Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation

Bondi, Giulia; Creamer, Rachel; Ferrari, Alessio; Fenton, Owen; Wall, David P.

doi:10.1016/j.geoderma.2017.11.035

Cited by 38 publications

(15 citation statements)

References 34 publications

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“…There are many positive examples of using data mining techniques for building predictive models in the field of agricultural and environmental sciences (Bondi et al 2018;Bui et al 2009;Debeljak et al 2007Debeljak et al , 2008Goldstein et al 2017;Kuzmanovski et al 2015;Shekoofa et al 2014;Trajanov 2011). Their biggest advantage is that they are applied on easily obtainable empirical data, and the parametrization of the data mining models is done automatically from the data; hence it is not influenced by the subjectivity of the modelers.…”

Section: Introductionmentioning

confidence: 99%

Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria

et al. 2018

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Primary productivity is in the foundation of farming communities. Therefore, much effort is invested in understanding the factors that influence the primary productivity potential of different soils. The International Long-Term Ecological Research (ILTER) is a network that enables valuable comparisons of data in understanding environmental change. In this study, we investigate three ILTER cropland sites and one long-term field experiment (LTE) outside of the ILTER network. The focus is on the influence of different management practices (tillage, crop residue incorporation, and compost amendments) on primary productivity. Data mining analyses of the experimental data were carried out in order to investigate trends in the productivity data. We generated predictive models that identify the influential factors that govern primary productivity. The data mining models achieved very high predictive performance (r > 0.80) for each of the sites. Preceding crop and crop of the current year were crucial for primary productivity in the tillage LTE and compost LTE, respectively. For both crop residue incorporation LTEs, plant-available Mg affected productivity the most, followed by properties such as soil pH, SOM, and the crop residue management. The results obtained by data mining are in line with previous studies and enhance our knowledge about the driving forces of primary productivity in arable systems. Hence, the models are considered very suitable and reliable for predicting the primary productivity at these ILTER sites in the future. They may also encourage researcherfarmer-advisor-stakeholder interaction, and thus create enabling environment for cooperation for further research around these ILTER sites.

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Section: Introductionmentioning

confidence: 99%

Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria

et al. 2018

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“…This parameter is often used in agronomic studies, as it indicates the presence of compacted layers. As such, it is commonly considered as a suitable trait for efficient measurement of soil carbon and nutrient stocks (Bondi et al, 2018). There is only little knowledge about bulk density, since the measurement of this parameter is demanding, as it is pointed out in the work by Premrov et al (2017).…”

Section: Resultsmentioning

confidence: 99%

Functional diversity of soil microorganisms in the conditions of an ecological farming system

Fazekašová

Fazekaš

2019

Folia Oecologica

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In the current study, we investigate the relationships among the soil functional diversity, physicochemical properties and heavy metals presence in an ecological farming system. The soil samples were collected from permanent research sites, from A soil horizons, at a depth from 0.05 m to 0.15 m, in June 2018. In fresh soil samples, we evaluated the metabolic profiles of their microbial communities, using Biolog® Eco Plates. The research showed that the soil physical properties got adjusted after a long-term application of an ecological farming system and that the measured values were stabilised, reaching the levels comparable with the average values for the relevant soil type. It is necessary to devote a continual attention to soil reaction, because soil is naturally acidified through acid atmospheric fallout as well as through calcium uptake-off by plants. The values of the selected heavy metals in the monitored period did not exceed the limit values specified in the Act No. 220/2004 Coll. Based on the results of Shannon’s diversity, we can conclude that the diversity in the investigated sites was low, from moderate to medium. The differences (3.26–3.36) among all 11 study localities were very small and not significant. There were determined the correlations between the soil functional diversity, soil physicochemical properties, and heavy metal contents. The average well colour development (AWCD) positively correlated with soil reaction and with Mg content and significantly negatively correlated with contents of Hg, Zn and Cu; equitability significantly positively correlated with soil reaction, Mg, AWCD and Shannon’s diversity. Spearman’s correlation coefficients confirmed the positive correlation between Shannon’s diversity and soil reaction, AWCD and Mg. In our study, no correlation was found between the functional diversity of microorganisms and the soil physical properties.

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“…This can partly be solved by weighing the different responses, as by Rutgers et al (2012). Machine learning is an alternative way of obtaining domain knowledge from empirical data (Trajanov et al, 2015(Trajanov et al, , 2018Idé, 2016;Bondi et al, 2018). Machine learning algorithms for rule and tree induction are a useful framework for extracting knowledge from data and representing it in a format that can be directly used in constructing decision support models.…”

Section: Combining Expert Knowledge With Machine Learningmentioning

confidence: 99%

“…This includes important attributes that can be used to optimize predictions, such as on primary productivity. Machine learning has now been utilized (i) to predict single soil attributes or study what governs them (Hobley et al, 2015;Hobley and Wilson, 2016;Chang et al, 2017;Bondi et al, 2018), (ii) for continental or even global soil property predictions (Henderson et al, 2005;Hashimoto et al, 2017;Hengl et al, 2017), and (iii) to classify soils in digital soil mapping (McBratney et al, 2003;Heung et al, 2016). Trajanov et al (2018) successfully used data mining to generate predictive models that identify the key factors governing primary productivity (r > 0.80).…”

Section: Introductionmentioning

confidence: 99%

Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France

Sandén

Trajanov

Spiegel

et al. 2019

Front. Environ. Sci.

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Agricultural soils provide society with several functions, one of which is primary productivity. This function is defined as the capacity of a soil to supply nutrients and water and to produce plant biomass for human use, providing food, feed, fiber, and fuel. For farmers, the productivity function delivers an economic basis and is a prerequisite for agricultural sustainability. Our study was designed to develop an agricultural primary productivity decision support model. To obtain a highly accurate decision support model that helps farmers and advisors to assess and manage the provision of the primary productivity soil function on their agricultural fields, we addressed the following specific objectives: (i) to construct a qualitative decision support model to assess the primary productivity soil function at the agricultural field level; (ii) to carry out verification, calibration, and sensitivity analysis of this model; and (iii) to validate the model based on empirical data. The result is a hierarchical qualitative model consisting of 25 input attributes describing soil properties, environmental conditions, cropping specifications, and management practices on each respective field. An extensive dataset from France containing data from 399 sites was used to calibrate and validate the model. The large amount of data enabled data mining to support model calibration. The accuracy of the decision support model prior to calibration supported by data mining was ∼40%. The data mining approach improved the accuracy to 77%. The proposed methodology of combining decision modeling and data mining proved to be an important step forward. This iterative approach yielded an accurate, reliable, and useful decision support model for the assessment of the primary productivity soil function at the field level. This can assist farmers and advisors in selecting the most appropriate crop management practices. Embedding this decision support model in a set of complementary models for four adjacent soil functions, as endeavored in the H2020 LANDMARK project, will help take the integrated sustainability of arable cropping systems to a new level.

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Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation

Cited by 38 publications

References 34 publications

Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria

Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria

Functional diversity of soil microorganisms in the conditions of an ecological farming system

Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France

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