Department of Biology, University of Vermont, Burlington, VT 05405.
Gund Institute for Environment, University of Vermont, Burlington, VT 05405.
Proc Natl Acad Sci U S A. 2024 Oct 15;121(42):e2402195121. doi: 10.1073/pnas.2402195121. Epub 2024 Oct 7.
Crop switching, in which farmers grow a crop that is novel to a given field, can help agricultural systems adapt to changing environmental, cultural, and market forces. Yet while regional crop production trends receive significant attention, relatively little is known about the local-scale crop switching that underlies these macrotrends. We characterized local crop-switching patterns across the United States using the US Department of Agriculture (USDA) Cropland Data Layer, an annual time series of high resolution (30 m pixel size) remote-sensed cropland data from 2008 to 2022. We found that at multiple spatial scales, crop switching was most common in sparsely cultivated landscapes and in landscapes with high crop diversity, whereas it was low in homogeneous, highly agricultural areas such as the Midwestern corn belt, suggesting a number of potential social and economic mechanisms influencing farmers' crop choices. Crop-switching rates were high overall, occurring on more than 6% of all US cropland in the average year. Applying a framework that classified crop switches based on their temporal novelty (crop introduction versus discontinuation), spatial novelty (locally divergent versus convergent switching), and categorical novelty (transformative versus incremental switching), we found distinct spatial patterns for these three novelty dimensions, indicating a dynamic and multifaceted set of cropping changes across US farms. Collectively, these results suggest that innovation through crop switching is playing out very differently in various parts of the country, with potentially significant implications for the resilience of agricultural systems to changes in climate and other systemic trends.
作物轮作，即农民在特定农田中种植新颖的作物，可以帮助农业系统适应不断变化的环境、文化和市场力量。然而，尽管区域作物生产趋势受到了广泛关注，但对于支撑这些宏观趋势的本地规模作物轮作，人们知之甚少。我们使用美国农业部（USDA）的农田数据层，该数据层是一个从 2008 年到 2022 年的高分辨率（30 米像素大小）遥感农田数据的年度时间序列，对美国各地的本地作物轮作模式进行了描述。我们发现，在多个空间尺度上，作物轮作最常见于耕种稀疏的景观和作物多样性高的景观，而在同质化、高度农业化的地区，如中西部玉米带，轮作则较少，这表明有许多潜在的社会和经济机制影响着农民的作物选择。总体而言，作物轮作率很高，在平均每年超过 6%的美国农田上发生。我们应用了一种基于时间新颖性（作物引入与停止）、空间新颖性（局部发散与收敛轮作）和类别新颖性（变革性与增量轮作）对作物轮作进行分类的框架，发现这三个新颖性维度具有不同的空间模式，表明美国农场的作物种植发生了动态和多方面的变化。总的来说，这些结果表明，通过作物轮作进行创新在该国不同地区的表现方式非常不同，这可能对农业系统应对气候和其他系统趋势变化的弹性产生重大影响。
