Lameck O. Odhiambo

EVALUATION OF METHODS FOR ESTIMATING DAILY REFERENCE CROP EVAPOTRANSPIRATION AT A SITE IN THE HUMID SOUTHEAST UNITED STATES

Estimated daily reference crop evapotranspiration (ETo) is normally used to determine the water requirement of crops using the crop factor method. Many ETo estimation methods have been developed for different types of climatic data, and the accuracy of these methods varies with climatic conditions. In this study, pair-wise comparisons were made between daily ETo estimated from eight different ETo equations and ETo measured by lysimeter to provide information helpful in selecting an appropriate ETo equation for the Cumberland Plateau located in the humid Southeast United States. Based on the standard error of the estimate (Syx), the relationship between the estimated and measured ETo was the best using the FAO-56 Penman-Monteith equation (coefficient of determination (r2) = 0.91, Syx = 0.31 mm d-1, and a coefficient of efficiency (E) = 0.87), followed by the Penman (1948) equation (r2 = 0.91, Syx = 0.34 mm d-1, and E = 0.88), and Turc’s equation (r2 = 0.90, Syx = 0.36 mm d-1, and E = 0.88). The FAO-24 Penman and Priestly-Taylor methods overestimated ETo, while the Makkink equation underestimated ETo. The results for the Hargreaves-Samani equation showed low correlation with lysimeter ETo data (r2 = 0.51, Syx = 0.68 mm d-1, and E = 0.20), while those for the Kimberly Penman were reasonable (r2 = 0.87, Syx = 0.40 mm d-1, and E = 0.87). These results support the adoption of the FAO-56 Penman-Monteith equation for the climatological conditions occurring in the humid Southeast. However, Turc’s equation may be an attractive alternative to the more complex Penman-Monteith method. The Turc method requires fewer input parameters, i.e., mean air temperature and solar irradiance data only.

OPTIMIZATION OF FUZZY EVAPOTRANSPIRATION MODEL THROUGH NEURAL TRAINING WITH INPUT–OUTPUT EXAMPLES

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 (Syx) 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 Syx 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.

Modeling water balance components in relation to field layout in lowland paddy fields. I. Model development

Abstract 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.

Modeling water balance components in relation to field layout in lowland paddy fields. II: Model application

Abstract In a companion paper, the development of a water balance model to predict the changes in water balance components in relation to field layout in lowland paddy fields was outlined. In this study, the model was applied to a large irrigated area to assess and compare the water balance of intensive and extensive field layouts. The results show significant differences in water balance which are attributed to the layouts and water management practices. The field layouts influenced the equity of water allocation, effective rainfall, water use efficiencies and relative proportion of various water balance components. Eight indices are computed to evaluate the effects of layout under different land and hydrological conditions. Appropriate measurement scales for ranking the indices are proposed.

Investigation of a Fuzzy-Neural Network Application in Classification of Soils Using Ground-Penetrating Radar Imagery

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.

Estimation of reference crop evapotranspiration using fuzzy state models

Microirrigation can potentially “spoon feed” nutrients to a crop. Accurately supplying the crop’s nitrogen (N) needs throughout the season can enhance crop yields and reduce the potential for groundwater contamination from nitrates. A 2–year study (1990–1991) was conducted on a Keith silt loam soil (Aridic Argiustoll) to examine combinations of both preplant surface application (30 cm band in center of furrow) and in–season fertigation of N fertilizer for field corn (Zea mays L.) at three different levels of water application (75%, 100%, and 125% of seasonal evapotranspiration) using a subsurface drip irrigation (SDI) system. The method of N application did not significantly affect corn yields, apparent plant nitrogen uptake, or water use efficiency, but all three factors were generally influenced by the combined total N amount. The N application method did have an effect on the amount and distribution of total soil N and nitrate–N in the soil profile following harvest. In both years, nearly all of the residual nitrate–N after corn harvest was within the upper 0.3 m of the soil profile for the treatments receiving only preplant–applied N, regardless of irrigation regime. In contrast, the nitrate–N concentrations increased with increasing rates of N injected by the SDI system and migrated deeper into the soil profile with increased irrigation. The results suggest that N applied with an SDI system at a depth of 40–45 cm redistributes differently in the soil profile than surface–applied preplant N banded in the furrow.

Evaluating the Impact of Daily Net Radiation Models on Grass and Alfalfa-Reference Evapotranspiration Using the Penman-Monteith Equation in a Subhumid and Semiarid Climate

Net radiation Rn is the main driving force of evapotranspiration ET and is a key input variable to the Penman-type combination and energy balance equations. However, Rn is not commonly measured. This paper analyzes the impact of 19 net radiation models that differ in model structure and intricacy on estimated grass and alfalfa-reference ET ETo and ETr, respectively and investi- gates how climate, season and cloud cover influence the impact of the Rn models on ETo and ETr. Datasets from two locations Clay Center, Nebraska, subhumid; and Davis, California, a Mediterranean-type semiarid climate were used. Rn values computed from the 19 models were used in the standardized ASCE-EWRI Penman-Monteith equation to estimate ETo and ETr on a daily time step. The influence of seasons on the estimation of Rn and on estimated ETo and ETr was investigated in winter November-March and summer May-September months. To analyze the influence of clouds on the impact of Rn models, relative shortwave radiation Rrs was used as a means to express the cloudiness of the days as: 0 Rrs0.35 for completely cloudy days; 0.35 Rrs0.70 for partially cloudy days; and 0.70 Rrs1.0 for clear sky days. The performances of Rn models showed variations at the same location and between the locations for the same model based on methods used to calculate various model parameters. The most significant impact of Rn on estimated ETo and ETr was related to the methods used to calculate atmospheric emissivity rather than methods used to calculate clear sky solar radiation Rso or cloud adjustment factor f. Rn models that used average air temperature to compute and an estimated f resulted in good performances at both locations. Empirical models that assumed f=1.0 showed poor to average performances at both locations. While model performances varied based on methods used to calculate Rso, f, and , there were significant seasonal variations in performances of models that calculated as a function of actual vapor pressure of the air ea. The seasonal variations in performances of these models were greater under subhumid climate at Clay Center than in semiarid climate at Davis, Calif. The models that calculated as a function of ea performed better under completely cloudy days than on other days, more so at Clay Center. Methods used to calculate have a significant impact on the Rn model performance, especially in unstable climatic conditions such as at Clay Center where there are frequent and rapid changes in climatic variables in a given day and throughout the year. The results of this study can be used as a reference tool to provide practical information on which method to select based on the data availability for reliable estimates of daily Rn relative to the ASCE-EWRI Rn method in subhumid and semiarid climates similar to Clay Center, Neb. and Davis, Calif.

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

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 paper 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 paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world data. 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-time soil profile clustering and classification during field surveys.

NONINTRUSIVE MAPPING OF NEAR-SURFACE PREFERENTIAL FLOW

A unique survey protocol has been developed that maps near-subsurface preferential flow using integrated ground-penetrating radar (GPR) and a differential geographical positioning system (DGPS). The survey protocol consists of a mobile GPR system that spirals outward along a prescribed course, continuously gathering subsurface data for an extended period. Metered water is applied to a centrally located water-ponding ring, after first capturing the initial dry-state pattern signatures. The water radiates outward beneath the surface as it follows preferential flow pathways, which the GPR instrumentation spiraling above highlights. After data are collected, pre- and post-water time-elapsed images profiles are segmented by pattern dissimilarities. The specific locales that exhibit pattern shifts from the initial dry state are identified as dynamic water movement. Locales that exhibit pattern shifts are mapped to indicate the rate and direction of preferential flow about the near surface.

Soil-Water Dynamics, Evapotranspiration, and Crop Coefficients of Cover-Crop Mixtures in Seed Maize Cover-Crop Rotation Fields. I: Soil-Water Dynamics and Evapotranspiration

AbstractCover crops are incorporated into row crop production systems as rotational crops because of their potential contributions to soil and water conservation. However, extremely limited data an...