Kosmowski Frédéric, Abebe Ayale, Ozkan Daglar
CGIAR Standing Panel on Impact Assessment, Addis Ababa, Ethiopia.
National Soil Testing Center, Addis Ababa, Ethiopia.
Geoderma. 2020 Oct 1;375:114500. doi: 10.1016/j.geoderma.2020.114500.
While the importance of soils in agriculture cannot be overlooked, plot level soil data remain scarce in the current data landscape. Large-scale household surveys efforts are increasing in low-income countries and assessing the accuracy, scalability and cost-effectiveness of available methods is crucial. Here, we firstly explore soil data requirements for a set of objectives that include identifying a soil constraint, improving recommendation domain studies and capturing soil metrics as covariates, or as outcomes. We then expose the lessons learned from a methodological experiment in rural Ethiopia, where different approaches - farmer's self-elicitation and miniaturized spectrometers - are compared against laboratory benchmarks for a set of soil parameters: soil texture, soil pH and soil organic C. With the exception of soil particle sizes, we find that soil parameters captured through farmer's elicitation do not converge with objective metrics. Miniaturized spectrometers can provide reasonably accurate data for the identification of soil constraints - soil acidity, low organic C or sandy soils. Approximate quantitative predictions can also be delivered for soil pH (R = 0.72) and organic C (R = 0.60). The additional costs of plot sampling and analysis are in the range of $19-$23 per sample, with the additional percentage of plots with correct data equivalent to 10% for the identification of sandy soils, 75% for low organic C and 89% of acidic soils.
虽然土壤在农业中的重要性不容忽视，但在当前的数据环境中，地块层面的土壤数据仍然稀缺。低收入国家的大规模家庭调查工作正在增加，评估现有方法的准确性、可扩展性和成本效益至关重要。在这里，我们首先探讨了一组目标的土壤数据需求，这些目标包括识别土壤限制因素、改进推荐领域研究以及将土壤指标作为协变量或结果进行记录。然后，我们介绍了在埃塞俄比亚农村进行的一项方法学实验中吸取的经验教训，在该实验中，将不同的方法——农民自我评估和小型光谱仪——与一组土壤参数（土壤质地、土壤pH值和土壤有机碳）的实验室基准进行了比较。除了土壤颗粒大小外，我们发现通过农民评估获得的土壤参数与客观指标不一致。小型光谱仪可以为识别土壤限制因素（土壤酸度、低有机碳或沙质土壤）提供相当准确的数据。还可以对土壤pH值（R = 0.72）和有机碳（R = 0.60）进行近似定量预测。地块采样和分析的额外成本在每个样本19美元至23美元之间，对于识别沙质土壤，具有正确数据的地块额外比例为10%，对于低有机碳土壤为75%，对于酸性土壤为89%。
