Access the full text.
Sign up today, get DeepDyve free for 14 days.
C. Brunsdon, S. Fotheringham, M. Charlton (1998)
Geographically weighted regression - Modelling spatial non-stationarityThe Statistician, 47
L. Anselin, H. Kelejian (1997)
Testing for Spatial Error Autocorrelation in the Presence of Endogenous RegressorsInternational Regional Science Review, 20
J. Whitney, Q. Ling, T. Wheaton, W. Miller (2001)
A Citrus Harvesting Labor Tracking and Yield Mapping SystemApplied Engineering in Agriculture, 17
O Schabenberger, CA Gotway (2005)
Statistical methods for spatial data analysis. Chapter 8: Non-stationary covariance
G Matheron (1965)
Les variables régionalisées et leur estimation
R. Beran, P. Hall (1992)
Estimating Coefficient Distributions in Random Coefficient RegressionsAnnals of Statistics, 20
O. Schabenberger, C. Gotway (2004)
Statistical Methods for Spatial Data Analysis
S. Tumbo, J. Whitney, W. Miller, T. Wheaton (2002)
DEVELOPMENT AND TESTING OF A CITRUS YIELD MONITORApplied Engineering in Agriculture, 18
L.F Johnson, D.E Roczen, S. Youkhana, R. Nemani, D. Bosch (2003)
Mapping vineyard leaf area with multispectral satellite imageryComputers and Electronics in Agriculture, 38
R. Kerry, M. Oliver (2007)
Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihoodGeoderma, 140
S. Lahiri, Yoon‐dong Lee, N. Cressie (2002)
On asymptotic distribution and asymptotic efficiency of least squares estimators of spatial variogram parametersJournal of Statistical Planning and Inference, 103
(2008)
GenStat for windows (11th Edition) introduction
N. Cressie (1994)
Statistics for Spatial Data, Revised Edition.Biometrics, 50
R. Bramley, R. Hamilton (2004)
Understanding variability in winegrape production systemsAustralian Journal of Grape and Wine Research, 10
V. Prasad (2006)
Statistical Methods for Spatial Data AnalysisPhotogrammetric Record, 21
C. Brunsdon, S. Fotheringham, M. Charlton (1998)
Geographically Weighted RegressionThe Statistician, 47
E. Pardo‐Igúzquiza (1998)
Inference of spatial indicator convariance parameters by maximum likelihood using MLREMLComputers & Geosciences, 24
G. Matheron (1963)
Principles of geostatisticsEconomic Geology, 58
H. Akaike (1974)
A new look at the statistical model identificationIEEE Transactions on Automatic Control, 19
L. Pozdnyakova, D. Giménez, P. Oudemans (2005)
Spatial Analysis of Cranberry Yield at Three ScalesAgronomy Journal, 97
P. Eggermont, V. LaRiccia (2009)
Smoothing Parameter Selection
A. Castrignanò, G. Buttafuoco, R. Puddu (2008)
Multi-scale assessment of the risk of soil salinization in an area of south-eastern Sardinia (Italy)Precision Agriculture, 9
D. Lamb, M. Weedon, R. Bramley (2008)
Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolutionAustralian Journal of Grape and Wine Research, 10
A. Hope (1968)
A Simplified Monte Carlo Significance Test ProcedureJournal of the royal statistical society series b-methodological, 30
O Schabenberger, CA Gotway (2005)
Statistical methods for spatial data analysis. Chapter 2: Some theory on random fields
Chenggang Wang, R. Färe, C. Seavert (2006)
Revenue Capacity Efficiency of Pear Trees and Its DecompositionJournal of the American Society for Horticultural Science, 131
J. Whitney, Q. Ling, W. Miller, T. Wheaton (2001)
A DGPS Yield Monitoring System for Florida CitrusApplied Engineering in Agriculture, 17
C. Hurvich, J. Simonoff, Chih-Ling Tsai (1998)
Smoothing parameter selection in nonparametric regression using an improved Akaike information criterionJournal of the Royal Statistical Society: Series B (Statistical Methodology), 60
N. Cressie (1993)
Statistics for Spatial Data: Cressie/Statistics
R. Plant (2001)
Site-specific management: the application of information technology to crop productionComputers and Electronics in Agriculture, 30
G. Matheron (1965)
Les variables régionalisées et leur estimation : une application de la théorie de fonctions aléatoires aux sciences de la nature
R. Bramley (2005)
Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintagesAustralian Journal of Grape and Wine Research, 11
SA Fotheringham, C Brunsdon, M Charlton (2002)
Geographically weighted regression: The analysis of spatially varying relationships
B. Whelan, A. McBratney (2000)
The “Null Hypothesis” of Precision Agriculture ManagementPrecision Agriculture, 2
C. Deutsch, A. Journel (1993)
GSLIB: Geostatistical Software Library and User's Guide
A. Fotheringham, C. Brunsdon, M. Charlton (2000)
Quantitative Geography: Perspectives on Spatial Data Analysis
E. Pardo‐Igúzquiza (1997)
MLREML: a computer program for the inference of spatial covariance parameters by maximum likelihood and restricted maximum liklihoodComputers & Geosciences, 23
L. Anselin (2010)
Local Indicators of Spatial Association—LISAGeographical Analysis, 27
P. Moran (1948)
The Interpretation of Statistical MapsJournal of the royal statistical society series b-methodological, 10
(1974)
Monitoring the vernal
Qamar-uz-zaman, A. Schumann (2006)
Nutrient Management Zones for Citrus Based on Variation in Soil Properties and Tree PerformancePrecision Agriculture, 7
AD Cliff, JK Ord (1981)
Spatial properties: Models and applications
We examined the spatial structure of fruit yield, tree size, vigor, and soil properties for an established pear orchard using Moran’s I, geographically weighted regression (GWR) and variogram analysis to determine potential scales of the factors affecting spatial variation. The spatial structure differed somewhat between the tree-based measurements (yield, size and vigor) and the soil properties. Yield, trunk cross-sectional area (TCSA) and normalized difference vegetation index (NDVI, used as a surrogate for vigor) were strongly spatially clustered as indicated by the global Moran’s I for these measurements. The autocorrelation between trees (determined by applying a localized Moran’s I) was greater in some areas than others, suggesting possible management by zones. The variogram ranges for TCSA and yield were 30–45 m, respectively, but large nugget variances indicated considerable variability from tree to tree. The variogram ranges of NDVI varied from about 14–27 m. The soil properties copper, iron, organic matter and total exchange capacity (TEC) were spatially structured, with longer variogram ranges than those of the tree characteristics: 31–95 m. Boron, pH and zinc were not spatially correlated. The GWR analyses supported the results from the other analyses indicating that assumptions of strict stationarity might be violated, so regression models fitted to the entire dataset might not be fitted optimally to spatial clusters of the data.
Precision Agriculture – Springer Journals
Published: Mar 21, 2009
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.