Dinâmica e variabilidade espacial de plantas daninhas em sistemas de mobilização do solo em sorgo forrageiro

Nagahama, H.J.; Cortez, Jorge Wilson; Concenço, Germani; Araújo, Victor Fonseca de; Honorato, Alan Cunha

doi:10.1590/s0100-83582014000200003

Cited by 6 publications

(9 citation statements)

References 14 publications

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“…2018 agricultural and forestry machineries (Guerrieri et al, 2017). The studies about the impact of mechanization through soil tillage on the behavior of weeds were efficient based on the similarity (CA) of the weeds (Boscardin et al, 2016;Nagahama et al, 2014). Azevedo et al (2015) and Silva & Lima (2012) investigated, respectively, the selection of lettuce cultivars and the nutritional status and productivity of coffee plants, and showed the efficiency of mult ivariate analysis, because the effects of multip le variables (random and interrelated) could not be interpreted separately.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Peanut Mechanized Harvesting

et al. 2018

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The peanuts harvesting mechanization is affected by the soil physical characteristics and it may increase the losses due to the production of pods in subsurface. The objective of the experiment was to identify the clusters through multivariate exploratory approaches from similarity in six soil textures (very clayey, clayey, silty clayey loam, clayey loam, sandy loam and sandy) in the state of São Paulo, Brazil, determining the main agronomic variables that most influenced the clustering division to assist the decision-making process in peanuts mechanized harvesting. The data were analyzed by the multivariate exploratory that is performed to simplify the description of a set of interrelated variables, using: yield, maturity, soil and pod moisture content, windrow width and height, visible and invisible digging losses, and gathering losses, as agronomic indicators of quality. The low and high clay content were grouped into clusters I and III, respectively, according to the agronomic traits of the peanut crop. The principal components analysis (PC) allowed a single distribution of accesses since only two eigenvalues were higher than "one": the highest eigenvalues of 4.51 and 1.79, resulted in a Biplot that explained 70% of the original variability, 50.11% and 19.89% of which in the PC1 and PC2, respectively. The multivariate analysis indicated that high peanut yields in soils with low clay are correlated with the losses during the peanut mechanized harvesting operation.

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Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Peanut Mechanized Harvesting

et al. 2018

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“…The first assumption was inspired by the fact that, in models with sill, there is similarity between the sample variance and the sample sill (Trangmar, Yost, Uehara, 1985;Lima, G. et al, 2014;Nagahama et al, 2014;Lima, J. et al, 2014;Jordão et al, 2015). So that, it was considered as possible to extend this idea to the power model, turning the sample variance approximately equal to the value of sample equivalent sill, in other words, the sample variance could be considered as the estimate of the equivalent sill.…”

Section: Methodsmentioning

confidence: 99%

Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

et al. 2017

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Abstract:In Geostatistics, the use of measurement to describe the spatial dependence of the attribute is of great importance, but only some models (which have second-order stationarity) are considered with such measurement. Thus, this paper aims to propose measurements to assess the degree of spatial dependence in power model adjustment phenomena. From a premise that considers the equivalent sill as the estimated semivariance value that matches the point where the adjusted power model curves intersect, it is possible to build two indexes to evaluate such dependence. The first one, SPD * , is obtained from the relation between the equivalent contribution (α) and the equivalent sill (C * = C0 + α), and varies from 0 to 100% (based on the calculation of spatial dependence areas). The second one, SDI * , beyond the previous relation, considers the equivalent factor of model (FM * ), which depends on the exponent β that describes the force of spatial dependence in the power model (based on spatial correlation areas). The SDI * , for β close to 2, assumes its larger scale, varying from 0 to 66.67%. Both indexes have symmetrical distribution, and allow the classification of spatial dependence in weak, moderate and strong.Keywords: Geostatistics; Variographic analysis; Semivariogram without sill; Spatial dependence indexes. Resumo:Em geoestatística, a utilização de medidas que descrevam a dependência espacial do atributo é de grande importância, porém apenas alguns modelos (que possuem estacionariedade de segunda ordem) são contemplados com tais medidas. Assim, este trabalho tem como objetivo propor medidas para avaliação do grau de dependência espacial em fenômenos com ajuste de modelo Barbosa, I. C. et al. 462 Bull. Geod. Sci, Articles section, Curitiba, v. 23, n°3, p.461 -475, Jul -Sept, 2017. potência. A partir de uma premissa que considera o patamar-equivalente como o valor de semivariância que coincide com o ponto em que as curvas ajustadas do modelo potência se interceptam, pode-se construir dois índices para avaliação de tal dependência. O primeiro, DE * , é obtido a partir da relação entre a contribuição-equivalente (α) e o patamar-equivalente (C * = C0 + α), e varia de 0 a 100% (baseado no cálculo de áreas de dependência espacial). O segundo, IDE * , além da relação anterior, considera um fator de modelo equivalente (FM * ), que depende do expoente β, o qual descreve a força da dependência espacial no modelo potência (baseado em áreas de correlação espacial). O IDE * , para β próximo de 2, assume sua maior escala, variando de 0 a 66.67%. Ambos os índices possuem distribuição simétrica, e permitem a classificação da dependência espacial em fraca, moderada e forte.

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“…In the first one, there was less straw and more disturbance by the fertilizer shank. Soil mobilization, regardless of the method, promotes greater weed density, indicating a greater care in additional control prior to sowing in order to avoid reduced yield caused by weeds (Nagahama et al, 2014). Regarding the species Raphanus raphanistrum, in the 2015/16 crop, the highest density also occurred in the chopped straw, which was not repeated in the following year, where the straw management had no effect on the occurrence of this species.…”

Section: /9mentioning

confidence: 99%

Management of superficial straw associate with strategy of physical improvement of the soil in the emergence of weeds

Rosa

Nunes

Alves

et al. 2021

Agraria

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The objective of this work was to qualify the improvement of the physical properties of the soil and the reduction of weeds by combining the management of the superficial cover associated with a strategy of physical improvement soil in the drilling, seeking the maintenance of the no-tillage system. The experiment was carried out in the 2015/16 and 2017/18 harvests, using a randomized block design in a two-factorial scheme (3 2) with three managements of the ground cover (chopped, rolled and natural) and two depths of planter's shank (7 and 12 cm). Counts and identification of weeds emerged in the row and between the lines of the soybean crop at 20 and 40 days after emergence, bulk density, mechanical resistance of the soil to penetration and total soil porosity in the flowering of the soybean culture were measured. The management of the straw cover combined with the depth of action of the planter shank influenced the emergence of weeds, as well as, the increase in the depth of the furrow improvement of the physical environment of the soil in the sowing line, reducing penetration resistance, bulk density and increasing total porosity.

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Dinâmica e variabilidade espacial de plantas daninhas em sistemas de mobilização do solo em sorgo forrageiro

Cited by 6 publications

References 14 publications

Multivariate Analysis of Peanut Mechanized Harvesting

Multivariate Analysis of Peanut Mechanized Harvesting

Proposal of the Spatial Dependence Evaluation From the Power Semivariogram Model

Management of superficial straw associate with strategy of physical improvement of the soil in the emergence of weeds

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