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Land Use Change and Livelihoods in the Plantation Nouvelle de Ngambe Tikar (Pnnt) Community Forest, Center Region of Cameroon
Nghobuoche Frankline,
Tsafack Ngoufo Serge,
Akoni Innocent Ngwainbi,
Godwill Tobouah Nyanchi,
Tieminie Robinson
Issue:
Volume 6, Issue 3, September 2020
Pages:
31-42
Received:
3 July 2020
Accepted:
17 July 2020
Published:
20 August 2020
Abstract: This study aims at identifying changes in land use composition, structure and distribution in PNNT Community Forest in the Center Region of Cameroon, from 2001 to 2020 and to investigate the impact of the search for livelihoods on the change. Obtained results revealed that there are four major land uses in the PNNT community forest; Moist evergreen forest, degraded forest, savanna and bare soils. Land use change analysis indicated that, the surface area covered by moist evergreen forest reduced by 5% while that of savanna reduced by 15.4% from 2001 to 2020. The findings indicated that 255,87 hectares of moist evergreen forest was lost between 2001 and 2020; an average of 13.47 hectares per year. The surface area of savanna reduced significantly from 1919.28 hectares in 2001, 1658.39 hectares in 2011 and to 1124,91 hectares in 2020. Results further revealed a substantial increase in the surface area of bare soil by 17% and degraded forest by 3.4% over 19 years. The total surface area of degraded forest increased by 174.4 hectares from 2001 to 2020. The land use with the most significant positive change was bare soils which increased from 396.61 hectares in 2001 to 1272.45 in 2020; a global increase of 875.84 hectares. Agricultural expansion, increased logging and harvesting of non-timber forest were identified as drivers of land use change in the community forest.
Abstract: This study aims at identifying changes in land use composition, structure and distribution in PNNT Community Forest in the Center Region of Cameroon, from 2001 to 2020 and to investigate the impact of the search for livelihoods on the change. Obtained results revealed that there are four major land uses in the PNNT community forest; Moist evergreen...
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Physicochemical Quality and Heavy Metals Contamination of Drinking Water Used in Poultry Farms at Maritime Region of Togo
Soumaoro Idrissa,
Pitala Were,
Gnandi Kissao,
Oke Emmanuel
Issue:
Volume 6, Issue 3, September 2020
Pages:
43-49
Received:
16 July 2020
Accepted:
13 August 2020
Published:
7 October 2020
Abstract: This study investigated heavy metals and physicochemical quality of drinking water used in poultry farms at the Maritime region of Togo. Information was gathered by face-to-face interviewing and samples from different drinking water sources (Wells, drillings and tanks) were collected and analysed for physicochemical parameters such as total hardness, pH, nitrate, sulphate and some heavy metals: Hg, Cd, Cu, Cr, Fe, As, Ni, Pb and Zn using standard methods Atomic Absorption Spectrometer Thermo Electron S series. The results showed that most of poultry farms in Maritime region were located within potential provenances sources of water pollution. These sources comprised agricultural, residential and industrial areas. The analysis indicated that concentrations of the metals were mostly far below the maximum recommended concentration. However, the concentrations of Iron, Cadmium, Lead and Mercury in some of the water samples were higher than the international standard values. Also the concentration of nitrate and sulphate in some water samples were higher than the levels recommended by WHO. The water samples were soft and moderately hard. The pH of all analysed samples was within the allowable limit. It was concluded that the contamination of drinking water in the poultry farms of the Maritime region of Togo was moderate except in industrial areas which was considerably high.
Abstract: This study investigated heavy metals and physicochemical quality of drinking water used in poultry farms at the Maritime region of Togo. Information was gathered by face-to-face interviewing and samples from different drinking water sources (Wells, drillings and tanks) were collected and analysed for physicochemical parameters such as total hardnes...
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Quantity and Quality of Shallot (Allium ascalonicum L.) as Influenced by Water Hyacinth Compost on Fluventic Eutrudepts
Rizkyani Remona,
Emma Trinurani Sofyan,
Benny Joy,
Rija Sudirja,
Anni Yuniarti,
Jajang Sauman Hamdani
Issue:
Volume 6, Issue 3, September 2020
Pages:
50-57
Received:
20 September 2020
Accepted:
5 October 2020
Published:
17 October 2020
Abstract: One of the problems with Fluventic Eutrudepts is that it has a low level of soil fertility because the soil surface is effortless to wash. The solution to this problem is the addition of one or more available nutrients to optimize soil fertility so that it can increase quantity and quality plant. This experiment aims to find the best dosage of water hyacinth compost and N, P, K, S fertilizer against changes in sulfur, quantity and quality of Batu Ijo Variety of shallots. This experiment was carried out at the Laboratory of Soil Chemistry and Plant Nutrition Experimental Garden, Faculty of Agriculture, Universitas Padjadjaran, Jatinangor, Sumedang from February to August 2020. The research approach used was a randomized block design (RBD) consisting of seven treatments and repeated four times, namely: control; N, P, K, S recommendations (¾, 1); dosage of fertilizer N, P, K, S (¾, 1); and the dosage of water hyacinth compost (½, 1, 1½). The results showed that the treatment dose of 25 t/ha of water hyacinth compost and 150 kg/ha Urea, 225 kg/ha SP-36, 150 kg/ha KCl, 375 kg/ha ZA was able to provide the best effect in increasing sulfur content, quantity, and the quality of shallots at Fluventic Eutrudepts.
Abstract: One of the problems with Fluventic Eutrudepts is that it has a low level of soil fertility because the soil surface is effortless to wash. The solution to this problem is the addition of one or more available nutrients to optimize soil fertility so that it can increase quantity and quality plant. This experiment aims to find the best dosage of wate...
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Earthquake Damage Prediction Using Random Forest and Gradient Boosting Classifier
Sourav Pandurang Adi,
Vivek Bettadapura Adishesha,
Keshav Vaidyanathan Bharadwaj,
Abhinav Narayan
Issue:
Volume 6, Issue 3, September 2020
Pages:
58-63
Received:
26 September 2020
Accepted:
13 October 2020
Published:
21 October 2020
Abstract: Earthquake is a major natural disaster that causes casualties in millions and leaving many more in trauma. Analyzing the consequences of such consequences gives one a better stand-in for potential catastrophe occurrences. It is important to establish a methodology that can assist in forecasting these earthquakes, as they can help prevent the severity of the damage. This paper discusses a machine learning model that can predict the damage grade severity caused by life-threatening earthquake that hit Nepal in the year 2015. The dataset is derived from the live competition hosted by Driven Data. The data was collected through the surveys conducted by the Kathmandu Living Labs and the Central Bureau of Statistics, which operates under the National Planning Commission Secretariat of Nepal. To accomplish the defined goal, we used the Random Forest Classifier and Gradient Boosting Classifier. The Random Forest Classifier algorithm demonstrated in this study was outperformed by the Gradient Boosting Classifier. With necessary parameter tuning using the Random Forest Classifier, the F1-Score achieved was 72.95%. The next technique was to perform winsorization on some attributes to handle outliers which improved the F1-score to 74.33% along with gradient boosting classifier. The last techniqueinvolved only hyper-parameter tuning with gradient boosting classifier achieved the best F1-Score of 74.42%.
Abstract: Earthquake is a major natural disaster that causes casualties in millions and leaving many more in trauma. Analyzing the consequences of such consequences gives one a better stand-in for potential catastrophe occurrences. It is important to establish a methodology that can assist in forecasting these earthquakes, as they can help prevent the severi...
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