Climate change impact on herbicide efficacy: A model to predict herbicide dose in common bean under different moisture and temperature conditions

In Iran, farmers sow common bean (Phaseolus vulgaris L.) from mid-April to early July. We used different sowing times to mimick the changing conditions expected under climate change to assess future herbicide efficacy. Filed experiments were carried out during 2016–2018 in split plot arrangements with main plots of moisture regimes (MR) consisting of 100, 80, 60% of bean water requirement, and sub-plots of 0, 25, 50, 75 and 100% of the recommended dose (RD) of the herbicide imazethapyr. We found that 1) July plantings resulted in a higher weed biomass and a higher yield loss, 2) weed biomass under 100% MR was higher than with 80 and 60% MR, and 3) 75% of RD decreased weed biomass to less than 10 g m−2 under 100% MR, while with 80 and 60% MR, 100% RD could not decrease weed biomass below 100 g m−2. We used a logistic model (M1) to predict weed biomass (W) changes with herbicide dose (D) at each MR%. Parameter W0 (weed biomass with no herbicide application) showed a linear increase with increasing moisture, while ED50 (the dose to reduce W0 by 50%) and B (the slope parameter) decreased. We replaced W0, ED50 and B with their linear relationships vs. MR% and obtained a more developed model (M2) that describes W with changing D and MR%. We then used M2 as a sub-model in a hyperbolic model for predicting D with any given MR%. The model suggests higher herbicide doses with delayed sowing time and lack of moisture. However, the economic and environmental impacts and high phytotoxic effect on crops prohibits higher herbicide doses demanding an integrated weed management approach to alleviate the reduced herbicide efficacy under future climate conditions.