Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications

Abstract

In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.

Modelling the Effect and Variability of Integrated Weed Management of Phalaris minor in Rice-Wheat Cropping Systems in Northern India

“…The fast development of machinelearning techniques presents a special opportunity in dealing with these multimodality, multifidelity data, revealing the correlations between intertwined phenomena [40]. Although current machine-learning models in weed science focus mainly on image analysis and physiological predictions of plant growth [41], they have the potential to go further and make more complicated predictions on evolutionary processes such as resistance. A hybrid model between data-driven approaches and knowledge-based mechanistic effect models is a promising direction, as proved by the preliminary success in adjacent disciplines such as hydrology [42], biomedical and human health [40], earth system sciences [43] and environmental sciences [44].…”

“…In addition to herbicide resistance, weeds also show evolutionary adaption to nonchemical control methods [45] (e.g., Echinochloa crus-galli adapted to hand weeding by mimicking the morphological characteristics of rice, A. myosuroides adapted to spring cropping), although this is more likely to happen over a longer time scale, considering the moderate selection pressure as compared to herbicides. Therefore, recurrent parameterisation and recalibration of weed models will be necessary as the environment changes and weed control tactics evolve [15,24,41]. Finally, factors other than weed control and resistance evolution are also important in sustainable agriculture, and an ideal digital tool will also model and balance land value, soil health, water quality, biodiversity and ecosystems.…”

Exploring alternatives for assessing and improving herbicide use in intensive agroecosystems of South Asia: A review

“…Thus, an early detection of herbicide resistance through various techniques viz. hydroponically grown weeds for rapid access to root and shoot growth behaviors, use of selected marker genes which help in identification of those genes which have conferred resistance to various herbicides, or models for better understanding of the management scenarios, and early prediction and risk assessment through longterm field trials studying weed population dynamics for better understanding and timely decision making could prevent or delay the development of herbicide resistance (Bagavathiannan et al, 2020). Physical management of HR weeds includes soil solarization, deep plowing, selection of clean crop seeds, and soil mulching.…”

Review of the Influence of Crop Rotation and Primary Tillage Operations on Weed Seed Bank

“…Regeneration of plants from seed requires that a portion of the seed are at the right time and are physiologically capable to germination Buhler, Hartzler, and Forcella (1997). Prediction of weed seedling emergence is important to estimate stages of growth and weed interference intensities Bagavathiannan et al (2020), which aids to determine the precise time of weed management. Light may be a requirement for germination in most weed seed in the soil seed bank Juroszek et al (2017).…”