Search Results (8)

Rust is one of the main diseases affecting wheat crops in Spain, causing significant yield and quality losses. Research on its identification and quantification in the air is a worldwide priority due to the importance of this crop as a source of food and feed. The objective of this study is to determine the temporal variation of airborne spores of Puccinia and their relationship with meteorological variables and the phenological development of a wheat crop in Northwestern Spain during two growing seasons. The study was conducted in A Limia, Ourense, located in Northwestern Spain, during the wheat growing seasons of 2021 and 2022. The Lanzoni VPPS 2010 spore trap was used to collect airborne spores, which were identified using optical microscopy. The wheat growing season was less than 95 days during both years, and wheat rust spores were detected during all phenological stages of the crop. Concentrations were higher than 100 spores/m³ from the booting stage to senescence, mainly in 2021. Statistical analyses showed that temperature was the meteorological variable that most influenced Puccinia concentrations in the air in both years. The modification of a prediction model proposed by other authors for wheat rust, which takes into account mean temperature (10–25 °C), dew point temperature (<5 °C), and nighttime temperature (10–20 °C), allowed us to tentatively predict the increase in Puccinia concentrations in the year 2022 when these conditions occurred for four or five consecutive days. This research is the first in Spain to report the presence of rust-causing Puccinia spores in the air during all phenological stages of the wheat crop and provides useful information for designing management strategies, considering temperature values. Full article

Protecting wheat yield is a top priority in agricultural production, and one of the important measures to preserve yield is the control of wheat diseases. With the maturity of computer vision technology, more possibilities have been provided to achieve plant disease detection. In this study, we propose the position attention block, which can effectively extract the position information from the feature map and construct the attention map to improve the feature extraction ability of the model for the region of interest. For training, we use transfer learning to improve the training speed of the model. In the experiment, ResNet built on positional attention blocks achieves 96.4% accuracy, which is much higher compared to other comparable models. Afterward, we optimized the undesirable detection class and validated its generalization performance on an open-source dataset. Full article

Rye (Secale cereale subsp. cereale L.) has long been exploited as a valuable alternative genetic resource in wheat (Triticum aestivum L.) breeding. Indeed, the introgression of rye genetic material led to significant breakthroughs in the improvement of disease and pest resistance of wheat, as well as a few agronomic traits. While such traits remain a high priority in cereal breeding, nutritional aspects of grain crops are coming under the spotlight as consumers become more conscious about their dietary choices and the food industry strives to offer food options that meet their demands. To address this new challenge, wheat breeding can once again turn to rye to look for additional genetic variation. A nutritional aspect that can potentially greatly benefit from the introgression of rye genetic material is the dietary fibre content of flour. In fact, rye is richer in dietary fibre than wheat, especially in terms of arabinoxylan content. Arabinoxylan is a major dietary fibre component in wheat and rye endosperm flours, and it is associated with a variety of health benefits, including normalisation of glycaemic levels and promotion of the gut microbiota. Thus, it is a valuable addition to the human diet, and it can represent a novel target for wheat–rye introgression breeding. Full article

Some studies have investigated the potential of genomic selection (GS) on stripe rust, leaf rust, Fusarium head blight (FHB), and leaf spot in wheat, but none of them have assessed the effect of the reaction norm model that incorporated GE interactions. In addition, the prediction accuracy on common bunt has not previously been studied. Here, we investigated within-population prediction accuracies using the baseline M1 model and two reaction norm models (M2 and M3) with three random cross-validation (CV1, CV2, and CV0) schemes. Three Canadian spring wheat populations were evaluated in up to eight field environments and genotyped with 3158, 5732, and 23,795 polymorphic markers. The M3 model that incorporated GE interactions reduced residual variance by an average of 10.2% as compared with the main effect M2 model and increased prediction accuracies on average by 2–6%. In some traits, the M3 model increased prediction accuracies up to 54% as compared with the M2 model. The average prediction accuracies of the M3 model with CV1, CV2, and CV0 schemes varied from 0.02 to 0.48, from 0.25 to 0.84, and from 0.14 to 0.87, respectively. In both CV2 and CV0 schemes, stripe rust in all three populations, common bunt and leaf rust in two populations, as well as FHB severity, FHB index, and leaf spot in one population had high to very high (0.54–0.87) prediction accuracies. This is the first comprehensive genomic selection study on five major diseases in spring wheat. Full article

In recent years, leaf rust (LR) and stem rust (SR) have become a serious threat to bread wheat production in Kazakhstan. Most local cultivars are susceptible to these rusts, which has affected their yield and quality. The development of new cultivars with high productivity and LR and SR disease resistance, including using marker-assisted selection, is becoming an important priority in local breeding projects. Therefore, the search for key genetic factors controlling resistance in all plant stages, including the seedling stage, is of great significance. In this work, we applied a genome-wide association study (GWAS) approach using 212 local bread wheat accessions that were phenotyped for resistance to specific races of Puccinia triticina Eriks. (Pt) and Puccinia graminis f. sp. tritici (Pgt) at the seedling stages. The collection was genotyped using a 20 K Illumina iSelect SNP assay, and 11,150 polymorphic SNP markers were selected for the association mapping. Using a mixed linear model, we identified 11 quantitative trait loci (QTLs) for five out of six specific races of Pt and Pgt. The comparison of the results from this GWAS with those from previously published work showed that nine out of eleven QTLs for LR and SR resistance had been previously reported in a GWAS study at the adult plant stages of wheat growth. Therefore, it was assumed that these nine common identified QTLs were effective for all-stage resistance to LR and SR, and the two other QTLs appear to be novel QTLs. In addition, five out of these nine QTLs that had been identified earlier were found to be associated with yield components, suggesting that they may directly influence the field performance of bread wheat. The identified QTLs, including novel QTLs found in this study, may play an essential role in the breeding process for improving wheat resistance to LR and SR. Full article

Fusarium and late blight (fungal diseases of cereals and potatoes) are among the main causes of crop loss worldwide. A key element of success in the fight against phytopathogens is the timely identification of infected plants and seeds. That is why the development of new methods for identifying phytopathogens is a priority for agriculture. The terahertz time-domain spectroscopy (THz-TDS) is a promising method for assessing the quality of materials. For the first time, we used THz-TDS for assessing the infection of seeds of cereals (oats, wheat and barley) with fusarium and potato tubers of different varieties (Nadezhda and Meteor) with late blight. We evaluated the refractive index, absorption coefficient and complex dielectric permittivity in healthy and infected plants. The presence of phytopathogens on seeds was confirmed by microscopy and PCR. It is shown, that Late blight significantly affected all the studied spectral characteristics. The nature of the changes depended on the variety of the analyzed plants and the localization of the analyzed tissue relative to the focus of infection. Fusarium also significantly affected all the studied spectral characteristics. It was found that THz-TDS method allows you to clearly establish the presence or absence of a phytopathogens, in the case of late blight, to assess the degree and depth of damage to plant tissues. Full article

This review summarizes research on virus diseases of cereals and oilseeds in Australia since the 1950s. All viruses known to infect the diverse range of cereal and oilseed crops grown in the continent’s temperate, Mediterranean, subtropical and tropical cropping regions are included. Viruses that occur commonly and have potential to cause the greatest seed yield and quality losses are described in detail, focusing on their biology, epidemiology and management. These are: barley yellow dwarf virus, cereal yellow dwarf virus and wheat streak mosaic virus in wheat, barley, oats, triticale and rye; Johnsongrass mosaic virus in sorghum, maize, sweet corn and pearl millet; turnip yellows virus and turnip mosaic virus in canola and Indian mustard; tobacco streak virus in sunflower; and cotton bunchy top virus in cotton. The currently less important viruses covered number nine infecting nine cereal crops and 14 infecting eight oilseed crops (none recorded for rice or linseed). Brief background information on the scope of the Australian cereal and oilseed industries, virus epidemiology and management and yield loss quantification is provided. Major future threats to managing virus diseases effectively include damaging viruses and virus vector species spreading from elsewhere, the increasing spectrum of insecticide resistance in insect and mite vectors, resistance-breaking virus strains, changes in epidemiology, virus and vectors impacts arising from climate instability and extreme weather events, and insufficient industry awareness of virus diseases. The pressing need for more resources to focus on addressing these threats is emphasized and recommendations over future research priorities provided. Full article

As a new type of crop protection machinery, the Crop Protection Unmanned Aerial System (CPUAS) has developed rapidly and been widely used in China; currently, how to use the CPUAS scientifically has become a top priority. However, the relationships between the operating parameters of the CPUAS and the effective spraying width (ESW), droplet distribution characteristics, and control effects of insect pests and diseases are not clear yet. Therefore, three levels of flight speed (FS) as 3, 4, and 5 m/s, three levels of flight height (FH) as 1.5, 2.0, and 2.5 m, and spraying volume 2.0 L/min experiments were carried out to investigate the effects of FS and FH on the ESW, droplet deposition uniformity (DDU), and droplet penetration rate (DPR) by using an electric single-rotor CPUAS CE20. Based on the obtained results, combined with the insect pests and diseases occurrence agronomic laws, the optimal operation parameters of the CPUAS were selected to control the wheat aphids, powdery mildew, and head blight. The results showed that the ESW of CE20 was not consistent, the maximum value was 5.78 m, and the minimum one was 2.51 m. The FS had a highly significant impact on ESW (p = 0.0033 < 0.01), while the FH and the interaction between FS and FH had no significant impact on ESW. The coefficients of variation (CV) of the droplet deposition were between 23.3% and 34.4%, which meant good deposition uniformity. The FH (p = 0.0019) and the interaction between FS and FH (p = 0.02) had significant impacts on the DDU. The control effects on aphids were 78.71% (1 day), 84.88% (3 days), and 90.42% (7 days), the control effects on powdery mildew were 77.17% (7 days) and 82.83% (14 days), and the control effect on head blight was 88.32% (20 days). This study proved that by the optimization of parameters and the combination of agronomy, good control effects for insect pests and diseases could be achieved by the CPUAS. The research results would provide some technical supports for CPUAS application. Full article