Abstract
Abstract
Background
Mapping of soil nutrients using different covariates was carried out in northern Morocco. This study was undertaken in response to the region's urgent requirement for an updated soil map. It aimed to test various covariates combinations for predicting the variability in soil properties using ordinary kriging and kriging with external drift.
Methods
A total of 1819 soil samples were collected at a depth of 0–40 cm using the 1-km grid sampling method. Samples were screened for their pH, soil organic matter (SOM), potassium (K2O), and phosphorus (P2O5) using standard laboratory protocols. Terrain attributes (T) computed using a 30-m resolution digital elevation model, bioclimatic data (C), and vegetation indices (V) were used as covariates in the study. Each targeted soil property was modeled using covariates separately and then combined (e.g., pH ~ T, pH ~ C, pH ~ V, and pH ~ T + C + V). k = tenfold cross-validation was applied to examine the performance of each employed model. The statistical parameter RMSE was used to determine the accuracy of different models.
Results
The pH of the area is slightly above the neutral level with a corresponding 7.82% of SOM, 290.34 ppm of K2O, and 100.86 ppm of P2O5. This was used for all the selected targeted soil properties. As a result, the studied soil properties showed a linear relationship with the selected covariates. pH, SOM, and K2O presented a moderate spatial autocorrelation, while P2O5 revealed a strong autocorrelation. The cross-validation result revealed that soil pH (RMSE = 0.281) and SOM (RMSE = 9.505%) were best predicted by climatic variables. P2O5 (RMSE = 106.511 ppm) produced the best maps with climate, while K2O (RMSE = 209.764 ppm) yielded the best map with terrain attributes.
Conclusions
The findings suggest that a combination of too many environmental covariates might not provide the actual variability of a targeted soil property. This demonstrates that specific covariates with close relationships with certain soil properties might perform better than the compilation of different environmental covariates, introducing errors due to randomness. In brief, the approach of the present study is new and can be inspiring to decision-makers in the region and other world areas as well.
Funder
Fakultu Agrobiologie, Potravinových a Prírodních Zdrojů, Česká Zemědělská Univerzita v Praze
Publisher
Springer Science and Business Media LLC
Subject
Ecological Modeling,Ecology
Reference50 articles.
1. Agyeman PC, Ahado SK, Borůvka L, Biney JKM, Sarkodie VYO, Kebonye NM, Kingsley J (2021) Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review. Environ Geochem Health 43:1715–1739. https://doi.org/10.1007/s10653-020-00742-9
2. Aksoy E, Panagos P, Montanarella L (2012) Spatial prediction of soil organic carbon of Crete by using geostatistics. In: Minasny B, Malone BP, McBratney AB, eds. Digital soil assessments and beyond—proceedings of the Fifth Global Workshop on digital soil mapping, pp 149–153. https://doi.org/10.1201/b12728-31
3. Allali A, Rezouki S, Lougraimzi H, Touati N, Eloutassi N, Fadli M (2020) Agricultural traditional practices and risks of using insecticides during seed storage in Morocco. Plant Cell Biotechnol Mol Biol 21(40):29–37
4. Amalu UC, Isong IA (2015) Land capability and soil suitability of some acid sand soil supporting oil palm (Elaeis guinensis Jacq) trees in Calabar, Nigeria. Niger J Soil Sci 25:92–109
5. Balkovič J, Rampašeková Z, Hutár V, Sobocká J, Skalský R (2013) Digital soil mapping from conventional field soil observations. Soil Water Res 8:13–25
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献