Using Data Mining to Predict Soil Quality after Application of Biosolids in Agriculture

Cortet, Jérôme; Kocev, Dragi; Ducobu, Caroline; Džeroski, Sašo; Debeljak, Marko; Schwartz, Christophe

doi:10.2134/jeq2011.0155

Cited by 10 publications

(1 citation statement)

References 45 publications

(61 reference statements)

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“…To address the second goal, we propose the use of datamining techniques (Cortet et al, 2011;Debeljak and Džeroski, 2011), which have proved useful for this kind of problem in the field of agricultural and environmental sciences (Debeljak et al, 2007(Debeljak et al, , 2008Trajanov, 2011 No. 1107/2009(FOCUS, 2001.…”

Section: Related Workmentioning

confidence: 99%

Estimating Drainage Periods for Agricultural Fields from Measured Data: Data‐Mining Methodology and a Case Study (La JailliÈRe, France)

Trajanov

Kuzmanovski

Leprince

et al. 2015

Irrigation and Drainage

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The identification of intensive drainage periods is important for determining mitigation strategies for protecting water against pollution with plant protection products (PPPs). Most attempts to estimate the start, duration and the end of a drainage period are based either on mechanistic modelling approaches or on empirical knowledge about tile drainage. Mechanistic modelling requires many parameters, while the empirical approach does not allow for making the simulations and predictions needed for proposing reliable mitigation measures. In order to complement these two approaches, we have used a data-mining approach on data from 25 (1987-2011) agricultural seasons (campaigns) from the experimental station La Jaillière, France. The models for estimating the start and the end of the intensive drainage period for a particular campaign have the form of decision trees and tell us which factors influence these dates the most. The start of a drainage period depends mostly on the cumulative drainage and the cumulative rainfall since the beginning of the campaign and the average air temperature of the last 7 days. For estimating the end of a drainage period, the most important variables are the cumulative rainfall of the last 7 days and the average air temperature of the following 7 days. Copyright © 2015 John Wiley & Sons, Ltd.key words: tile drainage; drainage period; empirical data; data mining; decision trees Received 13 January 2014; Revised 16 January 2015; Accepted 16 January 2015 RÉSUMÉ L'identification des périodes d'écoulement, et plus particulièrement la saison de drainage intense, est importante pour mettre en oeuvre des stratégies de limitation du risque de transfert des produits phytosanitaires dans les eaux. La plupart des démarches appliquées pour estimer le début, la durée et la fin de la période de drainage sont basées sur des modèles mécanistes ou sur la connaissance empirique du fonctionnement des réseaux de drainage. Les modèles mécanistes requièrent généralement de nombreux paramètres, et l'approche empirique ne permet pas de faire des simulations et des prédictions, indispensables pour la mise en place de mesures efficaces d'atténuation du risque. Dans le but de compléter ces deux approches, nous avons utilisé une méthodologie basée sur la fouille de données (data mining), en valorisant les informations recueillies durant 25 campagnes (1987 à 2011) par ARVALIS Institut du végétal sur le dispositif expérimental de La Jaillière à la Chapelle-Saint-Sauveur (Ouest de la France). Les modèles développés pour simuler les dates de début et fin de drainage prennent la forme d'arbres de décision qui hiérarchisent les facteurs ayant le plus d'influence sur la détermination de ces dates. Ainsi, la date de début du drainage dépend principalement de la quantité de pluies cumulées depuis le début de la campagne (1er septembre), mais aussi du cumul d'eau drainée, ainsi que de la température moyenne durant les sept jours précédents. La simulation de la date

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Section: Related Workmentioning

confidence: 99%

Estimating Drainage Periods for Agricultural Fields from Measured Data: Data‐Mining Methodology and a Case Study (La JailliÈRe, France)

Trajanov

Kuzmanovski

Leprince

et al. 2015

Irrigation and Drainage

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Heavy metals in composts of China: historical changes, regional variation, and potential impact on soil quality

Ding

Liu

et al. 2016

Environ Sci Pollut Res

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Composts are considered one of major sources that contribute heavy metals to the environment. The objective of this study was to investigate historical changes and spatial variation of metal concentrations in Chinese composts by analysing representative compost samples and published data from 2002 to 2013. Mean concentrations of cadmium (Cd), chromium (Cr), lead (Pb), arsenic (As), mercury (Hg), zinc (Zn), nickel (Ni), and copper (Cu) in the composts were 2.42, 35.52, 32.38, 16.33, 0.32, 258.1, 9.71, and 72.24 mg kg, respectively. The percentages of samples with metal concentration exceeding critical levels of Chinese standards for organic fertilizers (NY525-2012) were 28.7, 18.3, 9.6, 1.7, and 0.9%, respectively, for Cd, As, Pb, Cr, and Hg. Modelling predicts that it would take 18, 24, 29, and 48 years for Cu, Zn, Hg, and Cd to reach Category I Environmental Capacity of China (GB15618-1995) according to current application rate. During the 2002-2013 period, concentration of Cd in composts tended to decrease with time, whereas those of Zn, Cu, and As were opposite, which is likely due to less or no control for these elements. Concentrations of Cu, Zn, As, and Cd were generally higher in composts from the economically developed regions in China, where more commercial farms were built that use metal-spiked feeds. Manures from the commercial farms generally had higher metal concentration, as compared to the subsistence farms. Further research is needed to monitor metal concentrations from source materials to composts and assess long-term impact of compost application on soil quality.

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Long-term impact of 19 years’ farmyard manure or sewage sludge application on the structure, diversity and density of the protocatechuate-degrading bacterial community

Azhari

Lainé

Sappin‐Didier³

et al. 2012

Agriculture, Ecosystems & Environment

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Using Data Mining to Predict Soil Quality after Application of Biosolids in Agriculture

Cited by 10 publications

References 45 publications

Estimating Drainage Periods for Agricultural Fields from Measured Data: Data‐Mining Methodology and a Case Study (La JailliÈRe, France)

Estimating Drainage Periods for Agricultural Fields from Measured Data: Data‐Mining Methodology and a Case Study (La JailliÈRe, France)

Heavy metals in composts of China: historical changes, regional variation, and potential impact on soil quality

Long-term impact of 19 years’ farmyard manure or sewage sludge application on the structure, diversity and density of the protocatechuate-degrading bacterial community

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