Title: Detailed soil classification, mapping of some selected soil characters, and land suitability evaluation for rice & recommended second crops in Gav-Dasht research station-Mazandaran province
Author: Shahram MahmoudSoltani
Soil maps on a detailed scale are frequently used as the basic level of information for regional land use assessment. However, they are impractical for suitability predictions concerning quality crops, which require an enlargement of details that can be guaranteed only by detailed soil survey and mapping. Therefore, the Lack of detailed soil data has been a major constraint for making agronomic decisions in the farms (especially research farms). A soil survey was carried out to characterize and classify the soils of around the 400 ha of Gavedasht, Amol, Mazandaran province using the soil taxonomy (USDA) and the world reference base for soil resources (WRB) classification systems. The soil map produced at a scale of 1:5000 using FAO/UNESCO legend showed just one soil type. The soils belong to one major soil order: Alfisols (USDA), and are classified in the family name of Fine, mixed, superactive, thermic Typic Haploxeralfs. This soil covered about 100% of the area and supports crop productions that make plantation agriculture almost suitable.
Land suitability evaluation
Regarding the importance of rice as a strategic product and the importance of land suitability assessment studies in the optimal and sustainable use of land, it is necessary to employ the multi-criteria decision-making strategy and the capabilities of fuzzy systems in order to assess land suitability. The main objectives of this research are to determine the most reliable method of land suitability assessment for rice using integrated fuzzy decision making and optimum soil depth estimation that is used in land suitability evaluation for irrigated rice. After taking soil samples from four depths of 0 to 25, 25 to 50, 50 to 75, and 75 to 100 cm in 50 observation points, and from genetic horizons of representative pedon, which excavated in the region, the parameters needed for land suitability evaluation of rice were measured. The grain yield was harvested at a 1 m2 plot at each site. Then the land index was calculated and compared using parametric, fuzzy, Fuzzy-AHP, and Fuzzy-AHP-OWA methods at four depths. According to the results, the highest correlation coefficient (R2 = 0.37) was obtained for the Fuzzy-AHP-OWA method with a half quantifier, which indicates the superiority of this method compared with other methods. The results showed that there is a low correlation between the actual and predicted yield by all methods for each of the four depths. The predicted performance for each studied depth was correlated positively with land indices. Also, the actual performance, except the depth of 0 to 25 cm for the other three depths, has shown such a relation. Considering the similarity of the results obtained for depths of 0 to 50 and 0 to 100 cm in all methods, 0 to 50 cm soil depth might be a relevant alternative for the optimal depth to evaluate land suitability for rice in paddy fields in the Goldasht area of Amol.
Rice is one of the strategic agricultural crops of the Iranian growing population, which provides a high percentage of their dietary protein. With increasing rapid population growth and its consequent increase in food demands, it is necessary to keep the focus on rice yield and soil condition relationships, because the soil and many of its characteristics have a significant effect on plant growth and yield. Soil quality index (SQI) is a useful concept when assessing the sustainability of agricultural activities. Therefore, the objective of the present study is to evaluate the relationships of yield and soil quality in order to provide guidance regarding proper soil management practices for the sustainable development of Goldasht research paddy fields, south of Amol city, Mazandaran province. One hundred and twenty- eight surface soil samples were collected to measure some important physical and chemical soil properties. Also, rice grain yield was measured at 14% moisture content during the harvesting stage on related soil sample points. Firstly, to determine the SQI, the characteristics that have the highest effect on the SQI of the region were separated by the principal component analysis. The fuzzy logic method was used to convert quantitative soil properties to qualitative ranking, and finally, the indices were combined using the concept of coefficient of variation. The average recorded rice grain yield of the studied area is 3498 kg ha-1 and SQI vary between 0.47 and 0.97. Contrary to expectation, there isn’t a significant correlation between performance and SQI in the studied area (reasons were explained in the discussion completely). According to the results of SQI classification, available phosphorous is the most important limiting factor for soil quality. Direct comparison among yield maps and soil quality indices indicates that proper soil conditions along with proper management of the farm can be an effective solution to maximize rice yield. Thus, proper management of farmers can remedy shortcomings and somewhat inappropriate soil conditions. However, in many cases, proper soil quality alone cannot compensate for crop management shortcomings.
Keywords: Soil classification, Land suitability evaluation, Spatial variability, Rice, Multi-Criteria Decision Making, Fuzzy set theories