Modeling Weed Seedling Emergence for Time-Specific Weed Management: A Systematic Review

• Published in: Weed science 2024-07, Vol.72 (4), p.313-329
• Main Authors: Marschner, Caroline A., Colucci, Isabella, Stup, Rebecca S., Westbrook, Anna S., Brunharo, Caio A. C. G., DiTommaso, Antonio, Mesgaran, Mohsen B.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Base temperature; base water potential; Climate change; Climate models; Context; Dormancy; Effectiveness; Environmental conditions; Environmental management; Germination; hydrothermal time prediction; Neural networks; Review; REVIEWS; seed dormancy; seed germination; Seedlings; Seeds; Soft computing; Systematic review; Temperature requirements; thermal time; Water potential; Weed control; Weeds
• ISSN: 0043-1745; 1550-2759
• DOI: 10.1017/wsc.2024.25

Understanding the timing of weed emergence is crucial to effective management. Management practices implemented too early may fail to completely control late-emerging seedlings, whereas management practices implemented too late will suffer from low efficacy. Weed emergence times reflect biological factors, such as seed dormancy and germination requirements, as well as environmental conditions. We conducted a systematic review of studies that developed models to predict weed emergence temporal patterns. We screened 1,854 studies, 98 of which were included in the final dataset. Most of the studies included were conducted in North America (51%) or Europe (30%). A wide variety of weed species (102) and families (21) were included, and many studies modeled several weeds. Grass weeds (Poaceae) were modeled most frequently (83 instances). Most weeds (40%) had base temperature Tb values between 0 and 5 C, and 38% had base water potential cb ranging from –1.0 to –0.5 MPa. Most studies used empirical parametric models, such as Weibull (40%) or Gompertz (30%) models. Nonparametric and mechanistic models were also represented. Models varied in their biological and environmental data requirements. In general, empirical parametric models based on hydrothermal time (i.e., time above base temperature and water potential thresholds) represented a good balance between ease of use and prediction accuracy. Soft computing approaches such as artificial neural networks demonstrated substantial potential in situations with complex emergence patterns and limited data availability, although they (soft computing approaches) can be susceptible to overfitting. Our study also demonstrated variability in model performance and limited generalizability across species and regions. This finding underscores the need for context-specific and well-validated weed emergence models to inform management, especially in the context of climate change.