The use of climatic data for maize grain yield predictions.

Abstract

The development and testing of a mathematical model for maize grain yield predictions is described. The model is based upon daily considerations of soil moisture, atmospheric evaporative demand and stage of crop development. Final yield predictions depend upon a knowledge of yield decrement due to moisture stress and the number of occasions that stress is recorded. This information was determined in the following manner:- (i) Stress imposed in lysimeters before and after anthesis was found to reduce grain yields by 3,2% and 4,2% per stress day respectively. (ii) A stress day was identified with the aid of mass-measuring lysimeters and a U.S. Weather Bureau Class A evaporation pan for measuring atmospheric evaporative demand. A nomogram constructed in terms of evaporative demand and available soil moisture, which discriminates between stress and non-stress days, was obtained for the Doveton soil used in the lysimeters. The model was applied to Cedara rainfall and evaporation data and yield probability patterns for three planting dates were obtained. It was found that highest yields (8,5 Mg ha(-1)) and least seasonal yield variation, may be expected from the earliest planting data 15/10. The Cedara : Doveton yield prediction model was also applied to climatic records for two other Natal stations (Estcourt and Newcastle) and six stations outside Natal (Bethlehem, Potchefstroom, Hoopstad, Standerton, Ermelo and Krugersdorp). Interesting comparison of the suitability of their respective climates for maize production was obtained. A method which uses the predicted number of stress days and the resultant yield decrement to determine the most effective and economic irrigation scheduling is developed and described. The effect of moisture holding characteristics of various soils upon the shape of the discriminating curve is discussed, and a method of obtaining discriminating curves for other soils by modifying the Doveton curve is described.