Assessing the effect of spatial-temporal droughts on dominant crop yield changes in Central Malawi.

Central Malawi has intensely been subjected to different climate-related shocks such as floods, dry spells, and droughts, resulting in decreases in crop yields. Due to their recurrence arising from the effects of climate change, drought characterization, monitoring, and prediction are crucial in guiding agriculture-water users and planners to prepare drought risk management plans and early warning systems. This research analyzed droughts, using multiple drought indices and their impacts on dominant crops over Central Malawi. Forty years of hydro-meteorological data (1977-2017) from nine rain-gauging stations and crop yield data from 1983 to 2017 from four districts were analyzed. The study discovered that drought events in the Agricultural Development Division (ADD) are highly a function of rainfall deficit and high temperatures. The results highlighted that the rainfall patterns in the area are not dependable, calling for the utilization of climate-smart irrigation systems such as drip irrigation and rainwater harvesting technologies. Furthermore, we achieved that crops such as cassava and groundnuts must be promoted to withstand the long water stress duration. These crops also have a multiplier effect; hence, they can enhance food security in the region. This study recommends that using more robust variables in drought analysis studies is necessary for effective drought monitoring and early warning systems. In corroboration with disaster management NGOs, it is recommended that the government should be proactive in developing integrated drought management policies and planning strategies for drought adaptation and mitigation.

Testing Porter and pollution haven hypothesis via economic variables and CO2 emissions: a cross-country review with panel quantile regression method.

Nowadays, determining the factors influencing carbon dioxide emissions is a crucial issue for policymakers. So, this study examines Porter and pollution haven's hypothesis via foreign direct investment, financial development, and energy consumption in 14 countries of the MENA region during 2004-2016, using panel quantile regression that estimated the impact of these factors in quantiles of 0.1, 0.25, 0.5, 0.75, and 0.9. Also, the effect of population, trade openness, and economic growth variables has been investigated as controlling variables on CO2 emissions. The results of the research show that the impact of energy consumption, economic growth, and total population on all quantiles of carbon dioxide emission is positive and significant. Still, the effect of direct foreign investment on the amount of CO2 emissions is negative and only significant at 0.1, 0.5, and 0.75 quantiles, which supports Porter's hypothesis. Based on this hypothesis, the foreign direct investment entrance helps reduce the environmental pollution of the host country. Also, the effect of financial development on 0.25, 0.5, 0.75, and 0.9 quantile carbon dioxide emissions is negative and significant. Finally, the trade openness variable has a positive and significant effect on the quantiles of 0.1 and 0.9 CO2 emissions.

Climate data clustering effects on arid and semi-arid rainfed wheat yield: a comparison of artificial intelligence and K-means approaches.

Clustering algorithms are critical data mining techniques used to analyze a wide range of data. This study compares the utility of ant colony optimization (ACO), genetic algorithm (GA), and K-means methods to cluster climatic variables affecting the yield of rainfed wheat in northeast Iran from 1984 to 2010 (27 years). These variables included sunshine hours, wind speed, relative humidity, precipitation, maximum temperature, minimum temperature, and the number of wet days. Seven climatic factors with higher correlations with detrended rainfed wheat yield were selected based on Pearson correlation coefficient significance (P value < 0.1). Three variables (i.e., sunshine hours, wind, and average relative humidity) were excluded for clustering. In the next step based on Pearson correlation (P value < 0.05) between the yield, and the seven climate attributes, fitness function, and silhouette index, only four attributes with higher correlation in its cluster were selected for reclustering. Four climate attributes had an extensive association with yield, so we used four-dimensional clustering to describe the common characteristics of low-, medium-, and high-yielding years, and this is the significance of this research that we have done four-dimensional clustering. The silhouette index showed that the best number of clusters for each station was equal to three clusters. At the last step, reclustering was done through the best-selected method. The results yielded that GA was the best method.