m. bagheri bodaghabadi

The use of accurate and up-to-date information is crucial for sustainable resource management in agriculture. Satellite imagery provides the opportunity for the comprehensive monitoring of resources, and enables precise planning not only for rural development and agricultural sectors but also for national development programs and the implementation of food and water security policies in the country. It also helps prevent land use changes and their degradation. In this research, an attempt has been made to calculate the cultivable land area in Shahr-e-Kord Plain located in Chaharmahal-va-Bakhtiari Province. The imagery data of Landsats 7-8 and Sentinel-2 satellites were obtained in the form of NDVI index during the period from 2013 to 2022, which processed and validly classified using unsupervised algorithms in GIS environment. All existing features was differentiated based on digital number values and green area surfaces identified for different time periods. The sound and secure areas of cultivation were considered based on the frequencies of plantation during the 2013-2022. Average of the agricultural lands approximated 9,392 ha. Due to recurrent water shortages in recent years, only about 2,603 ha of agricultural lands in the study region have been under permanent cultivation over the 8-year study period and the rest have been abandoned over time or left for fallow and livestock grazing. Accordingly, as a basis for the integrated land-water-crop system planning and recommending policy for conservation of permanently cultivable land resources in the agricultural system of the region, the map of their spatiotemporal distribution was prepared and presented with a pixel specific precision, by different years of cultivation.

Information about the state of vegetation is very important for environmental planning, land preparation and achieving sustainable development. In this study normalized differential vegetation index (NDVI) values were calculated based on Landsat 8 satellite images in order to show temporal and spatial changes in the vegetation cover of agricultural lands in Sistan plain over ten years (2011 to 2020) using the Google Earth Engine platform. Additionally, the NDVI index were classified using decision tree algorithm in order to analyze vegetation changes using thematic change workflow method. By comparing classified images with reference samples which collected from ground sampling, validation was carried out. Then, in order to assess accuracy of vegetation maps, the error matrix was prepared, the overall accuracy and kappa indices were determined. The values of overall accuracy and kappa indices indicated optimal accuracy and it can be stated that there is moderate agreement between ground samples and the classified images (i.e., kappa index is 0.48 to 0.7). The central areas of Sistan plain have a decline in vegetation, whereas areas in northern and eastern have an increase. The cover vegetation on lands of Sistan plain decreased in 19260.4 ha while increased over 25633.2 ha throughout ten years. Examination of NDVI index shows instability of production in this area due to aforementioned factors.

 The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the natural resources. However, farmers and stakeholders need to be aware that through their management practices, they affect soil quality and, with the short-term goal of production and greater profitability, lead to soil degradation. They can both use the land economically and improve and maintain soil quality by balancing production inputs and refining their management approaches. There are different management systems of productivity in agricultural lands in Neyshabour plain in northeastern Iran. In addition to the water and soil limitations in the study area, the prevalence of the smallholder system and the unwillingness of farmers to integrate smallholder, has further increased the destruction of soils in the study area. The objective of this study was to assess the changes in soil quality index in surface soil and profile (0-100 cm) and calculate the correlation between soil quality index and alfalfa and rapeseed yield in rangeland and agricultural areas managed by smallholders, total owners, and Binalood Company in the study area.

 A total of 21 soil profiles were described in the total owner, smallholder and Binalood company management system and sampled from the alfalfa and rapeseed lands. Questionnaires were prepared with the help of farmers and experts in the study area based on Analytic Hierarchical analysis (AHP) method. The physical and chemical characteristics of the soil samples were determined. The important soil characteristics affecting plant growth were determined by interviewing farmers and experts study area. Soil quality index in the minimum data set (MDS) was calculated by two methods of principal component analysis (PCA) and expert opinion (EO), by additive and weighted methods in surface soil and profile. To achieve a single value for each soil properties in the soil profile, two methods of weighted mean and weighted factor were used. To evaluate the accuracy of the assessment, the correlation between soil quality index and alfalfa and rapeseed yield was investigated of the various management system.

The results showed that the highest additive and weighted soil quality index at both surface and soil profile in both PCA and EO methods were in rangeland. It was due to lack of cultivation and maintaining organic matter comparing to agricultural land. The total owner management system due to its economic power and the use of appropriate and scientific methods comparing to smallholder management system, showed the highest additive and weighted soil quality index. In all management system, the EO-calculated weight index by weighted factor method had the highest value due to assigning the suitable weight for soil characteristics. The correlation analyses soil quality indices with canola and alfalfa indicated that the EO soil quality calculated by weighted factor for the soil profile were more correlated than surface soil in total owner system and the Binalood company. Weight coefficient method due to the application of different weights to each layer based on their importance, showed a higher soil quality index in both EO and PCA sets than the weighted average method. The reason for better EO performance probably is that the PCA is a reducing the dimensions, meanwhile, the minimum data selection in the EO method is based on regional experts which are familiar with cause-and-effect relationship of the soil properties. Due to the relatively good correlation of the yield of the studied products, with the soil quality index, an appropriate management needs to maintain and improve soil quality, especially in the smallholder system, as well as meeting the nutritional needs of these products.

 Soil quality assessment in this study indicated that calculation of the soil quality index only considering the surface soil properties may not provide complete information for the farmers and land managers. Then inclusion of both surface and profile soil properties with farmers' knowledge and study area experts are essential for sustainable soil management. On the other hand, the differences in the management system also affected the soil quality index. Although the smallholder management system due to low input, especially chemical fertilizers, water and agricultural implements, had a high potential concerning environmental issues, but in terms of production, total owner and Binalood company management systems because of their high economic strength had the higher soil quality index. The farmers and stakeholders of the total owner management systems should be considered despite the proper management, however due to high inputs of fertilizer and water, especially in the Binalood company, the production may not be sustainable. Therefore, for further studies, calculating the water consumption in the desired management systems is recommended.

Land evaluation plays a decisive role in determination of land suitability for the intended uses. For this purpose, various approaches have been proposed, among which the parametric approach is an important one. In this approach, the land index (LI) is calculated using the Khidir (the square root) and/or the Storrie methods, and then the land suitability classes were determined based on the LI. Unfortunately, in many land suitability studies, the LI has been used without any correction, called uncorrected land index (ULI) that led to different results in evaluation of land suitability approaches. The current study shows the importance of the corrected land index (CLI) and its effect on land suitability classes.

In this study land suitability classes were determined by four methods including 1-simple limitation, 2- number and intensity of limitations, 3- Kiddir (square root) and 4- Storrie, using two cases i.e. the CLI and ULI. Properties and criteria for determining land suitability classes are shown in Table1. Simple limitation method is based on the Liebig’s law or the law of the minimum. Land classes are defined according to the lowest class level of the land characteristics. Number and intensity of limitation method has been described in Table 1. In parametric approach, the ULI is calculated using Kiddir and Storrie methods as shown in equations 1 and 2, respectively. The relationships between ULI and CLI are presented in Table 2. (1) (2) Then, a simulation process was done for the eight characteristics involved in calculating the land suitability index. For this purpose, one million random values were considered for each of the S1 to N2 classes; so that the minimum rating (Rmin) was a random number for each class in own defined range (Rating in Table 1) and the other seven characteristics were random numbers between Rmin and 100. For example, in the S2 class, a minimum random number is in the range of 60 to 85 and seven other characteristics were between this Rmin and 100. Finally, a total of five million random simulations were considered.

Table 3 shows the results of five million simulations for S1 to N2 classes. Based on the minimum, maximum and mean values obtained, it can be seen that the simulation process is acceptable. These numbers show that the simulations have simulated almost all the cases that may occur in reality, from the best to the worst. Based on the results, it is clear that the mean values of the ULIs or the Storrie method are much lower than the Khiddir ones (Table 3), but there was no significant difference between mean values both in Storrie and Khiddir methods using CLIs. These results are sufficient to conclude the importance of using CLIs and to show the difference between the results obtained from the CLIs and ULIs. Tables 4 to 8 show the results of one million simulations for each suitability class. The present study revealed that the results of the four employed methods using the CLIs are much closer, especially for the Storrie and Khiddir methods. All together, the simple limitation method was more consistent with the Khiddir method. On the other hand, the employed methods differed greatly when the ULIs were used. The analysis of five million simulations has shown that the contradictory results of land evaluation methods in various studies can be quite mathematically logical, but with a different probability.

According to the findings of the current study, it can be illustrated that it is very important and necessary to use the CLIs to determine the land suitability class. The study showed that using the CLIs leads to the closeness of the results of different methods, so that there was no significant difference between Storrie and Khiddir methods. In general, the results of the Khidir method are closer to the simple constraint method compared to Storrie. There was a significant difference between the Khiddir and Storrie methods using the ULIs, but the difference was too small and insignificant using the CLIs. Totally, the results of the ULIs may be largely inaccurate, misleading and unrealistic. Therefore, it is strongly suggested that the CLIs be used in determining the suitability classes, and then the results be compared with the observations in the reality.