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Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used w...
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Citations
... USDA Soil Texture Triangle(Groenendyk et al. 2015). ...
Rafflesia, a holoparasitic and endophytic plant, depends on its host, Tetrastigma spp., for survival, thus highlighting the critical interdependence between these species. Given the endangered status of Rafflesia due to anthropogenic pressures and narrow distribution, comprehensive conservation efforts are crucial. Ecological data on edaphic conditions, particularly the presence of the host, are important for effective conservation strategies. This study assessed soil properties across Rafflesia habitats on Sumatera, Borneo, and Java islands, revealing similarities in pH, carbon, nitrogen, cation exchange capacity (CEC), while the soil texture varied. These findings contribute valuable insights for informed conservation initiatives, both in-situ and ex-situ.
... The concentrations of exchangeable cations (potassium, sodium, calcium, and magnesium) were determined by employing the ammonium acetate method (Thomas, 1983). Soil's texture experiment was determined by the hydrometer method (Huluka & Miller, 2014), and the type of soil was determined using the United States Department of Agriculture (USDA) Natural Resources Conservation Service's Soil Textural triangle (Groenendyk et al., 2015). Soil electrical conductivity was measured by a unified standard (soil/ water, 1:5) (FAO, 2021b). ...
The increasing proximity of the Dudumbia dumpsite, an open dumpsite in Navrongo, Ghana, to human settlements necessitates an investigation of the soil quality to safeguard the environment from heavy metal toxicity. This study examined the impact of waste dumping activities on the physicochemical properties of the soil, as well as the level of heavy metal (Pb, Cd, Ni, Cr, As, Hg, Cu, Mn, and Zn) contamination and associated risks. Various contamination and risk assessment tools were used, including the geoaccumulation index (Igeo), pollution load index (PLI), potential ecological risk (Er), and potential ecological risk index (PERI). The study found significant improvements in notable soil attributes such as phosphorus (P), organic carbon (C), total nitrogen (N), calcium (Ca), magnesium (Mg), potassium (K), sodium (Na), and effective cation exchange capacity, with percentage increases ranging from 50.8 to 2078.3%. Igeo values ranged from 2.07 to 6.20, indicating contamination levels from moderate to extreme. The PLI and PERI values were 16.241 and 1810, respectively. The Er values for the heavy metals ranged from 36 to 607, indicating ecological risk levels from low to very high, with Cd and Hg posing very high risks. These results suggest that while the dumpsite soil shows improvements in some characteristics favourable for plant cultivation, waste dumping significantly contributes to heavy metal contamination. The soil at the dumpsite is deteriorated and poses significant health risks, particularly due to Cd and Hg. Therefore, remediation efforts should prioritise mitigating the risks posed by Cd and Hg.
... 2014;Gangwar et al. 2019). Finally, the soil texture was determined using the USDA textural triangle having the percentages of sand, silt and clay soils (Groenendyk et al. 2015). ...
Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.
... Before the experiment was set up, the soil properties were determined. The soil at a 0-30 cm depth contained 72.3% silt, 26.3% sand, and 1.4% clay and was classified as a sandy loam [35]. The soil pH was near neutral (pHKCl 6.9), and it contained 0.23 g kg −1 plant-available phosphorus (P2O5) and 0.37 g kg −1 plant-available potassium (K2O). ...
Growing perennial grasses is often cited as one of the possible and most affordable solutions for mitigating climate change. This practice is also recommended for sustainable soil management in agriculture. Our experiment involved timothy grass (Phleum pratense L.), red clover (Trifolium pratense L.), and their mixture; tall oat grass (Arrhenatherum elatius L.), alfalfa (Medicago sativa L.), and their mixture, with the aim of diversifying the annual rotation; and periodical, twice-per-season cultivated plots in the same area (the bare soil fallow). Soil samples were collected in late October after plant vegetation’s first, second, and third growth years from three field replicates at the soil layers 0–0.1 m, 0.1–0.2 m, and 0.2–0.3 m and plant roots—at the beginning of November in the second cultivation year. After three years, the SOC content increased in all the study areas occupied by plants, regardless of their species composition, while it decreased in fallow plots. Grass roots were characterized by the highest C/N ratio (38.2 and 45.5). The roots of the red clover–timothy grass mixture also reached a C/N ratio greater than 30. Based on our research, choosing a combination of at least two plants, such as legumes and grasses, is possibly more effective for enriching the soil with carbon compounds in a short period.
... Then, a hydrometer method was used to determine the soil texture proportions less than 2 mm in diameter (Fig. 3c). Finally, the soil texture was determined using the United States Department of Agriculture (USDA) textural triangle having the percentages of sand, silt and clay soils (Groenendyk et al. 2015). ...
Determination of infiltration capacity is a very important parameter during the design and evaluation of irrigation systems. Accurate estimation of infiltration rates helps in optimizing irrigation scheduling, preventing soil erosion, and improving water use efficiency. This study was conducted to evaluate and compare selected infiltration models for estimating water infiltration rates of five soil textures in the Shillanat-iv- irrigation scheme in northern Ethiopia. Soil samples were taken from selected sites in the irrigation scheme for determining soil textural classes using the hydrometer texture laboratory analysis and the USDA textural triangle. Soil textural map of the irrigation area was prepared using inverse distance weight interpolation technique in ArcGIS version 10.4. The double ring infiltrometer was used to measure the infiltration rates of different soil textures in the irrigation scheme. Six selected infiltration models namely Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel models were used to estimate infiltration rates for five soil textural classes namely, clay loam, loam, sandy clay loam, clay and sandy loam soils. To evaluate the performance of the models, infiltration rate was measured in 38 points of the study area, out of which 70% of the data was calibrating model parameters and 30% of the data was used for model validation. Parameters values of the infiltration models were optimized using the least-squares errors in SPSS statistical software. Five statistical parameters including the Coefficient of determination (R ² ), Maximum absolute error (MAE), Bias, Root mean square error (RMSE) and Percentage average error (PAE) were used to evaluate the performance of the infiltration models. Results indicated that the Revised Modified Kostiakov’s, Modified Kostiakov’s, and Novel’s infiltration models had better capability in estimating infiltration rates for clay loam, loam and sandy loam soil textures respectively. Similarly, the Hortons’s model had better performances in estimating infiltration rates of both sandy clay loam and clay soil textures compared to other models. In the design of the irrigation system, the best fitted infiltration models can be used for estimating the infiltration rate of soil textures in the selected irrigation scheme and other sites with similar soil textures.
... The K factor was estimated using the soil type map acquired from the World Soil Information Service Snapshot-2019 (Batjes et al., 2020), which was extracted according to the study area in ArcGIS software. The physical properties of each soil type, mainly silt, sand, and clay as well as the organic matter was extracted following the soil texture classification scheme (Groenendyk et al.,2015)which was used to generate the K factor values using a formula adopted from published literature (Sharpley and Williams, 1990). ...
Soil erosion is a serious issue, causing loss of agricultural productivity, increase in sediment deposit in the riverbeds, and damage to the ecological balance of the affected areas. Proper assessment of the rate of soil erosion is essential for the management of natural resources. The present study employs GIS-based RUSLE (Revised Universal Soil Loss Equation) model for the estimation of annual soil loss in Majuli River Island of Assam, India. To identify the soil erosion susceptible areas, annual average rainfall, soil properties, topographic characteristics, and LULC were taken as inputs. The result revealed that annual soil loss of the study area ranges between 0 to 711 t ha−1 yr−1, with a mean annual soil loss of 23.02 t ha−1 yr−1. The entire region was classified into six soil loss severity classes, around 90 % of the area was found to be very slightly affected (< 5 t ha−1 yr−1) by soil erosion, around 5 % slightly affected (5 – 10 t ha−1 yr−1), roughly 3% moderately affected (10 – 20 t ha−1 yr−1), around 1% moderate high (20 – 40 t ha−1 yr−1), nearly 0.3 % area affected severely (40 -80 t ha−1 yr−1) and very severely affected areas (> 80 t ha−1 yr−1) contributes 0.1 %. A total of six priority levels of conservation were demarcated village-wise, priority level I requires immediate attention, and so on. The outcome of the research can help in the effective implementation of conservation and management practices to check soil erosion in the study area.
Keywords: Soil erosion, RUSLE, Remote sensing, GIS, Majuli Island.
... Our soil model is based on the static soil properties of growth resistance , permeability , and water capacity , as well as dynamic soil properties of water and nutrients . It has been observed that different soil types have different hydraulic properties [Groenendyk et al. 2015]. For example, water in sandy soil infiltrates quickly, whereas in clay the flow is very slow. ...
Computer graphics has dedicated a considerable amount of effort to generating realistic models of trees and plants. Many existing methods leverage procedural modeling algorithms - that often consider biological findings - to generate branching structures of individual trees. While the realism of tree models generated by these algorithms steadily increases, most approaches neglect to model the root system of trees. However, the root system not only adds to the visual realism of tree models but also plays an important role in the development of trees. In this paper, we advance tree modeling in the following ways: First, we define a physically-plausible soil model to simulate resource gradients, such as water and nutrients. Second, we propose a novel developmental procedural model for tree roots that enables us to emergently develop root systems that adapt to various soil types. Third, we define long-distance signaling to coordinate the development of shoots and roots. We show that our advanced procedural model of tree development enables - for the first time - the generation of trees with their root systems.
... Based on the soil textural triangle [8,16], the Yala soil was classified as clay loam while the Tindinyo soil was classified as sandy clay loam. ...
... This means that higher OC does not necessarily translate to higher availability of organic matter, and emphasizes the importance of DOC determination in routine soil analysis. Soil organic matter usually puts together heavy metals such as copper through reactions with ions, and eventually forms complexes [13,16,27]. Complexation by organic matter has been reported as the preferential and most effective mechanism of Cu retention in soils (Bradl [9]), and copper in soil solution forms complexes with soluble organic matter. ...
... Previous studies have demonstrated a diverse array of applications for this ternary diagram, including the representation of hydraulic information (Groenendyk et al., 2015) and subsurface soil properties (Hu et al., 2004), the collection of agricultural data for dynamic soil texture prediction (Aarthi and Sivakumar, 2020), and the assessment of sensor Fig. 1. USDA soil texture triangle (NRCS, 1993). ...
Soil forms the foundation for biotic and abiotic activities that shape landscapes over time. Effective communication and understanding of soil profiles, contents, and interactions with other systems such as vegetation and climate are crucial for multidisciplinary research and projects involving soil. A robust, comprehensible, and extendable visualization system is required to enhance communication across diverse disciplines, including landscape architecture, agronomy, and ecology.
This paper introduces the BeingAliveLanguage, an innovative, extensible visualization system for soil-centric information within a multidisciplinary communication framework. The system employs a fractal-based visual language to effectively convey vital soil information to professionals in various fields engaged in architecture, landscape design, and urban planning projects. The corresponding software, developed as a plugin for the Rhino-Grasshopper CAD environment, allows users to automatically generate easily understandable soil-centered diagrams using a node-based programming language. Designed to enhance communication in landscape, geoscience, and agriculture-related fields, the system provides critical information to support the design and decision-making process. We showcase the system’s efficacy through two extensions and by utilizing the tool in multiple real-world projects.
... This characteristic enhances the water holding capacity at the microscopic level, ensuring water availability for plant roots over extended periods, particularly during drought or water stress. This property contributes to increased water supply to the root zone, enabling plants to withstand prolonged water stress and enhance their resilience [24,51]. ...
Climate variability is a major threat to maize (Zea mays) crops in Colombia, posing a risk to food security and compliance with Sustainable Development Goals. Therefore, this study aimed to assess the physical vulnerability of maize crops to climate variability in the semi-arid region of the Department of Cesar, Colombia, using the AquaCrop-OS model. Specifically, the study evaluated the vulnerability of maize crops to three typical meteorological years (dry, intermediate, and wet) adjusted to the growth cycle in two semesters of the year (periods I and II). An analysis of 43 years of data (1980–2022) revealed that most years in the area can be classified as intermediate, whereas the number of wet years was higher than the number of dry years. However, under the intermediate typical meteorological year scenario, maize cultivation in the department of Cesar experienced severe drought conditions during periods I and II. The study’s vulnerability curve showed an increasing rate within the yield loss index when the average water stress index was between 0.5 and 0.8 for period I. The rate of increase slowed when the index exceeded 0.9. For period II, the maize crop presented a lower degree of vulnerability, with 64% of the area experiencing a yield loss rate between 5% and 30%. The study also determined the required irrigation depth of water for optimal yields, which ranged from 70 to 160 mm during the growing season for all maize crop scenarios in the Department of Cesar. The results of this study can contribute to the consolidation of a database of physical vulnerability and threats of precipitation anomalies on regional and national scales. Overall, this study’s evaluation of the physical vulnerability of maize crops can help producers develop better strategies to mitigate the impacts of climate variability and ensure regional food security.
