Search

Saturated and near-saturated soil hydraulic conductivities Kh (mmh-1) determine the partitioning of precipitation into surface runoff and infiltration and are fundamental to soils' susceptibility to preferential flow. Recent studies found indications that climate factors influence Kh , which is highly relevant in the face of climate change. In this study, we investigated relationships between pedoclimatic factors and Kh and also evaluated effects of land use and soil management. To this end, we collated the Open Tension-disk Infiltrometer Meta-database (OTIM), which contains 1297 individual data entries from 172 different publication sources. We analysed a spectrum of saturated and near-saturated hydraulic conductivities at matric potentials between 0 and 100 mm. We found that methodological details like the direction of the wetting sequence or the choice of method for calculating infiltration rates to hydraulic conductivities had a large impact on the results. We therefore restricted ourselves to a subset of 466 of the 1297 data entries with similar methodological approaches. Correlations between Ks and Kh at higher supply tensions decreased especially close to saturation, indicating a different flow mechanism at and very close to saturation than towards the dry end of the investigated tension range. Climate factors were better correlated with topsoil near-saturated hydraulic conductivities at supply tensions ≥ 30 mm than soil texture, bulk density and organic carbon content. We find it most likely that the climate variables are proxies for soil macropore networks created by the respective biological activity, pedogenesis and climate-specific land use and management choices. Due to incomplete documentation in the source publications of OTIM, we were able to investigate only a few land use types and agricultural management practices. Land use, tillage system and soil compaction significantly influenced Kh , with effect sizes appearing comparable to the ones of soil texture and soil organic carbon. The data in OTIM show that experimental bias is present, introduced by the choice of measurement time relative to soil tillage, experimental design or data evaluation procedures. The establishment of best-practice rules for tension-disk infiltrometer measurements would therefore be helpful. Future studies are needed to investigate how climate shapes soil macropore networks and how land use and management can be adapted to improve soil hydraulic properties. Both tasks require large numbers of new measurement data with improved documentation on soil biology and land use and management history. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summarizing information from large bodies of scientific literature is an essential but work-intensive task. This is especially true in environmental studies where multiple factors (e.g., soil, climate, vegetation) can contribute to the effects observed. Meta-analyses, studies that quantitatively summarize findings of a large body of literature, rely on manually curated databases built upon primary publications. However, given the increasing amount of literature, this manual work is likely to require more and more effort in the future. Natural language processing (NLP) facilitates this task, but it is not clear yet to which extent the extraction process is reliable or complete. In this work, we explore three NLP techniques that can help support this task: topic modeling, tailored regular expressions and the shortest dependency path method. We apply these techniques in a practical and reproducible workflow on two corpora of documents: the Open Tension-disk Infiltrometer Meta-database (OTIM) and the Meta corpus. The OTIM corpus contains the source publications of the entries of the OTIM database of near-saturated hydraulic conductivity from tension-disk infiltrometer measurements (https://github.com/climasoma/otim-db , last access: 1 March 2023). The Meta corpus is constituted of all primary studies from 36 selected meta-analyses on the impact of agricultural practices on sustainable water management in Europe. As a first step of our practical workflow, we identified different topics from the individual source publications of the Meta corpus using topic modeling. This enabled us to distinguish well-researched topics (e.g., conventional tillage, cover crops), where meta-analysis would be useful, from neglected topics (e.g., effect of irrigation on soil properties), showing potential knowledge gaps. Then, we used tailored regular expressions to extract coordinates, soil texture, soil type, rainfall, disk diameter and tensions from the OTIM corpus to build a quantitative database. We were able to retrieve the respective information with 56 % up to 100 % of all relevant information (recall) and with a precision between 83 % and 100 %. Finally, we extracted relationships between a set of drivers corresponding to different soil management practices or amendments (e.g., "biochar", "zero tillage") and target variables (e.g., "soil aggregate", "hydraulic conductivity", "crop yield") from the source publications' abstracts of the Meta corpus using the shortest dependency path between them. These relationships were further classified according to positive, negative or absent correlations between the driver and the target variable. This quickly provided an overview of the different driver–variable relationships and their abundance for an entire body of literature. Overall, we found that all three tested NLP techniques were able to support evidence synthesis tasks. While human supervision remains essential, NLP methods have the potential to support automated evidence synthesis which can be continuously updated as new publications become available. [ABSTRACT FROM AUTHOR]

Climate change will most likely lead to an increase of extreme weather events, including heavy rainfall with soil surface runoff and erosion. Adapting agricultural management practices that lead to increased infiltration capacities of soil has potential to mitigate these risks. However, effects of agricultural management practices (tillage, cover crops, amendment, …) on soil variables (hydraulic conductivity, aggregate stability, …) often depend on the pedo-climatic context. Hence, the only possibility to gather information needed to advise stakeholders on suitable management practices is to quantify such dependencies using meta-analyses of studies investigating this topic. As a first step, structured information from scientific publications needs to be extracted to build a meta-database, which then can be analyzed and recommendations can be given in dependence to the pedo-climatic context. Manually building such a database by going through all publications is very time-consuming. Given the increasing amount of literature, this task is likely to require more and more effort in the future. Natural language processing (NLP) facilitates this task, but it is not clear yet to which extent the extraction process is reliable or complete. In this work, two corpora of documents were used, which we refer to as the OTIM and the Meta corpus in the following. The OTIM corpus contains the source publications of the entries of the OTIM database of near-saturated hydraulic conductivity from tension-disk infiltrometer measurements (https://github.com/climasoma/otim-db). The Meta corpus is constituted of all primary studies from 36 selected meta-analyses on the impact of agricultural practices on sustainable water management in Europe. We focused on three NLP techniques: topic modeling, tailored regular expressions and dictionaries and the shortest dependency path. We used topic modeling to sort the individual source-publications of the Meta corpus into 6 topics (e.g. related to cover crops, biochar, …) with a coherence metric Cv ranging from 0.7 to 0.9; Then, we used tailored regular expressions and dictionaries to extract coordinates, soil texture, soil type, rainfall, disk diameter and tensions on the OTIM corpus. We found that the respective information could be retrieved with 56 % up to 100 % of all relevant information (recall) and with a precision between 83 % and 100 %. Finally, we extracted relationships between a set of practices keywords (e.g. 'biochar', 'zero tillage', …) and soil variables (e.g. 'soil aggregate', 'hydraulic conductivity', 'crop yield',…) from the source-publications' abstracts of the Meta corpus using the shortest dependency path between them. These relationships were further classified according to positive, negative or absent correlations between the driver and soil property. This quickly provided an overview of the different driver-variable relationships and their abundance for an entire body of literature. Overall, we found that all three tested NLP techniques were able to support evidence synthesis tasks such as selecting relevant publications on a topic, extracting specific information to build databases for meta-analysis and providing an overview of relationships found in the corpus. While human supervision remains essential, NLP methods have the potential to support fully automated evidence synthesis that can be continuously updated as new publications become available. [ABSTRACT FROM AUTHOR]

Adopting soil and crop management practices that conserve or enhance 1 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 agro9 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 non-leguminous 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, 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. [ABSTRACT FROM AUTHOR]

Electrical Resistivity Tomography (ERT) has become an important tool to study soil water fluxes in cropped field. ERT results translates to water content via empirical pedophysical relations that take soil physical properties into account, usually ignoring the impact of roots. Studies shows high root dense soils behaves quite differently than less root dense soils in terms of bulk electrical conductivity. Yet, we do not completely understand the impact of root segments on the ERT measurements. In this numerical study, we coupled an electrical model with a plant-soil water flow model to investigate the impact of plant root growth and water uptake on the ERT virtual experiment. The electrical properties of roots were explicitly accounted in the finite element mesh and we obtained the electrical conductivities of root segments by conducting specific experiments on real maize plants. The contrast between electrical conductivity of roots and soil depends on factors such as root density, irrigation, root age, and root water uptake pattern. Root growth and water uptake processes thus affect this contrast together with the soil electrical properties. Model results indicate a non-negligible anisotropy in bulk electrical conductivity induced by root processes. We see a greater anisotropy in a sandy medium when compared to a loamy medium. We find that the water uptake process dominates the bulk electrical properties. The Gauss-Newton type ERT inversion of virtual rhizotron data demonstrate that, when root-soil electrical conductivity contrasts are high, it can lead to error in water content estimates since the electrical conductivity is partly due to root. Thus, incorporating the impact of root in the pedophysical relations is very important to interpret ERT results directly as water content. [ABSTRACT FROM AUTHOR]

In a landfill, the biodegradation of the organic fraction of the waste is driven by its moisture. Once the waste dump is covered, the infiltration through the cover layer is a source of additional water. In this study, the changes in water content in a landfill cover layer were investigated with long time-lapse electrical resistivity tomography (ERT) profiles. Measurements carried out during a rainfall event allowed the detection of zones with large infiltration events. We addressed all interpretation limits inherent to such a large scale, low resolution investigation with the use of synthetic modelling. Firstly, the smoothed resistivity changes distribution failed to detect small infiltration areas and induced a misestimation of the infiltration depth and volume in large infiltration zones. Secondly, when the background water content is not homogeneous, relative changes, as commonly used in literature, reflect both the background moisture distribution and the monitored changes. Thirdly, the temperature and pore fluid conductivity distribution are needed to compute the absolute water content changes. The latter better reflects the infiltration pattern. Lastly, rain water infiltration results in pore water dilution. When the dilution effect is not considered, the infiltrated volume is underestimated. This approach enabled us to detect a large infiltration zone that could be responsible for an important part of the annual water infiltration. [ABSTRACT FROM AUTHOR]