Project description:This study examined the effects of late sowing, water restrictions, and interannual weather variations on wheat grain yield and quality through field trials in Spain over two growing seasons. Delayed sowing and water scarcity significantly reduced yields, with grain quality mainly affected under rainfed conditions. Early-maturing varieties performed better in these conditions, benefiting from lower temperatures and extended grain-filling periods, leading to higher solar radiation interception, potentially increased photosynthetic activity, and improved yields. These varieties also saved water through reduced total cumulative evapotranspiration from sowing to maturity (ETo TOT), which was advantageous in water-limited environments. In contrast, late-maturing varieties were exposed to higher maximum temperatures during grain filling and experienced greater ETo TOT, leading to lower yields, reduced hectoliter weight, and a lower P/L ratio (tenacity/extensibility). This study highlighted the importance of optimizing temperature exposure and evapotranspiration for improved grain yield and quality, especially under climate change conditions with higher temperatures and water shortages. Notably, it established, for the first time, the importance of phenology on wheat quality of different varieties, suggesting that targeted selection for specific phenology could mitigate the negative impacts of heat stress not only on grain yield but also on grain quality.
Project description:Crop production is sensitive to anomalous weather conditions, but vegetable crops can be highly sensitive to environmental changes. Using sweet corn data collected on 16,040 fields over a 27-year period, we: (a) estimate yield sensitivities to changes in growing season temperature and total precipitation, (b) estimate critical thresholds in non-linear temperature effects on sweet corn yield across diverse environments, and (c) quantify yield losses from surpassing the upper temperature threshold during anthesis in sweet corn. Our results show growing-season temperatures exceeding 30 [Formula: see text] were detrimental to crop yield. Each additional degree day spent above 30 [Formula: see text] during anthesis reduced crop yields by 0.5% and 2% in irrigated and rainfed fields, respectively. This study shows evidence for sweet corn yield losses across broad spatial domains in the wake of climate change and underscores the urgency to accelerate crop adaptation strategies to sustain production of this highly popular crop.
Project description:Genome-wide association studies (GWAS) were undertaken to identify SNP markers associated with yield and yield-related traits in 123 Pakistani historical wheat cultivars evaluated during 2011-2014 seasons under rainfed field conditions. The population was genotyped by using high-density Illumina iSelect 90K single nucleotide polymorphism (SNP) assay, and finally 14,960 high quality SNPs were used in GWAS. Population structure examined using 1000 unlinked markers identified seven subpopulations (K = 7) that were representative of different breeding programs in Pakistan, in addition to local landraces. Forty four stable marker-trait associations (MTAs) with -log p > 4 were identified for nine yield-related traits. Nine multi-trait MTAs were found on chromosomes 1AL, 1BS, 2AL, 2BS, 2BL, 4BL, 5BL, 6AL, and 6BL, and those on 5BL and 6AL were stable across two seasons. Gene annotation and syntey identified that 14 trait-associated SNPs were linked to genes having significant importance in plant development. Favorable alleles for days to heading (DH), plant height (PH), thousand grain weight (TGW), and grain yield (GY) showed minor additive effects and their frequencies were slightly higher in cultivars released after 2000. However, no selection pressure on any favorable allele was identified. These genomic regions identified have historically contributed to achieve yield gains from 2.63 million tons in 1947 to 25.7 million tons in 2015. Future breeding strategies can be devised to initiate marker assisted breeding to accumulate these favorable alleles of SNPs associated with yield-related traits to increase grain yield. Additionally, in silico identification of 454-contigs corresponding to MTAs will facilitate fine mapping and subsequent cloning of candidate genes and functional marker development.
Project description:In this article, we outline the set-up and the application of an eco-hydrological box model, with the aim to describe the water balance of deciduous (Fagus Sylvatica L.) forest stands. The water balance model (WBM) uses standard meteorological parameters as input variables and runs on a daily time step. It consists of two modules. The aboveground module (1) comprises routines for fog precipitation generation, precipitation interception and snowfall/snowmelt dynamics. Covered belowground processes (2) are bypass flow, percolation, soil evaporation and transpiration, where the latter two processes are considered separately. Preceding to the WBM, a routine is introduced, specifying the intra-annual foliage dynamics of beech. Emphasis is also laid on the inter-annual variation of beech phenology. Leaf sprouting and leaf senescence are calculated as functions of day-length and air temperature. The WBM was applied to four European beech dominated forest stands in the northeastern part of Austria. They are located on a gradient of declining annual precipitation (from west to east). The two easterly sites are located close to the (dry) limit of the natural distribution of beech. Records of soil moisture were used for the adjustment of 26 parameters. On all sites the calibration process (simulated annealing) delivered good predictions of soil moisture (Nash-Sutcliffe efficiency≥ 0.925). Then, the obtained parameterization was used to apply different scenarios of global warming. The temperature was increased step-wisely up to 4 °C. All scenarios were run (1) with present phenological conditions and (2) with phenology responding to higher temperatures. This way, we wanted to assign the effect of higher temperatures and longer growing seasons on the water dynamics of the forest stands. A warming of 1 °C corresponded roughly to an elongation of the growing season of 4.5 days, where the start of the growing season was affected more strongly than the end. Apparently, higher temperatures led to drier soils. The strongest change was observed in early summer, also amplified by an earlier start of the growing season. Rising temperatures led to lower export fluxes of liquid water, simultaneously increasing evapotranspiration (ET). The gain in ET was almost entirely assignable to increased soil evaporation. Drier soils led to a sharp depression of transpiration during summer months. This decline was compensated by the effect of elongated growing seasons. The risk of severe drought was increased by higher temperatures, but here the contribution of growing season length was negligible. Drier soils seem to hamper the stands' productivity. For all warming scenarios, the estimated increase of the gross primary production, caused by longer periods of assimilation, is nullified by the effect of soil water deficit in mid-summer.
Project description:Grain yield (YLD) is affected by thousand kernel weight (TKW) which reflects the combination of grain length (GL), grain width (GW) and grain area (AREA). Grain weight is also influenced by heading time (HT) and plant height (PH). To detect candidate genes and quantitative trait loci (QTL) of yield components, a durum wheat recombinant inbred line (RIL) population was evaluated in three field trials. The RIL was genotyped with a 90K single nucleotide polymorphism (SNP) array and a high-density genetic linkage map with 5134 markers was obtained. A total of 30 QTL were detected including 23 QTL grouped in clusters on 1B, 2A, 3A, 4B and 6B chromosomes. A QTL cluster on 2A chromosome included a major QTL for HT co-located with QTL for YLD, TKW, GL, GW and AREA, respectively. The photoperiod sensitivity (Ppd-A1) gene was found in the physical position of this cluster. Serine carboxypeptidase, Big grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing QTL for seed size. This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate QTL cluster detection. This was a requisite to physically map QTL on durum genome and to identify candidate genes affecting grain yield.
Project description:Grain size is a dominant component of grain weight in cereals. Earlier studies have shown that OsGS5 plays a major role in regulating both grain size and weight in rice via promotion of cell division. In this study, we isolated TaGS5 homoeologues in wheat and mapped them on chromosomes 3A, 3B and 3D. Temporal and spatial expression analysis showed that TaGS5 homoeologues were preferentially expressed in young spikes and developing grains. Two alleles of TaGS5-3A, TaGS5-3A-T and TaGS5-3A-G were identified in wheat accessions, and a functional marker was developed to discriminate them. Association analysis revealed that TaGS5-3A-T was significantly correlated with larger grain size and higher thousand kernel weight. Biochemical assays showed that TaGS5-3A-T possesses a higher enzymatic activity than TaGS5-3A-G. Transgenic rice lines overexpressing TaGS5-3A-T also exhibited larger grain size and higher thousand kernel weight than TaGS5-3A-G lines, and the transcript levels of cell cycle-related genes in TaGS5-3A-T lines were higher than those in TaGS5-3A-G lines. Furthermore, systematic evolution analysis in diploid, tetraploid and hexaploid wheat showed that TaGS5-3A underwent strong artificial selection during wheat polyploidization events and the frequency changes of two alleles demonstrated that TaGS5-3A-T was favoured in global modern wheat cultivars. These results suggest that TaGS5-3A is a positive regulator of grain size and its favoured allele TaGS5-3A-T exhibits a larger potential application in wheat high-yield breeding.
Project description:Transgenic rice plants expressing the Arabidopsis phloem-specific sucrose transporter AtSUC2, which loads Suc into the phloem, showed an increase in grain yield of up to 16% relative to wild-type plants in field trials. The goal was to reveal how expressed AtSUC2 in rice leads to increased grain yield analyzing global gene expression.
Project description:Grain size is potentially yield determining in wheat, controlled by the ubiquitin pathway and negatively regulated by ubiquitin receptor DA1. We analyzed whether increased thousand grain weight in wheat da1 mutant is translated into higher grain yield and whether additional carbon provided by elevated (e)CO2 can be better used by the da1, displaying higher grain sink strength and size. Yield-related, biomass, grain quality traits, and grain dimensions were analyzed by two-factorial mixed-model analysis, regarding genotype and eCO2. da1 increased grain size but reduced spikes and grains per plant, grains per spike, and spikelets per spike, independent of eCO2 treatment, leaving total grain yield unchanged. eCO2 increased yield and grain number additively and independently of da1 but did not overcome the trade-off between grain size and number observed for da1. eCO2 but not da1 impaired grain quality, strongly decreasing concentrations of several macroelement and microelement. In conclusion, intrinsic stimulation of grain sink strength and grain size, achieved by da1, is not benefitting total yield unless trade-offs between grain size and numbers can be overcome. The results reveal interactions of yield components in da1-wheat under ambient and eCO2, thereby uncovering limitations enhancing wheat yield potential.
Project description:Lentil (Lens culinaris Medikus) is a protein-rich cool-season food legume with an excellent source of protein, prebiotic carbohydrates, minerals, and vitamins. With climate change, heat, and drought stresses have become more frequent and intense in lentil growing areas with a strong influence on phenology, grain yield, and nutritional quality. This study aimed to assess the impact of heat and drought stresses on phenology, grain yield, and nutritional quality of lentil. For this purpose, 100 lentil genotypes from the global collection were evaluated under normal, heat, and combined heat-drought conditions. Analysis of variance revealed significant differences (p < 0.001) among lentil genotypes for phenological traits, yield components, and grain quality traits. Under no stress conditions, mineral concentrations among lentil genotypes varied from 48 to 109 mg kg-1 for iron (Fe) and from 31 to 65 mg kg-1 for zinc (Zn), while crude protein content ranged from 22.5 to 32.0%. Iron, zinc, and crude protein content were significantly reduced under stress conditions, and the effect of combined heat-drought stress was more severe than heat stress alone. A significant positive correlation was observed between iron and zinc concentrations under both no stress and stress conditions. Based on grain yield, crude protein, and iron and zinc concentrations, lentil genotypes were grouped into three clusters following the hierarchical cluster analysis. Promising lentil genotypes with high micronutrient contents, crude protein, and grain yield with the least effect of heat and drought stress were identified as the potential donors for biofortification in the lentil breeding program.
Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.