巴西东北部中部地区玉米产量的预测潜力

F. D. S. Silva, Ivens Coelho Peixoto, R. L. Costa, H. Gomes, H. B. Gomes, Jório Bezerra Cabral Júnior, Rodrigo Martins de Araújo, D. Herdies
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摘要

巴西东北部大部分地区(NEB)的玉米生产系统以家庭耕作为基础,没有先进的技术,完全依赖自然降雨量,该地区大部分地区的降雨量集中在 4 至 5 个月。这就意味着,在 NEB,这种作物的生产率很低。在 NEB 的北部中区,降雨集中在 1 月至 6 月,在 NEB 的东部集中在 4 月至 9 月,在 NEB 的西部集中在 10 月至 3 月。生长季节就在这几个季节。有鉴于此,我们的目标是建立一个基于典型相关分析 (CCA) 的模型,以每学期累积降水量为预测变量,预测 1981 年至 2010 年期间东北亚盆地中区的玉米产量(单位:公斤/公顷)。结果表明,与累积降水量与产量之间的直接关系相比,CCA 模型在观测产量和模拟产量之间呈现出更高的相关性。使用的另外两个指标 RMSE 和 NRMSE 表明,在大多数中区,模拟误差平均约为 200 千克/公顷,但准确度主要处于中等水平,在大多数中区约为 29%,其中 6 个中区的数值低于 20%,表明模型准确度较高,2 个中区的数值高于 50%,表明准确度较低。此外,我们还研究了对东北亚降水有直接影响的两种气候变异模式的不同组合如何影响这 30 年的产量,其中厄尔尼诺和正大西洋偶极子的组合对收成的损害最大,而拉尼娜和负大西洋偶极子共同作用的年份对收成最有利。尽管所开发的模型取得了令人满意的结果并具有实际应用性,但应该指出的是,只使用降雨量这一个预测因子对更好地进行模型模拟是一个限制因素,因为其他气象变量和非气候因子对作物有重大影响。不过,该模型的简易性和可喜的结果可以帮助组成 NEB 的所有州的农业管理者做出决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Potential of Maize Yield in the Mesoregions of Northeast Brazil
Most of the northeastern region of Brazil (NEB) has a maize production system based on family farming, with no technological advances and totally dependent on the natural rainfall regime, which is concentrated in 4 to 5 months in most parts of the region. This means that the productivity of this crop is low in the NEB. In the northern mesoregions of the NEB, rainfall is concentrated between January and June, in the east of the NEB from April to September, and in the west of the NEB from October to March. The growing season takes place during these semesters. With this in mind, our objective was to develop a model based on canonical correlation analysis (CCA) to predict corn production in the mesoregions of the NEB between 1981 and 2010, using accumulated precipitation per semester as the predictor variable and predicting the observed production in kg/ha. Our results showed that the CCA model presented higher correlations between observed and simulated production than that obtained simply from the direct relationship between accumulated rainfall and production. The other two metrics used, RMSE and NRMSE, showed that, on average, in most mesoregions, the simulation error was around 200 kg/ha, but the accuracy was predominantly moderate, around 29% in most mesoregions, with values below 20% in six mesoregions, indicative of better model accuracy, and above 50% in two mesoregions, indicative of low accuracy. In addition, we investigated how the different combinations between two modes of climate variability with a direct influence on precipitation in the NEB impacted production in these 30 years, with the combination of El Niño and a positive Atlantic dipole being the most damaging to harvests, while years when La Niña and a negative Atlantic dipole acted together were the most favorable. Despite the satisfactory results and the practical applicability of the model developed, it should be noted that the use of only one predictor, rainfall, is a limiting factor for better model simulations since other meteorological variables and non-climatic factors have a significant impact on crops. However, the simplicity of the model and the promising results could help agricultural managers make decisions in all the states that make up the NEB.
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