Land surface albedo is the ratio of reflected to incident solar radiation. It is a function of several surface parameters including soil color, moisture, roughness and vegetation cover. A better understanding of albedo and how it changes in relation to variations in these parameters is important in order to help improve our ability to model the effects of land surface modifications on climate. The objectives of this study were 1) To determine empirical relationships between smooth bare soil albedo and soil color, 2) To develop statistical relationships between albedo and ground-based thematic mapper (TM) measurements of spectral reflectances, 3) To determine how increased surface roughness caused by tillage reduces bare soil albedo and 4) To empirically relate albedo with TM data and other physical characteristics of mixed grass/shrubland sites at Walnut Gulch Watershed. Albedos, colors and spectral reflectances were measured by Eppley pyranometer, Chroma Meter CR-200 and a Spectron SE-590, respectively. Measurements were made on two field soils (Gila and Pima) at the Campus Agricultural Center (CAC), Tucson, AZ. Soil surface roughness was measured by a profile meter developed by the USDA/ARS. Additional measurements were made at the Maricopa Agricultural Center (MAC) for statistical model testing. Albedos of the 15 smooth, bare soils (plus silica sand) were determined by linear regression to be highly correlated (r²=0.93, p>0.01) with color values for both wet and dry soil conditions. Albedos of the same smooth bare soils were also highly correlated (r²≥0.86, p>0.01) with spectral reflectances. Testing of the linear regression equations relating albedo to soil color and spectral reflectances using the data from MAC showed a high correlation. A general nonlinear relationship given by y=8.3661n(x)+37.802 r²=0.71 was determined between percent reduction in albedo (y) and surface roughness index (x) for wet and dry Pima and Gila field soils. Measurements of albedo, color and spectral reflectance at the Walnut Gulch Watershed indicated that albedo values were highly correlated with percent rock & gravel, color value and reflectance data (TM bands 1-4).

This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

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Water is a scarce resource used by various stakeholders. Agriculture is one of the users of this resource especially for growing plants. Plants need to take up carbon dioxide to prepare their own food. For this purpose plants have stomatal openings. These same openings are used for transpiration. Quantifying transpiration is important for efficient water resource management and crop production because it is closely related to dry matter production. Transpiration could be measured using a number of methods or calculated indirectly through quantification of the soil water balance components using environmental instruments. The use of models such as the Decision Support System for Agrotechnology Transfer (DSSAT v3.5) is, however, much easier than environmental instruments. Nowadays, with increased capabilities of computers, the use of crop simulation modelling has become a common practice for various applications. But it is important that models, such as DSSAT v3.5, be calibrated and verified before being used for various applications such as long-term risk assessment, evaluation of cultural practices and other applications. In this study the model inputs have been collected first Then the model was calibrated and verified. Next sensitivitY analysis was carried to observe the model behavior to changes in inputs. Finally the model has been applied for long-term risk assessment and evaluation of cultural practices. In this study, the data collected formed the basis forthe minimum dataset needed for running the DSSAT v3.5 model. In addition, the factory given transmission of shading material over a tomato crop was compared to actual measurements. Missing weather data (solar irradiance, minimum and maximum air temperature and rainfall) were completed after checking that it was homogeneous to measurements from nearby automatic weather station. It was found that factory-given transmission value of 0.7 of the shade cloth was different from the actual one of 0.765. So this value was used for conversion of solar irradiance measured outside the shade cloth to solar irradiance inside the shade cloth. Conventional laboratory procedures were used for the analysis of soil physical and chemical properties. Soil water content limits were determined using texture and bulk density regression based equations. Other model inputs were calculated using the DSSAT model. Crop management inputs were also documented for creation of the experimental details file. The DSSATv3.5 soil water balance model was calibrated for soil, plant and weather conditions at Ukulinga by modifying some of its inputs and then simulations of the soil water balance components were evaluated against actual measurements. For this purpose half of the data available was used for calibration and the other half for verification. Model simulations of soil water content (150 to 300 mm and 450 to 600 mm) improved significantly after calibration. In addition, simulations of leaf area index (LA!) were satisfactory. Simulated evapotranspiration (E1) had certain deviations from the measured ET because the latter calculated ET by multiplying the potential ET with constant crop multiplier so-called the crop coefficient. Sensitivity analysis and long-term risk assessments for yield, runoff and drainage and other model outputs were carried out for soil, plant and weather conditions at Ukulinga. For this purpose, some of the input parameters were varied individually to determine the effect on seven model output parameters. In addition, long-term weather data was used to simulate yield, biomass at harvest, runoff and drainage for various initial soil water content values. The sensitivity analysis gave results that conform to the current understanding of the soil-plant atmosphere system. The long-term assessment showed that it is risky to grow tomatoes during the winter season at Ukulinga irrespective of the initial soil water content unless certain measures are taken such as the use of mulching to protect the plants from frost. The CROPGRO-Soya bean model was used to evaluate the soil water balance and gro'W1:h routines for soil, plant and weather conditions at Cedara. In addition, cultural practices such as row spacing, seeding rate and cultivars were also evaluated using longterm weather data. Simulations of soil water content were unsatisfactory even after calibration of some of the model parameters. Other model parameters such as LAI, yield and flowering date had satisfactory agreement with observed values. Results from this study suggest that the model is sensitive to weather and cultural practices such as seeding rates, row spacing and cultivar maturity groups. The general use of decision support systems is limited by various factors. Some of the factors are: unclear definition of clients/end users; no end user input prior to or during the development of the DSS; DSS does not solve the problems that the client is experiencing; DSS do not match their decision-making style; producers see no reason to change the current management practices; DSS does not provide benefit over current decision-making system; limited computer ownership amongst producers; lack of field testing; producers do not trust the output due to the lack of understanding of the underlying theories of the models utilized; cannot access the necessary data inputs; lack of technical support; lack of training in the development ofDSS software; marketing and support constraints; institutional resistances; short shelf-life of DSS software; technical constraints, user constraints and other constraints. For successful use of DSS, the abovementioned constraints have to be solved before their useful impacts on farming systems could be realized. This study has shown that the DSSAT v3.5 model simulations of the soil water balance components such as evapotranspiration and soil water content were unsatisfactory while simulations of plant parameters such as leaf area index, yield and phonological stages were simulate to a satisfactory standard. Sensitivity analysis gave results that conform to the current understanding of the soil-plant -atmosphere system. Model outputs such as yield and phonological stages were found to sensitive to weather and cultural practices such as seeding rates, row spacing and cultivar maturity groups. It ha been further investigated that the model could be used for risk assessment in various crop management practices and evaluation of cultural practices. However, before farmers can use DSSAT v3.5, several constraints have to be solved. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.