Rust In Winter Wheat Research Articles

Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method

AbstractWheat (Triticum aestivum L.) leaf rust is the most common and widely distributed wheat disease. Non‐destructive and real‐time methods for monitoring wheat leaf rust can help prevent and control plant diseases in agricultural production. In this study, we obtained multispectral imagery of the wheat canopy acquired by an unmanned aerial vehicle, selected the vegetation index using the K‐means algorithm (KA) and genetic algorithm (GA), and established a wheat leaf rust monitoring model based on the backpropagation neural network (BPNN) method. The results showed that the R2 and RMSE of the KA‐BPNN model were 0.902% and 5.45% for the modeling set, respectively, and 0.784% and 4.76% for the validation set, respectively; and the R2 and RMSE of the GA‐BPNN model was 0.922% and 4.88% for the modeling set, respectively, and 0.780% and 4.28% for the validation set, respectively. The prediction model after optimizing the variables using KA and GA had higher accuracy than the BPNN model, implying that using variable dimensionality reduction methods and complex machine learning algorithms to construct estimation models can improve model accuracy significantly. These models accurately monitored leaf rust in winter wheat, providing a theoretical basis and technical support for assessing plant diseases and screening disease‐resistant wheat varieties.

Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future.

Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks.

Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.

Prevalence and harmfulness of winter wheat brown leaf rust (Puccinia recondita Rob. ex desm. f. sp. tritici) in the Southern Steppe of Ukraine

Apart from making farmers adapt agricultural technologies to new, more arid conditions in order to obtain sustainable and high yields, global climate change is also known to alter the pathogenicity of plant pathogens, the biological cycle of which is tightly associated with environmental factors. One of the most common diseases of winter wheat is brown leaf rust, caused by Puccinia recondita Rob. ex Desm. f. sp. tritici. In terms of its harmfulness and prevalence among other leaf and stem diseases, brown leaf rust is comparable to yellow and stem rust, septoria leaf blotch and other leaves blotches. Under arid conditions of the Southern Steppe of Ukraine, the disease manifests itself once every five years provided that sufficient moisture, indicated by the hydro-thermal index above 1.0, is present during the spring and summer vegetation periods. The main factor influencing the intensity of pathogen development is moisture supply from April to June (ΣTP> 50 mm). In 2019, the amount of precipitation during this period was 194 mm, which led to the disease development at the level of 18.4% with a spread of 27.2%. Productivity of winter wheat varieties such as Antonivka, Blago and Maria was reduced by 10.8-11.3%, when infected in the phase of milk grain stage (stages 73-77 on the BBCH scale), while net photosynthesis productivity decreased by 14.2-14.3%, which translates to lower average yields of 2.3-3.1 t/ha. To maximize the productive potential of varieties and effectively control the development of the causative agent of brown leaf rust in winter wheat under favourable conditions for epiphytic development, the application of systemic fungicides with long-term protective effect imparted by the chemical group of triazoles (Colossal e.c., 1.0 l/ha ) is recommended in stages 69-71 on the BBCH scale. This provides not only reliable crop protection (Eff.: 77.9-82.0%), but also yields at the level of 3.3 - 4.6 t/ha, depending on the variety under non-irrigated conditions.

Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in improved disease management. RGB images were acquired at four representative wheat-producing sites in the Grand Duchy of Luxembourg. Diseased leaf areas were determined based on the digital numbers (DNs) of green and red spectral bands for wheat stripe rust (WSR), and the combination of DNs of green, red, and blue spectral bands for wheat leaf rust (WLR). WSR and WLR caused alterations in the typical reflectance spectra of wheat plants between the green and red spectral channels. Overall, good agreements between UAV-based estimates and observations were found for canopy cover, WSR, and WLR severities, with statistically significant correlations (p-value (Kendall) < 0.0001). Correlation coefficients were 0.92, 0.96, and 0.86 for WSR severity, WLR severity, and canopy cover, respectively. While the estimation of canopy cover was most often less accurate (correlation coefficients < 0.20), WSR and WLR infected leaf areas were identified satisfactorily using the RGB imagery-derived indices during the critical period (i.e., stem elongation and booting stages) for efficacious fungicide application, while disease severities were also quantified accurately over the same period. Using such a UAV-based RGB imagery method for monitoring fungal foliar diseases throughout the cropping season can help to identify any new disease outbreak and efficaciously control its spread.

In-field proximal sensing of septoria tritici blotch, stripe rust and brown rust in winter wheat by means of reflectance and textural features from multispectral imagery

During its growth, winter wheat (Triticum aestivum L.) can be impacted by multiple stresses involving fungal diseases that are responsible for high yield losses. Enhancing the breeding and the identification of resistant cultivars could be achieved by collecting automated and reliable information at the plant level. This study aims to estimate the severity of stripe rust (SR), brown rust (BR) and septoria tritici blotch (STB) in natural conditions and to highlight wavebands of interest, based on images acquired through a multispectral camera embedded on a ground-based platform. The severity of the three diseases has been assessed visually in an agronomic trial involving five wheat cultivars with or without fungicide treatment. An acquisition system using multispectral imagery covering the visible and near-infrared range has been set up at the canopy level. Based on spectral and textural features, estimations of area under disease progress curve (AUDPC) were performed by means of artificial neural networks (ANN) and partial least squares regression (PLSR). Supervised classification was also implemented by means of ANN. The ANN performed better at estimating disease severity with R2 of 0.72, 0.57 and 0.65 for STB, SR and BR respectively. Discrimination in two classes below or above 100 AUDPC reached an accuracy of 81% ( κ = 0.60) for STB. This study, which combined the effect of date, cultivar and multiple disease infections, managed to highlight a few wavebands for each disease and took a step further in the development of a machine vision-based approach for the characterisation of fungal diseases in natural conditions.

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

Genetic architecture of yellow and stem rust resistance in a durum wheat diversity panel

Winter durum (Triticum turgidum var. durum) growing is favored in Germany, Austria, and Hungary. With the invasion of the aggressive Warrior race with a wider virulence spectrum of the yellow rust (YR) causing pathogen, YR came into focus of durum breeders. Accordingly, a local epidemic of stem rust in winter wheat 2013 gained much attention. Therefore, we aimed to analyze the genetic architecture of resistance to YR and stem rust (SR) in a diversity panel of 328 durum lines genotyped by 12,550 mapped markers. Infections were successful in three environments for YR and one environment for SR. Additionally, cumulative ratings of leaf and ear health were conducted in five and three environments, respectively. The genome-wide association analysis revealed six to eight quantitative trait loci (QTL) per trait with an explained total genotypic variance of 42% (YR) to 75% (ear health). Sequence comparison with a reference genome indicated that three YR (Yr10, Yr51, YrH9014) and two SR (Sr56, Sr8155B1) resistance genes might be present in the diversity panel. Other QTL with explained genotypic variances ranging from 1.5 to 21% were detected. The difference between marker-assisted selection and genomic prediction was the highest for those traits with lower explained genetic variance (YR, leaf health) indicating that an array of non-detected QTL was used for genomic prediction. Genomics-assisted breeding could greatly help in achieving complex resistances in cultivars.

Early optical detection of infection with brown rust in winter wheat by chlorophyll fluorescence excitation spectra

Puccinia recondita f. sp. tritici, the causative agent of brown rust disease is the reason for major yield losses in winter wheat plants (Triticum aestivum L.). Early detection of fungal infections would allow a specific fungicide application. Aim of the current study was to develop an automated and computer based device that differentiate infected and healthy plants non-invasively in an early stage of infection. To achieve the target an optical sensor device has been designed (“MultiDetExc”), which excites chlorophyll fluorescence in discrete wavelengths and detects the induced emissions. Wheat plants were infected artificially with brown rust in a climate chamber experiment to survey the capability of the instrument. The chlorophyll fluorescence excitation spectra of whole wheat plants were measured on several days after infection. As reference methods, HPLC and qPCR analysis were included in the current study to measure the polyphenol content of the leaves and the level of infection.The recently developed sensor device is an efficient technique to differentiate the infected and not infected wheat plants as soon as four days after inoculation. The measured fluorescence quotients correlate high positive with the polyphenol contents and the relative amount of fungal DNA. An untreated healthy control was measured parallelly, in order to associate the increased synthesis of polyphenols to the fungal infection definitely.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared (NIR) shoulder (750-900 nm) region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region (NSRI), defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices (water index and normalized difference water index) and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index (NDVI), water index (WI) and normalized difference water index (NDWI), with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

Selection of Spectral Channels for Satellite Sensors in Monitoring Yellow Rust Disease of Winter Wheat

Remote sensing has great potential to serve as a useful means in crop disease detection at regional scale. With the emerging of remote sensing data on various spectral settings, it is important to choose appropriate data for disease mapping and detection based on the characteristics of the disease. The present study takes yellow rust in winter wheat as an example. Based on canopy hyperspectral measurements, the simulative multi-spectral data was calculated by spectral response function of ten satellite sensors that were selected on purpose. An independent t-test analysis was conducted to access the disease sensitivity for different bands and sensors. The results showed that the sensitivity to yellow rust varied among different sensors, with green, red and near infrared bands been identified as disease sensitive bands. Moreover, to further assess the potential for onboard data in disease detection, we compared the performance of most suitable multi-spectral vegetation index (MVI)-GNDVI and NDVI based on Qu...

Development, evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat

In most cases, statistical models for monitoring the disease severity of yellow rust are based on hyperspectral information. The high cost and limited cover of airborne hyperspectral data make it impossible to apply it to large scale monitoring. Furthermore, the established models of disease detection cannot be used for most satellite images either because of the wide range of wavelengths in multispectral images. To resolve this dilemma, this paper presents a novel approach by constructing a spectral knowledge base (SKB) of diseased winter wheat plants, which takes the airborne images as a medium and links the disease severity with band reflectance from environment and disaster reduction small satellite images (HJ-CCD) accordingly. Through a matching process with a SKB, we estimated the disease severity with a disease index (DI) and degrees of disease severity. The proposed approach was validated against both simulated data and field surveyed data. Estimates of DI (%) from simulated data were more accurate, with a coefficient of determination (R 2) of 0.9 and normalized root mean square error (NRMSE) of 0.2. The overall accuracy of classification reached 0.8, with a kappa coefficient of 0.7. Validation of the estimates against field measurements showed that there were some errors in the DI value with the NRMSE close to 0.5. The result of the classification was more encouraging with an overall accuracy of 0.77 and a kappa coefficient of 0.58. For the matching process, Mahalanobis distance performed better than the spectral angle (SA) in all analyses in this study. The potential of SKB for monitoring the incidence and severity of yellow rust is illustrated in this study.

Identification and mapping QTL for high-temperature adult-plant resistance to stripe rust in winter wheat (Triticum aestivum L.) cultivar ‘Stephens’

High-temperature adult-plant (HTAP) resistance from the winter wheat (Triticum aestivum) cultivar 'Stephens' has protected wheat crops from stripe rust caused by Puccinia striiformis f. sp. tritici for 30 years. The objectives of this study were to identify quantitative trait loci (QTL) for HTAP resistance in Stephens through genetic linkage analysis and identify DNA markers linked to the QTL for use in marker-assisted breeding. Mapping populations consisted of 101 recombinant inbred lines (RILs) through single-seed descent from 'Stephens' (resistant) x 'Michigan Amber' (susceptible). F(5), F(6) and F(7) RILs were evaluated for stripe rust resistance at Pullman, WA in 1996, 1997 and 1998, respectively, whereas F(8) RILs were evaluated at Mt Vernon, WA, USA in 2005. The 101 F(8) RILs were evaluated with 250 resistance gene analog polymorphism (RGAP), 245 simple sequence repeat (SSR) and 1 sequence tagged site (STS) markers for genetic linkage map construction. Two QTL, which explained 48-61% of the total phenotypic variation of the HTAP resistance in Stephens, were identified. QYrst.wgp-6BS.1 was within a 3.9-cM region flanked by Xbarc101 and Xbarc136. QYrst.wgp-6BS.2 was mapped in a 17.5-cM region flanked by Xgwm132 and Xgdm113. Both two QTL were physically mapped to the short arm of chromosome 6B, but in different bins. Validation and polymorphism tests of the flanking markers in 43 wheat genotypes indicated that the molecular markers associated with these QTL should be useful in marker-assisted breeding programs to efficiently incorporate HTAP resistance into new wheat cultivars.

Framework to support decisions on chemical pest control under uncertainty, applied to aphids and brown rust in winter wheat

An approach is presented for supporting decision making on chemical pest control in an uncertain environment. Timing of chemical control is considered as a decision problem, which is analysed in terms of objectives, strategies, selection criteria, and a model of the system dynamics. Return on expenditure and insurance are considered to be two generally applicable objectives in pest control. These objectives are made operational by defining the profitability of strategy A compared with strategy B as the probability that A results in lower costs than B, and the risk associated with a strategy as the 0.90 quantile of the probability distribution of costs associated with that strategy, respectively. The authors propose to express the performance of relevant strategies in terms of profitability and risk. Risk-neutral damage thresholds where expected costs of no chemical control at any time just exceed those of immediate application of a pesticide, constitute a yardstick for assessing a strategy's performance. The approach is illustrated using a deterministic model of the pathosystem aphid-brown rust-winter wheat. Uncertainty is modelled as random inputs. No control and immediate pesticide application represent the only relevant strategies, as postponing chemical control decreases profitability and increases risk. The decline in risk attributable to pesticide application is larger for brown rust than for aphids. At risk-neutral damage thresholds the probability of positive returns on pesticide application amounts to only 30%. The causes of this bias to chemical control in recommendations for risk-neutral decision makers are discussed. Current recommendations for both aphids and brown rust appear to presume that farmers become more risk-averse as crop development advances.

Uncertainty analysis applied to supervised control of aphids and brown rust in winter wheat. Part 1. Quantification of uncertainty in cost-benefit calculations

Information on the uncertainty about predicted costs and benefits of chemical control is an indispensable, but usually ignored, aspect of decision support in crop protection. The consequences of ignoring uncertainty are illustrated using a model for evaluating financial loss associated with different strategies of chemical control of aphids (especially Sitobion avenae) and/or brown rust ( Puccinia recondita) in winter wheat in The Netherlands. Crop development, population dynamics and damage are simulated as a function of predicted average daily temperature and a tittle sequence of decisions on chemical control. The model is initialized with temperature sum and incidences of aphids and brown rust. Uncertainty about model output is computed from uncertainty about model parameters and inputs which are quantified using field data. The probability distribution of financial loss is estimated by stratified sampling from the input distributions followed by Monte Carlo simulation. Compared to no control, chemical control of both aphids and brown rust may reduce the range of possible financial loss. Because the probability distributions of financial loss associated with no chemical control have long right tails, uncertainty leads to damage thresholds well below the deterministic thresholds, even when indifference to risk is assumed. In comparison, the damage thresholds currently used in the decision support system EPIPRE are risk-avoiding for aphids, and approximately risk-neutral for brown rust.

Uncertainty analysis applied to supervised control of aphids and brown rust in winter wheat. Part 2. Relative importance of different components of uncertainty

The components of an existing model for supervised control of aphids (especially Sitobion avenae) and brown rust ( Puccinia recondita) in winter wheat contain uncertainty. Their contribution to uncertainty about model output is assessed. The model simulates financial loss associated with a time sequence of decisions on chemical control as a function of crop development, population growth, and damage. Four sources of uncertainty were quantified: model parameters, incidence sample estimates, future average daily temperature, and white noise. Uncertainty about the first two sources is controllable because it decreases when more information is collected. Uncertainty about the last two sources is uncontrollable, given the structure of the model. Uncertainty about model output, characterized by its variance, is calculated by repeatedly drawing realizations of the various sources of uncertainty, and calculating financial loss after each draw. By processing new realizations of these sources one by one, the contribution of each component to total variance can be assessed using an adapted Monte Carlo procedure. For most relevant initial conditions and decision strategies the sources of uncontrollable uncertainty cause more than half of the uncertainty about model output. White noise in the relative growth rates of aphids and brown rust is the most important source of uncertainty. Resources for improvement of the model are most effectively allocated to studies of the population dynamics of aphids and brown rust.

Deposition characteristics and biological effectiveness of fungicides applied to winter wheat and the hazards of drift when using different types of hydraulic nozzles

Hydraulic-fan cone, low-pressure fan and two-chamber cone nozzles were used at 1.5, 3.0 and 6.0 bar (150, 300 and 600 kPa) to deliver 140, 200 and 3001 ha −1 respectively of a fungicide spray to control mildew and yellow rust in winter wheat. No significant differences in biological effect were obtained although fan nozzles deposited significantly more on the upper parts of the stems, and two-chamber cone nozzles deposited less on the flag leaf and on the upper part of the stems. Because of their higher deposit and more even distribution, fan nozzles should be preferred, operated at a pressure of 1.5–3 bar to deliver a spray volume of 140–190 lha −1 at 7 km h −1 and to obtain an acceptable biological effect. Higher pressure (6 bar) and volume rates gave no improvement in fungicide performance. Adding a surfactant or penetration oil did not change the volume median diameter or proportion of spray liquid in droplets < 150 μm. Less drift was found when using two-chamber cone nozzles at 3 bar than with flat-fan spray nozzles at 1.5 bar. It is concluded that the two-chamber cone nozzle is useful for fungicide application when drift must be avoided.

Simulation of yield reduction by leaf rust in winter wheat, applied to the analysis of genetic variation in partial resistance

Differences in yield reduction among seven winter wheat genotypes due to leaf rust, as observed in a field experiment, were analysed using a simulation model. In this model, the effects of the disease on crop growth and yield were described on the basis of light interception, photosynthesis, respiration and assimilate partitioning. The model properly described the observed yield difference between the genotypes, both in the absence and in the presence of leaf rust. According to the model, 66% of total yield reduction was due to an accelerated decrease in green leaf area and 18% was due to light capture by dead leaf tissue at the top of the canopy. Genetic variation in yield reduction was, therefore, mainly explained by variation in leaf senescence. Leaf rust did not affect the photosynthetic rate of the remaining green leaf area. Opportunities of selection for individual damage components were assessed from their simulated effect on grain yield, together with their estimated genetic and environmental variance. Verschillen tussen zeven wintertarwegenotypen in opbrengstderving door bruine roest, zoals waargenomen in een veldexperiment, werden geanalyseerd met een simulatiemodel. In dit model werd het effect van de ziekte op gewasgroei en opbrengst beschreven op basis van lichtinterceptie, fotosynthese, ademhaling en assimilatenverdeling. Het model gaf een goede beschrijving van de waargenomen opbrengstverschillen tussen de genotypen, zowel bij aanwezigheid als bij afwezigheid van bruine roest. Van de totale opbrengstreductie werd, volgens modelberekeningen, 66% veroorzaakt door versnelde afname van groen bladoppervlak en 18% door het wegvangen van licht door dood bladweefsel boven in het gewas. De genetische variatie in opbrengstreductie werd zodoende vooral verklaard uit de waargenomen verschillen in bladveroudering tussen de genotypen. De fotosynthese per eenheid resterend groen bladoppervlak werd niet beïnvloed door bruine roest. Respectieven voor selectie op afzonderlijke schadecomponenten werden geëvalueerd op basis van hun gesimuleerde effect op korrelopbrengst, in combinatie met hun genetische en toevalsvariatie.

Genetic variation in partial resistance to leaf rust in winter wheat: disease progress, foliage senescence and yield reduction

Relations between leaf rust progress, foliage senescence and yield reduction were studied for seven winter wheat genotypes, differing in their partial resistance to leaf rust. Leaf rust accelerated leaf and ear senescence. Photosynthesis measurements showed, however, that the photosynthetic capacity of the remaining green surface was not affected. Differences between genotypes in yield reduction were largely explained by differences in leaf senescence and, therefore, in cumulative light interception (r=0.83) and crop photosynthesis. Reduction in cumulative light interception was closely related to the area under the disease-progress-curve (r=0.85), which was also closely associated with yield reduction (r=0.88). However, the time taken to reach a 50% reduction of green leaf area and the pustule density on 1 July (i.e. halfway through the epidemic) were just as good predictors of yield reduction as cumulative light interception and area under disease-progress-curve, suggesting that the former are useful criteria in the selection for partial resistance to leaf rust. De relatie stussen de epidemiologie van bruine roest, bladveroudering en oprengstderving werden bestudeerd bij zeven wintertarwegenotypen die verschilden in partiële resistentie tegen bruine roest. Bruine roest versnelde de veroudering van blad en aar. Fotosynthesemetingen wezen echter uit dat de fotosynthesecapaciteit van het resterende groene oppervlak niet werd beïnvloed. Verschillen tussen de genotypen in opbrengstderving werden voornamelijk veroorzaakt door verschillen in bladveroudering en daarmee in cumulatieve lichtinterceptie (r=0.83) en gewasfotosynthese. De reductie in cumulatieve lichtinterceptie was nauw gerelateerd aan de cumulatieve ziektedruk (in puistdagen) (r=0.85), welke ook nauw was geassocieerd met de opbrengstderving (r=0.88). De opbrengstreductie werd echter even goed voorspeld door de tijdsduur tot 50% reductie van het groene bladoppervlak en de puistdichtheid halverwege de epidemie als door cumulatieve lichtinterceptie en cumulatieve ziektedruk, waardoor beide eerste criteria gebruikt kunnen worden in de selectie voor partiële resistentie tegen bruine roest.

Some effects of applied sodium and potassium chloride on yellow rust in winter wheat

SUMMARYIn glasshouse experiments with plants in pots, applications of potassium chloride to the soil at 0.5 g/plant, 4 days before inoculation with Puccinia striiformis, decreased the severity of yellow rust on several winter wheat cultivars in comparison with untreated plants. Conversely, yellow rust was encouraged by applications of sodium nitrate. Sodium chloride in solution (8.6 g/l) reduced yellow rust when applied to the soil at the rate of 20 ml/plant but not when it was sprayed on to the leaves.In small‐plot field experiments, sodium and potassium chlorides applied to the soil as dry powders in the spring at rates of 376, 1130 or 2260 kg/ha, significantly decreased the severity of yellow rust on most of the winter wheat cvs examined at each rate. The chlorides at these rates did not adversely affect the overall growth or yield in the absence of yellow rust.