Statistical analysis of two-dimensional variation in variety yield trials

Abstract

SUMMARYMethods of adjustment for two-dimensional spatial heterogeneity of grain yield were investigated for 224 UK cereal trials. The methods used row and column ‘block’ analysis of plot yields and neighbour analysis based on first differences of plot yields. In 36% of trial analyses for block models and 30% for neighbour models the average variance of variety differences was reduced by more than 10% compared with the better of the one-dimensional row or column models. Compared with complete blocks, 2-D block analysis had a mean efficiency of 153% whereas the conventional 1-D block analysis (by rows) had a mean efficiency of 127%. Similarly, 2-D neighbour analysis had a mean efficiency of 159% whereas the 1-D analysis had a mean efficiency of 137%. Recently, general statistical methods have been developed for two-dimensional design and analysis; their wider use should lead to major gains in the precision of variety trials.

Experimental designs for evaluation of genetic variability and selection of ancient grapevine varieties: a simulation study

“…In the agricultural field, studies have reported greater effectiveness of balanced incomplete block and a designs compared to RCB designs (Patterson et al, 1978;Patterson and Silvey, 1980;Patterson and Hunter, 1983;Kempton et al, 1994;Yau, 1997;Qiao et al, 2000), and in the area of forestry similar results have been obtained, for example by Fu et al (1998Fu et al ( , 1999 and Gezan et al (2006).…”

Efficiency of Alpha Lattice Design in Rice Field Trials in Pakistan

“…The CV after using alpha lattice design was still too high suggesting that experiment was not carried out efficiently. The efficiency of alpha design in second experiment could be improved by using neighbor or spatial analysis or row and column analyses [4,6,11,[16][17][18][19][20]. The rank orders of mean based on Alpha design (least square means) and RCBD (simple means) also change, which is relevant when selecting genotypes for the purpose of recommendations for the farmers.…”

Novo método de classificação de coeficientes de variação para a cultura do arroz de terras altas

“…Nas situações onde o número de tratamentos é elevado, a utilização do BI reduz o erro experimental, uma vez que este delineamento consegue acomodar um grande número de tratamentos em pequenos blocos, mais homogêneos, mesmo em detrimento de se usar um menor número de repetições, embora o número de repetições possa influir também na correta avaliação da precisão dos experimentos (Pimentel-Gomes, 1991;Amaral et al, 1997). A maior dificuldade de análise dos delineamentos em blocos incompletos, como também a maior perda de graus de liberdade no resíduo, são freqüentemente compensados por uma grande redução no quadrado médio residual, de sorte que, não raro, obtêm-se experimentos mais precisos e mais eficientes (Patterson & Hunter, 1983;Pimentel-Gomes, 1985;Kempton et al, 1994).…”