NASM-Data: Input Data for the Nigerian Agricultural Sector Model (NASM) and Extra-Information/Results
Description
This Data Article accommodates secondary data, comprising of the available historic and current farm production data in Nigeria such as crop type, yields, prices, crop production inputs’ (e.g. labour, fertilizer, pesticide, seed, cash capital) as well as the Nigerian food consumption demand, and international commodity trade (import and export) data. The data were collected from reputable online databases such as FAOSTAT, World Bank, IMF, USDA, NBS, IITA, etc., and through personal research visits to Nigerian government agencies such as Federal and State Ministries of Agricultural and Rural Development, State Agricultural Development Agency (ADP) in Nigeria, National (Nigerian) Bureau of Statistics (NBS) and others; and were assembled, screened, processed (tabulated them according to GAMS table requirements). The data were applied via General Algebraic Modelling System (GAMS) to calibrate the ever first developed Nigerian Agricultural Sector Model, regionalised into existing six geo-political zones of Nigeria, in order to develop a reliable empirical tool for evidence-based agricultural development planning and policymaking in Nigeria. It is the Base (or Calibration) model, upon which the Nigerian Energy-Food Model (NEFM) is further built and applied to analyse the Nigerian Biofuels policy (i.e. bioethanol production potential), and its impacts on the Nigerian food and energy securities, job creation, rural economy development, and the entire Nigerian economy. Therefore, NASM-Data share some data with Mendeley Data (NEFM), while they both retain some data that are unique to them. Ndukwe Agbai Dick collected all the data, processed and applied them through GAMS to first develop NASM model that calibrates exactly the existing crop production environment and/or data in Nigeria before extending the model further to develop the NEFM, and finally applied the NEFM to analyse the Nigerian Biofuels Potential during his PhD research studies at Newcastle University, England, between October 2010 and September, 2014. The research visit took place between March 1st and June, 2012. Research findings from the analysis of these data using GAMS are currently being documented in paper titled "The Nigerian Agricultural Sector Model (NASM): A Sectoral Agricultural Policymaking Tool & An Empirical Model for Optimizing Food Production and Boosting Food Security in Nigeria", for submission to an international peer-reviewed and reputable high impact journal – Agricultural Systems (AS).
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Steps to reproduce
1. Literature review to identify the required analytical model to employ for your data analysis, 2. Literature review to identify data needs, 3. Data mapping to link required data with the potential sources, 4. Internet screening of reputable and relevant online databases, 5. Data evaluation to identify the relevance of data prior to data harvesting, 6. Data harvesting/download, 7. Data saving into relevant folders, 8. Data pre-processing (data screen to identify outliers, uniformity, and normal distribution), 9. Data processing (e.g. data assembling, data tabulation, charts, graphs, etc.), 10. Data incorporation and alignment to GAMS.