Pedometric Techniques in Spatialisation of Soil Properties  for Agricultural Land Evaluation

Tanasă, Iuliana Cornelia; Niculiţă, Mihai; Roșca, Bogdan; Pîrnău, Radu Gabriel

doi:10.15835/buasvmcn-agr:5095

Cited by 2 publications

(2 citation statements)

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“…Other studies focused on comparative applications of DSM techniques in different regions of the country. [ 24 ] used kriging and regression-kriging to derive quantitative soil parameters and logistic regression–kriging to derive occurrence spatial probabilities for soil horizons in a part of Iaşi County; [ 11 ] tested the used of logistic regression and fuzzy techniques for the prediction of qualitative soil variables; [ 25 ] applied comparatively several geostatistical methods including ordinary kriging, geographically weighted regression, regression-kriging to map soil quantitative properties; [ 26 ] investigated the effect of scale and resolution on DSM; [ 27 ] used regression-kriging to map soil heavy metals in Cluj County; [ 28 ] explored the potential of land-surface segmentation technique to create predictors for DSM of qualitative soil parameters (eg. soil taxonomic units, textural classes); [ 29 ] used RK and OK to interpolate pXRF-derived soil chemical data in an archaeological site.Among DSM methods, kriging was one of the first to be used for the interpolation of soil properties in the 80’s [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning

Patriche,

Roşca,

Pîrnău

et al. 2023

PLoS ONE

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Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R2 of 0.417–0.469, depending on the method), organic carbon (R2 of 0.302–0.443), calcium carbonates (R2 of 0.300–0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R2 of 0.155–0.331), while the lowest prediction characterizes the phosphorous content (R2 of 0.015–0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies.

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Section: Introductionmentioning

confidence: 99%

Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning

Patriche,

Roşca,

Pîrnău

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Land suitability assessment can identify constraints, opportunities and potential of the land resource for a given use (Mohammed, 2011;Mokarram and Aminzadeh, 1996). It also plays an important role in sustainable agricultural practice and management (Tanasȃ et al, 2010) as it provides information to farmers, extension staff, policy makers and other stake holders on how suitable the land is in terms of agronomic (such as soil), climatic and other limitations (Olaleye et al, 2002). It has been integrated in studies as an aid to land use planning (Johnson et al, 1994).…”

Section: Introductionmentioning

confidence: 99%