Considering soil compaction problem in sugarcane fields due to using heavy harvester and haulout equipment under unsuitable moisture conditions, this research aims to assess soil compaction in sugarcane fields located in Da’bal Khazaei Plantation unit of Sugarcane Development and By-product Company, Ahvaz. Undisturbed soil samples from the furrow (wheel tracks) were collected for measuring soil water content and bulk density. Considering the changes in soil texture of sugarcane fields, for expressing the degree of soil compactness, in addition to soil bulk density (BD), relative bulk density (BD divided by reference BD) was also determined. The change in soil mechanical resistance with depth was determined by a cone penetrometer. Results showed that most of soil BD values measured in the sugarcane fields were in the range of small root development scale (high limitation). Comparing the calculated RBD values with optimum value (0.85), it was observed that most of the values were higher than the optimum values recommended for root growth. This shows excessive soil compaction in the sugarcane fields. The values of cone indices measured in soil profiles indicated that most of the values were higher than either limiting (2 MPa) or critical (3 MPa) values for root growth. Therefore, for improving soil physical fertility and achieving sustainability in crop production, management of farm machinery traffic in sugarcane fields, especially at the harvest time, needs to be reconsidered.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

Background and Objectives: Soil bulk density (BD) is important because of its direct effect on soil properties such as porosity, soil moisture availability, and hydraulic conductivity and its indirect effects on root growth and crop yield. Environmental processes and agronomic practices induce soil bulk density to vary greatly in both space and time. On the other hand, measuring it on a large scale requires a lot of time and is not economical. As a result, indirect methods are used to measure the bulk density when performing large-scale field activities. Pedotransfer Functions (PTFs) have been broadly implemented as indirect cost-effective and time-saving methods in predicting soil bulk density. The purpose of this study is to evaluate the existing Pedotransfer functions in order to determine the bulk density for different soils of Sistan region as well as calibration and provide new Pedotransfer functions for the study area. Materials and Methods: After reviewing different reference, 64 different Pedotransfer functions (PTFs) published in different sources were selected to estimate the bulk density. These Pedotransfer functions were selected in such a way that 1)in a wide range of time scale (from 1957 up to date), 2) from wide regional, 3) from various soil land uses 4) from all types of regression techniques and 5) only using common and easily measured predictors such as sand, silt, clay and organic carbon. The soil samples collected in this study was 220 data, which was obtained from 110 points at two depths of 0-15 and 15-30. Three indicators of absolute mean error (ME), root mean square error (RMSE) and standard deviation of the predicted error (SDPE) were used to evaluate. Results: Among the existing Pedotransfer functions, Benites et al. (2007) with ME value equal to-0. 0008, RMSE value equal to 0. 1038 and SDPE equal to 0. 1033 had the best results. Based on the RMSE value of Yang et al. (2007) with a value of 0. 1038 with a rank of 1 and based on SDPE function with a value between 0. 0976 Leonaviciute (2000) had the best results. For the study area, 5 presented relationships including linear relationship between BD and OC, linear relationship between OC and BD squares, exponential relationship between BD and OC, linear relationship between BD and OC logarithm and polynomial relationship between OC and BD were presented. Conclusion: Based on the results it can be concluded that soil organic carbon (OC) is the most important factor in predicting soil bulk density and using soil organic carbon alone, soil bulk density can be predicted with relative accuracy. It can also be concluded that the 5 relationships developed in this study can be used to obtain the apparent density in the study area.

Background and Objectives: Spatial and temporal variations of soil characteristics occur in large and small scales. Study of these variations is very time-consuming and costly especially in large scales. In order to the fast and reliable determination of soil properties, various interpolation techniques have been developed and applied. The most widely used interpolation techniques in various sciences is the Kriging types. The copula function is one of the new interpolation techniques that are widely used in sciences such as hydrology. Thus, the aim of this study was to evaluate the spatial variations of some soil physical properties using copula function and to compare with geostatistics techniques. Materials and Methods: Sampling by regular networking was done in an area of 484 ha located in 10 km from the west of Baft city, Kerman province and finally, 121 surface soil samples were collected. After air drying, the apparent bulk density was determined using the Hunk, then the soil samples were passed through a 2 mm sieve to determine the percentage of sand. To interpolate, four functions of the Archimedean copula including the Clayton, Frank, Gumbel and Joe functions, and geostatistics techniques including simple, ordinary, universal and disjunctive Kriging and the Inverse Distance Weighting (IDW) method were used. The results were analyzed using Root Mean Square Error (RMSE), determination coefficient (R2), Mean Absolute Error (MAE) and Mean Bias Error (MBE). Results: Based on the descriptive statistics, soil bulk density and soil sand followed a normal and skewed distribution, respectively. In order to fit the copula function, the distribution functions of the studied variables were firstly determined. The results showed that the sand and bulk density followed the Frechet (3P) and Wakeby distribution functions, respectively. Also, based on the Pearson correlation coefficient, the correlation between pairs of points was determined in distances less than 2000 m and distances more than 2000 m were known as an independent distance. The estimation efficiency based on the determination coefficient (R2) showed that value of determination coefficient for copula function for the sand variable, 6% and for bulk density 8%, more than conventional geostatistics techniques were obtained. Also, the estimation error of copula function was minimum that indicate good performance of copula function to estimate the spatial variation of soil physical properties. Conclusion: The results of study showed that copula function, especially the median copula, have the better performance for estimation the studied soil properties. One of the most important reasons for this superiority is the ability to fit the marginal distribution function on the data in copula, while it is not possible in geostatistics techniques. Other reasons include the ability to express the correlation between the data at different intervals and the lack of sensitivity to outlier data in copula relative to conventional geostatistics techniques. Due to the skewness nature of soil data, as well as the need for more accurate analysis and interpretation of actual soil data, copula functions can be widely used to estimate of soil properties.

Introduction: In recent years, production techniques and equipment have been developed for conservation of tillage systems that have been adopted by many farmers. With proper management, overall yield averages for conventional and reduced tillage systems are nearly identical. Sometimes, field operations can be combined by connecting two or more implements. Combined operations reduce both fuel consumption, and time and labor requirements by eliminating at least one individual trip over the field. …

Soil bulk density measurements are often required as an input parameter for models that predict soil processes. Nonparametric approaches are being used in various fields to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm was introduced and tested to estimate soil bulk density from other soil properties, including soil textural fractions, EC, pH, SP, OC and TNV. As many as eight nearest neighbors, based on cross validation technique were selected to perform bulk density prediction from the attributes of 136 soil samples. The nonparametric k-NN technique mostly performed equally well using Pearson correlation coefficient (r=0.86), root-mean-squared errors (RMSE=2.5) maximum error (ME=0.15), coefficient of determination (CD=1.3), modeling efficiency (EF=0.75) and coefficient of residual mass (CRM=0.001) statistics. It can be concluded that the k-NN technique is an alternative to other techniques such as pedotransfer functions (PTFs).

The precision of soil data at any region depends largely on the nature of their variation which often is described by studying of their spatial variability. The location should be considered when analyzing such data because variation of soil properties depends on many process that act in space and time scale, therefore, potentially depends on a scale. This research was carried out to study the effect of scale on estimation precession of organic carbon and bulk density in paddy soils. Three different scales of study were selected at the field of the Rice Research Institute Iran in Rasht. At the first scale, 172 soil samples were taken from the center of 200×50 meter plots. At the second and third scales, 357 and 72 samples were taken from the center of 100×50 and 3×4m plots, respectively. The measured soil properties were organic carbon content and bulk density. All data had non-normal distribution, except bulk density in the third scale. Organic carbon in the second scale and bulk density in the third scale had the maximum and minimum coefficient of variation, respectively. A spherical model was fitted to the varigrams of organic carbon in all scales, which indicated that a single main factor affected organic carbon variations. In the other hand for bulk density, a linear model was fitted to the variograms of first and second scales, a spherical model was fitted to the third scale. Results showed that the first scale with the sampling distance of 200m may be the best scale for studying of organic carbon, and the second scale of 100m distance seems the best for the bulk density.

To obtain soil-moisture characteristic curve experimentally is time-consuming and usually subject to considerable errors. So, many investigators have tried to predict soil-moisture characteristic curve by different models. One of these models predicts soil moisture characteristic curve based on soil particle size distribution and bulk density. In this model, soil particle size distribution curve is divided into a number of segments, each with a specific particle radius and cumulative particle mass greater than that of the radius. Using these data, soil-moisture characteristic curve was estimated. In this model, a scale factor, a , is used which may be considered as a constant, or obtained by logistic or linear procedures. The average values of a for clay, silty clay, sandy loam, two loam soils, and two silty clay loam soils were 1.159, 1.229, 1.494, 1.391, 1.393, 1.253 and 1.254, respectively. For most conditions, soil particle size distribution curve is not available, but only the percentages of clay, silt, and sand could be obtained using soil textural data, which is not enough to draw a precise soil particle size distribution curve. In this situation, a precise soil particle size distribution curve must be initially developed on the basis of which the soil moisture characteristic curve can be predicted. In this study, using soil textural data of seven different soils, soil moisture characteristic curve of each was estimated. In these estimations, logistic and linear methods were used to obtain the a value. Then, the results were compared with those of measured soil moisture characteristic curve. For estimation of soil particle size distribution curve, two extreme values for soil particle radius, 125 and 999 mm, were used.The results indicated that using particle radius of 999 mm is more appropriate. On the other hand, it was found that for clay, silty clay, and sitly clay loam texture, it is more appropriate to employ a linear equation to determine a for estimating soil-moisture characteristic curve while the logistic equation can be more appropriately used for loam and sand loam textures.

This research, carried out in order to evaluation of the inverse distance weighting method and kriging, (ordinary and lognormal kriging) for estimate Organic carbon and Bulk density in paddy fields of the Iranian Institute of Rice Research in Rasht. Spatial variability characteristics of variables were determined by semivariograms. The function used for quantifying the structure of regional variable. Estimation of Kriging carried out by 6 than 40 neighbors in 70 percentage range of search radius. For estimation variable used to method inverse distance weighting of exponent value 1 than 5. The best models for organic carbon and bulk density were spherical. Four statistics of mean error, root mean square error, reduced variance and percent error were used to compare the methods. Exponent value was in the estimation inverse distance weighting for Organic carbon 1 and Bulk density 4. Results show that kriging is the accuracy better of inverse distance. Beside the best estimator was ordinary kriging for Organic carbon and lognormal kriging for Bulk density.