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Abstract:In this study, different interpolation techniques are presented, assessed, and compared for the estimation of soil iron (Fe) contents in locations where observations were not available. Initially, 400 soil samples from the Kozani area, which is near Polifitou Lake in northern Greece, were randomly collected from 2013 to 2015 and were analysed in the laboratory to determine the soil Fe concentrations and pH. The soil Fe concentrations were examined for spatial autocorrelation, and semivariograms were used to determine whether pH and Fe exhibited spatial cross correlation. Three interpolation methods, including Ordinary Kriging, Universal Kriging, and Co-Kriging, were applied, and their results were compared with the use of two different cross-validation methods. In the current study, there was evidence of spatial cross correlation of soil Fe and pH for each year, which was subsequently used to improve the interpolation results in locations where there were no measurements. In nearly all cases, Co-Kriging, which takes advantage of the covariance between the two regionalized variables (Fe and pH), outperformed the other interpolation techniques each year.

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In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates). The study was located in the area of Grevena, Greece, where 266 disturbed soil samples were collected from randomly selected locations and analyzed in the laboratory of the Soil and Water Resources Institute. The different models that were assessed were random forests (RF), random forests kriging (RFK), gradient boosting (GB), gradient boosting kriging (GBK), neural networks (NN), and neural networks kriging (NNK) and finally, multiple linear regression (MLR), ordinary kriging (OK), and regression kriging (RK) that although they are not ML models, they were used for comparison reasons. Both the GB and RF models presented the best results in the study, with NN a close second. The introduction of OK to the ML models’ residuals did not have a major impact. Classical geostatistical or hybrid geostatistical methods without ML (OK, MLR, and RK) exhibited worse prediction accuracy compared to the models that included ML. Furthermore, different implementations (methods and packages) of the same ML models were also assessed. Regarding RF and GB, the different implementations that were applied (ranger-ranger, randomForest-rf, xgboost-xgbTree, xgboost-xgbDART) led to similar results, whereas in NN, the differences between the implementations used (nnet-nnet and nnet-avNNet) were more distinct. Finally, ML models tuned through a random search optimization method were compared with the same ML models with their default values. The results showed that the predictions were improved by the optimization process only where the ML algorithms demanded a large number of hyperparameters that needed tuning and there was a significant difference between the default values and the optimized ones, like in the case of GB and NN, but not in RF. In general, the current study concluded that although RF and GB presented approximately the same prediction accuracy, RF had more consistent results, regardless of different packages, different hyperparameter selection methods, or even the inclusion of OK in the ML models’ residuals.

The aim of this study is to develop an integrated approach to soil quality and fertility assessment in high-yielding rice agro-ecosystems threatened due to overexploitation of soil resources by intensive agriculture. The proposed approach is implemented considering representative pilot fields allocated throughout a study area based on the assumption that soils of similar general properties present a similar nutritional status due to common long-term management practices. The analysis includes (a) object-based image analysis for land zonation, (b) hot-spot analysis for sampling scheme evaluation, (c) setting of critical thresholds in soil parameters for detecting nutrient deficiencies and soil quality problems, and (d) Redundancy Analysis, TITAN analysis, and multiple regression for identifying individual or combined effects of general soil properties (e.g., organic matter, soil texture, pH, salinity) or non-soil parameters (e.g., topographic parameters) on soil nutrients. The approach was applied using as a case study the large rice agro-ecosystem of Thessaloniki plain in Greece considering some site specificities (e.g., high rice yields, calcareous soils) when setting the critical thresholds in soil parameters. The results showed that (a) 62.5% of the pilot fields’ coverage has a simultaneous deficiency in Zn, Mn, and B, (b) organic matter (OM) was the most significant descriptor of nutrients’ variance, and its cold spots (clustered regions of low OM values) showed important overlapping with the cold spots of K, Mg, Zn, Mn, Cu, and B, (c) a higher rate of availability increase in P, K, Mg, Mn, Zn, Fe, Cu, and B was observed when the OM ranged between 2 and 3%, and (d) the multiple regression models that assess K and P concentrations based on general soil properties showed an adequate performance, allowing their use for general assessment of their soil concentrations in the fields of the whole agro-ecosystem.

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