Analysis of high yielding maize production – a study based on a commercial crop
C. J. Birch A , G. McLean B and A. Sawers CA School of Land, Crop and Food Science, The University of Queensland, Gatton Campus, Gatton, Qld 4343, Australia.
B Queensland Department of Primary Industries and Fisheries, Agricultural Production Systems Research Unit, Tor Street, Toowoomba, Qld 4350, Australia.
C Sawers Farms, Boort, Vic. 3537, Australia.
D Corresponding author. Email: c.birch@uq.edu.au
Australian Journal of Experimental Agriculture 48(3) 296-303 https://doi.org/10.1071/EA06103
Submitted: 16 March 2006 Accepted: 7 August 2007 Published: 4 February 2008
Abstract
This paper reports on the use of APSIM – Maize for retrospective analysis of performance of a high input, high yielding maize crop and analysis of predicted performance of maize grown with high inputs over the long-term (>100 years) for specified scenarios of environmental conditions (temperature and radiation) and agronomic inputs (sowing date, plant population, nitrogen fertiliser and irrigation) at Boort, Victoria, Australia. It uses a high yielding (17 400 kg/ha dry grain, 20 500 kg/ha at 15% water) commercial crop grown in 2004–05 as the basis of the study. Yield for the agronomic and environmental conditions of 2004–05 was predicted accurately, giving confidence that the model could be used for the detailed analyses undertaken. The analysis showed that the yield achieved was close to that possible with the conditions and agronomic inputs of 2004–05. Sowing dates during 21 September to 26 October had little effect on predicted yield, except when combined with reduced temperature. Single year and long-term analyses concluded that a higher plant population (11 plants/m2) is needed to optimise yield, but that slightly lower N and irrigation inputs are appropriate for the plant population used commercially (8.4 plants/m2). Also, compared with changes in agronomic inputs increases in temperature and/or radiation had relatively minor effects, except that reduced temperature reduces predicted yield substantially. This study provides an approach for the use of models for both retrospective analysis of crop performance and assessment of long-term variability of crop yield under a wide range of agronomic and environmental conditions.
Additional keywords: irrigation modelling, nitrogen optimisation, plant population, radiation, sowing date, temperature, yield, yield reliability, Zea mays.
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