A Threshold-Based Weather Model for Predicting Stripe Rust Infection in Winter Wheat.

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999 to 2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity >92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤0.1 mm (with the dekad having these conditions for 5 to 20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥0.90 and false alarm ratios were ≤0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications.

Plant Disease Severity Assessment-How Rater Bias, Assessment Method, and Experimental Design Affect Hypothesis Testing and Resource Use Efficiency.

The effect of rater bias and assessment method on hypothesis testing was studied for representative experimental designs for plant disease assessment using balanced and unbalanced data sets. Data sets with the same number of replicate estimates for each of two treatments are termed "balanced" and those with unequal numbers of replicate estimates are termed "unbalanced". The three assessment methods considered were nearest percent estimates (NPEs), an amended 10% incremental scale, and the Horsfall-Barratt (H-B) scale. Estimates of severity of Septoria leaf blotch on leaves of winter wheat were used to develop distributions for a simulation model. The experimental designs are presented here in the context of simulation experiments which consider the optimal design for the number of specimens (individual units sampled) and the number of replicate estimates per specimen for a fixed total number of observations (total sample size for the treatments being compared). The criterion used to gauge each method was the power of the hypothesis test. As expected, at a given fixed number of observations, the balanced experimental designs invariably resulted in a higher power compared with the unbalanced designs at different disease severity means, mean differences, and variances. Based on these results, with unbiased estimates using NPE, the recommended number of replicate estimates taken per specimen is 2 (from a sample of specimens of at least 30), because this conserves resources. Furthermore, for biased estimates, an apparent difference in the power of the hypothesis test was observed between assessment methods and between experimental designs. Results indicated that, regardless of experimental design or rater bias, an amended 10% incremental scale has slightly less power compared with NPEs, and that the H-B scale is more likely than the others to cause a type II error. These results suggest that choice of assessment method, optimizing sample number and number of replicate estimates, and using a balanced experimental design are important criteria to consider to maximize the power of hypothesis tests for comparing treatments using disease severity estimates.

Advancing Methodologies for Investigating PM2.5 Removal Using Green Wall System.

Combustion processes are the primary source of fine particulate matter in indoor air. Since the 1970s, plants have been extensively studied for their potential to reduce indoor air pollution. Leaves can retain particles on their surfaces, influenced by factors such as wax content and the presence of hairs. This study introduces an innovative experimental approach using metal oxide particles in an office-like environment to evaluate the depolluting effect of plant walls. Two plant walls were installed in a controlled room, housing three plant species: Aglaonema commutatum 'Silver Bay', Dracaena fragrans, and Epipremnum aureum. Metal oxide particles were introduced via a compressed air blower positioned between the two walls. The concentration of these particles was monitored using PM2.5 sensors, and the deposition of iron (Fe) on the leaves was quantified through Inductively Coupled Plasma Mass Spectrometry (ICP-MS). This novel methodology effectively demonstrated the utility of both real-time sensors and ICP-MS in quantifying airborne particle concentrations and leaf deposition, respectively. The results revealed that Dracaena fragrans had a 44% higher Fe particle retention rate compared to the control (wallpaper). However, further validation through methodological replication is necessary to confirm the reproducibility of these findings.

Unveiling the Synergistic Effects of Phosphorus Fertilization and Organic Amendments on Red Pepper Growth, Productivity and Physio-Biochemical Response under Saline Water Irrigation and Climate-Arid Stresses.

In regions facing water scarcity and soil salinity, mitigating these abiotic stresses is paramount for sustaining crop production. This study aimed to unravel the synergistic effects of organic matter and phosphorus management in reducing the adverse effect of saline water for irrigation on red pepper (Capsicum annuum L.) production, fruit quality, plant physiology, and stress tolerance indicators. The study was carried out in the arid Tadla region of Morocco and involved two key experiments: (i) a field experiment during the 2019 growing season, where red pepper plants were subjected to varying phosphorus fertilizer rates (120, 140, and 170 kg of P2O5.ha-1) and saline water irrigation levels (0.7; 1.5; 3; and 5 dS.m-1); and (ii) a controlled pot experiment in 2021 for examining the interaction of saline water irrigation levels (EC values of 0.7, 2, 5, and 9 dS.m-1), phosphorus rates (30, 36, and 42 kg of P2O5.ha-1), and the amount of organic matter (4, 8, 12, and 16 t.ha-1). The field study highlighted that saline irrigation significantly affected red pepper yields and fruit size, although phosphorus fertilization helped enhance productivity. Additionally, biochemical markers of stress tolerance, such as proline and glycine betaine, along with stomatal conductance, were impacted by increasing salinity levels. The pot experiment showed that combining organic amendments and phosphorus improved soil properties and stimulated red pepper growth and root weight across all salinity levels. The integration of phosphorus fertilization and organic amendments proved instrumental for counteracting salinity-induced constraints on red pepper growth and yield. Nonetheless, caution is necessary as high salinity can still negatively impact red pepper productivity, necessitating the establishment of an irrigation water salinity threshold, set at 5 dS.m-1.

Unleashing the Potential of Bacterial Isolates from Apple Tree Rhizosphere for Biocontrol of Monilinia laxa: A Promising Approach for Combatting Brown Rot Disease.

Monilinia laxa, a notorious fungal pathogen responsible for the devastating brown rot disease afflicting apples, wreaks havoc in both orchards and storage facilities, precipitating substantial economic losses. Currently, chemical methods represent the primary means of controlling this pathogen in warehouses. However, this study sought to explore an alternative approach by harnessing the biocontrol potential of bacterial isolates against brown rot in apple trees. A total of 72 bacterial isolates were successfully obtained from the apple tree rhizosphere and subjected to initial screening via co-cultivation with the pathogen. Notably, eight bacterial isolates demonstrated remarkable efficacy, reducing the mycelial growth of the pathogen from 68.75 to 9.25%. These isolates were subsequently characterized based on phenotypic traits, biochemical properties, and 16S rRNA gene amplification. Furthermore, we investigated these isolates' production capacity with respect to two enzymes, namely, protease and chitinase, and evaluated their efficacy in disease control. Through phenotypic, biochemical, and 16S rRNA gene-sequencing analyses, the bacterial isolates were identified as Serratia marcescens, Bacillus cereus, Bacillus sp., Staphylococcus succinus, and Pseudomonas baetica. In dual culture assays incorporating M. laxa, S. marcescens and S. succinus exhibited the most potent degree of mycelial growth inhibition, achieving 68.75 and 9.25% reductions, respectively. All the bacterial isolates displayed significant chitinase and protease activities. Quantitative assessment of chitinase activity revealed the highest levels in strains AP5 and AP13, with values of 1.47 and 1.36 U/mL, respectively. Similarly, AP13 and AP6 exhibited the highest protease activity, with maximal enzyme production levels reaching 1.3 and 1.2 U/mL, respectively. In apple disease control assays, S. marcescens and S. succinus strains exhibited disease severity values of 12.34% and 61.66% (DS), respectively, highlighting their contrasting efficacy in mitigating disease infecting apple fruits. These findings underscore the immense potential of the selected bacterial strains with regard to serving as biocontrol agents for combatting brown rot disease in apple trees, thus paving the way for sustainable and eco-friendly alternatives to chemical interventions.

Beneficial Microorganisms as Bioprotectants against Foliar Diseases of Cereals: A Review.

Cereal production plays a major role in both animal and human diets throughout the world. However, cereal crops are vulnerable to attacks by fungal pathogens on the foliage, disrupting their biological cycle and photosynthesis, which can reduce yields by 15-20% or even 60%. Consumers are concerned about the excessive use of synthetic pesticides given their harmful effects on human health and the environment. As a result, the search for alternative solutions to protect crops has attracted the interest of scientists around the world. Among these solutions, biological control using beneficial microorganisms has taken on considerable importance, and several biological control agents (BCAs) have been studied, including species belonging to the genera Bacillus, Pseudomonas, Streptomyces, Trichoderma, Cladosporium, and Epicoccum, most of which include plants of growth-promoting rhizobacteria (PGPRs). Bacillus has proved to be a broad-spectrum agent against these leaf cereal diseases. Interaction between plant and beneficial agents occurs as direct mycoparasitism or hyperparasitism by a mixed pathway via the secretion of lytic enzymes, growth enzymes, and antibiotics, or by an indirect interaction involving competition for nutrients or space and the induction of host resistance (systemic acquired resistance (SAR) or induced systemic resistance (ISR) pathway). We mainly demonstrate the role of BCAs in the defense against fungal diseases of cereal leaves. To enhance a solution-based crop protection approach, it is also important to understand the mechanism of action of BCAs/molecules/plants. Research in the field of preventing cereal diseases is still ongoing.

Assessing the Interplay between Weather and Septoria Leaf Blotch Severity on Lower Leaves on the Disease Risk on Upper Leaves in Winter Wheat.

Septoria leaf blotch (SLB) is among the most damaging foliar diseases of wheat worldwide. In this study, data for seven cropping seasons (2003−2009) at four representative wheat-growing sites in the Grand-Duchy of Luxembourg (GDL) were used to assess SLB risk on the three upper leaves (L3 to L1, L1 being the flag leaf) based on the combination of conducive weather conditions, simulated potential daily infection events by Zymoseptoria tritici, and SLB severity on lower leaves between stem elongation and mid-flowering. Results indicated that the variability in SLB severity on L3 to L1 at soft dough was significantly (p < 0.05) influenced by the disease severity on the lower leaf L5 at L3 emergence and the sum of daily mean air temperature between stem elongation and mid-flowering. Moreover, analyzing the predictive power of these variables through multiple linear regression indicated that the disease severity on L5 at L3 emergence and mild weather conditions between stem elongation and mid-flowering critically influenced the progress of SLB later in the season. Such results can help fine tune weather-based SLB risk models to guide optimal timing of fungicide application in winter wheat fields and ensure economic and ecological benefits.

Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium.

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beet-growing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84%, ≥78%, and ≤15%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH > 90% during the first four hours and RH > 60% during the following 9 h, daytime T > 16 °C and nighttime T > 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season.

Spatio-temporal distribution of DMI and SDHI fungicide resistance of Zymoseptoria tritici throughout Europe based on frequencies of key target-site alterations.

BACKGROUND: Over the past decade, demethylation inhibitor (DMI) and succinate dehydrogenase inhibitor (SDHI) fungicides have been extensively used to control to septoria tritici blotch, caused by Zymoseptoria tritici on wheat. This has led to the development and selection of alterations in the target-site enzymes (CYP51 and SDH, respectively). RESULTS: Taking advantage of newly and previously developed qPCR assays, the frequency of key alterations associated with DMI (CYP51-S524T) and SDHI (SDHC-T79N/I, C-N86S and C-H152R) resistance was assessed in Z. tritici-infected wheat leaf samples collected from commercial crops (n = 140) across 14 European countries prior to fungicide application in the spring of 2019. This revealed the presence of a West to East gradient in the frequencies of the most common key alterations conferring azole (S524T) and SDHI resistance (T79N and N86S), with the highest frequencies measured in Ireland and Great Britain. These observations were corroborated by sequencing (CYP51 and SDH subunits) and sensitivity phenotyping (prothioconazole-desthio and fluxapyroxad) of Z. tritici isolates collected from a selection of field samples. Additional sampling made at the end of the 2019 season confirmed the continued increase in frequency of the targeted alterations. Investigations on historical leaf DNA samples originating from different European countries revealed that the frequency of all key alterations (except C-T79I) has been gradually increasing over the past decade. CONCLUSION: Whilst these alterations are quickly becoming dominant in Ireland and Great Britain, scope still exists to delay their selection throughout the wider European population, emphasizing the need for the implementation of fungicide antiresistance measures. © 2021 Society of Chemical Industry.

Brown rust disease control in winter wheat: II. Exploring the optimization of fungicide sprays through a decision support system.

A decision support system (DSS) involving an approach for predicting wheat leaf rust (WLR) infection and progress based on night weather variables (i.e., air temperature, relative humidity, and rainfall) and a mechanistic model for leaf emergence and development simulation (i.e., PROCULTURE) was tested in order to schedule fungicide time spray for controlling leaf rust progress in wheat fields. Experiments including a single fungicide treatment based upon the DSS along with double and triple treatment were carried out over the 2007-2009 cropping seasons in four representative Luxembourgish wheat field locations. The study showed that the WLR occurrences and severities differed according to the site, cultivar, and year. We also found out that the single fungicide treatment based on the DSS allowed a good protection of the three upper leaves of susceptible cultivars in fields with predominant WLR occurrences. The harvested grain yield was not significantly different from that of the double and triple fungicide-treated plots (P < 0.05). Such results could serve as basis or be coupled to cost-effective and environmentally friendly crop management systems in operational context.

Brown rust disease control in winter wheat: I. Exploring an approach for disease progression based on night weather conditions.

An empirical approach for simulating the infection and progress of leaf rust (caused by Puccinia triticina) during stem elongation on winter wheat was analysed for the 2000 to 2006 growing seasons. The approach was elaborated based on night weather conditions (i.e., air temperature, relative humidity and rainfall) and leaf rust occurrences. Data from three consecutive cropping seasons (2000-2002) at four representative sites of the Grand-Duchy of Luxembourg were used in the set-up phase. The capability to correctly simulate the occurrence expression of P. triticina infections on the upper leaf layers was then assessed over the 2003-2006 period. Our study revealed that the development of leaf rust required a period of at least 12 consecutive hours with air temperatures ranging between 8 and 16 °C, a relative humidity greater than 60 % (optimal values being 12-16 °C and up to 80 % for air temperatures and relative humidity, respectively) and rainfall less than 1 mm. Moreover, leaf rust occurrences and infections were satisfactorily simulated. The false alarm ratio was ranged from 0.06 to 0.20 in all the study sites. The probability of detection and critical success index for WLR infection were also close to 1 (perfect score).

Fusarium head blight and associated mycotoxin occurrence on winter wheat in Luxembourg in 2007/2008.

Fusarium head blight (FHB) is among the major causes of reduced quality in winter wheat and its products. In addition, the causal fungi produce a variety of toxins. A relatively high FHB infection rate in winter wheat was observed in 2007 and 2008 in Luxembourg. A fusariotoxin survey was carried out in 17 different geographical locations. Three groups of Fusarium mycotoxins (trichothecenes A and B and zearalenone) were analysed by a multi-detection HPLC-MS/MS method. Fusarium strains were also investigated by morphological and molecular methods. In addition, questionnaires relating to cultural practices were sent to the farmers managing the 17 fields investigated. FHB prevalence ranged from 0.3 to 65.8% (mean: 8.5%) in 2007 and from 0 to 24.5% (mean: 8.3%) in 2008. Results of morphological and molecular identification showed that the most common species isolated from diseased wheat spikes was F. graminearum (33.1%), followed by F. avenaceum (20.3%) and F. poae (17.8%). The chemical analysis revealed that 75% of the investigated fields were contaminated by deoxynivalenol (DON, range 0-8111 microg/kg). The preceding crop was highly and significantly correlated to the number of grains infected and had a significant impact on disease prevalence (p = 0.025 and 0.017, respectively, Fisher's F-test). A trend was found for maize as the preceding crop (p = 0.084, Tukey's test) to predict the amount of DON in the fields. This is the first report on the occurrence of DON and ZON in naturally infected wheat grains sampled from Luxembourg.