Analyzing the Impact of Storm ‘Daniel’ and Subsequent Flooding on Thessaly’s Soil Chemistry through Causal Inference

Miltiadis Iatrou (), Miltiadis Tziouvalekas, Alexandros Tsitouras, Elefterios Evangelou, Christos Noulas, Dimitrios Vlachostergios, Vassilis Aschonitis, George Arampatzis, Irene Metaxa, Christos Karydas and Panagiotis Tziachris
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Miltiadis Iatrou: Soil and Water Resources Institute, Hellenic Agricultural Organization “DIMITRA”, 57001 Thessaloniki, Greece
Miltiadis Tziouvalekas: Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “DIMITRA”, 41335 Larissa, Greece
Alexandros Tsitouras: Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “DIMITRA”, 41335 Larissa, Greece
Elefterios Evangelou: Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “DIMITRA”, 41335 Larissa, Greece
Christos Noulas: Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “DIMITRA”, 41335 Larissa, Greece
Dimitrios Vlachostergios: Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “DIMITRA”, 41335 Larissa, Greece
Vassilis Aschonitis: Soil and Water Resources Institute, Hellenic Agricultural Organization “DIMITRA”, 57001 Thessaloniki, Greece
George Arampatzis: Soil and Water Resources Institute, Hellenic Agricultural Organization “DIMITRA”, 57001 Thessaloniki, Greece
Irene Metaxa: Soil and Water Resources Institute, Hellenic Agricultural Organization “DIMITRA”, 57001 Thessaloniki, Greece
Christos Karydas: Ecodevelopment S.A., 57010 Thessaloniki, Greece
Panagiotis Tziachris: Soil and Water Resources Institute, Hellenic Agricultural Organization “DIMITRA”, 57001 Thessaloniki, Greece

Agriculture, 2024, vol. 14, issue 4, 1-18

Abstract: 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 CaCO 3 concentration in the soil. Also, causal relationships were identified between CaCO 3 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 CaCO 3 , P, and Mn. The results revealed quadratic equations for CaCO 3 , P, and Mn as follows: 0.001XCaCO 3 2 + 0.08XCaCO 3 + 6.42, 0.004XP 2 − 0.26XP + 12.29, and 0.003XMn 2 − 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.

Keywords: causal machine learning; soil analysis; causal discovery; crop fertilization; flood; agriculture; deposition; climate change (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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