Utilising the Total or Cultivation (TFC) and Multiple Regression (MR) Models to Examine the Factors Influencing Rice Cultivation Farms in the Bombali District, Sierra Leone

Authors

  • Conteh Alhaji Mohamed Hamza Department of Mathematics and Statistics, Njala University, Freetown, Sierra Leone
  • Gegbe Brima Department of Mathematics and Statistics, Njala University, Freetown, Sierra Leone
  • Isaac Tamba Issa Department of Mathematics and Statistics, Njala University, Freetown, Sierra Leone

Keywords:

Influential Factors, Rice Farming, Rice Farmers, Bombali District, Examine

Abstract

Many obstacles to attaining rice self-sufficiency in Sierra Leone, is the stumpy cultivation of this staple food crop which transformed to inadequate rice availability for the entire sierra Leonean population. Several determinations have been made by the successive Sierra Leone governments towards rice self-sufficiency. Much attention is constantly given to improving the inputs utilised by the rice cultivators, and improvement of better-quality rice seed varieties whereas the main reasons of the low rice cultivation are left unaddressed. Consequently, this study focussed on the examination of the main factors that influence rice cultivation farms in Bombali district, North-Eastern of Sierra Leone. Through a multi-stage sampling technique, approximately 600 rice farms were selected.

This study used primary source with structured questionnaire for the collection of data. The data were analysed by using mean, percentage, standard deviation, Total Factor Cultivation (TFC) and Multiple Regression (MR) techniques. The result from data analyses confirms that the size of rice farm, household size, and extension visitation positively and significantly influenced cultivation of Bombali district rice farms, whereas the herbicide practise and age of the farmer negatively influenced cultivation of Bombali district rice farms. Additionally, the result disclosed that a 1% surge in herbicide amount significantly reduced the cultivation of rice farms by 3.84 %. Consequently, Sierra Leone Agricultural Research Institute (SLARI), and other agricultural agencies that are responsible for the training of farmers should strengthen effort in the herbicides

practise. Systematic and thorough soil analysis ought to be conducted in Bombali district rice farms to establish the compatibility of soil and herbicide practice.

References

. Rada, N., W. Liefert, and O. Liefert, Evaluating agricultural productivity and policy in Russia. Journal of Agricultural Economics, 2020. 71(1): p. 96-117.

. Gong, B., Agricultural productivity convergence in China. China Economic Review, 2020. 60: p. 101423.

. Sood, A. and A. Kumar, NATURE AND EXTENT OF CROP DIVERSIFICATION IN THE TRIBAL ECONOMY OF HIMACHAL PRADESH. EPRA International Journal of Agriculture and Rural Economic Research (ARER), 2022. 10(6): p. 73-79.

. Mwaura, F., M. Ngigi, and G. Obare, Agricultural Productivity and Labour Allocation Trade-Off Crises for Agriculture, Cooking Energy Sourcing and Off-Farm Employment in Developing Countries: Evidence from Western Kenya. African Journal of Education, Science and Technology, 2022. 7(1): p. 277-293.

. Damilola, A.T. and A.O. Emmanuela, IMPACT OF AGRICULTURAL COOPERATIVE IN PROMOTING FOOD SECURITY IN KWARA STATE, NIGERIA. Studies, 2022. 5(2): p. 13-23.

. Constantin, M., et al., A perspective on agricultural labor productivity and greenhouse gas emissions in context of the Common Agricultural Policy exigencies. ????????? ????????????, 2021. 68(1): p. 53-67.

. Gebre, G.G., et al., Gender differences in agricultural productivity: Evidence from maize farm households in southern Ethiopia. GeoJournal, 2021. 86(2): p. 843-864.

. Alban Singirankabo, U. and M. Willem Ertsen, Relations between land tenure security and agricultural productivity: Exploring the effect of land registration. Land, 2020. 9(5): p. 138.

. Thornton, P., et al., The impacts of climate change on Southern African food systems. 2022.

. Lu, F., et al., The non-linear effect of agricultural insurance on agricultural green competitiveness. Technology Analysis & Strategic Management, 2022: p. 1-16.

. Iwegbu, O. and L.B. de Mattos, Financial development, trade globalisation and agricultural output performance among BRICS and WAMZ member countries. SN Business & Economics, 2022. 2(8): p. 1-27.

. Leone, S.S., Sierra Leone Demographic and Health Survey 2019. Key Indicators Report. 2019.

. Moutouama, F.T., et al., Farmers’ Perception of Climate Change and Climate-Smart Agriculture in Northern Benin, West Africa. Agronomy, 2022. 12(6): p. 1348.

. Anwar, A., et al., Agricultural practices and quality of environment: evidence for global perspective. Environmental Science and Pollution Research, 2019. 26(15): p. 15617-15630.

. Mashabatu, M., Determining crop coefficients for irrigated fruit tree crops using readily available data sources. 2022.

. Jayne, T.S. and P.A. Sanchez, Agricultural productivity must improve in sub-Saharan Africa. Science, 2021. 372(6546): p. 1045-1047.

. Mosha, D.B., J. Jeckoniah, and G. Boniface, Does women’s engagement in sunflower commercialization empower them? Experience from Singida region, Tanzania. Gender, Technology and Development, 2022: p. 1-14.

. Ergat, M., The Effect of Nonfarm Income on Poverty And Inequality in Rural Ethiopia. 2022.

. Lazíková, J., et al., Crop diversity and common agricultural policy—the case of Slovakia. Sustainability, 2019. 11(5): p. 1416.

. Yang, Y. and W. Liu, THE INFLUENCE OF PUBLIC PHYSICAL EDUCATION CURRICULUM ON COLLEGE STUDENTS'PHYSICAL HEALTH. Revista Brasileira de Medicina do Esporte, 2021. 27: p. 83-86.

. van Engelenhoven, J., Taking smallholder farming to the next level: Evaluating the Farm Incubator Model on its applicability in Sub-Saharan Africa. A mixed-methods approach. 2022.

. Ouyang, H., X. Wei, and Q. Wu, Agricultural commodity futures prices prediction via long-and short-term time series network. Journal of Applied Economics, 2019. 22(1): p. 468-483.

. Khanal, A.R., et al., Modeling post adoption decision in precision agriculture: A Bayesian approach. Computers and Electronics in Agriculture, 2019. 162: p. 466-474.

. Tabor, G., et al., Effects of spacing on yield and head characteristics of cabbage (Brassica oleracea var. capitata L.) in two agro-ecologies of Ethiopia. African Journal of Agricultural Research, 2022. 18(5): p. 322-329.

. He, Y., Agricultural population urbanization, long-run economic growth, and metropolitan electricity consumption: An empirical dynamic general equilibrium model, in The Institutional Paradigm of Economic Geography. 2022, Springer. p. 79-102.

Downloads

Published

2022-09-04

How to Cite

Conteh Alhaji Mohamed Hamza, Gegbe Brima, & Isaac Tamba Issa. (2022). Utilising the Total or Cultivation (TFC) and Multiple Regression (MR) Models to Examine the Factors Influencing Rice Cultivation Farms in the Bombali District, Sierra Leone. International Journal of Sciences: Basic and Applied Research (IJSBAR), 63(2), 187–195. Retrieved from https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/14456

Issue

Section

Articles