Brian R. Cullis’s research while affiliated with University of Wollongong and other places

Smith et al. (2021) introduced the interaction class (iClass) approach for multi-environment trials (METs) to address the problem of summarising variety performance across environments in the presence of variety by environment interaction. They developed the approach with respect to a factor analytic linear mixed model (FALMM) in which the genetic effects for different varieties were assumed independent. It is well known that the accuracy of variety selection is greatly enhanced by considering the genetic relatedness of varieties, either via ancestral or genomic information. In the current paper we therefore extend the iClass approach for FALMMs which incorporate such information. We illustrate the utility of the approach for selection of both parental and potential commercial varieties using an early stage breeding MET data-set from Chickpea Breeding Australia.

Faba beans (Vicia faba L.) constitute a partially outcrossing species requiring an isolation distance to maintain genetic purity when more than one variety is grown in field conditions. This information is crucial for seed growers and faba bean breeders. A study was conducted at the University of Sydney’s Plant Breeding Institute, Narrabri, over two years to examine the extent of natural outcrossing using a creamy white flower characteristic as a morphological marker, which is controlled by a single recessive gene. The white-flowered genotype (IX225c) was grown in paired rows of 150 m length in four directions from a central 480 m2 plot of the normal flowered genotype PBA Warda. A beehive was placed in the central plot at the flowering time and natural pollination was allowed. At maturity, seed samples were taken from the white-flowered genotype at designated intervals along each axis and 100 seeds from each sample were grown in the glasshouse/birdcage to the 4–5 leaf stage and the proportion of plants displaying a stipule spot pigmentation (normal flower color and spotted stipule are linked) was used to determine the percentage of outcrossing. Maximum outcrossing of 2.28% occurred where both genotypes were grown side by side (0 m) and the degree of outcrossing decreased as the distance along each axis from the central plot increased. At a 6 m distance, the outcrossing was less than 1%; however, on occasion, it increased to 1% beyond a distance of 100 m, indicating the volatile and unpredictable nature of bee flights. Distance had a major effect on outcrossing but the direction and its interaction had no effect. The results suggest that to limit outcrossing to below 0.5%, a distance of more than 150 m between plots of different faba beans cultivars would be required. It also indicated that Australian faba bean genotypes are mostly self-fertile and a relatively narrow isolation distance will ensure self-fertilization in seed production and breeding programs.

Faba bean (Vicia faba L.) is a partially outcrossing species requiring an isolation distance to maintain genetic purity when more than one variety is grown in field conditions. This information is crucial for seed growers and faba bean breeders. A study was conducted at the University of Sydney’s Plant Breeding Institute, Narrabri over two years to examine the extent of natural outcrossing using a creamy white flower characteristic as a morphological marker, which is controlled by a single recessive gene. The white flowered genotype (IX225c) was grown in paired rows of 150 m length in four directions from a central 480 m2 plot of the normal flowered genotype PBA Warda. A beehive was placed in the central plot at flowering time and natural pollination was allowed. At maturity, seed samples were taken from the white flowered genotype at designated intervals along each axis and 100 seeds from each sample were grown in the glasshouse/birdcage to the 4-5 leaf stage and the proportion of plants displaying stipule spot pigmentation (normal flower colour and spotted stipule are linked) was used to determine the percentage of outcrossing. Maximum outcrossing of 2.28% occurred where both genotypes were grown side by side (0 m) and the degree of outcrossing decreased as the distance along each axis from the central plot increased. At 6 m distance the outcrossing was less than 1%, however on occasion it increased to 1% beyond a distance of 100 m, indicating the volatile and unpredictable nature of bee flights. Distance had the major effect on outcrossing, but direction and its interaction had no effect. The results suggest that to limit outcrossing to below 0.5%, a distance of more than 150 m between plots of different faba beans cultivars would be required. It also indicated that Australian faba bean genotypes are mostly self-fertile and a relatively narrow isolation distance will ensure self-fertilization in seed production and breeding programs.

Canola varieties exhibit discernible variation in drought avoidance and drought escape traits, reflecting adaptation to water‐deficit environments. Our understanding of underlying genes and their interaction across environments in improving crop productivity is limited. A doubled haploid (DH) population was analysed to identify QTL associated with water use efficiency (WUE) related traits. High WUE in the vegetative phase was associated with low seed yield. Based on the resequenced parental genome data, we developed sequence‐capture‐based markers and validated their linkage with carbon isotope discrimination (Δ13C) in an F2 population. mRNA‐Seq was performed to determine the expression of candidate genes underlying Δ13C QTL. QTL contributing to main and QTL × Environment interaction effects for Δ13C and yield were identified. One multiple‐trait QTL for Δ13C, days to flower, plant height, and seed yield was identified on chromosome A09. Interestingly, this QTL region overlapped with a homoeologous exchange event (HE), suggesting its association with the multiple traits. Transcriptome analysis revealed 121 significantly differentially expressed genes underlying Δ13C QTL on A09 and C09, including in HE regions. Sorting out the negative relation between vegetative WUE and seed yield is a priority. Genetic and genomic resources and knowledge so developed could improve canola WUE and yield. This article is protected by copyright. All rights reserved.

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

Canola varieties exhibit discernible variation in drought avoidance and drought escape traits, suggesting its adaptation to water-deficit environments. However, the underlying mechanisms are poorly understood. A doubled haploid (DH) population was analysed to identify QTL associated with water use efficiency (WUE) related traits. Based on the resequenced parental genome data, we developed sequence-capture based markers for fine mapping. mRNA-Seq was performed to determine the expression of candidate genes underlying QTL for carbon isotope discrimination (Δ ¹³ C). QTL contributing to main and QTL × Environment interaction effects for Δ ¹³ C and for agronomic WUE were identified. One multi-trait QTL for Δ ¹³ C, days to flower, plant height and seed yield was identified on chromosome A09, in the vicinity of ERECTA . Interestingly, this QTL region was overlapped with a homoeologous exchange event (HE), suggesting its association with the major QTL. Transcriptome analysis revealed several differentially expressed genes between parental lines, including in HE regions. This study provides insights into the complexity of WUE related genes in the context of canola adaptation to water-deficit conditions. Our results suggest that alleles for high Δ ¹³ C contribute positively to canola yield. Genetic and genomic resources developed herein could be utilised to make genetic gains for improving canola WUE.

White (Carcharodon carcharias L.), bull (Carcharhinus leucas, Müller & Henle) and tiger (Galeocerdo cuvier, Péron & Lesueur) sharks are the primary species responsible for unprovoked shark bites. Historically, management practices were based on culling “target” shark species (i.e. white, bull and tiger sharks), which resulted in high levels of bycatch and mortality. Shark‐Management‐Alert‐in‐Real‐Time (SMART) drumlines were trialled in New South Wales, Australia, aiming to optimise the capture of target shark species while minimising bycatch and mortality. Target shark species accounted for 70% of the total catch, with white sharks contributing 298 of the 350 sharks that were caught. Four animals died, and bycatch consisted of 13 species including two threatened species. Generalised linear mixed models (GLMMs) revealed a significant spatial, temporal, environmental and gear influence on white shark catch rates. SMART drumlines are a useful tool for catching target shark species with low bycatch and mortality relative to historical bather protection methods.

Tolerance to the cereal disease Fusarium crown rot (FCR) was investigated in a set of 34 durum wheat genotypes, with Suntop, (bread wheat) and EGA Bellaroi (durum) as tolerant and intolerant controls, in a series of replicated field trials over four years with inoculated (FCR-i) and non-inoculated (FCR-n) plots of the genotypes. The genotypes included conventional durum lines and lines derived from crossing durum with 2–49, a bread wheat genotype with the highest level of partial resistance to FCR. A split plot trial design was chosen to optimize the efficiency for the prediction of FCR tolerance for each genotype. A multi-environment trial (MET) analysis was undertaken which indicated that there was good repeatability of FCR tolerance across years. Based on an FCR tolerance index, Suntop was the most tolerant genotype and EGA Bellaroi was very intolerant, but some durum wheats had FCR tolerance indices which were comparable to Suntop. These included some conventional durum genotypes, V101030, TD1702, V11TD013*3X-63 and DBA Bindaroi, as well as genotypes from crosses with 2–49 (V114916 and V114942). The correlation between FCR tolerance and FCR-n yield predictions was moderately negative indicating it could be somewhat difficult to develop FCR-tolerant genotypes that are high yielding under low disease pressure. However, FCR tolerance showed a positive correlation with FCR-i yield predictions in seasons of high disease expression indicating it could be possible to screen for FCR tolerance using only FCR-i treatments. These results are the first demonstration of genetic diversity in durum germplasm for FCR tolerance and they provide a basis for breeding for this trait.

Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of “contemporary groups,” which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of “data bands,” which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.

A set of durum wheat genotypes from New South Wales (NSW, Durum Breeding Australia (DBA) Northern Program), South Australia (SA, DBA Southern Program and Australian Grain Technology), ICARDA and CIMMYT (International Centre for Research in Dryland Agriculture and International Centre for Maize and Wheat Improvement) was evaluated over 3 years (2012–2014) in field trials containing rainfed and watered blocks in Narrabri, NSW, Australia. Data on yield and other agronomic traits were analysed using a multi-environment trial approach that accommodated the factorial treatment structure (genotype by irrigation regime) within individual trials. Considerable variation was observed in the durum germplasm for productivity and grain quality traits. DBA Bindaroi (NSW) and 101042 (ICARDA) were the top yielders in watered and rainfed blocks, respectively. The yield was positively and strongly related to both harvest index and grains/m ² , but grains/m ² was negatively related to thousand grain weight (TGW) and positively related to screenings. TGW and screenings were strongly negatively related and TGW and grains/m ² showed a weak positive relationship. Promising genotypes were identified, with superior traits to both the bread wheat check, EGA Gregory and the durum check, Caparoi. Overall, lines from SA and ICARDA were superior for yield but those from NSW were superior for quality parameters including TGW and screenings. These results suggested the possibility of developing high yielding high-quality durum varieties by crossing NSW lines with SA, CIMMYT and ICARDA lines through simultaneous selection for yield, TGW and low screenings. The results also suggested that productivity in rainfed conditions was positively related to productivity under watering, but further research is required to establish this.