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

Agronomy

Paul Neve¹  GuillermoR. Chantre²  Rodrigo Werle³  RamonG. Leon⁴  BrianJ. Schutte⁵  ReneVan Acker⁶  MuthukumarV. Bagavathiannan⁷  SantiagoL. Poggio⁸  JoseL. Gonzalez-Andujar⁹  HughJ. Beckie^1,10  GayleJ. Somerville^1,11 

[1] Agriculture & Horticulture Development Board, Stoneleigh Park, Kenilworth CV8 2EQ, UK;Departamento de Agronomía/CERZOS, Universidad Nacional del Sur/CONICET, Bahía Blanca, Buenos Aires 8000, Argentina;Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA;Department of Crop and Soil Sciences, Center for Environmental Farming Systems, Genetic Engineering and Society Center, North Carolina State University, Raleigh, NC 27695, USA;Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM 88003, USA;Department of Plant Agriculture, Ontario Agricultural College, University of Guelph, Guelph, ON N1G 2W1, Canada;Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA;IFEVA, Universidad de Buenos Aires, CONICET. Facultad de Agronomía, Cátedra de Producción Vegetal, Av. San Martín 4453, Buenos Aires C1417DSE, Argentina;Instituto de Agricultura Sostenible (CSIC), 14004 Cordoba, Spain;School of Agriculture and Environment, The University of Western Australia, Perth 6009, Western Australia, Australia;Sustainable Agriculture Sciences, Rothamsted Research, North Wyke EX20 2SB, UK;

关键词: weed seedling emergence;    crop-weed competition;    weed population dynamics;    gene flow;    herbicide resistance;    decision-support tools;   

DOI  :  10.3390/agronomy10101611

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown