Phenotypic Plasticity Index as a Strategy for Selecting Water-Stress-Adapted Coffee Genotypes.

The adaptive potential of plants is commonly used as an indicator of genotypes with higher breeding program potential. However, the complexity and interaction of plant metabolic parameters pose a challenge to selection strategies. In this context, this study aimed to explore phenotypic plasticity within the germplasm of Hybrid Timor coffee.

IBA and melatonin increase trigonelline and caffeine during the induction and initiation of adventitious roots in Coffea arabica L. cuttings.

Caffeine and trigonelline are found in Coffea arabica, and show antioxidant roles and growth and development functions. However, there are no reports on trigonelline and caffeine in relation to coffee rooting. The aim was to evaluate the impact of application of indole-3-butyric acid (IBA) and melatonin on caffeine and trigonelline at different stages of adventitious rooting in cuttings.

Purple acid phosphatases in coffee-genome-wide identification and the transcriptional profile of selected members in roots upon Pi starvation.

Unlabelled: Phosphorus (P) availability is determinant for crop productivity and, despite the sufficient amount of this nutrient in arable land, most of it remains insoluble, leading to the need of high fertilizer input. To cope with P scarcity forecasts and also for cropping costs alleviation, genotypes better adapted to promote P solubilization and absorption are required, especially for perennial crops. Coffee is one of these important perennial crops cultivated in soils with low P availability, and thus we aimed to study adaptive strategies to coffee genotypes in acquire phosphorus.

Influence of postharvest processing on the quality and sensory profile of groups of arabica coffee genotypesc.

Background: This study combined qualitative and quantitative approaches to obtain a better understanding of the sensory quality of beverages made from Arabic coffee genotypes subjected to different processing methods. Over 3 consecutive years, 270 accessions of Arabic coffee from the germplasm collection of Minas Gerais State, Brazil, were sensorially characterized after dry postharvest processing. At the end of this period, the 26 genotypes with the greatest potential for the production of specialty coffees were subjected to dry and wet processing.

Photochemical efficiency correlated with candidate gene expression promote coffee drought tolerance.

The aim of this study was to identify the correlation between photochemical efficiency and candidate genes expression to elucidate the drought tolerance mechanisms in coffee progenies (Icatu Vermelho IAC 3851-2 × Catimor UFV 1602-215) previously identified as tolerant in field conditions. Four progenies (2, 5, 12 and 15) were evaluated under water-deficit conditions (water deficit imposed 8 months after transplanting seedlings to the pots) and under irrigated system. Evaluations of physiological parameters and expression of candidate genes for drought tolerance were performed.

Discrimination of genotypes coffee by chemical composition of the beans: Potential markers in natural coffees.

The chemical composition of coffee beans varies according to the cultivar used, but there is little information about the differences among them resulting in different sensory profiles. The purpose was to find potential chemical markers to discriminate genealogical groups of coffee using regression analysis by partial least squares. Twenty-two accessions of C.

Leaf water potential of coffee estimated by landsat-8 images.

Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential (ΨW) throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the ΨW by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R2) of 0.