The interpretation of trends from multiple yield maps

By S. Blackmore. 2000

205 citations recorded by the source corpus.

Subfields: Environmental Chemistry, Environmental Engineering, General Agricultural and Biological Sciences

View Paper: https://www.semanticscholar.org/paper/94e6b26b0a648fc697c0c8db8f9dd40289d5d494

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In Environmental Chemistry

In Environmental Engineering

In General Agricultural and Biological Sciences

Citations in the corpus

A comparison of different algorithms for the delineation of management zones (2010, background): “Another approach is based on crop yield maps (Blackmore 2000; Diker et al. 2004; Basso et al. 2007) and aerial photographs of growing crops (Basso et al.”
Classification of Crop Yield Variability in Irrigated Production Fields (2003, methodology): “MCV-3 Empirical categorization of three yield classes based onMSD-4: Four yield classes were arbitrarily defined using mean and CV of yield (Blackmore, 2000) the maps of mean relative yield and its SD among years at MSD-3 Empirical categorization of three yield classes based on mean and standard…”
Evaluating Management Zone Optimal Nitrogen Rates with a Crop Growth Model (2006, background): “Many approaches to MZ delineation have been proposed and evaluated (Mulla, 1991; Fleming et al., 2000, 2004; Blackmore, 2000; Fraisse et al., 2001a; Khosla et al., 2002; Diker et al., 2004; Chang et al., 2004; Miao et al., 2005); however, the biggest challenge facing the producer is how to manage in”
Exploring spatial dependence of cotton yield using global and local autocorrelation statistics (2004, methodology): “To make the cotton yields comparable among different years, cotton lint yields were first standardized by their average for a given year (Blackmore, 2000) by using Eq.”
Management Effects on Relationships of Crop Yields with Topography Represented by Wetness Index and Precipitation (2008, methodology): “For analyses that involved comparisons among the crops, the original yields were standardized (Blackmore, 2000; Perez-Quezada et al., 2003) as Y si = R si /Y s [1] where Y si is the standardized yield of the ith cell in the sth year, R si is the actual yield for the ith cell in the sth year, and Y…”
Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System (2014, background): “Another approach is using yield maps, providing information about spatial and temporal variability of yields in previous years [8].”