CONTEXT

Although rice production has increased significantly in the last decade in West Africa, the region is far from being rice self-sufficient. Inland valleys (IVs) with their relatively higher water content and soil fertility compared to the surrounding uplands are the main rice-growing agroecosystem. They are being promoted by governments and development agencies as future food baskets of the region. However, West Africa's crop production is estimated to be negatively affected by climate change due to the strong dependence of its agriculture on rainfall.

OBJECTIVE

The main objective of the study is to apply a set of machine learning models to quantify the extent of climate change impact on land suitability for rice using the presence of rice-only data in IVs along with bioclimatic indicators.

METHODS

We used a spatially explicit modeling approach based on correlative Ecological Niche Modeling. We deployed 4 algorithms (Boosted Regression Trees, Generalized Linear Model, Maximum Entropy, and Random Forest) for 4-time periods (the 2030s, 2050s, 2070s, and 2080s) of the 4 Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8) from an ensemble set of 32 spatially downscaled and bias-corrected Global Circulation Models climate data.

RESULTS AND CONCLUSIONS

The overall trend showed a decrease in suitable areas compared to the baseline as a function of changes in temperature and precipitation by the order of 22–33% area loss under the lowest reduction scenarios and more than 50% in extreme cases. Isothermality or how large the day to night temperatures oscillate relative to the annual oscillations has a large impact on area losses while precipitation increase accounts for most of the areas with no change in suitability. Strong adaptation measures along with technological advancement and adoption will be needed to cope with the adverse effects of climate change on inland valley rice areas in the sub-region.

SIGNIFICANCE

The demand for rice in West Africa is huge. For the rice self-sufficiency agenda of the region, “where” and “how much” land resources are available is key and requires long-term, informed planning. Farmers can only adapt when they switch to improved breeds, providing that they are suited for the new conditions. Our results stress the need for land use planning that considers potential climate change impacts to define the best areas and growing systems to produce rice under multiple future climate change uncertainties.

中文翻译：

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气候变化导致西非内陆山谷景观农业用地适宜性降低

语境

尽管过去十年西非的稻米产量显着增加，但该地区远未实现稻米自给自足。与周围高地相比，内陆河谷（IVs）的含水量和土壤肥力相对较高，是主要的水稻种植农业生态系统。政府和发展机构正在将它们作为该地区未来的食物篮子加以推广。然而，由于其农业对降雨的强烈依赖，据估计，西非的作物生产受到气候变化的负面影响。

客观的

该研究的主要目的是应用一组机器学习模型，利用 IV 中仅存在水稻数据以及生物气候指标来量化气候变化对水稻土地适宜性的影响程度。

方法

我们使用了基于相关生态位模型的空间显式建模方法。我们在 4 个代表性浓度路径（RCP2.6、RCP4.5）的 4 个时间段（2030 年代、2050 年代、2070 年代和 2080 年代）部署了 4 种算法（增强回归树、广义线性模型、最大熵和随机森林） 、RCP6.0 和 RCP8）来自 32 个空间缩小和偏差校正的全球环流模型气候数据的集合。

结果和结论

总体趋势显示，与基线相比，适宜面积随着温度和降水的变化而减少，在最低减少情景下面积减少 22-33%，在极端情况下减少超过 50%。等温度或相对于年度波动的昼夜温度波动幅度对面积损失有很大影响，而降水增加占大多数地区的适宜性没有变化。需要采取强有力的适应措施以及技术进步和采用，以应对气候变化对该次区域内陆河谷稻区的不利影响。

意义

西非对大米的需求巨大。对于该地区的稻米自给自足议程，“在哪里”和“有多少”可用的土地资源是关键，需要长期、知情的规划。农民只有在改用改良品种时才能适应，前提是它们适合新的条件。我们的研究结果强调了考虑潜在气候变化影响的土地利用规划的必要性，以确定在多种未来气候变化不确定性下生产水稻的最佳地区和种植系统。