Geophysical image to root function

Our agroecosystems are challenged by climate-induced stresses and the need to increase food production for a growing global population. Improving their resilience and sustainability are key challenges for tomorrow’s agriculture. Alternative agricultural practices (e.g. reduced tillage, cover crops, etc.) and selection of more robust crop varieties have the potential to help meet these challenges. To fully assess the effectiveness of such practices, an improved understanding of the soil-plant-water interactions is needed, however, such improvement is constrained by existing field-based measurement methods. In this work, we used a combination of time-lapse electrical resistivity tomography (ERT) and electromagnetic induction (EMI) to study soil moisture dynamics for a range of agricultural settings. In a study of cover crops, it was found that tap-rooted cover crops had larger impact on the soil drying than shallow-rooted ones. Additionally, the effect on soil drying of long-term cover crops (grown over two seasons) was larger than that over one season. However, in both cases the effect of the cover crops on the soil drying quickly vanished after their destruction. Soil compaction is another issue that might impact crop water availability. In this work, time-lapse ERT measurements enabled the imaging of the restricted drying depth of traffic-induced compacted plots compared to non-compacted ones under potatoes. The impact of tillage and nitrogen levels was also investigated using time-lapse EMI surveys under winter wheat. It was found that nitrogen levels only had an ephemeral effect on the change in electrical conductivity (EC) measured independently of the tillage treatment. Also direct drilled plots showed a smaller drying compared to mouldboard ploughed plots over the season. Aside from different agricultural practices, efforts have also been undergoing to breed varieties of crops more resilient to water stresses. Part of this resilience lies with the root system of the crop and its capacity to extract soil moisture. However, acquiring information on the variety traits (phenotypes) in a field-scale setup is one of the major bottlenecks of crop breeding, especially for below-ground traits. Time-lapse geophysical methods have been successfully used to discriminate soil drying between different winter wheat varieties in large field-scale trials. However, the results here show that the discriminating power of this approach can, under some conditions, be hindered by local variation of the pedophysical relationship used to convert change in EC to change in soil moisture. This study shows that this can have important impacts on the ranking of the varieties; alternative models and experimental procedure are proposed to further account for this heterogeneity. Finally, the emergence of automated field phenotyping platforms offers a unique means for crop breeders to screen large number of varieties in a controlled field environment. These platforms can provide a wealth of above-ground measurements, but they usually lack below-ground data. In this work, time intensive geoelectrical monitoring was performed on four plots of winter wheat with two different nitrogen levels in a field phenotyping platform. The measured seasonal dynamics of the soil EC appear to be related to the evolution of above-ground variables but are also impacted by the different nitrogen levels applied. Large decreases in the soil EC observed after large rainfall events are associated with both crop nitrogen and soil moisture uptake. At the hourly scale, time series analysis enables the identification of diurnal patterns that could be linked to root water uptake. Overall, time-lapse geophysical monitoring has proven to be a useful tool to monitor root zone processes with minimal invasiveness. The work, however, also demonstrates the limitations of the approach; a range of perspectives for future improvement are discussed. While not sufficient on their own, geophysical methods remain a useful tool for the emerging field of agrogeophysics and can provide valuable insights for shaping future agricultural practices

Potential of natural language processing for metadata extraction fromenvironmental scientific publications

Summarizing information from large bodies of scientific literature is anessential but work-intensive task. This is especially true in environmentalstudies where multiple factors (e.g., soil, climate, vegetation) cancontribute to the effects observed. Meta-analyses, studies thatquantitatively summarize findings of a large body of literature, rely onmanually curated databases built upon primary publications. However, giventhe increasing amount of literature, this manual work is likely to requiremore and more effort in the future. Natural language processing (NLP)facilitates this task, but it is not clear yet to which extent theextraction process is reliable or complete. In this work, we explore threeNLP techniques that can help support this task: topic modeling, tailoredregular expressions and the shortest dependency path method. We apply thesetechniques in a practical and reproducible workflow on two corpora ofdocuments: the Open Tension-diskInfiltrometer Meta-database (OTIM) and the Meta corpus. The OTIM corpus contains the sourcepublications of the entries of the OTIM database of near-saturated hydraulicconductivity from tension-disk infiltrometer measurements(https://github.com/climasoma/otim-db, last access: 1 March 2023). The Meta corpus is constituted ofall primary studies from 36 selected meta-analyses on the impact ofagricultural practices on sustainable water management in Europe. As a firststep of our practical workflow, we identified different topics from theindividual source publications of the Meta corpus using topic modeling.This enabled us to distinguish well-researched topics (e.g., conventionaltillage, cover crops), where meta-analysis would be useful, from neglectedtopics (e.g., effect of irrigation on soil properties), showing potentialknowledge gaps. Then, we used tailored regular expressions to extractcoordinates, soil texture, soil type, rainfall, disk diameter and tensionsfrom the OTIM corpus to build a quantitative database. We were able toretrieve the respective information with 56 % up to 100 % of allrelevant information (recall) and with a precision between 83 % and100 %. Finally, we extracted relationships between a set of driverscorresponding to different soil management practices or amendments (e.g.,"biochar", "zero tillage") and target variables (e.g., "soilaggregate", "hydraulic conductivity", "crop yield") from thesource publications' abstracts of the Meta corpus using the shortestdependency path between them. These relationships were further classifiedaccording to positive, negative or absent correlations between the driverand the target variable. This quickly provided an overview of the differentdriver-variable relationships and their abundance for an entire body ofliterature. Overall, we found that all three tested NLP techniques were ableto support evidence synthesis tasks. While human supervision remainsessential, NLP methods have the potential to support automated evidencesynthesis which can be continuously updated as new publications becomeavailable

EMagPy:open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data

Frequency domain electromagnetic induction (EMI) methods have had a long history of qualitative mapping for environmental applications. More recently, the development of multi-coil and multi-frequency instruments is such that the focus has shifted toward inverting data to obtain quantitative models of electrical conductivity. However, whilst collection of EMI data is relatively straightforward, the inverse modeling is more complicated. Furthermore, although several commercial and open-source inversion codes, exist, there is still a need for a user-friendly software that can bring EMI inversion to non-specialist audience. Here the open-source EMagPy software is presented as an intuitive approach to modeling EMI data. It comprises a graphical user (GUI) interface and a Python application programming interface (API). EMagPy implements both cumulative sensitivity and Maxwell-based forward operators and can model data for 1D and quasi-2D/3D cases using either deterministic or probabilistic methods. The EMagPy GUI has a logical ‘tab-based’ layout to lead the user through data importing, data filtering, inversion, and plotting of raw and inverted data. In addition, a dedicated forward modeling tab is presented to generate synthetic data. In this publication necessary considerations, and background, of EMI theory are described before EMagPy’s capabilities are presented through a series of synthetic and field-based case studies. Firstly, the performance of cumulative sensitivity and Maxwell-based forward models, and the influence of measurement noise are assessed for synthetic cases. Then the importance of data calibration for a riparian wetland dataset, the ability to include a priori information for a river-borne survey and the potential for monitoring soil moisture in a time-lapse example are all investigated. It is anticipated that EMagPy offers a user-friendly tool suitable for novice and experienced practitioners alike, and its intuitive nature mean it can provide a useful tool for teaching purposes

Soil and crop management practices and the water regulation functions of soils: a qualitative synthesis of meta-analyses relevant to European agriculture

Adopting soil and crop management practices that conserve or enhance soil structure is critical for supporting the sustainable adaptation of agriculture to climate change, as it should help maintain agricultural production in the face of increasing drought or water excess without impairing environmental quality. In this paper, we evaluate the evidence for this assertion by synthesizing the results of 34 published meta-analyses of the effects of such practices on soil physical and hydraulic properties relevant for climate change adaptation in European agriculture. We also review an additional 127 meta-analyses that investigated synergies and trade-offs or help to explain the effects of soil and crop management in terms of the underlying processes and mechanisms. Finally, we identify how responses to alternative soil–crop management systems vary under contrasting agro-environmental conditions across Europe. This information may help practitioners and policymakers to draw context-specific conclusions concerning the efficacy of management practices as climate adaptation tools.Our synthesis demonstrates that organic soil amendments and the adoption of practices that maintain “continuous living cover” result in significant benefits for the water regulation function of soils, mostly arising from the additional carbon inputs to soil and the stimulation of biological processes. These effects are clearly related to improved soil aggregation and enhanced bio-porosity, both of which reduce surface runoff and increase infiltration. One potentially negative consequence of these systems is a reduction in soil water storage and groundwater recharge, which may be problematic in dry climates. Some important synergies are reductions in nitrate leaching to groundwater and greenhouse gas emissions for nonleguminous cover crop systems. The benefits of reducing tillage intensity appear much less clear-cut. Increases in soil bulk density due to traffic compaction are commonly reported. However, biological activity is enhanced under reduced tillage intensity, which should improve soil structure and infiltration capacity and reduce surface runoff and the losses of agro-chemicals to surface water. However, the evidence for these beneficial effects is inconclusive, while significant trade-offs include yield penalties and increases in greenhouse gas emissions and the risks of leaching of pesticides and nitrate.Our synthesis also highlights important knowledge gaps on the effects of management practices on root growth and transpiration. Thus, conclusions related to the impacts of management on the crop water supply and other water regulation functions are necessarily based on inferences derived from proxy variables. Based on these knowledge gaps, we outlined several key avenues for future research on this topic

ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling

Electrical resistivity tomography (ERT) and induced polarization (IP) methods are now widely used in many interdisciplinary projects. Although field surveys using these methods are relatively straightforward, ERT and IP data require the application of inverse methods prior to any interpretation. Several established non-commercial inversion codes exist, but they typically require advanced knowledge to use effectively. ResIPy was developed to provide a more intuitive, user-friendly, approach to inversion of geoelectrical data, using an open source graphical user interface (GUI) and a Python application programming interface (API). ResIPy utilizes the mature R2/cR2 inversion codes for ERT and IP, respectively. The ResIPy GUI facilitates data importing, data filtering, error modeling, mesh generation, data inversion and plotting of inverse models. Furthermore, the easy to use designof ResIPy and the help provided inside makes it an effective educational tool. This paper highlights the rationale and structure behind the interface, before demonstrating its capabilities in a range of environmental problems. Specifically, we demonstrate the ease at which ResIPy deals with topography, advanced data processing, the ability to fix and constrain regions of known geoelectrical properties, time-lapse analysis and the capability for forward modeling and survey design

Time-intensive geoelectrical monitoring under winter wheat

Several studies have explored the potential of electrical resistivity tomography to monitor changes in soil moisture associated with the root water uptake of different crops. Such studies usually use a set of limited below-ground measurements throughout the growth season but are often unable to get a complete picture of the dynamics of the processes. With the development of high-throughput phenotyping platforms, we now have the capability to collect more frequent above-ground measurements, such as canopy cover, enabling the comparison with below-ground data. In this study hourly DC resistivity data were collected under the Field Scanalyzer platform at Rothamsted Research with different winter wheat varieties and nitrogen treatments in 2018 and 2019. Results from both years demonstrate the importance of applying the temperature correction to interpret hourly electrical conductivity (EC) data. Crops which received larger amounts of nitrogen showed larger canopy cover and more rapid changes in EC, especially during large rainfall events. The varieties showed contrasted heights although this does not appear to have influenced EC dynamics. The daily cyclic component of the EC signal was extracted by decomposing the time series. A shift in this daily component was observed during the growth season. For crops with appreciable difference in canopy cover, high frequency DC resistivity monitoring was able to distinguish the different below-ground behaviors. The results also highlight how coarse temporal sampling may affect interpretation of resistivity data from crop monitoring studies

The application of electromagnetic induction methods to reveal the hydrogeological structure of a riparian wetland

Understanding ecologically sensitive wetlands often require non-invasive methods to characterize their complex structure (e.g. deposit heterogeneity) and hydrogeological parameters (e.g. hydraulic conductivity). Here, electrical conductivities of a riparian wetland were obtained using frequency-domain electromagnetic induction (EMI) methods. The wetland was previously characterized by extensive intrusive measurements and 3D electrical resistivity tomography (ERT) and hence offers an ideal opportunity to objectively assess EMI methods. Firstly, approaches to obtain structural information (e.g. elevation and thickness of alluvium) from EMI data and models were assessed. Regularized and sharp inversion algorithms were investigated for ERT calibrated EMI data. Moreover, the importance of EMI errors in inversion was investigated. The hydrological information content was assessed using correlations with piezometric data and petrophysical models. It was found that EMI data were dominated by the thickness of peaty alluvial soils and relatively insensitive to topography and total alluvial thickness. Furthermore, although error weighting in the inversion improved the accuracy of alluvial soil thickness predictions, the multi-linear regression method performed the best. For instance, an iso-conductivity method to estimate the alluvial soil thickness in the regularized models had a normalized mean absolute difference (NMAD) of 21.4%, and although this performed better than the sharp inversion algorithm (NMAD = 65.3%), the multi-linear regression approach (using 100 intrusive observations) achieved a NMAD = 18.0%. In terms of hydrological information content, correlations between EMI results and piezometric data were poor, however robust relationships between petrophysically derived porosity and hydraulic conductivity were observed for the alluvial soils and gravels

Accounting for heterogeneity in θ-σ relationship:application to wheat phenotyping using ΕMI

Geophysical methods, such as electromagnetic induction (EMI), can be effective for monitoring changes in soil moisture at the field scale, particularly in agricultural applications. The electrical conductivity (σ) inferred from EMI needs to be converted to soil moisture content (θ) using an appropriate relationship. Typically, a single global relationship is applied to an entire agricultural field, however, soil heterogeneity at the field scale may limit the effectiveness of such an approach. One application area that may suffer from such an effect is crop phenotyping. Selecting crop varieties based on their root traits is important for crop breeding and maximizing yield. Hence, high throughput tools for phenotyping the root system architecture and activity at the field-scale are needed. Water uptake is a major root activity and, under appropriate conditions, can be approximated by measuring changes in soil moisture from time-lapse geophysical surveys. We examine here the effect of heterogeneity in the θ-σ relationship using a crop phenotyping study for illustration. In this study, the θ-σ relationship was found to vary substantially across a field site. To account for this, we propose a range of local (plot specific) θ-σ models. We show that the large number of parameters required for these models can be estimated from baseline σ and θ measurements. Finally, we compare the use of global (field scale) and local (plot scale) models with respect to ranking varieties based on the estimated soil moisture content change

Time-lapse geophysical assessment of agricultural practices on soil moisture dynamics

Geophysical surveys are now commonly used in agriculture for mapping applications. High-throughput collection of geophysical properties such as electrical conductivity (inverse of resistivity), can be used as a proxy for soil properties of interest (e.g. moisture, texture, salinity). Most applications only rely on a single geophysical survey at a given time. However, time-lapse geophysical surveys have greater capabilities to characterize the dynamics of the system, which is the focus of this work. Assessing the impact of agricultural practices through the growth season can reveal important information for the crop production. In this work, we demonstrate the use of time-lapse electrical resistivity tomography (ERT) and electromagnetic induction (EMI) surveys through a series of three case studies illustrating common agricultural practices (cover crops, compaction with irrigation, tillage with nitrogen fertilization). In the first case study, time-lapse EMI reveals the initial effect of cover crops on soil drying and the absence of effect on the subsequent main crop. In the second case study, compaction, leading to a shallower drying depth for potatoes was imaged by time-lapse ERT. In the third case study, larger change in electrical conductivity over time were observed in conventional tillage compared to direct drill using time-lapse EMI. In addition, different nitrogen application rates had significant effect on the yield and leaf area index but only ephemeral effects on the dynamics of electrical conductivity mainly after the first application. Overall, time-lapse geophysical surveys show great potential for monitoring the impact of different agricultural practices that can influence crop yield