Using natural product to reduce stress effects of herbicides in the Amur region
- Authors: Gretchenko A.E.1, Mikhailova M.P.1
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Affiliations:
- Russian Research Institute of Soybean
- Issue: Vol 19, No 4 (2024)
- Pages: 566-577
- Section: Crop production
- URL: https://agrojournal.rudn.ru/agronomy/article/view/20124
- DOI: https://doi.org/10.22363/2312-797X-2024-19-4-566-577
- EDN: https://elibrary.ru/AIGNMK
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Abstract
The relevance of the study is determined by the need for timely assessment of spring wheat varieties, including newly created ones, depending on changing weather factors in the cultivation area. The purpose of the study was to analyze the indicators of the ear of spring wheat varieties, which is the main element of sowing productivity. The materials for the research were yield data, indicators of the yield structure of spring soft wheat varieties obtained in field experiments conducted in the conditions of the Orenburg Urals during 2019—2020 and 2022—2023. The research methods included field experiments, structural analysis of accounting sheaf material, ranking of varieties by years of experiments and their final ranking, correlation and regression analysis of dependence of yield on ear productivity. The growing conditions of spring wheat in the years of research were characterized by significant aridity. The grade assessment of the varieties showed a significant dependence of their productivity on weather conditions and their ecological adaptation. The graphs showed that the theoretical yield in the range from 14.9 c/ha to 19.1 c/ha corresponds to: 25.7 grains per ear, 35.8 g — weight of 1000 grains, 13.6 spikelets per ear, 0.75 g — weight of grains per ear. The actual indicators of ear productivity elements were given by years of experiments with an analysis of their varietal differences and depending on the conditions of the year. We can conclude that weather factors cause differences in the response of varieties to their variability, expressed in their yield level under similar conditions. Varieties created at a later time are characterized by greater ecological plasticity with increasing yields and formation of a much more complete ear. Such varieties include Ulyanovskaya 105, Orenburgskaya 30, Tulaykovskaya zolotistaya. The varieties least adapted to the stress factors of the weather include Uchitel, Saratovskaya 42.
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Table 1
Meteorological data for the soybean growing season in 2021–2023
Month | Average daily air temperature, °C | Amount of precipitation, mm | ||||||
2021 | 2022 | 2023 | Average | 2021 | 2022 | 2023 | Average | |
May | 11.9 | 11.9 | 13.4 | 12.4 | 96.1 | 61.0 | 41.0 | 39.0 |
June | 20.2 | 19.5 | 18.7 | 18.8 | 57.1 | 100.0 | 79.0 | 85.0 |
July | 23.4 | 23.4 | 22.5 | 21.5 | 104.2 | 38.0 | 73.0 | 106.0 |
August | 18.8 | 18.8 | 20.3 | 19.2 | 194.2 | 121.0 | 194.0 | 103.0 |
September | 14.2 | 13.1 | 13.2 | 12.4 | 35.9 | 39.0 | 102.0 | 66.0 |
Source: created by A.E. Gretchenko, M.P. Mikhailova.
Table 2
Effect of bioproduct and herbicides application on amino acid composition of protein, %, in seeds of soybean cv. Nevesta
Treatment of | Total protein | including amino acids | ||||
seed | vegetative plants | Lysine | Histidine | Valin | Methyl histidine | |
Control (without treatment) | 36.88 ± 0.22 | 6.74 ± 0.11 | 5.99 ± 0.26 | 7.05 ± 0.49 | 1.99 ± 0.21 | |
Water | Stratos Ultra (1 L/ha) + | 38.37 ± 0.20 | 6.74 ± 0.11 | 5.74 ± 0.28 | 7.01 ± 0.56 | 1.92 ± 0.10 |
Water | Stratos Ultra (1 L/ha) + | 39.20 ± 0.42 | 6.68 ± 0.25 | 4.15 ± 0.42 | 6.86 ± 0.55 | 1.87 ± 0.20 |
Source: created by A.E. Gretchenko, M.P. Mikhailova.
Table 3
The effect of bioproduct and herbicides on quantitative and qualitative composition of fat, %, in seeds of soybean cv. Nevesta
Treatment | Fat | Unsaturated fatty acids | ||||
before sowing | of vegetative plants | Linolenic | Linoleum | Oleic acid | Stearic acid | |
Control (without treatment) | 18.42 ± 0.61 | 8.84 ± 0.19 | 51.46 ± 0.47 | 21.73 ± 0.66 | 3.25 ± 0.24 | |
Water | Stratos Ultra (1 L/ha) + Bison (1.5 L/ha) | 18.18 ± 0.42 | 9.17 ± 0.19 | 51.18 ± 0.12 | 21.57 ± 0.69 | 3.27 ± 0.17 |
Water | Stratos Ultra (1 L/ha) + Bison (1.5 L/ha) + | 19.25 ± 0.43 | 7.80 ± 0.36 | 51.46 ± 0.45 | 24.80 ± 0.50 | 3.23 ± 0.07 |
Source: created by A.E. Gretchenko, M.P. Mikhailova.
Table 4
The plant density and survival of ‘Nevesta’ soybean plants in 2021–2023
Variant | Seedling density, plants per m2 | Germination, % | Plant density before harvesting, | Survival, % | |
Control (no treatment) | 46.3 | 90.5 | 44.2 | 95.5 | |
Distilled water | Stratos Ultra (1 L/ha) + Bison (1.5 L/ha) | 47.8 | 91.0 | 44.6 | 93.3 |
Distilled water | Stratos Ultra (1 L/ha) + Bison (1.5 L/ha) + | 48.7 | 90.8 | 48.1 | 98.8 |
Source: created by A.E. Gretchenko, M.P. Mikhailova.
Table 5
Biometric indicators of ‘Nevesta’ soybean plants in 2021–2023
Variant | Number per plant | Seed weight per plant, g | ||
Seed treatment before sowing | Treatment of vegetative plants | beans | seeds | |
Control (no treatment) | 16.0 | 38.0 | 5.61 | |
Distilled water | Stratos Ultra (1 L/ha) + Bison (1.5 L/ha) | 15.9 | 37.3 | 5,60 |
Distilled water | Stratos Ultra (1 L/ha) + Bison (1.5 L/ha) + Bio-Algo (5 ml/L) | 19.8 | 48.1 | 7,27 |
LSD05 | 3.4 | 7.2 | 1.33 |
Source: created by A.E. Gretchenko, M.P. Mikhailova.
Biological yield of soybean cv. Nevesta in 2021–2023, t/ha: 1 — control; 2 — herbicides (treatment of vegetative plants); 3 — herbicides + bioproduct (treatment of vegetative plants)
Source: created by A.E. Gretchenko, M.P. Mikhailova.
About the authors
Alina E. Gretchenko
Russian Research Institute of Soybean
Author for correspondence.
Email: gae@vniisoi.ru
ORCID iD: 0000-0003-3930-5672
SPIN-code: 3750-4348
Researcher, Laboratory of Plant Physiology and Biochemistry
19 Ignatievskoe Highway, Amur region, Blagoveshchensk, 675027, Russian FederationMaria P. Mikhailova
Russian Research Institute of Soybean
Email: mmp@vniisoi.ru
SPIN-code: 9142-3480
Senior Researcher, Laboratory of Plant Physiology and Biochemistry 19 Ignatievskoe Highway, Amur region, Blagoveshchensk, 675027, Russian Federation
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Supplementary files
Source: created by A.E. Gretchenko, M.P. Mikhailova.
