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Application of Predictive Model for Efficient Cassava (Manihot esculenta Crantz) Yield in the Face of Climate Variability in Enugu State, Nigeria

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American Journal of Climate Change 2024年第2期13卷 361-389页

作者： Emeka Bright Ogbuene Tonia Nkiru Nwobodo Obianuju Gertrude Aloh Achoru Fred Emeka Josiah C. Ogbuka vivian amarachi ozorme Andrew M. Oroke Obiageli Jacinta Okolo Anwara Obianuju Amara E. S. Enemuo Centre for Environmental Management and Control (CEMAC) University of Nigeria Nsukka Nigeria Department of Geography University of Nigeria Nsukka Nigeria Department of Geography and Meteorology Enugu State University of Science and Technology (ESUT) Enugu Nigeria School of Civil Engineering Newcastle University Newcastle upon Tyne UK

Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a

关键词： Climate Variability Cassava (Manihot esculenta Crantz) Predictive Model Yield

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Application of Machine Learning for Flood Prediction and Evaluation in Southern Nigeria

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Atmospheric and Climate Sciences 2024年第3期14卷 299-316页

作者： Emeka Bright Ogbuene Chukwumeuche Ambrose Eze Obianuju Getrude Aloh Andrew Monday Oroke Damian Onuora Udegbunam Josiah Chukwuemeka Ogbuka Fred Emeka Achoru vivian amarachi ozorme Obianuju Anwara Ikechukwu Chukwunonyelum Anthonia Nneka Nebo Obiageli Jacinta Okolo Centre for Environmental Management and Control (CEMAC) University of Nigeria Enugu Nigeria Department of Geography Enugu State University of Science and Technology ESUT Enugu Nigeria School of Civil Engineering Newcastle University Newcastle upon Tyne UK

This study explored the application of machine learning techniques for flood prediction and analysis in southern Nigeria. Machine learning is an artificial intelligence technique that uses computer-based instructions to analyze and transform data into useful information to enable systems to make predictions. Traditional methods of flood prediction and analysis often fall short of providing accurate and timely information for effective disaster management. More so, numerical forecasting of flood disasters in the 19th century is not very accurate due to its inability to simplify complex atmospheric dynamics into simple equations. Here, we used Machine learning (ML) techniques including Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), and Neural Networks (NN) to model the complex physical processes that cause floods. The dataset contains 59 cases with the goal feature “Event-Type”, including 39 cases of floods and 20 cases of flood/rainstorms. Based on comparison of assessment metrics from models created using historical records, the result shows that NB performed better than all other techniques, followed by RF. The developed model can be used to predict the frequency of flood incidents. The majority of flood scenarios demonstrate that the event poses a significant risk to people’s lives. Therefore, each of the emergency response elements requires adequate knowledge of the flood incidences, continuous early warning service and accurate prediction model. This study can expand knowledge and research on flood predictive modeling in vulnerable areas to inform effective and sustainable contingency planning, policy, and management actions on flood disaster incidents, especially in other technologically underdeveloped settings.

关键词： Machine Learning Flood Prediction Evaluation Southern Nigeria

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A Case Study of Multi Drug-Resistant Tuberculosis (MDR-TB), HIV and Diabetes Mellitus (Dm) Comorbidity: Triple Pathology;Challenges and Prospects

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Advances in Infectious Diseases 2017年第3期7卷 70-79页

作者： Okorie Onuka John Ahukanna Chukwudi Okebaram Patrick Dakum Aderonke Agbaje vivian Ibeziako Gidado Mustapha Nkem Chukwueme Chux Anago Emperor Ubochioma amarachi Okorie Odume Bethrand Nwafor Charles Abia State Ministry of Health Umuahia Nigeria Department of Pharmacology Abia State University Okigwe Nigeria Institute of Human Virology Abuja Nigeria Challenge KNCV Abuja Nigeria Clinton Health Access Initiative Abuja Nigeria National Tuberculosis and Leprosy Control Program Abuja Nigeria School of Midwifery Umuahia Nigeria Department of Family Medicine National Hospital Abuja Nigeria German Leprosy and Tuberculosis Relief Association Enugu Nigeria

Tuberculosis (TB), diabetes mellitus and HIV co-morbidity is a rare and interrelated health condition with associated high morbidity and mortality especially in developing countries with high prevalence of TB. It has become an emerging concern to epidemiologists and TB control programs due to complexities in its control and management. Managing MDR-TB, DM and HIV comorbidity is challenging, with risk of unfavorable outcome;consequently, close monitoring is necessary. Individuals with weak immunity resulting from diseases such as uncontrolled Diabetes Mellitus (DM) and HIV have a higher risk of developing TB or progression from latent to active TB. We present a 65-year old known diabetic patient who presented to Royal Cross Hospital Ugwueke Abia State, Nigeria with a one-year history of recurrent productive cough with associated night sweats, low grade fever and marked weight loss. A diagnosis of drug-resistant TB with DM/HIV co-morbidity was made and co-managed by experts from the respective clinics and the State TB control program. The patient was declared cured (7 months consecutive negative cultures each taken 30 days apart) after completing 20 months of conventional MDR-TB treatment. The patient showed remarkable clinical improvement including weight gain, good diabetic control and significant increase in CD4 (700 cells). Managing MDR-TB patients with diabetes and HIV is challenging, however, appropriate treatment, psychosocial support, adequate blood sugar control as well as monthly monitoring of patients with requisite investigations are vital in achieving good treatment outcome.

关键词： Co-Morbidity-Diabetes-Multi Drug Resistant TB and HIV

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Effectiveness of Contact Tracing of Index Tuberculosis Cases in Nigeria

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Advances in Infectious Diseases 2018年第4期8卷 173-199页

作者： Okorie Onuka Ikpeazu Okezie John Ahukanna Chukwudi Okebaram Patrick Dakum Aderonke Agbaje vivian Ibeziako Gidado Mustapha Nkem Chukwueme Emperor Ubochioma amarachi Okorie Nwafor Charles Chux Anago TB Control Program Ministry of Health Abia State Nigeria Abia State Government House Umuahia Abia State Nigeria Ministry of Health Abia State Nigeria Department of Pharmacology and Therapeutics Abia State University Uturu Nigeria Institute of Human Virology Abuja Nigeria KNCV Abuja Nigeria National TB Control Program Abuja Nigeria School of Midwifery Ministry of Health Umuahia Nigeria GLRA Enugu Enugu Nigeria Clinton Health Access Initiative Abuja Nigeria

Tuberculosis (TB) is a chronic disease that has impacted negatively on human history. The biology of the mycobacterium tuberculosis is complicated and has affected the control as well as elimination of the disease. Nigeria is one of the countries with high incidence and prevalence of TB. The late and low case detection has been a major problem with National TB control program, caused by passive case finding strategy practiced by the country. A shift from the passive and active case search has been recommended for detection of missing cases of TB and improved program performance. The proximity of TB contact is a major determinant of disease transmission. However, maximizing early case detection and prompt treatment of notified cases is very useful in (TB) control especially in high burden countries. The tracking of TB contacts provides a good platform for early diagnosis, educating the household on TB disease and infection control as well as breaking the chain of transmission. The objective of the study is to ascertain effectiveness of contact tracing on Tuberculosis case detection. The study is a retrospective quasi experimental with quantitative arm. The study was conducted in Abia State, one of the South Eastern States of Nigeria. A pre-tested questionnaire was used for data collection and analyzed with SPSS. A total of 168 and 162 index cases of TB were recruited for both intervention and control facilities. The 168 index TB cases yielded 301 TB contacts. The result revealed 55% contact/index ratio, presumptive TB yield of 130 (43.2%) and TB yield of 68 (22.6%). The cases registered in the State increased from 336 to 417 and p value = 0.001, while presumptive TB and BCG vaccination were major predictors. The study shows that tracing contact of index TB cases is an effective and complementary method of finding undetected cases of TB.

关键词： Contact tracing Index TB cases

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