Tag Archives: geostatistics

xxx L. M. dos Santos, G.A.S. Ferraz, H.J.P. Alves, J.D.P. Rodrigues, S. Camiciottoli, L. Conti and G. Rossi
Comparison of spatial-temporal analysis modelling with purely spatial analysis modelling using temperature data obtained by remote sensing
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Comparison of spatial-temporal analysis modelling with purely spatial analysis modelling using temperature data obtained by remote sensing

L. M. dos Santos¹*, G.A.S. Ferraz¹, H.J.P. Alves², J.D.P. Rodrigues³, S. Camiciottoli⁴, L. Conti⁴ and G. Rossi⁴

¹Federal University of Lavras, Department of Agricultural Engineering, University Campus, BR37.200-000 Lavras-MG, Brazil
²Institute of Applied Economic Research- IPEA, Rio de Janeiro, BR 20071-900
Rio de Janeiro, Brazil
³Geoprocessing Analyst, Bracell, Lençois Paulistas, BR17120-000 São Paulo, Brazil
⁴University of Firenze, Department of Agriculture, Food, Environment and Forestry, Via San Bonaventura, 13, Firenze, Italy
*Correspondence: luanna_mendess@yahoo.com.br

Abstract:

Variations in climatic elements directly affect the productivity of agricultural activities. Temperature is one of the climatic elements that varies in space and time. Therefore, understanding spatial variations in temperature is essential for many activities. Given the above, the objective of this work was to compare the performance of the proposed spatiotemporal analysis model with that of purely spatial analysis using temperature data obtained by remote sensing. The experimental data were arranged in a grid with 403 spatial locations, with 22 samples collected in a 24-hour period. The statistical software R Core Team (2020) was used to perform the analysis. The packages used in the analyses were ‘geoR’, ‘CompRandFld’, ‘scatterplot3d’, and ‘fields’. For making the maps, the software ArcGIS was used. The behavioural analysis of spatiotemporal dependence indicated, through the covariogram graph of the data, that there is a strong spatial dependence. For the cases of purely spatial analysis of phenomena, a separate spatial model for each time is justified because this type of model presents a smaller prediction error and requires simpler processing than the space-time model. It was possible to compare the space-time analysis with the purely spatial analysis using temperature data obtained by remote sensing images. The data modelled with the purely spatial analysis had, on average, lower error than those with the space-time model.

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783–796 C.E.A. Oliveira, F.A. Damasceno, P.F.P. Ferraz, J.A.C. Nascimento, G.A.S. Ferraz and M. Barbari
Geostatistics applied to evaluation of thermal conditions and noise in compost dairy barns with different ventilation systems
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Geostatistics applied to evaluation of thermal conditions and noise in compost dairy barns with different ventilation systems

C.E.A. Oliveira¹, F.A. Damasceno¹*, P.F.P. Ferraz¹, J.A.C. Nascimento¹, G.A.S. Ferraz¹ and M. Barbari²

¹Federal University of Lavras, Engineering Department, BR37200-000, Lavras - Minas Gerais, Brazil
²University of Florence, Department of Agriculture, Food, Environment and Forestry, Via San Bonaventura, 13, IT50145 Firenze, Italy
*Correspondence: flavio.damasceno@deg.ufla.br

Abstract:

The objective of this work was to evaluate the spatial distribution of thermal conditions and bed variables in compost dairy barns with different ventilation systems, through the technique of geostatistics. The experiment was conducted in April 2017, in farms located in Madre de Deus, Minas Gerais, Brazil. Three facilities were evaluated with different ventilation systems: natural (NV); mechanical of low volume and high speed (LVHS); and mechanical of high volume and low speed (HVLS). The interior of the premises was divided into 40 meshes equidistant points, in which air temperature, relative humidity and air speed were manually collected. Geostatistics technique was used to assess the spatial dependence of the variables. The results showed the occurrence of dependence and spatial variability of the variables evaluated. Based on thermal comfort indexes, it was concluded that dairy cows were under stress conditions during the hottest hours of the day in the three animal facilities evaluated. The results obtained allow us to understand that the thermal environment is more influenced by the ventilation system adopted.

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385–395 F.A. Damasceno, C.E.A Oliveira, G.A.S Ferraz, J.A.C Nascimento, M Barbari and P.F.P Ferraz
Spatial distribution of thermal variables, acoustics and lighting in compost dairy barn with climate control system
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Spatial distribution of thermal variables, acoustics and lighting in compost dairy barn with climate control system

F.A. Damasceno¹*, C.E.A Oliveira¹, G.A.S Ferraz¹, J.A.C Nascimento¹, M Barbari² and P.F.P Ferraz¹

¹Federal University of Lavras, Engineering Department, BR37200-000 Lavras, Minas Gerais, Brazil
²University of Florence, Department of Agriculture, Food, Environment and Forestry, Via San Bonaventura, 13, IT50145 Firenze, Italy
*Correspondence: flavio.damasceno@deg.ufla.br

Abstract:

The main objective of this research was to evaluate the spatial distribution of the thermal variables, acoustics and lighting in climate controlled compost dairy barn. The experiment was conducted in October 2017, in a farm located in the west of Minas Gerais state, Brazil. For the study, the interior of the animal facility was divided into 120 meshes equidistant points, in which air temperature (tdb), relative humidity (RH), noise, illuminance, and air speed (Vair) were manually collected. The technique of geostatistics was used to evaluate the distribution and spatial dependence of variables. Spatial distribution maps showed the occurrence of high variability of attributes and content within the animal facility. Thermal environment variables showed alert situations throughout practically the entire facility. The noise and luminance levels were within the recommended values.

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1630–1638 G.A.S. Ferraz, R.C. Avelar, N.L. Bento, F.R. Souza, P.F.P. Ferraz, F.A. Damasceno and M. Barbari
Spatial variability of soil fertility attributes and productivity in a coffee crop farm
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Spatial variability of soil fertility attributes and productivity in a coffee crop farm

G.A.S. Ferraz¹*, R.C. Avelar², N.L. Bento¹, F.R. Souza³, P.F.P. Ferraz¹, F.A. Damasceno¹ and M. Barbari⁴

¹Federal University of Lavras – UFLA, Departament of Agricultural Engineering, University Campus, Postal Code 37200-000 Lavras-MG, Brazil
²AVELAR ASSESSORIA LTDA - Consultoria em Cafeicultura – Monte Carmelo – MG, Brazil
³Rural University of Rio de Janeiro – UFRJ, BR-465, Km 7, Postal Code 23.897000 Seropédica-RJ, Brazil
⁴Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze, Via San Bonaventura, 13, IT50145 Firenze, Itália
*Correspondence: gabriel.ferraz@ufla.br

Abstract:

Coffee cultivation is of great importance to Brazilian agribusiness, as coffee occupies extensive production areas and is one of the most exported Brazilian products. To maintain coffee production numbers, productive techniques must be adopted that optimize productive system use. The objective of this work was to apply geostatistical techniques in the evaluation of soil fertility attributes to construct maps of variability in soil fertility parameters and the productivity of a coffee crop in the municipality of Monte Carmelo, Minas Gerais (MG), Brazil. The work was developed with coffee of the cultivar Mundo Novo 379/19, and 19 sample points were georeferenced in Universal Transverse Mercator coordinates. Spatial dependence of the fertility and productivity parameters was analysed via classic semivariogram fitting and interpolation by ordinary kriging using the statistical computer system, R. All parameters evaluated showed high degrees of spatial dependence. The attribute values varied along the sampling points, except for the sodium (Na) contents, which had similar values in all samplings. The studied parameters ranged from 80 to 200 metres. It is conclusion, the use of productivity maps linked to soil chemical attributes can be useful for determining the occurrence of variable productivity rates throughout the area, allowing the adoption of corrective practices for subsequent crops and thus making the maps very useful tools for producers.

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408–417 P.F.P. Ferraz, G.A.S. Ferraz, L. Schiassi, V.H.B. Nogueira, M. Barbari and F.A. Damasceno
Spatial variability of litter temperature, relative air humidity and skin temperature of chicks in a commercial broiler house
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Spatial variability of litter temperature, relative air humidity and skin temperature of chicks in a commercial broiler house

P.F.P. Ferraz¹*, G.A.S. Ferraz¹, L. Schiassi¹, V.H.B. Nogueira¹, M. Barbari² and F.A. Damasceno¹

¹Federal University of Lavras, Agricultural Engineering Departament, Campus Universitário, PO Box 3037, Lavras, Minas Gerais, Brazil
²University of Firenze, Department of Agriculture, Food, Environment and Forestry, Via San Bonaventura, 13, IT50145 Firenze, Italy
*Correspondence: patricia.ponciano@ufla.br

Abstract:

The thermal environment inside a broiler house has a great influence on animal welfare and productivity during the production phase. Among the importance of the chicken litter is the function of absorbing moisture, provide thermal insulation and provide a soft surface for broilers. The skin temperature is an important physiological parameter to quantify the thermal comfort of animals, its variations may occur as a function of thermal variables. So, the aim of this work was to analyse the magnitude and spatial variability of chicken litter temperature and relative humidity of the air and to correlate them with the spatial distribution of chicks’ skin surface temperature throughout the broiler house during the 7th, 14th and 21st days of the chicks’ life, using geostatistical techniques. The experiment was performed in a commercial broiler house located in the western mesoregion of Minas Gerais, Brazil, where 28,000 male Cobb chicks were housed. The heating system consisted of an industrial indirect-fired biomass furnace. The heated air was inflated by an AC motor, 2,206 W of power, 1,725 RPM. Geostatistical techniques were used through semivariogram analysis and isochore maps were generated through data interpolation by kriging. The semivariogram was fitted by the restricted maximum likelihood method. The used mathematical model was the spherical one. After fitting the semivariograms, the data were interpolated by ordinary kriging. The semivariograms along with the isochore maps allowed identifying the non-uniformity of spatial distribution of the broiler litter temperature throughout the broiler house for 3 days of chicks’ life. It was observed that skin surface presented a positive correlation with the litter temperature and a negative correlation with the air humidity. The semivariograms along with the isochore maps allowed identifying the non-uniformity of spatial distribution of the litter temperature, air humidity and skin temperature of chicks throughout the broiler aviary for the three days. In addition, the use of geostatistics and distribution maps made possible to identify different environmental conditions in regions inside the broiler house that may harm the development of chicks.

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