Proximal soil sensors and geostatistical tools in precision agriculture applications

Recognition of spatial variability is very important in precision agriculture applications. The use of proximal soil sensors and geostatistical techniques is highly recommended worldwide to detect spatial variation not only in fields but also within-field (micro-scale). This study involves, as a first step, the use of visible and near infrared (vis-NIR) spectroscopy to estimate soil key properties (6) and obtain high resolution maps that allow us to model the spatial variability in the soil. Different calibration models were developed using partial least square regression (PLSR) for different soil properties. These calibration models were evaluated by both cross-validation and independent validation. Results show good to excellent calibration models for most of soil properties under study in both cross-validation and independent validation. The on-line maps created using the collected on-line spectra and the calibration models previously estimated for each soil property were compared with three different maps (measured, predicted, error). The second step uses multivariate geostatistical analysis to develop three different geostatistical models (soil, spectral, fusion). The soil model includes 8 soil properties, spectral model includes 4 soil properties and the fusion model includes 12 soil properties. The three models were evaluated by cross-validation and the results show that the goodness of fitting can be considered as satisfactory for the soil model, whereas the performance of the spectral model was quite poor. Regarding the fusion model, it performed quite well, though the model generally underestimated the high values and overestimated the low values. An independent validation data set was used to evaluate the performance of the three models calculating three statistics: mean error (ME), as an indicator of bias; mean standardized squared error (MSSE), as an indicator of accuracy, and root mean squared error (RMSE), as an indicator of precision of estimation. Synthetically, the two, soil and fusion, models performed quite similarly, whereas the performance of the spectral model was much poorer. With regard to delineation of management zones (MZs), the factor cokriging analysis was applied using the three different models. The first factor (F1) for the soil and fusion models was related to soil properties that affect soil fertility, whereas for the spectral model was related to P (-0.88) and pH (-0.42). Based on the first factor of the soil and fusion models, three management zones were delineated and classified as low, medium and high fertility zones using isofrequency classes. Spatial similarity between the yield map and delineated MZs maps based on F1 for the soil and fusion models was calculated. The overall accordance between the two maps was 40.0 % for the soil model and 38.6 % for the fusion model. The two models performed quite similarly. These results can be interpreted as more than 50% of the yield variation was ascribable to more dynamic factors than soil parameters not included in this study, such as agro-meteorological conditions, plant diseases, nutrition stresses, etc. However, the results are quite promising for the application of the proposed approach in site-specific management.</br

Paleoenvironmental implications through the study of an Eemian paleosol in northwestern Sardinia (Italy)

The aim of this work is to define the paleoenvironmental changes related to a soil belonging to the studied succession, by means of an in-depth micromorphological study. In particular, the presence of this paleosol is associated to the fast climatic fluctuations that took place between MIS5e and MIS5c

Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI

This paper explores the use of a novel nonlinear parametric modelling technique based on a Volterra Non-linear Regressive with eXogenous inputs (VNRX) method to quantify the individual, interaction and overall contributions of six soil properties on crop yield and normalised difference vegetation index (NDVI). The proposed technique has been applied on high sampling resolution data of soil total nitrogen (TN) in %, total carbon (TC) in %, potassium (K) in cmol kg−1, pH, phosphorous (P) in mg kg−1 and moisture content (MC) in %, collected with an on-line visible and near infrared (VIS-NIR) spectroscopy sensor from a 18 ha field in Bedfordshire, UK over 2013 (wheat) and 2015 (spring barley) cropping seasons. The on-line soil data were first subjected to a raster analysis to produce a common 5 m by 5 m grid, before they were used as inputs into the VNRX model, whereas crop yield and NDVI represented system outputs. Results revealed that the largest contributions commonly observed for both yield and NDVI were from K, P and TC. The highest sum of the error reduction ratio (SERR) of 48.59% was calculated with the VNRX model for NDVI, which was in line with the highest correlation coefficient (r) of 0.71 found between measured and predicted NDVI. However, on-line measured soil properties led to larger contributions to early measured NDVI than to a late measurement in the growing season. The performance of the VNRX model was better for NDVI than for yield, which was attributed to the exclusion of the influence of crop diseases, appearing at late growing stages. It was recommended to adopt the VNRX method for quantifying the contribution of on-line collected soil properties to crop NDVI and yield. However, it is important for future work to include additional soil properties and to account for other factors affecting crop growth and yield, to improve the performance of the VNRX model

Semi-Automatic Method for Early Detection of Xylella fastidiosa in Olive Trees Using UAV Multispectral Imagery and Geostatistical-Discriminant Analysis

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Assessment and Mapping of Soil Salinization Risk in an Egyptian Field Using a Probabilistic Approach

The assessment of soil salinization risk at the field scale requires modeling of the spatial variability of soil salinity. This paper presents a probabilistic approach to estimate and map a risk index using all available auxiliary information. A probabilistic methodology is proposed to estimate the conditional probability of exceeding the assigned threshold value of a generic indicator of soil salinity. A geostatistical non-parametric technique, probability kriging, was used to assess the risk of soil salinization and delineate different hazard zones within a field. The technique relies on indicator coding of information. The approach was applied to soil electrical conductivity measurements collected in an experimental field located in the Nile Delta region in Egypt, and submitted over time to trials with different fertilization treatments. The application of the method allowed delineation of a north-eastern zone in the field with a high risk of soil salinization due to its lack of cultivation for a long time and nearness to buildings that prevent water infiltration. The method proved to be quite promising from the perspective of precision agriculture and it is easily extendable to any sort of remote and proximal sensing auxiliary information, including information on the deepest layers of soil

Buried palaeosols of NW Sardinia (Italy) as archives of the Late Quaternary climatic fluctuations

A multi-disciplinary approach was performed to investigate two compound geosols included between windblown deposits at the top, and interglacial (MIS 5) beach sediments at the bottom, located along the Alghero coast (North-western Sardinia, Italy). A sedimentological andmorphological studywas carried out on the profile in the field, and samples collected on the main pedomembers were subjected to several laboratory analyses, consisting of physical and chemical determinations on bulk samples, mineralogy (XRD), micromorphology on undisturbed samples (thin Section, SEM), and EDAX-micro probe analyses. Dating was performed by means of Optically Stimulated Luminescence (OSL). The studied geosols show the evidence of a complex pedosedimentary evolution. Around 80 to 70 ka the lower geosol underwent weathering and clay illuviation (wet and warm conditions), followed by calcification-recalcification processes (dry-contrasted), and finally by strong bioturbation. Around 70 ka the onset of the glacial period (MIS 4) is marked by the deposition of a sand dune, capping the lower geosol. These results indicate that the coastal area of the central Mediterranean kept the relatively warm conditions typical of the interglacial climate for most of the Early Würm and reached cold conditions only at about 70 ka, possibly in relation to the rapid cooling of the Heinrich event H7. The upper geosol developed on colluvial material including abundant pedorelicts and reddish earthmaterial, deposited around 50 ka. Before being buried by aeolian sand around 43 ka, this deposit underwent pedogenesis phases possibly associated to Middle Würm interstadial events, indicating that in the study area these events were intense enough to influence pedogenesis