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Increasing phosphorus fertilizer value of recycled iron phosphates by prolonged flooding and organic matter addition

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Pedosphere 2024年第3期34卷 631-640页

作者： Rochelle Joie SARACANLAO Hannah VAN RYCKEL Mieke VERBEECK Maarten EVERAERT erik smolders Division of Soil and Water Management Department of Earth and Environmental ScienceKU LeuvenKasteelpark Arenberg 20Heverlee B-3001(Belgium) Soil Service of Belgium.Willem De Croylaan 48 Leuven 3001(Belgium)

Iron(Fe)minerals are commonly used to remove phosphorus(P)from waste streams,producing P-loaded Fe(Ⅲ)oxides or Fe(Ⅱ)phosphate minerals(e.g.,vivianite).These minerals may be used as fertilizers to enhance P circularity if solubilized in ***,we tested the P fertilizer value of recycled Fe phosphates(FePs)in a pot trial and in an incubation experiment,hypothesizing that P release from FePs is possible under Fe(Ⅲ)-reducing ***,a pot trial was set up with rice(Oryza sativa)in all combinations of soil flooding or not,three P-deficient soils(acid,neutral,and calcareous),and six FePs(three Fe(Ⅲ)Ps and three Fe(Ⅱ)Ps)referenced to triple superphosphate(TSP)or zero *** P uptake responded to TSP application in all treatments but only marginally to *** redox potential did not decrease to-200 mV by flooding for a brief period(13 d)during the pot trial.A longer incubation experiment(60 d)was performed,including a treatment of glutamate addition to stimulate reductive conditions,and P availability was assessed with CaCl_(2)extraction of *** addition and/or longer incubation lowered soil redox potential to<-100 *** the longer term,Fe(Ⅲ)minerals released P,and adequate P was reached in the calcareous soil and in the neutral soil amended with Fe(Ⅲ)*** can be concluded that prolonged soil flooding and organic matter addition can enhance the P fertilizer efficiency of ***,application of FeP in powder form may enhance P availability.

关键词： agronomic efficiency circular economy P-loaded Fe(Ⅲ)oxides paddy soil vivianite redox

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Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning:From prediction to explainability

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Artificial Intelligence in Agriculture 2022年第1期6卷 230-241页

作者： Franck Albinet Yi Peng Tetsuya Eguchi erik smolders Gerd Dercon Independent Researcher&Consultant GuétharyFrance Soil and Water Management&Crop Nutrition Laboratory Joint FAO/IAEA Centre of Nuclear Techniques in Food and AgricultureSeibersdorfAustria Global Soil Partnership Food and Agriculture Organization of the United NationsViale delle Terme di Caracalla00153 RomeItaly Agricultural Radiation Research Center Tohoku Agricultural Research CenterNational Agriculture and Food Research OrganizationFukushimaJapan Soil and Water Management Unit KU LeuvenBelgium

The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in *** this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of *** Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting *** in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders.

关键词： High-throughput soil characterization Machine learning Convolutional neural network Agriculture Nuclear emergency response Remediation Interpretability

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