Innovative Data-Driven Analysis of Water Management for Effective Agricultural Practices

Objective: To measure, investigate, analyse variables and factors that influences water resources management as used in the agricultural sector, and how water management techniques, systems, decision making processes can be optimized for a more efficient and effective water-agriculture-food nexus. Methods: Using current and historical real world data from validated open source data stores; analysis was carried out on agricultural, socio-economic, demographic, geo-climatic, gender, wireless technological factors and variables; that influence available and needed water capacity for farming (Wc) in selected African Countries and Globally. The methodical and data-driven analyses were carried out using Analytics, Machine Learning and Wireless Cooperative Communications algorithms. Results: The available and needed water capacity for farming (Wc) was calculated and predicted using factors and independent variables of real world data that were shown to influence Wc, and that were statistically measured and analysed. Time based, qualitative, quantitative, predictive, simulative, clustering, statistical data analyses confirmed that available water resources, socio-economy, demography, agricultural factors, Gender diversity & inclusion, Climate Change and Wireless Communication technologies; can influence water availability and water management for agriculture. Conclusion: Modern data-driven, cost effective analytical processes can be used to productively analyse and develop strategies, processes, systems and technologies for innovative, efficient and effective water management for improved agricultural practices and a sustainable environment.