regression analysis

Land degradation due to its impact on agricultural production, environment, security and quality of life in the 21st century has been raised as a global issue. It provides evidence such as biodiversity loss and climate pollution. The purpose of this study is to investigate the effects of socio-human factors on land degradation in the coasts of Bushehr, Hormozgan and Sistan and Baluchestan provinces. For this purpose, during the period 1988-2019, Landsat satellite data including OLI, ETM+ and TM sensors and population census information were used. To prepare the land use map, first geometric and radiometric corrections were made. Then, the supervised classification method and the maximum probability algorithm were used. In order to determine the role of effective variables, multivariate regression analysis method was used. SAVI and SI1 were considered as dependent variables, use of man-made areas and population density were also considered as independent variables. Based on the results of the change detection, the land use class of man-made areas and the population variable are associated with an increase of 2.7 and 2.3 times, respectively. Population density in the study period has increased from 96 to 194 people per square kilometer. According to the results of regression analysis, the use of man-made areas was selected as a more influential variable on the SAVI and SI1 indices. On the other hand, indicates the impact of socio-human factors on the process of land degradation by 37%, that the socio-human variables are an important factor and part of the process of land degradation. Therefore, the findings of this study will enable the adoption of fundamental decisions in the formulation of management policies for planners and managers in the field of natural resources and the environment for the sustainability and evaluation of coastal areas.

In order to study the relationship between quantitative and qualitative traits with sesame grain yield in nutrient treatments as chemical, biological and nano fertilizers and drought stress, an experiment was conducted as factorial split plot based on randomized complete block design with three repetitions. The experiment factors including irrigation regimes, irrigation withholding at different stages of development based on BBCH, full irrigation and irrigation up 65 and 75 BBCH, three nitrogen fertilizers as nitroxin, urea and combination with nitroxin and 50 % urea and four combinations of potassium fertilizer, as non-fertilized as control, spraying and soil application of potassium nano-chelate (2 ppt and 2 kg.ha-1 respectively) and potassium dioxide (2 l.ha-1). The highest grain yield was obtained in irrigation up to 50 % seed ripping in non-application of potassium and use of nitroxin with average of 1340.5 kg.ha-1 that about 35 % increased the average of this trait compared to the control treatment. The grain yield was positively and significantly correlated with stem diameter, plant dry weight, number of capsules, number of seeds per capsule, 1000-seed weight and oil percentage and yield. There was no significant correlation between morphological and yield traits. The results of stepwise regression showed that the maximum grain yield difference can be attributed to the total number of capsules, 1000 seed weight, number of seeds per capsule, oil yield and oil percentage which explained 91.1 % of the grain yield changes. The application of potassium nano- chelate in irrigation withholding at 50 % seed ripening (equivalent to 75 BBCH) and using of nitroxin bio-fertilizer had the highest grain yield in second cultivation after wheat and is suitable for dry and semi-arid climates.

Agronomic approaches to weed management are effective, low-cost, environmentally-friendly options without adverse effects on human health. These approaches should be emphasized to develop a sustainable agriculture (Zimdahl, 2007). A living cover crop can affect weed competition by competing for growth resources and the residue of cover crops may affect weed growth and competition with their allopathic potential (Karbalaei-Khiavi et al., 2016). Fenugreek plants, with their shallow root system, ability of nitrogen-fixing as well as allopathic potential, were chosen as a cover crop to study the effects of various densities of fenugreek on growth and weed competition.
An experiment was conducted in a randomized complete block design with six treatments and three replications at the experimental station of Torbat-e Jam University in 2016. The treatments included the seeding of 0, 10, 20, 30 and 40 fenugreek plants per square meter as a cover crop and a control treatment. The destructive sampling was taken to determine the dry matter production in sunflower, fenugreek and weed from 60 cm of rows. After examining the residuals for normality, analysis of variance was done using SAS software.
The highest and lowest weed dry matter were obtained from the densities of 0 and 20 fenugreek plants per square meter, respectively. The results showed that the planting of fenugreek, regardless of its density, at the same time as the sunflower planting reduced growth of competing weeds by at least 50%. This reduction can also be increased by up to 78% by establishing the appropriate density of fenugreek. The highest and lowest leaf area, dry matter production and seed yield in the sunflowers was obtained from densities of 20 and 40 cover crop plants per m-2, respectively. The presence of cover crops in all densities, except for 20 fenugreek plants per m-2, significantly reduced sunflower yield compared with the control. Regression analysis also showed that the response of sunflower growth and yield to increased cover crop density follows a quadratic trend. The sunflower growth and yield was reduced at lower and higher densities of cover crop compared to its optimum density. The role of cover crop densities higher than the optimum level, however, was greater than the lower one in reducing competing weeds. Thus, the establishment of a suitable plant density of fenugreek would occupy an existing vacant niche, reduce available growth resources, and ultimately reduce the growth of competing weeds (Mohammadi et al., 2015).
Due to the reduction of competing weeds in the presence of fenugreek and the economic value of fenugreek production, this plant can be used as an effective cover crop in sunflower fields as an option in a comprehensive weed management system.

Whereas climatic parameters find importance due to their impacts on water resources, therefore this research was done carried out for investigating of meteorological drought and its impacts on Kerman groundwater resources in the eighteen- year’s period (1997-2014). In this regard Standardized Precipitation Index (SPI) values in Kerman meteorological station and Groundwater Resource Index (GRI) of plain in different time scales (1, 3, 6, 9, 12, 18, 24 and 48 months) was calculated. The results of correlation test between GRI index in different time scales and SPI index in the time period under research without any delay and with a time lag of one to twelve months showed that 6 and 18 months GRI index have significantly related to 48 month SPI index with 0.628 and 0.631 Pearson coefficients, respectively and also 48 months GRI index was correlated to 48 months SPI index with 6 month lag time and its Pearson coefficient was 0.686. The results of regression model showed that up to 57 percent of variations in GRI index explained and justified by SPI index. This is due to other factors such as uncontrolled exploitation of groundwater resources on the loss of groundwater and groundwater index linked. Iso-decline maps showed that the decline in groundwater levels across the plain, an average of 20 meters and rising in ground water levels in the range of Kerman city by 14 meters.

Having the richest plants biodiversity, Hyrcanian natural mixed-beech forests contribute to the huge carbon pool in the different soil layers. This research aims to develop modeling soil carbon sequestration in terms of the plant biodiversity indices to manage soil carbon stock with respect to trend of sustainability, fertility, carbon cycle, and planning to face with climate change in local/ regional scales. After measuring plants biodiversity indices and soil carbon factor over the field operations, simple and multiple linear regressions as well as curve estimation regression were applied in the process of modeling. According to Adj.R2, SEE and AIC, simple and multiple linear regressions had no considerable accuracy (AICmin = 힝.74). Analysis of non-linear models showed that model S including index of species dominance belonging to herbal coverage was the best predictor with the least error and highest certainty (AICmin= -171.23) to estimate soil carbon pool in the studied forests. In the following, the results showed that although the log-transformed models with increasing the parameters and adding the correlated explanatory variables were valid (VIF