Scientia Agrícola
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/12094
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Item Agronomic practices toward coffee sustainability. A review(Escola Superior de Agricultura "Luiz de Queiroz", 2023-10-23) Martinez, Herminia Emilia Prieto; Andrade, Sara Adrián López de; Santos, Ricardo Henrique Silva; Baptistella, João Leonardo Corte; Mazzafera, PauloThe coffee sector is estimated to have a retail market value in excess of USD 83 billion, and over 125 million jobs have been created in the global coffee chain. The coffee specialty market has recently increased significantly, generating opportunities to certify coffee beans produced by sustainable practices. This avoids practices potentially harmful to the environment. Agroforestry, organic farming, intercropping, and soil conservation strategies are examples of sustainable alternatives in the production of coffee. In this review, we focus on practices for the sustainable management of coffee plantations that can help farmers fight problems caused by global warming. More specifically, we address soil organic matter and microbiota, the use of Urochloa grass as intercrop in coffee plantations, shading systems (including agroforestry), and organic coffee production. We concluded that from the agronomic viewpoint, we already have production techniques that can replace traditional ones with significant advantages accruing to the quality of coffee orchard ecosystems. Nevertheless, we need scientific research efforts to deal with the existing gaps and the engagement of the whole coffee chain as a means of guaranteeing an adequate profit to those smallholders who adopt and maintain sustainable practice and are capable of bringing several positive changes to the coffee crop, including the use of microbia-based commercial products and new organic sources of nutrients to complement chemical fertilizers and improve coffee quality.Item Initial performance and genetic diversity of coffee trees cultivated under contrasting altitude conditions(Escola Superior de Agricultura "Luiz de Queiroz", 2023-08-14) Senra, João Felipe de Brites; Silva, Josimar Aleixo da; Ferreira, Adésio; Esposti, Marlon Dutra Degli; Ferrão, Maria Amélia Gava; Fassarella, Kamila Machado; Silva, Uliana Ribeiro; Milheiros, Idalina Sturião; Silva, Fernanda Gomes daThis work evaluated the initial performance and genetic diversity of Coffea canephora genotypes cultivated in environments at contrasting altitudes. Fourteen morphophysiological traits and seven descriptors of the genus Coffea spp. of coffee trees cultivated at altitudes of 140 m and 700 m were evaluated. The design used was Federer’s augmented block in a 2 × 112 factorial scheme with six blocks. The first factor was the two environments, and the second was the 112 genotypes, with eight common treatments, being five conilon coffee clones and three arabica coffee cultivars. The data were analyzed by the method of REML/BLUP and genetic correlation method. Genetic diversity was evaluated by estimating the distance matrix, applying the Gower methodology followed by the clustering method by Tocher and UPGMA. The phenotypic means were higher in the environment at an altitude of 700 m, except for plant height, number of leaves, and canopy height (CH). Genotypic effects were significant for most traits except for leaf width, CH, unit leaf area, and total leaf area. A wide genetic diversity was verified, with distances varying from 0.037 to 0.593 for the pairs of genotypes 26 × 93 and T7 × 76, respectively. Most of the traits studied showed high genotypic correlation with the environment and expressive genetic correlation between the evaluated traits thereby demonstrating the possibility of indirect selection. There is an adaptation of conilon coffee genotypes to high altitudes and the possibility of developing a specific cultivar for the southern state of Espírito Santo.Item Coffee crops adaptation to climate change in agroforestry systems with rubber trees in southern Brazil(Escola Superior de Agricultura "Luiz de Queiroz", 2022-04-13) Zaro, Geovanna Cristina; Caramori, Paulo Henrique; Wrege, Marcos Silveira; Caldana, Nathan Felipe da Silva; Virgens Filho, Jorim Sousa das; Morais, Heverly; Yada Junior, George Mitsuo; Caramori, Daniel CamposAdaptation to climate change is a strategy for crops to cope with the scenario of rising temperatures worldwide. In the case of Coffea arabica L., the use of agroforestry systems (AFS) with woody species is a promising practice to reduce excessive heat during the day. This study aimed to 1) evaluate air temperature changes that occur in an AFS of coffee and double alleys of rubber trees (Hevea brasiliensis Müell. Arg.) and 2) carry out an analysis of future warming scenarios by comparing the cultivation of Arabic coffee in full sun and in an AFS of double alleys of rubber trees. The microclimatic variables were measured between two rows of coffee trees at 1.0 m of height from June 2016 to June 2018. The results indicate that the AFS with double alleys of rubber trees spaced 16 m apart had an average temperature reduction from 1.4 to 2.5 °C from 10h00 to 16h00. The study also simulated temperature increases of 1.7, 2.6, 3.1, and 4.8 °C from 2018 to 2099, according to scenarios predicted by the Intergovernmental Panel on Climate Change (IPCC), and the impact in coffee production in Paraná State, Brazil. Using the climatic generator PGECLIMA_R, simulations suggest a progressive reduction of traditional areas suitable for open-grown coffee in the state. Production conditions can be maintained through the AFS, since the systems attenuate mean temperatures by 1-2 °C. We conclude that the AFS of coffee and rubber trees contribute to coffee crop adaptations to a future warmer environment.Item Detection of coffee fruits on tree branches using computer vision(Escola Superior de Agricultura "Luiz de Queiroz", 2022-09-12) Bazame, Helizani Couto; Molin, José Paulo; Althoff, Daniel; Martello, MaurícioCoffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.Item Soil morphostructural characterization and coffee root distribution under agroforestry system with Hevea Brasiliensis(Escola Superior de Agricultura "Luiz de Queiroz", 2021) Nunes, Amanda Letícia Pit; Cortez, Glassys Louise de Souza; Zaro, Geovanna Cristina; Zorzenoni, Thiago Ometto; Melo, Thadeu Rodrigues de; Figueiredo, Alex; Aquino, Gisele Silva de; Medina, Cristiane de Conti; Ralisch, Ricardo; Caramori, Paulo Henrique; Guimarães, Maria de FátimaLand use and tillage practices may change soil structure and undermine sustainable agriculture; however, such changes are hardly identified in the short term. In this sense, agroforestry systems have been used to reduce soil degradation and promote sustainable production in coffee plantations. These areas are expected to have well-structured soils and hence improved root distribution. This study aimed to evaluate soil quality by the morphostructural and root distribution analyses comparing open-grown coffee and coffee in agroforestry systems with rubber trees for 19 years, in an Oxisol in northern Paraná State (Brazil). Treatments consisted of open-grown coffee (OG), coffee partially shaded by rubber trees (PSH), and coffee fully shaded by rubber trees (FSH). The mapping of morphostructural features and soil resistance to penetration in “cultural profile” walls identified changes in soil structure resulting from different tillage systems. Root distribution was better in coffee plants grown in PSH and FSH systems. At greater depths, cultural profiles of FSH and PSH showed a larger numbers of roots compared to OG. Among the three systems, PSH provided a better environment for root growth and distribution. This result could be attributed to the high biological activity and interaction between roots and aggregates in that profile. The FSH agroforestry system provided less compact morphological structures and more roots throughout the soil profile. The agroforestry systems presented fewer soil structural changes by tillage operations and lower values of soil penetration resistance. Coffee root distribution was an effective indicator of soil quality and consistent with the morphostructural characterization of cultural profile.Item Receptor-Like Kinase (RLK) as a candidate gene conferring resistance to Hemileia vastatrix in coffee(Escola Superior de Agricultura "Luiz de Queiroz", 2021) Almeida, Dênia Pires de; Castro, Isabel Samila Lima; Mendes, Tiago Antônio de Oliveira; Alves, Danúbia Rodrigues; Barka, Geleta Dugassa; Barreiros, Pedro Ricardo Rossi Marques; Zambolim, Laércio; Sakiyama, Ney Sussumu; Caixeta, Eveline TeixeiraThe biotrophic fungus Hemileia vastatrix causes coffee leaf rust (CLR), one of the most devastating diseases in Coffea arabica. Coffee, like other plants, has developed effective mechanisms to recognize and respond to infections caused by pathogens. Plant resistance gene analogs (RGAs) have been identified in certain plants as candidates for resistance (R) genes or membrane receptors that activate the R genes. The RGAs identified in different plants possess conserved domains that play specific roles in the fight against pathogens. Despite the importance of RGAs, in coffee plants these genes and other molecular mechanisms of disease resistance are still unknown. This study aimed to sequence and characterize candidate genes from coffee plants with the potential for involvement in resistance to H. vastatrix. Sequencing was performed based on a library of bacterial artificial chromosomes (BAC) of the coffee clone ‘Híbrido de Timor’ (HdT) CIFC 832/2 and screened using a functional marker. Two RGAs, HdT_ LRR_RLK1 and HdT_LRR_RLK2, containing the motif of leucine-rich repeat-like kinase (LRR-RLK) were identified. Based on the presence or absence of the HdT_LRR_RLK2 RGA in a number of differential coffee clones containing different combinations of the rust resistance gene, these RGAs did not correspond to any resistance gene already characterized (SH1-9). These genes were also analyzed using qPCR and demonstrated a major expression peak at 24 h after inoculation in both the compatible and incompatible interactions between coffee and H. vastatrix. These results are valuable information for breeding programs aimed at developing CLR-resistant cultivars, in addition to enabling a better understanding of the interactions between coffee and H. vastatrix.Item Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms(Escola Superior de Agricultura "Luiz de Queiroz", 2021) Sousa, Ithalo Coelho de; Nascimento, Moysés; Silva, Gabi Nunes; Nascimento, Ana Carolina Campana; Cruz, Cosme Damião; Silva, Fabyano Fonseca e; Almeida, Dênia Pires de; Pestana, Kátia Nogueira; Azevedo, Camila Ferreira; Zambolim, Laércio; Caixeta, Eveline TeixeiraGenomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.