Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus

Agriculture, Journal Year: 2022, Volume and Issue: 12(7), P. 1062 - 1062

Published: July 20, 2022

Predicting soil chemical properties such as organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC Ava-P influenced by both natural anthropogenic factors. This study aimed at (1) predicting a piedmont plain Northeast Iran using the Random Forests (RF) Cubist mathematical models hybrid (Regression Kriging), (2) comparing models’ results, (3) identifying key variables that influence spatial dynamics under agricultural practices. machine learning were trained with 201 composite surface samples 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) features (S) according to SCORPAN digital mapping framework, which can predictively represent formation factors spatially. Clay, one most well-known relationship SOC, was important predictor followed open-access multispectral satellite images-based vegetation indices. had similar set effective variables. Hybrid approaches did not improve model accuracy significantly, but they reduce map uncertainty. In validation set, calculated RF algorithm normalized root mean square (NRMSE) 96.8, while an NRMSE 94.2. These values change when technique for Ava-P; however, changed just 1% SOC. management supply activities be guided maps. Produced maps scientist plays active role used identify concentrations are high need protected, uncertainty sampling required further monitoring.

Language: Английский

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Spatial assessment of topsoil zinc concentrations in Europe

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 892, P. 164512 - 164512

Published: June 1, 2023

Zinc (Zn) is essential to sustain crop production and human health, while it can be toxic when present in excess. In this manuscript, we applied a machine learning model on 21,682 soil samples from the Land Use Coverage Area frame Survey (LUCAS) topsoil database of 2009/2012 assess spatial distribution Europe Zn concentrations measured by aqua regia extraction, identify influence natural drivers anthropogenic sources concentrations. As result, map was produced showing at resolution 250 m. The mean predicted concentration 41 mg kg-1, with root squared error around 40 kg-1 calculated for independent samples. We identified clay content as most important factor explaining overall Europe, lower coarser soils. Next texture, low were found soils pH (e.g. Podzols), well above 8 (i.e., Calcisols). presence deposits mining activities mainly explained occurrence relatively high 167 (the one percentile highest concentrations) within 10 km these sites. addition, higher levels grasslands regions livestock density may point manure significant source developed study used reference eco-toxicological risks associated areas deficiency. provide baseline future policies context pollution, nutrition.

Language: Английский

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Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102294 - 102294

Published: Sept. 11, 2023

Language: Английский

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Realising the circular phosphorus economy delivers for sustainable development goals

npj Sustainable Agriculture, Journal Year: 2023, Volume and Issue: 1(1)

Published: Oct. 4, 2023

Abstract While it remains debated if mineral deposits mined for phosphorus fertilizer are running out, insecurity is an emerging global issue. We explore how linked to the current linear economy (LPE) and historic implications. The problems multifold: there geopolitical concerns over held only by a few nations, sharply rising costs of fertilizers, heavy metal contaminants affecting soil food, problematic mining wastes, environmental degradation caused inefficiencies. Here we argue that new can resolve these problems. Transitioning sustainable use demands circular (CPE). A CPE supports several Sustainable Development Goals enables greater autonomy. illustrate with case studies outline opportunities change. will feature recovery facilities, waste valorisation technologies, improved formulations customized target crops crop systems. highlight examples rapidly advancing essential agriculture.

Language: Английский

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Spatial prediction of soil properties using random forest, k-nearest neighbors and cubist approaches in the foothills of the Ural Mountains, Russia

Modeling Earth Systems and Environment, Journal Year: 2023, Volume and Issue: 9(3), P. 3461 - 3471

Published: Jan. 25, 2023

Language: Английский

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Ensemble Machine Learning Geostatistical Hybrid Models for Grade Control

Mathematical Geosciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Language: Английский

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Bayesian calibration of management practices for a crop model implemented in a subsistence farming region

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 164, P. 127524 - 127524

Published: Feb. 4, 2025

Language: Английский

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A China dataset of soil properties for land surface modelling (version 2, CSDLv2)

Earth system science data, Journal Year: 2025, Volume and Issue: 17(2), P. 517 - 543

Published: Feb. 7, 2025

Abstract. Accurate and high-resolution spatial soil information is crucial for efficient sustainable land use, management, conservation. Since the establishment of digital mapping (DSM) GlobalSoilMap working group, significant advances have been made in terms availability quality globally. However, accurately predicting variation over large complex areas with limited samples remains a challenge, especially China, which has diverse landscapes. To address this we utilised 11 209 representative multi-source legacy profiles (including Second National Soil Survey World Information Service, First regional databases) soil-forming environment characterisation. Using advanced ensemble machine learning high-performance parallel-computing strategy, developed comprehensive maps 23 physical chemical properties at six standard depth layers from 0 to 2 m China 90 resolution (China dataset surface modelling version 2, CSDLv2). Data-splitting independent-sample validation strategies were employed evaluate accuracy predicted maps' quality. The results showed that significantly more accurate detailed compared traditional type linkage methods (i.e. CSDLv1, first dataset), SoilGrids 2.0, HWSD 2.0 products, effectively representing across China. prediction all intervals ranged good moderate, median model efficiency coefficients most ranging 0.29 0.70 during data-splitting 0.25 0.84 validation. wide range between 5 % lower 95 upper limits may indicate substantial room improvement current predictions. relative importance environmental covariates predictions varied property depth, indicating complexity interactions among multiple factors formation processes. As used study mainly originate conducted 1970s 1980s, they could provide new perspectives on changes, together existing based 2010s. findings make important contributions project can also be Earth system better represent role hydrological biogeochemical cycles This freely available https://www.scidb.cn/s/ZZJzAz (last access: 17 November 2024​​​​​​​) or https://doi.org/10.11888/Terre.tpdc.301235 (Shi Shangguan, 2024).

Language: Английский

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An ensemble machine learning bioavailable strontium isoscape for Eastern Canada

FACETS, Journal Year: 2025, Volume and Issue: 10, P. 1 - 17

Published: Jan. 1, 2025

Bioavailable strontium isotope ratios ( 87 Sr/ 86 Sr) distribution across the landscape mainly follow underlying lithology, making Sr baseline maps (isoscapes) powerful tools for provenance studies. has already been used in Eastern Canada (EC) to track food and human remains origins, or reconstruct animal mobility. While bioavailable isoscapes EC can be extrapolated from global datasets using random forest modelling (RF), no regionally calibrated isoscape exists. Here, we produce a by analysing plants collected at 136 sites EC, incorporating updated geological variables applying novel ensemble machine learning (EML) framework. We generated compared traditional RF EML approaches. Adding local dataset significantly improved model prediction with drastic increase of predicted increased spatial uncertainty northern Canadian craton. produced similar predictions but tighter distribution. Regionally outperformed isoscape, confirming requirement collecting data data-poor regions. This provides monitor manage movements agricultural products, natural resources, endangered/harmful migratory species, archaeological artifacts.

Language: Английский

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The global land-water-climate nexus of drought-tolerant succulent plants for bioenergy in abandoned croplands and arid marginal lands

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 379, P. 124747 - 124747

Published: March 6, 2025

Language: Английский

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