ENSO, seasonal rainfall patterns and simulated maize yield variability in Zimbabwe

Publication Status is "Submitted" Or "In Press: 
LDEO Publication: 
Publication Type: 
Year of Publication: 
Journal Title: 
Agricultural and Forest Meteorology
Journal Date: 
Place Published: 
Tertiary Title: 
Section / Start page: 
ISBN Number: 
ISSN Number: 
Short Title: 
Accession Number: 
LDEO Publication Number: 
Call Number: 

A correlation between ENSO (El Nino/Southern Oscillation) and rainfall in southern Africa has been recognized for at least a decade. This recognition has led to the expectation that ENSO-based climate predictions will have significant applications in agricultural management. This study is an analysis of the potential for using ENSO predictions to reduce risk in agricultural production associated with rainfall variability at the site level. Records of sea-surface temperatures in the equatorial Pacific during November, December and January were used to define El Nino, La Nina and neutral years. Climate data from four sites in four of the five agroecological zones (AEZ) in Zimbabwe were analyzed with respect to ENSO phases and used to drive a maize growth simulation model parameterized for soil conditions typical of each area, using two nitrogen fertilizer treatments and three planting dates. The four sites (Karoi, AEZ II; Gweru, AEZ III; Masvingo, AEZ IV; and Beitbridge, AEZ V) all showed a decrease in seasonal precipitation associated with the El Nino phase, compared to both neutral and La Nina years. At sites in zones II and III, within-season rainfall variability increased for both El Nino and La Nina years relative to neutral years. While average simulated maize yields were generally lowest in El Nino years, variability in rainfall pattern and standard deviation of yields at the site level was high within each ENSO phase, indicating that more precise seasonal climate predictions would be necessary for forecasts to be valuable in crop management decisions in Zimbabwe. However, simulation results point towards the relative importance of predicting favorable cropping seasons as opposed to poor ones with respect to better nitrogen management and yield improvement for the more marginal sites. (C) 1998 Elsevier Science B.V.


Zk358Times Cited:39Cited References Count:23