英文文献：Generating Plausible Crop Distribution Maps For Sub-Sahara Africa Using Spatial Allocation Model-利用空间分配模型生成撒哈拉以南非洲的合理作物分布图
英文文献作者：You, Liangzhi,Wood, Stanley,Wood-Sichra, Ulrike
英文文献摘要：
Spatial data, which are data that include the coordinates (either by latitude/longitude or by other addressing methods) on the surface of the earth, are essential for agricultural development. As fundamental parameters for agriculture policy research agricultural production statistics by geopolitical units such as country or sub-national entities have been used in many econometric analyses. However, collecting sub-national data is quite difficult in particular for developing countries. Even with great effort and only on regional scales, enormous data gaps exist and are unlikely to be filled. On the other hand, the spatial scale of even the subnational unit is relatively large for detailed spatial analysis. To fill these spatial data gaps we proposed a spatial allocation model. Using a classic cross-entropy approach, our spatial allocation model makes plausible allocations of crop production in geopolitical units (country, or state) into individual pixels, through judicious interpretation of all accessible evidence such as production statistics, farming systems, satellite image, crop biophysical suitability, crop price, local market access and prior knowledge. The prior application of the model to Brazil shows that the spatial allocation has relative good or acceptable agreement with actual statistic data. The current paper attempts to generate crop distribution maps for Sub-Sahara Africa for the year 2000 using the spatial allocation model. We modified the original model in the following three aspects: (1) Handle partial subnational statistics; (2) Include the irrigation map as another layer of information in the model; (3) Add subsistence portion of crops in addition to the existing three input and management levels (irrigated, high-input rainfed and low-input rainfed). With the modified spatial allocation model we obtain 5 by 5 minutes resolution maps for the following 20 major crops in Sub-Sahara Africa: Barley, Beans, Cassava, Cocoa, Coffee, Cotton, Cow Peas, Groundnuts, Maize, Millet, Oil Palm, Plantain, Potato, Rice, Sorghum, Soybeans, Sugar Cane, Sweet Potato, Wheat, Yam. This approach demonstrates that remote sensing technology such as satellite imagery could be quite useful in improved understanding of the spatial variation of land cover, agricultural production, and natural resources.

空间数据是包括地球表面的坐标(按纬度/经度或其他寻址方法)的数据，对农业发展至关重要。作为农业政策研究的基本参数，国家或地方实体等地缘政治单位的农业生产统计数据已被用于许多计量经济分析。然而，收集地方数据是相当困难的，特别是对于发展中国家。即使付出很大的努力，而且只是在区域范围内，仍然存在巨大的数据缺口，而且不太可能被填补。另一方面，即使是次国家单位的空间尺度对于详细的空间分析来说也比较大。为了填补这些空间数据空白，我们提出了一个空间分配模型。使用经典的熵方法,我们的空间分配模型使作物生产的合理分配地缘政治单位(国家,或国家)成单个像素,通过明智地解释所有可访问的证据,如生产统计、农业系统、卫星图像、作物生物物理适用性,农作物价格、当地市场准入和先验知识。模型在巴西的预先应用表明，空间分配与实际统计数据有较好的一致性或可接受的一致性。本论文试图利用空间分配模型绘制2000年撒哈拉以南非洲的作物分布图。我们从以下三个方面对原模型进行了修改:(1)处理部分地方统计;(2)在模型中加入灌溉图作为另一层信息;(3)在现有的三个投入和管理水平(灌溉、高投入雨养和低投入雨养)基础上增加作物的生存部分。利用改进的空间分配模型，我们获得了撒哈拉以南非洲20种主要作物的5×5分钟分辨率图:大麦、豆类、木薯、可可、咖啡、棉花、豇豆、花生、玉米、小米、油棕、大蕉、马铃薯、大米、高粱、大豆、甘蔗、甘薯、小麦、山药。这种方法表明，诸如卫星图像之类的遥感技术在改善对土地覆盖、农业生产和自然资源的空间变化的了解方面可能非常有用。