Physical and chemical properties, pectinases activity, and cell wall pectin of Acidulus, Momordica, Inodorus and Cantalupensis melons with different ripening degree at harvest

The objective of the present study was to evaluate the physical and chemical changes, pectinases activity, and cell wall pectin in melon varieties Acidulus (access 16), Momordica (access 2), Inodorus (cv. ‘Iracema’) and Cantalupensis (cv. ‘Olympic’), in the relation of ripening degree at harvest. Melon fruits were planted and evaluated with different ripening degree at harvest, from 15 to 35 days after anthesis (DAA). The fruits, arranged in a completely randomized design, had been evaluated on the harvest days to physical and chemical characteristics. We evaluate pectin methylesterase, polygalacturonase, beta-galactosidase, and pectin contents (water-soluble, chelate soluble, and sodium carbonate soluble). The ideal harvest for each melon was, 35 days after anthesis for cv ‘Iracema’, 30 days after anthesis for cv. ‘Olympic’, 30 days after anthesis for access 16, and 20 days after anthesis for access 2. High pulp firmness of access 16 is associated with the high levels of sodium carbonate soluble pectin and low levels of polygalacturonase and beta-galactosidase activity. Momordica melon fruit cracking is related to the high levels of pectinases activity, as well as pectin degradation

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

Silicato de potasio asociado a podas de producción en calidad postcosecha de higos (Ficus carica L.)

La higuera es un fruto de clima templado cultivado en todo el mundo, con características como rusticidad y resistencia, adaptándose a diferentes condiciones edafoclimáticas. El cultivar "Roxo de Valinhos" se destaca siendo producido en todo Brasil por tener alto vigor y alta producción de frutos. Sin embargo, uno de los grandes retos para la expansión de su cultivo en condiciones naturales es la alta susceptibilidad al deterioro la poscosecha, por problemas como la pudrición y la deshidratación. El objetivo de este trabajo fue evaluar las características químicas de frutos de higuera según la aplicación de dosis de silicato de potasio asociado a diferentes ramas productivas. El diseño experimental utilizado fue en bloques al azar, en esquema factorial 5 x 3, con cinco bloques. Los tratamientos consistieron en cinco dosis (0, 0,5%, 1%, 1,5% y 2%) de silicato de potasio aplicado vía foliar y tres tipos de podas productivas de 3, 6 y 9 ramas productivas. Las variables analizadas fueron: Sólidos solubles (SS), Acidez titulable (AT) y relación SS/TA. El contenido de sólidos solubles varió en los diferentes tipos de poda y dosis estudiadas. Las plantas con 3 ramas productivas presentaron los mayores valores a la dosis del 1% (15,73%). 6 ramas productivas, los niveles más altos se observaron en la dosificación de 0,5% de silicato de potasio (14,56%). plantas con 9 ramas productivas, presentaron los mayores niveles de sólidos solubles a la dosis más alta de 2%, alcanzando 13,74%. La relación SS/AT también fue influenciada por los factores estudiados. Las plantas con 3 ramas productivas presentaron valores más altos en la dosis de 0% (115,19). Las plantas con 6 ramas productivas presentaron los valores más altos a la dosis de 0,5% (87,64). Las plantas con 9 ramas presentaron valores superiores a la dosis del 2% (90,40). La acidez titulable no fue influenciada significativamente por los factores estudiados. La fertilización foliar aumenta la disponibilidad y eficiencia fotosintética de la planta, resultando en una mayor producción de azúcares son traslocados a los frutos, aumentando el contenido de sólidos solubles. La aplicación de silicato de potasio en dosis de 0,5% y/o 1% mejoró las características organolépticas de los frutos de higuera. Los frutos producidos con 3 ramas productivas tendieron a concentrar mayor cantidad de compuestos que las otras formas de conducción de las plantas. En consecuencia, correspondieron a frutos con los mayores niveles de sólidos solubles y la mayor acumulación de ácidos orgánicos.Facultad de Ciencias Agrarias y Forestale

Predição do potencial genético de populações segregantes de arroz de terras altas Prediction of genetic potential of segregating upland rice population

O objetivo deste trabalho foi avaliar o método de Jinks & Pooni na predição do potencial genético de populações segregantes de arroz e estudar o efeito da interação populações segregantes x ambiente na seleção destas populações. Utilizaram-se 23 populações segregantes de arroz de terras altas e duas testemunhas, avaliadas em um látice 5x5, com três repetições, no ano agrícola de 1996/97. O estudo foi conduzido em dois locais em Minas Gerais, Lavras e Patos de Minas, em três épocas distintas de semeadura. Os resultados encontrados em relação à probabilidade de extrair linhagens superiores a um determinado padrão indicaram como mais promissoras as populações CNAx 5496 e CNAx 6001, e menos promissoras, CNAx 6063 e CNAx 6102. A previsão do potencial genético das populações segregantes a partir do método de Jinks & Pooni mostrou-se uma alternativa viável na escolha das populações mais promissoras, permitindo ao melhorista concentrar maiores esforços na avaliação das famílias superiores. A ocorrência das interações populações segregantes x épocas e locais x épocas mostraram a importância de se avaliar as populações em mais de um ambiente. A escolha das populações que apresentam um comportamento estável frente às oscilações ambientais é importante dentro de um programa de melhoramento.<br>This work aimed to evaluate the Jinks & Pooni's method to predict genetic potential of the segregating upland rice population and to study the effect of the segregating population x environment interaction in the selection of those populations. In this work 23 segregating populations of upland rice with two checks were used. They were evaluated in a5x5 lattice, with three replicates, in the agricultural year of 1996/97. The populations were conducted in two sites in Minas Gerais State, Brazil, Lavras and Patos de Minas, at three distinct sowing times. The results found in relation to the probability of extracting superior lines from a certain population indicated as the most promising the populations CNAx 5496 and CNAx 6001 and as the least promising CNAx 6063 and CNAx 6102. The Jinks & Pooni's method was a feasible alternative in the choice of populations most promising permitting the breeder to concentrate his effort in the evaluation of a higher family. The occurrence of the segregating population x sowing time and site x sowing time interactions showed the importance of evaluating the populations in more than one environment. The choice of the populations which presented a steady behavior against the environmental drifts is an important step within an improvement program and the chance to obtain success in this phase rises