Hemileia Vastatrix Research Articles

Producción de café biodiverso en México: calidad, beneficios y controversia sobre el cambio de variedades por la roya

Intervention in any territory requires a careful assessment of environmental and cultural aspects, especially in the framework of territorial development. The objective of this study was to highlight the importance of conserving biological and cultural biodiversity in the main coffee growing of Mexico, and to analyze the challenges facing biodiverse coffee production. An updated regional typology was used to analyze the socio-economic dynamics of four states that will account for 90 % of national coffee production in 2020. The study examines the biological and cultural diversity of these areas in the context of public policies implemented between 2012 and 2016, which promoted varietal conversion as a phytosanitary measure to combat coffee rust (Hemileia vastatrix Berk & Br., 1869). This policy has generated an ongoing debate among producers, markets and specialists, who discuss the dilemma between maximizing production and preserving the quality and biodiversity of the territories. Finally, arguments are presented in favour of biodiverse coffee production driven by local communities, and the available legal framework for its protection, highlighting the right to be consulted in the implementation of future projects and policies.

Economical-epidemiological analysis of the coffee trees rust pandemic.

Coffee leaf rust is a prevalent botanical disease that causes a worldwide reduction in coffee supply and its quality, leading to immense economic losses. While several pandemic intervention policies (PIPs) for tackling this rust pandemic are commercially available, they seem to provide only partial epidemiological relief for farmers. In this work, we develop a high-resolution spatiotemporal economical-epidemiological model, extending the Susceptible-Infected-Removed model, that captures the rust pandemic's spread in coffee tree farms and its associated economic impact. Through extensive simulations for the case of Colombia, a country that consists mostly of small-size coffee farms and is the second-largest coffee producer in the world, our results show that it is economically impractical to sustain any profit without directly tackling the rust pandemic. Furthermore, even in the hypothetical case where farmers perfectly know their farm's epidemiological state and the weather in advance, any rust pandemic-related efforts can only amount to a limited profit of roughly 4% on investment. In the more realistic case, any rust pandemic-related efforts are expected to result in economic losses, indicating that major disturbances in the coffee market are anticipated.

Coffee Leaf Rust and Berry Borer Management in Agroforestry Systems: A Systematic Literature Review

Coffee Leaf Rust and Berry Borer Management in Agroforestry Systems: A Systematic Literature Review

Validation of the chemical control for coffee leaf rust in the susceptible variety Caturra in the south of the department of Huila.

CLR (Hemileia vastatrix) is the disease with the greatest economic and social impact on coffee production in the world, causing losses of up to 50% of productivity in susceptible varieties without proper and timely management. The department of Huila is the current largest producer with approximately 19% of production, going from 136,000 ha planted in 2012 with 61% in susceptible varieties, to about 150,000 ha in 2020 and 30% in susceptible varieties, mainly in the southern area of ??the department, indicating the advancement of the department's coffee sector. However, there is still an important area under threat from coffee rust, which must be controlled and reduced. The objective of this work was to validate the chemical management strategy for rust in commercial crops of the susceptible variety Caturra in the south of the department of Huila (Colombia). The evaluation was carried out in two commercial crops of var. Caturra at two altitudes at 1.410 and 1.280 m, where each batch was divided into three plots to monitor the blooms and the percentage of incidence of the disease from 2016 to 2018. The validation criteria consisted of: fixed calendars (CC1), adjustment of the main flowering (CC2), farmer (CA3), and finally without disease control (SC4). The incidence in critical periods was above 30% of the level of economic damage, directly accompanying the physiology of production. The criteria (CC1-CC2) and with the use of the recommended fungicide showed a lower incidence (AUDPC) compared to the other criteria evaluated, presenting a positive effect on the control of the disease, allowing a retention of foliage and delaying the curve of the disease reducing to 30% the incidence in critical periods. In addition, the risk of Colombian coffee growing has increased in recent years, especially due to the climatic variability that generates advances or delays in the peaks of the disease and generating reductions in the incubation periods.

Fungal Diversity Detected by ITS-5.8S from Coffea arabica Leaves Infected by Rust (Hemileia vastatrix) in Southern Ecuador

Fungal Diversity Detected by ITS-5.8S from Coffea arabica Leaves Infected by Rust (Hemileia vastatrix) in Southern Ecuador

Recovery of Novel Sequence Variants in Chemically Mutagenized Seed and Vegetatively Propagated Coffea arabica L.

The negative effects of climate change impact both farmers and consumers. This is exemplified in coffee, one of the most widely consumed beverages in the world. Yield loss in high-quality Coffea arabica L., due to the spread of coffee leaf rust (Hemileia vastatrix), results in lower income for subsistence farmers and volatile prices in markets and cafes. Genetic improvement of crops is a proven approach to support sustainable production while mitigating the effects of biotic and abiotic stresses and simultaneously maintaining or improving quality. However, the improvement of many species, including coffee, is hindered by low genetic diversity. This can be overcome by inducing novel genetic variation via treatment of seeds or cells with mutagens. To evaluate this approach in coffee, mutant populations created by incubating seed or embryogenic calli with the chemical mutagens ethyl methanesulphonate or sodium azide were subject to reduced-representation DNA sequencing using the ddRADseq approach. More than 10,000 novel variants were recovered. Functional analysis revealed hundreds of sequence changes predicted to be deleterious for gene function. We discuss the challenges of unambiguously assigning these variants as being caused by the mutagenic treatment and describe purpose-built computational tools to facilitate the recovery of novel genetic variation from mutant plant populations.

Biocontrol of Hemileia vastatrix, the Causal Agent of Coffee Leaf Rust, by Paenibacillus sp. NMA1017.

Coffee leaf rust (CLR), caused by Hemileia vastatrix, is considered a highly important phytosanitary problem in Mexico. Currently, there are few microorganisms used as biocontrol alternatives to chemical control of CLR in organic coffee fields in Mexico. This study evaluates the use of Paenibacillus sp. NMA1017 as a biocontrol agent to inhibit the development of H. vastatrix in in vitro and in vivo (greenhouse) experiments. Hemileia vastatrix urediniospores were placed on water agar plates, and then Paenibacillus sp. NMA1017 was inoculated simultaneously or 8 h later. Urediniospores germination rate was reduced by 94% when the NMA1017 strain was inoculated simultaneously with the urediniospores and reduced by 38% when NMA1017 was inoculated 8 h later. Experiments with 8-month-old Bourbon coffee plants that were infected with H. vastatrix showed that disease incidence was reduced by 38, 90, and 50% when NMA1017 was applied 8 days before, simultaneously, or 8 days after the application of H. vastatrix, respectively. Paenibacillus sp. NMA1017 also reduced the severity of CLR on the leaves by up to 62%. The germination urediniospores of other rust pathogens such as Puccinia sorghi (maize leaf rust), Puccinia triticina (wheat leaf rust), Puccinia graminis f. sp. tritici (black stem rust of wheat), Uromyces striatus (alfalfa leaf rust), and Phragmidium sp. (rosebush leaf rust) were also inhibited. Use of the potential biocontrol agent Paenibacillus sp. NMA1017 might help reduce the application of chemical fungicides for the control of CLR, making coffee a more sustainable crop and providing management options for organic coffee growers.

Spatial trajectories of coffee harvesting in large-scale plantations: Ecological and management drivers and implications

CONTEXTCoffee is produced under different management systems and scales of production categorized as Syndromes of Production. The “Capitalist Syndrome” is characterized by large-scale and high-density planting farms that may promote the development of plant pathogens like coffee leaf rust (CLR). Harvesting dynamics are also affected by the syndrome of production and generate spatial trajectories that could contribute to the dispersal of pathogens across and within plantations. However, these spatial trajectories have not yet been described, nor their relationship with the syndrome of production, and even less its potential ecological implications for pathogen dispersal. OBJECTIVEDescribe and analyze the daily spatial movement of coffee harvesters in two large-scale capitalist plantations, an organic and a conventional plantation, and systematize the drivers that might explain the differences in the spatial trajectories. METHODSUsing State-Space Models, we recorded and analyzed the spatial movements of harvesters. We then constructed a driver tree for harvest dynamics, which incorporated qualitative variables related to the environment, coffee biology, and management aspects reported by the harvesters or in previous studies. RESULTS AND CONCLUSIONSThe model differentiated two kinds of movements: 1) when trees have berries, harvesters remain in the coffee rows or areas nearby (Collect state; 94–98 % of the steps); 2) when not, harvesters make longer steps within the harvesting location or move to another area (Search state; 2–6 % of the steps). In the organic plantation, the Search state had a longer-tailed step-length distribution than in the conventional plantation, resulting in a significantly larger visited area per worker (p < 0.05). This might be directly related to the lower interplant ripening percentage or smaller harvesting locations (“pantes”) per number of harvesters. The number of harvested trees might be affected by the fruit load or the coffee variety, among others. Harvesting movements that explore a larger area, either by visiting more plants or by changing locations on the same day, could create more foci of CLR infection across the plantation. SIGNIFICANCEThese results constitute an initial analysis of harvesting trajectories and highlight practices that can reduce the potential impact of human dispersal of pathogens, like shorter harvesting trajectories by working fewer hours a day or avoiding harvesting at the end of the maturation season when few trees have berries and harvesters have to travel medium to long distances. This calls for organic coffee management that could prevent diseases and guarantee just and safe conditions for workers.

Respuesta de la roya (Hemileia vastatrix) frente a condiciones climáticas y aplicaciones de fungicidas en el cultivo de café (Coffea arabica) bajo sombra

Coffee leaf rust (Hemileia vastatrix) was first reported in 1869 on a plantation on the island of Sri Lanka, Asia, and has since spread to all coffee-growing regions far from its center of origin. The coexistence between the coffee plant, primarily Coffea arabica, and the fungus Hemileia vastatrix leads to coevolution, which manifests in significant genetic variability in the plant’s resistance to the disease and the fungus’s virulence towards the plant (Avelino and Rivas, 2013). The trophic relationships between the fungus and the host are influenced by soil moisture, fruit load, genetic resistance, precipitation, relative humidity, and temperature; these are the main factors affecting plant colonization (Filho and Astorga, 2015). In this study, the behavior of H. vastatrix was recorded through sampling to describe the relationship between disease incidence and environmental conditions, such as precipitation, temperature, and relative humidity, over an 18-month period at the Santa Lucía Experimental Farm (FESL) in Heredia, Costa Rica. The main findings demonstrated that the use of triazole fungicides and copper hydroxide can maintain disease incidence below 7%, despite contributions to its development from precipitation and increased temperatures, but not from increased relative humidity. Proper management of coffee leaf rust and the presence of shade can help reduce the incidence of the disease in the coffee plantation at FESL.

Future climate suitability of Hemileia vastatrix in arabica coffee under CMIP6 scenarios.

Coffee (Coffea arabica L.) is one of the most important commodities today, with a high economic value worldwide. Coffee leaf rust (Hemileia vastatrix Berk. et Br.) has been showing a high impact on Brazilian coffee trees among the various diseases that attack coffee. The climate has a great influence on the development of diseases, especially when fungi are the causal agents. This study aimed to carry out the zoning of climate favorability for coffee leaf rust in the traditional and main coffee-producing regions of Brazil. The study was conducted in 13 locations in the states of Paraná, São Paulo, Rio de Janeiro, Espírito Santo, Minas Gerais, Goiás and Bahia. Air temperature and daily precipitation data for the current scenario were collected using the WorldClim version 2.1 platform for the last climatological normal and future climate change data. The ideal climate conditions for coffee leaf rust consist of a mean air temperature ranging from 21 to 25 °C and precipitation >30 mm per month. The Coupled Model Intercomparison Projects scenarios associated with the Intergovernmental Panel on Climate Change reports consisted of the Shared Socio-Economic Pathways SSP-1 2.6, SSP-2 4.5, SSP-3 7.0 and SSP-5 8.5, the latter being considered one of the most catastrophic. All steps to carry out the suitability zoning were performed in a tool using the QGIS geographic information system software. Zoning for coffee leaf rust had three classes: favorable, relatively favorable and unfavorable. Currently, the largest coffee-producing region in Brazil has 49.1% of its analyzed area classified as favorable, 39.2% as relatively favorable and 11.7% as unfavorable. In the current scenario, Patrocínio and Três Pontas are locations with high coffee production in which the favorable class is predominant. The state of Minas Gerais has an annual mean of 55.3% of its entire territory apt for the disease, with the highest occurrence between September and March. Climate change has a negative impact on the development of coffee leaf rust, mainly in the long term, as in the period of 2081-2100, in which the SSP-5 8.5 scenario led to a decrease in the favorable and unfavorable areas and an increase in the relatively favorable areas of 9.8%, 18.6% and 71.5% for the Brazilian territory, respectively. © 2024 Society of Chemical Industry.

Use of artificial intelligence in the detection of coffee rust: An exploratory systematic review

Coffee rust, caused by the fungus Hemileia vastatrix, is a fungal disease that affects coffee production and quality, so its early detection is crucial to prevent massive outbreaks and protect production. This article analyzes the most effective factors, the algorithms used, the accuracy of the models, and the challenges in the detection of coffee rust, through an exploratory systematic review of 35 empirical articles obtained from Scopus, IEEE Xplore and SciELO. The review identifies that the most determinant factors for detection include humidity, temperature and the presence of shade. The most commonly used algorithms are Convolutional Neural Networks (CNN), Support Vector Machines (SVM) and Random Forest, highlighting CNN for its ability to process and analyze images with an accuracy of 99.57%, followed by Artificial Neural Networks (ANN) with 98% and SVM with 96%. However, it is concluded that challenges remain such as the need for high quality labeled datasets, variability in environmental conditions and implementation costs. This study provides a comprehensive overview of recent advances and areas for improvement in coffee rust detection, providing information for researchers, practitioners and decision makers in the agricultural sector.

ConvNext as a Basis for Interpretability in Coffee Leaf Rust Classification

The increasing complexity of deep learning models can make it difficult to interpret and fit models beyond a purely accuracy-focused evaluation. This is where interpretable and [ eXplainable Artificial Intelligence (XAI)explainable Artificial Intelligence (AI) come into play to facilitate an understanding of the inner workings of models. Consequently, alternatives have emerged, such as [class activation mapping (CAM) techniquesClass Activation Maps (CAM) aimed at identifying regions of importance for an image classification model. However, the behavior of such models can be highly dependent on the type of architecture and the different variants of convolutional neural networks. Accordingly, this paper evaluates three Convolutional Neural Network (CNN) architectures (VGG16, ResNet50, ConvNext-T) against seven CAM models (GradCAM, XGradCAM, HiResCAM, LayerCAM, GradCAM++, GradCAMElementWise, and EigenCAM), indicating that the CAM maps obtained with ConvNext models show less variability among them, i.e., they are less dependent on the selected CAM approach. This study was performed on an image dataset for the classification of coffee leaf rust and evaluated using the RemOve And Debias (ROAD) metric.

Climatic Favorability to the Occurrence of Hemileia vastatrix in Apt Areas for the Cultivation of Coffea arabica L. in Brazil

Climatic Favorability to the Occurrence of Hemileia vastatrix in Apt Areas for the Cultivation of Coffea arabica L. in Brazil

Fungicidal activity and molecular docking of glycerol‐derived triazole compounds for controlling coffee leaf rust

AbstractCoffee leaf rust (CLR), caused by Hemileia vastatrix, poses a significant threat to global coffee production, resulting in substantial economic losses. This study explores the effectiveness of triazole derivatives synthesized from glycerol in controlling CLR in conilon coffee (Coffea canephora). In vitro assays evaluated the inhibitory effect of triazole derivatives (4a–4q) at concentrations of 10, 25, 50, 75 and 100 ppm on H. vastatrix urediniospore germination, followed by greenhouse experiments to assess their preventive and curative potential. Preventive and curative sprayings of the four most efficient triazoles were conducted on seedlings inoculated with H. vastatrix at concentrations ranging from 0 to 400 ppm, evaluating severity, sporulation, incubation and latent periods. Triazole derivatives 4b, 4d, 4f and 4o effectively suppressed H. vastatrix urediniospore germination, achieving reductions between 9.9% and 78.8%. In greenhouse trials, these compounds demonstrated preventive efficacy by prolonging incubation and latent periods, reducing sporulation and mitigating rust severity, with triazole 4f exhibiting the highest efficiency. Furthermore, the derivatives showed effectiveness in curative treatments, particularly triazole 4f. Physiological analyses indicated no significant alterations in coffee plant metabolism, suggesting the safety of these compounds for agricultural use. Molecular docking studies elucidated their mechanism of action, suggesting their potential as antifungal agents by interacting with the HvCYP51 enzyme involved in ergosterol biosynthesis. Overall, the study underscores the promising efficacy of glycerol‐derived triazole derivatives in managing CLR, providing sustainable solutions for disease control in agriculture.

Distribution and Current Status of Coffee Leaf Rust in Western Oromia, Ethiopia

Distribution and Current Status of Coffee Leaf Rust in Western Oromia, Ethiopia

Gene pyramiding for achieving enhanced disease and insect multiple resistance in Coffea arabica

AbstractDeveloping a new coffee cultivar can take up to 25 years using traditional approaches. The implementation of molecular tools into the coffee breeding pipeline can speed up the development of high‐yielding varieties with multiple resistance to pests and diseases. This study aimed to verify the presence of gene pyramiding for resistance to coffee berry disease (CBD) and coffee leaf rust and access the breeding potential of a breeding population of coffee (Coffea arabica L.) that presents partial phenotypic resistance to coffee leaf miner. A progeny of 128 individuals was evaluated in 2019 during the coffee maturation stage for 11 agronomic traits. The individuals were genotyped for four loci related to rust and CBD resistance. Gene pyramiding was verified based on the presence of markers linked with the resistance alleles. Genetic parameters were estimated, and a selection index was used to rank the individuals based on the phenotypic evaluation. Gene pyramiding for rust and CBD was detected in ∼82% of the individuals. Moreover, the phenotypic evaluation of rust indicated high levels of resistance, confirmed by the presence of the resistance alleles. Ninety‐five percent of the 38 top‐ranked individuals presented resistance alleles and could be used in an interpopulation recurrent selection scheme with marker‐assisted selection. The studied coffee population has the potential to be used in coffee breeding programs since it presents vigorous individuals with low levels of coffee leaf miner incidence (CLMI) and gene pyramiding for CBD and rust resistance, the main pest and diseases of this crop.

Use of Detached Leaf Inoculation Method for the Early Selection of Coffea arabica L. for Resistance to Hemileia vastatrix Berk and Broome

Use of Detached Leaf Inoculation Method for the Early Selection of Coffea arabica L. for Resistance to Hemileia vastatrix Berk and Broome

Assessment of Hemileia vastatrix resistance in chemically mutagenized Coffea arabica L. leaf discs and the emergence of a novel resistance scale

Assessment of Hemileia vastatrix resistance in chemically mutagenized Coffea arabica L. leaf discs and the emergence of a novel resistance scale

A Method Based on Artificial Intelligence That Predicts Arabica Coffee Yield by Analyzing Abiotic Factors and the Prevalence of Coffee Leaf Rust

Coffee is a perennial crop that harbors infections throughout the plant, which may worsen illness under favorable circumstances. Coffee leaf rust, a widespread coffee disease and Arabica is susceptible to leaf rust. Disease incidence and severity depend on abiotic variables. Cloudy and constant South-West monsoon weather (June–September) promotes coffee leaf rust growth. In this five-model study, an Extra Tree and Gradient Boosting regression model predicted coffee crop output in Chikamagaluru, Karnataka, with the least error utilizing biotic and abiotic factors. We investigated additional tree, gradient boosting, RF, Decision Tree, and KNN models using biotic and abiotic predictors. Used the independent testing dataset's MSE, MAE, RMSE Root mean square errors, and R-squared errors to compare model performance. The extra tree (R²=0.98 kg/ha ˉ¹ and RMSE = 7.96 kg/ha ˉ¹) and gradient boosting (R²=0.96 kg/ha ˉ¹ and RMSE = 10.96 kg/ha ˉ¹) regression models used Group 1 and 2 characteristics as predictor variables and different parameter fine tuning functions to estimate coffee yield most accurately. Compared to the less precise probabilistic models utilized in this work, such as Random Forest, decision tree, and KNN models, shown in the results section. The optimum weather parameter for coffee production forecasts and biotic-CLR incidence data outperformed random forest, decision tree, and K-Nearest Neighbor models.

Shocked into side-selling? Production shocks and organic coffee farmers’ marketing decisions

Peru is the world’s leading exporter of organic coffee. Peruvian organic coffee is largely marketed through farmers’ cooperatives which have helped small farmers transition to organic production, earn price premiums over conventional coffee, and access extension services and finance. However, rising temperatures, increasingly volatile rainfall patterns, and the proliferation of pests and diseases make organic production riskier, as organic farmers cannot rely on agrochemicals to protect their farms against production shocks. If members of organic coffee cooperatives respond to production shocks by increasing their sales to private buyers (‘side-selling’), then cooperatives’ financial health could be threatened through reduced bargaining power with buyers and a decrease in scale economies. This paper explores the theoretical incentives for members to side-sell in response to production shocks and gives empirical evidence of how production shocks influence side-selling using panel data from members of two Peruvian specialty coffee cooperatives in 2013 and 2015. The study period coincides with a widespread production shock – the Coffee Leaf Rust (CLR) epidemic of 2012/13 – that decimated coffee production in Peru. We find suggestive evidence that the incidence of CLR on farms is correlated with increased side-selling. Particularly, members with high risk tolerance and high levels of non-coffee income increase side-selling when affected by production shocks. This paper contributes to a growing literature on the determinants of side-selling in agricultural cooperatives by examining the role of production shocks, extending existing theoretical frameworks, and analyzing determinants using panel data methods.