Probabilidade De Ocorrência Dos Atributos Químicos Em Um Latossolo Sob Plantio Direto

Dias, Márcio José; Alves, Sandra; Reis, Elton Fialho dos; Oliveira, Danilo Gomes de

doi:10.1590/1983-21252015v28n420rc

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…The coefficients of variation (Table 1) for Al 3+ and m% were above 200%, which is a high value. Dias et al (2015) also verified high CVs for Al 3+ , as well as mean and median values equal to zero. On the other hand, pH, V%, and sand content presented low CVs, and the other properties showed medium values as classified by Warrick and Nielsen (1980) for field experiments.…”

Section: Resultssupporting

confidence: 65%

“…Likewise, Dias et al (2015) found similar values for pH in the State of Goiás, when performing experiments in Latosols (Oxisols), with a mean of 5.46, a median of 5.50, and a CV of 3.72%. In the same research, these authors also observed CVs for K and V% that are similar to those observed here, but with distinct mean, median, maximum and minimum values.…”

Section: Resultssupporting

confidence: 55%

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Estimation of Physical and Chemical Soil Properties by Artificial Neural Networks

2018

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Soil physical and chemical analyses are relatively high-cost and time-consuming procedures. In the search for alternatives to predict these properties from a reduced number of soil samples, the use of Artificial Neural Networks (ANN) has been pointed out as a great computational technique to solve this problem by means of experience. This tool also has the ability to acquire knowledge and then apply it. This study aimed at using ANNs to estimate the physical and chemical properties of soil. The data came from the physical and chemical analysis of 120 sampling points, which were submitted to descriptive analysis, geostatistical analysis, and ANNs training and analysis. In the geostatistical analysis, the semivariogram model that best fitted the experimental variogram was verified for each soil property, and the ordinary kriging was used as an interpolation method. The ANNs were trained and selected based on their assertiveness in the mapping of considered standards, and then used to estimate all soil properties. The mean errors of ordinary kriging estimates were compared to those of ANNs and then compared to the original values using Student's t-Test. The results showed that the ANN had an assertiveness compatible with ordinary kriging. Therefore, such technique is a promising tool to estimate soil properties using a reduced number of soil samples.

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Section: Resultssupporting

confidence: 65%

Section: Resultssupporting

confidence: 55%

Estimation of Physical and Chemical Soil Properties by Artificial Neural Networks

2018

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“…The spatial variability of soil attributes occurs naturally due to the morphological characteristics of the relief and the complex physical-chemicalbiological interactions in its interior that result from the process of formation, in addition, this variation or heterogeneity increases through management practices of livestock activities (ARTUR et al, 2014;DIAS et al, 2015;SANTOS et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Sampling density to detect spatial dependence of potassium, calcium and magnesium in sandy soils

Batista

Oliveira

Folador

et al. 2022

Reveng

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The sampling grid density for georeferenced soil collection must be large enough to allow the identification of the spatial dependence of attributes with representative accuracy of the cultivated area, but not large enough to make fertility mapping unfeasible. The objective of this study was to define, from the evaluation of geostatistical parameters obtained from a super dense soil sampling, an efficient grid for detecting the spatial dependence of potassium (K+), calcium (Ca2+), and magnesium (Mg2+) in a sandy soil. The experiment was conducted in a 3.2 hectare annatto crop (Bixa orellana L.), in 2017. The geostatistical grid consisted of 31 points per hectare, totaling 101 georeferenced points in an 18x18 m spacing. Soil was sampled at the depths of 0-0.20 m and 0.20-0.40 m. A strong spatial dependence was found for all soil attributes in both depths, while the semivariograms fitted to the spherical model with good coefficients of determination (R²) indicating a spatial correlation between the attributes. The range of spatial dependence was close to 100 m for all attributes in both layers. In sandy soils, an efficient sampling grid to detect the spatial dependence of K+, Ca2+ and Mg2+ must consider a semivariogram range of approximately 100 meters.

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“…As áreas de cultivo no sistema de plantio direto vêm aumentando consideravelmente em razão dos benefícios, uma vez que o não revolvimento do solo e a manutenção da palhada, proporcionam maior infiltração de água e mantém o solo úmido (DIAS et al, 2015). Entretanto, os produtores que estão utilizando essa prática, vem alcançado altas produtividades com as culturas: o milho (Zea mays L.), o trigo (Triticum sativum L.) e a soja (Glycine max L.) que são de grande importância econômica (MARTIN et al, 2011).…”

Section: Introductionunclassified

Sistema De Plantio Direto Utilizando a Bidens Pilosa Com Diferentes Profundidades De Semeadura E Influência Sob a Qualidade Fisiológica

Santos

Hendges

Bernardi

et al. 2020

BJD

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Bidens pilosa L. is a weed that directly interferes with the potential of crops, competing for water, light and nutrients, reducing quality and productivity. The adoption of management practices aims to position the crop in a competitive and advantageous situation in relation to weeds, which is an alternative to eliminate the use of herbicides. For this reason, several studies use straw on the surface of the soil, as well as efficiency in depth in the deposition of seeds, as these factors directly influence the physiology of weeds, and contribute to the maximum yield of the crops. Therefore, the present study aimed to evaluate the no-tillage system using B. pilosa at different sowing depths and the influence on the physiological quality. The experiment was conducted in a greenhouse belonging to the Pontifical Catholic University of Paraná, Campus Toledo-PR. Sowing was performed manually with 50 seeds per vase with a capacity of 5 liters, containing soil, sand and organic matter in a 2:1:1 ratio. The straws were collected in commercial crops and dried in a forced circulation oven at 62 °C for 96 hours, using 3 t ha-1 for wheat, 6 t ha-1 for corn, considering the area of the pots and arranged superficially. For seven days, the germination test (G) was performed. On the 24 th day after implantation of the experiment, emergence (E), the total length of the seedlings (TLS) and the dry mass (DM) of the seedlings were performed. The design was in randomized blocks, with a 3x4 factorial, with three types of cover (without straw, corn straw and wheat straw) and four sowing depths (0, 2, 4 and 6 cm) and four replications. The results were submitted to analysis of variance and when significant. For the non-significant interaction, the qualitative data were submitted to the Tukey test (p≤0.05) and the quantitative data, the regression test (p≤0.01) was performed in the SISVAR ® Statistical Program. However, studies on no-tillage and the correlation with sowing depths show important evaluations in the physiology of weeds, for example: germination and seedling emergence, which are growth variables that reflect on the inference of the effect of the depth of sowing and compromise the expression of seed vigor and seedling length, since they are important assessments for estimating growth and development, as well as their direct effects on crops, and from these results, attempts are made to seek solutions within alternative management, in order to reduce growth and consequently try to control weeds, in order to ensure efficiency in the yield of economically important crops. The no-tillage system using corn straw at greater sowing depths influenced the lower performance in the physiology of Bidens pilosa.

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Probabilidade De Ocorrência Dos Atributos Químicos Em Um Latossolo Sob Plantio Direto

Cited by 10 publications

References 16 publications

Estimation of Physical and Chemical Soil Properties by Artificial Neural Networks

Estimation of Physical and Chemical Soil Properties by Artificial Neural Networks

Sampling density to detect spatial dependence of potassium, calcium and magnesium in sandy soils

Sistema De Plantio Direto Utilizando a Bidens Pilosa Com Diferentes Profundidades De Semeadura E Influência Sob a Qualidade Fisiológica

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