Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications
Muthukumar V. Bagavathiannan,
Hugh J. Beckie,
Guillermo R. Chantre,
Jose L. Gonzalez-Andujar,
Ramon G. Leon,
Paul Neve,
Santiago L. Poggio,
Brian J. Schutte,
Gayle J. Somerville,
Rodrigo Werle,
Rene Van Acker
Affiliations
Muthukumar V. Bagavathiannan
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
Hugh J. Beckie
School of Agriculture and Environment, The University of Western Australia, Perth 6009, Western Australia, Australia
Guillermo R. Chantre
Departamento de Agronomía/CERZOS, Universidad Nacional del Sur/CONICET, Bahía Blanca, Buenos Aires 8000, Argentina
Jose L. Gonzalez-Andujar
Instituto de Agricultura Sostenible (CSIC), 14004 Cordoba, Spain
Ramon G. Leon
Department of Crop and Soil Sciences, Center for Environmental Farming Systems, Genetic Engineering and Society Center, North Carolina State University, Raleigh, NC 27695, USA
Paul Neve
Agriculture & Horticulture Development Board, Stoneleigh Park, Kenilworth CV8 2EQ, UK
Santiago L. Poggio
IFEVA, Universidad de Buenos Aires, CONICET. Facultad de Agronomía, Cátedra de Producción Vegetal, Av. San Martín 4453, Buenos Aires C1417DSE, Argentina
Brian J. Schutte
Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM 88003, USA
Gayle J. Somerville
Sustainable Agriculture Sciences, Rothamsted Research, North Wyke EX20 2SB, UK
Rodrigo Werle
Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
Rene Van Acker
Department of Plant Agriculture, Ontario Agricultural College, University of Guelph, Guelph, ON N1G 2W1, Canada
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.
