Z. Nemati; A. Hemmat; M. R. Mosaddeghi
Abstract
Introduction The compaction of soil by agricultural equipment has become a matter of increasing concern because compaction of arable lands may reduce crop growth and yield, and it also has environmental impacts. In nature, soils could be compacted due to its own weights, external loads and internal forces ...
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Introduction The compaction of soil by agricultural equipment has become a matter of increasing concern because compaction of arable lands may reduce crop growth and yield, and it also has environmental impacts. In nature, soils could be compacted due to its own weights, external loads and internal forces as a result of wetting and drying processes. Soil compaction in sugarcane fields usually occurs due to mechanized harvesting operations by using heavy machinery in wet soils. Adding plant residues to the soil can improve soil structure. To improve soil physical quality of sugarcane fields, it might be suggested to add the bagasse and filter cake, which are the by-products of the sugar industry, to the soils. When a soil has been compacted by field traffic or has settled owing to natural forces, a threshold stress is believed to exist such that loadings inducing lower than the threshold cause little additional compaction, whilst loadings inducing greater stresses than the threshold cause much additional compaction. This threshold is called pre-compaction stress (σpc). The σpc is considered as an index of soil compactibility, the maximum pressure a soil has experienced in the past (i.e. soil management history), and the maximum major principal stress a soil can resist without major plastic deformation and compaction. Therefore, the main objective of this study was to investigate the effects of wetting and drying cycles, soil water content, residues type and percent on stress at compaction threshold (σpc). Materials and Methods In this research, the effect of adding sugarcane residues (i.e., bagasse and filter cake) with two different rates (1 and 2%) on pre-compaction stress (σpc) in a silty clay loam soil which was prepared at two relative water contents of 0.9PL (PL= plastic limit, moist) and 1.1PL (wet) with or without wetting and drying cycles. This study was conducted using a factorial experiment in a completely randomized design with three replications. A composite disturbed sample of topsoil (0–200 mm deep) of a silty clay loam soil was collected from Isfahan province (32 31.530 N; 51 49.40E) in center of Iran. The mean annual precipitation and temperature of the region are about 160 mm and 16 C, respectively. Sugarcane residues (bagasse and filter cake) were obtained from the sugarcane fields in Ahvaz, Khuzestan province (Iran). The samples were air-dried and passed through a 2-mm sieve. Soil treated by bagasse and filter cake in different rates was poured and knocked lightly into cylinders with diameter and height of 25 and 8 cm, respectively. Large air-dry disturbed soil samples were prepared and some of them were exposed to five wetting and drying cycles. Finally, the soil surface was covered by a plastic sheet and was left overnight in the laboratory (for 24 hours) to enable the moisture to equilibrate. The loading tests were performed the next day. The pre-compaction stress was determined by plate sinkage test (PST). The loading test for PST was performed using CBR apparatus. The compression for PST was continuous at the same constant displacement rate of the CBR (i.e. 1 mm min-1). Determination of the σpc was done using Casagrande’s graphical estimation procedure (Casagrande, 1936) in a program written in MatLab software. Results and Discussion The results showed that σpc was significantly decreased by adding residues to the soil at both water contents, and with/without wetting and drying process. For untreated treatments (control), the σpc decreased with increasing water content. Although σpc decreased with adding the residues to the soil, however, the effect of residue types and percentages and soil water content on σpc was not significant for the soil samples treated with residues. Conclusion In order to prevent re-compaction of the soil and improve its structure, it is suggested that traffic control system with permanent routes for the movement of machinery to be used in sugar cane plantations and the residues (after desalination) to be added into strips that are placed under cultivation.
M. A. Rostami; H. Afzali Gorouh
Abstract
Introduction Preserving of crop residues in the field surface after harvesting crops, making difficult farm operations. The farmers for getting rid of crop residues always choose the easiest way, i.e. burning. Burning is one of the common disposal methods for wheat and corn straw in some region of the ...
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Introduction Preserving of crop residues in the field surface after harvesting crops, making difficult farm operations. The farmers for getting rid of crop residues always choose the easiest way, i.e. burning. Burning is one of the common disposal methods for wheat and corn straw in some region of the world. Present study was aimed to investigate the accurate methods for monitoring of residue management after wheat harvesting. With this vision, the potential of Landsat 8 sensor was evaluated for monitoring of residue burning, using satellite spectral indices and Linear Spectral Unmixing Analysis. For this purpose, correlation of ground data with satellite spectral indices and LSUA data were tested by linear regression. Materials and Methods In this study we considered 12 farms where remained plants were burned, 12 green farm, 12 bare farms and 12 farms with full crop residue cover were considered. Spatial coordinates of experimental fields recorded with a GPS and fields map were drawn using ArcGissoftware, version of 10.1. In this study,t wo methods were used to separate burned fields from other farms including Satellite Spectral Indices and Linear Spectral unmixing analysis. In this study, multispectral landsat 8 image was acquired over 2015 year. Landsat 8 products are delivered to the customer as radiometric, sensor, and geometric corrections. Image pixels are unique to Landsat 8 data, and should not be directly compared to imagery from other sensors. Therefore, DN value must be converted to radiance value in order to change the radiance to the reflectance, which is useful when performing spectral analysis techniques, such as transformations, band ratios and the Normalized Difference Vegetation Index (NDVI), etc. In this study, a number of spectral indices and Linear Spectral Unmixing Analysis data were imported/extracted from Landsat 8 image. All satellite image data were analyzed by ENVI software package. The spectral indices used in this study were NDVI, BAI, NBR and NBRT. Classification accuracy was evaluated and expressed by confusion matrix and Kappa coefficient. Natural surfaces are rarely composed of a single uniform material. Spectral mixing occurs when materials with different spectral properties are represented by a single image pixel. The condition where scale of the mixing is large (macroscopic), mixing would occur in a linear fashion. However for microscopic situations, the mixing is generally nonlinear. The linear model ahich wasadopted in this study, assumes that there is no interaction between materials. Assumption of LSUA is that each pixel on the surface is a physical mixture of several constituents weighted by surface abundance, and the spectrum of the mixture is a linear combination of the endmember reflectance spectra. Within the context of this study, LSUA is a classification method that can determine contribution of each material (or endmember) such as soil or residue for each image pixel. Results and Discussion The spectral response curve extracted from Landsat 8 image used as input into the LSUA model in ENVI software. As expected, crop burned residue (Ash) spectra had lower reflectance when compared to the soil, residue and green plant spectra. The contrast between residue, green plant, soil and residue ash spectra was particularly evident in the NIR and SWIR bands. It is suggested that these bands are essential for residue discrimination. Differences of reflectance in the visible bands were minimal, providing little discrimination between residue, green plant, soil and residue ash. Burned area estimated by LSUA method from Landsat 8 image was correlated against the ground data (measured coincident to the ground data). The overall accuracy of classification with BAI index and LSUA method was 91.7 and 88.3 and Kappa coefficient was 0.89 and 0.84 respectively. Results indicated that burned field area can be located and its area can be estimated using Landsat 8 images. The Index BAI was selected as discernment index for burned/unburned fields in Landsat 8 images. Conclusion Present study was aimed to evaluate the accurate methods for monitoring residue management after wheat harvesting. With this vision, the potential of Landsat 8 sensor local data for monitoring residue burning was evaluated using satellite spectral indices and Linear Spectral Unmixing Analysis. Results indicate that residue ash spectra had lower reflectance when compared to the residue, soil and green plant except NIR band spectra. The contrast between residue, soil, green plant and residue ash spectra was particularly evident in the NIR bands. Results indicated that burned field area can be located and its area can be estimated using Landsat 8 images. The Index BAI was selected as discernment index for burned/unburned fields in Landsat 8 images.
