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In the Ethiopian Highlands, research projects were often measuring soil attributes of spatially structured point data but soil variability at a watershed scale is not clearly defined. This study was conducted to assess the correlation among selected soil attributes and to illustrate the spatial pattern and dependence of neighboring observations. Th...
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... elevation of the watershed varies from 1923 m at the outlet to 2851 m in the north and the mean elevation is 2237.54 m above sea level, which exhibit vast topographical variations over the research site (Figure 2). Meanwhile, the majority of the study watershed (90% of the study area) is mountainous and consists of dis- sected terrain with steep slopes, and the remaining 10% has an undulated topography with gentle slopes. ...
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... Based on 20 year's (2001-2020) data, annual rainfall is strongly seasonal, variable and unreliable with a mean of 1,225 mm (range 1,026 to 1,499 mm) (Figure 1). The soil types are predominantly Cambisols and Leptosols, which are found in the upper and central part of the watershed, whereas Vertisols are found in the lower catchment (Addis et al., 2015). The topography of the area ranges from gentle slopes to very steep slopes (Yonas et al., 2010). ...
Intercropping legume forages in cereal crops is a widely proposed strategy to improve land use efficiency, and maximize the economic value of the farming system, especially in developing countries with restricted resources. The current study was carried out during the successive rainy seasons of 2019 and 2020 in the Gumara-Maksegnit watershed, Gondar Zuria district, Ethiopia to evaluate the feasibility of forage legume inter-cropping in a food sorghum-based inter-cropping system. The experiment was laid down in a randomized complete block design with 3 replications. Local sorghum variety 'Kucho' and the vetch Vicia villosa were used for the experiment. Sole sorghum at 75 cm row spacing (T1); sorghum at 75 cm row spacing plus vetch with simultaneous planting (T2); sorghum at 75 cm row spacing plus vetch planted 2 weeks after sorghum (T3); sorghum at 75 cm row spacing plus vetch planted 3 weeks after sorghum (T4); sorghum at 150 cm row spacing plus vetch with simultaneous planting (T5); sorghum at 150 cm row spacing plus vetch planted 2 weeks after sorghum (T6); and sorghum at 150 cm row spacing plus vetch planted 3 weeks after sorghum (T7) were applied as study treatments, on plant height (cm), grain yield (t ha-1), Stover yield (t ha-1) and thousand seed weight(g) of sorghum, plant height (cm) and dry biomass yield (t ha-1) of Vetch. In addition, an economic analysis was made for the feasibility of under-sowing vetch in sorghum crops. At 75 cm row spacing sorghum was planted at 1 seed per hill and at 150 cm row spacing at 2 seeds per hill to maintain a constant population across treatments. Results revealed that the highest grain and Stover yields of sorghum were obtained from T1, T3, T4 and T7. Sorghum planted at 75 cm row spacing plus vetch with simultaneous planting (T2) produced the highest vetch dry matter yield during both years. Earlier vetch planting significantly depressed both grain and Stover yields of sorghum relative to a pure stand of sorghum. On the other hand, the earlier the vetch was planted the greater the dry matter yields of vetch produced. The economic assessment revealed that sowing sorghum at 75 cm row spacing and under-sowing of vetch at the same time was more profitable than pure sorghum with 15,550.00 ETB ha-1 gross margins. With the ultimate acceptance of the technology sorghum in 75 cm row spacing plus Vetch with simultaneous planting (T2) was found economically profitable. Based on the results of this study it is suggested that to get higher economic return from a given area of land, nutritious and high-quality fodder and soil improvement through nitrogen fixation, farmers should adopt the practice of intercropping sorghum with forage legumes, preferably sorghum in 75 cm row spacing plus Vetch with simultaneous planting. The testing of this system on a field scale would either confirm or reject the hypothesis that it is more profitable than the conventional pure sorghum planting system. Soil analyses to determine possible increased soil N levels could demonstrate additional benefits. Feeding studies to show the benefits of feeding vetch hay to livestock in comparison with sorghum stover would confirm other potential benefits.
... The spatial attribute is defined by the nugget/sill ratio or spatial dependence (SPD) Co/(Co + C). When the value of Co/(Co + C) is less than 0.25, the variable is said to have a strong spatial dependency; when it is between 0.25 and 0.75, it is considered to have a moderate geographic dependence; and when it exceeds 0.75, it is considered to have a weak spatial dependence [48,49,58,59]. Table 2 shows the semivariogram obtained from the geostatistical study, illustrating the various geographical distribution models and levels of Table 3 The areal extent of the different classes of selected soil chemical properties with respect to sampling sites. ...
Purpose: Salt-affected soils have significant enough salt concentrations to impact other land and
soil resource uses, plant health, soil characteristics, and water quality. Consequently, a study was
carried out in the South Ethiopian Rift Valley area around the lakes of Abaya and Chamo to
determine the intensity and the types of salt-affected soil and map their spatial distributions.
Methods: At 0–20 cm depths, a grid soil sampling scheme was employed to gather data from
agricultural soils affected by salt. An adequately spaced grid cell of 200 m*200 m or seven
transects, with seven samples collected every 200 m on each sampling site, was generated by the
QGIS software’s Fishnet tool, and an auger collected 226 soil samples from the proposed 245 soil
sampling points. The analysis and interpretation of the data were done using both statistical and
geostatistical methods. The un-sampled surface was predicted and mapped from laboratory point
data using the standard Kriging algorithm in QGIS.
Results: According to the results, the soil in the study area was rated as strongly alkaline and
moderately alkaline in the reaction. The coefficient of variation (CV) was the lowest for soil pH.
Except for the Ganta Kanchama site, low CV (<10 %) confirmed the similarity of pH values
throughout all research areas. The EC values depicted that the study area is slightly saline except
for the Ganta Kanchame site, which rated moderately saline to strongly saline. The variability of
soil EC rated moderate to strong variation for the studied area. The exchangeable sodium percentage
(ESP) values distribution between the study sites demonstrates considerable variability
and difference. The area is dominated by low to high-risk rate soil sodicity, as evidenced by the
soil ESP CV of the studied area, which was >100 % and showed significant variability among the
samples. Out of 2274.65ha of the studied area, the type of salt 62.28 %, 26.09 %, 10.99 %, and
0.63 % were categorized as non-saline non-sodic, saline-sodic, sodic, and saline, respectively.
Following saline-sodic, sodic, and saline soils, respectively, non-saline and non-sodic soils
comprise most of the investigated areas.
Conclusions: The result indicates almost all the salt-affected areas were situated in relatively lower
slope areas exhibiting a flat to almost flat slope (0–2%). The study’s findings are that the studied
area needs specific soil management strategies to boost the salinity and sodicity problems around
the study area and recommended reclamation techniques as the extent of the problems.
... However, the high variability of OC and EC in the study area may be due to pedogenic processes that were influenced by micro-topographic variations operating in different periods (Świtoniak 2014;). Addis et al. (2015), who found the highest CV value for SOC (CV above 37%), while pH varies the least (CV below 7%), reported similar results. ...
Soil chemical properties have a major impact on both agriculture and the environment, particularly with regard to soil fertility, soil quality, and crop production. However, little research has been done to address the spatial patterns of soil nutrients in the northwestern highlands of Amhara region of Ethiopia. The objective of this research was therefore to explore spatial variability of selected soil chemical properties that covers a broader area of 4 districts covering 560,085.2 ha of land to assess the spatial variability in the northwestern highlands of Amhara region in Ethiopia. Using Global Positioning System (GPS) (3 m precision), 363 (0–30 cm depth) soil point data were collected. Soil organic carbon (SOC), available P (AvP), electrical conductivity (EC), and soil acidity (pH) were taken into account when we explore the spatial variability. Inverse distance weighting (IDW), ordinary kriging (OK) methods, and geostatistical analysis (GS + 10) were used to analyze the spatial variability patterns of the SOC, EC, AvP, and pH concentrations. The results showed that the chemical properties vary considerably; the highest and lowest coefficients of variation (CV) were AvP (82.64%) and pH (9.12%), respectively. Moderate spatial dependencies (48.13–24.91%) were typically observed (SOC, EC, and pH), while AvP (17.84%) was strong. Cross-validation analysis showed that OK performed better (AvP, EC, and pH) than IDW (SOC). A semivariogram of log-transformed data for soil AvP, EC, and SOC was fitted with a spherical model whereas exponential model for pH. Spatial patterns of a pH map showed that the southeastern region is characterized by higher soil pH and high fertility potential for food crops. The research result/map could give useful tool for effective integrated land management to minimize soil acidification by policymakers, agriculturalists, and other stakeholder groups. We also recommended future routine update on the size and distribution of surface soil acidity, AvP, and SOC in the study area.
... For effective mitigation and control of soil erosion, soil conservationists and ecologists have been primarily concerned with the estimation of soil erosion. Either the Universal Soil Loss Equation USLE or the Revised Universal Soil Loss Equation RUSLE are commonly used for the determination of soil loss [20,21]. ...
... The global positioning system (GPS) device is deployed to navigate to the sample point and adjusted to the center of the area and checking the appropriate orientation which would be the sampled area's mid-point. Garmin explorer GPS of ± 3 m accuracy was used to locate the topographical coordinates of the sampling points, and 2 kg of disturbed soil samples were excavated for testing using a bucket auger [21]. At each sampling point, three soil samples were collected at the following depth: 0 -10 cm, 10-20 cm, and 20-30 cm (Table 1). ...
... It represents the rate at which soil will erode when exposed to a given amount of rainfall or runoff. Several factors such as soil organic matter content, permeability class index, structural class index, particle size distribution, shear strength, bulk density, porosity etc. influence soil erodibility factor which affect the soil's ability to resist detachment and transport by water [21,38]. The Universal Soil Loss Equation (USLE) is commonly used for the calculation of soil erodibility (Eq. 1) [20,21]. ...
This study evaluates the spatial distribution of soil erodibility factor (K) in Umuahia, Abia State, Nigeria using the Universal Soil Loss Equation (USLE) nomograph. The soils used for the study was sampled from 14 erosion prone areas in Umuahia, Southeast, Nigeria. The topsoil samples collected at depths of 0–10 cm, 10–20 cm, and 20–30 cm around the middle of each location identified with the aid of a GPS. The percentages of sand, silt, clay, moisture content, saturated hydraulic conductivity, and organic matter (OM) were all examined. The Gaussian ordinary kriging model for the determination of K-factor was compared with the Inverse Distance Weighting method. The K-factor’s coefficient of variation (CV) was 0.29 and the K-factor value of the nuggets to sill ratio (0.44) indicates a moderate spatial distribution. The Gaussian semi-variogram approach yielded the best estimate accuracy and model fitting effects, meaning that the Gaussian ordinary Kriging model is better for K-factor estimation. The root mean squared error (RMSE) was 0.0079 and the mean squared deviation ratio (MSDR) was 0.89, implying that the Gaussian model was unbiased and adequately captured the experimental variation. The K-factor values were lower in the north of the research region ranging from 0.0250 to 0.0197 Mg h MJ−1 mm−1 compared to east with the K-factor ranging from 0.0399 to 0.0423 Mg h MJ−1 mm−1. The estimated K-factor was relatively unbiased since the root mean square error was extremely small and the mean error was nearly equal to 0. The determination of soil erodibility (k-factor) influenced by factors such as soil texture, structure, and organic matter is crucial in assessing the vulnerability of land area to soil erosion. The spatial distribution of these factors affects the k-factor at different locations in a landscape, which enables accurate estimation of k-factor. Nutrient levels impact soil erodibility and distribution. Low nitrogen limits growth, favoring erosion. Phosphorus aids stability. Optimal potassium benefits growth and erosion control. Spatial distribution is vital, emphasizing precise nutrient management for effective soil health.
... Z(x i ) and Z (x i + h) are measured values of the variable Z at the corresponding locations. For the spatial autocorrelation process as defined by Addis et al. [29], the semivariogram model with the smallest reduced sum of squares (RSS) was used in this research. This is one of the finest criteria for parameter and model selection; the RSS assesses the total discrepancy between observed data and the estimated values by a prediction model [30]. ...
Soil erodibility is one of the most crucial factors used to estimate soil erosion by applying
modeling techniques. Soil data from soil maps are commonly used to create maps of soil erodibility
for soil conservation planning. This study analyzed the spatial variability of soil erodibility by using a
digital elevation model (DTM) and surface soil sample data at the Rhirane catchment (Algeria). A total
of 132 soil samples were collected of up to 20 cm in depth. The spatial distributions of the K-value and
soil physical properties (permeability, organic matter, and texture) were used to elaborate ordinary
Kriging interpolation maps. Results showed that mean values of soil organic matter content were
statistically different between Chromic Cambisols (M = 3.4%) vs. Calcic Cambisols (M = 2.2%). The
analysis of variance of the organic matter provided a tool for identifying significant differences when
comparing means between the soil types. The soil granulometry is mainly composed of silt and fine
sand. The soil erodibility showed values varying between 0.012 and 0.077 with an average of 0.034,
which was greater in soils with calcic horizons. Statistical evaluation by using Pearson’s correlation
revealed positive correlations between erodibility and silt (0.63%), and negative correlations with sand
(−0.16%), clay (−0.56%), organic matter (−0.32%), permeability (−0.41%), soil structure (−0.40%),
and the soil stability index (−0.26%). The variability analysis of the K-factor showed moderate spatial
dependency with the soil erodibility map indicating moderate to highly erodible risk in cropland
and sparse grassland land uses. Overall, the study provides scientific support for soil conservation
management and appropriate agricultural food practices for food supply.
Keywords: GIS; K-USLE; Kriging; land cover; soil erosion
... They are not restricted to being applied on homogenous regions only and can be applied in a wide range of settings. This is important since most agricultural farmlands often have a high amount of variability in a given spatial context (Addis et al. 2015). We do notice some unexpected model behavior in the last row of Fig. 13 where irrigated/farmland regions (toward the center of the image) have lower soil moisture than the neighboring regions. ...
We develop a deep learning–based convolutional-regression model that estimates the volumetric soil moisture content in the top ∼5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imagery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from ∼1000 in situ sensors globally over the period 2015–21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m ³ m ⁻³ , and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.
Significance Statement
Soil moisture is a key variable in various agriculture and water management systems. Accurate and high-resolution estimates of soil moisture have multiple downstream benefits such as reduced water wastage by better understanding and managing the consumption of water, utilizing smarter irrigation methods and effective canal water management. We develop a deep learning–based model that estimates the volumetric soil moisture content in the top ∼5 cm of soil at a nominal 320-m resolution. Our results demonstrate that machine learning is a useful tool for fusing different modalities with ease, while producing high-resolution models that are not location specific. Future work could explore the possibility of using temporal input sources to further improve model performance.
... This rate of erosion is increasing due to the continuous expansion of the agricultural frontier (deforestation), overgrazing and urbanization [2]. Therefore, it is indicated that erosion should be considered as a global problem with environmental and social impacts [1,29,31]. In the world, 25% of the land used for agriculture is seriously degraded, reducing productivity [27,37]. ...
... Once the study area was defined, the USLE equation was applied as a direct parametric method. The USLE Equation [1] is a methodology that allows quantifying annual quantities of eroded soil per unit area that was proposed by [50]. ...
The drinking water intake of the city of Guayaquil is located on the Daule River, supplying 2.7 million inhabitants who are affected by the turbidity of the water, even leading INERGAGUA to suspend the service with high economic losses. Given this, water erosion and the effects of land use were calculated using the Universal Soil Loss Equation (USLE), estimating the Erosivity factor (R) through the Fournier climatic aggressiveness index. Mean monthly and annual rainfall data from 14 stations in the basin were used. The other variables such as soil erodibility factor (K), slope length and gradient (LS), coverage factor (C), use factor and soil management (P), were quantified with a mathematical analysis of the variables. Initially, the sub-basin was considered as a global model and then as a distributed model, subdividing it into 88 micro-basins, allowing a more accurate estimate of an average annual water erosion of 130.04 t/ha/year. To determine the type and level of erosion, the FAO scale was used, observing 14 micro-basins with erosion rates ranging from 154.3487 t/ha/year to 846.3418 t/ha/year, whose range of erosion goes from very severe to catastrophic, which results to the requirement of immediate intervention. Applying the sediment delivery factor (SDF), it was estimated that 5.12% of the total eroded, that is, 8,089 tons/ha/year reaches the river.KeywordsWater erosionSedimentsSub-basinMicro-basinLevel of erosion
... They are not restricted to being applied on homogenous regions only and can be applied in a wide range of settings. This is important since most agricultural farmlands, etc. often have a high amount of variability in a given spatial context (Addis, Klik, and Strohmeier 2015). ...
We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical variables from SoilGrids and modelled soil moisture fields from GLDAS. The model was trained and evaluated on data from ~1300 in-situ sensors globally over the period 2015 - 2021 and obtained an average per-sensor correlation of 0.727 and ubRMSE of 0.054, and can be used to produce a soil moisture map at a nominal 320m resolution. These results are benchmarked against 13 other soil moisture works at different locations, and an ablation study was used to identify important predictors.
... In the Ethiopian Highlands, deforestation for crop production dramatically increased the vulnerability of the soils to rainfall-driven erosion (Nyssen et al., 2000;Melaku et al. 2017;Klik et al. 2017;Melaku et al. 2018). Intensive rainfalls during the rainy season (June to September) threaten the mountainous regions with severe land degradation especially the steep-sloped and unprotected areas (Addis et al., 2015). ...
... The watershed is dominated by steep slopes and ranges from about 1,920 m above sea level to 2,860 m above sea level in altitude. It covers an area of 54 sq km and is located between 12°24' N and 12°31' N and between 37°33' E and 37°37' E. The watershed drains into the Gumara River, which finally reaches Lake Tana (Addis et al., 2015). The two sub-watersheds are located in the southern lower part of Gumara-Maksegnit watershed between 12°25'26'' N and 12°25'46'' N and between 37°34'56'' E and 37°35'38'' E ( Figure 2). ...
... The geographical location is lied between 120 23' 53'' to 120 30' 49'' latitude and 370 33' 39'' to 37037' 14'' longitude. This mountainous agricultural watershed, which covers an area of 53.7 km 2 , is one of the most severely eroded parts of the Ethiopian highlands (Addis et al., 2015). The study watershed has a very rugged mountainous topography, with an average slope of 22.1% and most the study watershed (more than 90% of the area) is composed of gullies and ridges. ...
... The study watershed has a very rugged mountainous topography, with an average slope of 22.1% and most the study watershed (more than 90% of the area) is composed of gullies and ridges. The elevations of the watershed range from 1920 to 2850 m above sea level (Addis et al., 2015). The area geology is predominated by Trapp series of Tertiary volcanic eruptions (Addis et al., 2015). ...
... The elevations of the watershed range from 1920 to 2850 m above sea level (Addis et al., 2015). The area geology is predominated by Trapp series of Tertiary volcanic eruptions (Addis et al., 2015). The soil types of the watershed are predominately classified as Cambisols and Leptosols, which covered in the upper and central part of the area, while Vertisols in the lower parts of the watershed near the outlet. ...
Soil fertility improvement using sole application of artificial fertilizer is not commendable due to inaccessibility and financial stress for a farmer living in developing countries. Thus, the objective of this study was to evaluate ability of locally available tree species for soil fertility maintenance in Gummara-maksegnit watershed, Ethiopia. Questionnaires were administered to a total of 385 respondents with aim to determine three topmost preferred tree species for soil fertility enhancement. Leaf litter from the selected tree species and soil samples from both under the canopy of the selected tree species and open (controlled) area was collected for soil chemical properties analysis. Croton macrostachyus, Cordia africana and Olea europaea was perceived as to be the best by 42%, 32% and 26% of the respondents respectively. Leaf litter of C. macrostachyus, C. africana, and O. europaea had significantly different (P < 0.05) nitrogen value. In addition, leaf litter of C. macrostachyus and C. africana also showed higher concentration of P and K than O. europaea. Soil properties under the canopy of all the selected tree species significantly differ (P<0.05) from the open (controlled) sampling point mainly because of nutrient addition from the fallen leaf litter to the underneath soil. Therefore, it is necessary to advise farmers to let these tree species grow on their farms and improve soil condition to achieve maximum production.
