Abstract
The purpose of research is to determine the share of influence of factors in the overall variability of barley yield and assess stability using parametric and non-parametric statistical methods in the conditions of the south of the Moscow Region. According to the results of the study (2020–2022), significant contributions to yield variability were identified: environmental conditions – 81.9 %; genotypic characteristics – 7.4 and genotype × environment interaction – 8.2 %. 7 samples (280.7–345.2 g/m2) were classified as excee¬ding the standard. Evaluation by parametric (bi – regression coefficient; S2dᵢ – deviation from regression; θi – average variance component; θ(i) – GE variance component; Wi2 – environmental valence; σi2 – stability variance; CV – coefficient of variation) and nonparametric (S(1,2,3,6) – rank statistics; NP(i) – non-parametric stability statistics; KR – rank sum) stability indicators allowed us to identify differences in the response of the sample under study to environmental conditions. A high correlation was noted between S(3) and NP(4) (r = 0,90), S(1) and S(2) (r = 0,96), S(6) and NP(4) (r = 0,98), Wi2 and σi2, (r = 1,00), Wᵢ2 and θᵢ (r = 1,00), σ2ᵢ and θᵢ (r = 1,00), Wᵢ2 and θ(i) (r = –1,00), σ2ᵢ, and θ(i) (r = –1,00), θᵢ and θ(i) (r = –1,00). Relationship with productivity in such indicators as S(1), S(2), S(3), NP(1), Wᵢ2, σ2ᵢ, S2dᵢ, CV, θᵢ, θ(ᵢ) was weak, the greatest association was noted with bᵢ (r = 0,60), NP(3) (r = –0,67), NP(4) (r = –0,67), S(6) (r = –0,69), KR (r = –0,71), NP(2) (r = –0,78). Samples with a combination of stability and high yield were identified: Alei (k-31363, Russia), Maximus (k-31366, Russia), Austris (k-31368, Latvia), Povolzhsky Luch (k-31392, Russia).
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call