Energy-economic efficiency of growth of grain-crop cultures in conditions of right-bank forest-steppe zone of Ukraine
Bibliographic record
Abstract
The article aims to develop the theoretical framework and test the practical measures on increasing the efficiency of pea and lupine white breeding by cultivar selection, technological substantiation of basic tillage, and optimizing nutrition, in the conditions of the Forest-steppe zone. The results of the research in studying the peculiarities of growing, development and formation grain productivity of peas and lupine white depending on the pre-treatment of seeds and foliar nutrition usage are determined as well as economic, bioenergetic valuation and the evaluation of the technology on the competitiveness is given. In addition to soil introduction of the calculated doses of mineral fertilizers, it is imperative to use highly effective fertilizers for foliar fertilization on peas and lupine, in particular and microplant. It is also advisable to conduct the treatment before sowing peas and lupine with biopreparations on the basis of nodule bacteria and also carry out the treatment of seeds with biopharmaceuticals based on phosphate-mobilizing bacteria and a bacterial preparation for the prevention of fungal disease of the root system of plants. Using the obtained theoretical and experimental data, the breeding peculiarities and the development of the assimilation apparatus and the effectiveness of the PAPs usage, functioning of the symbiotic apparatus, formation of individual, and grain productivity of peas and lupine varieties depending on the method of basic soil cultivation and feeding systems were established. The economic and energy efficiency of peas and lupine grain production using the proposed elements of the technology of growing these leguminous crops in the conditions of the Forest-steppe zone was also calculated. Keywords: Peas; lupine white; pre-treatment of seeds; foliar nutrition; grain productivity; grain quality; economic and bioenergetic efficiency References: Lapinskas, E. (1998). Nitrogen fixation of biological nitrogen. Academy (Kedainiai district). Hunt, S., & Layzell, D. B. (1993). Gas exchange of legume nodules and the regulation of nitrogenase activity. Annual Review of Plant Biology, 44(1), 483-511. Davis Tim, D., & Narenda, S. (1986). Soybean photosynthesis and growth as influenced by flurprimidol. Compar Physiology and Ecology, 11(4), 166-169. Eviner, V. T., & Chapin III, F. S. (1997). Plant–microbial interactions. Nature, 385(6611), 26. Bohlool, B. B., Ladha, J. K., Garrity, D. P., & George, T. (1992). Biological nitrogen fixation for sustainable agriculture: A perspective. Plant and soil, 141(1-2), 1-11. Bollman, M. I., & Vessey, J. K. (2006). Differential effects of nitrate and ammonium supply on nodule initiation, development, and distribution on roots of pea (Pisum sativum). Botany, 84(6), 893-903. Brelles-Marino, G., & Boiardi, J. L. (1996). Nitrogen limitation of chemostat-grown Rhizobium etli elicits higher infection-thread formation in Phaseolus vulgaris. Microbiology, 142(5), 1067-1070. Brevedan, R. E., & Egli, D. B. (2003). Short periods of water stress during seed filling, leaf senescence, and yield of soybean. Crop Science, 43(6), 2083-2088. Novak, K., Slajs, M., Biedermannova, E., & Vondrys, J. (2005). Development of an Asymbiotic Reference Line for Pea cv. Bohatýr. Djekoun, A., & Planchon, C. (1991). Water status effect on dinitrogen fixation and photosynthesis in soybean. Agronomy Journal, 83(2), 316-322. Dorcinvil, R., Sotomayor-Ramirez, D., & Beaver, J. (2010). Agronomic performance of common bean (Phaseolus vulgaris L.) lines in an Oxisol. Field Crops Research, 118(3), 264-272. Duke, S. H., & Collins, M. (1985). Role of potassium in legume dinitrogen fixation. Potassium in agriculture, (potassiuminagri), 443-465. Elkins, D. M., Hamilton, G., Chan, C. K. Y., Briskovich, M. A., & Vandeventer, J. W. (1976). Effect of Cropping History on Soybean Growth and Nodulation and Soil Rhizobia 1. Agronomy Journal, 68(3), 513-517. Mateos, P. F., Baker, D. L., Petersen, M., Velazquez, E., Jimenez-Zurdo, J. I., Martinez-Molina, E., Andrea, S., Guy, O., David, H. H., & Dazzo, F. B. (2001). Erosion of root epidermal cell walls by Rhizobium polysaccharide-degrading enzymes as related to primary host infection in the Rhizobium legume symbiosis. Canadian journal of microbiology, 47(6), 475-487. Furseth, B. J., Conley, S. P., & Ane, J. M. (2011). Soybean response to rhizobia on previously flooded sites in southern Wisconsin. Agronomy journal, 103(3), 573-576. Furseth, B. J., Conley, S. P., & Ane, J. M. (2012). Soybean response to soil rhizobia and seed-applied rhizobia inoculants in Wisconsin. Crop science, 52(1), 339-344.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".