Lameck Odhiambo | University of Nebraska Lincoln (original) (raw)

Papers by Lameck Odhiambo

2000 ASAE Annual International Meeting, Milwaukee, Wisconsin, USA, 9-12 July 2000., 2000

Transactions of the ASABE, 2007

Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similar... more Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similarity within ground-penetrating radar (GPR) profiles. We have observed that the intricate and often visually indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar subsurface characteristics. In this study, we therefore examined the feasibility of using textural features extracted from GPR data to automate subsurface characterization. The textural features were matched to a "fingerprint" database of previous subsurface classifications of GPR textural features and the corresponding physical probings of subsurface conditions. Four textural features (energy, contrast, entropy, and homogeneity) were selected as inputs into a neural-network classifier. This classifier was tested and verified using GPR data obtained from two distinctly different field sites. The first data set contained features that indicate the presence or lack of sandstone bedrock in the upper 2 m of a shallow soil profile of fine sandy loam and loam. The second data set contained columnar patterns that correspond to the presence or the lack of vertical preferential flow paths within a deep loessial soil. The classifier automatically grouped each data set into one of the two categories. Comparing the results of classification using extracted textural features to the results obtained by visual interpretation found 93.6% of the sections that lack sandstone bedrock correctly classified in the first set of data, and 90% of the sections that contain pronounced columnar patterns correctly classified in the second set of data. The classified profile sections were mapped using integrated GPR and GPS data to show ground surface boundaries of different subsurface conditions. These results indicate that textural features extracted from GPR data can be utilized as inputs in a neural network classifier to rapidly characterize and map the subsurface into categories associated with known conditions with acceptable levels of accuracy. This approach of GPR imagery classification is to be considered as an alternative method to traditional human interpretation only in the classification of voluminous data sets, wherein the extensive time requirement would make the traditional human interpretation impractical.

Applied Engineering in Agriculture, 2004

Errors associated with visual inspection and interpretations of radargrams often inhibit the inte... more Errors associated with visual inspection and interpretations of radargrams often inhibit the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this article presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profiles using GPR imagery. The classifier clusters and classifies soil profile strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture, and structure of the horizons; and relative arrangement of the horizons, etc.). This article illustrates this classification procedure by its application on GPR data, both simulated and actual. Results show that the procedure is able to classify the profile into zones that corresponded with the classifications obtained by visual inspection and interpretation of radar grams. Application of F-NN to a study site in southwest Tennessee gave soil groupings that are in close correspondence with the groupings obtained in a previous study, which used the traditional methods of complete soil morphological, chemical, and physical characterization. At a crossover value of 3.0, the F-NN soil grouping boundary locations fall within a range of +2.7 m from the soil groupings determined by the traditional methods. These results indicate that F-NN can supply accurate real-time soil profile clustering and classification during field surveys.

Transactions of the ASAE, 2001

In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accura... more In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman-Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial-and-error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy-neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input-output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass-covered lysimeters and estimations with the FAO Penman-Monteith equation. The model-estimated ET vs. lysimeter-measured ET gave a coefficient of determination (r 2) value of 0.88 and a standard error of the estimate (S yx) of 0.48 mm d-1. For the same set of independent data, the FAO Penman-Monteith-estimated ET vs. lysimeter-measured ET gave an r 2 value of 0.85 and an S yx value of 0.56 mm d-1. These results show that the optimized fuzzy-neural-model is reasonably accurate, and is comparable to the FAO Penman-Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models.

Transactions of the ASAE, 2001

Daily evapotranspiration (ET) rates are needed for irrigation scheduling. Owing to the difficulty... more Daily evapotranspiration (ET) rates are needed for irrigation scheduling. Owing to the difficulty of obtaining accurate field measurements, ET rates are commonly estimated from weather parameters. A few empirical or semi-empirical methods have been developed for assessing daily reference crop ET, which is converted to actual crop ET using crop coefficients. The FAO Penman-Monteith method, which is now accepted as the standard method for the computation of daily reference ET, is sophisticated. It requires several input parameters, some of which have no actual measurements but are estimated from measured weather parameters. In this study, we examined the suitability of fuzzy logic for estimating daily reference ET with simpler and fewer parameters. Two fuzzy evapotranspiration models, using two or three input parameters, were developed and applied to estimate grass ET. Independent weather parameters from sites representing arid and humid climates were used to test the models. The fuzzy estimated ET values were compared with direct ET measurements from grass-covered weighing lysimeters, and with ET estimations obtained using the FAO Penman-Monteith and the Hargreaves-Samani equations. The estimated ET values from a fuzzy model using three input parameters (S yx = 0.54 mm, r 2 = 0.90) were found to be comparable to ET values estimated with the FAO Penman-Monteith equation (S yx = 0.50 mm, r 2 = 0.91) and were more accurate than those obtained by the Hargreaves-Samani equation (S yx = 0.66 mm, r 2 = 0.53). These results show that fuzzy evapotranspiration models with simpler and fewer input parameters can yield accurate estimation of ET.

The authors are solely responsible for the content of this technical presentation. The technical ... more The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural Engineers (ASAE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASAE editorial committees; therefore, they are not to be presented as refereed publications.

Agronomy Journal, Jul 16, 2020

Temperature‐based grass‐reference evapotranspiration (ETo) estimation methods (e.g., Hargreaves−S... more Temperature‐based grass‐reference evapotranspiration (ETo) estimation methods (e.g., Hargreaves−Samani [HS] model) present advantages over combination‐based methods that require full‐suite weather data. The U.S. High Plains region has scarce and short‐term full‐suite weather sites. This data scarcity presents challenges for combination‐based ETo estimation. The performance of HS model against the American Society of Civil Engineers (ASCE) standardized Penman−Monteith (PM) model was assessed using long‐term data at 124 full‐suite weather sites across nine states in the U.S. High Plains. The HS model underestimated ETo at arid (mean bias error [MBE] = −1.68 mm d−1), semi‐arid (MBE = −0.34 mm d−1), and dry subhumid sites (MBE = −0.16 mm d−1) and overestimated ETo at humid sites (MBE = 0.14 mm d−1). There was a significant relationship (p < .01) between HS model performance and aridity index. The HS model performed better (27% lower root mean squared difference [RMSD]) in summer months than the rest of the year at semi‐arid and dry subhumid sites. The model performance was non‐ideal during the summer months in subhumid climates. Spatio‐temporal annual zonal (climate division), monthly zonal, annual site‐specific, and monthly site‐specific calibration resulted in 12, 16, 20, and 26% reduction in RMSD and 11, 16, 17, and 23% reduction in relative error, respectively. Monthly site‐specific calibration performed the best and was used to quantify annual and growing season ETo across the region. The research characterized performance patterns of the HS model over an important agroecosystem‐dominated region. Practical data‐driven strategies were proposed to better estimate PM ETo using limited weather data at any given site (with similar aridity) and time of the year.

Journal of Irrigation and Drainage Engineering-asce, Nov 1, 2015

AbstractThis research evaluated the relative evaporative losses and water balance components in t... more AbstractThis research evaluated the relative evaporative losses and water balance components in two soybean [Glycine max (L.) Merr.] fields under subsurface drip irrigation (SDI) and center pivot irrigation (CPI) systems in south-central Nebraska. Meteorological and surface energy balance components, including actual evapotranspiration (ET), above the crop canopy was measured using Bowen ratio energy balance systems installed at the center of both fields. Crop transpiration (T) was estimated based on the variable stomatal resistances using the Penman-Monteith equation in conjunction with fractional green canopy cover. Evaporation (E) losses were estimated as the difference between measured ET and estimated T. Average soil water content (ASWC) in the crop root zone and effective rainfall were estimated using the water balance method. The relative evapotranspiration (ETrel) was 99.8% for the SDI field in both years (2007 and 2008), and it was 103.4% in 2008 and 93.9% in 2010 for the CPI field. The mean ETre...

Journal of Irrigation and Drainage Engineering-asce, Feb 1, 2011

Agricultural Water Management

Irrigation Science, Apr 1, 2004

Agricultural Water Management, Apr 1, 1996

In this study, a water balance model applicable to lowland paddy fields was developed. The model ... more In this study, a water balance model applicable to lowland paddy fields was developed. The model inputs consist of irrigation supply, climatic data, soil parameters and layout dimensions. The model is formulated to simulate various processes such as evapotranspiration, seepage and percolation, and surface runoff as they occur in the field water balance system. The model is able to predict the changes in water balance components under different land management and hydrological conditions. The model can be applied either for plot-to-plot or independent plot layouts. It was validated using data collected from controlled plot experiments. The details of model development and validation are outlined in this paper.

Agricultural Water Management, Apr 1, 1996

In this study, a water balance model applicable to lowland paddy fields was developed. The model ... more In this study, a water balance model applicable to lowland paddy fields was developed. The model inputs consist of irrigation supply, climatic data, soil parameters and layout dimensions. The model is formulated to simulate various processes such as evapotranspiration, seepage and percolation, and surface runoff as they occur in the field water balance system. The model is able to predict the changes in water balance components under different land management and hydrological conditions. The model can be applied either for plot-to-plot or independent plot layouts. It was validated using data collected from controlled plot experiments. The details of model development and validation are outlined in this paper.

Agricultural Water Management

Evapotranspiration (ET) is an important component of the hydrologic cycle and involves the exchan... more Evapotranspiration (ET) is an important component of the hydrologic cycle and involves the exchange of water between the surrounding water bodies, soil, crop surfaces, and the atmosphere. Crop growth and yields are largely affected by the rate of ET, especially in semi-arid areas where the rate of ET is high and rainfall is not sufficient and reliable to add more water into the soil for crop use. Solar radiation, relative humidity, air temperature, rainfall, and wind velocity are some of the meteorological factors that affect ET. Therefore, this research was aimed at determining ET and its trend across Rwanda using climatic data measured at 5 sites. With the research, we accessed meteorology data measured at synaptic stations in the five provinces of Rwanda (Kigali city/central, Kawangire/Eastern, Ruberengera/Western, Ruhengeri/Northern and Gikongoro/ and used the data to calculate reference evapotranspiration (ET) for the recent 10 years (2010-2018). Equations were used to calculat...

SSRN Electronic Journal

First and foremost, I want to express my sincere thanks to my supervisor Dr. Nick Brozović, for h... more First and foremost, I want to express my sincere thanks to my supervisor Dr. Nick Brozović, for his continuous support, mentorship, enthusiasm, and encouragement. Working with Nick has been an indelible experience on both academic and personal levels. I want to thank my co-advisors, Dr. Derek Heeren and Dr. Lameck Odhiambo, for providing their valuable inputs and guidance, which were instrumental in shaping my research. I wish to show my gratitude to my committee member Dr. Daran Rudnick for bringing his expertise and advice to the research thesis. This thesis would not have been possible without my wife, Ishani. I would like to thank her for personal support and for helping me with my research on various levels. I want to thank my parents, family members, and friends for always supporting me and praying for me. Mom and Dad, thank you for enabling me to pursue my dream in a distant land and never giving up on me despite all my failures. I would like to acknowledge the financial, academic, and technical support from Robert B. Daugherty Water for Food Global Institute (DWFI) and Nebraska Center for Energy Sciences Research (NCESR). I would like to pay my special regards to DWFI Policy team members-Kate Gibson, Ellen Emanuel, Caleb Milliken, and Vivian Nguyen, for continuously helping me with my research right from the beginning. I want to acknowledge the help and support of the Rwanda Agricultural Board (RAB), Rwanda Meteorology Agency (RMA), and Farm Fresh (a Rwanda based cooperative) in providing the soil and weather data. Grace Mukarusagara deserves a special mention for her hard work and cooperation in fieldwork. I also want to acknowledge the help of our field team members in Rwanda (Ngabo, Vedasthe, and Bigirimana) for their contribution and hard work in collecting field-level data for this research.

Evaluation of the impact of surface residue cover on single and dual crop coefficient for estimat... more Evaluation of the impact of surface residue cover on single and dual crop coefficient for estimating soybean actual evapotranspiration

Errors associated with visual inspection and interpretation of radargrams often inhibits the inte... more Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-...

2000 ASAE Annual International Meeting, Milwaukee, Wisconsin, USA, 9-12 July 2000., 2000

Transactions of the ASABE, 2007

Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similar... more Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similarity within ground-penetrating radar (GPR) profiles. We have observed that the intricate and often visually indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar subsurface characteristics. In this study, we therefore examined the feasibility of using textural features extracted from GPR data to automate subsurface characterization. The textural features were matched to a "fingerprint" database of previous subsurface classifications of GPR textural features and the corresponding physical probings of subsurface conditions. Four textural features (energy, contrast, entropy, and homogeneity) were selected as inputs into a neural-network classifier. This classifier was tested and verified using GPR data obtained from two distinctly different field sites. The first data set contained features that indicate the presence or lack of sandstone bedrock in the upper 2 m of a shallow soil profile of fine sandy loam and loam. The second data set contained columnar patterns that correspond to the presence or the lack of vertical preferential flow paths within a deep loessial soil. The classifier automatically grouped each data set into one of the two categories. Comparing the results of classification using extracted textural features to the results obtained by visual interpretation found 93.6% of the sections that lack sandstone bedrock correctly classified in the first set of data, and 90% of the sections that contain pronounced columnar patterns correctly classified in the second set of data. The classified profile sections were mapped using integrated GPR and GPS data to show ground surface boundaries of different subsurface conditions. These results indicate that textural features extracted from GPR data can be utilized as inputs in a neural network classifier to rapidly characterize and map the subsurface into categories associated with known conditions with acceptable levels of accuracy. This approach of GPR imagery classification is to be considered as an alternative method to traditional human interpretation only in the classification of voluminous data sets, wherein the extensive time requirement would make the traditional human interpretation impractical.

Applied Engineering in Agriculture, 2004

Errors associated with visual inspection and interpretations of radargrams often inhibit the inte... more Errors associated with visual inspection and interpretations of radargrams often inhibit the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this article presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profiles using GPR imagery. The classifier clusters and classifies soil profile strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture, and structure of the horizons; and relative arrangement of the horizons, etc.). This article illustrates this classification procedure by its application on GPR data, both simulated and actual. Results show that the procedure is able to classify the profile into zones that corresponded with the classifications obtained by visual inspection and interpretation of radar grams. Application of F-NN to a study site in southwest Tennessee gave soil groupings that are in close correspondence with the groupings obtained in a previous study, which used the traditional methods of complete soil morphological, chemical, and physical characterization. At a crossover value of 3.0, the F-NN soil grouping boundary locations fall within a range of +2.7 m from the soil groupings determined by the traditional methods. These results indicate that F-NN can supply accurate real-time soil profile clustering and classification during field surveys.

Transactions of the ASAE, 2001

In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accura... more In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman-Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial-and-error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy-neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input-output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass-covered lysimeters and estimations with the FAO Penman-Monteith equation. The model-estimated ET vs. lysimeter-measured ET gave a coefficient of determination (r 2) value of 0.88 and a standard error of the estimate (S yx) of 0.48 mm d-1. For the same set of independent data, the FAO Penman-Monteith-estimated ET vs. lysimeter-measured ET gave an r 2 value of 0.85 and an S yx value of 0.56 mm d-1. These results show that the optimized fuzzy-neural-model is reasonably accurate, and is comparable to the FAO Penman-Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models.

Transactions of the ASAE, 2001

Daily evapotranspiration (ET) rates are needed for irrigation scheduling. Owing to the difficulty... more Daily evapotranspiration (ET) rates are needed for irrigation scheduling. Owing to the difficulty of obtaining accurate field measurements, ET rates are commonly estimated from weather parameters. A few empirical or semi-empirical methods have been developed for assessing daily reference crop ET, which is converted to actual crop ET using crop coefficients. The FAO Penman-Monteith method, which is now accepted as the standard method for the computation of daily reference ET, is sophisticated. It requires several input parameters, some of which have no actual measurements but are estimated from measured weather parameters. In this study, we examined the suitability of fuzzy logic for estimating daily reference ET with simpler and fewer parameters. Two fuzzy evapotranspiration models, using two or three input parameters, were developed and applied to estimate grass ET. Independent weather parameters from sites representing arid and humid climates were used to test the models. The fuzzy estimated ET values were compared with direct ET measurements from grass-covered weighing lysimeters, and with ET estimations obtained using the FAO Penman-Monteith and the Hargreaves-Samani equations. The estimated ET values from a fuzzy model using three input parameters (S yx = 0.54 mm, r 2 = 0.90) were found to be comparable to ET values estimated with the FAO Penman-Monteith equation (S yx = 0.50 mm, r 2 = 0.91) and were more accurate than those obtained by the Hargreaves-Samani equation (S yx = 0.66 mm, r 2 = 0.53). These results show that fuzzy evapotranspiration models with simpler and fewer input parameters can yield accurate estimation of ET.

The authors are solely responsible for the content of this technical presentation. The technical ... more The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural Engineers (ASAE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASAE editorial committees; therefore, they are not to be presented as refereed publications.

Agronomy Journal, Jul 16, 2020

Temperature‐based grass‐reference evapotranspiration (ETo) estimation methods (e.g., Hargreaves−S... more Temperature‐based grass‐reference evapotranspiration (ETo) estimation methods (e.g., Hargreaves−Samani [HS] model) present advantages over combination‐based methods that require full‐suite weather data. The U.S. High Plains region has scarce and short‐term full‐suite weather sites. This data scarcity presents challenges for combination‐based ETo estimation. The performance of HS model against the American Society of Civil Engineers (ASCE) standardized Penman−Monteith (PM) model was assessed using long‐term data at 124 full‐suite weather sites across nine states in the U.S. High Plains. The HS model underestimated ETo at arid (mean bias error [MBE] = −1.68 mm d−1), semi‐arid (MBE = −0.34 mm d−1), and dry subhumid sites (MBE = −0.16 mm d−1) and overestimated ETo at humid sites (MBE = 0.14 mm d−1). There was a significant relationship (p < .01) between HS model performance and aridity index. The HS model performed better (27% lower root mean squared difference [RMSD]) in summer months than the rest of the year at semi‐arid and dry subhumid sites. The model performance was non‐ideal during the summer months in subhumid climates. Spatio‐temporal annual zonal (climate division), monthly zonal, annual site‐specific, and monthly site‐specific calibration resulted in 12, 16, 20, and 26% reduction in RMSD and 11, 16, 17, and 23% reduction in relative error, respectively. Monthly site‐specific calibration performed the best and was used to quantify annual and growing season ETo across the region. The research characterized performance patterns of the HS model over an important agroecosystem‐dominated region. Practical data‐driven strategies were proposed to better estimate PM ETo using limited weather data at any given site (with similar aridity) and time of the year.

Journal of Irrigation and Drainage Engineering-asce, Nov 1, 2015

AbstractThis research evaluated the relative evaporative losses and water balance components in t... more AbstractThis research evaluated the relative evaporative losses and water balance components in two soybean [Glycine max (L.) Merr.] fields under subsurface drip irrigation (SDI) and center pivot irrigation (CPI) systems in south-central Nebraska. Meteorological and surface energy balance components, including actual evapotranspiration (ET), above the crop canopy was measured using Bowen ratio energy balance systems installed at the center of both fields. Crop transpiration (T) was estimated based on the variable stomatal resistances using the Penman-Monteith equation in conjunction with fractional green canopy cover. Evaporation (E) losses were estimated as the difference between measured ET and estimated T. Average soil water content (ASWC) in the crop root zone and effective rainfall were estimated using the water balance method. The relative evapotranspiration (ETrel) was 99.8% for the SDI field in both years (2007 and 2008), and it was 103.4% in 2008 and 93.9% in 2010 for the CPI field. The mean ETre...

Journal of Irrigation and Drainage Engineering-asce, Feb 1, 2011

Agricultural Water Management

Irrigation Science, Apr 1, 2004

Agricultural Water Management, Apr 1, 1996

In this study, a water balance model applicable to lowland paddy fields was developed. The model ... more In this study, a water balance model applicable to lowland paddy fields was developed. The model inputs consist of irrigation supply, climatic data, soil parameters and layout dimensions. The model is formulated to simulate various processes such as evapotranspiration, seepage and percolation, and surface runoff as they occur in the field water balance system. The model is able to predict the changes in water balance components under different land management and hydrological conditions. The model can be applied either for plot-to-plot or independent plot layouts. It was validated using data collected from controlled plot experiments. The details of model development and validation are outlined in this paper.

Agricultural Water Management, Apr 1, 1996

In this study, a water balance model applicable to lowland paddy fields was developed. The model ... more In this study, a water balance model applicable to lowland paddy fields was developed. The model inputs consist of irrigation supply, climatic data, soil parameters and layout dimensions. The model is formulated to simulate various processes such as evapotranspiration, seepage and percolation, and surface runoff as they occur in the field water balance system. The model is able to predict the changes in water balance components under different land management and hydrological conditions. The model can be applied either for plot-to-plot or independent plot layouts. It was validated using data collected from controlled plot experiments. The details of model development and validation are outlined in this paper.

Agricultural Water Management

Evapotranspiration (ET) is an important component of the hydrologic cycle and involves the exchan... more Evapotranspiration (ET) is an important component of the hydrologic cycle and involves the exchange of water between the surrounding water bodies, soil, crop surfaces, and the atmosphere. Crop growth and yields are largely affected by the rate of ET, especially in semi-arid areas where the rate of ET is high and rainfall is not sufficient and reliable to add more water into the soil for crop use. Solar radiation, relative humidity, air temperature, rainfall, and wind velocity are some of the meteorological factors that affect ET. Therefore, this research was aimed at determining ET and its trend across Rwanda using climatic data measured at 5 sites. With the research, we accessed meteorology data measured at synaptic stations in the five provinces of Rwanda (Kigali city/central, Kawangire/Eastern, Ruberengera/Western, Ruhengeri/Northern and Gikongoro/ and used the data to calculate reference evapotranspiration (ET) for the recent 10 years (2010-2018). Equations were used to calculat...

SSRN Electronic Journal

First and foremost, I want to express my sincere thanks to my supervisor Dr. Nick Brozović, for h... more First and foremost, I want to express my sincere thanks to my supervisor Dr. Nick Brozović, for his continuous support, mentorship, enthusiasm, and encouragement. Working with Nick has been an indelible experience on both academic and personal levels. I want to thank my co-advisors, Dr. Derek Heeren and Dr. Lameck Odhiambo, for providing their valuable inputs and guidance, which were instrumental in shaping my research. I wish to show my gratitude to my committee member Dr. Daran Rudnick for bringing his expertise and advice to the research thesis. This thesis would not have been possible without my wife, Ishani. I would like to thank her for personal support and for helping me with my research on various levels. I want to thank my parents, family members, and friends for always supporting me and praying for me. Mom and Dad, thank you for enabling me to pursue my dream in a distant land and never giving up on me despite all my failures. I would like to acknowledge the financial, academic, and technical support from Robert B. Daugherty Water for Food Global Institute (DWFI) and Nebraska Center for Energy Sciences Research (NCESR). I would like to pay my special regards to DWFI Policy team members-Kate Gibson, Ellen Emanuel, Caleb Milliken, and Vivian Nguyen, for continuously helping me with my research right from the beginning. I want to acknowledge the help and support of the Rwanda Agricultural Board (RAB), Rwanda Meteorology Agency (RMA), and Farm Fresh (a Rwanda based cooperative) in providing the soil and weather data. Grace Mukarusagara deserves a special mention for her hard work and cooperation in fieldwork. I also want to acknowledge the help of our field team members in Rwanda (Ngabo, Vedasthe, and Bigirimana) for their contribution and hard work in collecting field-level data for this research.

Evaluation of the impact of surface residue cover on single and dual crop coefficient for estimat... more Evaluation of the impact of surface residue cover on single and dual crop coefficient for estimating soybean actual evapotranspiration

Errors associated with visual inspection and interpretation of radargrams often inhibits the inte... more Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-...