Soil fertility, organic carbon and fractions of the organic matter at different distances from eucalyptus stumps

Knowledge on variations in vertical, horizontal and temporal characteristics of the soil chemical properties under eucalyptus stumps left in the soil is of fundamental importance for the management of subsequent crops. The objective of this work was to evaluate the effect of eucalyptus stumps (ES) left after cutting on the spatial variability of chemical characteristics in a dystrophic Yellow Argisol in the eastern coastal plain region of Brazil. For this purpose, ES left for 31 and 54 months were selected in two experimental areas with similar characteristics, to assess the decomposition effects of the stumps on soil chemical attributes. Soil samples were collected directly around these ES, and at distances of 30, 60, 90, 120 and 150 cm away from them, in the layers 0-10, 10-20 and 20-40 cm along the row of ES, which is in-between the rows of eucalyptus trees of a new plantation, grown at a spacing of 3 x 3 m. The soil was sampled in five replications in plots of 900 m² each and the samples analyzed for pH, available P and K (Mehlich-1), exchangeable Al, Ca and Mg, total organic carbon (TOC) and C content in humic substances (HS) and in the free light fraction. The pH values and P, K, Ca2+, Mg2+ and Al3+ contents varied between the soil layers with increasing distance from the 31 and 54-monthold stumps. The highest pH, P, K, Ca2+ and Mg2+ values and the lowest Al3+ content were found in the surface soil layer. The TOC of the various fractions of soil organic matter decreased with increasing distance from the 31 and 54-month-old ES in the 0-10 and 10-20 cm layers, indicating that the root (and stump) cycling and rhizodeposition contribute to maintain soil organic matter. The C contents of the free light fraction, of the HS and TOC fractions were higher in the topsoil layer under the ES left for 31 months due to the higher clay levels of this layer, than in those found under the 54-month-old stumps. However, highest C levels of the different fractions of soil organic matter in the topsoil layer reflect the deposition and maintenance of forest residues on the soil surface, mainly after forest harvest.

Inter-relation between soybean yield and soil compaction under degraded pasture in Brazilian Savannah

The Cerrado (Brazilian Savannah) plays an important economic and financial role in the nation, since the pastures of this biome feed cattle for half of the domestic bovine meat productivity, and its agricultural fields produce a third of the country's grain. The variability and spatial dependence between the soil physical attributes and soybean yield were evaluated in a crop rotation planted on a degraded brachiaria pasture, on a dystroferric Red Latosol of an experimental farm of the State University of São Paulo (UNESP), in the 2005/2006 growing season. The linear and spatial correlations between these attributes were also studied, to determine conditions that would allow increased agricultural productivity. In the above pasture area, a grid was installed with 124 plots, spaced 10.0 x 10.0 m and 5.0 x 5.0 m apart, in a total area of 7,500 m². From the linear and spatial point of view, the high grain yield can be explained by the number of grains per plant and soil macroporosity. The high variability observed for most soil properties indicated that the crop - livestock integration system results in environmental heterogeneity of the soil.

Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation

The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI), remaining phosphorus (P-rem), and micronutrients (Zn, Fe, Mn, Cu and B). The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

Strategy of specification of management areas: rice grain yield as related to soil fertility

It is well-known nowadays that soil variability can influence crop yields. Therefore, to determine specific areas of soil management, we studied the Pearson and spatial correlations of rice grain yield with organic matter content and pH of an Oxisol (Typic Acrustox) under no- tillage, in the 2009/10 growing season, in Selvíria, State of Mato Grosso do Sul, in the Brazilian Cerrado (longitude 51º24' 21'' W, latitude 20º20' 56'' S). The upland rice cultivar IAC 202 was used as test plant. A geostatistical grid was installed for soil and plant data collection, with 120 sampling points in an area of 3.0 ha with a homogeneous slope of 0.055 m m-1. The properties rice grain yield and organic matter content, pH and potential acidity and aluminum content were analyzed in the 0-0.10 and 0.10-0.20 m soil layers. Spatially, two specific areas of agricultural land management were discriminated, differing in the value of organic matter and rice grain yield, respectively with fertilization at variable rates in the second zone, a substantial increase in agricultural productivity can be obtained. The organic matter content was confirmed as a good indicator of soil quality, when spatially correlated with rice grain yield.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

Fuzzy logic applied to the modeling of water dynamics in an Oxisol in northeastern Brazil

Modeling of water movement in non-saturated soil usually requires a large number of parameters and variables, such as initial soil water content, saturated water content and saturated hydraulic conductivity, which can be assessed relatively easily. Dimensional flow of water in the soil is usually modeled by a nonlinear partial differential equation, known as the Richards equation. Since this equation cannot be solved analytically in certain cases, one way to approach its solution is by numerical algorithms. The success of numerical models in describing the dynamics of water in the soil is closely related to the accuracy with which the water-physical parameters are determined. That has been a big challenge in the use of numerical models because these parameters are generally difficult to determine since they present great spatial variability in the soil. Therefore, it is necessary to develop and use methods that properly incorporate the uncertainties inherent to water displacement in soils. In this paper, a model based on fuzzy logic is used as an alternative to describe water flow in the vadose zone. This fuzzy model was developed to simulate the displacement of water in a non-vegetated crop soil during the period called the emergency phase. The principle of this model consists of a Mamdani fuzzy rule-based system in which the rules are based on the moisture content of adjacent soil layers. The performances of the results modeled by the fuzzy system were evaluated by the evolution of moisture profiles over time as compared to those obtained in the field. The results obtained through use of the fuzzy model provided satisfactory reproduction of soil moisture profiles.

USE OF SCALED SEMIVARIOGRAMS IN THE PLANNING SAMPLE OF SOIL PHYSICAL PROPERTIES IN SOUTHERN AMAZONAS, BRAZIL

There is a great lack of information from soil surveys in the southern part of the State of Amazonas, Brazil. The use of tools such as geostatistics may improve environmental planning, use and management. In this study, we aimed to use scaled semivariograms in sample design of soil physical properties of some environments in Amazonas. We selected five areas located in the south of the state of Amazonas, Brazil, with varied soil uses, such as forest, archaeological dark earth (ADE), pasture, sugarcane cropping, and agroforestry. Regular mesh grids were set up in these areas with 64 sample points spaced at 10 m from each other. At these points, we determined the particle size composition, soil resistance to penetration, moisture, soil bulk density and particle density, macroporosity, microporosity, total porosity, and aggregate stability in water at a depth of 0.00-0.20 m. Descriptive and geostatistical analyses were performed. The sample density requirements were lower in the pasture area but higher in the forest. We concluded that managed-environments had differences in their soil physical properties compared to the natural forest; notably, the soil in the ADE environment is physically improved in relation to the others. The physical properties evaluated showed a structure of spatial dependence with a slight variability of the forest compared to the others. The use of the range parameter of the semivariogram analysis proved to be effective in determining an ideal sample density.

USE OF SCALED SEMIVARIOGRAMS IN THE PLANNING SAMPLE OF SOIL CHEMICAL PROPERTIES IN SOUTHERN AMAZONAS, BRAZIL

The lack of information concerning the variability of soil properties has been a major concern of researchers in the Amazon region. Thus, the aim of this study was to evaluate the spatial variability of soil chemical properties and determine minimal sampling density to characterize the variability of these properties in five environments located in the south of the State of Amazonas, Brazil. The five environments were archaeological dark earth (ADE), forest, pasture land, agroforestry operation, and sugarcane crop. Regular 70 × 70 m mesh grids were set up in these areas, with 64 sample points spaced at 10 m distance. Soil samples were collected at the 0.0-0.1 m depth. The chemical properties of pH in water, OM, P, K, Ca, Mg, H+Al, SB, CEC, and V were determined at these points. Data were analyzed by descriptive and geostatistical analyses. A large part of the data analyzed showed spatial dependence. Chemical properties were best fitted to the spherical model in almost all the environments evaluated, except for the sugarcane field with a better fit to the exponential model. ADE and sugarcane areas had greater heterogeneity of soil chemical properties, showing a greater range and higher sampling density; however, forest and agroforestry areas had less variability of chemical properties.

Regional deterministic characterization of fracture networks and its application to GIS-based rock fall risk assessment

The study investigates the possibility to incorporate fracture intensity and block geometry as spatially continuous parameters in GIS-based systems. For this purpose, a deterministic method has been implemented to estimate block size (Bloc3D) and joint frequency (COLTOP). In addition to measuring the block size, the Bloc3D Method provides a 3D representation of the shape of individual blocks. These two methods were applied using field measurements (joint set orientation and spacing) performed over a large field area, in the Swiss Alps. This area is characterized by a complex geology, a number of different rock masses and varying degrees of metamorphism. The spatial variability of the parameters was evaluated with regard to lithology and major faults. A model incorporating these measurements and observations into a GIS system to assess the risk associated with rock falls is proposed. The analysis concludes with a discussion on the feasibility of such an application in regularly and irregularly jointed rock masses, with persistent and impersistent discontinuities.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Fly Ash Soil Stabilization for Non-Uniform Subgrade Soils, Volume II: Influence of Subgrade Non-Uniformity on PCC Pavement Performance, TR-461, 2005

To provide insight into subgrade non-uniformity and its effects on pavement performance, this study investigated the influence of non-uniform subgrade support on pavement responses (stress and deflection) that affect pavement performance. Several reconstructed PCC pavement projects in Iowa were studied to document and evaluate the influence of subgrade/subbase non-uniformity on pavement performance. In situ field tests were performed at 12 sites to determine the subgrade/subbase engineering properties and develop a database of engineering parameter values for statistical and numerical analysis. Results of stiffness, moisture and density, strength, and soil classification were used to determine the spatial variability of a given property. Natural subgrade soils, fly ash-stabilized subgrade, reclaimed hydrated fly ash subbase, and granular subbase were studied. The influence of the spatial variability of subgrade/subbase on pavement performance was then evaluated by modeling the elastic properties of the pavement and subgrade using the ISLAB2000 finite element analysis program. A major conclusion from this study is that non-uniform subgrade/subbase stiffness increases localized deflections and causes principal stress concentrations in the pavement, which can lead to fatigue cracking and other types of pavement distresses. Field data show that hydrated fly ash, self-cementing fly ash-stabilized subgrade, and granular subbases exhibit lower variability than natural subgrade soils. Pavement life should be increased through the use of more uniform subgrade support. Subgrade/subbase construction in the future should consider uniformity as a key to long-term pavement performance.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Interacting populations in heterogeneous environments

To optimally manage a metapopulation, managers and conservation biologists can favor a type of habitat spatial distribution (e.g. aggregated or random). However, the spatial distribution that provides the highest habitat occupancy remains ambiguous and numerous contradictory results exist. Habitat occupancy depends on the balance between local extinction and colonization. Thus, the issue becomes even more puzzling when various forms of relationships - positive or negative co-variation - between local extinction and colonization rate within habitat types exist. Using an analytical model we demonstrate first that the habitat occupancy of a metapopulation is significantly affected by the presence of habitat types that display different extinction-colonization dynamics, considering: (i) variation in extinction or colonization rate and (ii) positive and negative co-variation between the two processes within habitat types. We consequently examine, with a spatially explicit stochastic simulation model, how different degrees of habitat aggregation affect occupancy predictions under similar scenarios. An aggregated distribution of habitat types provides the highest habitat occupancy when local extinction risk is spatially heterogeneous and high in some places, while a random distribution of habitat provides the highest habitat occupancy when colonization rates are high. Because spatial variability in local extinction rates always favors aggregation of habitats, we only need to know about spatial variability in colonization rates to determine whether aggregating habitat types increases, or not, metapopulation occupancy. From a comparison of the results obtained with the analytical and with the spatial-explicit stochastic simulation model we determine the conditions under which a simple metapopulation model closely matches the results of a more complex spatial simulation model with explicit heterogeneity.

Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management

Precision Viticulture (PV) is a concept that is beginning to have an impact on the wine-growing sector. Its practical implementation is dependant on various technological developments: crop sensors and yield monitors, local and remote sensors, Global Positioning Systems (GPS), VRA (Variable-Rate Application) equipment and machinery, Geographic Information Systems (GIS) and systems for data analysis and interpretation. This paper reviews a number of research lines related to PV. These areas of research have focused on four very specific fields: 1) quantification and evaluation of within-field variability, 2) delineation of zones of differential treatment at parcel level, based on the analysis and interpretation of this variability, 3) development of Variable-Rate Technologies (VRT) and, finally, 4) evaluation of the opportunities for site-specific vineyard management. Research in these fields should allow winegrowers and enologists to know and understand why yield variability exists within the same parcel, what the causes of this variability are, how the yield and its quality are interrelated and, if spatial variability exists, whether site-specific vineyard management is justifiable on a technical and economic basis.

L'aprofitament ramader a la vall del Madriu-Perafita-Claror (Principat d'Andorra)

The farming exploitation in the Madriu-Perafita-Claror Valley (Principality of Andorra). The Madriu-Perafita-Claror Valley, a natural space located in the Principality of Andorra, has kept a high ecological and landscape value through time. At present, the Valley is considered in the cultural landscape category of the UNESCO World-wide Heritage. A study of the spatial variability of pastures in the Valley conducted from 1994 to 2003 concluded that there was an optimistic future for livestock. This future was mainly explained by new policies in the country, as well as by the new hopes of the tockbreeders. The study also stated that cattle and horse movements within the Valley did not varied over the study period, although entrance and exit points changed. Sheep only fed in the Madriu-Perafita-Claror Valley, but it wouldbe convenient its introduction in other areas where horses and cattle did not pasture. The study concluded that the use of the Valley by the stockbreeders contributed to the development of the vegetation and the landscape, and that the livestock is very important to keep natural and landscape values of the Valley.