Rust Severity Research Articles

Field efficacy of novel fungicides against chickpea rust (Uromyces ciceris-arietini)

The host resistance in present-day chickpea cultivars against rust disease is lacking thus, the present study was carried out during the winter (rabi) seasons of 2019–20 and 2020–21 at Regional Research Station, ICAR-Indian Institute of Pulses Research, Dharwad, Karnataka to explore effective fungicides for the management of chickpea rust. All fungicides found to reduce chickpea rust severity, but differences in efficacy were observed throughout the study. Present studies revealed that two sprays of 0.1% azoxystrobin 18.2% + difenoconazole 11.4% sc fungicide at 15 days interval minimizes the rust severity to the tune of >77% and achieved a benefit cost (B:C) ratio of 2.32 i.e. in terms of monetary value farmers could get a net income of ₹35268/ha. Thus, timely spraying of identified modern fungicide combination can be taken advantage for better control of chickpea rust.

Bacillus‐based product protects soybean, improves grain yield and produces metabolites able to promote plant growth and antagonize pathogenic fungi

AbstractSoybean is frequently affected by pathogenic fungi, which are usually managed by chemicals. Foliar sprays with biological agents have been introduced as new allies to reduce plant diseases. This study verified foliar sprays of Bacillus subtilis‐based product applied for three consecutive cropping seasons associated or not with chemical fungicides to control Asian rust (Phakopsora pachyrhizi) and the effect of B. subtilis‐produced metabolites against pathogenic fungi and on soybean growth. Field treatments were as follows: (i) Foliar application of B. subtilis‐based product, followed by chemical sprays; (ii) chemical management alone; (iii) B. subtilis‐based product alone, and (iv) control treatment with water sprays. In the three years, B. subtilis‐based product alone or followed by chemicals reduced at least 75% the Asian rust severity, similar to chemical management. Moreover, soybean grain yield increased when B. subtilis‐based product was followed by chemicals. Cell‐free filtrates and volatile organic compounds (VOCs) from B. subtilis isolate or from the formulated bioproduct were tested against fungi pathogens Fusarium cf. incarnatum/equiseti and Macrophomina phaseolina. All filtrates and VOCs emission reduced more than 40% the mycelial growth of both fungi. Also, the filtrates stimulated the soybean growth at a rate similar to the bioproduct, indicating the potential of the B. subtilis compounds to control fungi pathogens and promote plant growth. Therefore, the B. subtilis‐based product showed a satisfactory protection and yield increment for soybean and B. subtilis‐produced metabolites antagonized pathogenic fungi and stimulated the soybean plant growth.

Multi-environments Evaluation of Zn and Fe Enhanced Bread Wheat Genotypes in Optimum Areas of Ethiopia

A multi-location experiment was conducted during June, 2020–October, 2021 under rainfed condition at Kulumsa, Asasa, Adet, Holeta, and Sinana Research centers, Ethiopia to evaluate the genotype-by-environment interaction effect and grain yield stability of Zn and Fe enhanced bread wheat genotypes grown. The treatments constituted 21 advanced genotypes and two standard checks were evaluated in an alpha lattice design replicated three times and data analysis was carried using R software. The results showed that genotypes and genotype x environmental interaction had a significant (p<0.001) effect on days to 50% heading, days to 90% maturity, plant height, grain yield, and 1000 kernel weight. The bread wheat lines BW172862, EBW193416, BW172864, and EBW193414 were high-yielding across most test environments, whereas genotype EBW192455 and Hidasse were low-yielding ones. From stability analysis genotypes BW172862, and EBW193416 were identified to be the most adapted bread wheat genotypes. “BW172862 produced 24.43% and 86.38% yield advantage over the standard check (Lemu) and local check (Hidasse), respectively. The second candidate genotype EBW193416 also produced 23.58% and 85.1% yield advantage over the standard check (Lemu) and local check (Hidasse), respectively. EBW193416 and BW172862 have also exhibited lower Yellow and Stem rust severity compared to others across the test environments. Thus, BW172862 and EBW193416 were selected as best parent to recycle for bread wheat population improvement in 2023 for medium agro-ecology of Ethiopia.

Virulence phenotypes of Puccinia graminis on barley, wheat and oat in Canada from 2020 to 2022

Stem rust of barley and wheat is caused by the obligate pathogen Puccinia graminis f. sp. tritici (Pgt) and stem rust of oat is caused by P. graminis f. sp. avenae (Pga). Stem rust can be a very destructive disease of cereal crops and, until the development of resistant host varieties, was responsible for millions of dollars of losses to producers. Genetic host resistance is the most common and effective method to control stem rust on cereals. Knowledge of the virulence genes and races present in the pathogen population is important to identify effective host resistance genes for use by cereal plant breeders. The objectives of this study were to determine the incidence and severity of P. graminis in wheat, barley and oat in Manitoba, Canada, and assess the virulence frequencies and races of isolates collected from Manitoba, Saskatchewan and Ontario, Canada during 2020 to 2022. Stem rust incidence and severity was very light in Manitoba in 2020, 2021 and 2022. Virulence was not observed on resistance genes Sr30, Sr24 and Sr31 from Canadian collections of Pgt isolates made during 2020 to 2022. Race QFCSC was the most common race of Pgt in Canada, and races MCCFC and TPMKC were also common in Manitoba in 2022. Virulence to resistance genes Pg6, Pg10 and Pg16 was not observed among Pga isolates. The most common race of Pga from Manitoba and Saskatchewan was TGN, followed by SGB and TJS. The most frequent Pga races were TDJ, TDD and TGN in Ontario.

Comparisons of stripe rust response, grain yield and quality between fungicide sprayed and non-sprayed treatments for newly developed wheat lines carrying different genes for adult-plant resistance to stripe rust

Wheat genes Yr39, Yr48, Yr52, Yr59, and Yr62 conferring different levels of adult-plant resistance (APR) to stripe rust have been introgressed into Chinese wheat cultivars. To determine the effects of five newly developed wheat lines carrying these APR genes on reduction of stripe rust severity, grain yield, and quality, field experiments including non-sprayed and fungicide sprayed plots were conducted in the 2021 and 2022 growth seasons. Compared to the 100% relative area under the disease progress curve (rAUDPC) of a susceptible check cultivar, the resistant lines had relatively low rAUDPC values, ranging from 0.38% in SWUST-445 (Yr62) to 42.04% in SWUST-006 (Yr39 + Yr26) on average of the two years in the non-sprayed plots. The two-time fungicide applications almost completely controlled stripe rust on the susceptible check and the resistant lines in both years. Stripe rust significantly reduced yield by 29.96% in 2021 and by 84.40% in 2022, whereas the yield differences of the resistant lines were not significant in both years, except SWUST-006 and SWUST-445 in 2022. Stripe rust significantly reduced the thousand kernel weight (TKW) of the susceptible check by 30.36% on the average of both years but did not cause significant TKW reduction in the resistant lines. Except few cases, the various measurements for grain quality did not show significant differences for the susceptible check and resistant lines. The results showed that the resistant lines are useful for further development of wheat cultivars with adequate and durable resistance to stripe rust.

Effectiveness of Lr34 gene in reducing leaf rust severity in wheat cultivar BRW 934 transferred through marker-assisted backcross

Effectiveness of Lr34 gene in reducing leaf rust severity in wheat cultivar BRW 934 transferred through marker-assisted backcross

Elucidating the Etiology and Temporal Progress of Rust on Physic Nut Genotypes and Their Relationship with Environmental Conditions in Ecuador

Physic nut (Jatropha curcas L.) has emerged as a promising fruit crop in Ecuador, but the recent identification of rust poses a potential threat to its productive development. This study focused on elucidating the morphological aspects of the basidiomycete and assessing rust intensity across different canopy levels of physic nut hybrids and genotypes under field and semi-controlled conditions in Manabí, Ecuador. For the first time, this study confirms that Phakopsora arthuriana should be responsible for rust on physic nut in Ecuador based on the characteristics of the fungal structures. Rust incidence was 100% across all canopy layers, with the lower and middle canopies exhibiting higher severity and lesion numbers than the upper canopy. Using the Weibull nonlinear distribution model, we epidemiologically modeled disease progression, revealing that hybrid JAT 001100 displayed the highest temporal progress, recording 15% severity and an area under the disease progression curve of 3228.9 units. Promising genotypes CP-041 and CP-052 demonstrated lower rust intensity. Environmental parameters, including dew point, temperature, precipitation, and relative humidity, were correlated with rust severity and lesion numbers. In greenhouse assays, hybrid JAT 001165 showed higher severity, whereas JAT 001103 and JAT 001164 had more lesions than other genotypes. In contrast, promising genotypes CP-041 and CP-052 consistently exhibited lower rust intensity in both field and greenhouse environments. This study demonstrated that P. arthuriana could be epidemiologically modeled with the Weibull model, providing crucial insights into the dynamic interplay between rust infection and physic nut hybrids and genotypes under diverse conditions in the Manabí region of Ecuador.

Effectiveness of Rhodococcus erythropolis strain OPI-01 on the fungal development in winter wheat

Abstract. Behzad A, Diakite S, Astarkhanova TS, Pakina EN, Allen D, Mohammadi PM, Diarra O, Saquee FS. 2024. Effectiveness of Rhodococcus erythropolis strain OPI-01 on the fungal development in winter wheat. Biodiversitas 25: 1063-1070. One of the means of sustainable wheat production is using bacterial pesticides, which can synthesize phytohormonal substances with antibacterial and antifungal action. Therefore, the work aimed to study the effect of different doses of a new growth regulator Restart, ZH (Rhodococcus erythropolis D strain OPI-01) on winter wheat yield. The findings of field experiments on the effect of plant growth regulator R. erythropolis strain OPI-01 method of pre-sowing treatment of seeds (at 0.1 L/t and 0.2 L/t), and soil spraying just before sowing (-0.5 l/ha) revealed that on average, during the 2020-2021 and 2021-2022 seasons, the number of productive stems increased by a margin of 23-30, the number of grains in 1 ear increased in the experimental units by 16.2-19.4%, and the length of the ear by 0.3-0.7cm, gluten varied from 32.3 to 33.8%, yield increased from 27.8-33.0% relative to the control. Disease severity and incidence of powdery mildew, brown rust and Septoria disease in plots amended withR. erythropolis strain OPI-01 was 2-fold less than in the control plots. Research trials show the efficacy of R. erythropolis strain OPI-01 under field conditions as a plant growth regulator and a potential microbial fungicide against some foliar diseases of winter wheat. It would be exciting to study the mechanism of R. erythropolis on wheat pathogens in vitro and in planta which are still less developed in the literature.

Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor

Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process.

Impact of climate on a host–hyperparasite interaction on Arabica coffee in its native range

Abstract Natural enemies of plant pathogens might play an important role in controlling plant disease levels in natural and agricultural systems. Yet, plant pathogen–natural enemy interactions might be sensitive to climatic changes. Understanding the relationship between climate, plant pathogens and their natural enemies is thus important for developing climate‐resilient, sustainable agriculture. To this aim, we recorded shade cover, daily minimum and maximum temperature, relative humidity, coffee leaf rust and its hyperparasite at 58 sites in southwestern Ethiopia during the dry and wet season for 2 years. Coffee leaf rust severity was positively related to the maximum temperature. Hyperparasite severity was higher when the minimum temperature was low (i.e. in places with cold night temperatures). While canopy cover did not have a direct effect on rust severity, it reduced rust severity indirectly by lowering the maximum temperature. Canopy cover had a direct positive effect on the hyperparasite severity during one surveying period. Synthesis and applications. Our findings highlight that coffee leaf rust and its hyperparasite are both affected by shade cover and temperature, but in different ways. On the one hand, these niche differences lead to the worrying prediction that levels of coffee leaf rust will increase, and its hyperparasite will decrease, with climate change. On the other hand, these niche differences between coffee leaf rust and its hyperparasite provide opportunities to develop strategies to manage the environment (such as shade cover and microclimate) in such a way that the rust is disfavoured and the hyperparasite is favoured.

Preventive application of Si to control orange rust on varieties of sugarcane seedlings cultivated under greenhouse conditions

Among the several factors limiting sugarcane production, the occurrence and severity of diseases such as orange rust is one of the reason for replacing cultivars and the use of silicon (Si) can reduce the intensity of diseases caused by pathogens. This study was carried out to evaluate the effect of a preventive application of Si to suppress the intensity of orange rust caused by Puccinia kuehnii on sugarcane seedlings. Three experiments under house conditions have been conducted using a Randomized Block Design with two in a 2x5 factorial scheme (soil with and without Si application x five foliar treatments), with variety RB92-579 and SP81-3250, and a third study with 13 treatments (soil and foliar treatments) with variety SP-015503. RB92-579 showed higher dry mass without Si application in the soil whereas higher levels of Si content in plants and soil with soil treated with Si was verified. Foliar treatments showed higher values of height, dry mass, and Si uptake. The severity level of orange rust on SP81-3250 with soil treated with Si was lower than on untreated soil. SP-015503 showed a dry mass and Si uptake increase with foliar treatments, especially with silicic acid. K silicate and silicic acid showed lower severity of orange rust than the control and fungicide. Si was able to reduce the severity of orange rust and improve plant development even though it does not have effectiveness as a preventive treatment to control orange rust in one of the studied varieties.

Molecular Markers Help with Breeding for Agronomic Traits of Spring Wheat in Kazakhstan and Siberia.

The Kazakhstan-Siberia Network for Spring Wheat Improvement (KASIB) was established in 2000, forming a collaboration between breeding and research programs through biannual yield trials. A core set of 142 genotypes from 15 breeding programs was selected, genotyped for 81 DNA functional markers and phenotyped for 10 agronomic traits at three sites in Kazakhstan (Karabalyk, Shortandy and Shagalaly) and one site in Russia (Omsk) in 2020-2022. The study aim was to identify markers demonstrating significant effects on agronomic traits. The average grain yield of individual trials varied from 118 to 569 g/m2. Grain yield was positively associated with the number of days to heading, plant height, number of grains per spike and 1000-kernel weight. Eight DNA markers demonstrated significant effects. The spring-type allele of the Vrn-A1 gene accelerated heading by two days (5.6%) and was present in 80% of the germplasm. The winter allele of the Vrn-A1 gene significantly increased grain yield by 2.7%. The late allele of the earliness marker per se, TaMOT1-D1, delayed development by 1.9% and increased yield by 4.5%. Translocation of 1B.1R was present in 21.8% of the material, which resulted in a 6.2% yield advantage compared to 1B.1B germplasm and a reduction in stem rust severity from 27.6 to 6.6%. The favorable allele of TaGS-D1 increased both kernel weight and yield by 2-3%. Four markers identified in ICARDA germplasm, ISBW2-GY (Kukri_c3243_1065, 3B), ISBW3-BM (TA004946-0577, 1B), ISBW10-SM2 (BS00076246_51, 5A), ISBW11-GY (wsnp_Ex_c12812_20324622, 4A), showed an improved yield in this study of 3-4%. The study recommends simultaneous validation and use of selected markers in KASIB's network.

Impact of managing yellow rust (Puccinia striiformis f.sp. tritici) by the use of fungicide sprays and resistant varieties on the physiochemical qualities of bread wheat

AbstractYellow rust disease caused by Puccinia striiformis f. sp. tritici is one of the major threats to wheat production in Ethiopia. In this paper, the impact of yellow rust on the physical qualities of bread wheat was studied. For this purpose, combinations of four bread wheat varieties (Digalu, Hidase, Danda'a, and Honkolo) and two fungicides (Nativo SC 300 and Tilt® 250E.C.) were tested at two different hotspot areas (Albazar and Alicho) in the Silte Zone, Southern Ethiopia, under natural epidemics of the disease during the 2018 growing season. A randomized complete block design in factorial arrangement with three replications was used to set up the experiment. A significant (p < .01) increase in hectoliters weight and wet gluten content were observed due to the application of Nativo SC 300; however, grain protein content did not respond significantly (p > .01) at both locations. Wheat varieties with high yellow rust severity exhibited maximum values of area under the disease progress curve and physical quality loss percentage (%). The highly susceptible wheat variety Digalu exhibited the highest values of hectoliter weight losses (%), and the actual losses ranged from 5.5% to 29.5%. Except for grain protein content, there was a significant negative correlation between the values of terminal rust severity, average coefficient infection, and area under the disease progress curve and that of hectoliters weight and wet gluten content, with correlation coefficients ranging from −0.85** to −0.33**. It is possible to improve the physiochemical properties of bread wheat through the application of foliar fungicides by selecting the appropriate genotypes that have an acceptable level of resistance to yellow rust.

StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust.

Wheat stripe rust poses a marked threat to global wheat production. Accurate and effective disease severity assessments are crucial for disease resistance breeding and timely management of field diseases. In this study, we propose a practical solution using mobile-based deep learning and model-assisted labeling. StripeRust-Pocket, a user-friendly mobile application developed based on deep learning models, accurately quantifies disease severity in wheat stripe rust leaf images, even under complex backgrounds. Additionally, StripeRust-Pocket facilitates image acquisition, result storage, organization, and sharing. The underlying model employed by StripeRust-Pocket, called StripeRustNet, is a balanced lightweight 2-stage model. The first stage utilizes MobileNetV2-DeepLabV3+ for leaf segmentation, followed by ResNet50-DeepLabV3+ in the second stage for lesion segmentation. Disease severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel area. StripeRustNet achieves 98.65% mean intersection over union (MIoU) for leaf segmentation and 86.08% MIoU for lesion segmentation. Validation using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual scores. To address the challenges in manual labeling, we introduce a 2-stage labeling pipeline that combines model-assisted labeling, manual correction, and spatial complementarity. We apply this pipeline to our self-collected dataset, reducing the annotation time from 20 min to 3 min per image. Our method provides an efficient and practical solution for wheat stripe rust severity assessments, empowering wheat breeders and pathologists to implement timely disease management. It also demonstrates how to address the "last mile" challenge of applying computer vision technology to plant phenomics.

Evaluation of Spinach Cultivars for Resistance to Stemphylium Leaf Spot (Stemphylium vesicarium) and White Rust (Albugo occidentalis)

Stemphylium leaf spot, caused by Stemphylium vesicarium, and white rust, caused by Albugo occidentalis, can cause significant losses in spinach production. Management of these foliar diseases of spinach has become increasingly challenging with the development of fungicide resistance in some pathogen populations, high planting density and overhead irrigation used for baby leaf spinach production, and the fact that >60% of fresh market spinach production in the United States is certified organic. To identify spinach cultivars with resistance to Stemphylium leaf spot and white rust, a field trial was performed near Crystal City, TX, USA, in 2021 (79 cultivars), 2022 (87 cultivars), and 2023 (63 cultivars). Each year, the plants were inoculated with S. vesicarium and rated for disease severity. Plants were also rated for white rust severity that resulted from natural infection during the 2021 and the 2022 trials. During each trial, 11% to 27% of the cultivars were identified as resistant to Stemphylium leaf spot, and another 29% to 48% had moderately resistant reactions. In contrast, only 5 of 79 cultivars (6%) in the 2021 trial did not develop symptoms of white rust, and all 87 cultivars evaluated in the 2022 trial had symptoms of white rust. Although there was no significant correlation between mean Stemphylium leaf spot ratings and mean white rust ratings during these trials, the cultivars Colusa, Kodiak, PV-1569, and PV-1664 displayed resistant or moderately resistant responses to both diseases in at least two trials. Therefore, processing and fresh market spinach growers have resistant cultivars from which to select to reduce the economic impacts of Stemphylium leaf spot and white rust.

Field-testing whitebark pine resistance to white pine blister rust: A simple, effective approach to progeny testing for restoration

The endangered conifer whitebark pine (Pinus albicaulis), native to high elevation forests of western North America, is declining mainly due to the introduced pathogen Cronartium ribicola, causing the disease white pine blister rust. Low levels of genetic resistance to blister rust are present in whitebark pine populations. Traditional methods of inoculating seedlings to determine resistance to the rust are costly, labour intensive, and have limited capacity due to facilities. This presents a bottleneck in the development of resistant material for restoring stands that have been heavily infected by the rust. Here we test an alternative approach to controlled inoculations that could screen large numbers of families with lower costs and requiring fewer specialized resources. We screened 214 open-pollinated families from 44 provenances from British Columbia (BC), Washington (WA), Oregon (OR), Idaho (ID), Montana (MT), and Nevada (NV) to determine: (1) the effectiveness of natural rust inoculation from Ribes nigrum in a common garden; (2) family- and provenance-level resistance to blister rust; and (3) climate variables related to population-level rust resistance. Eighty-one of these families were previously screened by the US Forest Service in Cottage Grove, Oregon using standard artificial inoculation methods. The natural inoculation was effective, with 73 % of seedlings displaying stem symptoms of the disease, and 95 % showing rust infection. A clear relationship was found between distance from the nearest Ribes nigrum plant and severity of blister rust. Linear mixed models with spatial correlations were fitted to rust infection data using ASReml-R to estimate breeding values, heritability, and among-population differentiation (QST) for rust resistance. Population differentiation for rust resistance was moderate, with the highest resistance in provenances from the Cascade Mountains of Washington, and the most susceptible families from the BC Chilcotin and Okanagan regions. Heritability of rust resistance was moderate (0.23). The field testing was broadly consistent with classifications of families into resistant, moderate, and susceptible groups based on artificial inoculations. This method of screening could be used at a broader scale to determine families resistant to white pine blister rust without specialized testing facilities and increase the availability of resistant seedlings for restoration.

Soybean and grapevine rusts accelerate the defoliation rates of host plants

AbstractGrapevine leaf rust (GLR) and soybean rust (SBR), caused by Neophysopella tropicalis and Phakopsora pachyrhizi, respectively, may lead to early defoliation of the host, depending on disease severity level. The rate of defoliation is an important parameter in mechanistic models aimed at simulating yield loss, but such knowledge is not available for these rust diseases. This work aimed to (i) relate the temporal dynamics of GLR and SBR to defoliation; and (ii) estimate the relative rates of defoliation and model their relationship with rust severities. Grapevine and soybean plants were inoculated in the field at increasing concentrations of urediniospore suspensions of the respective causal agent. Control plots of the vineyard and the soybean field were protected with sequential fungicide sprays to evaluate natural defoliation. Rust severity (proportion of area affected) of each leaf or leaflet was evaluated every three or four days on 1323 grapevine leaves and 655 soybean leaflets, respectively. The relative rates of defoliation were estimated as the slope parameters of linear regression of the Napierian logarithm of the number of alive leaves or leaflets over time. Defoliation rates in grapevine and soybean incremented logarithmically with the increase of rust severity. Defoliation rates on symptomless grapevine and soybean leaves were 0.018 and 0.05 day−1, respectively, while they averaged 0.033 day−1 on diseased grapevine leaves (rust severity between 5% and 12%), and 0.12 day−1 on diseased soybean leaflets (rust severity between 25% and 60%). Thus, a quantitative relationship was established between rust severity and defoliation on grapevine and soybean.

Bazı Ekmeklik ve Makarnalık Buğday Çeşitlerinde Sarı Pas Hastalığının Fenolojik Dönemlere göre Hastalık-Verim İlişkisinin Çok Bantlı (Hiperspektral) Veriler Kullanılarak Araştırılması

The aim of this study was to evaluate the severity of yellow rust in different phenological periods by 
 subjecting bread (Bayraktar 2000, Demir 2000, Eser and Kenanbey) and durum (Çeşit-1252, Eminbey, Kızıltan 91 and Mirzabey 2000) wheat varieties to different spore doses (0%, 25%, 50% and 100%) under controlled epidemic conditions. The research was conducted in Yenimahalle, Ankara, Turkey during the 2018-2019 growing season. In the study, the morphological changes in yellow rust severity were determined at different phenological developmental stages of the test materials with the reflectance values obtained by using handheld spectroradiometer in different spore dose applications during the period from tillering to stalk emergence. These reflectance values were converted into vegetation index values expressed by mathematical formulae and used in determining yield estimates. Considering the results obtained, it was determined that the spectral indices calculated especially in the early flowering period (25 May 2019, Feekes 10.5.1) were effective in yield estimation for all bread varieties except Kenanbey variety (15 June 2019, Feekes 10.5.4). It was determined that the spectral band region of 25 May 2019 (Feekes 10.5.1), which includes all indices determined to predict yield in all bread and durum varieties and which is the beginning of flowering, was effective. In grain yield estimation, it was determined that there was a decrease in the correlation values of the spectral indices starting from the early flowering period (Feekes 10.5.1) towards the grain setting period (Feekes 10.5.3) and milk maturity period 
 (Feekes 10.5.4). When the correlations between these index values and yield values were examined, it was 
 determined that prominent phenological periods and high correlation indices could be calculated for these periods. Nowadays, with the use of optical sensor technology instead of traditional disease surveillance methods, the way has paved the way for the development of new approaches for early, fast and accurate yield estimation as a result of the verification of images taken by unmanned aerial vehicles on which multispectral and hyperspectral cameras are located with ground data using artificial intelligence and deep learning techniques.

Identification of novel sources of partial and incomplete hypersensitive resistance to rust and associated genomic regions in common bean

Common bean (Phaseolus vulgaris) is one of the legume crops most consumed worldwide and bean rust is one of the most severe foliar biotrophic fungal diseases impacting its production. In this work, we searched for new sources of rust resistance (Uromyces appendiculatus) in a representative collection of the Portuguese germplasm, known to have accessions with an admixed genetic background between Mesoamerican and Andean gene pools. We identified six accessions with incomplete hypersensitive resistance and 20 partially resistant accessions of Andean, Mesoamerican, and admixed origin. We detected 11 disease severity-associated single-nucleotide polymorphisms (SNPs) using a genome-wide association approach. Six of the associations were related to partial (incomplete non-hypersensitive) resistance and five to incomplete hypersensitive resistance, and the proportion of variance explained by each association varied from 4.7 to 25.2%. Bean rust severity values ranged from 0.2 to 49.1% and all the infection types were identified, reflecting the diversity of resistance mechanisms deployed by the Portuguese germplasm.The associations with U. appendiculatus partial resistance were located in chromosome Pv08, and with incomplete hypersensitive resistance in chromosomes Pv06, Pv07, and Pv08, suggesting an oligogenic inheritance of both types of resistance. A resolution to the gene level was achieved for eight of the associations. The candidate genes proposed included several resistance-associated enzymes, namely β-amylase 7, acyl-CoA thioesterase, protein kinase, and aspartyl protease. Both SNPs and candidate genes here identified constitute promising genomics targets to develop functional molecular tools to support bean rust resistance precision breeding.

Research on the Method of Identifying the Severity of Wheat Stripe Rust Based on Machine Vision

Research on the Method of Identifying the Severity of Wheat Stripe Rust Based on Machine Vision