巴西东北部玉米作物生长、产量和土壤水分动态的模型评估

M. Santos, A. L. Carvalho, J. L. Souza, Maurício Bruno Prado da Silva, Rui Palmeira Medeiros, R. A. F. Junior, G. Lyra, Iêdo Teodoro, G. B. Lyra, M. Lemes
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引用次数: 2

摘要

本研究旨在评价APSIM-Maize模型的性能,将其作为决策工具,帮助巴西东北部地区提高产量、降低生产成本并评估气候变化对作物产量的潜在影响。模拟中使用的作物、土壤和天气数据来自2008年和2011年在巴西东北部阿拉戈斯州两个不同的气候区对玉米作物进行的田间试验。我们使用的方法探索了APSIM模拟玉米品种生长变量和土壤水分动态的能力(AL Bandeirante)。在参数化过程中,我们对品种和土壤有机质进行了一些调整,以更好地表征生长和土壤水分动态。APSIM-Maize模型预测叶面积指数的RMSE(均方根误差)在0.14 ~ 1.06 cm2 cm-2之间,生物量产量的RMSE在2.30 ~ 3.34 Mg ha-1之间。预测土壤体积含水量的RMSE范围为0.02 ~ 0.08 mm mm-1。结果表明,该模型是一个有用的决策工具,可以作为旨在改善区域生产的气候风险管理和政策的支持,前提是该模型先前已通过独立数据集验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A modelling assessment of the maize crop growth, yield and soil water dynamics in the Northeast of Brazil
The present study aims to evaluate the APSIM-Maize model performance to use it as a decision-making tool to help improve production rates, reduce production costs and assess the potential impacts of climate change on crop yields in the Northeast of Brazil. The crop, soil and weather data used in the simulations were obtained from field experiments carried out in maize crops in 2008 and 2011 in two different edaphoclimatic regions in Alagoas State, Northeast Brazil. The approach we used explored the ability of APSIM to simulate growth variables and soil water dynamics of a maize variety (AL Bandeirante). During parametrization, we made some adjustments regarding the variety and soil organic matter to attain a better representation of the growth and soil water dynamics, respectively. The APSIM-Maize model predicted the leaf area index with a RMSE (Root Mean Square Error) ranging between 0.14 and 1.06 cm2 cm-2 and the biomass production with an RMSE between 2.30 and 3.34 Mg ha-1. The volumetric soil water content was satisfactorily predicted with RMSE ranging between 0.02 and 0.08 mm mm-1. Results showed that this model is a useful tool for decision-making, which can be potentially used as a support in climate risk management and policies, aiming to improve regional production, provided it has been previously validated with independent datasets.
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