高产玉米产量分析——基于一种经济作物的研究

C. Birch, G. McLean, A. Sawers
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引用次数: 10

摘要

本文报道了在澳大利亚维多利亚州的Boort,使用APSIM - Maize对高投入、高产玉米作物的性能进行回顾性分析,并分析了在特定环境条件(温度和辐射)和农学投入(播种日期、植物种群、氮肥和灌溉)的情况下,高投入种植的玉米在长期(100 - 100年)的预测性能。它使用2004- 2005年种植的高产(17 400公斤/公顷干粮,20 500公斤/公顷水占15%)商业作物作为研究的基础。对2004- 2005年农艺和环境条件下的产量进行了准确预测,使人们相信该模型可以用于所进行的详细分析。分析表明,产量接近2004-05年条件和农艺投入所能达到的产量。9月21日至10月26日的播种日期对预测产量影响不大,除非与温度降低相结合。单年度和长期分析得出的结论是,需要更高的植物种群(11株/m2)来优化产量,但对于商业使用的植物种群(8.4株/m2)来说,稍微低一点的氮和灌溉投入是合适的。此外,与农艺投入的变化相比,温度和/或辐射增加的影响相对较小,但温度降低会大大降低预测产量。本研究提供了一种方法,可以使用模型对作物性能进行回顾性分析,并在广泛的农艺和环境条件下评估作物产量的长期变异性。
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Analysis of high yielding maize production – a study based on a commercial crop
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.
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