Linking satellite derived LAI patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements

Patterns in crop development and yield are often directly related to lateral and vertical changes in soil texture causing changes in available water and resource supply for plant growth, especially under dry conditions. Relict geomorphologic features, such as old river channels covered by shallow sediments can challenge assumptions of uniformity in precision agriculture, subsurface hydrology, and crop modeling. Hence a better detection of these subsurface structures is of great interest. In this study, the origins of narrow and undulating leaf area index (LAI) patterns showing better crop performance in large scale multi-temporal satellite imagery were for the first time interpreted by proximal soil sensor data. A multi-receiver electromagnetic induction (EMI) sensor measuring soil apparent electrical conductivity (ECa) for six depths of exploration (DOE) ranging from 0–0.25 to 0–1.9 m was used as reconnaissance soil survey tool in combination with selected electrical resistivity tomography (ERT) transects, and ground truth texture data to investigate lateral and vertical changes of soil properties at ten arable fields. The moderate to excellent spatial consistency (R2 0.19–0.82) of ECa patterns and LAI crop marks that indicate a higher water storage capacity as well as the increased correlations between large-offset ECa data and the subsoil clay content and soil profile depth, implies that along this buried paleo-river structure the subsoil is mainly responsible for better crop development in drought periods. Furthermore, observed stagnant water in the subsoil indicates that this paleo-river structure still plays an important role in subsurface hydrology. These insights should be considered and implemented in local hydrological as well as crop models

Infants with esophageal atresia and right aortic arch: Characteristics and outcomes from the Midwest Pediatric Surgery Consortium

Purpose Right sided aortic arch (RAA) is a rare anatomic finding in infants with esophageal atresia with or without tracheoesophageal fistula (EA/TEF). In the presence of RAA, significant controversy exists regarding optimal side for thoracotomy in repair of the EA/TEF. The purpose of this study was to characterize the incidence, demographics, surgical approach, and outcomes of patients with RAA and EA/TEF. Methods A multi-institutional, IRB approved, retrospective cohort study of infants with EA/TEF treated at 11 children's hospitals in the United States over a 5-year period (2009 to 2014) was performed. All patients had a minimum of one-year follow-up. Results In a cohort of 396 infants with esophageal atresia, 20 (5%) had RAA, with 18 having EA with a distal TEF and 2 with pure EA. Compared to infants with left sided arch (LAA), RAA infants had a lower median birth weight, (1.96 kg (IQR 1.54–2.65) vs. 2.57 kg (2.00–3.03), p = 0.01), earlier gestational age (34.5 weeks (IQR 32–37) vs. 37 weeks (35–39), p = 0.01), and a higher incidence of congenital heart disease (90% vs. 32%, p  0.29). Conclusion RAA in infants with EA/TEF is rare with an incidence of 5%. Compared to infants with EA/TEF and LAA, infants with EA/TEF and RAA are more severely ill with lower birth weight and higher rates of prematurity and complex congenital heart disease. In neonates with RAA, surgical repair of the EA/TEF is technically feasible via thoracotomy from either chest. A higher incidence of anastomotic strictures may occur with a right-sided approach

Large‐scale detection and quantification of harmful soil compaction in a post‐mining landscape using multi‐configuration electromagnetic induction

Fast and accurate large-scale localization and quantification of harmfully compacted soils in recultivated post-mining landscapes are of particular importance for mining companies and the following farmers. The use of heavy machinery during recultivation imposes soil stress and can cause irreversible subsoil compaction limiting crop growth in the long term. To overcome or guide classical point-scale methods to determine compaction, fast methods covering large areas are required. In our study, a recultivated field of the Garzweiler mine in North Rhine-Westphalia, Germany, with known variability in crop performance was intensively studied using non-invasive electromagnetic induction (EMI) and electrode-based electrical resistivity tomography (ERT). Additionally, soil bulk density, volumetric soil water content and soil textures were analysed along two transects covering different compaction levels. The results showed that the measured EMI apparent electrical conductivity (ECa) along the transects was highly correlated (R2 > .7 for different dates and depths below 0.3 m) to subsoil bulk density. Finally, the correlations established along the transects were used to predict harmful subsoil compaction within the field, whereby a spatial probabilistic map of zones of harmful compaction was developed. In general, the results revealed the feasibility of using the EMI derived ECa to predict harmful compaction. They can be the basis for quick monitoring of the recultivation process and implementation of necessary melioration to return a well-structured soil with good water and nutrient accessibility, and rooting depths for increased crop yields to the farmers

Large-scale soil mapping using multi-configuration EMI and supervised image classification

Reliable and high-resolution subsurface characterization beyond the field scale is of great interest for precision agriculture and agro-ecological modelling because the shallow soil (~1–2m depth) is responsible for the storageof moisture and nutrients that are accessible to crops. This can potentially be achieved with a combination of direct sampling and Electromagnetic Induction (EMI) measurements, which have shown great potential for soilcharacterization due to their non-invasive nature and high mobility. However, only a few studies have used EMI beyond the field scale because of the challenges associated with a consistent interpretation of EMI data frommultiple fields and acquisition days. In this study, we performed a detailed EMI survey of an area of 1 km2 divided in 51 agricultural fields where previous studies showed a clear connection between crop performanceand soil properties. In total, nine apparent electrical conductivity (ECa) values were measured at each location with a depth of investigation ranging between 0–0.2 to 0–2.7 m. Based on the combination of ECa maps andavailable soil maps, an a priori interpretation was performed and four sub-areas with characteristic sediments and ECa were identified. Then, a supervised classification methodology was used to divide the ECa maps intoareas with similar soil properties. In a next step, soil profile descriptions to a depth of 2m were obtained at 100 sampling locations and 552 samples were analyzed for textural characteristics. The combination of the classifiedmap and ground truth data resulted in a 1m resolution soil map with eighteen units with a typical soil profile and texture information. It was found that the soil profile descriptions and texture of the EMI-based soil classes were significantly different when compared using a two-tailed t-test. Moreover, the high-resolution soil map corresponded well with patterns in crop health obtained from satellite imagery. It was concluded that this novel EMI data processing approach provides a reliable and cost-effective tool to obtain high-resolution soil maps to support precision agriculture and agro-ecological modelling