摘要
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 withmortality 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 theMediterranean 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 themethod 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 howresistance development under an herbicide rotation strategy is likely to be higher than expected.
基金
This studywas partially funded by the Computational Biology Research Initiative (CBRI)of the University of Melbourne,Australia.
