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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.