Meeting the burgeoning global demand for both food and energy requires substantial yield increases through the efficient use of inputs like fertilizers. Prompted by the result of a soil survey expedition in the late 1950s, which signaled a widespread deficiency of nitrogen (N) and phosphorus (P), plant nutrition research in Ethiopia began in the 1960s, focusing on the response of prioritized cereals – tef (Eragrostis tef), wheat (Triticum aestivum), and maize (Zea mays) – to the application of N and P fertilizers. Nationwide on-farm trials conducted in the early 1970s led to a blanket recommendation of 64 kg N ha−1 and 20 kg P ha−1, irrespective of the crop and soil types, which were applied in the form of di-ammonium phosphate (18-46-0) and urea (46-0-0), respectively. Research conducted in the 1980s across agro-ecological and edaphic spectrum recommended 30–138 kg N ha−1 and 0–50 kg P ha−1, respectively. However, studies show that only 30–40% of the smallholder farmers use fertilizers at a rate less than recommended (on average at 37–40 kg ha−1). This rate reflects limited supply, high prices, and the low and declining crop response to fertilizers. As a result, cereal yields increased only 10% despite a fivefold increase in fertilizer application since the 1980s. Owing to the limited and declining crop response and the increased price of fertilizer in the 1990s, research on the integrated application of inorganic and organic sources of fertilizers was initiated. Although the integrated use resulted in increased yield and better economic benefits, it was not mainstreamed into the national agricultural extension system. The soil survey expedition that began in 2011 culminated in the mapping of the soil nutrient status using literature-based critical limits. The maps have persistently revealed the deficiency of N, P, potassium, sulfur, zinc, and boron across the surveyed areas. Despite the above efforts, the data sets generated through the soil surveys conducted at different times during the last half-century and the agronomic research during the same period have never been fully exploited. It is believed that the recent development in data mining and machine-learning approaches creates the opportunities to use the data sets in conjunction with other covariates in order to generate evidence that helps to make better decisions both at strategic and operational levels. The development of decision support tools based on such large datasets and analytical capacity is believed to facilitate better-informed decisions that lead to increased resource use efficiency and sustainability.

满足全球对粮食和能源日益增长的需求需要通过有效使用化肥等投入物来大幅提高产量。受 1950 年代后期土壤调查考察结果的影响，这表明氮 (N) 和磷 (P) 普遍缺乏，埃塞俄比亚的植物营养研究于 1960 年代开始，重点关注优先谷物——tef (tef) 的反应。Eragrostis tef )、小麦 ( Triticum aestivum ) 和玉米 ( Zea mays ) – 用于施用 N 和 P 肥料。1970 年代初期进行的全国性农场试验得出了 64 kg N ha -1和 20 kg P ha -1的全面建议，不考虑作物和土壤类型，分别以磷酸二铵 (18-46-0) 和尿素 (46-0-0) 的形式施用。1980 年代对农业生态和土壤光谱进行的研究分别推荐 30-138 kg N ha -1和 0-50 kg P ha -1。然而，研究表明，只有 30–40% 的小农使用化肥的比例低于推荐值（平均为 37–40 kg ha -1）。这一比率反映了供应有限、价格高企以及作物对化肥的低反应和下降反应。结果，尽管自 1980 年代以来施肥量增加了五倍，但谷物产量仅增加了 10%。由于 1990 年代作物反应有限和下降以及肥料价格上涨，开始了无机和有机肥料综合施用的研究。综合利用虽然提高了产量和经济效益，但并未纳入国家农业推广体系的主流。2011 年开始的土壤调查考察最终使用基于文献的临界限值绘制土壤养分状况图。这些地图持续显示整个调查区域的氮、磷、钾、硫、锌和硼的缺乏。尽管做出了上述努力，但通过过去半个世纪不同时间进行的土壤调查和同期农艺研究产生的数据集从未得到充分利用。人们相信，数据挖掘和机器学习方法的最新发展创造了将数据集与其他协变量结合使用的机会，以生成有助于在战略和运营层面做出更好决策的证据。基于如此大的数据集和分析能力开发决策支持工具被认为有助于做出更明智的决策，从而提高资源使用效率和可持续性。过去半个世纪不同时期进行的土壤调查和同期农艺研究所产生的数据集从未得到充分利用。人们相信，数据挖掘和机器学习方法的最新发展创造了将数据集与其他协变量结合使用的机会，以生成有助于在战略和运营层面做出更好决策的证据。基于如此大的数据集和分析能力开发决策支持工具被认为有助于做出更明智的决策，从而提高资源使用效率和可持续性。过去半个世纪不同时期的土壤调查和同一时期的农艺研究所产生的数据集从未得到充分利用。人们相信，数据挖掘和机器学习方法的最新发展创造了将数据集与其他协变量结合使用的机会，以生成有助于在战略和运营层面做出更好决策的证据。基于如此大的数据集和分析能力开发决策支持工具被认为有助于做出更明智的决策，从而提高资源使用效率和可持续性。人们相信，数据挖掘和机器学习方法的最新发展创造了将数据集与其他协变量结合使用的机会，以生成有助于在战略和运营层面做出更好决策的证据。基于如此大的数据集和分析能力开发决策支持工具被认为有助于做出更明智的决策，从而提高资源使用效率和可持续性。人们相信，数据挖掘和机器学习方法的最新发展创造了将数据集与其他协变量结合使用的机会，以生成有助于在战略和运营层面做出更好决策的证据。基于如此大的数据集和分析能力开发决策支持工具被认为有助于做出更明智的决策，从而提高资源使用效率和可持续性。