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Precision Agriculture (precision + agriculture)

Distribution by Scientific Domains

Mathematics and Statistics	50%

Selected Abstracts

The economic potential of precision nitrogen application with wheat based on plant sensing

AGRICULTURAL ECONOMICS, Issue 4 2009
Jon T. Biermacher
Nitrogen fertilizer; Precision agriculture; Stochastic plateau; Wheat Abstract Plant-based precision nitrogen fertilizer application technologies have been developed as a way to predict and precisely meet nitrogen needs. Equipment necessary for precision application of nitrogen, based on sensing of growing wheat plants in late winter, is available commercially, but adoption has been slow. This article determines the expected profit from using a plant-sensing system to determine winter wheat nitrogen requirements. We find that plant-sensing systems have the potential to be more profitable than traditional nonprecise systems, but the existing system simulated was roughly breakeven with a traditional system. [source]

Simulating the spatial distribution of clay layer occurrence depth in alluvial soils with a Markov chain geostatistical approach

ENVIRONMETRICS, Issue 1 2010
Weidong Li
Abstract The spatial distribution information of clay layer occurrence depth (CLOD), particularly the spatial distribution maps of occurrence of clay layers at depths less than a certain threshold, in alluvial soils is crucial to designing appropriate plans and measures for precision agriculture and environmental management in alluvial plains. Markov chain geostatistics (MCG), which was proposed recently for simulating categorical spatial variables, can objectively decrease spatial uncertainty and consequently increase prediction accuracy in simulated results by using nonlinear estimators and incorporating various interclass relationships. In this paper, a MCG method was suggested to simulate the CLOD in a meso-scale alluvial soil area by encoding the continuous variable with several threshold values into binary variables (for single thresholds) or a multi-class variable (for all thresholds being considered together). Related optimal prediction maps, realization maps, and occurrence probability maps for all of these indicator-coded variables were generated. The simulated results displayed the spatial distribution characteristics of CLOD within different soil depths in the study area, which are not only helpful to understanding the spatial heterogeneity of clay layers in alluvial soils but also providing valuable quantitative information for precision agricultural management and environmental study. The study indicated that MCG could be a powerful method for simulating discretized continuous spatial variables. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Multivariate calibration of hyperspectral ,-ray energy spectra for proximal soil sensing

EUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2007
R. A. Viscarra Rossel
Summary The development of proximal soil sensors to collect fine-scale soil information for environmental monitoring, modelling and precision agriculture is vital. Conventional soil sampling and laboratory analyses are time-consuming and expensive. In this paper we look at the possibility of calibrating hyperspectral ,-ray energy spectra to predict various surface and subsurface soil properties. The spectra were collected with a proximal, on-the-go ,-ray spectrometer. We surveyed two geographically and physiographically different fields in New South Wales, Australia, and collected hyperspectral ,-ray data consisting of 256 energy bands at more than 20 000 sites in each field. Bootstrap aggregation with partial least squares regression (or bagging-PLSR) was used to calibrate the ,-ray spectra of each field for predictions of selected soil properties. However, significant amounts of pre-processing were necessary to expose the correlations between the ,-ray spectra and the soil data. We first filtered the spectra spatially using local kriging, then further de-noised, normalized and detrended them. The resulting bagging-PLSR models of each field were tested using leave-one-out cross-validation. Bagging-PLSR provided robust predictions of clay, coarse sand and Fe contents in the 0,15 cm soil layer and pH and coarse sand contents in the 15,50 cm soil layer. Furthermore, bagging-PLSR provided us with a measure of the uncertainty of predictions. This study is apparently the first to use a multivariate calibration technique with on-the-go proximal ,-ray spectrometry. Proximally sensed ,-ray spectrometry proved to be a useful tool for predicting soil properties in different soil landscapes. [source]

On Estimation and Prediction for Spatial Generalized Linear Mixed Models

BIOMETRICS, Issue 1 2002
Hao Zhang
Summary. We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. We develop a Monte Carlo version of the EM gradient algorithm for maximum likelihood estimation of model parameters. A by-product of this approach is that it also produces the MMSE estimates for the realized random effects at the sampled sites. This method is illustrated through a simulation study and is also applied to a real data set on plant root diseases to obtain a map of disease severity that can facilitate the practice of precision agriculture. [source]