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.

Genomic selection for genotype performance and environmental stability in Coffea canephora.

Coffee is one of the most important beverages and trade products in the world. Among the multiple research initiatives focused on coffee sustainability, plant breeding provides the best means to increase phenotypic performance and release cultivars that could meet market demands. Since coffee is well adapted to a diversity of tropical environments, an important question for those confronting the problem of evaluating phenotypic performance is the relevance of genotype-by-environment interaction. As a perennial crop with a long juvenile phase, coffee is subjected to significant temporal and spatial variations. Such facts not only hinder the selection of promising materials but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled and therefore is predictable using molecular information. To test it, we used genome-based methods to predict stability metrics computed with the primary goal of selecting coffee genotypes that combine high phenotypic performance and stability for target environments. Using 2 populations of Coffea canephora, evaluated across multiple years and locations, our contribution is 3-fold: (1) first, we demonstrated that the number of harvest evaluations may be reduced leading to accelerated implementation of molecular breeding; (2) we showed that stability metrics are predictable; and finally, (3) both stable and high-performance genotypes can be simultaneously predicted and selected. While this research was carried out on representative environments for coffee production with substantial crossover in genotypic ranking, we anticipate that genomic prediction can be an efficient tool to select coffee genotypes that combine high performance and stability across years and the target locations here evaluated.

Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence.

Several anthropic activities, especially mining, have contributed to the exacerbation of contents of potentially toxic elements in soils around the world. Mines can release a large amount of direct sources of contaminants into the environment, and even after the mines are no longer being exploited, the environmental liabilities generated may continue to provide contamination risks. Potentially toxic elements (PTEs), when present in the environment, can enter the food chain, promoting serious risks to human health and the ecosystem. Several methods have been used to determine the contents of PTEs in soils, but most are laborious, costly and generate waste. In this study, we use a methodological framework to optimize the prediction of levels of PTEs in soils. We used a total set of 120 soil samples, collected at a depth of 0-10 cm. The covariate database is composed of variables measured by proximal sensors, physical and chemical soil characteristics, and morphometric data derived from a DEM with a spatial resolution of 30 m. Five machine learning algorithms were tested: Random Forests, Cubist, Linear Model, Support Vector Machine and K Nearest Neighbor. In general, the Cubist algorithm produced better results in predicting the contents of Pb, Zn, Ba and Fe compared to the other tested models. For the Al contents, the Support Vector Machine produced the best prediction. For the Cr contents, all models showed low predictive power. The most important covariates in predicting the contents of PTEs varied according to the studied element. However, x-ray fluorescence measurements, textural and morphometric variables stood out for all elements. The methodology structure reported in this study represents an alternative for fast, low-cost prediction of PTEs in soils, in addition to being efficient and economical for monitoring potentially contaminated areas and obtaining quality reference values for soils.

Molecular characterization of parents and hybrid progenies of conilon coffee.

The objective of the present work was the molecular characterization of 11 parents and 101 hybrid progenies of conilon coffee, obtained through diallel crosses from the breeding program of the Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural (Incaper, ES, Brazil). The analyses were performed with 18 Simple Sequence Repeat (SSR) molecular markers, obtaining a total of 32 alleles. SSR markers were classified as moderately informative (PIC = 0.37), being efficient in characterizing individuals. High genetic diversity was verified in the 112 genotypes, based on the greater values of observed heterozygosity about to the expected heterozygosity (0.55 and 0.44, respectively), negative values for the fixation index (F) (-0.14), and the formation of distinct groups by UPGMA. These results indicate high genetic variability among the conilon coffee genitors, which remained similar and persisting in the progenies. The average dissimilarity between parents was 0.29 and between progenies 0.34. The progenies 38 and 40 and the parent P11 were considered the most divergent in the study. The genetic variability found can be explored in the genetic breeding of the conilon coffee and guide crossings between diversified and compatible genetic materials, for the composition of novel cultivars for the state of Espírito Santo.

Chemical and sensory profile of new genotypes of Brazilian Coffea canephora.

The study of Brazilian Conilon coffee genotypes with unknown chemical composition and sensory quality is extremely important since these data may contribute to the launching of new coffee cultivars in the international market with high cup quality. The present study aimed to investigate the metabolic profile of 3 genotypes of Conilon and compared them to Robusta Tropical and Arabica coffees, all collected at 3 different levels of ripeness. The extracts were analysed by ESI-LTQ-ORBITRAP, and 11 attributes were evaluated by sensory analysis. To correlate sensory, composition and maturation, chemometric analysis was used. The metabolites trigonelline, caffeine, caffeoylquinic acid and sugars revealed higher concentrations in genotypes 105 and 108. According to the sensorial analysis, genotype 108 showed the highest final score (82), which was even higher than the Arabica coffees. Among the new coffees studied, genotype 108 presented promising characteristics, sparking interest in its national and international commercialization.

Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models.

Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee-production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.

Diversity among genotypes of conilon coffee selected in Espírito Santo state / Diversidade entre genótipos de café conilon selecionados no estado do Espírito Santo

The use of multivariate techniques for factor analysis is an efficient alternative for coffee breeding programs. This study aimed to evaluate the genetic diversity of 60 genotypes of conilon coffee based on agronomic performance in the northern state of Espírito Santo and to estimate the relative contribution of different agronomic characteristics towards the diversity of the species. The data were collected in an experiment conducted on the Experimental Farm of Bananal do Norte (Instituto Capixaba de Pesquisa, Assistência Técnica e Extenção Rural ­ INCAPER) in the southern state of Espírito Santo, and 12 agronomic characteristics were evaluated over four sequential harvests (4 years). Significant differences between the genotypes were observed for all of the characteristics, indicating the possibility of exploiting the high genetic variability to classify the genotypes into different groups based on their similarities. Of the agronomic characteristics, the duration of the ripening cycle was the variable that contributed the most to the variability among the 60 genotypes, with a relative contribution of 70.02%.

A utilização de técnicas multivariadas de análise de fatores é uma alternativa eficiente utilizada no melhoramento genético do cafeeiro. O presente trabalho objetivou avaliar a divergência genética de 60 clones de café conilon, selecionados pelo seu desempenho no norte do Estado do Espírito Santo, e estimar a contribuição relativa de diferentes características agronômicas para a diversidade da espécie. Os dados foram coletados em experimento conduzido na Fazenda Experimental Bananal do Norte (INCAPER), considerando 12 características agronômicas, avaliadas através de médias de quatro safras. Diferenças significativas entre os genótipos foram observadas para todas as características avaliadas, indicando a possibilidade de exploração da alta variabilidade genética para a classificação dos genótipos em diferentes grupos homogêneos, baseado em suas similaridades. Dentre as características agronômicas, a duração do ciclo de maturação foi a variável que mais contribuiu para a variabilidade entre os 60 genótipos, com contribuição relativa de 70,02%.

Tolerance of arabica coffee cultivars for aluminum in nutritive solution

This work aimed to evaluate the Coffea arabica cultivars for aluminum toxicity tolerance, in modified Hoagland solution. A completely randomized design with five repetitions in a factorial 4 x 4 (cultivar x combinations of aluminum) was used. After 44 days of the sowing, were transferred ten seedlings each cultivar germinated in the absence of Al3+ to solution without Al3+, and ten for solution with Al3+; ten seedlings each cultivar germinated in presence of Al3+ to solution without Al3+, and ten for solution with Al3+. In the treatment with aluminum, the element was added to the nutritive solution in the concentration of 0.83 mmol L-1 as Al2(SO4)3.16H2O. The cultivars Catuaí Amarelo IAC 62 and Iapar 59 were tolerant to the aluminum; cultivar Oeiras presented intermediate tolerance, while cultivar Obatã IAC 1669-20 was sensitive. The tolerance of the coffee cultivars to the aluminum during the initial development of the seedlings did not depend on the presence of aluminum in the germination phase.