Hemileia Vastatrix Research Articles

Breeding Programs Against Coffee Leaf Rust in Brazil: A Review

Leaf rust caused by Hemileia vastatrix Berk & Br. is the main disease that attack coffee plants all over the world. The disease causes 35-50% of yield loss in average in Brazil. The disease is present in all the countries that cultivate coffee (Coffea arabica L. and C. canephora L.). Resistance of coffee to leaf rust is the main method of disease control. But the great variability of the pathogen makes it very difficult. In the world and in Brazil there are more than 50 and 16 races of the pathogen, respectively. In Brazil there are seven research institutions that have been working with breeding programs against coffee leaf rust. The main source of resistance to coffee leaf rust used by all the research institutions in Brazil came from ‘Centro de Investigação das Ferrugens do Cafeeiro-CIFC’ in Oeiras, Portugal. They are: Híbrido de Timor (HdT) CIFC 832-1 and HdT CIFC 832 and HdT CIFC 2570. Crosses were made at CIFC with a variety Caturra and Vila Sarchi and sent to Brazil, Colombia and some countries of Central America and Caribean. The countries that received the germplasm of CIFC back crossed them with varieties Catuai and Mundo Novo. Crossing HdT with Caturra originated the group Catimor and HdT with Vila Sarchi the group Sarchimor. Other source of resistance to leaf rust came from Coffea canephora crossed with C. arabica originating the cultivar Icatu. But the great majority of the resistant cultivar came from Catimor and Sarchimor. More than 50 coffee varieties have been launched with resistance to the leaf rust in the last 40 years in Brazil. But only few of them remain with vertical resistance to all the races of H. vastatrix. Horizontal resistance is more common on C. canephora var. conilon and C. canephora var. robusta than in C. arabica. This revision has the purpose to relate to the scientific comunity the breeding programs against coffee leaf rust in Brazil.

Leaf surface microbiota transplantation confers resistance to coffee leaf rust in susceptible Coffea arabica.

Coffee leaf rust, caused by the fungus Hemileia vastatrix, has become a major concern for coffee-producing countries. Additionally, there has been an increase in the resistance of certain races of the fungus to fungicides and breeding cultivars, making producers use alternative control methods. In this work, we transplanted the leaf surface microbiota of rust-resistant coffee species (Coffea racemosa and Coffea stenophylla) to Coffea arabica and tested whether the new microbiota would be able to minimize the damage caused by H. vastatrix. It was seen that the transplant was successful in controlling rust, especially from C. stenophylla, but the protection depended on the concentration of the microbiota. Certain fungi, such as Acrocalymma, Bipolaris, Didymella, Nigrospora, Setophaeosphaeria, Simplicillium, Stagonospora and Torula, and bacteria, such as Chryseobacterium, Sphingobium and especially Enterobacter, had their populations increased and this may be related to the antagonism seen against H. vastatrix. Interestingly, the relative population of bacteria from genera Pantoea, Methylobacterium and Sphingomonas decreased after transplantation, suggesting a positive interaction between them and H. vastatrix development. Our findings may help to better understand the role of the microbiota in coffee leaf rust, as well as help to optimize the development of biocontrol agents.

Ant communities and their trophobionts shape the incidence of pests and diseases in Indonesia’s coffee agroforestry system

Abstract. Muhammad FN, Rizali A, Rahardjo BT. 2024. Ant communities and their trophobionts shape the incidence of pests and diseases in Indonesia’s coffee agroforestry system. Biodiversitas 25: 1127-1134. Ants play various roles in a coffee agroecosystem, and their presence may impact biological control in coffee plantations. Despite the pantropical economic importance of coffee, most research is focused on the Americas. This study investigates how ant communities and their trophobionts shape the incidences of pests and diseases in the Indonesian shade coffee agroecosystem. Three 30 m x 30 m plots consisting of a total of 256 coffee plants were observed from January to March 2022. Ant communities and their trophobiont were observed using three methods: visual, tuna bait, and live bait. The ants found were classified into dominant and non-dominant based on their nesting behavior and dominance in the bait trap. Pest damage and disease intensity observation were also conducted three times. Generalized linear models were used to test the relationships between ant, hemipteran, coffee berry borer, twig borer damage and leaf rust disease intensity. Twenty-one species of ants were found, and five of them were categorized as dominant ants. Ants correlate coffee pest damage and disease intensity, their hemipteran trophobionts, or both. Dolichoderus thoracicus ant positively correlated with all trophobiont hemipteran and negatively correlated with twig borer damage. Technomyrmex albipes positively correlated with aphids but negatively with flatids. Meanwhile, Tetraponera sp.1 ant only positively correlated with flatids. In trophobiont hemipteran group, all except aphids positively correlated with coffee pests and disease. Scale insect Coccus viridis positively correlated with coffee leaf rust and twig borer infestation intensity, while flatid presence even positively correlated with all coffee plant pest and disease intensity. Indirectly, ants may reduce coffee pests and diseases through hemipteran suppression. The results suggest that the interaction of ants and their trophobiont hemipteran can act as a biological control agent for coffee pests and diseases.

Using local knowledge to reconstruct climate‐mediated changes in disease dynamics and yield—A case study on Arabica coffee in its native range

Societal Impact StatementAdapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships.Summary While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies. We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee. The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution. Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist.

Achievements and Challenges in Controlling Coffee Leaf Rust (Hemileia vastatrix) in Hawaii

In this case study, the current situation faced by coffee growers attempting to control coffee leaf rust (Hemileia vastatrix) in Hawaii is reported. CLR is considered the most devastating disease affecting coffee crops worldwide and was detected in Hawaii in 2020. Three small coffee farms from the South Kona district of Hawaii Island were selected. The goals of this case study were to: (1) assist coffee growers in the early detection of CLR incidence, and consequently support farmers with recommendations for control, (2) record agronomic information and management practices, and (3) estimate the cost to control CLR during 2021 and 2022 seasons. Low CLR incidence (<4%) was initially observed at all farms (January–June 2021), but increased as the harvest began, ending the season (December 2021) at 77%, 21% and 6% incidence at farms 1, 2 and 3, respectively. At the end of 2022 season (December), CLR incidence reached 43%, 20% and 3% at farms 1, 2 and 3, respectively. The number of sprays per season (5–10), the type of fungicides applied (preventive, curative), the timing of sprays, the efficacy of applications and weather conditions all played a role in determining the infection rates at each farm. Effective control of CLR is possible in Hawaii if the sprays of fungicides are carried out with the right products, appropriate timing and good coverage.

An optimal control model for Coffee Berry Disease and Coffee Leaf Rust co-infection

In the 1980s, coffee production in Kenya was peaking at an average of 1.7 million bags annually. Since then, this production has been declining to the current production of below 0.9 million bags annually. Coffee berry disease (CBD) and Coffee leaf rust (CLR) are some of the causes of this decline. This is due to a lack of sufficient knowledge on optimal control strategies for co-infection of CBD and CLR. In this research, we derive a system of ODEs from the mathematical model for co-infection of CBD and CLR with prevention of CBD infection, prevention of CLR infection, the treatment of CBD-infected coffee plants, the treatment of CLR-infected coffee plants, the treatment of CBD-CLR Co-infected coffee plants, elimination of Colletotrichum kahawae pathogens and elimination of Hemileia vastatrix pathogens to perform optimal control analysis. An optimal control problem is formulated andsolved using Pontryagin’s maximum principle. The outcomes of the model’s numerical simulations indicate that combining all controls would be the best strategy for slowing the spread of the CBD-CLR co-infection.

Reaction of Gidami Coffee Collections against the Major Diseases

Ethiopia's largest export commodity is coffee arabica, one of the most significant goods that significantly contribute to the national economy. However, diseases including coffee berry, coffee leaf rust, and coffee wilt have been significantly reducing its yield. In order to assess Gidami coffee arabica accessions’ reaction to serious fungal infections, this study was started. The total of 100 genotypes was evaluated under Gera and Haru field situations for these major diseases. Whereas, 92 accessions with two checks were tested under greenhouse condition for coffee wilt. The results showed a highly significant variation (p<0.001) among genotypes in both conditions with 0–51%, 4–36%, and 0–100% disease severity ranges respectively. Except the four accessions namely G67/13, G71/13, G54/13, and G66/13, most of them showed susceptible reactions to CBD under Gera conditions. As well, 36 and 12 coffee accessions revealed <10% CLR reactivity at Haru and Gera conditions respectively. But none of the accessions had resistance levels higher than the two checks (Challa and 8136) at either of the locations. In other word, Gera had much greater levels of CBD and CLR pressure than Haru. On the other hand, only two accessions, G57 and G20, showed 100% CWD survival rate in greenhouse environments. This study showed how plant genetics and environmental variables affect diseases development and demonstrated that the response of Gidami coffee accessions to the major coffee diseases highly influenced by geographical and genetic variables. Therefore, continual and massive genotype screening across the locations must be taken into account in order to get resistance genotypes as the best disease mitigation to overcome the coffee diseases problem.

Development of a loop-mediated isothermal amplification assay for rapid and sensitive detection of Hemileia vastatrix in coffee plantations

Development of a loop-mediated isothermal amplification assay for rapid and sensitive detection of Hemileia vastatrix in coffee plantations

Antifungal Activity of Phyllospheric Bacteria Isolated from Coffea arabica against Hemileia vastatrix.

Peru is one of the leading countries that produce and export specialty coffees, favorably positioned in the international markets for its physical and organoleptic cup qualities. In recent years, yellow coffee rust caused by the phytopathogenic fungus Hemileia vastatrix stands out as one of the main phytosanitary diseases that affect coffee culture yields. Many studies have demonstrated bacteria antagonistic activity against a number of phytopathogen fungi. In this context, the aim of this work was to select and characterize phyllospheric bacteria isolated from Coffea arabica with antagonistic features against coffee rust to obtain biocontrollers. For that purpose, a total of 82 phyllospheric bacteria were isolated from two coffee leaf rust-susceptible varieties, typica and caturra roja, and one tolerant variety, catimor. Of all the isolates, 15% were endophytic and 85% were epiphytes. Among all the isolates, 14 were capable of inhibiting the mycelial radial growth of Mycena citricolor, and Colletotrichum sp. 16S rRNA gene sequence-based analysis showed that 9 isolates were related to Achromobacter insuavis, 2 were related to Luteibacter anthropi and 1 was related to Rodococcus ceridiohylli, Achromobacter marplatensis and Pseudomonas parafulva. A total of 7 representative bacteria of each group were selected based on their antagonistic activity and tested in germination inhibition assays of coffee rust uredinospores. The CRRFLT7 and TRFLT8 isolates showed a high inhibition percentage of urediniospores germination (81% and 82%, respectively), similar to that obtained with the chemical control (91%). An experimental field assay showed a good performance of both strains against rust damage too, making them a promising alternative for coffee leaf rust biocontrol.

Diversity of Leaf Fungal Endophytes from Two Coffea arabica Varieties and Antagonism towards Coffee Leaf Rust.

Coffee has immense value as a worldwide-appreciated commodity. However, its production faces the effects of climate change and the spread of severe diseases such as coffee leaf rust (CLR). The exploration of fungal endophytes associated with Coffea sp. has already found the existence of nearly 600 fungal species, but their role in the plants remains practically unknown. We have researched the diversity of leaf fungal endophytes in two Coffea arabica varieties: one susceptible and one resistant to CLR. Then, we conducted cross-infection essays with four common endophyte species (three Colletotrichum sp. and Xylaria sp. 1) and Hemileia vastatrix (CLR) in leaf discs, to investigate the interaction of the endophytes on CLR colonisation success and severity of infection. Two Colletotrichum sp., when inoculated 72 h before H. vastatrix, prevented the colonisation of the leaf disc by the latter. Moreover, the presence of endophytes prior to the arrival of H. vastatrix ameliorated the severity of CLR. Our work highlights both the importance of characterising the hidden biodiversity of endophytes and investigating their potential roles in the plant-endophyte interaction.

Coffee disease detection and classification using image processing: A Literature review

Coffee, as one of the world's most consumed beverages, sustains livelihoods for millions across more than 50 nations. The vulnerability of coffee plants to diseases, particularly Coffee Leaf Rust and Coffee Berry Disease, poses a significant threat to global production and quality. Leveraging advancements in image processing and computer vision, researchers have explored diverse classification algorithms, ranging from traditional Support Vector Machines to state-of-the-art Deep Convolutional Neural Networks (DCNNs). This review literature addresses the challenges of coffee disease detection, emphasizing the need for precise and early identification. Notable studies have achieved commendable accuracies, such as SVMs reaching 96% and DCNNs demonstrating precision but with extended training times. Innovations like feature concatenation, transfer learning, and ensemble methods have emerged as strategies to overcome classification limitations. Recent breakthroughs showcase impressive results, including DenseNet models achieving a classification accuracy of 99.57% and MobileNetV2 reaching 99.93%. Additionally, Convolutional Neural Networks and VGG-19 architecture demonstrated a promising F1-Score of 90% in classifying various coffee leaf diseases. This concludes with a vision for ongoing advancements, emphasizing the fusion of image processing and machine learning technologies to safeguard the global coffee industry by enabling early and accurate disease detection.

Survival models: a tool for managing and analyzing the incidence of rust in irrigated coffee trees

Studies related to the characteristics and health of coffee make it possible to manage and enhance the product, directly influencing its quality. Such studies are crucial for strategic marketing reasons, encompassing cost reduction, loss, and production optimization. In addition to these aspects, there are concerns about improving product quality. Thus, studying the variable time until the development of coffee rust, considering different irrigation systems, is relevant, given that this disease causes early leaf fall and branch drying, diminishing both production and product quality. In light of the above, the objective is to demonstrate that the main survival analysis models can be employed as instruments for managing and analyzing the incidence of coffee rust, considering different irrigation systems. The analysis revealed that the drip irrigation system was more effective than the others.

Advancing Coffee Leaf Rust Disease Management: A Deep Learning Approach for Accurate Detection and Classification Using Convolutional Neural Networks

Coffee Leaf Rust (CLR), caused by the fungus Hemileia vastatrix, poses a severe threat to global coffee production. Timely detection is critical for effective control measures. This study employs Convolutional Neural Networks (CNNs) to enhance CLR detection accuracy. Traditionally, this task relies on expert assessment. DL emerges as a promising approach, capable of autonomously extracting salient features. Our model, trained on a diverse dataset, accurately identifies CLR. Using 1365 meticulously curated images, the model undergoes rigorous preprocessing and augmentation. The DL-based approach achieves remarkable accuracy (98.89%), precision (99.00%), recall (98.07%), and an F1 score of (98.55%). These outcomes establish the CNN model as a proficient system for precise, real-time CLR diagnosis. This study contributes to the creation of an efficient system, safeguarding coffee orchard vitality and productivity.

Fungi Tryptophan Synthases: What Is the Role of the Linker Connecting the α and β Structural Domains in Hemileia vastatrix TRPS? A Molecular Dynamics Investigation.

Tryptophan synthase (TRPS) is a complex enzyme responsible for tryptophan biosynthesis. It occurs in bacteria, plants, and fungi as an αββα heterotetramer. Although encoded by independent genes in bacteria and plants, in fungi, TRPS is generated by a single gene that concurrently expresses the α and β entities, which are linked by an elongated peculiar segment. We conducted 1 µs all-atom molecular dynamics simulations on Hemileia vastatrix TRPS to address two questions: (i) the role of the linker segment and (ii) the comparative mode of action. Since there is not an experimental structure, we started our simulations with homology modeling. Based on the results, it seems that TRPS makes use of an already-existing tunnel that can spontaneously move the indole moiety from the α catalytic pocket to the β one. Such behavior was completely disrupted in the simulation without the linker. In light of these results and the αβ dimer's low stability, the full-working TRPS single genes might be the result of a particular evolution. Considering the significant losses that Hemileia vastatrix causes to coffee plantations, our next course of action will be to use the TRPS to look for substances that can block tryptophan production and therefore control the disease.

Response of arabica coffee populations on coffee leaf rust in two weather conditions in North Sumatra, Indonesia

Coffee leaf rust (CLR) disease is seriously threatening the sustainability of coffee production in many countries. The incidence and severity as CLR parameters generally depend on the coffee plant, pathogen, and environment. Therefore, this study aimed to determine the effect of arabica coffee populations and weather conditions on CLR parameters. The study was carried out using a field experiment with seven groups of arabica coffee populations in different weathers in North Sumatra Province, Indonesia. The results showed that the arabica coffee population P7 from District Toba had high resistance to wet weather. Furthermore, all the population groups showed higher degree of CLR parameters in dry than wet weather, while a significant interaction between the population and the weather was observed on branch rust incidence, leaf rust incidence, and severity. Moreover, H. vastatrix races were probably varied and had different adaptability to weather changes, which was the most decisive factor for the dispersal and severity of CLR. The CLR parameters in the dry weather did not correlate with the parameters in the wet weather and vice versa. The total rainfall and the number of the rainy day reduced the CLR dispersal and severity.

Impact of ontogenic changes on the dynamics of a fungal crop disease model motivated by coffee leaf rust.

Ontogenic resistance has been described for many plant-pathogen systems. Conversely, coffee leaf rust, a major fungal disease that drastically reduces coffee production, exhibits a form of ontogenic susceptibility, with a higher infection risk for mature leaves. To take into account stage-dependent crop response to phytopathogenic fungi, we developed an SEIR-U epidemiological model, where U stands for spores, which differentiates between young and mature leaves. Based on this model, we also explored the impact of ontogenic resistance on the sporulation rate. We computed the basic reproduction number [Formula: see text], which classically determines the stability of the disease-free equilibrium. We identified forward and backward bifurcation cases. The backward bifurcation is generated by the high sporulation of young leaves compared to mature ones. In this case, when the basic reproduction number is less than one, the disease can persist. These results provide useful insights on the disease dynamics and its control. In particular, ontogenic resistance may require higher control efforts to eradicate the disease.

Distribution and Intensity of Coffee Berry Diseases (Colletotrichum Kahawae Waller and Bridge) in Gurage Zone

In worldwide more than 125 million people and in Ethiopia around 15-16 million peoples were derive their income from Coffee. Coffee berry disease (CBD), coffee wilt disease (CLD) and coffee leaf rust (CLR) are most important coffee diseases in Ethiopia. Previous research works have little information on CBD distribution, status and associated factors in Gurage zone southern parts of Ethiopia. This study was designed to carry out the CBD disease intensity. The assessment was done in Enemorina Ener, Ezha and Cheha districts of Gurage zone during July to August 2017. The study result indicated that CBD was prevalent in all inspected farmers’ field with a disease incidence range of 100% and 86.66%. Percent Severity Index was recorded from 38.89 to 59.44% in all assessed areas. Pearson correlation analysis showed that disease incidence (DI) and percent severity index (PSI) have positive and strong relationship with altitude (r=0.46) and (r=0.73), respectively. It is good when the study was repeated in one more production year and with including other coffee producing districts of Gurage zone. The present study gives clues to generate CBD disease management strategies as short term and develop resistant cultivars for the study area as long term strategy to increase county foreign currency.

First Description of Simplicillium lanosoniveum, a Potential Antagonist of the Coffee Leaf Rust from Cuba

(1) The fungal genus Simplicillium (Cordycipitaceae: Hypocreales) has an extensive distribution and a broad spectrum of hosts and substrates. The species Simplicillium lanosoniveum is a mycoparasite with potential for biological control of coffee leaf rust, Hemileia vastatrix. Morphologically, Simplicillium closely resembles mycoparasitic and entomopathogenic Lecanicillium fungi, often resulting in misidentification. A fungal isolate was obtained from leaf-rust-infested coffee plants from Cienfuegos Province, Cuba. (2) Combined analyses of morphology and molecular markers (ITS, LSU, EF-1alpha) were used for fungal identification. (3) In the NJ, ML, and BI phylogenies which were reconstructed, the isolate LBSim-01 was located in the Simplicillium lanosoniveum clade. This species-level identification was supported by morphological features. (4) The isolate LBSim-01 was assigned to the species Simplicillium lanosoniveum. This is the first description of a Simplicillium fungus associated with coffee leaf rust in Cuba. The presented results hold implications for the biological control of this economically relevant plant disease.

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.

First Occurrence of Coffee Leaf Rust Caused by Hemileia vastatrix on Coffee in Saudi Arabia

Coffee leaf rust (CLR) is a major disease of Arabica coffee caused by the biotrophic fungus Hemileia vastatrix. Jazan region in Saudi Arabia has long been one of the last coffee-producing regions in the world free of this disease. In August 2023, CLR was initially observed in coffee plantations located in Fyfa district one of the main coffee-producing mountains of Jazan region. The source of the infection is unknown, however CLR could have entered Jazan from the nearest coffee-producing locations that haven the pathogen. During a scheduled survey in August 2023, symptoms were observed including yellowish-orange lesions that frequently combined to form chlorotic lesions and exhibited the powdery appearance of yellow uredospores on the abaxial surface of leaves. The uredospores and teliospores were microscopically examined and their morphology matched the previously published description for H. vastatrix. The identity of H. vastatrix specimens was further confirmed based on PCR amplification and sequencing of ITS, sharing a 99–100% identity with previously published sequences, as belonging to H. vastatrix. The pathogenicity of H. vastatrix specimens was investigated on Coffee arabica plants under growth chamber conditions, and all were pathogenic relative to control, with 100% of disease incidence, therefore fulfilling Koch’s postulates. Based on our findings, this is the first documentation of H. vastatrix causing CLR in Saudi Arabia.