarian joint decision-making households, especially female-headed ones.