Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

Internet use, spatial variation and its determinants among reproductive age women in Ethiopia

BACKGROUND

The Internet is the preferred source of health information for retrieving relevant information. In Ethiopia, the Internet penetration rate is improving year to year, but it is still at a low level compared to the rest of the world and neighboring African countries. Due to a lack of adequate information, it is important to assess Internet use, spatial variation, and determinants of Internet use among reproductive-age group women in Ethiopia.

METHOD

Secondary data from EDHS 2016 were used to analyze 15,683 women aged 15-49 years. Spatial analysis was performed using ArcGIS 10.7. The Bernoulli model was used by applying Kuldorff's methods using SaTScan 10.1.2 software to analyze the purely spatial clusters of Internet use. A multilevel mixed-effect logistic regression was applied to estimate community variance to identify individual- and community-level factors associated with Internet use. All models were fitted in STATA version 17.0, and finally, the adjusted odds ratio (AOR) with a corresponding 95% confidence interval (CI) was reported.

RESULT

The magnitude of Internet use was 4.97% ± 95% CI (4.63-5.32). The overall average age of women was 24.21 ± 8.06 years, with the age range 15-24 years constituting the larger group (39.2%). Women with secondary and above education [AOR = 6.47; 95% CI (5.04, 8.31)], unmarried [AOR = 2.60; 95% CI (1.89, 3.56)], rich [AOR = 1.95; 95% CI (1.00, 3.80)], own a mobile phone [AOR = 3.74; 95% CI (2.75, 5.09)], media exposure [AOR = 2.63; 95% CI (2.03, 3.42)], and urban [AOR = 1.80; 95% CI (1.08, 3.01)] had higher odds of Internet use. The spatial variation in Internet use was found to be nonrandom (global Moran's I = 0.58, p value < 0.001). Fifty-seven primary clusters were identified that were located in Addis Ababa city with a relative likelihood of 10.24 and a log-likelihood ratio of 425.16.

CONCLUSIONS

Internet use among reproductive-age women in Ethiopia is 4.97 and has significant spatial variation across the country. Both community- and individual-level factors affect Internet use in Ethiopia. Therefore, educating women, improving access to media, encouraging women to use family planning, and supporting household wealth could improve women's Internet use.

Mapping and predicting open defecation in Ethiopia: 2021 PMA-ET study

INTRODUCTION

There has been extensive research conducted on open defecation in Ethiopia, but a notable gap persists in comprehensively understanding the spatial variation and predictors at the household level. This study utilizes data from the 2021 Performance Monitoring for Action Ethiopia (PMA-ET) to address this gap by identifying hotspots and predictors of open defecation. Employing geographically weighted regression analysis, it goes beyond traditional models to account for spatial heterogeneity, offering a nuanced understanding of geographical variations in open defecation prevalence and its determinants. This research pinpoints hotspot areas and significant predictors, aiding policymakers and practitioners in tailoring interventions effectively. It not only fills the knowledge gap in Ethiopia but also informs global sanitation initiatives.

METHODS

The study comprised a total weighted sample of 24,747 household participants. ArcGIS version 10.7 and SaT Scan version 9.6 were used to handle mapping, hotspots, ordinary least squares, Bernoulli model analysis, and Spatial regression. Bernoulli-based model was used to analyze the purely spatial cluster detection of open defecation at the household level in Ethiopia. Ordinary Least Square (OLS) analysis and geographically weighted regression analysis were employed to assess the association between an open defecation and explanatory variables.

RESULTS

The spatial distribution of open defecation at the household level exhibited clustering (global Moran's I index value of 4.540385, coupled with a p-value of less than 0.001), with significant hotspots identified in Amhara, Afar, Harari, and parts of Dire Dawa. Spatial analysis using Kuldorff's Scan identified six clusters, with four showing statistical significance (P-value < 0.05) in Amhara, Afar, Harari, Tigray, and southwest Ethiopia. In the geographically weighted regression model, being male [coefficient = 0.87, P-value < 0.05] and having no media exposure (not watching TV or listening to the radio) [coefficient = 0.47, P-value < 0.05] emerged as statistically significant predictors of household-level open defecation in Ethiopia.

CONCLUSION

The study revealed that open defecation at the household level in Ethiopia varies across the regions, with significant hotspots identified in Amhara, Afar, Harari, and parts of Dire Dawa. Geographically weighted regression analysis highlights male participants lacking media exposure as substantial predictors of open defecation. Targeted interventions in Ethiopia should improve media exposure among males in hotspot regions, tailored sanitation programs, and region-specific awareness campaigns. Collaboration with local communities is crucial.

Spatiotemporal distribution of migraine in China: analyses based on baidu index

BACKGROUND

In recent years, innovative approaches utilizing Internet data have emerged in the field of syndromic surveillance. These novel methods aim to aid in the early prediction of epidemics across various scenarios and diseases. It has been observed that these systems demonstrate remarkable accuracy in monitoring outbreaks even before they become apparent in the general population. Therefore, they serve as valuable complementary tools to augment existing methodologies. In this study, we aimed to investigate the spatiotemporal distribution of migraine in China by leveraging Baidu Index (BI) data.

METHODS

Migraine-related BI data from January 2014 to December 2022 were leveraged, covering 301 city-level areas from 31 provincial-level regions by using the keyword "migraine ()". Prevalence data from the Global Burden of Disease study (GBD) were attracted to ensure the reliability of utilizing migraine-related BI data for research. Comprehensive analytical methods were then followed to investigate migraine's spatiotemporal distribution. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal distribution. Spatial distribution was explored using the Getis-Ord Gi* statistic, standard deviation ellipse analysis, Moran's Index, and Ordinary Kriging. The top eight migraine-related search terms were analyzed through the Demand Graph feature in the Baidu Index platform to understand the public's concerns related to migraine.

RESULTS

A strong association was observed between migraine-related BI and the prevalence data of migraine from GBD with a Spearman correlation coefficient of 0.983 (P = 4.96 × 10- 5). The overall trend of migraine-related BI showed a gradual upward trend over the years with a sharp increase from 2017 to 2019. Seasonality was observed and the peak period occurred in spring nationwide. The middle-lower reaches of the Yangtze River were found to be hotspots, while the eastern coastal areas had the highest concentration of migraine-related BI, with a gradual decrease towards the west. The most common search term related to migraine was "How to treat migraine quickly and effectively ()".

CONCLUSIONS

This study reveals important findings on migraine distribution in China, underscoring the urgent need for effective prevention and management strategies.

Prediction of spatial distribution characteristics of ecosystem functions based on a minimum data set of functional traits of desert plants

The relationship between plant functional traits and ecosystem function is a hot topic in current ecological research, and community-level traits based on individual plant functional traits play important roles in ecosystem function. In temperate desert ecosystems, which functional trait to use to predict ecosystem function is an important scientific question. In this study, the minimum data sets of functional traits of woody (wMDS) and herbaceous (hMDS) plants were constructed and used to predict the spatial distribution of C, N, and P cycling in ecosystems. The results showed that the wMDS included plant height, specific leaf area, leaf dry weight, leaf water content, diameter at breast height (DBH), leaf width, and leaf thickness, and the hMDS included plant height, specific leaf area, leaf fresh weight, leaf length, and leaf width. The linear regression results based on the cross-validations (FTEI_{W - L}, FTEI_{A - L}, FTEI_{W - NL}, and FTEI_{A - NL}) for the MDS and TDS (total data set) showed that the R² (coefficients of determination) for wMDS were 0.29, 0.34, 0.75, and 0.57, respectively, and those for hMDS were 0.82, 0.75, 0.76, and 0.68, respectively, proving that the MDSs can replace the TDS in predicting ecosystem function. Then, the MDSs were used to predict the C, N, and P cycling in the ecosystem. The results showed that non-linear models RF and BPNN were able to predict the spatial distributions of C, N and P cycling, and the distributions showed inconsistent patterns between different life forms under moisture restrictions. The C, N, and P cycling showed strong spatial autocorrelation and were mainly influenced by structural factors. Based on the non-linear models, the MDSs can be used to accurately predict the C, N, and P cycling, and the predicted values of woody plant functional traits visualized by regression kriging were closer to the kriging results based on raw values. This study provides a new perspective for exploring the relationship between biodiversity and ecosystem function.

Priority planting area planning for cash crops under heavy metal pollution and climate change: A case study of Ligusticum chuanxiong Hort

INTRODUCTION

Soil pollution by heavy metals and climate change pose substantial threats to the habitat suitability of cash crops. Discussing the suitability of cash crops in this context is necessary for the conservation and management of species. We developed a comprehensive evaluation system that is universally applicable to all plants stressed by heavy metal pollution.

METHODS

The MaxEnt model was used to simulate the spatial distribution of Ligusticum chuanxiong Hort within the study area (Sichuan, Shaanxi, and Chongqing) based on current and future climate conditions (RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios). We established the current Cd pollution status in the study area using kriging interpolation and kernel density. Additionally, the three scenarios were used in prediction models to simulate future Cd pollution conditions based on current Cd pollution data. The current and future priority planting areas for L. chuanxiong were determined by overlay analysis, and two levels of results were obtained.

RESULTS

The results revealed that the current first- and secondary-priority planting areas for L. chuanxiong were 2.06 ×10³ km² and 1.64 ×10⁴ km², respectively. Of these areas, the seven primary and twelve secondary counties for current L. chuanxiong cultivation should be given higher priority; these areas include Meishan, Qionglai, Pujiang, and other regions. Furthermore, all the priority zones based on the current and future scenarios were mainly concentrated on the Chengdu Plain, southeastern Sichuan and northern Chongqing. Future planning results indicated that Renshou, Pingwu, Meishan, Qionglai, Pengshan, and other regions are very important for L. chuanxiong planting, and a pessimistic scenario will negatively impact this potential planting. The spatial dynamics of priority areas in 2050 and 2070 clearly fluctuated under different prediction scenarios and were mainly distributed in northern Sichuan and western Chongqing.

DISCUSSION

Given these results, taking reasonable measures to replan and manage these areas is necessary. This study provides. not only a useful reference for the protection and cultivation of L. chuanxiong, but also a framework for analyzing other cash crops.

Prediction of the concentration of antimony in agricultural soil using data fusion, terrain attributes combined with regression kriging

Potentially toxic elements in agricultural soils are primarily derived from anthropogenic and geogenic sources. This study aims to predict and map antimony (Sb) concentration in soil using multiple regression kriging in two distinct modeling approaches, namely Sb prediction using data fusion coupled with regression kriging (scenario 1) and Sb prediction using data fusion, terrain attributes, and regression kriging (scenario 2). Cubist regression kriging (cubist_RK), conditional inference forest regression kriging (CIF_RK), extreme gradient boosting regression kriging (EGB_RK) and random forest regression kriging (RF_RK) were the modeling techniques used in the estimation of Sb concentration in agricultural soil. The validation results suggested that in scenario 1, EGB_RK was the optimal modeling approach for Sb prediction in agricultural soil with root mean square error (RMSE) = 1.31 and mean absolute error (MAE) = 0.61, bias = 0.37, and high coefficient of determination R² = 0.81. Similarly, the EGB_RK was also the optimal modeling approach in scenario 2, with the highest R² = 0.76, RMSE = 0.90, bias = 0.06, and MAE = 0.48 values than the other regression kriging modeling approaches. The cumulative assessment suggested that the EGB_RK in scenario 2 yielded optimal results compared to the respective modeling approach in scenario 1. The uncertainty propagated by the modeling approaches in both scenarios indicated that the degree of uncertainty during the modeling process was distributed across the study area from a low to a moderate uncertainty level. However, cubist_RK in scenario 2 exhibited some elevated spots of uncertainty levels. As a result, the combination of data fusion, terrain attributes, and regression kriging modeling approaches produces optimal results with a high R² value, minimal errors as well as bias. Furthermore, combining terrain attributes with data fusion is promising for reducing model error, bias and yielding high-accuracy predictions.

Spatial variation of overweight/obesity and associated factor among reproductive age group women in Ethiopia, evidence from EDHS 2016

BACKGROUND

Globally, at least 4.7 million people die from being overweight or obese. In Ethiopia, the level of overweight and obesity among women grew from 3% to 8%. However, as far as my literature searching, studies concerning the spatial variation of overweight/obesity and factors associated are not researched in Ethiopia using geospatial techniques. Therefore, this study aimed to explore the spatial variation of overweight/obesity and factor associated among reproductive age group women in Ethiopia using geospatial techniques.

MOTHED

A total weighted sample of 10,928 reproductive age women were included in the study. ArcGIS version10.7 was used to explore the spatial variation of overweight/obesity. Bernoulli based model was used to analyze the purely spatial cluster detection of overweight/obesity through SaTScan version 9.6.1 software. Ordinary Least Square analysis and geographically weighted regression analysis was employed to assess the association between an outcome variable and explanatory variables by using ArcGIS 10.7 software. P value of less than 0.05 was used to declare statically significant.

RESULT

The spatial distribution of overweight/obesity in Ethiopia was clustered. Statistically, a significant-high hot spot overweight/obesity was identified at Addis Ababa, harrari, Dire Dawa. SaTScan identified 66 primary spatial clusters (RR = 4.17, P < 0.001) located at Addis Ababa, southeast amhara, central part of oromia region and northern part of SNNP region. In geographically weighted regression, rich wealth index, women's age (35-39 and 40-44 years), watching TV, internet use and not working were statistically significant that affecting spatial variation of overweight/obesity.

CONCLUSION

In Ethiopia, overweight/obesity varies across the region. Statistically, significant-high hot spots of overweight/obesity were detected in Addis Ababa, Harari, Dire Dawa, some parts of Amhara and afar region, most of the Oromia and Somalia region, and the South Nation Nationality and People region of Ethiopia. Therefore, the ministry of health and the Ethiopian public health institute, try to initiate policies and practices that could include providing funding for physical education as well as recreational centers in communities most in need. In addition, public and private mass media create awareness of healthy lifestyles is promoted by health education regarding increased physical activity and reduced sedentary behavior through various media platforms.

High fluoride in groundwater and associated non-carcinogenic risks at Tiruvannamalai region in Tamil Nadu, India

The present investigation in the Tiruvannamalai region is about high fluoride contamination of groundwater samples from bore wells and open wells. About 75% of groundwater samples were found predominantly containing the fluoride content greater than the acceptable limit of 1.5 mg/L in the ranges 1.51 - 2.00 mg/L (23%), 2.01 - 3.00 mg/L (36%) and greater than or equal to 3.01 mg/L (16%) as per WHO. The other water quality parameters were found within the permissible limit of WHO. Taking the groundwater sources into consideration, the non - carcinogenic risk due to high fluoride concentration in groundwater sources revealed that teen - aged (98%), Children (92%) and Infant (98%) categories were at greater risk than those under Men (50%) and Women (69%) categories. The mapping was done on the spatial distribution of fluoride concentration in groundwater and the associated health risk by Ordinary Kriging. The correlation coefficients among the parameters witnessed that the hydro-chemical facies are interdependent. Box - Whisker plots illustrated the dispersion of various water quality parameters. The WQI data represented the quality of groundwater in view of potable nature due to dissolved ions. The Gibbs, bivariate mixing and the scatter plots ascribed the dissolution of carbonate and silicate minerals which dominate the groundwater chemistry. The factor analysis detailed the extracted loadings of different parameters of groundwater sources and differentiated the percentage variance values between bore well and open well sources.

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.

Optimal Design of IPMSM for EV Using Subdivided Kriging Multi-Objective Optimization

In this paper, subdivided kriging multi-objective optimization (SKMOO) is proposed for the optimal design of interior permanent magnet synchronous motor (IPMSM). The SKMOO with surrogate kriging model can obtain a uniform and accurate pareto front set with a reduced computation cost compared to conventional algorithms which directly adds the solution in the objective function area. In other words, the proposed algorithm uses a kriging surrogate model, so it is possible to know which design variables have the value of the objective function on the blank space. Therefore, the solution can be added directly in the objective function area. In the SKMOO algorithm, a non-dominated sorting method is used to find the pareto front set and the fill blank method is applied to prevent premature convergence. In addition, the subdivided kriging grid is proposed to make a well-distributed and more precise pareto front set. Superior performance of the SKMOO is confirmed by compared conventional multi objective optimization (MOO) algorithms with test functions and are applied to the optimal design of IPMSM for electric vehicle.

An Integrated Use of GIS, Geostatistical and Map Overlay Techniques for Spatio-Temporal Variability Analysis of Groundwater Quality and Level in the Punjab Province of Pakistan, South Asia

The rapidly changing climatic scenario is demanding periodic evaluation of groundwater quality at the temporal and spatial scale in any region for its effectual management. The statistical, geographic information system (GIS), geostatistical, and map overlay approaches were applied for investigating the spatio-temporal variation in groundwater quality and level data of 242 monitoring wells in Punjab, Pakistan during pre-monsoon and post-monsoon seasons of the years 2015 and 2016. The analysis indicated the higher variation in data for both the seasons (pre-monsoon and post-monsoon) as coefficient of variation (CV) values were found in the range of 84–175% for groundwater quality parameters. Based on the t-test values, the marginal improvement in groundwater electrical conductivity (EC), sodium absorption ratio (SAR) and residual sodium carbonate (RSC) and decrease in groundwater level (GWL) were observed in 2016 as compared to 2015 (p = 0.05). The spatial distribution analysis of groundwater EC, SAR and RSC indicated that the groundwater quality was unfit for irrigation in the lower south-east part of the study area. The groundwater level (GWL) was also higher in that part of the study area during the pre-monsoon and post-monsoon seasons in 2015 and 2016. The overlay analysis also indicated that the groundwater EC, RSC and GWL values were higher in south-east parts of the study area during pre-monsoon and post-monsoon seasons of 2015 and 2016. Hence, there is an instant need to apply groundwater management practices in the rest of the region (especially in the lower south-east part) to overcome the future degradation of groundwater quality.

Machine learning and multispectral data-based detection of soil salinity in an arid region, Central Iran

In recent years, indirect methods have been used to estimate soil salinity in agricultural lands. In this research, the electrical conductivity of 93 soil samples from 0 to 30 cm and 0 to 100 cm was measured using the hypercube technique at Sharifabad-Saveh Plain, Iran. Land area parameters such as TWI, TCI, STP, DEM, and LS were used as topographic variables and spatial indices of salinity and vegetation were derived from Landsat 8 images. Soil salinity off crops and gardens was determined at 0-30 cm and 0-100 cm. The data were divided into two series: the training set (70%) and the test set (30%). In order to model and predict salinity, models such as an artificial neural network (ANN), integration of neural network and genetic algorithm (ANN-GA), PLSR, and decision tree (DT) were used. The results of the models' evaluation based on MSE and R² indices showed that the ANN-GA model has the highest accuracy in predicting soil properties. This model improved the accuracy of soil salinity prediction by 28%, 42%, and 23% in 0-30 cm and by 20%, 28%, and 25% at 100 cm than ANN, PLSR, and DT. The result showed the 2 dS/m EC at alfalfa and cucurbits farmlands while pistachio orchards have low salinity and bare lands have moderate and high salinity.

Spatial mapping and size distribution of oxidative potential of particulate matter released by spatially disaggregated sources

The ability of particulate matter (PM) to induce oxidative stress is frequently estimated by acellular oxidative potential (OP) assays, such as ascorbic acid (AA) and 1,4-dithiothreitol (DTT), used as proxy of reactive oxygen species (ROS) generation in biological systems, and particle-bound ROS measurement, such as 2',7'-dichlorodihydrofluorescein (DCFH) assay. In this study, we evaluated the spatial and size distribution of OP results obtained by three OP assays (OP^AA, OP^DCFH and OP^DTT), to qualitative identify the relative relevance of single source contributions in building up OP values and to map the PM potential to induce oxidative stress in living organisms. To this aim, AA, DCFH and DTT assays were applied to size-segregated PM samples, collected by low-pressure cascade impactors, and to PM₁₀ samples collected at 23 different sampling sites (about 1 km between each other) in Terni, an urban and industrial hot-spot of Central Italy, by using recently developed high spatial resolution samplers of PM, which worked in parallel during three monitoring periods (February, April and December 2017). The sampling sites were chosen for representing the main spatially disaggregated sources of PM (vehicular traffic, rail network, domestic heating, power plant for waste treatment, steel plant) present in the study area. The obtained results clearly showed a very different sensitivity of the three assays toward each local PM source. OP^AA was particularly sensitive toward coarse particles released from the railway, OP^DCFH was sensible to fine particles released from the steel plant and domestic biomass heating, and OP^DTT was quite selectively sensitive toward the fine fraction of PM released by industrial and biomass burning sources.

Regional Geotechnical Mapping Employing Kriging on Electronic Geodatabase

A regional geotechnical map was developed by employing kriging using spatial and s geostatistical analysis tools. Many studies have been carried out in the field of topography, digital elevation modeling, agriculture, geological, crop, and precipitation mapping. However, no significant contribution to the development of geotechnical mapping has been made. For the appraisal of a geotechnical map, extensive field explorations were carried out throughout the geotechnically diversified plateau spread over an area of approximately 23,000 km2. In total, 450 soil samples were collected from 75 data stations to determine requisite index properties and soil classification for the subsequent allowable bearing capacity evaluation. The formatted test results, along with associated geospatial information, were uploaded to ArcMap, which created an initial input electronic database. The kriging technique of geostatistical analysis was determined to be more feasible for generating a geotechnical map. The developed map represents the distribution of soil in the region as per the engineering classification system, allowable bearing capacity, and American Association of State Highway and Transportation Officials (AASHTO) subgrade rating for 1.5-, 3.0-, and 4.5-m depths. The accuracy of the maps generated using kriging interpolation technique under spatial analyst tools was verified by comparing the values in the generated surface with the actual values measured at randomly selected validation points. The database was primarily created for the appraisal of geotechnical maps and can also be used for preliminary geotechnical investigations, which saves the cost of soil investigations. In addition, this approach allows establishing useful correlations among the geotechnical properties of soil.

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

Multi-Criteria Decision Analysis for the Land Evaluation of Potential Agricultural Land Use Types in a Hilly Area of Central Vietnam

Land evaluation is a process that is aimed at the sustainable development of agricultural production in rural areas, especially in developing countries. Therefore, land evaluation involves many aspects of natural conditions, economic, and social issues. This research was conducted in a hilly region of Central Vietnam to assess the land suitability of potential agricultural land use types that are based on scientific and local knowledge. In the frame of this research, Participatory Rural Appraisal (PRA); Analytical Hierarchy Analysis (AHP); Geographic Information System (GIS); and, scoring based scientific literature and local knowledge were applied for Multi-Criteria Decision Analysis (MCDA) for land use evaluation. The results of the PRA survey reveal that five plants offer great agricultural potential in the research area, namely rice, cassava, acacia, banana, and rubber. The land suitability of each plant type varies, depending on physical conditions as well as economic and social aspects. Acacia and cassava represent the most suitable plant types in the research area. Recommendations regarding agricultural land use planning in the A Luoi district are brought forward based on the land evaluation results. The combination of scientific and local knowledge in land assessment based on GIS technology, AHP, and PRA methods is a promising approach for land evaluation.