Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Abstract

Spatial analyses of yield trials allow adjustment of cultivar means for spatial variation, improving the statistical precision of yield estimation. While the relative efficiency of spatial analysis has been frequently reported in several yield trials, its application to long‐term Lolium spp. forage yield trials has not been characterized. The objective of this study was to evaluate the trend analysis, nearest‐neighbor analysis (NNA), and correlated error (CE) models for their ability to account for spatial variability in 138 Lolium spp. forage yield trials. This case study was performed on data from five locations and 11 yr (2001–2011) using randomized complete block design (RCBD) trials conducted by the Department of Agriculture, Food and Marine (DAFM) in Ireland. The relative efficiencies of trend, NNA, and CE models compared with RCBD models were 129, 143, and 193% for analysis by trial × year, and 121, 125, and 171% for analysis by trial, respectively. When the top one, two, three, four, or five cultivar(s) were compared between CE and RCBD models, the agreement between two models to find common cultivars varied from 66% for the top cultivar to 28% for the top five cultivars. Using CE models, four replicates were sufficient to detect mean yield differences between cultivars of 7% of the mean and 80% power. Spatial analysis should be added to the routine DAFM testing programs, not only to improve the precision of yield estimates, but also to reduce the risk of missing potential candidate cultivars, given the existence of spatial variation.