spatial regression

Plant transpiration is a process through which part of the water in the plant is transfered out of the pores. The amount of plant transpiration data is useful in developing strategies for water sustainability. Field measurement of plant transpiration is pointwise and discontinious and has some difficulties. The purpose of this study is to provide a plant transpiration map using satellite images and spatial modeling to identify the impact of environmental variables on transpiration in western Iran. First, by using the algorithm written in Google Earth cloud system, the plant vegetation translocation map was extracted as a dependent variable, and then the layers of solar radiation, water vapor pressure, wind speed and maximum temperature, vegetation index were selected as independent variables for modeling. The results showed that the prevalence of plant transpiration in the studied area range from 0 to 2.6 mm per day. Field data collected from 16 typical farms of Agricultural Research Center of Kermanshah and Kurdistan Provinces were used for validation and by comparing the map pixels and the ground data, the root mean square error and the Nash Sutcliffe coefficient were obtained to be 0.71 and 0.63, respectively. After implementing general regression and spatial regression models based on evaluation criteria, the spatial regression showed better explanatory and estimation power than the general regression. Based on this model, the coefficients of each variable were estimated spatially, making it possible to determine the spatial variation of the relationships between the variables. Also, the results of both models showed that the vegetation indices and water vapor pressure deficiency in western of Iran have the most positive effect on vegetation transpiration. Using the results of this study, areas prone to severe plant transpiration can be identified for improving the management of irrigation systems and providing intelligent agricultural services.

Identifying the impact of consumption of different inputs on agricultural production helps to provide the right information to the policymaker. This study aimed at estimating the production function of two basic products (irrigated and rainfed wheat, and rice) using the three-dimensional spatial panel pattern (product, province and time). The required information was collected for the 30 provinces of Iran in the period from 2006 to 2018; and according to the selection criteria of the production functions, the translog function was selected as the superior function. Due to spatial effects and diagnostic tests, the spatial correlation model was evaluated as the optimal model. The study results showed that the elasticity of cultivated area, consumption of pesticides, tractors and combine harvesters to the production of irrigated wheat, rainfed wheat and rice products was positive and the elasticity of labor, consumption of seeds and fertilizers to the production of these products was negative; in addition, the elasticity of production inputs did not change significantly over time, and the trend of impacts of the production inputs on the production of the studied products was almost constant. Due to the surplus of labor in the production of these three products, the withdrawal of labor from production in this sector should be on the agenda and more investment should be made in investment factors such as combine harvesters and tractors to increase production so that the country can achieve self- sufficiency.

The CO2 emission is one of the components of global warming which has been more attention paid. Globalization as a phenomenon in the world has been affected the economic and social aspects of people. Therefore, given that globalization and the growing share of countries in the global trade network require extensive economic and productive activities, there is doubt about the impact of globalization on CO2 emissions. Considering the lack of consensus, the present study seeks to answer the question of whether globalization will increase or reduce CO2 emissions. To achieve the aim spatial panel data is used. The results of this study showed that globalization has a positive and significant effect on CO2 emissions in the MENA region. In other words, the growing trend of globalization has led to increasing CO2 emissions. Also, due to the spatial relationship between the MENA countries, it is suggested that countries begin to more cooperation and common policies in order to reduce the negative effects of economic activities on the environment.

Understanding the behavior of phenomena requires attention to all its dimensions, and one way to understand their complexities is modeling. Soil surface moisture is a key variable for describing drought, water, and energy exchanges between Korea and the air, as well as assessing crop conditions. Soil moisture is affected by both environmental variables and many environmental variables such as runoff, soil erosion, and crop production, but is highly variable due to unstable spatial and temporal conditions. The purpose of this paper is to investigate, extract and evaluate the spatial model of soil moisture dispersal after more than normal rainfall in 1979-98 in Kurdistan province. In this regard, after analyzing soil moisture dispersion as dependent variable and precipitation variables, snow water equivalent, topographic moisture index and vegetation index were selected as independent variables. Then, using a general regression model (OLS) and geographically weighted regression (GWR), spatial modeling was performed. Based on the evaluation criteria, the results showed that the GWR model with R2 = 0.74 has better explanatory power and better estimation than the general regression model with R2 = 0.68. According to the results of the GWR model, snow water equivalent variable in the northern mountainous regions had the highest effect on evapotranspiration and the least effect on soil moisture. The obtained spatial model can identify low or moist soil areas in order to identify environmental potentials and improve decision making, allocation and spatial distribution of agricultural services.