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outdated production technology that needs to change, restricted market access due to poor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.
