Using of pH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network

Monitoring of groundwater quality is one of the important tools to provide adequate information about water management. In the present study, artificial neural network (ANN) with a feed-forward back-propagation was designed to predict groundwater salinity, expressed by total dissolved solids (TDS), using pH as an input parameter. Groundwater samples were collected from a 36 m depth well located in the experimental farm of the City of Scientific Researches and Technological Applications (SRTA City), New Borg El-Arab City, Alexandria, Egypt. The network structure was 1–5–3–1 and used the default Levenberg–Marquardt algorithm for training. It was observed that, the best validation performance, based on the mean square error, was 14819 at epoch 0, and no major problems or over-fitting occurred with the training step. The simulated output tracked the measured data with a correlation coefficient (R-value) of 0.64, 0.67 and 0.90 for training, validation and test, respectively. In this case, the network response was acceptable, and simulation could be used for entering new inputs

Climate Change Factors' Impact on the Egyptian Agricultural Sector

Climate change is the greatest threat to agriculture and food security, particularly in developing countries. Climate change occurs as CO2 levels in the atmosphere rise, causing changes in wind patterns and rainfall and rising temperatures. This study assumes that climate change will have a long-run impact on Egypt's agricultural sector. So, an autoregressive distributed lag model (ARDL) was applied to examine the effects of climate change factors and other economic factors on Egyptian agricultural GDP in the short and long run from 1990 to 2020. The findings indicate that climate change factors have a long-run impact on Egypt's agricultural sector. In the long run, CO2 is the primary cause of Egypt's increasing temperatures. In the short run, climate change occurs because CO2 levels in the atmosphere increase, resulting in global warming, storms, floods, and rising sea levels. The result is that rising temperatures have reduced agricultural GDP

Climate Change Factors' Impact on the Egyptian Agricultural Sector

Climate change is the greatest threat to agriculture and food security, particularly in developing countries. Climate change occurs as CO2 levels in the atmosphere rise, causing changes in wind patterns and rainfall and rising temperatures. This study assumes that climate change will have a long-run impact on Egypt's agricultural sector. So, an autoregressive distributed lag model (ARDL) was applied to examine the effects of climate change factors and other economic factors on Egyptian agricultural GDP in the short and long run from 1990 to 2020. The findings indicate that climate change factors have a long-run impact on Egypt's agricultural sector. In the long run, CO2 is the primary cause of Egypt's increasing temperatures. In the short run, climate change occurs because CO2 levels in the atmosphere increase, resulting in global warming, storms, floods, and rising sea levels. The result is that rising temperatures have reduced agricultural GDP

Evaluation of Barley Cultivated Areas' Actual Status in Egyptian Newly Reclaimed Lands

Barley is a globally important strategic cereal crop, which grows well under various climatic and drought-stress conditions. In Egypt, barley is a major winter crop cultivated in old and newly reclaimed lands that suffer from a lack of irrigation, low soil fertility, and salinity of both soil and water. However, there is a lack of awareness of the nutritional role of barley for both humans and animals. Therefore, this paper aims to evaluate the actual status of cultivated areas of barley, especially in newly reclaimed lands in Egypt during the period (2004/2005–2018/2019). The study is based on descriptive and quantitative analysis using means, growth rates, relative importance, and robust regression. Results show that barley cultivated areas in newly reclaimed lands represented about 76.9% of total cultivated areas during (2004/2005–2018/2019). It means that barley is more adaptable in dry and marginal areas, meaning it is a sustainable plant that can face drought, land degradation, and climate change. Also, production costs, farm prices, and net return of barley are the most important factors that affect the producer’s decision to cultivate barley during the study period. In addition, there is excessive use of some variables during the study period; after estimating the production function of barley using robust regression, it is shown that it is necessary to reduce these variables in the production process to achieve economic efficiency

Evaluation of Barley Cultivated Areas' Actual Status in Egyptian Newly Reclaimed Lands

Barley is a globally important strategic cereal crop, which grows well under various climatic and drought-stress conditions. In Egypt, barley is a major winter crop cultivated in old and newly reclaimed lands that suffer from a lack of irrigation, low soil fertility, and salinity of both soil and water. However, there is a lack of awareness of the nutritional role of barley for both humans and animals. Therefore, this paper aims to evaluate the actual status of cultivated areas of barley, especially in newly reclaimed lands in Egypt during the period (2004/2005–2018/2019). The study is based on descriptive and quantitative analysis using means, growth rates, relative importance, and robust regression. Results show that barley cultivated areas in newly reclaimed lands represented about 76.9% of total cultivated areas during (2004/2005–2018/2019). It means that barley is more adaptable in dry and marginal areas, meaning it is a sustainable plant that can face drought, land degradation, and climate change. Also, production costs, farm prices, and net return of barley are the most important factors that affect the producer’s decision to cultivate barley during the study period. In addition, there is excessive use of some variables during the study period; after estimating the production function of barley using robust regression, it is shown that it is necessary to reduce these variables in the production process to achieve economic efficiency