Abstract: Background: In recent years, the use of simultaneous pesticides in tank mixtures has greatly increased due to cost reduction, improved efficacy and minimizing environmental impact. Additionally, it has been shown that the detoxification of chemicals in plants can be enhanced by the inhibitory capability of some organophosphorus insecticides. Objective: This study aims to assess the joint action of binary mixtures of an insecticide and common herbicides using the additive dose model (ADM) as a reference. Methods: Four separate greenhouse experiments were conducted on Alhagi pseudalhagi (Bieb.) Desv. using seven doses of each of the following herbicides: malathion, 2,4-D, glyphosate, glufosinate-ammonium, and paraquat. Dose-response curves were analyzed with a three-parameter log-logistic model for pure and mixed ratios (100:0, 80:20, 60:40, 50:50, 40:60, 20:80, 0:100) to obtain ED50 values in R software, and ED80 and ED90 values in Excel®. Results: A potent synergism was found for mixtures of malathion with paraquat and/or glufosinate-ammonium on A. pseudalhagi, with sums of toxic units for the 50:50% effect mixture (ΣTU50:50) as low as 0.32 and 0.37, respectively. The mixture of malathion with glyphosate showed moderate synergism with ΣTU50:50 of 0.75. The binary mixture of malathion with 2,4-D followed ADM (additive effects), though it showed a slight synergism (λ-value > 1). Conclusion: This study highlights the importance of understanding synergistic pesticide interactions for more efficient weed management. By optimizing pesticide combinations, we can minimize resistance, reduce environmental impact, and achieve better control. Further research is needed to address limitations of the analysis tools and explore synergistic interactions further. Abstract Background years reduction impact Additionally insecticides Objective (ADM reference Methods Bieb. Bieb (Bieb. Desv 2,4D, 24D D 2,4 D, 2 4 glufosinateammonium, glufosinateammonium glufosinate ammonium, ammonium Doseresponse Dose response threeparameter three parameter loglogistic log logistic 1000, 1000 100 0, 0 (100:0 8020, 8020 80 20, 20 80:20 6040, 6040 60 40, 40 60:40 5050, 5050 50 50, 50:50 4060, 4060 60, 40:60 2080, 2080 80, 20:80 0100 0:100 ED ED5 software ED8 ED9 Excel Excel® Results andor or 5050% 50% ΣTU5050 ΣTU ΣTU50 (ΣTU50:50 032 32 0.3 037 37 0.37 respectively ΣTU50:5 075 75 0.75 2,4D effects, effects , effects) λvalue λ value 1. 1 . 1) Conclusion management combinations resistance control further (Bieb 4D 24 2, 10 (100: 802 8 80:2 604 6 60:4 505 5 50:5 406 40:6 208 20:8 010 0:10 ΣTU505 ΣTU5 (ΣTU50:5 03 3 0. ΣTU50: 07 7 0.7 (100 80: 60: 50: 40: 20: 01 0:1 (ΣTU50: (10 0: (ΣTU50 (1 (ΣTU5 ( (ΣTU