Quantidade absorvida e concentrações de micronutrientes em tomateiro sob cultivo protegido

A produção sob cultivo protegido é uma prática relativamente recente no Brasil e a área com tomate nesse ambiente vem crescendo a cada ano, e junto com esse crescimento, cresce também a falta de informações técnicas apropriadas para as nossa condições. Este trabalho avaliou a quantidade absorvida e os teores de micronutrientes pelo tomateiro em ambiente protegido. Foram utilizados diferentes fertilizantes, via solo, foliar e fertirrigação, foi utilizada a cultivar Lúcia, o experimento foi instalado em Piracicaba-SP, no período de dez/95 a março/96. O delineamento utilizado foi o aleatorizados em blocos, com três tratamentos e seis repetições. Em média, a planta apresentou na época do florescimento (35 dias após transplante) a seguinte concentração em mg kg-1: 56,1 de B; 107,8 de Cu, 440,4 de Fe; 313,8 de Mn e 194,9 de Zn. Para uma produção de 10.2 kg m-2 a planta extraiu em g m-2: 0,0274 de B; 0,0826 de Cu; 0,1694 de Fe; 0,1702 de Mn e 0,1133 de Zn.Production under protected cultivation is a relatively new practice in Brazil, and the area planted to tomatoes in such an environment is increasing year by year. Along with this growth comes a lack of proper technical information regarding our present stand. This work evaluates the grade and the quantity of micronutrients absolved by the tomato plant in a protected environment. Fertilizers were applied via soil, leaves and fertirrigating, using the cultivar Lucia. The experiment was carried out in Piracicaba, SP, Brazil, from December/95 to March/96. A completely random design was used with three treatments and six replications. At the flowering stage, plants presented the following micronutrient concentrations in mg kg-1: 56.1 of B; 107.8 of Cu, 440.4 of Fe; 313.8 of Mn e 194.9 of Zn. For a yield of 10.2 kg m-2, plants extracted the following quantities of nutrient in g m-2: 0.0274 of B; 0.0826 of Cu; 0.1694 of Fe; 0.1702 of Mn e 0.1133 of Zn

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

Hydroponics cultivation of tomato crop (Lycopersicon esculentum Mill.) using two application systems of the nutrient solution with differents ratios of K:N

O experimento foi conduzido no período de julho a dezembro de 1998, na área experimental do Departamento de Produção Vegetal Horticultura, ESALQ-USP. Plantas de tomate (Lycopersicon esculentum Mill, híbrido Carmem), foram conduzidas em dois sistemas hidropônicos diferentes: NFT (Nutrient Film Technique) e NNT (Nutrient Nebulization Technique), onde as raízes das plantas recebiam solução de forma nebulizada, dentro de tubulações de PVC. Também foram testadas duas relações K:N (2:1 e 3:1). Estas duas relações foram utilizadas durante todo o ciclo e combinadas da seguinte maneira: (2:1/3:1 e 3:1/2:1) sendo uma relação até o início do florescimento e outra até o fim do ciclo. Todas as quatro soluções foram utilizadas nos dois sistemas mencionados totalizando 8 combinações. As plantas foram conduzidas até a 6ª penca e a produção foi avaliada através dos parâmetros peso, número, diâmetro e % de refugo de frutos que foram classificados de acordo com dois sistemas de comercialização: sistema CEAGESP e sistema supermercado). Nas condições do experimento, não houve diferença significativa entre sistemas. Para o sistema NFT, as relações K:N 2:1/2:1, 3:1/3:1 e 2:1/3:1, respectivamente, proporcionaram os melhores resultados. Para o sistema NNT, as relações K:N 3:1/3:1 e 2:1/3:1, respectivamente, proporcionaram os melhores resultadosThe trial was conducted from JuIy to December of 1998, at an experimental area of the Vegetable Production Department, São Paulo University. Tomato plants were grown on two different hydroponics systems: NFT (Nutrient Film Technique) and NNT (Nutrient Nebulization Technique). In NNT, the root systems of the plants received nebulized solution inside PVC pipeline. Two K:N ratios (2: 1 and 3: 1) also were evaluated. These two ratios were used during all cycle and combined as follows: one of the ratio untiI beginning of flowering and the other, until the end of the cycle. Each mentioned systems (NFT and NNT), received four proposed soIutions, totaling eight treatment combinations. All pIants were conducted UntiI 6th raceme. Production characteristics were evaluated based on: weight, number, diameter and cull fruits (%). Two market classification systems were also used (CEAGESP and supermarket ). In the experimental conditions, there was not found any statistical difference at P<0.05 between hydroponics systems. The ratios K:N 2:1/2:1 and 3:1/3:1 (all cycle) and the combination K:N 2:113:1, respectively, showed the best results, for NFT. The ratios K:N 3:1/3:1 (all cyc1e) and the combination 2:1/3:1, respectively, presented the best results, for NNT

Hydroponics cultivation of tomato crop (Lycopersicon esculentum Mill.) using two application systems of the nutrient solution with differents ratios of K:N

O experimento foi conduzido no período de julho a dezembro de 1998, na área experimental do Departamento de Produção Vegetal Horticultura, ESALQ-USP. Plantas de tomate (Lycopersicon esculentum Mill, híbrido Carmem), foram conduzidas em dois sistemas hidropônicos diferentes: NFT (Nutrient Film Technique) e NNT (Nutrient Nebulization Technique), onde as raízes das plantas recebiam solução de forma nebulizada, dentro de tubulações de PVC. Também foram testadas duas relações K:N (2:1 e 3:1). Estas duas relações foram utilizadas durante todo o ciclo e combinadas da seguinte maneira: (2:1/3:1 e 3:1/2:1) sendo uma relação até o início do florescimento e outra até o fim do ciclo. Todas as quatro soluções foram utilizadas nos dois sistemas mencionados totalizando 8 combinações. As plantas foram conduzidas até a 6ª penca e a produção foi avaliada através dos parâmetros peso, número, diâmetro e % de refugo de frutos que foram classificados de acordo com dois sistemas de comercialização: sistema CEAGESP e sistema supermercado). Nas condições do experimento, não houve diferença significativa entre sistemas. Para o sistema NFT, as relações K:N 2:1/2:1, 3:1/3:1 e 2:1/3:1, respectivamente, proporcionaram os melhores resultados. Para o sistema NNT, as relações K:N 3:1/3:1 e 2:1/3:1, respectivamente, proporcionaram os melhores resultadosThe trial was conducted from JuIy to December of 1998, at an experimental area of the Vegetable Production Department, São Paulo University. Tomato plants were grown on two different hydroponics systems: NFT (Nutrient Film Technique) and NNT (Nutrient Nebulization Technique). In NNT, the root systems of the plants received nebulized solution inside PVC pipeline. Two K:N ratios (2: 1 and 3: 1) also were evaluated. These two ratios were used during all cycle and combined as follows: one of the ratio untiI beginning of flowering and the other, until the end of the cycle. Each mentioned systems (NFT and NNT), received four proposed soIutions, totaling eight treatment combinations. All pIants were conducted UntiI 6th raceme. Production characteristics were evaluated based on: weight, number, diameter and cull fruits (%). Two market classification systems were also used (CEAGESP and supermarket ). In the experimental conditions, there was not found any statistical difference at P<0.05 between hydroponics systems. The ratios K:N 2:1/2:1 and 3:1/3:1 (all cycle) and the combination K:N 2:113:1, respectively, showed the best results, for NFT. The ratios K:N 3:1/3:1 (all cyc1e) and the combination 2:1/3:1, respectively, presented the best results, for NNT

Quantidade absorvida e concentrações de micronutrientes em tomateiro sob cultivo protegido

A produção sob cultivo protegido é uma prática relativamente recente no Brasil e a área com tomate nesse ambiente vem crescendo a cada ano, e junto com esse crescimento, cresce também a falta de informações técnicas apropriadas para as nossa condições. Este trabalho avaliou a quantidade absorvida e os teores de micronutrientes pelo tomateiro em ambiente protegido. Foram utilizados diferentes fertilizantes, via solo, foliar e fertirrigação, foi utilizada a cultivar Lúcia, o experimento foi instalado em Piracicaba-SP, no período de dez/95 a março/96. O delineamento utilizado foi o aleatorizados em blocos, com três tratamentos e seis repetições. em média, a planta apresentou na época do florescimento (35 dias após transplante) a seguinte concentração em mg kg-1: 56,1 de B; 107,8 de Cu, 440,4 de Fe; 313,8 de Mn e 194,9 de Zn. Para uma produção de 10.2 kg m-2 a planta extraiu em g m-2: 0,0274 de B; 0,0826 de Cu; 0,1694 de Fe; 0,1702 de Mn e 0,1133 de Zn.Production under protected cultivation is a relatively new practice in Brazil, and the area planted to tomatoes in such an environment is increasing year by year. Along with this growth comes a lack of proper technical information regarding our present stand. This work evaluates the grade and the quantity of micronutrients absolved by the tomato plant in a protected environment. Fertilizers were applied via soil, leaves and fertirrigating, using the cultivar Lucia. The experiment was carried out in Piracicaba, SP, Brazil, from December/95 to March/96. A completely random design was used with three treatments and six replications. At the flowering stage, plants presented the following micronutrient concentrations in mg kg-1: 56.1 of B; 107.8 of Cu, 440.4 of Fe; 313.8 of Mn e 194.9 of Zn. For a yield of 10.2 kg m-2, plants extracted the following quantities of nutrient in g m-2: 0.0274 of B; 0.0826 of Cu; 0.1694 of Fe; 0.1702 of Mn e 0.1133 of Zn