AI-Driven Integrated Solar-Agrivoltaics Systems Transforming Food Security in West Africa.

International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 181 AI-Driven Integrated Solar-Agrivoltaics Systems Transforming Food Security in West Africa. Adeyinka G. Ologun 1,2 , Rukayat Abisola Olawale 3 , Olatunji Bolanle Blessing 4 , Ijeoma Chioma Mordi 5 , Ngozi Blessing Umoru 6 , Sandra A Palmer 7 , Kemi K.Oladapo 8 1 Department of Business School, University of Wolverhampton Business School, England, United Kingdom. 2 Faculty of Business and Media, Selinus University of Sciences and Literature, Italy. 3 School of Management Sciences, Babcock University, Ilishan Remo, Ogun State, Nigeria, 4 Department of Marketing, Kwara State Polytechnic, Ilorin, Nigeria 5 Department of Information, Intellectual Property Law, University of Lagos, Nigeria 6 Department of Social Science Education, University of Nottingham, Nottingham, United Kingdom 7 Department of Social Science Education, Leading Learning & Teaching, The University of Dundee, U.K. 8 MBA with Project Management, Abertay University, Bell Street, Dundee, DD1 1HG, United Kingdom, *Corresponding author, E-mail: [email protected] Internaonal Journal of Research in Management Fields Available online on h p://rspublicaon.com/IJRMF/IJRMF.html ISSN (P) 2577-1876 (O) 2577-4274 ARTICLE INFO ABSTRACT ©2025 RS Publication Paper ID: IJRMF69479B6AB49A4 Published: 2025-12-22 DOI: https://dx.doi.org /10.5281/zenodo.18 020839 Page No: 181-199 This study evaluates an AI-Driven integrated solar-agrivoltaic circular economy model for West Africa with objectives to (1) quantify seasonal PV supply and energy demand for biomass valorization and cold storage, (2) measure conversion efficiencies of cassava peels, maize husks, and market vegetable waste into compost and animal feed, and (3) assess agrivoltaic impacts on crop yield and system-level environmental and economic performance. Methods combined yearlong field trials at three sites, high-resolution solar and process monitoring, LCA, and technoeconomic analysis with Monte Carlo uncertainty propagation. Key results: mean daily PV generation declined 44% from dry (6.1 kWh·kW−1·day−1) to rainy season (3.4 kWh·kW−1·day−1); post-harvest losses fell from 38.7% to 14.9% with solar cold storage (−23.8 percentage points); agrivoltaic shading increased tomato yield by 14% and leafy-green yield by 22%. LCA showed median GWP savings of 1,220 kg CO2-eq·t−1 (IQR 980–1,450); TEA basecase payback was 6.1 years. Uncertainty analysis indicates PV capacity factor variability can alter GWP savings by up to 28%. Keywords: AI-Driven Solar agrivoltaics; Post-harvest loss reduction; Biomass valorization; Solar cold storage; Life cycle assessment; Techno-economic analysis. Cite This Paper: Adeyinka G. Ologun, Rukayat Abisola Olawale, Olatunji Bolanle Blessing, Ijeoma Chioma Mordi, Ngozi Blessing Umoru, Sandra A Palmer and Kemi K.Oladapo (2025). "AI-Driven Integrated Solar-Agrivoltaics Systems Transforming Food Security in West Africa". INTERNATIONAL JOURNAL OF RESEARCH IN MANAGEMENT FIELDS (IJRMF), vol. 9, no. 6, 2025, pp. 181-199,. DOI: https://dx.doi.org/10.5281/zenodo.18020839 International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 182 1. Introduction Global food systems face mounting pressures from population growth, climate change, and energy insecurity, producing large volumes of food waste while simultaneously straining natural resources and livelihoods in vulnerable regions [1][2]. Post-harvest losses across subSaharan Africa can reach substantial proportions for high-value perishables such as tomatoes, leafy vegetables, fish, and roots, with consequences for food security, farmer incomes, and greenhouse gas emissions [3][4]. At the same time, West Africa possesses significant solar energy potential, with average solar irradiance levels that create an opportunity to align renewable energy deployment with localized food-system interventions [5][6]. Integrating solar energy into food-waste valorization and agricultural production offers a pathway to reduce losses, recover resources, and advance circular-economy objectives in the region [7][8]. Recent studies demonstrate promising outcomes from campus-scale and pilot implementations of solar-powered biomass valorization, showing conversion of post-consumer organic waste into pet food, compost, and energy products, while achieving measurable reductions in CO2equivalent emissions and operational costs [1][9]. However, many of these efforts remain geographically and operationally limited, lacking comprehensive assessments of seasonal variability, socio-economic scalability, and integration with productive land uses such as agrivoltaics [10][11]. Reviews of food-waste valorization highlight persistent gaps, including fragmented evaluation frameworks, a lack of data standardization, and limited system-level integration across technological constraints that hamper transferability to diverse contexts, such as West Africa [12][13]. There is therefore a pressing need for interdisciplinary, regionally tailored research that couples renewable energy systems with circular food-waste management and climate-smart agriculture. Agrivoltaics—the co-location of photovoltaic systems and crop production—has emerged as a promising approach to increase land-use productivity and create beneficial microclimates that can reduce evaporative losses and heat stress on crops [14][15]. When combined with on-site biomass valorization (conversion of collected food waste into compost, animal feed, or bioenergy) and solar-driven cold-chain storage, an AI-driven integrated system can address multiple failure points in West African value chains: spoilage at markets, inadequate storage, and underutilized organic residues [16][17]. Life cycle and techno-economic analyses from the literature suggest that multi-technology coupling and circular designs yield higher resource International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 183 efficiency and lower greenhouse gas intensity than single-technology stand-alone solutions, yet empirical LCA studies specific to AI-driven integrated agrivoltaic–valorization systems in tropical West African settings are scarce [12][18]. Seasonal dynamics in both feedstock composition and solar resource availability represent critical uncertainties for solar-powered valorization and agrivoltaic systems. West Africa's marked dry and rainy seasons alter the quantity and quality of food waste generated by markets and households, and cloud cover and aerosol loading during the rainy season can substantially reduce PV output, creating temporal mismatches between energy supply and processing demand [6][19]. These mismatches can undermine the continuous operation of digesters, dryers, and cold storage, thereby diminishing conversion efficiencies and economic returns, unless buffering strategies (storage, hybridization, demand management) are designed into system architectures [20][1]. Consequently, sensitivity analyses and resilience metrics that quantify how conversion efficiency, crop yield, and cold-chain reliability respond to seasonal variability are essential for credible scaling pathways. Socio-economic and institutional dimensions further mediate technology adoption and impact. Smallholder and market-level actors in West Africa operate under constrained capital, informal market structures, and varied regulatory environments, all of which influence investment decisions, feedstock collection logistics, and acceptance of upcycled products such as feed and compost [3][11]. Business models that combine revenue streams—energy savings, product sales, reduced losses—and leverage mechanisms like energy performance contracting or cooperative ownership have shown promise in other contexts but require adaptation and testing in West African cultural and market conditions [1][7]. Additionally, policymakers need evidence-based evaluation frameworks that integrate techno-economic analysis, LCA, and social acceptance metrics to prioritize interventions that deliver equitable and sustainable outcomes. This study therefore develops, implements, and evaluates an AI-driven integrated solaragrivoltaic circular economy model tailored for West African agro-ecosystems, with the following objectives: (i) quantify seasonal PV output and energy-demand profiles for biomass valorization and cold storage across dry and rainy seasons; (ii) measure conversion efficiencies of locally dominant residues (cassava peels, maize husks, market vegetable waste) into International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 184 compost and animal feed under solar-driven processing; (iii) assess agrivoltaic impacts on crop yield and soil moisture; and (iv) perform a combined LCA and techno-economic analysis including sensitivity tests for seasonal variability and business-model scenarios. By addressing the coupled technical, environmental, and socio-economic questions identified in the literature, this work aims to provide empirically grounded guidelines and a replicable evaluation framework for scaling solar-AI-driven integrated circular food systems in West Africa [12][20]. Figure 1 visually compares the dry and rainy seasons in West Africa, showing high solar PV output (~6.1 kWh/kW/day) during sunny conditions and reduced output (~3.4 kWh/kW/day) under cloudy skies. In comparison, biomass energy demand remains constant at ~5 kWh/day across both seasons. Figure 1: Seasonal Variation in Solar PV Output and Energy Demand for Biomass Valorisation in West Africa 2. Methodology This investigation adopts a mixed methodological framework combining field measurements, process-based simulation, and life-cycle modelling. Three West African ecological zones were selected to represent distinct climatic regimes: a humid coastal system (Ghana), a sub-humid transition belt (Nigeria), and a Sahelian dryland zone (Senegal). At each site, paired plots were established for the agrivoltaic and open-field control conditions, along with experimental units for food waste collection and biomass utilisation. The study spans two full agricultural seasons, International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 185 allowing seasonal variability in solar resources, crop responses, and biomass flows to be captured. All analytical procedures, equations, and datasets are provided to ensure independent reproducibility. 2.1. Data Collection and Instrumentation Continuous solar irradiance, module-level AC output, and microclimate variables were monitored at 5-minute intervals using calibrated pyranometers, data loggers, and inverter records. Biomass feedstocks (market rejects, cassava peels, and maize residues) were sampled daily for mass, moisture (TS/VS), C:N ratio, and calorific value. Agronomic observations included soil moisture dynamics, intercepted PAR, and crop yield. Cold-chain units and processing equipment were equipped with smart meters to quantify energy demand and spoilage reduction. Metadata and timestamps were unified across all devices; datasets are stored in CSV with full variable descriptions. 2.2. AI-driven integrated System Representation and Mass–Energy Balance Models. The system is represented as a dynamic, coupled mass–energy network in which solar generation, biomass conversion, and processing loads interact. The energy balance at any time t follows: Where:   PV = PV AC output,   aux = auxiliary/grid/diesel energy,    = energy load of process p,   exp = exported surplus,   loss = inverter + wiring losses. Material conversion is expressed as: International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 186 where   is feedstock inflow,   are functional product flows, and   is unconverted residue. For multi-stage processes, a vectorized formulation is applied: Matrices A encode transformation coefficients estimated from lab assays. 2.3. Solar PV Modelling and Uncertainty Quantification PV output is simulated using a temperature-corrected irradiance model: Panel temperature is estimated with the NOCT relation. Uncertainty in , , and  inv is propagated using a 1,000-run Monte Carlo simulation to provide site-specific confidence intervals. 2.4. Biomass Conversion Models Biodegradable fractions are modelled using first-order degradation: While nutrientor microbial-limited processes follow the Monod equation: Thermal drying demand is estimated from: Laboratory batch reactors provide parameter estimates via nonlinear regression. International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 187 2.5. Agrivoltaic Crop Response Modelling Crop biomass accumulation is expressed using a radiation-use efficiency (RUE) structure: Where shading modifies RUE via: Water-stress factor   is derived from a soil-moisture response curve. Model calibration uses least-squares fitting; 5-fold cross-validation assesses accuracy. 2.6. Economic Evaluation (TEA) Project viability is assessed using discounted cash-flow equations: IRR, payback period, and sensitivity analyses are computed across variations in energy prices, biomass availability, and crop market conditions. 2.7. Life-Cycle Assessment (LCA) A cradle-to-gate LCA is conducted in accordance with ISO 14040/44. The functional unit is "1 tonne of stabilized agricultural produce." Impact categories include GWP100, cumulative energy demand, and land use. Inventory flows are modelled using log-normal distributions and 1,000-run uncertainty propagation. 2.8. Reproducible Literature Identification Protocol A reproducible, script-driven literature search was conducted using Scopus, Web of Science, AGRIS, PubMed, and Google Scholar. Each search records exact query strings, timestamps, and hit counts. Screening follows a PRISMA protocol with dual-reviewer validation. Extracted data include system boundaries, performance metrics, LCA/TEA assumptions, and International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 188 uncertainty treatment. All RIS/BibTeX files, screening sheets, and scripts are stored in a public repository. Figure 2 (a) shows that cassava peels convert to compost at ~42% efficiency and to animal feed at ~29%, while maize husks convert to compost at ~38% and to animal feed at ~33% under solar-powered systems. (b) shows that shaded plots under agrivoltaic systems significantly improve both tomato yield and soil moisture retention compared to unshaded plots in Nigeria. 3. Results Field deployments and system monitoring across the three West African sites produced quantified outcomes for solar generation, biomass collection and conversion, agrivoltaic crop performance, cold storage operations, and AI-driven integrated environmental and economic indicators for the system. Solar PV arrays exhibited evident seasonal variability: mean daily AC generation during the dry season averaged 6.1 kWh·kW−1·day−1 (±0.8), while the rainy season mean fell to 3.4 kWh·kW−1·day−1 (±0.9), resulting in a 44% seasonal reduction in available on-site renewable energy. Measured inverter and balance-of-system losses averaged 8.5% of DC production, consistent with expected inverter efficiencies and wiring losses, leaving usable energy for processes that matched modelled expectations within ±7% over monthly aggregates. Market and farm feedstock collection yielded an annualized average of 4.5 t·site−1·yr−1 of organic residues per site, dominated by cassava peels (45% by mass), maize husks (30%), and mixed vegetable waste (25%). Moisture content varied by feedstock and season: cassava peels averaged 58% (wet basis) in the rainy season and 44% in the dry season, while maize husks showed smaller seasonal swings (35–28%). Volatile solids (VS) content measurements indicated higher biodegradable fractions in vegetable wastes (VS/TS ≈ 0.85) and lower in cassava peels (VS/TS ≈ 0.62), informing conversion yields in downstream processes. International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 189 a b Figure 2: (a) Conversion efficiency of cassava peels and maize husks into compost and animal feed under a solar-powered system. (b) Impact of Agrivoltaic Shading on Tomato Yield and Soil Moisture Retention in Nigeria. Biomass valorization units configured for combined composting and small-scale dry fermentation produced distinct product yields. Composting of mixed market waste reached stable maturity in 8–10 weeks under the managed windrow protocol, with mass reduction factors of 0.42±0.05 and final nutrient concentrations (N-P-K) sufficient for local crop amendment. Dry fermentation targeted animal-feed upgrading (drying + pelletizing) achieved mass retention of 38% for vegetable-rich mixes and 29% for cassava-dominant mixes after moisture reduction to <12%. Laboratory assays indicated crude protein increases of approximately 18% (relative) after enzymatic treatment and pelletization for vegetabledominant feedstock. Process energy intensity for drying and pelletizing averaged 1.2 kWh·kg−1 of water removed, which translated to daily energy demands that could be met on average 68% of dry-season days by PV alone but only 32% of rainy-season days without backup or storage. Agrivoltaic trials on tomato and leafy-vegetable plots demonstrated consistent microclimatic benefits and yield responses. Shaded plots beneath 30% adequate panel coverage experienced mean daytime canopy temperatures 1.8 °C lower than fully exposed controls during peak afternoon hours and maintained soil volumetric water content 9–12 percentage points higher at 10 cm depth across dry-season irrigation cycles. Tomato yield per m2 increased by 14% (95% CI: 9–19%) in shaded agrivoltaic plots relative to unshaded controls under the same management practices; leafy greens showed a 22% yield increase (95% CI: 16–28%), likely International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18020839 Original Article ©2025 RS Publicaon, rspublica[email protected]m 196 systems. The originality lies in combining energy generation, waste-to-resource conversion, and post-harvest preservation within a single framework, thereby addressing multiple sustainability challenges simultaneously. Quantified results highlight the model's technical and agronomic potential. Seasonal solar PV variability was evident, with output declining by approximately 44% between dry and rainy seasons, reducing the share of days when PV alone met energy demand from 68% to 32%. Agrivoltaic shading improved crop yields, with tomatoes increasing by 14% and leafy greens by 22%, demonstrating the microclimatic benefits of moderated canopy coverage. Biomass valorization achieved conversion efficiencies of 29–38% for compost and 33% for animal feed, though cassava-dominant mixes showed lower retained mass due to their higher moisture content. Cold storage reduced post-harvest losses from 39% to 15%, improving food preservation by 24 percentage points. Error margins from sensitivity analyses indicated up to a 28% loss in greenhouse gas reduction benefits under a 20% drop in PV performance, underscoring the importance of system reliability. 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