Genomic breeding for food, environment and livelihoods

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.

Legume Pangenome: Status and Scope for Crop Improvement

In the last decade, legume genomics research has seen a paradigm shift due to advances in genome sequencing technologies, assembly algorithms, and computational genomics that enabled the construction of high-quality reference genome assemblies of major legume crops. These advances have certainly facilitated the identification of novel genetic variants underlying the traits of agronomic importance in many legume crops. Furthermore, these robust sequencing technologies have allowed us to study structural variations across the whole genome in multiple individuals and at the species level using 'pangenome analysis.' This review updates the progress of constructing pangenome assemblies for various legume crops and discusses the prospects for these pangenomes and how to harness the information to improve various traits of economic importance through molecular breeding to increase genetic gain in legumes and tackle the increasing global food crisis.

Pathways to sustainable land use and food systems in Canada

Meeting global sustainability targets under the United Nations Sustainable Development Goals and the Paris Agreement requires paying attention to major land-use sectors such as forestry and agriculture. These sectors play a large role in national emissions, biodiversity conservation, and human well-being. There are numerous possible pathways to sustainability in these sectors and potential synergies and trade-offs along those pathways. This paper reports on the use of a model for Canada's land use to 2050 to assess three different pathways (one based on current trends and two with differing levels of ambition for meeting sustainability targets). This was done as part of a large international consortium, Food, Agriculture, Biodiversity, Land and Energy (FABLE), which allows for incorporating international trade in meeting both national and global sustainability targets. The results show not only the importance of increasingly stringent policies in meeting the targets, but also the role that population and consumption (e.g., diets) play in meeting the targets. Both the medium and high ambition sustainability pathways can drastically reduce greenhouse gas emissions while protecting forestland.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-022-01213-z.

Global Diversity of the Brachypodium Species Complex as a Resource for Genome-Wide Association Studies Demonstrated for Agronomic Traits in Response to Climate

The development of model systems requires a detailed assessment of standing genetic variation across natural populations. The Brachypodium species complex has been promoted as a plant model for grass genomics with translation to small grain and biomass crops. To capture the genetic diversity within this species complex, thousands of Brachypodium accessions from around the globe were collected and genotyped by sequencing. Overall, 1897 samples were classified into two diploid or allopolyploid species, and then further grouped into distinct inbred genotypes. A core set of diverse B. distachyon diploid lines was selected for whole genome sequencing and high resolution phenotyping. Genome-wide association studies across simulated seasonal environments was used to identify candidate genes and pathways tied to key life history and agronomic traits under current and future climatic conditions. A total of 8, 22, and 47 QTL were identified for flowering time, early vigor, and energy traits, respectively. The results highlight the genomic structure of the Brachypodium species complex, and the diploid lines provided a resource that allows complex trait dissection within this grass model species.

Deep phenotyping: deep learning for temporal phenotype/genotype classification

BACKGROUND

High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care.

METHODS

In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available.

CONCLUSION

The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.

Deep phenotyping: deep learning for temporal phenotype/genotype classification

BACKGROUND

High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care.

METHODS

In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available.

CONCLUSION

The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.

Resistance Genes in Global Crop Breeding Networks

Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Large Differences in Gene Expression Responses to Drought and Heat Stress between Elite Barley Cultivar Scarlett and a Spanish Landrace

Drought causes important losses in crop production every season. Improvement for drought tolerance could take advantage of the diversity held in germplasm collections, much of which has not been incorporated yet into modern breeding. Spanish landraces constitute a promising resource for barley breeding, as they were widely grown until last century and still show good yielding ability under stress. Here, we study the transcriptome expression landscape in two genotypes, an outstanding Spanish landrace-derived inbred line (SBCC073) and a modern cultivar (Scarlett). Gene expression of adult plants after prolonged stresses, either drought or drought combined with heat, was monitored. Transcriptome of mature leaves presented little changes under severe drought, whereas abundant gene expression changes were observed under combined mild drought and heat. Developing inflorescences of SBCC073 exhibited mostly unaltered gene expression, whereas numerous changes were found in the same tissues for Scarlett. Genotypic differences in physiological traits and gene expression patterns confirmed the different behavior of landrace SBCC073 and cultivar Scarlett under abiotic stress, suggesting that they responded to stress following different strategies. A comparison with related studies in barley, addressing gene expression responses to drought, revealed common biological processes, but moderate agreement regarding individual differentially expressed transcripts. Special emphasis was put in the search of co-expressed genes and underlying common regulatory motifs. Overall, 11 transcription factors were identified, and one of them matched cis-regulatory motifs discovered upstream of co-expressed genes involved in those responses.

Climate change increases the risk of herbicide-resistant weeds due to enhanced detoxification

Global warming will increase the incidence of metabolism-based reduced herbicide efficacy on weeds and, therefore, the risk for evolution of non-target site herbicide resistance. Climate changes affect food security both directly and indirectly. Weeds are the major biotic factor limiting crop production worldwide, and herbicides are the most cost-effective way for weed management. Processes associated with climatic changes, such as elevated temperatures, can strongly affect weed control efficiency. Responses of several grass weed populations to herbicides that inhibit acetyl-CoA carboxylase (ACCase) were examined under different temperature regimes. We characterized the mechanism of temperature-dependent sensitivity and the kinetics of pinoxaden detoxification. The products of pinoxaden detoxification were quantified. Decreased sensitivity to ACCase inhibitors was observed under elevated temperatures. Pre-treatment with the cytochrome-P450 inhibitor malathion supports a non-target site metabolism-based mechanism of herbicide resistance. The first 48 h after herbicide application were crucial for pinoxaden detoxification. The levels of the inactive glucose-conjugated pinoxaden product (M5) were found significantly higher under high- than low-temperature regime. Under high temperature, a rapid elevation in the level of the intermediate metabolite (M4) was found only in pinoxaden-resistant plants. Our results highlight the quantitative nature of non-target-site resistance. To the best of our knowledge, this is the first experimental evidence for temperature-dependent herbicide sensitivity based on metabolic detoxification. These findings suggest an increased risk for the evolution of herbicide-resistant weeds under predicted climatic conditions.