Socioeconomic Factors Influencing Agricultural Production among Cooperative Farmers in Anambra State, Nigeria

Anigbogu, Theresa U.; Agbasi, Obianuju Emmanuela; Okoli, Ikechukwu Moses

doi:10.6007/ijarems/v4-i3/1876

Cited by 27 publications

(40 citation statements)

References 5 publications

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“…Table 4. Selected Socio-economic factors using Logit, Probitand Proposed Extra-tree Learning Technique Used Selected Socio-economic Factors Logit (17,19) FD(RC), FD(KC), CF, FD(V), WRF, FarmTools, CRS, CA, TLU, AE, ICS, FT, PTO, EQHHH, and ATI Probit (21) FD(RC), FD(KC), CF, FD(V), WRF, FarmTools, CA, CRS, TLU, AE, ICS, FT, PTO, EQHHH, and ATI Proposed Extra-tree Learning AH, EQHHH,HS, HPF, HPQ, EQHQ, NOD, ALU(ACRE), FRT, LS, LT(I), LT(NI), AvailGS, LL, and UF https://www.indjst.org/ The AO prediction performance of the proposed prediction model (Algorithm 2) has been proposed and its performance has been compared with eleven standard machine learning based models: DT, K-Nearest Neigbhor (36) , Naïve Bayes (37) , Random Forest (38) , Multi-Layer Perceptron (39) , Linear Discriminant Analysis (LDA) (40) , Linear Regression (LR) (41) , Quadratic Discriminant Analysis (QDA) (42) and Stochastic Gradient Descent (SGD) (43) . Various performance metrics such as precision, F1-score, ROC-AUC, and recall are considered to compare all the models.…”

Section: Results and Analysismentioning

confidence: 99%

“…Production of food in Nigeria is no longer maintained with population growth. To examine the recognized problem of apparently rejected food production in Nigeria, Anibogu et.al (19) performed a study of socioeconomic factors influencing agricultural production among cooperative farmers in Anambra State, Nigeria. Their results are vigorous with varying insightful consequences.…”

Section: Introductionmentioning

confidence: 99%

“…Socio-economic factor analysis for agricultural productivity has attracted many researchers in this field and other allied fields of science and engineering due to the social impact of this study. It is evident from literature survey that, various statistical model and few machine learning model have been applied in the field such as Descriptive statistics (13,16) , multiple regression analysis and descriptive statistics (14) , Descriptive and inferential statistics (15,18) , logit model (regression model) (17,19) and Probit (21) . This study offers an advanced machine learning based model for socio-economic factor analysis and efficient ensemble learning based model for prediction of agricultural productivity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extra-tree learning based Socio-economic factor analysis and multi-class adaptive boosting meta-estimator for prediction of agricultural productivity

Naik¹

2020

IJST

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extra-tree learning based Socio-economic factor analysis and multi-class adaptive boosting meta-estimator for prediction of agricultural productivity

Naik¹

2020

IJST

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“…Karakteristik sosial ekonomi responden yang terdiri dari tingkat pendidikan, jumlah tanggungan, tingkatan usia, luas lahan (Safa, 2005;Ogunleye, Oluwafemi, Zacchaeus, Arowolo & Odegbile, 2015) lama pengusahaan sagu, dan tingkat penghasilan (Anigbogu, Agbasi, & Okoli, 2015) dianalisis lebih lanjut untuk mengetahui hubungan preferensi dengan faktor-faktor demografi tersebut. Alat analisis yang digunakan adalah korelasi spearman rank.…”

Section: Teknik Pengumpulan Dan Analisis Dataunclassified

People’s Preference in Managing Sago in Asahan District, and Its Influencing Factors

Situmorang

Harianja²

2018

JPSEK

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Lack of attention to the non-staple food sectors and changes in consumption pattern have resulted in decreasing of farmers' preference in cultivating sago, which ultimately results in the conversion of sago land including in Asahan District. This study aims to measure the level of community preference in managing sago and to reveal the factors that influence the level of people's preference. Data were collected from 98 respondents by using questionnaire method and analyzed by descriptive statistical analysis, frequency tab, and spearman rank correlation. Research was conducted from May to December 2015 in Silo Bonto Village, Sei Paham Village, and Sungai II Hulu Village. The results showed that level of people's preference in managing sago was moderate with an average value of 27.92. Ecological benefit factor from sago cultivation dominated the reason of community's interest in managing sago (40.48%), followed by economic factor (33.97%,) and sago consumption (25.54%). Socioeconomic characteristics of respondents such as age, number of family dependents, farmland area, sago stands, and length of sago business have a significant correlation to the level of preference. Older farmers, fewer family dependents, sufficient farmland, and longer experience in sago palm utilization seem to prefer to manage sago cultivation.

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“…If this persists, there may be a wide gap in the demand and supply of food as already observed in food deficits marked with an upsurge in food market prices. This deficit and price upsurge could resultantly lead to hunger, malnutrition and food security accompanied with falling standards of living [5].…”

Section: Introductionmentioning

confidence: 99%

An Analysis on the Effect of Farmer’s Health on Production Performance in the Kumba Municipality of Cameroon

Oyang¹,

Tabe-Ojong²,

Molua³

2018

AJRD

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Advances in productivity analysis show the importance of human capital in driving productivity and reducing inefficiency in production. Based on this premise, we profile and analyze the effect of farmer's health on production performance in the Kumba municipality of the Southwest region of Cameroon. We sought to assess the share of poor health conditions on farm performance. Problems like malnutrition, poor values and habits amongst others, usually lead to farmer's poor health. We tested this hypothesis with the use of robust statistics and the Ordinary Least square technique to capture the relationship between farmers output, poor health and other important determinants. The study found the health conditions of farmers to be a critical and positive determinant of farm performance. The amount of labour hours is also observed to positively influence farm production. Other significant variables are farm size and amount of pesticide used. We therefore recommend to policy, increased investment in the health sector to urgently and increasingly address the health conditions of farmers in a bid to improve their health conditions while targeting food production in the long run.

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Socioeconomic Factors Influencing Agricultural Production among Cooperative Farmers in Anambra State, Nigeria

Cited by 27 publications

References 5 publications

Extra-tree learning based Socio-economic factor analysis and multi-class adaptive boosting meta-estimator for prediction of agricultural productivity

Extra-tree learning based Socio-economic factor analysis and multi-class adaptive boosting meta-estimator for prediction of agricultural productivity

People’s Preference in Managing Sago in Asahan District, and Its Influencing Factors

An Analysis on the Effect of Farmer’s Health on Production Performance in the Kumba Municipality of Cameroon

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