Developing pedotransfer functions to harmonize extractable soil phosphorus content measured with different methods: A case study across the mainland of France

Autor: Hocine Bourennane, Bifeng Hu, Pascal Denoroy, Dominique Arrouays, Nicolas Saby, Blandine Lemercier
Přispěvatelé: InfoSol (InfoSol), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Science du Sol (Orléans) (URSols), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Geoderma
Geoderma, Elsevier, 2021, 381, pp.114645. ⟨10.1016/j.geoderma.2020.114645⟩
ISSN: 0016-7061
1872-6259
DOI: 10.1016/j.geoderma.2020.114645⟩
Popis: International audience; Phosphorus (P) is a nutrient essential to living organisms and ecosystems. Accurate information regarding extractable soil P is necessary for agricultural management and environmental quality. Direct measurements of extractable soil P at large scales are usually impeded by considerable time, labour, and economic resources required for implementation. To meet agronomic and environmental monitoring needs, multiple extraction methods have been developed worldwide to estimate the different components of soil P. In France, three extraction methods are used, namely the Dyer method for acidic soils, Joret-Hébert for calcareous soils, and Olsen for all soils. Therefore, it is difficult to compare data obtained nationwide for monitoring purposes. Consequently, it is of significant importance to develop pedotransfer functions (PTFs) to harmonise extractable soil P data obtained from different extraction methods with the assistance of other easily available predictors from soil information systems. In this study, we used an extensive dataset from the French soil-monitoring programme for the calibration and evaluation of PTFs. We implemented the partial least squares regression to relate extractable P measured by the Dyer or Joret-Hébert method to extractable P determined by the Olsen method considering 14 soil properties (total P2O5, pH, cation exchange capacity (CEC), CaCO3, soil texture (clay, silt and sand contents), total organic carbon, and exchangeable Fe, Al, CaO, Mn, MgO, and K2O). We constructed patrimonial models by selecting the most important predictors. According to the results of 10 iterations cross-validation, the average R2, root mean-square error (RMSE), and mean error (ME) of the PTF of calcareous soils were 0.66, 25.81, and −0.11 mg kg−1, whereas those of acidic soils were 0.70, 24.02, and −0.87 mg kg1, respectively. The Joret-Hébert P2O5, silt, pH, total P2O5, CEC, and K were the most important predictors for estimating Olsen P2O5 in calcareous soils, whereas Dyer P2O5, exchangeable Al, K, and pH were the most important predictors for estimating Olsen P2O5 in acidic soils. We observed that the explanatory power of the soil properties was more important in calcareous than in acidic soils. As expected, the proxies of Olsen P2O5, namely, Dyer P2O5 and Joret-Hébert P2O5, were the most important variables in modelling Olsen P2O5 variations. In addition, the relationship between Olsen P2O5 and Dyer P2O5 was much stronger than that between Olsen P2O5 and Joret-Hébert P2O5. The results confirmed the feasibility of estimating extractable P in soil by PTFs that were constructed using statistical methods, such as partial least squares regression. The addition of more predictors that are related to agricultural practices and topography attributes may improve the prediction accuracy.
Databáze: OpenAIRE
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