Yield and fruit quality of Santa Cruz and Italian tomatoes depending on fruit thinning

O manejo do tomateiro cultivado em ambiente protegido, com ênfase no raleio de frutos, pode contribuir de forma significativa para a produção de frutos de qualidade superior, expressando assim o potencial de cada cultivar. Foi avaliado o efeito do raleio de frutos na produtividade e seus componentes e em alguns atributos de qualidade de frutos de genótipos de tomateiro dos segmentos Santa Cruz e Italiano de crescimento indeterminado, visando ao consumo in natura. Avaliaram-se 12 genótipos de tomate de mesa (seis híbridos experimentais e seis cultivares) e dois modos de condução (plantas conduzidas com e sem raleio de frutos). O experimento foi com parcelas subdivididas, distribuídas em blocos completos ao acaso com três repetições. As parcelas foram representadas pelos genótipos e as subparcelas pelos modos de condução. Avaliaram-se o número de frutos por planta, produtividade comercial de frutos, produtividade de frutos não-comercializáveis, massa média do fruto, comprimento e largura do fruto, pH, teor de sólidos solúveis totais (SS), acidez titulável (AT), relação entre SS e AT, teor de ácido ascórbico e teor de licopeno. O raleio dos frutos proporcionou incremento da produtividade comercial, massa média, comprimento e largura do fruto para os híbridos THX-02 e THX-03, do segmento Santa Cruz, e THX-04, THX-05 e Netuno, do segmento Italiano e não mostrou vantagens para a produção e seus componentes para os demais genótipos e características avaliadas. Considerando o raleio de frutos, os genótipos Giuliana e Sahel obtiveram maior produtividade comercial e massa média do fruto. Sem o raleio de frutos, 'Netuno' alcançou maior número de frutos por planta, porém, o híbrido Sahel foi quem se destacou por apresentar maior produtividade comercial e massa média do fruto. O raleio não influenciou a qualidade organoléptica dos genótipos avaliados. 'Avalon' apresentou maior teor de ácido ascórbico que 'Netuno' e 'Sahel'. 'Débora Max', THX-01, THX-02 e THX-04 foram semelhantes entre si quanto ao teor de licopeno e superaram 'Giuliana', 'Sahel', THX-03 e THX-06.The management of the tomato plant under greenhouse, with emphasis on fruit thinning, contributes significantly to the production of fruits of superior quality, expressing the potential of each cultivar. We determined the effect of fruit thinning on yield and its components in tomato hybrids of the Santa Cruz and Italian types of indeterminate growth, and we performed the qualitative characterization of the fruits, aiming in natura market. Twelve fresh-market tomato genotypes (six experimental hybrids and six commercial cultivars) and two training methods (with and without manual fruit thinning) were evaluated. A randomized complete block design was used in this trial, with split-plots and three replications. The plots were represented by genotypes and the subplots were constitued by training methods. Number of fruits per plant, yield of marketable and not marketable fruits, average fruit weight, fruit length and fruit width were measured. For quantitative characterization, pH, soluble solids concentration (SS), tritratable acidity (AT), ascorbic acid content, SS and AT ratio and lycopene content were measured. The fruit thinning increased marketable yield, average weight, length and width for the Santa Cruz hybrids THX-02 and THX-03 and for THX-04, THX-05 and Netuno, classified as Italian type. The fruit thinning did not provide benefits for yield and its components for the other evaluated characteristics and genotypes. Giuliana and Sahel genotypes presented higher marketable yield and average fruit weight. Without fruit thinning, 'Netuno' reached the highest number of fruits per plant, but the hybrid Sahel showed higher marketable yield and average fruit weight. Thinning did not affect the organoleptic quality of the genotypes. 'Avalon' showed higher ascorbic acid content than 'Netuno' and 'Sahel', and 'Débora Max', 'THX-01', 'THX-02' and 'THX-04', which were similar to each other in the content of lycopene, overcame 'Giuliana', 'Sahel', 'THX-03', and 'THX-06'.FAPESPCoordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Influência de condições ambientais e da irrigação na composição química e na qualidade de bebida do café

The influence of environmental conditions and irrigation on the chemical composition of green coffee beans and the relationship of these parameters to the quality of the beverage were investigated in coffee plantations in the regions of Adamantina, Mococa and Campinas, in the state of São Paulo, Brazil. The chemical composition and physical aspects of green coffee beans produced in the three regions were related through Principal Component Analyses (PCA) to the quality of beverage, as determined by sensorial and electronic analyses. The chemical composition was affected by the environmental conditions. Some differences in cup quality were detected by the electronic method but not by cup tasting. Irrigation was not a major factor affecting chemical composition, since there were few differences in relation to non-irrigated coffee plants. The production site appeared to be the main influencing factor on biochemical composition. A pronounced difference was observed in Adamantina, where annual average air temperature was 1.6-2.4ºC warmer than in the other two areas and about 3.5ºC above to the optimal limit for coffee cultivation.A influência das condições ambientais e do manejo da irrigação na composição química do grão de café verde e sua relação com a qualidade de bebida foram investigadas em cafeeiros cultivados nas regiões paulistas de Adamantina, Mococa e Campinas. A composição química e alguns aspectos físicos dos grãos de café verde produzidos nos diferentes tratamentos e locais de cultivo foram relacionados à qualidade de bebida determinada por análises sensoriais e eletrônicas. Os dados tratados por análises de componentes principais demonstraram que a composição química dos grãos permitiu a diferenciação do café produzido em diferentes regiões, sendo as variações na composição das sementes perceptíveis na análise da bebida pelo método eletrônico, mas não pelas análises sensoriais. A irrigação não foi fator condicionante na composição química dos cafés, com pouca ou nenhuma diferença significativa em relação a cafeeiros não irrigados, sendo tais diferenças condicionadas pelo local de cultivo, as quais, acredita-se, seriam influenciadas pela temperatura do ar, que foi, em média, 1,6 a 2,4ºC mais alta em Adamantina em relação, respectivamente, a Mococa e Campinas, e cerca de 3,5ºC acima do limite de temperatura considerado ótimo para o cultivo do cafeeiro.229238Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq