A review on biogas production as the alternative source of fuel

Challenges related to energy shortages are increasingly frequent both at the local and global scale due to population growth and the desire for a greater standard of living. The growing demand for oil and natural gas caused by high consumption levels is one of the current major problems faced by the world population. Therefore, new forms of energy generation must be investigated that would eventually allow the diversification of the present energy matrix, which has an almost 90% dependence on fossil fuels the world over. This coupled with long-term economic and environmental concerns have resulted in a great amount of research in the past decades on renewable sources of liquid fuels to replace fossil fuels. Burning fossil fuels such as coal and oil releases carbon dioxide (CO2), which is a major cause of global warming. It is anticipated that not a single source of alternative energy but a mix of various energy sources and carriers will contribute to the energy system of the future. Among the various sources been explored, biogas offer one of the best alternative options as they present a viable option for improving sustainable development through energy security and reducing the emission of greenhouse gases. This paper elaborates on Biogas production as the alternative source of fuel. The paper also studies the importance of Biogas production as a means of reducing problem of power energy, environmental vandalism, loss of resources, climate change and also reduce environmental pollution caused by burning of woods, cars, motorcycle and industrial activities

Some aspects of fisheries ecology in Thomas dam, Kano Nigeria

The diversity, length-weight relationship and condition factor of fish species of Thomas Dam, Dambatta Kano were studied fortnightly between November, 2016 and February, 2017. Fish species were collected using line nets, cast nets, hooks and traps; weighted to the nearest gram and standard length measured to the nearest centimeter. A total 313 fishes comprising of 7 families and 11 species were identified. Family cichlidae was predominant(36.7%) represented by T. zilli (21.7%) and Oreochromis niloticus (15.0%). Family Claridae was the second highest in abundance with 24.7% represented by C. garipienus (8.9%), Clarias anguillaris (8.9%) and Heterobranchus sp. (6.7) while Protopteridae represented by Protopterus sp. was the least with 2.8%. Species diversity determined by Shannon Weiner index of diversity, Evenness index and Margalef’s index which revealed the highest value at site A of 1.45, 0.78 and 2.66 while site D had the least with 1.1, 0.64 and 1.72 respectively. Growth coefficient b of the length weight relationship ranged from 0.9 to 2.7 inHeterobranchussp. andClariasgariepinus. The b values of the all the fish species is less than the mean exponent b =3, indicating a negative allometric growth. Condition factor (K) for all fish species differed significantly (P<0.05) as the highest value was recorded in Mormyrus rume and Heterobranchus sp. with 1.9 each and the least were C. gariepinus and C. anguillaris, protopterus sp. and Labeo senegalensis each had 0.9 values respectively. The mean condition factor (K) by species was greater than 1, indicating that the fish species were not in good condition.KEY WORDS: Species diversity, Length-weight relationship, Condition factor, Allometric growth, Thomas Dam, Dambatta Kano State

Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning

Identifying suitable watersheds is a prerequisite to operationalizing planning interventions for agricultural development. With the help of geospatial tools, this paper identified suitable watersheds across Nigeria using biophysical parameters to aid agricultural planning. Our study included various critical thematic layers such as precipitation, temperature, slope, land-use/land-cover (LULC), soil texture, soil depth, and length of growing period, prepared and modeled on the Google Earth Engine (GEE) platform. Using expert knowledge, scores were assigned to these thematic layers, and a priority map was prepared based on the combined weighted average score. We also validated priority watersheds. For this, the study area was classified into three priority zones ranging from ‘high’ to ‘low’. Of the 277 watersheds identified, 57 fell in the high priority category, implying that they are highly favorable for interventions. This would be useful for regional-scale water resource planning for agricultural landscape development

Performance Analysis of Intelligent Computational Algorithms for Biomass Higher Heating Value Prediction

 Higher heating value (HHV) is an essential parameter to consider when evaluating and choosing biomass substrates for combustion and power generation. Traditionally, HHV is determined in the laboratory using an adiabatic oxygen bomb calorimeter. Meanwhile, this approach is laborious and cost-intensive. Hence, it is essential to explore other viable options. In this study, two distinct artificial intelligence-based techniques, namely, a support vector machine (SVM) and an artificial neural network (ANN) were employed to develop proximate analysis-based biomass HHV prediction models. The input variables comprising ash, volatile matter, and fixed carbon were paired to form four separate inputs to the prediction models. The overall findings showed that both the ANN and the SVM tools can guarantee accurate prediction in all the input combinations. The optimal prediction performances were observed when fixed carbon and volatile matter were paired as the input combination. This combination showed that the ANN outperformed the SVM, having presented the least root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study, therefore, concluded that the ANN is more preferred compared to SVM for biomass HHV prediction based on the proximate analysis

Predicting In-season Sorghum Yield Potential Using Remote Sensing Approach:A Case Study Of Kano In Sudan Savannah Agro- Ecological Zone, Nigeria

Estimating crop yield prior to harvest using remote sensing techniques has proven to be successful. However, accuracy of estimation still varies across crops and landscapes. This study was conducted to examine the applicability of Sentinel-2B for estimating sorghum yield during the 2018 rainy season in three locations (Bebeji, Dawakin Kudu and Rano) within the Sudan Savannah agro-ecological zone of Nigeria. SAMSORG 45 (an early maturing improved sorghum variety) was established in five (5) randomly selected farmer plots in each of the three LGAs. The relationship among different vegetation indices, Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Ratio Vegetation Index (RVI) and grain yield were determined using linear regression analysis. Models at different growth stages were then compared using root mean square error (RMSE), coefficient of variations (CV) and coefficient of determination (R2) respectively. The results from the statistical analysis showed that NDVI was superior to GNDVI and RVI for grain yield estimation, indicating low RMSE, high R2 and low CV values at early vegetative (40 days after sowing, DAS), reproductive stage, and entire crop-life cycle. The estimate at 40DAS, reproductive stage, and entire crop-life cycle showed RMSE of 0.04, 0.03, 0.02, R2 (0.75, 0.77 0.93), CV (13.7%, 27.3%, 39.2%) respectively. In addition, RVI had the best fit for stalk yield estimates, having RMSE (0.06, 0.04, 0.01), R2 (0.5, 0.83, 0.98) and CV (15.7%, 19.9% 38.5%) at 70DAS, reproductive stage, and entire crop-life cycle respectively. This study therefore concludes that sorghum yield could be accurately predicted in-season with NDVI and RVI for grain and stalk yields using Sentinel-2B