|SRIPATHI, RAGHU - University Of Wisconsin|
|CONAGHAN, PATRICK - Teagasc (AGRICULTURE AND FOOD DEVELOPMENT AUTHORITY)|
|GROGAN, D - Teagasc (AGRICULTURE AND FOOD DEVELOPMENT AUTHORITY)|
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/17/2017
Publication Date: 1/17/2017
Citation: Sripathi, R., Conaghan, P., Grogan, D., Casler, M.D. 2017. Spatial variability effects on precision and power of forage yield estimation. Crop Science. 57:1383-1393.
Interpretive Summary: Livestock producers rely heavily on public researchers to recommend only the best varieties for use in pasture and hay production. Public researchers use an extensive system of field trials to sort out the best varieties for these recommendations. However, the sorting and the final recommendations are only as good as the trials themselves. This study was based on 11 years of trial data from five locations for perennial ryegrass varieties grown in Ireland, with the goal to determine how best to improve the field trials. Rankings of varieties, including the choice of the top variety, were strongly influenced by uncontrolled errors in the field trials. This indicates that the trials are not as effective as they should be. Scarce public resources are currently being squandered on creating erroneous rankings in some situations. This can be corrected by the use of more complex field designs with proper randomization and blocking of varieties, combined with statistical modeling of the spatial variation that exists within these field trials. These approaches will be put into place in the Irish trials beginning with the trials to be planted in 2016.
Technical Abstract: Spatial analyses of yield trials are important, as they adjust cultivar means for spatial variation and improve the statistical precision of yield estimation. While the relative efficiency of spatial analysis has been frequently reported in several yield trials, its application on long-term forage yield trials conducted by the Department of Agriculture, Food and Marine (DAFM) in Ireland has not been studied. The objective of this study was to evaluate the trend analysis, nearest-neighbor analysis (NNA), correlated error (CE) models for their ability to account for spatial variability in 138 DAFM forage yield trials. These trials were evaluated across five locations and eleven sowing years from 2001 to 2011 in randomized complete block designs (RCBD). Different trend, NNA, and CE models were evaluated by trial and trial x year analyses. The relative efficiencies of trend, NNA, CE models compared to RCBD models were 129, 143, and 193% for analysis by trial x year, and 121, 125, and 171% for analysis by trial, respectively. The CE models had relatively more precise estimates than trend analysis and NNA. When top one, two, three, four or five cultivars between CE and RCBD models were compared, the agreement between two models to find common cultivars varied from 66% for top cultivar to 28% for 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. Our results suggest that spatial analysis must 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.