Natural landscapes in the Mediterranean ecosystem have experienced extensive changes over the last two centuries due to wildfire activity. Resulting interactions between climatic warming, vegetation species, soil natural, and meteorological condition before and after a wildfire create substantial abrupt landscape alterations. This study investigates the evolution (2 days, 3, 6, 9, and 12 months after a fire) of topsoil (0–5 cm) chemical properties in burned Cambisols (Zadar County, Croatia) with respect to different wildfire severities (HS—high severity, MS—medium severity, C—unburned) and vegetation species (Quercus pubescens Willd. and Juniperus communis L.). Soil pH, electrical conductivity (EC), calcium carbonates (CaCO3), total organic carbon (TOC), total nitrogen (TN), total sulphur (TS), copper (Cu) and zinc (Zn) were significantly higher in HS than in MS and C. Total soil potassium (TK), Fe and Ni were significantly higher in C than in HS. The increase of TOC and TN was more pronounced in Quercus p. than Juniperus c., especially in the first three months. Soil pH, EC, CaCO3, TOC, TN, and TS were most affected by wildfire severity. The distinction between C, MS and HS categories was less visible 9 and 12 months post-fire, indicating the start of the recovery of the soil system. Post-fire management and temporal recovery of the soil system should consider the obvious difference in soil disturbance under HS and MS between vegetation species.
The research objective was to use proximal spectroscopy in visible and near infrared (VNIR) spectra to determine the total leaf nitrogen (TN) content and the above-ground biomass of Miscanthus × giganteus (MxG) grown in the open-roof greenhouse experiment on soil contaminated with cadmium and mercury (100 mg Cd/kg soil; 20 mg Hg/kg soil), in dependence of different soil amendments in four treatments (I-soil without amendment; II-sludge; III-mycorrhizae; IV-MxG ash). Leaf reflectance was acquired using a field spectroradiometer (350–1050 nm) at the end of the vegetation period between 2018 and 2019 (n = 24). TN content was determined using the dry combustion method, while biomass was weighted immediately after the harvest. In terms of the treatment effect, sludge showed the greatest contribution in TN content. Regarding the biomass quantity, MxG ash revealed the best results as soil amendment. Applying the partial least squares regression, complete correlation and low root mean squared error (RMSE) were obtained between predicted and measured values for the validation dataset of TN content (R2 = 0.87, RMSE = 0.139%), while a strong correlation was calculated for biomass (R2 = 0.53, RMSE = 0.833 t/ha). As an additional tool with analytical methods, proximal spectroscopy is suitable to integrate the optical and physiological properties of MxG, and to assess nutrient stress in crop grown on contaminated soils.
This paper aims to evaluate the ability of VNIR proximal soil spectroscopy to determine post-fire soil chemical properties and discriminate fire severity based on soil spectra. A total of 120 topsoil samples (0–3 cm) were taken from 6 ha of unburned (control (CON)) and burned areas (moderate fire severity (MS) and high fire severity (HS)) in Mediterranean Croatia within one year after the wildfire. Partial least squares regression (PLSR) and an artificial neural network (ANN) were used to build calibration models of soil pH, electrical conductivity (EC), CaCO3, plant-available phosphorus (P2O5) and potassium (K2O), soil organic carbon (SOC), exchangeable calcium (exCa), magnesium (exMg), potassium (exK), sodium (exNa), and cation exchange capacity (CEC), based on soil reflectance data. In terms of fire severity, CON samples exhibited higher average reflectance than MS and HS samples due to their lower SOC content. The PCA results pointed to the significance of the NIR part of the spectrum for extracting the variance in reflectance data and differentiation between the CON and burned area (MS and HS). DA generated 74.2% correctly classified soil spectral samples according to the fire severity. Both PLSR and ANN calibration techniques showed sensitivity to extract information from soil features based on hyperspectral reflectance, most successfully for the prediction of SOC, P2O5, exCa, exK, and CEC. This study confirms the usefulness of soil spectroscopy for fast screening and a better understanding of soil chemical properties in post-fire periods.
Soil and water loss due to traditional intensive types of agricultural management is widespread and unsustainable in Croatian croplands. In order to mitigate the accelerated land degradation, we studied different cropland soil management strategies to obtain feasible and sustainable agro-technical practices. A rainfall simulation experiment was conducted at 58 mm h–1 over 30 min on 10 paired plots (0.785 m2), bare and straw covered (2 t ha−1). The experiment was carried out in maize cultivation (Blagorodovac, Croatia) established on Stagnosols on slopes. Measurements were conducted during April (bare soil, after seeding), May (five-leaves stage), and June (intensive vegetative growth) making 60 rainfall simulations in total. Straw reduced soil and water losses significantly. The highest water, sediment loss, and sediment concentrations were identified in tillage plots during May. Straw addition resulted in delayed ponding (for 7%, 63%, and 50% during April, May and June, respectively) and runoff generation (for 37%, 32%, and 18% during April, May and June, respectively). Compared with the straw-mulched plot, tillage and bare soil increased water loss by 349%. Maize development reduced the difference between bare and straw-mulched plots. During May and June, bare plots increase water loss by 92% and 95%, respectively. The straw mulch reduced raindrop kinetic energy and sediment detachment from 9, 6, and 5 magnitude orders in April, May, and June, respectively. Overall, the straw mulch was revealed to be a highly efficient nature-based solution for soil conservation and maize cultivation protection.
<p>Fire is an important element of the ecosystems, nevertheless, high severity fires can have negative impacts on the ecosystems as consequence of the high temperatures reaches. High temperatures normally have detrimental impacts on soil properties. The objective of this work was to determine the relationship of spectral reflectance and soil pH, electrical conductivity (EC), carbonates (CaCO<sub>3</sub>) and total carbon (TC) content after a medium to high severity wildfire occurred in Croatia using linear and nonlinear calibration models.</p><p>Soils were sampled 2 days after a medium to high severity wildfire in Zadar County, Croatia. A total of 120 soil samples (0-5 cm) were collected from three different treatments (n= 40 per treatment): control (C), mean severity (MS), high severity (HS). Soil pH, EC, CaCO<sub>3</sub> and TC content were determined using standard laboratory methods. &#160;Soil spectral measurements were carried out using a portable spectroradiometer (20 per treatment, 60 in total). Linear statistical model - partial least squares regression (PLSR) and non-linear - artificial neural network (ANN) were generated to estimate changes in soil pH, EC, CaCO<sub>3</sub> and TC content based on the original spectral reflectance and its first derivative in form of principal components (PC). One-way ANOVA revealed pH values were significantly different in all three treatments. EC, CaCO<sub>3</sub> and TC were significantly higher in HS plots compared with the other treatments.</p><p>Different wildfire severity indicated very collinear soil spectral response, but with certain variations of reflectance intensity. Control samples showed a higher reflectance than MS and HS samples. This is attributed to the low pH and TC content. Low reflectance of MS and HS samples could be explained by their increased pH and TC values. Soil pH was the only parameter that showed a high R<sup>2</sup> and low root mean squared error (RMSE) after Savitzky Golay smoothing and the first derivation. In PLSR model, strong to very strong correlation and low RMSE were obtained. ANN model also showed a high R<sup>2</sup> and lower RMSE for all properties except pH. Both models showed satisfactory results for prediction of the studied soil properties. ANN model predicted EC, CaCO<sub>3</sub>, and TC better, while PLSR proved to be a better model for pH prediction.</p><p><strong>Key words:</strong> soil reflectance, fire severity, principal components, partial least squares regression, neural networks</p><p>Acknowledgements</p><p>This work was supported by Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO).</p>
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