Yield component analysis with SYCA and TDP in fodder pea.

The paper presents some theoretical assumptions of the SYCA (Sequential Yield Component Analysis) and the application of SYCA followed by TDP (Two-dimensional Partitioning) to analysis of the data from a plant breeding experiment with fodder pea. Partially balanced incomplete block design with 25 morphologically different breeding forms in 4 replications was applied. In both methods of data analysis plant height was the first trait in a sequence of independent traits, followed by different traits depending on the method.The results of the analyses proved that in a morphologically highly differentiated population of fodder pea the contribution of plant height to the yield variability is reduced, with plant height to the first pod being one of the traits that have a significant effect on yield. According to the SYCA method, when the pea forms were divided into groups of plants similar in height, the effect of plant height as the first yield component was high and significant. Generally, the higher were the plants in the groups, the smaller was the share of the trait in the yield, although still relatively high and significant. For the purpose of yield component analysis in pea it is recommendable to divide the breeding material to groups of plants of a similar height.As for the other yield components, the highest contribution into the final yield was attributed to the number of nodes with pods by plant height and seed weight by number of seeds calculated according to the SYCA and the number of nodes with pods calculated according to the TDP method, respectively.The authors, who have used the two yield component analyses for several years, have gained enough experience to claim that the two methods can become effective statistical tools for the elaboration of yield components. Moreover, they can be useful not only in plant breeding studies but also in many other types of agricultural experimentation.