CONTEXT

The COVID-19 pandemic has resulted in immediate and wide impacts on human and agricultural systems. While some of the positive and negative impacts of COVID-19 on the environment and economies are emerging, there is not a comprehensive understanding of the potential impacts of COVID-19 on the most vulnerable farmers.

OBJECTIVE

The purpose of this study is to evaluate the immediate impacts of COVID-19 on agricultural and food systems in the United States. Our aim is to quantify the impacts on labor productivity in crops and livestock production considering the heterogenous vulnerability of different farmworkers. We are interested in measuring the production that is not realized due to COVID-19.

METHODS

In this paper, we introduce IMLAP, Immediate impact Model of Local Agricultural Production. This model is an economic framework considering short-term agricultural production responses to economic, environmental, and policy changes. We investigate the potential impacts of COVID-19 on the farmers in the U.S. for each county with a special focus on female, Hispanic, black and African American, and small-scale producers.

RESULTS AND CONCLUSIONS

Considering the impacts of COVID-19 on labor, the findings of this study suggest a decline in agricultural output in all the U.S. counties ranging from 1.18% to 7.14% of total production. Our simulation results show that counties with a higher number of small-scale farms, non-white farmers, and female-operated farms are the most vulnerable to COVID-19. Also, we argue that the stimulus policies and support packages must target these communities of producers to ensure that their livelihood is protected. The findings suggest that productivity growth (technological improvements) and international trade can eliminate the negative impacts of pandemics.

SIGNIFICANCE

The proposed quantitative framework of this study is a simple yet novel model that empowers diverse research communities to provide a quick analysis of the impacts of unprecedented events. It offers a holistic framework to evaluate the response of agricultural production to changes in availability and productivity of labor, machinery & equipment, land, fertilizer, seeds, and other inputs. This study presents new foundations for agricultural research communities to provide solutions to agricultural resilience challenges and highlights the significance of demand drivers, technological growth, and international trade in strengthening the food system.

中文翻译：

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使用当地农业生产的直接影响模型 (IMLAP) 评估 COVID-19 对美国县的影响

语境

COVID-19 大流行对人类和农业系统造成了直接而广泛的影响。虽然 COVID-19 对环境和经济的一些正面和负面影响正在显现，但人们对 COVID-19 对最脆弱农民的潜在影响还没有全面的了解。

客观的

本研究的目的是评估 COVID-19 对美国农业和粮食系统的直接影响。我们的目标是在考虑到不同农场工人的异质性脆弱性的情况下，量化对作物和畜牧业劳动生产率的影响。我们有兴趣衡量因 COVID-19 而未实现的产量。

方法

在本文中，我们介绍了 IMLAP，即当地农业生产的即时影响模型。该模型是一个经济框架，考虑了短期农业生产对经济、环境和政策变化的反应。我们调查了 COVID-19 对美国每个县农民的潜在影响，特别关注女性、西班牙裔、黑人和非裔美国人以及小规模生产者。

结果和结论

考虑到 COVID-19 对劳动力的影响，这项研究的结果表明美国所有县的农业产量下降了 1.18% 到 7.14% 不等。我们的模拟结果表明，小型农场、非白人农民和女性经营的农场数量较多的县最容易受到 COVID-19 的影响。此外，我们认为刺激政策和支持计划必须针对这些生产者社区，以确保他们的生计得到保护。研究结果表明，生产力增长（技术改进）和国际贸易可以消除流行病的负面影响。

意义

本研究提出的定量框架是一个简单而新颖的模型，它使不同的研究团体能够快速分析前所未有的事件的影响。它提供了一个整体框架来评估农业生产对劳动力、机械和设备、土地、肥料、种子和其他投入的可用性和生产力变化的反应。本研究为农业研究团体提供新的基础，以提供应对农业弹性挑战的解决方案，并强调需求驱动因素、技术增长和国际贸易在加强粮食系统方面的重要性。