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• Published: 26 November 2020

A holistic approach in herbicide resistance research and management: from resistance detection to sustainable weed control

• Chun Liu   ORCID: orcid.org/0000-0001-5886-5785 1 ,
• Lucy V. Jackson 1 ,
• Sarah-Jane Hutchings   ORCID: orcid.org/0000-0001-6853-5229 1 ,
• Daniel Tuesca 2 ,
• Raul Moreno 3 ,
• Eddie Mcindoe 1 &
• Shiv S. Kaundun   ORCID: orcid.org/0000-0002-7249-2046 1  

Scientific Reports volume  10 , Article number:  20741 ( 2020 ) Cite this article

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• Agroecology
• Ecological modelling
• Genetic variation
• Plant evolution

Agricultural weeds can adapt rapidly to human activities as exemplified by the evolution of resistance to herbicides. Despite its multi-faceted nature, herbicide resistance has rarely been researched in a holistic manner. A novel approach combining timely resistance confirmation, investigation of resistance mechanisms, alternative control solutions and population modelling was adopted for the sustainable management of the Amaranthus palmeri weed in soybean production systems in Argentina. Here, we show that resistance to glyphosate in the studied population from Cordoba province was mainly due to a P106S target-site mutation in the 5-enolpyruvylshikimate 3-phosphate synthase ( EPSPS ) gene, with minor contributions from EPSPS gene duplication/overexpression. Alternative herbicides, such as fomesafen, effectively controlled the glyphosate-resistant plants. Model simulations revealed the tendency of a solo herbicidal input to primarily select for a single resistance mechanism and suggested that residual herbicides, alongside chemical diversity, were important for the sustainable use of these herbicides. We also discuss the value of an interdisciplinary approach for improved understanding of evolving weeds.

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Introduction.

Weeds are highly diverse organisms capable of evolving traits to survive detrimental stresses. Agricultural fields are one of the most modified ecosystems where weed populations undergo frequent and intensive selection pressure imposed by anthropogenic activities, primarily via the use of herbicides. The large seed production of some weeds, such as Amaranthus palmeri , gives rise to the standing genetic variation in field populations 1 , 2 . Multiple mechanisms could therefore evolve to confer survival against herbicidal treatments. Herbicides applied at high doses usually select for target-site (TS) mutations while low doses usually boost quantitative resistance, including target gene duplication/overexpression and non-target-site resistance (NTSR) 3 . Different weed control practices may select for different resistance mechanisms towards a same herbicide in the same weed species. For instance, resistance to glyphosate in A. palmeri could be endowed, either individually or simultaneously, by reduced uptake, EPSPS gene amplification, or a P106S target-site mutation 4 , 5 , 6 . The long-term sustainability of weed control practices becomes difficult to predict under laboratory or field settings due to the diverse nature of weeds, the complex resistance mechanism, and the interfering human activities, as well as the interactions among these factors. In this respect, computer-based population models are useful tools for the cost-effective prediction of the evolutionary dynamics of weed populations under different management programs. So far the majority of resistance models have mainly focused on demonstrating principles and been used as educational tools 7 . This is mainly due to the lack of sufficient knowledge about the weed biology and resistance mechanisms, and the difficulty in representing spatio-temporal variations in the models; the former hinders accurate model predictions and the latter hinders field-specific recommendations.

Multiple biological and anthropogenic factors are involved in the evolutionary process of herbicide resistance, and therefore research has often been advanced from various perspectives. Key areas of study include (i) investigation of poor weed control in the field and geographic distribution of resistant populations 8 , 9 , (ii) confirmation of resistance using glasshouse and laboratory tests 10 , 11 , 12 , (iii) analysis of resistance mechanisms at the genetic and molecular levels 5 , 13 , (iv) exploration of alternative control methods of resistant weeds 14 , 15 , and (v) evaluation of long-term sustainability of weed management strategies using population models 16 , 17 . Most of the studies have focused on a specific aspect and are therefore incomplete. A holistic approach is needed for a more comprehensive understanding and timely management of resistance.

Here, we have combined the different research aspects, from resistance detection to population modelling, and present a case study on the control of the highly damaging A. palmeri in soybean agroecosystems in Argentina.

Rapid confirmation of resistance to glyphosate

The standard sensitive A. palmeri population (ApS1) was fully controlled at 25 μM in the Syngenta RISQ (Resistance In-Season Quick) test and at 200 g ae ha −1 in the whole plant pot test (discriminating rates). Both the standard resistant population (ApR) and the field samples collected from Villa Valeria (VV), Argentina survived the discriminating rates of glyphosate (Fig.  1 ).

Shift in responses to glyphosate in the A. palmeri samples collected in Villa Valeria (VV), Argentina, as compared to the standard sensitive (ApS1) and standard resistant (ApR) populations. Photos from the agar-based RISQ test (left) and dose response from the whole plant pot test (right) are shown.

Mechanism of glyphosate resistance

There was little evidence to suggest any difference in glyphosate uptake between the VV and ApS1 populations ( p  = 0.2965). Impaired translocation was not observed in the VV population, and contrary to expectations for an impaired translocation concept, the movement to meristem was higher in the VV population than in ApS1 ( p  = 0.0141). Recoveries of glyphosate were over 72% at 96 h after treatment, indicating low metabolic degradation (Fig.  2 ).

Biokinetics experiment results showing uptake of glyphosate in the whole plant, and radiochemical recovered, expressed as percentage glyphosate absorbed, in different parts of the plant measured at 24, 48 and 96 h after treatment in the ApS1 and VV populations.

An increase in EPSPS gene copy number and expression levels was observed in the VV compared to the ApS2 populations. However, the magnitude of the difference in all cases was relatively small (~ 1.5-fold) (Table 1 ), as compared to the difference between ApR and ApS1 (~ 9.0-fold) in Kaundun et al. 6 .

Sanger sequencing identified the known cytosine (CCA) to thymine (TCA) target-site resistance mutation at EPSPS codon 106 in the VV population. The frequencies of the genotypes were 6% for homozygous wild-type PP106, 22% for heterozygous mutant PS106 and 72% for homozygous mutant SS106.

Therefore, resistance to glyphosate in the VV population was endowed by multiple mechanisms, namely, a P106S TS mutation and a low level of EPSPS gene duplication/overexpression (quantitative resistance).

Simulation of evolved glyphosate resistance endowed by different mechanisms

The dose responses of PP, PS and SS genotypes (for the EPSPS codon 106) in the population model were parameterised based on the VM1 samples (collected from Vicuña Mackenna, Cordoba, Argentina) in Kaundun et al. 6 . This was justified by the similar glyphosate resistance mechanisms in the two populations which were collected near each other (75 km) and had similar previous weed control practices. The model predicted that weed density exceeded control threshold (1 plant m −2 ) in an average of 9.7 years for a pristine population with the two resistance mechanisms implemented simultaneously and glyphosate used as a solo treatment applied twice post-emergence. Evolved TS mutation-based resistance exceeded 20% in an average of 7.4 years, whilst the evolved quantitative resistance was below 0.04% in all of the 100 replicates. For both TS mutation-based and quantitative resistance, the resulting resistance level was positively correlated to the existing number of resistant plants in the population (i.e. initial resistance frequency). However, the level of evolved TS mutation is negatively correlated to the initial % quantitative resistance, and vice versa (Fig.  3 ). In other words, TS mutation-based resistance and quantitative resistance showed a tendency of being selected in opposite manners. When the population underwent a selection posed by a single herbicide, in this case, glyphosate, a single resistance mechanism would be selected primarily, instead of multiple mechanisms being selected simultaneously. This is because a high initial proportion of individuals with one of the mechanisms would mean the absolute majority of the population that survived the herbicide application carry this particular mechanism (e.g. P106S TS mutation in this case), among which the statistical distribution of the other mechanism (quantitative glyphosate resistance in this case) is nevertheless unskewed, and hence unselected for. This conclusion only holds when a single herbicide is involved. The resistance mechanism to be selected will depend on the relative initial frequency of the candidate mechanisms.

Evolution of glyphosate resistance endowed by target-site (TS) mutation and quantitative resistance respectively, in the surviving plants, with varying initial % resistance (quantitative resistance across columns and TS mutation across different lines). Number of replicates = 100.

Alternative herbicides

All alternative herbicides tested, including protoporphyrinogen oxidase (PPO) inhibitors lactofen and fomesafen, and residual herbicides S -metolachlor and metribuzin controlled the glyphosate-resistant VV population with 100% efficacy at field application rates.

Sustainability of PPO-based herbicides

The predicted outcome of weed control was affected by both the % exposure to herbicides and the evolution of PPO resistance for populations where glyphosate resistance was commonplace and weed control relied predominantly on PPO-based herbicides. The % exposure is a result of % weed emergence vs. herbicide application timing. A glyphosate + fomesafen mixture at a high application rate, and hence longer residual activity from fomesafen (Table 2 ), provided sufficient herbicide exposure. However, evolved glyphosate resistance together with partial PPO resistance (10 −2 ) led to weed control failure within six years (Fig.  4 , S1). Similarly, lactofen, which does not have residual activity, but sequentially applied twice post-emergence, also ensured adequate level of exposure. Using a single herbicide mode of action, however, bore high risk of resistance evolution and led to control failure within three years in partially PPO-resistant populations (S2). In contrast, in programs S3 and S4, S -metolachlor + fomesafen was used only once, and so did not control all cohorts of the season. Program S4 was slightly better than S3 because when the mixture of S -metolachlor + fomesafen was applied pre-emergence, weed control was from the residual activity of both herbicides. When the mixture was applied post-emergence, the control of early-emerging cohorts relied solely on fomesafen, resulting in high selection pressure. Applying this herbicide mixture both pre-emergence and post-emergence ensured better weed control for a longer period of time (S5) and lower resistance risk than the one-time post-emergence only program (S3). As a further improvement, program S6 incorporated more chemical diversity by replacing fomesafen with metribuzin, a different herbicide mode of action, in the pre-emergence application. In partially PPO-resistant fields (e.g. 10 –2 ), S6 effectively remedied PPO resistance evolution from early onset and to a lower level, as compared to S5.

Simulated time series of weed density (upper panel) and % PPO resistance in the soil seedbank (lower panel) under different herbicide programs (S1–S6) in a partially PPO-resistant population (initial proportion of PPO resistance = 10 −2 ). Number of replicates in each scenario = 100. GLY glyphosate, FMS fomesafen, LAC lactofen, SMOC S -metolachlor, MBZ metribuzin, PRE pre-emergence application, POST post-emergence application, fb followed by. For more detail of the scenarios, see Table 2 .

Our holistic study reveals the cause of resistance to glyphosate in the A. palmeri population from Argentina, confirms the effectiveness of alternative PPO-based herbicides, and suggests sustainable ways of using the PPO herbicides so that their longevity is maximised. Resistance to glyphosate in this population was due to a major contribution of P106S TS mutation and a minor contribution of EPSPS gene duplication/overexpression. This result was different to what is commonly found in the glyphosate-resistant A. palmeri populations in the USA which are predominantly characterised by gene duplication/overexpression 5 , 18 , 19 . Kaundun et al. tested 115 A. palmeri populations from the Midwestern USA, and none of them contained the P106S mutation 6 . This reflects the use of different weed control practices in Argentina and the USA, although recently, the P106S mutation was also found in Conyza canadensis in the USA 20 . This may be due to sequential resistance evolution in the herbicide mixture partners. As simulated in the glyphosate-only scenario (Fig.  3 ), a single herbicidal input was likely to select for a single resistance mechanism, either TS mutation-based or a quantitative trait. This explained the weak contribution of EPSPS gene duplication/overexpression in the VV population, whereas P106S TS mutation played the predominant role. In another population of A. palmeri from Cordoba province, resistance to glyphosate was exclusively endowed by NTSR of reduced absorption and impaired translocation 21 . Examples in other weed species include a glyphosate-resistant Poa annua population from the USA [resistance index (RI) = 17.9], where a sevenfold EPSPS gene copy number and a weak P106L mutation coexist, with dominant contribution from gene duplication 22 ; and more recently, a glyphosate-resistant Eleusine indica population from China (RI = 13.4), where a P106A mutation and a low level of gene duplication/overexpression coexist, with dominant contribution from the TS mutation 23 . In some occasions of resistance to herbicides of other modes of action, such as diclofop-methyl (acetyl-CoA carboxylase inhibitor) and metribuzin (photosystem II inhibitor), TS mutation and NTSR mechanisms had concurrently strong contributions 24 , 25 . These are likely due to selection pressure from multiple herbicides with the same or different modes of action used in the cropping systems, as opposed to the rather uniform use of solo glyphosate in Argentina. In addition to the high dose vs. low dose regimes 3 , the model showed that initial frequencies of the resistance mechanisms are paramount. It is likely that in the VV population, before A. palmeri was subject to herbicidal treatments, the naturally existing P106S mutation was more frequent than the EPSPS gene amplification. The P106S mutation was also found in the native A. quitensis in the form of a triple EPSPS mutation (TAP‐IVS: T102I, A103V and P106S), causing an extremely high level of glyphosate resistance (RI > 300) 26 . Conversely, gene amplification was believed to come from the USA 27 , which could be rare and primarily diluted by the higher frequency of TS mutations. Nonetheless, recent discovery of the extra chromosomal circular DNA-based gene amplification as well as other potentially new mechanisms, such as mobile genetic element insertion, may give rise to higher genome plasticity and more rapid adaptive evolution of the quantitative traits 28 , 29 . The modelling indications as well as real-life examples provided some different perspective to the appeal that herbicide discovery should aim for multi-target inhibitors 30 , which may not be ubiquitously necessary, considering the common weed control practices in some specific cropping systems. Prior to this work, a population model of a similar weed species, A. tuberculatus was built , assuming glyphosate resistance as a quantitative trait 31 and the pace of resistance evolution was faster than the current case of A. palmeri in Argentina. The different resistance mechanisms in this Argentine population, together with the insights from the segregation and dose responses of PP, PS and SS genotypes 6 , indicated that it is not always feasible to borrow information from similar or even the same species from other geographic locations for model parameterisation. At the population level, genetic diversity within a weed species could lead to differentiated evolutionary responses to herbicidal as well as agronomic weed control methods 32 , 33 ; at the community level, species diversity could lead to flora changes and succession of dominant weed species 34 . The complex nature of agroecosystems and variations in anthropogenic practices make it difficult and risky to recommend generalised weed management strategies 2 , 35 . Future weed management programs would benefit from a more localised design, with the help of quick resistance detection methods, molecular assays and predictive modelling, as demonstrated by the holistic approach in this study.

As an alternative to glyphosate, the PPO herbicides effectively controlled the VV population. However, it is important to note that resistance to PPO herbicides has already been documented in Amaranthus spp. in the USA and in Euphorbia heterophylla and Conyza sumatrensis in Brazil 36 , and if not used properly, their sustainability will be at stake (Fig.  4 , S1–S3). Model simulations indicated that chemical diversity and the use of residual herbicides were key to the sustainable use of PPO herbicides. With the starting population being partially resistant (initial PPO resistance = 10 −2 ), the large variation of resulting PPO resistance in the most effective programs (S5 and S6) warned that resistance could continue to build up even though it may not have been noticeable at the weed density level. If growers switch to a less effective program or a suboptimal dose, the contained high resistance frequency within the seedbank is likely to increase rapidly. Generally, evolutionary processes happen over a much longer time scale than ecological processes 37 . However, the large population size and strong anthropogenic selection pressure embrace contemporary evolution in weed populations at an ecological timescale, which makes it ever more important to detect and take science-based actions at early onset of resistance. To conquer the evolving weeds and weed control challenges, new technologies and concepts, such as weed seed harvest, precision application, artificial intelligence and gene drive systems have attracted growing attention 38 , 39 , 40 , 41 . Meanwhile, another level of innovation stems from making use of what is already available in the toolbox and combining the strengths of multiple tools, as demonstrated by this study. Similarly, interdisciplinary research has been proposed to address several ecological issues, such as climate change, fishery management, forestry retention, soil health and disease emergence 42 , 43 , 44 , 45 , 46 . However, few comprehensive examples exist in practice, for it requires a skilful balance between inputs from different and sometimes conflicting perspectives. The majority of collaborations have been conducted in the form of workshops or training consortiums 46 , 47 . We have hereby demonstrated a mini version of an interdisciplinary study achieved within a functional research team. Resistance detection and mechanism investigation informed the parameterisation of the population model. Meanwhile, the model set the frame of the study system and guided each piece of the puzzle to be filled by the relevant upstream studies. It has brought more ecological realism and evolutionary relevance into weed science experiments. As Cousens pointed out recently, statistically non-significant results do not necessarily designate no effect in biological sense 48 . From an evolutionary perspective, several non-significant differences in individual measures may jointly lead to a significant difference at the population level, and the effect is likely to be further signified through iterating generations. Population models, by integrating the segmented information, can help capture the subtle but biologically meaningful differences and translate them into long-term consequences. Additionally, the model selection approach 49 enabled a feedback loop from dry to wet experiments and filtered unrealistic scenarios, which aided the prioritisation of laboratory work. Finally, the interactions with the knowledge base inspired the model structure to be designed flexibly to allow the incorporation of novel technologies into the system. Further expansion of the holistic approach may include the integration of environmental science, economics, science communication as well as digital tools.

Plant material and growth conditions

The A. palmeri samples were collected from a soybean field in Villa Valeria, Cordoba province in Argentina. The field was under soybean monoculture and treated with continuous glyphosate applications for five years prior to seed collection. A standard sensitive (ApS1) population purchased from Azlin Seed Service (USA), a second standard sensitive (ApS2) population and a resistant (ApR) population both sourced from Georgia, USA, were used in glasshouse, molecular and biokinetic experiments. The ApR sample was characterised by EPSPS gene duplication/overexpression (ninefold relative to ApS1) 6 . Seeds were sown into trays containing equal portions of compost and peat and maintained in controlled glasshouse conditions of 24/18 °C day/night temperatures and 65% relative humidity. Seedlings at the 1–3 leaf stage were transplanted onto agar for the Resistance In-Season Quick (RISQ) test 10 . Two-centimetre-tall seedlings were transplanted into 75-mm diameter pots and irrigated and fertilised as necessary.

Glyphosate resistance confirmation tests

The agar-based RISQ test plates contained 0, 25, 50 and 100 µM of glyphosate respectively. Each plate had 10 transplanted A. palmeri seedlings and was kept in the glasshouse. Survivorship was assessed 7 to 12 days after transplanting, based on observation of new root and leaf development. In the whole plant pot test, each treatment had 14 individually potted A. palmeri . When the plants reached 8 cm in height, they were sprayed with 0, 100, 200, 400, 800, 1600, 3200 and 6400 g ae ha −1 of glyphosate. The pots were randomised and maintained in the same glasshouse conditions as mentioned above. Survivorship was assessed 21 days after transplanting.

Mechanism of resistance to glyphosate

Experiments followed the protocols detailed in Kaundun et al. 6 and are briefly summarised below.

To test the uptake and translocation of glyphosate, plants from the VV and ApS1 populations were treated with [phosphonomethylene]- 14 C glyphosate at the 4-leaf stage. Each plant received 20 μg glyphosate, with 5 kBq radioactivity. Plants were sampled at 0, 24, 48 and 96 h after treatment, with four replicates for each time point. Treated leaves were painted with cellulose acetate, left to dry, and then dissolved in 1 ml acetone. Radioactivity in the solution was measured by liquid scintillation counting (LSC) using a Perkin Elmer Tricarb 2900TR (Perkin Elmer, Waltham, Ma, USA). Treated plants were freeze-dried and separated into treated leaf, meristem and rest of plant which were then individually combusted in a Harvey OX 500 Biological Oxidiser with attached Zinsser robot (R. J. Harvey Instruments, Frankfurt, Germany). Radioactivity was measured by LSC. Uptake was calculated as percentage of glyphosate applied while translocation to the meristem and rest of plants was evaluated as percentage of total glyphosate absorbed.

Sixteen untreated plants from the VV populations were analysed with quantitative real-time PCR (qPCR) for EPSPS gene duplication and overexpression. The ApS2 population were used as sensitive control. DNA was extracted from fresh leaf tissues of each plant with a KINGFISHER Flex Purification System (ThermoFisher Scientific, Leicestershire, UK) and a Wizard Magnetic 96 DNA Plant System (Promega, WI, USA), and used in the gene duplication test. RNA was extracted with the RNeasy Plant Mini Kit (Qiagen, Manchester, UK) and employed in the gene expression analysis. The RNA samples were cleaned with a DNAse at 37 °C for 2 h, after which the enzyme was inactivated at 75 °C for 5 min. Corresponding cDNAs were synthesised using High-Capacity cDNA Reverse Transcription Kit (ThermoFisher Scientific, Leicestershire, UK). Acetolactate synthase ( ALS ) and carbamoyl phosphate synthetase ( CPS ) were used as reference genes in the tests. The primers consisted of ALS -forward 5′-TTCCTCGACATGAACAAGGTG-3′, ALS -reverse 5′-CCAACGCGTCCAGTAGCA-3′ and ALS- probe 5′-TTTTCGCTGCTGAAGGCTACGCTC-3′; CPS -forward 5′-TGCGGCAATTTTAAGAGCAT-3′, CPS -reverse 5′-GATGAGCTGAAGATTGAACAACCT-3′ and CPS -probe 5′-AGCTTCACTCCTAGCGATGCCTCCC-3′; EPSPS -forward 5′-GTCTAAAGCAACTTGGTTCAGATGT-3′, EPSPS -reverse 5′-CCCTGGAAGGCCTCCTTT-3′ and EPSPS -probe 5′-TGTTTTCTTGGCACAAATTGCCCTCC-3′. Reactions contained 1× Sigma JumpStart Taq ReadyMix, 300 nM of forward and reverse primers, 100 nM Probe, 3 µl of DNA or cDNA, and distilled H 2 O to 10 µl. Two replicates were used in the tests. All samples were placed in a completely randomised design on the 384-well plate and analysed in a QuantStudio 7 Flex Real-Time PCR System (ThermoFisher Scientific, Leicestershire, USA) using the following settings: 5 min at 95 °C, 40 cycles of 5 s at 95 °C, and 30 s at 60 °C.

Eighteen untreated plants from the VV population were sequenced for known EPSPS gene mutations around codon positions 102 and 106. DNA was extracted as described above. Forward (5′ATGTTGGACGCTCTCAGAACTCTTGGT3′) and reverse (5′TGAATTTCCTCCAGCAACGGCAA3′) primers were used to amplify a 195 bp fragment. The reaction was prepared in 25 µl units, each containing 10–50 ng DNA and 20 pmol primers. PCR program was set as follows: 5 min at 95 °C, 30 cycles of 30 s at 95 °C, 30 s at 60 °C, 1 min at 72 °C, and a final 10 min at 72 °C, using a Master Cycle Gradient Thermocycler Model 96 (Eppendorf, UK). Direct Sanger sequencing (Genewiz LLC, USA) was carried out on the PCR product and sequencing reads were aligned and compared using the Seqman software (DNASTAR Lasergene 10, DNASTAR, USA).

Statistical analysis

The gene copy number (DNA measures) and expression (cDNA measures) from the qPCR experiment were analysed separately. Analysis of variance was performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), by fitting:

where y ij denotes the difference between the average C T for EPSPS and the reference genes for plant i of population j, μ denotes the overall true mean, γ j denotes the effect of population j, and ε ij denotes the random error associated with plant i of population j. A t-test was performed to compare the gene duplication and overexpression levels between the populations, with p -value < 0.05 indicating a significant difference.

Similarly, biokinetic measures of glyphosate uptake and translocation were tested by factorial analysis of variance:

where i represents replicate, j represents population and k represents time point, τ k denotes the true effect for time k, (γτ) jk denotes the population × time interaction. Where there was evidence of a population × time interaction, the difference between populations were analysed separately at each time point. Otherwise, the comparison was made based on average values across time.

Model and simulation settings

The model structure and algorithms followed the model of Liu et al. 31 . The individual-based model accounted for the annual life cycle and resistance profiles of the A. palmeri population. Herbicide applications removed weed plants from the population, the proportion of which was dependent on the overlap between application time and weed emergence, as well as the standard control efficacy of the herbicide (Table 2 ). Time-series population dynamics and evolution of resistance emerged from the 20-year iterations. The model was implemented in NetLogo 6.0 50 . Parameters that were specifically updated for A. palmeri in the VV population include:emergence curve

seed production per plant

and annual mortality rate per plant

where x denotes days after the start of the season (DASS). Sigma of the log-normal distribution of glyphosate quantitative resistance was 0.4656.

The model tested seven herbicidal weed control scenarios (Table 2 ), one with glyphosate-only program (S0, Fig.  3 ) and the rest with PPO-based herbicides (S1–S6, Fig.  4 ). Soybean was planted on 10th November (71 DASS). All weed plants that emerged before sowing were assumed to be controlled by pre-plant burndown applications. Pre-emergence treatments were applied on the same day as planting, i.e. 0 days after planting (DAP). In S0, glyphosate was applied twice post-emergence, 20 DAP and 40 DAP, respectively. Plants that emerged before the application dates were exposed to the herbicide treatment. If the individual plant had a resistance phenotype of the quantitative trait that was higher than the application rate, the plant would survive the treatment; otherwise the survivorship followed the dose–response curves of segregated PP, PS and SS populations described in Kaundun et al. 6 (Table 2 ). In S1–S6, two PPO-based herbicides, lactofen and fomesafen were tested. Resistance to PPO-herbicides was assumed to be endowed by a target-site mutation with a dominance value of 0.75 51 , 52 , 53 . The exposure to lactofen was similar to that of glyphosate and survivorship was dependent on the genotype, i.e. 100% of the homozygous resistant individuals and 75% of the heterozygous resistant individuals survived. Fomesafen’s effect was implemented in a similar way to lactofen, with additional residual activity which controlled plants that were to emerge after the application date. Other herbicides included S -metolachlor and metribuzin, which only had residual activities. Post-emergence treatments in S5 and S6 were applied on 39 DAP and 32 DAP, respectively. The initial level of resistance to glyphosate was varied to represent different field situations, whilst all populations were assumed to be susceptible to S -metolachlor and metribuzin based on current knowledge 36 .

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Keeley, P. E., Carter, C. H. & Thullen, R. J. Influence of planting date on growth of Palmer amaranth ( Amaranthus palmeri ). Weed Sci. 35 , 199–204 (1987).

Article   Google Scholar  

Christoffers, M. J. Genetic aspects of herbicide-resistant weed management. Weed Technol. 13 , 647–652 (1999).

Gressel, J. Low pesticide rates may hasten the evolution of resistance by increasing mutation frequencies. Pest Manage. Sci. 67 , 253–257 (2011).

Article   CAS   Google Scholar  

Dominguez-Valenzuela, J. A. et al. First confirmation and characterization of target and non-target site resistance to glyphosate in Palmer amaranth ( Amaranthus palmeri ) from Mexico. Plant Physiol. Biochem. 115 , 212–218 (2017).

Article   CAS   PubMed   Google Scholar  

Gaines, T. A. et al. Gene amplification confers glyphosate resistance in Amaranthus palmeri . Proc. Natl. Acad. Sci. 107 , 1029–1034 (2010).

Article   ADS   CAS   PubMed   Google Scholar  

Kaundun, S. S. et al. Evolution of target-site resistance to glyphosate in an Amaranthus palmeri population from Argentina and its expression at different plant growth temperatures. Plants 8 , 512 (2019).

Article   CAS   PubMed Central   Google Scholar  

Lacoste, M. & Powles, S. Beyond modelling: considering user-centred and post-development aspects to ensure the success of a decision support system. Comput. Electron. Agric. 121 , 260–268 (2016).

Culpepper, A., Whitaker, J., MacRae, A. & York, A. Distribution of glyphosate-resistant Palmer amaranth ( Amaranthus palmeri ) in Georgia and North Carolina during 2005 and 2006. J. Cotton Sci. 12 , 306–310 (2008).

Google Scholar  

Patzoldt, W. L., Tranel, P. J. & Hager, A. G. A waterhemp ( Amaranthus tuberculatus ) biotype with multiple resistance across three herbicide sites of action. Weed Sci.   53 , 30–36 (2005).

Kaundun, S., Hutchings, S. J., Dale, R., Bailly, G. & Glanfield, P. Syngenta ‘RISQ’ test: a novel in-season method for detecting resistance to post-emergence ACCase and ALS inhibitor herbicides in grass weeds. Weed Res. 51 , 284–293 (2011).

Koger, C. H. et al. Assessment of two nondestructive assays for detecting glyphosate resistance in horseweed ( Conyza canadensis ). Weed Sci. 53 , 559–566 (2005).

Norsworthy, J. K., Scott, R. C., Smith, K. L. & Oliver, L. R. Response of Northeastern Arkansas Palmer amaranth ( Amaranthus palmeri ) accessions to glyphosate. Weed Technol. 22 , 408–413 (2008).

Mithila, J., Hall, J. C., Johnson, W. G., Kelley, K. B. & Riechers, D. E. Evolution of resistance to auxinic herbicides: historical perspectives, mechanisms of resistance, and implications for broadleaf weed management in agronomic crops. Weed Sci. 59 , 445–457 (2011).

Spaunhorst, D. J. Utilization of dicamba for the control of glyphosate-resistant giant ragweed ( Ambrosia trifida L.) and waterhemp ( Amaranthus rudis Sauer.) Masters thesis, University of Missouri-Columbia, (2013).

Steckel, L. E., Craig, C. C. & Hayes, R. M. Glyphosate-resistant horseweed ( Conyza canadensis ) control with glufosinate prior to planting no-till cotton ( Gossypium hirsutum ). Weed Technol. 20 , 1047–1051 (2006).

Lindsay, K. et al. PAM: decision support for long-term Palmer amaranth ( Amaranthus palmeri ) control. Weed Technol. 31 , 915–927 (2017).

Neve, P., Norsworthy, J. K., Smith, K. L. & Zelaya, I. A. Modelling evolution and management of glyphosate resistance in Amaranthus palmeri . Weed Res. 51 , 99–112 (2011).

Mohseni-Moghadam, M., Schroeder, J. & Ashigh, J. Mechanism of resistance and inheritance in glyphosate resistant Palmer amaranth ( Amaranthus palmeri ) populations from New Mexico, USA. Weed Sci. 61 , 517–525 (2013).

Singh, S., Singh, V., Lawton-Rauh, A., Bagavathiannan, M. V. & Roma-Burgos, N. EPSPS gene amplification primarily confers glyphosate resistance among Arkansas Palmer amaranth ( Amaranthus palmeri ) populations. Weed Sci. 66 , 293–300 (2018).

Beres, Z. T. et al. Target-site EPSPS Pro-106-Ser mutation in Conyza canadensis biotypes with extreme resistance to glyphosate in Ohio and Iowa, USA. Sci. Rep. 10 , 7577. https://doi.org/10.1038/s41598-020-64458-7 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Palma-Bautista, C. et al. Reduced absorption and impaired translocation endows glyphosate resistance in Amaranthus palmeri harvested in glyphosate-resistant soybean from Argentina. J. Agric. Food Chem. 67 , 1052–1060 (2019).

Brunharo, C. A. D. C. G., Morran, S., Martin, K., Moretti, M. L. & Hanson, B. D. EPSPS duplication and mutation involved in glyphosate resistance in the allotetraploid weed species Poa annua L.. Pest Manage. Sci. 75 , 1663–1670. https://doi.org/10.1002/ps.5284 (2019).

Chen, J. et al. Glyphosate resistance in Eleusine indica : EPSPS overexpression and P106A mutation evolved in the same individuals. Pestic. Biochem. Physiol. 164 , 203–208. https://doi.org/10.1016/j.pestbp.2020.01.014 (2020).

Kaundun, S. S. An aspartate to glycine change in the carboxyl transferase domain of acetyl CoA carboxylase and non-target-site mechanism (s) confer resistance to ACCase inhibitor herbicides in a Lolium multiflorum population. Pest Manage. Sci. 66 , 1249–1256 (2010).

Lu, H., Yu, Q., Han, H., Owen, M. J. & Powles, S. B. Metribuzin resistance in a wild radish ( Raphanus raphanistrum ) population via both psbA gene mutation and enhanced metabolism. J. Agric. Food Chem. 67 , 1353–1359 (2019).

Perotti, V. E. et al. A novel triple amino acid substitution in the EPSPS found in a high-level glyphosate-resistant Amaranthus hybridus population from Argentina. Pest Manage. Sci. 75 , 1242–1251 (2019).

Heap, I. & Duke, S. O. Overview of glyphosate-resistant weeds worldwide. Pest Manage. Sci. 74 , 1040–1049 (2018).

Koo, D.-H. et al. Extrachromosomal circular DNA-based amplification and transmission of herbicide resistance in crop weed Amaranthus palmeri . Proc. Natl. Acad. Sci. 115 , 3332–3337 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Gaines, T. A., Patterson, E. L. & Neve, P. Molecular mechanisms of adaptive evolution revealed by global selection for glyphosate resistance. New Phytol. 223 , 1770–1775 (2019).

Article   PubMed   Google Scholar  

Gressel, J. Perspective: present pesticide discovery paradigms promote the evolution of resistance—learn from nature and prioritize multi-target site inhibitor design. Pest Manage. Sci. 76 , 421–425. https://doi.org/10.1002/ps.5649 (2020).

Liu, C. et al. A generalised individual-based algorithm for modelling the evolution of quantitative herbicide resistance in arable weed populations. Pest Manage. Sci. 73 , 462–474 (2017).

Ashworth, M. B., Walsh, M. J., Flower, K. C., Vila-Aiub, M. M. & Powles, S. B. Directional selection for flowering time leads to adaptive evolution in Raphanus raphanistrum (Wild radish). Evol. Appl. 9 , 619–629 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Bagavathiannan, M. V. & Davis, A. S. An ecological perspective on managing weeds during the great selection for herbicide resistance. Pest Manage. Sci. 74 , 2277–2286 (2018).

Booth, B. D. & Swanton, C. J. Assembly theory applied to weed communities. Weed Sci. 50 , 2–13 (2002).

Délye, C., Jasieniuk, M. & Le Corre, V. Deciphering the evolution of herbicide resistance in weeds. Trends Genet. 29 , 649–658 (2013).

Article   PubMed   CAS   Google Scholar  

Heap, I. The International Herbicide-Resistant Weed Database (accessed 29 May 2020). www.weedscience.org .

Carroll, S. P., Hendry, A. P., Reznick, D. N. & Fox, C. W. Evolution on ecological time-scales. Funct. Ecol. 21 , 387–393 (2007).

Neve, P. Gene drive systems: do they have a place in agricultural weed management?. Pest Manage. Sci. 74 , 2671–2679 (2018).

Partel, V., Charan Kakarla, S. & Ampatzidis, Y. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput. Electron. Agric. 157 , 339–350 (2019).

Singh, V. et al. Chapter three—unmanned aircraft systems for precision weed detection and management: prospects and challenges. In Advances in Agronomy (ed. Sparks, D. L.) 93–134 (Academic Press, Cambridge, 2020).

Walsh, M. J. et al. Opportunities and challenges for harvest weed seed control in global cropping systems. Pest Manage. Sci. 74 , 2235–2245 (2018).

Daszak, P. et al. Interdisciplinary approaches to understanding disease emergence: the past, present, and future drivers of Nipah virus emergence. Proc. Natl. Acad. Sci. 110 , 3681–3688 (2013).

Phillipson, J. & Symes, D. Science for sustainable fisheries management: an interdisciplinary approach. Fish. Res. 139 , 61–64 (2013).

Shaman, J., Solomon, S., Colwell, R. R. & Field, C. B. Fostering advances in interdisciplinary climate science. Proc. Natl. Acad. Sci. 110 , 3653–3656 (2013).

Storch, I. et al. Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary, multi-scale approach. Ecol. Evol. 10 , 1489–1509 (2020).

Totsche, K. U. et al. Biogeochemical interfaces in soil: the interdisciplinary challenge for soil science. J. Plant Nutr. Soil Sci. 173 , 88–99 (2010).

Grimm, V. et al. CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals. Environ. Sci. Pollut. Res. 16 , 614–617 (2009).

Cousens, R. D. A question of logic: experiments cannot prove lack of an herbicide-resistance fitness penalty. Weed Sci. 68 , 197–198. https://doi.org/10.1017/wsc.2020.24 (2020).

Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19 , 101–108 (2004).

Wilensky, U. NetLogo . (accessed 20 May 2020). http://ccl.northwestern.edu/netlogo/ (1999).

Patzoldt, W. L., Hager, A. G., McCormick, J. S. & Tranel, P. J. A codon deletion confers resistance to herbicides inhibiting protoporphyrinogen oxidase. Proc. Natl. Acad. Sci. U.S.A. 103 , 12329–12334 (2006).

Rangani, G. et al. A novel single-site mutation in the catalytic domain of protoporphyrinogen oxidase IX (PPO) confers resistance to PPO-inhibiting herbicides. Front. Plant Sci. 10 , 568. https://doi.org/10.3389/fpls.2019.00568 (2019).

Salas, R. A. et al. Resistance to PPO-inhibiting herbicide in Palmer amaranth from Arkansas. Pest Manage. Sci. 72 , 864–869 (2016).

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Acknowledgements

The authors would like to thank Elisabetta Marchegiani, Anushka Howell, Penny Sherwood, Jonathan Galloway, Ryan Carlin, Weining Gu, Wenling Wang and Joe Downes for their help with the experiments, and three anonymous reviewers for their helpful comments.

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S.S.K. conceived the ideas; C.L., L.V.J., S.J.H., D.T., R.M. and S.S.K. designed the methodology; L.V.J. and S.J.H. conducted and coordinated the biological experiments; E.M. analysed the experimental data; D.T. and R.M. provided information on weed biology and agricultural practices in Argentina; C.L. constructed the model, ran simulation experiments, analysed the results, and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

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Liu, C., Jackson, L.V., Hutchings, SJ. et al. A holistic approach in herbicide resistance research and management: from resistance detection to sustainable weed control. Sci Rep 10 , 20741 (2020). https://doi.org/10.1038/s41598-020-77649-z

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REVIEW article

Weeds in a changing climate: vulnerabilities, consequences, and implications for future weed management.

• 1 Indian Council of Agricultural Research – Indian Institute of Soil Science, Bhopal, India
• 2 Department of Agronomy, Muhammad Nawaz Shareef University of Agriculture, Multan, Pakistan
• 3 The Centre for Plant Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD, Australia
• 4 Department of Agronomy, University of Agriculture, Faisalabad, Pakistan
• 5 Ayub Agricultural Research Institute, Faisalabad, Pakistan
• 6 Centre for Environmental Management, Faculty of Science and Technology, Federation University Australia, Mount Helen, VIC, Australia

Whilst it is agreed that climate change will impact on the long-term interactions between crops and weeds, the results of this impact are far from clear. We suggest that a thorough understanding of weed dominance and weed interactions, depending on crop and weed ecosystems and crop sequences in the ecosystem, will be the key determining factor for successful weed management. Indeed, we claim that recent changes observed throughout the world within the weed spectrum in different cropping systems which were ostensibly related to climate change, warrant a deeper examination of weed vulnerabilities before a full understanding is reached. For example, the uncontrolled establishment of weeds in crops leads to a mixed population, in terms of C 3 and C 4 pathways, and this poses a considerable level of complexity for weed management. There is a need to include all possible combinations of crops and weeds while studying the impact of climate change on crop-weed competitive interactions, since, from a weed management perspective, C 4 weeds would flourish in the increased temperature scenario and pose serious yield penalties. This is particularly alarming as a majority of the most competitive weeds are C 4 plants. Although CO 2 is considered as a main contributing factor for climate change, a few Australian studies have also predicted differing responses of weed species due to shifts in rainfall patterns. Reduced water availability, due to recurrent and unforeseen droughts, would alter the competitive balance between crops and some weed species, intensifying the crop-weed competition pressure. Although it is recognized that the weed pressure associated with climate change is a significant threat to crop production, either through increased temperatures, rainfall shift, and elevated CO 2 levels, the current knowledge of this effect is very sparse. A few models that have attempted to predict these interactions are discussed in this paper, since these models could play an integral role in developing future management programs for future weed threats. This review has presented a comprehensive discussion of the recent research in this area, and has identified key deficiencies which need further research in crop-weed eco-systems to formulate suitable control measures before the real impacts of climate change set in.

Introduction

To sustain food production for the world’s burgeoning human population ( Parry et al., 2005 ), there is an urgent need to discover vulnerabilities and adaptive measures in managed ecosystems ( Howden et al., 2007 ). It is unequivocal that food security, be either availability, accessibility, utilization, and/or system stability, is dependent on climate ( Killman, 2008 ). Food security is potentially vulnerable to climate change since climate plays a pivotal role in determining growth, development, and perpetuation of all organisms. Climate is defined as the sum of weather conditions of a given area, quantified as long-term statistics of meteorological variables ( World Meteorological Organization, 1992 ). These variables include temperature, wind, precipitation, and sunshine hours, all of which are essential for growth, development, and productivity of vegetation and in turn, human welfare.

In recent decades, changes in climate have caused significant impacts on natural and human ecosystems 1 . These impacts of climate change, irrespective of their cause, illustrate the sensitivity of natural as well as human ecosystems to variations in the function of climatic systems, interaction between its components, or changes in external forces either naturally or due to anthropogenic reasons ( IPCC, 1995 ). Of particular interest here is that agriculture may be jeopardized by climate change ( Kang and Banga, 2013 ; Chauhan et al., 2014 ), since changes in weather factors have a significant effect on growth of all plant species, including crops and weeds. Rising atmospheric CO 2 and temperature are expected to pose both direct and indirect consequences for agricultural production, sustainability, water availability and, therefore, food security ( Sinha and Swaminathan, 1991 ; Chauhan et al., 2014 ). However, in many ways, extremes of weather events associated with climate change are a more serious concern from farmers’ perspectives on crop management as compared with more subtle changes brought by the actual increases in temperature, CO 2 levels, water availability and associated weather events. To cope with these extreme changes, future development needs to make adjustments in technology, management practices, and legislation ( Bhat and Jan, 2010 ).

At the more subtle level, it is recognized that weeds are aggressive, troublesome, and competitive elements within croplands. Contrary to other pests, weeds share a similar trophic level with crop plants, and by competing for scarce resources they cause enormous crop yield losses. The focus of this paper is on the dynamics of weed-crop competition and how they are influenced by climate, since this has important regional and global implications for food production. For example, the incessant rains during the kharif season (June–September) in India have made weed management a challenge, particularly in soybean-based cropping systems. Abrupt changes in climatic variables are likely to result in stressed crop plants, which are vulnerable to attack by insect pests and pathogens ( Reddy, 2013 ), and makes them less competitive against weeds ( Patterson, 1995 ). It is important to note here that this area is extremely complex, as is shown by the work of Chen and McCarl (2001) , who found that higher temperatures increase pesticide cost variance for maize ( Zea mays L.), potatoes ( Solanum tuberosum L.), and wheat ( Triticum aestivum L.), while decreasing it for soybeans ( Glycine max (L.) Merr.). They also reported that rainfall was directly proportional to unit land pesticide usage costs for these crops in the USA.

With a lack of precise information on the effects of climate change on agricultural pests, understanding of this issue remains a major obstacle for remedial measures. The ecological, environmental, and economic costs of not understanding these interactions can be substantial ( Ziska and McConnell, 2015 ). These authors provided a comprehensive review of work done on weeds in a changing climate, with a particular emphasis on the vulnerabilities of crops and cropping systems to weed pressure in changing climate regimes.

Climate Change and Weeds

The current atmospheric burden of the two most important greenhouse gases (carbon dioxide and methane), are unprecedented ( Petit et al., 1999 ) and have emerged as the greatest ecological challenge of the 21 st century ( Kang and Banga, 2013 ). The impact of climate change on weedy vegetation may be manifested in the form of geographic range expansions (migration or introduction to new areas), alterations in species life cycles, and population dynamics. Migration of weeds will subsequently result in a differential structure and composition of weed communities within natural and managed ecosystems. Through the lens of climate change, Peters et al. (2014) outlined three distinct types of shifts in weedy vegetation (range, niche, and trait shifts), occurring at different scales (landscape, community, and population scales), respectively. Changes in weed biology, ecology, and interference potential, in the wake of climate change, will result in complex crop-weed interactions that necessitate alternative adaptive mechanisms. There is a general perception that climate change will result in a differential growth pattern between crops and weeds, as major weeds of the world have the C 4 pathway and they will become more competitive, although this is certainly not a simple matter due to the adaptive mechanisms in weedy species.

Weed Response to Increasing CO 2 Levels

There is an ever-growing consensus that the earth’s climate is changing, and despite the efforts made to reduce CO 2 emissions, there is an increasing pressure to identify adaptive mechanisms in agro-ecosystems ( Howden et al., 2007 ). The record of atmospheric CO 2 obtained from Mauna Loa observatory at Hawaii indicated a 20% increase from 311 ppm in the mid-1950s to 375 ppm in 2001 ( Keeling and Whorf, 2004 ), even though Mauna Loa and other global monitoring sites are situated in areas well away from regions of rapid CO 2 production. Previous studies have quantified a difference of 80 ppm in the CO 2 concentration between urban and suburban areas ( Idso et al., 1988 , 2001 ; Ziska et al., 2001 ). This observation suggests that although data from Mauna Loa observatory mirrors the global increase, regional increases may be even more substantial due to rapid urbanization and intensive cropping, especially in Asia. This increase will continue in the near future, with estimates suggesting that it may reach 600 ppm ( Schimel et al., 1996 ), while the Inter-Governmental Panel on Climate Change have suggested, as a conservative estimate, 700 ppm by the end of the century ( IPCC, 2007 ).

The projected increase in atmospheric CO 2 is known to favor net photosynthesis in C 3 plants (three quarters of global agriculture is represented by C 3 crops; Kimbal, 1983 ) by limiting the loss of CO 2 via photorespiration and increasing the CO 2 concentration gradient from air to the leaf interior ( Ziska, 2000 ). By contrast, plants with a C 4 photosynthetic pathway manifest little response to elevated CO 2 as they have an internal mechanism to concentrate CO 2 at the site of CO 2 carboxylation. Thus, from this perspective, ongoing increase in the atmospheric CO 2 concentration will have crucial implications for weed-crop competition and crop yield losses. Numerous studies have addressed weed-crop interactions by evaluating the comparative growth and physiology of C 3 crops and C 4 weeds, and concluded that an elevated CO 2 concentration generally favors the vegetative growth of C 3 plant species over those with C 4 pathways ( Patterson, 1995 ). However, not all crops are based on C 3 pathways, and not all weeds are C 4 based ( Ziska et al., 2010 ). Hence, while the above concept is relevant for C 3 cereals such as rice, which compete, in the main, with C 4 grassy and broad-leaved weeds, this situation is not universal. There are many C 4 crops of economic significance, such as maize, sugarcane, and sorghum, which have competition from important C 3 weeds, for example, Chenopodium album L. ( Ziska, 2000 ). This implies that weed-related yield losses of C 4 crops will tend to increase under elevated CO 2 , but this will not occur with C 3 crops, as elevated CO 2 will be a crucial factor in realizing the potential benefits of CO 2 fertilization.

Notwithstanding this understanding, the abundance and appearance of weeds varies according to regions, crops, and management systems, which complicates management approaches. For example, Phalaris minor Retz., a C 3 species, is a problematic weed in wheat in the Indo-Gangetic Plains of North India. The C 3 weeds in other areas include Avena fatua L., Chenopodium album L., Cirsium arvense (L.) Scop., Convolvulus arvensis L., and Ludwigia hyssopifolia (G. Don) Exell. Weedy rice ( Oryza sativa L.) is also a C 3 weed in rice in many Asian countries, including Vietnam, Sri Lanka, Malaysia, Thailand, the USA, and the Philippines ( Chin et al., 2013 ; Chauhan, 2013 ). The third important grain crop, maize, is a C 4 species, and as noted, although there were some variations in the experimental conditions, C 3 species generally responded more favorably than C 4 species to an increased concentration of CO 2 ( Patterson, 1995 ). Of further interest is that Ziska and McClung (2008) indicated a greater physiological plasticity and genetic diversity among weedy (red) rice relative to cultivated rice, which may impact weed-crop competition with increased atmospheric CO 2 .

It is becoming clear that predicting competitive outcomes based on species grown in isolation, may not adequately quantify crop-weed competition as a function of increasing CO 2, as weeds usually occur in a mixture ( Ziska, 2001 ). Therefore, there is a need to evaluate the effects of weed competition on crops in an environment of mixed of weeds and crops since most of the studies on the effect of CO 2 on crops and weeds have included weed and crop species in isolation. Only a few studies have examined the response of crops and weeds to CO 2 in competitive environments ( Ziska, 2004 ) and a very little attention has been given to the effect of elevated CO 2 on the geographical distribution of weeds in managed ecosystems ( McDonald et al., 2009 ). In a study by Ziska et al. (2010) , an increase in CO 2 concentration resulted in a significant decrease in plant relative seed yield of a cultivated rice (C 3 crop species) variety, but the reverse for a weedy rice biotype, also a C 3 species. It is thought that this was probably due to a greater physiological plasticity and genetic diversity between a wild and cultivated lines ( Treharne, 1989 ).

Ziska et al. (2011) opined that the increase in atmospheric CO 2 might also change the biology of invasive weeds. Presumably, an increase in CO 2 concentration stimulated growth and development of many invasive plant species, for example Cirsium arvense L., an invasive perennial C 3 weed species, had registered a 70% increase in growth with elevated CO 2 ( Ziska et al., 2011 ). The authors suggested that increasing CO 2 levels may also increase wind dispersal of weed seeds by either increasing the height of the weed plant or by increasing the plant size. Some of these wind-dispersed invasive weed species are Cirsium arvense. Sonchus arvensis L., Sonchus oleraceus L., and Carduus nutans L.

Changes in weed communities in response to crop establishment methods, wet or dry moisture conditions, tillage regimes, and other management practices are well established in the literature ( Nichols et al., 2015 ; Ramesh, 2015 ). Nevertheless, very few studies have focused on changes in weed communities in the backdrop of elevated CO 2 ( Koizumi et al., 2004 ). Variations in the weed competitiveness response to elevated CO 2 among diverse lines of rice may necessitate screening of vulnerable and resistant cultivars for wider adoption. Although rice could benefit from CO 2 fertilization, the greater response of its wild relatives, particularly the weedy biotypes of rice, could offset the associated benefits and competitive outcomes as crop yield losses could increase ( Ziska, 2008 ).

Herbicide efficacy may also decrease as CO 2 concentrations increase ( Ziska and Teasdale, 2000 ; Ziska et al., 2004 ). An increase in CO 2 induces morpho-physiological and anatomical changes in plants affecting the rate of uptake and translocation of herbicides ( Ziska and Teasdale, 2000 ; Manea et al., 2011 ). C 3 plants showed a decrease in stomata number and conductance and increased leaf thickness, which might interfere with foliar uptake of herbicides ( Nowak et al., 2004 ; Ainsworth and Long, 2005 ), as well as an increase in starch accumulation on the leaf surface ( Patterson, 1995 ). Moreover, perennial weeds may become even more noxious, if vegetative growth is stimulated as a result of increased photosynthesis in response to elevated CO 2 . These changes are expected to reduce the efficacy of the most commonly used herbicides, such as glyphosate, due to a dilution effect, although the precise mechanism conducive to increased tolerance to glyphosate remains elusive ( Manea et al., 2011 ). This could be due to less translocation as the root system becomes vigorous. In addition, an increase in the root-shoot ratio may play a critical role in herbicide efficacy ( Ziska et al., 2004 ). The authors concluded that CO 2 -induced increase in root biomass could make perennial weeds harder to control in a higher CO 2 environment. Thus, research is needed to assess the comparative growth and physiological response of C 3 and C 4 weeds of different age groups (seedlings or mature plants), and their molecular and biochemical bases to herbicide tolerance under ambient and elevated CO 2 concentrations. Allocation of resources to below ground parts, source-sink relationships, and mitochondrial respiration also need to be reassessed in the wake of climate change scenarios.

Weed Response to Elevated Temperature

Atmospheric temperature is regarded as an important indicator of weed species distribution in a geographical area ( Patterson et al., 1999 ). Its rise could alter weed proliferation and competitive behavior in weedy vegetation as well as in crop stands. The indicated likely climate change may favor C 4 over C 3 weeds ( Tubiello et al., 2007 ), since under conditions of elevated CO 2 , reduced CO 2 solubility, and decreased affinity of RUBISCO for CO 2 would deter C 3 photosynthesis ( Patterson, 1995 ). As a result, a variation in weed distribution will affect the world’s most important cropping systems, for example, rice-based cropping systems through weed shifts to high latitudes and altitudes. In addition, Striga spp. might extend their range to moderate climatic zones ( Mohamed et al., 2006 , 2007 ). However, Striga asiatica (L.) Kuntze is relatively insensitive to temperature, and changes in the geographical range of the host plants seem to play a critical role in its distribution rather than the direct effects of temperature ( Patterson et al., 1982 ). If this concept is generalized for all parasitic weeds in the Orobanchaceae ( Phoenix and Press, 2005 ), these weeds could pose a serious threat to global crop production, especially in fodders, in the near future. Many C 4 weeds, such as Amaranthus retroflexus L., Setaria sp., Digitaria sp., Sorghum halepense (L.) Pers., and Paspalum dichotomiflorum (L.) Michx., are expected to expand further north ( Weber and Gut, 2005 ; Clements and DiTommaso, 2011 ), which would have a more pronounced effect in the northern Europe, where the number of weed species is lower than in the south ( Fried et al., 2010 ). Milder and wetter winters would tend to increase the survival of winter annual weeds, while thermophile summer annuals will grow more profusely in areas with warmer summers under prolonged growing seasons, enabling them to grow further north ( Walck et al., 2011 ; Hanzlik and Gerowitt, 2012 ).

Patterson (1995) predicted that climate change would spread arable weed species. For example, Datura stramonium L., which needs high temperature for profuse growth ( Cavero et al., 1999 ), would become a more competitive candidate under the climate change scenarios. Warm temperature regimes augmented the abundance of Hieracium aurantiacum L. in Australia through accelerated growth and reproduction ( Brinkley and Bomford, 2002 ).

Under warmer conditions, Setaria viridis (L.) P. Beauv. germinated later in the (August) season ( Dekker, 2003 ). This was a beneficial temporal non-synchrony with emergence of a maize crop, avoiding crop-weed competition. In contrast, a recent study indicated that this species would be a problematic weed in maize-based cropping systems elsewhere, through synchrony with maize emergence, which is probably due to stimulation by increased temperature ( Peters and Gerowitt, 2014 ). Therefore, S. viridis would become a competitor of maize at enhanced temperatures at the time of emergence. This has implications for the northern part of the Central Europe, where temperatures are still below the optimum for this species ( Walck et al., 2011 ). Similarly, Rottboelliia cochinchinensis (Lour.) Clayton could invade the central Midwest of the USA and California from Gulf Coast states, with a 3°C rise in temperature ( Patterson, 1995 ). If new weeds are introduced into a non-native area, new and effective herbicides may be needed.

Lee (2011) opined that increased temperature had a greater effect on plant phenological development than elevated CO 2 . The author observed that increasing temperature by 4°C advanced the emergence timing of C. album and S. viridis by 26 and 35 days, respectively, and flowering time by 50 and 31.5 days. Increased temperatures strongly affected the biomass accumulation by annual grass species during their reproductive phase as compared with the vegetative phase, and such effects are more pronounced in C 3 than C 4 plant species. However, the increased temperature was believed to offset potential benefits of elevated CO 2 by reducing the reproductive output. The uptake and translocation of herbicides in plants and their persistence in soil will also be affected by rising temperatures ( Rodenburg et al., 2011 ). In addition to these effects, temperature will also affect the rate of water absorption and movement, which affects the rate of leaf development, cuticle thickness, and stomatal number and their aperture, thus indirectly affecting herbicide selectivity and efficacy ( Bailey, 2004 ; Chandrasena, 2009 ; Rodenburg et al., 2011 ).

Weed Response to Variation in Rainfall and Drought Spells

A variation in rainfall pattern and increased aridity consistent with a warming climate, could alter weed distribution and their impact on crop production. In the near future, aridity is expected to increase in many agronomically important areas, since an anticipated increase in temperature (1–5°C) is expected with each doubling of the atmospheric CO 2 level. Rising temperatures also causes greater evaporation, and global trends in rainfall variability suggest that the monsoon regions will become drier ( Giannini et al., 2008 ), leading to a 5–8% increase in drought-susceptible areas ( Rodenburg et al., 2011 ). Trends in future rainfall prediction are difficult to predict, except to forecast more erratic rainfall and consequently, drought and flood would become recurrent phenomena. Under such a scenario, the distribution and prevalence of weeds will be problematic in crop ecosystems, and in particular summer droughts will affect weed management in spring-sown crops ( Peters and Gerowitt, 2014 ). Rodenburg et al. (2010) postulated that under prolonged drought spells, C 4 and parasitic weeds like S. hermonthica will thrive better. Under excess water environments, weeds such as Rhamphicarpa fistulosa (Hochst.) Benth. will be favored. A change in rainfall patterns would favor hydromorphic weeds while prolonged drought spells will benefit C 4 over C 3 weeds. Under rainfed or dryland environments, little or no rainfall will hamper adequate land preparation for wet season rice as a result of limited water availability for flooding, especially early in the season when the rice is most susceptible to weed competition. This will limit traditional weed management in flooded rice and necessitate the use of herbicides. Rice yield losses are expected to be higher under such circumstances. Asch et al. (2005) emphasized that drought-tolerant rice cultivars would be required to prevent water stress-induced yield losses and to increase rice competitiveness against weeds under rainfed conditions. A change from transplanting to direct seeding of rice, in relation to water saving in South Asia, has already resulted in increased weed competition and changed weed dynamics ( Matloob et al., 2015a ).

Competition of cotton with Abutilon theophrasti Medic. and Anoda cristata (L.) Schltdl. increased under drought conditions ( Patterson and Highsmith, 1989 ). A yield reduction due to Xanthium strumarium L. was more pronounced in well-watered soybeans compared with water-stressed soybeans ( Mortensen and Coble, 1989 ). An increase in rainfall provided greater competition to wheat growth and yield against C. arvense ( Donald and Khan, 1992 ). According to Patterson (1995) , weed competition had little effect on crops under water deficit conditions, as the potential crop yield was already reduced by water stress. This was confirmed by Chauhan and Abugho (2013) , who showed that rice could not survive under water stress conditions. By contrast, Amaranthus spinosus and Leptochloa chinensis (L) Nees survived under water stress conditions and produced a significant number of tillers/branches and leaves even at the lowest soil water content.

Increased rainfall frequency and intensity will have an adverse effect on uptake, retention, and activity of soil-applied herbicides ( Bailey, 2004 ; Rodenburg et al., 2011 ). Increased cuticle thickness and leaf pubescence in response to drought, will reduce herbicide entry into leaves ( Patterson, 1995 ). These attributes can also affect growth and recovery of crops and weeds following herbicide application. Increasing aridity and drought will increase herbicide volatilization, and, moreover, frequent rain showers will reduce the “rain safe periods” available for herbicide application in a given cropping system posing multidimensional challenges for weed management. An unprecedented increase in rainfall (either as a single rain event or annually) may promote leaching of soil-applied herbicides, and subsequent ground water contamination ( Froud-Williams, 1996 ). A general conclusion that can be drawn from the above discussion is that an increase in rainfall would lead to additional weed pressure, thus increasing the herbicide costs and overall cost of production of major crops.

Weed Response to the Interactive Effects of Climatic Variables

Climate change causes extinctions and alters species distributions of flora and fauna, and exerts inescapable impacts on various antagonistic and mutualistic interactions among terrestrial species ( Tylianakis et al., 2008 ). As noted earlier, the conventional concept that CO 2 enrichment favors C 3 plant species over C 4 by stimulating net photosynthesis, is modified by other associated climate variables affecting this (simple) response ( Prior et al., 2003 ; Hikosaka et al., 2005 ). The interactive effect of the CO 2 enrichment will affect weed-crop competition simultaneously or sequentially in a complex manner, quite differentially from its effect on the photosynthetic pathway alone. Past research on climate change has focused on manipulating the plant response to the CO 2 concentration and not on the associated increases in temperature or drought ( Bunce and Ziska, 2000 ; Fuhrer, 2003 ). These anticipated changes in temperature and moisture projected under changing climates ( IPCC, 2007 ) have obvious implications for germination and the spatial and temporal emergence of weed seeds and seedlings, which require more holistic investigation. For example, dormancy, which is considered one of the major constraints to weed emergence, is expected to be broken earlier or sooner due to greater moisture availability and warmer temperatures ( Ooi et al., 2014 ; Jaganathan and Liu, 2015 ). Dormancy cycles observed in some species are known to be regulated mainly by soil temperature in temperate environments where water is not seasonally restricted ( Batlla and Benech-Arnold, 2004 ), irrespective of their CO 2 response. Wand et al. (1999) and Ward et al. (1999) demonstrated that the combined effects of soil water and nutrient stress limited the response of C 3 plants to elevated CO 2 , but not C 4 species. Belote et al. (2003) suggested that water availability is a crucial factor that mediates species and community responses to rising CO 2 concentrations. Information regarding the interactive effects of elevated CO 2 with sub-ambient temperatures in either C 4 weeds or crops is scarce. Few studies are available that have examined the growth and reproductive response of C 3 weeds to the combined effect of temperature and CO 2 . Weeds like Senna obtusifolia and Anoda cristata exhibited a higher range of growth stimulation under elevated CO 2 and temperature, however, other weeds ( Triticum repen L. and Abutilon theophrasti ) did not respond similarly ( Patterson et al., 1988 ; Tremmel and Patterson, 1993 ). Moreover, differential enhancement of C 3 crops and weeds by elevated CO 2 at sub-optimal temperatures should receive attention. Alberto et al. (1996) found that competitiveness of a C 3 crop species (rice) relative to a C 4 weed species ( Echinochloa glabrescens Munro ex Hook.f.) could be enhanced by elevated CO 2 alone, but a simultaneous increase in CO 2 and temperature still favored the weed. Remarkably, the interactive effect of CO 2 with water availability has been exclusively studied for crop species ( Tyree and Alexander, 1993 ; Bunce, 2004 ), with little emphasis placed on quantifying differences between crops and weeds of the same photosynthetic pathway. Patterson (1986) found that rising CO 2 levels favored the growth of both a C 3 crop (soybean) and C 4 weeds [ Echinochloa crus-galli (L.) P.Beauv., Eleusine indica (L.) Gaertn., and Digitaria ciliaris (Retz.) Koeler)] by improving water-use efficiency under drought, although greater growth stimulation in the C 3 crop was expected. Studies reporting the interactive effects of rising CO 2 levels, drought, and weed-crop competition are not common. It could be speculated that if CO 2 decreases, the water requirement of C 4 weeds relative to C 3 crops, C 4 weeds could still potentially compete successfully with C 3 crops under a high CO 2 /drought situation ( Knapp et al., 1993 ). Ironically, few studies have focused on how CO 2 -induced changes in phenological development could be modified by other climatic factors such as water supply and/or temperature ( Springer and Ward, 2007 ).

The opinion of Rosenzweig and Hillel (1998) that rising temperature and CO 2 levels could make crop plants less competitive with weeds, together with a similar prediction a decade later by Wolfe et al. (2008) that weeds would benefit more than cash crops, were both found to be true. Amaranthus retroflexus produced more seeds in barley cropping, albeit the growth of barley as well as the weed was reduced in southern Finland ( Hyvonen, 2011 ).

Patterson and Flint (1982) found growth stimulation in soybean and two associated weeds [ Senna obtusifolia (L.) H. S. Irwin and Barneby and Crotalaria spectabilis Roth] with increasing CO 2 to 675 ppm in Hoagland’s solution. Zhu et al. (2008) , while investigating the effect of nutrient and CO 2 on weed-crop competition using a C 3 crop (rice) and a C 4 weed ( E. crus-galli) model system, found a proportionate increase in rice biomass compared with E. crus-galli (in response to 200 ppm increase in CO 2 ) under an optimum nitrogen supply. In contrast, at a sub-optimum nitrogen level, elevated CO 2 reduced the competitive ability of rice against E. crus-galli . Hence, an increase in atmospheric CO 2 will exacerbate rice yield losses under low soil nitrogen status, owing to C 4 weed competition. Systemic investigations are needed to appraise the interactive effects of key environmental variables on different weed species and communities under diverse ecosystems.

Several modeling studies do not account for the impacts of an increased climatic variability ( Tubiello et al., 2007 ; Orlandini et al., 2008 ) which is a necessity in weed-crop interactions under the climate change scenario. It is a general perception that the only role of the endogenous process has been emphasized in the weed population dynamics models, which ignores exogenous variables such as climate ( Lima et al., 2012 ). The authors considered that the population dynamics of weeds are a function of ecological interactions within and between plant populations, nutrient and water limitation, rainfall, temperature and stochastic forces. Using a reproduction function ( R -function), they concluded that interactions between endogenous and exogenous factors are important for management of weed and invasive plants and climate change mitigation. Since predicting the impact of a weed under cultivated conditions at local scale requires a process-based modeling approach integrating local environmental conditions with the differential responses of the crop and weeds, Stratonovitch et al. (2012) have developed a simulation model for winter wheat and a competing weed Alopecurus myosuroides in UK.

Herbicide-Climate Interactions

Herbicide effectiveness is dependent on the local climate/microclimate, and herbicides are no exceptions, particularly foliage applied post-emergence ( Kudsk and Kristensen, 1992 ). A rise in temperature will increase volatility of certain herbicides such as trifluralin, rendering it less efficient. In the 1970s, rapid volatilization of surface-applied alachlor, butachlor, and propachlor occurred from continuously moist soils exposed to a constant 21°C ( Beestman and Deming, 1974 ). Temperatures (day/night 32/22°C and 26/16°C) were not as critical as that of relative humidity in influencing acifluorfen (diphenylether group) phytotoxicity on Xanthium strumarium L. and Ambrosia artemisiifolia L. ( Ritter and Coble, 1981 ). However, temperature had a significant effect on the degradation of imazapyr (imidazolinone group), flumetsulam (sulfonanilide family), and thifensulfuron (sulfonylurea group) in soil ( Mcdowell et al., 1997 ). Glyphosate absorption is dependent on the atmospheric temperature, as evident from Desmodium tortuosum (Sw.) DC., a C 3 weed ( Sharma and Singh, 2001 ). An increase in temperature or relative humidity increased the efficacy of mesotrione on X. strumarium and A. theophrastii three-fold ( Johnson and Young, 2002 ). The efficacy of the herbicide pyrithiobac (pyrimidinylthiobenzoic acid group) on Amaranthus palmeri L. was reduced at temperatures outside the range of 20-34°C ( Mahan et al., 2004 ). Anderson et al. (1993) found that relative humidity had the most significant effect on the phytotoxic action of glufosinate-ammonium, since this is attributed to changes in cuticle hydration and droplet drying ( Ramsey et al., 2005 ). Studies under controlled environmental chambers in Australia using varying night/day temperatures of 5/10, 15/20, and 20/25°C showed that Raphanus raphanistrum L., grown under cooler temperatures of 5/10°C, was poorly controlled with 1,200 g ai ha -1 of glufosinate. By comparision, 100% mortality was achieved under 15/20 and 20/25°C for the same dose ( Kumaratilake and Preston, 2005 ), suggesting enhanced efficacy of glufosinate under enhanced atmospheric temperature.

Implications of Climate Change for Weed-Crop Interactions

Uncertainty in agricultural productivity under a climate change scenario, can be the result of plant-plant interactions through direct effects of a change in temperature and atmospheric CO 2 , or indirect effects at the system level through shifts in crop–weed interactions ( Fuhrer, 2003 ) and other biotic stresses.

Shifts in Weed Abundance, Distribution, and Competitive Balance

Under ambient conditions, water availability and temperature are the principal determinants of species distribution ( Patterson et al., 1999 ), but there is the recent addition to this list of CO 2 concentrations through the lens of climate change ( Patterson, 1995 ; Chauhan et al., 2014 ). The changing climate variables may either increase the distribution range of weed species in response to a change in atmospheric temperature, or allow some non-potent weeds to dominate weed abundance as crop-weed interactions may increasingly favor C 3 weeds ( Bazzaz et al., 1985 ). Other than geographical distribution, the projected climate change might impact their population biology ( Patterson et al., 1999 ; Ziska and Goins, 2006 ), causing them to move to new areas lying at higher altitudes and latitudes ( Patterson, 1995 ; Ziska and Dukes, 2011 ). Such effects have been proposed for Striga sp., which are expected to extend their geographic range ( Mohamed et al., 2006 ). Climate change will alter the distribution of plant species and overall functioning and productivity of ecosystems. For example, increased abundance of woody vines as a consequences of rising CO 2 levels has been associated with an increased tree mortality and reduced tree regeneration in forests throughout the world ( Phillips et al., 2002 ). Similarly, an increase in parasitic weeds would become a serious threat to productivity of rice and sorghum crops under rainfed agriculture ( Rodenburg et al., 2011 ). According to Holm et al. (1997) , most of the troublesome C 3 and C 4 weeds of the arable land are limited to tropical and subtropical regions, primarily due to low temperatures at higher latitudes. Preliminary data showed an increased tolerance in many weeds to low temperatures under elevated CO 2 ( Boese et al., 1997 ), which suggests the possibility of polar-ward expansion for many weed species ( Bradley and Mustard, 2005 ; McDonald et al., 2009 ; Ziska and Dukes, 2011 ).

Species either have to adapt in situ to new climatic conditions or undergo shifts in their distribution to more favorable locals. McDonald et al. (2009) proposed that if climate change forecasts are realized, damaging endemic weed species of major cropping systems might experience a significant transformation in their host range, besides an overall increase in the chance of invasion by exotic invasive weed species. Besides agronomic weeds, there are also certain non-native weeds whose introduction to new areas can pose ecological and environmental hazards ( Mooney and Hobbs, 2000 ). Several studies have demonstrated that such weeds often benefit from carbonaceous fertilization ( Polley et al., 2002 ; Belote et al., 2003 ; Ziska and George, 2004 ). It is believed that under a climate change scenario, these invasive plants would be able to extend their geographic range as well as spread to new areas, including currently agriculturally productive regions ( Ziska and Dukes, 2011 ). An expansion in the geographic range proposed for weeds such as Lonicera semperviens L. and Pueraria lobata (Lour.) Merr. in the past has now become a reality ( Patterson, 1995 ). Range expansion of arable and invasive weeds in connection with climate change must be studied as an integral part of crop-weed interactions.

In a composite stand of weeds in a cropped field (C 3 and C 4 plants), dynamics in insurgence and shifts of the weed populations in favor of specific species is expected over time ( Das et al., 2012 ). These authors further argued that climate change is likely to trigger differential growth in crops and weeds and will have significant implications for weed management across crops and cropping systems. The abundance, competitive ability, and survival of perennial weeds are expected to be higher, since a rise in CO 2 stimulates tuber and rhizome growth ( Chandrasena, 2009 ). Climate change will result in a greater frequency of extreme weather events such as frequent droughts and cold spells, so that weeds with less phenotypic plasticity may experience population declines ( Peters et al., 2014 ). Lack of rainfall and prolonged drought will limit growth of arable crops and pastures, resulting in a lack of vegetation cover and bare ground, thus allowing invasion by more resilient drought-tolerant weeds. Increasing CO 2 could alter the competitive balance in a weed-crop mixture through its effect on photosynthesis and stomatal physiology, which is linked with the competitive balance between crops and weeds in a cropping system ( Alberto et al., 1996 ). The range of growth stimulation in response to elevated CO 2 needs to be determined for both crops and weeds with contrasting carbon fixation pathways, growing in variable densities and species compositions. Under conditions of higher temperature and drought, C 4 weeds such as A. retroflexus tend to dominate C 3 crops (e.g., soybean). The infestation of P. minor is expected to worsen in wheat fields with CO 2 increase ( Mahajan et al., 2012 ). Likewise, weedy rice will compete more strongly with cultivated rice ( Ziska et al., 2010 ). Exploring differential mechanisms and responses that govern the success of weeds to invade new areas/cropping systems and their ability to utilize growth resources, will be helpful in understanding the implications of rising CO 2 levels on plant-plant interactions. This also requires characterizing their damage niche ( McDonald et al., 2009 ).

Effectiveness of Weed Management and Adoption of Best Agronomic Practices

Climate change will indirectly affect the adoption and success of weed management strategies. Looming water crises have been recognized as a major threat to agricultural productivity ( Sandhu et al., 2012 ) notably in irrigated rice ( Soomro, 2004 ; Farooq et al., 2011 ) with long-term consequences for regional and global food security ( Braun and Bos, 2005 ; Seck et al., 2012 ). Water requirements of irrigated rice are approximately 2–3 times higher than for any other upland cereal ( Bouman et al., 2007 ; Bouman, 2009 ; Pathak et al., 2011 ). Aerobic rice is a potential water-use efficient production system, but a high weed infestation (up to 90% yield reduction; Gowda et al., 2009 ) has threatened its sustainability, which demands efficient and cost-effective weed management techniques ( Anwar et al., 2012 ). Frequent drought spells and erratic rainfall will affect productivity and sustainability of upland and low land rice production systems. There will be a trade-off between water-use efficient rice production methods and weed management. In upland rice, drought tolerance will be needed not only to cope with water scarcity but also to safeguard production losses against weeds by maintaining or improving a competitive edge ( Asch et al., 2005 ). In aerobic or dry-seeded rice, the switch over from transplanting in respect of water saving, induces qualitative and quantitative changes in rice weed flora ( Matloob et al., 2015a ). The inherent size differential of transplanted rice seedlings in conjunction with flooded environments provided a distinct competitive advantage, i.e., an earlier growth and germination over a wide range of weed species that otherwise are quite problematic in aerobic rice. With a dwindling water supply and more severe drought spells, flooding will not be available as a potential weed management tool in the near future. Hand weeding was 35% higher when the flooding regime was altered from permanent to temporary flooding ( Latif et al., 2005 ). This means that farmers lacking alternate means and resources to combat weeds will suffer significant yield losses. Moreover, dry tillage practices, alternate wetting and drying regimes, and extended periods during which soil is not flooded, will result in the insurgence of non-native and difficult-to-control weeds ( Chauhan et al., 2014 ). Under drought conditions, rice (C 3 ) is already a poor weed competitor ( Saito, 2010 ) and will be under greater pressure due to increased competition from C 4 weeds, which comprise the majority of weed flora infesting rice fields ( Caton et al., 2010 ). In rainfed rice, a lack of rainfall early in wet seasons may compel farmers to adjust their timing of land preparation and subsequent planting. This might affect synchronization of rice sensitive growth periods with emergence and active growth period of troublesome weeds. Hence, it seems that strategies aimed at mitigating climate change effects on crop production like drought-tolerant rice germplasm and water saving rice cultivation, will also have implications for weed management ( Rodenburg et al., 2011 ).

Increasing interest in conservation agriculture has created a reliance on glyphosate for weed management ( Shaner, 2000 ), and the continuous use of this herbicide may result in evolution of resistant biotypes of major weeds. In wheat, resource conservation technologies, such as no-till systems, have emerged as an important breakthrough ( Erenstein et al., 2008 ). However, adoption of no-till approaches which are characterized by minimal soil disturbance, may affect the abundance and floristic composition of weeds ( Matloob et al., 2015b ). Hardy weeds, such as Rumex sp., are expected to be higher in zero-till wheat fields ( Chauhan et al., 2014 ).

Ziska et al. (1999) and Ziska and Teasdale (2000) have shown that herbicides (e.g., glyphosate) will be rendered less effective against weeds under CO 2 levels anticipated in the near future. Increased tolerance to glyphosate under elevated CO 2 has been recorded for both agricultural and invasive weed species ( Ziska and McConnell, 2015 ). These alarming findings revealed a sustained increase in photosynthesis and growth of perennial weeds such as Elymus repens (L.) Gould. with a concurrent decrease in herbicide efficacy and increased potential of invasion and competition ( Ziska and Teasdale, 2000 ). Differential tolerance to glyphosate exhibited by certain weeds under elevated CO 2 is also an issue. Whilst the response of weeds such as A. retroflexus was not affected by elevated CO 2 , Chenopodium album and Cirsium arvensis manifested a significant glyphosate tolerance ( Ziska et al., 1999 ; Ziska et al., 2004 ). A variable response to glyphosate was observed even for invasive grass species possessing the same carbon fixation pathway ( Manea et al., 2011 ). Hence, it can be inferred that some weeds will be more problematic in the near future in glyphosate-tolerant crops or under conservation agriculture. Another difficulty will be the knockdown of perennial weeds if glyphosate efficacy is reduced due to climate change. An increase in rhizome and tuber growth, coupled with an increase in biomass, would cause a dilution effect on any herbicide application, causing an increase in weed control costs. Direct effects of climate change on plant physiology, anatomy, and morphology will indirectly affect herbicide efficacy by influencing uptake, translocation, and metabolism. Changes in physical environments, such as drought spells or prolonged rainy seasons, may limit the field conditions necessary for herbicide applications. Climate change will have implications for all dimensions of chemical weed management including application, spray drift, persistence, metabolism, and herbicide efficacy. This justifies diversifying current weed management tactics as well as the urgency of a sound knowledge regarding the ecology and biology of weeds in a changing climate.

After a catastrophic climatic events such as drought or flood, weeds will have a greater chance to colonize and invade disturbed habitats. Chemical control measures may become less effective due to a change in the external environment (drier and warmer conditions) or changes in anatomy, growth physiology, and phenology of the target weed flora ( Chauhan et al., 2014 ; Ziska and McConnell, 2015 ). Asexual reproduction through below-ground parts is always conducive to spread, irrespective of water availability. Extremes of moisture availability, viz., flood as well as drought, hinder physical management methods such as hoeing, inter-cultivation, etc. It seems that growers will have to carefully synchronize the timing of their control measures with the weed life cycle since these will also respond to climate change. The opinion of Chandrasena (2009) that adaptive responses should be based on a better knowledge on how plant communities will respond to climate change rather than ad hoc responses, is therefore valid in the current context.

Conclusion and Future Research Needs

Research is needed to unravel whether the so-called CO 2 fertilization could compensate for other negative effects of climate change on crop-weed competition. Moreover, the response of agricultural and invasive weeds to other climatic factors and associated parameters such as temperature, drought, rainfall, and an extended growing period should be explicitly assessed in conjunction with an anticipated rise in CO 2 concentration to predict a wider picture of competitive outcomes in managed and natural ecosystems. The effect of climate change on the geographic distribution of invasive weeds will be a subject of interest in the near future. Research efforts are also needed to explore the adaptive mechanisms/practices to facilitate crop production with changing conditions under climate change scenarios and, at the same time, asses their effectiveness, required time span, and economic and ecological costs.

Climate change is a looming global crisis and its impacts on agricultural weeds have not been well explored. Conventional thinking around carbon pathways in plants and nutrient management in crops could partially solve the climate change implications, but weed problems could also be aggravated in the wake of increasing CO 2 concentration, high temperature, and most significantly by water stress. These conditions might necessitate the adoption of new agronomic practices to enhance weed competitiveness. As crops and weeds share the same trophic level, the stimulatory or inhibitory behavior of the climate variables on crops should generally hold true for weeds. An increase in atmospheric temperature was found to favor weed growth as well as herbicide efficacy. Although there is a dominance of C 4 weeds in agriculture, C 3 and C 3 -C 4 intermediate pathways of prominent weeds would pose severe crop-weed competition in the years to come. Importantly, due to species interaction, there is a need to study all possible combinations of plant-weed carbon fixation pathways, C 3 crops and C 3 weeds, C 4 crops and C 4 weeds, C 3 crops and C 4 weeds, and C 4 crops and C 3 weeds, while studying the impact of climate change on crop-weed competitive interactions. Several weeds will exert additional pressure for crop-weed competition under the climate change scenario. More adaptive research studies, including complex research conditions, could yield useful solutions for managing yield reduction in the ensuing decades.

Author Contributions

BSC developed the initial concept and outline. KR and AM took lead in expanding the content. FA, SKF, and BSC contributed and edited the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Ainsworth, E. A., and Long, S. P. (2005). What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372. doi: 10.1111/j.1469-8137.2004.01224.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Alberto, A. M., Ziska, L. H., Cervancia, C. R., and Manalo, P. A. (1996). The influence of increasing carbon dioxide and temperature on competitive interactions between a C3 crop, rice ( Oryza sativa ) and a C4 weed ( Echinochloa glabrescens ). Aust. J. Plant Physiol. 23, 795–802. doi: 10.1071/PP9960795

CrossRef Full Text | Google Scholar

Anderson, D. M., Swanton, C. J., Hall, J. C., and Mersey, B. G. (1993). The influence of temperature and relative humidity on the efficacy of glufosinate-ammonium. Weed Res. 33, 139–147. doi: 10.1111/j.1365-3180.1993.tb01927.x

Anwar, M. P., Juraimi, A. S., Puteh, A., Man, A., and Rahman, M. M. (2012). Efficacy, phytotoxicity and economics of different herbicides in aerobic rice. Acta Agric. Scand. B Soil Plant Sci. 62, 604–615. doi: 10.1080/09064710.2012.681060

Asch, F., Dingkuhn, M., Sow, A., and Audebert, A. (2005). Drought-induced changes in rooting patterns and assimilate partitioning between root and shoot in upland rice. Field Crops Res. 93, 223–236. doi: 10.1016/j.fcr.2004.10.002

Bailey, S. W. (2004). Climate change and decreasing herbicide persistence. Pest Manage. Sci. 60, 158–162. doi: 10.1002/ps.785

Batlla, D., and Benech-Arnold, R. L. (2004). Seed dormancy loss assessed by changes in population hydrotime parameters. Dev. Predictive Model. Seed Sci. Res. 14, 277–286. doi: 10.1079/SSR2004177

Bazzaz, F. A., Garbutt, K., and Williams, W. E. (1985). “Effect of increased atmospheric carbon dioxide concentration on plant communities,” in Direct Effects of Increasing Carbon Dioxide on Vegetation , eds B. R. Strain and J. D. Cure (Washington, DC: United States Department of Energy), 168.

Google Scholar

Beestman, G. B., and Deming, J. M. (1974). Dissipation of acetanilide herbicides from soils. Agron. J. 66, 308–311. doi: 10.2134/agronj1974.00021962006600020035x

Belote, R. T., Weltzin, J. F., and Norby, R. J. (2003). Response of an understory plant community to elevated CO2 depends on differential responses of dominant invasive species and is mediated by soil water availability. New Phytol. 161, 827–835. doi: 10.1111/j.1469-8137.2004.00977.x

Bhat, N. H., and Jan, S. (2010). Impact of climate change on crop productivity: need of adjustment in agriculture. Res. J. Agric. Sci. 1, 483–486.

Boese, S. R., Wolfe, D. W., and Melkonian, J. (1997). Elevated CO2 mitigates chilling-induced water stress and photosynthetic reduction during chilling. Plant Cell Environ. 20, 625–632. doi: 10.1111/j.1365-3040.1997.00082.x

Bouman, B. A. M. (2009). How much water does rice use? Rice Today 8, 28–29.

Bouman, B. A. M., Humphreys, E., Tuong, T. P., and Barker, R. (2007). Rice and water. Adv. Agron. 92, 187–237. doi: 10.1016/S0065-2113(04)92004-4

Bradley, B. A., and Mustard, J. F. (2005). Identifying land cover variability distinct from land cover change: cheatgrass in the Great Basin. Remote Sens. Environ. 94, 204–213. doi: 10.1016/j.rse.2004.08.016

Braun, J., and Bos, M. S. (2005). “The changing economics and politics of rice: Implications for food security, globalization, and environmental sustainability,” in Proceedings of the World Rice Research Conference Held in Tokyo and Tsukuba , eds K. Toriyama, K. L. Heong, and B. Hardy (Tsukuba: Japan International Research Center for Agricultural Sciences), 7–20.

Brinkley, T. R., and Bomford, M. (2002). Agricultural Sleeper Weeds: What is the Potential Threat? Kingston, ACT: Bureau of Rural Sciences.

Bunce, J. A. (2004). Carbon dioxide effects on stomatal responses to the environment and water use by crops under field conditions. Oecologia 140, 1–10. doi: 10.1007/s00442-003-1401-6

Bunce, J. A., and Ziska, L. H. (2000). “Crop ecosystem responses to climatic change: crop/weed interactions,” in Climate Change and Global Crop Productivity , eds K. R. Reddy and H. F. Hodges (New York, NY: CABI), 333–348.

Caton, B. P., Mortimer, M., Hill, J. E., and Johnson, D. E. (2010). A Practical Field Guide to Weeds of Rice in Asia , 2nd Edn. Los Baños: International Rice Research Institute, 118.

Cavero, J., Zaragoza, C., Suso, M. L., and Pard, A. (1999). Competition between maize and Datura stramonium in an irrigated field under semi-arid conditions. Weed Res. 39, 225–240. doi: 10.1046/j.1365-3180.1999.00140.x

Chandrasena, N. (2009). How will weed management change under climate change? Some perspectives. J. Crop Weed 5, 95–105.

Chauhan, B. S. (2013). Strategies to manage weedy rice in Asia. Crop Prot. 48, 51–56. doi: 10.1016/j.cropro.2013.02.015

Chauhan, B. S., and Abugho, S. B. (2013). Effect of water stress on the growth and development of Amaranthus spinosus. Leptochloa chinensis , and rice. Am. J. Plant Sci. 4, 989–998. doi: 10.4236/ajps.2013.45122

Chauhan, B. S., Prabhjyot-Kaur, Mahajan, G., Randhawa, R. J., Singh, H., and Kang, M. S. (2014). Global warming and its possible impact on agriculture in India. Adv. Agron. 123, 65–121. doi: 10.1017/S1751731116002706

Chen, C. C., and McCarl, B. A. (2001). An investigation of the relationship between pesticide usage and climate change. Climatic Change 50, 475–487. doi: 10.1023/A:1010655503471

Chin, D. V., Thien, T. C., Bi, H. H., Nhiem, N. T., and Son, T. T. N. (2013). “Weed and weedy rice control by imidazolinone herbicides in Clearfield paddy in Vietnam,” in Proceedings of the 4th Tropical Weed Science Conference , Chiang Mai, 98.

Clements, D. R., and DiTommaso, A. (2011). Climate change and weed adaptation: can evolution of invasive plants lead to greater range expansion than forecasted? Weed Res. 51, 227–240. doi: 10.1111/j.1365-3180.2011.00850.x

Das, T. K., Sharma, A. R., and Pathak, H. (2012). “Crop-weed balance studies under climate change,” in Climate Change Impact, Adaptation and Mitigation in Agriculture: Methodology for Assessment and Application , eds H. Pathak, P. K. Aggarwal, and S. D. Singh (New Delhi: Indian Agricultural Research Institute), 131.

Dekker, J. (2003). “Evolutionary biology of the Foxtail ( Setaria ) species-group,” in Weed Biology and Management , ed. Inderjit (Dordrecht: Kluwer Academic Publishers), 65–114.

Donald, W. W., and Khan, M. (1992). Yield loss assessment for spring wheat ( Triticum aestivum ) infested with Canada thistle ( Cirsium arvense ). Weed Sci. 40, 590–598.

Erenstein, O., Farooq, U., Malik, R. K., and Sharif, M. (2008). On-farm impacts of zero tillage wheat in South Asia’s rice-wheat systems. Field Crops Res. 105, 240–252. doi: 10.1016/j.fcr.2007.10.010

Farooq, M., Siddique, K. H. M., Rehman, H., Aziz, T., Lee, D., and Wahid, A. (2011). Rice direct seeding: experiences, challenges and opportunities. Soil Till. Res. 111, 87–98. doi: 10.1016/j.still.2010.10.008

Fried, G., Petit, S., and Reboud, X. (2010). A specialist-generalist classification of the arable flora and its response to changes in agricultural practices. BMC Ecol. 10, 1–11. doi: 10.1186/1472-6785-10-20

Froud-Williams, R. J. (1996). Weeds and climate change: implications for their ecology and control. Asp. Appl. Biol. 45, 187–196.

Fuhrer, J. (2003). Agroecosystem responses to combination of elevated CO2, ozone and global climate change. Agric. Ecosys. Environ. 97, 1–20. doi: 10.1016/S0167-8809(03)00125-7

Giannini, A., Biasutti, M., Held, I. M., and Sobel, A. H. (2008). A global perspective on African climate. Climatic Change 90, 359–383. doi: 10.1007/s10584-008-9396-y

Gowda, P. T., Shankaraiah, C., Jnanesh, A. C., Govindappa, M., and Murthy, K. N. K. (2009). Studies on chemical weed control in aerobic rice ( Oryza sativa L.). J. Crop Weed 5, 321–324.

Hanzlik, K., and Gerowitt, B. (2012). Occurrence and distribution of important weed species in German winter oilseed rape fields. J. Plant Dis. Prot. 119, 107–120. doi: 10.1007/BF03356429

Hikosaka, K., Onoda, Y., Kinugasa, T., Nagashima, H., Anten, N. P. R., and Hirose, T. (2005). Plant responses to elevated CO2 concentration at different scales: leaf, whole plant, canopy, and population. Ecol. Res. 20, 243–253. doi: 10.1007/s11284-005-0041-1

Holm, L. G., Doll, J., Holm, E., Pancho, J., and Herverger, J. (1997). Worlds Weeds: Natural Histories and Distribution . New York, NY: John Wiley & Sons, 1129.

Howden, S. M., Soussana, J. F., Tubiello, F. N., Chhetri, N., Dunlop, M., and Meinke, H. (2007). Adapting agriculture to climate change. Proc. Natl. Acad. Sci. U.S.A. 104, 19691–19696. doi: 10.1073/pnas.0701890104

Hyvonen, T. (2011). Impact of temperature and germination time on the success of a C4 weed in a C3 crop. Amaranthus retroflexus and spring barley. Agric. Food Sci. 20, 183–190. doi: 10.2137/145960611797215664

CrossRef Full Text

Idso, C. D., Idso, S. B., and Balling, R. C. Jr. (1988). The urban CO2 dome of Phoenix, Arizona. Physical. Geogr. 19, 95–108.

PubMed Abstract | Google Scholar

Idso, C. D., Idso, S. B., and Balling, R. C. Jr. (2001). An intensive two week study of an urban CO2 dome in Phoenix Arizona, USA. Atmos. Environ. 35, 995–1000. doi: 10.1016/S1352-2310(00)00412-X

IPCC (1995). Climate Change: A Glossary by the Intergovernmental Panel on Climate Change . Geneva: IPPCC.

IPCC (2007). Climate Change: Impacts, Adaptation and Vulnerability . Geneva: IPCC Secretariat, 986.

Jaganathan, G. K., and Liu, B. (2015). Role of seed sowing time and microclimate on germination and seedling establishment of Dodonaea viscosa (Sapindaceae) in a seasonal dry tropical environment — an insight into restoration efforts. Botany 93, 23–29.

Johnson, B. C., and Young, B. G. (2002). Influence of temperature and relative humidity on the foliar activity of mesotrione. Weed Sci. 50, 157–161. doi: 10.1614/0043-17452002050[0157:IOTARH]2.0.CO;2

Kang, M. S., and Banga, S. S. (2013). “Global agriculture and climate change: a perspective,” in Combating Climate Change: An Agricultural Perspective , eds M. S. Kang and S. S. Banga (Boca Raton, FL: CRC Press), 11–25.

Keeling, C. D., and Whorf, T. P. (2004). “Atmospheric CO 2 records from sites in the SIO air sampling network,” in Trends 93: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center , eds T. A. Boden, D. P. Kaiser, R. J. Sepanski, and F. W. Stoss (Oak Ridge, TN: Oak Ridge National Laboratory, U.S. Department of Energy).

Killman, W. (2008). “Foreword,” in Climate Change and Food Security: A Framework Document . Rome: FAO of the United Nations, iii.

Kimbal, B. A. (1983). Carbon diooxide and agricultural yield. An assemblage and analysis of 430 prior observations. Agron. J. 75, 779–788. doi: 10.2134/agronj1983.00021962007500050014x

Knapp, A. K., Hamerlyn, C. K., and Owensby, C. E. (1993). Photosynthetic and water relations response to elevated CO2 in the C4 grass, Andropogon gerardii. Int. J. Plant Sci. 154, 459–466. doi: 10.1086/297129

Koizumi, H., Kibe, T., Nakada, T., Yazaki, Y., Adachi, M., Inatomi, M., et al. (2004). “Effect of free-air CO 2 enrichment on structures of weed communities and CO 2 exchange at the flood-water surface in a rice paddy field,” in Global Environmental Change in the Ocean and on Land , eds M. Shiyomi, H. Kawahata, H. Koizumi, A. Tsuda, and Y. Awaya 473–485.

Kudsk, P., and Kristensen, J. L. (1992). “Effect of environmental factors on herbicide performance,” in Proceedings of the First International Weed Control Congress , Melbourne, VIV, 173–186.

Kumaratilake, A. R., and Preston, C. (2005). Low temperature reduces glufosinate activity and translocation in wild radish ( Raphanus raphanistrum ). Weed Sci. 53, 10–16. doi: 10.1614/WS-03-140R

Latif, M. A., Islam, M. R., Ali, M. Y., and Saeque, M. A. (2005). Validation of the system of rice intensification (SRI) in Bangladesh. Field Crops Res. 93, 281–292. doi: 10.1016/j.fcr.2004.10.005

Lee, J. (2011). Combined effect of elevated CO2 and temperature on the growth and phenology of two annual C3 and C4 weedy species. Agric. Ecosys. Environ. 140, 484–491. doi: 10.1016/j.agee.2011.01.013

Lima, M., Navarrete, L., and González-Andujar, J. L. (2012). Climate effects and feedback structure determining weed population dynamics in a long-term experiment. PLoS ONE 7:e30569. doi: 10.1371/journal.pone.0030569

Mahajan, G., Singh, S., and Chauhan, B. S. (2012). Impact of climate change on weeds in the rice-wheat cropping system. Curr. Sci. 102, 1254–1255.

Mahan, J. R., Dotray, P. A., and Light, G. G. (2004). Thermal dependence of enzyme function and inhibition; implications for, herbicide efficacy and tolerance. Physiol. Plant. 20, 187–195. doi: 10.1111/j.0031-9317.2004.0255.x

Manea, A., Leishman, M. R., and Downey, P. O. (2011). Exotic C4 grasses have increased tolerance to glyphosate under elevated carbon dioxide. Weed Sci. 59, 28–36. doi: 10.1614/WS-D-10-00080.1

Matloob, A., Khaliq, A., and Chauhan, B. S. (2015a). Weeds of rice in Asia: problems and opportunities. Adv. Agron. 130, 291–336. doi: 10.1016/bs.agron.2014.10.003

Matloob, A., Khaliq, A., Tanveer, A., Husssain, S., Aslam, F., and Chauhan, B. S. (2015b). Weed dynamics in dry direct-seeded fine rice as influenced by tillage system, sowing time and weed competition duration. Crop Prot. 71, 25–38. doi: 10.1016/j.cropro.2015.01.009

McDonald, A., Riha, S., DiTommaso, A., and DeGaetano, A. (2009). Climate change and the geography of weed damage: analysis of U.S. maize systems suggests the potential for significant range transformations. Agric. Ecosys. Environ. 130, 131–140. doi: 10.1016/j.agee.2008.12.007

Mcdowell, R. W., Condron, L. M., Main, B. E., and Dastgheib, F. (1997). Dissipation of imazapyr, flumetsulam and thifensulfuron in soil. Weed Res. 37, 381–389. doi: 10.1046/j.1365-3180.1997.d01-73.x

Mohamed, K. I., Bolin, J. F., Musselman, L. J., and Peterson, T. A. (2007). “Genetic diversity of Striga and implications for control and modelling future distributions,” in Integrating New Technologies for Striga Control–Towards Ending the Witch-Hunt , eds G. Ejeta and J. Gressel (Singapore: World Scientific), 71–84.

Mohamed, K. I., Papes, M., Williams, R., Benz, B. W., and Peterson, T. A. (2006). Global invasive potential of 10 parasitic witchweeds and related Orobanchaceae. Ambio 35, 281–288. doi: 10.1579/05-R-051R.1

Mooney, H. A., and Hobbs, R. J. (2000). Invasive Species in a Changing World. Washington, DC: Island Press, 457.

Mortensen, D. A., and Coble, H. D. (1989). The influence of soil water content on common cocklebur ( Xanthium strumarium ) interference in soybeans ( Glycine max ). Weed Sci. 37, 76–83.

Nichols, V., Verhulst, N., Cox, R., and Govaerts, B. (2015). Weed dynamics and conservation agriculture principles: a review. Field Crops Res. 183, 56–68. doi: 10.1016/j.fcr.2015.07.012

Nowak, R. S., Ellsworth, D. S., and Smith, S. D. (2004). Functional responses of plants to elevated atmospheric CO2: do photosynthetic and productivity data from FACE experiments support early predictions? New Phytol. 162, 253–280. doi: 10.1111/j.1469-8137.2004.01033.x

Ooi, M. K. J., Denham, A. J., Santana, V. M., and Auld, T. D. (2014). Temperature thresholds of physically dormant seeds and plant functional response to fire: variation among species and relative impact of climate change. Ecol. Evolut. 4, 656–671. doi: 10.1002/ece3.973

Orlandini, S., Nejedlik, P., Eitzinger, J., Alexandrov, V., Toulios, L., Calanca, P., et al. (2008). Impacts of climate change and variability on european agriculture. Results of inventory analysis in COST 734 countries. Trends Dir. Clim. Res. 1146, 338–353. doi: 10.1196/annals.1446.013

Parry, M., Rosenzweig, C., and Livermore, M. (2005). Climate change, global food supply and risk of hunger. Philos. Trans. R. Soc. B 360, 2125–2138. doi: 10.1098/rstb.2005.1751

Pathak, H., Tewari, A. N., Sankhyan, S., Dubey, D. S., Mina, U., Singh, V. K., et al. (2011). Direct-seeded rice: potential, performance and problems – a review. Curr. Adv. Agric. Sci. 3, 77–88.

Patterson, D. T. (1986). Responses of soybean ( Glycine max ) and three C4 grass weeds to CO2 enrichment during drought. Weed Sci. 34, 203–210.

Patterson, D. T. (1995). Weeds in a changing climate. Weed Sci. 43, 685–701.

Patterson, D. T., and Flint, E. P. (1982). Interacting effects of CO2 and nutrient concentration. Weed Sci. 30, 389–394.

Patterson, D. T., and Highsmith, M. T. (1989). Competition of spurred anoda ( Anoda cristata ) and velvet leaf ( Albutilon theophrasti ) with cotton ( Gossypium hirsutum ) during simulated drought and recovery. Weed Sci. 37, 658–664.

Patterson, D. T., Highsmith, M. T., and Flint, E. P. (1988). Effects of temperature and CO2 concentration on the growth of cotton ( Gossypium hirsutum ), spurred anoda ( Anoda cristata ), and velvetleaf ( Abutilon theophrasti ). Weed Sci. 36, 751–757.

Patterson, D. T., Musser, R. L., Flint, E. P., and Eplee, R. E. (1982). Temperature responses and potential for spread of witchweed ( Striga lutea ) in the United States. Weed Sci. 30, 87–93.

Patterson, D. T., Westbrook, J. K., Joyce, R. J. V., Lingren, P. D., and Rogasik, J. (1999). Weeds, insects, and diseases. Climate Change 43, 711–727. doi: 10.1023/A:1005549400875

Peters, K., Breitsameter, L., and Gerowitt, B. (2014). Impact of climate change on weeds in agriculture: a review. Agron. Sustain. Dev. 34, 707–721. doi: 10.1007/s13593-014-0245-2

Peters, K., and Gerowitt, B. (2014). Important maize weeds profit in growth and reproduction from climate change conditions represented by higher temperatures and reduced humidity. J. Appl. Bot. Food Qual. 87, 234–242.

Petit, J. R., Jouzel, J., Raynaud, D., Barkov, N. I., Barnola, J. M., Basile, I., et al. (1999). Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436. doi: 10.1038/20859

Phillips, O. L., Martinez, R. V., Arroyo, L., Baker, T. R., Killeen, T., Lewis, S. L., et al. (2002). Increasing dominance of large lianas in Amazonian forests. Nature 418, 770–774. doi: 10.1038/nature00926

Phoenix, G. K., and Press, M. C. (2005). Effects of climate change on parasitic plants: the root hemiparasitic Orobanchaceae. Folia Geobotanica 40, 205–216. doi: 10.1007/BF02803235

Polley, H. W., Johnson, H. B., and Tischler, C. R. (2002). Woody invasion of grasslands: evidence that CO 2 enrichment indirectly promotes establishment of Prosopis glandulosa. Plant Ecol. 164, 85–94. doi: 10.1023/A:1021271226866

Prior, S. A., Rogers, H. H., Mullins, G. L., and Runion, G. B. (2003). The effects of elevated atmospheric CO2 and soil P placement on cotton root deployment. Plant Soil 255, 179–187. doi: 10.1023/A:1026143410238

Ramesh, K. (2015). Weed problems, ecology and management options in conservation agriculture: issues and perspectives. Adv. Agron. 131, 251–280. doi: 10.1016/bs.agron.2014.12.003

Ramsey, R. J. L., Stephenson, G. R., and Hall, J. C. (2005). A review of the effects of humidity, humectants, and surfactant composition on the absorption and efficacy of highly water-soluble herbicides. Pesticide Biochem. Physiol. 82, 162–175. doi: 10.1016/j.pestbp.2005.02.005

Reddy, P. P. (2013). Impact of climate change on insect pests, pathogens and nematodes. Pest. Manag. Hortic. Ecosys. 19, 225–233.

Ritter, R. L., and Coble, H. D. (1981). Influence of temperature and relative humidity on the activity of acifluorfen. Weed Sci. 29, 480–485.

Rodenburg, J., Meinke, H., and Johnson, D. E. (2011). Challenges for weed management in African rice systems in a changing climate. J. Agric. Sci. 149, 427–435. doi: 10.1017/S0021859611000207

Rodenburg, J., Riches, C. R., and Kayeke, J. M. (2010). Addressing current and future problems of parasitic weeds in rice. Crop Prot. 29, 210–221. doi: 10.1016/j.cropro.2009.10.015

Rosenzweig, C., and Hillel, D. (1998). Climate Change and the Global Harvest. Oxford: Oxford University Press, 1–324.

Saito, K. (2010). Weed pressure level and the correlation between weed competitiveness and rice yield without weed competition: an analysis of empirical data. Field Crops Res. 117, 1–8. doi: 10.1016/j.fcr.2010.02.009

Sandhu, N., Jain, S., Battan, K. R., and Jain, R. K. (2012). Aerobic rice genotypes displayed greater adaptation to water-limited cultivation and tolerance to polyethyleneglycol-6000 induced stress. Physiol. Mol. Biol. Plants 18, 33–43. doi: 10.1007/s12298-011-0094-2

Schimel, D., Alves, D., Enting, I., Heimann, M., Joos, F., Raynaud, D., et al. (1996). “Radiative forcing of climate change,” in Climate Change 1995: The Science of Climate Change , eds J. T. Houghton, L. G. M. Filho, B. A. Callander, N. Harris, A. Kattenberg, and K. Maskell (Cambridge: Cambridge University Press), 65–131.

Seck, P. A., Diagne, A., Mohanty, S., and Wopereis, M. C. S. (2012). Crops that feed the world 7: rice. Food Sec. 4, 7–24. doi: 10.1007/s12571-012-0168-1

Shaner, D. L. (2000). The impact of glyphosate-tolerant crops on the use of other herbicides and on resistance management. Pest Manage. Sci. 56, 320–326. doi: 10.1002/(SICI)1526-4998(200004)56:4<320::AID-PS125<3.0.CO;2-B

Sharma, S. D., and Singh, M. (2001). Environmental factors affecting absorption and bio-efficacy of glyphosate in Florida beggar weed ( Desmodium tortuosum ). Crop Prot. 20, 511–516. doi: 10.1016/S0261-2194(01)00065-5

Sinha, S. K., and Swaminathan, M. S. (1991). Deforestation, climate change and sustainable nutri tion security: a case study of India. Clim. Change 19, 201–209. doi: 10.1007/BF00142227

Soomro, B. (2004). Paddy and water environment related issues, problems and prospects in Pakistan. Paddy Water Environ. 2, 41–44. doi: 10.1007/s10333-004-0045-4

Springer, C. J., and Ward, J. K. (2007). Flowering time and elevated atmospheric CO2. New Phytol. 176, 243–255. doi: 10.1111/j.1469-8137.2007.02196.x

Stratonovitch, P., Storkey, J., and Semenov, M. A. (2012). A process-based approach to modelling impacts of climate change on the damage niche of an agricultural weed. Glob. Change Biol. 18, 2071–2080. doi: 10.1111/j.1365-2486.2012.02650.x

Treharne, K. (1989). “The implications of the ‘greenhouse effect’ for fertilizers and agrochemicals,” in The Greenhouse Effect and UK Agriculture, CAS Paper 19 , ed. R. C. Bennet (Reading: University. of Reading Press), 67–78.

Tremmel, D. C., and Patterson, D. T. (1993). Responses of soybean and five weeds to CO2 enrichment under two temperature regimes. Can. J. Plant Sci. 73, 1249–1260. doi: 10.4141/cjps93-164

Tubiello, F. N., Soussana, J. F., and Howden, S. M. (2007). Crop and pasture response to climate change. Proc. Natl. Acad. Sci. U.S.A. 104, 19686–19690. doi: 10.1073/pnas.0701728104

Tylianakis, J. M., Didham, R. K., Bascompte, J., and Wardle, D. A. (2008). Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363. doi: 10.1111/j.1461-0248.2008.01250.x

Tyree, M. T., and Alexander, J. D. (1993). Plant water relations and the effects of elevated CO2: a review and suggestions for future research. Vegetation 10, 47–62. doi: 10.1007/BF00048144

Walck, J. L., Hidayati, S. N., Dixon, K. W., Thompson, K., and Poschlod, P. (2011). Climate change and plant regeneration from seed. Glob. Change Biol. 17, 2145–2161. doi: 10.1111/j.1365-2486.2010.02368.x

Wand, S. J., Midgley, G. F., Jones, M. H., and Curtis, P. S. (1999). Response of wild C4 and C3 (Poaceae) species to elevated atmospheric CO2 concentration: a meta analytic test of current theories and perceptions. Glob. Change Biol. 5, 723–741. doi: 10.1046/j.1365-2486.1999.00265.x

Ward, J. K., Tissue, D. T., Thomas, R. B., and Strain, B. R. (1999). Comparative responses of model C3 and C4 plants to drought in low and elevated CO2. Glob. Change Biol. 5, 857–867. doi: 10.1046/j.1365-2486.1999.00270.x

Weber, E., and Gut, D. (2005). A survey of weeds that are increasingly spreading in Europe. Agron. Sustain. Dev. 25, 109–121. doi: 10.1051/agro:2004061

Wolfe, D. W., Ziska, L., Petzoldt, C., Seaman, A., Chase, L., and Hayhoe, K. (2008). Projected change in climate thresholds in the North eastern U.S.: implications for crops, pests, livestock, and farmers. Mitigat. Adapt. Strat. Glob. Change 13, 555–575. doi: 10.1007/s11027-007-9125-2

World Meteorological Organization (1992). International Meteorological Vocabulary , 2nd Edn. Geneva: World Meteorological Organization.

Zhu, C., Zeng, Q., Ziska, L. H., Zhu, J., Xie, Z., and Liu, G. (2008). Effect of nitrogen supply on carbon dioxide induced changes in competition between rice and barnyard grass ( Echinochloa crus-galli ). Weed Sci. 56, 66–71. doi: 10.1614/WS-07-088.1

Ziska, H., Ghannoum, O., Baker, J. T., Conroy, J., Bunce, J. A., Kobayashi, K., et al. (2001). A global perspective of ground level, ‘ambient’ carbon dioxide for assessing the response of plants to atmospheric CO2. Glob. Change Biol. 7, 789–796. doi: 10.1111/j.1365-2486.2001.00436.x

Ziska, L. H. (2000). The impact of elevated CO2 on yield loss from a C3 and C4 weed in field-grown soybean. Glob. Change Biol. 6, 899–905. doi: 10.1046/j.1365-2486.2000.00364.x

Ziska, L. H. (2001). Changes in competitive ability between a C4 crop and a C3 weed with elevated carbon dioxide. Weed Sci. 49, 622–627. doi: 10.1614/0043-17452001049[0622:CICABA]2.0.CO;2

Ziska, L. H. (2004). “Rising carbon dioxide and weed ecology,” in Weed Biology and Management , ed. Inderjit (Dordrecht: Kluwer Publishing), 159–176.

Ziska, L. H. (2008). Rising atmospheric carbon dioxide and plant biology: the overlooked paradigm. DNA Cell Biol. 27, 165–172. doi: 10.1089/dna.2007.0726

Ziska, L. H., Blumenthal, D. M., Runion, G. B., Hunt, E. R. Jr., and Diaz-Soltero, H. (2011). Invasive species and climate change: an agronomic perspective. Clim. Change 105, 13–42. doi: 10.1371/journal.pone.0081510

Ziska, L. H., and Dukes, J. S. (2011). Weed Biology and Climate Change. Ames, IA: Blackwell Publishing Ltd, 68–205.

Ziska, L. H., Faulkner, S. S., and Lydon, J. (2004). Changes in biomass and root : shoot ratio of field-grown Canada thistle ( Cirsium arvense ), a noxious, invasive weed, with elevated CO2: implications for control with glyphosate. Weed Sci. 52, 584–588. doi: 10.1614/WS-03-161R

Ziska, L. H., and George, K. (2004). Rising carbon dioxide and invasive, noxious plants: potential threats and consequences. World Res. Rev. 16, 427–447.

Ziska, L. H., and Goins, E. W. (2006). Elevated atmospheric carbon dioxide and weed populations in glyphosate treated soybean. Crop Sci. 46, 1354–1359. doi: 10.2135/cropsci2005.10-0378

Ziska, L. H., and McClung, A. (2008). Differential response of cultivated and weedy (red) rice to recent and projected increases in atmospheric carbon dioxide. Agron. J. 100, 1259–1263. doi: 10.1371/journal.pone.0037522

Ziska, L. H., and McConnell, L. L. (2015). Climate change, carbon dioxide, and pest biology: monitor, mitigate, management. J. Agric. Food Chem. 64, 6–12. doi: 10.1021/jf506101h

Ziska, L. H., and Teasdale, J. R. (2000). Sustained growth and increased tolerance to glyphosate observed in a C3 perennial weed, quackgrass ( Elytrigia repens ), grown at elevated carbon dioxide. Aust. J. Plant Physiol. 27, 159–164.

Ziska, L. H., Teasdale, J. R., and Bunce, J. A. (1999). Future atmospheric carbon dioxide may increase tolerance to glyphosate. Weed Sci. 47, 608–6015.

Ziska, L. H., Tomecek, M. B., and Gealy, D. R. (2010). Competitive interactions between cultivated and red rice as a function of recent and projected increases in atmospheric carbon dioxide. Agron. J. 102, 118–123. doi: 10.2134/agronj2009.0205

Keywords : weed, climate change, crops, agricultural, management crop, vulnerabilities

Citation: Ramesh K, Matloob A, Aslam F, Florentine SK and Chauhan BS (2017) Weeds in a Changing Climate: Vulnerabilities, Consequences, and Implications for Future Weed Management. Front. Plant Sci. 8:95. doi: 10.3389/fpls.2017.00095

Received: 03 October 2016; Accepted: 17 January 2017; Published: 13 February 2017.

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Copyright © 2017 Ramesh, Matloob, Aslam, Florentine and Chauhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Singarayer K. Florentine, [email protected] Bhagirath S. Chauhan, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 25 March 2021

Drone and sensor technology for sustainable weed management: a review

• Marco Esposito 1   na1 ,
• Mariano Crimaldi 1   na1 ,
• Valerio Cirillo   ORCID: orcid.org/0000-0002-2929-5485 1 ,
• Fabrizio Sarghini 1 &
• Albino Maggio 1  

Chemical and Biological Technologies in Agriculture volume  8 , Article number:  18 ( 2021 ) Cite this article

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Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts.

Introduction

Biotic threats such as insects, weeds, fungi, viruses, and bacteria can broadly affect crop yield and quality. Among these, weeds are the most impacting problem causing remarkable yield loss worldwide [ 1 ]. The most characterized effect of weeds is competition for resources such as light [ 2 ], water [ 3 ], space [ 4 ], and nutrients [ 5 ]. In addition, specific chemical signals and/or toxic molecules produced by weeds may interfere with a normal crop development [ 6 ]. A distinctive trait of wild species, including weeds, is their high physiological, morphological, and anatomical plasticity which makes them more tolerant than crop species to environmental stressors [ 7 , 8 , 9 , 10 ]. Moreover, weeds interact with other biological components of the environment, acting as refuge for plant pests such as insects, fungi, and bacteria that can harm close in crops [ 11 , 12 , 13 ]. For example, wild oats ( Avena fatua L.) can harbor the etiological agents of the powdery mildew in crops such as wheat ( Triticum aestivum L.), oats, and barley ( Hordeum vulgar e L.) [ 14 ]; altamisa ( Parthenium hysterophorus L.) can be a secondary host of the common hairy caterpillar ( Diacrisia obliqua Walk.) [ 15 , 16 ]; Cyperus rotundus can host the root-knot Meloidogyne graminicola and, therefore, can contribute to their spreading in the field [ 17 ]. Finally, weed infestation may affect fresh and processed products quality such as beer, wine, forage [ 18 , 19 ]. In this respect, weed residuals may cause accumulation of off-flavors products [ 20 , 21 ], or in some cases, can make them harmful to humans and animals [ 22 , 23 ]. Weeds may also contain high levels of allergens and/or toxic metabolites that, if ingested, can cause asthma, skin rash, and other reactions [ 24 , 25 ].

Most weed research aims at developing strategies that can reduce the deleterious impact of the interspecific competition between crops and weeds and recent technological advances may further contribute to this scope, while improving the sustainability of weed control [ 26 , 27 , 28 ]. Worldwide, weed competition causes severe yield reduction in all major crops, such as wheat (23%), soybean (37%), rice (37%), maize (40%), cotton (36%), and potato (30%) [ 1 ]. Yearly, weeds cause 50% yield losses of corn and soybean productivity in North America. For corn, this equates to a loss of 148 million tons for an economical loss of over $26.7 billion [ 29 ]. In Australia, yield loss due to weeds accounts for 2.76 million tons of grain from different plants, including wheat, barley, oats, canola, sorghum, and pulses [ 30 ]. The annual global economic loss caused by weeds has been estimated to be more than $100 billion U.S. dollars [ 31 ], despite worldwide annual herbicide sales in the range of $25 billion [ 32 ]. In Europe, herbicides are the second most-sold pesticides. They accounted for 35% of all pesticide sales in 2018, overcoming insecticides and acaricides (Fig.  1 ) [ 33 ].

Percentage (of total volume in kilograms) of pesticide sales by category in Europe in 2018 [ 33 ]

Weed management requires an integrated approach

In 2050, the world population will quadruplicate, reaching 9.15 billion people [ 34 ]. However, the predicted increase in food demand will be hardly met by the current production system [ 35 ]. Also, climate change will be an additional challenge for the human food supply in the near future [ 36 ]. Among all the processes affecting crop productivity, weed management will be one of the hardest challenges [ 37 ]. Mechanical and chemical weed control has disadvantages that probably will impede them to be effective for future weed management [ 38 , 39 , 40 , 41 , 42 , 43 ]. Mechanical methods are scarcely efficient, and herbicides have a high ecological impact. An approach that minimizes the drawbacks of mechanical and chemical weed control is Integrated Weed Management (IWM). IWM combines chemical, biological, mechanical, and/or crop management methods, and represents a model to improve the efficiency and sustainability of weed control [ 3 , 44 ]. In contrast to traditional methods, IWM integrates several agro-ecological aspects such as the role of conservation tillage and crop rotation on weeds seed bank dynamics [ 10 ], the ability to forecast the critical period of weed interference and their competition with crops [ 45 , 46 ], and the specific critical levels of crops/weeds interaction [ 47 ]. Therefore, an effective IWM must rely on a thorough knowledge of crop-weeds competition dynamics, which currently represents one of the most active research areas in weed science [ 48 , 49 ].

New technologies for site-specific weed management

Precision agriculture relies on technologies that combine sensors, information systems, and informed management to optimize crop productivity and to reduce the environmental impact [ 50 ]. Nowadays, precision agriculture has a broad range of applications and it is employed in different agricultural contexts including pests control [ 51 ], fertilization, irrigation [ 52 , 53 ], sowing [ 54 ] and harvesting [ 55 ]. Precision agriculture can be effectively applied to IWM also. In the last decade, precision agriculture has rapidly advanced because of technological innovations in the areas of sensors [ 56 ], computer hardware [ 57 ], nanotechnology [ 58 ], unmanned vehicles systems and robots [ 59 ] that may allow for specific identification of weeds that are present in the field [ 47 ]. Unmanned aerial vehicles (UAV) are one of the most successful technologies applied in precision agriculture [ 60 ]. Unmanned Vehicles systems are mobile Aerial (UAV) or Terrestrial (UTV) platforms that provide numerous advantages for the execution and monitoring of farming activities [ 61 ]. UAVs can be highly valuable since they allow for Site-Specific Weed Management (SSWM) (Fig.  2 ). SSWM is an improved weed management approach for highly efficient and environmentally safe control of weed populations [ 28 ], enabling precise and continuous monitoring and mapping of weed infestation. SSWM consents to optimize weed treatments for each specific agronomical situation [ 62 ]. The combination of UAVs with advanced cameras and sensors, able to discern specific weeds [ 63 ], and GPS technologies, that provide geographical information for field mapping, can help in precisely monitoring large areas in a few minutes. Thanks to more accurate planning of weed management that can increase mechanical methods effectiveness and/or reduce herbicide spread [ 64 ], the potential agro-ecological and economic implications of SSWM are remarkable, yielding lower production costs, reducing the onset of weed resistance, improving biodiversity, and containing environmental impacts [ 65 ]. The application of UAVs to weed control can, therefore, contribute to improve the sustainability of future agricultural production systems that must comply with an increasing world population [ 34 , 35 ].

Site-specific weed management (SSWM) scheme realized by drones and its economical and agro-ecological implications

UAVs remote sensing techniques and sensors

UAVs have become a common tool in precision agriculture [ 66 , 67 ]. Thanks to their affordability, user-friendliness and versatility, UAVs are often the primary choice for fast and precise in situ remote sensing or survey operations. Despite their versatility, these systems may be used for different purposes, depending on the sensors they carry on. Ongoing research is looking at the best solutions to integrate data collected from sensors on UAVs, ground sensors and other data sources for better management of punctual operations in the field, with a particular focus on smart agriculture and big data management [ 68 , 69 ].

Although UAVs systems do not offer the same territorial coverage as satellites, they offer a spatial and temporal resolution that other systems do not [ 70 , 71 ]. From an economic point of view, the use of drones requires the investment to buy a UAV system with at least a 0.1 cm /px resolution RGB camera, a trained pilot for flight management and post-processing software capabilities. The initial UAV investment is compensated by the repeatability of flights, which increases the frequency of datasets delivered, and the higher resolution compared to other systems [ 72 , 73 ]. UAVs systems also have further advantages: (1) the possibility to collect easily deployable data in real time (excluding post-processing); (2) they can be used to survey areas with high level of hazard and/or difficult to reach; (3) they allow operators to collect data even with unfavorable weather conditions, such as in very cloudy or foggy days, under which satellite detection systems fail or produce very altered datasets [ 71 ]. The most important sensors available as payload are mainly categorized into three classes depending on the spectral length and number they can record:

RGB (Red, Green, Blue) or VIS (Visible) sensors

Multispectral sensors

Hyperspectral sensors, rgb/vis sensors.

The RGB or VIS sensors are the most common and largely available commercial cameras (Table 1 ). Their possible applications have been the focus of most research for years due to their potential and low-cost operational requirements [ 74 , 75 ].

These sensors are used to calculate vegetation indices such as the Green/Red Vegetation Index (GRVI), Greenness Index (GI) and Excessive Greenness (ExG) with acceptable or high levels of accuracy [ 76 , 77 ]. Also, RGB sensors have been increasingly used for machine learning techniques in object recognition, phenology, pathologies, and similar purposes. The typical workflow of processing RGB images from UAVs for remote sensing is: 1. pre-flight planning, 2. flight and image acquisition, 3. post-processing and indexes or dataset extrapolation [ 71 ]. Phase 1 is critical and essential to collect data of useful quality for the purpose. In the pre-flight planning phase, the parameters to consider are the definition of the study area, the flight altitude, site topography, weather forecast and local regulations for unmanned flights. In phase 2, it is recommended to keep the data flow sufficient to store data and to check if the acquisition platform can acquire the amount of data required. It could be possible to encounter I/O errors due to the inadequacy of the platform with consequent loss of information or abortion of the mission. In phase 3, for RGB sensors, there is no need to perform radiometric calibration, which is the case when using multispectral and hyperspectral sensors. RGB data can be used per se or to create a georeferenced orthomosaic. In this case, the individual images are rectified, georeferenced using GPS data and stitched together to form a single image (orthomosaic) covering the entire study area. Orthomosaics can be generated either with RGB values as they are or after calculating the desired vegetation indices [ 77 ]. If RGB images are to be used in machine learning algorithms, the workflow is different [ 78 , 79 , 80 , 81 ]. In this case, it is necessary to collect a large dataset of images for the training and testing of the algorithm [ 82 ]. This dataset may already be available from third-party sources, such as PlantVillage [ 83 ] or PlantDoc [ 84 ]. Alternatively, it can be created from scratch if the purpose of the research is not covered by existing datasets [ 85 ]. In this case, the acquisition, selection and processing of the images are critical, because the final dataset can affect both the training and the use of the neural network, with risks of producing biased results [ 86 ].

The multispectral sensors are used for a wider range of calculations of vegetation indices as they can rely on a higher number of radiometric bands. A comparison of the most common multispectral sensors, specific for UAV systems, is shown in Table 2 .

With multispectral sensors, the range of vegetation indices that can be monitored is considerably extended compared to those that can be calculated with only three RGB bands. Moreover, the workflow has minor variations. For these sensors, in phase 1, the radiometric calibration and atmospheric correction phases are strictly required. Many multispectral sensors, such as the Micasense RedEdge series or the Parrot Sequoia  + , have downwelling irradiance sensors and a calibrated reflectance panel to address some of the requirements for radiometric calibration [ 87 ]. Due to a lower resolution of the sensors compared to RGB ones, a lower flight altitude and an adequate horizontal and vertical overlap of recorded images must be taken into account to obtain an adequate ground resolution for the surveyed objective and to avoid missing data [ 88 ]. In phase 2, having a higher number of radiometric bands to record, the dataflow will be higher so is critical to avoid I/O errors, missing data or mission failures [ 89 ]. Due to multi-lenses nature of the sensors in phase 3, the data collected suffer from the parallax problem. As a consequence, images have to be rectified, georeferenced and must be stacked to generate a single image with different radiometric levels, and calibrated with the downwelling irradiance sensors data acquired during the flight [ 90 ]. After this procedure, it is possible to generate a multispectral orthomosaic and then calculate the requested indexes [ 91 ]. Multispectral images are also used in machine learning applications [ 80 , 85 , 92 ] taking into account the multi-camera nature of sensors and the different bands recorded. Thanks to the availability of a higher number of radiometric bands, the machine learning algorithms can be extended to not-visible recognition such as early stage plant disease, field quality assessment, soil water content, and more [ 91 ].

The hyperspectral sensors can record hundreds to thousands of narrow radiometric bands, usually in visible and infrared ranges. To deal with hyperspectral applications, the choice of number and radiometric range of bands is critical. Each band or combination of bands, being very narrow, can detect a specific field characteristic. Each hyperspectral sensor can detect only a certain number of bands, so the aim of survey must be very clear to choose the right sensor. Although hyperspectral sensors have decreased in price in recent years, they are still an important starting investment since they are much more expensive than RGB and multispectral sensors. In addition, they are heavier and bigger than other sensors, often making their use on UAV systems difficult and/or excessively onerous in terms of payload. Some of most used hyperspectral sensors in UAVs application and their main characteristics are shown in Table 3 .

In this case, the workflow for radiometric calibration is more complex compared to other sensors. Some calibration methods needed for these sensors are derived from manned aircraft hyperspectral platforms, based on artificial targets to assess data quality, to correct radiance, and to generate a high-quality reflectance data-cube [ 93 ]. In phase 1, the planning must also be carried out in time and not only in space because, in addition to the spectrometric resolution, hyperspectral sensors have a temporal resolution due to the different acquisition method. In phase 2, it should be considered that both images’ size and data flow are bigger than multispectral/RGB images. Moreover, these sensors may acquire a large amount of data, but the payload limitations of UAVs may not allow the transport of adequate file storage systems. Phase 3 for hyperspectral images is critical: quality assessment is one of the critical issues of hyperspectral data and some problems associated with the quality of the images have not been completely overcome. Among those, the stability of the sensor itself (due to the nature of UAV platforms) and the vibrations involved can comprise a good calibration of the sensor. Subsequently, on post-processed data, it is possible to calculate narrowband indices such as chlorophyll absorption ratio index (CARI), greenness index (GI), greenness vegetation index (GVI), modified chlorophyll absorption ratio index (MCARI), modified normalized difference vegetation index (MNDVI), simple ratio (SR), transformed chlorophyll absorption ratio index (TCARI), triangular vegetation index (TVI), modified vegetation stress ratio (MVSR), modified soil-adjusted vegetation index (MSAVI) and photochemical reflectance index (PRI) [ 94 ].

Applications of UAVs to weed management

UAVs are ideal to identify weed patches. The main advantages of UAVs compared to UTVs are the shorter monitoring/surveying time they require and optimal control in the presence of obstacles, which is critical when working between crop rows [ 95 ]. In a few minutes, UAVs can cover many hectares flying over the field, thus providing the photographic material for weed patches identification [ 61 ]. These images are processed via deep neural network [ 78 ], convolutional neural network, and object-based image analysis [ 96 , 97 ]. Based on a systematic review of the literature concerning weed identification by UAVs, it can be concluded that mainly three types of cameras are used for weed patches identification: RGB, multispectral and hyperspectral cameras (Table 4 ). These cameras are very similar in terms of information obtained for the purpose of weeds identification. Indeed, the three camera types can recognize weed patches with good accuracy depending on flying altitude, camera resolution and UAV used. UAVs have been mainly tested on important crops such as Triticum spp. , Hordeum vulgare , Beta vulgaris , Zea mays [ 98 , 99 , 100 , 101 ] . These are among the most cultivated crops worldwide and are highly susceptible to weed competition especially in early phenological stages. In these crops, it was possible to identify several dicotyledonous weeds including Amaranthus palmeri , Chenopodium album and Cirsium arvense [ 102 , 103 , 104 ], as well as different monocotyledonous such as Phalaris spp., Avena spp. and Lolium spp. [ 105 , 106 ]. These weed species are widespread globally and can be a serious threat to different crops [ 107 , 108 ]. Therefore, the combined use of UAVs and image processing technologies may contribute to effectively control different weed species interfering with the crops with relevant environmental benefits [ 28 , 109 ].

The use of UAVs and machine learning techniques allow for the identification of weed patches in a cultivated field with accuracy and can improve weed management sustainability [ 97 ]. Weed patches identification by UAVs can facilitate integrated weed management (IWM), reducing both the selection pressure vs herbicide-resistant weeds and herbicides diffusion in the environment [ 64 ]. Recent research has shown that new technologies are able to discern single weed species in open fields [ 63 , 106 , 126 ]. If integrated with weed management planning, this information gathered via remote imaging analysis can contribute to sustainably improve weed management. In addition, imaging analysis can help in the study of weed dynamics in the field, as well as their interaction with the crop, which both represent a necessary step to define new strategies for weed management based on interspecific crop–weed interactions [ 127 , 128 , 129 ]. Recent studies demonstrate that some weed communities are actually not detrimental to crop yield and quality [ 127 , 128 ]. In winter wheat cultivation, a highly diversified weed community caused lower yield losses than a less diversified one [ 129 ]. In soybean, through a combination of field experiments in which weed species were manipulated in composition and abundance, it has been shown that increasing levels of weed competition resulted in an increase in seed protein content without impairing yield [ 130 ].

Most likely, the integration of known and emerging technologies in this field will greatly improve the sustainability of weed control, following the SSWM approach. By image analysis, different machine learning techniques will be able to provide a reliable overview of the level and type of infestation. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot (AWR), via herbicide spray or mechanical means [ 131 ]. Also, the creation of a specific weed images dataset is crucial to achieve this goal. This approach must necessarily rely on a dataset of photographs taken in dedicated experimental fields, labeled in extended COCO/POCO (Common Objects in COntext/ Plant Objects in COntext) format [ 86 ] and integrated with images from PlantVillage dataset [ 83 ] or other existing ones.

New insights on weed population dynamics and their competition with crops are needed in order to extend this approach to real agricultural contexts, so as to specifically recognize and eliminate only harmful weed species. The overall objective is to overcome the consequences of biological vacuum around the crop, which has been proved to be highly impacting for both biotic and the abiotic components of the environment [ 132 , 133 ], with long-term consequences on human safety on earth.

Availability of data and materials

Not applicable.

Abbreviations

Integrated Weed Management

Unmanned Aerial Vehicles

Unmanned Terrestrial Vehicles

Specific Weed Management

Red Green Blue

Green/Red Vegetation Index

Greenness Index

Excessive Greenness

Global Positioning System

Chlorophyll Absorption Ratio Index

Modified Chlorophyll Absorption Ratio Index

Modified Normalized Difference Vegetation Index

Simple Ratio

Transformed Chlorophyll Absorption Ratio Index

Triangular Vegetation Index

Modified Vegetation Stress Ratio

Modified Soil-Adjusted Vegetation Index

Photochemical Reflectance Index

Autonomous Weeding Robot

Common Objects in COntext/ Plant Objects in COntext

Oerke EC. Crop losses to pests. Sci: J Agric; 2006.

Book   Google Scholar  

Guglielmini AC, Verdú AMC, Satorre EH. Competitive ability of five common weed species in competition with soybean. Int J Pest Manag. 2017;63:30–6.

Article   Google Scholar  

Kaur H, Brar SG, Shete PP. A REVIEW on different weed management approaches. Int J Curr Microbiol Appl Sci. 2019; 8: 2854.

Korav S, Ram V, Ray LIP, Krishnappa R, Singh NJ, Premaradhya N. Weed pressure on growth and yield of groundnut ( Arachis hypogaea L.) in Meghalaya, India. Int J Curr Microbiol Appl Sci. 2018;7:2852–8.

Article   CAS   Google Scholar  

Thompson CR, Dille JA, Peterson DE. Weed Competition and Management in Sorghum. In Sorghum. Ciampitti IA, Vara Prasad P, eds. 2019.

Zohaib A, Abbas T, Tabassum T. Weeds cause losses in field crops through allelopathy. Not Sci Biol. 2016;8:47–56.

Cirillo V, Masin R, Maggio A, Zanin G. Crop-weed interactions in saline environments. Eur J Agron. 2018;99:51–61.

Hauvermale AL, Sanad MNME. Phenological plasticity of wild and cultivated plants. Plant communities and their environment. London: IntechOpen; 2019.

Google Scholar  

Guo L, Qiu J, Li L-F, Lu B, Olsen K, Fan L. Genomic clues for crop-weed interactions and evolution. Trends Plant Sci. 2018;23:1102–15.

Article   CAS   PubMed   Google Scholar  

Kumar A, Choudhary T, Das S, Meena SK. Weed Seed Bank: impacts and management for future crop production BT—agronomic crops, Vol 2. Management Practices. Hasanuzzaman M, editor. 2019. p. 207–23.

Smith JD, Dubois T, Mallogo R, Njau E-F, Tua S, Srinivasan R. Host range of the invasive tomato pest tuta absoluta meyrick (Lepidoptera: Gelechiidae) on solanaceous crops and weeds in Tanzania. Fla Entomol. 2019;101:573.

Roshan P, Kulshreshtha A, Hallan V. Global weed-infecting geminiviruses BT-geminiviruses: impact, challenges and approaches. Berlin: Springer; 2019. p. 103–21.

Srinivasan R, Cervantes FA, Alvarez JM. Aphid-borne virus dynamics in the potato-weed pathosystem. Insect Pests Potato. 2013.

Eshed N, Wahl I. Role of wild grasses in epidemics of powdery mildew. Phytopathology. 1975;65:57–63.

Adkins S, Shabbir A. Biology, ecology and management of the invasive parthenium weed ( Parthenium hysterophorus L.). Pest Manag Sci. 2014;70:1023–9.

Tanveer A, Khaliq A. Mahajan G. Interference and management of parthenium: The world ’ s most important invasive weed. J Crop Prot; 2015. p. 68.

Khan MR, Somvanshi VS, Rao U. Emerging nematode pest of rice, wheat and onion. Rice Root-Knot Nematode Popular Kheti. 2017;3:53–5.

Ersin A, Gargin S, Esitken A, Guzel NP, Atay AN, Altindal M, et al. The effect of weed competition on apple fruit quality. Not Bot Horti Agrobot Cluj-Napoca. 2017;45:120–5.

Yadav T, Chopra NK, Chopra NK, Kumar R, Soni PG. Assessment of critical period of crop-weed competition in forage cowpea ( Vigna unguiculata ) and its effect on seed yield and quality. Indian J Agron. 2018;63:124–7.

Hall CW. Vacuum treatment of milk. in: vacuum technology transactions. Pergamon; 2013.

Sabra FS, Mahmoud MS. Utilization of herbicidal treatments to overcome weed problems in utilization of herbicidal treatments to overcome weed problems in peppermint ( Mentha piperita L.) cultivation under Egyptian conditions. Asian J Agric Food Sci. 2015;03:1–7.

Mantle P. Comparative ergot alkaloid elaboration by selected plectenchymatic mycelia of Claviceps purpurea through sequential cycles of axenic culture and plant parasitism. Biology. 2020;9:41.

Article   CAS   PubMed Central   Google Scholar  

Kumar R, Katiyar R, Kumar S, Kumar T, Singh V. Lantana camara : an alien weed, its impact on animal health and strategies to control. J Exp Biol Agric Sci. 2016;4:321–37.

Denisow-Pietrzyk M, Pietrzyk Ł, Denisow B. Asteraceae species as potential environmental factors of allergy. Environ Sci Pollut Res. 2019;26:6290–300.

Allan S, Shi B, Adkins SW. Impact of parthenium weed on human and animal health. Parthenium Weed Biol Ecol Manag. 2018;7:105.

Korres NE, Burgos NR, Travlos I, Vurro M, Gitsopoulos TK, Varanasi VK, et al. New directions for integrated weed management: modern technologies, tools and knowledge discovery. Adv Agron. 2019;155:243–319.

Partel V, Charan Kakarla S, Ampatzidis Y. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput Electron Agric. 2019;157:339–50.

Hunter JE, Gannon TW, Richardson RJ, Yelverton FH, Leon RG. Integration of remote-weed mapping and an autonomous spraying unmanned aerial vehicle for site-specific weed management. Pest Manag Sci. 2020;76:1386–92.

Soltani N, Dille JA, Burke IC, Everman WJ, VanGessel MJ, Davis VM, et al. Potential Corn yield losses from weeds in North America. Weed Technol. 2016;30:979–84.

Llewellyn R, Ronning D, Ouzman J, Walker S, Mayfield A, Clarke M. Impact of Weeds on Australian Grain Production: the cost of weeds to Australian grain growers and the adoption of weed management and tillage practices Report for GRDC. CSIRO. 2016.

Appleby AP, Müller F, Carpy S. Weed control. In Ullmann's Encyclopedia of Industrial Chemistry, (Ed.). https://doi.org/10.1002/14356007.a28_165

Swanton CJ, Nkoa R, Blackshaw RE. Experimental methods for crop-weed competition studies. Weed Sci. 2015;63:2–11.

EUSROSTAT. Statistics Eurostat. https://ec.europa.eu/eurostat/databrowser/view/aei_fm_salpest09/default/table?lang=en .

Alexandratos N, Bruinsma J. World agriculture towards 2030/2050: the 2012 revision. 2012.

Ray DK, Mueller ND, West PC, Foley JA. Yield trends are insufficient to double global crop production by 2050. PLoS ONE. 2013;8:e66428.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Mall RK, Gupta A, Sonkar G. 2—effect of climate change on agricultural crops. In: Dubey SK, Pandey A, Sangwan RSBT-CD in B and B, editors. Elsevier. 2017. p. 23–46.

Westwood JH, Charudattan R, Duke SO, Fennimore SA, Marrone P, Slaughter DC, et al. Weed management in 2050: perspectives on the future of weed science. Weed Sci. 2018;66:275–85.

Dong W, Liu E, Yan C, Tian J, Zhang H, Zhang Y. Impact of no tillage vs. conventional tillage on the soil bacterial community structure in a winter wheat cropping succession in northern China. Eur J Soil Biol. 2017;80:35–42.

Duke S, Heap I. Evolution of weed resistance to herbicides: what have we learned after 70 years. Biol Physiol Mol Biol Weeds. 2017;63–86.

Hicks HL, Comont D, Coutts SR, Crook L, Hull R, Norris K, et al. The factors driving evolved herbicide resistance at a national scale. Nat Ecol Evol. 2018;2:529–36.

Article   PubMed   Google Scholar  

Büchi L, Wendling M, Amossé C, Necpalova M, Charles R. Importance of cover crops in alleviating negative effects of reduced soil tillage and promoting soil fertility in a winter wheat cropping system. Agric Ecosyst Environ. 2018;256:92–104.

Jackson L, Calderon F, Steenwerth K, Scow K, Rolston D. Responses of soil microbial processes and community structure to tillage events and implications for soil quality. Geoderma. 2003;114:305–17.

Muola A, Fuchs B, Laihonen M, Rainio K, Heikkonen L, Ruuskanen S, et al. Risk in the circular food economy: glyphosate-based herbicide residues in manure fertilizers decrease crop yield. Sci Total Environ. 2020;750:141422.

Article   PubMed   CAS   Google Scholar  

Riemens MM, Van Der Weide RY, Bleeker PO, Lotz LAP. Effect of stale seedbed preparations and subsequent weed control in lettuce (cv. Iceboll) on weed densities. Weed Res. 2007;47:149–56.

Bajwa AA, Walsh M, Chauhan BS. Weed management using crop competition in Australia. Crop Prot. 2017;95:8–13.

Swanton CJ, Weise SF. Integrated weed management: the rationale and approach. Weed Technol. 1991;5:657–63.

Young SL, Pitla SK, Van Evert FK, Schueller JK, Pierce FJ. Moving integrated weed management from low level to a truly integrated and highly specific weed management system using advanced technologies. Weed Res. 2017;57:1–5.

Bonny S. Genetically modified herbicide-tolerant crops, weeds, and herbicides: overview and impact. Environ Manage. 2016;57:31–48.

Rose MT, Cavagnaro TR, Scanlan CA, Rose TJ, Vancov T, Kimber S, et al. Impact of Herbicides on Soil Biology and Function. In: Sparks DL, editor. Adv Agro. Academic Press. 2016; pp. 133–220.

Shannon D, Clay DE, Sudduth KA. An introduction to precision agriculture. Precis Agric Basics. 2018.

Mahlein AK. Plant disease detection by imaging sensors—parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 2016;100:241–51.

Nawar S, Corstanje R, Halcro G, Mulla D, Mouazen AM. Delineation of soil management zones for variable-rate fertilization: a review. Adv Agron. 2017;143:175–245.

Lama GFC, Errico A, Francalanci S, Solari L, Preti F, Chirico GB. Evaluation of flow resistance models based on field experiments in a partly vegetated reclamation channel. Geosciences. 2020;10:47.

Gautam PV, Kushwaha HL, Kumar A, Kumar D. Mechatronics application in precision sowing: a review. Int J Curr Microbiol Appl Sci. 2019;8:1793–807.

Chlingaryan A, Sukkarieh S, Whelan B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput Electronics Agric. 2018;151:61–9.

Murray SC. Optical sensors advancing precision in agricultural production. Photonics Spectra. 2017.

Dyshekov AI, Smirnov IG, Mirzaev MA, Shereuzhev MA. Principles of functioning of the autonomous device for weed control for precision agriculture. IOP Conf Ser Mater Sci Eng. 2020.

Duhan JS, Kumar R, Kumar N, Kaur P, Nehra K, Duhan S. Nanotechnology: the new perspective in precision agriculture. Biotechnol Rep. 2017;15:11–23.

Mogili UR, Deepak BBVL. Review on application of drone systems in precision agriculture. Procedia Comput Sci. 2018;133:502–9.

Raj R, Kar S, Nandan R, Jagarlapudi A. precision agriculture and unmanned aerial vehicles (UAVs) BT unmanned aerial vehicle: applications in agriculture and environment. In: Avtar R, Watanabe T, editors. Cham: Springer International Publishing; 2020.

Krishna KR. Agricultural drones: a peaceful pursuit. Taylor Francis. 2018.

John KN, Valentin V, Abdullah B, Bayat M, Kargar MH, Zargar M. Weed mapping technologies in discerning and managing weed infestation levels of farming systems. Res Crops. 2020.

Lottes P, Khanna R, Pfeifer J, Siegwart R, Stachniss C. UAV-based crop and weed classification for smart farming. Proc IEEE Int Conf Robot Autom. 2017.

López-Granados F, Torres-Sánchez J, Serrano-Pérez A, de Castro AI, Mesas-Carrascosa Fco-J, Peña J-M. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precis Agric. 2016;17:183–99.

Jiménez-Brenes FM, López-Granados F, Torres-Sánchez J, Peña JM, Ramírez P, Castillejo-González IL, et al. Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management. PLoS ONE. 2019.

Daponte P, Vito LD, Glielmo L, Iannelli L, Liuzza D, Picariello F, et al. A review on the use of drones for precision agriculture. IOP Conf Ser Earth Environ Sci. 2019;275:012022.

Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios I. A compilation of UAV applications for precision agriculture. Comput Netw. 2020;172:107148.

Mancini A, Frontoni E, Zingaretti P. Satellite and UAV data for Precision Agriculture Applications. In: 2019 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE. 2019.

Brook A, De Micco V, Battipaglia G, Erbaggio A, Ludeno G, Catapano I, et al. A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: proof of concept on Aglianico vineyard. Remote Sens Environ. 2020;240:111679.

Watts AC, Ambrosia VG, Hinkley EA. Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens. 2012;4:1671–92.

Manfreda S, McCabe ME, Miller PE, Lucas R, Madrigal VP, Mallinis G, et al. On the use of unmanned aerial systems for environmental monitoring. Remote Sens. 2018;10:641.

Anderson K, Gaston KJ. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ. 2013;11:138–46.

Whitehead K, Hugenholtz CH. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges. J Unmanned Veh Syst. 2014;02:69–85.

Torres-Sánchez J, Peña JM, de Castro AI, López-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput Electron Agric. 2014;103:104–13.

Jannoura R, Brinkmann K, Uteau D, Bruns C, Joergensen RG. Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosyst Eng. 2015;129:341–51.

Motohka T, Nasahara KN, Oguma H, Tsuchida S. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens. 2010;2:2369–87.

Xue J, Su B. Significant remote sensing vegetation indices: a review of developments and applications. J Sens. 2017.

Crimaldi M, Cristiano V, De Vivo A, Isernia M, Ivanov P, Sarghini F. Neural network algorithms for real time plant diseases detection using UAVs. In: Coppola A, Di Renzo GC, Altieri G, D’Antonio P, editors. Innov Biosyst Eng Sustain Agric For Food Prod. 2020. p. 827–35.

Pflanz M, Schirrmann M, Nordmeyer H. Drone based weed monitoring with an image feature classifier. Julius-Kühn-Archiv. 2018;458:379–84.

Etienne A, Saraswat D. Machine learning approaches to automate weed detection by UAV based sensors. In: Autonomous air and ground sensing systems for agricultural optimization and phenotyping IV. International Society for Optics and Photonics, 2019. p. 110080R.

Wang S, Han Y, Chen J, Pan Y, Cao Y, Meng H, et al. A deep-learning- based low-altitude remote sensing algorithm for weed classification in ecological irrigation area. In: International Conference on Intelligent Technologies and Applications. Springer, Singapore, 2018. p. 451–460.

Bah MD, Hafiane A, Canals R. Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote Sens. 2018;10:1690.

Hughes DP, Salathe M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv151108060 Cs. 2016. http://arxiv.org/abs/1511.08060

Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 2020. p. 249–253.

Sa I, Popović M, Khanna R, Chen Z, Lottes P, Liebisch F, et al. WeedMap: a large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sens. 2018;10:1–25.

Wspanialy P, Brooks J, Moussa M. An image labeling tool and agricultural dataset for deep learning. ArXiv200403351 Cs Eess. 2020. http://arxiv.org/abs/2004.03351

Micasense Inc. Automatic Calibration Panel Detection (QR Mode). MicaSense Knowl. Base. 2020. https://support.micasense.com/hc/en-us/articles/360018618774

Micasense Inc. Best practices: Collecting Data with MicaSense Sensors. MicaSense Knowl. Base. 2020. https://support.micasense.com/hc/en-us/articles/224893167 .

Laliberte AS, Goforth MA, Steele CM, Rango A. Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments. Remote Sens. 2011;3:2529–51.

Barrero O, Perdomo SA. RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Agric: Precis; 2018.

Candiago S, Remondino F, De Giglio M, Dubbini M, Gattelli M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 2015;7:4026.

Mazzia V, Comba L, Khaliq A, Chiaberge M, Gay P. UAV and machine learning based refinement of a satellite-driven vegetation index for precision agriculture. Sensors. 2020;20:2530.

Article   PubMed Central   Google Scholar  

Brook A, Ben-Dor E. Supervised Vicarious Calibration (SVC) of multi-source hyperspectral remote-sensing data. Remote Sens. 2015;7:6196–223.

Adao T, Hruska J, Padua L, Bessa J, Peres E, Morais R, et al. Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 2017;9:1110.

Hassanein M, El-Sheimy N. An efficient weed detection procedure using low-cost UAV imagery system for precision agriculture applications. Int Arch Photogramm Remote Sens Spat Inf Sci ISPRS Arch. 2018;42:181–7.

Tsouros DC, Bibi S, Sarigiannidis PG. A review on UAV-based applications for precision agriculture. Inf Switz. 2019;10:349.

Maes WH, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 2019;24:152–64.

Fawakherji M, Potena C, Bloisi DD, Imperoli M, Pretto A, Nardi D. UAV Image based crop and weed distribution estimation on embedded GPU boards BT computer analysis of images and patterns. Berlin: Springer; 2019.

Rasmussen J, Nielsen J, Streibig JC, Jensen JE, Pedersen KS, Olsen SI. Pre-harvest weed mapping of Cirsium arvense in wheat and barley with off-the-shelf UAVs. Precis Agric. 2019;20:983–99.

Franco C, Guada C, Rodríguez JT, Nielsen J, Rasmussen J, Gómez D, et al. Automatic detection of thistle-weeds in cereal crops from aerial RGB images BT information processing and management of uncertainty in knowledge-based systems. Applications. 2018. p. 441–52.

Louargant M, Jones G, Faroux R, Paoli JN, Maillot T, Gée C, et al. Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information. Remote Sens. 2018;10:1–18.

Huang Y, Reddy KN, Fletcher RS, Pennington D. UAV low-altitude remote sensing for precision weed management. Weed Technol. 2018;32:2–6.

Hansen KD, Garcia-Ruiz F, Kazmi W, Bisgaard M, La Cour-Harbo A, Rasmussen J, et al. An autonomous robotic system for mapping weeds in fields. IFAC Proc Vol IFAC-Pap. 2013;8:217–24.

Louargant M, Villette S, Jones G, Vigneau N, Paoli JN, Gée C. Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images. Precis Agric. 2017;1–20.

López-Granados F, Jurado-Expósito M, Peña-Barragán JM, García-Torres L. Using remote sensing for identification of late-season grass weed patches in wheat. Weed Sci. 2006;54:346–53.

Sanders JT, Everman WJ, Austin R, Roberson GT, Richardson RJ. Weed species differentiation using spectral reflectance land image classification. ProcSPIE. 2019.

Davis S, Mangold J, Menalled F, Orloff N, Miller Z, Lehnhoff E. A Meta-analysis of Canada Thistle ( Cirsium arvense ) management. Weed Sci. 2018;66:548–57.

Alshallash KS. Germination of weed species ( Avena fatua , Bromus catharticus , Chenopodium album and Phalaris minor ) with implications for their dispersal and control. Ann Agric Sci. 2018;63:91–7.

De Castro AI, Torres-Sánchez J, Peña JM, Jiménez-Brenes FM, Csillik O, López-Granados F. An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens. 2018;10:285.

Reddy KN, Huang Y, Lee MA, Nandula VK, Fletcher RS, Thomson SJ, et al. Glyphosate-resistant and glyphosate-susceptible Palmer amaranth ( Amaranthus palmeri S. Wats.): hyperspectral reflectance properties of plants and potential for classification. Pest Manag Sci. 2014;70:1910–7.

Lass LW, Prather TS, Glenn NF, Weber KT, Mundt JT, Pettingill J. A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed ( Centaurea maculosa ) and babysbreath ( Gypsophila paniculata ) with a hyperspectral sensor. Weed Sci. 2005;53:242–51.

Tiwari O, Goyal V, Kumar P, Vij S. An experimental set up for utilizing convolutional neural network in automated weed detection. Proc 2019 4th Int Conf Internet Things Smart Innov Usages IoT-SIU 2019. 2019;1–6.

Tay JYL, Erfmeier A, Kalwij JM. Reaching new heights: can drones replace current methods to study plant population dynamics? Plant Ecol. 2018;219:1139–50.

Sandino J, Gonzalez F. A Novel Approach for invasive weeds and vegetation surveys using UAS and artificial intelligence. 2018 23rd Int Conf Methods Models Autom Robo. 2018.

Scherrer B, Sheppard J, Jha P, Shaw JA. Hyperspectral imaging and neural networks to classify herbicide-resistant weeds. J Appl Remote Sens. 2019;13:1.

Svensgaard J, Jensen SM, Westergaard JC, Nielsen J, Christensen S, Rasmussen J. Can reproducible comparisons of cereal genotypes be generated in field experiments based on UAV imagery using RGB cameras? Eur J Agron. 2019;106:49–57.

Garcia-Ruiz FJ, Wulfsohn D, Rasmussen J. Sugar beet ( Beta vulgaris L.) and thistle ( Cirsium arvensis L.) discrimination based on field spectral data. Biosyst Eng. 2015;139:1–15.

Shapira U, Herrmann I, Karnieli A, Bonfil JD. Weeds detection by ground-level hyperspectral data. ISPRS Archive Vol. XXXVIII. 2010;38:27–33.

Rasmussen J, Nielsen J, Garcia-Ruiz F, Christensen S, Streibig JC. Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Res. 2013;53:242–8.

Slaughter DC, Giles DK, Fennimore SA, Smith RF. Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technol. 2008;22:378–84.

Norasma N, Ya C, George D. Spectral discrimination of weeds using hyperspectral. 2013.

López-Granados F, Peña-Barragán JM, Jurado-Expósito M, Francisco-Fernández M, Cao R, Alonso-Betanzos A, et al. Multispectral classification of grass weeds and wheat ( Triticum durum ) using linear and nonparametric functional discriminant analysis and neural networks. Weed Res. 2008;48:28–37.

de Castro AI, Peña JM, Torres-Sánchez J, Jiménez-Brenes F, López-Granados F. Mapping Cynodon dactylon in vineyards using UAV images for site-specific weed control. Adv Anim Biosci. 2017;8:267–71.

Fawakherji M, Potena C, Bloisi DD, Imperoli M, Pretto A, Nardi D. UAV image based crop and weed distribution estimation on embedded GPU boards. Commun Comput Inf Sci. 2019;1089:100–8.

Peña JM, Torres-Sánchez J, de Castro AI, Kelly M, López-Granados F. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE. 2013;8:1–11.

Pantazi X-E, Moshou D, Bravo C. Active learning system for weed species recognition based on hyperspectral sensing. Biosyst Eng. 2016;146:193–202.

Gibson DJ, Young BG, Wood AJ. Can weeds enhance profitability? Integrating ecological concepts to address crop-weed competition and yield quality. J Ecol. 2017;105:900–4.

Millar KDL, Ebbs SD, Gibson DJ, Wood AJ, Young BG. Evaluation of physiological parameters for the prediction of seed yield and quality for soybean ( Glycine max ) plants grown in the presence of weed competition. Plant Biosyst. 2011;145:1–11.

Adeux G, Vieren E, Carlesi S, Bàrberi P, Munier-Jolain N, Cordeau S. Mitigating crop yield losses through weed diversity. Nat Sustain. 2019;2:1018–26.

Millar K, Gibson DJ, Young BG, Wood AJ. Impact of interspecific competition on seed development and quality of five soybean cultivars. Aust J Exp Agric. 2007;47:1455–9.

Talaviya T, Shah D, Patel N, Yagnik H, Shah M. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif Intell Agric. 2020;4:58–73.

Lanz B, Dietz S, Swanson T. The expansion of modern agriculture and global biodiversity decline: an integrated assessment. Ecol Econ. 2018;144:260–77.

Erisman JW, van Eekeren N, de Wit J, Koopmans C, Cuijpers W, Oerlemans N, et al. Agriculture and biodiversity: a better balance benefits both. AIMS Agric Food. 2016;1:157–74.

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Marco Esposito, Mariano Crimaldi, Valerio Cirillo, Fabrizio Sarghini & Albino Maggio

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ME and MC wrote the original draft and produced all the tables and the figures. AM, FS and VC revised the text for the final version. VC and ME conceived the idea of writing the review. All authors read and approved the final manuscript.

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Esposito, M., Crimaldi, M., Cirillo, V. et al. Drone and sensor technology for sustainable weed management: a review. Chem. Biol. Technol. Agric. 8 , 18 (2021). https://doi.org/10.1186/s40538-021-00217-8

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Results and discussion, weed biology, weed detection, preventative weed management, weed control vs. iwm, journals publishing weed management method articles, author affiliations publishing weed management method articles, countries publishing weed management method articles, proportional iwm publishing on the basis of population, arable land, and crop production, iwm components and combinations, research implications.

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Integrated weed management (IWM) can be defined as a holistic approach to weed management that integrates different methods of weed control to provide the crop with an advantage over weeds. It is practiced globally at varying levels of adoption from farm to farm. IWM has the potential to restrict weed populations to manageable levels, reduce the environmental impact of individual weed management practices, increase cropping system sustainability, and reduce selection pressure for weed resistance to herbicides. There is some debate as to whether simple herbicidal weed control programs have now shifted to more diverse IWM cropping systems. Given the rapid evolution and spread of herbicide-resistant weeds and their negative consequences, one might predict that IWM research would currently be a prominent activity among weed scientists. Here we examine the level of research activity dedicated to weed control techniques and the assemblage of IWM techniques in cropping systems as evidenced by scientific paper publications from 1995 to June 1, 2012. Authors from the United States have published more weed and IWM-related articles than authors from any other country. When IWM articles were weighted as a proportion of country population, arable land, or crop production, authors from Switzerland, the Netherlands, New Zealand, Australia, and Canada were most prominent. Considerable evidence exists that research on nonherbicidal weed management strategies as well as strategies that integrate other weed management systems with herbicide use has increased. However, articles published on chemical control still eclipse any other weed management method. The latter emphasis continues to retard the development of weed science as a balanced discipline.

El manejo integrado de malezas (IWM) puede ser definido como un enfoque holístico del manejo de malezas que integra diferentes métodos de control para brindar al cultivo una ventaja sobre las malezas. Esto es practicado globalmente con niveles de adopción que varían de finca a finca. El IWM tiene el potencial de restringir las poblaciones de malezas a niveles manejables, reducir el impacto ambiental de prácticas individuales de manejo de malezas, incrementar la sostenibilidad de los sistemas de cultivos y reducir la presión de selección sobre la resistencia a herbicidas de las malezas. Existe cierto debate acerca de si programas de control de malezas basados simplemente en herbicidas, ahora se han convertido a sistemas de cultivos con IWM más diversos. Dada la rápida evolución y dispersión de malezas resistentes a herbicidas y sus consecuencias negativas, uno podría predecir que la investigación en IWM sería actualmente una actividad prominente entre científicos de malezas. Aquí examinamos el nivel de actividad investigativa dedicada a técnicas de control de malezas y al ensamblaje de técnicas de IWM en sistemas de cultivos, usando como evidencia la publicación de artículos científicos desde 1995 al 1 de Junio, 2012. Autores de los Estados Unidos han publicado más artículos relacionados a malezas y a IWM que autores de cualquier otro país. Cuando se ajustó el peso de los artículos de IWM como proporción de la población del país, tierras arables o producción de cultivos, autores de Suiza, Holanda, Nueva Zelanda, Australia y Canadá fueron los más prominentes. Existe considerable evidencia de que ha incrementado la investigación sobre estrategias no-herbicidas de manejo de malezas y también sobre las estrategias que integran otros sistemas de manejo de malezas con el uso de herbicidas. Sin embargo, los artículos publicados sobre control químico todavía eclipsan cualquier otro método de manejo de malezas. Este último énfasis continúa retrasando el desarrollo de la ciencia de malezas como una disciplina balanceada.

Herbicides are the dominant tool used for weed control in modern agriculture; they are highly effective on most weeds but are not a complete solution to the complex challenge that weeds present. The overuse of herbicides has led to the rapid evolution of herbicide-resistant (HR) weeds ( Beckie 2006 ; Egan et al. 2011 ; Powles and Yu 2010 ). Globally, there are 383 HR weed biotypes among 208 HR weed species ( Heap 2012 ). Weeds resistant to the most widely used herbicide in the world, glyphosate, have been confirmed in 20 countries (23 species) ( Heap 2012 ). In addition, multiple herbicide resistance within single biotypes is widespread. Ever-increasing populations of HR weeds, especially those with multiple herbicide resistance, have pressured weed researchers to develop management systems that are less dependent on herbicides ( Powles and Matthews 1992 ). Given HR weed issues and consistent public pressure to reduce overall pesticide use, herbicide alternatives and true integrated weed management (IWM) strategies are urgently required now more than ever.

The importance of using alternatives to chemicals for weed control was recognized long ago. In 1929, in a meeting where spraying with sulfuric acid for mustard control was considered practicable, the chairman made the following conclusion: “The destruction of weeds by chemicals must of course be supplementary to crop rotation, summer fallowing and other control methods, which will always have a prominent place” ( NRC 1929 ). Although summer fallow is no longer a desirable weed management practice in many areas, at such an early era of chemical weed management, the chairman was certainly astute in suggesting that chemicals must be supplementary to other weed control methods. Unfortunately, in modern agriculture, nonherbicidal weed control methods have not always held “a prominent place.”

Many others have challenged weed researchers to increase emphasis on IWM systems and alternatives to herbicides. In 1992, Wyse suggested the need for “more emphasis on principles-based research that can provide the basic knowledge required to develop new weed control technology.” He also stated that “non-chemical methods of weed control have not been researched extensively for almost 30 yr,” an observation supported by other weed scientists ( Altieri and Liebman 1988 , Buhler 1999 , Roush et al. 1990 , Thill et al. 1991 ). Buhler (1999 ) and Hamill et al. (2004 ) suggested that a common goal for weed science should be to develop systems that give producers more flexibility and options. Buhler (1999 ) further challenged researchers with the statement that “we seldom examine the causes of the perpetual presence of weeds.” Maxwell and O'Donovan ( 2007 ) stressed the need to identify the first principles of weed ecology and biology that relate to crop–weed interactions and demonstrate how they can be used to assess weed management alternatives including nonchemical approaches. Others have suggested the need to incorporate the various components of IWM into cropping, including user-friendly decision support systems ( O'Donovan 1996 ; Swanton et al. 2008 ). More recently, Egan et al. (2011 ) noted that a diversity of chemical and nonchemical practices reduces herbicide use and offers a more robust weed-control system. Clearly, a diverse suite of weed research and outreach activities will be necessary for long-term weed management successes ( Harker 2004 ).

Wyse (1992 ) noticed that most resources devoted to weed science have been directed to herbicide research. Therefore, he suggested that “weed science is currently perceived by many to be the science of herbicides rather than the science of weeds.” Similarly, Thill et al. (1991 ) observed that many U.S. weed scientists publish more on herbicide-related research than all other areas combined. Since these comments were made, one might ask if resources and efforts of researchers have shifted from weed control with herbicides to the science of weeds and alternative strategies for weed management. Have nonchemical weed control methods been extensively researched? Is the discipline of weed science still perceived as the discipline of herbicides? Is the weed science contribution to agriculture still “truncated by intensive specialization and narrow expertise” ( Zimdahl 1994 )? Radosevich and Ghersa (1992 ) suggest that “weed scientists…need to think about how they think.” Zimdahl (1994 ) suggested that weed scientists should ask themselves: “who are you and where are you going?” The analysis of weed control, weed management, and IWM-related publications in this review helps answer these questions and provides one measure of how well weed researchers have responded to the above challenges.

Weed “management” implies more than weed “control” and is an important choice of terms and direction ( Buhler 1996 ; Zimdahl 1994 ). The “ruthless fight to the last weed” ( Zimdahl 1994 ) is part of the weed control paradigm, whereas a weed management paradigm suggests greater consideration of thresholds, critical periods, environment, and possibly even social outcomes, before weed management methods are imposed. The next logical step is to integrate multiple weed management strategies into IWM systems. Numerous definitions have been applied to IWM and the broader area of integrated pest management (IPM). Some definitions incorporate economic and ecological goals in addition to the goal of integrating several weed management approaches. For example, Prokopy (2003 ) summarized the essence of many IPM definitions as follows: “…a decision-based process involving coordinated use of multiple tactics for optimizing the control of all classes of pests (insects, pathogens, weeds, vertebrates) in an ecologically and economically sound manner.” An IWM definition from Australia has an HR weed focus: “to reduce selection pressure for resistance to any single control agent and to manage herbicide resistant weeds within a profitable system” ( Sutherland 1991 ). However, for the purposes of this review, IWM is defined as the use of more than one weed management tactic (biological, chemical, cultural, or physical) during or surrounding a crop life cycle in a given field.

Successful IWM techniques are most likely to be discovered after biological characteristics and ecological behaviors of weeds have been elucidated. Many authors agree that the study of weeds themselves (weed biology and ecology) is absolutely essential to the development of useful IWM strategies ( Altieri and Liebman 1988 ; Buhler 1996 ; Holt 1994 ; Liebman et al. 2001 ; Maxwell and Luschei 2004 ; Mortensen et al. 2000 ; Navas 1991 ; Radosevich and Ghersa 1992 ; Swanton et al. 2008 ; Wyse 1992 ; Zimdahl 1994 ). For example, Smith et al. (2009 ) recently united ecological and traditional crop–weed competition theories into a resource pool diversity hypothesis suggesting that more-diverse soil resource pools in more-diverse cropping systems increase crop competitiveness with weeds compared with less-diverse cropping systems. Radosevich and Ghersa (1992 ) observed that if we wish our cropping systems to be successful, stable, and profitable, weed researchers will also need to extend their influence into the basic disciplines of economics and sociology.

IWM systems may combine several different combinations of weed control methods. Although few of these systems combine all weed management methods ( Figure 1 A), many current IWM systems involve chemical and physical (especially tillage) ( Figure 1 B) or chemical and cultural ( Figure 1 C) methods. Unfortunately, the “integration” consisting of only chemical control components is common in modern cropping systems ( Figure 1 D). In this example, weed practitioners recommend several ways of applying herbicides or recommend applying more than one herbicidal mode of action. Although these techniques are important, they are not IWM. The latter method ( Figure 1 D) is sometimes touted as an IWM program, but it is nothing more than a more complex form of managing weeds solely with herbicides, also known as integrated herbicide management ( Harker et al. 2012 ). Perhaps the reason that so many weed scientists continue to only recommend herbicide solutions for weed resistance problems ( Harker et al. 2012 ) is because they have the misguided feeling that IWM is simply a new term for herbicidal weed control ( Walker and Buchanan 1982 ).

One might inappropriately conclude that IWM implementation means that herbicides should be avoided in preference for other weed management methods. However, IWM should not be about the exclusion of one method for another as much as it is about overall technique diversity. Any weed management method that is continuously repeated provides heavy selection pressure for weed adaptation and resistance to that practice. Intense and continuous barnyardgrass [ Echinochloa crus-galli (L.) Beauv.] hand-weeding in rice ( Oryza sativa L.) allowed the selection of rice-mimic biotypes that “resisted” hand-weeding efforts ( Barrett 1983 ). Therefore, weeds will likely resist any often-repeated weed management technique. In an IWM program, using a diversity of weed management methods is more important than striving to exclude any single method.

Our objective was to analyze weed-related articles published after 1994 to determine if IWM articles have increased relative to articles on weed control with herbicides, and to determine where IWM-related research has been conducted and published. We also considered articles published on weed biology, weed detection, and different methods of weed control as they provide knowledge and techniques for IWM systems. Our search was by no means intended to be an exhaustive review of all IWM techniques and systems. We utilized Scopus (  http://www.scopus.com/search/form.url?zone=TopNavBar&origin=searchbasic ) for our publication queries given its excellent built-in analysis feature and publication coverage during the period we were interested in (1995 to June 1, 2012). Scopus “content coverage” details can be accessed on the same web page. For example, in May 2012, Scopus queries covered 19,500 titles, 18,500 of which were peer-reviewed journals. We conducted the following queries to access publication numbers and sources related to weed management, weed biology, weed detection, and several methods of weed management.

TITLE-ABS-KEY(" weed control ”) AND PUBYEAR > 1994

TITLE-ABS-KEY(" weed management ”) AND PUBYEAR > 1994

TITLE-ABS-KEY(" integrated weed management ”) AND PUBYEAR > 1994

TITLE-ABS-KEY(weed* AND ( biology OR ecology OR allelo* OR competition OR interference OR “critical period” OR duration OR population OR “spatial distribution”)) AND PUBYEAR > 1994

TITLE-ABS-KEY(" weed detect *” OR (weed AND (vision OR sense OR robot*))) AND PUBYEAR > 1994

TITLE-ABS-KEY( prevent * AND (weed* OR “weed control” OR “weed management”)) AND PUBYEAR > 1994

TITLE-ABS-KEY( biological AND ("weed control” OR “weed management”)) AND PUBYEAR > 1994

TITLE-ABS-KEY(( chemical OR herbicid*) AND ("weed control” OR “weed management”)) AND PUBYEAR > 1994

TITLE-ABS-KEY(( cultural OR “competitive cultivar” OR “competitive variety” OR “seed vigo*” OR “seed* rate*” OR “sow* rate*” “crop densit*” OR “cover crop*” OR “smother crop*” OR “green manur*” OR “row spac*” OR “crop rotation*” OR “crop diversity” OR intercrop* OR “clean* seed” OR “certified seed” OR fertili*) AND ("weed control” OR “weed management”)) AND PUBYEAR > 1994

TITLE-ABS-KEY(( physical OR mechanical OR till* OR cultivat* OR hoe* OR mow* OR thermal OR flam* OR steam* OR “seed destruct*”) AND ("weed control” OR “weed management”)) AND PUBYEAR > 1994

TITLE-ABS-KEY(( alternative OR organic OR holistic OR “low input” OR harvest*) AND ("weed control” OR “weed management”)) AND PUBYEAR > 1994

We chose to limit our search to those articles published after 1994. The time period after 1994 allowed us to access weed scientists' response to the challenges issued by Wyse (1992 ) and Zimdahl (1994 ). Furthermore, the beginning of 1995 immediately precedes the introduction of HR crops ( Duke 2005 ) and also marks the beginning of the relatively rapid increase of weed resistance to acetolactate synthase- and acetyl coenzyme A carboxylase-inhibitor herbicides ( Heap 2012 ). After the queries were executed, we used the “analyze results” feature in Scopus to obtain details on query results (publication source, author affiliations, country, and article type).

For our analysis, the vast majority of all publications we considered were scientific papers in refereed journals. For example, for the queries involving just “weed control,” “weed management,” and “IWM” articles, 84% were scientific articles (11,490), 9% were conference proceedings (1,259), and 5% were reviews (694). The remaining publications (2%) included short surveys, notes, articles in press, undefined contributions, books, etc.

As mentioned above, knowledge of weed biology and ecology is essential to the development of successful IWM systems. Since 1994, Weed Science has published more weed biology and ecology articles than any other scientific publication source ( Table 1 ). U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) authors published more than twice the weed biology articles than authors from any other research affiliation. Authors from the United States (all affiliations), Australia, Canada, the United Kingdom, and India all published a substantial number of weed biology articles (top five author-affiliated countries).

Weed detection technology provides essential “scouting” information in many weed management systems. Publishing sources and affiliations for this subject area include a wider variety of participants than any other research area in the review ( Table 2 ). Typical weed journals rank third ( Weed Technology ) and fifth ( Weed Research ) in this area. Although United States authors published the most weed detection articles, authors from China were also significant contributors.

Preventative weed management is a key strategy in IWM systems ( Hamill et al. 2004 ); it is the first and probably most important consideration for new weed populations, particularly invasive species. Maxwell and O'Donovan ( 2007 ) suggested that a first principle of nonchemical weed management could be that the higher the uncertainty in a crop–weed interaction, the more management emphasis should be placed on prevention and less on causing weed mortality. Since 1994, Weed Technology , Weed Science , and Weed Research all contributed at least 60 preventative weed management articles ( Table 3 ). Given the fact that Invasive Plant Science and Management was first published in January 2008, it is impressive that 20 preventative weed management articles have now been published in that journal. Authors from the USDA-ARS contributed the most articles in this category (44), whereas authors from Wageningen University and Research Centre published 30 articles. Although U.S. authors published the most preventative weed management articles (390), Australian authors published almost one-third of that number (121). Considering much lower arable land levels and agricultural scientist numbers in Australia vs. the United States, relatively early and severe weed resistance issues have likely led to relatively greater awareness, research interest, and publishing efforts related to preventative weed management strategies in Australia.

Thill et al. (1991 ) noted that weed scientists may have responded positively to Shaw's (1982 ) request for more IWM research made during the 1981 Weed Science Society of America IWM Symposium. They observed that before the Symposium, from 1960 to 1981, “about 4% of all Weed Science articles dealt with IWM” and that from 1982 to 1989 “about 8% of the articles of the articles published in Weed Science have dealt with IWM” ( Thill et al. 1991 ). However, Thill et al. (1991 ) noted that the IWM publishing trend from 1982 to 1989 was a definite regression. Apparently, the plea for more IWM research was either reconsidered or quickly forgotten. More than 20 yr later, depending on one's perspective, things may or may not have improved. Although there is a slight but consistent upward trend in published “IWM” articles in the Scopus database, the upward trend in “weed control” articles is much stronger ( Figure 2 ). Furthermore, during the 17-yr period from 1995 to 2011, “weed control” articles outnumbered “IWM” articles by a ratio of 14 to 1 (10,359 to 720).

One purpose for our simple queries of “weed control” and “weed management” ( Figure 2 ) was to determine if weed scientists were shifting their thinking in terms of how weeds should be managed. If IWM is to become more widely implemented in research programs as well as on the farm, replacing a weed control mentality with a weed management mentality will be necessary.

The evolution of strategies from weed control to weed management to IWM in research publications suggests a necessary shift in research emphasis toward more sustainable weed management techniques. Nevertheless, weed control articles from the last 17 years outnumbered weed management articles by a ratio of almost 4 to 1 (9,964 to 2,708) ( Figure 2 ). During the same time period weed management articles outnumbered “integrated weed management” articles by a similar 4-to-1 ratio (2,708 to 697). However, titles, abstracts, and key words do not always tell the full story. For example, in 1987, two weed papers were published, with one mentioning “management” ( Bridges and Walker 1987 ) and the other mentioning “control” ( Cardina et al. 1987 ); both papers were excellent examples of real IWM.

Given its mandate to determine “how” weeds are managed, Weed Technology publishes more weed management method papers than any other scientific journal ( Table 4 ). The only weed management category where Weed Technology falls behind some other journals is biological control. In the latter category, Biocontrol Science and Technology , Biological Control, and Weed Science all publish more articles than Weed Technology .

USDA ARS authors published the most weed management articles and led all other affiliations in five of six weed management categories ( Table 5 ). Agriculture and Agri-Food Canada authors led other affiliations in publishing cultural weed management articles. Wageningen University and Research Centre, University of Florida, Commonwealth Scientific and Industrial Research Organisation Entomology, North Carolina State University, and Michigan State University authors were also among the top three affiliations publishing weed management method articles.

U.S. authors published the most weed management articles and led all other countries in all six weed management categories ( Table 6 ). The number of weed scientists in the United States is probably greater than in any other country. Authors from Canada, India, Australia, and the United Kingdom were also among the top three countries with authors publishing weed management method articles.

Country comparisons were more interesting, and perhaps more fairly compared, when articles published were on the basis of country population, arable land, and crop production ( Table 7 ). In the article-population chart ( Figure 3 ), the number of published IWM articles from the top 10 countries is expressed as a percentage of country population (# 1,000,000 −1 ). The top three countries with authors publishing IWM articles on the basis of population proportion were Australia, Canada, and New Zealand. In the article arable land chart ( Figure 4 ), the number of published IWM articles from the top 10 countries is expressed as a percentage of country arable land (km 2 1,000 −1 ). The top three countries with authors publishing IWM articles on the basis of arable land proportion were Switzerland, the Netherlands, and New Zealand. In the article crop production chart ( Figure 5 ), the number of published IWM articles from the top 10 countries is expressed as a percentage of country crop production (Mt 1,000,000 −1 ). The top three countries with authors publishing IWM articles on the basis of crop production proportion were New Zealand, Switzerland, and Canada. Therefore, although U.S. authors have published more IWM articles than authors from any other country, when IWM article numbers were weighted as a proportion of country population, arable land, or crop production, authors from New Zealand, Canada, Switzerland, Australia, and the Netherlands were most prominent.

Given the rather low number of IWM articles vs. weed control articles shown in Figure 2 , it is encouraging to observe the steady increase in nonchemical articles published since 1994 ( Figure 6 ). Clearly, the knowledge required for the discovery of new IWM components and the study of the components themselves has been substantially augmented. Nonchemical articles provide knowledge regarding IWM components that have been somewhat neglected in the past ( Altieri and Liebman 1988 ; Buhler 1999 ; Roush et al. 1990 ; Thill et al. 1991 ; Wyse 1992 ). In addition, that nonchemical weed management articles were published at a more rapid pace than HR weed biotypes are confirmed is encouraging, if the idea for such a comparison was not so menacing ( Figure 6 ). Encouragement, however, should be tempered by the fact that some articles were counted in more than one subject area (i.e., total articles numbers were lower). Nevertheless, observing the rate that nonchemical weed management and IWM system articles are published in the future will be interesting.

Some argue that nonchemical and other alternative weed management strategies are not sufficiently efficacious or economical. Although current tools may not be highly effective for growers right now, researchers should not abandon their quest for future weed management alternatives. Furthermore, given the rapid expansion of HR weed populations, the economic feasibility of alternative weed management strategies may substantially improve in the near future. Thomas et al. (2010 ) concluded that cultural weed management practices can both complement and substitute for herbicides. Forward-thinking researchers ignore some “low efficacy” and “low profit” comments by farmers and other researchers to develop new weed management techniques that will contribute future “little hammers” for weeds ( Liebman and Gallandt 1997 ). “Weed scientists who want to change things” face a difficult task ( Zimdahl 1994 ).

Many researchers have answered the challenge to develop true IWM systems that include more than one method of weed management ( Buhler 1999 ; Swanton and Weise 1991 ) and to raise the profile of nonchemical weed management research. The principle of using “many little hammers” to manage crop–weed interactions ( Liebman and Gallandt 1997 ) also appears to be gaining more momentum. Weed management tools such air-propelled corn grit ( Forcella 2012 ), weed-suppressing Brassica seed meal ( Handiseni et al. 2011 ), cryogenic salts ( Jitsuyama and Ichikawa 2011 ), early-cut barley silage ( Harker et al. 2003b ), crop rotation ( Liebman and Dyck 1993 ), chaff and weed seed collection ( Shirtliffe and Entz 2005 ), the Harrington seed destructor ( Walsh et al. 2012 ), higher crop seeding rates ( O'Donovan et al. 2006 ), sized crop seed ( Xue and Stougaard 2006 ), robotic weeders ( Blasco et al. 2002 ), band-steaming ( Melander et al. 2002 ), planting patterns ( Mahajan and Chauhan 2011 ), competitive species ( Beres et al. 2010 ), competitive cultivars ( Drew et al. 2009 ), intercropping ( Nelson et al. 2012 ), and some of the many techniques developed in Australia against GR rigid ryegrass ( Lolium rigidum Gaudin) ( Llewellyn et al. 2004 ) can be among the useful tools used together in global IWM systems.

Not all IWM tools are new techniques. The relatively recent focus on herbicidal weed control and HR crops effectively caused us to forget many historically effective nonchemical weed management techniques. A paper on suppressing weeds with higher crop seeding rates was published in 1935 ( Godel 1935 ), and crop rotation has been advocated for centuries to manage weeds. However, novelty and innovation occur when these tools are combined with other tools in modern cropping systems. There are numerous examples showing the benefit of combining multiple tools in IWM systems ( Anderson 2000 , 2003 , 2005 , Barton et al. 1992 ; Blackshaw et al. 1999 , 2005 , 2008 ; Harker et al. 2003a , 2009 ; Holm et al. 2006 ; Kolb et al. 2012 ; Melander et al. 2005 ; O'Donovan et al. 2007; Wang et al. 2012 ; Westerman et al. 2005 ; Young et al. 2010 ). For example, Anderson (2000 ) showed that, in the absence of herbicides, combining high seeding rates with seed-banded nitrogen fertilizer and a tall proso millet ( Panicum miliaceum L.) cultivar eliminated crop yield loss due to weeds.

Perhaps the most rapid discovery, reinvention, and adoption of alternatives to herbicides in modern agricultural cropping systems has been in Australia where multiple-resistant rigid ryegrass has forced growers and researchers to look for herbicide alternatives. Llewellyn et al. (2004 ) list 18 nonherbicidal IWM practices that have been relatively recently used by Western Australian grain growers to reduce rigid ryegrass populations. Similar innovation and IWM research are likely to occur in the immediate future where glyphosate-resistant weeds are beginning to dominate some cropping regions. As Beckie (2006 ) suggests, increased grower adoption of IWM techniques usually only occurs after HR weeds have been confirmed on their farm. Therefore, one might expect a resurgence in IWM technique development and grower adoption of those techniques in southeastern United States cotton ( Gossypium hirsutum L.)-growing regions where Palmer amaranth ( Amaranthus palmeri S. Wats.), the major weed in cotton, is now resistant to glyphosate ( Culpepper et al. 2006 ; Norsworthy et al. 2008 ; Steckel et al. 2008 ) and to herbicides with other mechanisms of action ( Heap 2012 ).

Although new weed management method and IWM component combination papers are needed much more than yet another herbicide efficacy paper, new management method and IWM papers usually require more time, resources, and innovation to develop and publish. Comparatively, herbicide efficacy papers can be relatively easy to publish and offer a quick pathway to career success. However, discovering and utilizing weed management practices in addition to herbicides is essential to achieve true IWM and preserve the efficacy of valuable herbicide tools ( Beckie 2006 , 2007 ; Buhler 1999 ; Duke 2011 ; Hamill et al. 2004 ; Powles and Yu 2010 ).

Research funding opportunities often determine research direction. Weed scientists are not solely responsible for the promotion and adoption of IWM techniques and systems. The search for new weed management techniques and answers to basic weed biology and ecology questions leading to successful IWM systems ( Harker et al. 2012 ; Radosevich and Ghersa 1992 ; Swanton et al. 2008 ; Thill et al. 1991 ; Wyse 1992 ; Zimdahl 1994 ) requires visionary and long-term research funding by multinationals, grower-funded organizations, and various levels of government. Complex, system-based research programs require many years of study that can be less amenable to short-term funding priorities as well as scientific career advancement. Perhaps a shift in how researchers are evaluated would advance IWM research as much as anything.

The publication record reviewed here suggests that some weed scientists are shifting more emphasis to weed biology and ecology, as well as developing weed management tools other than herbicides ( Figure 6 ). Swanton and Murphy (1996 ) suggest that IWM research needs to focus on indicators of agroecosystem health to help determine long-term consequences and constraints of IWM adoption. We hope this article will stimulate new avenues of IWM research and reduce the 14-to-1 ratio of published weed control to IWM articles.

Finally, greater grower adoption of IWM practices would likely stimulate research funding and increase interest levels among weed researchers. However, growers, consultants, and industry representatives like relatively quick prescriptive solutions for weed problems with low uncertainty. The certainty associated with recommending a highly efficacious herbicide or herbicide mixture is likely to be greater than that associated with nonchemical management recommendations, at least in the short term (Maxwell and O'Donovan 2007). The key to generating more interest and innovation in IWM research may lie in educating growers and industry on the long-term benefits of more holistic principles of weed management rather than relying solely on more rapid prescriptive solutions. Nonmarket-based programs that attempt to internalize environmental externalities such as the U.S. Environmental Quality Incentives Program may also help to increase IWM funding, research, and grower adoption.

If nothing else, this article may provide impetus for researchers to include adequate key words in titles, abstracts, and key word sections of their manuscripts to ensure that their work is fully credited and discovered. This article is somewhat biased toward those that were careful to add appropriate key words to their articles. It is also likely that some articles purporting to be about IWM are really only integrated herbicide management ( Harker et al. 2012 ) similar to the “other IPM” ( Ehler 2006 ), and therefore should not be included as IWM articles. Nevertheless, if IWM-related key words are not mentioned in titles, abstracts, or key words, they are probably not important to the research or the article.

Much progress developing alternative weed management techniques, and the integration of these and other techniques into real IWM systems, has been made. Some organizations and countries have been more successful than others. Nevertheless, there is still much more that can and should be done. More IWM-focused priorities, increased IWM funding levels from various agencies, and greater grower education as to the long-term benefits of IWM could facilitate crucial changes in research direction. Wyse's lament 20 years ago (1992) that: “a large portion of the resources devoted to weed science have been devoted to herbicide research” may be outdated; perhaps many weed scientists have listened and responded. But a continued overemphasis on chemical weed control by many weed scientists will continue to retard “the development of weed science as a balanced discipline” ( Wyse 1992 ).

Current and future weed scientists will determine whether weed science will continue to be perceived as a discipline that studies only herbicides. Hamill et al. (2004 ) suggest that weed science has shifted from an early emphasis on herbicides to a more complete integration of several control methods that are determined on ecological as well as economic goals. The potential promise that real IWM brings to agricultural sustainability is dependent upon a continued focus on weed biology, weed ecology, developing new management tactics, and studying and implementing diverse combinations of IWM systems.

Literature Cited

Some forms of true integrated weed management (IWM) (A–C) in contrast to integrated herbicide management (D).

Weed research articles published with “weed control” (WC), “weed management” (WM), or “integrated weed management” (IWM) listed in the title, abstract, or key words from 1995 to 2011 (Scopus query). Total articles published in the specified time period were 9,964, 2,708, and 697 for WC, WM, and IWM, respectively.

Top 10 countries with authors publishing “integrated weed management” papers as a percentage of country population (1,000,000 −1 ) (see Table 7 ).

Top 10 countries with authors publishing “integrated weed management” papers as a percentage of country arable land (1,000 km −2 ) (see Table 7 ).

Top 10 countries with authors publishing “integrated weed management” papers as a percentage of country crop production (1,000,000 Mt −1 ) (see Table 7 ).

Chemical control and nonchemical (biological, cultural, and physical combined) weed management articles published from 1995 to 2011 (Scopus queries). The numbers of herbicide-resistant (HR) weed biotypes confirmed over the same time period are also illustrated ( Heap 2012 ).

The top five weed biology a article sources and numbers from 1995 to June 1, 2012 (Scopus query). Total articles published = 10,705.

The top five weed detection a article sources and numbers from 1995 to June 1, 2012 (Scopus query). Total articles published = 479.

The top five preventative weed management a article sources and numbers from 1995 to June 1, 2012 (Scopus query). Total articles published = 1,270.

Article numbers in the top five journals publishing weed management methods from 1995 to June 1, 2012 (Scopus query). a

Article numbers for the top five affiliations publishing weed management methods from 1995 to June 1, 2012 (Scopus query). a

Article numbers for the top five countries publishing weed management methods from 1995 to June 1, 2012 (Scopus query). a

Population, arable land, and crop production (total of top five crops) data for the top 10 countries publishing “integrated weed management” articles (listed in the title, abstract, or key words) from 1995 to June 1, 2012 (Scopus query).

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Submersed herbicides and thrips biological control effectively reduce biomass of alligatorweed ( Alternanthera philoxeroides ), a widespread aquatic invasive plant

• Published: 01 May 2024

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• Samuel A. Schmid   ORCID: orcid.org/0000-0001-9523-5459 1 ,
• Gray Turnage   ORCID: orcid.org/0000-0001-6337-6329 2 &
• Gary N. Ervin   ORCID: orcid.org/0000-0002-7086-9794 1  

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Alligatorweed [ Alternanthera philoxeroides (Mart.) Griseb.; Amaranthaceae] is a globally problematic, aquatic invasive weed with a long history as a target for control efforts. Although chemical and biological control methods have been widely studied to manage alligatorweed infestations, many research questions remain unanswered. This paper seeks to assess the efficacy of two understudied alligatorweed control methods: submersed herbicide applications and biological control with alligatorweed thrips ( Amynothrips andersoni O’Neill 1968; Thysanoptera: Phlaeothripidae). These assessments were carried out in mesocosm experiments, in two stages. The first stage tested five herbicides applied as submersed injections at two different rates, and the second tested the same five herbicides alone and in combination with alligatorweed thrips biological control. The submersed herbicides used in this study were penoxsulam, bispyribac-sodium, imazamox, fluridone, and topramezone. The control effect of these treatments was measured as percent biomass reduction 12 weeks after treatment. These data showed that, with the exception of bispyribac-sodium, submersed herbicide application was generally successful at reducing alligatorweed biomass. Also, thrips biological control was broadly effective at reducing alligatorweed biomass. However, these data did not identify a specific herbicide whose control was significantly benefitted by thrips biological control at the rates these herbicides were applied. While the results of this study show promise for combining submersed herbicides and alligatorweed thrips for integrated alligatorweed management, questions remain regarding this combined control strategy including whether or not these results translate to the field.

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Data availability.

Data used in this study is deposited at the Mississippi State University institutional repository and is available at: https://doi.org/10.54718/GLZZ3432 .

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Code used in this study is deposited at the Mississippi State University institutional repository and is available at: https://doi.org/10.54718/GLZZ3432 .

Bhalla T, Kumar S, Rana SS (2022) Evaluation of herbicides to control alligator weed ( Alternanthera philoxeroides ) in mid hills of Himachal Pradesh. Himachal J Agric Res 48:205–209

Google Scholar  

Bowmer KH, Eberbach PL, McCorkelle G (1993) Uptake and translocation of 14C-glyphosate in Alternanthera philoxeroides (Mart.) Griseb. (alligator weed) I. Rhizome concentrations required for inhibition. Weed Res 33:53–57

Article   CAS   Google Scholar  

Buckingham GR (1996) Biological control of alligatorweed, Alternanthera philoxeroides , the world’s first aquatic weed success story. Castanea 61:232–243

Buckley YM, Rees M, Sheppard AW, Smyth MJ (2005) Stable coexistence of an invasive plant and biocontrol agent: a parameterized coupled plant–herbivore model. J Appl Ecol 42:70–79

Article   Google Scholar  

Burns EE, Prischmann-Voldseth DA, Gramig GG (2013) Integrated management of Canada thistle ( Cirsium arvense ) with insect biological control and plant competition under variable soil nutrients. Invasive Plant Sci Manag 6:512–520

Clements D, Dugdale TM, Butler KL, Hunt TD (2014) Management of aquatic alligator weed ( Alternanthera philoxeroides ) in an early stage of invasion. Manag Biol Invasions 5:327–339

Clements D, Dugdale TM, Butler KL, Florentine SK, Sillitoe J (2017) Herbicide efficacy for aquatic Alternanthera philoxeroides management in an early stage of invasion: integrating above-ground biomass, below-ground biomass and viable stem fragmentation. Weed Res 57:257–266

Cofrancesco AF (1988) Alligatorweed survey of ten southern states. Environmental Laboratory (U.S.), Libertyville

Cornell JH (1949) Eradication of emergent aquatic vegetation with 2,4-D: particular emphasis upon alligatorweed. Prog Fish-Culturist 11:113–118

Coulson JR (1977) Biological control of alligatorweed, 1959-1972: A review and evaluation. United States Department of Agriculture.

Cox MC, Wersal RM, Madsen JD (2014) Evaluations of foliar applied herbicides for alligatorweed ( Alternanthera philoxeroides ) control. J Aquat Plant Manage 52:27–30

Cuda JP (2021) Introduction to biological control of aquatic plants. In: Gettys LA, Haller WT, Petty DG (eds) Biology and control of aquatic plants: a best management practices handbook, 4 th edn. Aquatic Ecosystem Restoration Foundation, Marietta, pp 139–144

Deguine J-P, Aubertot J-N, Flor RJ, Lescourret F, Wyckhuys KAG, Ratnadass A (2021) Integrated pest management: good intentions, hard realities. Rev Agron Sustain Dev 41:38

Dugdale TM, Champion PD (2012) Control of alligator weed with herbicides: a review. Plant Prot Q 27:70–82

CAS   Google Scholar  

Emerine SE, Richardson RJ, True SL, West AM, Roten RL (2010) Greenhouse response of six aquatic invasive weeds to imazamox. J Aquat Plant Manage 48:105–111

Fox J, Weisberg S, Price B, Adler D, Bates D, Baud-Bovy G, Bolker B, Ellison S, Firth D, Friendly M, Gorjanc G, Graves S, Heiberger R, Krivitsky P, Laboissiere R, Maechler M, Monette G, Murdoch D, Nilsson H, Ogle D, Ripley B, Short T, Venables W, Walker S, Winsemius D, Zeileis A, R-Core (2023) car: Companion to applied regression. https://cran.r-project.org/web/packages/car/index.html

Gettys LA, Thayer KL, Heilman MA, Van Goethem EM (2021) Effect of florpyrauxifen-benzyl concentration-exposure time on hygrophila and rotala. J Aquat Plant Manag 59:79–84

Goode ABC, Tipping PW, Gettys LA, Knowles BK, Pokorny E, Salinas LS (2022) Integrating herbicide rates, coverage, and classical biological control insects ( Megamelus scutellaris, Neochetina eichhorniae, and Neochetina bruchi ) to manage Pontederia ( Eichhornia ) crassipe s. Biol Control 170:104930

Goolsby JA, Makinson J, Purcell M (2000) Seasonal phenology of the gall-making fly Fergusonina sp. (Diptera: Fergusoninidae) and its implications for biological control of Melaleuca quinquenervia . Aust J Entomol 39:336–343

Haller WT (2021) Aquatic herbicide application methods. In: Gettys LA, Haller WT, Petty DG (eds) Biology and control of aquatic plants: a best management practices handbook, 4 th edn. Aquatic Ecosystem Restoration Foundation, Marietta, pp 191–196

Harms NE, Shearer JF (2017) Early-season dynamics of alligatorweed biological control by Agasicles hygrophila in Louisiana and Mississippi. J Aquat Plant Manag 55:89–95

Herrera-Reddy AM, Carruthers RI, Mills NJ (2012) Integrated management of Scotch broom ( Cytisus scoparius ) using biological control. Invasive Plant Sci Manag 5:69–82

Hoffmann JH, Moran VC, Zimmermann HG, Impson FAC (2020) Biocontrol of a prickly pear cactus in South Africa: Reinterpreting the analogous, renowned case in Australia. J Appl Ecol 57:2475–2484

Hofstra DE, Champion PD (2010) Herbicide trials for the control of alligatorweed. J Aquat Plant Manag 48:79–83

Howell AW, Hofstra DE, Heilman MA, Richardson RJ (2022) Response of parrotfeather watermilfoil and alligatorweed to foliar florpyrauxifen-benzyl applications. J Aquat Plant Manage 60:53–61

Julien MH, Bourne AS, Low VHK (1992) Growth of the weed Alternanthera philoxeroides (Martius) Grisebach, (alligator weed) in aquatic and terrestrial habitats in Australia. Plant Prot Q 7:102–108

Julien MH, Skarratt B, Maywald GF (1995) Potential geographical distribution of alligator weed and its biological control by Agasicles hygrophila . J Aquat Plant Manage 33:55–60

Knight IA, Harms NE (2022) Improving biological control of the invasive aquatic weed, Alternanthera philoxeroides : cold tolerance of Amynothrips andersoni (Thysanoptera: Phlaeothripidae) and the short-term feeding impact on different host haplotypes. BioControl 67:443–454

Lavergne S, Molofsky J (2006) Control strategies for the invasive reed canarygrass ( Phalaris arundinacea L.) in North American wetlands: the need for an integrated management plan. Nat Areas J 26:208–214

Lemon J, Bolker B, Oom S, Klein E, Rowlingson B, Wickham H, Tyagi A, Eterradossi O, Grothendieck G, Toews M, Kane J, Turner R, Witthoft C, Stander J, Petzoldt T, Duursma R, Biancotto E, Levy O, Dutang C, Solymos P, Engelmann R, Hecker M, Steinbeck F, Borchers H, Singmann H, Toal T, Ogle D, Baral D, Groemping U, Venables B, The CRAN Team, Murdoch D (2023) plotrix: Various plotting functions. https://cran.r-project.org/web/packages/plotrix/index.html

Maddox DM (1968) Bionomics of an alligatorweed flea beetle, Agasicles sp. in Argentina. Ann Entomol Soc Am 61:1299–1305

Maddox DM, Mayfield A (1979) Biology and life history of Amynothrips andersoni , a thrip for the biological control of alligatorweed. Ann Entomol Soc Am 72:136–140

Mudge CR, Harms NE, Nachtrieb JG (2013) Interactions of herbicides, surfactants, and the giant salvinia weevil ( Cyrtobagous salviniae ) for control of giant salvinia ( Salvinia molesta ). J Aquat Plant Manage 51:77–83

Netherland MD, Getsinger KD, Turner EG (1993) Fluridone concentration and exposure time requirements for control of Eurasian watermilfoil and hydrilla. J Aquat Plant Manage 31:189–194

R Core Team (2021) R: A language and environment for statistical computing. https://www.r-project.org/

Racelis AE, Goolsby JA, Moran P (2009) Seasonality and movement of adventive populations of the arundo wasp (Hymenoptera: Eurytomidae), a biological control agent of giant reed in the Lower Rio Grande Basin in south Texas. Southwest Entomol 34:347–357

Rejmánková E (1992) Ecology of creeping macrophytes with special reference to Ludwigia peploides (H. B. K.) Raven. Aquat Bot 43:283–299

Richardson RJ, Haug EJ, Netherland MD (2016) Response of seven aquatic plants to a new arylpicolinate herbicide. J Aquat Plant Manage 54:26–31

RStudio Team (2020) RStudio: Integrated developement for R. https://www.rstudio.com/

Sánchez-Restrepo AF, Reche VA, Cabrera N, Pan X, Pratt P, Sosa AJ (2023) What distribution models of alligator weed in its native and invaded ranges tell us about its invasion story and biological control. Entomol Exp Appl 171:1009–1018

Steinbauer MJ, Edwards PB, Hoskins M, Schatz T, Forno IW (2000) Seasonal abundance of insect biocontrol agents of Mimosa pigra in the northern territory. Aust J Entomol 39:328–335

Stiling P, Cornelissen T (2005) What makes a successful biocontrol agent? A meta-analysis of biological control agent performance. Biol Control 34:236–246

Tanveer A, Ali HH, Manalil S, Raza A, Chauhan BS (2018) Eco-biology and management of alligator weed [ Alternanthera philoxeroides ) (Mart.) Griseb.]: a review. Wetlands 38:1067–1079

Tipping PW, Gettys LA, Minteer CR, Foley JR, Sardes SN (2017) Herbivory by biological control agents improves herbicidal control of waterhyacinth ( Eichhornia crassipes ). Invasive Plant Sci Manag 10:271–276

US Environmental Protection Agency (2008) Integrated vegetation management. US EPA Office of Pesticide Programs

Vogt GB, McGuire, JU, Cushman AD (1979) Probable evolution and morphological variation in South American disonychine flea beetles (Coleoptera: Chrysomelidae) and their amaranthaceous hosts. United States Department of Agriculture,

Vogt GB, Qumbly, PC, Kay SH (1992) Effects of weather on the biological control of alligatorweed in the lower Mississippi Valley Region, 1973–83. United States Department of Agriculture

Wahl CF, Mudge CR, Diaz R (2018) Does the aquatic herbicide 2,4-D and a nonionic surfactant affect survival of salvinia weevil? J Aquat Plant Manage 56:113–119

Wang Y, Ismail M, Huang W, Wang Y, Ding J (2019) Population dynamics and overwintering of a biological control beetle, Agasicles hygrophila , on a nontarget plant Alternanthera sessilis, along a latitudinal gradient. J Pest Sci 92:835–845

Wickham H, Chang W, Henry L, Pederson TL, Takahashi K, Wilke C, Woo K, Yutani H, Dunnington D, Posit, PBC (2023) ggplot2: Create elegant data visualisations using the grammar of graphics. https://cran.r-project.org/web/packages/ggplot2/index.html

Wickham H (2023) tidyverse: Easily install and load the “tidyverse”. https://cran.r-project.org/web/packages/tidyverse/index.html

Willingham SD, Senseman SA, McCauley GN, Chandler JM (2008) Effect of temperature and propanil on penoxsulam efficacy, absorption, and translocation in alligatorweed ( Alternanthera philoxeroides ). Weed Sci 56:780–784

Willingham SD, Bagavathiannan MV, Carson KS, Cogdill TJ, McCauley GN, Chandler JM (2015) Evaluation of herbicide options for alligatorweed ( Alternanthera philoxeroides ) control in rice. Weed Technol 29:793–799

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Acknowledgements

We thank the Gulf States Marine Fisheries Commission for providing funding for this and other studies on alligatorweed ecology and management (grant # FWS-801-037-2021-MSU). We thank the MidSouth and South Carolina chapters of the Aquatic Plant Management Society whose scholarships helped fund this and other studies on alligatorweed ecology and management. We thank the following employees of the Aquatic Plant Research Facility whose work helped with the execution of this study: Jordan Besson, Dylan Crum, Christopher Grant, Joseph Kauppi, Maxwell Gebhart, Jillian Skidmore, Esther St. Pierre, and Phillip Wittman. We thank Drs. Ian Knight and Nathan Harms for providing insight into the logistics and biology of Amynothrips andersoni rearing and research. Finally, we thank the two anonymous reviewers whose critical reviews and constructive comments helped improve this manuscript.

This research on invasive alligatorweed was funded by the Gulf States Marine Fisheries Commission (grant # FWS-801-037-2021-MSU).

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GT and GNE secured funding necessary to perform this study. All authors contributed to the study conception and design. SAS performed methods, collected data, and analyzed results. SAS drafted the initial version of the manuscript. All authors critically revised subsequent versions of the manuscript. All authors read and approved the final manuscript.

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Schmid, S.A., Turnage, G. & Ervin, G.N. Submersed herbicides and thrips biological control effectively reduce biomass of alligatorweed ( Alternanthera philoxeroides ), a widespread aquatic invasive plant. BioControl (2024). https://doi.org/10.1007/s10526-024-10262-5

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Received : 18 December 2023

Accepted : 13 April 2024

Published : 01 May 2024

DOI : https://doi.org/10.1007/s10526-024-10262-5

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University of Illinois 2024 Weed Science Field Research Tour

Department of Crop Sciences University of Illinois

The weed science program at the University of Illinois invites all weed management practitioners to our annual weed science field tour on Wednesday, June 26 at the Department of Crop Sciences field research location known as the Clem Farm, located at 1114 County Road 1200 East, Champaign. Registration begins at 8:00 a.m. and the tour will start at 9:00 a.m. Preregistration is not required, but please let us know in advance if you will be bringing a large group of participants so we can plan accordingly for meals.

Similar to past years, we will walk/drive to the fields where participants can join in a guided (but informal) tour format. The tour will provide ample opportunity to look at research plots and interact with weed science faculty, staff, and graduate students. Participants can compare their favorite corn and soybean herbicide programs to other commercial programs and get an early look at a few new products that soon will be on the market. The tour will conclude around noon with lunch.

Cost for the weed science tour is $10, which will help defray the cost of the field tour book, refreshments and box lunch. We will apply for 2 hours of CCA credit under the IPM category.  If you have any questions about the weed science tour, please feel free to contact Caleb Wepprecht (815-671-1050, [email protected] ), Jasmine Mausbach (402-885-0745, [email protected] ), or Aaron Hager (217-621-8963, [email protected] ).

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Computer Science > Machine Learning

Title: a comprehensive survey of research towards ai-enabled unmanned aerial systems in pre-, active-, and post-wildfire management.

Abstract: Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although some of the existing survey papers have explored various learning-based approaches, a comprehensive review emphasizing the application of AI-enabled UAV systems and their subsequent impact on multi-stage wildfire management is notably lacking. This survey aims to bridge these gaps by offering a systematic review of the recent state-of-the-art technologies, highlighting the advancements of UAV systems and AI models from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.

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