
%\usepackage{caption}
\section{appendix}
\subsection{Technical Details on Modeling Approaches and Adaptations}
\label{app:modeling}

\subsubsection{CGE Model Hierarchy and F-G-W Adaptations}
CGE models differ in scope, sectoral disaggregation, dynamic structure, and capacity to represent institutional and social heterogeneity. The hierarchy-from foundational recursive-dynamic models like MAMS to agriculture-resource-focused models like DEMETRA, regional extensions (PEP), global models (GTAP), and spatially explicit hybrids like SIMPLE-G-requires systematic adaptations to fully operationalize the F-G-W nexus.

MAMS (Maquette for MDG Simulations) combines a standard CGE core with an MDG/ODD module endogenizing public spending in social sectors (health, education, water, sanitation) on human capital and productivity \citep{Hans2013}. Its recursive-dynamic structure captures accumulation effects through capital stock equations. Water and land enter as regionally constrained factors with endogenous prices. Widely applied in African contexts for MDG/ODD strategies and water investments \citep{Lofgren2019}. For F-G-W, MAMS requires gender extensions: differentiated labor markets with sector-specific access barriers and wage gaps, time allocation constraints creating binding limits on women’s paid labor supply, and gender-disaggregated water-productivity linkages \citep{Blackden2006}.

DEMETRA (Dynamic Equilibrium Model for Economic Development, Resources and Agriculture) advances this by modeling agriculture dependent on depletable natural capital stocks (land quality, water availability, forest cover) that depreciate under overuse and appreciate via conservation. Agricultural production functions employ varied forms to capture substitution and biophysical constraints: constant elasticity of substitution (CES) for flexible substitution between land, water, labor, capital \citep{Britz2011}; Cobb-Douglas for unitary elasticities; Leontief (fixed-proportions) for rigid constraints in water-scarce or land-limited contexts. Land and water are endogenously allocated subject to relative returns and resource constraints.

Adapting DEMETRA to the F-G-W nexus (DEMETRA-FGW) requires five systematic extensions:

1. Gender-disaggregated production functions and labor markets, with differentiated male/female productivities reflecting unequal access to land, credit, extension, and inputs \citep{FAO2018, Doss2015}. These enter as productivity wedges calibrated from gender-disaggregated yield gap estimates.

2. Time-use accounting and time poverty constraints. Gender-specific time-use matrices must be embedded in the underlying Social Accounting Matrix, capturing how women’s unpaid labor subsidizes household reproduction. Water collection time becomes endogenous (driven by infrastructure, scarcity, deforestation), imposing a binding constraint formalized as:
\begin{equation}
\bar{T}_w = T_w^{\text{agric}} + T_w^{\text{wage}} + T_w^{\text{water}} + T_w^{\text{care}} + T_w^{\text{leisure}}
\label{eq:time_constraint}
\end{equation}
where \(\bar{T}_w\) is the total time endowment per woman. A reduction in \(T_w^{\text{water}}\) (via piped water or closer sources) or \(T_w^{\text{care}}\) (via childcare) reallocates time toward agricultural or wage activities, increasing female labor supply and household income-multiplier effects absent from standard CGE formulations \citep{Blackden2006, Fontana2009, geere2020, Cicowiez2024}. This reallocation can be further linked to agricultural output via a gender-specific production function:
\begin{equation}
Q^{\text{agric}}_w = A_w \left( L_w^{\text{agric}} \right)^{\alpha} \left( K_w \right)^{\beta} \left( \text{Land}_w \right)^{\gamma} \left( \text{Water}_w \right)^{\delta},
\label{eq:prod_fem}
\end{equation}
where increased \(L_w^{\text{agric}}\) (from time freed) amplifies output beyond the male contribution.

3. Land governance and tenure heterogeneity, distinguishing statutory from customary regimes, with differential investment responses, credit access, and displacement risks. Land conflicts enter via transaction cost parameters that rise with tenure insecurity \citep{Deininger2011, WBGU2021}.

4. Water governance institutions and access constraints, represented through governance effectiveness parameters and participation constraints (e.g., women’s exclusion from water user associations creates gaps between potential and realized irrigation gains) \citep{Cleaver2021, MeinzenDick2019}.

5. Deforestation feedbacks and ecosystem service linkages, treating forest cover as a natural capital stock that provides water regulation, soil fertility, and fuelwood services. These are formalized via ecosystem service production functions linking forest loss to downstream water yield, soil quality, and women’s fuelwood collection distance \citep{Agarwal2009, Biggs2015, IPBES2019}.
\begin{table}[!htb]
\small
\centering
\begin{tabular}{p{2.5cm}p{2.5cm}p{3cm}p{5.5cm}}
\toprule
\textbf{Model} & \textbf{Scale} & \textbf{Core Innovation} & \textbf{F-G-W Adaptation Required} \\
\midrule
\textit{MAMS} & National & Social investment-productivity linkages & + Gender-SAM, time-use matrices, gender-disaggregated MDG water-productivity effects \\
\textit{DEMETRA → DEMETRA-FGW} & National & Natural capital depreciation/appreciation in agriculture & + Gender labor/productivity differentiation, time poverty constraint (\ref{eq:time_constraint}), tenure heterogeneity, deforestation-ecosystem feedbacks \\
\textit{PEP Multi-regional} & Transboundary basin & Inter-regional trade and factor mobility & + Hydrological upstream-downstream constraints, gender-disaggregated benefit-sharing \\
\textit{GTAP} & Global & International trade and virtual flows & + Coupling with national models, gender impacts of trade liberalization \\
\textit{SIMPLE-G + F-G-W} & Global + Grid (0.5°) & Global-local biophysical coupling & + Grid-level gender land/time access, deforestation-fuelwood links, institutional water constraints \\
\textit{Spatial SAR/SEM/SDM} & District/Grid & Local spillovers & Calibrate with gender land ownership, time poverty indices, tenure security, deforestation \\
\textit{Hybrid Nested F-G-W} & Grid → National → Basin → Global & Multi-scale parameter flow & Full integration: gender-land-water-deforestation-governance-capability dynamics \\
\bottomrule
\end{tabular}
\caption{Models Hierarchy with F-G-W Nexus Adaptations}
\label{tab:cge_fgw_hierarchy}
\par \small\textit{Source: Authors' elaboration.}
\end{table}
Regional CGE models (e.g., PEP-network extensions) add multi-regional trade and factor mobility, essential for transboundary basins. They must incorporate basin-scale hydrological constraints and gender-disaggregated upstream-downstream impacts, including benefit-sharing and compensation mechanisms \citep{Ringler2013, Cascao2010}. Global models like GTAP capture international trade and virtual water flows but require coupling with higher-resolution national models (e.g., DEMETRA-FGW) due to coarse African disaggregation. SIMPLE-G represents a frontier by coupling global CGE coherence with grid-cell (typically 0.5°) biophysical detail. For F-G-W operationalization, it must extend grid-level land-use and water decisions to include gender-differentiated access, tenure security, and time poverty effects, linking local deforestation to women’s fuelwood burdens and institutional water access constraints \citep{Baldos2020, Hertel2025, Haqiqi2022, Haqiqi_2022}.

These systematic modeling adaptations-particularly the explicit treatment of gender-disaggregated time poverty constraints, land tenure heterogeneity, deforestation-ecosystem feedbacks, and institutional governance parameters-enable hybrid spatial-CGE frameworks to embody the three co-equal pillars of wise agricultural systems: economic viability and resilience, environmental sustainability, and social justice and equity \citep{Parra2026}.


\subsection{Spatial Econometric Specifications for Micro-Level Heterogeneity}
Spatial econometric models capture fine-scale geographic heterogeneity and spillover effects inadequately represented by aggregate CGE models, even grid-based ones like SIMPLE-G. They are essential to operationalize spatial resilience and environmental inequalities in the F-G-W framework.

The Spatial Autoregressive Model (SAR) captures direct spatial dependence in the dependent variable:
\begin{equation}
FS_i = \rho \sum_{j} w_{ij} FS_j + X_i \beta + \varepsilon_i
\label{eq:SAR}
\end{equation}
where $FS_i$ is food security in region $i$, $\rho$ measures spatial spillovers, $X_i$ includes F-G-W variables (female land ownership, time poverty indices, tenure security, forest cover, water access, governance quality), and $w_{ij}$ is the spatial weight matrix (geographic distance, extendable to institutional or conflict networks) \citep{Ngoma2015, Chamberlin2014}.

The Spatial Error Model (SEM) addresses unobserved spatial heterogeneity:
\begin{equation}
FS_i = X_i \beta + u_i, \quad u_i = \lambda \sum_{j} w_{ij} u_j + \varepsilon_i
\label{eq:SEM}
\end{equation}

The Spatial Durbin Model (SDM) combines both:
\begin{equation}
FS_i = \rho \sum_{j} w_{ij} FS_j + X_i \beta + W X_i \theta + \varepsilon_i
\label{eq:SDM}
\end{equation}
capturing direct and indirect effects (e.g., neighboring female land ownership influencing local bargaining power via demonstration and social learning).

These models are estimated via maximum likelihood or GMM, with robustness checks on weight matrices. Empirical calibration requires variables operationalizing F-G-W dimensions: gender-disaggregated tenure, time poverty from surveys, distance to water, water user association membership, georeferenced conflicts, remote-sensed deforestation, governance scores \citep{Ngoma2015, Mulwa2017, Alhassan2022, LiuBrouwer2022, Deininger2011}.

\begin{landscape}
\centering
\small
\begin{longtable}{p{18cm} c}
\caption{General scopus and WoSSearch Queries Covering the Food–Gender–Water Nexus and Thematic Scope} \label{tab:scopus_queries} \\
\toprule
\textbf{Query} & \textbf{Articles} \\
\midrule
\endfirsthead

\toprule
\textbf{Query} & \textbf{Articles} \\
\midrule
\endhead

\bottomrule
\endfoot

( "nexus" OR "interlinkage" OR "interconnection" OR "integration" OR "synergy" OR "coupled systems" OR "food security" OR "food insecurity" OR "nutrition security" OR "hunger" OR "malnutrition" OR "agri-food systems" OR "sustainable food systems" OR "food availability" OR "food access" OR "food systems" OR "agricultural production" OR "food supply" OR "food policy" OR "crop production" OR "household food security" OR "Land productivity" OR "resilience" )
AND ( "gender inequality" OR "gender equity" OR "women's empowerment" OR "gender disparity" OR "gender justice" OR "gender gap" OR "female participation" OR "gender roles" OR "feminization of agriculture" OR "gender-sensitive" OR "gender mainstreaming" OR "rural women" OR "women in agriculture" OR "intersectionality" )
AND ("water management" OR "water governance" OR "water scarcity" OR "water security" OR "water resources" OR "water access" OR "irrigation systems" OR "hydraulic infrastructure" OR "IWRM" OR "integrated water resources management" OR "water-use efficiency" OR "water policy" OR "rural water supply" OR "water governance framework" OR "governance") & 4847 \\

\end{longtable}
\par \small\textit{Source: Authors' own elaboration.}
\end{landscape}





\begin{landscape}
%\small
\centering
\begin{longtable}{p{20cm} c}
\caption{Methodologically Refined Search Queries Incorporating CGE, Spatial Models, and Quantitative Indices} 
\label{tab:scopus_queries_} \\
\toprule
\textbf{Query} & \textbf{Articles} \\
\midrule
\endfirsthead
\toprule
\textbf{Query} & \textbf{Articles} \\
\midrule
\endhead
\bottomrule
\endfoot
"water management" AND "gender inequality" OR "gender gap" OR "women empowerment" AND "spatial modeling" OR "spatial econometrics" OR "general equilibrium model" OR "computable general equilibrium" OR "CGE model" & 9 \\
"nutrition" AND "gender inequality" OR "gender gap" OR "women empowerment" AND "water management" OR "water resources" OR "irrigation" AND "general equilibrium model" OR "computable general equilibrium" OR "CGE model" OR "spatial modeling" OR "spatial econometrics" & 9 \\
"food security" AND "gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers" AND "water management" OR "water resources" OR "irrigation" OR "water governance" AND "general equilibrium model" OR "computable general equilibrium" OR "CGE model" OR "spatial modeling" OR "spatial econometrics" OR "spatial analysis" AND (LIMIT-TO(SUBJAREA, "ENVI") OR LIMIT-TO(SUBJAREA, "SOCI") OR LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "AGRI") OR LIMIT-TO(SUBJAREA, "BUSI") OR LIMIT-TO(SUBJAREA, "MULT")) & 114 \\
"agriculture" AND "gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers" AND "water management" OR "water resources" OR "irrigation" AND "general equilibrium model" OR "computable general equilibrium" OR "CGE model" OR "spatial modeling" OR "spatial econometrics" & 23 \\
("food security" OR "agriculture") AND ("gender inequality" OR "women empowerment") AND ("water management" OR "irrigation") AND ("CGE model" OR "spatial econometrics" OR "spatial analysis") & 42 \\
("dynamic stochastic general equilibrium") AND ("food security" OR "water management") AND ("gender inequality" OR "women empowerment") & 2 \\
"spatial econometric" AND "climate change" AND "women empowerment" & 11 \\
"spatial econometric" AND "climate change" & 77 \\
("food security" OR "agriculture") AND ("water management" OR "irrigation") AND ("CGE model" OR "spatial econometrics") AND ("gender inequality" OR "women empowerment") & 7 \\
("nexus" OR "OLG" OR "GTAP" OR "WEF" OR "FWE²" OR "WELF" OR "food security" OR "LAND" OR "climate change" OR "gender inequality" OR "women empowerment" OR "water management" OR "water governance") AND ("CGE model" OR "spatial modeling") AND PUBYEAR > 1994 AND PUBYEAR < 2026 AND (LIMIT-TO(SRCTYPE, "j")) AND (LIMIT-TO(OA, "all")) AND (LIMIT-TO(PUBSTAGE, "final")) AND (LIMIT-TO(SUBJAREA, "ECON")) AND (LIMIT-TO(DOCTYPE, "ar")) AND (LIMIT-TO(LANGUAGE, "English") OR LIMIT-TO(LANGUAGE, "French")) & 248 \\
TITLE-ABS-KEY("water management" OR "water resources" AND "gender inequality" OR "gender gap" OR "women empowerment") AND ("spatial modeling" OR "CGE model") & 10 \\
"spatial econometric" AND "gender" & 15 \\
"spatial econometric" AND "food security" & 15 \\
"CGE" AND "africa" AND "gender" & 12 \\
"CGE" AND "africa" AND "food security" & 19 \\
"CGE" AND "Food Price" & 37 \\
"DSGE" AND "Food Price" & 8 \\
"DSGE" AND "water" & 3 \\
"spatial econometric" AND "water" AND "women empowerment" & 10 \\
"spatial econometric" AND "food security" AND "women empowerment" & 9 \\
"general equilibrium model" AND "food security" AND "gender inequality" & 12 \\
"spatial econometric" AND "food security" AND "gender inequality" & 6 \\
"spatial econometric" AND "gender inequality" AND "water" & 8 \\
"spatial econometric" AND "gender inequality" AND "climate change" & 16 \\
"general equilibrium model" AND "food security" AND "women empowerment" & 8 \\
"dynamic stochastic general equilibrium" AND "food security" & 57 \\
"dynamic stochastic general equilibrium" AND "gender inequality" & 4 \\
"dynamic stochastic general equilibrium" AND "irrigation" & 14 \\
"dynamic stochastic general equilibrium" AND "gender" & 98 \\
"dynamic stochastic general equilibrium" AND "gender inequality" & 3 \\
"dynamic stochastic general equilibrium" AND "gender equality" & 6 \\
("food security" OR "nutrition" OR "agriculture") AND ("gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers" OR "WEMNS") AND ("water management" OR "water governance" OR "irrigation" OR "water access") AND ("computable general equilibrium" OR "CGE model" OR "general equilibrium model" OR "spatial modeling" OR "spatial econometrics" OR "spatial analysis") AND (LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "EART") OR LIMIT-TO(SUBJAREA, "ENVI") OR LIMIT-TO(SUBJAREA, "SOCI") OR LIMIT-TO(SUBJAREA, "AGRI") OR LIMIT-TO(SUBJAREA, "BUSI") OR LIMIT-TO(SUBJAREA, "MULT")) & 146 \\
("food security" OR "agriculture") AND ("water management" OR "irrigation") AND ("CGE model" OR "spatial econometrics") & 409 \\
("food security" OR "nutrition" OR "agriculture") AND ("gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers") AND ("water management" OR "water governance" OR "irrigation" OR "water access") AND ("computable general equilibrium" OR "CGE model" OR "general equilibrium model" OR "spatial modeling" OR "spatial econometrics" OR "spatial analysis") & 103 \\
("gender inequality" OR "women empowerment") AND ("water governance" OR "irrigation") AND ("CGE model" OR "spatial econometrics") & 4 \\
("food security" OR "nutrition") AND ("gender inequality" OR "female farmers") AND ("CGE model" OR "spatial modeling") & 14 \\
("food security" OR "nutrition") AND ("gender inequality" OR "female farmers") AND ("CGE" OR "spatial modeling") & 25 \\
("Food Security Index" OR "Global Hunger Index" OR "HFIAS" OR "FIES" OR "Dietary Diversity Score" OR "WEMNS" OR "WEAI" OR "WEF Nexus Index") AND ("food security" OR "gender inequality" OR "water management") AND ("CGE model" OR "spatial econometrics") AND (LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "SOCI")) AND (LIMIT-TO(DOCTYPE, "ar")) AND (LIMIT-TO(LANGUAGE, "English") OR LIMIT-TO(LANGUAGE, "French")) & 10 \\
("food security" OR "nutrition" OR "agriculture") AND ("gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers") AND ("water management" OR "water governance" OR "irrigation" OR "water access") AND ("computable general equilibrium" OR "CGE" OR "CGE model" OR "general equilibrium model" OR "spatial modeling" OR "spatial econometrics" OR "spatial analysis") & 52 \\
("Food Security Index" OR "Global Hunger Index" OR "HFIAS" OR "FIES" OR "Dietary Diversity Score" OR "WEMNS" OR "WEAI" OR "WEF Nexus Index") AND ("food security" OR "gender inequality" OR "water management") AND ("CGE model" OR "spatial econometrics") AND (LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "SOCI") OR LIMIT-TO(SUBJAREA, "ENVI") OR LIMIT-TO(SUBJAREA, "AGRI") OR LIMIT-TO(SUBJAREA, "DECI") OR LIMIT-TO(SUBJAREA, "COMP")) AND (LIMIT-TO(DOCTYPE, "ar")) AND (LIMIT-TO(LANGUAGE, "English") OR LIMIT-TO(LANGUAGE, "French")) & 21 \\
(("food security" OR "nutrition" OR "agriculture" OR "Food Security Index" OR "Global Hunger Index" OR "HFIAS" OR "FIES" OR "Dietary Diversity Score" OR "sustainable nutrition security") AND ("gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers" OR "WEMNS" OR "WEAI" OR "gender index") AND ("water management" OR "water resources" OR "irrigation" OR "water governance" OR "water access") AND ("CGE model" OR "computable general equilibrium" OR "spatial modeling" OR "spatial econometrics" OR "spatial analysis" OR "GTAP") AND ("nexus" OR "water-energy-food nexus" OR "WEF nexus" OR "WEFE nexus" OR "WEF Nexus Index" OR "resource security index" OR "sustainability metrics")) AND (LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "ENVI") OR LIMIT-TO(SUBJAREA, "SOCI")) AND (LIMIT-TO(DOCTYPE, "ar")) AND (LIMIT-TO(LANGUAGE, "English") OR LIMIT-TO(LANGUAGE, "French")) & 21 \\
("food security" OR "water management") AND ("gender inequality" OR "women empowerment") AND ("spatial econometrics" OR "CGE model") & 32 \\
"food security" OR "nutrition" OR "agriculture" AND "gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers" AND "water management" OR "water governance" OR "irrigation" OR "water access" AND "computable general equilibrium" OR "CGE" OR "CGE model" OR "DSGE" OR "Spatial" OR "general equilibrium model" OR "spatial modeling" OR "spatial econometrics" OR "spatial analysis" OR "SAR" OR "SEM" OR "SDM" OR "SARAR" & 7 \\
("food security" OR "nutrition" OR "agriculture") AND ("gender inequality" OR "gender gap" OR "women empowerment" OR "female farmers") AND ("water management" OR "water resources" OR "irrigation" OR "water governance" OR "water access") AND ("computable general equilibrium" OR "CGE model" OR "spatial modeling" OR "spatial econometrics" OR "spatial analysis") AND ("nexus" OR "water-energy-food nexus" OR "integrated approach") AND (LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "ENVI") OR LIMIT-TO(SUBJAREA, "SOCI")) AND (LIMIT-TO(DOCTYPE, "ar")) AND (LIMIT-TO(LANGUAGE, "English") OR LIMIT-TO(LANGUAGE, "French")) & 31 \\
TITLE-ABS-KEY(("food security" OR "nutrition" OR "malnutrition" OR "undernutrition" OR "safe nutritious food" OR "Food Security Index" OR "FIES" OR "Dietary Diversity Score") AND ("gender inequality" OR "gender gap" OR "women empowerment" OR "WEMNS" OR "WEAI" OR "gender index") AND ("water management" OR "water resources" OR "irrigation" OR "water governance") AND ("CGE model" OR "computable general equilibrium" OR "spatial modeling" OR "spatial econometrics" OR "GTAP") AND ("nexus" OR "water-energy-food nexus" OR "WEF nexus" OR "WEF Nexus Index" OR "sustainability metrics" OR "financial inclusion" OR "economic empowerment agriculture" OR "sustainable consumption")) AND (LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "ENVI") OR LIMIT-TO(SUBJAREA, "SOCI") OR LIMIT-TO(SUBJAREA, "AGRI")) AND (LIMIT-TO(DOCTYPE, "ar")) AND (LIMIT-TO(LANGUAGE, "English") OR LIMIT-TO(LANGUAGE, "French")) & 7 \\
(("food security" OR "nutrition" OR "agriculture") AND ("gender inequality" OR "women empowerment" OR "female farmers" OR "WEMNS" OR "WEAI" OR "leadership agriculture" OR "decision making agriculture" OR "social norms agriculture") AND ("water management" OR "water resources" OR "irrigation" OR "water access") AND ("CGE model" OR "computable general equilibrium" OR "spatial modeling" OR "spatial econometrics" OR "GTAP") AND ("nexus" OR "water-energy-food nexus" OR "WEF nexus" OR "access to technology agriculture" OR "land tenure" OR "land rights" OR "time poverty agriculture" OR "unpaid care")) AND (LIMIT-TO(SUBJAREA, "ECON") OR LIMIT-TO(SUBJAREA, "ENVI") OR LIMIT-TO(SUBJAREA, "SOCI") OR LIMIT-TO(SUBJAREA, "AGRI")) AND (LIMIT-TO(DOCTYPE, "ar")) AND (LIMIT-TO(LANGUAGE, "English") OR LIMIT-TO(LANGUAGE, "French")) & 9 \\
\bottomrule

\end{longtable}
\par \small\textit{Source: Authors' own elaboration.}

\end{landscape}



% tab 3 et 4

\begin{landscape} 
\begin{table}[h!] 
\centering 
\renewcommand{\arraystretch}{1.12} 
\begin{tabular}{|p{3.2cm}|p{15cm}|} 
\hline 
\textbf{PICO Element} & \textbf{Applied Criteria and Illustrative Examples} \\ 
\hline 
\textbf{Population} & 
Regions analyzed included North Africa (Morocco, Algeria, Tunisia, Egypt), West Africa (Senegal, Mali, Burkina Faso, Côte d’Ivoire, Ghana, Nigeria), Central Africa (Cameroon, Chad, DRC, Gabon), East Africa (Ethiopia, Kenya, Uganda, Tanzania), Southern Africa (South Africa, Zimbabwe, Zambia, Namibia, Mozambique), and transboundary regions (Sahel, SADC, IGAD, river basins).
Vulnerable subgroups of interest were rural women, female-headed households, smallholders, herders, fishers, climate migrants, indigenous peoples, rural youth, the elderly, peri-urban communities, and cooperatives.
Units of analysis spanned individual, household, community, district, watershed, province, country, region, and agroecological zone levels.
Exclusions comprised studies outside Africa, urban affluent populations, and those lacking a gender, water, or food security dimension. \\ 
\hline 
\textbf{Intervention} & 
Strategies analyzed were climate change adaptation, integrated water resource management (IWRM), irrigation, land reform, access to credit, gender-sensitive governance, subsidies, agricultural extension, and microfinance.
Technological innovations included climate-smart agriculture (CSA), drought-resistant seeds, rainwater harvesting, solar pumping, early warning systems, mobile platforms, CGE models, and spatial econometrics.
Social and institutional interventions involved gender quotas, women’s empowerment programs, savings groups, and participatory governance structures.
Exclusions were interventions outside the nexus, such as pure energy policies not related to food, water, or gender security. \\ 
\hline 
\end{tabular} 
\caption{Detailed PICOST grid (Part 1) for the Africa nexus review: Population and Intervention.} 
\label{tab:pico_population_intervention_en} 
\par \small\textit{Source: Authors' own elaboration.}

\end{table} 

\end{landscape} 

\begin{landscape} 
\begin{table}[h!] 
\centering 
\renewcommand{\arraystretch}{1.12} 
\begin{tabular}{|p{3.2cm}|p{15cm}|} 
\hline 
\textbf{PICO Element} & \textbf{Applied Criteria and Illustrative Examples} \\ 
\hline 
\textbf{Comparison} & 
Spatial comparisons were made between regions, countries, climate zones, and agroecological zones.
Temporal comparisons included historical series (1995-2005, 2006-2015, 2016-2024), before/after intervention analyses, and longitudinal cohort tracking.
Population and social comparisons involved women vs. men, rural vs. peri-urban, female- vs. male-headed households, and technology adopters vs. non-adopters.
Institutional and policy comparisons contrasted inclusive vs. neutral governance and land reform vs. status quo scenarios.
Exclusions were studies lacking a control group or relevant social or spatial differentiation. \\ 
\hline 
\textbf{Outcome} & 
Objective indicators encompassed food security metrics, water security measures, agricultural yield, technology adoption rates, and economic indicators like household income and poverty.
Subjective or qualitative indicators included perceived women’s empowerment, beneficiary satisfaction, participation in governance, and perceived resilience.
Health and well-being indicators measured waterborne disease prevalence, malnutrition rates, and reductions in labor burden.
Institutional and policy indicators assessed the effectiveness of public policies, adoption of new regulations, and impact on relevant SDGs (2, 5, 6, 13).
Exclusions were studies with no measurable indicators, purely conceptual analyses, or non-nexus results. \\ 
\hline 
\end{tabular} 
\caption{Detailed PICOST grid (Part 2) for the Africa nexus review: Comparison and Outcome.} 
\label{tab:pico_comparison_outcome_en} 
\par \small\textit{Source: Authors' own elaboration.}
\end{table} 
\end{landscape} 
%\end{flushright}
% tab 5
\begin{table}[h!] % On garde l'environnement table standard
    \centering
    \renewcommand{\arraystretch}{1.3}
    \setlength{\tabcolsep}{6pt}
    \begin{tabular}{|p{2.5cm}|p{10.5cm}|} % J'ai ajusté la largeur pour occuper la page
    \hline
    \textbf{Region} & \textbf{Key Climate Impacts} \\
    \hline
    \textbf{North Africa (Maghreb)} & Water scarcity, recurrent droughts, decreasing precipitation (-10 to -20\% by 2050), increasing temperatures (+2.5°C by 2050), reduced groundwater recharge \citep{OMM2022}. \\
    \hline
    \textbf{Sahel} & Severe desertification, rising temperatures (+3.5°C by 2100), more frequent droughts, food production decline (10–25\% for cereals by 2050), forced climate migration \citep{CILSS2023}. \\
    \hline
    \textbf{West Africa} & Coastal erosion, saltwater intrusion in agricultural lands, extreme rainfall variability causing both droughts and floods, food insecurity hotspots in urban slums \citep{CEDEAO2023}. \\
    \hline
    \textbf{Central Africa} & Deforestation (rate of 0.5M hectares/year), biodiversity loss, increased rainfall variability, higher risks of flooding, pressure on subsistence agriculture \citep{UNEP2022}. \\
    \hline
    \textbf{East Africa} & Transboundary water conflicts (Nile Basin), prolonged droughts, erratic rainfall patterns disrupting pastoralist communities, locust infestations exacerbating food shortages \citep{IGAD2023}. \\
    \hline
    \textbf{Southern Africa} & Prolonged droughts, loss of soil fertility, urban water crises (Cape T, Lusaka), declining agricultural yields (-30\% for maize by 2050), cyclones increasing in frequency \citep{SADC2023}. \\
    \hline
    \end{tabular}
    \caption{Regional Climate Impacts on Agricultural Systems and Food Security. Source: Author’s elaboration.}
    \par \small\textit{Source: Authors' own elaboration.}
    \label{tab:regional_climate_impacts}
\end{table}
% fig 7 et 8 
\begin{figure}[!htbp] 
    \centering 
    \includegraphics[width=0.8\textwidth]{co-authorship_per_avrg_pub_time by Contries.png}
    \caption{Co-authorship network by country, illustrating the evolution of international collaborations in nexus research (1995-2024). Source: Author’s elaboration.} 
    \label{fig:coauthorship_country} 
    \par \small\textit{Source: Authors' own elaboration.}
\end{figure} 

\begin{figure}[!htbp] 
    \centering 
    \begin{subfigure}[b]{0.32\textwidth} 
        \includegraphics[width=\textwidth]{P1-co-authorship-in-time-by-contries-3-clust.png} 
        \caption{P1 (1995–2005)} 
        \label{fig:P1-coauth} 
    \end{subfigure} 
    \hfill 
    \begin{subfigure}[b]{0.32\textwidth} 
        \includegraphics[width=\textwidth]{P2-co-authorship-by-contries-4clus.png} 
        \caption{P2 (2006–2015)} 
        \label{fig:P2-coauth} 
    \end{subfigure} 
    \hfill 
    \begin{subfigure}[b]{0.32\textwidth} 
        \includegraphics[width=\textwidth]{P3-co-autorship-in-by-contries-8clust.png} 
        \caption{P3 (2016–2024)} 
        \label{fig:P3-coauth} 
    \end{subfigure} 
    \caption{Temporal evolution of co-authorship networks by country across the three periods, showing increasing complexity and density of collaborations.} 
    \label{fig:coauthorship_evolution} 
    \par \small\textit{Source: Authors' own elaboration.}
\end{figure} 

%fig 10 
\begin{figure}[!htbp] 
    \centering 
    \includegraphics[width=0.85\textwidth]{co-occurence-keyword-index-by-5-Clusters(keyword&region).png}
    \caption{Keyword density clustering map (1995–2024) showing thematic overlap and regional focus. } 
    \par \small\textit{Source: Authors' own elaboration.}

    \label{fig:keyword_clusters} 
\end{figure} 


% fig 4
\begin{figure}[!htbp]
    \centering
    \includegraphics[width=0.80\textwidth]{Thematic_network_visualization-4847ar -5cluster.png}
    \caption{Keyword co-occurrence network from the broad exploratory corpus (n=4,847 articles, 1995–2024), showing the overall structure of thematic clusters.}
    \label{fig:biblio_timeline}
    \par \small\textit{Source: Authors' own elaboration.}
\end{figure}
% fig 14 
\begin{figure}[!htbp]
\centering
\includegraphics[width=0.66\textwidth]{Prev_of_undernour_iof_3_reg.png}
\caption{\textit{Prevalence of undernourishment in Africa, Asia, and the World (2000--2023).}}
   % \insertsource

\label{fig:Undernourishment_Trends}
\par \small\textit{Source: Authors' own elaboration.}

\end{figure}


%\vspace{2mm}
%\begin{flushright} % alignement à droite
%\small\textit{Source: Author's  elaboration using Vosviewer.}
% fig 16 
\begin{figure}[!htbp]
    \centering
    \includegraphics[width=0.9\textwidth]{gender_GAP_FAO_2024.png}
    \caption{Prevalence of Food Insecurity by Gender in Africa and Other World Regions (2015-2023). The data highlights the consistently higher rates of moderate or severe food insecurity among women compared to men in Africa.}
    \label{fig:gender_gap_africa}
    \par \small\textit{Source: Authors' own elaboration based on FAO 
    DATA.}
\end{figure}
