Theoretical assessment of persistence and adaptation in weeds with complex life cycles

Abstract

Perennial weed species like Sorghum halepense are a bane of agriculture and lead to significant economic losses. Such weeds can spread quickly and are particularly hard to control due to their ability to reproduce sexually via seeds and asexually through rhizomes. The growing problem of herbicide resistance imposes an additional major challenge on the weed management. Our theoretical study of Sorghum halepense incorporates the complete complex life cycle along with control measures of herbicide application and tillage. We evaluate the extent of control that the different strategies provide and the evolution of herbicide resistance. Our analysis indicates that the natural frequency of target-site resistance mainly depends on the resistance cost and less on its dominance. The sexual phase of the life cycle, including self-pollination and seed bank dynamics, contributes substantially to the persistence and rapid adaptation of the weed. For example, the seed bank significantly increases the probability of escape from control while maintaining genetic variation. We also extend our analysis to study herbicide mixtures, rotations and treatment combinations and how they contribute to reducing control failure. Our model is rooted in observational and experimental data on Sorghum halepense. Still, the general methodology developed herein is extendable to other perennial weeds comprising a complex life cycle. This approach shows how an integrated interdisciplinary view can synergistically contribute to understanding and tackling the global threat to food production presented by resistant weeds.

Optimizing crop rotations via Parrondo’s paradox for sustainable agriculture

“…The general result can be tailored to the specific properties of the crops, such as the threshold soil quality required to grow the cash crop and how fast the cover crop can replenish the soil quality. The probabilities of profit making are often complex combinations of social, economic and evolutionary parameters such as the presence and intensity of crop pests like pathogens and weeds [ 8 , 28 ]. For crop systems, these probabiltities can then be estimated to develop a system-specific model.…”