State University of São Paulo (UNESP), PPG-Ciência Do Solo, Jaboticabal, São Paulo, Brazil.
State University of São Paulo (UNESP), PPG-Ciência Do Solo, Jaboticabal, São Paulo, Brazil.
Environ Res. 2023 Jun 15;227:115729. doi: 10.1016/j.envres.2023.115729. Epub 2023 Mar 21.
The emission of soil carbon dioxide (CO) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of soil CO emissions and its relationship with related variables, such as the CO flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO emissions and soil temperature (-0.240) and CO flux model (-0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO flux model variable. Soil CO emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 μmol m s) and low-yield potential soybean areas (from 0.943 to 5.686 μmol m s) and higher for native forests (from 2.279 to 8.131 μmol m s), whereas the opposite was true for the CO flux model.
农业区土壤二氧化碳(CO)的排放是气候、土壤和土地管理实践等多种因素相互作用的结果。农业实践直接影响土壤与大气之间的碳动态。在此,我们评估了土壤 CO 排放的时间变化(2020/2021 作物季)及其与相关变量的关系,如来自轨道数据的 CO 通量模型、增强植被指数(EVI)、总初级生产力(GPP)和叶面积指数(LAI),以及来自原地收集的土壤温度、土壤湿度和土壤 CO 排放、原生林、生产力牧场、退化牧场以及高产量潜力大豆和低产量潜力大豆生产区。所有变量以及不同土地利用和占用类型之间都观察到显著影响(p<0.01)。9 月和 10 月土壤 CO 排放量较低,土壤湿度和土壤温度均值较低,各处理之间无差异。另一方面,在生产力牧场、高产量潜力大豆区和低产量潜力大豆区 CO 通量模型有显著影响。模型中 CO 通量值最高的月份(无论土地利用和土地覆盖类型如何)是 10 月和 11 月,这是雨季的开始。土壤 CO 排放与 GPP(0.208)、LAI(0.354)、EVI(0.363)和土壤湿度(0.280)呈正相关,与土壤 CO 排放与土壤温度(-0.240)和 CO 通量模型(-0.314)值呈负相关。土地利用和土地覆盖与这些变量呈负相关,除了 CO 通量模型变量。高产量潜力大豆区(平均值为 0.834 至 6.835 μmol m s)和低产量潜力大豆区(0.943 至 5.686 μmol m s)的土壤 CO 排放值较低,而原生林(2.279 至 8.131 μmol m s)的土壤 CO 排放值较高,而 CO 通量模型则相反。