Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments.

Increasing grain number through fine-tuning duration of the late reproductive phase (LRP; terminal spikelet to anthesis) without altering anthesis time has been proposed as a genetic strategy to increase yield potential (YP) of wheat. Here we conducted a modelling analysis to evaluate the potential of fine-tuning LRP in raising YP in irrigated mega-environments. Using the known optimal anthesis and sowing date of current elite benchmark genotypes, we applied a gene-based phenology model for long-term simulations of phenological stages and yield-related variables of all potential germplasm with the same duration to anthesis as the benchmark genotypes. These diverse genotypes had the same duration to anthesis but varying LRP duration. Lengthening LRP increased YP and harvest index by increasing grain number to some extent and an excessively long LRP reduced YP due to reduced time for canopy construction for high biomass production of pre-anthesis phase. The current elite genotypes could have their LRP extended for higher YP in most sites. Genotypes with a ratio of the duration of LRP to pre-anthesis phase of about 0.42 ensured high yields (≥95% of YP) with their optimal sowing and anthesis dates. Optimization of intermediate growth stages could be further evaluated in breeding programmes to improve YP.

Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop growth model with a radiative transfer model to introduce biological constraints in a synthetic training dataset. In addition to the comparison of two datasets without and with biological constraints, we also investigated the effects of observation geometry, retrieval method, and wavelength range on estimation accuracy of four crop traits (leaf area index, leaf chlorophyll content, leaf dry matter, and leaf water content) of wheat. The theoretical analysis demonstrated potential advantages of adding biological constraints in synthetic training datasets as well as the capability of deep learning. Additionally, the predictive models were validated on real unmanned aerial vehicle-based multispectral images collected from wheat plots contrasting in canopy structure. The predictive model trained over a synthetic dataset with biological constraints enabled the prediction of leaf water content from using wavelengths in the visible to near infrared range based on the correlations between crop traits. Our findings presented the potential of the proposed conceptual framework in simultaneously retrieving multiple crop traits from canopy reflectance for applications in precision agriculture and plant breeding.

Isolation and sequencing of a single copy of an introgressed chromosome from a complex genome for gene and SNP identification.

KEY MESSAGE: This manuscript describes the identification, isolation and sequencing of a single chromosome containing high value resistance genes from a complex polyploid where sequencing the whole genome is too costly. The large complex genomes of many crops constrain the use of new technologies for genome-assisted selection and genetic improvement. One method to simplify a genome is to break it into individual chromosomes by flow cytometry; however, in many crop species most chromosomes cannot be isolated individually. Flow sorting of a single copy of a chromosome has been developed in wheat, and here we demonstrate its use to identify markers of interest in an Erianthus/Sacchurum hybrid. Erianthus/Saccharum hybrids are of interest because Erianthus is known to be highly resistant to soil borne diseases which cause extensive sugarcane yield losses in Australia. Sugarcane (Saccharum) cultivars are autopolyploids with a highly complex genome and over 100 chromosomes. Flow cytometry for sugarcane, as in most crops, does not resolve individual chromosomes to a karyotype peak for sorting. To isolate a single chromosome, we used genomic in situ hybridization (GISH) to identify the flow karyotype region containing the Erianthus chromosomes, flow sorted single chromosomes from this region, PCR screened for the Erianthus chromosomes and sequenced them. One Erianthus chromosome amplified and sequenced well, and from this data we could identify 57 resistant type genes and SNPs in nearly half of these genes. We developed KASP SNP assays and demonstrated that the identified SNP markers segregated as expected in a small introgression population. The pipeline we developed here to flow sort and sequence single chromosomes could be used in any crop with a large complex genome to rapidly discover and develop markers to important loci.

Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments.

To maximize the grain yield of spring wheat, flowering needs to coincide with the optimal flowering period (OFP) by minimizing frost and heat stress on reproductive development. This global study conducted a comprehensive modelling analysis of genotype, environment, and management to identify the OFPs for sites in irrigated mega-environments of spring wheat where the crop matures during a period of increasing temperatures. We used a gene-based phenology model to conduct long-term simulation analysis with parameterized genotypes to identify OFPs and optimal sowing dates for sites in irrigated mega-environments, considering the impacts of frost and heat stress on yield. The validation results showed that the gene-based model accurately predicted wheat heading dates across global wheat environments. The long-term simulations indicated that frost and heat stress significantly advanced or delayed OFPs and shrank the durations of OFPs in irrigated mega-environments when compared with OFPs where the model excluded frost and heat stress impacts. The simulation results (incorporating frost and heat penalties on yield) also showed that earlier flowering generally resulted in higher yields, and early sowing dates and/or early flowering genotypes were suggested to achieve early flowering. These results provided an interpretation of the regulation of wheat flowering to the OFP by the selection of sowing date and cultivar to achieve higher yields in irrigated mega-environments.

A reduced-tillering trait shows small but important yield gains in dryland wheat production.

Reducing the number of tillers per plant using a tiller inhibition (tin) gene has been considered as an important trait for wheat production in dryland environments. We used a spatial analysis approach with a daily time-step coupled radiation and transpiration efficiency model to simulate the impact of the reduced-tillering trait on wheat yield under different climate change scenarios across Australia's arable land. Our results show a small but consistent yield advantage of the reduced-tillering trait in the most water-limited environments both under current and likely future conditions. Our climate scenarios show that whilst elevated [CO2 ] (e[CO2 ]) alone might limit the area where the reduced-tillering trait is advantageous, the most likely climate scenario of e[CO2 ] combined with increased temperature and reduced rainfall consistently increased the area where restricted tillering has an advantage. Whilst long-term average yield advantages were small (ranged from 31 to 51 kg ha-1  year-1 ), across large dryland areas the value is large (potential cost-benefits ranged from Australian dollar 23 to 60 MIL/year). It seems therefore worthwhile to further explore this reduced-tillering trait in relation to a range of different environments and climates, because its benefits are likely to grow in future dry environments where wheat is grown around the world.

Modelling impact of early vigour on wheat yield in dryland regions.

Early vigour, or faster early leaf area development, has been considered an important trait for rainfed wheat in dryland regions such as Australia. However, early vigour is a genetically complex trait, and results from field experiments have been highly variable. Whether early vigour can lead to improved water use efficiency and crop yields is strongly dependent on climate and management conditions across the entire growing season. Here, we present a modelling framework for simulating the impact of early vigour on wheat growth and yield at eight sites representing the major climate types in Australia. On a typical soil with plant available water capacity (PAWC) of 147 mm, simulated yield increase with early vigour associated with larger seed size was on average 4% higher compared with normal vigour wheat. Early vigour through selection of doubled early leaf sizes could increase yield by 16%. Increase in yield was mainly from increase in biomass and grain number, and was reduced at sites with seasonal rainfall plus initial soil water <300 mm. Opportunities exists for development of early vigour wheat varieties for wetter sites. Soil PAWC could play a significant role in delivering the benefit of early vigour and would require particular attention.

Improving process-based crop models to better capture genotype×environment×management interactions.

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

Optimizing soil-coring strategies to quantify root-length-density distribution in field-grown maize: virtual coring trials using 3-D root architecture models.

Background and Aims: Root distribution has a major influence on soil exploration and nutrient and water acquisition by plants. Soil coring is a well-known way to estimate root distribution. However, identifying an optimal core-sampling strategy is important if one is to strike the right balance between the high cost of making field estimates of root length density (RLD) vs. the need for accurate estimates. Virtual assessment of competing soil-coring strategies, based on three-dimensional (3-D) models of root system architecture (RSA), is a highly effective way to find that balance. Methods: The trajectories of the axile roots of two maize cultivars having contrasting axile root angles were measured in the field using in situ 3-D digitization. Lateral roots were also measured by recording topological and geometrical parameters. Based on the measurement dataset obtained, contrasting 3-D RSA models of individual maize plants were constructed in which the different lateral rooting angles were represented. Using these RSA models the accuracies of various core-sampling strategies for estimating RLD were assessed in a series of virtual experiments. Key Results: Substantial biases occur if a one-core sampling strategy is used to estimate RLD. The biases largely remain for two-core sampling, although a weighting method can reduce these. However, given that identification of an optimal weighting method is difficult in practice, a new sampling strategy is proposed based on an area-weighting algorithm. In this way low deviations in RLD estimation can be achieved by sampling between rows and also by using larger-diameter (7.5 or 10 cm) cores. Conclusions: A 3-D root architecture model based on a detailed measurement dataset provides an ideal platform for assessing a range of soil-coring strategies. The improved two-core sampling strategy, based on an area-weighting algorithm, shows considerable promise as a cost-efficient way of obtaining good quality RLD estimates for maize.

Projected impact of future climate on water-stress patterns across the Australian wheatbelt.

Drought frequently limits Australian wheat production, and the expected future increase in temperatures and rainfall variability will further challenge productivity. A modelling approach captured plant×environment×management interactions to simulate water-stress patterns experienced by wheat crops at representative locations across the Australian wheatbelt for 33 climate model projections, considering the 'business as usual' emission scenario RCP8.5. The results indicate that projections of future water-stress patterns are region specific. Significant variations in projected impacts were found across climate models, providing local ranges of uncertainty to consider in planning efforts. Most climate models projected an increase in the frequency of severe water-stress conditions in the Western area, the largest producing region, and fewer severe water stresses in other regions. Where found, reductions in water-stress conditions were largely due to shorter crop cycles (a result of warmer temperatures), increased water use efficiency (resulting from increased CO2 levels), and, in some cases, increased local rainfall. Overall, simulations indicate that all areas of the Australian wheatbelt will continue to experience severe water-stress conditions (43.9, 42.6, and 40.2% for 2030, 2050, and 2070 compared with 42.8% for 1990). Given projected frequencies of severe water stress and warmer conditions, efforts towards maintaining or improving yields are essential.

EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions.

Understanding interactions of genotype, environment, and management under field conditions is vital for selecting new cultivars and farming systems. Image analysis is considered a robust technique in high-throughput phenotyping with non-destructive sampling. However, analysis of digital field-derived images remains challenging because of the variety of light intensities, growth environments, and developmental stages. The plant canopy coverage (PCC) ratio is an important index of crop growth and development. Here, we present a tool, EasyPCC, for effective and accurate evaluation of the ground coverage ratio from a large number of images under variable field conditions. The core algorithm of EasyPCC is based on a pixel-based segmentation method using a decision-tree-based segmentation model (DTSM). EasyPCC was developed under the MATLAB® and R languages; thus, it could be implemented in high-performance computing to handle large numbers of images following just a single model training process. This study used an experimental set of images from a paddy field to demonstrate EasyPCC, and to show the accuracy improvement possible by adjusting key points (e.g., outlier deletion and model retraining). The accuracy (R² = 0.99) of the calculated coverage ratio was validated against a corresponding benchmark dataset. The EasyPCC source code is released under GPL license with benchmark datasets of several different crop types for algorithm development and for evaluating ground coverage ratios.

Velocity of temperature and flowering time in wheat - assisting breeders to keep pace with climate change.

By accelerating crop development, warming climates may result in mismatches between key sensitive growth stages and extreme climate events, with severe consequences for crop yield and food security. Using recent estimates of gene responses to vernalization and photoperiod in wheat, we modelled the flowering times of all 'potential' genotypes as influenced by the velocity of climate change across the Australian wheatbelt. In the period 1957-2010, seasonal increases in temperature of 0.012 °C yr(-1) were recorded and changed flowering time of a mid-season wheat genotype by an average -0.074 day yr(-1) , with flowering 'velocity' of up to 0.95 km yr(-1) towards the coastal edges of the wheatbelt; this is an estimate of how quickly the given genotype would have to be 'moved' across the landscape to maintain its original flowering time. By 2030, these national changes are projected to accelerate by up to 3-fold for seasonal temperature and by up to 5-fold for flowering time between now and 2030, with average national shifts in flowering time of 0.33 and 0.41 day yr(-1) between baseline and the worst climate scenario tested for 2030 and 2050, respectively. Without new flowering alleles in commercial germplasm, the life cycle of wheat crops is predicted to shorten by 2 weeks by 2030 across the wheatbelt for the most pessimistic climate scenario. While current cultivars may be otherwise suitable for future conditions, they will flower earlier due to warmer temperatures. To allow earlier sowing to escape frost, heat and terminal drought, and to maintain current growing period of early-sown wheat crops in the future, breeders will need to develop and/or introduce new genetic sources for later flowering, more so in the eastern part of the wheatbelt.

Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches.

Stresses from heat and drought are expected to increasingly suppress crop yields, but the degree to which current models can represent these effects is uncertain. Here we evaluate the algorithms that determine impacts of heat and drought stress on maize in 16 major maize models by incorporating these algorithms into a standard model, the Agricultural Production Systems sIMulator (APSIM), and running an ensemble of simulations. Although both daily mean temperature and daylight temperature are common choice of forcing heat stress algorithms, current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for daily mean temperature. Different drought algorithms (i.e., a function of soil water content, of soil water supply to demand ratio, and of actual to potential transpiration ratio) simulated considerably different patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. Using the selected combination of algorithms, our simulations show that maize yield reduction was more sensitive to drought stress than to heat stress for the US Midwest since the 1980s, and this pattern will continue under future scenarios; the influence of excessive heat will become increasingly prominent by the late 21st century. Our review of algorithms in 16 crop models suggests that the impacts of heat and drought stress on plant yield can be best described by crop models that: (i) incorporate event-based descriptions of heat and drought stress, (ii) consider the effects of nighttime warming, and (iii) coordinate the interactions among multiple stresses. Our study identifies the proficiency with which different model formulations capture the impacts of heat and drought stress on maize biomass and yield production. The framework presented here can be applied to other modeled processes and used to improve yield predictions of other crops with a wide variety of crop models.

Frost trends and their estimated impact on yield in the Australian wheatbelt.

Radiant spring frosts occurring during reproductive developmental stages can result in catastrophic yield loss for wheat producers. To better understand the spatial and temporal variability of frost, the occurrence and impact of frost events on rain-fed wheat production was estimated across the Australian wheatbelt for 1957-2013 using a 0.05 ° gridded weather data set. Simulated yield outcomes at 60 key locations were compared with those for virtual genotypes with different levels of frost tolerance. Over the last six decades, more frost events, later last frost day, and a significant increase in frost impact on yield were found in certain regions of the Australian wheatbelt, in particular in the South-East and West. Increasing trends in frost-related yield losses were simulated in regions where no significant trend of frost occurrence was observed, due to higher mean temperatures accelerating crop development and causing sensitive post-heading stages to occur earlier, during the frost risk period. Simulations indicated that with frost-tolerant lines the mean national yield could be improved by up to 20% through (i) reduced frost damage (~10% improvement) and (ii) the ability to use earlier sowing dates (adding a further 10% improvement). In the simulations, genotypes with an improved frost tolerance to temperatures 1 °C lower than the current 0 °C reference provided substantial benefit in most cropping regions, while greater tolerance (to 3 °C lower temperatures) brought further benefits in the East. The results indicate that breeding for improved reproductive frost tolerance should remain a priority for the Australian wheat industry, despite warming climates.

The shifting influence of drought and heat stress for crops in northeast Australia.

Characterization of drought environment types (ETs) has proven useful for breeding crops for drought-prone regions. Here, we consider how changes in climate and atmospheric carbon dioxide (CO2 ) concentrations will affect drought ET frequencies in sorghum and wheat systems of northeast Australia. We also modify APSIM (the Agricultural Production Systems Simulator) to incorporate extreme heat effects on grain number and weight, and then evaluate changes in the occurrence of heat-induced yield losses of more than 10%, as well as the co-occurrence of drought and heat. More than six million simulations spanning representative locations, soil types, management systems, and 33 climate projections led to three key findings. First, the projected frequency of drought decreased slightly for most climate projections for both sorghum and wheat, but for different reasons. In sorghum, warming exacerbated drought stresses by raising the atmospheric vapor pressure deficit and reducing transpiration efficiency (TE), but an increase in TE due to elevated CO2 more than offset these effects. In wheat, warming reduced drought stress during spring by hastening development through winter and reducing exposure to terminal drought. Elevated CO2 increased TE but also raised radiation-use efficiency and overall growth rates and water use, thereby offsetting much of the drought reduction from warming. Second, adding explicit effects of heat on grain number and grain size often switched projected yield impacts from positive to negative. Finally, although average yield losses associated with drought will remain generally higher than that for heat stress for the next half century, the relative importance of heat is steadily growing. This trend, as well as the likely high degree of genetic variability in heat tolerance, suggests that more emphasis on heat tolerance is warranted in breeding programs. At the same time, work on drought tolerance should continue with an emphasis on drought that co-occurs with extreme heat.

Climate change enhances stability of wheat-flowering-date.

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments.

Heading time is a major determinant of the adaptation of wheat to different environments, and is critical in minimizing risks of frost, heat, and drought on reproductive development. Given that major developmental genes are known in wheat, a process-based model, APSIM, was modified to incorporate gene effects into estimation of heading time, while minimizing degradation in the predictive capability of the model. Model parameters describing environment responses were replaced with functions of the number of winter and photoperiod (PPD)-sensitive alleles at the three VRN1 loci and the Ppd-D1 locus, respectively. Two years of vernalization and PPD trials of 210 lines (spring wheats) at a single location were used to estimate the effects of the VRN1 and Ppd-D1 alleles, with validation against 190 trials (~4400 observations) across the Australian wheatbelt. Compared with spring genotypes, winter genotypes for Vrn-A1 (i.e. with two winter alleles) had a delay of 76.8 degree days (°Cd) in time to heading, which was double the effect of the Vrn-B1 or Vrn-D1 winter genotypes. Of the three VRN1 loci, winter alleles at Vrn-B1 had the strongest interaction with PPD, delaying heading time by 99.0 °Cd under long days. The gene-based model had root mean square error of 3.2 and 4.3 d for calibration and validation datasets, respectively. Virtual genotypes were created to examine heading time in comparison with frost and heat events and showed that new longer-season varieties could be heading later (with potential increased yield) when sown early in season. This gene-based model allows breeders to consider how to target gene combinations to current and future production environments using parameters determined from a small set of phenotyping treatments.

A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background.

It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m2 m-2) (root mean square error (RMSE): 0.23 to 0.89 m2 m-2), including for sparse canopy (LAI less than 0.3 m2 m-2) grown on different soil types (RMSE: 0.02 to 0.25 m2 m-2). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.

Responses of wheat kernel weight to diverse allelic combinations under projected climate change conditions.

Introduction: In wheat, kernel weight (KW) is a key determinant of grain yield (GY). However, it is often overlooked when improving wheat productivity under climate warming. Moreover, little is known about the complex effects of genetic and climatic factors on KW. Here, we explored the responses of wheat KW to diverse allelic combinations under projected climate warming conditions. Methods: To focus on KW, we selected a subset of 81 out of 209 wheat varieties with similar GY, biomass, and kernel number (KN) and focused on their thousand-kernel weight (TKW). We genotyped them at eight kompetitive allele-specific polymerase chain reaction markers closely associated with TKW. Subsequently, we calibrated and evaluated the process-based model known as Agricultural Production Systems Simulator (APSIM-Wheat) based on a unique dataset including phenotyping, genotyping, climate, soil physicochemistry, and on-farm management information. We then used the calibrated APSIM-Wheat model to estimate TKW under eight allelic combinations (81 wheat varieties), seven sowing dates, and the shared socioeconomic pathways (SSPs) designated SSP2-4.5 and SSP5-8.5, driven by climate projections from five General Circulation Models (GCMs) BCC-CSM2-MR, CanESM5, EC-Earth3-Veg, MIROC-ES2L, and UKESM1-0-LL. Results: The APSIM-Wheat model reliably simulated wheat TKW with a root mean square error (RMSE) of < 3.076 g TK-1 and R2 of > 0.575 (P < 0.001). The analysis of variance based on the simulation output showed that allelic combination, climate scenario, and sowing date extremely significantly affected TKW (P < 0.001). The impact of the interaction allelic combination × climate scenario on TKW was also significant (P < 0.05). Meanwhile, the variety parameters and their relative importance in the APSIM-Wheat model accorded with the expression of the allelic combinations. Under the projected climate scenarios, the favorable allelic combinations (TaCKX-D1b + Hap-7A-1 + Hap-T + Hap-6A-G + Hap-6B-1 + H1g + A1b for SSP2-4.5 and SSP5-8.5) mitigated the negative effects of climate change on TKW. Discussion: The present study demonstrated that optimizing favorable allelic combinations can help achieve high wheat TKW. The findings of this study clarify the responses of wheat KW to diverse allelic combinations under projected climate change conditions. Additionally, the present study provides theoretical and practical reference for marker-assisted selection of high TKW in wheat breeding.

Quantification of the Cumulative Shading Capacity in a Maize-Soybean Intercropping System Using an Unmanned Aerial Vehicle.

In intercropping systems, higher crops block direct radiation, resulting in inevitable shading on the lower crops. Cumulative shading capacity (CSC), defined as the amount of direct radiation shaded by higher crops during a growth period, affects the light interception and radiation use efficiency of crops. Previous studies investigated the light interception and distribution of intercropping. However, how to directly quantify the CSC and its inter-row heterogeneity is still unclear. Considering the canopy height differences (Hms, obtained using an unmanned aerial vehicle) and solar position, we developed a shading capacity model (SCM) to quantify the shading on soybean in maize-soybean intercropping systems. Our results indicated that the southernmost row of soybean had the highest shading proportion, with variations observed among treatments composed of strip configurations and plant densities (ranging from 52.44% to 57.44%). The maximum overall CSC in our treatments reached 123.77 MJ m-2. There was a quantitative relationship between CSC and the soybean canopy height increment (y = 3.61 × 10-2×ln(x)+6.80 × 10-1, P < 0.001). Assuming that the growth status of maize and soybean was consistent under different planting directions and latitudes, we evaluated the effects of factors (i.e., canopy height difference, latitude, and planting direction) on shading to provide insights for optimizing intercropping planting patterns. The simulation showed that increasing canopy height differences and latitude led to increased shading, and the planting direction with the least shading was about 90° to 120° at the experimental site. The newly proposed SCM offers a quantitative approach for better understanding shading in intercropping systems.

Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?

Extreme climate, especially temperature, can severely reduce wheat yield. As global warming has already begun to increase mean temperature and the occurrence of extreme temperatures, it has become urgent to accelerate the 5-20 year process of breeding for new wheat varieties, to adapt to future climate. We analyzed the patterns of frost and heat events across the Australian wheatbelt based on 50 years of historical records (1960-2009) for 2864 weather stations. Flowering dates of three contrasting-maturity wheat varieties were simulated for a wide range of sowing dates in 22 locations for 'current' climate (1960-2009) and eight future scenarios (high and low CO2 emission, dry and wet precipitation scenarios, in 2030 and 2050). The results highlighted the substantial spatial variability of frost and heat events across the Australian wheatbelt in current and future climates. As both 'last frost' and 'first heat' events would occur earlier in the season, the 'target' sowing and flowering windows (defined as risk less than 10% for frost (<0 °C) and less than 30% for heat (>35 °C) around flowering) would be shifted earlier by up to 2 and 1 month(s), respectively, in 2050. A short-season variety would require a shift in target sowing window 2-fold greater than long- and medium-season varieties by 2050 (8 vs. 4 days on average across locations and scenarios, respectively), but would suffer a lesser decrease in the length of the vegetative period (4 vs. 7 days). Overall, warmer winters would shorten the wheat season by up to 6 weeks, especially during preflowering. This faster crop cycle is associated with a reduced time for resource acquisition, and potential yield loss. As far as favourable rain and modern equipment would allow, early sowing and longer season varieties (i.e. in current climate) would be the best strategies to adapt to future climates.