Proposed statistical techniques for combining parameter estimates: a case of food production in sub-saharan Africa

Abstract

The underperforming agricultural sector in Sub-Saharan Africa (SSA) has left African countries with insufficient food production in the face of challenges related to climate change, diseases and increasing population growth. The agricultural sector is the main source of food, generates income, employs a large portion of the population, and produces raw materials for agribusinesses. The improvement of agricultural food production contributes to food security, poverty alleviation, the development of trade, and a country's economy. The challenges facing the SSA countries include ineffective farming system, loss of soil fertility, limited access to land, climate change, water scarcity, outdated production technology that needs to change, restricted market access due to poor infrastructure, and high transaction costs among others. To address these challenges, the combination of multiple nutrients was proposed to increase grain yield of crop simply because of the contribution of each nutrient rather than the use of a single fertiliser. Research conducted in SSA with the aim of improving food production miss the opportunity to share the findings across the various sectors. This points out the lack of appropriate statistical techniques to address the challenges. We can understand better the real situation on food production by developing a comprehensive scientific and statistical approach that can gather all published single information to a unified finding. The process of collecting and combining research outputs require the use of meta analysis (MA) to provide precise estimates on various parameters associated with food production. Various factors can be considered in making significant contribution to agricultural food production such as fertiliser, access to market, energy use, trade, etc. To establish the diverse set of relationships that can be developed among the factors, structural equation model (SEM) statistical technique is used. In some conditions, this procedure can be more restrictive and inflexible since the approach requires the specification of latent variables in the mix of a huge diversity of sets of variables. In the body of this work, we propose a more suitable, flexible and accurate approach in determining the number of linear regressions based on the observed data in a clear and precise manner through factor analysis and principal component analysis (PCA). In addition, to test the large number of variables or factors of the parameters obtained in SEM, we propose to synthesise all this information by integrating MA into SEM. The incorporation of MA into SEM allows us to account simultaneously all effects of factors of the food production in a single model. In MA, the effect sizes are assumed independent from each study and univariate MA is used. A single study could involve multiple tests of the same hypothesis, resulting in reporting multiple outcomes (MOs). In such situation, the researcher developed MOs approach to determine the multiple linear regression model that tested and analysed the relations between the factors of interests in the food production. The results of MA were expressed in terms of fixed- and random-effects. The fixed-effects models were more appropriate simply because of the presence of homogenous effects in the studies. The random effect models helped to control unobserved heterogeneity when the between-studies variance was large. It was more productive to apply the combined inorganic fertilizer by the raisin yield grain of maize. The findings of SEM provide efficient results in the evaluation of the relations among variables and for testing a statistical theoretical model. The findings from the integration approach of MA into SEM permitted to combine parameter estimates within a single model. Researchers in agricultural and related field can use these techniques positively. We hope that many researchers can benefit from the methodological approach to estimate and draw inference in addressing the food production situation. The outcomes of this work contribute to science by providing scientifically comprehensive statistical approaches to evaluate and synthesise the more suitable results. The benefit can be extended to the development of suitable food production.