The Effects of Different Tillage Methods on Available Soil Potassium Measured by Various Extractors in a Soil with High Specific Surface Area

Introduction: The effects of any tillage method on soil properties, depends on location (soil, water and air) and the number of (years) their implementation. Soil compaction reduces yield through increased soil mechanical resistance against root growth and lower water and nutrient use efficiency (Gamda et al. 18 & Ishagh et al 23). Soil surface and sub surface compaction both reduce yield due to limited root growth and plant potassium uptake (Doulan et al. 14). Sabt et al. (50) reported that in the study area, which the lands are mostly illite clay (high specific surface area) with sufficient nitrogen, soil potassium is the most important limiting factor for the growth of wheat.Considering the point that loess soils in Golestan Province have a high specific surface area,they can provide potassium for plants to produce crop, but for a higher production, potassium fertilizers should be used. Previous studies indicated that production of wheat is limited due to potassium deficiency (4, 49, 54 and 57). In these soils with a high specific surface area, the speed of movement of potassium from the soil solution is low, and doing solimits wheat yield.In loess soils containing high illite and high specific surface area (eg, soilsin the series of Rahmat Abad of Gorgan), ammonium acetate measured potassium on exchange and solution surfaces, which is highly correlated with grain yield (54) . There is a high correlation between grain yield with overload of potassium and Na TPB extraction (57). The aim of this study was to absorb potassium (limiting factor for plant growth) with different tillage systemsat different depths. International recommendations towards reducing the depth and intensity of tillage (from minimum tillage to no-tillage) in order to reduce erosion and oxidation of organic substances plays an important role in determining the amount of greenhouse gases. If potassium absorption does not reduceafter reducing tillage intensity,low or no-tillage methods are preferred. Otherwise no choice but to continue conventional tillage. The second objective is to assess the effects of the treatments (different tillage systems) on the growth and size of the roots and to predict nutrient uptake by plants. Materials and Methods: This research was a field experiment during 2009-2010 in estates of Gorgan University of Agricultural Sciences and Natural Resources (Seyed Miran Area) with 5 treatments and 4 replications which used completely randomized block design. Treatments were 5 tillage methods including moldboard-ploughing (20-25 cm depth) followed by disking, rotivator (12-17 cm depth), disking (8-10 cm depth), chisel (25- 30 cm depth) and no-tillage. Row spacing, distance between seeds in a rowand the amount of seeding was 20 cm 1.5 cm and 268.5 kg ha respectively (planting was done by hands). The consumption of fertilizers based on soil test results and the results reported by other researchers were added to the soil surface before planting (54). In all treatments, 350 kg per hectare of ammonium phosphate and 200 kg of potassium sulfate before planting and by hands were added. For treated moldboard,rotary cultivator, disc and chisel were used, and for no-tillage system by disc plow and sweep were used.Main parameters measured were soil mechanical resistance at 6 stages during wheat growth using a cone penetrometer (0-8 cm soil depth), soil potassium at two stages during plant growth (before heading and harvest) using sodium tetraphenyl boron(12), ammonium acetate(28) and ammonium nitrate as extractents and using potassium surface excess(8) determination method and also bulk soil solution potassium concentration(2). Yield of wheat and its components were also determined at harvest. Data analysis include the analysis of variance and mean comparisons using LSD and correlations which carried out using SAS software. Results and Discussion: Results show there was a significant difference between treatments with respect to extractible soil potassium using sodium tetraphenyl boron at 5 percent level and ammonium acetate at 1 percent level, both before wheat heading. Soil potassium content did not differ significantly in this stage when potassium excess method was used. With all methods of soil potassium determination, soil potassium did not differ significantly at harvest. Soil potassium with moldboard-ploughing was less than all other tillage methods at before plant heading. Thomas et al. (55) and Martin Rhoda et al.(40) also stated that soil potassium was greater with no-tillage method. Lopez Phando & Pardo. (34) similarly stated that soil potassium with no-tillage method was greater than moldboard ploughing. According to results of the current experiment, soil mechanical resistance was further reduced as tillage intensity was increased. Soil mechanical resistance with moldboard ploughing was less than other tillage methods between early heading stage and harvest. Lower mechanical resistance with increased tillage intensity increased root growth and soil potassium uptake by wheat grain and straw, leading to greater yield production in accordance with results by Fakori (16). Conclusions Soil tillage with moldboard ploughing reduced mechanical resistance, increased root density (and possibly soil-root contact surface area) and soil potassium uptake which results a greater wheat head density and yield and also a lower soil potassium with different methods (potassium excess determination and bulk soil solution potassium concentration methods and also using soidium tetraphenyl boron, ammonium acetate extractants) at before heading which is the stage for maximal growth and nutrient accumulation rate. Soil extractants maybe used for plant nutrient uptake and yield predictions in a plant canopy, when plant nutrient uptake has a positive significant correlation with soil potassium and treatments do not affect root growth and the mentioned correlation

Survey and Zoning of Soil Physical and Chemical Properties Using Geostatistical Methods in GIS (Case Study: Miankangi Region in Sistan)

Introduction: In order to provide a database, it is essential having access to accurate information on soil spatial variation for soil sustainable management such as proper application of fertilizers. Spatial variations in soil properties are common but it is important for understanding these changes, particularly in agricultural lands for careful planning and land management. Materials and Methods: To this end, in winter 1391, 189 undisturbed soil samples (0-30 cm depth) in a regular lattice with a spacing of 500 m were gathered from the surface of Miankangi land, Sistan plain, and their physical and chemical properties were studied. The land area of the region is about 4,500 hectares; the average elevation of studied area is 489.2 meters above sea level with different land uses. Soil texture was measured by the hydrometer methods (11), Also EC and pH (39), calcium carbonate equivalent (37) and the saturation percentage of soils were determined. Kriging, Co-Kriging, Inverse Distance Weighting and Local Polynomial Interpolation techniques were evaluated to produce a soil characteristics map of the study area zoning and to select the best geostatistical methods. Cross-validation techniques and Root Mean Square Error (RMSE) were used. Results and Discussion: Normalized test results showed that all of the soil properties except calcium carbonate and soil clay content had normal distribution. In addition, the results of correlation test showed that the soil saturation percentage was positively correlated with silt content (r=0.43 and

Doing more with less: A comparative assessment between morphometric indices and machine learning models for automated gully pattern extraction (A case study: Dashtiari region, Sistan and Baluchestan Province)

Deep gullies in the Dashtiari Region prompted us to couple different morphometric indices obtained from a UAV-derived DEM to automatically extract gully signatures. The extraction of gully signatures is commonly undertaken via pattern recognition techniques, whose recent advancements seem to require more data and rather cumbersome modeling processes, making it even more difficult for those who are not well-versed in such contexts. Among these methods, object-based image analysis (OBIA), machine learning, and deep learning techniques are the most common. Conversely, here we took advantage of simple morphometric indices and their combinations for gully extraction, including valley depth (VD), topographic position index (TPI), positive openness (PO), red relief image map (RRIM), elevation, slope degree, and the coupled PO-DEM. Furthermore, we compared the automatically derived gully patterns to the manually extracted ones (treated as the ground truth), and their spatial autocorrelation was investigated. Additionally, the application of the classification tree (CT) as a powerful machine learning model was comparatively assessed for morphometric indices. The performance of the adopted pattern extraction techniques was estimated using four different metrics: precision index, true skill statistics (TSS), Cohen's kappa, and Matthews correlation coefficient (MCC). The results revealed that the single use of PO, TPI, and RRIM indices failed to reliably capture the gullies’ pattern, leading to partial success. Notably, combinations of indices showed that the coupled PO-DEM could successfully classify the gully presence locations from the absences and outperform the CT model in terms of both goodness-of-fit and generalization capacity (prediction power), considering all four-performance metrics. Hence, comparing the amount of time spent for manual delineation of gullies, the application of simple morphometric indices, and machine learning models is beyond comparison