Flowering time is an important trait in wheat breeding as it affects adaptation and yield potential. The aim of this study was to investigate the genetic architecture of flowering time in European winter bread wheat cultivars. To this end a population of 410 winter wheat varieties was evaluated in multi-location field trials and genotyped by a genotyping-by-sequencing approach and candidate gene markers. Our analyses revealed that the photoperiod regulator Ppd-D1 is the major factor affecting flowering time in this germplasm set, explaining 58% of the genotypic variance. Copy number variation at the Ppd-B1 locus was present but explains only 3.2% and thus a comparably small proportion of genotypic variance. By contrast, the plant height loci Rht-B1 and Rht-D1 had no effect on flowering time. The genome-wide scan identified six QTL which each explain only a small proportion of genotypic variance and in addition we identified a number of epistatic QTL, also with small effects. Taken together, our results show that flowering time in European winter bread wheat cultivars is mainly controlled by Ppd-D1 while the fine tuning to local climatic conditions is achieved through Ppd-B1 copy number variation and a larger number of QTL with small effects.
In contrast to high-throughput genotyping which can manage a large number of plants at relatively low cost, phenotyping of many individual genotypes in field trials is still laborious and expensive. Early plant vigour, as an early selection criterion, is a trait that is visually scored due to a lack of suitable phenotyping methods for an accurate detection of this trait in large field trials. A high-throughput phenotyping technique for scoring early plant vigour would enhance the breeding process. This study was conducted to develop a method for scoring phenotypic differences in early plant vigour of 50 winter wheat ( L.) cultivars in a 2-years experiment using a vehicle based multispectral active sensor and two commercially available active sensors, GreenSeeker and CropCircle. Pixel analysis of RGB images revealed to be the most feasible and superior method compared to other possible reference methods. A comparison between the two years 2011 and 2012 confirmed that early plant vigour was affected by genotypic differences. A novel spectral plant vigour index (EPVI) was found to accurately reflect the plant vigour at tillering. Different methods were applied to identify optimal combinations of wavelengths to predict early plant vigour, including multivariate modelling and prediction, contour maps for identifying all possible simple ratios and testing of combined indices. The EPVI and the relative amount of green pixels (RAGP) derived from digital images were significantly related with = 0.98 to each other in both years. A total of 200 plots, 12 m in length, could be measured within 75 min. The EPVI was shown to be an accurate scoring method for the high-throughput screening of large field trials. The rapidity and accuracy of this novel method may contribute to enhanced selection at early growth stages.
A powdery mildew resistance gene was introgressed from Aegilops speltoides into winter wheat and mapped to chromosome 5BL. Closely linked markers will permit marker-assisted selection for the resistance gene. Powdery mildew of wheat (Triticum aestivum L.) is a major fungal disease in many areas of the world, caused by Blumeria graminis f. sp. tritici (Bgt). Host plant resistance is the preferred form of disease prevention because it is both economical and environmentally sound. Identification of new resistance sources and closely linked markers enable breeders to utilize these new sources in marker-assisted selection as well as in gene pyramiding. Aegilops speltoides (2n = 2x = 14, genome SS), has been a valuable disease resistance donor. The powdery mildew resistant wheat germplasm line NC09BGTS16 (NC-S16) was developed by backcrossing an Ae. speltoides accession, TAU829, to the susceptible soft red winter wheat cultivar ‘Saluda’. NC-S16 was crossed to the susceptible cultivar ‘Coker 68-15’ to develop F2:3 families for gene mapping. Greenhouse and field evaluations of these F2:3 families indicated that a single gene, designated Pm53, conferred resistance to powdery mildew. Bulked segregant analysis showed that multiple simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers specific to chromosome 5BL segregated with the resistance gene. The gene was flanked by markers Xgwm499, Xwmc759, IWA6024 (0.7 cM proximal) and IWA2454 (1.8 cM distal). Pm36, derived from a different wild wheat relative (T. turgidum var. dicoccoides), had previously been mapped to chromosome 5BL in a durum wheat line. Detached leaf tests revealed that NC-S16 and a genotype carrying Pm36 differed in their responses to each of three Bgt isolates. Pm53 therefore appears to be a new source of powdery mildew resistance.
Biotic stresses including diseases (leaf, stem and stripe rusts), arthropods (greenbug [GB], Hessian fly [Hf], Russian wheat aphid [RWA], and wheat curl mite [WCM]) and their transmitted viral diseases significantly affect grain yield and end-use quality of hard winter wheat (Triticum aestivum L.) in the U. S. Great Plains. Many genes or quantitative trait loci (QTL) have been identified for seedling or adult-plant resistance to these stresses. Molecular markers for these genes or QTL have been identified using mapping or cloning. This study summarizes the markers associated with various effective genes, including genes or QTL conferring resistances to arthropods, such as GB (7), RWA (4), Hf (9), and WCM (4) and diseases including leaf, stem and stripe rusts (26) and Wheat streak mosaic virus (WSMV; 2); genes or QTL for end-use quality traits such as high (3) and low (13) molecular weight glutenin subunits, gliadin (3), polyphenol oxidase (2), granule-bound starch synthase (3), puroindoline (2), and preharvesting sprouting (1); genes on wheat-rye (Secale cereale L.) chromosomal translocations of 1AL. 1RS and 1BL. 1RS; and genes controlling plant height (12), photoperiod sensitivity (1), and vernalization (2). A subset of the markers was validated using a set of diverse wheat lines developed by breeding programs in the Great Plains. These analyses showed that most markers are diagnostic in only limited genetic backgrounds. However, some markers developed from the gene sequences or alien fragments are highly diagnostic across various backgrounds, such as those markers linked to Rht-B1, Rht-D1, Ppd-D1, Glu-D1, Glu-A1, and 1AL. 1RS. Knowledge of both genotype and phenotype of advanced breeding lines could help breeders to select the optimal parents to integrate various genes into new cultivars and increase the efficiency of wheat breeding.
Many spectral indices have been proposed to derive plant nitrogen (N) nutrient indicators based on different algorithms. However, the relationships between selected spectral indices and the canopy N content of crops are often inconsistent. The goals of this study were to test the performance of spectral indices and partial least square regression (PLSR) and to compare their use for predicting canopy N content of winter wheat. The study was conducted in cool and wet southeastern Germany and the hot and dry North China Plain for three winter wheat growing seasons. The canopy N content of winter wheat varied from 0.54% to 5.55% in German cultivars and from 0.57% to 4.84% in Chinese cultivars across growth stages and years. The best performing spectral indices and their band combinations varied across growth stages, cultivars, sites and years. Compared with the best performing spectral indices, the average value of the for the PLSR models increased by 76.8% and 75.5% in the calibration and validation datasets, respectively. The results indicate that PLSR is a potentially useful approach to derive canopy N content of winter wheat across growth stages, cultivars, sites and years under field conditions when a broad set of canopy reflectance data are included in the calibration models. PLSR will be useful for real-time estimation of N status of winter wheat in the fields and for guiding farmers in the accurate application of their N fertilisation strategies.
The yield gap (YG) between the potential yields (Yp) and the average on-farm yields (Ya) is an indicator of the potential improvement for crop production. Understanding how large the current gap is and how this gap has changed over the past few decades is essential for increasing wheat production to meet increased food demand in China. This paper describes a study conducted using an APSIM-Wheat model and farm-level crop yield to analyze the spatio-temporal distribution of the yield gap of winter wheat from 1981 to 2010 in the North China Plain. Nine varieties were calibrated and evaluated based on the data from 16 agro-meteorological experimental sites and then potential yields were estimated considering cultivar replacement. In addition, a trend pattern analysis of on-farm yields for the period 1981–2010 was conducted. Results revealed an estimated yield gap across the entire North China Plain region of 1140–6810 kg ha , with a weight average of 3630 kg ha in 1981–2010. Expressed as a relative yield (yield gap % of potential yields), the range was 15–80%, and the weight average was 45%. Despite the negative effects of increasing temperature and decreasing radiation, the potential yields significantly increased by 45 kg ha per year due to cultivar improvement. On-farm yields increased even more notably because of new cultivar selection, increased fertilizer application and other management improvements, but were stagnating in 32.3% of wheat areas, located mainly in Hebei province, Shandong province, Beijing and Tianjin. The improvement of on-farm yields have substantially contributed to yield gap spatio-temporal variation. As a result, the yield gap decreased from 4200 kg ha (56%) in 1981–1990 to 3000 kg ha (35%) in 2001–2010 at a rate of −69 kg ha per year. However, yields stagnation will expand to the northern Henan province without cultivar potential productivity improving, where yield gap was close to or less than 20% of the potential yields and proved difficult to reduce. To further improve the total production of winter wheat in the coming decades, efforts should be paid to break the potential ceiling and reduce the yield gap by breeding higher yield variety and introduction of new agricultural technology.
High resolution climate data, derived from the ENSEMBLES Regional Climate Models outputs, were used to assess the impacts of climate change on crop water and irrigation requirements and yield of winter wheat and tomato in the Mediterranean region. Data, based on the A1B emission scenario, were arranged purposefully to represent the years 2000 and 2050. Over this 50-years span, an overall reduction of annual precipitation of 39.1 ± 55.1 mm and an increase of air temperature of 1.57 ± 0.27 °C (from 0.84 to 2.31 °C) are predicted. The consequent increase of annual reference evapotranspiration is 92.3 ± 42.1 mm (6.7%). The potentially cultivable areas of winter wheat and tomato may increase by 7 and 24%, respectively, and might be extended prevalently in the Northern Mediterranean countries. The average length of growing season was estimated to be shorter in 2050 by 15 and 12 days for wheat and tomato, respectively. Due to anticipation and shortening of growing season, the crop evapotranspiration is foreseen to be reduced by 6 and 5% for wheat and tomato, respectively. The net irrigation requirements (NIR) under optimal water supply may decrease by 11% for wheat and 5% for tomato. Under moderate deficit irrigation, NIR are foreseen to decrease by 14 and 7% respectively for wheat and tomato. As a whole, a slight increase of relative yield losses (RYL) is expected for rainfed wheat, particularly in the Northern Mediterranean. Overall, tomato RYL are not expected to change in the future. The foreseen impact of precipitation decrease is more relevant for winter-spring crops. Hence, the adoption of supplemental irrigation for winter wheat could become more widespread also in the northern Mediterranean countries. Differently, for tomato, cropped in most areas out of the rainy season, the irrigation strategies are expected to remain similar as today.
This article covers detailed characterization and naming of QSr.sun - 5BL as Sr56 . Molecular markers linked with adult plant stem rust resistance gene Sr56 were identified and validated for marker-assisted selection. The identification of new sources of adult plant resistance (APR) and effective combinations of major and minor genes is well appreciated in breeding for durable rust resistance in wheat. A QTL, QSr.sun-5BL, contributed by winter wheat cultivar Arina providing 12–15 % reduction in stem rust severity, was reported in an Arina/Forno recombinant inbred line (RIL) population. Following the demonstration of monogenic segregation for APR in the Arina/Yitpi RIL population, the resistance locus was formally named Sr56. Saturation mapping of the Sr56 region using STS (from EST and DArT clones), SNP (9 K) and SSR markers from wheat chromosome survey sequences that were ordered based on synteny with Brachypodium distachyon genes in chromosome 1 resulted in the flanking of Sr56 by sun209 (SSR) and sun320 (STS) at 2.6 and 1.2 cM on the proximal and distal ends, respectively. Investigation of conservation of gene order between the Sr56 region in wheat and B. distachyon showed that the syntenic region defined by SSR marker interval sun209-sun215 corresponded to approximately 192 kb in B. distachyon, which contains five predicted genes. Conservation of gene order for the Sr56 region between wheat and Brachypodium, except for two inversions, provides a starting point for future map-based cloning of Sr56. The Arina/Forno RILs carrying both Sr56 and Sr57 exhibited low disease severity compared to those RILs carrying these genes singly. Markers linked with Sr56 would be useful for marker-assisted pyramiding of this gene with other major and APR genes for which closely linked markers are available.
Yellow rust ( f. sp. Tritici), powdery mildew ( ) and wheat aphid ( F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent -test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher's linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level.
Winter wheat is the main crop on the North China Plain (NCP), and in this region the most limiting factor for the crop is water. The objective of this study was to adapt and test the ability of the FAO-developed AquaCrop model (v3.1) to simulate winter wheat grain yield, biomass, actual evapotranspiration (ET ) and total soil water content (0–120 cm). Field experiments were conducted under deficit irrigation at the Luancheng Agro-ecosystem station (NCP) in 1998–2001, and the AquaCrop model was calibrated with treatment D (1999–2000); the rest of the data was used for validation of the model. The AquaCrop model was revalidated with data on measured grain yield from the experimental station for 1990–2010, considering actual field conditions. The second revalidation was done with the statistical grain yield for 1995–2010 in the study region. For the model validation, the significant differences between simulated and observed grain yield, biomass and ET were in the order of: rainfed treatment > well-watered treatment > moderate water stress. Total soil water simulated by AquaCrop tends to follow closely the trend in the measured data, but with slight underestimations for irrigated treatments and significant overestimations for rainfed treatments. In general, errors in the model's evaluation such as RMSE and Willmot's statistics were for grain yield (0.58 Mg ha , 0.92), biomass (0.87 Mg ha , 0.95), ET (33.2 mm, 0.93) and soil water content (24.5–37.6 mm, 0.85–0.90). The overall results based on extensive validation and revalidation showed that AquaCrop is a valid model and can be used with a reliable degree of accuracy for optimizing winter wheat grain yield production and water requirement on the NCP.