Parstoo Aslani; Masoud Davari; Mohammad Ali Mahmoodi; Farzad Hosseinpanahi; Naser Khaleghpanah
Abstract
Introduction Soil quality is one aspect of sustainable agroecosystem management. The application of zeolite minerals alone or in combination with other soil amendments (organic and inorganic fertilizers) can, directly or indirectly, affect soil quality indicators. Considering the unique characteristics ...
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Introduction Soil quality is one aspect of sustainable agroecosystem management. The application of zeolite minerals alone or in combination with other soil amendments (organic and inorganic fertilizers) can, directly or indirectly, affect soil quality indicators. Considering the unique characteristics of zeolites, such as the low-cost and abundance of its mines in Iran and the large area of wheat cultivation in Kurdistan province, the need to study the effect of zeolite application on soil properties and wheat yield becomes apparent. Although there is a lot of research on the impact of zeolite on improving soil properties and increasing the yield of various crops, few studies have been done on its residual effects. Therefore, in this study, we investigated the effect of zeolite and nitrogen (N) application on some basic soil properties, N efficiency, and wheat yield under field conditions after two years of zeolite application. Materials and MethodsBefore conducting the research, a composite soil sample from the soil surface (0 to 30 cm depth) was collected and analyzed to assess the farm's soil properties. The experiment was laid out in a split-plot based on a randomized complete block design with three replications at the University of Kurdistan research farm in Dehgolan. The main plots consisted of natural zeolite at four levels (0, 5, 10, and 15 ton. ha-1). Within each main plot, subplots were subjected to nitrogen applications at five levels (0, 50, 100, 150, and 200 kg. ha-1). Urea fertilizer was used to supply the required nitrogen. Zeolite was only utilized in 2018 and mixed into the surface layer of soil. The experiment was repeated in 2019 except for no addition of zeolite. The field was under potato cultivation in the first year of the experiment and followed by wheat crop in the second year. Wheat cultivation (Pishgam cultivar) was done in 2019 by grain seeders in plots with dimensions of 4.5 × 8.25 m. At the end of cultivation season, harvest was done from each plot, and some plant traits (grain protein, thousand-grain weight, spike number, grain number in spike, an economic yield of the plant, biological yield of plant, harvest index, and chlorophyll concentration) were measured. In order to investigate the effect of zeolite on basic soil properties, soil samples were collected from plots in the second year after harvest, and a number of physical and chemical properties of the soil were measured (dry bulk density (ρb), particle density (ρp), total porosity (f), saturated hydraulic conductivity (Ks), electrical conductivity (EC), soil reaction (pH), cation exchange capacity (CEC), and total soil nitrogen (TN)). Statistical analysis of data was performed using SAS 8.02 software.Results and DiscussionThe results from the second year indicated that the applications of zeolite or nitrogen alone or in combination with each other decreased dry bulk density and particle density of soil, but increased total porosity, saturated hydraulic conductivity, electrical conductivity, soil reaction, and cation exchange capacity. The porous structure of zeolite helps improve soil structure and increase porosity, thereby reducing the bulk density of the soil. Also, zeolites can affect the soil hydraulic conductivity due to channels in their structure. Zeolite is not acidic but marginally alkaline, and its use with fertilizers can help buffer soil pH levels. The very open structure of the zeolite and the similar pore network create a high specific surface area for the storage and exchange of nutrients. Therefore, different salts can be absorbed or desorbed from the zeolite structure. Desorption of salts from the zeolite can increase EC in the soil. The high cation exchange capacity and porosity of zeolite increase soil CEC, which increases the soil's ability to retain nutrients such as ammonium. The results also revealed that the grain protein, thousand-grain weight, spike number, grain number in spike, an economic yield of the plant, biological yield of plant and harvest index, with mean increasing about 37%, 6%, 30%, 15%, 43%, 26% and 7%, respectively, compared with the control, were significantly affected by zeolite and nitrogen applications, and also zeolite and nitrogen interaction. However, the chlorophyll concentration was not meaningfully influenced by them. Increased grain yield can be attributed to reduced nitrogen leaching and increased soil water holding capacity in the presence of zeolite, which improves nitrogen status and the availability of water for growth. Drought stress significantly affects grain yield, harvest index, thousand-grain weight, spike number, grain number in spike, and plant height. The use of zeolite can maintain soil moisture for a longer period and mitigate the adverse effects of drought stress on the crops.ConclusionThe improved agronomic traits and enhanced grain yield potentials induced by zeolite amendment were related to decreased drought stress in wheat crops and the increase in soil quality indicators and N uptake. The zeolite application probably enhanced NH4+–N retention in the topsoil and prevented NO3-–N from leaching into the subsoil. In general, the results showed that the combined application of zeolite and N can be a beneficial approach for increasing nitrogen fertilizer efficiency and improving the sustainability of agricultural systems.
Kamran Azizi; Kamal Nabiollahi; Masoud Davari
Abstract
Introduction Soil salinity and alkalization are recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. To manage these soils a lot of data are needed and laboratory measurement is costly and time-consuming. Therefore, indirect methods that are cheap, fast and ...
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Introduction Soil salinity and alkalization are recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. To manage these soils a lot of data are needed and laboratory measurement is costly and time-consuming. Therefore, indirect methods that are cheap, fast and easy to access are one of the research priorities. One of these methods is visible near infrared diffuse reflectance spectroscopy. Visible and near infrared diffuse reflectance spectroscopy is a time and cost-effective approach that has been successfully used for characterizing soil properties. Materials and Methods The study area is located in Kurdistan Province, about 20 km northeast of Ghorveh city, west of Iran, and covers 260 km2. Average annual precipitation and temperature are 369.8mm and 10.8 °C, respectively. Soil moisture and temperature regimes are Xeric and Mesic, respectively. In the study area, 100 soil samples were collected (0–30 cm depth). The main land use types consist of cropland and rangeland. The soil samples were air-dried at room temperature and then, passed through a 2mm sieve. EC, pH, SAR, OC, CaCO3 and ΔMWD were measured. Sodium Adsorption Ratio (SAR) was calculated using results from the saturated paste extracts of sodium, calcium, and magnesium. The stability aggregate was measured using the difference between distributions of particle size in dry and wet sieve methods. Spectral analysis of soil samples was done using a spectrophotometric instrument with a wavelength of 350 to 2500 nm and recorded using RS3 software. After recording the spectra, different preprocessing methods were evaluated. Two models of multiple linear regression and artificial neural network were used to predict soil properties using spectral data. Results and Discussion The soil salinity of the study area ranged between low and high. The highest amount of salinity was observed in the center, south and southwest of the study area and the least amount of salinity was observed in northwest, southeast, northeast and north. The maximum amounts of acidity and sodium adsorption ratio showed that the central part of the study area has saline and sodium soils. The results showed that the best method for preprocessing of spectral data is the 1st Derivative + Savitzky-Golay filter + Mean center + SNV. The Pearson correlation coefficient between the soil properties and the spectral reflection values for each wavelength in the range of 2450-400 nm showed that there is a relatively high correlation between the measured characteristics and the spectral values of the soil. The results showed that the correlation coefficient can be positive or negative. The maximum positive correlation coefficients for electrical conductivity, soil acidity, sodium adsorption, organic carbon, calcium carbonate and aggregate stability at the wavelengths 1229, 2397, 2399, 1298, 2090, 2014, and two spectra 2257 and 660 were 0.45**, 0.43**, 0.46**, 0.61**, 0.53** and 0.40**, respectively. The maximum negative correlation coefficients for electrical conductivity, soil acidity, sodium adsorption ratio, organic carbon, calcium carbonate and aggregate stability at the wavelengths 630, 2289, 630, 1904, 1379 and 2107 were -0.47**, -0.42**, -0.44**, -0.46**, -0.55** and -0.44**, respectively. Based on the determination coefficient statistic, artificial neural network model (0.88, 0.25, 0.59, 0.68, 0.52 and 0.48 to electrical conductivity, PH, SAR, calcium carbonate and aggregate stability, respectively) had better results compared to the multiple linear regression model (0.45, 0.13, 0.23, 0.66, 0.48 and 0.28 to electrical conductivity, PH, SAR, calcium carbonate and aggregate stability, respectively). Conclusion In this study, visible near infrared diffuse reflectance spectroscopy was evaluated to estimate some properties of salt-affected soils. After recording the spectral data, the continuity curve and pre-processing of spectral data were performed. The results showed that the best method for pre-processing of spectral data is the first derivative + Savitzky filter and Glair + Mid filter + Normal standard variable. Multiple linear regression and artificial neural network models were used to estimate some properties of salt-affected soils (EC, pH, SAR, OC, CaCO3 and ΔMWD) using spectral data. Based on the statistics of mean error, root mean squared error, and correlation coefficient, the artificial neural network model had better results in estimateing the properties of salt-affected soils compared to the multiple linear regression model. Therefore, based on these findings it is suggested that soil spectral data be used as an indirect method to the estimate soil properties.
