Drainage Class Research Articles

Drainage gradient versus seasonal cycles: Differential response of microbial community composition to variations in soil moisture

AbstractThe variation in the soil microbial community composition over time was assessed at monthly time steps for 1 year in three neighboring Mollisols spanning a drainage gradient. This was done to distinguish between natural oscillations in the community composition versus lasting adaptations to environmental factors such as soil water availability. To isolate soil water availability as a controlling factor, we selected three soils sharing the same soil order (fine‐silty, superactive Argixerolls/Argialbolls); slope (0%–1%); temperature regime (mesic); moisture regime (xeric); and land use history (continuous grassland for the past 10 years) but differing in drainage class (well‐drained vs. moderately well‐drained vs. poorly drained). Changes in microbial diversity were quantified by monitoring the bacterial community at monthly intervals for 1 year. Within individual soils, α‐diversity varied little with season and drainage classes. Despite the three soils experiencing the same climate regime and vegetation/land use, they exhibited distinct community composition and turnover, which we attribute to differences in moisture availability across drainage and seasons. We posit that a seasonal recurring drop in soil redox potential induced by seasonal water saturation in the poorly drained soil is the most probable cause setting the microbial community of that soil apart from those in the better drained soils. Our investigation suggests that not all indicators of microbial diversity share the same sensitivity to seasonal and drainage‐related soil moisture variations.

Using MIR and XRF spectroscopy to develop a heavy metal leaching potential model in Irish top soils

The European Union aim to have all soils healthy by 2050. However, a major challenge to soil health monitoring is identifying key metrics for soil health indicators. Moreover, how to analyse numerous soil properties which are time-consuming, labour intensive and expensive. MIR spectroscopy is a rapid, non-destructive cheaper alternative to wet chemical methods. Here, we combined known soil properties that limit transport of heavy metals (i.e. drainage class, depth, organic matter, particle size/texture, bulk density and cation-exchange-capacity) to develop a topsoil (≤50 cm) leaching potential model (n = 3,515). The study area consisted of mostly grassland soils which had mainly high and moderately low leaching potential (43 and 36 %, respectively), with lower coverage of high and intermediate classes (10 and 11 %, respectively). However, known topsoil prediction models of 5–25 cm (n = 4759) were extrapolated to deeper samples 30–50 cm. As a result, 26 % of samples were identified as ‘out of control’ in peatland transition areas. For full spatial coverage for environmental modelling from spectral data, reference values are needed for the deeper samples in peatland transition areas.Furthermore, this study used a geological survey of the northern half of Ireland at ≤4 km2 resolution to map regions of naturally high levels of As, Cd, Cr, Cu, Ni and Pb by ICP and XRF spectroscopy. Geospatial clipping of heavy metal thresholds showed high coverage of As and Ni in Eastern regions, and Cd in the Midland regions of Ireland. Therefore, it would be useful to include fertiliser loadings, transport pathways or any source/recipient data to assess heavy metal movement throughout the soil profile, particularly in these regions.

Vertical Distribution of Carbon and Nitrogen in Pastures Fertilized with Broiler Litter or Mineral Fertilizer with Two Drainage Classes

Vertical Distribution of Carbon and Nitrogen in Pastures Fertilized with Broiler Litter or Mineral Fertilizer with Two Drainage Classes

Mapping global distributions of clay-size minerals via soil properties and machine learning techniques

Clay-size mineral is a vital ingredient of soil that influences various environment behaviors. It is crucial to establish a global distribution map of clay-size minerals to improve the recognition of environment variations. However, there is a huge gap of lacking some mineral contents in poorly accessible remote areas. In this work, machine learning (ML) approaches were conducted to predict the mineral contents and analyze their global abundance changes through the relationship between soil properties and mineral distributions. The average content of kaolinite, illite, smectite, vermiculite, chlorite, and feldspar were predicated to be 28.69 %, 22.30 %, 12.42 %, 5.43 %, 5.03 %, and 1.44 % respectively. Model interpretation showed that topsoil bulk density and drainage class were the most significant factors for predicting all six minerals. It could be seen from the feature importance analysis that bulk density notably reflected the distribution of 2:1 layered minerals more than that of 1:1 mineral. High drainage favored secondary minerals development, while low drainage was more benefited for primary minerals. Moreover, the content variation of different minerals aligned with the distribution of corresponding soil properties, which affirmed the accuracy of established models. This study proposed a new approach to predict mineral contents through soil properties, which filled a necessary step of understanding the geochemical cycles of soil-related processes.

Reconstruction of a deep-time critical zone in southern patagonia (mid-high paleolatitude) during the mid-cretaceous greenhouse

The mid-Cretaceous was a period characterized by a global greenhouse climate. Paleosols can be used to reconstruct Critical Zones of the Earth́s past and enhance our comprehension of such greenhouse conditions. Through macro- and micromorphological, clay-mineralogy, and bulk geochemical analyses of the paleosols, we conducted a deep-time Critical Zone reconstruction at mid-high paleolatitudes of the mid-Cretaceous Puesto El Moro Formation in the southern Patagonia (Austral-Magallanes Basin). Paleosols were classified as hydromorphic Histosol-like paleosols (Kachaike pedotype), Alfisols like-paleosols (La Lila pedotype), and Vertisol like-paleosols (La Bernarda pedotype). The analysis of these paleosols facilitated the reconstruction of the deep-time Critical Zone and enables us to infer the four scientific spheres (lithosphere, hydrosphere, biosphere, and atmosphere). Different types of paleosols developed under different parent materials with specific soil-moisture regime and drainage class. Overall, paleosols took place during temperate and humid conditions with a marked seasonality of the precipitations. The paleoclimate reconstruction obtained here provides new data from mid-high paleolatitudes and agrees with the current mid-Cretaceous climate reconstructions in the context of a greenhouse world.

Impact of Controlled Drainage on Crop Yield Including Within-Field Variability

Highlights A 10-m grid system was developed for spatial and temporal analysis of crop yield impacts of controlled drainage. Yields were analyzed by annual wetness classification, soil drainage class, and elevation within the field. Overall, controlled drainage significantly increased corn yield by an average of 2.3%, but not soybean yield. The grid-based analysis with a linear mixed model identified characteristics influencing CD yield effect. Abstract. Controlled drainage is the practice of using a water control structure to raise the outlet in agricultural fields during periods when drainage is unnecessary, with the primary purpose of reducing nitrate loss. The use of this practice may increase crop yields compared to free drainage because controlled drainage can capture water from precipitation as soil water and store it for crop use later in the growing season. However, results from published studies of controlled drainage have shown mixed impacts on crop yields, and the effects of field characteristics and annual weather variation on these impacts are not well understood. To analyze crop yield impacts, two controlled and two free draining plots in eastern Indiana were compared. A grid system consisting of 10 by 10 meter cells was created to obtain a balanced data set and used to analyze crop yield by year, annual wetness classification, soil drainage class, and elevation. Controlled drainage significantly increased the corn yield in six out of nine years, and the nine-year corn yield average increased by 2.3%. Soybean yield was significantly higher under controlled drainage in three out of four years, but there was no significant difference in the four-year soybean average. Years were further classified as wet, normal, or dry based on growing season precipitation, and results indicated controlled drainage had the greatest significant impact on corn yield in the dry years. Analyzing yield by soil drainage class determined that yield had the greatest response to controlled drainage on the very poorly drained soils. Controlled drainage had a significant yield benefit in corn during dry years in areas in which the elevation is more than 30 cm above the surface elevation at the outlet, and did not lead to significant differences in normal or wet years at any elevation. The grid-based analysis with a linear mixed model accounting for autocorrelation was useful for understanding within-field variability of yield response. Keywords: Controlled drainage, Crop yield, Drainage water management, Soil drainage class, Subsurface drainage.

Estimating natural soil drainage classes in the Wisconsin till plain of the Midwestern U.S.A. based on lidar derived terrain indices: Evaluating prediction accuracy of multinomial logistic regression and machine learning algorithms

Accurate spatial prediction of natural soil drainage condition is not only important for agriculture and hydrological modeling but also for installing subsurface drainage and onsite waste disposal systems. For this research, 154 sites were selected based on a stratified random sampling method. For each site, drainage class was identified based on visual examination of soil cores. A digital elevation model developed from lidar data was used to derive seven terrain indices. Terrain indices were used to predict drainage class using four prediction models: multinomial logistic regression and three machine learning algorithms (random forest, C5.0, and artificial neural network). Based on 30% random hold-back validation data, all digital soil mapping (DSM) models provided similar results. The artificial neural network provided relatively higher overall accuracy of 70%, a kappa coefficient of 0.59, and a Brier score of 0.34. The rest of the DSM models as well as SSURGO provided an accuracy that ranged between 64 and 66%, a kappa coefficient that ranged between 0.52 and 0.54, and a Brier score that ranged between 0.41 and 0.46.

Penilaian Kesesuaian Lahan untuk Tanaman Jagung dan Kedelai di Lahan Rawa Lebak Desa Tanjung Sejaro Kecamatan Indralaya Kabupaten Ogan Ilir

This study aims to assess the suitability of land for corn and soybean plants. The research has been carried out in January to March 2020 in the swampy land of Tanjung Sejaro Village, Indralaya District, Ogan Ilir Regency. This research uses an intensive level survey method with an area of 5 ha. Observation and measurement were made directly to the field using a 1: 5000 scale map. The sample points consist of 10 points taken by the grid method, at each point representing 0.5 ha. Soil samples taken at a depth of 0-20 cm from the ground surface. The variables observed in the field were that the drainage class was rather poor, the effective depth was generally >90 cm, the topography was flat (<3%). The parameters observed in the laboratory were chemical properties in the form of very acidic soil pH in the range of 3.97-4.37, CEC classified as low with a range of 7.50-15.00 cmol/kg, Total nitrogen was very low with a range of 0.01-0.08 %, Phosphorus available was classified as very low with a range of 0.25-4.60 ppm, Potassium available was classified as very low with a range of 0.19-0.38 cmol/kg. The results showed the actual suitability for corn and soybean plants was N (not suitable) with several factors limiting nutrient retention and available nutrients. If liming and fertilizing inputs are given, the potential suitability for corn and soybean plants is S2.

Topsoil and subsoil bacterial community assemblies across different drainage conditions in a mountain environment

BackgroundHigh mountainous environments are of particular interest as they play an essential role for life and human societies, while being environments which are highly vulnerable to climate change and land use intensification. Despite this, our knowledge of high mountain soils in South America and their microbial community structure is strikingly scarce, which is of more concern considering the large population that depends on the ecosystem services provided by these areas. Conversely, the Central Andes, located in the Mediterranean region of Chile, has long been studied for its singular flora, whose diversity and endemism has been attributed to the particular geological history and pronounced environmental gradients in short distances. Here, we explore soil properties and microbial community structure depending on drainage class in a well-preserved Andean valley on the lower alpine vegetation belt (~2500 m a.s.l.) at 33.5˚S. This presents an opportunity to determine changes in the overall bacterial community structure across different types of soils and their distinct layers in a soil depth profile of a highly heterogeneous environment.MethodsFive sites closely located (<1.5 km) and distributed in a well preserved Andean valley on the lower alpine vegetation belt (~2500 m a.s.l.) at 33.5˚S were selected based on a pedological approach taking into account soil types, drainage classes and horizons. We analyzed 113 soil samples using high-throughput sequencing of the 16S rRNA gene to describe bacterial abundance, taxonomic composition, and co-occurrence networks.ResultsAlmost 18,427 Amplicon Sequence Variant (ASVs) affiliated to 55 phyla were detected. The bacterial community structure within the same horizons were very similar validating the pedological sampling approach. Bray-Curtis dissimilarity analysis revealed that the structure of bacterial communities in superficial horizons (topsoil) differed from those found in deep horizons (subsoil) in a site-specific manner. However, an overall closer relationship was observed between topsoil as opposed to between subsoil microbial communities. Alpha diversity of soil bacterial communities was higher in topsoil, which also showed more bacterial members interacting and with higher average connectivity compared to subsoils. Finally, abundances of specific taxa could be considered as biological markers in the transition from topsoil to subsoil horizons, like Fibrobacterota, Proteobacteria, Bacteroidota for shallower soils and Chloroflexi, Latescibacterota and Nitrospirota for deeper soils.ConclusionsThe results indicate the importance of the soil drainage conditions for the bacterial community composition, suggesting that information of both structure and their possible ecological relationships, might be useful in clarifying the location of the edge of the topsoil-subsoil transition in mountainous environments.

Soil organic carbon stock prediction using multi-spatial resolutions of environmental variables: How well does the prediction match local references?

We evaluated how the spatial resolution of environmental variables (n = 47) altered their ability to predict soil organic carbon (SOC) stocks (0–30 cm depth) using training data from Gridded Soil Survey Geographic-gSSURGO and SoilGrids databases. Training and validation subsamples (1,629) were selected using a conditioned Latin hypercube sampling (cLHS) design based on environmental variables in Vermont, U.S. The predictive relationships between environmental variables and SOC stock (t C ha−1) were developed using machine learning algorithms. The algorithms were trained (70 %) and evaluated (30 %) using a random subset of database subsamples, respectively, with an additional evaluation step using local, independent SOC reference data (n = 272). The Random Forest (RF) algorithm outperformed other algorithms at all spatial resolutions in estimating SOC stocks. As spatial resolution increased, model performance with the gSSURGO database increased (R2 = 0.33–0.62 and RMSE = 42.42–34.92), while no such trend was observed for the SoilGrids database. The best SOC stock model prediction using the SoilGrids database was achieved with a 10 m resolution (R2 = 0.54 and RMSE = 4.67). Evaluation of modeled results using the external, or independent, reference data showed a significant decrease compared to the internal validation in prediction accuracy (R2 = 0.11–0.14 for gSSURGO and, R2 = −0.19 for SoilGrids). The gSSURGO database showed that soil maps (including suborders, drainage classes, temperature, and moisture) and geology/landform maps had a greater influence than other environmental variables at all spatial resolution scales. In contrast, climatic- and DEM-related variables were more significant for the SoilGrids database. Our study suggested that the origin of the SOC stock database and the sampling scheme largely affects the importance of environmental variables assigned in the machine learning algorithm. Our results confirmed that the variable and data sources, model type, and combination of environmental variables significantly influenced prediction accuracy. In conclusion, DSM products should be re-evaluated with local references when used for spatial extents that are different from those for which they were initially designed.

Confirmation of causality of climatic and soil physical factors affecting the corn (Zea mays L.) yield for silage in the climate-soil-yield network

Abstract This study aimed to confirm the causality of climatic and soil physical factors on silage corn yield based on the climatesoil-yield network in Korea. The climatic variables were growing degree days, high temperature, low temperature, surface temperature, rainfall, relative humidity, wind speed, and sunshine duration before and after silking. The soil physical variables were the effective depth, slope and drainage class of the soil. The yield variables were total digestible nutrients, dry matter and fresh matter yields. The network was constructed using structural equations and neural network models. In the result of the network, three causalities were remarkable. First, all longitudinal climatic causality before and after the silking stages were significant. It implies that the effect of climate in the vegetative stage reaches to yield through themselves in the reproductive stage. Second, there was the causality between climatic and soil physical factors based on indirect effects. Thus, it is likely to lead to an offset between the direct and indirect effects of soil physical factors. Finally, the effects of drought and heavy rainfall were clear before and after silking stages, respectively. It indicates that stress can damage the corn yield for silage. Here, the damage caused by the drought could be recovered due to various indirect effects, while the damage caused by heavy rainfall was fatal because there was a lack of an indirect path to recover from. This study contributed to identifying how various climatic and soil physical factors can affect production in the network. Furthermore, the climate-soil-yield network for silage corn in this study will help extend the structure with various factors in future studies.

An approach to provide maps of the N2O emission risks by soils at the regional scale: A case-study at the Haut-Loir watershed, France

Nitrous oxide (N2O) is often emitted by soils after nitrogen fertilization when the reduction of nitrate into N2 is incomplete and the soil is in hydromorphic condition. To take action to reduce N2O emissions, it is necessary to identify and locate areas that present a risk of N2O emissions. In this study, an approach to map N2O emission risk by soils was therefore developed based on soil properties. The risk of N2O emission was assessed through two components linked to static properties, independent on climate and agricultural practices; the Vulnerability: the ability of the soil to reduce N2O and the Hazard: the probability of soil water-logging. This approach was tested in the Haut-Loir watershed (3600 km2), a highly cropped area in the French Center Region. Vulnerability and hazard were estimated using French soil databases. The databases contain the drainage class information which allowed inferring the hazard. They also have measurements of pH, CEC and clay content which allowed estimating vulnerability through a pedotransfer function. In this watershed, contrasting risks were highlighted between different soil types and agricultural regions. High risk soils (∼2% of the studied area) were generally found in valleys and were not under crop because of their hydromorphy and acidity. However, attention should be given to medium risk soils (∼32% of the area) which were mainly found in the western region. Oppositely, soils of the eastern region present generally no risk of N2O emissions. Some former field studies have been reported in the studied watershed: they generally supported this soil risk classification. For medium-risk soils, different actions of mitigation depending on the degree of risk were suggested: liming or adjusting nitrogen input periods. This risk mapping approach could be applied in other cropland regions to help mitigation strategy.

Physical Land Suitability Assessment for Tea Cultivation Using GIS-Based Multi-Criteria Approach at Dabus Basin of Oromia Region, Southwest Ethiopia

Tea is economically important export commodity for Ethiopia Predicting the physical land suitability of tea is vital to avoid a sightless expansion of tea plantations and significant to recognize the potential suitable area for tea to disseminate of tea plantations in the country. Land suitability analysis is pre-request for assigning specific land for specific purposes. The study incorporate applications of Geographic Information System, Remote Sensing and Analytical Hierarchical Process (AHP) to allocate suitability weights to criteria that influence tea plant's growth to produce a predictive suitability map for its cultivation. Topography, Soils, climatic and land use features were included in the process as an important contributing factor for tea plant's growth. Each of the evaluations criteria layers were classified into four suitability class of not suitable, less suitable, suitable, and highly suitable. The results indicated that important influential factors affecting tea cultivation suitability evaluation were Rainfall (23.9%) followed by soils pH (18.05%) Elevation (12.72%), land use (10.79%), Aspect (9.07%), soils texture (6.96%), Slope (6.43), soils Depth (5.25%) temperature (3.42%) and drainage class (3.42%) respectively with 8.23% consistency index. The results revealed that the moderately suitable suitability class occupied the largest proportion of Land (84.92%), followed by highly suitable area (11.42%) and the unsuitable area (3.66%). the study area were precipitation, soil reaction, Altitude, Aspect and current Land use were identified as main limiting factors for Tea productions.

The influence of inherent soil factors and agricultural management on soil organic matter

AbstractThe accumulation of soil organic matter (SOM) is vital to the agronomic and environmental functioning of agroecosystems, yet the relative influence of inherent soil properties and agricultural management practices on SOM dynamics are not often addressed in individual studies. Using a network of 218 operating farm fields across Wisconsin and southern Minnesota, USA, this research employs single variable analysis (ANOVA and regression) and regression tree analysis to assess the effects of soil properties (texture, drainage class, and pH) and management variables related to crop rotation, tillage, cover cropping, and manure application on SOM, as well as total organic carbon (TOC) and total nitrogen (TN) in the upper 15 cm. Single variable analysis revealed that greater SOM, TOC, and TN were associated with poorly drained soil, tile‐drained fields, high clay content soil, and high biomass crop rotations. SOM and TOC were strongly related (R2 = 0.71), but different regression trees were produced; SOM was most influenced by clay content, while TOC was most influenced by drainage class. Future assessment for the building of SOM or TOC should be conducted with drainage and texture class categories and on a regional basis, given that these factors influence the practices that occur within landscapes. A rapid building of datasets through unstructured sampling, including an abundance of metadata, should be a research priority in agricultural science to identify practices to build SOM on a regional basis.

Revealing the sources of arsenic in private well water using Random Forest Classification and Regression.

Exposure to arsenic through private drinking water wells causes serious human health risks throughout the globe. Water testing data indicates there is arsenic contamination in private drinking water wells across New Jersey. To reduce the adverse health risk due to exposure to arsenic in drinking water, it is necessary to identify arsenic sources affecting private wells. Private wells are not regulated by any federal or state agencies through the Safe Drinking Water Act and therefore information is often lacking. To this end, we have developed machine learning algorithms including Random Forest Classification and Regression to decipher the factors contributing to higher arsenic concentration in private drinking water wells in west-central New Jersey. Arsenic concentration in private drinking water wells served as a response variable while explanatory variables were geological bedrock type, soil type, drainage class, land use/cover, and presence of orchards, contaminated sites, and abandoned mines within the 152.4-meter (500 ft) radius of each well. Random Forest Classification and Regression achieved 66 % and 55 % prediction accuracies for arsenic concentration in private drinking water wells, respectively. Overall, both models identify that bedrock, soil, land use/cover, and drainage type (in descending order) are the most important variables contributing to higher arsenic concentration in well water. These models further identify bedrock subgroups at a finer scale including Passaic Formation, Lockatong Formation, Stockton Formation contributing significantly to arsenic concentration in well water. Identification of sources of arsenic contamination in private drinking water wells at such a fine scale facilitates development of more targeted outreach as well as mitigation strategies to improve water quality and safeguard human health.

Assessment of Agricultural Land Suitability for Surface Irrigation Using Geospatial Techniques in the Lower Omo Gibe Basin, Ethiopia

Land suitability assessment for irrigation is critical to inform as well as manage current and future irrigated agriculture production systems. Land suitability analysis determines whether a given land area could potentially be used for specific crop production. The objective of this study was to identify the availability of suitable land for surface irrigation systems for the production of millet, sorghum, sugarcane, and wheat production in the Lower Omo Gibe plain, Southern Ethiopia. Land suitability analysis was performed by a parametric method using factors such as soil texture, effective soil depth, Calcium Carbonate (CaCO3), soil electrical conductivity (ECe), drainage class, and slope. Five land suitability classes were identified that include highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2). Results showed that 6.6, 7.5, 6.6, and 6.6% of the study area mostly located in the western part of the basin, were highly suitable (S1) for irrigated millets, sorghum, sugarcane, and wheat crops production, respectively. However, the mountainous areas in the central part of the basin were classified as N2 due to the steep slope and shallow soil depth. Overall, the results of the study revealed that the use of various suitability analysis techniques could assist in identifying suitable land for irrigated agriculture.

The effects of combined throughfall reduction and snow removal on soil physical properties across a drainage gradient in aspen forests of northern Minnesota, USA

Climate change is projected to alter precipitation patterns across northern latitudes, with decreased snow accumulation and summer rainfall predicted. These changes may alter soil physical properties such as soil strength, which would have implications for the feasibility of forest management activities. Reductions in summer and winter precipitation were simulated using a paired-plot design with throughfall reduction and snow removal as treatments across four soil drainage classes (well, moderately well, somewhat poor, and poorly drained) at each of three locations in northern Minnesota, USA. Snow removal caused large reductions in soil temperature and significantly deeper penetration of frost that varied by drainage class, where frost depth decreased with decreasing (wetter) drainage. There was a positive relationship between air freezing index and frost depth, where the rate of frost development was much higher in the snow removal treatment compared to the control (r2 of treatment = 0.8, slope = 0.093, p < 0.001; r2 of control = 0.18, slope = 0.012, p < 0.001). Throughfall reduction had limited effects on soil water content (SWC) and inconsistent effects on soil strength; relationships between SWC and strength were positive, negative, or non-existent. Based on these findings, changes in soil physical properties with altered precipitation are likely to manifest primarily in winter. Drainage class and air freezing index may be used to predict when sufficient soil frost is present for forest management activities to occur without detrimental effects to soil functions.

GIS and fuzzy logic integration in land suitability assessment for surface irrigation: the case of Guder watershed, Upper Blue Nile Basin, Ethiopia

Before planning the use of existing land resources for irrigation, it is necessary to determine their availability. The primary goal of this research was to examine the Guder watershed's land resource potential for irrigation development and to create a geo-referenced map of these resources using a geographic information system coupled with fuzzy logic. Irrigation suitability criteria such as slope, land use, proximity to water body, rainfall deficit, soil texture class, soil depth, soil drainage classes, and proximity to road were considered when evaluating prospective irrigable properties. The criteria maps were divided into four suitability classes using a natural break interval range technique. According to the study's findings, 39.8% of the watershed area is Highly suitable, 34.5% is moderately acceptable, 24.5% is marginally suitable, and 1.2% is not suitable for the aforementioned reasons. According to the irrigation suitability study of these characteristics, 70.42% of the slope, 15.57% of the slope, 10.6% of the slope, and 3.95% of the slope are Highly, moderately, and marginally suitable for surface irrigation, respectively. In addition, 15% of the soil in the study Area is suitable for a surface irrigation system. In terms of land cover and use, 75% is highly favorable, whereas 0.3% is not suitable for irrigation development. GIS and remote sensing offer a straightforward and powerful framework for combining spatially complicated field variables for land suitability research. This study demonstrates the effectiveness of the fuzzy logic technique combined with GIS as an effective model for finding prospective irrigable land on a continental scale.

Limited Effects of Precipitation Manipulation on Soil Respiration and Inorganic N Concentrations across Soil Drainage Classes in Northern Minnesota Aspen Forests

It is critical to gain insight into the responses of forest soils to the changing climate. We simulated future climate conditions with growing season throughfall reduction (by 50%) and winter snow removal using a paired-plot design across a soil drainage class gradient at three upland, Populus-dominated forests in northern Minnesota, USA. In situ bulk soil respiration and concentrations of extractable soil N were measured during the summers of 2020–2021. Soil respiration and N concentrations were not affected by throughfall reduction and snow removal, which was largely attributed to the limited treatment effects on soil moisture content and soil temperature. Drainage class was only a significant factor during the spring thaw period in 2021. During this period, the poorly drained plots had lower respiration rates compared to the well-drained plots, which was associated with the drainage class effects on soil temperature. The results of the companion laboratory incubation with varying levels of soil moisture also indicated no effect of the treatment on soil respiration, but effects of drainage class and moisture content on respiration were observed. Our results indicate that the combined effects of reduced summer and winter precipitation on soil respiration and N dynamics may be limited across the range of conditions that occurred in our study.

A proposed framework for assigning soil drainage classes to non-redoximorphic soils in the Canadian System of Soil Classification

Drainage refers to the frequency and duration of periods of saturation, and how quickly excess water is removed from the soil profile. It is one of the central concepts used to differentiate soil series within the Canadian System of Soil Classification (CSSC). Currently, seven drainage classes are recognized in the CSSC: very rapid, rapid, well, moderately well, imperfect, poor, and very poor. In redoximorphic soils (imperfect, poor, and very poor drainage classes), drainage is typically differentiated based on morphological features (i.e., the presence of gleying and mottles). Non-redoximorphic soils (very rapid, rapid, and well-drained classes) do not display such morphological features but are differentiated based on available water holding capacity (AWHC) as inferred from soil texture and particle size. Moderately well-drained soils are intermediate, in some cases defined by the presence of redoximorphic characteristics, but in other cases inferred based on texture. In effect, drainage in materials without redoximorphic features is estimated based on AWHC as related to texture class, which should include sand subfractions. Values for AWHC were calculated using a published pedotransfer function for combinations of sand, silt, and clay-sized particles, including various combinations of very fine to very coarse sand separates as input. Calculated values were compared with currently assigned drainage classes and several inconsistencies were identified. Revisions are proposed to textural assessment of soil drainage for non-redoximorphic soils.