ABSTRACT
Soil is polluted with plastic waste from macro to submicron level. Our understanding of macroplastics (> 5 mm) occurrence and behavior has remained comparatively elusive, mainly due to a lack of a tracing mechanism. This study set up a methodology to trace macroplastic displacement, which combined magnetic iron oxide-tagged soil and macroplastic pieces tagged by an adhesive passive radiofrequency identification transponder. By utilizing these techniques, a field study was carried out to analyze the effect of tillage implement and plastic sizes on plastic displacement, to understand the fate of macroplastics in arable land. Results indicated that the displacement of macroplastics did not depend upon plastic sizes but did depend upon the tillage implement used. The mean macroplastics displacement per tillage pass was 0.36 ± 0.25 m with non-inversion chisel tillage and 0.15 ± 0.13 m with inversion disk tillage, which was similar to bulk soil displacement. However, only inversion disk tillage caused fragmentation (41 %) of macroplastics and generated microplastics (< 5 mm). In contrast, both tillage implements drove to similar burial of surface macroplastics into the tilled layer (74 % on average). These results highlight that tillage is a major process for macroplastics fate in arable soils, being one of the first studies to investigate it.
ABSTRACT
Centre-pivot systems are widely used for irrigation in agriculture. However, excessive water application rates under low pressure centre-pivot systems can lead to soil erosion, which degrades soil structure and increases crop vulnerability to droughts. Although efforts have been deployed to measure soil erosion underneath individual centre pivots, a large-scale systematic assessment of extent and severity of soil erosion in centre-pivot irrigated fields is currently lacking. Here we used Google Earth™ satellite images to provide first evidence of widespread, severe soil erosion in centre-pivot irrigated agricultural land. We focused on the municipality of Cristalina (6154 km2), in the Brazilian Central Highlands, where centre pivots irrigate approximately 60,000 ha of cropland. The study area is in the Cerrado biome, which is one of the most important grain-producing regions in the world and Brazil's main centre-pivot irrigation area. By mapping erosion features under centre pivots, we found that 29 % of centre-pivot fields displayed signs of rill erosion, with individual rills up to a length of 1200 m. Most erosion features were identified during the dry season of the Brazilian Cerrado, which coincided with the period of greater satellite-image availability. Moreover, we found that compacted centre-pivot-wheel tracks often triggered rill incision and that eroding centre-pivot fields displayed higher slope gradients and were better connected to surface waters than the non-eroding fields. Ultimately, the frequent identification of severe erosion features in the centre-pivot fields during the dry season indicates that irrigation causes and/or aggravates soil erosion in Cristalina and likely in other parts of the Brazilian Cerrado. This first systematic evidence of widespread soil erosion underneath centre-pivot systems highlights that irrigation erosion is an important but neglected driver of land degradation, and that urgent action is required to protect affected soils for future generations.
ABSTRACT
The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.