TÜRKİYE'NİN YABANCI OTLARI VE ÖZELLİKLERİ: AYÇİÇEĞİ
Bibliographic record
Abstract
The global production of sunflower is greatly affected by various factors, including the increase in production costs, such as energy, chemical, and labor costs. Additionally, emerging geopolitical concerns and the possible consequences of climate change have also a significant impact on sunflower production worldwide. This phenomenon leads to substantial increase or fluctuation in the price of sunflower, which is the primary source of vegetable oil in Turkiye, as well as in products derived from sunflower.The sunflower plant has a high susceptibility to weed competition, particularly during its early development stages. The failure of effective weed management practices can result in significant reductions in crop productivity, with potential yield losses reaching as high as 70%. This can render the cultivation of sunflowers nearly unviable. Hence, the management of weeds assumes an essential role in preventing the negative impact on crop yield and quality. Nevertheless, the occurrence of herbicide-induced phytotoxicity, the increasing incidence of herbicide resistance in weeds, and the difficulties encountered in effectively controlling certain weed species that belong to the same family as sunflowers pose significant challenges to weed management. Consequently, the successful management of weeds in sunflower cultivation requires the adoption of a holistic strategy that includes a variety of control techniques, such as cultural, mechanical, and chemical methods. When developing management strategies, it is essential to to initially address the challenge in insufficient knowledge regarding troubling weed species in the agro-ecosystem. This includes understanding the biological and ecological characteristics of weeds, as well as monitoring changes in weed populations within the field over a specified timeframe. The existing literature on the occurrence and characteristics of noxious weed species in sunflower cultivation in Türkiye is inadequate, given the gradual expansion of sunflower cultivation across Türkiye. This review assesses the results of weed control studies conducted in sunflower production regions in Türkiye, and evaluates the findings of surveys conducted to identify weed species within the specified time frame (1973 - 2023). The literature findings were compared with technical instructions and relevant books. A comprehensive synthesis of various studies was conducted to compile a detailed list of weed species prevalent in sunflower cultivation areas across the entire nation. The resulting compilation provided an overview of the general characteristics of these weeds. The prominent weed species found in sunflower fields in Turkey have also been highlighted.An extensive inventory revealed the presence of 316 distinct weed species within sunflower cultivation regions across Türkiye. However, the quantity of weed species encountered frequently was approximately 80. The most problematic weed species (15 species) in sunflower fields were bindweed (Convolvulus arvensis), lamb's quarters (Chenopodium album), wild mustard (Sinapis arvensis), redroot pigweed (Amaranthus retroflexus), common cocklebur (Xanthium strumarium), Canada thistle (Cirsium arvense), cockspur grass (Echinochloa crus-galli), black nightshade (Solanum nigrum), common purslane (Portulaca oleraceae), foxtail species (Seteria spp.), common knotgrass (Polygonum aviculare), European heliotrope (Heliotropium europaeum), Bermuda grass (Cynodon dactylon), jimsonweed (Datura stramonium), and saltbush species (Atriplex spp.). Inaddition, a total of four species of broomrape (Orobanche spp.) and 2 species of frass (Cuscuta spp.) were identified. The prevalence of parasitic species from two distinct genera was recorded in 11% and 5% of sunflower fields in Türkiye, respectively. Significant temporal and spatial/regional differences have been reported among weed species and their densities in sunflower production areas accross Türkiye. The observed phenomenon can be attributed to the variation of ecological conditions, as well as changes in the production system, including practices such as crop rotation, tillage, fertilization, and irrigation. Additionally, differences in weed management strategies employed also contribute to the sevariations. Hence, it is crucial to develop region- or field- specific weed management strategies within the context of integrated weed control in sunflower cultivation regions, as opposed to relying solely on conventionally employed calendar-based weed control methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.126 | 0.045 |
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".