Effects of pH-Induced Changes in Soil Physical Characteristics on the Development of Soil Water Erosion

Matsumoto, Shinji; Ogata, Shunta; Shimada, Hideki; Sasaoka, Takashi; Hamanaka, Akihiro; Kusuma, Ginting Jalu

doi:10.3390/geosciences8040134

Cited by 26 publications

(20 citation statements)

References 24 publications

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“…6 ). Significantly high pH in the Gleysol-dominated Philippines soil samples might indicate the washout of plant-based biomass resulting from water erosion or topsoil erosion 48 , 49 .…”

Section: Discussionmentioning

confidence: 99%

Uncovering multi-faceted taxonomic and functional diversity of soil bacteriomes in tropical Southeast Asian countries

Likhitrattanapisal

Siriarchawatana

Seesang

et al. 2021

Sci Rep

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Environmental microbiomes encompass massive biodiversity and genetic information with a wide-ranging potential for industrial and agricultural applications. Knowledge of the relationship between microbiomes and environmental factors is crucial for translating that information into practical uses. In this study, the integrated data of Southeast Asian soil bacteriomes were used as models to assess the variation in taxonomic and functional diversity of bacterial communities. Our results demonstrated that there were differences in soil bacteriomes across different geographic locality with different soil characteristics: soil class and pH level. Such differences were observed in taxonomic diversity, interspecific association patterns, and functional diversity of soil bacteriomes. The bacterial-mediated biogeochemical cycles of nitrogen, sulfur, carbon, and phosphorus illustrated the functional relationship of soil bacteriome and soil characteristics, as well as an influence from bacterial interspecific interaction. The insights from this study reveal the importance of microbiome data integration for future microbiome research.

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“…6 ). Significantly high pH in the Gleysol-dominated Philippines soil samples might indicate the washout of plant-based biomass resulting from water erosion or topsoil erosion 48 , 49 .…”

Section: Discussionmentioning

confidence: 99%

Uncovering multi-faceted taxonomic and functional diversity of soil bacteriomes in tropical Southeast Asian countries

Likhitrattanapisal

Siriarchawatana

Seesang

et al. 2021

Sci Rep

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“…In addition, the combination of H + -toxicity, nutrient deficiency and reduced water uptake influences growth negatively [10]. Many studies associate soil acidification with nitrogen fertilizer over-application [11][12][13], acid rain [13][14][15], changes in soil physical properties [16], plant residues [17], and changes in soil chemistry [18] as the main common reasons worldwide.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Soil Acidity on the Physiological Responses of Two Bread Wheat Cultivars

Tóth

Juhász

Labuschagne

et al. 2020

Plants

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The recent study was conducted to examine the influence of acidic soil on the activities of ascorbate (APX) and guaiacol peroxidase (POD), proline, protein as well as malon-dialdehyde (MDA) content, in two commercial spring wheat cultivars (PAN3497 and SST806) at different growth stages (tillering and grain filling). A cultivar effect was significant only for MDA content, while the treatment effect was highly significant for proline, protein, and MDA. The sampling time effect was significant for most characteristics. MDA, antioxidative capacity, as well as protein content increased with maturity. At grain filling, MDA and proline contents were significantly higher at pH 5 than pH 6 and 7 for both cultivars, with the highest content in SST806. Similarly, SST806 had significantly higher APX and POD when growing at pH 5. There were no significant differences in protein content at grain filling between either genotype or treatments affected by low pH. This study showed that growth stage and soil pH influence the rate of lipid peroxidation as well as the antioxidative capacity of wheat, with a larger effect at grain filling, at pH 5. Although SST806 had higher proline, POD, and APX content than PAN3497 at this growth stage, this coincided with a very high MDA content. This shows that the high antioxidative capacity observed here, was not associated with a reduction of lipid peroxidation under low soil pH. Further research should, therefore, be done to establish the role of the induced antioxidant system in association with growth and yield in wheat.

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“…Since all mutual information values are not null, the subsequent model establishment phase should take into account all of the ten factors. is study shows that mutual information of slope (factor 2) is lower than pH (factor 4), which may be explained by change in pH causing physical properties of the soil to change in the clayey soil rich in Al, Ca, and Mg [34] leading to the development of soil water erosion [71].…”

Section: A Preliminary Analysis On the Relevancy Of Input Factors With Mutual Informationmentioning

confidence: 88%

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Dinh

Hoang

Tran

2021

Applied and Environmental Soil Science

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Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.

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Effects of pH-Induced Changes in Soil Physical Characteristics on the Development of Soil Water Erosion

Cited by 26 publications

References 24 publications

Uncovering multi-faceted taxonomic and functional diversity of soil bacteriomes in tropical Southeast Asian countries

Uncovering multi-faceted taxonomic and functional diversity of soil bacteriomes in tropical Southeast Asian countries

The Influence of Soil Acidity on the Physiological Responses of Two Bread Wheat Cultivars

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

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