Working Document Series Open Access 2012 RUF-31925

Measuring Maize (Zea mays L.) Genetic Coefficients for Modeling Water-Limited Potential Yield and Yield Gaps in the Wami-Ruvu River Basin, Tanzania: An Overview

S.K. MouriceA. NyambililaC.L. RweyemamuS. Tumbo

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Abstract

70 words

This research establishes observed data for validating an artificial neural network model for estimating the cultivar coefficients for the popular maize varieties in Tanzania. The genetic coefficients are fundamental in calibrating the DSSAT crop system model for estimating potential yield and yield gap under rain fed conditions. Leaf area index, plant height, aboveground biomass and grain growth rate varied significantly among the maize varieties. This paper summarizes the obtained results.

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