Diseases of Edible Oilseed Crops

Diseases of Edible Oilseed Crops presents an unprecedentedly thorough collection of information on the diseases of cultivated annual oilseed crops, including peanut, rapeseed-mustard, sesame, soybean, sunflower, and safflower. Written by internationally recognized researchers, this book covers and integrates worldwide literature in the field up to 2014, setting it apart from other books that are only of regional importance. The book focuses on major diseases of economic importance to each crop. Each chapter is devoted to a type of crop and a profile of affecting diseases according to geographical occurrence, epidemiology, symptoms, causal pathogens, host-pathogen interactions, biotechnological aspects, and the latest approaches to understanding host-pathogen interactions. It also includes discussions on developments on controversial subjects in research in order to stimulate thinking and further conversation with an eye toward improvements and resolutions. Research on oilseed crop diseases has expanded tremendously in the past 30 years, primarily as an effort to reduce losses to various stresses, including crop diseases. In the war against hunger and malnutrition, it is necessary to enhance and update knowledge about crop diseases and managing them. By compiling decades of information from previously scattered research into a single globally minded volume, Diseases of Edible Oilseed Crops provides these much-needed updates and enhancements

Production of oilseed rape with increased seed shattering resistance

Rapeseed (Brassica napus) production is limited by the crop’s natural propagation mechanism which involves growing siliques that dry out upon maturity and break easily. The resulting pre-harvest yield loss makes shatter resistance an important breeding goal. Studies on Arabidopsis thaliana revealed a set of transcription factors controlling dehiscence zone establishment. INDEHISCENT (IND) and ALCATRAZ (ALC) are major regulators of tissue differentiation in the critical parts of the silique. While ALC is required for the development of a partially degraded separation layer, IND also regulates the essential lignification of neighboring cells, probably through induction of NAC SECONDARY WALL THICKENING PROMOTING FACTOR 1 and 3 (NST1/3) expression. This study aimed at producing rapeseed lines with robust siliques through the use of Bnalc, Bnind, and Bnnst1 mutations. CRISPR/Cas9-mediated gene editing of the two BnALC homoeologs of cultivar ‘Haydn’ efficiently yielded four mutant alleles in a single transgenic T1 plant which were stably inherited. A tensile force test suggested the increased shatter resistance of T2 double mutants. However, the effect was masked by the innate silique robustness of ‘Haydn’. The Bnalc phenotype was therefore confirmed with EMS-induced mutant alleles in the shatter-prone cultivar ‘Express’. Bnind mutations derived from the same ‘Express’ mutant population were utilized for detailed analyses of shatter mechanics. Three phenotyping tests consistently identified a double mutant with especially robust siliques. No lignification defects were observed. Instead, shatter resistance was accounted to a broader replum-valve joint area in combination with smaller cells therein. CRISPR/Cas9-induced mutagenesis of four BnNST1 homoeologs yielded a chimeric T1 plant with multiple mutant alleles per gene copy which now have to be fixed in the progeny. Altogether, the developed mutant material provides novel variation for shatter resistance breeding

Identification and characterization of novel genes contributing to wheat grain yield

Grain yield is one of the most important aspects of wheat breeding. Being a polygenic trait, wheat grain yield is regulated by multiple genes and influenced by environmental factors. It is a complex trait which is linked to several traits such as seed number, thousand kernels’ weight etc. The interaction of these yield components with environmental stimulus are poorly understood. In the current study, to improve our understanding, phenotypic plasticity of contributing traits to the grain yield was explored. The phenotypic plasticity is the variations in the expressed phenotype by an individual genotype under environmental influences. The experiment consisted of 225 Westonia-Kauz double haploid (DH) lines and evaluated in five environmental conditions. The result demonstrated that, across the DH lines, the spikelets/spike was the most plastic trait. The least plastic character was the grain protein content. Yield plasticity was found higher at favourable conditions. An increase in yield plasticity by 0.1 units was associated with an increase in maximum yield by 4.45 kg ha−1 (p≤0.001). The generated knowledge regarding trait plasticity will be useful in dissecting the genetics for yield improvement particularly at the situation of rapid climate change. Identifying quantitative trait loci (QTL) and incorporating them in the breeding program has been a widely used approach for genetic improvement of yield and its components. QTL mapping suggests a considerable size of chromosomal location harbouring genes contributing to the trait which also contains many non-target genes. Thus, a more precise identification of contributing gene would be much helpful for an efficient breeding approach. However, functional confirmation of each individual gene of a QTL region is quite laborious and expensive work. In-silico approach provides the opportunity to reduce the down-stream workload by reducing the number of candidate genes in a systematic approach. Apart from the trait plasticity research, the current study also used a pipeline combining bioinformatics and laboratory approaches to identify the contributing genes of a grain yield QTL from a double haploid (DH) population of Westonia × Kauz. Assembling the QTL region on the International Wheat Genome Sequence Consortium (IWGSC) whole-genome sequence using the flanking 90K SNP markers identified the genomic region of 20 Mbp. Gene annotation revealed 16 high confidence genes and 41 low confidence genes in that genomic region. Further functional gene annotation, ontology investigation, pathway exploration, and gene network study using publicly available expressional data enabled short-listing of four genes for down-stream functional confirmation. Complete sequencing of those four genes demonstrated that only two genes namely ferredoxin-like protein and tetratricopetide-repeat (TPR) protein gene are polymorphic between the parental cultivars. Two single nucleotide polymorphism (SNP) variations were observed in the exon for both genes, and one SNP resulted in changes in amino acid sequence. The qPCR-based gene expression showed that both genes were highly expressed in the high-yielding double haploid lines. In contrast, gene expression was significantly lower in low-yielding lines. Results indicate that these two genes are potentially the underlying genes for the grain yield QTL. To investigate the association of the selected genes with grain yield and yield components at a wider level, further genetic and phenotyping experiments were conducted on a set of 143 historical wheat cultivars of Australia. For both genes, the identified alleles in the parental cultivars have been named as Westonia and Kauz allele. Characterising the allelic composition of the genes demonstrated that, for ferredoxin gene, 34.9% cultivars possessed Westonia allele and 16.9% cultivars possessed Kauz allele. In case of TPR gene, 20.9% cultivars possessed Westonia allele and 23.8% cultivars possessed Kauz allele. For both genes, cultivars having Westonia allele showed significantly higher seed width, thousand kernels’ weight and grain yield at different environmental conditions which clearly indicated that these genes are playing important roles in determining grain yield. For further level of functional confirmation, CRISPR-Cas9 based genome editing experiment was carried out on the TPR gene in Arabidopsis using orthologous gene. Agrobacterium mediated floral dip transformation was performed using immature inflorescence containing Cas9 gene. Knock-out mutants were selected by sequencing the target gene. Phenotypic data were collected from T2 generation on leaf length, stem length, number of branches on the main stem, days to flowering, days to maturity, pods/plant, and pod length. A significant reduction was observed in pods/plant, leaf length, and days to flowering and maturity. Gene expression analyses was performed on the selected genes responsible for increased seed size, seed number, and vegetative growth, in transgenic lines of Arabidopsis. Significant reduction in gene expression was observed for ARGOS, GRF1 and GW2 genes suggesting the role of TPR gene in downregulating essential growth regulator genes and its involvement in grain yield indirectly. Overall, this study demonstrated successful use of multiple research approaches in identification of a novel candidate genes of a yield related QTL. This approach can be utilised in exploring the candidate genes of other QTLs. The identified novel genes demonstrated the potential of improving the wheat grain yield which might be included in the breeding program for further yield improvement

Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements

The properties related to market value, milling, classification, storage, and transportation of bread wheat are determined by using some important physical quality characteristics such as weight, shape, dimensions, and hardness of wheat kernels. It is possible to measure all these features using single kernel characterization system (SKCS). Classification of wheat genotypes using computer-based algorithms is crucial to determine the most accurate physical quality classification for breeding studies. In this paper, four commercial wheat cultivars (Altay-2000, Bezostaja-1, Harmankaya-99, and Kate A-1) and six doubled haploid (DH) wheat genotypes are studied to classify wheat cultivars and DH wheat genotypes separately. In the classification stage, feature vectors constructed from measured characters namely, kernel weight, diameter, hardness, and moisture are applied to well-known classifiers such as Common Vector Approach (CVA), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Satisfactory results especially for the training set are obtained from the experimental studies. Classification results are compared with single linkage hierarchical cluster (SLHC) analysis, which is the most widely used in breeding studies. Recognition of clustered genotypes in all three classification methods and dendrograms present similar results. The SVM model is found to be outperformed over other methods for studied characters and could therefore effectively be utilized for characterizing, classifying and/or identifying the wheat genotypes

Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements

985-991The properties related to market value, milling, classification, storage, and transportation of bread wheat are determined by using some important physical quality characteristics such as weight, shape, dimensions, and hardness of wheat kernels. It is possible to measure all these features using single kernel characterization system (SKCS). Classification of wheat genotypes using computer-based algorithms is crucial to determine the most accurate physical quality classification for breeding studies. In this paper, four commercial wheat cultivars (Altay-2000, Bezostaja-1, Harmankaya-99, and Kate A-1) and six doubled haploid (DH) wheat genotypes are studied to classify wheat cultivars and DH wheat genotypes separately. In the classification stage, feature vectors constructed from measured characters namely, kernel weight, diameter, hardness, and moisture are applied to well-known classifiers such as Common Vector Approach (CVA), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Satisfactory results especially for the training set are obtained from the experimental studies. Classification results are compared with single linkage hierarchical cluster (SLHC) analysis, which is the most widely used in breeding studies. Recognition of clustered genotypes in all three classification methods and dendrograms present similar results. The SVM model is found to be outperformed over other methods for studied characters and could therefore effectively be utilized for characterizing, classifying and/or identifying the wheat genotypes

Understanding Trichoderma bio-inoculants in the root ecosystem of Pinus radiata

Oral presentation on understanding Trichoderma bio-inoculants in the root ecosystem of Pinus radiat

Organic farming and gene transfer from genetically modified crops

This is the final report of MAFF/Defra project OF0157. Genetically modified (GM) crops cannot be released into the environment and used as food, feed, medicines or industrial processing before they have passed through a rigorous and internationally recognised regulatory process designed to protect human and animal health, and the environment. The UK body that oversees standards in organic farming, the United Kingdom Register of Organic Food Standards (UKROFS), has ruled that genetically modified (GM) crops have no role to play in organic farming systems. They, therefore, have concerns about the possibility and consequences of the mixing of GM crops with organic crops. The two main sources of mixing are through pollen and seed. Pollen from GM crops may pollinate an organic crop. Seed from a GM crop, or plants established from them, may become mixed with organic crops or their products. Minimising genetic mixing is an important feature of the production of all high quality seed samples of plant varieties supplied to farmers. Extensive experience has been obtained over many decades in the production of high purity seed samples. Crop isolation distances, and crop rotational and management practices are laid down to achieve this. These procedures for the production of seed of high genetic purity could be used for the production of organic crops. No system for the field production of seed can guarantee absolute genetic purity of seed samples. Very rarely long distance pollination or seed transfer is possible, so any criteria for organic crop production will need to recognise this. There has always been the possibility of hybridisation and seed mixing between organic crops and non-organic crops. Organic farming systems acknowledge the possibility of spray or fertiliser drift from non-organic farming systems, and procedures are established to minimise this. In practice, detecting the presence of certain types of GM material in organic crops, especially quantification, is likely to be difficult. Some seed used by organic farmers are currently obtained from abroad. After January 2001, or a modified deadline thereafter, UK organic farmers will be required to sow seed produced organically. There is little or no organic seed produced in the UK at present, so it has to be obtained from abroad. Seed obtained from outside the UK or the European Union, may have different seed production criteria. This may make it difficult to guarantee that it is absolutely free from any GM material. Organic farmers and/or GM crop producers will need to ensure that their crops are isolated from one another by an appropriate distance or barrier to reduce pollen transfer if the crop flowers. To reduce seed mixing, shared equipment will need to be cleaned and an appropriate period of time allowed before organic crops are grown on land previously used for GM crops. Responsibility for isolation will need to be decided before appropriate measures can be implemented. The report highlights the need for acceptable levels of the presence of GM material in organic crops and measures identified to achieve this

Recent Advances in Genetics and Breeding of Major Staple Food Crops

To meet the global food demand of an increasing population, food production has to be increased by 60% by 2050. The main production constraints, such as climate change, biotic stresses, abiotic stresses, soil nutrition deficiency problems, problematic soils, etc., have to be addressed on an urgent basis. More than 50% of human calories are from three major cereals: rice, wheat, and maize. The harnessing of genetic diversity by novel allele mining assisted by recent advances in biotechnological and bioinformatics tools will enhance the utilization of the hidden treasures in the gene bank. Technological advances in plant breeding will provide some solutions for the biofortification, stress resistance, yield potential, and quality improvement in staple crops. The elucidation of the genetic, physiological, and molecular basis of useful traits and the improvement of the improved donors containing multiple traits are key activities for variety development. High-throughput genotyping systems assisted by bioinformatics and data science provide efficient and easy tools for geneticists and breeders. Recently, new breeding techniques applied in some food crops have become game-changers in the global food crop market. With this background, we invited 18 eminent researchers working on food crops from across the world to contribute their high-quality original research manuscripts. The research studies covered modern food crop genetics and breeding