The main goal of this study was to investigate the genetic basis of yield and grain quality traits in winter wheat genotypes using association mapping approach, and identify linked molecular markers for marker assisted selection. A total of 120 elite facultative/winter wheat genotypes were evaluated for yield, quality and other agronomic traits under rain-fed and irrigated conditions for two years (2011-2012) at the Tel Hadya station of ICARDA, Syria. The same genotypes were genotyped using 3,051 Diversity Array Technologies (DArT) markers, of which 1,586 were of known chromosome positions. The grain yield performance of the genotypes was highly significant both in rain-fed and irrigated sites. Average yield of the genotypes ranged from 2295 to 4038 kg/ha and 4268 to 7102 kg/ha under rain-fed and irrigated conditions, respectively. Protein content and alveograph strength (W) ranged from 13.6-16.1% and 217.6-375 Jx10-4, respectively. DArT markers wPt731910 (3B), wPt4680 (4A), wPt3509 (5A), wPt8183 (6B), and wPt0298 (2D) were significantly associated with yield under rain-fed conditions. Under irrigated condition, tPt4125 on chromosome 2B was significantly associated with yield explaining about 13% of the variation. Markers wPt2607 and wPt1482 on 5B were highly associated with protein content and alveograph strength explaining 16 and 14% of the variations, respectively. The elite genotypes have been distributed to many countries using ICARDA's International system for potential direct release and/or use as parents after local adaptation trials by the NARSs of respective countries. The QTLs identified in this study are recommended to be used for marker assisted selection after through validation using bi-parental populations.
Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
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.
Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head.
Data on grain yield from field trials on winter wheat under conventional farming, harvested between 1992 and 2008, were combined with daily weather data available for 44 grids covering Denmark. Nine agroclimatic indices were calculated and used for describing the relation between weather data and grain yield. These indices were calculated as average temperature, radiation and precipitation during winter (1 October-31 March), spring (1 April-15 June) and summer (16 June-31 July), and they were included as linear and quadratic covariates in a mixed regression model. The model also included an effect of year to describe the change in yield caused by unrecorded variables such as management changes. The final model included all effects that were significant for at least one of the two soil types (sandy and loamy soils). Seven of the nine agroclimatic indices were included in the final model that was used to predict the wheat grain yield under five climate scenarios (a baseline for 1985 and two climate change projections for 2020 and 2040) for two soil types and two locations in Denmark. The agroclimatic index for summer temperature showed the strongest effect causing lower yields with increasing temperature, whereas yield increased with increasing radiation during summer and spring. Winter precipitation and spring temperature did not affect grain yield significantly. Grain yield responded non-linearly to mean winter temperature with the highest yield at 4.4 degrees C and lower yields both below and above this inflection point. The application of the model predicted that the average yield would decrease under projected climate change. The average decrease varied between 0.1 and 0.8 t/ha (comparable to a relative reduction of 1.6-12.3%) depending on the climate projection, location and soil type. On average, the grain yield decreased by about 0.25 t/ha (c. 3.6%) from 1985 to 2020 and by about 0.55 t/ha (c. 8.0%) from 1985 to 2040. The predicted yield decrease depended on climate projection and was larger for wheat grown in West Zealand than in Central Jutland and in most cases also larger for loamy soils than for sandy soils. The inter-annual variation in grain yield varied greatly between climate projections. The coefficient of variation (CV) varied between 0.16 and 0.46 and was smallest for wheat grown on loamy soils in Central Jutland in the baseline climate and largest for winter wheat grown under one of the 2040 climate projections. The increase in CV is not so much an effect of increased climatic variability under the climate change projections, but more an effect of increased winter temperature, where more extreme winter temperatures (lower or higher than the inflection point at 4.4 degrees C) increased the effect of winter temperatures.
Interseeding annual clovers in cereal grains may help organic producers reduce use of tillage following cereal harvest. Using clovers that winterkill would minimize need for tillage in the spring also. The objective of this study was to evaluate seedling emergence and survival of berseem clover (Trifolium alexandrinum L.) in winter wheat (Triticum aestivum L.). Berseem clover (hereafter, referred to as berseem) was planted 0, 2 and 4 weeks after initiation of winter wheat growth in the spring. Berseem density was highest when planted on April 12, 2 weeks after winter wheat broke dormancy. Establishment density was 40-80% less with the other planting dates. A dry interval during the 5 weeks preceding winter wheat harvest reduced seedling survival of berseem, killing more than 80% of seedlings. Winter wheat yield was reduced at the last planting date of berseem, which was attributed to mechanical injury to winter wheat by the drill when planting berseem. Berseem may not be viable for interseeding at this location or in drier regions. Clover species that are more drought tolerant will be needed.
A suitable planting pattern and irrigation strategy are essential for optimizing winter wheat yield and water use efficiency (WUE). The study aimed to evaluate the impact of planting pattern and irrigation frequency on grain yield and WUE of winter wheat. During the 20132014 and 2014-2015 winter wheat growing seasons in the North China Plain, the effects of planting patterns and irrigation frequencies were determined on tiller number, grain yield, and WUE. The two planting patterns tested were wide-precision and conventional-cultivation. Each planting pattern had three irrigation regimes: irrigation (120 mm) at the jointing stage; irrigation (60 mm) at both the jointing and heading stages; and irrigation (40 mm) at the jointing, heading, and milking stages. In our study, tiller number was significantly higher in the wide-precision planting pattern than in the conventional-cultivation planting pattern. Additionally, the highest grain yields and WUE were observed when irrigation was applied at the jointing stage (120 mm) or at the jointing and heading stages (60 mm each) in the wide-precision planting pattern. These results could be attributed to higher tiller numbers as well as reduced water consumption due to reduced irrigation frequency. In both growing seasons, applying 60 mm of water at jointing and heading stages resulted in the highest grain yield among the treatments. Based on our results, for winter wheat production in semi-humid regions, we recommend a wide-precision planting pattern with irrigation (60 mm) at both the jointing and heading stages.
Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO) algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI) was the best spectral index for estimating biomass, with the coefficient of determination (R-2), root mean square error (RMSE), and relative RMSE (RRMSE) values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R-2 = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60%) achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R-2 = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%). This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data.
Water shortage and nitrogen (N) deficiency are the key factors limiting agricultural production in arid and semi-arid regions, and increasing agricultural productivity under rain-fed conditions often requires N management strategies. A field experiment on winter wheat (Triticum aestivum L.) was begun in 2004 to investigate effects of long-term N fertilization in the traditional pattern used for wheat in China. Using data collected over three consecutive years, commencing five years after the experiment began, the effects of N fertilization on wheat yield, evapotranspiration (ET) and water use efficiency (WUE, i.e. the ratio of grain yield to total ET in the crop growing season) were examined. In 2010, 2011 and 2012, N increased the yield of wheat cultivar Zhengmai No. 9023 by up to 61.1, 117.9 and 34.7%, respectively, and correspondingly in cultivar Changhan No. 58 by 58.4, 100.8 and 51.7%. N-applied treatments increased water consumption in different layers of 0-200 cm of soil and thus ET was significantly higher in N-applied than in non-N treatments. WUE was in the range of 1.0-2.09 kg/m(3) for 2010, 2011 and 2012. N fertilization significantly increased WUE in 2010 and 2011, but not in 2012. The results indicated the following: (1) in this dryland farming system, increased N fertilization could raise wheat yield, and the drought-tolerant Changhan No. 58 showed a yield advantage in drought environments with high N fertilizer rates; (2) N application affected water consumption in different soil layers, and promoted wheat absorbing deeper soil water and so increased utilization of soil water; and (3) comprehensive consideration of yield and WUE of wheat indicated that the N rate of 270 kg/ha for Changhan No. 58 was better to avoid the risk of reduced production reduction due to lack of precipitation; however, under conditions of better soil moisture, the N rate of 180 kg/ha was more economic.
The production and cultivation of hybrid wheat is a possible strategy to close the yield gap in wheat. Efficient hybrid wheat seed production largely depends on high rates of cross-pollination which can be ensured through high anther extrusion (AE) by male parental lines. Here, we report the AE capacity and elucidate its genetics in 514 elite European winter wheat varieties via genome-wide association studies (GWAS). We observed a wide range of variation among genotypes and a high heritability (0.80) for AE. The whole panel was genotyped with the 35k Affymetrix and 90k iSELECT single nucleotide polymorphism (SNP) arrays plus Ppd-D1, Rht-B1 and Rht-D1 candidate markers. GWAS revealed 51 marker-trait associations (MTAs) on chromosomes 1A, 1B, 2A, 4D and 5B, with Rht-D1 (4D) being the most significant marker. Division of whole panel according to the Rht-D1 genotype resulted in 212 and 294 varieties harboring Rht-D1a and Rht-D1b allele, respectively. The presence of Rht-D1a compared to Rht-D1b (mutant) allele had a large phenotypic influence on AE resulting in its similar to 17% increase. GWAS performed on the sub-panels detected novel MTAs on chromosomes 2D, 3B and 6A with increased phenotypic variance imparted by individual markers. Our study shows that AE is a highly quantitative trait and wild type Rht-D1a allele greatly improves AE. Moreover, demarcating the quantitative trait loci regions based on intra-chromosomal linkage disequilibrium revealed AE's candidate genes/genomic regions. Understanding the genetics of AE in elite European wheat and utilizing the linked markers in breeding programs can help to enhance cross-pollination for better exploitation of heterosis.