Time Series Analysis on Selected Rainfall Stations Data in Louisiana Using ARIMA Approach

Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values at specific stations is key for environmental and agricultural decision making. This research developed Autoregressive Integrated Moving Average (ARIMA) models for selected stations with Integrated component and Autoregressive Moving Average (ARMA) for selected stations without Integrated component at Louisiana State. The ARIMA module is represented as ARIMA(p, d, q)(P,D,Q). The selected lag order for the Autoregressive (AR) component is represented with p and P for seasonal AR component, while the integrated form (number of times data were differenced) is d and D for seasonal differencing, and the Moving Average (MA) lag order is q and Q for seasonal MA component. Data from 1950 to 2020 were employed in this research. Results of the analysis indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)12), Abbeville (ARMA (0,0,1) (0,0,2)12), Monroe Regional (ARMA (0,0,1) (0,0,0)12), New Orleans Airport (ARMA (1,0,0) (0,0,2)12), Alexandria (ARMA (1,0,1) (0,0,0)12), Logansport (ARIMA (0,1,2) (0,0,0)12), New Orleans Audubon (ARMA (1,0,0) (0,0,0)12), Lake Charles Airport (ARMA (2,0,2) (0,0,0)12) are the best ARIMA models for predicting precipitation in Louisiana. The models were used to predict the average monthly rainfall at each station. The highest precipitation observed in Louisiana was recorded in 1991. The Precipitation in Louisiana fluctuated over the years but has adopted a decreasing trend from the year 2000 to 2020. It was recommended that the government, researchers, and individuals take note of these models to make future plans to help increase the production of agricultural commodities and prevent destructions caused by excessive precipitation.

Reaction of Sorghum Differentials to Grain Mold Infection in Puerto Rico

Grain mold, associated with many fungi, is the most important disease of sorghum, causing both yield and quality losses. In this study, 23 sorghum differentials used in pathotype characterization of anthracnose and head smut pathogens were evaluated for grain mold resistance under favorable conditions in Isabela, Puerto Rico. Lines BTx643 and IS18760 exhibited the lowest grain mold severity, indicating that these two may possess genes for grain mold resistance. These two lines also recorded the highest germination rates 94.7% and 97.6%, respectively, and their seed weight was among the heaviest. In conclusion, these two lines can be utilized in breeding programs to develop grain mold-resistant hybrid lines.

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Techniques of Remote Sensing and GIS as Tools for Visualizing Impact of Climate Change-Induced Flood in the Southern African Region

Flooding is a major problem facing Southern African region. The region has been experiencing flood for the past two decades. This flood event has been exacerbated in recent years by global weather pattern known as La Ni?a which cools ocean waters in the equatorial Pacific and changes rainfall patterns across the world. This change in weather pattern has resulted in increased rainfall over Southern Africa causing flash floods resulting in extensive socioeconomic loses, casualties and environmental damage. This study employs remote sensing and geographical information systems (GIS) data to visualize the impact of climate change caused by flooding in the Southern African region in order to assist decision makers’ plans for future occurrences. To achieve these objectives, the study used Digital Elevation Model (DEM), temporal Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites data obtained from the United States Geological Survey (USGS) and NASA’s Earth Observatory websites in order to show the spatial dimensions of the damage and the flooded area. Results of the study revealed notable damages to social and natural environments as well as flood risk zones and watercourses in the study area. The paper concludes by outlining policy recommendations in the form of the need for building drainage ditches on the flat plains identified in this study to accommodate flood flows, the design of a comprehensive Regional Emergency Information System (REIS) with support from the governments in the study area and the neighboring countries. Building such system, the paper concludes could offer decision-makers access to the appropriate spatio-temporal data for monitoring climate change induced emergencies related to seasonal floods.

Land Resource Areas and Spatial Analysis of Potential Location of Bioenergy Crops Production in Mississippi

Mississippi State is renowned for its land resource areas (LRA) and production of bioenergy crops which generate both agricultural and economic benefits. Agricultural commodities play a key role in economic growth, therefore the ability to produce more would enhance development. This paper offers an analysis of the production of bioenergy crops in Mississippi. Relative measures, time series graphs and descriptive statistics coupled with geographic information systems (GIS) mapping using ArcMap were employed to generate the outcome of this research. The outcome of the statistical analysis indicated that corn and soybeans were the most produced crops in Agricultural Districts 10 and 40. These districts produced more bioenergy crops than the other districts. GIS mapping results also showed that the potential area for bioenergy crops is in zone 131 of the Mississippi Land Resource Area (MLRA). This zone has an absolute advantage in the production of these crops which includes the diversity of biomass production such as corn, cotton, soybeans, wheat, rice, barley, grain sorghum, canola, camelina, algae, hardwoods, and softwood. The paper recommends a constant GIS mapping and land management systems for each agricultural district in Mississippi to enable researchers and farmers to determine the factors which contribute towards the increasing and decreasing trends in the production of the bioenergy crops.

Modeling the Risks of Climate Change and Global Warming to Humans Settled in Low Elevation Coastal Zones in Louisiana, USA

This paper seeks to identify high risk areas that are prone to flooding, caused by sea level rise because of high impacts of global climate change resulting from global warming and human settlements in low-lying coastal elevation areas in Louisiana, and model and understand the ramifications of predicted sea-level rise. To accomplish these objectives, the study made use of accessible public datasets to assess the potential risk faced by residents of coastal lowlands of Southern Louisiana in the United States. Elevation data was obtained from the Louisiana Statewide Light Detection and Ranging (LiDAR) with resolution of 16.4 feet (5 m) distributed by Atlas. The data was downloaded from Atlas website and imported into Environmental Systems Research Institute’s (ESRI’s) ArcMap software to create a single mosaic elevation image map of the study area. After mosaicking the elevation data in ArcMap, Spatial Analyst extension software was used to classify areas with low and high elevation. Also, data was derived from United States Geological Survey (USGS) Digital Elevation Model (DEM) and absolute sea level rise data covering the period 1880 to 2015 was acquired from United States Environmental Protection Agency (EPA) website. In addition, population data from U.S. Census Bureau was obtained and coupled with elevation data for assessing the risks of the population residing in low lying areas. Models of population trend and cumulative sea level rise were developed using statistical methods and software were applied to reveal the national trends and local deviations from the trends. The trends of population changes with respect to sea level rise and time in years were modeled for the low land coastal parishes of Louisiana. The expected years for the populations in the study area to be at risk due to rising sea level were estimated by models. The geographic information systems (GIS) results indicate that areas of low elevation were mostly located along the coastal Parishes in the study area. Further results of the study revealed that, if the sea level continued to rise at the present rate, a population of approximately 1.8 million people in Louisiana’s coastal lands would be at risk of suffering from flooding associated with the sea level having risen to about 740 inches by 2040. The population in high risk flood zone was modeled by the following equation: y = 6.6667x - 12,864, with R squared equal to 0.9964. The rate of sea level rise was found to increase as years progressed. The slopes of models for data for time periods, 1880-2015 (entire data) and 1970-2015 were found to be, 4.2653 and 6.6667, respectively. The increase reflects impacts of climate change and land management on rate of sea level rise, respectively. A model for the variation of years with respect to cumulative sea level was developed for use in predicting the year when the cumulative sea level would equal the elevation above sea level of study area parishes. The model is given by the following equation: y = 0.1219x + 1944.1 with R square equal to 0.9995.