Dryland Wheat variety selection in the Texas High Plain

Selecting the best wheat varieties affects producers’ profit and financial risk. This study identifies the optimal wheat variety selection using the portfolio approach at various risk aversion levels. Results showed that the optimal wheat variety selection was significantly affected by changes in levels of risk aversion of decision makersDryland, Portfolio, risk, wheat Variety, Farm Management,

Physiology and transcriptomics of water-deficit stress responses in wheat cultivars TAM 111 and TAM 112

Citation: Reddy, S. K., Liu, S., Rudd, J. C., Xue, Q., Payton, P., Finlayson, S. A., … Lu, N. (2014). Physiology and transcriptomics of water-deficit stress responses in wheat cultivars TAM 111 and TAM 112. Retrieved from http://krex.ksu.eduHard red winter wheat crops on the U.S. Southern Great Plains often experience moderate to severe drought stress, especially during the grain filling stage, resulting in significant yield losses. Cultivars TAM 111 and TAM 112 are widely cultivated in the region, share parentage and showed superior but distinct adaption mechanisms under water-deficit (WD) conditions. Nevertheless, the physiological and molecular basis of their adaptation remains unknown. A greenhouse study was conducted to understand the differences in the physiological and transcriptomic responses of TAM 111 and TAM 112 to WD stress. Whole-plant data indicated that TAM 112 used more water, produced more biomass and grain yield under WD compared to TAM 111. Leaf-level data at the grain filling stage indicated that TAM 112 had elevated abscisic acid (ABA) content and reduced stomatal conductance and photosynthesis as compared to TAM 111. Sustained WD during the grain filling stage also resulted in greater flag leaf transcriptome changes in TAM 112 than TAM 111. Transcripts associated with photosynthesis, carbohydrate metabolism, phytohormone metabolism, and other dehydration responses were uniquely regulated between cultivars. These results suggested a differential role for ABA in regulating physiological and transcriptomic changes associated with WD stress and potential involvement in the superior adaptation and yield of TAM 112

QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.Horticulture and Landscape Architectur

Genetic mapping of quantitative trait loci for end-use quality and grain minerals in hard red winter wheat

To meet the demands of different wheat-based food products, traits related to end-use quality become indispensable components in wheat improvement. Thus, markers associated with these traits are valuable for the timely evaluation of protein content, kernel physical characteristics, and rheological properties. Hereunder, we report the mapping results of quantitative trait loci (QTLs) linked to end-use quality traits. We used a dense genetic map with 5199 SNPs from a 90K array based on a recombinant inbred line (RIL) population derived from ‘CO960293-2’/‘TAM 111’. The population was evaluated for flour protein concentration, kernel characteristics, dough rheological properties, and grain mineral concentrations. An inclusive composite interval mapping model for individual and across-environment QTL analyses revealed 22 consistent QTLs identified in two or more environments. Chromosomes 1A, 1B, and 1D had clustered QTLs associated with rheological parameters. Glu-D1 loci from CO960293-2 and either low-molecular-weight glutenin subunits or gliadin loci on 1A, 1B, and 1D influenced dough mixing properties substantially, with up to 34.2% of the total phenotypic variation explained (PVE). A total of five QTLs associated with grain Cd, Co, and Mo concentrations were identified on 3B, 5A, and 7B, explaining up to 11.6% of PVE. The results provide important genetic resources towards understanding the genetic bases of end-use quality traits. Information about the novel and consistent QTLs provided solid foundations for further characterization and marker designing to assist selections for end-use quality improvements.Horticulture and Landscape Architectur

Supplementary File for Capturing wheat phenotypes at the genome level

Supplementary S1: Yield and related traits in bread wheat. Table S1: Examples of genomic regions, candidate and cloned genes for yield and related traits in bread wheat. Supplementary S2: Drought tolerance. Table S2: Examples of genomic regions and candidate genes for drought tolerance. Supplementary S3: Heat tolerance. Table S3. Examples of genomic regions and candidate genes for heat tolerance. Supplementary S4: salinity tolerance in bread wheat. Table S4. Examples of genomic regions and candidate genes for salinity tolerance in bread wheat. Supplementary S5: Frost tolerance. Supplementary S6: Disease resistance. Table S5. Examples of genomic regions, candidate and cloned genes mapped for disease resistance in wheat species. Supplementary S7 insect and mite resistance. Table S6. Examples of genomic regions and candidate genes mapped for insect and mite resistance. Supplementary S8: Quality traits. Table S7. Examples of genomic regions, candidate and cloned genes for quality traits.Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.Peer reviewe

Molecular mapping of greenbug resistance genes \u3ci\u3eGb2\u3c/i\u3e and \u3ci\u3eGb6\u3c/i\u3e in T1AL.1RS wheat-rye translocations

Two greenbug resistance genes Gb2 and Gb6 derived from the same donor rye line \u27Insave\u27, are present in wheat germplasm lines \u27Amigo\u27 and \u27GRS1201\u27, respectively as 1AL.1RS wheat-rye translocations. The allelic relationship between the two genes has not been determined, and no molecular markers for Gb6 are available. In this study, molecular mapping of Gb2 and Gb6 was performed in a mapping population derived from a cross between \u27N96L9970\u27 (Gb6Gb6) and \u27TAM 107\u27 (Gb2Gb2). Segregation among F2:3 families of host responses to infestation of greenbug biotypes E and KS-1 revealed that Gb2 and Gb6 were different linked loci in 1RS. Gb2 and Gb6 were 15.8 cM apart with Gb6 being distal to Gb2. Despite the low number of marker polymorphisms between the parental lines, eight markers linked with Gb2 and Gb6 were identified. The closest marker, XIA294, was 11.4 cM proximal to Gb2. Deletion mapping indicated that both Gb2 and Gb6 were physically located in the satellite region of 1RS

Dryland Wheat variety selection in the Texas High Plain

Selecting the best wheat varieties affects producers’ profit and financial risk. This study identifies the optimal wheat variety selection using the portfolio approach at various risk aversion levels. Results showed that the optimal wheat variety selection was significantly affected by changes in levels of risk aversion of decision maker

Thermal imaging to evaluate wheat genotypes under dryland conditions

Abstract Thermal imaging has been used to determine canopy temperature and study plant water relationships. The objective of this study was to investigate the potential use of infrared thermal imaging to determine crop canopy temperature (Tc) and evaluate wheat (Triticum aestivum L.) genotypes under drought conditions. Thermal images were acquired at anthesis and grain‐filling stages from 17 genotypes grown under dryland conditions in 2015 and 2016 winter wheat growing season at Bushland, TX. A handheld thermal camera was used to acquire thermal images and the images were processed using customized image processing software. The customized software filters out the background soil from the thermal images and calculates the mean Tc. A significant difference (p < .05) in Tc among genotypes was found during grain filling in 2015 and at anthesis in 2016. Genotypes TAM 111, TAM 114, PlainsGold Byrd, and Jagalene had cooler canopies, and Billings, TAM 304, and TAM 105 had warmer canopies in both years. There was a significant negative correlation between grain yield and Tc measured at anthesis (r =–.48, p < .05) and grain‐filling (r = –.33, p < .05). Infrared thermal imaging showed a promising method to obtain Tc, which can be used to evaluate genotypes for drought tolerance

Assessing the effect of drought on winter wheat growth using unmanned aerial system (UAS)-based phenotyping

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains

Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p &lt; 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p &lt; 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains