Limited water supplies are available to satisfy the increasing demands of crop production. It is therefore very important to conserve the water, which comes as rainfall, and water, which is used in irrigation. A proper irrigation water management system requires accurate, simple, automated, non-destructive method to schedule irrigations. Utilization of infrared thermometry to assess plant water stress provides a rapid, nondestructive, reliable estimate of plant water status which would be amenable to larger scale applications and would over-reach some of the sampling problems associated with point measurements. Several indices have been developed to time irrigation. The most useful is the crop water stress index (CWSI), which normalizes canopy to aIr temperature differential measurements, to atmospheric water vapour pressure deficit. A field experiment was conducted at Cedara, KwaZulu-Natal, South Africa, to determine the non-water-stressed baselines, and CWSI of cereal rye (Secale cereale L.) from 22 July to 26 September 2002, and aImual (Italian) ryegrass (Lolium multiflorum Lam.) from October 8 to December 4, 2002, when the crops completely covered the soil. An accurate measurement of canopy to air temperature differential is crucial for the determination of CWSI using the empirical (Idso et al., 1981) and theoretical (Jackson et al., 1981) methods. Calibrations of infrared thermometers, a Vaisala CS500 air temperature and relative humidity sensor and thermocouples were performed, and the reliability of the measured weather data were analysed. The Everest and Apogee infrared thermometers require correction for temperatures less than 15 QC and greater than 35 QC. Although the calibration relationships were highly linearly significant the slopes and intercepts should be corrected for greater accuracy. Since the slopes of the thermocouples and Vaisala CS500 air temperature sensor were statistically different from 1, multipliers were used to correct the readings. The relative humidity sensor needs to be calibrated for RH values less than 25 % and greater than 75 %. The integrity of weather data showed that solar irradiance, net irradiance, wind speed and vapour pressure deficit were measured accurately. Calculated soil heat flux was underestimated and the calculated surface temperature was underestimated for most of the experimental period compared to measured canopy temperature. The CWSI was determined using the empirical and theoretical methods. An investigation was made to determine if the CWSI could be used to schedule irrigation in cereal rye and annual rye grass to prevent water stress. Both the empirical and theoretical methods require an estimate or measurement of the canopy to air temperature differential, the non-waterstressed baseline, and the non-transpiring canopy to air temperature differential. The upper (stressed) and lower (non- stressed) baselines were calculated to quantify and monitor crop water stress for cereal rye and annual ryegrass. The non-water-stressed baselines were described by the linear equations Te - Ta = 2.0404 - 2.0424 * VPD for cereal rye and Te - Ta = 2.7377 - 1.2524 * VP D for annual ryegrass. The theoretical CWSI was greater than the empirical CWSI for most of the experimental days for both cereal rye and annual ryegrass. Variability of empirical (CWSI)E and theoretical (CWSI)T values followed soil water content as would be expected. The CWSI values responded predictably to rainfall and irrigation. CWSI values of 0.24 for cereal rye and 0.29 for annual ryegrass were found from this study, which can be used for timing irrigations to alleviate water stress and avoid excess irrigation water. The non-water-stressed baseline can also be used alone if the aim of the irrigator is to obtain maximum yields. However the non-water-stressed baseline determined using the empirical method cannot be applied to another location and is only valid for clear sky conditions. And the non-water-stressed baseline determined using theoretical method requires computation of aerodynamic resistance and canopy resistances, as the knowledge of canopy resistance, however the values it can assume throughout the day is still scarce. The baseline was then determined using a new method by Alves and Pereira (2000), which overcomes these problems. This method evaluated the infrared surface temperature as a wet bulb temperature for cereal rye and annual ryegrass. From this study, it is concluded that the infrared surface temperature of fully irrigated cereal rye and annual ryegrass can be regarded as a surface wet bulb temperature. The value of infrared surface temperature can be computed from measured or estimated values of net irradiance, aerodynamic resistance and air temperature. The non-water-stressed baseline is a useful concept that can effectively guide the irrigator to obtain maximum yields and to schedule irrigation. Surface temperature can be used to monitor the crop water status at any time of the day even on cloudy days, which may greatly ease the task of the irrigator. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003

A well-managed irrigation scheduling system needs a rapid, preCIse, simple, costeffective and non-destructive soil water content sensor. The PRl profile probe and Diviner 2000 were used to determine the timing and amount of irrigation of three cover crops (Avena sativa L., Secale cereale L. and Lolium multiflonlm Lam.), which were planted at Cedara, KwaZulu-Natal. The PRl profile probe was first calibrated in the field and also compared with the Diviner 2000. For the calibration of the PRl profile probe the factory-supplied parameters (aJ = 8.4 and ao = 1.6) showed good correlation· compared to the soil-estimated parameters (aJ = 11.04 and ao = 1.02). The factorysupplied parameters gave a linear regression coefficient (r2 ) of 0.822 and root mean square error (RMSE) of 0.062. The soil-estimated parameter showed a linear regression coefficient of 0.820 with RMSE of 0.085. The comparison between the soil water content measured using the PR1 profile probe and Diviner 2000 showed a linear regression coefficient of 0.947 to 0.964 with a range of RMSE of 0.070 to 0.109 respectively for the first 100 to 300 mm soil depths. The deeper depths (400, 600 and 1000 mm) showed a linear regression coefficient ofO.716to 0.810 with a range of 0.058 to 0.150 RMSE. These differences between the shallow and deeper depths could be due to soil variability or lack of good contact between the access tube and the surrounding soil. To undertake irrigation scheduling using the PRl profile probe and Diviner 2000, the soil water content limits were determined using field, laboratory and regression equations. The field method was done by measuring simultaneously the soil water content using the PR1 profile probe and soil water potential using a Watermark sensor and tensiometers at three depths (100, 300 and 600 mm) from a 1 m2 bare plot, while the soil dries after being completely saturated. The retentivity function was developed from these measurements and the drained upper limit was estimated to be 0.355 m3 m-3 when the drainage from the pre-wetted surface was negligible. The lower limit was calculated at -1500 kPa and it was estimated to be 0.316 m3m,3. The available soil water content, which is the difference between the upper and lower limit, was equal to 0.039 m3 m,3. In the laboratory the soil water content and matric potential were measured from the undisturbed soil samples taken from the edge of the 1 m2 bare plot before the sensors were installed. Undisturbed soil samples were taken using a core sampler from 100 to 1000 mm soil depth in three replications in 100 mm increments. These undisturbed soil samples were saturated and subjected to different matric potentials between -1 to -1500 kPa. In the laboratory, the pressure was increased after the cores attained equilibrium and weighed before being subjecting to the next matric potential. The retentivity function was then developed from these measurements. The laboratory method moved the drained upper limit to be 0.390 m3 m,3 at -33 kPa and the lower limit be 0.312 m3m-3 at -1500 kPa. The regression equation, which uses the bulk density, clay and silt percentage to calculate the soil water content at a given soil water potential, estimated the drained upper limit to be 0.295 m3m-3at -33 kPa and the lower limit 0.210 m3 m,3 at -1500 kPa. Comparison was made between the three methods using the soil water content measured at the same soil water potential. The fieldmeasured soil water content was not statistically the same with the laboratory and estimated soil water content. This was shown from the paired-t test, where the probability level (P) for the laboratory and estimated methods were 0.011 and 0.0005 respectively at 95 % level of significance. However, it showed a linear regression coefficient of 0.975 with RMSE of 0.064 when the field method was compared with the laboratory method. The field method showed a linear regression coefficient of 0.995 with RMSE of 0.035 when compared with the estimated method. The timing and amount of irrigation was determined using the PR1 profile probe and Diviner 2000. The laboratory measured retentivity function was used to define the fill (0.39 m3 m-3 ) and high refill point (0.34 m3 m-3 ). The soil water content was measured using both sensors two to three times per week starting from May 29 (149 day of year, 2002) 50 days after planting until September 20 (263 day of year) 11 days before harvesting. There were five irrigations and twenty rainfall events. The next date of irrigation was predicted graphically using, the PRl profile probe measurements, to be on 3 September (246 day of year) after the last rainfall event on 29 August (241 day of year) with 8 mm. When the Diviner 2000 was used, it predicted two days after the PRl profile probe predicted date. This difference appeared since the Diviner 2000-measured soil water content at the rooting depth was slightly higher than the PRl profile probe measurements. The amount of irrigation was estimated using two comparable methods (graphic and mathematical method). The amount of irrigation that should have been applied on 20, September (263 day of year) to bring the soil water content to field capacity was estimated to be 4.5 hand 23 mm graphically and 5.23 hand 20 mm mathematically. The difference between these two methods was caused due to the error encountered while plotting the correct line to represent the average variation in soil water content and cumulative irrigation as a function of time. More research is needed to find the cause for the very low soil water content measurements of the PRI profile probe at some depths. The research should be focused on the factors, which could affect the measurement of the PRl profile probe and Diviner 2000 like salinity, temperature, bulk density and electrical conductivity. Further research is also needed to extend the non-linear relationship between the electrical resistance of the sensor and soil water potential up to -200 kPa. This non-linear equation of the Watermark is only applicable within the range of soil water potential between -10 and -100 kPa. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.