1,720,997 research outputs found
Estimating within-field variation using a nonparametric density algorithm
No full textThe application of site-specific techniques and technologies in precision farming requires subdividing a field into a generally small number of contiguous homogeneous zones. The proposed algorithm of clustering is based on nonparametric density estimate, where a cluster is defined as a region surrounding a local maximum of the probability density function. Soil samples were collected in a 2-ha field of the experimental farm of the Agricultural Research Institute, located in Foggia (Southern Italy) and some of the most production-affecting soil properties were interpolated by using the geostatistical techniques of kriging and cokriging. The application of the clustering approach to the (co)kriged surface variables produced the subdivision of the field into five distinct classes. The proposed algorithm proves quite promising in identifying spatially contiguous zones, which are more homogeneous in soil properties than the whole-field. Its great advantage consists in giving an additional description of the residual variation within the class and such a piece of information is very useful in precision farming as a basis for the variable-rate application of agronomic inputs
How spatial and temporal variability can effect fertilization trial results
No full textThe objectives of this paper were to study the influence of nitrogen fertilization on crop production using a linear mixed effects model with a first order continuous autoregressive correlation structure. On a 2-ha field, the most relevant soil properties were determined. Four fertilizer treatments were applied in a completely randomised block design with four replications (blocks) and repeated crop measurements were made in three crop seasons. The most relevant sources of variation in wheat production might not be ascribed to management of soil fertilization but to soil intrinsic variation and between-season variability. More advanced methods of statistical analysis need to be used to separate the residual error from error sources
How spatial and temporal variability can affect fertilization trial results
No full textThe objectives of this paper were to study the influence of nitrogen fertilization on crop production using a linear mixed effects model with a first order continuous autoregressive correlation structure. On a 2-ha field, the most relevant soil properties were determined. Four fertilizer treatments were applied in a completely randomised block design with four replications (blocks) and repeated crop measurements were made in three crop seasons. The most relevant sources of variation in wheat production might not be ascribed to management of soil fertilization but to soil intrinsic variation and between-season variability. More advanced methods of statistical analysis need to be used to separate the residual error from error sources
Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field
No full textThe paper proposes a geostatistical approach for delineating management zones (MZs) based on multivariate geostatistics, showing the use of polygon kriging to compare durum wheat yield among the different MZs (polygons). The study site was a durum wheat field in southern Italy and yield was measured over three crop seasons. The first regionalized factor, calculated with factorial cokriging, was used to partition the field into three iso-frequency classes (MZs). For each MZ, the expected value and standard deviation of yield were estimated with polygon kriging over the three crop seasons. The yield variation was only in part related to soil properties but most of it might be ascribable to different patterns of meteorological conditions. Both components of variation (plant and soil) in a cropping system should then be taken into account for an effective management of rainfed durum wheat in precision agriculture. The proposed approach proved multivariate Geostatistics to be effective for MZ delineation even if further testing is required under different cropping systems and management
Assessment of groundwater salinisation risk using multivariate geostatistics
No full textThe risk assessment at regional scale requires modelling spatial variability of environmental variables. Traditional approach, based on estimating point environmental indicators, cannot be considered satisfactory for this purpose, because it does not take into account spatial dependence between variables. We propose the application of an approach to the problem of groundwater salinisation, in which multivariate geostatistics and GIS are combined to integrate primary information with exhaustive secondary information. The dataset consisted of 454 private wells used for irrigation and located in Apulia region (south Italy). Three variables were processed: concentration of chlorides and nitrates, as primary variables, and the distance from the coast, as auxiliary variable. The approach highlighted the widespread degradation of water resources in the Apulian groundwater. The maps of the global indicator allowed us to delineate the zones at high risk of groundwater contamination and also to identify those parameters most responsible for water degradation, so that a wiser management of water resources could be planned. This approach can be used as operational support to a wide range of activities and in decision making among several remediation alternatives
Modelling spatial uncertainty of soil erodibility factor using joint stochastic simulation
No full textSoil erosion varies greatly over space and is commonly estimated using the revised universal soil loss equation (RUSLE).
Neglecting information about estimation uncertainty, however, may lead to improper decision-making. One geostatistical
approach to spatial analysis is joint stochastic simulation, which draws alternative, equally probable, joint realizations of a
regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an
error propagation analysis.
The objective of this paper was to assess spatial uncertainty of a soil erodibility factor (K) model resulting from the
uncertainties in the input parameters (texture and organic matter). The 500 km2 study area was located in central-eastern Sardinia
(Italy) and 152 samples were collected. A Monte Carlo analysis was performed where spatial cross-correlation information
through joint turning bands simulation was incorporated. A linear coregionalization model was fitted to all direct and
cross-variograms of the input variables, which included three different structures: a nugget effect, a spherical structure with
a shorter range (3500 m) and a spherical structure with a longer range (10 000 m). The K factor was then estimated for each set of
the 500 joint realizations of the input variables, and the ensemble of the model outputs was used to infer the soil erodibility
probability distribution function. This approach permitted delineation of the areas characterized by greater uncertainty, to
improve supplementary sampling strategies and K value predictions
Delineating management zones using crop and soil variables with a multivariate geostatistic approach
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