Translating theory into practice: A flexible decision-making tool to support the design and implementation of climate-smart agriculture projects

IF 6.1 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Agricultural Systems Pub Date : 2024-07-11 DOI:10.1016/j.agsy.2024.104060

Conor Walsh , Mara Renn , Dominik Klauser , Alessandro de Pinto , Jeremy Haggar , Rouf Abdur , Richard J. Hopkins , Farhad Zamil

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Abstract

CONTEXT

Climate-smart agriculture (CSA) is a conceptual framework for responding climate-related risk in agriculture across the three pillars of Mitigation, Resilience, and Production. Existing tools have been developed which seek to operationalise the CSA concept to evaluate and benchmark progress; each of which have their own relative strengths and weaknesses.

OBJECTIVE

The translation of this concept into actionable projects/portfolios hence requires the careful evaluation of potential trade-offs and synergies between these three pillars. The hereby presented decision-making tool aims to offer a basis for a structured evaluation of such trade-offs and synergies.

METHODS

It does so by assessing five different outcome pathways on how they contribute to a project's performance across the three pillars of CSA. We aspire that the use of this tool will allow for more deliberate design and implementation of projects in agricultural development, increasing the resilience and productivity of farming systems whilst ensuring the sustainable use of the environmental resource-based agriculture depends on.

RESULTS AND CONCLUSIONS

This tool was applied in a workshop setting to evaluate the relative strengths and weaknesses of two distinct projects; demonstrating the utility in visualising the same performance in different ways. Of particular importance was ability to demonstrate how focusing on productivity and adaptation may trade-off mitigation activities.

SIGNIFICANCE

The results of the case study application demonstrated the challenge in meeting all the CSA requirements; particularly where the main objective of a project is to enhance and increase productivity. This reinforces how supporting all three pillars is challenging for a single project and therefore CSA is arguably more achievable when viewed in terms of a portfolio of activities which can collectively compensate for the limitations of a single project.

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将理论转化为实践：支持设计和实施气候智能型农业项目的灵活决策工具

背景气候智能型农业（CSA）是一个概念框架，用于应对农业中与气候相关的风险，包括减缓、抗灾和生产三大支柱。将这一概念转化为可操作的项目/组合需要仔细评估这三大支柱之间的潜在权衡和协同作用。本文介绍的决策工具旨在为有条理地评估此类权衡和协同作用奠定基础。方法通过评估五种不同的成果途径来实现这一目标，即这些途径如何促进项目在 "全面综合评估 "三大支柱方面的绩效。我们希望，通过使用该工具，能够更加审慎地设计和实施农业发展项目，提高农业系统的复原力和生产力，同时确保可持续地利用农业赖以生存的环境资源。结果与结论在研讨会上应用该工具评估了两个不同项目的相对优势和劣势；证明了以不同方式可视化相同绩效的实用性。特别重要的是，该工具能够展示关注生产率和适应性可能会对减缓活动产生的影响。意义案例研究的应用结果表明，要满足 CSA 的所有要求是一项挑战；尤其是在项目的主要目标是提高生产率的情况下。这进一步说明了支持所有三大支柱对于单个项目而言是多么具有挑战性，因此可以说，如果从活动组合的角度来看，全面综合评估更容易实现，因为这些活动可以共同弥补单个项目的局限性。

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来源期刊

Agricultural Systems 农林科学-农业综合

CiteScore

13.30

自引率

7.60%

发文量

174

审稿时长

30 days

期刊介绍： Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments. The scope includes the development and application of systems analysis methodologies in the following areas: Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making; The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment; Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems; Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.