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Frost can cause irreversible damage to plant tissue and can significantly reduce yields and quality. A thorough understanding of the freezing dynamics is crucial to developing strategies for frost protection and the prevention of freezing damage. This study investigated artificial intelligence machine learning (ML) models to capture the thermodynamic patterns of freezing based on infrared thermography (IRT) imagery, which would help to automate the image analysis process in real-time or post frost events. A small-scale dataset of IRT images was collected to capture the freezing process from sample droplets containing ice-nucleating bacterium. We performed several ML experiments on the data to detect the transitions in temperatures from the images. We evaluated five popular ML models, namely support vector machines, random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron, and convolutional neural networks. We analysed the dataset to classify adjacent temperature transitions. The results show that ML models can consistently capture the thermodynamics of frost events, i.e., ice-nucleation and freezing points on typical freezing curves. Amongst the ML models, RF and XGBoost achieved the best results, both with an average accuracy of 87–88% in classifying the temperatures. With 0.25 °C temperature transitions, RF model identified the ice nucleation and freezing points at around −2.25 °C to −2.75 °C and −4.25 °C to −4.75 °C, respectively. RF and XGBoost took about 7.3 ms and 5.5 ms time per image respectively, which indicates that these models can be used in real-time applications. Our study shows that ML models using IRT imagery can be used as an automatic real-time tool to accurately detect the critical temperatures for frost formation.

Reproductive stage frost poses a major constraint for wheat production in countries such as Australia. However, little progress has been made in identifying key genes to overcome the constraint. In the present study, a severe frost event hit two large-scale field trials consisting of six doubled haploid (DH) wheat populations at reproductive stage (young microspore stage) in Western Australia, leading to the identification of 30 robust frost QTL on 17 chromosomes. The major 18 QTL with the phenotype variation over 9.5% were located on 13 chromosomes including 2A, 2B, 2D, 3A, 4A, 4B, 4D, 5A, 5D, 6D, 7A, 7B and 7D. Most frost QTL were closely linked to the QTL of anthesis, maturity, Zadok stages as well as linked to anthesis related genes. Out of those, six QTL were repetitively detected on the homologous regions on 2B, 4B, 4D, 5A, 5D, 7A in more than two populations. Results showed that the frost damage is associated with alleles of Vrn-A1a, Vrn-D1a, Rht-B1b, Rht-D1b, and the high copy number of Ppd-B1. However, anthesis QTL and anthesis related genes of Vrn-B1a and TaFT3-1B on chromosomes 5B and 1B did not lead to frost damage, indicating that these early-flowering phenotype related genes are compatible with frost tolerance and thus can be utilised in breeding. Our results also indicate that wild-type alleles Rht-B1a and Rht-D1a can be used when breeding for frost-tolerant varieties without delaying flowering time.

The downside risk of crop production affects the entire supply chain of the agricultural industry nationally and globally. This also has a profound impact on food security, and thus livelihoods, in many parts of the world. The advent of high temporal, spatial and spectral resolution remote sensing platforms, specifically during the last 5 years, and the advancement in software pipelines and cloud computing have resulted in the collating, analysing and application of ‘BIG DATA’ systems, especially in agriculture. Furthermore, the application of traditional and novel computational and machine learning approaches is assisting in resolving complex interactions, to reveal components of ecophysiological systems that were previously deemed either ‘too difficult’ to solve or ‘unseen’. In this review, digital technologies encompass mathematical, computational, proximal and remote sensing technologies. Here, we review the current state of digital technologies and their application in broad-acre cropping systems globally and in Australia. More specifically, we discuss the advances in (i) remote sensing platforms, (ii) machine learning approaches to discriminate between crops and (iii) the prediction of crop phenological stages from both sensing and crop simulation systems for major Australian winter crops. An integrated solution is proposed to allow accurate development, validation and scalability of predictive tools for crop phenology mapping at within-field scales, across extensive cropping areas.

INTRODUCTION: Frost events lead to A$360 million of yield losses annually to the Australian wheat industry, making improvement of chilling and frost tolerance an important trait for breeding. OBJECTIVES: This study aimed to use metabolomics and lipidomics to explore genetic variation in acclimation potential to chilling and to identify metabolite markers for chilling tolerance in wheat. METHODS: We established a controlled environment screening assay that is able to reproduce field rankings of wheat germplasm for chilling and frost tolerance. This assay, together with targeted metabolomics and lipidomics approaches, were used to compare metabolite and lipid levels in flag leaves of two wheat varieties with contrasting chilling tolerance. RESULTS: The sensitive variety Wyalkatchem showed a strong reduction in amino acids after the first cold night, followed by accumulation of osmolytes such as fructose, glucose, putrescine and shikimate over a 4-day period. Accumulation of osmolytes is indicative of acclimation to water stress in Wyalkatchem. This response was not observed for tolerant variety Young. The two varieties also displayed significant differences in lipid accumulation. Variation in two lipid clusters, resulted in a higher unsaturated to saturated lipid ratio in Young after 4 days cold treatment and the lipids PC(34:0), PC(34:1), PC(35:1), PC(38:3), and PI(36:4) were the main contributors to the unsaturated to saturated ratio change. This indicates that Young may have superior ability to maintain membrane fluidity following cold exposure, thereby avoiding membrane damage and water stress observed for Wyalkatchem. CONCLUSION: Our study suggests that metabolomics and lipidomics markers could be used as an alternative phenotyping method to discriminate wheat varieties with differences in cold acclimation.

Kony2012 was a viral Internet video that attracted unprecedented online interest in promoting a campaign to arrest the leader of an African militant group. The current research considers the social psychological bases of social media‐based collective action. In three cross‐sectional surveys (N = 304) collected before, on, and after the key action date of 20 April 2012, we consider the nature (opinion based or global) and function (emergent or transforming) of social identity in modern forms of social action. Multigroup structural equation modelling showed that Kony2012 action was best captured by an emergent opinion‐based social identity. Moreover, the same factors that predicted Kony2012 action generally also predicted engagement in new repertoires of protest (involving the use of social media) and an observable traditional socio‐political action (signing a letter to a government minister). The results suggest that there is no sharp dividing line between traditional and new forms of collective action and that both may be understood as valid expressions of collective selfhood.

Genome enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under represented in the training set, whereas other parts will be over represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD), proposed by Rincent et al. (2012). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTLs and GBLUPs, and Reproducing Kernel Hilbert Spaces (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel

In wheat stems, the levels of fructan-dominated water-soluble carbohydrates (WSC) do not always correlate well with grain yield. Field drought experiments were carried out to further explain this lack of correlation. Wheat (Triticum aestivum) varieties, Westonia, Kauz and c. 20 genetically diverse double haploid (DH) lines derived from them were investigated. Substantial genotypic differences in fructan remobilization were found and the 1-FEH w3 gene was shown to be the major contributor in the stem fructan remobilization process based on enzyme activity and gene expression results. A single nucleotide polymorphism (SNP) was detected in an auxin response element in the 1-FEH w3 promoter region, therefore we speculated that the mutated Westonia allele might affect gene expression and enzyme activity levels. A cleaved amplified polymorphic (CAP) marker was generated from the SNP. The harvested results showed that the mutated Westonia 1-FEH w3 allele was associated with a higher thousand grain weight (TGW) under drought conditions in 2011 and 2012. These results indicated that higher gene expression of 1-FEH w3 and 1-FEH w3 mediated enzyme activities that favoured stem WSC remobilization to the grains. The CAP marker residing in the 1-FEH w3 promoter region may facilitate wheat breeding by selecting lines with high stem fructan remobilization capacity under terminal drought.

The major vernalisation genes of VRN1 are well understood at the molecular level. However, their quantitative contributions to flowering time and grain yield related traits are not clear. In this study, we used a double haploid population (225 lines) of Westonia × Kauz in which the Vrn-A1a (Westonia), Vrn-B1a (Westonia) and Vrn-D1a (Kauz) were segregating, and a high resolution genetic map of 1,159 loci, to determine the quantitative contributions of Vrn-A1a, Vrn-B1a and Vrn-D1a for the days to anthesis and grain yield related traits in diverse environments. The major quantitative trait loci (QTL) of spikelet number per spike and days to anthesis were contributed by the winter alleles of VRN1. The QTL of the time of grain filling were contributed by the spring alleles of VRN1. The wild genotype (vrn-A1vrn-B1vrn-D1) showed the latest flowering, the highest spikelet number per spike, lowest peduncle proportion and thousand grain weight in three environmental analyses, and the largest spikelet number per spike, which resulted in high kernel number per spike (KN) and grain weight (GW) in well-watered environments. One QTL of KN was located on 5B, contributed by winter allele of vrn-B1 in three environmental analyses, and one GW QTL was detected on 5A, contributed by the spring allele of Vrn-A1a in a drought environment. The results indicated that the genotype Vrn-A1avrn-B1Vrn-D1a would shorten the time to anthesis and give high GW and KN in drought environments. The early anthesis associated phenotype, peduncle proportion would provide an indicator in breeding programs.

Field evaluation of germplasm for performance under water and heat stress is challenging. Field environments are variable and unpredictable, and genotype × environment interactions are difficult to interpret if environments are not well characterised. Numerous traits, genes and quantitative trait loci have been proposed for improving performance but few have been used in variety development. This reflects the limited capacity of commercial breeding companies to screen for these traits and the absence of validation in field environments relevant to breeding companies, and because little is known about the economic benefit of selecting one particular trait over another. The value of the proposed traits or genes is commonly not demonstrated in genetic backgrounds of value to breeding companies. To overcome this disconnection between physiological trait breeding and uptake by breeding companies, three field sites representing the main environment types encountered across the Australian wheatbelt were selected to form a set of managed environment facilities (MEFs). Each MEF manages soil moisture stress through irrigation, and the effects of heat stress through variable sowing dates. Field trials are monitored continuously for weather variables and changes in soil water and canopy temperature in selected probe genotypes, which aids in decisions guiding irrigation scheduling and sampling times. Protocols have been standardised for an essential core set of measurements so that phenotyping yield and other traits are consistent across sites and seasons. MEFs enable assessment of a large number of traits across multiple genetic backgrounds in relevant environments, determine relative trait value, and facilitate delivery of promising germplasm and high value traits into commercial breeding programs.

The utilization of dwarfing genes Rht-B1b and Rht-D1b in wheat significantly increased grain yield and contributed to the "green revolution". However, the benefit of Rht-B1b and Rht-D1b in drought environments has been debated. Although quantitative trait loci (QTL) for kernel number per spike (KN) and thousand-grain weight (TGW) have been found to be associated with Rht-B1 and Rht-D1, the confounding effect of environmental variation has made a direct association difficult to find. In this study, we used a doubled haploid population (225 lines) of Westonia × Kauz, in which both Rht-B1b (Kauz) and Rht-D1b (Westonia) segregated. The purpose of the study was to determine the interaction of Rht-B1 and Rht-D1 with grain yield components, namely KN and TGW, and to investigate genotype-by-environment interactions in glasshouse and field trials conducted in 2010 and 2011 in Western Australia. A genetic map of 1,156 loci was constructed using 195 microsatellite markers, two gene-based markers for Rht-B1 and Rht-D1, and 959 single nucleotide polymorphisms. The major QTL for TGW and KN were strongly linked to Rht-B1 and Rht-D1 loci and the positive effects were associated with the wild-type alleles, Rht-B1a and Rht-D1a. The major QTL of TGW were on chromosome 2D and 4B. The significant genetic effects (14.6-22.9 %) of TGW indicated that marker-assisted selection for TGW is possible, and markers gwm192a (206 bp) or gwm192b (236 bp) can be used as indicators of high TGW. For KN, one major QTL was detected on chromosome 4D in the analysis across three environments. The association of the wild-type alleles Rht-B1a and Rht-D1a in drought environments is discussed.

Field evaluation of germplasm for performance under water and heat stress is challenging. Field environments are variable and unpredictable, and genotype x environment interactions are difficult to interpret if environments are not well characterised. Numerous traits, genes and quantitative trait loci have been proposed for improving performance but few have been used in variety development. This reflects the limited capacity of commercial breeding companies to screen for these traits and the absence of validation in field environments relevant to breeding companies, and because little is known about the economic benefit of selecting one particular trait over another. The value of the proposed traits or genes is commonly not demonstrated in genetic backgrounds of value to breeding companies. To overcome this disconnection between physiological trait breeding and uptake by breeding companies, three field sites representing the main environment types encountered across the Australian wheatbelt were selected to form a set of managed environment facilities (MEFs). Each MEF manages soil moisture stress through irrigation, and the effects of heat stress through variable sowing dates. Field trials are monitored continuously for weather variables and changes in soil water and canopy temperature in selected probe genotypes, which aids in decisions guiding irrigation scheduling and sampling times. Protocols have been standardised for an essential core set of measurements so that phenotyping yield and other traits are consistent across sites and seasons. MEFs enable assessment of a large number of traits across multiple genetic backgrounds in relevant environments, determine relative trait value, and facilitate delivery of promising germplasm and high value traits into commercial breeding programs.

The most common factor affecting wheat production and grain quality in Australia is water deficit - "drought." Drought and heat are considered the top priority among 15 abiotic stresses affecting wheat production in Western Australia (WA). In the WA wheatbelt, water deficit intensifies from anthesis to about 4 weeks later and severe drought usually occurs from a month after anthesis to maturity. Thus, terminal drought is a major problem for wheat production in WA and drought tolerance in wheat is a priority for research. A major limitation in this field is the lack of efficient approaches of drought tolerance screening and availability of drought tolerant germ plasm. Our study has found that stem water soluble carbohydrate (WSC) mobilization has great significance for converting biomass into the grain under terminal water-deficit. However, stem WSC level is not, on its own, a reliable criterion for drought tolerant. The genes involved in the mobilization of stem WSC, 1-FEHs have been studied and the gene expression data, as a potential drought tolerant marker, define their crucial role in contributing to water deficit tolerance. QTL recently discovered provide some evidence for the possible gene function of the contribution to the grain yield components. This and other markers of grain yield components may be useful for wheat breeding in the future. Lack of drought tolerant germplasm significantly slows down drought tolerant breeding in wheat. Introducing new germplasm from China will give great benefit to the Australian wheat breeding program. Hopefully the combination of genes from such diverse environments will enhance the production of new drought tolerant wheat in conventional breeding.

No abstract available