Abstract

For the rational use of degraded pastures and the fight against desertification, it is necessary to apply phytomelioration of territories more widely. The most effective will be the use of communities consisting of shrubs, semi-shrubs and perennial grasses. To identify on this further, the study was conducted at the experimental sites of the Agroecology Research Center of the Russian Academy of Sciences in the period from 2019 to 2022. In this study, artificially created models of long-term pasture ecosystems were utilized. In the arid conditions of the south of Russia, weather conditions had the greatest impact on the productivity of the created pasture models (share of influence is 81-85%), while the yield of dry fodder mass of the herbaceous-semi-shrub layer varied from 1.2 to 3.9 t/ha, depending on the seasonality of the pasture and the composition of the soil substrate. The highest yield of phytomass is formed on pastures created on a chernozem-like sandy loam substrate (humus horizon of chernozem-like sandy loam soil) on spring-summer pastures, the yield increase is 33% higher compared to spring-summer pastures on a light chestnut sandy substrate (a product of Aeolian processing of light chestnut soil). On all types of pastures, groups of cereals were leading in terms of biomass. Analysis of the multiple regression equation between the phytomass yield of model pastures (y3), temperature (x1) and precipitation of the warm period (x2) on pastures with a chernozem–like soil substrate showed that the multiple correlation coefficient R = 0.96 - this states a close relationship between the effective indicator and two factors. The multiple determination coefficient R2 = 0.98 means that 98% of the variation in the phytomass yield level is taken into account by the regression equation and 2% is accounted for by unaccounted factors on a light chestnut sandy substrate. The moderate correlation dependence of the yield of the fodder mass of model pastures (y3) on a plot with a light chestnut sandy substrate on the temperature in the warm period (x1), the component (r=0.52) indicates a significant effect on the yield of precipitation of the warm period (x2) with r=0.94.

Full Text
Published version (Free)

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