Abstract. A major obstacle to selecting the most appropriate crops and closing the yield gap in many areas of the world is a lack of site-specific soil information. Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields. However, acquiring accurate soil information is often difficult due to the high spatial and temporal variability of soil properties at fine scales and the cost and inaccessibility of laboratory-based soil analyses. With recent advancements in predictive soil mapping, there is a growing expectation that soil map predictions can provide much of the information needed to inform soil management. Yet, it is unclear how accurate current soil map predictions are at scales relevant to management. The main objective of this study was to address this issue by evaluating the site-specific accuracy of regional-to-global soil maps, using Ghana as a test case. Four web-based soil maps of Ghana were evaluated using a dataset of 6514 soil profile descriptions collected on smallholder farms using the LandPKS mobile application. Results from this study revealed that publicly available soil maps in Ghana lack the needed accuracy (i.e., correct identification of soil limitations) to reliably inform soil management decisions at the 1–2 ha scale common to smallholders. Standard measures of map accuracy for soil texture class and rock fragment class predictions showed that all soil maps had similar performance in estimating the correct property class. Overall soil texture class accuracies ranged from 8 %–14 % but could be as high as 38 %–64 % after accounting for uncertainty in the evaluation dataset. Soil rock fragment class accuracies ranged from 26 %–29 %. However, despite these similar overall accuracies, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps. To better understand the functional implications of these soil property differences, we used a modified version of the FAO Global Agro-Ecological Zone (GAEZ) soil suitability modeling framework to derive soil suitability ratings for each soil data source. Using a low-input, rain-fed, maize production scenario, we evaluated the functional accuracy of map-based soil property estimates. This analysis showed that soil map data significantly overestimated crop suitability for over 65 % of study sites, potentially leading to ineffective agronomic investments by farmers, including cash-constrained smallholders.

中文翻译：

---

用于农场决策的区域到全球土壤图的准确性：土壤图“足够好”吗？

摘要。在世界许多地区选择最合适的作物和缩小产量差距的一个主要障碍是缺乏特定地点的土壤信息。关于土壤特性的准确信息对于确定土壤限制和提高作物产量所需的管理实践至关重要。然而，由于精细尺度下土壤特性的高空间和时间变异性以及基于实验室的土壤分析的成本和不可获得性，获取准确的土壤信息通常很困难。随着预测性土壤绘图的最新进展，人们越来越期望土壤图预测可以提供土壤管理所需的大量信息。然而，目前尚不清楚当前土壤地图预测在与管理相关的尺度上有多准确。本研究的主要目的是以加纳作为测试案例，通过评估区域到全球土壤地图的特定地点准确性来解决这个问题。使用 LandPKS 移动应用程序在小农农场收集的 6514 份土壤剖面描述数据集评估了加纳的四张基于网络的土壤地图。这项研究的结果表明，加纳公开可用的土壤地图缺乏所需的准确性（即正确识别土壤限制），无法可靠地为小农常见的 1-2 公顷规模的土壤管理决策提供信息。土壤质地类别和岩石碎片类别预测的地图精度标准测量表明，所有土壤地图在估计正确的属性类别方面具有相似的性能。整体土壤质地类别的准确度范围为 8%–14%，但在考虑评估数据集中的不确定性后可能高达 38%–64%。土壤岩石碎片类精度范围为 26%–29%。然而，尽管总体精度相似，但四张地图在土壤特性预测方面存在显着差异，突出表明土壤地图误差在地图之间并不一致。为了更好地理解这些土壤特性差异的功能影响，我们使用了粮农组织全球农业生态区 (GAEZ) 土壤适宜性建模框架的修改版本来得出每个土壤数据源的土壤适宜性等级。使用低投入、雨养、玉米生产情景，我们评估了基于地图的土壤特性估计的功能准确性。