Soil–landscape variability: mapping and building detail information for soil management

Iticha, Birhanu; Takele, Chalsissa

doi:10.1111/sum.12404

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…The mean soil OC of 5.1 kg m −2 reported for conventionally cultivated land at 20 cm depth in Inner Mongolia of North China [54] is also higher than the value obtained during the present study. Limited crop rotation practices, use of crop residues for energy, animal feed, construction, etc., and slash and burn, which are the common practices in Ethiopia, contributed to the depletion of soil OC [55]. The mono-cropping system in the study area also resulted in apparently low soil OC similar to the findings of [56].…”

Section: Discussionsupporting

confidence: 83%

Spatial prediction of soil acidity and nutrients for site-specific soil management in Bedele district, Southwestern Ethiopia

2021

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Background Understanding the spatial variability of soil properties is useful to tailor site-specific agricultural inputs to enhance crop production on a sustainable basis. This study was aimed to assess and map the spatial patterns of soil acidity and nutrients using geostatistical methods and support site-specific lime and fertilizer recommendations in Bedele district, Southwestern Ethiopia. Methods Soil samples were collected from agricultural land at a depth of 20 cm using grid sampling technique. The semivariogram analysis was performed for accurate spatial prediction and the kriging technique was used for interpolation of soil parameters. Results Soil pH varied between 4.5 and 6.8. Soil organic carbon (OC) content ranged from 0.3 to 5.6% and the mean soil OC density was 0.81 kg m−2. Available phosphorus (AvP) ranged from 0.8 to 38.6 mg kg−1 and nearly 80.23% of the soils exhibited very low to low AvP that could be due to fixation by strong acidity. Soils of the study area exhibited very high exchangeable potassium (K), but very low exchangeable calcium (Ca) and magnesium (Mg). The potassium to magnesium ratio (K:Mg) ranged from 0.2:1 to 10.9:1, while the values of calcium to magnesium ratio (Ca:Mg) varied between 0.3 and 3.4. Among the soil parameters, exchangeable Ca (CV = 54%) and K:Mg ratio (CV = 57.62%) were more variable than other soil parameters. Spatial variability was lowest for soil pH (CV = 10%). Conclusions Major portions of the study site were affected by strong acidity (pH ≤ 5.5). Accordingly, about 89% of the soils require lime that varied between 0.09 and 3.6 tons ha−1. In addition to soil acidity, deficiency of available P, Ca, and Mg were the major liming factors affecting crop production in the study area. Digital soil mapping was used to show the spatial variability of soil acidity and nutrients across agricultural land and applied for efficient lime and nutrients advisory works.

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Section: Discussionsupporting

confidence: 83%

Spatial prediction of soil acidity and nutrients for site-specific soil management in Bedele district, Southwestern Ethiopia

2021

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“…Cover and management factor is important in developing conservation plans (Mohamed & Shantha, 2019). The other key significant factor used in RUSLE model is the support and conservation practices factor called P-factor (Iticha & Takele, 2018). This parameter estimates the support and conservation practices and the corresponding to a particular support practice in up and down slope farming (Markose & Jayappa, 2016).…”

Section: Support Practice (P) and Cover And Management Factors(c)mentioning

confidence: 99%

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

Wagari

Tamiru

2021

Air, Soil and Water Research

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In this study, Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) platforms were successfully applied to quantify the annual soil loss for the protection of soil erosion in Fincha catchment, Ethiopia. The key physical factors such as rainfall erosivity ( R-factor), soil erodibility ( K-factor), topographic condition (LS-factor), cover management ( C-factor), and support practice ( P-factor) were prepared in GIS environment from rainfall, soil, Digital Elevation Model (DEM), Land use/Land cover (LULC) respectively. The RUSLE equation was used in raster calculator of ArcGIS spatial tool analyst. The individual map of the derived factors was multiplied in the raster calculator and an average annual soil loss ranges from 0.0 to 76.5 t ha−1 yr−1 was estimated. The estimated annual soil loss was categorized based on the qualitative and quantitative classifications as Very Low (0–15 t ha−1 yr−1), Low (15–45 t ha−1 yr−1), Moderate (45–75 t ha−1 yr−1), and High (>75 t ha−1 yr−1). It was found from the generated soil erosion severity map that about 45% of the catchment area was vulnerable to the erosion with an annual soil loss of (>75 t ha−1 yr−1), and this demonstrates that the erosion reduction actions are immediately required to ensure the sustainable soil resources in the study area. The soil erosion severity map generated based on RUSLE model and GIS platforms have a paramount role to alert all stakeholders in controlling the effects of the erosion. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss protection practices.

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“…In general, this concept addresses differences in yields due to variability in nutrient supply to crops, both between fields or within a given field [19,20]. The main reason for the in-field differences in nutrient supply to the currently grown crop is variability in basic soil properties, consequently resulting in a spatial differentiation in water and nutrient supply to plants during the growing season [21,22]. In rain-fed agriculture, effective crop production, in fact, depends on field zones sensitive to water shortages during the growing season [23].…”

Section: Introductionmentioning

confidence: 99%

“…The main target of the field zonation is to recognize the in-season potential of different field zones to N release. It can be assumed that N release from soil resources and its subsequent supply to the currently grown crop differs between high-(yield gain) and low-yielding field zones (potential yield loss) [19,22].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability of Yield and Nitrogen Indicators—A Crop Rotation Approach

et al. 2020

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The division of an arable field into zones of different productivity requires a reliable, discriminatory tool. This hypothesis was validated by analyzing the spatial variability of yield and N indicators in the crop rotation of winter oilseed rape (WOSR)/winter triticale (WTR) during 2016/2017 and 2017/2018 in a field of 30 ha (Przebędowo, Poland). The direct, measurable variables were: yield, N accumulated in—seeds/grain and crop residues, mineral N in spring, and harvest. The basic N indicators were total N uptake (TN), N-partial factor productivity, and N balance (Nb). The attainable yields of WOSR and WTR were 4.93 and 6.51 t ha−1, and a yield gap of −2.04 and −2.10 t ha−1. The management of 50 kg of the non-used N by crops, i.e. nitrogen gap (NG) could cover 36% and 65% of the yield gap (YG), respectively. The Nb, based on N input (Nin = Nmin + Nf) and TN, was the key field indicator, defining both yield and NG. Geostatic parameters, i.e., the nugget to sill ratio, spatial dependence range, and mean correlation distance, were very stable (≤0.2–0.17; 94–100 m; 28 m for WOSR and WTR). The spatial stability of Nb, irrespective of the crop and growing conditions, corroborates its suitability for discriminating high and low-productivity field zones.

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Soil–landscape variability: mapping and building detail information for soil management

Abstract: The study of soil-landscape relationships at a detailed scale (

Cited by 15 publications

References 45 publications

Spatial prediction of soil acidity and nutrients for site-specific soil management in Bedele district, Southwestern Ethiopia

Spatial prediction of soil acidity and nutrients for site-specific soil management in Bedele district, Southwestern Ethiopia

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

Spatial Variability of Yield and Nitrogen Indicators—A Crop Rotation Approach

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