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首页> 外文期刊>Agricultural Water Management >Rice yield response forecasting tool (YIELDCAST) for supporting climate change adaptation decision in Sahel
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Rice yield response forecasting tool (YIELDCAST) for supporting climate change adaptation decision in Sahel

机译：水稻产量响应预测工具（MATECTCASS），用于支持萨赫尔气候变化适应决策

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Rice yield responses forecast (YIELDCAST) is a very useful decision support tool in climate adaptation in Sahel, where crops are purely rainfed climate-stressors sensitive. This study aims to construct upland rice yield responses forecasting algebraic formulation code referred as YIELDCAST by using gene-expression programming (GEP) based on observed rainfall and temperatures data (1979-2011), and forcing with global climate model (GCM) downscaled outputs under CO2 emission scenarios SR-A1B, A2 and B1 (2012-2100) over Bobo-Dioulasso, a Sahelian region. Statistically, GEP is a capable tool to downscale climate variables in the region (R = 0.746-0.949), and construct reliable rice YIELDCAST tool (R = 0.930; MSE = 0.037 ton/ha; MAE = 0.155 ton/ha, RSE = 0.137 ton/ha). Yields forecasted (2012-2100) showed a noticeable statistically significant difference between scenarios; however, fluctuating with no substantial increase (average below 1.60 ton/ha); suggesting that the increase observed in temperatures and decrease in rains will either reduced or hindered yield to largely increase in Sahel. With no such YIELDCAST tool to support adaptation decision, Sahel will still be under the trap of the broad array of adaptation strategy, which is a trial and error, less specific and costly. The model can help anticipate adaptation decision support on-farm water management, shift to suitable planting periods, and use of improved drought resistant and short duration varieties adapted to a local weather pattern.
机译：水稻产量响应预测（MapedCast）是萨赫尔气候适应中的一个非常有用的决策工具，其中作物纯粹雨水气候压力敏感。本研究旨在通过使用基于观察到的降雨和温度数据（1979-2011），构建普通水稻产量的响应预测代数制剂代码（GEP）（GEP）（1979-2011），并迫使全球气候模型（GCM）较低的输出CO2发射情景SR-A1B，A2和B1（2012-2100）在萨赫洛亚地区Bobo-Dioulasso。统计上，GEP是该地区（r = 0.746-0.949）的低级气候变量的能力工具（r = 0.746-0.949），并构建可靠的水稻培养工具（r = 0.930; MSE = 0.037吨/公顷; MAE = 0.155吨/公顷，RSE = 0.137吨/公顷）。收益率预测（2012-2100）在情景之间显示出明显的统计学意义差异;但是，波动无大幅增加（平均低于1.60吨/公顷）;建议在温度下观察到的增加和降低雨量将减少或阻碍莎草在大大增加的产量。没有这种支持适应决策的可用性工具，萨赫尔仍将受到广泛适应策略的陷阱，这是一种试验和错误，较少特定和昂贵。该模型可以帮助预测适应决策支持的农场水管理，转移到合适的种植期，以及使用适应当地天气模式的改进的抗旱和短持续时间变化。

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