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This study examines the dynamics of multidimensional poverty in Nigeria from 2003 to 2018, focusing on women and children, to understand the reality of poverty in Nigeria, where poverty reduction has been stagnant.

This study employed the first-order dominance (FOD) methodology to conduct a multidimensional analysis of poverty among households, women and children in Nigeria, using data from the Demographic and Health Surveys (DHS) conducted in 2003, 2008, 2013 and 2018. We examined how the relative position of multidimensional poverty in each zone has changed for approximately 20 years.

The results indicated that the north-south poverty gap in Nigeria persisted as of 2018 and, regarding within the north and south, changes in the relative pecking order of poverty between the zones have occurred considerably over the past two decades. Different trends were also observed for child and female poverty, suggesting the influence of the unique dimensions of poverty and cultural differences.

This study is the first poverty analysis to apply the FOD approach to children and women in Nigeria, the country with the highest poverty, over a relatively long period of 2003–2018.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.