Tracking Ideal Varieties and Cropping Techniques for Agroecological Weed Management: A Simulation-Based Study on Pea

• Published in: Frontiers in plant science Vol. 13; p. 809056
• Main Authors: Colbach, Nathalie, Felten, Emeline, Gée, Christelle, Klein, Antony, Lannuzel, Laura, Lecomte, Christophe, Maillot, Thibault, Strbik, Florence, Villerd, Jean, Moreau, Delphine
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers 04-04-2022; Frontiers Media S.A
• Subjects: Agricultural sciences; Agronomy; ideotype; Life Sciences; pea (Pisum sativum); Plant Science; Sciences and technics of agriculture; trait; Vegetal Biology; weed damage; yield gap; yield loss; multi-criteria decision; pea (Pisum sativum); weed damage; yield gap; trade-off; ideotype; trait; yield loss; Trade-off; Trait; Yield gap; Pea; Yield loss; Multi-criteria decision; Ideotype; Weed damage
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2022.809056

Pea or
L. is a key diversification crop, but current varieties are not very competitive against weeds. The objective was to identify, depending on the type of cropping system and weed flora, (1) the key pea parameters that drive crop production, weed control and weed contribution to biodiversity, (2) optimal combinations of pea-parameter values and crop-management techniques to maximize these goals. For this, virtual experiments were run, using FLORSYS, a mechanistic simulation model. This individual-based 3D model simulates daily crop-weed seed and plant dynamics over the years, from the cropping system and pedoclimate. Here, this model was parameterized for seven pea varieties, from experiments and literature. Moreover, ten virtual varieties were created by randomly combining variety-parameter values according to a Latin Hypercube Sampling (LHS) plan, respecting parameter ranges and correlations observed in the actual varieties. A global sensitivity analysis was run, using another LHS plan to combine pea varieties, crop rotations and management techniques in nine contrasting situations (e.g., conventional vs. organic, no-till, type of weed flora). Simulated data were analyzed with classification and regression trees (CART). We highlighted (1) Parameters that drive potential yield and competitivity against weeds (notably the ability to increase plant height and leaf area in shaded situations), depending on variety type (spring vs. winter) and cropping system. These are pointers for breeding varieties to regulate weeds by biological interactions; (2) Rules to guide farmers to choose the best pea variety, depending on the production goal and the cropping system; (3) The trade-off between increasing yield potential and minimizing yield losses due to weeds when choosing pea variety and management, especially in winter peas. The main pea-variety rules were the same for all performance goals, management strategies, and analyses scales, but further rules were useful for individual goals, strategies, and scales. Some variety features only fitted to particular systems (e.g., delayed pea emergence is only beneficial in case of herbicide-spraying and disastrous in unsprayed systems). Fewer variety rules should be compensated by more management rules. If one of the two main weed-control levers, herbicide or tillage, was eliminated, further pea-variety and/or management rules were needed.