TÜRKİYE'NİN YABANCI OTLARI VE ÖZELLİKLERİ: AYÇİÇEĞİ
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».