Quality of self-propelled sprayers through periodic inspection

The inspection of agricultural sprayers is a vital tool for the increment of quality of spray technology for phytosanitary products. The objective of this work was to evaluate the performance conditions of self-propelled sprayers on-farm, using the periodical inspection methodology for sprays and analyzing the climatic conditions during the spraying. The evaluations were carried on farms visited randomly. A questionnaire was filled out by the operator or farmers and the inspection itself of the sprayers. The items evaluated were the condition and location of hoses, presence of leaks, monitor performance, spacing between nozzles, spray nozzles, in-line filter, primary filter, the performance of anti-dripping gauges, and limiting environmental conditions. Most interviewed operators and farmers did not know the methodology for inspecting agricultural sprayers, demonstrating the need to create specific training programs in the region. Self-propelled sprayers have a few technical problems when they were new, however, spraying beyond the ideal weather conditions can reduce the spraying quality

Deposition of sprayed drops in soybean in function of sowing spacing

To reach all parts of the plants can be a difficult achievement faced by the drops in several crops. Strategies in crop management such as an increment in the plant spacing can contribute to the spray application's success. This study aimed to evaluate the droplets deposition in soybean, using three different spray nozzles and application rate, in two soybean rows spacing. The experimental design used was the randomized blocks, with treatments arranged in a split-plot scheme. The plots were the interrow spacing (0.45 and 0.76 m), the subplots were the spray nozzles (JA-2 and Magno 11002 BD), and the sub-subplots were the application rate (120, 200 and 280 L ha-1). Droplets coverage was evaluated in the upper, middle and lower thirds of soybean plants. Water-sensitive papers were installed in the adaxial part of plant leaves to analyze the spray technology and evaluated using E-Sprinkle® software. Spraying was performed in plants at the R5.3 soybean stage. This experiment evaluated the following parameters: the volume median diameter, the density of droplets per cm2, the droplet coverage area, and the droplet percentage less than 150 µm. The increase in the soybean row spacing combined with the spray volume increase provided greater droplet coverage in the middle third in soybean crop. The Magno 11002 BD droplet nozzle provided the higher droplet coverage in the row spacing of 0.76 m. The spray rate of 280 L ha-1 provided the highest density of droplets per cm2 in the lower third and greater coverage in the middle-third

Deposition of spray droplets by four spray nozzles and two working pressures

The deposition of spray droplets on the target can be influenced by the type spray nozzle used, as well as the employed working pressure. Thus, with the commercial availability of new spray nozzles, performance studies with the new models become necessary. This study was carried out to evaluate ground deposition of spray droplets by four models of spray nozzles, at two working pressures. The experiment was conducted in Dourados/MS, in September 2020, with strip design and 4x2 factorial scheme, with five repetitions. Four spray nozzles were used (single flat fan, ST-IA 02 model; angle flat fan MUG 02; hollow cone MGA 02; and double flat fan ST-IA/D 02), working at 30 and 50 psi pressures. The distance between each nozzle was 50 cm, 60 cm above the ground. Water-sensitive paper was used and, immediately after application, the paper was scanned using the DropScan® tool. Subsequently, the number of droplets, coverage, amplitude, dispersion,      Volume Median Diameter (VMD), Number Median Diameter (NMD), DV09, and DV01 were evaluated. The hollow cone spray nozzle provided a higher number of droplets and greater coverage compared to the other nozzles for the studied weather conditions

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Detecção de sementes de soja geneticamente modificada por meio de teste de germinação

O trabalho teve como objetivo a detecção de sementes de soja geneticamente modificadas utilizando teste de germinação. O experimento foi baseado no teste de germinação, utilizando como tratamento duas variedades de soja (BRS 245RR e CD202) e cinco concentrações da solução de glifosato (15, 30, 45, 60 e 75%) e uma testemunha (água destilada). As sementes de soja foram alocadas em papel germitest pré-embebidos em solução de glyphosate de acordo com os tratamentos e levadas ao germinador.  Foram avaliadas a porcentagem de germinação de plântulas normais, anormais e sementes mortas, comprimento de radícula, hipocótilo e epicótilo. A cultivar de soja convencional apresentou as menores porcentagens de plântulas normais e maiores de plântulas anormais e sementes mortas. Houve redução do comprimento de radícula, hipocótilo e epicótilo proporcional ao aumento da concentração de solução de glifosato, sendo mais drástica na cultivar não geneticamente modificada. A utilização de teste de germinação com substrato embebido em solução de glyphosate é eficiente para detectar sementes de soja geneticamente modificadas resistentes ao glyphosate. Recomenda-se a semeadura em substrato umidecido com solução de glyphosate na concentração de 45%

Desenvolvimento inicial do milho após diferentes manejos de nabo forrageiro

O nabo forrageiro tem sido utilizado como cobertura vegetal, entretanto, pouco se sabe sobre o manejo adequado desta espécie e seus efeitos sobre a cultura seguinte. Nesse sentido, objetivou-se neste trabalho avaliar o desenvolvimento inicial da cultura do milho após diferentes manejos do nabo forrageiro. O experimento foi realizado em Dourados-MS, em delineamento de blocos casualizados, com quatro repetições. Os tratamentos foram constituídos por quatro manejos do nabo forrageiro antecedendo a semeadura do milho: aplicação de herbicida três dias antes da semeadura (DS), aplicação de herbicida seis DS, manejo com Triton® três DS, manejo com Triton® seis DS e um tratamento sem a cobertura vegetal de nabo forrageiro. Foram realizadas avaliações de porcentagem de emergência acumulada, altura de plantas, teor de clorofila, área foliar e matéria seca das plantas de milho. O desenvolvimento inicial da cultura do milho é afetado pelo manejo do nabo forrageiro e a semeadura do milho deve ser realizada após seis dias do manejo do nabo forrageiro, seja por herbicida ou pelo desintegrador