Detailed soil information and soil maps are essential for the monitoring, management, conservation and restoration of natural ecosystems, rangelands and protected areas. Semi-automated mapping methods have advantages over conventional ones, and the selection of the best interpolation method and accurately predicted soil property maps are important for effective management and conservation strategies. Spatial soil information is important also for managing natural resources, predicting soil properties, improving sampling designs in future agro-ecological studies, and for assessing protected areas. We investigated the suitability of different interpolation methods for spatial variability predictions and for studying various soil properties within a rangeland ecosystem and the Sabalan National Natural Monument protected area, in northwestern Iran. Soil samples were collected randomly from a depth of 0-30 cm, and various properties were measured in the laboratory. Normality of data was examined and spatial statistics was applied to determine spatial variation of the properties. Interpolation methods of inverse distance weighting, Kriging and Cokriging were applied and compared for suitability. Results were evaluated using crossvalidation. The results of applying spatial statistics demonstrated that soil properties had spatial dependence; Cokriging emerged as the most accurate technique overall.
Aboveground Net Primary Production (ANPP) of rangeland ecosystems is driven by interactions among multiple environmental factors. This study aimed to model the combined effects of precipitation, elevation, and soil conditions on ANPP variation along an elevation gradient. Ground surveys and vegetation sampling were conducted in 2016 through 26 sampling sites along two elevation profiles in the rangelands of Moghan-Sabalan, Ardabil Province, Iran. At each sampling site, the ANPP of each plant functional type (PFT; grasses, forbs, and shrubs) was measured, and soil samples were taken from 0–15 to 15–30 cm depth. Regression analysis and structural equation modeling (SEM) were used to investigate the factors affecting both total and PFT ANPP. Soil variables were the best predictors of grass (R2 = 0.51), forb (R2 = 0.61), shrub (R2 = 0.71), and total (R2 = 0.76) ANPP. The SEM interpretation suggested that precipitation is the most important direct driver of ANPP with R2 values of 0.20 (Total), 0.30 (Shrubs), 0.26 (Grasses), and 0.10 (Forbs). Whereas soil factors were good predictors in the regression models, the SEM models demonstrated that soil factors were generally unimportant compared with climate, likely owing to the close links between soil-forming factors and climate. The results make it possible to estimate annual ANPP combined with climate forecasts and leads to more accurate estimates of future grazing capacity by policy makers and stakeholders.
As the utilization of rangelands in Iran has exceeded 2.2 times its carrying capacity, increasing numbers of livestock, especially in drought years, have dramatic effects on rangelands. In this regard, the prediction of forage production is an important management strategy to mitigate the consequences of drought. In Plour and Saveh Range Experimental Sites, climatic factors such as precipitation, temperature (mean, maximum and minimum), evapotranspiration, standardized precipitation index, reconnaissance drought index, and/or a combination of these factors were used to construct a predictive model of forage production. For each climatic variable and/or index, 33 time periods of 1–4, 6 and 9‐months were specified. We used principal components analysis, stepwise regression and best subset regression, to reduce the number of variables and then the appropriate time periods selected. To select a model, assessment statistics of correlation coefficient, mean of bias error, root mean of square error, mean of absolute relative error and ideal point error were used. Finally models derived from combined climatic factors and drought indices were selected for the prediction of forage production at both study areas. In the arid region of Saveh, production is more effected by precipitation, while in the humid and colder region of Plour, minimum temperature had more effects on plant growth. In Saveh, due to a lack of rainfall in February and minimum temperatures in March, production has direct and indirect relationships with drought and with maximum temperatures, respectively. In the Plour, production has a direct relationship with the maximum temperatures of March–June and drought of February–March and has an indirect relationship with the maximum temperatures of May–June.
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