Simulating changes in cropping practises in conventional and glyphosate-tolerant maize. I. Effects on weeds

Identification and Comprehensive Evaluation of Resistant Weeds Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Atrazine is one of the most widely used herbicides in weed management. However, the widespread use of atrazine has concurrently accelerated the evolution of weed resistance mechanisms. Resistant weeds were identified early to contribute to crop protection in precision agriculture before visible symptoms of atrazine application to weeds in actual field environments. New developments in unmanned aerial vehicle (UAV) platforms and sensor technologies promote cost-effective data collection by collecting multi-modal data at very high spatial and spectral resolution. In this study, we obtained multispectral and RGB images using UAVs, increased available information with the help of image fusion technology, and developed a weed spectral resistance index, WSRI = (RE-R)/(RE-B), based on the difference between susceptible and resistant weed biotypes. A deep convolutional neural network (DCNN) was applied to evaluate the potential for identifying resistant weeds in the field. Comparing the WSRI introduced in this study with previously published vegetation indices (VIs) shows that the WSRI is better at classifying susceptible and resistant weed biotypes. Fusing multispectral and RGB images improved the resistance identification accuracy, and the DCNN achieved high field accuracies of 81.1% for barnyardgrass and 92.4% for velvetleaf. Time series and weed density influenced the study of weed resistance, with 4 days after application (4DAA) identified as a watershed timeframe in the study of weed resistance, while different weed densities resulted in changes in classification accuracy. Multispectral and deep learning proved to be effective phenotypic techniques that can thoroughly analyze weed resistance dynamic response and provide valuable methods for high-throughput phenotyping and accurate field management of resistant weeds.

Evaluation of phytotoxic effects of cationic polyacrylamide polymers: implication for the use of sludges as organic soil amendments in assisted phytostabilization

Sludges, as biosolids, are organic soil amendments commonly used in assisted phytostabilitation. Extensive studies on their environmental impacts exists, particularly for improper land application and contents of trace elements, organic compounds and pathogens, but not for their content of cationic polyacrylamide polymers (C-PAMs). Direct toxicity of C-PAMs on aquatic organisms has been demonstrated but scarce information about plant toxicity is available. In this study, the effect of C-PAMs on early plant growth was evaluated by means of standard toxicity assays. Firstly, increasing doses of C-PAMs were applied as solutions to seeds of Avena sativa, Lactuca sativa and Solanum lycopersycum to evaluate germination and root elongation. Secondly, the effect of increasing doses of C-PAMs spiked in pig manure and mixed with sand (75 t ha^-1dry base) was evaluated on the emergence, radicle elongation and biomass of A. sativa. Results showed high phytotoxicity of C-PAMs in solutions above 1,000 mg L^-1, but no effect was detected when spiked into experimental substrate; a significant effect was only observed above 5,000 mg L^-1 for radicle elongation, aerial biomass, and radicle biomass. Results demonstrate direct phytotoxic effects of C-PAMs, which can be mitigated when spiked into an organic (pig manure) and mineral (sand) matrix.

Non-destructive Determination of Shikimic Acid Concentration in Transgenic Maize Exhibiting Glyphosate Tolerance Using Chlorophyll Fluorescence and Hyperspectral Imaging

The development of transgenic glyphosate-tolerant crops has revolutionized weed control in crops in many regions of the world. The early, non-destructive identification of superior plant phenotypes is an important stage in plant breeding programs. Here, glyphosate-tolerant transgenic maize and its parental wild-type control were studied at 2, 4, 6, and 8 days after glyphosate treatment. Visible and near-infrared hyperspectral imaging and chlorophyll fluorescence imaging techniques were applied to monitor the performance of plants. In our research, transgenic maize, which was highly tolerant to glyphosate, was phenotyped using these high-throughput non-destructive methods to validate low levels of shikimic acid accumulation and high photochemical efficiency of photosystem II as reflected by maximum quantum yield and non-photochemical quenching in response to glyphosate. For hyperspectral imaging analysis, the combination of spectroscopy and chemometric methods was used to predict shikimic acid concentration. Our results indicated that a partial least-squares regression model, built on optimal wavelengths, effectively predicted shikimic acid concentrations, with a coefficient of determination value of 0.79 for the calibration set, and 0.82 for the prediction set. Moreover, shikimic acid concentration estimates from hyperspectral images were visualized on the prediction maps by spectral features, which could help in developing a simple multispectral imaging instrument for non-destructive phenotyping. Specific physiological effects of glyphosate affected the photochemical processes of maize, which induced substantial changes in chlorophyll fluorescence characteristics. A new data-driven method, combining mean fluorescence parameters and featuring a screening approach, provided a satisfactory relationship between fluorescence parameters and shikimic acid content. The glyphosate-tolerant transgenic plants can be identified with the developed discrimination model established on important wavelengths or sensitive fluorescence parameters 6 days after glyphosate treatment. The overall results indicated that both hyperspectral imaging and chlorophyll fluorescence imaging techniques could provide useful tools for stress phenotyping in maize breeding programs and could enable the detection and evaluation of superior genotypes, such as glyphosate tolerance, with a non-destructive high-throughput technique.

Relationship between weed dormancy and herbicide rotations: implications in resistance evolution

It is suggested that selection for late germinating seed cohorts is significantly associated with herbicide resistance in some cropping systems. In turn, it is conceivable that rotating herbicide modes of action selects for populations with mutations for increased secondary dormancy, thus partially overcoming the delaying effect of rotation on resistance evolution. Modified seed dormancy could affect management strategies - like herbicide rotation - that are used to prevent or control herbicide resistance. Here, we review the literature for data on seed dormancy and germination dynamics of herbicide-resistant versus susceptible plants. Few studies use plant material with similar genetic backgrounds, so there are few really comparative data. Increased dormancy and delayed germination may co-occur with resistance to ACCase inhibitors, but there is no clear-cut link with resistance to other herbicide classes. Population shifts are due in part to pleiotropic effects of the resistance genes, but interaction with the cropping system is also possible. We provide an example of a model simulation that accounts for genetic diversity in the dormancy trait, and subsequent consequences for various cropping systems. We strongly recommend adding more accurate and detailed mechanistic modelling to the current tools used today to predict the efficiency of prevention and management of herbicide resistance. These models should be validated through long-term experimental designs including mono-herbicide versus chemical rotation in the field. © 2017 Society of Chemical Industry.

Simulating changes in cropping practices in conventional and glyphosate-resistant maize. II. Weed impacts on crop production and biodiversity

Overreliance on the same herbicide mode of action leads to the spread of resistant weeds, which cancels the advantages of herbicide-tolerant (HT) crops. Here, the objective was to quantify, with simulations, the impact of glyphosate-resistant (GR) weeds on crop production and weed-related wild biodiversity in HT maize-based cropping systems differing in terms of management practices. We (1) simulated current conventional and probable HT cropping systems in two European regions, Aquitaine and Catalonia, with the weed dynamics model FLORSYS; (2) quantified how much the presence of GR weeds contributed to weed impacts on crop production and biodiversity; (3) determined the effect of cultural practices on the impact of GR weeds and (4) identified which species traits most influence weed-impact indicators. The simulation study showed that during the analysed 28 years, the advent of glyphosate resistance had little effect on plant biodiversity. Glyphosate-susceptible populations and species were replaced by GR ones. Including GR weeds only affected functional biodiversity (food offer for birds, bees and carabids) and weed harmfulness when weed effect was initially low; when weed effect was initially high, including GR weeds had little effect. The GR effect also depended on cultural practices, e.g. GR weeds were most detrimental for species equitability when maize was sown late. Species traits most harmful for crop production and most beneficial for biodiversity were identified, using RLQ analyses. None of the species presenting these traits belonged to a family for which glyphosate resistance was reported. An advice table was built; the effects of cultural practices on crop production and biodiversity were synthesized, explained, quantified and ranked, and the optimal choices for each management technique were identified.