Herbicide Selectivity In The Early Development Of Alexander Palm And Peach Palm

Herbicides are an efficient weed-control method, and herbicide selectivity with regard to palm species is an important subject of agricultural research. Owing to a lack of studies in the literature regarding the use of herbicides on palm trees, especially during the early stages of growth, the present study aimed to evaluate the selectivity of some herbicides during the early development of Alexander palm (Archontophoenix alexandrae) and peach palm (Bactris gasipaes) seedlings. The study was conducted in two seasons in a completely randomized design with eight treatments and four repetitions. The herbicide treatments and dosages (g i.a. ha-1), were as follows: fluazifop-p-butyl (93.8), sethoxydim (184.0), quizalofop-p-ethyl (75.0) clethodim + fenoxaprop-p-ethyl (50.0 + 50.0), fomesafen (225.0), lactofen (168.0), and nicosulfuron (50.0), and a no-herbicide control was included. The seedlings of both types of trees were transplanted into 3.1-L plastic containers. In the first study, herbicide was applied to Alexander palm seedlings of 25-30 cm in height. In the second study, herbicide was applied to Alexander palm seedlings of 30-40 cm in height. Herbicide was applied to peach palm tree seedlings of 40-55 cm in height in both studies. In peach palms only, the herbicides caused slight visible damage during early development. Collectively, the results suggested that all herbicides used are selective and can be used on peach palms during the various stages of development when there are one to four leaves. For Alexander palms, fluazifop-p-butyl, quizalofop-p-ethyl, and lactofen were the only herbicides that did not affect early development during the stages when the plant had one to four leaves.3752891290

Use of Prohexadione Calcium on Grass Species Development

<div><p> ABSTRACT: This study has aimed to evaluate the effect of prohexadione-calcium as a plant growth regulator on growth and quality of Japanese lawn grass, Broadleaf carpet grass and Bermudagrass. The treatments and doses of the prohexadione-calcium tested with two three reapplications were 27.5, 55.0, 110.0, 165.0 and 165.0 g a.i. ha-1, and a control without application of a growth regulator. Visual injury evaluations were performed using a scale of scores and plant height and chipping dry matter were determined. We have evaluated the total thickness of the sod grass, root length and dry matter of this material at the end of the experimental period. The study was arranged in a completely randomized design with four replications. The results were submitted to analysis of variance by F-test and the averages of the treatments were compared by t test at 5% probability. The prohexadione-calcium plant growth regulator was visually selective and reduced shoot growth of the three species of grass and can thus be used in the management of lawns in gardens and sports areas. As for the production of sod grass, the prohexadione-calcium can be recommended for Japanese lawn grass and Broadleaf sod grass, especially doses 165.0+165.0 and 55.0+55.0+55.0 respectively, because they have provided a better quality sod grass. For Bermudagrass, the effect of the treatments was harmful for the quality of sod grass and is not recommended for production purposes.</p></div

Estimativa de estro em vacas leiteiras utilizando métodos quantitativos preditivos Dairy cows estrus estimation using predictive and quantitative methods

O Brasil é o sexto maior produtor de leite do mundo, sendo que essa produção cresce a uma taxa anual 4% superior aos demais países produtores. Parte desse aumento na produção de leite deve-se ao uso de diversas tecnologias desenvolvidas para o setor, principalmente, aquelas relacionadas à genética e ao manejo do rebanho. A detecção acurada do cio em vacas é um fator limitante na eficiência reprodutiva do rebanho leiteiro, sendo considerada uma das principais deficiências na reprodução bovina. Falha na identificação do estro com eficiência ocasiona perdas para o produtor. Métodos quantitativos preditivos, baseados em dados históricos e conhecimento especialista, permitem, a partir de uma base de dados organizada, a predição de padrões com baixa percentagem de erro. Este trabalho comparou a precisão das técnicas de estimativa de estro para vacas da raça Holandesa alojadas em galpão freestall, utilizando métodos quantitativos preditivos, por meio da interposição dos pontos intermediários provenientes de série histórica do rebanho. Uma base de regras foi formulada sendo que os valores dos pesos de cada afirmação pertencem a um intervalo de zero a um, e esses limites foram utilizados para gerar a função de pertinência Fuzzy, cuja saída era a predição de estro. Na etapa seguinte, foi aplicada a técnica de Data mining utilizando os parâmetros de movimentação, produção de leite, dias de lactação e comportamento de monta, sendo gerada uma árvore de decisão para analisar os parâmetros mais significativos na previsão de estro em vacas leiteiras. Os resultados indicaram que a presença de estro pode ser detectada com maior precisão usando a observação de movimentação das vacas (87%, erro estimado 4%) ou o comportamento de monta (78%, erro estimado 11%).<br>Brazil is the sixth world’s larger milk producer, increasing its production at an annual rate of 4% above other producer countries. Part of this raise in milk production was due to the use of several technologies that have being developed for the sector, mainly those related to genetics and herd management. Accurate estrus detection in dairy cows is a limiting factor in the reproduction efficiency of dairy cattle, and it has been considered the most important deficiency in the field of reproduction. Failing to detect estrus efficiently may cause losses for the producer. Quantitative predictive methods based on historical data and specialist knowledge may allow, from an organized data base, the prediction of estrus pattern with lower error. This research compared the precision of the estrus prediction techniques for freestall confined Holstein dairy cows using quantitative predictive methods, through the interpolation of intermediate points of historical herd data set. A base of rules was formulated and the values of weight for each statement is within the interval of 0 to 1; and these limits were used to generate a function of pertinence fuzzy that had as output the estrus prediction. In the following stage Data mining technique was applied using the parameters of movement rate, milk production, days of lactation and mounting behavior, and a decision tree was built for analyzing the most significant parameters for predicting estrus in dairy cows. The results indicate that the prediction of estrus incidence may be achieved either using the association of cow’s movement (87%, with estimated error of 4%) or the observation of mounting behavior (78%, with estimated error of 11%)