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首页> 外文期刊>Current Opinion in Cell Biology >Towards a complexity-aware theory of change for participatory research programs working within agricultural innovation systems
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Towards a complexity-aware theory of change for participatory research programs working within agricultural innovation systems

机译：朝着农业创新体系中工作的参与式研究计划的复杂性感知理论

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Agricultural innovation systems (AIS) are increasingly recognized as complex adaptive systems in which interventions cannot be expected to create predictable, linear impacts. Nevertheless, the logic models and theory of change (ToC) used by standard-setting international agricultural research agencies and donors assume that agricultural research will create impact through a predictable linear adoption pathway which largely ignores the complexity dynamics of AIS, and which misses important alternate pathways through which agricultural research can improve system performance and generate sustainable development impact. Despite a growing body of literature calling for more dynamic, flexible and "complexity-aware" approaches to monitoring and evaluation, few concrete examples exist of ToC that takes complexity dynamics within AIS into account, or provide guidance on how such theories could be developed. This paper addresses this gap by presenting an example of how an empirically-grounded, complexity-aware ToC can be developed and what such a model might look like in the context of a particular type of program intervention. Two detailed case studies are presented from an agricultural research program which was explicitly seeking to work in a "complexity-aware" way within aquatic agricultural systems in Zambia and the Philippines. Through an analysis of the outcomes of these interventions, the pathways through which they began to produce impacts, and the causal factors at play, we derive a "complexity-aware" ToC to model how the cases worked. This middle-range model, as well as an overarching model that we derive from it, offer an alternate narrative of how development change can be produced in agricultural systems, one which aligns with insights from complexity science and which, we argue, more closely represents the ways in which many research for development interventions work in practice. The nested ToC offers a starting point for asking a different set of evaluation and research questions which may be more relevant to participatory research efforts working from within a complexity-aware, agricultural innovation systems perspective.
机译：农业创新系统（AIS）越来越被认为是复杂的自适应系统，其中不能预期干预措施创造可预测的线性影响。然而，标准制定国际农业研究机构和捐助者使用的逻辑模型和变革理论（TOC）认为农业研究将通过可预测的线性采用途径产生影响，这主要忽略了AIS的复杂性动态，并错过了重要的交替农业研究可以提高系统性能，产生可持续发展影响的途径。尽管呼吁更加活跃，灵活性和“复杂性感知”的监测和评估方法，但TOC中存在一些具体示例，即在AIS中考虑复杂性动态，或提供如何制定这些理论的指导。本文通过呈现多种经验接地的复杂性无知的TOC的示例来解决这个差距，并且在特定类型的程序干预的上下文中可能看起来像这样的模型。从农业研究计划中提出了两个详细的案例研究，明确寻求以赞比亚和菲律宾的水生农业系统中的“复杂性感知”方式工作。通过分析这些干预措施的结果，他们开始产生影响的途径以及戏剧的因果因素，我们推出了“复杂性感知”的TOC来模拟案件的工作原理。这种中档模型以及我们从中获得的总体模型，提供了在农业系统中产生开发变化的替代叙述，其中一个与复杂性科学的见解一致，我们争论，更紧密地代表许多发展措施研究的方式在实践中工作。嵌套的TOC提供了询问不同一套评估和研究问题的起点，这些问题可能与从复杂性感知，农业创新系统的角度来看的参与性研究努力更相关。

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