A cebola é a segunda hortaliça mais produzida no mundo, alcançando a marca de 111 milhões de toneladas em 2022. Apesar da sua importância, ainda se desconhece as características do resíduo proveniente do beneficiamento da cebola (RBC). Este trabalho teve como objetivo (1) determinar a composição e teor de nutrientes no resíduo do beneficiamento da cebola, considerando suas frações; (2) classificar o mesmo segundo a legislação federal brasileira, visando sua possível utilização como fertilizante. A amostragem do RBC foi realizada em 15 diferentes pontos, geograficamente distribuídos dentro do Município de Aurora, SC. Estes incluíram as unidades de beneficiamento/classificação da cebola (UBCC), locais de descarte inapropriado e locais de descarte comuns. No processo de caracterização observou-se que o resíduo é constituído de duas frações, uma oriunda de restos de tecidos vegetais e uma fração de solo que vem aderida aos tecidos vegetais. Na sua forma integra, o RBC apresentou densidade de 302,82kg/m³, baixa relação C:N (15,8:1) e importante percentual de matéria orgânica (64,83%) e carbono orgânico (20,07%). Quando estratificado, cerca de 38% do resíduo correspondia à fração de solo e 62% à fração vegetal. O RBC apresentou teor de Nitrogênio de 1,27%, sugerindo potencial como fonte de N para fertilização de áreas. Em termos gerais, o teor de nutrientes é comparável a alguns resíduos vegetais (ex. cascas de café, mamona, amendoim) e animais (ex. lodo de esgoto, esterco sólido bovino).
The purpose of this study was to evaluate the efficiency of using fuzzy controllers as auxiliary tools for different adaptability and stability methods aiming at the selection of conilon coffee clones. Thirty-six conilon coffee clones were evaluated for grain yield (bag ha-1), within seven harvests (2018 to 2024). Adaptability and stability were evaluated using the hybrid fuzzy controller (Eberhart & Russell, 1966; Cruz et al., 1989; Annicchiarico, 1992). There was significance for the effects of crops, clones, and clone by crop interaction. The Eberhart & Russell method classified clones 3, 16, 24, and 33 as having general adaptability, and 32 and 36 as having adaptability to unfavorable environments. In the Annicchiarico method (1992), clones 4, 10, 24, and 26 were classified as general behavior. Clones 2 and 8 show high adaptability to adverse environments, while clones 10, 23, and 29 excel in favorable conditions. By Cruz et al. (1989), clones 28 and 31 did not correspond to any classification through this controller. In this methodology, clone 29 presented the highest stability index. The use of these methodologies is a viable alternative for the classification of clones. The differentiated behavior of clones is influenced by environmental conditions, which justifies the use of different adaptability and stability methodologies. The use of fuzzy controllers in the recommendation of conilon coffee clones is a useful and promising tool in plant breeding programs. Clones 2 and 8 showed high adaptability to adverse environments, while clones 10, 23, and 29 excelled in favorable conditions.
Tropical forest canopies are the biosphere's most concentrated atmospheric interface for carbon, water and energy1,2. However, in most Earth System Models, the diverse and heterogeneous tropical forest biome is represented as a largely uniform ecosystem with either a singular or a small number of fixed canopy ecophysiological properties3. This situation arises, in part, from a lack of understanding about how and why the functional properties of tropical forest canopies vary geographically4. Here, by combining field-collected data from more than 1,800 vegetation plots and tree traits with satellite remote-sensing, terrain, climate and soil data, we predict variation across 13 morphological, structural and chemical functional traits of trees, and use this to compute and map the functional diversity of tropical forests. Our findings reveal that the tropical Americas, Africa and Asia tend to occupy different portions of the total functional trait space available across tropical forests. Tropical American forests are predicted to have 40% greater functional richness than tropical African and Asian forests. Meanwhile, African forests have the highest functional divergence-32% and 7% higher than that of tropical American and Asian forests, respectively. An uncertainty analysis highlights priority regions for further data collection, which would refine and improve these maps. Our predictions represent a ground-based and remotely enabled global analysis of how and why the functional traits of tropical forest canopies vary across space.
Wood density is a critical control on tree biomass, so poor understanding of its spatial variation can lead to large and systematic errors in forest biomass estimates and carbon maps. The need to understand how and why wood density varies is especially critical in tropical America where forests have exceptional species diversity and spatial turnover in composition. As tree identity and forest composition are challenging to estimate remotely, ground surveys are essential to know the wood density of trees, whether measured directly or inferred from their identity. Here, we assemble an extensive dataset of variation in wood density across the most forested and tree-diverse continent, examine how it relates to spatial and environmental variables, and use these relationships to predict spatial variation in wood density over tropical and sub-tropical South America. Our analysis refines previously identified east-west Amazon gradients in wood density, improves them by revealing fine-scale variation, and extends predictions into Andean, dry, and Atlantic forests. The results halve biomass prediction errors compared to a naïve scenario with no knowledge of spatial variation in wood density. Our findings will help improve remote sensing-based estimates of aboveground biomass carbon stocks across tropical South America.
The aim of this study was to select Coffea canephora genotypes from the seminal propagation variety ‘ES8152’ with different harvest times. The experiment was conducted using a Federer augmented block design with three repetitions, evaluating 175 genotypes and four clonal witnesses in two harvests (2022 and 2023), and 20 morphoagronomic characteristics were evaluated. The data were analyzed using the REML/BLUP methodology with the Selegen software, where the variance components and genetic values were estimated. The selection was performed using the Mulamba-Rank index. The bottom sieve (BS) and top sieve (TS) characteristics had high heritability (0.5779 and 0.6694, respectively) and accuracy (0.7602 and 0.8182, respectively). TS also showed high repeatability (0.6827). The genotypic effects were significant at 1% level for days for fruit ripening, fruit size, vegetative vigor, yield per plant, TS, and BS; at 5% level for general scale; and at 10% level for incidence of rust, degree of inclination, and percentage of fruit float. It was possible to distinguish 20 superior genotypes in terms of maturation, among which the selection gains for the genotypic clusters were 46.14, 45.92 and 41.56% for indefinite, early, and late maturation, respectively, by applying a selection intensity of 11.43%. Genotypes 25, 26, 73, 93, and 100 could be used for early maturing varieties, whereas genotypes 155 and 189 could be used for late-maturing varieties. The most promising genotypes for composing a variety, regardless of the maturation period, were 20, 39, 90, 112, and 190, as these were among the five best genotypes ranked in the three selection processes, demonstrating that they added superior desired morphoagronomic characteristics. It is concluded that there is genetic variability among the 175 genotypes evaluated, as well as significant genetic effects to be explored in the pool gene of individuals originating from the 'ES8152' variety.
