George H. Hargreaves

Reference Crop Evapotranspiration from Temperature

MEASURED lysimeter evapotranspiration of Alta fescue grass (a cool season grass) is taken as an index of reference crop evapotranspiration (ETo). An equation is presented that estimates ETo from measured values of daily or mean values of maximum and minimum temperature. This equation is compared with various other methods for estimating ETo. The equation was developed using eight years of daily lysimeter data from Davis, California and used to estimate values of ETo for other locations. Comparisons with other methods with measured cool season grass evapotranspiration at Aspendale, Australia; Lompoc, California; and Seabrook, New Jersey; with lysimeter data from Damin, Haiti; and with the modified Penman for various locations in Bangladesh indicated that the method usually does not require local calibration and that the estimated values are probably as reliable and useable as those from the other estimating methods used for comparison. Considering the scarcity of complete and reliable climatic data for estimating crop water requirements in developing countries, this proposed method can do much to improve irrigation planning design and scheduling in the developing countries.

Estimating Solar Radiation from Temperature with Spatial and Temporal Calibration

A procedure is introduced to estimate daily solar radiation from maximum and minimum temperatures for areas where solar radiation is either unavailable or unreliable. The procedure uses historical temperature data for spatial and temporal self-calibration. The results from the self-calibrated procedure are compared with measured solar radiation data in one coastal and two interior regions. The self-calibrated procedure is also compared with an equation presented by Thornton and Running that also uses historical maximum and minimum temperature to estimate solar radiation. The comparison between measured and predicted solar radiation values for several years shows that the self-calibrated method can predict solar radiation with error ranging from 0–3%. The error from the Thornton and Running equation ranges from 7–11%. In general, the Thornton and Running equation tends to overestimate solar radiation especially for overcast sky conditions such as monsoon season.

Estimating Crop Yields from Simulated Daily Weather Data

A weather generating model called WMAKER was developed at Utah State University to simulate daily climatological values using monthly average values and standard deviations of precipitation, temperature, number of rainy days and potential evapotranspiration. The data generated by the WMAKER Model were used to simulate yield probability values for corn under rainfed and irrigated agriculture for stations of Quixeramobim and Brasilia (Brazil) and San Andres and La Union (El Salvador), using the PLANTGRO and CERES-Maize models. The yield probability values were then calculated using actual daily climatic data. It was concluded that the results obtained with actual data were in close agreement with those calculated by the WMAKER Model. The IBSNAT project will soon have yield models available for 10 crops. With adequate documentation, training programs and a monthly data base, these models can become powerful tools for increasing agricultural production, particularly in the developing countries.

Applications of a climatic data base for Africa

Abstract A climatic data base has recently been developed for 299 stations in Africa. This paper describes how this data base can be used for agricultural planning and management for Africa, including estimation of rainfall probabilities, yield prediction under rainfed and irrigated agriculture, extreme rainfall evaluation, estimation of global solar radiation and irrigation scheduling.

Estimating Rainfall Probabilities from Average Values

A method is presented to estimate the values of monthly rainfall amount at different probability levels using only average monthly rainfall. First, a relationship is derived between average monthly rainfall and monthly rainfall at 50% probability for the stations with available data. The rainfall estimated at 50% probability and the average rainfall is then used to calculate the shape parameter of the Gamma distribution through an iterative search procedure for stations for which only average values are available. After the shape parameter is calculated, then the rainfall at any other probability level can be estimated. The method is tested for countries of Brazil, India, Cameroon and Central African Republic. It was concluded that when past records are not available, this method can be used to estimate the rainfall probability distribution from average values with reasonable accuracy.