NUGGET EFFECT INFLUENCE ON SPATIAL VARIABILITY OF AGRICULTURAL DATA

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.

Download Full-text

Temporal variability of available phosphorus, potassium and magnesium in arable soil

Plant Soil and Environment ◽

10.17221/4072-pse ◽

2011 ◽

Vol 50 (No. 12) ◽

pp. 547-551 ◽

Cited By ~ 5

Author(s):

K. Štípek ◽

V. Vaněk ◽

J. Száková ◽

J. Černý ◽

J. Šilha

Keyword(s):

Spatial Variability ◽

Coefficient Of Variation ◽

Temporal Variability ◽

Soil Samples ◽

Arable Soil ◽

Soil Parameters ◽

Available Phosphorus ◽

Nugget Effect ◽

Available Nutrients ◽

Field Variability

The investigation is focused on the illustration of the temporal changes in spatial variability of the Mehlich III available phosphorus, potassium and magnesium in the 10.5 hapart of the whole 54 hafield, located near Český Brod. Soil is characterized as Orthic Luvisol. To estimate temporal variability of available nutrients, soil samples were taken in 2001–2003 four times per year (twice in spring and twice in the autumn periods). For the description of field variability of selected soil parameters coefficient of variation (CV), experimental variograms with fitted models and relative nugget effect parameters have been used. Three year results shows that the lowest temporal variability, characterized by relative nugget effect had available Mg (4–23%) and P (13–29%) and K (15–49%).

Download Full-text

A genetic algorithm for resizing and sampling reduction of non-stationary soil chemical attributes optimizing spatial prediction

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2021194-17877 ◽

2021 ◽

Vol 19 (4) ◽

pp. e0210-e0210

Author(s):

Tamara C. Maltauro ◽

Keyword(s):

Genetic Algorithm ◽

Spatial Variability ◽

Sample Size ◽

Simulated Data ◽

Optimization Process ◽

Dependence Structure ◽

Nugget Effect ◽

Accuracy Measure ◽

Chemical Attributes ◽

Sample Configuration

Aim of study: To evaluate the influence of the parameters of the geostatistical model and the initial sample configuration used in the optimization process; and to propose and evaluate the resizing of a sample configuration, reducing its sample size, for simulated data and for the study of the spatial variability of soil chemical attributes under a non-stationary with drift process from a commercial soybean cultivation area. Area of study: Cascavel, Brazil Material and methods: For both, the simulated data and the soil chemical attributes, the Genetic Algorithm was used for sample resizing, maximizing the overall accuracy measure. Main results: The results obtained from the simulated data showed that the practical range did not influence in a relevant way the optimization process. Moreover, the local variations, such as variance or sampling errors (nugget effect), had a direct relationship with the reduction of the sample size, mainly for the smaller nugget effect. For the soil chemical attributes, the Genetic Algorithm was efficient in resizing the sampling configuration, since it generated sampling configurations with 30 to 35 points, corresponding to 29.41% to 34.31% of the initial configuration, respectively. In addition, comparing the optimized and initial configurations, similarities were obtained regarding spatial dependence structure and characterization of spatial variability of soil chemical attributes in the study area. Research highlights: The optimization process showed that it is possible to reduce the sample size, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in future experiments.

Download Full-text

Sampling and mapping of heterogeneous surfaces: multi-resolution tiling adjusted to spatial variability

International Journal of Geographical Information Systems ◽

10.1080/026937996137756 ◽

1996 ◽

Vol 10 (7) ◽

pp. 851-876

Author(s):

Ferenc Csillag ◽

Miklos Kertesz ◽

Agnes Kummert

Keyword(s):

Spatial Variability ◽

Heterogeneous Surfaces

Download Full-text

Spatial variability induces generalization in contextual cueing.

Journal of Experimental Psychology Learning Memory and Cognition ◽

10.1037/xlm0000796 ◽

2020 ◽

Vol 46 (12) ◽

pp. 2295-2313

Author(s):

Yoko Higuchi ◽

Yoshiyuki Ueda ◽

Kazuhisa Shibata ◽

Jun Saiki

Keyword(s):

Spatial Variability ◽

Contextual Cueing

Download Full-text

Spatial variability in responses to environmental conditions in Southern Hemisphere long-finned pilot whales

Marine Ecology Progress Series ◽

10.3354/meps13109 ◽

2019 ◽

Vol 629 ◽

pp. 207-218 ◽

Cited By ~ 1

Author(s):

V Hamilton ◽

K Evans ◽

B Raymond ◽

E Betty ◽

MA Hindell

Keyword(s):

Spatial Variability ◽

Southern Hemisphere ◽

Environmental Conditions ◽

Pilot Whales

Download Full-text

DISTRIBUIÇÃO DA EVAPORAÇÃO EM ESTUFA PLÁSTICA NA PRIMAVERA

Irriga ◽

10.15809/irriga.2001v6n3p120-127 ◽

2001 ◽

Vol 6 (3) ◽

pp. 120-127

Author(s):

Reginaldo Ferreira Santos ◽

Antonio Evaldo Klar

Keyword(s):

Spatial Variability ◽

Pan Evaporation ◽

Spring Season ◽

Class A ◽

Agricultural Sciences ◽

E Mail ◽

Center Area ◽

Class A Pan

DISTRIBUIÇÃO DA EVAPORAÇÃO EM ESTUFA PLÁSTICA NA PRIMAVERA Reginaldo Ferreira SantosCentro de Ciências Exatas e Tecnológica da UNIOESTE- CP 711CEP 858114-110, Cascavel, PR - Fone: 0XX45 2203155. E-mail: [email protected] Evaldo KlarDepartamento de Engenharia Rural - Faculdade de Ciências Agronômica- UNESP - CEP 18603-970 - Botucatu, SP. CP: 237. E-mail: [email protected] 1 RESUMO O presente trabalho teve como objetivo avaliar a distribuição da evaporação no interior de uma estufa plástica, com uma cultura de pimentão, através da variabilidade espacial e comparar a evaporação dos microevaporímetros com os valores do Tanque classe "A". O experimento foi conduzido no Campus da Universidade Estadual Paulista - FCA/UNESP, no período de primavera, em estufa plástica de polietileno de baixa densidade (PEBD). Na distribuição da evaporação em estufa com orientação norte/sul, verificou-se que as maiores evaporações ocorreram nas extremidades sul e norte tendente ao lado oeste. Já as menores evaporações localizaram-se no centro. No período de primavera, a evaporação média nos microevaporímetros superestimou em 55% a evaporação determinada no Tanque classe "A". UNITERMOS: evaporação, geoestatística, estufa. SANTOS, R.F, KLAR, A.E. EVAPORATION DISTRIBUTION INSIDE A PLASTIC TUNNEL IN THE SPRING SEASON 2 ABSTRACT The main aim of this study was to verify the evaporation distribution inside a plastic tunnel, with pepper crop, oriented to north/south, through spatial variability and to compare Class A Pan evaporation to punctual evaporations of 40 equidistant microevaporimeters placed from 50cm the soil. The study was carried out at the College of Agricultural Sciences/UNESP, Botucatu – SP in the spring season. The highest evaporation occurred next to north and to south sides of the tunnel, with tendency to west. Consequently, the lowest evaporations occurred at the center area. The microevaporimeter evaporations were 55% higher than those obtained from Class A Pan. KEYWORDS: evaporation distribution, microevaporimeter.

Download Full-text