Isbn: 9783844393156 - application of spatial mixed model in agricultural field experiment: advantage of spatial mixed model over traditional statistical analysis (6 risultati)

Taschenbuch. Condizione: Neu. Application of Spatial Mixed Model in Agricultural Field Experiment | Advantage of Spatial Mixed Model over Traditional statistical Analysis | Dibaba Gemechu (u. a.) | Taschenbuch | 96 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844393156 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Field experiments in agronomy and related disciplines have traditionally been affected by soil heterogeneity. This is because the soil characteristics are typically non-random and show fertility trend, spatial autocorrelation or periodicity. In the same way that spatial modeling is getting popular, robust designs which utilize spatial information are now common. Spatial variation in fertility, moisture, intercepted light, and other environmental factors can bias variety contrasts and inflate residual variation. This book,therefore,is to evaluate the efficiency of spatial statistical analysis in field trials and, particularly,to demonstrate the benefits of the approach when experimental observations are spatially dependent. Three different data sets taken from Ethiopian Agricultural Research Organization were used for the analysis. The analysis should help agronomists and any one else who may be want to analyze spatially related data. 96 pp. Englisch. …

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gemechu DibabaGemechu B. Dibaba, Msc: Studied Biostatistics at Addis Ababa University. He is a lecture in the Department of Statistics at the University of Addis Ababa. He has presented different Statistical courses such as Statistic.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Field experiments in agronomy and related disciplines have traditionally been affected by soil heterogeneity. This is because the soil characteristics are typically non-random and show fertility trend, spatial autocorrelation or periodicity. In the same way that spatial modeling is getting popular, robust designs which utilize spatial information are now common. Spatial variation in fertility, moisture, intercepted light, and other environmental factors can bias variety contrasts and inflate residual variation. This book,therefore,is to evaluate the efficiency of spatial statistical analysis in field trials and, particularly,to demonstrate the benefits of the approach when experimental observations are spatially dependent. Three different data sets taken from Ethiopian Agricultural Research Organization were used for the analysis. The analysis should help agronomists and any one else who may be want to analyze spatially related data.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch. …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Field experiments in agronomy and related disciplines have traditionally been affected by soil heterogeneity. This is because the soil characteristics are typically non-random and show fertility trend, spatial autocorrelation or periodicity. In the same way that spatial modeling is getting popular, robust designs which utilize spatial information are now common. Spatial variation in fertility, moisture, intercepted light, and other environmental factors can bias variety contrasts and inflate residual variation. This book,therefore,is to evaluate the efficiency of spatial statistical analysis in field trials and, particularly,to demonstrate the benefits of the approach when experimental observations are spatially dependent. Three different data sets taken from Ethiopian Agricultural Research Organization were used for the analysis. The analysis should help agronomists and any one else who may be want to analyze spatially related data. …