The herbicide bensulfuron-methyl inhibits rice seedling development by blocking calcium ion flux in the OsCNGC12 channel.

Identification of translocator protein-related genes involved in bensulfuron-methyl (BSM) uptake and transport in rice could facilitate the development of herbicide-tolerant cultivars by inactivating them. This study found that the OsCNGC12 mutants not only reduced BSM uptake but also compromised the Ca2 ⁺ efflux caused by BSM in the roots, regulating dynamic equilibrium of Ca2 ⁺ inside the cell and conferring non-target-site tolerance to BSM.

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.

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.

Environmental and economic concerns surrounding restrictions on glyphosate use in corn.

Since the commercialization of transgenic glyphosate-tolerant (GT) crops in the mid-1990s, glyphosate has become the dominant herbicide to control weeds in corn, soybean, and other crops in the United States and elsewhere. However, recent public concerns over its potential carcinogenicity in humans have generated calls for glyphosate-restricting policies. Should a policy to restrict glyphosate use, such as a glyphosate tax, be implemented? The decision involves two types of tradeoffs: human health and environmental (HH-E) impacts versus market economic impacts, and the use of glyphosate versus alternative herbicides, where the alternatives potentially have more serious adverse HH-E effects. Accounting for farmers' weed management choices, we provide empirical evaluation of the HH-E welfare and market economic welfare effects of a glyphosate use restriction policy on US corn production. Under a glyphosate tax, farmers would substitute glyphosate for a combination of other herbicides. Should a 10% glyphosate tax be imposed, then the most conservative welfare estimate is a net HH-E welfare gain with a monetized value of US$6 million per annum but also a net market economic loss of US$98 million per annum in the United States, which translates into a net loss in social welfare. This result of overall welfare loss is robust to a wide range of tax rates considered, from 10 to 50%, and to multiple scenarios of glyphosate's HH-E effects, which are the primary sources of uncertainties about glyphosate's effects.

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.

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.

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.

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.

Homoeologous evolution of the allotetraploid genome of Poa annua L.

BACKGROUND: Poa annua (annual bluegrass) is an allotetraploid turfgrass, an agronomically significant weed, and one of the most widely dispersed plant species on earth. Here, we report the chromosome-scale genome assemblies of P. annua's diploid progenitors, P. infirma and P. supina, and use multi-omic analyses spanning all three species to better understand P. annua's evolutionary novelty. RESULTS: We find that the diploids diverged from their common ancestor 5.5 - 6.3 million years ago and hybridized to form P. annua ≤ 50,000 years ago. The diploid genomes are similar in chromosome structure and most notably distinguished by the divergent evolutionary histories of their transposable elements, leading to a 1.7 × difference in genome size. In allotetraploid P. annua, we find biased movement of retrotransposons from the larger (A) subgenome to the smaller (B) subgenome. We show that P. annua's B subgenome is preferentially accumulating genes and that its genes are more highly expressed. Whole-genome resequencing of several additional P. annua accessions revealed large-scale chromosomal rearrangements characterized by extensive TE-downsizing and evidence to support the Genome Balance Hypothesis. CONCLUSIONS: The divergent evolutions of the diploid progenitors played a central role in conferring onto P. annua its remarkable phenotypic plasticity. We find that plant genes (guided by selection and drift) and transposable elements (mostly guided by host immunity) each respond to polyploidy in unique ways and that P. annua uses whole-genome duplication to purge highly parasitized heterochromatic sequences. The findings and genomic resources presented here will enable the development of homoeolog-specific markers for accelerated weed science and turfgrass breeding.

Application of Moringa leaves as soil amendment to tiger-nut for suppressing weeds in the Nigerian Savanna.

BACKGROUND: The allelopathic effect of Moringa (Moringa oleifera Lam.) leaves applied as organic manure in tiger nut (Cyperus esculentus L.) production on associated weeds was investigated in the guinea savanna of South West Nigeria, during the 2014 (September - November) and 2015 (June - August) wet seasons. METHODS: Five Moringa leaves rates (0, 2.5, 5.0, 7.5 and 10 t/ha) and three tuber sizes (0.28 g, 0.49 g and 0.88 g dry weight) were laid out in the main plot and sub-plot, respectively in a split-plot arrangement fitted into randomized complete block design and replicated three times. RESULTS: Parameters measured, which include, weed cover score (WCS), weed density (WD) and weed dry matter production (WDMP) were significantly (p<0.05) influenced in both years by Moringa leaf. In 2015, WCS, WD and WDMP significantly (p<0.05) reduced by 25-73%, 35-78% and 26-70% on Moringa leaves-treated plots respectively. There were significant (p<0.05) interactions between quantity of Moringa leaves incorporated and tuber size. The bigger the tuber and the higher the quantity of Moringa leaves incorporated the lower the WCS, WD and WDMP. CONCLUSIONS: Consequently, application of 10 t.ha- 1 Moringa leaves and planting of large or medium-sized tubers were recommended for optimum weed suppression in tiger nut production in South West Nigeria.

Discrepancies between the drivers of alpha and beta plant diversity in arable field margins.

Field margins are major habitats for biodiversity conservation and ecosystem functioning in agricultural landscapes, but biotic homogenization of plant communities threatens their ecological and agronomic functions. Our objective is to determine the drivers of plant diversity in field margins for conservation and restoration purposes. To do so, we assessed the effects of field margin structure and long-term management over 20 years (1995-2015) on the taxonomic and functional α- and ß-diversity, and the functional composition of herbaceous plant communities. In 2015, we surveyed 302 field margins in bocage landscapes of Brittany, northwestern France. Results were very similar between taxonomic and functional diversity but revealed important discrepancies between the drivers of α- and ß-diversity. Deep ditches, mowing and grazing increased α-diversity but did not affect ß-diversity. Denser hedgerows had lower α-diversity than other field margins but strongly contributed to ß-diversity by harbouring more unique sets of species or life strategies. Long-term herbicide spraying in field margins and cropping intensity in adjacent habitats did not affect α-diversity, but had more complex effects on ß-diversity and selected for common weeds. All in all, preservation of dense hedgerows, abandonment of herbicide spraying, and protection against agrochemical drifts are key measures to prevent the establishment of common weeds and biotic homogenization of herbaceous plant communities in field margins. Above all, our study shows how important it is to go beyond α-diversity to make robust conservation and restoration decisions.

The blackgrass genome reveals patterns of non-parallel evolution of polygenic herbicide resistance.

Globally, weedy plants are a major constraint to sustainable crop production. Much of the success of weeds rests with their ability to rapidly adapt in the face of human-mediated management of agroecosystems. Alopecurus myosuroides (blackgrass) is a widespread and impactful weed affecting agriculture in Europe. Here we report a chromosome-scale genome assembly of blackgrass and use this reference genome to explore the genomic/genetic basis of non-target site herbicide resistance (NTSR). Based on our analysis of F2 seed families derived from two distinct blackgrass populations with the same NTSR phenotype, we demonstrate that the trait is polygenic and evolves from standing genetic variation. We present evidence that selection for NTSR has signatures of both parallel and non-parallel evolution. There are parallel and non-parallel changes at the transcriptional level of several stress- and defence-responsive gene families. At the genomic level, however, the genetic loci underpinning NTSR are different (non-parallel) between seed families. We speculate that variation in the number, regulation and function of stress- and defence-related gene families enable weedy species to rapidly evolve NTSR via exaptation of genes within large multi-functional gene families. These results provide novel insights into the potential for, and nature of plant adaptation in rapidly changing environments.