Spatial assessment of pest and disease vulnerability in smallholder farming systems using AHP and GIS

Spatial assessment of pest and disease vulnerability in smallholder farming systems using AHP and GIS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial assessment of pest and disease vulnerability in smallholder farming systems using AHP and GIS Sani Abubakar Mashi, Aminu Abdullahi Muye, Elizabeth Dorsuu Jenkwe, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8468265/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Pests and diseases pose critical challenges to agricultural productivity, particularly in smallholder agricultural systems of sub-Saharan Africa. Understanding and mapping vulnerability to these threats requires an integrated assessment of biophysical and land-use factors that influence pest and disease dynamics. Few studies combine Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS) for this purpose, despite their high potential. This study employs a combined Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) framework to evaluate agricultural pest and disease vulnerability in Gwagwalada Area Council, Nigeria. Six key determinants—land use and cover, soil properties, temperature, moisture, and topography—were analyzed to construct a composite vulnerability index and spatial risk map. The results indicate that land use and land cover exert the greatest influence on vulnerability patterns, while temperature and soil moisture also play critical roles. Validation using the Normalized Difference Vegetation Index (NDVI) confirmed the spatial accuracy of the derived vulnerability zones. The study demonstrates that integrating AHP with GIS provides a robust, participatory, and data-driven decision-support tool for sustainable pest and disease management. The findings highlight the importance of landscape-level monitoring and adaptive management to enhance agricultural resilience, promote sustainable intensification, and safeguard food security in vulnerable farming regions. analytic hierarchy process (AHP) geographic information system (GIS) crop pest and disease risk tailored management strategies agricultural resilience sustainable agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Global population expansion has placed unprecedented pressure on agricultural systems to increase food production per unit area, even as the availability of arable land per capita declines. This challenge is intensified by widespread land degradation resulting from deforestation, overgrazing, urbanization, and unsustainable management practices, alongside the growing impacts of climate change - manifested through rising temperatures, altered rainfall regimes, and more frequent extreme events (Lal et al. 1989 ; Ofori et al. 2021; Pickson et al. 2023 ; Brenya et al. 2024 ; Rashidi et al. 2024 ; Shahzad et al. 2024 ). These interacting pressures diminish soil fertility, reduce ecosystem stability, and increase the incidence of biotic stressors such as pests and diseases, thereby threatening food security and sustainable development (Rosenzweig and Parry 1994 ). Crop pests and diseases represent major biotic constraints to agricultural productivity, with significant economic, social, and ecological consequences. They cause substantial yield and quality losses, undermine livelihoods, and destabilize local economies (Oerke 2020 ; Pimentel and Pimentel 2019 ). Common pests - including aphids, nematodes, rodents, and whiteflies - and pathogenic agents such as fungi, bacteria, and viruses can devastate crops, sometimes leading to complete failure (Scott et al. 2021 ). Their effects extend beyond productivity losses to public health challenges, including contamination of food by pathogens such as E. coli and Salmonella , and exposure to mycotoxins from Fusarium species. Pests and diseases also contribute to biodiversity decline by altering community composition and displacing native species (Sharma et al. 2020 ; Benjamin et al. 2021; Lichtenberg et al., 2025 ). Addressing pest and disease threats is therefore critical to achieving several United Nations Sustainable Development Goals (SDGs), particularly those related to food security (SDG 2), health (SDG 3), economic growth (SDG 8), and ecosystem protection (SDG 15) (Saxena et al. 2021 ). Managing these biotic threats remains inherently complex because pest and disease occurrences are influenced by interacting climatic, edaphic, biological, and topographic factors (Altieri and Nicholls, 2009 ; Bing-fang et al., 2021 ). Conventional monitoring systems are often localized, reactive, and lack the spatial integration needed for early warning or adaptive management. Consequently, geospatial approaches have become indispensable tools for characterizing and predicting crop vulnerability to pests and diseases across heterogeneous landscapes (Brown and Funk 2008 ; Chuan et al., 2019 ; Dong et al. 2019 ; Kumar et al. 2024 ). Over the past two decades, Geographic Information Systems (GIS) have revolutionized the spatial analysis of crop–pest–environment interactions. They facilitate data integration, visualization, and modeling of spatial and temporal pest dynamics. GIS has been successfully employed to map pest distribution (Arampatzis et al., 2004 ; Eisen and Eisen, 2011 ; Thenkabail et al., 2012 ; Tran et al. 2013 ), simulate climate–pest interactions (Bielecka, 2020 ), and assess environmental determinants of pest risk (Hengl et al. 2017 ; Zhang et al., 2019 ). Furthermore, GIS-based monitoring using remote sensing data enables early detection of stress conditions such as moisture deficits or canopy health decline, which often precede pest and disease outbreaks (Wardlow et al. 2007 ; Mamatkulov et al. 2021 ). Network and scenario modeling within GIS environments also support evaluation of potential outbreak pathways and management options (Lindberg et al. 2024 ). Collectively, these capabilities underscore GIS’s value in risk zoning, decision support, and evidence-based management of crop health (Shen et al. 2023 ). However, a major limitation of GIS-based assessments lies in the subjective weighting of environmental and agronomic variables, which may bias decision outcomes (Mariye et al., 2023 ; Makadi et al., 2024 ). To overcome this, the Analytic Hierarchy Process (AHP) provides a structured, multi-criteria decision-making framework for assigning relative importance to variables based on expert judgment and consistency checks (Saaty 2008 ; Vaidya and Kumar 2006 ; Ebinne et al., 2025 ). The integration of GIS with AHP enables spatial multi-criteria evaluation (SMCE), combining the objectivity of systematic decision logic with the spatial analysis capabilities of GIS (Malczewski 2006; Diriba et al. 2024). This integration has been effectively applied to flood risk mapping, groundwater potential assessment, and land suitability analysis (Mokarram et al. 2023 ; Kucuker and Giraldo 2022 ). Yet, despite its versatility, GIS–AHP integration remains underutilized in assessing and visualizing crop pest and disease vulnerability, particularly within smallholder farming contexts. The neglect of spatial decision-support frameworks in pest and disease management is particularly concerning in sub-Saharan Africa, where smallholder farmers produce over 80% of food consumed but face severe capacity and information gaps. These farmers operate under conditions of limited access to credit, extension services, and modern technologies, making them especially susceptible to climate-induced biotic stresses. The absence of spatially informed pest surveillance systems hampers timely response and adaptive management, exacerbating yield losses and threatening livelihood sustainability. Developing locally relevant, spatially explicit vulnerability maps can thus support targeted interventions, improve resource allocation, and enhance the adaptive capacity of smallholder systems. This study addresses this critical gap by applying an integrated GIS–AHP framework to assess and map crop pest and disease vulnerability in Gwagwalada Area Council, Nigeria - a rapidly urbanizing agricultural region dominated by smallholder farmers. The area provides a suitable case for demonstrating how spatial multi-criteria evaluation can enhance agricultural resilience and sustainability under complex environmental and socio-economic conditions. The specific objectives of this study are to: identify and spatially characterize key environmental and land-use factors influencing crop pest and disease occurrence; assign relative weights to these factors using AHP for objective multi-criteria evaluation; integrate the weighted factors within a GIS environment to generate a composite vulnerability map; validate the vulnerability map using the Normalized Difference Vegetation Index (NDVI); and derive spatially explicit insights to guide site-specific pest and disease management, strengthen smallholder resilience, and promote sustainable agricultural intensification. By integrating biophysical, climatic, and land-use data through GIS–AHP modeling, this study contributes to the growing field of spatial agronomy aimed at supporting precision and sustainability in smallholder farming systems. It advances a replicable framework for agricultural vulnerability assessment that can inform early warning systems, optimize management decisions, and enhance food system resilience in the face of global environmental change. Study area Gwagwalada Area Council, situated 40 kilometers from Nigeria's capital, Abuja, spans from latitude 8 50' 00'' to 90 20' 00'' N and longitude 60 12' 00'' to 7 28' 00'. Covering approximately 6,500 hectares, it comprises ten wards, including Ibwa, Paiko Kore, Zuba, Tungamaje, Gwako, Kutunku, Dobi, Ikwa, Gwagwalada Central, and Quarters. The area features Precambrian granite and schist formations, alongside Quaternary alluvium deposits in the Usman River channel, offering fine sand for construction. Its topography is characterized by inselbergs, outliers, and iron-stone-capped ridges, as well as undulating plains dissected by seasonal river valleys. Elevations range from 213.3 meters to the north to 142.2 meters to the south, with gentle slopes prevailing. The region experiences two main seasons: rainy (April to October) and dry (November to March), with temperatures ranging from 17°C during Harmattan to 30–37 degrees Celsius annually. Relative humidity peaks at around 50% in the rainy season. With an annual rainfall between 1632mm and 1404mm, the area is conducive to rainfed farming. Population growth has been significant, increasing from 16,000 in 1978 to 384,700 in 2023. The environmental conditions of the Gwagwalada Area Council play a significant role in the occurrence, spread, and severity of crop pests and diseases. The region's warm temperatures, ranging from 17°C to 37°C, create a favorable environment for various pests and pathogens, particularly fungal and bacterial diseases that thrive in humid conditions. The seasonal variability, with a rainy season from April to October and a dry season from November to March, further influences pest and disease dynamics. While the rainy season provides optimal moisture for fungal infections such as blights and mildews, it also enhances nematode activity in wet soils. Conversely, the dry season, especially during Harmattan, reduces moisture availability, slowing fungal diseases but increasing the risk of viral infections spread by vectors like whiteflies and aphids. With an annual rainfall of 1404–1632mm and relative humidity peaking at 50% during the rainy season, the region is prone to fungal outbreaks in dense crop canopies where localized humidity levels may be higher. The area's diverse soil types, including Precambrian granite, schist formations, and Quaternary alluvium deposits, also influence disease prevalence. While the fertile alluvial soils along the Usman River promote crop growth, they also retain moisture, creating conditions conducive to root rot, damping-off in seedlings, and nematode infestations. Similarly, the undulating plains and seasonal river valleys impact pest distributions, as poorly drained areas can serve as breeding grounds for disease-carrying insects, while ridges and inselbergs create microclimates affecting disease severity. Additionally, rapid population growth from 16,000 in 1978 to 384,700 in 2023 has led to increased land use, deforestation, and agricultural expansion, disrupting natural predator-prey balances and exacerbating pest outbreaks. The heavy reliance on rainfed farming without controlled irrigation heightens vulnerability to climate variability, as periodic droughts weaken crops, making them more susceptible to insect infestations such as locusts, armyworms, and grasshoppers. Furthermore, the Harmattan winds, carrying dry, dust-laden air from the Sahara, can spread fungal spores over long distances, contributing to disease transmission across farms. Collectively, these environmental and anthropogenic factors create complex interactions that influence the prevalence and intensity of crop pests and diseases in the Gwagwalada Area Council. Conceptual and analytical framework Crop pest and disease risks are inherently complex and spatially heterogeneous phenomena that emerge from the dynamic interplay of environmental, biophysical, and anthropogenic factors (Ojiambo, 2025 ). Understanding these interactions requires a systematic framework that integrates spatial data, multi-criteria evaluation, and geospatial modeling to generate actionable intelligence for sustainable agricultural management (Dittmar et al., 2024 ). The conceptual foundation of this study rests on three interrelated principles: the spatial heterogeneity of environmental risk, the multi-factorial nature of pest and disease dynamics, and the strategic value of geospatial decision-support systems in managing environmental uncertainty. Together, these principles establish the basis for linking spatial data analysis with decision-making processes to improve the precision and effectiveness of pest and disease management in agricultural systems. At the conceptual level, the framework assumes that crop vulnerability to pests and diseases is a spatial function of several interacting parameters, including soil properties, land use and cover, slope, temperature, moisture, and vegetation vigor (Abbas et al., 2022 ; Lindell et al., 2023 ). Each of these parameters contributes uniquely to overall vulnerability, depending on its ecological role in shaping pest habitats and influencing crop resilience. To capture these complex interrelationships, a multi-criteria spatial modeling approach is adopted to integrate diverse environmental and biophysical influences within a coherent analytical structure. This approach ensures that the resulting model reflects both the direct and indirect drivers of pest and disease dynamics across space. The AHP provides the decision-analytic foundation of the framework by transforming expert judgments about the relative importance of criteria into quantitative weights through pairwise comparisons, eigenvalue derivation, and consistency verification. These weights express the proportional contribution of each factor to crop vulnerability, ensuring objectivity and comparability across variables. The GIS, on the other hand, operationalizes these weights by integrating spatial datasets representing each criterion to compute a composite vulnerability index. This process produces a continuous surface that reveals spatial variations in pest and disease risk. The integration of AHP and GIS, therefore, yields a Spatial Decision Support System (SDSS) capable of quantifying, visualizing, and communicating vulnerability patterns. This enables targeted and evidence-based agricultural decision-making while aligning with the broader Spatial Information Science (SIS) paradigm, which emphasizes spatial reasoning, geospatial data integration, and multi-criteria modeling as tools for addressing complex real-world problems (Eisen and Eisen, 2011 ; Makkulawu et al., 2023 ; Teixeira et al., 2023 ; Makadi et al., 2024 ). The analytical process developed in this study (Fig. 1 ) follows a structured six-step workflow typically implemented in carrying out a study of this kind (Makkulawu et al., 2024). The first step involves parameter selection and data acquisition, where environmental and land-use factors influencing pest and disease occurrence are identified through literature review and expert consultation. Relevant spatial datasets - such as soil maps, temperature, slope, Normalized Difference Vegetation Index (NDVI), and land cover - are then obtained from remote sensing and secondary geospatial sources. In the second step, data preprocessing and standardization, all datasets are projected to a uniform coordinate system, resampled to a common spatial resolution, and standardized through reclassification techniques to ensure comparability. The third step, weight derivation using AHP, employs expert judgments to construct a pairwise comparison matrix of all selected criteria, followed by eigenvalue computation and consistency ratio testing (CR ≤ 0.1) to ensure the reliability of derived weights. In the fourth step, weighted overlay and vulnerability mapping, the AHP-derived weights are integrated with standardized GIS layers using the Weighted Linear Combination (WLC) technique to generate a composite vulnerability index. The resulting map classifies the study area into low, moderate, and high pest and disease vulnerability zones. This is followed by validation of results, where the generated vulnerability map is tested using NDVI data to establish correlations between vegetation stress patterns and predicted high-vulnerability zones. The final step, interpretation and decision support, involves analyzing the spatial outputs to identify critical hotspots and prioritize intervention zones. These results provide a decision-support foundation for agricultural extension, integrated pest management, and adaptive land-use planning. Figure 1 visually illustrates how AHP and GIS are integrated into a coherent spatial decision-support system for pest and disease vulnerability mapping. The process begins with data input blocks comprising environmental and biophysical parameters such as soil, land cover, slope, temperature, and vegetation index. These data feed into two analytical streams: the AHP decision module, where criteria are subjected to pairwise comparison and weighted based on their importance, and the GIS spatial module, where standardized raster layers are prepared for analysis. The outputs from both streams converge at an integration node through a weighted overlay operation, producing a composite vulnerability surface that classifies the study area into various risk levels. A validation loop then compares model outputs with NDVI-based field indicators to evaluate accuracy and coherence. The final stage - decision support and application - translates the results into actionable insights for farmers, agricultural extension officers, and policymakers. The diagram’s arrows emphasize the iterative and interactive nature of the framework, allowing continuous feedback between analytical stages, such as refining AHP weights based on validation outcomes. This iterative design demonstrates how spatial information science harmonizes decision theory and geospatial analysis to transform diverse datasets into knowledge-driven spatial intelligence for sustainable agricultural management. This integrated framework significantly contributes to the advancement of Spatial Information Science (SIS) by embedding multi-criteria decision-making (AHP) within a geospatial modeling environment (GIS) to produce interpretable and dynamic spatial risk surfaces. It enhances data interoperability and analytical precision through knowledge-based weighting and standardization and demonstrates how geospatial intelligence can be applied to adaptive management in data-scarce smallholder farming systems (Agogue Feujio et al., 2024 ). Ultimately, the model offers a replicable and scalable template for spatial risk assessment. Data collection and analytical framework Figure 1 illustrates the integrated framework of the Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS) within a Spatial Decision Support System (SDSS) developed to evaluate crop pest and disease vulnerability in Gwagwalada Area Council, Nigeria. This framework combines expert-based decision-making with spatial analysis to identify high-risk zones, support evidence-based interventions, and inform sustainable agricultural management. Key environmental and biophysical parameters—including soil texture and pH, land cover, slope, temperature, moisture, and vegetation indices—were selected based on a literature review and their known influence on pest and disease incidence (Oerke 2020 ; Scott et al. 2021 ; Saxena et al. 2021 ). These parameters were assigned relative importance weights using the AHP, standardized, and spatially overlaid in GIS to generate a composite vulnerability surface. The resulting vulnerability map categorizes areas into low, moderate, and high risk, providing actionable insights for targeted management strategies. The approach integrates sustainability considerations by linking risk identification to interventions that reduce reliance on chemical inputs, optimize resource use, and enhance smallholder resilience. Data and data sources A review of the literature on GIS- and AHP-based crop pest and disease assessment identified seven key parameters influencing vulnerability: soil texture, soil pH, slope, temperature, moisture, land use/cover, and vegetation index. Data were obtained from multiple sources, including remote sensing products (Landsat and Sentinel imagery for NDVI), local meteorological stations (temperature, moisture, and humidity), and field surveys (soil and land-use validation). The selection of these parameters is supported by previous studies linking biophysical and environmental conditions to pest and disease outbreaks (Altieri and Nicholls 2009 ; Hengl et al. 2017 ; Zhang et al. 2019 ; Babaremu et al., 2024 ). Table 1 provides a comprehensive overview of the types of data collected for the study and the corresponding sources from which these data were obtained. Table 2 presents the rating scale applied to evaluate and qualify the status/level of each parameter, offering a standardized framework for interpreting measurements and facilitating consistent comparisons. Table 3 provides the normalized matrix used in the study. In AHP-based GIS analysis, a normalized matrix converts diverse criteria with different units and ranges into a dimensionless scale (0–1), ensuring comparability and preventing variables with larger values from dominating. Normalized values are then weighted according to AHP priorities and combined to produce a composite suitability map. This step is essential for accurate, unbiased integration of multiple spatial criteria in multi-criteria decision analysis. AHP implementation The AHP was applied through six structured phases: defining the decision hierarchy, conducting pairwise comparisons using Saaty’s 1–9 scale, normalizing the comparison matrix to obtain factor weights, checking consistency for reliability, aggregating weights to rank alternatives, and performing sensitivity analysis to test result stability. This systematic approach ensured objective and robust decision-making. The implementation was to ensure rigorous multi-criteria evaluation: Hierarchy construction : The main objective—assessing crop pest and disease vulnerability—was placed at the top, followed by criteria (environmental, climatic, and land-use factors) and alternatives (low, moderate, high vulnerability). Pairwise comparisons : Experts evaluated the relative importance of each criterion using Saaty’s 1–9 scale, where 1 indicates equal importance and 9 denotes extreme importance of one factor over another (Saaty 2008 ). Weight derivation : Normalized comparison matrices were used to calculate numerical weights for each criterion. Consistency checks : Consistency Ratios (CR) were computed to ensure reliability of expert judgments (CR < 0.1 considered acceptable). Aggregation : Weights were aggregated to derive overall scores for each vulnerability level. Sensitivity analysis : The robustness of the model was tested by varying criterion weights ± 10% to evaluate the stability of the vulnerability classification, thereby accounting for uncertainty in expert judgment. This rigorous approach ensures that the AHP model provides a transparent, replicable, and objective assessment of criteria importance. GIS-based risk mapping The study employed ArcGIS 8.0 to integrate AHP and GIS for mapping crop pest and disease risks. Key steps included identifying relevant risk criteria, conducting pairwise comparisons to determine their relative importance, and calculating corresponding weights. These weighted factors were incorporated into standardized GIS data layers (scaled 1–5) to represent varying vulnerability levels. A weighted overlay analysis then generated a composite vulnerability map, visually highlighting areas of low, moderate, and high risk. The resulting maps supported the identification of high-vulnerability zones and informed priority management strategies, demonstrating the effectiveness of the GIS–AHP integration for comprehensive agricultural risk assessment and decision-making. The workflow involved: Preparation of spatial data layers : All parameters were digitized and standardized to a common spatial resolution and vulnerability scale (1–5). Weighted overlay analysis : AHP-derived weights were applied to each raster layer using ArcGIS Pro 3.1, producing a composite vulnerability surface. Risk classification : The resulting raster was categorized into low, moderate, and high vulnerability zones for clear interpretation. Map visualization : The vulnerability map was visualized with GIS symbology for easy interpretation by stakeholders. Validation : Spatial coherence of the model was evaluated using NDVI data derived from Sentinel-2 imagery. Quantitative validation was conducted by comparing modeled high-risk zones with areas exhibiting vegetation stress, using correlation analysis to ensure predictive reliability. Scenario analysis : GIS was further used to simulate alternative scenarios of pest and disease risk under variations in environmental conditions, supporting adaptive management decisions. The integration of AHP and GIS enables a spatially explicit, evidence-based, and decision-oriented assessment, offering a transferable framework for other smallholder-dominated agricultural landscapes. By highlighting high-risk areas, the model supports targeted interventions, sustainable pest management practices, and resource-efficient agricultural planning. Sustainability and generalizability This methodology directly addresses sustainability objectives by: Supporting site-specific interventions, reducing blanket pesticide application and associated environmental impacts. Providing evidence-based guidance for smallholder farmers, enhancing productivity and resilience under climate variability. Offering a replicable framework that can be adapted to other crops, regions, and environmental contexts, facilitating broader adoption in Sub-Saharan Africa and similar agro-ecological zones. By linking spatial vulnerability mapping to practical management strategies, the methodology creates a bridge between analytical assessment and real-world application. This integration allows decision-makers to identify priority areas for intervention while aligning land-use planning with sustainability goals. In doing so, the approach ensures that the study not only maps areas of environmental stress but also provides a framework for promoting sustainable agricultural intensification, enhancing smallholder resilience to climate and land degradation risks, and advancing ecosystem-based management practices. These strategies collectively support the optimization of land resources, reduction of environmental vulnerability, and long-term livelihood security for farming communities. Results and discussion Environmental, climatic, and land-use factors influencing crop pest and disease outbreaks Figures 1 – 7 present the actual and reclassified maps of the seven parameters used in the AHP–GIS criteria decision process - land use/cover, soil moisture index, land surface temperature, relative humidity, soil texture, soil pH, and slope - while Table 6 summarizes their spatial coverage, AHP rankings, and importance levels. Agricultural land constitutes 51.5% of the area and is deemed extremely important, whereas vegetation covers 36.1% and is rated highly important. Bare and built-up surfaces occupy smaller proportions, with low to moderate ratings. Areas with soil moisture indices below 0.338 (31.6% of total coverage) are of extremely high importance, with decreasing relevance as moisture levels increase. High land surface temperatures (34 0 –36°C) dominate 61.2% of the area and are rated highly significant, while moderate relative humidity (32.1–35.1%) spans 46.2% and is considered moderately to highly important. Sandy loam soils (79.6%) are rated very high in importance compared to loam soils (20.4%), which are moderately important. Soils with pH ≥ 6.3 cover 75.6% of the area and are of utmost importance, while moderate slopes (2.6–6.0°) cover 40.3% and are rated low to moderately important, though steeper slopes carry higher weight. Collectively, these parameters strongly influence agricultural vulnerability to pests and diseases, providing essential insight for spatially targeted control strategies. The extensive agricultural and vegetated areas highlight the dominance of farming and natural vegetation in the study area - key environments for pest proliferation (Nansel et al. 2019; Abd El-Ghany et al. 2020 ; Childers et al. 2021 ). High soil moisture zones provide favorable conditions for pests and pathogens, particularly fungi (Bebber et al. 2014 ; Romero et al. 2022 ; Rodas and Madrigal 2025 ). Elevated temperatures encourage pest and disease spread in warmer climates, whereas cooler areas may suppress their activity (Porter et al. 2014 ; Jonathan and Mahendranathan, 2024 ; Yang et al. 2024 ). Moderate-to-high humidity levels foster disease transmission in damp environments (IPCC Secretariat 2021; Skendžić et al., 2021 ). Sandy loam soils, prevalent in the area, may support specific crops and pests, while soil pH indirectly affects plant health and vulnerability (Jackson and Meetei, 2018 ; Barrow and Hartemink, 2023 ). Slope variability also shapes erosion and moisture dynamics, influencing pest and disease patterns (Zhang et al. 2019 ; Magalhães 2023; Nyairo, 2024 ). Hence, high-priority zones - particularly those with dense vegetation, productive farmlands, and specific soil types - require frequent monitoring and adaptive pest management to mitigate agricultural risks. Results of ranking and weighing of risk factors using AHP to enhance decision-making in pest and disease management Figure 8 depicts the development of a crop pest and disease vulnerability map using AHP and GIS techniques. Figure 10 shows the validation results obtained by querying disease plants from NDVI. The vulnerability map combined values from the seven criteria outlined in Table 4. We classified each criterion and constructed a pairwise comparison matrix to assess their significance, resulting in rating scores. By applying weighted sums to causative criteria, a final susceptibility map with a consistency ratio (C.R.) of 0.08 (< 0.1, validated). Land use and land cover criteria exerted the highest influence at 50.02%, while soil moisture index, land surface temperature, relative humidity, soil texture, soil pH, and slope cover were less important, with weights ranging from 2% to 11.28%. We classified the 1,042.871 km2 study area into low and very low susceptible zones (11% and 4%, respectively) and very high, high, and moderate susceptible zones (20.46%, 37.89%, and 26.75%, respectively). The study has been developed for the Gwagwalada area council a crop pest and disease vulnerability map that has significant implications for understanding and mitigating risks to agricultural productivity. We can tailor strategies to manage these areas effectively, with land use and land cover criteria exerting the highest influence, accounting for 50.02% of the vulnerability. Other factors, such as soil moisture, temperature, humidity, soil texture, pH, and slope cover, also play critical roles. Areas categorized as very high and high susceptible zones occupy a substantial portion (around 58.35%) of the study area, indicating heightened risk. Farmers and agricultural authorities can use this information to prioritize resources and interventions, such as targeted pest control measures, crop selection, or irrigation practices, to mitigate the impact of pests and diseases. Moreover, understanding vulnerability patterns can aid in planning resilient agricultural systems and adapting to changing environmental conditions, thereby safeguarding food security and livelihoods in the region. Use of GIS and AHP to generate vulnerability maps highlighting high-risk zones for targeted pest and disease control interventions GIS was instrumental in generating vulnerability maps for targeted pest and disease control interventions in Gwagwalada Area Council by integrating multiple environmental and agricultural parameters using the Analytical Hierarchy Process (AHP). The combination of GIS and AHP is widely recognized for its effectiveness in spatial decision-making and multi-criteria analysis, particularly in agricultural risk assessments (Malczewski, 2010 ; Saaty, 1980). The study utilized seven key criteria—land use, land cover, soil moisture index, land surface temperature, relative humidity, soil texture, soil pH, and slope—to assess the spatial susceptibility of agricultural zones to pest and disease outbreaks. These parameters are crucial in pest and disease ecology, as environmental conditions significantly influence pathogen proliferation and pest population dynamics (Bebber et al., 2014 ; Romero et al. 2022 ). Figures 1 to 7 illustrate both the actual levels and the reclassified status of these parameters, while Table 6 details the extent covered by each class and their corresponding rankings within the AHP framework. Pattern of vulnerability and pest and disease risk zones in the study area The crop pest and disease vulnerability map of Gwagwalada Area Council (Fig. 8 ) reveals a spatially heterogeneous susceptibility pattern shaped by environmental, climatic, and land-use factors, consistent with earlier studies emphasizing the roles of land cover, soil, and climate in pest and disease outbreaks (Bebber et al. 2014 ; Porter et al. 2014 ). High and very high vulnerability zones dominate agriculturally intensive areas, where dense vegetation and extensive cropland create favorable microclimates for pest proliferation and disease spread (Nansel et al. 2019; Abd El-Ghany et al. 2020 ). Regions characterized by low soil moisture index, elevated land surface temperatures (340–360°C), and moderate-to-high humidity (32.12–35.1%) further promote pest survival and pathogen multiplication (Romero et al. 2022 ; Rodas and Madrigal 2025 ). Soil factors substantially influence vulnerability patterns. The predominance of sandy loam soils (79.6%) and higher soil pH (> 6.30 in 75.6% of the area) support specific pest species and disease agents by affecting crop nutrition and physiological resistance (Barrow and Hartemink 2023 ). Consequently, these high-risk zones require intensive monitoring, early warning systems, and targeted pest management to mitigate yield losses (IPCC Secretariat 2021). Moderate-risk areas—transitioning between high- and low-risk zones—reflect gradual shifts in soil, vegetation, and microclimatic conditions. These intermediate regions benefit from proactive pest management, such as crop rotation, intercropping, and soil improvement practices, to prevent escalation into higher vulnerability levels (Zhang et al. 2019 ; Magalhães 2023; Childers et al. 2021 ). Low and very low vulnerability zones correspond mainly to built-up and barren areas, which have reduced vegetation, higher runoff, and lower pest prevalence (Yang et al. 2024 ). Sloped terrains, where runoff prevents water stagnation, are less conducive to fungal and bacterial development (Zhang et al. 2019 ). The resulting vulnerability pattern underscores the importance of spatially targeted management. The vulnerability map serves as a decision-support tool for prioritizing high-risk zones, where biocontrol, resistant varieties, and surveillance can reduce pest outbreaks (Bebber et al. 2014 ), while moderate-risk areas may benefit from integrated pest management (IPM) and improved irrigation to strengthen resilience (Porter et al. 2014 ). This spatial approach enhances resource allocation efficiency, directing interventions toward areas of higher susceptibility (Magalhães 2023), and aligns with global findings on land use, soil, and climate influences on agricultural risks (Liu et al. 2020 ). Through AHP-based classification, each factor was weighted and ranked according to its contribution to pest and disease occurrence. Land use and land cover emerged as the most influential parameter, accounting for 50.02% of total vulnerability. Agricultural land (51.5%) and vegetation (36.1%) were the most critical exposure zones, corroborating findings that dense vegetation and extensive cultivation favor pest proliferation (Abd El-Ghany et al. 2020 ; Childers et al. 2021 ). Climatic factors such as low soil moisture, high temperature (340–360°C covering 61.2%), and moderate-to-high humidity (32.12–35.1% covering 46.2%) were strong determinants of pest activity (Bebber et al. 2014 ; Porter et al. 2014 ). Soil properties—especially sandy loam texture (79.6%) and high pH (> 6.30)—affected plant health and pest resistance (Barrow and Hartemink 2023 ), while slope influenced erosion and runoff, indirectly shaping pest–disease dynamics (Zhang et al. 2019 ). The AHP model, validated through NDVI-derived diseased plant observations (Fig. 10), yielded a consistency ratio (C.R.) of 0.08 (< 0.1), confirming model reliability. Approximately 58.35% of the total area was classified as highly or very highly susceptible, signifying the need for targeted interventions. The resulting map provides a robust spatial framework for decision-makers to prioritize surveillance, deploy biological and chemical control, and guide farmers in adopting pest-resistant crops and sustainable management practices. Ultimately, the GIS–AHP approach strengthens evidence-based pest and disease risk management, enhancing agricultural productivity and food security in Gwagwalada Area Council (Parsa et al. 2014 ; Bajocco et al. 2012 ). The validation of the pest and disease vulnerability map The validation process was essential to ensure the accuracy of the integrated AHP–GIS analysis. An NDVI (Normalized Difference Vegetation Index) map was generated for the study area and compared with the vulnerability map (Fig. 9 ). NDVI measures vegetation health by assessing chlorophyll content—higher NDVI values indicate healthy vegetation resistant to stressors such as pests and diseases (Horler et al., 1983 ; Moran et al., 1997 ; Pettorelli et al., 2005 ). However, dense or stressed vegetation may also exhibit high NDVI values, which can attract pests and diseases (Thenkabail et al., 2021). Thus, NDVI variations serve as early indicators of pest or disease presence, supporting proactive monitoring and integrated pest management to enhance agricultural productivity. The NDVI results (Fig. 10) show that 3.1% of the area had very low NDVI values, 7.3% low, 16.2% moderate, 31.2% high, and 42.2% very high. This means that over 70% of the region exhibits high to extremely high NDVI values, reflecting dense vegetation that may promote pest and disease spread. Similarly, the AHP–GIS–derived vulnerability map (Fig. 8 ) shows that about 60% of the study area faces high to very high risk of pest and disease occurrence. The high NDVI values indicate dense vegetation cover, which provides favorable conditions for pest and pathogen proliferation due to increased food and shelter availability. Previous studies confirm that dense vegetation, while beneficial for ecosystem health, can enhance pest and disease spread, posing challenges to agricultural productivity (Pettorelli et al., 2005 ; Wardlow and Egbert, 2008 ; Shanmugapriya et al., 2019 ; Raihan, 2024 ). The strong spatial agreement between the NDVI and AHP–GIS maps indicates that approximately 60% of the study area is highly susceptible to pest and disease outbreaks, underscoring a clear correlation between vegetative density and vulnerability. This consistency aligns with findings from other studies that used NDVI and GIS-based methods to validate agricultural risk models (de Resende et al., 2021 ; Patel et al., 2021 ; Hinnah et al., 2023 ; Opoku et al., 2024 ). Proposed integrated pest and disease management strategies to support sustainable agriculture and food security The validation process was essential to ensure the accuracy of the integrated AHP–GIS analysis. An NDVI (Normalized Difference Vegetation Index) map was generated for the study area and compared with the vulnerability map (Fig. 9 ). NDVI measures vegetation health by assessing chlorophyll content - higher NDVI values indicate healthy vegetation resistant to stressors such as pests and diseases (Horler et al., 1983 ; Moran et al., 1997 ; Pettorelli et al., 2005 ). However, dense or stressed vegetation may also exhibit high NDVI values, which can attract pests and diseases (Thenkabail et al., 2021). Thus, NDVI variations serve as early indicators of pest or disease presence, supporting proactive monitoring and integrated pest management to enhance agricultural productivity. The NDVI results (Fig. 10) show that 3.1% of the area had very low NDVI values, 7.3% low, 16.2% moderate, 31.2% high, and 42.2% very high. This means that over 70% of the region exhibits high to extremely high NDVI values, reflecting dense vegetation that may promote pest and disease spread. Similarly, the AHP–GIS–derived vulnerability map (Fig. 8 ) shows that about 60% of the study area faces high to very high risk of pest and disease occurrence. The high NDVI values indicate dense vegetation cover, which provides favorable conditions for pest and pathogen proliferation due to increased food and shelter availability. Previous studies confirm that dense vegetation, while beneficial for ecosystem health, can enhance pest and disease spread, posing challenges to agricultural productivity (Pettorelli et al., 2005 ; Wardlow and Egbert, 2008 ; Shanmugapriya et al., 2019 ; Raihan, 2024 ). The strong spatial agreement between the NDVI and AHP–GIS maps indicates that approximately 60% of the study area is highly susceptible to pest and disease outbreaks, underscoring a clear correlation between vegetative density and vulnerability. This consistency aligns with findings from other studies that used NDVI and GIS-based methods to validate agricultural risk models (de Resende et al., 2021 ; Patel et al., 2021 ; Hinnah et al., 2023 ; Gaudry, 2024 ; Opoku et al., 2024 ). Conclusion This study presents an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) framework for assessing crop pest and disease vulnerability in Gwagwalada, Nigeria—an approach that advances spatially explicit, data-driven agricultural risk management in tropical agroecosystems. By incorporating soil, topography, land use, and climatic parameters, the model systematically quantified the relative influence of each factor on pest and disease occurrence. Land use and land cover emerged as the most dominant determinants, contributing 50.02% to total vulnerability, with agricultural land (51.5%) and vegetated zones (36.1%) being the most affected. Climatic and soil variables such as low soil moisture (< 0.338), elevated land surface temperature (340°C–360°C), and moderate humidity (32–35%) were also critical in creating favorable conditions for pest and pathogen proliferation. The resulting AHP–GIS vulnerability map classified 58.35% of the area as highly or very highly vulnerable, underscoring the spatial concentration of risks within active farming zones. Validation using NDVI confirmed that over 70% of the study area exhibits high vegetative cover, which aligns with the mapped vulnerability hotspots, thereby confirming the robustness of the integrated model. High-risk zones corresponded primarily to agricultural and vegetated landscapes, while built-up and barren lands displayed lower susceptibility, demonstrating the strong ecological link between vegetative density and pest–disease prevalence. The novelty of this study lies in its integration of AHP-based multi-criteria analysis with NDVI validation to produce a scientifically rigorous and spatially explicit pest–disease risk model tailored to semi-arid agroecosystems. This dual-validation approach enhances predictive accuracy and provides a transferable framework for sustainable crop protection planning under climate variability. For sustainable pest and disease management, the study recommends the adoption of climate-smart and ecosystem-based interventions, including integrated pest management (IPM) practices, biological control, intercropping, crop rotation, and soil organic amendments. Additionally, establishing early warning systems that combine remote sensing data with farmer-based field monitoring will enable timely interventions. Capacity building for local farmers on the use of GIS-enabled forecasting tools, coupled with the creation of a digital decision-support platform, can enhance adaptive management and resilience. Ultimately, this research contributes to sustainable agronomy by linking spatial decision science with ecological processes that shape pest and disease dynamics. It demonstrates how integrated geospatial modeling can support evidence-based agricultural planning, strengthen food security, and promote climate-resilient farming systems in Sub-Saharan Africa. Limitations of the Study and Suggestions for Future Research While the integrated AHP–GIS framework provided valuable insights into spatial patterns of crop pest and disease vulnerability, several limitations should be acknowledged. First, the precision of the vulnerability map is constrained by the resolution, completeness, and temporal relevance of spatial datasets used for soil properties, climatic variables, and land use. Inaccuracies or outdated data may lead to spatial uncertainty and misclassification of risk zones. Second, validation based solely on NDVI, although effective in capturing vegetation health, does not fully represent dynamic ecological factors such as seasonal variability, pest migration, or vegetation phenology. This limits the model’s capacity to capture temporal shifts in pest–disease interactions. Third, the AHP-based weighting and ranking process involves expert judgment, which, despite its structured nature, introduces a degree of subjectivity and potential bias in factor prioritization. Lastly, the model assumes static environmental conditions and does not incorporate real-time monitoring or predictive simulation of pest and disease dynamics under projected climate change scenarios. Future research should seek to enhance the accuracy, adaptability, and predictive power of vulnerability models. Integrating high-resolution multispectral and hyperspectral satellite imagery with real-time meteorological and soil sensor data can improve spatial and temporal precision. The application of machine learning and artificial intelligence (AI) for predictive modeling would enable dynamic forecasting of pest outbreaks by incorporating variables such as rainfall anomalies, wind patterns, temperature fluctuations, and pest life cycles. Longitudinal studies capturing seasonal and interannual variations in pest and disease occurrences would further strengthen temporal understanding. Moreover, incorporating socioeconomic and behavioral dimensions—including farmers’ adaptation capacity, pesticide use intensity, and access to extension services—would provide a more holistic perspective on vulnerability and resilience. Developing a web-based, GIS-integrated decision-support platform that links satellite observations, field data, and mobile-based farmer feedback could revolutionize early warning and response systems. Finally, collaborative research frameworks involving scientists, policymakers, and local farming communities are essential to co-develop sustainable, climate-smart pest management strategies that enhance agricultural resilience in Sub-Saharan Africa. Declarations Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used ChatGPT in order to refine grammar and expression in the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Funding declaration No funding was received to conduct this study References Abbas M, Saleem M, Hussain D, Ramzan M, Jawad Saleem M, Abbas S, Hussain N, Irshad M, Hussain K, Ghouse G, Khaliq M (2022) Review on integrated disease and pest management of field crops. 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Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 07 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 28 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8468265","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602471435,"identity":"47dbabeb-9946-4aa0-b04f-54793593824b","order_by":0,"name":"Sani Abubakar Mashi","email":"data:image/png;base64,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","orcid":"","institution":"University of Abuja","correspondingAuthor":true,"prefix":"","firstName":"Sani","middleName":"Abubakar","lastName":"Mashi","suffix":""},{"id":602471436,"identity":"9366d048-7d23-4bc7-809a-0730a519a3bd","order_by":1,"name":"Aminu Abdullahi Muye","email":"","orcid":"","institution":"University of 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University","correspondingAuthor":false,"prefix":"","firstName":"Amina","middleName":"Ibrahim","lastName":"Inkani","suffix":""},{"id":602471440,"identity":"21e813d7-6775-4744-bf09-82c0ce5bf333","order_by":5,"name":"Safirat Sani","email":"","orcid":"","institution":"University of Abuja","correspondingAuthor":false,"prefix":"","firstName":"Safirat","middleName":"","lastName":"Sani","suffix":""}],"badges":[],"createdAt":"2025-12-29 01:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8468265/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8468265/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104525328,"identity":"74b37b28-293e-4c4d-ac03-f47ad1d9fad0","added_by":"auto","created_at":"2026-03-12 22:10:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":974391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual and analytical framework for GIS–AHP integration in crop pest and disease vulnerability mapping\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/0551493a2e16912a2db90697.png"},{"id":104525338,"identity":"159bbdc9-2263-4c69-a7a6-6f0c4a4f4ba8","added_by":"auto","created_at":"2026-03-12 22:10:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1307789,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. \u003c/strong\u003eAreal coverage of Land use landcoverclasses and ranking under the AHP framework in the study area\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/09abc51f727d56eb7467917f.png"},{"id":104525335,"identity":"51357d9d-9185-47e8-977e-eed1490e8494","added_by":"auto","created_at":"2026-03-12 22:10:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1423836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2. \u003c/strong\u003eAreal coverage of soil moistureindex classes and ranking under the AHP framework in the study area\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/36b6cd094d212f63833e1481.png"},{"id":104525332,"identity":"a45cf79f-791e-4a08-a46b-6ab24d1e6c35","added_by":"auto","created_at":"2026-03-12 22:10:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1443458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. \u003c/strong\u003eAreal coverage of land surface temperature classes and ranking under the AHP framework in the study area\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/d48e6bf54da8c406a0d8dce8.png"},{"id":104781844,"identity":"b4d67cd7-c2e3-4a0f-afc7-cc646858f547","added_by":"auto","created_at":"2026-03-17 07:56:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1407677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4. \u003c/strong\u003eAreal coverage of relative humidity classes and ranking under the AHP framework in the study area\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/377be0df8d308317101e737f.png"},{"id":104525329,"identity":"fa792be5-95f3-4c94-8fbb-e6baf0f8784a","added_by":"auto","created_at":"2026-03-12 22:10:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":688824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5. \u003c/strong\u003eAreal coverage of soil texture classes and ranking under the AHP framework in the study area\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/ed0e96b29d3f58d73e829426.png"},{"id":104525331,"identity":"7b71cfa5-298d-4fcf-bfcf-b1a10019bfbf","added_by":"auto","created_at":"2026-03-12 22:10:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":927380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6. \u003c/strong\u003eAreal coverage of soil pH classes and ranking under the AHP framework in the study area\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/06b7a914bc323ed8faec8cf0.png"},{"id":104780834,"identity":"b22d0a21-f5d5-4fa8-ba14-ac18bdf1ff33","added_by":"auto","created_at":"2026-03-17 07:54:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1378914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7. \u003c/strong\u003eAreal coverage of slope classes and ranking under the AHP framework in the study area\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/151699bbe2face5bddbbd5bd.png"},{"id":104525333,"identity":"501ca7a9-8315-4d21-a12b-163c9946b76d","added_by":"auto","created_at":"2026-03-12 22:10:13","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1636649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8. \u003c/strong\u003eCrop pest and disease vulnerability map of the study area\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/a9dc16d87daa47c15c970af2.png"},{"id":104525334,"identity":"a2da00d3-e7bf-40ea-9f26-c9a998d17d1c","added_by":"auto","created_at":"2026-03-12 22:10:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1888890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9. \u003c/strong\u003eValidation of the Crop pest and disease vulnerability map with queried disease plant from NDVI\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/bb4209ffb92908a09c83b10a.png"},{"id":105727649,"identity":"daf6d7f9-806e-45bd-bd25-f529aebe0c52","added_by":"auto","created_at":"2026-03-30 10:57:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13790789,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/f773f181-eb7a-4cfd-b170-f428488ed568.pdf"},{"id":104525336,"identity":"1b99a7e0-0d43-490c-a35e-41f2d6436672","added_by":"auto","created_at":"2026-03-12 22:10:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50365,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8468265/v1/6d063144a99e16503f07b823.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial assessment of pest and disease vulnerability in smallholder farming systems using AHP and GIS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal population expansion has placed unprecedented pressure on agricultural systems to increase food production per unit area, even as the availability of arable land per capita declines. This challenge is intensified by widespread land degradation resulting from deforestation, overgrazing, urbanization, and unsustainable management practices, alongside the growing impacts of climate change - manifested through rising temperatures, altered rainfall regimes, and more frequent extreme events (Lal et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Ofori et al. 2021; Pickson et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Brenya et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rashidi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shahzad et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These interacting pressures diminish soil fertility, reduce ecosystem stability, and increase the incidence of biotic stressors such as pests and diseases, thereby threatening food security and sustainable development (Rosenzweig and Parry \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrop pests and diseases represent major biotic constraints to agricultural productivity, with significant economic, social, and ecological consequences. They cause substantial yield and quality losses, undermine livelihoods, and destabilize local economies (Oerke \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pimentel and Pimentel \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Common pests - including aphids, nematodes, rodents, and whiteflies - and pathogenic agents such as fungi, bacteria, and viruses can devastate crops, sometimes leading to complete failure (Scott et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their effects extend beyond productivity losses to public health challenges, including contamination of food by pathogens such as \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e, and exposure to mycotoxins from \u003cem\u003eFusarium\u003c/em\u003e species. Pests and diseases also contribute to biodiversity decline by altering community composition and displacing native species (Sharma et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Benjamin et al. 2021; Lichtenberg et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Addressing pest and disease threats is therefore critical to achieving several United Nations Sustainable Development Goals (SDGs), particularly those related to food security (SDG 2), health (SDG 3), economic growth (SDG 8), and ecosystem protection (SDG 15) (Saxena et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Managing these biotic threats remains inherently complex because pest and disease occurrences are influenced by interacting climatic, edaphic, biological, and topographic factors (Altieri and Nicholls, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bing-fang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conventional monitoring systems are often localized, reactive, and lack the spatial integration needed for early warning or adaptive management. Consequently, geospatial approaches have become indispensable tools for characterizing and predicting crop vulnerability to pests and diseases across heterogeneous landscapes (Brown and Funk \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chuan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past two decades, Geographic Information Systems (GIS) have revolutionized the spatial analysis of crop\u0026ndash;pest\u0026ndash;environment interactions. They facilitate data integration, visualization, and modeling of spatial and temporal pest dynamics. GIS has been successfully employed to map pest distribution (Arampatzis et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Eisen and Eisen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thenkabail et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tran et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), simulate climate\u0026ndash;pest interactions (Bielecka, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and assess environmental determinants of pest risk (Hengl et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, GIS-based monitoring using remote sensing data enables early detection of stress conditions such as moisture deficits or canopy health decline, which often precede pest and disease outbreaks (Wardlow et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mamatkulov et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Network and scenario modeling within GIS environments also support evaluation of potential outbreak pathways and management options (Lindberg et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Collectively, these capabilities underscore GIS\u0026rsquo;s value in risk zoning, decision support, and evidence-based management of crop health (Shen et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, a major limitation of GIS-based assessments lies in the subjective weighting of environmental and agronomic variables, which may bias decision outcomes (Mariye et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Makadi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To overcome this, the Analytic Hierarchy Process (AHP) provides a structured, multi-criteria decision-making framework for assigning relative importance to variables based on expert judgment and consistency checks (Saaty \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vaidya and Kumar \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ebinne et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The integration of GIS with AHP enables spatial multi-criteria evaluation (SMCE), combining the objectivity of systematic decision logic with the spatial analysis capabilities of GIS (Malczewski 2006; Diriba et al. 2024). This integration has been effectively applied to flood risk mapping, groundwater potential assessment, and land suitability analysis (Mokarram et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kucuker and Giraldo \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet, despite its versatility, GIS\u0026ndash;AHP integration remains underutilized in assessing and visualizing crop pest and disease vulnerability, particularly within smallholder farming contexts.\u003c/p\u003e \u003cp\u003eThe neglect of spatial decision-support frameworks in pest and disease management is particularly concerning in sub-Saharan Africa, where smallholder farmers produce over 80% of food consumed but face severe capacity and information gaps. These farmers operate under conditions of limited access to credit, extension services, and modern technologies, making them especially susceptible to climate-induced biotic stresses. The absence of spatially informed pest surveillance systems hampers timely response and adaptive management, exacerbating yield losses and threatening livelihood sustainability. Developing locally relevant, spatially explicit vulnerability maps can thus support targeted interventions, improve resource allocation, and enhance the adaptive capacity of smallholder systems. This study addresses this critical gap by applying an integrated GIS\u0026ndash;AHP framework to assess and map crop pest and disease vulnerability in Gwagwalada Area Council, Nigeria - a rapidly urbanizing agricultural region dominated by smallholder farmers. The area provides a suitable case for demonstrating how spatial multi-criteria evaluation can enhance agricultural resilience and sustainability under complex environmental and socio-economic conditions. The specific objectives of this study are to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eidentify and spatially characterize key environmental and land-use factors influencing crop pest and disease occurrence;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eassign relative weights to these factors using AHP for objective multi-criteria evaluation;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eintegrate the weighted factors within a GIS environment to generate a composite vulnerability map;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003evalidate the vulnerability map using the Normalized Difference Vegetation Index (NDVI); and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ederive spatially explicit insights to guide site-specific pest and disease management, strengthen smallholder resilience, and promote sustainable agricultural intensification.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBy integrating biophysical, climatic, and land-use data through GIS\u0026ndash;AHP modeling, this study contributes to the growing field of spatial agronomy aimed at supporting precision and sustainability in smallholder farming systems. It advances a replicable framework for agricultural vulnerability assessment that can inform early warning systems, optimize management decisions, and enhance food system resilience in the face of global environmental change.\u003c/p\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eGwagwalada Area Council, situated 40 kilometers from Nigeria's capital, Abuja, spans from latitude 8 50' 00'' to 90 20' 00'' N and longitude 60 12' 00'' to 7 28' 00'. Covering approximately 6,500 hectares, it comprises ten wards, including Ibwa, Paiko Kore, Zuba, Tungamaje, Gwako, Kutunku, Dobi, Ikwa, Gwagwalada Central, and Quarters. The area features Precambrian granite and schist formations, alongside Quaternary alluvium deposits in the Usman River channel, offering fine sand for construction. Its topography is characterized by inselbergs, outliers, and iron-stone-capped ridges, as well as undulating plains dissected by seasonal river valleys. Elevations range from 213.3 meters to the north to 142.2 meters to the south, with gentle slopes prevailing. The region experiences two main seasons: rainy (April to October) and dry (November to March), with temperatures ranging from 17\u0026deg;C during Harmattan to 30\u0026ndash;37 degrees Celsius annually. Relative humidity peaks at around 50% in the rainy season. With an annual rainfall between 1632mm and 1404mm, the area is conducive to rainfed farming. Population growth has been significant, increasing from 16,000 in 1978 to 384,700 in 2023.\u003c/p\u003e \u003cp\u003eThe environmental conditions of the Gwagwalada Area Council play a significant role in the occurrence, spread, and severity of crop pests and diseases. The region's warm temperatures, ranging from 17\u0026deg;C to 37\u0026deg;C, create a favorable environment for various pests and pathogens, particularly fungal and bacterial diseases that thrive in humid conditions. The seasonal variability, with a rainy season from April to October and a dry season from November to March, further influences pest and disease dynamics. While the rainy season provides optimal moisture for fungal infections such as blights and mildews, it also enhances nematode activity in wet soils. Conversely, the dry season, especially during Harmattan, reduces moisture availability, slowing fungal diseases but increasing the risk of viral infections spread by vectors like whiteflies and aphids. With an annual rainfall of 1404\u0026ndash;1632mm and relative humidity peaking at 50% during the rainy season, the region is prone to fungal outbreaks in dense crop canopies where localized humidity levels may be higher. The area's diverse soil types, including Precambrian granite, schist formations, and Quaternary alluvium deposits, also influence disease prevalence. While the fertile alluvial soils along the Usman River promote crop growth, they also retain moisture, creating conditions conducive to root rot, damping-off in seedlings, and nematode infestations. Similarly, the undulating plains and seasonal river valleys impact pest distributions, as poorly drained areas can serve as breeding grounds for disease-carrying insects, while ridges and inselbergs create microclimates affecting disease severity. Additionally, rapid population growth from 16,000 in 1978 to 384,700 in 2023 has led to increased land use, deforestation, and agricultural expansion, disrupting natural predator-prey balances and exacerbating pest outbreaks. The heavy reliance on rainfed farming without controlled irrigation heightens vulnerability to climate variability, as periodic droughts weaken crops, making them more susceptible to insect infestations such as locusts, armyworms, and grasshoppers. Furthermore, the Harmattan winds, carrying dry, dust-laden air from the Sahara, can spread fungal spores over long distances, contributing to disease transmission across farms. Collectively, these environmental and anthropogenic factors create complex interactions that influence the prevalence and intensity of crop pests and diseases in the Gwagwalada Area Council.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eConceptual and analytical framework\u003c/h2\u003e \u003cp\u003eCrop pest and disease risks are inherently complex and spatially heterogeneous phenomena that emerge from the dynamic interplay of environmental, biophysical, and anthropogenic factors (Ojiambo, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Understanding these interactions requires a systematic framework that integrates spatial data, multi-criteria evaluation, and geospatial modeling to generate actionable intelligence for sustainable agricultural management (Dittmar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The conceptual foundation of this study rests on three interrelated principles: the spatial heterogeneity of environmental risk, the multi-factorial nature of pest and disease dynamics, and the strategic value of geospatial decision-support systems in managing environmental uncertainty. Together, these principles establish the basis for linking spatial data analysis with decision-making processes to improve the precision and effectiveness of pest and disease management in agricultural systems.\u003c/p\u003e \u003cp\u003eAt the conceptual level, the framework assumes that crop vulnerability to pests and diseases is a spatial function of several interacting parameters, including soil properties, land use and cover, slope, temperature, moisture, and vegetation vigor (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lindell et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Each of these parameters contributes uniquely to overall vulnerability, depending on its ecological role in shaping pest habitats and influencing crop resilience. To capture these complex interrelationships, a multi-criteria spatial modeling approach is adopted to integrate diverse environmental and biophysical influences within a coherent analytical structure. This approach ensures that the resulting model reflects both the direct and indirect drivers of pest and disease dynamics across space. The AHP provides the decision-analytic foundation of the framework by transforming expert judgments about the relative importance of criteria into quantitative weights through pairwise comparisons, eigenvalue derivation, and consistency verification. These weights express the proportional contribution of each factor to crop vulnerability, ensuring objectivity and comparability across variables. The GIS, on the other hand, operationalizes these weights by integrating spatial datasets representing each criterion to compute a composite vulnerability index. This process produces a continuous surface that reveals spatial variations in pest and disease risk. The integration of AHP and GIS, therefore, yields a Spatial Decision Support System (SDSS) capable of quantifying, visualizing, and communicating vulnerability patterns. This enables targeted and evidence-based agricultural decision-making while aligning with the broader Spatial Information Science (SIS) paradigm, which emphasizes spatial reasoning, geospatial data integration, and multi-criteria modeling as tools for addressing complex real-world problems (Eisen and Eisen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Makkulawu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Teixeira et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Makadi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analytical process developed in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) follows a structured six-step workflow typically implemented in carrying out a study of this kind (Makkulawu et al., 2024). The first step involves parameter selection and data acquisition, where environmental and land-use factors influencing pest and disease occurrence are identified through literature review and expert consultation. Relevant spatial datasets - such as soil maps, temperature, slope, Normalized Difference Vegetation Index (NDVI), and land cover - are then obtained from remote sensing and secondary geospatial sources. In the second step, data preprocessing and standardization, all datasets are projected to a uniform coordinate system, resampled to a common spatial resolution, and standardized through reclassification techniques to ensure comparability. The third step, weight derivation using AHP, employs expert judgments to construct a pairwise comparison matrix of all selected criteria, followed by eigenvalue computation and consistency ratio testing (CR\u0026thinsp;\u0026le;\u0026thinsp;0.1) to ensure the reliability of derived weights. In the fourth step, weighted overlay and vulnerability mapping, the AHP-derived weights are integrated with standardized GIS layers using the Weighted Linear Combination (WLC) technique to generate a composite vulnerability index. The resulting map classifies the study area into low, moderate, and high pest and disease vulnerability zones. This is followed by validation of results, where the generated vulnerability map is tested using NDVI data to establish correlations between vegetation stress patterns and predicted high-vulnerability zones. The final step, interpretation and decision support, involves analyzing the spatial outputs to identify critical hotspots and prioritize intervention zones. These results provide a decision-support foundation for agricultural extension, integrated pest management, and adaptive land-use planning.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e visually illustrates how AHP and GIS are integrated into a coherent spatial decision-support system for pest and disease vulnerability mapping. The process begins with data input blocks comprising environmental and biophysical parameters such as soil, land cover, slope, temperature, and vegetation index. These data feed into two analytical streams: the AHP decision module, where criteria are subjected to pairwise comparison and weighted based on their importance, and the GIS spatial module, where standardized raster layers are prepared for analysis. The outputs from both streams converge at an integration node through a weighted overlay operation, producing a composite vulnerability surface that classifies the study area into various risk levels. A validation loop then compares model outputs with NDVI-based field indicators to evaluate accuracy and coherence. The final stage - decision support and application - translates the results into actionable insights for farmers, agricultural extension officers, and policymakers. The diagram\u0026rsquo;s arrows emphasize the iterative and interactive nature of the framework, allowing continuous feedback between analytical stages, such as refining AHP weights based on validation outcomes. This iterative design demonstrates how spatial information science harmonizes decision theory and geospatial analysis to transform diverse datasets into knowledge-driven spatial intelligence for sustainable agricultural management.\u003c/p\u003e \u003cp\u003eThis integrated framework significantly contributes to the advancement of Spatial Information Science (SIS) by embedding multi-criteria decision-making (AHP) within a geospatial modeling environment (GIS) to produce interpretable and dynamic spatial risk surfaces. It enhances data interoperability and analytical precision through knowledge-based weighting and standardization and demonstrates how geospatial intelligence can be applied to adaptive management in data-scarce smallholder farming systems (Agogue Feujio et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ultimately, the model offers a replicable and scalable template for spatial risk assessment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Data collection and analytical framework","content":"\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the integrated framework of the Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS) within a Spatial Decision Support System (SDSS) developed to evaluate crop pest and disease vulnerability in Gwagwalada Area Council, Nigeria. This framework combines expert-based decision-making with spatial analysis to identify high-risk zones, support evidence-based interventions, and inform sustainable agricultural management. Key environmental and biophysical parameters\u0026mdash;including soil texture and pH, land cover, slope, temperature, moisture, and vegetation indices\u0026mdash;were selected based on a literature review and their known influence on pest and disease incidence (Oerke \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Scott et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saxena et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). These parameters were assigned relative importance weights using the AHP, standardized, and spatially overlaid in GIS to generate a composite vulnerability surface. The resulting vulnerability map categorizes areas into low, moderate, and high risk, providing actionable insights for targeted management strategies. The approach integrates sustainability considerations by linking risk identification to interventions that reduce reliance on chemical inputs, optimize resource use, and enhance smallholder resilience.\u003c/p\u003e\n\u003cp\u003eData and data sources\u003c/p\u003e\n\u003cp\u003eA review of the literature on GIS- and AHP-based crop pest and disease assessment identified seven key parameters influencing vulnerability: soil texture, soil pH, slope, temperature, moisture, land use/cover, and vegetation index. Data were obtained from multiple sources, including remote sensing products (Landsat and Sentinel imagery for NDVI), local meteorological stations (temperature, moisture, and humidity), and field surveys (soil and land-use validation). The selection of these parameters is supported by previous studies linking biophysical and environmental conditions to pest and disease outbreaks (Altieri and Nicholls \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hengl et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Babaremu et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Table 1 provides a comprehensive overview of the types of data collected for the study and the corresponding sources from which these data were obtained. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the rating scale applied to evaluate and qualify the status/level of each parameter, offering a standardized framework for interpreting measurements and facilitating consistent comparisons. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides the normalized matrix used in the study. In AHP-based GIS analysis, a normalized matrix converts diverse criteria with different units and ranges into a dimensionless scale (0\u0026ndash;1), ensuring comparability and preventing variables with larger values from dominating. Normalized values are then weighted according to AHP priorities and combined to produce a composite suitability map. This step is essential for accurate, unbiased integration of multiple spatial criteria in multi-criteria decision analysis.\u003c/p\u003e\n\u003cp\u003eAHP implementation\u003c/p\u003e \u003cp\u003eThe AHP was applied through six structured phases: defining the decision hierarchy, conducting pairwise comparisons using Saaty\u0026rsquo;s 1\u0026ndash;9 scale, normalizing the comparison matrix to obtain factor weights, checking consistency for reliability, aggregating weights to rank alternatives, and performing sensitivity analysis to test result stability. This systematic approach ensured objective and robust decision-making. The implementation was to ensure rigorous multi-criteria evaluation:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eHierarchy construction\u003c/em\u003e: The main objective\u0026mdash;assessing crop pest and disease vulnerability\u0026mdash;was placed at the top, followed by criteria (environmental, climatic, and land-use factors) and alternatives (low, moderate, high vulnerability).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePairwise comparisons\u003c/em\u003e: Experts evaluated the relative importance of each criterion using Saaty\u0026rsquo;s 1\u0026ndash;9 scale, where 1 indicates equal importance and 9 denotes extreme importance of one factor over another (Saaty \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eWeight derivation\u003c/em\u003e: Normalized comparison matrices were used to calculate numerical weights for each criterion.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eConsistency checks\u003c/em\u003e: Consistency Ratios (CR) were computed to ensure reliability of expert judgments (CR\u0026thinsp;\u0026lt;\u0026thinsp;0.1 considered acceptable).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eAggregation\u003c/em\u003e: Weights were aggregated to derive overall scores for each vulnerability level.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eSensitivity analysis\u003c/em\u003e: The robustness of the model was tested by varying criterion weights\u0026thinsp;\u0026plusmn;\u0026thinsp;10% to evaluate the stability of the vulnerability classification, thereby accounting for uncertainty in expert judgment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis rigorous approach ensures that the AHP model provides a transparent, replicable, and objective assessment of criteria importance.\u003c/p\u003e \u003cp\u003eGIS-based risk mapping\u003c/p\u003e \u003cp\u003eThe study employed ArcGIS 8.0 to integrate AHP and GIS for mapping crop pest and disease risks. Key steps included identifying relevant risk criteria, conducting pairwise comparisons to determine their relative importance, and calculating corresponding weights. These weighted factors were incorporated into standardized GIS data layers (scaled 1\u0026ndash;5) to represent varying vulnerability levels. A weighted overlay analysis then generated a composite vulnerability map, visually highlighting areas of low, moderate, and high risk. The resulting maps supported the identification of high-vulnerability zones and informed priority management strategies, demonstrating the effectiveness of the GIS\u0026ndash;AHP integration for comprehensive agricultural risk assessment and decision-making. The workflow involved:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePreparation of spatial data layers\u003c/em\u003e: All parameters were digitized and standardized to a common spatial resolution and vulnerability scale (1\u0026ndash;5).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eWeighted overlay analysis\u003c/em\u003e: AHP-derived weights were applied to each raster layer using ArcGIS Pro 3.1, producing a composite vulnerability surface.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eRisk classification\u003c/em\u003e: The resulting raster was categorized into low, moderate, and high vulnerability zones for clear interpretation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eMap visualization\u003c/em\u003e: The vulnerability map was visualized with GIS symbology for easy interpretation by stakeholders.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eValidation\u003c/em\u003e: Spatial coherence of the model was evaluated using NDVI data derived from Sentinel-2 imagery. Quantitative validation was conducted by comparing modeled high-risk zones with areas exhibiting vegetation stress, using correlation analysis to ensure predictive reliability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eScenario analysis\u003c/em\u003e: GIS was further used to simulate alternative scenarios of pest and disease risk under variations in environmental conditions, supporting adaptive management decisions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe integration of AHP and GIS enables a spatially explicit, evidence-based, and decision-oriented assessment, offering a transferable framework for other smallholder-dominated agricultural landscapes. By highlighting high-risk areas, the model supports targeted interventions, sustainable pest management practices, and resource-efficient agricultural planning.\u003c/p\u003e \u003cp\u003eSustainability and generalizability\u003c/p\u003e \u003cp\u003eThis methodology directly addresses sustainability objectives by:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSupporting site-specific interventions, reducing blanket pesticide application and associated environmental impacts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eProviding evidence-based guidance for smallholder farmers, enhancing productivity and resilience under climate variability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eOffering a replicable framework that can be adapted to other crops, regions, and environmental contexts, facilitating broader adoption in Sub-Saharan Africa and similar agro-ecological zones.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBy linking spatial vulnerability mapping to practical management strategies, the methodology creates a bridge between analytical assessment and real-world application. This integration allows decision-makers to identify priority areas for intervention while aligning land-use planning with sustainability goals. In doing so, the approach ensures that the study not only maps areas of environmental stress but also provides a framework for promoting sustainable agricultural intensification, enhancing smallholder resilience to climate and land degradation risks, and advancing ecosystem-based management practices. These strategies collectively support the optimization of land resources, reduction of environmental vulnerability, and long-term livelihood security for farming communities.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eEnvironmental, climatic, and land-use factors influencing crop pest and disease outbreaks\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e present the actual and reclassified maps of the seven parameters used in the AHP\u0026ndash;GIS criteria decision process - land use/cover, soil moisture index, land surface temperature, relative humidity, soil texture, soil pH, and slope - while Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes their spatial coverage, AHP rankings, and importance levels. Agricultural land constitutes 51.5% of the area and is deemed extremely important, whereas vegetation covers 36.1% and is rated highly important. Bare and built-up surfaces occupy smaller proportions, with low to moderate ratings. Areas with soil moisture indices below 0.338 (31.6% of total coverage) are of extremely high importance, with decreasing relevance as moisture levels increase. High land surface temperatures (34\u003csup\u003e0\u003c/sup\u003e\u0026ndash;36\u0026deg;C) dominate 61.2% of the area and are rated highly significant, while moderate relative humidity (32.1\u0026ndash;35.1%) spans 46.2% and is considered moderately to highly important. Sandy loam soils (79.6%) are rated very high in importance compared to loam soils (20.4%), which are moderately important. Soils with pH\u0026thinsp;\u0026ge;\u0026thinsp;6.3 cover 75.6% of the area and are of utmost importance, while moderate slopes (2.6\u0026ndash;6.0\u0026deg;) cover 40.3% and are rated low to moderately important, though steeper slopes carry higher weight. Collectively, these parameters strongly influence agricultural vulnerability to pests and diseases, providing essential insight for spatially targeted control strategies.\u003c/p\u003e \u003cp\u003eThe extensive agricultural and vegetated areas highlight the dominance of farming and natural vegetation in the study area - key environments for pest proliferation (Nansel et al. 2019; Abd El-Ghany et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Childers et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). High soil moisture zones provide favorable conditions for pests and pathogens, particularly fungi (Bebber et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Romero et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rodas and Madrigal \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Elevated temperatures encourage pest and disease spread in warmer climates, whereas cooler areas may suppress their activity (Porter et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jonathan and Mahendranathan, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moderate-to-high humidity levels foster disease transmission in damp environments (IPCC Secretariat 2021; Skendžić et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sandy loam soils, prevalent in the area, may support specific crops and pests, while soil pH indirectly affects plant health and vulnerability (Jackson and Meetei, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Barrow and Hartemink, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Slope variability also shapes erosion and moisture dynamics, influencing pest and disease patterns (Zhang et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Magalh\u0026atilde;es 2023; Nyairo, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, high-priority zones - particularly those with dense vegetation, productive farmlands, and specific soil types - require frequent monitoring and adaptive pest management to mitigate agricultural risks.\u003c/p\u003e \u003cp\u003eResults of ranking and weighing of risk factors using AHP to enhance decision-making in pest and disease management\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the development of a crop pest and disease vulnerability map using AHP and GIS techniques. Figure\u0026nbsp;10 shows the validation results obtained by querying disease plants from NDVI. The vulnerability map combined values from the seven criteria outlined in Table\u0026nbsp;4. We classified each criterion and constructed a pairwise comparison matrix to assess their significance, resulting in rating scores. By applying weighted sums to causative criteria, a final susceptibility map with a consistency ratio (C.R.) of 0.08 (\u0026lt;\u0026thinsp;0.1, validated). Land use and land cover criteria exerted the highest influence at 50.02%, while soil moisture index, land surface temperature, relative humidity, soil texture, soil pH, and slope cover were less important, with weights ranging from 2% to 11.28%. We classified the 1,042.871 km2 study area into low and very low susceptible zones (11% and 4%, respectively) and very high, high, and moderate susceptible zones (20.46%, 37.89%, and 26.75%, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study has been developed for the Gwagwalada area council a crop pest and disease vulnerability map that has significant implications for understanding and mitigating risks to agricultural productivity. We can tailor strategies to manage these areas effectively, with land use and land cover criteria exerting the highest influence, accounting for 50.02% of the vulnerability. Other factors, such as soil moisture, temperature, humidity, soil texture, pH, and slope cover, also play critical roles. Areas categorized as very high and high susceptible zones occupy a substantial portion (around 58.35%) of the study area, indicating heightened risk. Farmers and agricultural authorities can use this information to prioritize resources and interventions, such as targeted pest control measures, crop selection, or irrigation practices, to mitigate the impact of pests and diseases. Moreover, understanding vulnerability patterns can aid in planning resilient agricultural systems and adapting to changing environmental conditions, thereby safeguarding food security and livelihoods in the region.\u003c/p\u003e \u003cp\u003eUse of GIS and AHP to generate vulnerability maps highlighting high-risk zones for targeted pest and disease control interventions\u003c/p\u003e \u003cp\u003eGIS was instrumental in generating vulnerability maps for targeted pest and disease control interventions in Gwagwalada Area Council by integrating multiple environmental and agricultural parameters using the Analytical Hierarchy Process (AHP). The combination of GIS and AHP is widely recognized for its effectiveness in spatial decision-making and multi-criteria analysis, particularly in agricultural risk assessments (Malczewski, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Saaty, 1980). The study utilized seven key criteria\u0026mdash;land use, land cover, soil moisture index, land surface temperature, relative humidity, soil texture, soil pH, and slope\u0026mdash;to assess the spatial susceptibility of agricultural zones to pest and disease outbreaks. These parameters are crucial in pest and disease ecology, as environmental conditions significantly influence pathogen proliferation and pest population dynamics (Bebber et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Romero et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e to \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrate both the actual levels and the reclassified status of these parameters, while Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e6\u003c/span\u003e details the extent covered by each class and their corresponding rankings within the AHP framework.\u003c/p\u003e \u003cp\u003ePattern of vulnerability and pest and disease risk zones in the study area\u003c/p\u003e \u003cp\u003eThe crop pest and disease vulnerability map of Gwagwalada Area Council (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e8\u003c/span\u003e) reveals a spatially heterogeneous susceptibility pattern shaped by environmental, climatic, and land-use factors, consistent with earlier studies emphasizing the roles of land cover, soil, and climate in pest and disease outbreaks (Bebber et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Porter et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). High and very high vulnerability zones dominate agriculturally intensive areas, where dense vegetation and extensive cropland create favorable microclimates for pest proliferation and disease spread (Nansel et al. 2019; Abd El-Ghany et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Regions characterized by low soil moisture index, elevated land surface temperatures (340\u0026ndash;360\u0026deg;C), and moderate-to-high humidity (32.12\u0026ndash;35.1%) further promote pest survival and pathogen multiplication (Romero et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rodas and Madrigal \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil factors substantially influence vulnerability patterns. The predominance of sandy loam soils (79.6%) and higher soil pH (\u0026gt;\u0026thinsp;6.30 in 75.6% of the area) support specific pest species and disease agents by affecting crop nutrition and physiological resistance (Barrow and Hartemink \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, these high-risk zones require intensive monitoring, early warning systems, and targeted pest management to mitigate yield losses (IPCC Secretariat 2021). Moderate-risk areas\u0026mdash;transitioning between high- and low-risk zones\u0026mdash;reflect gradual shifts in soil, vegetation, and microclimatic conditions. These intermediate regions benefit from proactive pest management, such as crop rotation, intercropping, and soil improvement practices, to prevent escalation into higher vulnerability levels (Zhang et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Magalh\u0026atilde;es 2023; Childers et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLow and very low vulnerability zones correspond mainly to built-up and barren areas, which have reduced vegetation, higher runoff, and lower pest prevalence (Yang et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sloped terrains, where runoff prevents water stagnation, are less conducive to fungal and bacterial development (Zhang et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The resulting vulnerability pattern underscores the importance of spatially targeted management. The vulnerability map serves as a decision-support tool for prioritizing high-risk zones, where biocontrol, resistant varieties, and surveillance can reduce pest outbreaks (Bebber et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while moderate-risk areas may benefit from integrated pest management (IPM) and improved irrigation to strengthen resilience (Porter et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This spatial approach enhances resource allocation efficiency, directing interventions toward areas of higher susceptibility (Magalh\u0026atilde;es 2023), and aligns with global findings on land use, soil, and climate influences on agricultural risks (Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThrough AHP-based classification, each factor was weighted and ranked according to its contribution to pest and disease occurrence. Land use and land cover emerged as the most influential parameter, accounting for 50.02% of total vulnerability. Agricultural land (51.5%) and vegetation (36.1%) were the most critical exposure zones, corroborating findings that dense vegetation and extensive cultivation favor pest proliferation (Abd El-Ghany et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Childers et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climatic factors such as low soil moisture, high temperature (340\u0026ndash;360\u0026deg;C covering 61.2%), and moderate-to-high humidity (32.12\u0026ndash;35.1% covering 46.2%) were strong determinants of pest activity (Bebber et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Porter et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Soil properties\u0026mdash;especially sandy loam texture (79.6%) and high pH (\u0026gt;\u0026thinsp;6.30)\u0026mdash;affected plant health and pest resistance (Barrow and Hartemink \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while slope influenced erosion and runoff, indirectly shaping pest\u0026ndash;disease dynamics (Zhang et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe AHP model, validated through NDVI-derived diseased plant observations (Fig.\u0026nbsp;10), yielded a consistency ratio (C.R.) of 0.08 (\u0026lt;\u0026thinsp;0.1), confirming model reliability. Approximately 58.35% of the total area was classified as highly or very highly susceptible, signifying the need for targeted interventions. The resulting map provides a robust spatial framework for decision-makers to prioritize surveillance, deploy biological and chemical control, and guide farmers in adopting pest-resistant crops and sustainable management practices. Ultimately, the GIS\u0026ndash;AHP approach strengthens evidence-based pest and disease risk management, enhancing agricultural productivity and food security in Gwagwalada Area Council (Parsa et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bajocco et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe validation of the pest and disease vulnerability map\u003c/p\u003e \u003cp\u003eThe validation process was essential to ensure the accuracy of the integrated AHP\u0026ndash;GIS analysis. An NDVI (Normalized Difference Vegetation Index) map was generated for the study area and compared with the vulnerability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e). NDVI measures vegetation health by assessing chlorophyll content\u0026mdash;higher NDVI values indicate healthy vegetation resistant to stressors such as pests and diseases (Horler et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Moran et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Pettorelli et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, dense or stressed vegetation may also exhibit high NDVI values, which can attract pests and diseases (Thenkabail et al., 2021). Thus, NDVI variations serve as early indicators of pest or disease presence, supporting proactive monitoring and integrated pest management to enhance agricultural productivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe NDVI results (Fig.\u0026nbsp;10) show that 3.1% of the area had very low NDVI values, 7.3% low, 16.2% moderate, 31.2% high, and 42.2% very high. This means that over 70% of the region exhibits high to extremely high NDVI values, reflecting dense vegetation that may promote pest and disease spread. Similarly, the AHP\u0026ndash;GIS\u0026ndash;derived vulnerability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e8\u003c/span\u003e) shows that about 60% of the study area faces high to very high risk of pest and disease occurrence. The high NDVI values indicate dense vegetation cover, which provides favorable conditions for pest and pathogen proliferation due to increased food and shelter availability. Previous studies confirm that dense vegetation, while beneficial for ecosystem health, can enhance pest and disease spread, posing challenges to agricultural productivity (Pettorelli et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wardlow and Egbert, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shanmugapriya et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raihan, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The strong spatial agreement between the NDVI and AHP\u0026ndash;GIS maps indicates that approximately 60% of the study area is highly susceptible to pest and disease outbreaks, underscoring a clear correlation between vegetative density and vulnerability. This consistency aligns with findings from other studies that used NDVI and GIS-based methods to validate agricultural risk models (de Resende et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hinnah et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Opoku et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProposed integrated pest and disease management strategies to support sustainable agriculture and food security\u003c/p\u003e \u003cp\u003eThe validation process was essential to ensure the accuracy of the integrated AHP\u0026ndash;GIS analysis. An NDVI (Normalized Difference Vegetation Index) map was generated for the study area and compared with the vulnerability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e). NDVI measures vegetation health by assessing chlorophyll content - higher NDVI values indicate healthy vegetation resistant to stressors such as pests and diseases (Horler et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Moran et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Pettorelli et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, dense or stressed vegetation may also exhibit high NDVI values, which can attract pests and diseases (Thenkabail et al., 2021). Thus, NDVI variations serve as early indicators of pest or disease presence, supporting proactive monitoring and integrated pest management to enhance agricultural productivity.\u003c/p\u003e \u003cp\u003eThe NDVI results (Fig.\u0026nbsp;10) show that 3.1% of the area had very low NDVI values, 7.3% low, 16.2% moderate, 31.2% high, and 42.2% very high. This means that over 70% of the region exhibits high to extremely high NDVI values, reflecting dense vegetation that may promote pest and disease spread. Similarly, the AHP\u0026ndash;GIS\u0026ndash;derived vulnerability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e8\u003c/span\u003e) shows that about 60% of the study area faces high to very high risk of pest and disease occurrence. The high NDVI values indicate dense vegetation cover, which provides favorable conditions for pest and pathogen proliferation due to increased food and shelter availability. Previous studies confirm that dense vegetation, while beneficial for ecosystem health, can enhance pest and disease spread, posing challenges to agricultural productivity (Pettorelli et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wardlow and Egbert, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shanmugapriya et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raihan, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The strong spatial agreement between the NDVI and AHP\u0026ndash;GIS maps indicates that approximately 60% of the study area is highly susceptible to pest and disease outbreaks, underscoring a clear correlation between vegetative density and vulnerability. This consistency aligns with findings from other studies that used NDVI and GIS-based methods to validate agricultural risk models (de Resende et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hinnah et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gaudry, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Opoku et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) framework for assessing crop pest and disease vulnerability in Gwagwalada, Nigeria\u0026mdash;an approach that advances spatially explicit, data-driven agricultural risk management in tropical agroecosystems. By incorporating soil, topography, land use, and climatic parameters, the model systematically quantified the relative influence of each factor on pest and disease occurrence. Land use and land cover emerged as the most dominant determinants, contributing 50.02% to total vulnerability, with agricultural land (51.5%) and vegetated zones (36.1%) being the most affected. Climatic and soil variables such as low soil moisture (\u0026lt;\u0026thinsp;0.338), elevated land surface temperature (340\u0026deg;C\u0026ndash;360\u0026deg;C), and moderate humidity (32\u0026ndash;35%) were also critical in creating favorable conditions for pest and pathogen proliferation.\u003c/p\u003e \u003cp\u003eThe resulting AHP\u0026ndash;GIS vulnerability map classified 58.35% of the area as highly or very highly vulnerable, underscoring the spatial concentration of risks within active farming zones. Validation using NDVI confirmed that over 70% of the study area exhibits high vegetative cover, which aligns with the mapped vulnerability hotspots, thereby confirming the robustness of the integrated model. High-risk zones corresponded primarily to agricultural and vegetated landscapes, while built-up and barren lands displayed lower susceptibility, demonstrating the strong ecological link between vegetative density and pest\u0026ndash;disease prevalence.\u003c/p\u003e \u003cp\u003eThe novelty of this study lies in its integration of AHP-based multi-criteria analysis with NDVI validation to produce a scientifically rigorous and spatially explicit pest\u0026ndash;disease risk model tailored to semi-arid agroecosystems. This dual-validation approach enhances predictive accuracy and provides a transferable framework for sustainable crop protection planning under climate variability. For sustainable pest and disease management, the study recommends the adoption of climate-smart and ecosystem-based interventions, including integrated pest management (IPM) practices, biological control, intercropping, crop rotation, and soil organic amendments. Additionally, establishing early warning systems that combine remote sensing data with farmer-based field monitoring will enable timely interventions. Capacity building for local farmers on the use of GIS-enabled forecasting tools, coupled with the creation of a digital decision-support platform, can enhance adaptive management and resilience. Ultimately, this research contributes to sustainable agronomy by linking spatial decision science with ecological processes that shape pest and disease dynamics. It demonstrates how integrated geospatial modeling can support evidence-based agricultural planning, strengthen food security, and promote climate-resilient farming systems in Sub-Saharan Africa.\u003c/p\u003e"},{"header":"Limitations of the Study and Suggestions for Future Research","content":"\u003cp\u003eWhile the integrated AHP\u0026ndash;GIS framework provided valuable insights into spatial patterns of crop pest and disease vulnerability, several limitations should be acknowledged. First, the precision of the vulnerability map is constrained by the resolution, completeness, and temporal relevance of spatial datasets used for soil properties, climatic variables, and land use. Inaccuracies or outdated data may lead to spatial uncertainty and misclassification of risk zones. Second, validation based solely on NDVI, although effective in capturing vegetation health, does not fully represent dynamic ecological factors such as seasonal variability, pest migration, or vegetation phenology. This limits the model\u0026rsquo;s capacity to capture temporal shifts in pest\u0026ndash;disease interactions. Third, the AHP-based weighting and ranking process involves expert judgment, which, despite its structured nature, introduces a degree of subjectivity and potential bias in factor prioritization. Lastly, the model assumes static environmental conditions and does not incorporate real-time monitoring or predictive simulation of pest and disease dynamics under projected climate change scenarios.\u003c/p\u003e \u003cp\u003eFuture research should seek to enhance the accuracy, adaptability, and predictive power of vulnerability models. Integrating high-resolution multispectral and hyperspectral satellite imagery with real-time meteorological and soil sensor data can improve spatial and temporal precision. The application of machine learning and artificial intelligence (AI) for predictive modeling would enable dynamic forecasting of pest outbreaks by incorporating variables such as rainfall anomalies, wind patterns, temperature fluctuations, and pest life cycles. Longitudinal studies capturing seasonal and interannual variations in pest and disease occurrences would further strengthen temporal understanding. Moreover, incorporating socioeconomic and behavioral dimensions\u0026mdash;including farmers\u0026rsquo; adaptation capacity, pesticide use intensity, and access to extension services\u0026mdash;would provide a more holistic perspective on vulnerability and resilience. Developing a web-based, GIS-integrated decision-support platform that links satellite observations, field data, and mobile-based farmer feedback could revolutionize early warning and response systems. Finally, collaborative research frameworks involving scientists, policymakers, and local farming communities are essential to co-develop sustainable, climate-smart pest management strategies that enhance agricultural resilience in Sub-Saharan Africa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;During the preparation of this work, the authors used ChatGPT in order to refine grammar and expression in the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to conduct this study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas M, Saleem M, Hussain D, Ramzan M, Jawad Saleem M, Abbas S, Hussain N, Irshad M, Hussain K, Ghouse G, Khaliq M (2022) Review on integrated disease and pest management of field crops. 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Understanding and mapping vulnerability to these threats requires an integrated assessment of biophysical and land-use factors that influence pest and disease dynamics. Few studies combine Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS) for this purpose, despite their high potential. This study employs a combined Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) framework to evaluate agricultural pest and disease vulnerability in Gwagwalada Area Council, Nigeria. Six key determinants\u0026mdash;land use and cover, soil properties, temperature, moisture, and topography\u0026mdash;were analyzed to construct a composite vulnerability index and spatial risk map. The results indicate that land use and land cover exert the greatest influence on vulnerability patterns, while temperature and soil moisture also play critical roles. Validation using the Normalized Difference Vegetation Index (NDVI) confirmed the spatial accuracy of the derived vulnerability zones. The study demonstrates that integrating AHP with GIS provides a robust, participatory, and data-driven decision-support tool for sustainable pest and disease management. The findings highlight the importance of landscape-level monitoring and adaptive management to enhance agricultural resilience, promote sustainable intensification, and safeguard food security in vulnerable farming regions.\u003c/p\u003e","manuscriptTitle":"Spatial assessment of pest and disease vulnerability in smallholder farming systems using AHP and GIS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 22:10:08","doi":"10.21203/rs.3.rs-8468265/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"51106708701894560465578756198089491320","date":"2026-03-08T01:04:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T16:10:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T02:42:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-08T02:42:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Geomatics","date":"2025-12-29T01:17:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0f5e9820-c228-4d95-bb1b-cb07ab511bed","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T22:10:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 22:10:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8468265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8468265","identity":"rs-8468265","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}