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首页> 外文期刊>Archives of Agronomy and Soil Science >Neural networks in climate spatialization and their application in the agricultural zoning of climate risk for sunflower in different sowing dates
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Neural networks in climate spatialization and their application in the agricultural zoning of climate risk for sunflower in different sowing dates

机译：气候空间化的神经网络及其在不同播种日期向日葵气候风险的农业分区中的应用

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摘要

Sunflower is a species that is sensitive to local climate conditions. However, studies that use artificial neural networks (ANNs) to evaluate this influence and create tools such as agricultural zoning of climate risk (ZARC) have not been conducted for this species. Due to the importance of sunflower as a human food source and for biodiesel production, and also the necessity of conducting research to evaluate the suitability of this oleaginous species under different climatic conditions. Thus, we seek to construct a ZARC for sunflower in Brazil simulating sowing on different dates and using meteorological elements spatialized by ANNs. Climate data were used: air temperature (T), rainfall (P), relative air humidity (UR), solar radiation (MJ_m(-2)_d(-1)) and wind velocity (U-2). Climatic regions considered suitable for the cultivation of sunflower had average annual values for T between 20 and 28 degrees C, P between 500 and 1.500 mm per cycle, and soil water deficit (DEF) below 140 mm per cycle. A neural network is an efficient tool that can be used in spatialization of climate variables quickly and accurately. Sunflower sowing in the spring and summer are the ones that provide the largest suitable areas in southeastern Brazil, with 58.13 and 64.36% of suitable areas, respectively.
机译：向日葵是对局部气候条件敏感的物种。然而，使用人工神经网络（ANNS）来评估这种影响和创造诸如农业区划气候风险（ZARC）的工具（ZARC）的研究尚未对此物种进行。由于向日葵作为人类食物来源和生物柴油生产的重要性，以及进行研究的必要性，以评估这种含油物种在不同气候条件下的适用性。因此，我们寻求建造一个ZARC在巴西的向日葵模拟不同日期播种，并使用ANNS时空的气象元素。使用气候数据：空气温度（T），降雨（P），相对空气湿度（UR），太阳辐射（MJ_M（-2）_D（-1））和风速（U-2）。认为适用于向日葵的培养的气候区域在每周期为20至28℃，p的500至1.500 mm之间的平均年度值，每循环低于140 mm的土壤水分赤字（DEF）。神经网络是一种有效的工具，可以快速准确地用于气候变量的时空。向日葵播种在春天和夏天，是在巴西东南部的最大合适区域，分别为58.13和64.36％的合适区域。

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