Visualizing land‐use and management complexity within biogeochemical cycles of an agricultural landscape

摘要

Abstract Crop fields are cultivated across continuities of soil, topography, and local climate that drive biological processes and nutrient cycling at the landscape scale; yet land management and agricultural research are often performed at the field scale, potentially neglecting the context of the surrounding landscape. Adding to this complexity is the overlap of ecosystems and their biogeochemical legacies, as a patchwork of crops fields, natural grasslands, and forests develops across the landscape. Furthermore, as new technologies and policies are introduced, management practices change, including fertilization strategies, which further alter biological productivity and nutrient cycling. All of these environmental, biological, and historical legacies are potentially recorded in the isotopic signal of plant, soil, and sediment organic matter across the landscape. We mapped over 1500 plant, soil, and sediment isotopic values and generated an isotopic landscape (isoscape) over a 40‐km2 agricultural site in NE Germany. We observed distinct patterns in the isotopic composition of organic matter sampled from the landscape that clearly reflect the landscape complexity. C3 crop intrinsic water‐use efficiency reflected a precipitation gradient, while native forest and grassland plant species did not, suggesting that native plants are more adapted to predominant climatic conditions. δ13Csoil patterns reflected both the long‐term input of plant organic matter, which was affected by the local climate conditions, and the repeated cultivation of corn. Soil organic matter 15N isotopic values also revealed spatial differences in fertilization regimes. Forest fragments, in which the nitrogen cycle was relatively open, were more water‐use efficient. Sediments from small water bodies received substantial inputs from surrounding field vegetation but were also affected by seasonal drying. These isotopic maps can be used to visualize large spatial heterogeneity and complexity, and they are a powerful means to interpret past and current trends in agricultural landscapes.