The use of chemical fertilizers is among the main innovations brought by the 1960s Green Revolution. In Ethiopia, fertilizer application during the last four decades has led to significant yield gains, yet yield remains below its potential across much of the country. One of the main challenges responsible for low yield response to fertilizer application has been the use of ‘blanket’ recommendations, whereby no tailoring of fertilizer amount and frequency is done based on soil requirements. As a result, the amount of fertilizer applied ranges widely, and can be either sub- or supra-optimal. There is thus an increasing need for site-specific fertilizer recommendations which take into account site characteristics such as climate variables (temperature, rainfall, and solar radiation); soil factors (soil organic carbon, moisture, pH, texture, cation exchange capacity, and level of macro- and micronutrients); and topographic position indices. This article reports on a data-mining approach we developed on a large dataset of 6585 wheat (Triticum aestivum) field trials. The dataset includes detailed, site-specific biophysical variables to create nutrient response functions that can guide optimal site-specific fertilizer application. The approach used a machine-learning model (random forest) to capture the relationship between nutrients – nitrogen (N), phosphorous (P), potassium (K), and sulfur (S) – and wheat yield. The model explained about 83, 82, 47, and 69% of variances of yield for N, P, K, and S omission, respectively, with consistent performance across training and testing datasets. Expectedly, for N and P omission data, the most important explanatory variables are nutrient rate, followed by soil organic carbon and soil pH. For K and S, however, climatic variables played an important role alongside nutrient rates. The site-specific yield–fertilizer response curves derived from our model are highly variable from location to location, as they are affected by the climatic, soil, or topographic conditions of the site. Importantly, using principal component analysis, we showed that the shape of the fertilizer response curves is a result of the multiple environmental factors (including soil, topography, and climate) that are at play at a given site, rather than of a specific dominant one. The research output is expected to respond to the national policy demands for a sound method to identify the optimal fertilizer rate to increase economic returns of fertilizer investments and take fertilizer utilization research one step further.

化肥的使用是 1960 年代绿色革命带来的主要创新之一。在埃塞俄比亚，过去 40 年中施肥使单产显着提高，但该国大部分地区的单产仍低于其潜力。造成对施肥的低产量反应的主要挑战之一是使用“一揽子”建议，即没有根据土壤要求调整肥料的用量和频率。因此，施肥量的范围很广，可以是次优的，也可以是超优的。因此，越来越需要考虑到气候变量（温度、降雨量和太阳辐射）等场地特征的场地特定肥料建议；土壤因素（土壤有机碳、水分、pH、质地、阳离子交换能力，以及大量和微量营养素的水平）；和地形位置指数。本文报告了我们在 6585 小麦的大型数据集上开发的数据挖掘方法（小麦) 田间试验。该数据集包括详细的、特定地点的生物物理变量，以创建可以指导最佳特定地点施肥的营养响应函数。该方法使用机器学习模型（随机森林）来捕捉养分（氮 (N)、磷 (P)、钾 (K) 和硫 (S)）与小麦产量之间的关系。该模型分别解释了 N、P、K 和 S 遗漏的约 83%、82%、47% 和 69% 的产量方差，在训练和测试数据集上具有一致的性能。预计，对于 N 和 P 遗漏数据，最重要的解释变量是养分率，其次是土壤有机碳和土壤 pH 值。然而，对于 K 和 S，气候变量与营养率一起发挥了重要作用。从我们的模型中得出的特定地点的产量-肥料响应曲线因地点而异，因为它们受到地点的气候、土壤或地形条件的影响。重要的是，使用主成分分析，我们表明肥料响应曲线的形状是在给定地点起作用的多种环境因素（包括土壤、地形和气候）的结果，而不是特定的主导因素. 研究成果有望响应国家政策要求，找到合理的最佳施肥用量方法，提高化肥投资的经济效益，使化肥利用研究更上一层楼。重要的是，使用主成分分析，我们表明肥料响应曲线的形状是在给定地点起作用的多种环境因素（包括土壤、地形和气候）的结果，而不是特定的主导因素. 该研究成果有望响应国家政策要求，提出合理的方法确定最佳施肥量，以提高肥料投资的经济效益，使肥料利用研究更上一层楼。重要的是，使用主成分分析，我们表明肥料响应曲线的形状是在给定地点起作用的多种环境因素（包括土壤、地形和气候）的结果，而不是特定的主导因素. 该研究成果有望响应国家政策要求，提出合理的方法确定最佳施肥量，以提高肥料投资的经济效益，使肥料利用研究更上一层楼。