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首页> 外文期刊>Australian Journal of Crop Science >Agrometeorological models for forecasting yield and quality of sugarcane
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Agrometeorological models for forecasting yield and quality of sugarcane

机译：预测甘蔗产量和品质的农业气象模型

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Climate is an important factor in sugarcane production, and its study is fundamental for understanding the climatic requirements of the crop. We developed regional agro-meteorological models to forecast monthly yields in tonnes of sugarcane per hectare (TCH) and quality of the total recoverable sugar (ATR). We used monthly climatological data (air temperature, precipitation, water deficiency and surplus, potential and actual evapotranspiration, soil-water storage, and global solar irradiation) of the previous year to forecast TCH and ATR for the next year using multiple linear regression. The parameters of monthly climatological data were chosen for their small mean absolute percentage errors (MAPEs) and p < 0.05 and ability to model longer periods of prediction. Data for Jaboticabal, a major area of sugarcane production in the state of S?o Paulo, Brazil, from 2002-2009 were used for calibration, and data from 2010-2013 were used for validation. All calibrated models were significant (p < 0.05) and accurate, with a MAPE of 4.06% for the forecast of TCH in the ambient “C” for July. The model calibrated for November had variable water deficits in all environments, showing the importance of this variable to the crops. The monthly models tested performed well. For example, the forecast by TCHMAY in the AB environment (MAPE = 1.89% and adjusted coefficient of determination = 0.90) overestimated the average yield of 90.6 t ha-1 by only 1.7 t ha-1. The predictive period for forecasting TCHMAY was eight months when the last climatological parameter used in the model was DEFSEP.
机译：气候是甘蔗生产的重要因素，其研究对于了解作物的气候需求至关重要。我们开发了区域农业气象模型，以预测每公顷甘蔗吨产量（TCH）和总可采糖质量（ATR）的月度产量。我们使用上一年的每月气候数据（气温，降水，缺水和过剩，潜在和实际的蒸散量，土壤蓄水量以及全球太阳辐射量），使用多元线性回归来预测下一年的TCH和ATR。选择每月气候数据的参数是因为它们的平均绝对百分比误差（MAPE）小，p <0.05，并且能够对更长的预测时间进行建模。使用Jaboticabal（巴西圣保罗州甘蔗生产的主要地区）的2002-2009年数据进行校准，并使用2010-2013年的数据进行验证。所有校准的模型均具有显着性（p <0.05），且准确度较高，MAPE为4.06％，可预测7月环境“ C”中的TCH。十一月份校准的模型在所有环境中都有可变的缺水状况，显示了该变量对农作物的重要性。每月测试的模型表现良好。例如，TCHMAY在AB环境下的预测（MAPE = 1.89％，调整后的确定系数= 0.90）仅将90.6 t ha-1的平均产量高估了1.7 t ha-1。当模型中使用的最后气候参数为DEFSEP时，预测TCHMAY的预测期为八个月。

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