乌克兰草原区土壤和气候条件下向日葵杂交种的时空模式和植被预测

V. Pichura, Larysa Potravka, Ye.O. Domaratskiy, Spartakas Petrovas
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摘要

对耕作和作物管理进行长期研究,对于找出有助于实现可持续产量和利润的作物生产方式至关重要。在气候变化条件下,缺墒地区作物栽培系统的选择、预测和调整以及农业风险管理等问题尤为重要。因此,本研究旨在确定向日葵杂交种无性系发育的时空模式,并预测其在乌克兰草原的土壤和气候条件下的产量。在 "哨兵 2 号 "卫星设备空间图像的帮助下,对 2019-2021 年期间向日葵杂交作物归一化差异植被指数值的季节性变化进行了详细分析,然后使用 ArcGis 10.6 许可软件产品进行了处理。根据 NDVI 得出的不同植被阶段作物状况结果的可信度及其用于农作物产量预测的可能性已得到证实。确定了各种向日葵杂交种对 STeppe 土壤和气候条件的适应能力,特别是 Oplot、Hektor、DSL403、P64GE133、8X477KL 等杂交种。根据每年的水分供应水平,为每种向日葵杂交种建立了产量预测函数模型。预测模型的数据逼近程度为 97.2%-99.9%。建议使用专门为不同水分供应和植物营养条件开发的系统函数模型,以便根据特定情况预测向日葵杂交种的产量。研究结果可用于改进农作物植被研究方法、验证轮作、选择使用多功能生长调节物质的最佳实用方法、确定栽培品种和杂交种的气候调节、管理资源、开发农业和作物生产中的适应性气候技术、计算其效率、预测产量以及确保水分亏缺区农业生产和高风险农业管理的盈利能力。
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Spatiotemporal patterns and vegetation forecasting of sunflower hybrids in soil and climatic conditions of the Ukrainian Steppe zone
Long-term studies of tillage and crop management are essential in finding out which crop production practices would contribute to sustainable yields and profits. In the conditions of climate change, such issues as selection, forecasting and adjustment of crop cultivation systems in the zone of moisture deficit and agricultural risk management are especially relevant. Therefore, the aim of the study was to establish spatiotemporal patterns of vegetative development of sunflower hybrids and predict their productivity in the soil and climatic conditions of the Ukrainian Steppe. A detailed analysis of seasonal changes in the values of the normalized difference vegetation index in sunflower hybrid crops during the 2019-2021 time period was carried out with the help of space images from the Sentinel 2 satellite device, and then processed with the ArcGis 10.6 licensed software product. The credibility of the achieved results of the condition of crops in different phases of plant vegetation on the basis of NDVI and the possibility of their use for forecasting the yield of agricultural crops have been proven. The adjustment capabilities of various sunflower hybrids to the STeppe soil and climate conditions were determined, particularly in regards of such hybrids as Oplot, Hektor, DSL403, P64GE133, 8X477KL. A model of the yield forecasting function for each sunflower hybrid was developed according to the annual level of moisture supply. The level of data approximation of the forecasting models was 97.2-99.9%. It is suggested to use system functional models developed specifically for different moisture supply and plant nutrition conditions in order to forecast of the yield of sunflower hybrids according to a particular situation. The results can be used to improve the methodology of researching the vegetation of agricultural crops, to validate crop rotation, to choose the best practical ways for the use of multifunctional growth-regulating substances, to define the climatic adjustment of cultivars and hybrids, to manage resources, to develop adaptive climate technologies in agriculture and crop production, to calculate their efficiency, to forecast the yield and to ensure the profitability of agricultural production in the moisture deficit zone and managing a high-risk farming
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