Abstract
Herbicide resistance in agricultural weeds is a global problem with an increasing understanding that it is caused by multiple genes leading to quantitative resistance. These quantitative patterns of resistance are not easy to decipher with mortality assays alone, and there is a need for straightforward and unbiased protocols to accurately assess quantitative herbicide resistance. instaGraminoid—a computer vision and statistical analysis package—was developed as an automated and scalable method for quantifying herbicide resistance. The package was tested in rigid ryegrass (Lolium rigidum), the most noxious and highly resistant weed in Australia and the Mediterranean region. This method provides quantitative measures of the degree of chlorosis and necrosis of individual plants which was shown to accurately reflect herbicide resistance. We were able to reliably characterise resistance to four herbicides with different sites of action (glyphosate, sulfometuron, terbuthylazine, and trifluralin) in two L. rigidum populations from Southeast Australia. Cross-validation of the method across populations and herbicide treatments showed high repeatability and transferability. Significant positive correlations in resistance of individual plants were observed across herbicides, which suggest either the accumulation of herbicide-specific resistance alleles in single genotypes (multiple stacked resistance) or the presence of general broad-effects resistance alleles (cross-resistance). We used these quantitative estimates of cross-resistance to simulate how resistance development under an herbicide rotation strategy is likely to be higher than expected.
Highlights
Weeds are a major issue in modern cropping systems with yield losses due to infestation ranging from 7.5% to 10.5% in important crops, i.e., wheat, rice, maize, potato, soybean, and cotton [1, 2]
Based on binary survival data, the survival rates to the different herbicides differed significantly between populations (Table 3)
All the plants were resistant to trifluralin
Summary
Weeds are a major issue in modern cropping systems with yield losses due to infestation ranging from 7.5% to 10.5% in important crops, i.e., wheat, rice, maize, potato, soybean, and cotton [1, 2]. The most widely adopted resistance assays measure mortality by scoring the number of dead and surviving plants after herbicide application These methods generate useful and unambiguous binary data characterising levels of resistance; mortality assays may not be sensitive enough to reveal early signs of resistance that could be helpful for preemptive strategies. Measuring herbicide resistance of individual plants on a quantitative scale can provide the precision needed to finely monitor the development of resistance. This enables a better capture of the underlying genetic nature of resistance by increasing. These programs were written in Bash 4.3 [37] using GNU-parallel version 2016 [38], Python 2.7 [39], using the Open Source Computer Vision (OpenCV 3.4) [40] and Scientific Python libraries (numpy 1.14 and scipy 1.0) [41], R 3.4 [42], using the glmnet 2.0 [36] and ROCR 1.0 [43] packages
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