Impact of Digital Farming on Sustainable Development and Planning in Agriculture and Increasing the Competitiveness of the Agricultural Business
Bibliographic record
Abstract
To develop agriculture, it is crucial to introduce digital farming. This is a fundamentally new management strategy based on digital technologies associated with the use of geographic information systems of global positioning, onboard computers, and smart equipment, as well as managerial and executive processes that can differentiate the methods of farming, fertilization, and adding chemical ameliorants and plant protection products. The study aims at determining the applied aspects and key components within a system of digital farming as a tool for the sustainable development of the agricultural business. The authors chose a mixed type of research, with a predominance of qualitative research methods. In particular, to collect data, the authors analyzed scientific sources on the research problem and conducted an expert survey measuring the degree of consistency of expert opinions with mathematical processing of the results obtained. It was determined that in Russia, it is necessary to consistently introduce the use of digital farming. This includes the introduction of parallel stirring, the ability to turn off the sections of the seeder on the floors, the re-equipment of crop protection sprayers to turn off the sections on the floors, and the acquisition of new equipment for differentiated fertilization. The authors conclude that the introduction of digital farming by agricultural producers is a tool for sustainable development and planning in agriculture and increasing the competitiveness of the agricultural business since it increases the economic (increased yields, reduced crop losses, increased land bank efficiency), environmental (production in risky farming areas), and social (increasing the level of personnel qualification and social standards) efficiency of their activities.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".