Scale-Up of Coffea canephora Somatic Embryogenesis in Temporary Immersion System.

Somatic embryogenesis (SE) is a clear example of cellular totipotency. The SE of the genus Coffea has become a model for in vitro propagation for woody species and for the large-scale production of disease-free plants that provide an advantage for modern agriculture. Temporary immersion systems (TIS) are in high demand for the propagation of plants. The success of this type of bioreactor is based on the alternating cycles of immersion of the plant material in the culture medium, usually a few minutes, and the permanence outside the medium of the tissues for several hours. Some bioreactors are very efficient for propagating one species but not another. The efficiency of bioreactors depends on the species, the tissue used to propagate, the species' nutritional needs, the amount of ethylene produced by the tissue, and many more. In this protocol, we show how we produce C. canephora plants that are being taken to the field.

Transcriptomic Analysis During the Induction of Somatic Embryogenesis in Coffea canephora.

Omic tools have changed the way of doing research in experimental biology. The somatic embryogenesis (SE) study has not been immune to this benefit. The transcriptomic tools have been used to compare the genes expressed during the induction of SE with the genes expressed in zygotic embryogenesis or to compare the development of the different stages embryos go through. It has also been used to compare the expression of genes during the development of calli from which SE is induced, as well as many other applications. The protocol described here is employed in our laboratory to extract RNA and generate several transcriptomes for the study of SE on Coffea canephora.

Quality of Arabica coffee grown in Brazilian Savannah and impact of potassium sources.

Located in Brazil's Central Plateau, the Cerrado Savannah is an emerging coffee-growing region with significant potential for the national coffee market. This study investigated the impact of potassium fertilization on Arabica coffee quality in the Cerrado, using three potassium sources (K2SO4, KCl, and KNO3) and five cultivars (Arara, Aranãs, IPR103, Catiguá and Topázio) across two consecutive harvests. We focused on productivity, granulometry, chemical composition, and sensory characteristics. No significant difference in productivity across the cultivars studied or potassium sources as isolated factors were observed. Regarding chemical parameters, potassium sources only affected NO3- and SO42- levels in the grains. Cultivar-specific differences were noted in caffeine (CAF), citric acid (CA), and sucrose (SUC), highlighting a strong genetic influence. K2SO4 improved productivity in Arara (15 %) and IPR103 (11 %), while KNO3 reduced flat grain percentage to 70 % in Catiguá. Sensory evaluation showed that all potassium sources and cultivars produced specialty coffees, with the Arara cultivar treated with K2SO4 achieving the highest SCA score (83.3) while IPR 103 treated with KCl scored the lowest at 78. Only three treatments were below but very close to the threshold (80). Multivariate analysis indicated a trend where specific treatments correlated with higher productivity and quality. Despite the subtle differences in productivity and quality among potassium sources, a cost-benefit analysis may favor KCl due to its affordability, suggesting its viability as a potassium fertilization option in coffee cultivation. Future research is needed to confirm these trends and optimize potassium source selection to enhance coffee quality in the Cerrado.

Transcriptomic Analysis of Alternative Splicing Events during Different Fruit Ripening Stages of Coffea arabica L.

To date, genomic and transcriptomic data on Coffea arabica L. in public databases are very limited, and there has been no comprehensive integrated investigation conducted on alternative splicing (AS). Previously, we have constructed and sequenced eighteen RNA-seq libraries of C. arabica at different ripening stages of fruit development. From this dataset, a total of 3824, 2445, 2564, 2990, and 3162 DSGs were identified in a comparison of different fruit ripening stages. The largest proportion of DSGs, approximately 65%, were of the skipped exon (SE) type. Biologically, 9 and 29 differentially expressed DSGs in the spliceosome pathway and carbon metabolism pathway, respectively, were identified. These DSGs exhibited significant variations, primarily in S1 vs. S2 and S5 vs. S6, and they involve many aspects of organ development, hormone transduction, and the synthesis of flavor components. Through the examination of research findings regarding the biological functions and biochemical pathways associated with DSGs and DEGs, it was observed that six DSGs significantly enriched in ABC transporters, namely, LOC113712394, LOC113726618, LOC113739972, LOC113725240, LOC113730214, and LOC113707447, were continually down-regulated at the fruit ripening stage. In contrast, a total of four genes, which were LOC113732777, LOC113727880, LOC113690566, and LOC113711936, including those enriched in the cysteine and methionine metabolism, were continually up-regulated. Collectively, our findings may contribute to the exploration of alternative splicing mechanisms for focused investigations of potential genes associated with the ripening of fruits in C. arabica.

Coffee berry borer (Coleoptera: Scolytidae) population dynamics across Hawaii Island's diverse coffee-growing landscape: optimizing location-specific pesticide applications.

A major challenge to area-wide management of coffee berry borer (Hypothenemus hampei Ferrari) (Coleoptera: Scolytidae) is understanding how a heterogeneous coffee-growing landscape affects coffee berry borer population dynamics across temporal and spatial scales. We examined coffee phenology, weather, coffee berry borer flight activity, infestation, coffee berry borer position within the fruit, and management across 14 commercial coffee farms from 2016 to 2018 on Hawaii Island to characterize variation among districts and elevations. Here we aim to determine whether the timing of pesticide applications might be optimized based on specific locations. We observed larger populations of coffee berry borer at low-elevation farms and in the Kona district compared to mid- and high-elevation farms and the Ka'u district. Temperature, relative humidity, and rainfall all differed significantly across districts and elevations. We also observed a trend of higher fruit production at low-elevation farms compared to high-elevation farms, and differences in the timing of fruit development. Infestation increased with higher pest pressure and air temperatures and reduced fruit availability early and late in the season. Lastly, the timing and number of management interventions varied among districts and elevations. Combining information on trap catch, infestation, coffee berry borer position, and plant phenology, we present an optimized pesticide spray schedule for each location and find that the number of sprays could be reduced by 33-75% in comparison to the existing integrated pest management recommendations while maintaining effective control. Implementing a coordinated area-wide approach refined by small-scale optimization will lead to improved management of coffee berry borer on individual farms and a reduction in pest pressure across the coffee-growing landscape.

The floral development of the allotetraploid Coffea arabica L. correlates with a small RNA dynamic reprogramming.

Noncoding and coding RNAs are key regulators of plant growth, development, and stress responses. To investigate the types of transcripts accumulated during the vegetative to reproductive transition and floral development in the Coffea arabica L., we sequenced small RNA libraries from eight developmental stages, up to anthesis. We combined these data with messenger RNA and PARE sequencing of two important development stages that marks the transition of an apparent latent to a rapid growth stage. In addition, we took advantage of multiple in silico tools to characterize genomic loci producing small RNAs such as phasiRNAs, miRNAs, and tRFs. Our differential and co-expression analysis showed that some types of small RNAs such as tRNAs, snoRNAs, snRNAs, and phasiRNAs preferentially accumulate in a stage-specific manner. Members of the miR482/miR2118 superfamily and their 21-nucleotide phasiRNAs originating from resistance genes show a robust co-expression pattern that is maintained across all the evaluated developmental stages. Finally, the majority of miRNAs accumulate in a family stage-specific manner, related to modulated hormonal responses and transcription factor expression.

Microbiological attributes as indicators of soil quality in coffee growing systems in Southwest Bahia, Brazil.

Microorganisms are important indicators of soil quality due to their sensitivity to changes, reflecting the impacts caused by different land uses. The objective of this study was to evaluate the microbiological and physical-chemical attributes of the soil in areas cultivated with coffee under three different management systems (shaded coffee and full sun coffee with two spacings), as well as in adjacent areas under pasture and native forest, in Bahia, Brazil. The microbiological and physicochemical indicators evaluated were basal soil respiration (MBR), soil total organic carbon (TOC), microbial biomass carbon (MBC), metabolic quotient (qCO2), microbial quotient (qMic), enzyme activities (urease, acid phosphatase and fluorescein diacetate hydrolysis (FDA)). Physical and chemical indicators (particle size, texture, pH, P, K+, Ca2+, Mg2+, Al3+, and sum of bases) were also evaluated. Biological and chemical attributes were much more discriminative of study areas in the dry season. Microbial quotient (qMic) and metabolic quotient (qCO2) in the dry season showed that pasture is the most degraded land use. Conversely, nature forest and coffee with Grevillea were similar and were the best ones. In general, soil quality indicators were more sensitive to discriminate pasture and native forest from coffee systems, which, in turn, were not well discriminated among themselves.

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning.

BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (characteristics of the soil and the plant), mapping, and applying inputs according to the plants' needs. This differentiated management is precision coffee growing and it stands out for its increased yield and sustainability. RESULTS: This research aimed to predict yield in coffee plantations by applying machine learning methodologies to soil and plant attributes. The data were obtained in a field of 54.6 ha during two consecutive seasons, applying varied fertilization rates in accordance with the recommendations of soil attribute maps. Leaf analysis maps also were monitored with the aim of establishing a correlation between input parameters and yield prediction. The machine-learning models obtained from these data predicted coffee yield efficiently. The best model demonstrated predictive fit results with a Pearson correlation of 0.86. Soil chemical attributes did not interfere with the prediction models, indicating that this analysis can be dispensed with when applying these models. CONCLUSION: These findings have important implications for optimizing coffee management and cultivation, providing valuable insights for producers and researchers interested in maximizing yield using precision agriculture. © 2024 Society of Chemical Industry.

Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

BACKGROUND: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. RESULTS: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). CONCLUSION: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Like an "espresso" but not like a "cappuccino": landscape metrics are useful for predicting coffee production at the farm level but not at the municipality level.

Coffee farms receive ecosystem services that rely on pollinators and pest predators. Landscape-scale processes regulate the flow of these biodiversity-based services. Consequently, the coffee farms' surrounding landscape impacts coffee production. This paper investigates how landscape structure can influence coffee production at different scales. We also evaluated the predictive utility of landscape metrics in a spatial (farm level) and aspatial approach (municipality level). We tested the effect of landscape structure on coffee production for 25 farms and 30 municipalities in southern Brazil. We used seven landscape metrics at landscape and class levels to measure the effect of landscape structure. At the farm level, we calculated metrics in five buffers from 1 to 5 km from the farm centroid to measure their scale of effect. We conducted a model selection using the generalized linear model (GLM) with a Gamma error distribution and inverse link function to evaluate the impact of landscape metrics on coffee production in both spatial and aspatial approaches. The landscape intensity index had a negative effect on coffee production (AICc = 375.59, p < 0.001). The native forest patch density (AICc = 390.14, p = 0.011) and landscape diversity (AICc = 391.18, p = 0.023) had a positive effect on production. All significant factors had effects at the farm level in the 2 km buffer but no effects at the municipality level. Our findings suggest that the landscape composition in the immediate surroundings of coffee farms helps predict production in a spatially explicit approach. However, these metrics cannot detect the impact of the landscape when analyzed in an aspatial approach. These findings highlight the importance of the landscape spatial structure, mainly the natural one, in the stability of coffee production. This study enhanced the knowledge of coffee production dependence on landscape-level processes. This advance can help to improve the sustainability of land use and better planning of agriculture, ensuring food and economic safety. Furthermore, our framework provides a method that can be useful to scrutinize any cropping system with census data that is either spatialized or not.

Microclimate and development of Coffea canephora intercropped with Carica papaya: measures to mitigate climate change

Intercropped systems with Conilon coffee might provide a better environment for coffee production. The aim of this study was to assess the microclimate and development of Conilon coffee intercropped with papaya trees. Papaya was planted with spacing of 3.20 x 2.40 m. The coffee trees were planted after eight months, with spacing of 3.20 x 1.60 m, in-between papaya trees (in the same row). The measurements were taken 0, 40 and 80 cm away from the coffee plants, both in the north and south direction. Concomitantly, an adjoining full sunlight coffee system (not intercropped) was also assessed. The measurements included atmospheric parameters (temperature, irradiance, and relative humidity) and vegetative parameters for the coffee plants (leaf area, relative chlorophyll index, length of plagiotropic branches, length of orthotopic branches and number of nodes) in three periods of the year. The intercropped system of Conilon coffee and papaya trees led to a decrease in both irradiance and temperature, and higher means of relative humidity during daytime in all the periods assessed, which contributes to a better environment for coffee cultivation. The shadow provided by papaya trees in the coffee plants contributed to a higher leaf area but did not affect neither the growth of both plagiotropic and orthotopic branches, nor the number of nodes and the etiolation. The intercropped system of Conilon coffee and papaya trees may be potentially used as a farming system to mitigate climate change.

Expected global suitability of coffee, cashew and avocado due to climate change.

Coffee, cashew and avocado are of high socio-economic importance in many tropical smallholder farming systems around the globe. As plantation crops with a long lifespan, their cultivation requires long-term planning. The evaluation of climate change impacts on their biophysical suitability is therefore essential for developing adaptation measures and selecting appropriate varieties or crops. In this study, we modelled the current and future suitability of coffee arabica, cashew and avocado on a global scale based on climatic and soil requirements of the three crops. We used climate outputs of 14 global circulation models based on three emission scenarios to model the future (2050) climate change impacts on the crops both globally and in the main producing countries. For all three crops, climatic factors, mainly long dry seasons, mean temperatures (high and low), low minimum temperatures and annual precipitation (high and low), were more restrictive for the global extent of suitable growing regions than land and soil parameters, which were primarily low soil pH, unfavourable soil texture and steep slopes. We found shifts in suitable growing regions due to climate change with both regions of future expansion and contraction for all crops investigated. Coffee proved to be most vulnerable, with negative climate impacts dominating in all main producing regions. For both cashew and avocado, areas suitable for cultivation are expected to expand globally while in most main producing countries, the areas of highest suitability may decrease. The study reveals that climate change adaptation will be necessary in most major producing regions of all three crops. At high latitudes and high altitudes, however, they may all profit from increasing minimum temperatures. The study presents the first global assessment of climate change impacts on cashew and avocado suitability.

Can nonlinear agrometeorological models estimate coffee foliation?

BACKGROUND: The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25-year historical series (1995-2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987-2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four-parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees-day for 70 locations. RESULTS: The general model calibrated to estimate the arabica coffee foliation was accurate (mean absolute percentage error = 2.19%) and precise (R2 adj  = 0.99) and can be used to assist decision-making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax  = 97.63%, ymin  = 9%, Xo  = 3517.41 DD, and p = 6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. CONCLUSION: Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of -0.94. Therefore, coffee foliation can be estimated using degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer. © 2021 Society of Chemical Industry.

Cup quality profiles of Robusta coffee wilt disease resistant varieties grown in three agro-ecologies in Uganda.

BACKGROUND: The recently developed Robusta coffee wilt disease resistant (CWD-r) varieties in Uganda outperform the local landraces, both in yield and resilience. However, their uptake has been slow due to limited information on their cup worth. This study profiled the cup worth of the five most commonly grown CWD-r across the Lake Victoria Crescent, Western Mid-altitude farmland and Central Wooded Savannah agro-ecologies. RESULTS: Significant correlations (P ≤ 0.05) were observed between soil nutrients and coffee bean size but this was not the case for biochemical and cup quality. The proportion of coffee beans retained on screen 15; minimum acceptable size through coffee commercial markets, ranged from 58.09% in Mukono to 92.49% in Mityana. Interestingly, the bean size of variety KR4 was hardly influenced by environmental variations, with portions of beans retained on screen 15 being relatively the same (80.30% Ibanda, 89.50% Mukono, 98.20% Mityana). Coffee cup quality for most of the varieties was scored as premium (70-79%) across three agro-ecologies, with the exception of KR4, which was scored specialty grade (≥80%). Coffee blends generated were used to make coffee products with specialty score (82.25%) and a distinctive aroma complex. CONCLUSION: In this study, blends of CWD-r resulted in superior cup scores (76-82%). These findings show that CWD-r varieties have a high cup worth with potential for wide adaptation in Uganda's Robusta coffee growing agro-ecologies. Most importantly, variety KR4 has resilience across three agro-ecologies with a consistent high bean size and superior cup quality, making it a candidate variety for the market and breeding. © 2021 Society of Chemical Industry.

Osmotic stress-induced somatic embryo maturation of coffee Coffea arabica L., shoot and root apical meristems development and robustness.

Somatic embryogenesis (SE) is the most important plant biotechnology process for plant regeneration, propagation, genetic transformation and genome editing of coffee, Coffea arabica L. Somatic embryo (SEs) conversion to plantlets is the principal bottleneck for basic and applied use of this process. In this study we focus on the maturation of SEs of C. arabica var. Typica. SEs conversion to plantlet up to 95.9% was achieved under osmotic stress, using 9 g/L gelrite, as compared with only 39.34% in non-osmotic stress. Mature SEs induced in osmotic stress developed shoot and root apical meristems, while untreated SEs were unable to do it. C. arabica regenerated plants from osmotic stress were robust, with higher leaf and root area and internode length. To understand a possible regulatory mechanism, gene expression of key genes of C. arabica, homologous to sequences in the Arabidopsis thaliana genome, were analyzed. A set of two component system and cytokinin signaling-related coding genes (AHK1, AHK3, AHP4 and ARR1) which interact with WUSCHEL and WOX5 homedomains and morphogenic genes, BABY-BOOM, LEC1, FUS3 and AGL15, underwent significant changes during maturation of SEs of C. arabica var. Typica. This protocol is currently being applied in genetic transformation with high rate of success.

Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability.

Ecometabolic mixture design-fingerprinting in coffee cultivated under climate change was chemically explored using ComDim. Multi-blocks were formed using UV, NIRS, 1H NMR, SWV, and FT-IR data. ComDim investigated all these different fingerprints according to the extractor solvent and in virtue of atmospheric CO2 increase. Ethanol and ethanol-dichloromethane showed the best separations due to CO2 environment. 1H NMR loading indicate increases of fatty acids, caffeine, trigonelline, and glucose in beans under current CO2 levels, whereas quinic acid/chlorogenic acids, malic acid, and kahweol/cafestol increased in beans under elevated CO2 conditions. SWV indicated quercetin and chlorogenic acid as important compounds in coffee beans cultivated under current and elevated CO2, respectively. Based on the ethanol and ethanol-dichloromethane fingerprints, k-NN correctly classified the beans cultivated under different carbon dioxide environments and water availabilities, confirming the existence of metabolic changes due to climate changes. SWV proved to be promising compared with widely used spectrometric methods.

New species and records of Trichoderma isolated as mycoparasites and endophytes from cultivated and wild coffee in Africa.

A survey for species of the genus Trichoderma occurring as endophytes of Coffea, and as mycoparasites of coffee rusts (Hemileia), was undertaken in Africa; concentrating on Cameroon and Ethiopia. Ninety-four isolates of Trichoderma were obtained during this study: 76 as endophytes of healthy leaves, stems and berries and, 18 directly from colonized rust pustules. A phylogenetic analysis of all isolates used a combination of three genes: translation elongation factor-1α (tef1), rpb2 and cal for selected isolates. GCPSR criteria were used for the recognition of species; supported by morphological and cultural characters. The results reveal a previously unrecorded diversity of Trichoderma species endophytic in both wild and cultivated Coffea, and mycoparasitic on Hemileia rusts. Sixteen species were delimited, including four novel taxa which are described herein: T. botryosum, T. caeruloviride, T. lentissimum and T. pseudopyramidale. Two of these new species, T. botryosum and T. pseudopyramidale, constituted over 60% of the total isolations, predominantly from wild C. arabica in Ethiopian cloud forest. In sharp contrast, not a single isolate of Trichoderma was obtained using the same isolation protocol during a survey of coffee in four Brazilian states, suggesting the existence of a 'Trichoderma void' in the endophyte mycobiota of coffee outside of Africa. The potential use of these African Trichoderma isolates in classical biological control, either as endophytic bodyguards-to protect coffee plants from Hemileia vastatrix, the fungus causing coffee leaf rust (CLR)-or to reduce its impact through mycoparasitism, is discussed, with reference to the on-going CLR crisis in Central America.

Genetic diversity of native and cultivated Ugandan Robusta coffee (Coffea canephora Pierre ex A. Froehner): Climate influences, breeding potential and diversity conservation.

Wild genetic resources and their ability to adapt to environmental change are critically important in light of the projected climate change, while constituting the foundation of agricultural sustainability. To address the expected negative effects of climate change on Robusta coffee trees (Coffea canephora), collecting missions were conducted to explore its current native distribution in Uganda over a broad climatic range. Wild material from seven forests could thus be collected. We used 19 microsatellite (SSR) markers to assess genetic diversity and structure of this material as well as material from two ex-situ collections and a feral population. The Ugandan C. canephora diversity was then positioned relative to the species' global diversity structure. Twenty-two climatic variables were used to explore variations in climatic zones across the sampled forests. Overall, Uganda's native C. canephora diversity differs from other known genetic groups of this species. In northwestern (NW) Uganda, four distinct genetic clusters were distinguished being from Zoka, Budongo, Itwara and Kibale forests A large southern-central (SC) cluster included Malabigambo, Mabira, and Kalangala forest accessions, as well as feral and cultivated accessions, suggesting similarity in genetic origin and strong gene flow between wild and cultivated compartments. We also confirmed the introduction of Congolese varieties into the SC region where most Robusta coffee production takes place. Identified populations occurred in divergent environmental conditions and 12 environmental variables significantly explained 16.3% of the total allelic variation across populations. The substantial genetic variation within and between Ugandan populations with different climatic envelopes might contain adaptive diversity to cope with climate change. The accessions that we collected have substantially enriched the diversity hosted in the Ugandan collections and thus contribute to ex situ conservation of this vital genetic resource. However, there is an urgent need to develop strategies to enhance complementary in-situ conservation of Coffea canephora in native forests in northwestern Uganda.

Chemometrics-based aroma profiling for revealing origin, roasting indices, and brewing method in coffee seeds and its commercial blends in the Middle East.

Coffee is among the most consumed beverages worldwide. The present study reports on the aroma composition associated with coffee seeds brewing. Aroma of authentic coffee specimens of Coffea arabica and C. robusta alongside with typical products consumed in the Middle East were analyzed using HS-SPME coupled with GC-MS. In addition, multivariate data analysis (MVA) was employed. Results revealed for 102 volatiles with a distinct aroma profile between the different brewing methods. Infusion demonstrated higher esters level, while decoction and maceration were more abundant in sesquiterpenes and terpene alcohols, respectively. Besides, heat-induced products, i.e., 4-vinyl guaiacol was identified as potential roasting index in instant coffee and roasted C. robusta brews. Blending with cardamom further masked the smoky odor of such compounds by its fragrant terpinyl acetate. This study provides the first report on the chemical sensory attributes of Middle Eastern coffee blends and further reveal for the impact of brewing, roasting on its aroma composition.

Linking root and stem hydraulic traits to leaf physiological parameters in Coffea canephora clones with contrasting drought tolerance.

Knowing the key hydraulic traits of different genotypes at early seedling stages can potentially provide crucial information and save time for breeding programs. In the current study we investigated: (1) how root, stem and whole plant conductivities are linked to xylem traits, and (2) how the integrated hydraulic system impacts leaf water potential, gas exchange, chlorophyll a fluorescence and the growth of three coffee cultivars (clones of Coffea canephora Pierre ex Froehner cv. Conilon) with known differences in drought tolerance. The Conilon clones CL 14, CL 5 V and CL 109A, classified as tolerant, moderately tolerant, and sensitive to drought respectively, were grown under non-limiting soil-water supply but high atmospheric demand (i.e., high VPDair). CL 14 and CL 5 V displayed higher root and stem hydraulic conductance and conductivity, and higher whole plant conductivity than CL 109A, and these differences were associated with higher root growth traits. In addition, CL 109A exhibited a non-significant trend towards wider vessels. Collectively, these responses likely contributed to reduce leaf water potential in CL 109A, and in turn, reduced leaf gas exchange, especially during elevated VPDair. Even when grown under well-watered conditions, the elevated VPDair observed during this study resulted in key differences in the hydraulic traits between the cultivars corresponding to differences in plant water status, gas exchange, and photochemical activity. Together these results suggest that coffee hydraulic traits, even when grown under non-water stress conditions, can be considered in breeding programs targeting more productive and efficient genotypes under drought and high atmospheric demand.