R. A. Maksimov
Ural Agricultural Research Institute, branch of the Ural Federal Agrarian Scientific Center, Ural branch, Russian Academy of Sciences, ul. Glavnaya, 21, Istok, Ekaterinburg, 620061, Russian Federation
Abstract. The purpose of the studies was to determine the adaptability parameters in different periods of growth and development of spring barley plants for the development of a method for predicting the biological yield of grain, depending on the effects of quantitative traits. The experiments were conducted in 2011–2020 in the southwest of the Sverdlovsk region. We took into account yield and biometric indicators of quantitative traits of various genotypes of the culture (Veresk, Pamyati Chepeleva, Acha, Binom, Sonet, and Bagrets). Variations in biological grain yield by 89.3% were determined by environmental conditions; only 2.9% of its variability was due to the influence of genotypes; 3.7% was due to the genotype and environment interaction. To differentiate the yield depending on the effect of quantitative traits, we used an additive model of the relationship between biological yield and the elements of its structure. For Acha variety, it did not pass the mathematical test for use according to the last checkpoint (the coincidence of the signs of the regression coefficients bk and the correlation coefficients rхky of the relationship between the dependent and independent variables of the multiple regression model); b3 was 0.0095; rх3y was -0.315. Based on the proven additive models of the other five varieties, we determined point forecasts of biological yield in each of the environments (weather conditions in 2011–2020). The predicted yield values for different genotypes deviated from the actual ones in the range of ki of 5.7–7.1% (Veresk – 7.1%; Pamyati Chepeleva – 7.1%; Binom – 7.0%; Sonet – 5.7%; Bagrets – 6.1%). This forecast accuracy (ki was less than 15%), ultimately, made it possible to differentiate the point forecast of biological yield from the forecast, depending on the effect of quantitative traits. As a result, we created a matrix of yield forecast data (Ŷkci) for each environment (2011–2020).
Keywords: barley (Hordeum vulgare L.); quantitative traits; productivity; genotype; conditions.
Author Details: R. A. Maksimov, Cand. Sc. (Agr.), leading research fellow (е-mail: Этот адрес электронной почты защищён от спам-ботов. У вас должен быть включен JavaScript для просмотра.).
For citation: Maksimov RA. [Multiple regression analysis as a way to differentiate yield by the phases of growth and development of barley genotypes (Hordeum vulgare L.)]. Dostizheniya nauki i tekhniki APK. 2021;35(4):29-34. Russian. doi: 10.24411/0235-2451-2021-10404.