scholarly journals Evolutionary ecology of herbicide resistance and its future perspective

2021 ◽  
Vol 66 (2) ◽  
pp. 59-71
Author(s):  
Yuya Fukano ◽  
Chikara Hosoda ◽  
Noriko Maruyama
Keyword(s):  
Herbicide Resistance ◽  
Evolutionary Ecology ◽  
Future Perspective

Assessing Fitness Costs from a Herbicide-Resistance Management Perspective: A Review and Insight

Weed Science ◽  
2018 ◽  
Vol 67 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Eshagh Keshtkar ◽  
Roohollah Abdolshahi ◽  
Hamidreza Sasanfar ◽  
Eskandar Zand ◽  
Roland Beffa ◽  
...  
Keyword(s):  
Herbicide Resistance ◽  
Weed Management ◽  
Genetic Background ◽  
Management Practices ◽  
Evolutionary Ecology ◽  
Resistance Management ◽  
Single Population ◽  
Herbicide Resistant ◽  
Scientific Disciplines ◽  
Cost Studies

AbstractIn recent years, herbicide resistance has attracted much attention as an increasingly urgent problem worldwide. Unfortunately, most of that effort was focused on confirmation of resistance and characterization of the mechanisms of resistance. For management purposes, knowledge about biology and ecology of the resistant weed phenotypes is critical. This includes fitness of the resistant biotypes compared with the corresponding wild biotypes. Accordingly, fitness has been the subject of many studies; however, lack of consensus on the concept of fitness resulted in poor experimental designs and misinterpretation of the ensuing data. In recent years, methodological protocols for conducting proper fitness studies have been proposed; however, we think these methods should be reconsidered from a herbicide-resistance management viewpoint. In addition, a discussion of the inherent challenges associated with fitness cost studies is pertinent. We believe that the methodological requirements for fitness studies of herbicide-resistant weed biotypes might differ from those applied in other scientific disciplines such as evolutionary ecology and genetics. Moreover, another important question is to what extent controlling genetic background is necessary when the aim of a fitness study is developing management practices for resistant biotypes. Among the methods available to control genetic background, we suggest two approaches (single population and pedigreed lines) as the most appropriate methods to detect differences between resistant (R) and susceptible (S) populations and to derive herbicide-resistant weed management programs. Based on these two methods, we suggest two new approaches that we named the “recurrent single population” and “recurrent pedigreed lines” methods. Importantly, whenever the aim of a fitness study is to develop optimal resistance management, we suggest selecting R and S plants within a single population and evaluating all fitness components from seed to seed instead of measuring changes in the frequency of R and S alleles through multigenerational fitness studies.

scholarly journals Understanding the nexus of rising CO2, climate change, and evolution in weed biology

2019 ◽  
Vol 12 (02) ◽  
pp. 79-88 ◽  
Author(s):  
Lewis H. Ziska ◽  
Dana M. Blumenthal ◽  
Steven J. Franks
Keyword(s):  
Climate Change ◽  
Herbicide Resistance ◽  
Atmospheric Carbon Dioxide ◽  
Evolutionary Ecology ◽  
Current Data ◽  
Weed Species ◽  
Weed Science ◽  
Weed Biology ◽  
Short Period ◽  
Weed Distribution

AbstractRapid increases in herbicide resistance have highlighted the ability of weeds to undergo genetic change within a short period of time. That change, in turn, has resulted in an increasing emphasis in weed science on the evolutionary ecology and potential adaptation of weeds to herbicide selection. Here we argue that a similar emphasis would also be invaluable for understanding another challenge that will profoundly alter weed biology: the rapid rise in atmospheric carbon dioxide (CO2) and the associated changes in climate. Our review of the literature suggests that elevated CO2 and climate change will impose strong selection pressures on weeds and that weeds will often have the capacity to respond with rapid adaptive evolution. Based on current data, climate change and rising CO2 levels are likely to alter the evolution of agronomic and invasive weeds, with consequences for distribution, community composition, and herbicide efficacy. In addition, we identify four key areas that represent clear knowledge gaps in weed evolution: (1) differential herbicide resistance in response to a rapidly changing CO2/climate confluence; (2) shifts in the efficacy of biological constraints (e.g., pathogens) and resultant selection shifts in affected weed species; (3) climate-induced phenological shifts in weed distribution, demography, and fitness relative to crop systems; and (4) understanding and characterization of epigenetics and the differential expression of phenotypic plasticity versus evolutionary adaptation. These consequences, in turn, should be of fundamental interest to the weed science community.

New Technologies to Combat Herbicide Resistance

2020 ◽  
Vol 31 (2) ◽  
pp. 90-92
Author(s):  
Rob Edwards
Keyword(s):  
Winter Wheat ◽  
Herbicide Resistance ◽  
Cover Crops ◽  
New Technologies ◽  
Green Revolution ◽  
Arable Land ◽  
Limited Range ◽  
Cultural Control ◽  
Arable Farming ◽  
The Uk

Herbicide resistance in problem weeds is now a major threat to global food production, being particularly widespread in wild grasses affecting cereal crops. In the UK, black-grass (Alopecurus myosuroides) holds the title of number one agronomic problem in winter wheat, with the loss of production associated with herbicide resistance now estimated to cost the farming sector at least £0.5 billion p.a. Black-grass presents us with many of the characteristic traits of a problem weed; being highly competitive, genetically diverse and obligately out-crossing, with a growth habit that matches winter wheat. With the UK’s limited arable crop rotations and the reliance on the repeated use of a very limited range of selective herbicides we have been continuously performing a classic Darwinian selection for resistance traits in weeds that possess great genetic diversity and plasticity in their growth habits. The result has been inevitable; the steady rise of herbicide resistance across the UK, which now affects over 2.1 million hectares of some of our best arable land. Once the resistance genie is out of the bottle, it has proven difficult to prevent its establishment and spread. With the selective herbicide option being no longer effective, the options are to revert to cultural control; changing rotations and cover crops, manual rogueing of weeds, deep ploughing and chemical mulching with total herbicides such as glyphosate. While new precision weeding technologies are being developed, their cost and scalability in arable farming remains unproven. As an agricultural scientist who has spent a working lifetime researching selective weed control, we seem to be giving up on a technology that has been a foundation stone of the green revolution. For me it begs the question, are we really unable to use modern chemical and biological technology to counter resistance? I would argue the answer to that question is most patently no; solutions are around the corner if we choose to develop them.

FUTURE PERSPECTIVE OF PLANT BIOTECHNOLOGY: A REVIEW

2020 ◽  
Vol 1 (1) ◽  
pp. 36-41
Author(s):  
Gaurav Ranabhat ◽  
Ashmita Dhakal ◽  
Saurav Ranabhat ◽  
Ananta Dhakal ◽  
Rakshya Aryal
Keyword(s):  
Insect Pest ◽  
Ornamental Plants ◽  
Herbicide Tolerance ◽  
Plant Biotechnology ◽  
Future Perspective ◽  
New Development ◽  
Recent Developments ◽  
Modern Biotechnology ◽  
Stable Yield ◽  
As Stress

Modern biotechnology enables an organism to produce a totally new product which the organism does not or cannot produce normally through the incorporation of the technology of ‘Genetic engineering’. Biotechnology shows its technical merits and new development prospects in breeding of new plants varieties with high and stable yield, good quality, as well as stress tolerance and resistance. Some of the most prevailing problems faced in agricultural ecosystems could be solved with the introduction of transgenic crops incorporated with traits for insect pest resistance, herbicide tolerance and resistance to viral diseases. Plant biotechnology has gained importance in the recent past for increasing the quality and quantity of agricultural, horticultural, ornamental plants, and in manipulating the plants for improved agronomic performance. Recent developments in the genome sequencing will have far reaching implications for future agriculture. From this study, we can know that the developing world adopts these fast-changing technologies soon and harness their unprecedented potential for the future benefit of human being.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

2019 ◽  
Vol 20 (3) ◽  
pp. 170-176 ◽  
Author(s):  
Zhongyan Li ◽  
Qingqing Miao ◽  
Fugang Yan ◽  
Yang Meng ◽  
Peng Zhou
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Peptide Binding ◽  
Machine Learning Techniques ◽  
Future Perspective ◽  
Peptide Design ◽  
Human Interactome ◽  
Therapeutic Peptide ◽  
Peptide Affinity ◽  
Peptide Binding Affinity

Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Sign in / Sign up

Export Citation Format

Share Document