Bio

Dr. Omuto CT

                        Personal details

Name:                         Christian Thine Omuto

Nationality:     Kenya

Date of birth: 25th December 1973

Marital status: Married

Contact:          Box 30197-100, Nairobi, Kenya

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Publications


2014

CT, O, RR V.  2014.  Re-tooling of regression kriging in R for improved digital mapping of soil properties. Geosciences Journal. :DOI10.1007/s12303-014-0023-9.

2013

K, O, CT O.  2013.  Evaluation of Screening Level Technique (SLT) for pollutant removal in an engineered ecological system: a case study in Kenya . International Research Journal of Bioengineering an d Biomedical Science . 1(1):1-6.
Paron, P, Olago D, Omuto CT.  2013.  Kenya: A Natural Outlook Geo-Environmental Resources and Hazards. , Netherlands: Elsevier
Agullo, JO, Hassan MA, Omuto CT, Gumbe LO, Obiero JPO.  2013.  Development of Pedotransfer functions for saturated hydraulic conductivity. Website
CT, O.  2013.  HydroMe: R codes for estimating water retention and infiltration model parameters using experimental data. R Contributed Packages. Abstract

This package is version 2 of HydroMe v.1 package. It estimates the parameters in infiltration and water retention models by curve-fitting method. The models considered are those that are commonly used in soil science. It has new models for water retention characteristic curve and debugging of errors in HydroMe v.1

2012

THINE, DROMUTOCHRISTIAN.  2012.  Omuto CT. 2012. DSM for mapping soil classes in Somalia. In Vargas et al. Workshop Proceedings for "GSP / e-SOTER Workshop: Towards Global Soil Information: Activities within the GEO Task Global Soil Data". 20-23 March 2012. FAO. Rome. SR-CRSP Technical Report Series No. 43 pp. 64. (Co-authored with Rex Campbell and Herbert Lionberger).. : FAO Abstract
Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Soil occurs in nature in groups with unique response characteristics to its forming factors. These characteristics should ideally be represented as a family of curves in the models for producing soil maps. However, the current approaches for producing soil maps use a single model which either blocks/controls the grouping effects or do not statistically recognize the natural landscape groupings. This study tested mixed-effects modelling technique for ingenious recognition of soil groupings and consequent improvement of the accuracy of the resultant soil maps. Mixed-effects modelling is a form of regression analysis for simultaneous modelling of the average landscape characteristics and individual groups within the landscape. It can model a family of curves and potentially remove inadequacies inherent in the current models for soil mapping. Its potential in regression kriging of continuous and categorical soil attributes has been shown in this paper, where it produced about 60% accuracy with holdout validation. Compared to the current application of a single model in regression kriging, mixed-effects modelling produced about five times improvement of the mapping accuracy. It is anticipated that its adoption will contribute to improved soil mapping

2011

THINE, DROMUTOCHRISTIAN.  2011.  Paron P., Vargas Rojas R. and Omuto C.T. 2011. Integrated landform mapping: methodology and application for digital soil mapping in Somalia. IAG/AIG REGIONAL CONFERENCE 2011, . IAG/AIG REGIONAL CONFERENCE. : International Association of Geomorphologists Abstract
Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Soil occurs in nature in groups with unique response characteristics to its forming factors. These characteristics should ideally be represented as a family of curves in the models for producing soil maps. However, the current approaches for producing soil maps use a single model which either blocks/controls the grouping effects or do not statistically recognize the natural landscape groupings. This study tested mixed-effects modelling technique for ingenious recognition of soil groupings and consequent improvement of the accuracy of the resultant soil maps. Mixed-effects modelling is a form of regression analysis for simultaneous modelling of the average landscape characteristics and individual groups within the landscape. It can model a family of curves and potentially remove inadequacies inherent in the current models for soil mapping. Its potential in regression kriging of continuous and categorical soil attributes has been shown in this paper, where it produced about 60% accuracy with holdout validation. Compared to the current application of a single model in regression kriging, mixed-effects modelling produced about five times improvement of the mapping accuracy. It is anticipated that its adoption will contribute to improved soil mapping

2010

THINE, DROMUTOCHRISTIAN.  2010.  Omuto, CT, Vargas, RR, Alim,SM, Paron P. 2010. Mixed-effects modelling of time series NDVI-rainfall relationship for detecting human-induced loss of vegetation cover in drylands. Journal of Arid Environments, 74:1552-1563. Journal of Arid Environments. : Journal of Arid Environments Abstract
Many researchers have used time-series analysis of remotely sensed images to gain understanding of the dynamics of loss of vegetation cover in drylands. However, complex interactions between vegetation and climate still mask the potential of remote sensing signals to detect human-induced loss of vegetation cover. This paper presents mixed-effect modelling method for time-series NDVI-rainfall relationship to account for the complex interaction between vegetation and climate. Mixed-effects method is a form of statistical modelling that can simultaneously model environmental relationships for a population and for different groups within the population. In this study, it was used to model the NDVI-rainfall relationship in Somalia and for different vegetation types in the country. Its time-series application removed the interaction between vegetation and rainfall and identified areas experiencing human-induced loss of vegetation cover in the country. On average, it gave an accurate relationship between rainfall and NDVI (r2 > 60%) and detected areas with human-induced loss of vegetation cover (kappa = 75%). Although the potential of mixed-effects was shown using vegetation types, other factors such as soil types and land use can also be included in the method to improve accuracy of time-series NDVI images in detecting human-induced loss of vegetation cover in the drylands.

2009

THINE, DROMUTOCHRISTIAN.  2009.  Omuto CT, Vargas RR, Paron P. 2009. Soil erosion and sedimentation modelling of the areas between river Juba and Shabelle in South Somalia. Technical Report No. 16. FAO-SWALIM. Nairobi, Kenya. Land Degradation and Development. : FAO-SWALIM Abstract
Soil loss is a major concern for land managers due to its influence on biomass production, surface water quality and landscape beauty. In Somalia, the risk of soil loss is accelerated by the removal of vegetation, bad land use practices and negative impacts of urbanization. The political upheavals and consequent insecurity in the country are major limitations for detailed database and research in soil loss. This study tested opportunities in pedometrics, remote sensing, limited field data collection and the revised universal soil loss equation (RUSLE) to model the risk of soil loss in northwestern Somalia. The approach successfully predicted the risk of soil loss with accuracy of 79 per cent. It also showed that RUSLE is only relatively accurate and stable in identifying areas with low risk of soil loss and therefore is useful in modelling early warning signs of erosion. About 24 per cent of northwestern Somalia was depicted to have no significant human-induced soil loss while 68 per cent of the region is in threat of soil loss if no action is taken against the removal of vegetation, land use practices and policies on land tenure systems. About 8 per cent of the area is at high risk of soil loss due to negative effects of urbanization and lack of proper management of steep slopes. It is anticipated that this approach can be integrated in the assessment of soil erosion in areas with poor database.
THINE, DROMUTOCHRISTIAN.  2009.  Omuto, C.T., Vargas, R. R., Alim, M.S., Ismail, A., Osman, A., Iman. H.M. 2009. Land degradation assessment and a monitoring framework in Somalia. FA0-SWALIM Technical Report L-14, FAO-SWALIM, Nairobi, Kenya.. Land Degradation and Development. : FAO-SWALIM Abstract
Soil loss is a major concern for land managers due to its influence on biomass production, surface water quality and landscape beauty. In Somalia, the risk of soil loss is accelerated by the removal of vegetation, bad land use practices and negative impacts of urbanization. The political upheavals and consequent insecurity in the country are major limitations for detailed database and research in soil loss. This study tested opportunities in pedometrics, remote sensing, limited field data collection and the revised universal soil loss equation (RUSLE) to model the risk of soil loss in northwestern Somalia. The approach successfully predicted the risk of soil loss with accuracy of 79 per cent. It also showed that RUSLE is only relatively accurate and stable in identifying areas with low risk of soil loss and therefore is useful in modelling early warning signs of erosion. About 24 per cent of northwestern Somalia was depicted to have no significant human-induced soil loss while 68 per cent of the region is in threat of soil loss if no action is taken against the removal of vegetation, land use practices and policies on land tenure systems. About 8 per cent of the area is at high risk of soil loss due to negative effects of urbanization and lack of proper management of steep slopes. It is anticipated that this approach can be integrated in the assessment of soil erosion in areas with poor database.
THINE, DROMUTOCHRISTIAN.  2009.  Omuto, C.T. and L.O. Gumbe. 2009. Estimating water infiltration and retention characteristics using a computer program in R. Computers & Geosciences 35: 579-585.. Computers and Geosciences. : Computers and Geosciences Abstract
Infiltration and water retention functions are widely used soil hydraulic properties in the geosciences. They contain coefficients known as hydraulic parameters that are traditionally obtained through curve-fitting. Computer programs for the curve-fitting process are available for certain infiltration or water retention models. However, these programs are either not freely accessible or do not estimate certain hydraulic parameters. They are also inefficient and prone to errors for applications involving large datasets. This paper discusses the use of a freely accessible HydroMe package for fast, efficient, and accurate estimation of soil hydraulic parameters in some commonly used infiltration and water retention models. The package is executable in the freely downloadable R programming software. It contains a program for estimating the parameters in infiltration models previously proposed. The program is capable of estimating parameters from arrays of grouped data in one single pass without having to enter the data each time for the parameter estimation. It incorporates mixed-effects and covariate modelling techniques for improved estimation accuracy. These techniques are not common in any other computer programs in the geosciences. Through covariate modelling, the package provides opportunity to include environmental correlates in the estimation of soil hydraulic parameters. Therefore, HydroMe not only improves the estimation accuracy and efficiency, but also provides insight into environmental risk factors that influence the management of soil and water resources.
THINE, DROMUTOCHRISTIAN.  2009.  Omuto, C.T. and Vargas, R.R. 2009. Combining pedometrics, remote sensing and field observations for assessing soil loss in challenging drylands: a case study of northwestern Somalia. Land Degradation and Development 20: 101-115.. Land Degradation and Development. : Land Degradation and Development Abstract
Soil loss is a major concern for land managers due to its influence on biomass production, surface water quality and landscape beauty. In Somalia, the risk of soil loss is accelerated by the removal of vegetation, bad land use practices and negative impacts of urbanization. The political upheavals and consequent insecurity in the country are major limitations for detailed database and research in soil loss. This study tested opportunities in pedometrics, remote sensing, limited field data collection and the revised universal soil loss equation (RUSLE) to model the risk of soil loss in northwestern Somalia. The approach successfully predicted the risk of soil loss with accuracy of 79 per cent. It also showed that RUSLE is only relatively accurate and stable in identifying areas with low risk of soil loss and therefore is useful in modelling early warning signs of erosion. About 24 per cent of northwestern Somalia was depicted to have no significant human-induced soil loss while 68 per cent of the region is in threat of soil loss if no action is taken against the removal of vegetation, land use practices and policies on land tenure systems. About 8 per cent of the area is at high risk of soil loss due to negative effects of urbanization and lack of proper management of steep slopes. It is anticipated that this approach can be integrated in the assessment of soil erosion in areas with poor database.

2008

THINE, DROMUTOCHRISTIAN.  2008.  Vargas, RR, Omuto, CT, and Lewis, N. 2008. Land degradation assessment of a selected study area in Somaliland: application of the LADA/WOCAT approach at local level. National Land Degradation Workshop, 16-18 September, Pretoria, South Africa. National Land Degradation workshop. : FAO Abstract
Soil physical degradation is a gradual process of many steps beginning from structural deterioration and ending in differential loss of finer particles through erosion. Control of the degradation remains a challenge to many scientists due to lack of proper assessment protocols. This study developed a sequential protocol with emphasis on definition of physical degradation and successive soil testing to determine the stages of degradation development. The protocol was tested in Cambisols, Arenosols, and Ferralsols in Eastern Kenya. Soil physical degradation due to 10 years land use change was defined as more than 25% drop in infiltration and water retention characteristics and aggregate stability and more than 30% increase in bulk density and silt content. Then a soil testing model was sequentially applied to identify physical degradation phases. Visual assessment of degradation symptoms, RUSLE model, and diffuse infrared spectral reflectance were used in the soil testing model as predictors of physical degradation. Visual assessment was found to be cheap and fast method for identifying final stages of physical degradation with 60% accuracy. Visual assessment combined with RUSLE model improved the assessment accuracy to 80%. Infrared spectral reflectance, which is sensitive to subtle changes in soil physical conditions, was also found as a potential surrogate predictor of early-warning signs of soil physical degradation. Inclusion of spectra into the assessment model improved the accuracy to 95%. This protocol is effective in identifying phases of soil physical degradation, which are useful for planning degradation control and monitoring schemes. Its further testing and worldwide application is recommended.
THINE, DROMUTOCHRISTIAN.  2008.  Vargas RR and Omuto CT. 2008. National land degradation assessment in Somalia. Consultative Workshop on Land Degradation Assessment, Holiday-Inn, 8-12th September 2008, Nairobi, Kenya. Consultative Workshop on Land Degradation Assessment. : FAO Abstract
Soil physical degradation is a gradual process of many steps beginning from structural deterioration and ending in differential loss of finer particles through erosion. Control of the degradation remains a challenge to many scientists due to lack of proper assessment protocols. This study developed a sequential protocol with emphasis on definition of physical degradation and successive soil testing to determine the stages of degradation development. The protocol was tested in Cambisols, Arenosols, and Ferralsols in Eastern Kenya. Soil physical degradation due to 10 years land use change was defined as more than 25% drop in infiltration and water retention characteristics and aggregate stability and more than 30% increase in bulk density and silt content. Then a soil testing model was sequentially applied to identify physical degradation phases. Visual assessment of degradation symptoms, RUSLE model, and diffuse infrared spectral reflectance were used in the soil testing model as predictors of physical degradation. Visual assessment was found to be cheap and fast method for identifying final stages of physical degradation with 60% accuracy. Visual assessment combined with RUSLE model improved the assessment accuracy to 80%. Infrared spectral reflectance, which is sensitive to subtle changes in soil physical conditions, was also found as a potential surrogate predictor of early-warning signs of soil physical degradation. Inclusion of spectra into the assessment model improved the accuracy to 95%. This protocol is effective in identifying phases of soil physical degradation, which are useful for planning degradation control and monitoring schemes. Its further testing and worldwide application is recommended.
THINE, DROMUTOCHRISTIAN.  2008.  Omuto, C.T. 2008. Assessment of soil physical degradation in Eastern Kenya by use of a sequential soil testing protocol. Agriculture, Ecosystems & Environment 128:199-211.. Agriculture, Ecosystems and Environment. : Agriculture, Ecosystems and Environment Abstract
Soil physical degradation is a gradual process of many steps beginning from structural deterioration and ending in differential loss of finer particles through erosion. Control of the degradation remains a challenge to many scientists due to lack of proper assessment protocols. This study developed a sequential protocol with emphasis on definition of physical degradation and successive soil testing to determine the stages of degradation development. The protocol was tested in Cambisols, Arenosols, and Ferralsols in Eastern Kenya. Soil physical degradation due to 10 years land use change was defined as more than 25% drop in infiltration and water retention characteristics and aggregate stability and more than 30% increase in bulk density and silt content. Then a soil testing model was sequentially applied to identify physical degradation phases. Visual assessment of degradation symptoms, RUSLE model, and diffuse infrared spectral reflectance were used in the soil testing model as predictors of physical degradation. Visual assessment was found to be cheap and fast method for identifying final stages of physical degradation with 60% accuracy. Visual assessment combined with RUSLE model improved the assessment accuracy to 80%. Infrared spectral reflectance, which is sensitive to subtle changes in soil physical conditions, was also found as a potential surrogate predictor of early-warning signs of soil physical degradation. Inclusion of spectra into the assessment model improved the accuracy to 95%. This protocol is effective in identifying phases of soil physical degradation, which are useful for planning degradation control and monitoring schemes. Its further testing and worldwide application is recommended.

2007

THINE, DROMUTOCHRISTIAN.  2007.  Omuto, C.T. and P.D. Shrestha. 2007. Remote sensing techniques for rapid detection of soil physical degradation. International Journal of Remote Sensing 28, 4785. International Journal of Remote Sensing. : International Journal of Remote Sensing Abstract

Physical degradation undermines soils' ability to perform their many biophysical functions. Currently, there is lack of rapid methods to facilitate timely large-area assessment for effective control of the degradation. This study tested the combined applications of point-measurements of physical properties, soil spectral reflectance, and remote sensing for prediction of the degradation in a large watershed. Infiltration and water retention measurements at selected sites were used to aid definition of the degradation classes. A tree classification was then developed with diffuse spectral reflectance to predict the degradation classes. 93% accuracy with holdout cross-validation was achieved and the tree used to predict the degradation at multiple points in the study area. In addition, standardized deviations of land surface temperature (LST) and normalized difference vegetation index (NDVI) from long-term Landsat scenes were used to study the thermal and vegetation conditions at the sampled points. The deviations of LST and NDVI were effectively incorporated in the prediction of the degradation at other places with 80% accuracy of ground reference data. This approach has the potential as a useful tool for guiding policy decision on sustainable land management.

THINE, DROMUTOCHRISTIAN.  2007.  Omuto, C.T. 2007. HydroMe: Estimation of soil hydraulic parameters from experimental data.. Software. : R Development Core Team Abstract
HydroMe is a R Package that estimates the parameters in infiltration and water retention models by curve-fitting method. The models considered in this package those that are commonly used in soil science.

2006

THINE, DROMUTOCHRISTIAN.  2006.  Omuto CT, Shepherd KD, Coe R, Walsh MG, Gumbe LO. 2006. Rapid protocol for assessing soil physical degradation in arid and semi-arid areas. Highland2006 conference on environmental change, geomorphic processes, land degradation and rehabilitation in tropi. Highland2006 conference. : Mekelle University Abstract
Accurate estimation of soil hydraulic functions is an important topic in soil physics and hydrology. Soil scientists and hydrologists use experimental data to derive parameters of the hydraulic functions, and when measurements are not available they utilise pedotransfer functions. In all of the current methods, there is a lack of consideration for environmental covariates in the parameter estimation process. This paper presents nonlinear mixed effects (NLME) approach that incorporates various sources of information to make parameter estimates for population and individual-sites at the same time. Using likelihood approximations, NLME allows structured covariance matrices and estimated parameters to vary over sample-points thus giving more accurate description of the magnitudes and sources of inter-individual variations. This approach was used to estimate the hydraulic parameters from infiltration and water retention measurements that incorporate information about soil degradation. The performance of NLME was compared to: (i) average parameters obtained by considering whole dataset as one group and (ii) individual treatment of each sample-point independently. The best performance was achieved with NLME that gave the lowest residual standard error for the Philip’s infiltration and the van Genuchten’s water retention function. By including sampling structure and covariates in the parameter estimation process, NLME offers opportunity to study the mechanisms or factors producing a particular hydrologic response from different parts of the watershed. This information can be used for targeting recommendations in watershed management.
THINE, DROMUTOCHRISTIAN.  2006.  Omuto CT, Minasny B, McBratney AB, Biamah, EK. 2006. Nonlinear mixed effect modelling for improved estimation of water retention and infiltration parameters. Journal of Hydrology, 330:748-758. Journal of Hydrology. : Journal of Hydrology Abstract
Accurate estimation of soil hydraulic functions is an important topic in soil physics and hydrology. Soil scientists and hydrologists use experimental data to derive parameters of the hydraulic functions, and when measurements are not available they utilise pedotransfer functions. In all of the current methods, there is a lack of consideration for environmental covariates in the parameter estimation process. This paper presents nonlinear mixed effects (NLME) approach that incorporates various sources of information to make parameter estimates for population and individual-sites at the same time. Using likelihood approximations, NLME allows structured covariance matrices and estimated parameters to vary over sample-points thus giving more accurate description of the magnitudes and sources of inter-individual variations. This approach was used to estimate the hydraulic parameters from infiltration and water retention measurements that incorporate information about soil degradation. The performance of NLME was compared to: (i) average parameters obtained by considering whole dataset as one group and (ii) individual treatment of each sample-point independently. The best performance was achieved with NLME that gave the lowest residual standard error for the Philip’s infiltration and the van Genuchten’s water retention function. By including sampling structure and covariates in the parameter estimation process, NLME offers opportunity to study the mechanisms or factors producing a particular hydrologic response from different parts of the watershed. This information can be used for targeting recommendations in watershed management.
THINE, DROMUTOCHRISTIAN.  2006.  Omuto, CT and Shepherd KD. 2006. New methods for large-area assessment of soil degradation. 18th World Congress of Soil Science July 9-15, 2006 - Philadelphia, Pennsylvania, USA.. 18th World Congress of Soil Science July 9-15, 2006 - Philadelphia, Pennsylvania, USA. : WCSS Abstract
Accurate estimation of soil hydraulic functions is an important topic in soil physics and hydrology. Soil scientists and hydrologists use experimental data to derive parameters of the hydraulic functions, and when measurements are not available they utilise pedotransfer functions. In all of the current methods, there is a lack of consideration for environmental covariates in the parameter estimation process. This paper presents nonlinear mixed effects (NLME) approach that incorporates various sources of information to make parameter estimates for population and individual-sites at the same time. Using likelihood approximations, NLME allows structured covariance matrices and estimated parameters to vary over sample-points thus giving more accurate description of the magnitudes and sources of inter-individual variations. This approach was used to estimate the hydraulic parameters from infiltration and water retention measurements that incorporate information about soil degradation. The performance of NLME was compared to: (i) average parameters obtained by considering whole dataset as one group and (ii) individual treatment of each sample-point independently. The best performance was achieved with NLME that gave the lowest residual standard error for the Philip’s infiltration and the van Genuchten’s water retention function. By including sampling structure and covariates in the parameter estimation process, NLME offers opportunity to study the mechanisms or factors producing a particular hydrologic response from different parts of the watershed. This information can be used for targeting recommendations in watershed management.

2005

Biamah, E. K; Stroosnijder, OL; CT.  2005.  Watershed conservation in semi-arid Kenya. Abstract

Over the past three decades, agricultural watersheds in semi-arid Kenya have experienced some rapid decline in soil and crop productivity due to severe soil erosion, low soil water, low soil fertility and high soil crusting and compaction. Thus, the management of these watersheds requires some good understanding of agricultural drought, stratification of production zones according to slope, and suitable conservation options that include in-situ water conservation and runoff utilization. The planning of watershed conservation requires the application of runoff models in the selection of interventions that reduce upstream flood magnitude and downstream sedimentation. Successful interventions can be introduced under enabling conditions to farmers at various hierarchical policy levels. A few of these enabling conditions that are elaborated upon include agricultural policy, focus on smallholder agriculture and public¬community partnerships.

2003

Obiero, J.P.O; Thine, MCO; DO.  2003.  Rainwater Harvesting for Crop Production in Semi-arid Areas.
THINE, DROMUTOCHRISTIAN.  2003.  Omuto CT, Walsh MG, Shepherd KD, Coe R 2003. Prediction of field-measured infiltration rates using diffuse reflectance spectroscopy. Poster presented at ASA-CSSA-SSSA Annual Meetings, 2-6 November 2003, Denver, Colorado, USA. Annual Meeting Abstracts [CD-. ASA-CSSA-SSSA Annual Meetings, 2-6 November 2003, Denver, Colorado, USA. : ASA-CSSA-SSSA Abstract
Accurate estimation of soil hydraulic functions is an important topic in soil physics and hydrology. Soil scientists and hydrologists use experimental data to derive parameters of the hydraulic functions, and when measurements are not available they utilise pedotransfer functions. In all of the current methods, there is a lack of consideration for environmental covariates in the parameter estimation process. This paper presents nonlinear mixed effects (NLME) approach that incorporates various sources of information to make parameter estimates for population and individual-sites at the same time. Using likelihood approximations, NLME allows structured covariance matrices and estimated parameters to vary over sample-points thus giving more accurate description of the magnitudes and sources of inter-individual variations. This approach was used to estimate the hydraulic parameters from infiltration and water retention measurements that incorporate information about soil degradation. The performance of NLME was compared to: (i) average parameters obtained by considering whole dataset as one group and (ii) individual treatment of each sample-point independently. The best performance was achieved with NLME that gave the lowest residual standard error for the Philip’s infiltration and the van Genuchten’s water retention function. By including sampling structure and covariates in the parameter estimation process, NLME offers opportunity to study the mechanisms or factors producing a particular hydrologic response from different parts of the watershed. This information can be used for targeting recommendations in watershed management.

2001

THINE, DROMUTOCHRISTIAN.  2001.  Omuto, C. T., J .P .O. Obiero and S. C. Ondieki. 2001. Modelling Hydraulic Conductivity. Paper presented at the Kenya Society of Agricultural Engineers International Conference , Grand Regency Hotel, Nairobi, 2001. Kenya Society of Agricultural Engineers International Conference. : KSAE Abstract
Accurate estimation of soil hydraulic functions is an important topic in soil physics and hydrology. Soil scientists and hydrologists use experimental data to derive parameters of the hydraulic functions, and when measurements are not available they utilise pedotransfer functions. In all of the current methods, there is a lack of consideration for environmental covariates in the parameter estimation process. This paper presents nonlinear mixed effects (NLME) approach that incorporates various sources of information to make parameter estimates for population and individual-sites at the same time. Using likelihood approximations, NLME allows structured covariance matrices and estimated parameters to vary over sample-points thus giving more accurate description of the magnitudes and sources of inter-individual variations. This approach was used to estimate the hydraulic parameters from infiltration and water retention measurements that incorporate information about soil degradation. The performance of NLME was compared to: (i) average parameters obtained by considering whole dataset as one group and (ii) individual treatment of each sample-point independently. The best performance was achieved with NLME that gave the lowest residual standard error for the Philip’s infiltration and the van Genuchten’s water retention function. By including sampling structure and covariates in the parameter estimation process, NLME offers opportunity to study the mechanisms or factors producing a particular hydrologic response from different parts of the watershed. This information can be used for targeting recommendations in watershed management.

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