Scientia Agrícola
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/12094
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Resultados da Pesquisa
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.Item Host colonization differences between citrus and coffee isolates of xylella fastidiosa in reciprocal inoculation(Escola Superior de Agricultura "Luiz de Queiroz", 2008-05) Prado, Simone de Souza; Lopes, João Roberto Spotti; Demétrio, Clarice Garcia Borges; Borgatto, Adriano Ferreti; Almeida, Rodrigo Piacentini Paes deCitrus variegated chlorosis (CVC) and coffee stem atrophy (CSA) are important diseases in Brazil associated with closely-related strains of Xylella fastidiosa, but little is know about host overlapping and importance of citrus and coffee as inoculum sources of these strains. In this study, reciprocal-inoculation experiments were performed to determine if CVC and CSA isolates are biologically similar within citrus and coffee plants. These two hosts were mechanically inoculated with a CVC and a CSA isolate of X. fastidiosa at four concentrations ranging between10 3 and 10 9 colony forming units CFU mL -1 . At two, four and eight months after inoculation, the infection efficiency and bacterial populations of the isolates in each host were determined by culturing. The CVC isolate infected both citrus and coffee plants, but developed lower populations in coffee. The CSA isolate did not colonize citrus. Inoculation of coffee plants with the CVC isolate resulted in low rates of infection and required an inoculum concentration ten-fold higher than that necessary to obtain a similar (25%) rate of infection in citrus. The relatively low infection rates and bacterial numbers of the CVC isolate in coffee plants compared with those observed in citrus suggest that coffee is not a suitable host to serve as a source of inoculum of the CVC strain for primary spread to citrus or within coffee plantations. Key words: citrus variegated chlorosis, coffee leaf scorch, inoculum concentration, bacterial population, epidemiologyItem Growth, development, and fertilizer-15N recovery by the coffee plant(Escola Superior de Agricultura "Luiz de Queiroz", 2007-09) Fenilli, Tatiele Anete Bergamo; Dourado-Neto, Durval; Trivelin, Paulo César Ocheuze; Favarin, José Laercio; Costa, Flávio Murilo Pereira da; Bacchi, Osny Oliveira SantosThe relationship between growth and fertilizer nitrogen recovery by perenial crops such as coffee is poorly understood and improved understanding of such relations is important for the establishment of rational crop management practices. In order to characterize the growth of a typical coffee crop in Brazil and quantify the recovery of 15 N labeled ammonium sulfate, and improve information for fertilizer management practices this study presents results for two consecutive cropping years, fertilized with 280 and 350 kg ha -1 of N, respectively, applied in four splittings, using five replicates. Shoot dry matter accumulation was evaluated every 60 days, separating plants into branches, leaves and fruits. Labeled sub-plots were used to evaluate N-total and 15 N abundance by mass spectrometry. During the first year the aerial part reached a recovery of 71% of the fertilizer N applied up to February, but this value was reduced to 34% at harvest and 19% at the beginning of the next flowering period due to leaf fall and fruit export. For the second year the aerial part absorbed 36% of the fertilizer N up to March, 47% up to harvest and 19% up to the beginning of the next flowering period. The splitting into four applications of the used fertilizer rates was adequate for the requirements of the crop at these growth stages of the coffee crop.Item Spatio-temporal modelling of coffee berry borer infestation patterns accounting for inflation of zeroes and missing values(Escola Superior de Agricultura "Luiz de Queiroz", 2009-01) Cárdenas, Ramiro Ruiz; Assunçã, Renato Martins; Demétrio, Clarice Garcia BorgesThe study of pest distributions in space and time in agricultural systems provides important information for the optimization of integrated pest management programs and for the planning of experiments. Two statistical problems commonly associated to the space-time modelling of data that hinder its implementation are the excess of zero counts and the presence of missing values due to the adopted sampling scheme. These problems are considered in the present article. Data of coffee berry borer infestation collected under Colombian field conditions are used to study the spatio-temporal evolution of the pest infestation. The dispersion of the pest starting from initial focuses of infestation was modelled considering linear and quadratic infestation growth trends as well as different combinations of random effects representing both spatially and not spatially structured variability. The analysis was accomplished under a hierarchical Bayesian approach. The missing values were dealt with by means of multiple imputation. Additionally, a mixture model was proposed to take into account the excess of zeroes in the beginning of the infestation. In general, quadratic models had a better fit than linear models. The use of spatially structured parameters also allowed a clearer identification of the temporal increase or decrease of infestation patterns. However, neither of the space-time models based on standard distributions was able to properly describe the excess of zero counts in the beginning of the infestation. This overdispersed pattern was correctly modelled by the mixture space-time models, which had a better performance than their counterpart without a mixture component.