Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis.

Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both carnivores and granivores, and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.

Macronutrient application rescues performance of tolerant sorghum genotypes when infected by the parasitic plant striga.

BACKGROUND AND AIMS: Infection by the hemi-parasitic plant Striga hermonthica causes severe host plant damage and seed production losses. Increased availability of essential plant nutrients reduces infection. Whether, how and to what extent it also reduces striga-induced host plant damage has not been well studied. METHODS: The effects of improved macro- and micronutrient supply on host plant performance under striga-free and infected conditions were investigated in glasshouse pot assays. One striga-sensitive and two striga-tolerant genotypes were compared. Plants growing in impoverished soils were supplied with (1) 25 % of optimal macro- and micronutrient quantities, (2) 25 % macro- and 100 % micronutrients, (3) 100 % macro- and 25 % micronutrients, or (4) 100 % macro- and micronutrients. KEY RESULTS: Photosynthesis rates of striga-infected plants of the sensitive genotype increased with improved nutrition (from 12.2 to 22.1 µmol m-2 s-1) but remained below striga-free levels (34.9-38.8 µmol m-2 s-1). For the tolerant genotypes, increased macronutrient supply offset striga-induced photosynthesis losses. Striga-induced relative grain losses of 100 % for the sensitive genotype were reduced to 74 % by increased macronutrients. Grain losses of 80 % in the tolerant Ochuti genotype, incurred at low nutrient supply, were reduced to 5 % by improved nutrient supply. CONCLUSIONS: Increasing macronutrient supply reduces the impact of striga on host plants but can only restore losses when applied to genotypes with a tolerant background.

Bacterial allelopathy: An approach for biological control of weeds.

Weed infestation is one of the most damaging biotic factors to limit crop production by competing with the crop for space, water, and nutrients. Different conventional approaches are being used to cope with weed infestation including labor intensive manual removal and the use of soil-degrading and crop-damaging, and environment-deteriorating chemical herbicides. The use of chemicals for weed control has increased two-fold after the Green Revolution and their non-judicious use is posing serious threats to mankind, animals, and biodiversity. The detrimental effects of these approaches have shifted the researchers' attention from the last two decades towards alternate, sustainable, and ecofriendly approaches to cope with weed infestation. The recent approaches of weed control including plant and microbial allelopathy have gained popularity during last decade. Farmers still use conventional methods, but the majority of farmers are very passionate about organic agriculture and describe it as a slogan in the developed world. The effectiveness of these approaches lies in host specificity by selective bacteria and differential response towards weeds and crops. Moreover, the crop growth promoting effect of microorganisms (allelopathic bacteria) possessing various growth promoting traits i.e., mineral solubilization, phytohormone production and beneficial enzymatic activity, provide additional benefits. The significance of this review lies in the provision of a comprehensive comparison of the conventional approaches along with their potential limitations with advanced/biological weed control approaches in sustainable production. In addition, the knowledge imparted about weed control will contribute to a better understanding of biological control methods.

Analyzing the response of rice to tefuryltrione herbicide: Haplotype variation and evolutionary dynamics of the HIS1 gene.

Weeds pose multifaceted challenges in rice cultivation, leading to substantial economic losses through reduced yield and poor grain quality. Harnessing the natural genetic diversity in germplasm collections becomes crucial for identifying novel herbicide resistance loci in crops. A comprehensive analysis was conducted on 475 rice accessions from the KRICE depository, assessing their response to TFT (tefuryltrione) and probing the underlying HIS1 (HPPD INHIBITOR SENSITIVE 1) genotypic variations. The HIS1 gene, responsible for detoxifying benzobicyclon (BBC) and imparting broad-spectrum herbicide resistance, holds significant promise in rice breeding. This study explores the genetic landscape of HIS1 within Korean rice collection (KRICE), aiming to unveil genetic variations, haplotype diversity, and evolutionary relationships across diverse rice ecotypes. The indica ecotype showed the highest nucleotide diversity, while the wild and temperate japonica groups exhibited low diversity, hinting at selective sweeps and possible population expansion. Negative Tajima's D values in temperate japonica and wild groups indicate an excess of low-frequency mutations, potentially resulting from selective sweeps. In contrast, with positive Tajima's D values, admixture, indica, and aus groups suggest balancing selection. Furthermore, haplotype analysis uncovered 42 distinct haplotypes within KRICE, with four shared haplotypes between cultivated and wild accessions, four specific to cultivated accessions, and 34 specific to wild types. Phenotypic assessments of these haplotypes revealed that three haplotypes, viz., Hap_1 (predominant in japonica), Hap_2 (predominant in indica), and Hap_3 (specific to indica), displayed significant differences from aus-specific Hap_4 and indica-specific Hap_5. This study offers insights into genetic diversity, selective pressures, and ecotype-specific responses, ultimately paving the way for developing HPPD-inhibiting herbicide-resistant rice cultivars.

Crofton weed derived isomers of ageraphorone as potent antifeedant against Plutella xylostella (L.).

Global agricultural production is significantly hampered by insect pests, and the demand for natural pragmatic pesticides with environmental concern remains unfulfilled. Ageratina adenophora (Spreng.) also known as Crofton weed, is an invasive perennial herbaceous plant that is known to possess multiple bioactive compounds. In our study, two isomers of ageraphorone metabolites i.e, 10 Hα-9-oxo-ageraphorone (10HA) and 10 Hß-9-oxo-ageraphorone (10HB), were identified from Crofton weed, exhibiting potent antifeedant and larvicidal activities against Plutella xylostella. For antifeedant activity, the median effective concentration (EC50) values for 10HA and 10HB in the choice method were 2279 mg/L and 3233 mg/L, respectively, and for the no choice method, EC50 values were 1721 mg/L and 2394 mg/L, respectively. For larvicidal activity, lethal concentration (LC50) values for 10HA and 10HB were 2421 mg/L and 4109 mg/L at 48 h and 2101 mg/L and 3550 mg/L at 72 h. Furthermore, both in- vivo and in-vitro studies revealed that the isomers 10HA and 10HB exhibited potent detoxifying enzymes inhibition activity such as carboxylesterase and glutathione S-transferases. Molecular docking and MD simulation analysis provide insight into the possible interaction between isomers of ageraphorone metabolites and Carboxylic Ester Hydrolase protein (Gene: pxCCE016b) of P. xylostella, which led to a finding that CarEH protein plays a significant role in the detoxification of the two compounds in P. xylostella. Finally, our findings show that the primary enzymes undergoing inhibition by isomers of ageraphorone metabolites, causing toxicity in insects, are Carboxylesterase and glutathione S-transferase.

First Report of Leaf Spot Caused by Arthrinium arundinis on Amaranthus hybridus in Korea.

Amaranthus hybridus (=A. patulus), often called green amaranth, is an annual herbaceous plant of the Amaranthaceae. This plant is considered a harmful weed in the agricultural context of North America and has expanded its distribution to Asia and Europe. In Korea, it has become a problematic invasive issue, leading to economic losses due to reduced crop yields and rising weed management costs (Park et al., 2014), although its seeds and young leaves are edible and frequently consumed. In October 2020, we observed leaf spot symptoms on A. hybridus plants that were growing within perilla farms (Perilla frutescens var. japonica) located in Damyang (35°14'07"N, 126°59'40"E), Korea, with a disease incidence of 20 to 30% of the inspected plants. The initial signs appeared as grey to brown dots on the leaves, which gradually expanded into irregular, brown patches with a diameter of 2-3 cm. A single spore was isolated from the diseased leaf under a dissecting microscope, placed onto a 2% water agar plate, and incubated in darkness at 25°C for three days. Pure cultures were obtained by transferring single hyphal tips onto potato dextrose agar (PDA) plates. Five single-spore isolates were the same in the cultural and morphological examination, and a representative isolate (P309) was preserved at the Korean Agricultural Culture Collection (KACC49813), Korea. Colonies appeared light gray to white with regular margins and reached 4 to 5 cm in diameter after a week. After two weeks, black patches of spores were often visible in the aerial mycelia. Conidiophores were brown to pale brown, often branched, thick-walled, and measured 6.8 × 2.7 µm (n = 30). Conidia were single-celled, dark brown, globose to ellipsoid, and measured 6.8 × 5.0 µm (n = 50), with a ratio of length/width of 1.1 to 1.6 (n = 50). These morphological characteristics matched those of Arthrinium arundinis (Crous et al., 2013). For molecular identification, genomic DNA was extracted from conidia and mycelia on two-week-old PDA culture of the KACC49813. PCR was performed for the internal transcribed spacer (ITS) (primers ITS1/ITS4, White et al. 1990), the large subunit (LSU) rDNA (primers LROR/LR5, Vilgalys et al. 1990), the beta-tubulin gene (TUB) (primers T1/Bt-2b, O'Donnell and Cigelnik 1997), and the translation elongation factor 1-alpha (TEF) (primers EF1-728F/EF-2, Crous et al. 2013). A BLASTn search of the resulting sequences of ITS (560 bp; OL744431), LSU (881 bp; OL744432), TUB (790 bp; PP084934), and TEF (445 bp; PP084935) revealed 100 % similarity (e-value=0.0, coverage=100%) to previously reported sequences of Arthrinium arundinis (e.g. MF627422 for ITS, KF144930 for LSU, KF144973 for TUB, and KY705146 for TEF), confirming the identity of the Korean isolate. Pathogenicity assays were performed twice by spraying 1 ml of a conidial suspension (1.1 × 104 conidia per mL) onto the leaf surface of sixteen healthy A. hybridus plants. Sixteen control plants were sprayed with sterile water. All plants were kept in a growth chamber at 80% relative humidity and 23 °C with a 12-h light/dark cycle. Three weeks after the inoculation, initial symptoms mirroring the aforementioned ones appeared, while the control plants remained symptomless. Fungal isolates were successfully re-isolated from the inoculated leaves, and their identity as A. arundinis was confirmed by DNA sequencing, thus fulfilling Koch's postulates. To our knowledge, this is the first report of leaf spot caused by A. arundinis on Amaranthus hybridus, not only in Korea but globally. Arthrinium arundinis has also been reported as a plant pathogen on some agricultural crops (Ji et al. 2020; Liao et al. 2022; Farr and Rossman 2023), suggesting its polyphagous behavior. Then, this pathogen could represent a potential risk to the cultivation of edible amaranth in Korea and other crops where Amaranthus species are spread as weeds. For this reason, continuous monitoring is necessary to assess the impact of this fungus on Amaranthus and other crops.

Progress in the analysis of phytocannabinoids by HPLC and UPLC (or UHPLC) during 2020-2023.

INTRODUCTION: Organic molecules that bind to cannabinoid receptors are known as cannabinoids. These molecules possess pharmacological properties similar to those produced by Cannabis sativa L. High-performance liquid chromatography (HPLC) and ultra-performance liquid chromatography (UPLC, also known as ultra-high-performance liquid chromatography, UHPLC) have become the most widely used analytical tools for detection and quantification of phytocannabinoids in various matrices. HPLC and UPLC (or UHPLC) are usually coupled to an ultraviolet (UV), photodiode array (PDA), or mass spectrometric (MS) detector. OBJECTIVE: To critically appraise the literature on the application of HPLC and UPLC (or UHPLC) methods for the analysis of phytocannabinoids published from January 2020 to December 2023. METHODOLOGY: An extensive literature search was conducted using Web of Science, PubMed, and Google Scholar and published materials including relevant books. In various combinations, using cannabinoid in all combinations, cannabis, hemp, hashish, C. sativa, marijuana, analysis, HPLC, UHPLC, UPLC, and quantitative, qualitative, and quality control were used as the keywords for the literature search. RESULTS: Several HPLC- and UPLC (or UHPLC)-based methods for the analysis of phytocannabinoids were reported. While simple HPLC-UV or HPLC-PDA-based methods were common, the use of HPLC-MS, HPLC-MS/MS, UPLC (or UHPLC)-PDA, UPLC (or UHPLC)-MS, and UPLC (or UHPLC)-MS/MS was also reported. Applications of mathematical and computational models for optimization of protocols were noted. Pre-analyses included various environmentally friendly extraction protocols. CONCLUSION: During the last 4 years, HPLC and UPLC (or UHPLC) remained the main analytical tools for phytocannabinoid analysis in different matrices.

Evaluation of Inference Performance of Deep Learning Models for Real-Time Weed Detection in an Embedded Computer.

The knowledge that precision weed control in agricultural fields can reduce waste and increase productivity has led to research into autonomous machines capable of detecting and removing weeds in real time. One of the driving factors for weed detection is to develop alternatives to herbicides, which are becoming less effective as weed species develop resistance. Advances in deep learning technology have significantly improved the robustness of weed detection tasks. However, deep learning algorithms often require extensive computational resources, typically found in powerful computers that are not suitable for deployment in robotic platforms. Most ground rovers and UAVs utilize embedded computers that are portable but limited in performance. This necessitates research into deep learning models that are computationally lightweight enough to function in embedded computers for real-time applications while still maintaining a base level of detection accuracy. This paper evaluates the weed detection performance of three real-time-capable deep learning models, YOLOv4, EfficientDet, and CenterNet, when run on a deep-learning-enabled embedded computer, an Nvidia Jetson Xavier AGX. We tested the accuracy of the models in detecting 13 different species of weeds and assesses their real-time viability through their inference speeds on an embedded computer compared to a powerful deep learning PC. The results showed that YOLOv4 performed better than the other models, achieving an average inference speed of 80 ms per image and 14 frames per second on a video when run on an imbedded computer, while maintaining a mean average precision of 93.4% at a 50% IoU threshold. Furthermore, recognizing that some real-world applications may require even greater speed, and that the detection program would not be the only task running on the embedded computer, a lightweight version of the YOLOv4 model, YOLOv4-tiny, was tested for improved performance in an embedded computer. YOLOv4-tiny impressively achieved an average inference speed of 24.5 ms per image and 52 frames per second, albeit with a slightly reduced mean average precision of 89% at a 50% IoU threshold, making it an ideal choice for real-time weed detection.

YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion.

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields.

Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning-A Comparison between High-End and Low-Cost Multispectral Sensors.

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated.

Stereo Vision for Plant Detection in Dense Scenes.

Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.

A shift in habitat use patterns of brown trout (Salmo trutta): A behavioural response to macrophyte removal.

Mass development of macrophytes is an increasing problem worldwide and they are frequently removed where they are in conflict with local waterway users. Yet, macrophytes can provide important refuge and nursery habitats for fish. Little is known about the consequences of macrophyte removal for fish behavioural space use and habitat selection. We hypothesised that macrophyte removal would affect brown trout (Salmo trutta) movement during the partial removal of the aquatic plant Juncus bulbosus (L.) in an oligotrophic impounded Norwegian river.We tagged 94 brown trout and tracked them using passive acoustic telemetry for 10 months and mapped the cover of J. bulbosus. Trout behavioural patterns were quantified as space use (utilisation areas 50% and 95%) which was linked to habitat use and selection for J. bulbosus. Removal of J. bulbosus influenced space use of brown trout by reducing the core utilisation area by 22%. Habitat use and selection were likewise influenced by removal with increased use and selection of areas with low J. bulbosus cover (<25%) with corresponding reduction in high J. bulbosus cover (>25-75%). Finally, diurnal differences in space use and habitat use were found, with 19% larger utilisation areas at night and higher use of areas with low J. bulbosus during daytime. Yet, all effect sizes were relatively small compared to the size of the study area. This research provides a detailed case study on the effects of macrophyte removal on fish behavioural patterns in a section of a large Norwegian river with macrophyte mass development. We found no large effects of removal on trout behaviour but noted an increased use of areas with low macrophyte cover. This research is relevant for water managers and policy makers of freshwater conservation and provides a template for using acoustic telemetry to study the effects of macrophyte removal on fish.

Enhanced Adsorption of Methyl Orange from Aqueous Phase Using Chitosan-Palmer Amaranth Biochar Composite Microspheres.

To develop valuable applications for the invasive weed Palmer amaranth, we utilized it as a novel biochar source and explored its potential for methyl orange adsorption through the synthesis of chitosan-encapsulated Palmer amaranth biochar composite microspheres. Firstly, the prepared microspheres were characterized by scanning electron microscopy and Fourier transform infrared spectroscopy and were demonstrated to have a surface area of 19.6 m2/g, a total pore volume of 0.0664 cm3/g and an average pore diameter of 10.6 nm. Then, the influences of pH, dosage and salt type and concentration on the adsorption efficiency were systematically investigated alongside the adsorption kinetics, isotherms, and thermodynamics. The results reveal that the highest adsorption capacity of methyl orange was obtained at pH 4.0. The adsorption process was well fitted by a pseudo-second-order kinetic model and the Langmuir isotherm model, and was spontaneous and endothermic. Through the Langmuir model, the maximal adsorption capacities of methyl orange were calculated as 495.0, 537.1 and 554.3 mg/g at 25.0, 35.0 and 45.0 °C, respectively. Subsequently, the adsorption mechanisms were elucidated by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy investigations. It is indicated that electrostatic interactions, hydrogen bonding, π-π interactions and hydrophobic interactions between methyl orange and the composite microspheres were pivotal for the adsorption process. Finally, the regeneration studies demonstrated that after five adsorption-desorption cycles, the microspheres still maintained 93.6% of their initial adsorption capacity for methyl orange. This work not only presents a promising method for mitigating methyl orange pollution but also offers a sustainable approach to managing Palmer amaranth invasion.

Accurate recognition of rice plants based on visual and tactile sensing.

BACKGROUND: Crop recognition is the basis of intelligent agricultural machine operations. Visual perception methods have achieved high recognition accuracy. However, the reliability of such methods is difficult to guarantee because of the complex environment of paddy fields. Tactile sensing methods are not affected by background or environmental interference, and have high reliability. However, in an ideal environment, the recognition accuracy is not as high as that of the visual method. RESULTS: To balance the accuracy and reliability of rice plant recognition, a combined visual-tactile method was proposed in this study. A rice plant recognition device was developed with a poly(vinylidene fluoride) sensor embedded inside the device as a tactile perceptron and a graphic designed as a visual perceptron. The primary role of the tactile perceptron is to initially recognize rice plants and provide a time point for image capture for visual perception. The main role of the visual perceptron is to extract features from the captured images and recognize rice plants again. The results of tactile and visual recognition were eventually fused to achieve accurate recognition of rice plants. CONCLUSION: The contact speed between the recognition perceptron and rice-weed was selected for the field performance test based on the real situation of paddy field operation. The results showed that the accuracy and reliability of rice plant recognition decreased as the travelling speed of the paddy field operation machine increased. The results of this study provide a basis for intelligent farm machinery operations in rice fields. © 2024 Society of Chemical Industry.

Exploring the influence of invasive weed biochar on the sorption and dissipation dynamics of imazethapyr in sandy loam soil.

The management of invasive weeds on both arable and non-arable land is a vast challenge. Converting these invasive weeds into biochar and using them to control the fate of herbicides in soil could be an effective strategy within the concept of turning waste into a wealth product. In this study, the fate of imazethapyr (IMZ), a commonly used herbicide in various crops, was investigated by introducing such weeds as biochar, i.e., Parthenium hysterophorus (PB) and Lantana camara (LB) in sandy loam soil. In terms of kinetics, the pseudo-second order (PSO) model provided the best fit for both biochar-mixed soils. More IMZ was sorbed onto LB-mixed soil compared to PB-mixed soil. When compared to the control (no biochar), both PB and LB biochars (at concentrations of 0.2% and 0.5%) increased IMZ adsorption, although the extent of this effect varied depending on the dosage and type of biochar. The Freundlich adsorption isotherm provided a satisfactory explanation for IMZ adsorption in soil/soil mixed with biochar, with the adsorption process exhibiting high nonlinearity. The values of Gibb's free energy change (ΔG) were negative for both adsorption and desorption in soil/soil mixed with biochar, indicating that sorption was exothermic and spontaneous. Both types of biochar significantly affect IMZ dissipation, with higher degradation observed in LB-amended soil compared to PB-amended soil. Hence, the findings suggest that the preparation of biochar from invasive weeds and its utilization for managing the fate of herbicides can effectively reduce the residual toxicity of IMZ in treated agroecosystems in tropical and subtropical regions.

Does weed diversity mitigate yield losses?

While intensive control of weed populations plays a central role in current agriculture, numerous studies highlight the multifaceted contribution of weeds to the functionality and resilience of agroecosystems. Recent research indicates that increased evenness within weed communities may mitigate yield losses in contrast to communities characterized by lower diversity, since weed species that strongly affect crop yields, also dominate weed communities, with a concurrent reduction of evenness. If confirmed, this observation would suggest a paradigm shift in weed management towards promoting higher community diversity. To validate whether the evenness of weed communities is indeed linked to higher crop productivity, we conducted two field experiments: one analyzing the effects of a natural weed community in an intercrop of faba bean and oat, and the other analyzing the effects of artificially created weed communities, together with the individual sown weed species, in faba bean, oats and an intercrop of both crops. The evenness of the weed communities ranged from 0.2 to 0.9 in the natural weed community, from 0.2 to 0.7 in faba bean, from 0 to 0.8 in the intercrop and from 0.3 to 0.9 in oats. Neither the natural nor the artificial weed community showed significant effects of evenness on crop grain yield or crop biomass. The results of this study do not validate a positive relationship of crop productivity and weed evenness, possibly due to low weed pressure and the absence of competitive effects but suggest that also less diverse weed communities may be maintained without suffering yield losses. This is expected to have far reaching implications, since not only diverse weed communities, but also higher abundances of few weed species may contribute to ecosystem functions and may support faunal diversity associated with weeds.

Laser weeding of common weed species.

The massive use of herbicides since the 1950s has resulted in increasing problems with herbicideresistant weeds and pollution of the environment, including food, feed, and water. These side effects have resulted in political pressures to reduce herbicide application. The European Commission aims to reduce the use and risk of chemicals and more hazardous pesticides in the EU. Therefore, new weed control methods are in demand. Laser weeding might be an alternative to replace or supplement herbicides and other weed control methods in an Integrated Weed Management (IPM) strategy. This work aimed to investigate how increasing laser energy affected common weeds when the apical meristem was exposed to irradiation at the early stages of development. A 50 W thulium-doped fibre laser with a diameter of 2 mm and a wavelength of 2 µm was used. The highest efficacy of laser irradiation was achieved when the grass weed (Alopecurus myosuroides) had one leaf and the dicot species were at the cotyledon stage. There was a large difference between the species' susceptibility to irradiation probably caused by differences in morphology and growth habit. At the 4-leaf stage, most of the species regrew after irradiation. Laser weeding may be a solution to replace or supplement other weed control methods in some crops, but in general the weeds must be irradiated when they are at the cotyledon to 2-leaf stage to avoid regrowth.

Agronomic and Technical Evaluation of Herbicide Spot Spraying in Maize Based on High-Resolution Aerial Weed Maps-An On-Farm Trial.

Spot spraying can significantly reduce herbicide use while maintaining equal weed control efficacy as a broadcast application of herbicides. Several online spot-spraying systems have been developed, with sensors mounted on the sprayer or by recording the RTK-GNSS position of each crop seed. In this study, spot spraying was realized offline based on georeferenced unmanned aerial vehicle (UAV) images with high spatial resolution. Studies were conducted in four maize fields in Southwestern Germany in 2023. A randomized complete block design was used with seven treatments containing broadcast and spot applications of pre-emergence and post-emergence herbicides. Post-emergence herbicides were applied at 2-4-leaf and at 6-8-leaf stages of maize. Weed and crop density, weed control efficacy (WCE), crop losses, accuracy of weed classification in UAV images, herbicide savings and maize yield were measured and analyzed. On average, 94% of all weed plants were correctly identified in the UAV images with the automatic classifier. Spot-spraying achieved up to 86% WCE, which was equal to the broadcast herbicide treatment. Early spot spraying saved 47% of herbicides compared to the broadcast herbicide application. Maize yields in the spot-spraying plots were equal to the broadcast herbicide application plots. This study demonstrates that spot-spraying based on UAV weed maps is feasible and provides a significant reduction in herbicide use.

Early silique-shedding wild radish (Raphanus raphanistrum L.) phenotypes persist in a long-term harvest weed seed control managed field in Western Australia.

BACKGROUND: This study introduces a wild radish population collected from Yelbeni in the Western Australian grainbelt that evolved an early silique abscission (shedding) trait to persist despite long-term harvest weed seed control (HWSC) use. In 2017, field-collected seed (known herein as Yelbeni) was compared to surrounding ruderal and field-collected populations in a fully randomized common garden study. RESULTS: The Yelbeni population exhibited a higher rate of silique abscission when compared to the ruderal populations collected from the site before wheat (Triticum aestivum L.) harvest (assessed at soft dough stage, Zadoks 83). A similar common garden study was conducted in the subsequent season (2018) using progeny reproduced on a single site without stress. The HWSC-selected progeny (Yelbeni P) shed 1048 (±288) siliques before wheat maturity at the soft dough stage (Zadoks 83) compared to 25 (±7) siliques from the pooled control populations. The Yelbeni P population only flowered 6 days earlier (FT50 as determined by log-logistic analysis) than pooled control populations, which is unlikely to fully account for the increased rate of silique abscission. The Yelbeni P population also located its lowest siliques below the lowest height for harvest interception (10 cm), which is likely to increase HWSC evasion. The mechanism inducing early silique-shedding is yet to be determined; however, wild radish is known for its significant genetic variability and has demonstrated its capacity to adapt to environmental and management stresses. CONCLUSION: This study demonstrates that the repeated use of HWSC can lead to the selection of HWSC-avoidance traits including early silique-shedding before harvest and/or locating siliques below the harvest cutting height for interception. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Distribution, characterization and chemical management of noxious shrub weed (Dichapetalum stuhlmannii Engl) in cashew in southeastern Tanzania.

Weeds are among the limiting factors that influence low production of economically important crops including cashew (Anacardium occidental L.). Shrub weeds attribute to inter-competition for resources, hinder harvesting and ultimately reduce cashew yields in Tanzania. The current study determined the distribution, characteristics and chemical control option of Dichapetalum Engl in Lindi and Mtwara regions, Southeastern Tanzania. The distribution study involved a survey for weed presence along the areas of Lindi and Mtwara regions. The characterization included distilling, and assessing the growth and development properties of shrub weed. Chemical herbicides; glyphosate 480g. ai/l, 2, 4 D - Dichlorirophenoxyacetic 720g. ai/l, triclopyr 160g a.i./l and 1:1 mixture of glyphosate 480g a.i./l plus triclopyr 160g a.i./L at 15, 20 and 25 mls of formulated product/L of water per 4 m2 were tested on tender, mature and blooming growth stages of D. Stuhlmannii and three spraying frequencies. Findings revealed that the D.stuhlmannii, is a widely distributed shrub weed in Lindi and Mtwara regions. The weed was characterized with three main growth stages of seedling, mature and blooming with a long tap root and evergreen throughout the wet and dry seasons. The tested herbicides revealed the potential suppression of D. Stuhlmannii growth. Glyphosate and a mixture of glyphosate + triclopyr at 15 ml/L outperformed triclopyr and 2, 4 D across all the growth stages. Double spraying of glyphosate and its mixture bettered frequencies of triclopyr and 2, 4 D. The delayed regrowth of suppressed shrub weed took 90-120 days after application of herbicides. The current study recommends for single or double applications of glyphosate herbicides at 15 ml/L or 10,700 ml/ha on tender or mature D. Stuhlmannii in cashew farms. Further studies on the economic feasibility and effect on the microbiota of applied fungicides are required.