Search Results (51)

Teff [Eragrostis tef (Zucc.) Trotter] is attracting growing interest in Europe due to its nutritional qualities, gluten-free nature, and drought tolerance; however, its cultivation is hindered by its limited yield potential and the lack of authorised herbicides. This study evaluated chemical and mechanical weed-control strategies using two sowing methods to identify effective and sustainable solutions under central Italian conditions. Two field trials were conducted in 2023 and 2024 using a randomised block design. Post-emergence herbicides and mechanical control (split-hoe and finger-weeder) were assessed for weed suppression, crop selectivity, biomass production, and grain yield, comparing broadcast and wide-row sowing. The results showed that chemical control was the most effective option. The florasulam + fluroxypyr + pyroxsulam mixture achieved a nearly complete weed suppression with only mild and temporary phytotoxicity. Mechanical control provided a moderate and variable efficacy. The sowing pattern significantly influenced the crop performance: broadcast sowing reduced the weed competition and resulted in higher yields, whereas wide-row sowing led to a higher weed density and lower productivity. Despite the varying levels of infestation between years, teff maintained a remarkable competitive ability, with untreated plots often achieving acceptable yields. Integrating selective herbicides with appropriate sowing practices supports the development of efficient and sustainable weed-management strategies for teff cultivation. Full article

Steep olive orchards in northwest Syria are experiencing severe land degradation as a result of unsustainable uphill–downhill tillage, which accelerates erosion and reduces productivity. To address this problem, three tillage systems, no-till natural vegetation strips (NVSs), contour tillage, and uphill–downhill tillage, were evaluated at two research sites, Yakhour and Tel-Hadya, NW Syria. The adoption of no-till NVSs significantly increased soil organic matter (SOM) at both sites, outperforming uphill–downhill tillage. While contour tillage resulted in lower SOM levels than NVSs, it still performed better than the conventional uphill–downhill practice. Contour soil flux (CSF) was lower in Yakhour, where mule-drawn tillage on steep slopes (31–35%) was practiced, compared to higher CSF values in Tel-Hadya, where tractor tillage was applied on gentler slopes (11–13%), which highlights the influence of slope steepness on soil fluxes. Over four years, net soil flux (NSF) indicated greater soil loss under tractor tillage, confirming that mule-drawn tillage is less disruptive. Olive trees with no-till NVSs benefited from protected root systems, improved soil structure through SOM accumulation, reduced erosion risk, and improved surface runoff buffering, which resulted in increased water infiltration and soil water retention. This study was carried out using a participatory technology development (PTD) framework, which guided the entire research process, from diagnosing problems to co-designing, field testing, and refining soil conservation practices. In Yakhour, farmers actively identified the challenges of degradation. They collaboratively chose no-till natural vegetation strips (NVSs) and contour tillage as key interventions, valuing NVSs for their ability to conserve moisture, suppress weeds and pests, and increase olive productivity. The farmer–scientist co-learning network positioned PTD not only as an outreach tool but also as a core research method, enabling locally relevant and scalable strategies to restore soil functions and combat land degradation in northwest Syria’s hilly olive orchards. Full article

Crop–weed competition markedly reduces cereal yield. Integrative weed management approaches, involving the use of humic acid (HA) and seaweed extract (SWE), have gained attention as herbicide efficacy declines and environmental concerns grow. However, potential synergistic effects between HA and SWE have not yet been investigated. We evaluated the effects of HA, SWE, and their combination (HA+SWE) on the growth, yield, and competitive ability of cereals against wild weed beets (Beta vulgaris L.). A single-season field experiment was conducted using a split-plot design within a randomised complete block to assess the effects of treatment amendments on wheat, barley, and oats. The results showed that HA and HA+SWE organic amendments consistently improved grain yield and biomass across crop species. SWE responses varied across species, indicating species-dependent sensitivity. In addition, HA enhanced barley weed suppression, highlighting its dual roles in improving crop vigour and reducing weed proliferation. In contrast, SWE modestly increased spike length in oats, emphasising its effect on crop growth characteristics. Overall, these preliminary findings support targeted biostimulant use to enhance cereal yield and integrate weed management into sustainable cropping systems. Full article

Conventional agriculture, focusing on productivity rather than sustainability, have long abandoned hulled wheats. With them not only striking genetic diversity but valuable, health-promoting food sources became lost. Although einkorn and emmer—two of the most ancient wheat species—are generally considered good candidates of sustainable agriculture especially for pesticide-free cropping, they have remained largely unrecognized. To assess their agronomic potential in comparison with modern wheats grown under the same conditions, comprehensive research was conducted, combining multi-location participatory on-farm and small-plot trials. Our findings confirmed that most landraces of emmer and einkorn exhibited strong weed suppression ability, making them suitable for organic cultivation, and effective resistance against diseases—including Fusarium spp. and associated deoxynivalenol (DON) mycotoxin accumulation. Both species were entirely avoided by cereal leaf beetles (Oulema spp.) and had, on average, 2.6% more grain protein content than common wheat. Although they command significantly higher market prices, their (hulled) yields were comparable to modern wheat only in extreme years or at sites typically producing 3–5 t/ha of wheat. Nevertheless, the cultivation of emmer and einkorn presents a more sustainable "sow-and-harvest" alternative, free from pesticide and mycotoxin residue risks, while also enhances biodiversity from the field to the table. Full article

Despite our preliminary research about the inductive effect of exogenous abscisic acid (ABA) on the weed-suppressive activity of rice in a hydroponic system, there is a lack of knowledge regarding the induction mechanism for ABA application to enhance the ability for weed control underground. Here, two pot experiments using rice–barnyard grass mixed culture were conducted to investigate the effects of exogenous ABA treatment on weed inhibition strategies in both allelopathic rice PI312777 (PI) and non-allelopathic rice Lemont (Le). The largest observed weed inhibition changes in the two rice accessions both occurred with the 9 μmol/L ABA treatment. ABA induction on PI significantly increases the inhibitory effect on the plant height of barnyard grass with root contact and root segregation by 25.7% and 19.1%, respectively, with 23.5% increases observed in Le rice with root contact and no significant increases in plants with root segregation with nylon mesh. ABA induction also significantly increased the root distribution in the soil of Le. Compared with the uninduced group, ABA treatment significantly elevated the total amounts of reversibly adsorbed phenolic acids in the two soil layers of PI and the irreversibly adsorbed phenolic acids in Le soil layers. Furthermore, exogenous ABA could change the bacterial composition in rhizosphere soil of the two rice accessions, with the change in the species composition in the rhizosphere soil of the allelopathic rice PI being greater. Importantly, the bacterial compositions (Anaerolineales, Bacteroidales, and Myxococcale) in the PI rhizosphere soil of rice induced by ABA were more related to the contents of reversibly adsorbed phenolic acids in the soil. However, the core bacterial compositions that promote plant growth (Sphingomonadales, Cyanobacteriales, and Rhizobiales) in the Le rhizosphere soil were more related to the contents of irreversibly adsorbed phenolic acids in the soil. These findings suggested that the ABA induction mainly changed root distribution and core bacterial compositions in Le to enhance resource competition, whereas it stimulated the release of reversibly adsorbed phenolic acids to modulate the specific bacterial compositions in rhizosphere soil of PI and to strengthen allelopathic effects. Full article

Weeds are an inevitable element in agricultural production, and their significant negative impacts on crop growth make weed detection a crucial task in precision agriculture. The diversity of weed species and the substantial background noise in weed images pose considerable challenges for weed detection. To address these challenges, constructing a high-quality dataset and designing an effective artificial intelligence model are essential solutions. We captured 2002 images containing 10 types of weeds from cotton and corn fields, establishing the CornCottonWeed dataset, which provides rich data support for weed-detection tasks. Based on this dataset, we developed the MKD8 model for weed detection. To enhance the model’s feature extraction capabilities, we designed the CVM and CKN modules, which effectively alleviate the issues of deep-feature information loss and the difficulty in capturing fine-grained features, enabling the model to more accurately distinguish between different weed species. To suppress the interference of background noise, we designed the ASDW module, which combines dynamic convolution and attention mechanisms to further improve the model’s ability to differentiate and detect weeds. Experimental results show that the MKD8 model achieved mAP₅₀ and mAP_[50:95] of 88.6% and 78.4%, respectively, on the CornCottonWeed dataset, representing improvements of 9.9% and 8.5% over the baseline model. On the public weed dataset CottoWeedDet12, the mAP₅₀ and mAP_[50:95] reached 95.3% and 90.5%, respectively, representing improvements of 1.0% and 1.4% over the baseline model. Full article

Weeds are unwanted and invasive plants characterized by their rapid growth and ability to compete with crops for essential resources such as space, water, nutrients, and sunlight. This competition has a negative impact on crop quality and productivity. To reduce the influence of weeds, precision weeding is used, which uses image sensors and computational algorithms to identify plants and classify weeds using digital images. This study used images of maize (Zea mays L.) to detect four types of weeds (Lolium rigidum, Sonchus oleraceus, Solanum nigrum, and Poa annua). For this purpose, YOLO (You Only Look Once) architectures, YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s versions, were trained and compared, along with an architecture based on RT-DETR (Real-Time Detection Transformer), version RT-DETR-1. The YOLO architectures are noted for their real-time detection efficiency, and RT-DETR-l allows evaluation of the impact of an architecture that dispenses with Non-Maximum Suppression (NMS). The YOLOv9s model had the best overall performance, achieving a mAP@0.5 of 0.834 in 60 epochs and an F1-score of 0.78, which demonstrates a optimal balance between accuracy and recall, although with less confidence in its predictions. On the other hand, the RT-DETR-l model stood out for its efficiency in convergence, reaching a competitive performance in only 58 epochs with a mAP@0.5 of 0.828 and an F1-score of 0.80. Full article

In agricultural planting, early plant classification is an indicator of crop health and growth. In order to accurately classify early plants, this paper proposes a classification method combining a dual attention mechanism and multi-scale module. Firstly, the ECA module (Efficient channel attention) is added to enhance the attention of the network to plants and suppress irrelevant background noise; secondly, the MSFN (Multi-scale Feedforward Network) module is embedded to improve the ability to capture complex data features. Finally, CA (Channel attention) is added to further emphasize the extracted features, thus enhancing the discrimination ability and improving the accuracy of the model. The experimental results show an accuracy of 93.20%, precision of 94.53%, recall of 93.27%, and an F1 score of 93.39%. This study can realize the classification of early plants, and effectively distinguish crops from weeds, which is helpful to identify and realize accurate weeding, thus promoting the intelligent and modern process of agricultural production. Full article

To thrive as a successful weed in natural pastures, a plant must have not only highly competitive ability, but also the resilience to endure environmental stress and rapidly reclaim space once those stressors diminish and the other non-stress-tolerant plants die. Acanthostyles buniifolius [(Hook. ex Hook. & Arn.) R.M.King & H.Rob.], known as chirca, is a widely spread weed in South American natural pastures. It is known for its remarkable ability to withstand environmental stress and flourish in environments with prevalent stressors. The study evaluated the memory effect of water stress (drought) in chirca plants. The experiment was conducted in a greenhouse in a randomized block design with three replications. Treatments included Control = control plants without water deficit kept at 100% of the soil water-holding capacity (WHC); Primed plants = plants that were primed with water stress at 141 days after emergence (DAE) and received recurrent stress at 164 DAE; Naïve plants: plants that only experienced water stress at 164 DAE. To reach water stress, plants were not watered until the soil reached 15% of the soil’s WHC, which occurred ten days after water suppression in the priming stress and nine days after water suppression in the second stress. During periods without restriction, the pots were watered daily at 100% of the WHC. Primed plants exposed to water deficit better-maintained water status compared to the naïve plants; glycine betaine is an important defense mechanism against water deficit in chirca; naïve plants have a higher concentration of proline than plants under recurrent stress, demonstrating the greater need for protection against oxidative damage and needs greater osmotic regulation. Recurrent water deficits can prepare chirca plants for future drought events. These results show that chirca is a very adaptative weed and may become a greater threat to pastures in South America due to climate change, especially if drought becomes more frequent and severe. Full article

Wheat (Triticum aestivum) is grown on more arable acreage than any other food crop and has been well documented to produce allelochemicals. Wheat allelochemicals include numerous benzoxazinoids and their microbially transformed metabolites that actively suppress growth of weed seedlings. Production and subsequent release of these metabolites by commercial wheat cultivars, however, has not yet been targeted by focussed breeding programmes seeking to develop more competitive crops. Recently, the Commonwealth Scientific and Industrial Organisation (CSIRO), through an extensive recurrent selection programme investment, released numerous early-vigour wheat genotypes for commercial use, but the physiological basis for their improved vigour is under investigation. In the current study, we evaluated several early-vigour genotypes alongside common commercial and heritage wheat cultivars to assess the impact of improved early vigour on the production and release of targeted benzoxazinoids by field-grown wheat roots over a two-year period. Using UPLC coupled with triple quadrupole mass spectrometry (LC-MS QQQ), we quantified common wheat benzoxazinoids and their microbially produced metabolites (aminophenoxazinones) in soil collected from the rhizosphere and rhizoplane of wheat plants over two growing seasons in the Riverina region of New South Wales, Australia. The benzoxazolinone MBOA and several aminophenoxazinones were readily detected in soil samples, but actual soil concentrations differed greatly between years and among genotypes. In contrast to 2019, the concentration of aminophenoxazinones in wheat rhizosphere soil was significantly elevated in 2020, a year receiving adequate rainfall for optimal wheat growth. Aminophenoxazinones were detected in the rhizosphere of early-vigour genotypes and also parental lines exhibiting weed suppression, suggesting that improved early vigour and subsequent weed competitiveness may be related to increased root exudation and production of microbial metabolites in addition to changes in canopy architecture or other root-related early-vigour traits. As previously reported, MBOA was detected frequently in both the rhizoplane and rhizosphere of wheat. Depending on the year and genotype, we also observed enhanced biotransformation of these metabolites to several microbially transformed aminophenoxazinones in the rhizosphere of many of the evaluated genotypes. We are now investigating the role of early-vigour traits, including early canopy closure and biomass accumulation upon improved competitive ability of wheat, which will eventually result in more cost-effective weed management. Full article

Canola (Brassica napus L.) is an important oilseed crop that provides essential vegetable oil but faces significant competition from weeds that are influenced by various agronomic practices and environmental conditions. This study examines the complex interactions between canola stand density and weed intensity over three growing seasons, identifying a total of 27 weed species. It is important to establish a connection between the density of winter canola stands, the intensity of weeding and the response of individual weed species in real conditions. The case study was executed on plots located in the Přerov district (Olomouc region, Czech Republic). The assessment was carried out during two periods—autumn in October and spring in April. Canola plants (plant density) were counted in each evaluated area, weed species were identified, and the number of plants for each weed species was determined. Half of the plots were covered with foil before herbicide application to prevent these areas from being treated with herbicides. We used redundancy analysis (RDA) to evaluate the relationships between canola density and weed dynamics, both with and without herbicide treatment. The results show the ability of canola to compete with weeds; however, that is factored by the density of the canola stand. In dense stands (over 60 plants/m²), canola is able to suppress Galium aparine L., Geranium pusillum L., Lamium purpureum L., Papaver rhoeas L. and Chamomilla suaveolens (Pursh) Rydb. Nevertheless, there are weed species that grow well even in dense canola stands (Echinochloa crus-galli (L.) P. Beauv., Phragmites australis (Cav.) Steud., Tripleurospermum inodorum (L.) Sch. Bip. and Triticum aestivum L.). These findings highlight the potential for using canola stand density as a strategic component of integrated weed management to reduce herbicide reliance and address the growing challenge of herbicide-resistant weed populations. This research contributes significantly to our understanding of the dynamics of weed competition in canola systems and informs sustainable agricultural practices for improved crop yield and environmental stewardship. Full article

Canola (Brassica napus L.) is a profitable grain crop for Australian growers. However, weeds remain a major constraint for its production. Chemical herbicides are used for weed control, but this tactic also leads to the evolution of herbicide resistance in different weed species. The suppression of weeds by crop interference (competition and allelopathic) mechanisms has been receiving significant attention. Here, the weed suppressive ability and associated functional traits and stability of four selected canola genotypes (PAK85388-502, AV-OPAL, AV-GARNET, and BAROSSA) were examined at different locations in NSW, Australia. The results showed that there were significant effects of canola genotypes and of genotypes by crop density interaction on weed growth. Among the tested genotypes, PAK85388-502 and AV-OPAL were the most weed suppressive and, at a plant density of 10 plants/m², they reduced the weed biomass of wild radish, shepherd’s purse, and annual ryegrass by more than 80%. No significant differences were found in the primary root lengths among canola varieties; however, plants of the most weed-suppressive genotype PAK8538-502 exhibited a 35% increase in lateral root number relative to plants of the less weed-suppressive genotype BAROSSA. The analysis of variance revealed a significant influence of genotypes with PAK85388-502 and AV-OPAL performing the best across all the research sites. Results showed that canola genotypes PAK85388-502 and AV-OPAL were more weed suppressive than AV-GARNET and BAROSSA and may release specific bioactive compounds in their surroundings to suppress neighboring weeds. This study provides valuable information that could be utilised in breeding programs to select weed-suppressive varieties of canola in Australia. Thus, lateral root number could be a potential target trait for weed-suppressive varieties. Additionally, other root architecture traits may contribute to the underground allelopathic interaction to provide a competitive advantage to the crop. Full article

Due to the existence of cotton weeds in a complex cotton field environment with many different species, dense distribution, partial occlusion, and small target phenomena, the use of the YOLO algorithm is prone to problems such as low detection accuracy, serious misdetection, etc. In this study, we propose a YOLOv8-DMAS model for the detection of cotton weeds in complex environments based on the YOLOv8 detection algorithm. To enhance the ability of the model to capture multi-scale features of different weeds, all the BottleNeck are replaced by the Dilation-wise Residual Module (DWR) in the C2f network, and the Multi-Scale module (MSBlock) is added in the last layer of the backbone. Additionally, a small-target detection layer is added to the head structure to avoid the omission of small-target weed detection, and the Adaptively Spatial Feature Fusion mechanism (ASFF) is used to improve the detection head to solve the spatial inconsistency problem of feature fusion. Finally, the original Non-maximum suppression (NMS) method is replaced with SoftNMS to improve the accuracy under dense weed detection. In comparison to YOLO v8s, the experimental results show that the improved YOLOv8-DMAS improves accuracy, recall, mAP0.5, and mAP0.5:0.95 by 1.7%, 3.8%, 2.1%, and 3.7%, respectively. Furthermore, compared to the mature target detection algorithms YOLOv5s, YOLOv7, and SSD, it improves 4.8%, 4.5%, and 5.9% on mAP0.5:0.95, respectively. The results show that the improved model could accurately detect cotton weeds in complex field environments in real time and provide technical support for intelligent weeding research. Full article

Recently, there has been growing interest in the use of summer cover crops that can be grown during summer fallow periods of crop rotation. This study evaluates the potential of sunn hemp (Crotalaria juncea L.), velvetbean [Mucuna pruriens (L.) DC.] and cowpea [Vigna unguiculata (L.) Walp.]. as three annual legumes summer cover crops. The main objective of this review was to conduct global research comparing these summer cover crops to investigate the benefits, challenges, and trade-offs among ecosystems services when implementing these summer cover crops. In European agriculture, there are three main windows in crop rotation when these summer legumes can be grown: Around mid-spring after winter fallow, early summer after harvest of a winter crop, and mid- to late summer after harvest of an early-season crop. All three legumes can suppress weeds while they are actively growing. After termination, their mulch can create unfavorable conditions for weed emergence. Sunn hemp and velvetbean cover crops can cause a reduction in weed biomass of more than 50%. In addition to their ability to suppress weeds, sunn hemp, velvetbean, and cowpea provide a variety of ecosystem services, such as improving soil health, quality, and fertility, controlling pests, and sequestering carbon. The review highlights their promising role in weed suppression and their contribution to sustainable agricultural practices. However, further research is needed to evaluate their performance in weed management and their environmental impact in field trials under different soil-climatic conditions, as cover cropping is an effective practice but highly context-specific. Full article

Navua sedge (Cyperus aromaticus), a perennial plant native to Africa, poses a significant weed concern due to its capacity for seed and rhizome fragment dissemination. Infestations can diminish pasture carrying capacity, displacing desirable species. Despite the burgeoning interest in integrated weed management strategies, information regarding the efficacy of competitive interactions with other pasture species for Navua sedge management remains limited. A pot trial investigated the competitive abilities of 14 diverse broadleaf and grass pasture species. The results indicated a range of the reduction in Navua sedge dry biomass from 6% to 98% across these species. Subsequently, three broadleaf species—burgundy bean (Macroptilium bracteatum), cowpea (Vigna unguiculata), and lablab (Lablab purpureus), and three grass species—Gatton panic (Megathyrsus maximus), Rhodes grass (Chloris gayana), and signal grass (Urochloa decumbens) were chosen for a follow-up pot trial based on their superior dry biomass performance. These six species were planted at three varying densities (44, 88, and 176 plants/m²) surrounding a Navua sedge plant. Among the grass pasture species, Gatton panic and Rhodes grass exhibited high competitiveness, resulting in a minimum decrease of 86% and 99%, respectively, in Navua sedge dry biomass. Regarding the broadleaf species, lablab displayed the highest competitiveness, causing a minimum decrease of 99% in Navua sedge dry biomass. This study highlights the increasing efficacy of crop competition in suppressing weed growth and seed production, with the most significant suppression observed at a density of 176 plants/m². Full article