Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem

Şenol, Hüseyin; Alaboz, Pelin; Demir, Sinan; Dengi̇z, Orhan

doi:10.1007/s12517-020-06214-9

Cited by 31 publications

(7 citation statements)

References 32 publications

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“…Therefore, digital soil mapping techniques have been used to detect and define SQI of a large area or field-scale with minimal soil sampling effort [9]. Ordinary kriging interpolation methods, which is one of the digital soil mapping techniques, have been successfully applied by many studies to predict the distribution of SQI [28,52]. The manure applications obtained higher SQI compared to inorganic treatments.…”

Section: Soil Quality Assessment Under Manure and Inorganic Fertilize...mentioning

confidence: 99%

Field-Scale Evaluation of the Soil Quality Index as Influenced by Dairy Manure and Inorganic Fertilizers

et al. 2022

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Long-term addition of manure increases soil organic carbon (SOC), provides nutrient supply, enhances soil quality and crop yield (CY), but may also increase global warming potential (GWP). In this study, a long-term experiment was conducted to investigate impacts of organic dairy manure and inorganic fertilizer on the spatial distribution of soil quality indicators in field scale. The experiment was initiated in 2008 (seven years), and includes three manure and two inorganic fertilizer treatments along with a control (no manure or no inorganic fertilizer addition). The study was set into a randomized complete block design with six treatments and four replications in a total of 24 plots with an equal size each of 6 × 18 m (108 m2). Soil physical, chemical and biological properties (total 26 properties) were considered as the total data set and principal component analysis (PCA) was used to determine long-term organic and inorganic fertilizer-induced changes in soil quality. Ordinary kriging interpolation methods were used to predict the spatial distributions of soil quality index (SQI) and mean soil quality values were compared with fertilization treatments by using Duncan’s test. Results showed that most measured soil quality index parameters showed significant differences (p < 0.05). The long-term dairy manure applications had positive impacts on soil quality index parameters where overall SQI scores were higher under high manure (HM) compared to medium manure (MM), low manure (LM), medium fertilizer (MF), high fertilizer (HF), control (CK) by 25%, 27%, 47%, 55% and 92%. A similar trend was observed for CY and GWP. This indicates that long-term dairy manure can be an option to increase SQI values and provide higher CY, however, this may lead to greater GWP.

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Section: Soil Quality Assessment Under Manure and Inorganic Fertilize...mentioning

confidence: 99%

Field-Scale Evaluation of the Soil Quality Index as Influenced by Dairy Manure and Inorganic Fertilizers

et al. 2022

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“…The obtained values of land suitability can be considered reliable because they have been evaluated worldwide in many land and agricultural systems. Nevertheless, further research into these maps of observed IQI values compared with maps of predicted value generated by machine learning, or other types of techniques, as suggested by Şenol et al (2020) could be useful. The use of PCA can be satisfactory in the case of a lot of variables in which selecting a minimum data set is required (Chandel et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Developing scoring functions for soil quality to assess land suitability for irrigated wheat in Southern Algeria

Mihoub

Naima

Helimi

et al. 2021

Soil Use and Management

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Efficient land‐use management could be optimized through appropriate advice regarding land suitability and achieving a reasonable yield while preserving soil and environmental conditions. The main objective of this study was to determine and define those locations capable of wheat cultivation with irrigation in the municipality of N’goussa (Southern Algeria). Expert opinion (EO) method was used for selecting minimum soil data set (MDS‐EO), where six influencing soil properties (sand, pH, salinity, organic matter, gypsum and lime) and other three key parameters (slope, land use/land cover, and proximity to roads and electricity network) were considered important for irrigated wheat cultivation in the study area. Indicators were weighted according to principal component analysis (PCA) loadings and summed. PCA revealed that the selected nine indicators are appropriate for accounting 73.63% of the total variance. Two maps of land suitability were created here (expressed as a soil quality index within a range from 0 to 1) from multivariate analysis and already known standard scoring functions (SSFs) by using two data sets (pure and scored). The distribution of soil quality index (IQI) showed that above 70% of the land was moderately (MS) to highly suitable (HS) for irrigated wheat. The estimation accuracy of soil quality classes has been evaluated using random forests (RF), having as a dependent variable the final class of land suitability used in each map. It was determined that estimation accuracy could reach 60% (pure data set) and 65% (scored data set) at 0.5 cut‐off value for soil quality classes. Final prioritizing according to ranks of sensitivity index (SI) and efficiency ratio (ER) showed that IQI obtained from scored data set is the most suitable approach in soil quality assessment of arid areas. Therefore, it could be applied in southern Algeria for land management, integrated planning and environmental assessments.

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“…On the other hand, According to Tunçay et al (2018) determined the lowest mean absolute error (MAE) and mean squarer error (MSE) values with the regression kriging method in the creation of field capacity distribution maps, while the wilting point was obtained with the Cokriging method. Furthermore, the distribution of the observed values and the values estimated from the algorithms with different methods showed a similar pattern (Alaboz et al 2020;Şenol et al 2020).…”

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confidence: 81%

The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches

Alaboz

Dengi̇z

2023

J Agr Sci-Tarim Bili

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In this study, as a result of the determination of the Least limiting water range (LLWR) contents of alluvial lands with different soils, which are distributed in the Bafra Plain where intensive agricultural activities are carried out, the compression and aeration problems in the area are revealed with distribution maps. In addition, the predictability of LLWR was evaluated with the random forest (RF) algorithm, which is one of the machine learning algorithms, and the usability of the prediction values distribution maps was revealed. The LLWR contents of the soils varied in the range of 0.049- 0.273 cm3 cm-3 for surface soils. While there were aeration problems in 6.72%, compaction problems in 20.16%, both aeration and compaction problems in 0.8% of the surface soils examined in the study area, 72.32% were determined under optimal conditions. For the 20-40 cm depth, an aeration problem in the 5.88%, a compaction problem in the 28.57%, and both an aeration and a compaction problem in the 2.52% of the points were detected. In the estimation of LLWR with the random forest algorithm, the RMSE value obtained for 0-20 cm depth was determined as 0.0218 cm3 cm-3, and for 20-40 cm it was determined as 0.0247 cm3 cm-3 . In the distribution maps of the observed and predicted values obtained, the lowest RMSE value was determined by Simple Kriging interpolation methods for 0-20 cm depth and Ordinary Kriging interpolation methods for 20-40 cm. While the distribution of obtained and predicted values in surface soils was similar, variations were found in the distribution of areas with low LLWR below the surface. As a result of the study, it has been revealed that LLWR can be obtained with a low error rate with the RF algorithm, and distribution maps can be created with lower error in surface soils.

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Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem

Cited by 31 publications

References 32 publications

Field-Scale Evaluation of the Soil Quality Index as Influenced by Dairy Manure and Inorganic Fertilizers

Field-Scale Evaluation of the Soil Quality Index as Influenced by Dairy Manure and Inorganic Fertilizers

Developing scoring functions for soil quality to assess land suitability for irrigated wheat in Southern Algeria

The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches

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