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首页> 外文期刊>Agricultural Water Management >Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate
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Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate

机译：评估新灌溉决策支持系统，提高干旱气候棉花产量和水生产率

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Optimizing irrigation scheduling through a Decision Support System has shown promise to improve crop yield and water productivity in irrigated agriculture in an arid climate. The effects of an irrigation scheduling method on cotton (Gossypium hirsutum L.) yield and water productivity were investigated in Qira Oasis, China from 2016-2018. The Decision Support System for Irrigation Scheduling (DSSIS) was based on forecasted rainfall and water stress index simulated by the Root Zone Water Quality Model (RZWQM2). A field experiment was conducted to test the viability of the DSSIS in 2016. The design of the experiment was a randomized complete that included two factors and two levels for each factor: (i) irrigation scheduling method-DSSIS-based (DSS) and soil moisture sensor-based (SMS), and (ii) irrigation level-full irrigation (FI) and deficit irrigation (DI, 75 % of FI). Implementation of the DSS led to significant increases in seed cotton yield [1.05 Mg ha(-1) (32 %)] and water productivity [1.64 kg ha(-1) mm(-1) (20 %)] compared to the SMS. Compared to DI, FI significantly increased cotton yield [0.69 Mg ha(-1) (20 %)] but had no significant effect on water productivity. In general, the higher water productivity under DSS (vs. SMS) was attributed to the reduced water stress and increased seed cotton yield. While the DSS-FI treatment provided the greatest seed cotton yield (4.55 Mg ha(-1)) and net income (US $3427 ha(-1)), the highest water productivity (10.09 kg ha(-1) mm(-1)) was achieved under the DSS-DI treatment. Water use under DSS-DI treatment significantly decreased by 51 mm (10 %) and 23 mm (5 %), respectively, compared to DSS-FI and SMS-FI treatments. Therefore, our results demonstrated that the DSS with deficit irrigation could maintain cotton yield and improve water productivity under an arid desert climate.
机译：通过决策支持系统优化灌溉调度，并在干旱气候中提高了灌溉农业的作物产量和水生产率。 2016 - 2018年QIRA OASIS研究了灌溉调度方法对棉花（Gossypium hirsutum L.）产量和水生产率的影响。灌溉调度决策支持系统（DSSIS）是基于由根区水质模型（RZWQM2）模拟的预测降雨和水分应激指数。进行了田间实验以测试DSSIS在2016年的活力。实验的设计是随机完成，其中包括两个因素和两个水平的每个因素：（i）灌溉调度方法-DSSIS基（DSS）和土壤基于湿度传感器（SMS），和（ii）灌溉水平 - 全灌溉（FI）和缺陷灌溉（DI，75％）。与SMS相比，DSS的液体棉花产量[1.05mg ha（-1）（-1）]和水生产率（-1）mm（20％）]的显着增加导致了显着增加[1.64 kg ha（-1）mm（-1）（20％）] 。与DI，Fi显着增加棉花产量[0.69 mg ha（-1）（20％）]但对水生产率没有显着影响。通常，DSS（与SMS）下的水生产率较高，归因于降低的水分胁迫和种子棉产量增加。虽然DSS-FI处理提供了最大的种子棉花产量（4.55 mg HA（-1））和净收入（3427公顷（-1美元）），水生产率最高（10.09千克（-1）mm（-1 ））在DSS-DI处理下实现。与DSS-Fi和SMS-Fi处理相比，DSS-DI处理下的用水量显着降低51毫米（10％）和23毫米（5％）。因此，我们的结果表明，具有缺陷灌溉的DSS可以维持棉花产量，并在干旱的沙漠气候下提高水生产率。

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