The impact of crop rotation and tillage practices on weed flora of cotton in the southern fertile crescent

Abstract:

Background:  Weeds remain a key constraint in cotton systems of Southeastern Anatolia, and agronomic practices, including crop rotation and conservation tillage, may alter weed communities and pressure.

Objective:  The primary objective was to quantify the effects of crop rotation and tillage on weed flora (density/diversity, and key species) and cotton yield.

Methods:  Field studies were conducted at Harran University (Şanlıurfa, Türkiye) during 2021–2024 using three tillage methods [conventional tillage (CT), reduced tillage (RT), no tillage (NT)] and three cropping systems (cotton/cotton; cover crop [vetch + triticale]/cotton; wheat/maize/cotton). Weed species, their densities/diversity, and cotton yield were recorded.

Results:  A total of 69 weed species (23 families) were recorded. Amaranthus palmeri, Sorghum halepense, and Physalis angulata were the dominant weeds. Weed density/diversity was highest under cotton/cotton (≈50–120 plants m⁻²) and lowest under wheat/maize/cotton (≈20–60 plants m⁻²). Weed density was significantly altered by tillage practices, and the highest (≈60–140 plants m⁻²) and the lowest (≈20–70 plants m⁻²) densities were recorded for NT and CT, respectively. The highest cotton yield (≈5,940–6,670 kg ha⁻¹) was recorded for cover crop/cotton, whereas cotton/cotton resulted in the lowest yield (≈4,070–4,260 kg ha⁻¹).

Conclusions:  Crop diversification reduced weed pressure compared with continuous cotton, and CT generally minimized weed density relative to NT. However, crop rotation or tillage alone was insufficient for weed control. The findings support integrated weed management that combines cultural practices and herbicide programs. Nevertheless, longer-term trials (≥5 years) are needed to confirm the results.

Keywords:
Cotton production; Weed control; Tillage methods; Crop rotation; Cover crops; Sustainable agriculture

1. Introduction

Cotton (Gossypium hirsutum L.) is a fundamental raw material for the textile industry, accounting for approximately one-third of the global fiber trade. It is extensively cultivated across various regions, playing a crucial role in agricultural economies worldwide (Khan et al., 2020). China is the leading cotton producer, while Türkiye ranks seventh globally. Türkiye produced 1,017,500 tons of cotton on 573,161 hectares in 2023. The Southeastern Anatolia Region accounted for 59% of this production, followed by the Aegean Region (24%) and the Mediterranean Region (17%) (TÜİK, 2024).

Cotton production is affected by various biotic and abiotic constraints that can significantly affect yield and fiber quality. Weed infestation is one of the most serious concerns in cotton production (Rudell et al., 2023). Weeds compete with cotton plants for water, nutrients, and sunlight, leading to substantial reductions in yield and overall crop performance (Webster, Nichols, 2012). Additionally, weed infestation increases production costs by necessitating labor-intensive management strategies, including mechanical removal, herbicide applications, and cultural practices. Certain aggressive weed species can become dominant in cotton fields if left uncontrolled (Figure 1), intensifying competition and further diminishing fiber quality (Anwar et al., 2021; Munda et al., 2024; Agrawal, Singh, 2025). This makes weed control a major agronomic challenge in cotton production systems worldwide (Memon et al., 2025). Inadequate weed management strategies can result in substantial yield losses, ranging from 10% to 90% under severe infestations (Tariq et al., 2020; Süer, Tursun, 2024).

Figure 1
Cotton field under severe infestation of Johnsongrass (Sorghum halepense)

A combination of pre- and post-emergence herbicides and genetically modified (GM) and herbicide-tolerant cotton varieties has been employed for weed management in cotton (Iqbal et al., 2019). Glyphosate-resistant cotton has played a significant role in weed management (International Service for the Acquisition of Agri-biotech Applications, 2017; Iqbal et al., 2019). Despite advances in technology and the development of herbicide-resistant cotton varieties, increasing weed infestation is observed in cotton crop globally (Price et al., 2016). Several weed species, including Amaranthus palmeri, Commelina benghalensis, Conyza bonariensis, Conyza canadensis, and Sorghum halepense, have evolved resistance to herbicides in the regions cultivating glyphosate-resistant cotton varieties, leading to complex management challenges and yield losses (Iqbal et al., 2019; Vulchi et al., 2023; Agrawal, Singh, 2025). The evolution of herbicide resistance in weeds, combined with the potential environmental damage from herbicide residues and their adverse effects on biodiversity, necessitates urgent action to develop sustainable weed control strategies (Vulchi et al., 2023). Environmentally friendly practices, such as cultural methods, allelopathy, and tillage, have garnered increasing attention under persistent weed management challenges (Damalas, Koutroubas, 2025). Agronomic strategies, including crop rotation, cover cropping, conservation tillage, and initiatives to reduce the weed seed bank, not only suppress new infestations but also enhance the overall efficiency and sustainability of cotton production. These integrated approaches offer significant cost-effectiveness and environmental benefits, making them a critical component of modern weed management in cotton (Vulchi et al., 2023; Rudell et al., 2023; Billman et al., 2023; Agrawal, Singh, 2025; Damalas, Koutroubas, 2025).

Weeds continue to cause significant yield losses in cotton production in regions where glyphosate-resistant cotton varieties are prohibited, regardless of whether conventional or organic management practices are used (Süer, Tursun 2024). Integrated management approaches that combine multiple control techniques must be adopted to prevent yield losses from weeds and achieve high-quality and yield (Agrawal, Singh 2025). Effective weed control during critical growth stages, i.e., during the first 1–2 weeks after seedling emergence and again during weeks 7–10, is vital for optimizing crop performance (Tursun et al., 2016).

Şanlıurfa is one of the leading cotton-producing regions in Türkiye, where the expansion of irrigated land under the Southeastern Anatolia Project has led to widespread cotton cultivation (Aydogdu et al., 2018). However, conventional agricultural practices, cotton monoculture, and excessive use of herbicides and insecticides have declined soil productivity and posed serious environmental health concerns. Moreover, the widespread use of flood irrigation facilitates the dispersal of weed seeds to adjacent areas, resulting in substantial changes in the weed flora (Arslan 2018). Consequently, the adoption of integrated weed management strategies is essential for ensuring sustainable cotton production.

This study assessed the impacts of various crop rotation systems, i.e., continuous cotton, a cover crop system (triticale + vetch) followed by cotton, and a rotation involving cotton with winter wheat and grain maize, alongside different tillage practices, including conventional tillage (CT), reduced tillage (RT), and zero tillage (ZT), on the composition and density of weed species and cotton yield. It was hypothesized that (i) diversified cropping systems (cover crop/cotton and wheat/maize/cotton rotations) would reduce weed density and diversity compared with continuous cotton monoculture, (ii) CT would cause higher weed suppression than RT or ZT, and (iii) the combination of crop diversification and appropriate tillage would improve cotton yield relative to monoculture cotton under any single tillage method.

2. Materials and Methods

The study was conducted between 2021 and 2024 in the experimental fields of the Faculty of Agriculture, Harran University Sanliurfa, Türkiye. The soil of the experimental area belongs to the Kısas series, which is prevalent in the Harran Plain. These soils are classified as Typic Calciusterts in Soil Taxonomy and are characterized by high clay content. Detailed soil properties of the experimental site are provided in Table 1.

Table 1
Physical and chemical characteristics of the horizons in the soil profile opened in the experimental area

Horizon	Depth (cm)	pH	EC (dS/m)	Lime (%)	Organic Matter (%)	CEC (me/100g soil)	Clay (%)	Silt (%)	Sand (%)
Ap	0-14	7.84	0.67	28.0	1.72	41.2	67.2	19.8	13.0
Ad	14-34	7.82	0.82	29.2	1.49	38.5	68.7	21.0	10.2
Bwk	34-55	7.83	0.73	34.6	0.76	40.6	69.0	18.2	12.8
Bss	55-110	7.83	0.72	30.4	0.86	44.8	69.8	18.8	11.4
Bk	110-165	7.85	0.76	38.8	0.81	42.1	71.3	18.6	10.1

The soil pH ranged from 7.82 to 7.85, indicating a slightly alkaline nature. Electrical conductivity (EC) varied between 0.67 and 0.82 dS/m. Lime content ranged from 28.0% to 38.8%, while organic matter content varied between 0.76% and 1.72%. The cation exchange capacity (CEC) ranged from 38.5 to 44.8 me/100g soil. The soil had a high clay content, with clay ranging from 67.2% to 71.3% in the profile, silt from 18.2% to 21.0%, and sand from 10.1% to 13.0%.

The experiments were conducted under field conditions using a randomized block design with three replications. The net plot size was 12 x 30 m. Experimental design and treatments are presented in Figure 2.

Figure 2
Experimental Design: Tillage Treatments (CT: Conventional Tillage; RT: Reduced Tillage; ZT: Zero Tillage)

The crop rotation and tillage methods were employed following the procedures outlined below (Table 2). Additionally, herbicides were sprayed using a field sprayer to ensure weed control in the experimental area. A tractor-mounted boom sprayer (Hardi Navigator 3000, Denmark) equipped with an 8-meter boom was used to apply herbisices. The sprayer was fitted with XR TeeJet 11002 flat-fan nozzles spaced 50 cm apart, operating at 2.5 bar and a spray volume of 300 L ha⁻¹. The tractor speed during application was maintained at 6–7 km h⁻¹. In the ZT plots, glyphosate (Roundup, 360 g L⁻¹ a.i.) was applied at a rate of 1,440 g a.i. ha⁻¹ as a pre-sowing burndown treatment. A pre-emergence application of pendimethalin (Stomp Extra, 455 g L⁻¹ a.i.) was made at a rate of 1365 g a.i. ha⁻¹ immediately after cotton sowing.

Table 2
Soil tillage methods used in the sowing of the crops in the experiment

Soil tillage method	Summer sowing (Cotton)	Winter sowing (Wheat and vetch + triticale mixture)	Summer sowing (Maize)
Conventional Tillage (CT)	Shredding of crop residues from the previous crop Tillage with a moldboard plow (30–33 cm) Tillage with a goble disk harrow (18–20 cm) Rolling with a land roller Cotton planting with a pneumatic precision seeder	Shredding of crop residues from the previous crop Tillage with a moldboard plow (30–33 cm) Tillage with a goble disk harrow (18–20 cm) Rolling/leveling (2 times) Sowing of wheat or vetch+triticale using a grain drill Rolling with a land roller	Shredding of crop residues from the previous crop Tillage with a moldboard plow (30–33 cm) Tillage with a goble disk harrow (18–20 cm) Rolling/leveling Planting maize using a pneumatic precision seeder
Reduced Tillage (RT)	Shredding of crop residues from the previous crop Tillage with a goble disk harrow (18–20 cm) (2 times) Planting cotton using a pneumatic precision seeder	Shredding of crop residues from the previous crop Tillage with a goble disk harrow (18–20 cm) (2 times) Sowing of wheat or vetch+triticale using a grain drill Rolling with a land roller	Shredding of crop residues from the previous crop Tillage with a goble disk harrow (18–20 cm) (2 times) Rolling/leveling Planting maize using a pneumatic precision seeder
Zero Tillage (ZT)	Shredding of crop residues from the previous crop Herbicide application Planting cotton using a pneumatic precision seeder	Shredding of crop residues from the previous crop Herbicide application Direct sowing of wheat or vetch+triticale using a grain drill Rolling with a land roller	Shredding of crop residues from the previous crop Herbicide application Direct sowing of maize using a pneumatic precision seeder

2.1 Planting and Agronomic Practices

A mixture comprising 75% Hungarian vetch (Vicia pannonica) and 25% triticale was used as the cover crop. In subsequent years, this same cover crop mixture was replanted in the same plots following the cotton harvest. Cover crop was mowed at a height of 10 cm above the soil surface at the end of vegetative growth in the cover crop/cotton (CC/C) rotation. No herbicides were used for desiccation. The residues were incorporated into the soil in the CT and RT treatments, following the tillage methods described in Table 2. The mowed residues were left on the soil surface as mulch in the ZT treatment, and a burndown herbicide (glyphosate at 1,440 g a.i. ha⁻¹) was applied to terminate any regrowth before cotton planting. Mowing was performed at a height of 10 cm above the soil surface to enhance the cover crop's contribution of organic matter. Soil preparation in the CT and RT treatments was carried out using the specified tillage methods (Table 2).

The widely cultivated cotton variety ‘May 455’ in the Southeastern Anatolia Region was used in this study. The crop was sown with a row spacing of 70 cm and a sowing depth of 3–4 cm. Cotton was planted between April 25 and May 20, depending on prevailing climatic conditions. A pre-emergence herbicide (pendimethalin at 1365 g a.i. ha⁻¹) was applied, followed by mechanical hoeing between the rows to loosen the root zone and control weed growth when the plants reached a height of 15–40 cm. Additionally, broadleaf weeds were manually removed three times to ensure more effective weed management. Soil moisture levels were regularly monitored, and irrigation was carried out using a drip irrigation system in the first year, while a sprinkler irrigation system was utilized in the subsequent two years.

Fertilizers were applied based on soil analysis and the nutrient requirements of each crop. A total of 160 kg N ha⁻¹ (as ammonium sulfate, 21% N), 80 kg P₂O₅ ha⁻¹ (as triple superphosphate, 46% P₂O₅), and 80 kg K₂O ha⁻¹ (as potassium sulfate, 50% K₂O) were applied to cotton crop. Half of the nitrogen and the full amounts of phosphorus and potassium were incorporated at sowing, while the remaining nitrogen was supplied as topdressing in two equal applications during the vegetative growth and flowering stages. For wheat, 140 kg N ha⁻¹ and 60 kg P₂O₅ ha⁻¹ were applied, whereas 200 kg N ha⁻¹, 80 kg P₂O₅ ha⁻¹, and 80 kg K₂O ha⁻¹ were applied to maize following the same split-application strategy.

Wheat was sown in plots prepared following the cotton harvest. The ‘Zivago’ wheat variety was planted at a depth of 3–4 cm, with a row spacing of 15 cm and a seeding rate of 250 kg per hectare. The 2,4-D amine (Mustang, 720 g L⁻¹ a.i.) herbicide (720 g a.i. ha⁻¹) was applied at the tillering stage of wheat to control broadleaf weeds. A second application was done at the stem-elongation stage, as necessary. Following the wheat harvest, the ‘MAY Capuzi’ maize variety was sown on June 22. Maize was planted at a depth of 3–5 cm with a row spacing of 70 cm and a plant spacing of 18 cm, using a seeding rate of 30 kg per hectare. A total of 9 irrigations were applied using the sprinkler irrigation method. A post-emergence herbicide was applied during the 2–4-leaf stage of weeds to control weeds in the maize crop.

2.2 Weed Monitoring, Species Identification, and Density Assessment

Weeds were monitored throughout the vegetation period during 1^st year. However, monitoring was conducted during the early growth stages of cotton to evaluate the effects of cover crops on weed species composition and diversity in subsequent years. Weed species and densities were assessed at four distinct locations within each plot at the end of the third week following cotton emergence. For this purpose, 50 × 50 cm quadrats were used to quantify the total number of weeds per square meter and their frequency of occurrence.

The observed weed species were classified into four categories based on their distribution range, invasiveness status, and ecological significance in the agricultural systems of Türkiye (Serim et al., 2023). Species that were recently observed in the region with a narrow distribution range were regarded as invasive alien plants (IAP) (A). Weeds reaching significant densities in certain ecosystems and crops in Türkiye and/or other regions were regarded as significant weeds (B). Species that can be found in various cropping systems, different climatic conditions, and across a range of crops were classified as cosmopolitan species (C). Species rarely seen in agricultural areas that exhibit invasive traits but have not yet been conclusively proven to cause ecological, economic, and/or social problems (suspected species) were regarded as rare species (W). The names of the identified species were checked from World Flora Online for consistency (https://www.worldfloraonline.org/). Similarly, EPPO codes were retrieved from the European and Mediterranean Plant Protection Organization database (https://gd.eppo.int/).

2.3 Statistical Analysis

Weed density and species diversity data were collected over three cotton-growing seasons (2022, 2023, and 2024). Because crop rotation and tillage treatments were established in 2021, the first season data (2022) served as the baseline for subsequent comparisons. Weed density data from each year were analyzed separately using a two-factor analysis of variance (ANOVA) (Steel et al., 1997), with tillage method (CT, RT, ZT) as the main factor and cropping system (C/C, CC/C, C/W/M) as the subplot factor. Mean comparisons were performed using the Least Significant Difference (LSD) test at α = 0.05. Additionally, a combined ANOVA across years was conducted, treating year as a random factor, to assess year × tillage and year × cropping system interactions. Weed species composition data from the three-year monitoring period were pooled for the descriptive floristic analysis. Cotton yield data were analyzed using the same ANOVA model, with LSD multiple comparison tests. All statistical analyses were performed using SPSS. The data were presented using chord diagrams in Origin Pro.

3. Results and Discussion

3.1 Weed Species Identified in the Experimental Plots and Their General Characteristics

A total of 69 weed species from 23 families were identified (Figure 3, Table 3). Among these species, 51 were annuals, 7 were either annuals or biennials, and 11 were perennials. Additionally, 7 of the identified weed species are recognized as invasive in Türkiye, posing a potential threat to local agroecosystems.

Figure 3
Distribution of the weed species observed in the experimental area according to families

Table 3
Weeds detected in the experimental plots between 2021 and 2024 and their general characteristics

Family	Scientific name	EPPO code	General Characteristics	Weed category
Amaranthaceae	Amaranthus albus L.	AMAAL	Annual, IAP	C
Amaranthaceae	Amaranthus blitoides S.Wats.	AMABL	Annual	B
Amaranthaceae	Amaranthus palmeri S.Watson	AMAPA	Annual, IAP	A
Amaranthaceae	Amaranthus retroflexus L.	AMARE	Annual, IAP	C
Amaranthaceae	Chenopodium album L.	CHEAL	Annual	C
Apiaceae	Bifora radians M.Bieb.	BIFRA	Annual	B
Apiaceae	Bupleurum sp.	BUPSS	Annual	B
Apiaceae	Echinophora tenuifolia L.	EHPTS	Perennial	B
Apiaceae	Scandix pecten-veneris L.	SCAPV	Annual	B
Apiaceae	Turgenia latifolia (L.) Hoffm.	CUCLF	Annual	B
Asteraceae	Carduus pycnocephalus L.	CRUPY	Annual	W-B
Asteraceae	Centaurea hyalolepis Boiss.	CENHY	Annual, biennial and perennial	W
Asteraceae	Crepis sancta (L.) Bornm.	CVPSA	Annual	B
Asteraceae	Filago pyramidata L.	FILPY	Annual	B
Asteraceae	Lactuca serriola L.	LACSE	Biennial	C
Asteraceae	Notobasis syriaca (L.) Cass.	NOBSY	Annual	B
Asteraceae	Senecio vernalis Waldst. & Kit.	SENVE	Annual	B
Asteraceae	Senecio vulgaris L.	SENVU	Annual	B-C
Asteraceae	Silybum marianum (L.) Gaertn.	SLYMA	Annual and biennial	B
Asteraceae	Sonchus asper (L.) Hill	SONAS	Annual and biennial	B
Asteraceae	Sonchus oleraceus L.	SONOL	Annual and biennial	B-C
Asteraceae	Xanthium strumarium L.	XANST	Annual, IAP	C
Asteraceae	Filago pyramidata L.	FILPY	Annual	C
Boraginaceae	Buglossoides arvensis subsp. sibthorpiana (Griseb.) R.Fern.	LITAS	Annual	B
Brassicaceae	Calepina irregularis (Asso) Thell.	CPAIR	Annual	B
Brassicaceae	Capsella bursa-pastoris (L.) Medik.	CAPBP	Annual	B-C
Brassicaceae	Sinapis arvensis L.	SINAR	Annual	C
Brassicaceae	Sisymbrium orientale L.	SSYOR	Annual	B-C
Caryophyllaceae	Agrostemma githago L.	AGOGI	Annual	B
Caryophyllaceae	Silene latifolia Poir.	SILLT	Annual, biennial and perennial	W
Caryophyllaceae	Stellaria media (L.) Vill.	STEME	Annual	B-C
Caryophyllaceae	Vaccaria hispanica (Mill.) Rauschert	VAAPY	Annual	B
Convolvulaceae	Convolvulus arvensis L.	CONAR	Perennial, IAP	C
Convolvulaceae	Convolvulus galaticus Rost. ex Choisy	CONGA	Perennial	B
Convolvulaceae	Cuscuta sp.	CVCSS	Annual	A
Convolvulaceae	Ipomoea spp.	IPOSS	Annual	A
Cucurbitaceae	Cucumis melo var. agrestis Naudin	CUMMG	Annual	B
Euphorbiaceae	Euphorbia serpens Kunth	EPHSN	Annual	B
Fabaceae	Astragalus hamosus L.	ASAHA	Annual	C
Fabaceae	Coronilla scorpioides (L.) W.D.J.Koch	CZRSC	Annual	B
Fabaceae	Medicago polymorpha L.	MEDPO	Annual	B
Fabaceae	Prosopis farcta (Banks & Sol.) J.F.Macbr.	PRCST	Perennial	B-C
Fabaceae	Trifolium campestre Schreb.	TRFCA	Annual	W-B
Fabaceae	Trifolium tomentosum L.	TRFTO	Annual	W-B
Fabaceae	Vicia sp.	VICSS	Annual	W-B
Geraniaceae	Erodium cicutarium subsp. cicutarium (L.) L Hér.	EROCI	Annual	W-B
Hypericaceae	Hypericum triquetrifolium Turra	HYPTR	Perennial	W
Lamiaceae	Lamium amplexicaule var. amplexicaule L.	LAMAM	Annual	B-C
Malvaceae	Malva neglecta Wallr.	MALNE	Annual	B-C
Malvaceae	Malvella sherardiana (L.) Jaub. & Spach	FUMOF	Perennial	W-B
Papaveraceae	Fumaria officinalis L.	FUMOF	Annual	B-C
Papaveraceae	Papaver rhoeas L.	PAPRH	Annual	B-C
Plantaginaceae	Plantago coronopus L.	PLACO	Perennial	B
Plantaginaceae	Veronica persica Poir.	VERPE	Annual	B-C
Poaceae	Cynodon dactylon (L.) Pers.	CYNDA	Perennial	C
Poaceae	Bromus tectorum L.	BROTE	Perennial	B
Poaceae	Digitaria sanguinalis (L.) Scop.	DIGSA	Annual	B-C
Poaceae	Echinochloa colonum (L.) Link.	ECHCO	Annual	C
Poaceae	Echinochloa crus-galli (L.) P. Beauv.	ECHCG	Annual	C
Poaceae	Sorghum halepense var. halepense (L.) Pers.	SORHA	Perennial	C
Polygonaceae	Polygonum aviculare L.	POLAV	Annual and biennial	C
Polygonaceae	Polygonum cognatum Meissn.	POLCG	Perennial	W
Portulacaceae	Portulaca oleracea L.	POROL	Annual	C
Ranunculaceae	Ranunculus arvensis L.	RANAR	Annual	B
Rubiaceae	Galium aparine L.	GALAP	Annual	C
Solanaecae	Physalis philadelphica Lam.	PHYIX	Annual	B
Solanaecae	Physalis angulata L.	PHYAN	Annual	B
Solanaecae	Solanum americanum Mill.	SOLAM	Annual	C
Zygophyllaceae	Tribulus terrestris L.	TRBTE	Annual	C

• A: invasive alien plant; B: significant densities; C: cosmopolitan species; W: rare in agricultural systems

3.2 The Effect of Sowing Methods and Soil Tillage on Weed Infestation

The average weed densities are shown in Figures 4, 5, and 6, categorized by crop rotations and tillage methods. A total of 18 different weed species were identified in the experimental area. Among these, the most significant weed species affecting cotton yield and quality were Amaranthus palmeri, Convolvulus arvensis, Physalis angulata, Physalis philadelphica, Portulaca oleracea, Sorghum halepense, and Xanthium strumarium. These species exhibited the highest occurrence frequency and density, posing substantial challenges to crop production.

Figure 4
Changes in weed densities per square meter based on soil tillage methods in the cover crop/cotton treatment

Figure 5
Changes in weed densities per square meter based on soil tillage methods in the cotton/cotton treatment

Figure 6
Changes in weed densities per square meter based on soil tillage methods in the wheat/maize/cotton application

These findings are consistent with previous studies reporting weed flora in Şanlıurfa province. Bükün (2005) reported that Amaranthus spp., Chenopodium spp., Echinochloa spp., Physalis spp., Setaria spp., and Sorghum halepense were the most frequently observed weed species in cotton fields. Similarly, Arslan (2018) identified S. halepense, Xanthium strumarium, Solanum americanum, Physalis philadelphica, and Portulaca oleracea as the predominant weeds in these agricultural areas. Recently, Süer, Tursun (2024) highlighted Amaranthus retroflexus, Convolvulus arvensis, Cyperus rotundus, Echinochloa colonum, Physalis spp., P. oleracea, S. halepense, Solanum nigrum, and X. strumarium as the most prevalent weed species in cotton cultivation systems. These studies collectively reinforce the present study's observations regarding the dominant weed species affecting cotton production in the region.

Previously identified major weed species, except for Cyperus rotundus, were also observed as significant weeds in the current study (Figures 4, 5, and 6). These weeds pose considerable challenges to cotton cultivation, not only in Türkiye but also globally (Webster, Nichols, 2012; Usman et al., 2013; Tursun et al., 2016; Akhil et al., 2024; Damalas, Koutroubas, 2025). Their widespread occurrence and competitive nature highlight the necessity for effective weed management strategies to mitigate yield and quality losses in cotton production.

The relationship between crop rotations and weed species diversity revealed significant changes in weed composition and diversity. The highest and the lowest diversity were observed in C/C and W/M/C rotations, respectively. However, rotations did not alter the prevalence of problematic weed species (Figures 4, 5, and 6). These findings suggest that while crop rotation influences weed diversity, it may not be sufficient on its own to control dominant, highly competitive weed species in cotton. This is consistent with a recent study (Vulchi et al., 2023) demonstrating that even with crop rotation (cotton:sorghum:cotton), high-input herbicide programs were necessary to achieve >90% control of A. palmeri across multiple tillage types.

The integration of cover crops effectively suppressed weeds compared to sole cotton cultivation. Cover crops have been reported to suppress A. palmeri in cotton farming, and increase cotton fiber yields (Price et al., 2016). Similarly, another stuydy indicated that no cover crop alone was sufficient to control weeds effectively without herbicide application (Reeves et al., 2005). However, incorporating black oat or rye cover crops alongside herbicide treatments enhanced cotton productivity while reducing overall herbicide usage. This suggests that the weed-suppressing effects of winter cover crops diminish over time, particularly in summer crops such as cotton, necessitating additional weed management strategies to maintain effective control. This temporal limitation of cover crop-mediated weed suppression was also observed in the present study, where the weed-suppressing effects of the vetch + triticale cover crop diminished as the cotton growing season progressed.

Different tillage methods had a significant impact on weed species composition and diversity. The highest weed densities were observed under ZT, followed by RT and CT. Despite the implementation of different sowing and tillage methods, problematic weed species, including Amaranthus palmeri, Convolvulus arvensis, Echinochloa colonum, Physalis spp., Portulaca oleracea, Sorghum halepense, Sorghum americanum, and Xanthium strumarium, were observed across all treatments. These species are known to cause significant yield and quality losses in cotton. However, their densities varied widely depending on the tillage treatment. Overall, weed density was lowest in the CT treatment and highest in the ZT treatment (Figures 4, 5, and 6), indicating that tillage intensity influences weed proliferation. The higher densities under ZT can be attributed to the concentration of weed seeds in the upper soil layers (0–10 cm). NT systems have dense and abundant weed seed bank in the 0–10 cm layer compared with the 10–20 cm layer, leading to higher weed emergence (Forte et al., 2018). The lack of mechanical burial of weed seed through plowing in ZT explains the elevated weed pressure observed in the present study. In contrast, CT distributes weed seeds throughout the soil profile, reducing the proportion available for germination near the soil surface. CT methods have been reported to improve soil quality and cotton yield over time. However, suppressing invasive and dominant weed species such as Cynodon dactylon, Conyza canadensis, Tribulus terrestris, and Cyperus rotundus remains a challenge in the short term (Usman et al., 2013). Similarly Bilalis et al. (2001) indicated that Malva sp., Convolvulus arvensis, and Solanum nigrum were the most prevalent weed species. Malva sp. was the dominant species in ZT, while C. arvensis was not detected. Conversely, S. nigrum had the highest population density in CT, significantly surpassing that of other tillage systems.

Figure 4 illustrates the impact of different soil tillage methods on weed species composition and density within a CC/C rotation system. Overall, Sorghum halepense (SORHA) emerged as the most dominant species across all three tillage methods. While the RT and ZT methods led to reduced densities of certain weed species, they did not demonstrate a suppressive effect on Amaranthus palmeri (AMAPA), Physalis species (PHYSS), or Portulaca oleracea (POROL) (Figure 4).

Sorghum halepense (SORHA) was the most dominant weed species in C/C rotation, with particularly high densities under the ZT and RT. The CT demonstrated greater effectiveness in suppressing certain weeds, such as Physalis species (PHYS) and Portulaca oleracea (POROL), while Conyza canadensis (CONAR) and Xanthium strumarium (XANST) remained at consistently low densities across all tillage methods. Overall, the ZT resulted in higher weed densities, suggesting that shallow tillage may be insufficient for effectively controlling certain weed species (Figure 5).

Weed densities varied across tillage methods in the C/W/M rotation. Sorghum halepense (SORHA) was the dominant species, particularly under ZT (93.06 plants/m²), showing its inefficiency in weed control. The density of Physalis species (PHYS) under the CT was low, whereas Amaranthus palmeri (AMAPA) and Portulaca oleracea (POROL) showed the highest increases under RT. Overall, the CT method demonstrated greater effectiveness in weed suppression (Figure 6).

3.3 The Effects of Crop Rotations and Tillage Methods on Cotton Yield

Crop rotation and soil tillage significantly influenced cotton yield (p = 0.001). The highest yield was observed in CC/C rotation, while the lowest yields were recorded in the C/C rotation with RT.

The CC/C rotation consistently produced the highest yields across different tillage methods. In CT, CC/C had the highest yields (6,673 kg/ha in 2022 and 6,225 kg/ha in 2024), significantly outperforming other treatments (p = 0.001). ZT and RT resulted in slightly lower yields than CT, with CC/C + ZT yielding 6,036 kg/ha in 2022 and 5,617 kg/ha in 2024, while CC/C + RT showed a sharper decline from 5,825 kg/ha to 4,942 kg/ha. These results suggest that while conservation tillage methods (ZT and RT) in CC/C can sustain relatively high yields, CT remains the most effective option for maximizing cotton production in this rotation (Table 4).

Table 4
The average cotton yields (kg/ha) in 2022 and 2024 under different soil tillage methods in cover crop/cotton (CC/C), cotton/cotton (C/C), and cotton/wheat/maize (C/W/M) rotations

Treatments	2022	2024
CC/C + CT	6,673 a	6,225 a
C/W/M + CT	5,847 bc	5,760 bc
C/C + CT	6,070 b	4,665 e
C/C + ZT	6,078 b	4,755 e
CC/C + ZT	6,036 b	5,617 c
C/W/M + ZT	5,588 bcd	5,900 b
C/W/M + RT	5,337 d	6,160 a
CC/C + RT	5,825 bc	4,942 d
C/C + RT	5,448 cd	4,260 f
Soil Tillage	0.001	0.001
Crop Rotation	0.001	0.001
Soil Tillage x Crop Rotation	0.271	0.001

• ZT: Zero tillage; RT: Reduced tillage; CT: Conventional tillage.

The C/W/M rotation exhibited a unique trend in which RT led to a substantial increase in yield over time. In CT, yields remained relatively stable (5847 kg/ha in 2022 and 5760 kg/ha in 2024). However, under ZT, yields improved from 5588 kg/ha in 2022 to 5900 kg/ha in 2024. The yield stability observed in C/W/M + ZT further reinforces the potential benefits of conservation tillage in diversified cropping systems. The C/W/M + RT treatment experienced a significant increase in yield over time, rising from 5337 kg/ha in 2022 to 6160 kg/ha in 2024, making it one of the highest-yielding treatments in the later stage of the study. These findings suggest that conservation tillage methods, particularly RT, may enhance long-term productivity in diversified rotations through cumulative improvements in soil structure, organic matter content, and nutrient cycling.

The C/C rotation exhibited the lowest yields among all crop rotations, particularly under RT. Under CT, C/C yielded 6070 kg/ha in 2022, but this declined sharply to 4665 kg/ha in 2024. Similarly, C/C + ZT followed a comparable pattern, with yields decreasing from 6078 kg/ha to 4755 kg/ha. The most significant decline was observed in the C/C + RT treatment, where yields dropped from 5448 kg/ha to 4260 kg/ha, making it the lowest-yielding treatment in the study. This suggests that continuous cotton cropping without diversification depletes soil nutrients, exacerbates pest and weed pressures, and contributes to soil degradation over time. The particularly poor performance of C/C + RT indicates that RT alone is not sufficient to maintain productivity in monoculture systems and must be complemented by other sustainable practices, such as crop rotation or integrated weed management. This suggests that continuous cotton monoculture depletes soil nutrients, exacerbates weed pressures, and contributes to soil degradation over time, consistent with findings in long-term crop rotation studies (Rudell et al. 2023).

The interaction between soil tillage and crop rotation was not statistically significant in 2022 (p = 0.271), but by 2024, it became highly significant (p = 0.001), indicating that the combined effects of these management practices became more pronounced over time. The findings align with previous research. While variations in average cotton yields were observed across different soil tillage methods, the overall yield from the RT and ZT methods did not show a significant decline compared to the CT method (Price et al. 2016).

A five-year study reported that cotton yields were higher under the RT compared to the CT during the first three years (Blaise 2003). However, in the subsequent two years, yields in the RT became comparable to those in the CT. Similarly, a separate study found that monocotyledonous and dicotyledonous weed densities were significantly lower in RT than in CT (Blaise 2006). The findings of the present study suggest that crop rotations and tillage methods can influence weed species composition and density, potentially affecting cotton yields either positively or negatively. Given these dynamics, there is a critical need for long-term studies, spanning at least five years, to comprehensively assess the effects of cover crops and tillage methods on weed populations and densities, as well as their overall impact on crop yield.

Conclusions

The results partially support the proposed hypotheses. Diversified cropping systems (CC/C and C/W/M) reduced weed density and species diversity compared with continuous cotton (C/C), supporting the first hypothesis. The W/M/C rotation showed the lowest weed densities (≈20–60 plants m⁻²), while cotton/cotton exhibited the highest (≈50–120 plants m⁻²). CT consistently resulted in the lowest weed densities across all cropping systems, confirming the second hypothesis. ZT showed the highest weed densities (≈60–140 plants m⁻²), largely driven by the proliferation of Sorghum halepense and Amaranthus palmeri in undisturbed soil. The CC/C rotation combined with CT produced the highest cotton yields (6,225–6,673 kg ha⁻¹), partially supporting the third hypothesis. However, the W/M/C rotation under RT also showed yield increase over time (from 5,337 to 6,160 kg ha⁻¹), indicating that conservation tillage can enhance productivity in diversified rotations over the medium term.

Importantly, neither crop rotation nor tillage alone was sufficient to control the dominant problematic weed species (A. palmeri, S. halepense, Physalis spp., P. oleracea, and X. strumarium), which persisted across all treatments. These findings necessitate integrated weed management that combines cultural practices (crop rotation, cover crops) with targeted herbicide applications. Longer-term studies (≥5 years) are recommended to evaluate the sustainability of these combined approaches under the semi-arid conditions of the Southeastern Anatolia Region.

• Funding
  This study was supported by the Scientific and Technological Research Council of Türkiye under the project titled "Effects of Crop Rotation and Tillage Practices on Soil Quality and Greenhouse Gas Emissions of Cotton Cultivated Areas and Modeling of Future Cotton Yields with CropGro-Cotton" (Project number: 121 O 433).

Data Availability

The data will be available from the corresponding author on request.

Acknowledgements

This study was supported by the Scientific and Technological Research Council of Türkiye under the project titled "Effects of Crop Rotation and Tillage Practices on Soil Quality and Greenhouse Gas Emissions of Cotton Cultivated Areas and Modeling of Future Cotton Yields with CropGro-Cotton" (Project number: 121 O 433).

References

• Agrawal A, Singh M. Agronomic Interventions for Sustainable Weed Management in Cotton. J Cotton Sci. 2025;29:1–8. Available from: https://doi.org/10.56454/SNPU9646
  » https://doi.org/10.56454/SNPU9646
• Akhil K, Reddy MS, Kadasidappa M, Vikram N. Integrated weed management in cotton: a review. Int J Adv Biochem Res. 2024;8(10S):1548-54. Available from: https://doi.org/10.33545/26174693.2024.v8.i10Ss.2777
  » https://doi.org/10.33545/26174693.2024.v8.i10Ss.2777
• Anwar MP, Islam AKMM, Yeasmin S, Rashid MdH, Juraimi AS, Ahmed S et al. Weeds and their responses to management efforts in a changing climate. Agronomy. 2021;11(10):1921. Available from: https://doi.org/10.3390/agronomy11101921
  » https://doi.org/10.3390/agronomy11101921
• Arslan ZF. [Changes, problems and solution of weeds in cotton fields after irrigation in Şanlıurfa Province]. Harran Tar Gıda Bilim Derg. 2018;22(1):109–25. Turkish. Available from: https://doi.org/10.29050/harranziraat.306920
  » https://doi.org/10.29050/harranziraat.306920
• Aydogdu MH, Karli B, Parlakci Dogan H, Sevinc G, Eren ME et al. Economic analysis of agricultural water usage efficiency in the GAP-Harran plain: cotton production sampling, Sanliurfa-Turkey. Int J Adv Agric Sci. 2018;3(12):12–9.
• Bilalis D, Efthimiadis P, Sidiras N. Effect of three tillage systems on weed flora in a 3-year rotation with four crops. J Agron Crop Sci. 2001;186(2):135-41. Available from: https://doi.org/10.1046/j.1439-037X.2001.00458.x
  » https://doi.org/10.1046/j.1439-037X.2001.00458.x
• Billman ED, Campbell BT, Reay-Jones FPF. Using perennial groundcover crops to suppress weeds and thrips in the southeast cotton belt. Crop Sci. 2023;63(5):3037-50. Available from: https://doi.org/10.1002/csc2.21048
  » https://doi.org/10.1002/csc2.21048
• Blaise D. Effect of tillage systems on weed control, yield and fibre quality of upland (Gossypium hirsutum L.) and Asiatic tree cotton (G. arboreum L.). Soil Tillage Res. 2006;91(1–2). Available from: https://doi.org/10.1016/j.still.2005.12.005
  » https://doi.org/10.1016/j.still.2005.12.005
• Blaise D. Influence of tillage and residue management on growth and yield of cotton grown on a vertisol over 5 years in a semi-arid region of India. Soil Tillage Res. 2003;70(2):163-73. Available from: https://doi.org/10.1016/S0167-1987(02)00153-8
  » https://doi.org/10.1016/S0167-1987(02)00153-8
• Bükün B. Weed flora changes in cotton growing areas during the last decade after irrigation of Harran plain in Şanliurfa, Turkey. Pak J Bot. 2005;37(3):667–72.
• Damalas CA, Koutroubas SD. Current status of herbicide resistance in global cotton production: trends and perspectives. Cro-Prot. 2025;197:1-10. Available from: https://doi.org/10.1016/j.cropro.2025.107320
  » https://doi.org/10.1016/j.cropro.2025.107320
• Forte CT, Galon L, Beutler AN, Basso FJM, Nonemacher F, Reichert Júnior FW et al. Soil management systems and their effect on the weed seed bank. Pesq Agropecu Bras. 2018;53(4):435-42. Available from: https://doi.org/10.1590/s0100-204x2018000400005
  » https://doi.org/10.1590/s0100-204x2018000400005
• International Service for the Acquisition of Agri-biotech Applications – ISAAA. Global status of commercialized biotech/GM crops in 2017: Biotech crop adoption surges as economic benefits accumulate in 22 years. ISAAA briefs 53. 2017.
• Iqbal N, Manalil S, Chauhan BS, Adkins SW. Investigation of alternate herbicides for effective weed management in glyphosate-tolerant cotton. Arch Agron Soil Sci. 2019;65(13):1885-99. Available from: https://doi.org/10.1080/03650340.2019.1579904
  » https://doi.org/10.1080/03650340.2019.1579904
• Khan MA, Wahid A, Ahmad M, Tahir MT, Ahmed M, Ahmad S et al. World cotton production and consumption: an overview. In: Ahmad S, Hasanuzzaman M, editors. Cotton production and uses: agronomy, crop protection, and postharvest technologies. Singapore: Springer; 2020[access Nov 11, 2025]. p. 1–7. Available from: http://link.springer.com/10.1007/978-981-15-1472-2_1
  » http://link.springer.com/10.1007/978-981-15-1472-2_1
• Memon MS, Chen S, Shen B, Liang R, Tang Z, Wang S et al. Automatic visual recognition, detection and classification of weeds in cotton fields based on machine vision. Crop Prot. 2025;187:106966. Available from: https://doi.org/10.1016/j.cropro.2024.106966
  » https://doi.org/10.1016/j.cropro.2024.106966
• Munda S, Nayak BK, Das SR, Dey S, Pradhan A, Swain CK et al. Understanding the effects of changing climate on weeds and their management. In: Pathak H, Chatterjee D, Saha S, Das B, editors. Climate change impacts on soil-plant-atmosphere continuum. Berlin: Springer; 2024. p. 405–25.
• Price AJ, Monks CD, Culpepper AS, Duzy LM, Kelton JA, Marshall MW et al. High-residue cover crops alone or with strategic tillage to manage glyphosate-resistant Palmer amaranth (Amaranthus palmeri) in southeastern cotton (Gossypium hirsutum). J Soil Water Conserv. 2016;71(1):1–11. Available from: https://doi.org/10.2489/jswc.71.1.1
  » https://doi.org/10.2489/jswc.71.1.1
• Reeves DW, Price AJ, Patterson MG. Evaluation of three winter cereals for weed control in conservation-tillage nontransgenic cotton. Weed Technol. 2005;19(3):731–6. Available from: https://doi.org/10.1614/WT-04-245R1.11
  » https://doi.org/10.1614/WT-04-245R1.11
• Rudell EC, Zanrosso BA, Frandaloso D, Giacomini AJ, Spadotto D V, Vargas L et al. Integrated weed management strategies in a long-term crop rotation system. Adv Weed Sci. 2023;41:1-9. Available from: https://doi.org/10.51694/AdvWeedSci/2023;41:00026
  » https://doi.org/10.51694/AdvWeedSci/2023;41:00026
• Serim AT, Özkil M, Üremiş İ, Uludag A. Changes in the weed flora of cotton fields in the Eastern Mediterranean region of Turkiye. Mustafa Kemal Üniver Tarım Bilim Derg. 2023;28(3):633-48. Available from: https://doi.org/10.37908/mkutbd.1325888
  » https://doi.org/10.37908/mkutbd.1325888
• Steel RGD, Torrie JH, Dickey D. Principles and procedure of statistics: a biometrical approach. 3rd Ed. New York: McGraw HillBook. 1997. p. 352-8.
• Süer İE, Tursun N. Weeds in the cotton growing areas in the Southeastern Anatolia Region. Harran Tarım Gıda Bilim Derg. 2024;28(2):209–21. Available from: https://doi.org/10.29050/harranziraat.1406951
  » https://doi.org/10.29050/harranziraat.1406951
• Tariq M, Abdullah K, Ahmad S, Abbas G, Rahman MH ur, Khan MA. Weed Management in Cotton. In: Ahmad S, Hasanuzzaman M, editors. Cotton production and uses: agronomy, crop protection, and postharvest technologies. Singapore: Springer; 2020[access Oct 28, 2025]. p. 145–61. Available from: http://link.springer.com/10.1007/978-981-15-1472-2_9
  » http://link.springer.com/10.1007/978-981-15-1472-2_9
• Trkiye statistik Kurumu (TÜİK) (TR). Trkiye statistik Kurumu, Ankara; 2024 [access Nov 10, 2025]. Turkish. Available from:http://www.tuik.gov.tr/UstMenu.do?metod=temelist
  » http://www.tuik.gov.tr/UstMenu.do?metod=temelist
• Tursun N, Datta A, Budak S, Kantarci Z, Knezevic SZ. Row spacing impacts the critical period for weed control in cotton (Gossypium hirsutum). Phytoparasitica. 2016;44: 139-49. Available from: https://doi.org/10.1007/s12600-015-0494-x
  » https://doi.org/10.1007/s12600-015-0494-x
• Usman K, Khan N, Khan MU, Rehman A ur, Ghulam S. Impact of tillage and herbicides on weed density, yield and quality of cotton in wheat based cropping system. J Integr Agric. 2013;12(9):1568-79. Available from: https://doi.org/10.1016/S2095-3119(13)60339-1
  » https://doi.org/10.1016/S2095-3119(13)60339-1
• Vulchi R, Nolte S, McGinty J, McKnight B. Herbicide programs, cropping sequences, and tillage-types: a systems approach for managing Amaranthus palmeri in dicamba-resistant cotton. Front Agron. 2023;5:1-13. Available from: https://doi.org/10.3389/fagro.2023.1277054
  » https://doi.org/10.3389/fagro.2023.1277054
• Webster TM, Nichols RL. Changes in the prevalence of weed species in the major agronomic crops of the Southern United States: 1994/1995 to 2008/2009. Weed Sci. 2012;60(2):145-57. Available from: https://doi.org/10.1614/WS-D-11-00092.1
  » https://doi.org/10.1614/WS-D-11-00092.1

Edited by

• Editor in Chief:
  Carol Ann Mallory-Smith

• Associate Editor:
  Anderson Gabardo Nunes

Publication Dates

• Publication in this collection
  24 July 2026
• Date of issue
  2026

History

• Received
  15 Dec 2025
• Accepted
  31 Mar 2026