Coffee Breeding Research Articles

English

Coffee breeding programs aim at developing superior genotypes, which entail exploring the genetic variability existing in conilon/robusta coffee. This study aimed at examining the variability and estimate genetic param of physical and physiological nature in Coffea canephora seeds in order to provide bases for the selection of genotypes for superior seeds quality. Forty-three C. canephora genotypes were evaluated for 11 physical and physiological seed traits. Estimates of genetic param, environmental variance, phenotypic variance, genotypic variance, broad-sense heritability, variation index and Pearson's correlation were obtained. The Dissimilarity between genotypes was obtained by generalized Mahalanobis distance. The dendrogram was constructed using unweighted pair group method with arithmetic mean (UPGMA). C. canephora genotypes presented high germination potential and exhibited genetic diversity for all seed traits, with estimates of genetic variance greater than environmental variance. Heritability reached values above 96.0% and all genetic variation indices found were higher than unity. The UPGMA hierarchical method grouped the genotypes in three groups. The physical traits (circumference, area and roundness) were those that contributed the most to explaining the dissimilarity between genotypes. Genotypes Alecrim, AP, Bamburral, Z39 and Tardio C were the most divergent. The results revealed the presence of genetic variability between C. canephora genotypes and greater genetic than environmental influence on the characters of seeds, indicating that selection for the studied traits showed favorable conditions in terms of immediate gain.

Spatial variation in current and historical management of Arabica coffee across forests in its indigenous distribution

Societal Impact StatementArabica coffee grows in the forests and woodlands of Ethiopia. These Arabica coffee populations contain a reservoir of the highest genetic variation of this species, which is extremely valuable from a biodiversity conservation and coffee breeding point of view. Knowledge about the distribution of these populations, including how and to what extent they have been managed by local communities now and in the past, is limited. We reveal insights into the history, distribution, and management of Arabica coffee populations in the remaining forests of Ethiopia, which is essential for developing effective conservation policies for this globally important resource.Summary To guide conservation of forest biodiversity in a broad sense, we need to understand the landscape‐level variation in current and historical management practices of agroforestry systems. We collected data on coffee management practices across a large forested landscape in Ethiopia within Arabica coffee's indigenous distribution, using interviews in 11 villages and field surveys and interviews at 78 coffee sites in the forest. There was a gradient in coffee density (higher), liana cover (lower), and canopy cover (lower) from sites with high management intensity to sites without management. Recently, management intensity has increased in the forest edges. Interviews suggest that substantial areas of currently unmanaged coffee forests are a legacy of reforestation of abandoned (semi‐) open landscapes in the late 19th century. Despite a dynamic history of coffee cultivation across these areas, the conservation of forest biodiversity, including unmanaged populations of genetically diverse Arabica coffee, should be a priority, given the few such remaining areas in Ethiopia and elsewhere in the world.

Coffee sustainability: leveraging collaborative breeding for variety improvement

The sustainability of global coffee supply is threatened by production challenges worsening with every decade since the turn of the 21st century. Coffee is among the most popular beverages in the world, and its production supports livelihoods of millions of people, mostly smallholder farmers. With a global market value exceeding US$130 billion, the coffee industry is a source of export revenue for producing countries, the majority of which are developing nations in the Global South. Climate change, pests, and diseases continue to hamper productivity, profitability, and countries’ competitiveness in the global market. At the same time, the global demand for coffee continues to rise. A crucial pillar to safeguarding the future of coffee is continuously developing improved varieties suitable for the changing environment, and that address the needs of farmers and the industry. Yet, most national breeding programs are grossly out of touch with climate change reality and market dynamics. Working in isolation, these programs are constrained by unstable funding, limited sources of genetic variability, and inaccessibility of modern technologies necessary to accelerate the breeding process. We review here key production challenges, status of genetic diversity, and breeding approaches. Additionally, we explore opportunities in leveraging a pre-competitive collaborative breeding approach encompassing sharing of germplasm, and cross-border multi-environment trials in developing next-generation cultivars. We argue that breeding efforts should be informed by the needs of all the stakeholders in the supply chain including researchers, smallholder farmers, small and medium-sized enterprises investing in coffee processing and roasting, and consumers. Using emerging and reviving producers in Southeast Asia and Eastern Africa as a model, we explore how a paradigm shift in coffee breeding from the historically nation-centric approach to a more forward-looking collaborative model could improve the efficiency of variety improvement in face of climate change and growing market demand.

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.

Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

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.

A comparison of genomic and phenomic selection methods for yield prediction in Coffea canephora

AbstractGenomic prediction has been proposed as the standard method to predict the genetic merit of unphenotyped individuals. Despite the promising results reported in the plant breeding literature, its routine implementation remains difficult for some crops. This is the case with Coffea canephora, in which costs and availability of molecular tools are major challenges for most breeding programs. To circumvent this, the use of near‐infrared spectroscopy (NIR) has been recently proposed as an alternative to complement marker‐assisted selection. The so‐called phenomic selection relies on the reflectance spectrum to capture similarities between individuals and emerges as a valid approach for prediction. With promising results reported in multiple annual crops, we hypothesize that phenomic prediction could be a cost‐efficient approach to incorporate into a practical coffee breeding program. To test it, we relied on a diverse population of C. canephora, evaluated for yield production, in two geographical locations over four harvest seasons. Our contributions in this paper are twofold: (i) We compared phenomic and genomic selection results, and showed large predictive abilities when NIR is used as a predictor for within and across‐location predictions, and (ii) we presented a critical view of how both information sets could be combined into a contemporaneous coffee breeding program. Altogether, our results show how multi‐omic information could be integrated in the same framework to leverage genetic gains in the long term.

Unravelling the role of key genes involved coffee leaf rust resistance

The biotrophic fungus Hemileia vastatrix is the pathogen responsible for coffee leaf rust, a devastating disease in several coffee-producing countries. Despite the importance of studying the interaction between Coffea and H. vastatrix, a more comprehensive understanding of the mechanisms involved in this pathosystem is necessary. The role of eight candidate genes was analyzed aiming at identifying and validating new important coffee resistance genes and understanding their interaction with H. vastatrix. These genes were identified in the most important sources of coffee resistance, the Híbrido de Timor CIFC 832/2 and CIFC 832/1. Previous works found six resistance genes and, in our research, other two new genes were identified in BAC clones and validated by RT-qPCR during compatible and incompatible interactions between Coffea and H. vastatrix. An interactome approach was performed using Coffea-H. vastatrix and Coffea-Coffea proteins to better understand the biological process and the interaction of the host-pathogen. Two networks of interactions from the compiled data were built focused on candidate genes associated with pre-haustorial resistance (12 and 24 h.a.i) in coffee plants against the pathogen. The results showed, for the first time, differentially expressed proteins (DEPs) positively regulated in the incompatible interaction Coffea-H. vastatrix. These coffee proteins interact with each other and with secreted and/or transmembrane pathogen proteins. The obtained results also show that DEPs found are involved in important plant defense pathways such as pathways associated with the response to wounds, signaling, regulation of the innate immune response and the transmembrane receptor protein serine/threonine kinase pathway. The present work shows the involvement of genes in both pathogen recognition and signaling cascades, which act in pre-haustorial defense mechanisms of HdT coffee. Therefore, the candidate genes analyzed, together with the biological processes elucidated, have the potential to contribute to the development of new control strategies against the fungus H. vastatrix within coffee breeding programs aiming to develop cultivars with durable resistance.

Over View of Coffee (Arabica Coffee) Breeding in Ethiopia

Over View of Coffee (Arabica Coffee) Breeding in Ethiopia

Coffee breeding in a time of crisis: F1 hybrids in Central America since 1990

Social Impact StatementCentral America is renowned for producing some of the world's finest coffee, prized for its quality and flavor. With coffee being a major export crop for many countries, the region's economic, social, and cultural well‐being is closely linked to the success of its coffee industry. Coffee breeding supports the industry's long‐term sustainability and competitiveness by developing new varieties that are more productive, disease‐resistant, climate resilient, and adapted to the coffee producer's realities. Ongoing efforts in coffee breeding have significant implications for the livelihoods of thousands of coffee farmers and their families, the economies of Central American countries, and the preservation of the region's rich coffee tradition and culture.Summary The central objective is to understand how coffee breeding programs have evolved to help coffee farmers adapt to the rapidly changing economic, political, institutional, and environmental conditions since 1990. Before then, coffee breeders focused primarily on productivity, developing dwarf varieties bred for intensive monoculture production. Since 1990, breeders have shifted their work to address climate change, new diseases and pests, a growing industry interest in cup quality, and unprecedented price volatility. Much of the breeding work has shifted from public institutions to international technical assistance agencies, international non‐governmental organizations (NGOs), and private companies. Their new programs focus on developing F1 coffees designed for agroforestry production and, which, can be developed quickly in response to changing conditions. This case study also complicates narratives about the place of hybrid coffees in agricultural modernization during the 20th and 21st centuries. Analyze documents produced over the past 30 years by key breeding organizations, governments, trade organizations, to assess the evolution of breeding programs in historical context, and the progress of the new coffees in the field. Some F1 hybrids have shown considerable promise, but challenges surrounding their propagation, distribution, and cost—as well as their suitability for the specialty coffee market—have so far prevented farmers from adopting them on a large scale. The switch to F1 coffee breeding represents a deep strategic shift in coffee breeding in Central America, more attuned to the needs of coffee farmers. While the F1 coffees can help farmers address many of the farmers' technical challenges, cost and logistical issues remain a challenge.

The Exploration and Utilization of Coffee (Coffea arabica L.) Genetic Resource in Its Center of Origin

Coffee, which is the most popular soft drink in the world, belongs to the genus Coffea in the Rubiaceae family. About 124 species are known in this genus, whereas Coffea arabica L. and Coffea canephora are the two most widely cultivated species worldwide. Coffea arabica is the only tetraploid species in the genus coffea and is self-fertile, while other species are diploid and generally self-incompatible. Ethiopia is the center of origin and diversity for arabica coffee, which is the only species cultivated in the country. The availability of wide genetic variability among the indigenous coffee and the diverse agro-ecologies in the country provided immense possibilities to improve the crop for desirable agronomic and breeding interest traits. Cognizant of this and the occurrence of coffee berry disease (CBD) in Ethiopia for the first time, a coffee breeding program was initiated in the 1970’s aiming at exploiting the available genetic diversity and developing productive varieties combining disease resistance, best quality, and other desirable characteristics. Coffee genetic resource collection, characterization, evaluation, conservation and genetic studies are among the major breeding activities undertaken so far by designing and implementing different breeding strategies following the conventional breeding approach. So far, outstanding achievements have been recorded in collecting 7130 germplasm accessions, generating basic information on the genetics aspects, and developing 44 improved varieties of which seven are F1 hybrids and identifying 149 elite promising genotypes for further evaluation to develop and release a new varieties. Extensive collection of coffee genetic resources from unaddressed areas, and application of molecular breeding techniques such as marker-assisted selection and breeding for stress tolerance, among others, would be the main focus areas of future coffee breeding programs.

Genetic diversity of Coffea arabica L. mitochondrial genomes caused by repeat- mediated recombination and RNA editing.

Coffea arabica L. is one of the most important crops widely cultivated in 70 countries across Asia, Africa, and Latin America. Mitochondria are essential organelles that play critical roles in cellular respiration, metabolism, and differentiation. C. arabica's nuclear and chloroplast genomes have been reported. However, its mitochondrial genome remained unreported. Here, we intended to sequence and characterize its mitochondrial genome to maximize the potential of its genomes for evolutionary studies, molecular breeding, and molecular marker developments. We sequenced the total DNA of C. arabica using Illumina and Nanopore platforms. We then assembled the mitochondrial genome with a hybrid strategy using Unicycler software. We found that the mitochondrial genome comprised two circular chromosomes with lengths of 867,678 bp and 153,529 bp, encoding 40 protein-coding genes, 26 tRNA genes, and three rRNA genes. We also detected 270 Simple Sequence Repeats and 34 tandem repeats in the mitochondrial genome. We found 515 high-scoring sequence pairs (HSPs) for a self-to-self similarity comparison using BLASTn. Three HSPs were found to mediate recombination by the mapping of long reads. Furthermore, we predicted 472 using deep-mt with the convolutional neural network model. Then we randomly validated 90 RNA editing events by PCR amplification and Sanger sequencing, with the majority being non-synonymous substitutions and only three being synonymous substitutions. These findings provide valuable insights into the genetic characteristics of the C. arabica mitochondrial genome, which can be helpful for future study on coffee breeding and mitochondrial genome evolution. Our study sheds new light on the evolution of C. arabica organelle genomes and their potential use in genetic breeding, providing valuable data for developing molecular markers that can improve crop productivity and quality. Furthermore, the discovery of RNA editing events in the mitochondrial genome of C. arabica offers insights into the regulation of gene expression in this species, contributing to a better understanding of coffee genetics and evolution.

The chloroplast protein HCF164 is predicted to be associated with Coffea SH9 resistance factor against Hemileia vastatrix

To explore the connection between chloroplast and coffee resistance factors, designated as SH1 to SH9, whole genomic DNA of 42 coffee genotypes was sequenced, and entire chloroplast genomes were de novo assembled. The chloroplast phylogenetic haplotype network clustered individuals per species instead of SH factors. However, for the first time, it allowed the molecular validation of Coffea arabica as the maternal parent of the spontaneous hybrid “Híbrido de Timor”. Individual reads were also aligned on the C. arabica reference genome to relate SH factors with chloroplast metabolism, and an in-silico analysis of selected nuclear-encoded chloroplast proteins (132 proteins) was performed. The nuclear-encoded thioredoxin-like membrane protein HCF164 enabled the discrimination of individuals with and without the SH9 factor, due to specific DNA variants linked to chromosome 7c (from C. canephora-derived sub-genome). The absence of both the thioredoxin domain and redox-active disulphide center in the HCF164 protein, observed in SH9 individuals, raises the possibility of potential implications on redox regulation. For the first time, the identification of specific DNA variants of chloroplast proteins allows discriminating individuals according to the SH profile. This study introduces an unexplored strategy for identifying protein/genes associated with SH factors and candidate targets of H. vastatrix effectors, thereby creating new perspectives for coffee breeding programs.

Genetic diversity of ex-situ conserved Arabica cofee (Coffea arabica L.) accessions in Ethiopia as revealed by simple sequence repeats markers

So far, there has been limited use of molecular markers in arabica coffee breeding program in Ethiopia. The objectives of this study were to explore the effectiveness of simple sequence repeats markers (SSRs) in detecting polymor- phism and to assess the extent of genetic diversity and relationships among ex-situconserved Arabica coffee accessions. Sixty-two forest coffee accessions planted in the experimental plot of the Ethiopian coffee breeding program were evaluated using 14 SSR markers. These markers amplified a total of 100 alleles, varying from four to ten alleles per locus, with an average of 7.2 across all loci. The rate of polymorphism ranged from 75 to 100, with a mean value of 96.4 across the accessions. The polymorphic information content (PIC) varied from 0.26 to 0.92, with a mean value of 0.70. The genetic similarity coefficient values between 72% possible pair-wise combinations ranged from 0.18 to 0.50, with overall mean value of 0.44.The unweighted pair group method with arithmetic mean (UPGMA) dendrogram based on Jaccard’s genetic similarity grouped the accessions into five main clusters and two singletons at <0.47 similarity coefficient value. These results indicate the effectiveness of the SSR markers in detecting polymorphism and the presence of a high level of genetic diversity and distant relatedness among the studied coffee accessions. The observed diversity could be exploited in the future coffee breeding program to develop heterotic hybrid coffee varieties through crossing of divergent parental lines. The highly informative SSRs markers can be also used in genetic analysis of Arabica coffee germplasm conserved in the field gene bank at Jimma Agricultural Research Center (JARC) to establish core collections for effective conservation, management and utilization purposes

Agro‐morphological characterization and diversity analysis of Coffea arabica germplasm collection from INIA, Peru

AbstractCoffee (Coffea arabica L.) plays a major role in the economy of Peru and the world. The present study aims to elucidate the agro‐morphological variability of coffee genotypes maintained in the National Institute of Agrarian Innovation (INIA) germplasm collection. Therefore, 20 vegetative, reproductive, and phytosanitary traits of 162 coffee accessions of INIA's germplasm collection were evaluated and analyzed. Correlation results indicate that a simultaneous selection of characters, such as number of branches per plant, number of nodes per branch, leaf area, and weight of a hundred fruits, can contribute to increase coffee yields. Additionally, coffee yield was negatively correlated with the incidence and severity of coffee leaf rust, and interestingly the occurrence of small and compact coffee plants with high resistance to the disease was also found. The analysis of Tocher and Mahalanobis D2 determined the formation of 10 groups of divergent coffee accessions; where clusters 1 (accession codes 20, 29, 38, 54, 67, 71, 117, 24, 26, and 27), 5 (accession codes 46 and 53), 9 (accession code 159), and 10 (accession code 203) group promising accessions that can be used in breeding programs. Principal component analysis showed that at least five of its principal components managed to explain 70.01% of the total variation in the collection. Finally, the high coefficients obtained for the phenotypic, genotypic, and heritability variation confirm the existence of additive genes in the evaluated population, that would ensure the success of coffee breeding programs based on the selection of traits of agronomic importance.

Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia

Abstract Small-scale coffee farming in Indonesia faces low productivity due to poor farming practices and low-quality planting materials. It highlights the need for improved coffee varieties that meet farmers’ preferences. Given the vulnerability of coffee farming to climate change, participatory breeding programs that involve collaboration between researchers and farmers to develop a climate-adapted variety are essential. This study used survey data from Gayo Highland, Aceh, Indonesia, to investigate farmers’ perception of the importance of climate-related attributes in a coffee variety, including those developed through a participatory breeding program, and the determinant factors. The result shows that farmers rated climate-related attributes as the least important (average score 0.36) compared to others, such as coffee productivity (1.57) and quality (1.22), resistance to pests and diseases (0.96), and input-use efficiency (0.57). This finding suggests a lack of awareness among farmers about the importance of climate issues in coffee farming. The estimation results also indicate that coffee extension activities have a negative association with farmers’ perceptions of the importance of climate attributes. This study recommends inclusive and targeted climate campaigns to increase farmers’ concern, awareness, and knowledge about the threats of climate change to coffee farming through strengthening advisory services.

Genomic-assisted breeding for climate-smart coffee.

Coffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from different geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.

Diversity and structure of Coffea canephora from old seminal crops in Espírito Santo, Brazil: genetic resources for coffee breeding

Diversity and structure of Coffea canephora from old seminal crops in Espírito Santo, Brazil: genetic resources for coffee breeding

New coffee varieties as a climate adaptation strategy: Empirical evidence from Costa Rica

Adapting to climate change in vulnerable coffee regions is crucial to maintain rural livelihoods. Among the solutions, coffee breeding strategies aim to produce coffee varieties with higher output performance than traditional varieties while reducing competition for land. This paper investigates the output performance of hybrid coffee (e.g., Starmaya and Centroamericano – H1), introgressed (e.g., Marsellesa and obatá) and traditional coffee (e.g., Caturra and Villa Sarchi) varieties. By using plot-level panel data among commercial farms in Costa Rica, we estimate the output performance of the three coffee varieties using pooled ordinary least squares and random effects models. We find that hybrid coffee varieties give 29–61% higher output than traditional coffee varieties. The results remain robust even after controlling for factor and climate inputs. Notwithstanding the larger productivity, hybrid coffee varieties demand more labor and inorganic fertilizers. While pesticide use may be reduced by hybrid's pest resistance, agroecological approaches for nutrient management are still needed to improve livelihoods and environmental outcomes. Headed towards longer-term studies, our paper presents the first evidence on the output performance of hybrid coffee varieties outside on-farm trials or experimental plots. These results suggest the potential of hybrid coffee varieties in promoting sustainable agriculture by improving the livelihood of coffee farmers, enhancing their adaptation against climate change and decreasing competition for land.

Multiple-trait model by Bayesian inference applied to environment efficient Coffea arabica with low-nitrogen nutrient

Identifying Coffea arabica cultivars that are more efficient in the use of nitrogen is an important strategy and a necessity in the context of environmental and economic impacts attributed to excessive nitrogen fertilization. Although Coffea arabica breeding data have a multi-trait structure, they are often analyzed under a single trait structure. Thus, the objectives of this study were to use a Bayesian multitrait model, to estimate heritability in the broad sense, and to select arabica coffee cultivars with better genetic potential (desirable agronomic traits) in nitrogen-restricted cultivation. The experiment was carried out in a greenhouse with 20 arabica coffee cultivars grown in a nutrient solution with low-nitrogen content (1.5 mM). The experimental design used was in randomized blocks with three replications. Six agromorphological traits of the arabica coffee breeding program and five nutritional efficiency indices were used. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The agromorphological traits were considered highly heritable, with a credibility interval (95% probability): H2 = 0.9538 – 5.89E-01. The Bayesian multitrait model presents an adequate strategy for the genetic improvement of arabica coffee grown in low-nitrogen concentrations. Coffee arabica cultivars Icatu Precoce 3282, Icatu Vermelho IAC 4045, Acaiá Cerrado MG 1474, Tupi IAC 1669-33, Catucaí 785/15, Caturra Vermelho and Obatã IAC 1669/20 demonstrated greater potential for cultivation in low-nitrogen concentration.

Germplasm characterization of half‐sib families from seminal matricies of Coffea canephora

AbstractThe genetic diversity of Coffea canephora Pierre ex A. Froehner present in old seminal crops must be characterized because it represents a source of genetic variability for breeding programs. In this study, the genetic diversity of 388 half‐sib families of C. canephora, collected from Espírito Santo, Brazil, were assessed for their phenotypic traits. The data were obtained in a trial planted in April 2018 with five commercial clones as controls. Vegetative development was evaluated based on plant height, stem diameter, and average height and diameter growth rates. Abiotic (drought) and biotic (rust, leaf miner, and mealybug) stresses were evaluated by a rating scale. The phenotypic variability of the population was detected for all traits, and divergent clusters of families were identified. Negative correlations indicated that adverse conditions hampered the early development of the coffee plants. Groups of families should be investigated for breeding programs according to adequate development even under stressful conditions. These results show the importance of preserving the germplasm of old crops in the south of Espírito Santo as a source of genetic resources for coffee breeding programs. This valuable resource should be maintained and conserved as a strategy to guarantee the sustainability of coffee production.