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A new approach for modeling crop-weed interaction targeting management support in operational contexts: A case study on the rice weeds barnyardgrass and red rice

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  • Movedi, Ermes
  • Valiante, Daniele
  • Colosio, Alessandro
  • Corengia, Luca
  • Cossa, Stefano
  • Confalonieri, Roberto

Abstract

Despite their potential to support the optimization of weed management, available ecophysiological models for the simulation of crop-weed interaction are still not adopted in operational contexts. For some of them the reasons deal with the insufficient validation in farming conditions, whereas others are either too complex for being used in operational contexts or too empiric for being free from site- or context-specific effects. Here we present a new approach (WeedyCoSMo) to support strategic decisions on weed management, derived from the CoSMo process-based model for the simulation of phytocoenosis dynamics. The model dynamically reproduces on a yearly basis the interaction between crop and weeds at canopy level through the daily quantification of the suitability of each species to weather conditions and management practices, as well as to the simulated system state variables. Dynamically predicted outputs are the relative abundance of crop and weeds and state variables for each species like, e.g., aboveground biomass, biomass of different plant organs, grain yield, leaf area index, plant height. WeedyCoSMo was calibrated and validated using data from different sites (in the Jiangsu province, China, and in Arkansas, USA) and years (from 1982 to 2014), where different rice varieties and two major rice weeds–i.e., red rice (Oryza sativa L., var. sylvatica) and barnyardgrass (Echinocloa crus-galli L.) – were grown in monoculture or mixture. Model performances were satisfying: for rice crops grown in interaction with weeds, relative root mean square error never exceeded 25.2%, regardless of the variable considered, and Nash-Sutcliffe modeling efficiency was always higher than 0.63. Despite the low number of inputs and parameters needed to run the simulations, the degree of accuracy was similar to the ones achieved with other models for crop-weed interaction. This allows considering WeedyCoSMo as a promising approach in light of the possible integration in decision support systems targeting operational farming conditions.

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  • Movedi, Ermes & Valiante, Daniele & Colosio, Alessandro & Corengia, Luca & Cossa, Stefano & Confalonieri, Roberto, 2022. "A new approach for modeling crop-weed interaction targeting management support in operational contexts: A case study on the rice weeds barnyardgrass and red rice," Ecological Modelling, Elsevier, vol. 463(C).
    • Handle: RePEc:eee:ecomod:v:463:y:2022:i:c:s0304380021003434
    DOI: 10.1016/j.ecolmodel.2021.109797
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    1. Marios Vasileiou & Leonidas Sotirios Kyrgiakos & Christina Kleisiari & Georgios Kleftodimos & George Vlontzos & Hatem Belhouchette & Panos M. Pardalos, 2024. "Transforming weed management in sustainable agriculture with artificial intelligence: a systematic literature review towards weed identification and deep learning," Post-Print hal-04297703, HAL.
    2. Movedi, Ermes & Paleari, Livia & Argenti, Giovanni & Vesely, Fosco M. & Staglianò, Nicolina & Parrini, Silvia & Confalonieri, Roberto, 2024. "The application of a plant community model to evaluate adaptation strategies for alleviating climate change impacts on grassland productivity, biodiversity and forage quality," Ecological Modelling, Elsevier, vol. 488(C).

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