Accuracy of regional-to-global soil maps for on-farm decision making: Are soil maps “good enough”?

TitleAccuracy of regional-to-global soil maps for on-farm decision making: Are soil maps “good enough”?
Publication TypeJournal Article
Year of Publication2022
AuthorsMaynard J, Yeboah E, Owusu S, Buenemann M., Neff J., Herrick JE
Date Published5/19/2022
ARIS Log Number384883
Keywordsaccuracy, decision making, on-farm, regional-to-global, soil maps

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 6,514 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 showed that all soil maps had similar performance in estimating the correct property class, with overall accuracies ranging from 8–39 % for soil texture classes and 26–33 % for soil rock fragment classes. Furthermore, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps despite their similar overall accuracies. 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 modelling 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.