M. Mosapour; A. Forghani; A. Sabouri
Abstract
Introduction: Potassium is the second essential nutrient for plants. Potassium has a high abundance in the soil, but only a small part of it can be used. The principal forms of potassium in the soil are solution potassium, exchangeable potassium, non-exchangeable potassium, and structural potassium. ...
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Introduction: Potassium is the second essential nutrient for plants. Potassium has a high abundance in the soil, but only a small part of it can be used. The principal forms of potassium in the soil are solution potassium, exchangeable potassium, non-exchangeable potassium, and structural potassium. To evaluate the state of potassium in the soil, three forms of soluble, exchangeable, and non-exchangeable are used. The Q/I curve is used to describe the availability of potassium, due to the competition between calcium, potassium, and magnesium ions by soil exchange sites. This curve represents the supply power of soil potassium. The objective of this study was to investigate soil potassium Q/I curve and relationships between its parameters and soil characteristics in some calcareous soils of Lorestan province.
Materials and Methods: In this study, 16 topsoil samples (0-30 cm) were obtained from the calcareous soils of Lorestan province. The experiment was carried out by a completely randomized design with three replications. To prepare the Q/I curve, six suspensions were prepared from each soil sample containing 1 g of soil and 10 ml of calcium chloride 0.01 M and 10 milliliters of potassium chloride with concentrations of 0.33, 0.66, 1, 1.33, 2 and 2.5 mmol. The solutions were shaken for one hour. They were then left for 20 hours to reach the balance. The samples were centrifuged and the soluble and solid phase were separated and then the soluble potassium solution was read using a potassium flame photometry. Calcium and magnesium concentrations were measured by titration with EDTA. Then, 20 ml of 1 M ammonium acetate (NH4OAC) was added to the solid phase of each sample. Then, the concentration of exchangeable potassium was measured using a flame photometer. Then the Q/I curve was plotted for each sample. In addition, the association analysis was performed using a stepwise multivariable regression method.
Results and Discussion: According to the Q/I curve, ARK0 (potassium activity ratio at equilibrium) ranged from 0.087-0.047 (mmol.L-1). The maximum amount of PBCK (potential buffering capacity) was observed in soil No.11 with value of 45.834 (mmol.kg-1)/(mmol.L-1)0.5 and the lowest value obtained for soil No.13 with value of 23.329 (mmol.kg-1)/(mmol.L-1)0.5. In fact, in soils with PBCK, the soluble potassium activity has a lower oscillation and is better buffered. The low amount of PBCK in soil No.13 indicates low soil power to supply potassium and the necessity of using potassium fertilizers. The lowest and most easily converted easy potassium (ΔK0) were observed for soil No.12 and 4 with a value of 1.269 and 23.243 (mmol.kg-1) respectively. There was a negative correlation between PBCK and ARK, suggesting those high-PBCK soils, lower ARK, are more stable than those with lower PBCK. The KL (available Potassium) with ΔK0 and Kx (Hardly exchangeable K) showed a significant and positive relationship (r=0.70, p<0.01). Therefore, it can be concluded that by increasing each of the two parameters ΔK0 and KX, the amount of potassium (KL) is increased. Also, a positive and significant correlation was found between potassium potential buffering capacity with clay content (r=0.79, p<0/01) and the cation exchange capacity (r=0.73, p<0.01). Therefore, the cation exchange capacity of soils can be used to estimate the buffering capacity of soils and therefore recommend potassium fertilizers. Available potassium (KL) showed a positive and significant correlation with soil organic matter because its organic material is a part of potassium. Also, organic matter can alter the amount of potassium by changing the pH value. Other Q/I curve parameters, such as ARK0, Kx, and ΔK0 did not show any significant correlation with any soil properties. According to regression analysis, it was determined among all soil characteristics the only amount of clay can be used as a proper attribute in order to estimate the potential of potassium in soil according to the following equation: PBCK=17.857+0.482 Clay R2 = 0.631. Also, the amount of organic carbon (O.C) was determined as the proper variable for estimating the KL value according to the following model: KL=14.468+9.017 O.C (R2 = 0.318).
Conclusion: Due to potential buffering capacity (PBCK) in these soils, it seems that soils can be able to provide the absorbable potassium relatively. Therefore, fertilizer recommendation can be performed by considering the amount of determined variables by the Q/I curve.
