Journal of Crop Production and Processing

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This study addresses the methodology of studying spatial variability of soil salinity. The information used is based on a semi-detailed soil survey, followed by a free survey, conducted in Ramhormoz, Khuzestan. The study of soil salinity variations was carried out using about 600 sampling points with an average distance of 500 m, at three depths of 0-50, 50-100, and 100-150 cm. To determine the spatial variability of soil salinity at different depths, the variogram which is a statistical function for the spatial variability analysis of the geographical variables was used. The results indicate that all variograms show almost the same range of 12 - 13 km which is closely related to the geographical distribution of the soil parent materials in the area. Ordinary block kriging was used to map salinity at different depths for a block dimension of 500 × 500 m. A comparison between the kriged estimates and the soil salinity map, produced during the soil survey, showed that the overall similarity between the test data and the classified kriging estimates was 40%, while the overall agreement between the test data and the soil survey salinity map was 36%. A detailed similarity calculation showed that the reliability of the classified kriging estimates representing the lowest salinity classes (S0, S1) is larger (75%) than the reliability of the soil survey salinity map representing these classes (50%). Consequently, the results indicate that geostatistical tools can be used to support the present-day procedures of soil salinity mapping.

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The analysis of the EC data set indicated that the spatial distribution of EC data of different depths are closely related to one another. It means that they are spatially cross correlated on one another and can be considered to be co-regionalized. It also implies that EC values at a particular depth contain useful information about the other depths which can be used to improve their estimation. In this research, we aimed to investigate the effects of using relevant ancillary information in the estimation procedure. To do this, cokriging was used. To evaluate this algorithm as a potential tool for mapping EC, its performance on the independent test data was evaluated and compared with the results obtained from studies using kriging. The results of the co-regionalization of EC at different depths indicated that cokriging the salinity data, although more rigorous from theoretical point of view, displayed no advantage over independent ordinary kriging at each depth. The results confirmed that cokriging improves little over ordinary kriging if the primary and auxiliary variables are almost equally sampled and all the variograms are identical. Also, ordinary kriging showed to be quite self-consistent since the predicted average salinity profile over the three depths was almost identical to the one predicted by cokriging. Considering the complexity of the cokriging and the LMC modeling, it is clear that there is no gain in using co-regionalization.

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Point data of weather stations are not important in and by themselves. Therefore, it is necessary to change these point data into regional information. Undesirable distribution of weather stations and their data deficiency hinder the direct determination of the regional information, unless sufficient data in the study area could be provided. Providing extra data using the geostatistical methods is practical, scientific, simple and quick, but adopting a suitable method is the basic question. The objective of the present study is to find a suitable method to estimate monthly rainfall in the central region of Iran. In this regard, the methods of kriging (ordinary kriging, log-kriging, co-kriging), weighted moving average (WMA, with the power of 1 to 5), thin plate smoothing splines (TPSS, with the power of 2 and 3 and with covariable) were used. Cross validation technique was used to compare these methods. Based on the variography analysis, the range of influence of monthly rainfall in the central region is about 450 km. The results show that TPSS, with the power of 2 and with elevation as a covariable, was the most accurate method to estimate monthly rainfall. In addition, it is preferable to use the selected interpolation method in the sub-basins with homogeneous climates instead of considering the whole region.

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This study was conducted in a sugar beet field at Collage of Agriculture Experimental Station, Ferdowsi University of Mashhad, Iran. In order to describe the pattern of spatial variations and density of Chenopodium album, Solanum nigrum, Amaranthus sp., Portulaca oleracea, Echinochla crus-galli, and Convulvulus arvense as the main prevalent annual and perennial weeds of sugar beet fields, geostatistic methods were used. Samples were taken by systematic method from the corners of (7m × 7m) grids, using (0.5m × 0.5m) quadrates in three stages (before application of herbicides, after herbicide treatment, and before harvesting sugar beets). The integrity of spatial variation of variables was determined by using variogram functions and distribution maps of species. The variograms indicated that variations of all variables did not happen by chance. The maximum and minimum ranges of variation were observed in Solanum nigrum (by 142.7m) and Portulaca oleracea (by 1.5m), respectively. Both maximum and minimum ranges of variations were related to pre herbicide application. The highest and the lowest spatial correlations were related to Amaranthus sp. (in the third sampling treatment) and Solanum nigrum (in the first stage of sampling), respectively. The spatial distribution maps confirmed the patchiness distribution of the weeds. The patch of weed was constructed from a dense point at the center, gradually tapering toward the edges. The patches were skewed across the rows and irrigation channels. The structure of patches altered during the growing season. Any information on the distribution of weeds in the fields can be useful to improve decision makings in relation to applying the herbicides, selecting the herbicide type or applying the amount of herbicide. Also it can be useful to better design of weed control programs.

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Evaluating agricultural land management practices requires a thorough knowledge of soil spatial variability and understanding their relationships. This study was conducted at a traditionally operated wheat field in Sorkhankalateh district, located about 25 km northeast of Gorgan, in Golestan province, Iran. Soil samples of the 0-30 cm depth were collected right after planting at the end of autumn 2004 , 100 × 180m plot in a nested grid pattern (n=101). A 1 m² plot of wheat was harvested at each of 101 sites previously sampled at the end of spring. Statistical results showed that frequency distribution of all data was normal. The highest and lowest CV was related to grain yield (20.40%) and pH (0.59%) respectively. Variogram analysis showed that all parameters had spatial structure and the range values showed considerable variability among the measured parameters. The ranges of spatial dependence showed a variation from 23.99m for total N up to 93.92m for K. Among the parameters, total N and ESP had stronger spatial dependence while P had the lower spatial dependence. Interpolated maps of Kriging demonstrated that crop and soil properties did not have a random pattern but had a spatial distribution. The spatial distribution of total N was similar to organic matter and also there was similarity between spatial distribution of harvest index and available P. The results demonstrated that, the spatial distribution and spatial dependence level of soil properties can be different even within similarly managed farms. Variography and Kriging can be useful tools for designing soil sampling strategies, characterizing management zones and variable application rates of inputs in the precision agriculture.

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This study was performed in two barley fields, in Experimental Station, Agricultural College of Ferdowsi University of Mashhad in 2003. Sampling was done by systematic method in which samples were taken from the corners of 7m*7m grids using 0.5m 0.5m size quadrates in three stages (pre herbicide, post herbicide and pre harvesting stages). The results indicted that the density of annual weed seedlings in sugar beet- barley rotation was more than fallow- barley rotation, and the density of perennial weed seedlings in fallow-barley rotation was more than sugar beet- barley rotation. Map of species distribution and density confirmed patchiness distribution of the weeds. The shape and size of patches differed based on the field and weed species, but spatial distribution did not change considerably before and after the application of herbicide. Percentage of free weeds area was 11.5% and 1.5% in fallow-barley rotation and 0.6% and 0% in sugar beet- barley rotation in the first and second sampling stages, respectively. These results indicate beside emphasis on weed infestation. The result also indicates inefficacy of sugarbeet-barley rotation compared to follow-barley rotation. Apparently, the evaluation of management and paying special attention to weed dispersal within the field assist in the implementation of appropriate management strategy, which includes high efficacy, and profit for farmers as well as least damage to crops.

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In this research, using geographic information system (GIS) and different geostatistical methods including the kriging and co-kriging (ordinary, simple and universal) as well as the radial basis functions, the spatial distributions of runoff coefficient were evaluated in Hamedan province. To this end, the annual runoff were calculated in 18 existing hydrometery stations and another 11 auxiliary points, using digital elevation model (DEM) and 11 years available data of the stations. The performance criteria for evaluating the methods were mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), and general standard deviation (GSD) along with the cross validation examination. A high regression between the runoff coefficient and watershed average slope was selected as auxiliary variable. The results showed that the runoff coefficient of the region changes between 3.5 and 85%. The findings also indicated that the universal co-krigings with spherical semi-variogram model had better performance with the values of MBE (-0.0014), MAE (0.036), RMSE (0.054) and GSD (20.152). The universal and simple kriging with spherical model were equal in runoff estimation of the region and were ranked as the second methods to this propose.