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农业生态杂草管理的理想品种追踪与种植技术:基于豌豆的模拟研究

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

作者信息

Colbach Nathalie, Felten Emeline, Gée Christelle, Klein Antony, Lannuzel Laura, Lecomte Christophe, Maillot Thibault, Strbik Florence, Villerd Jean, Moreau Delphine

机构信息

Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, F-21000, Dijon, France.

出版信息

Front Plant Sci. 2022 Apr 4;13:809056. doi: 10.3389/fpls.2022.809056. eCollection 2022.

DOI:10.3389/fpls.2022.809056
PMID:35444680
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9014269/
Abstract

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.

摘要

豌豆(Pisum sativum L.)是一种关键的多样化作物,但目前的品种在与杂草竞争方面竞争力不强。目标是根据种植系统类型和杂草群落,确定(1)驱动作物产量、杂草控制以及杂草对生物多样性贡献的关键豌豆参数,(2)豌豆参数值与作物管理技术的最佳组合,以实现这些目标的最大化。为此,使用了基于机理的模拟模型FLORSYS进行虚拟实验。这个基于个体的三维模型模拟多年来每日作物 - 杂草种子和植株动态,涵盖种植系统和土壤气候条件。在此,根据实验和文献对该模型针对七个豌豆品种进行了参数化。此外,根据拉丁超立方抽样(LHS)计划,在尊重实际品种中观察到的参数范围和相关性的前提下,通过随机组合品种参数值创建了十个虚拟品种。使用另一个LHS计划在九种对比情况下(例如,传统种植与有机种植、免耕、杂草群落类型)组合豌豆品种、作物轮作和管理技术,进行了全局敏感性分析。使用分类和回归树(CART)对模拟数据进行了分析。我们强调了(1)根据品种类型(春播与冬播)和种植系统,驱动潜在产量和与杂草竞争力的参数(特别是在遮荫情况下增加株高和叶面积的能力)。这些是通过生物相互作用培育调控杂草品种的指标;(2)根据生产目标和种植系统指导农民选择最佳豌豆品种的规则;(3)在选择豌豆品种和管理方式时,特别是对于冬豌豆,在提高产量潜力与最小化因杂草造成的产量损失之间的权衡。对于所有性能目标、管理策略和分析尺度,主要的豌豆品种规则是相同的,但针对个别目标、策略和尺度还有其他有用的规则。一些品种特征仅适用于特定系统(例如,豌豆出苗延迟仅在喷施除草剂的情况下有益,而在未喷施除草剂的系统中则是灾难性的)。较少的品种规则应由更多的管理规则来补充。如果消除两种主要杂草控制手段之一(除草剂或耕作),则需要进一步的豌豆品种和/或管理规则。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f36/9014269/1d756b72aa51/fpls-13-809056-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f36/9014269/cd3e3a40a322/fpls-13-809056-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f36/9014269/2f7112c85054/fpls-13-809056-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f36/9014269/1d756b72aa51/fpls-13-809056-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f36/9014269/cd3e3a40a322/fpls-13-809056-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f36/9014269/2f7112c85054/fpls-13-809056-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f36/9014269/1d756b72aa51/fpls-13-809056-g003.jpg

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