In classic resilience thinking, there is an implicit focus on controlling functional variation to maintain system stability. Modern approaches to resilience thinking deal with complex, adaptive system dynamics and true uncertainty; these contemporary frameworks involve the process of learning to live with change and make use of the consequences of transformation and development. In a socio-environmental context, the identification of metrics by which resilience can be effectively and reliably measured is fundamental to understanding the unique vulnerabilities that characterize coupled human and natural systems. We developed an innovative procedure for stakeholder-friendly quantification of socio-environmental resilience metrics. These metrics were calculated and analyzed through the application of discrete disturbance simulations, which were produced using a dynamically coupled, biophysical-socioeconomic modeling framework. Following the development of a unique shock-response assessment regime, five metrics (time to baseline-level recovery, rate of return to baseline, degree of return to baseline, overall post-disturbance perturbation, and corrective impact of disturbance) describing distinct aspects of systemic resilience were quantified for three agroecosystem variables (farm income, water-table depth, and crop revenue) over a period of 30 years (1989–2019) in the Rechna Doab basin of northeastern Pakistan. Using this procedure, we determined that farm income is the least resilient variable of the three tested. Farm income was easily diverted from the “normal” functional paradigm for the Rechna Doab socio-environmental system, regardless of shock type, intensity, or duration combination. Crop revenue was the least stable variable (i.e., outputs fluctuated significantly between very high and very low values). Water-table depth was consistently the most robust and resistant to change, even under physical shock conditions. The procedure developed here should improve the ease with which stakeholders are able to conduct quantitative resilience analyses.

中文翻译：

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量化动态农业生态系统变量的瞬态冲击响应，以改善社会环境适应能力

在经典的弹性思想中，隐含地关注控制功能变化以维持系统稳定性。弹性思维的现代方法处理复杂的，自适应的系统动力学和真实的不确定性；这些当代框架涉及学习适应变化的过程，并利用变革和发展的后果。在社会环境环境中，识别可以有效和可靠地衡量弹性的指标，对于理解表征人类和自然系统耦合的独特漏洞至关重要。我们开发了一种创新的程序，用于对利益相关者友好的社会环境弹性指标的量化。这些指标是通过应用离散干扰模拟来计算和分析的，它们是使用动态耦合的生物物理-社会经济模型框架制作的。在开发了独特的冲击响应评估机制之后，描述了以下方面的五个指标（到基线水平的恢复时间，回到基线的比率，回到基线的程度，整体扰动后的扰动和矫正的影响）。在巴基斯坦东北部Rechna Doab盆地的30年期间（1989-2019年），对三个农业生态系统变量（农业收入，地下水位深度和农作物收入）的系统适应力进行了量化。使用此程序，我们确定农场收入是三个测试中弹性最小的变量。农场收入很容易从Rechna Doab社会环境系统的“正常”功能范式中转移出来，而不论休克类型，强度，或持续时间组合。作物收入是最不稳定的变量（即，产出在极高和极低之间波动很大）。地下水位深度始终是最坚固的，即使在物理冲击条件下也能抵抗变化。此处开发的程序应提高利益相关者进行定量弹性分析的便利性。