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Storm ‘Daniel’ caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. The causal relationships between the soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. The results of the causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and the available phosphorus (P-Olsen), as well as those between the sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25, and 30 cm). Then, linear regression equations were fitted across the different levels of deposit depth to determine the effect of the deposit depth on CaCO3, P, and Mn. The results revealed quadratic equations for CaCO3, P, and Mn as follows: 0.001XCaCO32 + 0.08XCaCO3 + 6.42, 0.004XP2 − 0.26XP + 12.29, and 0.003XMn2 − 0.08XMn + 22.47, respectively. The statistical analysis indicated that corn growing in soils with a sediment over 10 cm requires a 31.8% increase in the P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.

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.