Per annum growth rates of exogenous drivers for the historical period 1961–2006

Table 1. Per annum growth rates of exogenous drivers for the historical period 1961–2006. Abstract Global agricultural models are becoming indispensable in the debate over climate change impacts and mitigation policies. Therefore, it is becoming increasingly important to validate these models and identify critical areas for improvement. In this letter, we illustrate both the opportunities and the challenges in undertaking such model validation, using the SIMPLE model of global agriculture. We look back at the long run historical period 1961–2006 and, using a few key historical drivers—population, incomes and total factor productivity—we find that SIMPLE is able to accurately reproduce historical changes in cropland use, crop price, crop production and average crop yields at the global scale. Equally important is our investigation into how the specific assumptions embedded in many agricultural models will likely influence these results. We find that those global models which are largely biophysical—thereby ignoring the price responsiveness of demand and supply—are likely to understate changes in crop production, while failing to capture the changes in cropland use and crop price. Likewise, global models which incorporate economic responses, but do so based on limited time series estimates of these responses, are likely to understate land use change and overstate price changes.

表1. 1961-2006年历史时期外生驱动因素的年均增长率。摘要 全球农业模型在气候变化影响与减缓政策的相关研讨中，已成为不可或缺的核心工具。因此，对这类模型开展验证工作，并明确其亟需优化的关键领域，其重要性与日俱增。在本研究通讯中，我们借助全球农业SIMPLE模型（SIMPLE），阐述了开展此类模型验证所面临的机遇与挑战。我们回溯了1961-2006年这一长期历史时段，选取人口、收入与全要素生产率（Total Factor Productivity）三大核心历史驱动因素展开分析，结果显示，SIMPLE能够精准复刻全球尺度下的耕地利用、作物价格、作物总产量以及作物单产的历史变化趋势。同样值得关注的是，我们还探究了诸多农业模型中嵌入的特定假设，将如何对上述分析结果产生影响。研究发现，那些以生物物理机制为核心的全球模型——即忽略供需价格响应性的模型——往往会低估作物总产量的变化幅度，同时无法准确捕捉耕地利用与作物价格的变动情况。同理，那些纳入了经济响应机制，但仅基于有限的时间序列估算来构建该机制的全球模型，则可能会低估土地利用变化的幅度，同时高估作物价格的变动幅度。