Soil organic matter and related soil properties in forest, grassland and cultivated land use types

Keen, Yeoh Chooi; Jalloh, Mohamadu Boyie; Ahmed, Osumanu Haruna; Sudin, Mahmud; Besar, Normah Awang

doi:10.5897/ijps11.427

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Logging equipment can have an impact on carbon stocks and plant nutrition elements. Organic matter is associated with the properties of soils, as it is an accumulator of nutrients and determines the temperature and hydrothermal regimes of soils [52,53]. Despite the fact that the changes in carbon stocks in the soil were at the level of a trend, the studied sites formed series in descending order in the upper (O) horizon of CL > SLCC > control, in the E horizon of SLCC > CL > control and in the BF horizon of CL > The studied soils are acidic.…”

Section: Discussionmentioning

confidence: 99%

The Influence of Logging Equipment on the Content, Stock and Stratification Coefficient of Elements of the Mineral Nutrition of Plants in the Soils of the Taiga Zone of Karelia

Медведева

Ananyev

2023

Forests

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This study was carried out in the territories of Northern Europe, in the middle taiga subzone of Karelia. The paper presents the results of a study of an experiment on logging to study the impact of controlled logging using supervised logging with controlling cutting (SLCC) and conventional logging (CL) on the properties of soils (horizons O, E and BF) in a spruce forest 15 years after logging. Virgin forest (VF) was used as a control. The volume weight of soils, the contents of carbon, nitrogen and potassium in different soil layers (layers O, E and BF), as well as reserves of C, N and K and their stratification coefficients SRs (SR1 [O:E], SR2 [O:BF] and SR3 [E:BF]) were studied. The results showed a tendency to increase the volume weight of soils of anthropogenically disturbed (CL and SLCC) areas can be measured. The obtained data demonstrated that there was no sharp change in the contents or stocks of the studied elements between the anthropogenically disturbed (CL and SLCC) and undisturbed areas (VF). The largest reserves of carbon, nitrogen and potassium were noted in the upper horizons of the soils of all sites, averaging 35.6, 1.27 and 0.073 t/ha, respectively. In the lower horizons of the studied soils, the values were lower. The values of the stratification coefficients in the studied soils were arranged in decreasing order as SR2 > SR1 > SR3. At the same time, the general trend of unidirectional changes in the SR values for carbon and potassium in soils was noted; the data for nitrogen were somewhat different. The results showed a marked decrease in SOC concentration with an increase in soil depth. Higher rates of cellulose decomposition were observed in anthropogenically disturbed areas (CL—69.0 ± 3.6%; SLCC—57.4 ± 3.5%) compared with virgin forest (VF) (53.7 ± 3.1%), which is consistent with the results of other studies in the taiga zone. The data obtained indicate the importance of a more accurate assessment of the contents and stocks of elements, as well as the need to use tests for soil biological activity.

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Section: Discussionmentioning

confidence: 99%

The Influence of Logging Equipment on the Content, Stock and Stratification Coefficient of Elements of the Mineral Nutrition of Plants in the Soils of the Taiga Zone of Karelia

Медведева

Ananyev

2023

Forests

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“…This study was conducted within a limited timeframe, potentially overlooking the influence of seasonal changes and land use dynamics on the SOM [76]. To mitigate this limitation, continuous data collection spanning at least a year would enable the training of more robust and generalized ML algorithms capable of considering the time series nature of the data to predict the SOM variations.…”

Section: Limitations and Prospectsmentioning

confidence: 99%

Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction

Uddin,

Sherrell,

Emami

et al. 2024

Sensors

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Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.

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“…Previous studies showed that SOM is influenced by land use [63], climate and topography [64] and varies in different soils. In order to understand the influence of land use on SOM estimation.…”

Section: Limitations and Prospectsmentioning

confidence: 99%

Estimating Soil Organic Matter Content Using Sentinel-2 Imagery by Machine Learning in Shanghai

Wang

Han²,

Yao

et al. 2021

IEEE Access

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Soil organic matter (SOM) plays an important role in the field of climate change and terrestrial ecosystems. SOM in large areas, especially in urban areas, is difficult to monitor and estimate by traditional methods. Urban land structure is complex, and soil is a mixture of organic and inorganic constituents with different physical and chemical properties. Previous studies showed that remote sensing techniques that provide diverse data in the visible-near-infrared (VNIR)-shortwave infrared (SWIR) spectral range, are promising in the prediction of SOM content on a large scale. Sentinel-2 covers the important spectral bands (VNIR-SWIR) for SOM prediction with a short revisit time. Thus, this article aimed to evaluate the capacity of Sentinel-2 for SOM prediction in an urban area (i.e., Shanghai). 103 bare soil samples filtrated from 398 soil samples at a depth of 20 cm were selected. Three methods, partial least square regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The root mean square error (RMSE) of modelling (mRMSE) and the coefficient of determination (R 2 ) of modelling (mR 2 ) were used to reflect the accuracy of the model. The results show that PLSR has the poorest performance. ANN has the highest modelling accuracy (mRMSE=7.387 g kg −1 , mR 2 =0.446). The ANN prediction accuracy of RMSE (pRMSE) is 4.713 g kg −1 and the prediction accuracy of R 2 (pR 2 ) is 0.723. For SVR, the pRMSE is 4.638 g kg −1 , and the pR 2 is 0.732. The prediction accuracy of SVR is slightly higher than that of ANN. The spatial distribution of SOM demonstrates that the value obtained by ANN is the closest to the range of the bare soil samples, and ANN performs better in vegetation-covered areas. Therefore, Sentinel-2 can be used to estimate SOM content in urban areas, and ANN is a promising method for SOM estimation.INDEX TERMS Soil organic matter estimation; Shanghai; Sentinel-2; indexes; artificial neural network; support vector machine This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Soil organic matter and related soil properties in forest, grassland and cultivated land use types

Cited by 8 publications

References 29 publications

The Influence of Logging Equipment on the Content, Stock and Stratification Coefficient of Elements of the Mineral Nutrition of Plants in the Soils of the Taiga Zone of Karelia

The Influence of Logging Equipment on the Content, Stock and Stratification Coefficient of Elements of the Mineral Nutrition of Plants in the Soils of the Taiga Zone of Karelia

Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction

Estimating Soil Organic Matter Content Using Sentinel-2 Imagery by Machine Learning in Shanghai

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