Cover crops, hormones and herbicides: Priming an integrated weed management strategy

Study on Design, Synthesis and Herbicidal Activity of Novel 6-Indazolyl-2-picolinic Acids

Thirty-eight new 4-amino-3,5-dicholo-6-(1H-indazolyl)-2-picolinic acids and 4-amino-3,5-dicholo-6-(2H-indazolyl)-2-picolinic acids were designed by scaffold hopping and synthesized to discover potential herbicidal molecules. All the new compounds were tested to determine their inhibitory activities against Arabidopsis thaliana and the root growth of five weeds. In general, the synthesized compounds exhibited excellent inhibition properties and showed good inhibitory effects on weed root growth. In particular, compound 5a showed significantly greater root inhibitory activity than picloram in Brassica napus and Abutilon theophrasti Medicus at the concentration of 10 µM. The majority of compounds exhibited a 100% post-emergence herbicidal effect at 250 g/ha against Amaranthus retroflexus and Chenopodium album. We also found that 6-indazolyl-2-picolinic acids could induce the up-regulation of auxin genes ACS7 and NCED3, while auxin influx, efflux and auxin response factor were down-regulated, indicating that 6-indazolyl-2-picolinic acids promoted ethylene release and ABA production to cause plant death in a short period, which is different in mode from other picolinic acids.

Divergent Cross-Adaptation of Herbicide-Treated Wheat and Triticale Affected by Drought or Waterlogging

Widely used agrochemicals that do not exert negative effects on crops and selectively target weeds could influence plant resilience under unfavorable conditions. The cross-adaptation of wheat (Triticum aestivum L.) and triticale (×Triticosecale Wittm.) exposed to two environmental abiotic stressors (drought and waterlogging) was evaluated after treatment with a selective herbicide (Serrate®, Syngenta). The ambivalent effects of the herbicide on the two studied crops were particularly distinct in waterlogged plants, showing a significant reduction in wheat growth and better performance of triticale individuals exposed to the same combined treatment. Histochemical staining for the detection of reactive oxygen species (ROS) confirmed that the herbicide treatment increased the accumulation of superoxide anion in the flooded wheat plants, and this effect persisted in the younger leaves of the recovered individuals. Comparative transcript profiling of ROS scavenging enzymes (superoxide dismutase, peroxidase, glutathione reductase, and catalase) in stressed and recovered plants revealed crop-specific variations resulting from the unfavorable water regimes in combination with the herbicide treatment. Short-term dehydration was relatively well tolerated by the hybrid crop triticale and this aligned with the considerable upregulation of genes for L-Proline biosynthesis. Its drought resilience was diminished by herbicide application, as evidenced by increased ROS accumulation after prolonged water deprivation.

Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials

The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies' workers. Intoxication with these herbicides dysregulates the microbiome-gut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.

Applications and Prospects of CRISPR/Cas9-Mediated Base Editing in Plant Breeding

The clustered regularly interspaced short palindromic repeats (CRISPR)/associated protein 9 system (Cas9) has been used at length to optimize multiple aspects of germplasm resources. However, large-scale genomic research has indicated that novel variations in crop plants are attributed to single-nucleotide polymorphisms (SNPs). Therefore, substituting single bases into a plant genome may produce desirable traits. Gene editing by CRISPR/Cas9 techniques frequently results in insertions-deletions (indels). Base editing allows precise single-nucleotide changes in the genome in the absence of double-strand breaks (DSBs) and donor repair templates (DRTs). Therefore, BEs have provided a new way of thinking about genome editing, and base editing techniques are currently being utilized to edit the genomes of many different organisms. As traditional breeding techniques and modern molecular breeding technologies complement each other, various genome editing technologies have emerged. How to realize the greater potential of BE applications is the question we need to consider. Here, we explain various base editings such as CBEs, ABEs, and CGBEs. In addition, the latest applications of base editing technologies in agriculture are summarized, including crop yield, quality, disease, and herbicide resistance. Finally, the challenges and future prospects of base editing technologies are presented. The aim is to provide a comprehensive overview of the application of BE in crop breeding to further improve BE and make the most of its value.

Metabolomics as a Prospective Tool for Soybean (Glycine max) Crop Improvement

Global demand for soybean and its products has stimulated research into the production of novel genotypes with higher yields, greater drought and disease tolerance, and shorter growth times. Genetic research may be the most effective way to continue developing high-performing cultivars with desirable agronomic features and improved nutritional content and seed performance. Metabolomics, which predicts the metabolic marker for plant performance under stressful conditions, is rapidly gaining interest in plant breeding and has emerged as a powerful tool for driving crop improvement. The development of increasingly sensitive, automated, and high-throughput analytical technologies, paired with improved bioinformatics and other omics techniques, has paved the way for wide characterization of genetic characteristics for crop improvement. The combination of chromatography (liquid and gas-based) with mass spectrometry has also proven to be an indisputable efficient platform for metabolomic studies, notably plant metabolic fingerprinting investigations. Nevertheless, there has been significant progress in the use of nuclear magnetic resonance (NMR), capillary electrophoresis, and Fourier-transform infrared spectroscopy (FTIR), each with its own set of benefits and drawbacks. Furthermore, utilizing multivariate analysis, principal components analysis (PCA), discriminant analysis, and projection to latent structures (PLS), it is possible to identify and differentiate various groups. The researched soybean varieties may be correctly classified by using the PCA and PLS multivariate analyses. As metabolomics is an effective method for evaluating and selecting wild specimens with desirable features for the breeding of improved new cultivars, plant breeders can benefit from the identification of metabolite biomarkers and key metabolic pathways to develop new genotypes with value-added features.

A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae

Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.

An assessment of potential pesticide transmission, considering the combined impact of soil texture and pesticide properties: A meta-analysis

Pesticides are widely employed as a cost-effective means of reducing the impacts of undesirable plants and animals. The aim of this paper is to develop a risk ranking of transmission of key pesticides through soil to waterways, taking into account physico-chemical properties of the pesticides (soil half-life and water solubility), soil permeability, and the relationship between adsorption of pesticides and soil texture. This may be used as a screening tool for land managers, as it allows assessment of the potential transmission risks associated with the use of specified pesticides across a spectrum of soil textures. The twenty-eight pesticides examined were differentiated into three groups: herbicides, fungicides and insecticides. The highest risk of pesticide transmission through soils to waterways is associated with soils containing <20% clay or >45% sand. In a small number of cases, the resulting transmission risk is not influenced by soil texture alone. For example, for Phenmedipham, the transmission risk is higher for clay soils than for silt loam. The data generated in this paper may also be used in the identification of critical area sources, which have a high likelihood of pesticide transmission to waterways. Furthermore, they have the potential to be applied to GIS mapping, where the potential transmission risk values of the pesticides can be layered directly onto various soil textures.

Novel Galactosyl Moiety-Conjugated Furylchalcones Synthesized Facilely Display Significant Regulatory Effect on Plant Growth

The expansion of weed infestation has increased the demand on new herbicides. A series of novel galactosyl moiety-conjugated furylchalcones was facilely synthesized in which the furyl group (A ring) was combined with the substituted benzene group (B ring), and a galactosyl moiety was introduced. All these galactosyl furylchalcones were predicted to be phloem-mobile. Most of the galactosyl furylchalcones significantly promoted early seedling growth of sorghum and barnyardgrass under dark conditions, but all of them revealed considerable anti-growth ability on illuminated pot plants; especially, 1-(3'-(4″-O-β-d-galactopyranosyl)furyl)-3-(4″-nitrophenyl)-2-en-1-one (B₁₁) had a better herbicidal activity against rapeseed and Chinese amaranth than haloxyfop-R-methyl. The median efficient concentrations (EC₅₀) of compound B₁₁ against cucumber and wheat were 9.55 and 26.97 mg/L, respectively, also showing a stronger suppressing capacity than 2,4-D. Molecular docking with phosphoenolpyruvate carboxylase protein showed a stable binding conformation in which the galactosyl group interacted with LYS363 and GLU369, the furan ring and carbonyl bound with ARG184, and the crosslink of the nitro group with GLU240 formed a salt bridge. The results demonstrate that galactosyl furylchalcones possess the great potential as new herbicides for weed management, and further evaluations on more weeds are required for practical application.

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.