Sidney Rosa Vieira

VARIABILIDADE ESPACIAL DE ARGILA, SILTE E ATRIBUTOS QUÍMICOS EM UMA PARCELA EXPERIMENTAL DE UM LATOSSOLO ROXO DE CAMPINAS (SP)

The majority of experiments on soil management and soil fertility uses experimental and statistical procedures which require statistically independent samples and uniform experimental plots. This hypothesis can only be satisfied and verified in practice with geostatistical calculations such as semivariograms. The objective of this paper was to study the spatial variability, within an experimental plot, and to show the use of geostatistical analysis. The field selected for this study is located at the Centro Experimental de Campinas, Instituto Agronomico, Campinas (SP), Brazil, on a Dusky Red Latosol, which had been on conventional tillage for the previous 10 years. A 30 m by 30 m plot was marked on a square grid of 5 m, resulting in 49 sampling points, in which bulk soil samples were collected at the depths of 0-25 cm and 25-50 cm, taken to the laboratory, air dried, sieved on the 2 mm sieve, and submitted to routine chemical and particle size analysis in order to obtain the clay content, silt content, delta pH, sum of bases, cation exchange capacity, and base saturation. Geostatistics was used for the spatial variability analysis. The coefficients of variation found indicate higher variability for all properties at 0-25 cm than at 25-50 cm, probably due to soil tillage depth. Spatial dependence was also stronger at 0-25 cm than at 25-50 cm depth. It was concluded that the spatial variability for the chemical properties was high considering the plot size of 30 m by 30 m and that random sampling would fail to detect it.

Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in Sao Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol) and a Red Yellow Latossol (Ultisol). Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

Topography and spatial variability of soil physical properties

Among the soil formation factors, relief is one of the most used in soil mapping, because of its strong correlation with the spatial variability of soil attributes over a landscape. In this study the relationship between topography and the spatial variability of some soil physical properties was evaluated. The study site, a pasture with 2.84 ha, is located near Seropedica, Rio de Janeiro State, Brazil, where a regular square grid with 20 m spacing was laid out and georreferenced. In each sampling point, altitude was measured and undisturbed soil samples were collected, at 0.0-0.1, 0.1-0.2, and 0.2-0.3 m depths. Organic carbon content, soil texture, bulk density, particle density, and soil water retention at 10 (Field Capacity), 80 (limit of tensiometer reading) and 1500 kPa (Permanent Wilting Point) were determined. Descriptive statistics was used to evaluate central tendency and dispersion parameters of the data. Semivariograms and cross semivariograms were calculated to evaluate the spatial variability of elevation and soil physical attributes, as well as, the relation between elevation and soil physical attributes. Except for silt fraction content (at the three depths), bulk density (at 0.2-0.3 m) and particle density (at 0.0-0.1 m depth), all soil attributes showed a strong spatial dependence. Areas with higher elevation presented higher values of clay content, as well as soil water retention at 10, 80 and 1500 kPa. The correlation between altitude and soil physical attributes decreased as soil depth increased. The cross semivariograms demonstrated the viability in using altitude as an auxiliary variable to improve the interpolation of sand and clay contents at the depth of 0.0-0.3 m, and of water retention at 10, 80 and 1500 kPa at the depth of 0.0-0.2 m.

Variabilidade espacial do potencial de erosão das chuvas do Estado de São Paulo

Utilizaram-se dados de 292 pluviometros da rede oficial do Estado de Sao Paulo para o calculo do fator R da equacao universal de perdas de solo, segundo modelo disponivel na literatura. O objetivo deste trabalho foi usar a geoestatistica para estudar a variabilidade espacial do potencial de erosao das chuvas e construir mapas de isolinhas com precisao. Calculou-se a dependencia espacial atraves do semivariograma para os periodos das secas (abril a setembro), das aguas (outubro a marco) e para a erosividade media anual. Os alcances dos semivariogramas variaram de 50 a 70 km, porem a autovalidacao das estimativas mostrou que o numero de vizinhos utilizados em uma estimativa nao deve ultrapassar 16 para que as hipoteses a respeito dos dados nao sejam invalidadas. Estimou-se um total de 2.460 valores para todo o Estado, com uma separacao de 20 km entre pontos para boa precisao na construcao dos mapas de isolinhas. Os mapas de isolinhas mostraram que, no periodo das secas, existe aumento de erosividade na direcao sul do Estado; no periodo das aguas, da-se o inverso: o aumento da erosividade e na direcao norte.

Variabilidade de atributos físicos do solo determinados por métodos diversos

The spatial variability of soil physical attributes has been the focus of many studies in the last few years. However, little has been discussed of differences of these attributes due to sampling methods. The objective of this study was to analyze the spatial variability of soil physical attributes sampled by different methods in Rhodic Eutrudox, in Campinas (SP, Brazil). Sampling was performed between January and May of 2005, at 302 points of a 10 x 10 m grid. Soil bulk density was measured by the volumetric soil core and the clod methods. Total soil porosity was estimated using the relation between soil bulk density and particle density 2,65 kg dm-3 described in scientific literature for mineral soil and 2,84 kg dm-3 found in the study area. Soil water content was measured by the volumetric core, gravimetric and TDR methods. Bulk density measured soil core presented the lesser value of coefficient of variation (CV) and effect nugget (C0), respectively 7,13% and 0,005. The measured total porosity is presented as the method most practical for determination of this attribute of the soil. The humidity sampling volumetric presented results close to the overall average comparative to the too much methods of sampling of the soil water content.

GEOSTATISTICAL ANALYSIS FOR SOIL MOISTURE CONTENT UNDER THE NO TILLAGE CROPPING SYSTEM

Experiments in agriculture usually consider the topsoil properties to be uniform in space and, for this reason, often make inadequate use of the results. The objective of this study was to assess the variability for soil moisture content using geostatistical techniques. The experiment was carried out on a Rhodic Ferralsol (typic Haplorthox) in Campinas, SP, Brazil, in an area of 3.42 ha cultivated under the no tillage system, and the sampling was made in a grid of 102 points spaced 10 m x 20 m. Access tubes were inserted down to one meter at each evaluation point in order to measure soil moisture contents (cm3 cm-3) at depths of 30, 60 and 90 cm with a neutron moisture gauge. Samplings were made between the months of August and September of 2003 and in January 2004. The soil moisture content for each sampling date was analyzed using classical statistics in order to appropriately describe the central tendency and dispersion on the data and then using geostatistics to describe the spatial variability. The comparison between the spatial variability for different samplings was made examining scaled semivariograms. Water content was mapped using interpolated values with punctual kriging. The semivariograms showed that, at the 60 cm depth, soil water content had moderate spatial dependence with ranges between 90 and 110 m. However, no spatial dependence was found for 30 and 90 cm depths in 2003. Sampling density was insufficient for an adequate characterization of the spatial variability of soil moisture contents at the 30 and 90 cm depths.

Variabilidade espacial da taxa de infiltração de água e da espessura do horizonte A, em um Argissolo Vermelho-Amarelo, sob diferentes usos

The spatial variability of soil water infiltration rate and A horizon thickness were studied in a Red-Yellow Podzolic soil (Kandudalf) showing erosion signs, located in Vera Cruz, SP. Field measurements were taken in regular 5 meter spacing square grid, resulting in 64 sampling points where the infiltration rate at the A, E and B horizon, and A horizon thickness were determined in coffee, pasture and natural vegetation. Statistical analysis showed high variability in the soil water infiltration rate and in the A horizon thickness. A semivariogram showed the existence of spatial dependence on infiltration rate in the E horizon in all land uses. A cross-semivariogram showed spatial correlation between A horizon soil thickness and soil water infiltration rate in coffee crop and natural vegetation. The natural vegetation cover protected A horizon soil loss by erosion, as shown by its greater thickness and lesser spatial variation.

Jack knifing for semivariogram validation

The semivariogram function fitting is the most important aspect of geostatistics and because of this the model chosen must be validated. Jack knifing may be one the most efficient ways for this validation purpose. The objective of this study was to show the use of the jack knifing technique to validate geostatistical hypothesis and semivariogram models. For that purpose, topographical heights data obtained from six distinct field scales and sampling densities were analyzed. Because the topographical data showed very strong trend for all fields as it was verified by the absence of a sill in the experimental semivariograms, the trend was removed with a trend surface fitted by minimum square deviation. Semivariogram models were fitted with different techniques and the results of the jack knifing with them were compared. The jack knifing parameters analyzed were the intercept, slope and correlation coefficient between measured and estimated values, and the mean and variance of the errors calculated by the difference between measured and estimated values, divided by the square root of the estimation variances. The ideal numbers of neighbors used in each estimation was also studied using the jack knifing procedure. The jack knifing results were useful in the judgment of the adequate models fitted independent of the scale and sampling densities. It was concluded that the manual fitted semivariogram models produced better jack knifing parameters because the user has the freedom to choose a better fit in distinct regions of the semivariogram.

Spatial distribution of Pseudaletia sequax Franclemlont in triticale under no-till management

The knowledge of the spatial distribution of insect populations in crops allows the estimation of the population density in space, the classification of damage impact on yield as well as decisions to be made about control measures. To evaluate the spatial distribution of the wheat armyworm Pseudaletia sequax Franclemont, in relation to the development and yield of triticale (Triticum secale Wittmack) under no-till management, samplings were taken at 302 points in a square grid measuring 10 x 10 m, in Campinas, SP, Brazil. The caterpillar population was evaluated by counting their number per meter at the base of triticale plants. Grain yield (kg ha-1), cover of the above-ground part (kg ha-1), mean plant height (m), and ground cover percentage were evaluated after the triticale crop was mechanically harvested, with the measurement of the mass of straw left on soil surface. Geostatistics was used to analyze spatial variability, by means of semivariogram analysis, and interpolation of data by both ordinary and indicator kriging, considering the injury threshold level of ten caterpillars per square meter to construct isoline maps. Spatial dependence occurred for all variables, demonstrating an aggregate spatial distribution pattern of the caterpillar in triticale. Random sampling in this area would fail to reveal that grain yield was affected at the location with the greatest occurrence of the caterpillar. At that location, the economic injury level was exceeded, which would justify the adoption of control measures against the pest.

Detrending non stationary data for geostatistical applications

The use of geostatistics requires at least that the intrinsic hypothesis be satisfied. The presence of a trend in the data invalidates this hypothesis. One of the ways of solving this problem is by subtracting a function fitted to the original data and working with the residuals. This technique also represents a change to a smaller scale of the variability and surface roughness. This paper describes the detrending technique of subtracting a trend surface fitted by the least squares method and discusses the results using topographical data as examples. The objective is to show how the detrending technique works for different scales and degrees of trend and how to interpret the results. It is shown that the simplest the surfaces fitted that does the work of removing the trend the best are the results obtained. The use of jack knifing is proved useful to validate the resulting semivariograms. For most of the applications and depending upon the scale, a linear or a parabolic surface works reasonably well. The back transformation of the data afterwards is very easily done by adding back the subtracted trend surface.