Assessment of the soil fertility status in Benin (West Africa) – Digital soil mapping using machine learning

Hounkpatin, Ozias; Bossa, Aymar Yaovi; Yira, Yacouba; Igue, Mouïnou A.; Sinsin, Brice

doi:10.1016/j.geodrs.2021.e00444

Cited by 33 publications

(18 citation statements)

References 65 publications

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“…Two important parameters mtry (number of prediction factors) and ntree (number of trees) were optimized through cross‐validation using caret package to enhance the prediction capability of the model (Kuhn et al., 2016). The QRF model has also been used in many studies for predicting SOC (Dharumarajan et al., 2021; Hounkpatin et al., 2022; Lamichhane et al., 2022; Matinfar et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

Reza,

Chattaraj,

Mukhopadhyay

et al. 2023

Soil Use and Management

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Delineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM) and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using the quadratic spline method (equal‐area) was carried out prior to quantile regression forest (QRF) algorithm‐based modelling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy k‐means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha−1 in the top layer (0–5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha−1 in the depths of 5–15, 15–30, 30–60, 60–100 and 100–200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R2, which ranged from .67 to .83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site‐specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.

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Section: Methodsmentioning

confidence: 99%

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

Reza,

Chattaraj,

Mukhopadhyay

et al. 2023

Soil Use and Management

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“…Environmental data consisted in climate variables (humidity in %, rainfall in mm, rainfall regime and temperature in °C) and soil pH were obtained from Meteorological Agency of Benin and previously published data (Igue et al, 2013;Hounkpatin et al, 2021), respectively.…”

Section: Data Collectionmentioning

confidence: 99%

Mapping actual distribution of invasive species siam weed and its abundance across biogeographical zones in Benin (West Africa)

Dandjlessa¹,

Ezin²,

Zossou³

et al. 2021

Advances in Weed Science

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Background: Invasive weeds pose a serious threat to agricultural systems worldwide and the management of such species requires information on their distribution. Objective: This research work aimed at assessing the evolution in the distribution of Siaw weed across biogeographical zones in Benin as first step toward the development of management policy and to give insight into the threat it poses to agriculture and biodiversity. Methods: Field and roadside surveys were carried out to record the presence of the species. Geographical coordinates were collected and incorporated in GIS software to map the distribution of the plant. Abundance data were submitted to analysis of variance to compare infestation level across biogeographical zones, and correlation of the species distribution with environmental factors was examined. Results:The extent of occurrence of the species has increased and fell within latitudes 6°30' and just beyond 10° North and range from about 0 m to 438 m in elevation, covering the entire area from south to center, and in part the north of the country. This weed is significantly more abundant in sudano-guinean zone (7.28 ± 0.18) than guinean zone (4.72 ± 0.20) and sudanian zone (3.5 ± 0.5). The species distribution is significantly correlated to the major climate variables and soil pH. Conclusion:The distribution of Siam weed mapped here shows progress in its occurrence points. Further studies are needed to better investigate the importance of soil and climate variables in its distribution and the vulnerability of the center zone ecosystems.

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“…Commonly used data mining models, including stepwise linear regression (SLR), support vector machine, artificial neural network, gradient boosting regression tree, and random forest (RF), are global models that assume that the relationship between the dependent variable and covariates is homogeneous across the region [23][24][25][26][27][28]. However, many studies have found that the relationship between soil properties and environmental variables is often moderated by third-party variables [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…The Cubist model is also notable because of its high accuracy. Several studies have found that the Cubist model outperforms the RF model in the spatial estimation of soil properties [26,[37][38][39][40]. However, scholars mainly focus on the high prediction accuracy of the Cubist model, ignoring its vital role in revealing stratified heterogeneous relationships, and failing to explore the determinants of SOC in different subregions.…”

Section: Introductionmentioning

confidence: 99%

Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

Chen

Yang

et al. 2022

Remote Sensing

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Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.

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Assessment of the soil fertility status in Benin (West Africa) – Digital soil mapping using machine learning

Cited by 33 publications

References 65 publications

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

Mapping actual distribution of invasive species siam weed and its abundance across biogeographical zones in Benin (West Africa)

Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

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