Colbach Nathalie
INRA, UMR 1210 Biologie et Gestion des Adventices, BP 86510, 17 rue Sully, 21065, Dijon Cedex, France.
Environ Sci Pollut Res Int. 2009 May;16(3):348-60. doi: 10.1007/s11356-008-0080-6. Epub 2008 Dec 9.
BACKGROUND, AIM AND SCOPE: Agricultural landscapes comprise cultivated fields and semi-natural areas. Biological components of these compartments such as weeds, insect pests and pathogenic fungi can disperse sometimes over very large distances, colonise new habitats via insect flight, spores, pollen or seeds and are responsible for losses in crop yield (e.g. weeds, pathogens) and biodiversity (e.g. invasive weeds). The spatiotemporal dynamics of these biological components interact with crop locations, successions and management as well as the location and management of semi-natural areas such as roadverges. The objective of this investigation was to establish a modelling and simulation methodology for describing, analysing and predicting spatiotemporal dynamics and genetics of biological components of agricultural landscapes. The ultimate aim of the models was to evaluate and propose innovative cropping systems adapted to particular agricultural concerns. The method was applied to oilseed rape (OSR) volunteers playing a key role for the coexistence of genetically modified (GM) and non-GM oilseed rape crops, where the adventitious presence of GM seeds in non-GM harvests (AGMP) could result in financial losses for farmers and cooperatives.
A multi-year, spatially explicit model was built, using field patterns, climate, cropping systems and OSR varieties as input variables, focusing on processes and cultivation techniques crucial for plant densities and pollen flow. The sensitivity of the model to input variables was analysed to identify the major cropping factors. These should be modified first when searching for solutions limiting gene flow. The sensitivity to model processes and species life-traits were analysed to facilitate the future adaptation of the model to other species. The model was evaluated by comparing its simulations to independent field observations to determine its domain of validity and prediction error.
The cropping system study determined contrasted farm types, simulated the current situation and tested a large range of modifications compatible with each farm to identify solutions for reducing the AGMP. The landscape study simulated gene flow in a large number of actual and virtual field patterns, four combinations of regional OSR and GM proportions and three contrasted cropping systems. The analysis of the AGMP rate at the landscape level determined a maximum acceptable GM OSR area for the different cropping systems, depending on the regional OSR volunteer infestation. The analysis at the field level determined minimum distances between GM and non-GM crops, again for different cropping systems and volunteer infestations.
The main challenge in building spatially explicit models of the effects of cropping systems and landscape patterns on species dynamics and gene flow is to determine the spatial extent, the time scale, the major processes and the degree of mechanistic description to include in the model, depending on the species characteristics and the model objective.
These models can be used to study the effects of cropping systems and landscape patterns over a large range of situations. The interactions between the two aspects make it impossible to extrapolate conclusions from individual studies to other cases. The advantage of the present method was to produce conclusions for several contrasted farm types and to establish recommendations valid for a large range of situations by testing numerous landscapes with contrasted cropping systems. Depending on the level of investigation (region or field), these recommendations concern different decision-makers, either farmers and technical advisors or cooperatives and public decision-makers.
The present simulation study showed that gene flow between coexisting GM and non-GM varieties is inevitable. The management of OSR volunteers is crucial for containing gene flow, and the cropping system study identified solutions for reducing these volunteers and ferals in and outside fields. Only if these are controlled can additional measures such as isolation distances between GM and non-GM crops or limiting the proportion of the region grown with GM OSR be efficient. In addition, particular OSR varieties contribute to limit gene flow. The technical, organisational and financial feasibility of the proposed measures remains to be evaluated by a multi-disciplinary team.
背景、目的与范围：农业景观由耕地和半自然区域组成。这些区域中的生物成分，如杂草、害虫和致病真菌，有时能远距离扩散，通过昆虫飞行、孢子、花粉或种子在新栖息地定殖，从而导致作物产量损失（如杂草、病原体）和生物多样性丧失（如入侵杂草）。这些生物成分的时空动态与作物布局、轮作及管理，以及半自然区域（如路边）的位置和管理相互作用。本研究的目的是建立一种建模与模拟方法，用于描述、分析和预测农业景观生物成分的时空动态及遗传特性。这些模型的最终目标是评估并提出适应特定农业需求的创新种植系统。该方法应用于对转基因和非转基因油菜作物共存起关键作用的油菜自生苗，因为非转基因收获物中偶然出现转基因种子（无意混杂）会给农民和合作社造成经济损失。
构建了一个多年期、具有空间明确性的模型，将田间格局、气候、种植系统和油菜品种作为输入变量，重点关注对植株密度和花粉传播至关重要的过程及栽培技术。分析了模型对输入变量的敏感性，以确定主要种植因素。在寻找限制基因流动的解决方案时，应首先对这些因素进行调整。分析了模型对过程和物种生活特性的敏感性，以便未来使模型能适用于其他物种。通过将模型模拟结果与独立的田间观测数据进行比较来评估模型，以确定其有效范围和预测误差。
种植系统研究确定了不同类型的农场，模拟了当前状况，并测试了一系列与每个农场相匹配的调整措施，以找出减少无意混杂的解决方案。景观研究在大量实际和虚拟田间格局、区域油菜与转基因油菜比例的四种组合以及三种不同种植系统中模拟了基因流动。在景观层面分析无意混杂率，根据区域油菜自生苗侵染情况，确定了不同种植系统下转基因油菜的最大可接受种植面积。在田间层面进行分析，同样针对不同种植系统和自生苗侵染情况，确定了转基因作物与非转基因作物之间的最小距离。
构建关于种植系统和景观格局对物种动态及基因流动影响的具有空间明确性的模型时，主要挑战在于根据物种特性和模型目标，确定模型应涵盖的空间范围、时间尺度、主要过程以及机制描述程度。
这些模型可用于研究多种情况下种植系统和景观格局的影响。这两个方面的相互作用使得无法将个别研究的结论外推至其他情况。本方法的优点是针对多种不同类型的农场得出结论，并通过测试众多具有不同种植系统的景观，建立适用于多种情况的建议。根据研究层面（区域或田间）的不同，这些建议针对不同的决策者，即农民和技术顾问或合作社及公共决策者。
当前的模拟研究表明，共存的转基因和非转基因品种之间的基因流动是不可避免的。控制油菜自生苗对于遏制基因流动至关重要，种植系统研究找出了减少田间内外自生苗和野生植株的解决方案。只有控制住这些，诸如转基因作物与非转基因作物之间的隔离距离或限制种植转基因油菜的区域比例等额外措施才会有效。此外，特定的油菜品种有助于限制基因流动。所提出措施在技术、组织和财务方面的可行性仍有待多学科团队进行评估。
