{"paper_id":"4e384a55-1146-45d9-b409-325ae0ebea89","body_text":"Agrorac: A web-based tool focused on knowledge management for identifying agroclimatic risks in equatorial agrosystems | 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 Agrorac: A web-based tool focused on knowledge management for identifying agroclimatic risks in equatorial agrosystems Angela María Castaño Marín, Gerardo Antonio Góez Vinasco, LUIS FERNANDO GOMEZ GIL, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7457303/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper introduces AGRORAC, a robust and user-friendly web tool designed to assess future climate risks in equatorial agrosystems, such as excess or deficient water and extreme air temperatures. The tool uses climate prediction data from the Colombian meteorological service (IDEAM), soil data on water storage capacity, and agroecosystem susceptibility to extreme rainfall and temperature events. The primary programming language used was R version 4.4.0, in conjunction with the Shiny framework (version 1.8.1.1). Agrorac enables data visualization, analysis, and risk assessment by integrating climate scenarios with agroecosystem vulnerabilities related to crops phenological stages. Case studies on banana crops (Dominico Hartón, in Montenegro – Quindío) and potato crops (Diacol Capiro, in Mosquera – Cundinamarca) demonstrate Agrorac’s capability to generate detailed spatial maps and customized risk lists based on crop development stages. These functionalities enhance spatial resolution beyond that of traditional agroclimatic bulletins, providing useful information for precise, localized agronomic planning. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction According to Henry ( 2005 ), northern South America, specifically Colombia, Venezuela, Ecuador, the northern states of Brazil, and the north of Peru, is part of the equatorial region. This area is known for its unique climatic characteristics, primarily due to slight variations in air temperature and day length throughout the year. Additionally, the variation in global solar radiation in this region is influenced more by the presence or absence of cloud cover than by the position of the Sun throughout the annual cycle. In the equatorial region, where rainfed agricultural production predominates, variations in rainfall determine crop management, including planting and harvesting times (Gallo et al., 2024 ; Ocampo et al., 2025). Furthermore, in tropical high-Andean areas, including equatorial zones, frost events become widespread during the dry seasons of the driest years (Pouteau et al., 2011), resulting in a higher loss of agricultural production (Gómez et al., 2021 ). Therefore, low air temperature variations can also enable year-round crop production, but this is only feasible in limited areas. While low air temperature variations can allow for year-round crop production, this is only feasible in limited areas. In Colombia, continuous production can occur in the 1,100,000 hectares of existing irrigation districts, just a tiny fraction of the nearly 18,500,000 hectares with irrigation potential (MADR, 2020). Furthermore, according to the Ministry of Agriculture and Rural Development (MADR), the current state of these irrigation districts is not satisfactory. Given this context and the lack of initiatives for constructing new irrigation districts, ADR) has promoted the establishment of Technical Agroclimatic Committees (TAC). One of the main functions of these committees is to develop tools and strategies aimed at mitigating the impacts caused by extreme rainfall variability (Loboguerrero et al., 2018 ). TACs strengthen the capacity of producers and other agricultural decision-makers to effectively use climate forecasts. This anticipatory knowledge of environmental conditions enables them to identify rainfall-related threats and design optimal crop and livestock management strategies, thus reducing the risk of failing to meet production goals due to fluctuations in precipitation. Recognizing hazards is just the first step; it is equally important to understand how sensitive and vulnerable production systems are to the impact of weather. While crop models are used in various agricultural contexts to evaluate the sensitivity and susceptibility of cultivated species (Sotelo et al., 2020 ), not all crop species at the national level have calibrated or adjusted models (Marín et al., 2023). This gap in adjusted and parameterized crop models makes the TAC even more relevant at the regional (department) scale. TACs bring together individuals involved in agricultural activities (experts) to share their experiences in identifying potential risks associated with future rainfall scenarios, specifically by assessing the sensitivity of their systems to extreme rainfall events. This strategy's challenge involves assessing the system's risk and vulnerability as an elicitation product, following the methodology outlined by Loboguerrero et al. ( 2018 ). It is vital to ensure that each TAC meeting, held monthly in every territory, includes at least one expert, be it a farmer, technician, or professional specialized in the relevant agricultural system (FAO, 2025 ). However, dependence on a single individual to evaluate the sensitivity of a production system can lead to significant uncertainties in our assessments (Peña Quiñones et al., 2023 ). Moreover, the absence of experts for specific agricultural systems at these meetings often results in missed opportunities for critical risk evaluation. To tackle this pressing challenge and elevate the TAC's influence as a key platform for disseminating agrometeorological information, we have developed Agrorac a powerful web-based tool designed to identify agroclimatic risks and facilitate the formulation of targeted adaptation strategies and measures. This innovative solution enables stakeholders to make informed decisions, thereby enhancing their resilience in the face of climate-related challenges. Agrorac identifies projected climate-related risks for agroecosystems, primarily water excess or deficit and air temperatures outside physiological ranges, using future precipitation and temperature scenarios. These risks are parametrized in advance through the co-creation of Agroclimatic Technical Datasheets (ATD), compiled from secondary sources (scientific literature, official databases) and the expert knowledge of local technicians, agronomists, extension agents, and producers (Peña Quiñones et al., 2023 ). For each phenological stage and cultural operation, from land preparation through harvest and, where applicable, post-harvest, the ATD specifies (i) the exposed elements (crop, infrastructure, inputs) and (ii) the associated risks, such as pest and disease outbreaks or nutrient leaching under excessive rainfall. To define hydric risks, the datasheet combines the crop’s water requirement (stage-specific Kc coefficient) with the soil’s water-holding capacity; consequently, a given projected rainfall event may be classified as a deficit or an excess depending on soil type and crop stage. Analogously, thermal thresholds are set using each species’ cardinal temperatures (minimum, optimum, and maximum); any projection outside this range is considered potentially detrimental to productivity. Once uploaded to the platform, the ATD allows Agrorac to overlay climate scenarios with system tolerances and produce spatially explicit hazard and vulnerability maps. These outputs offer objective, reproducible inputs that enable TACs and other stakeholders to prioritize interventions and, based on local context, crop, and management practices, devise the most appropriate adaptation or mitigation strategies, thereby reducing the uncertainty associated with single expert assessments. In addition to pinpointing agroclimatic risks, Agrorac serves as a strategic opportunity explorer for producers. By cross-referencing future precipitation scenarios with the conventional crop calendar, the platform can expose “non-traditional” windows of optimal moisture. Using these insights, farmers can make datadriven decisions to advance or delay sowing relative to conventional planting dates. Planting outside the standard calendar when timely rainfall is forecast enables harvest scheduling during atypical windows, capturing niche markets or avoiding supply gluts, and ultimately reducing reliance on historical climate patterns that may shift under increasing variability. 2. Methodology The development of Agrorac a web-based tool for identifying agroclimatic risks, was approached through a structured methodology encompassing system architecture design, selection of core technologies, integration of diverse geospatial and agricultural data, and implementation of advanced analytical procedures. This section details the technical specifications, data sources, and computational models employed. 2.1. System Overview Agrorac is an interactive web application designed to facilitate the evaluation of agroclimatic risks faced by specific productive systems, particularly those related to water excess and/or deficit, and air temperatures outside physiological ranges in user-defined areas. The tool integrates data on soil characteristics, historical climatology (1991–2020), and climate projections (up to six months ahead from IDEAM), alongside detailed information about productive systems, their water requirements (Kc coefficients), and associated vulnerabilities. The primary objective is to provide farmers, extensionists, agro-technical personnel, and researchers in Colombia with critical, spatially explicit information to make informed decisions and mitigate climate-related risks. 2.2 Architecture and Development Environment The application's architecture is robust and scalable, leveraging powerful tools for scientific computing and interactive web development. 2.2.1. Core Technologies Agrorac was developed using R version 4.4.0 (2024-04-24) as the primary programming language. The Shiny framework (version 1.8.1.1) was chosen for building the web application due to its strong integration with R's statistical and scientific computing capabilities, facilitating the creation of complex statistical, geostatistical models, and advanced visualizations without requiring extensive knowledge of HTML, CSS, or JavaScript. Shiny's ability to support real-time dynamic responses, update graphics and tables without page reloading, and offer a wide range of customizable widgets further enhanced user experience and application functionality. Key R packages utilized in the development include are listed in Table 1 : Table 1 R Libraries Utilized in the Development of the Agrorac Web Application Package Version Description Shiny 1.8.1.1 Core framework for building interactive web applications in R. shinythemes 1.2.0 Provides predefined themes for Shiny applications. shinyWidgets 0.8.6 Extends Shiny's input controls with advanced widgets. Shinycssloaders 1.0.0 Adds loading indicators to Shiny elements. ggplot2 3.5.1 Graphics system based on the grammar of graphics. gridExtra 2.3 Functions for arranging multiple plots on a single panel. Dplyr 1.1.4 Tools for efficient data manipulation and transformation. DT 0.33 Enables the creation of interactive tables in Shiny. Readxl 1.4.3 Facilitates reading Excel files (.xls and .xlsx). cowplot 1.1.3 Enhances the organization and presentation of plots with ggplot2. Fields 15.2 Tools for spatial analysis and interpolation functions. Maps 3.4.2 Provides support for map visualization in R. lubridate 1.9.3 Simplifies date and time handling and manipulation. Raster 3.6–26 Processes and analyzes spatial data in raster format. Sf 1.0–16 Handles spatial data in simple features format. Sp 2.1-4 Defines classes and methods for spatial data. Leaflet 2.2.2 Enables the creation of interactive maps in R. leaflegend 1.2.1 Adds custom legends to Leaflet maps. rworldxtra 1.01 Provides additional high-resolution geospatial data for maps. ggspatial 1.1.9 Improves the visualization of spatial data within ggplot2. Gstat 0.6.0 Supports spatial statistics and geostatistics. utf8 1.2.4 Manages and encodes UTF-8 character strings in R. Car 3.1-2 Provides advanced statistical analysis and regression tools. Plotly 4.10.4 Generates interactive graphics in R. Png 0.1-8 Reads and writes PNG image files. fontawesome 0.5.2 Allows the use of FontAwesome icons in Shiny applications. rmarkdown 1.12 Tools for dynamic report generation in various formats. htmlwidgets 1.6.4 Integrates HTML widgets into R. Knitr 1.46 Automates report generation in R. Officer 0.6.6 Creates and edits Microsoft Word and PowerPoint documents. officedown 0.3.1 Extends officer for advanced Word report generation. pandoc 0.2.0 Document conversion tool between different formats. 2.2.2. Application Structure and Components Agrorac's structure follows a typical web application design, divided into a frontend (UI) and a backend (server logic). The UI defines the application's appearance and user interaction using Shiny functions like fluidPage(), sidebarLayout(), sidebarPanel(), and mainPanel(), which organize interactive elements such as sliders, text boxes, dropdowns, and checkboxes. The backend, implemented in R, manages data processing, analysis, and dynamic updates of output elements based on user input, facilitated by input and output objects. The application's source code is organized into two main R files: app.R, which contains the UI and server logic, and FuncionesR which holds auxiliary functions for geospatial, climatic, and productive data processing. Data are stored in dedicated folders, including Climatología, DEM, Municipios_Colombia_shp, Proyecciones, Sistemas_Productivos, and Suelos_Antioquia, and are loaded into the application's internal data collection at startup, ensuring rapid and efficient access. AGRORAC does not use a traditional database engine due to the nature and organization of the information it processes and displays. 2.2.3. Deployment The application is deployed on Shinyapps.io with a standard license. Furthermore, a Dockerization process has been implemented to facilitate deployment on corporate servers for production environments, enhancing scalability and responsiveness for multiple simultaneous users. 2.3. Data Sources and Management Agrorac integrates various types of geospatial and agricultural data, pre-processed and structured for efficient analysis (Fig. 1 ). 2.3.1. Geospatial Base Data Colombia's Political Division: A digital file in .SHP format representing the current political division of Colombia at department and municipality scales, sourced from the official IGAC geoportal ( https://geoportal.igac.gov.co/contenido/datos-abiertos-cartografia-y-geografia ). This vector data, with Magna-Sirgas projection, is loaded once. Digital Elevation Model (DEM): A raster file (GeoTiff) of Colombia's digital elevation model, originally from the Shuttle Radar Topography Mission (SRTM) with 30m precision. To optimize computational time for real-time processing, the original DEM was resampled with an aggregation factor of 10 in both coordinates, resulting in a lower resolution (382.19m x 382.19m) but significantly smaller file size (68.9 MB from 7.64 GB). This resampled DEM (SRTM_600.tif) is critical for calculating reference evapotranspiration (ET 0 ). 2.3.2. Climatic Data All climatic raster files (GeoTiff format) use geographical coordinates (WGS84). Climatology Raster: Monthly multiannual averages (30 years, 1991–2020) for precipitation, maximum temperature, mean temperature, and minimum temperature are provided by IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales de Colombia). These 12 monthly files (e.g., CLIMA_ENE_PREC.tif) are loaded once into the application unless IDEAM updates the official climatology. Climate Projections Raster: Monthly climate projections for precipitation, maximum temperature, mean temperature, and minimum temperature are also provided by IDEAM, with a forecasting horizon of up to six subsequent months. These files (e.g., PROYECCION AAAA-MM.tif) are updated monthly in the application. 2.3.3. Soil Data Soil Raster: This dataset contains information from soil surveys conducted at a departmental scale, obtained from the official IGAC website, and presented at a 1:100,000 scale. The study involved analyzing specific soil profiles to extract textural and practical root depth data. Typical values for the sand, silt, and clay soils were generated, assuming a 2.5% organic matter content. Using the equations described by Saxton & Rawls (2006), soil hydraulic parameters such as water storage capacity (mm) were calculated. Four GeoTiff raster files (Agua aprovechable (mm), fracción de arcilla (%), fracción de arena (%), fracción de limo (%)) store this processed soil property data. Currently, this dataset only covers the department of Antioquia because funding was specifically allocated for this region; expansion to other regions will depend on the availability of additional resources. User-Defined Soil Data: The application also allows users to input specific soil physics data (e.g., sand, clay, organic matter, salinity, gravel, compaction, horizon A depth) to estimate hydraulic parameters like field capacity, permanent wilting point, saturation, and available water, and to generate a customized water retention curve. 2.3.4. Productive Systems Data Agroclimatic Technical Datasheets (ATDs) contain comprehensive information for each crop or productive system. These are stored as .xlsx files in the Sistemas_Productivos folder. For the current version of the Agrorac application, the tool is fundamentally designed to manage a diverse array of preloaded productive systems. Users can select from an extensive list of available crops to conduct their agroclimatic risk analyses. This list, provided for context and not explicitly detailed within the given sources, includes: Hass avocado in productive phase (Antioquia), Hass avocado in vegetative phase (Antioquia), Sugarcane for panela (Northeast Antioquia), Bush bean, Climbing bean, Tahiti lime (Meta - Piedemonte), Maize (Yondó - Antioquia), Cashew in establishment phase (Vichada), Cashew in productive phase (Vichada), Potato (Cundinamarca), Plantain (Quindío), Soybean (Altillanura), and Carrot (Southwest Antioquia). This intrinsic capability to integrate various species and geographical territories is a cornerstone of Agrorac's architectural design and aligns with the application's long-term sustainability strategy, enabling future adaptation to additional species or territories as resources become available. Each ATD specifies: Identification: Species, variety, geographical context, altitudinal range, and full cycle duration. Phenology: Description of phenological stages with their duration in months. Crop Coefficient (Kc): Kc values for initial, vegetative, reproductive, and harvest stages. Vulnerability: It includes detailed lists of exposed elements (plant parts, cultural activities) and associated risks for specific climate threats (excess/deficit) across phenological stages. Co-creation Methodology for Vulnerability and Risk in Agroclimatic Technical Datasheets : Through co-creation sessions of Agroclimatic Technical Datasheets (ATDs), researchers, technical specialists, agronomists, extension agents, and producers collaboratively define the crop timeline, covering planting dates, harvest dates, and phenological stages, identify exposed elements (leaves, flowers, fruits, agronomic operations, etc.), and characterize the risks associated with water deficit or surplus, predetermined via crop coefficients (Kc), as well as temperatures exceeding the species-specific optimal thresholds. Subsequently, each ATD is integrated into Agrorac to enable spatial detection of agroclimatic threats and precise identification of the associated risks. 2.3.5. Analytical Framework and Key Functionalities Agrorac provides a suite of functionalities for data visualization, analysis, and risk assessment through its specialized modules. Area of Study Definition Users can define their area of interest through three methods: By Municipality: Selecting a department and a specific municipality from dropdown menus in Colombia. By User-Defined Area (Shapefile): Uploading a custom shapefile (requiring .shp, .shx, and .dbf files). By Point Selection: Clicking a point on an interactive map, which generates a square study area of 2 km side centered on the selected point. Once selected, the area is visualized using static (ggplot2) and interactive (leaflet) maps. Soil Properties Analysis The \"Soil Module\" offers functionalities for understanding soil characteristics: Official Soil Data Visualization: Users can visualize spatial information for soil properties (e.g., available water, clay, sand, silt fractions) for areas within the Antioquia department, based on preloaded official raster data. Hydro-Physical Parameter Estimation: For specific plots or farms, users can input detailed soil physics data (e.g., textural class, sand, clay, organic matter, salinity, gravel, compaction, depth of A horizon). The application then calculates and displays estimated hydro-physical parameters such as field capacity, permanent wilting point, saturation, and available water, along with a water retention curve. Climatic and Evapotranspiration Visualization The \"Climate Module\" allows users to consult and visualize climatic data: Climatology: Displays monthly multiannual averages (1991–2020) for precipitation, maximum, mean, and minimum temperatures. Users select the variable, month, and coordinate system, generating maps (static and interactive), descriptive statistics (mean, median, standard deviation, min, Q1, Q3, max), and frequency analysis (histograms). Climate Projections: Visualizes monthly climate projections from IDEAM for precipitation and temperatures up to six months in advance. Similar to climatology, outputs include maps, descriptive statistics, and frequency analysis. Reference Evapotranspiration (ET 0 ): Calculates and visualizes ETo for the selected study area, based on the resampled DEM. This includes maps and descriptive statistics. Productive System Characterization The \"Productive Systems Module\" allows for the management and visualization of crop-specific data: Loading Productive Systems: Users can select a preloaded system. Vulnerability Visualization: For a loaded system, an interactive matrix displays vulnerabilities and associated risks (excess or deficit) for specific exposed elements (e.g., roots, leaves, cultural operations) across the crop cycle, enabling the identification of critical periods. Water Requirement (Kc) Visualization: The module calculates and graphically displays the crop coefficient (Kc) curve over the productive cycle, showing the crop's water requirements at different phenological stages. Agroclimatic Risk Assessment This core functionality integrates all previously loaded data to perform a comprehensive risk analysis: Input Parameters: Users select the planting date or phase start, the desired soil water retention capacity (qualitative: high/low; or quantitative: based on Antioquia soil study or user-input physics data), and the study area. Risk Calculation: The system estimates the optimal precipitation range (minimum and maximum precipitation required for the crop, Ppmin and Ppmax), based on the crop's Kc and the soil's water retention capacity. It then compares this optimal range with the actual precipitation offer (either from climatology or IDEAM projections). Water Deficit: Occurs when precipitation is below Ppmin. Water Excess: Occurs when precipitation is above Ppmax. Output: The analysis generates: Maps of optimal precipitation range. Maps of water threat, thematicized by excess (blue), deficit (red), or no threat. A bar chart showing the percentage distribution of threat across the study area. A detailed table of latent agroclimatic risks, identifying exposed elements, detected threats, and the percentage of the area under risk. Report Generation A key feature of Agrorac is the ability to generate a detailed report of the agroclimatic risk analysis in Microsoft Word format. This report includes the threat map, risk table, and all relevant findings, facilitating documentation and communication of results to stakeholders. 2.3.6. User Interface Design The UI is designed to be clear, simple, and intuitive, adhering to principles that minimize complexity for end-users. Navigation elements, such as menus and buttons, are easily recognizable and accessible, allowing for a logical and natural flow through the interface. Customization with HTML and CSS is possible for enhanced visual appearance, and additional Shiny packages are integrated to extend interactivity and aesthetic appeal. 3. Results To demonstrate the functionality of AGRORAC, we designed two hypothetical case studies in Colombia. The first focused on the banana agrosystem in the municipality of Montenegro (Quindío Department). The second was set in the municipality of Mosquera (Cundinamarca Department). In the first case, a technical assistant (AGRORAC user) preemptively assessed the probability of agroclimatic risk. This involves evaluating the likelihood that weather and soil conditions will negatively impact crop production for a banana cultivar, Dominico Hartón (Musa AAB Simmonds), planted on 1 March 2025. To begin, the user enters the Agrorac URL ( https://agroclimatica.agrosavia.co/ ) (Fig. 2a) and selects the plot location in the Study Area module. This was done by choosing a georeferenced point (4.544276 N, − 75.785327 W) (Fig. 2b). Alternatively, the user can upload a shapefile, a digital map file format, or select the entire municipality (Fig. 2c). Next, in the Soil module, the user entered known edaphic (soil-related) parameters (Fig. 2d). These included loam texture, 8% organic matter, and a 40 cm A-horizon depth (the topsoil layer). Agrorac then calculated soil metrics based on this information. These included the permanent wilting point (the soil moisture level at which plants can no longer obtain water, in %), field capacity (the amount of soil moisture or water content held in the soil after excess water has drained, in %), saturation (the point at which soil pores are filled with water, in %), and plant-available water (the portion of water in soil that can be readily absorbed by plant roots, in mm) for the soil profile (Fig. 2e). Subsequently, in the Climate module, the user requested the precipitation forecast for September 2025, corresponding to the flower differentiation stage, which occurs seven months after establishment (Fig. 2f). The user obtained a forecast of 153 ± 2.63 mm. Next, in the Productive Systems module, they loaded the Agroclimatic Technical Datasheet (ATD), a summary of crop-specific environmental requirements and sensitivities, for banana cv. Dominico Harton (Fig. 2g). This action enabled Agrorac to provide further crop-specific information on general characteristics, developmental-stage vulnerability (the crop's sensitivity to climate at each growth stage), and water requirement, as determined by the crop coefficient (Kc, a factor used to estimate the water needs of a crop). In the Risk Analysis module, the user selected month 7 for review. The results included maps showing the optimal rainfall range for plant growth, a September rainfall forecast, a map highlighting areas at risk of excess water, and a list of primary risks associated with that growth stage (Fig. 2h). The Figure indicates that the location surveyed faces an excess water hazard, with projected precipitation levels exceeding optimal thresholds for flower development. In the second case, an agronomist advises on a specific cultivar of potatoes. To preemptively assess agroclimatic risks for the tuber-filling stage of a crop planted on July 15, 2025, across several farms in Mosquera (Cundinamarca Department), he uses the Study Area module, selecting “by municipality” (Mosquera)—although uploading a shapefile or drawing the area are alternatives. In the Soil module, he enters a sandy-loam texture, 2.3% organic matter, and a 30 cm A-horizon depth. AGRORAC then estimates relevant soil parameters (permanent wilting point, field capacity, saturation, plant-available water), producing a soil profile with low moisture-retention capacity. In the Climate module, he requested the precipitation projection for November 2025 (five months after crop establishment, corresponding to the tuber-filling phase—a key growth stage), obtaining approximately 97.1 mm. In the Productive Systems module, he loaded the potato cv. Diacol capiro entry is preconfigured in the Agroclimatic Technical Datasheet (ATD), a document specific to Cundinamarca that outlines crop-specific climate requirements. In the Risk Analysis module, he entered the sowing date and set month 5 as the analysis period. The system then generated spatial maps showing the optimum precipitation range for potato growth and the November rainfall forecast. Next, it produced a water-deficit/excess threat map, indicating risk areas for too little or too much rain across the municipality, and highlighted crop-specific risks during the tuber-filling stage. In this scenario, the water threat map for the municipality of Mosquera revealed a distinct pattern: approximately 1.4% of the territory exhibited risk of excess water (precipitation exceeding the optimal threshold for potato growth), primarily concentrated in the western sector of the municipality (Fig. 3 a). In contrast, 98.6% of the evaluated area remained within optimal hydric thresholds defined by the Agroclimatic Technical Datasheet, showing no signs of either deficit or excess (green zones). No portion of the study area displayed water-deficit threat (red zones, indicating rainfall below the optimal level) during this critical tuber-filling stage (Fig. 3 b). Both case studies presented here correspond to risk analyses for a single phenological window (a defined crop growth stage); however, to explore multiple scenarios. By varying the study site, sowing date, edaphic characteristics (soil-related properties such as texture, organic matter, A-horizon depth), and choosing between historical climatology (past weather data) or future projections, one can generate spatially explicit (location-based) risk diagnostics tailored to any phenological phase and local context, thus enabling truly personalized agronomic planning. (Fig. 4). Figure 4. Cashew producers, extensionists, instructors from the National Apprenticeship Service (SENA), and professionals from the Colombian Corporation for Agricultural Research (AGROSAVIA) defining vulnerability of the agrosystem in Puerto Carreño (Vichada) (a). Results of the elicitation process on the plantain production system in Chinchiná (Caldas) (b). 4. Discussion Crop models are indispensable tools for anticipating actions in diverse agronomic, climatic, and production scenarios. However, calibration can become a significant bottleneck, especially in countries like Colombia, which lack sufficient resources for experimentation. This challenge can limit the precision of model predictions and hinder the adoption of decision support systems (DSS). Seidel et al ( 2018 ) demonstrate that methodological heterogeneity and the lack of high-resolution field data can produce discrepancies among models and increase uncertainty in their outputs. Crop-model calibration involves adjusting parameters (e.g., phenological coefficients, growth rates, and responses to water and nutrient availability) so that simulations reproduce observed field data as closely as possible. In crop modeling, a range of calibration approaches exists, from manual empirical methods to automatic optimization algorithms, and many studies lack high-resolution datasets (such as detailed temporal yield series, precise phenological event dates, and local-station climate records). This combination of disparate methods and insufficient data yields widely divergent predictions among models that, in theory, describe the same crop under similar conditions, thereby significantly amplifying uncertainty in yield projections (Seidel et al., 2018 ). Similarly, Wallach et al. ( 2024 ) emphasize that the absence of standardized calibration protocols, clear procedures specifying which parameters to calibrate, which datasets to employ, which validation criteria to use, and how to report results, leads to inconsistent parameter estimates and impedes comparability across studies. This lack of uniformity obstructs rigorous cross-study comparisons and hinders the cumulative refinement of crop models. Ara et al. ( 2021 ) further note that much of the agricultural DSS landscape suffers from a “technology push” approach, in which advances in technological knowledge, whether substantial improvements to existing products or the development of entirely new technologies, drive innovation and bring these developments to market without explicit or structured demand from end users. Such tools also frequently omit clear quantification of uncertainty, diminishing both their tactical and strategic utility. Meanwhile, Iakovidis et al. ( 2024 ), through Q-methodology, reveal that DSS effectiveness and adoption also hinge on socio-technical factors: affordable cost, ongoing training, intuitive interfaces, and active stakeholder participation. In response to these challenges, Agrorac adopts a co-creation approach grounded in the conceptual framework of (Jakku & Thorburn, 2010 ), which identifies three critical elements for a successful DSS: (1) technological frames: Agrorac is developed through collaborative workshops that align the assumptions and objectives of researchers, technicians, and producers by collectively defining the components of Agroclimatic Technical Datasheets; (2) interpretative flexibility: the datasheets are designed to adapt to multiple use contexts, allowing each stakeholder to interpret and apply results based on their own experience; and (3) boundary objects: the datasheets serve as shared tools that translate scientific knowledge into practical parameters (planting dates, water requirements, identified vulnerabilities, and risk thresholds). 5. Conclusion Agrorac represents a progressive step forward in strengthening agroclimatic risk management in Colombia’s equatorial agroecosystems, which are characterized by a high dependence on rainfed agriculture, limited irrigation infrastructure, pronounced diurnal thermal variation, and short periods of intense climatic variability. These conditions make Agrorac’s capacity to deliver anticipatory knowledge particularly valuable in supporting agronomic decision-making. Its usability and accessibility oriented design allows technicians and field assistants in rural areas with limited technological infrastructure to incorporate climate and soil information into their planning processes. Agrorac has potential to complement conventional crop models, especially in contexts where precise model calibration still faces technical and data-related barriers. The tool enables the integration of multiple sources of information: climate, soil, phenology, and crop requirements, into a single operational platform, generating spatially explicit diagnostics that have shown practical utility in case studies. Moreover, its co-creation approach in the development of Agroclimatic Technical Datasheets (ATDs), which incorporate both scientific and local knowledge, enhances the contextual relevance of its outputs. While it is acknowledged that the tool’s accuracy depends on the quality of the input data and climate projections used, Agrorac constitutes a foundation upon which to continue building a more robust tool for climate-smart agricultural landscape management in Colombia. To achieve this, it will be necessary to improve its ability to assess output certainty, leverage available global and regional climate and soil databases, and expand the number of Agroclimatic Technical Datasheets covering the country’s main cropping systems. Declarations Competing interests: The authors have no relevant financial or non-financial interests to disclose Funding: This work was supported by the Orinoquia Biocarbono Project (World Bank) (Consultancy 065 of 2022) and the project Agroantioquia Exporta 4.0 Phase 3 – Code 1002653: Automated Agroclimatic Technical Datasheet for Hass Avocado in Antioquia.” Author Contribution A.M.C.M: Conceptualization, Methodology, Data curation, Validation, Writing original draft, Writing review & editing. G.A.G.V: Conceptualization, Methodology, Software, Data curation, Validation, Writing original draft, Writing review & editing. L.F.G.G: Conceptualization, Methodology, Validation, Writing, review & editing.D.L.C.M: Methodology, Writing original draft. J.H.B.R. Methodology, Writing review & editing. A.J.P.Q: Conceptualization, Methodology, Validation, Writing original draft, Writing review & editing. All authors read and approved the final manuscript. All authors contributed to the study conception, design, and reviewed the manuscript. Acknowledgement The authors acknowledge the Ministry of Agriculture and Rural Development of Colombia for its support. Special recognition is extended to Dr. Néstor Miguel Riaño Herrera for his contribution to the initial conceptualization of the Agrorac information system. The authors also acknowledge the valuable contributions of Bernardo Mejía, Lucas Cano, Mauricio Londoño, and Paula Andrea Aguilar, and the support of Claudia Patricia Rendón in the logistics and content development of the co-creation workshops for the Agroclimatic Technical Datasheets. Data availability The datasets generated during and/or analysed during the current study are not publicly available due to they are protected under the copyright of AGROSAVIA but are available on reasonable request. References Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. 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Eur J Agron 94(January):25–35. https://doi.org/10.1016/j.eja.2018.01.006 Sotelo S, Guevara E, Llanos-Herrera L, Agudelo D, Esquivel A, Rodriguez J, Ordoñez L, Mesa J, Muñoz Borja LA, Howland F, Amariles S, Rojas A, Valencia JJ, Segura CC, Grajales F, Hernández F, Cote F, Saavedra E, Ruiz F, Ramirez-Villegas J (2020) Pronosticos AClimateColombia: A system for the provision of information for climate risk reduction in Colombia. Comput Electron Agric 174:105486. https://doi.org/10.1016/j.compag.2020.105486 Wallach D, Buis S, Seserman DM, Palosuo T, Thorburn PJ, Mielenz H, Justes E, Kersebaum KC, Dumont B, Launay M, Seidel SJ (2024) A calibration protocol for soil-crop models. Environ Model Softw 180(July):106147. https://doi.org/10.1016/j.envsoft.2024.106147 Additional Declarations No competing interests reported. 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14:10:46\",\"extension\":\"png\",\"order_by\":26,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":3337,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/676db39b1a2a500d9712d8fc.png\"},{\"id\":93841941,\"identity\":\"fc9a159e-daae-4763-b0da-c36fa8d5e90f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-18 14:10:46\",\"extension\":\"xml\",\"order_by\":27,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":96838,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"c5583ab42b594317a99d51ec5dd7c6fb1structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/781c460bec64d316b27883e1.xml\"},{\"id\":93841945,\"identity\":\"3e1731c0-29e2-4167-a5c0-f748a7328a99\",\"added_by\":\"auto\",\"created_at\":\"2025-10-18 14:10:46\",\"extension\":\"html\",\"order_by\":28,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":105013,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/475bc148c0e4dac3e3718af9.html\"},{\"id\":93841916,\"identity\":\"545eddfe-9f7c-4350-8e8c-48b535c80387\",\"added_by\":\"auto\",\"created_at\":\"2025-10-18 14:10:46\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1678690,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAGRORAC´s database integration containing geopolitical (a), digital elevation model (b), climate (c), and soil (d) data for threat definition\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/4ba2d2a385bbccf8e7e57a00.png\"},{\"id\":93841920,\"identity\":\"67314cb9-9543-473b-acc3-851f64ab542e\",\"added_by\":\"auto\",\"created_at\":\"2025-10-18 14:10:46\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":565859,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAGRORAC outputs show the main page (a), location of the plot (b, c), soil parameters (d, e), climate threat (f), agroecosystem susceptibility (g), and main risks for the territory (municipality) (h).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/3c2efdda6636d3c1d8c001b4.png\"},{\"id\":93843534,\"identity\":\"ee2ad1b9-76a7-4cdf-ba55-098b0785dd65\",\"added_by\":\"auto\",\"created_at\":\"2025-10-18 14:26:46\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":290623,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial report of \\u0026nbsp;\\u0026nbsp;precipitation extremes (mm) threat in the analyzed territory for potato cv. \\u0026nbsp;\\u0026nbsp;Diacol Capiro (a), and report of the main risks associated with this threat, \\u0026nbsp;\\u0026nbsp;in this case water-excess risk for the crop, based on the November 2025 \\u0026nbsp;\\u0026nbsp;rainfall projection (b).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/e9abf886cbb216d2f193e6d0.png\"},{\"id\":93842539,\"identity\":\"b0a72864-eafb-4b45-ac57-dfbee504bdf7\",\"added_by\":\"auto\",\"created_at\":\"2025-10-18 14:18:46\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":926596,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCashew producers, extensionists, instructors from the National Apprenticeship Service (SENA), and professionals from the Colombian Corporation for Agricultural Research (AGROSAVIA) defining vulnerability of the agrosystem in Puerto Carreño (Vichada) (a). Results of the elicitation process on the plantain production system in Chinchiná (Caldas) (b).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/041cadf6db66e90ac72204bd.png\"},{\"id\":97136117,\"identity\":\"8ed257bf-353e-4c38-b0f8-842956ee1061\",\"added_by\":\"auto\",\"created_at\":\"2025-12-01 09:55:40\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3870464,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7457303/v1/bd44b478-fa81-49af-8e7b-2f78337976ab.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Agrorac: A web-based tool focused on knowledge management for identifying agroclimatic risks in equatorial agrosystems\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eAccording to Henry (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e), northern South America, specifically Colombia, Venezuela, Ecuador, the northern states of Brazil, and the north of Peru, is part of the equatorial region. This area is known for its unique climatic characteristics, primarily due to slight variations in air temperature and day length throughout the year. Additionally, the variation in global solar radiation in this region is influenced more by the presence or absence of cloud cover than by the position of the Sun throughout the annual cycle. In the equatorial region, where rainfed agricultural production predominates, variations in rainfall determine crop management, including planting and harvesting times (Gallo et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Ocampo et al., 2025). Furthermore, in tropical high-Andean areas, including equatorial zones, frost events become widespread during the dry seasons of the driest years (Pouteau et al., 2011), resulting in a higher loss of agricultural production (G\\u0026oacute;mez et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Therefore, low air temperature variations can also enable year-round crop production, but this is only feasible in limited areas.\\u003c/p\\u003e\\u003cp\\u003eWhile low air temperature variations can allow for year-round crop production, this is only feasible in limited areas. In Colombia, continuous production can occur in the 1,100,000 hectares of existing irrigation districts, just a tiny fraction of the nearly 18,500,000 hectares with irrigation potential (MADR, 2020). Furthermore, according to the Ministry of Agriculture and Rural Development (MADR), the current state of these irrigation districts is not satisfactory. Given this context and the lack of initiatives for constructing new irrigation districts, ADR) has promoted the establishment of Technical Agroclimatic Committees (TAC). One of the main functions of these committees is to develop tools and strategies aimed at mitigating the impacts caused by extreme rainfall variability (Loboguerrero et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). TACs strengthen the capacity of producers and other agricultural decision-makers to effectively use climate forecasts. This anticipatory knowledge of environmental conditions enables them to identify rainfall-related threats and design optimal crop and livestock management strategies, thus reducing the risk of failing to meet production goals due to fluctuations in precipitation.\\u003c/p\\u003e\\u003cp\\u003eRecognizing hazards is just the first step; it is equally important to understand how sensitive and vulnerable production systems are to the impact of weather. While crop models are used in various agricultural contexts to evaluate the sensitivity and susceptibility of cultivated species (Sotelo et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), not all crop species at the national level have calibrated or adjusted models (Mar\\u0026iacute;n et al., 2023). This gap in adjusted and parameterized crop models makes the TAC even more relevant at the regional (department) scale. TACs bring together individuals involved in agricultural activities (experts) to share their experiences in identifying potential risks associated with future rainfall scenarios, specifically by assessing the sensitivity of their systems to extreme rainfall events. This strategy's challenge involves assessing the system's risk and vulnerability as an elicitation product, following the methodology outlined by Loboguerrero et al. (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIt is vital to ensure that each TAC meeting, held monthly in every territory, includes at least one expert, be it a farmer, technician, or professional specialized in the relevant agricultural system (FAO, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). However, dependence on a single individual to evaluate the sensitivity of a production system can lead to significant uncertainties in our assessments (Pe\\u0026ntilde;a Qui\\u0026ntilde;ones et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Moreover, the absence of experts for specific agricultural systems at these meetings often results in missed opportunities for critical risk evaluation. To tackle this pressing challenge and elevate the TAC's influence as a key platform for disseminating agrometeorological information, we have developed Agrorac a powerful web-based tool designed to identify agroclimatic risks and facilitate the formulation of targeted adaptation strategies and measures. This innovative solution enables stakeholders to make informed decisions, thereby enhancing their resilience in the face of climate-related challenges.\\u003c/p\\u003e\\u003cp\\u003eAgrorac identifies projected climate-related risks for agroecosystems, primarily water excess or deficit and air temperatures outside physiological ranges, using future precipitation and temperature scenarios. These risks are parametrized in advance through the co-creation of Agroclimatic Technical Datasheets (ATD), compiled from secondary sources (scientific literature, official databases) and the expert knowledge of local technicians, agronomists, extension agents, and producers (Pe\\u0026ntilde;a Qui\\u0026ntilde;ones et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eFor each phenological stage and cultural operation, from land preparation through harvest and, where applicable, post-harvest, the ATD specifies (i) the exposed elements (crop, infrastructure, inputs) and (ii) the associated risks, such as pest and disease outbreaks or nutrient leaching under excessive rainfall. To define hydric risks, the datasheet combines the crop\\u0026rsquo;s water requirement (stage-specific \\u003cem\\u003eKc\\u003c/em\\u003e coefficient) with the soil\\u0026rsquo;s water-holding capacity; consequently, a given projected rainfall event may be classified as a deficit or an excess depending on soil type and crop stage. Analogously, thermal thresholds are set using each species\\u0026rsquo; cardinal temperatures (minimum, optimum, and maximum); any projection outside this range is considered potentially detrimental to productivity.\\u003c/p\\u003e\\u003cp\\u003eOnce uploaded to the platform, the ATD allows Agrorac to overlay climate scenarios with system tolerances and produce spatially explicit hazard and vulnerability maps. These outputs offer objective, reproducible inputs that enable TACs and other stakeholders to prioritize interventions and, based on local context, crop, and management practices, devise the most appropriate adaptation or mitigation strategies, thereby reducing the uncertainty associated with single expert assessments.\\u003c/p\\u003e\\u003cp\\u003eIn addition to pinpointing agroclimatic risks, Agrorac serves as a strategic opportunity explorer for producers. By cross-referencing future precipitation scenarios with the conventional crop calendar, the platform can expose \\u0026ldquo;non-traditional\\u0026rdquo; windows of optimal moisture. Using these insights, farmers can make datadriven decisions to advance or delay sowing relative to conventional planting dates. Planting outside the standard calendar when timely rainfall is forecast enables harvest scheduling during atypical windows, capturing niche markets or avoiding supply gluts, and ultimately reducing reliance on historical climate patterns that may shift under increasing variability.\\u003c/p\\u003e\"},{\"header\":\"2. Methodology\",\"content\":\"\\u003cp\\u003eThe development of Agrorac a web-based tool for identifying agroclimatic risks, was approached through a structured methodology encompassing system architecture design, selection of core technologies, integration of diverse geospatial and agricultural data, and implementation of advanced analytical procedures. This section details the technical specifications, data sources, and computational models employed.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1. System Overview\\u003c/h2\\u003e\\u003cp\\u003eAgrorac is an interactive web application designed to facilitate the evaluation of agroclimatic risks faced by specific productive systems, particularly those related to water excess and/or deficit, and air temperatures outside physiological ranges in user-defined areas. The tool integrates data on soil characteristics, historical climatology (1991\\u0026ndash;2020), and climate projections (up to six months ahead from IDEAM), alongside detailed information about productive systems, their water requirements (Kc coefficients), and associated vulnerabilities. The primary objective is to provide farmers, extensionists, agro-technical personnel, and researchers in Colombia with critical, spatially explicit information to make informed decisions and mitigate climate-related risks.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Architecture and Development Environment\\u003c/h2\\u003e\\u003cp\\u003eThe application's architecture is robust and scalable, leveraging powerful tools for scientific computing and interactive web development.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.1. Core Technologies\\u003c/h2\\u003e\\u003cp\\u003eAgrorac was developed using R version 4.4.0 (2024-04-24) as the primary programming language. The Shiny framework (version 1.8.1.1) was chosen for building the web application due to its strong integration with R's statistical and scientific computing capabilities, facilitating the creation of complex statistical, geostatistical models, and advanced visualizations without requiring extensive knowledge of HTML, CSS, or JavaScript. Shiny's ability to support real-time dynamic responses, update graphics and tables without page reloading, and offer a wide range of customizable widgets further enhanced user experience and application functionality.\\u003c/p\\u003e\\u003cp\\u003eKey R packages utilized in the development include are listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e:\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eR Libraries Utilized in the Development of the Agrorac Web Application\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" 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widgets.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eShinycssloaders\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAdds loading indicators to Shiny elements.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eggplot2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.5.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eGraphics system based on the grammar of graphics.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003egridExtra\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFunctions for arranging multiple plots on a single panel.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDplyr\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.1.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTools for efficient data manipulation and transformation.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDT\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eEnables the creation of interactive tables in Shiny.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eReadxl\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.4.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eFacilitates reading Excel files (.xls and .xlsx).\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ecowplot\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eEnhances the organization and presentation of plots with ggplot2.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFields\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e15.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTools for spatial analysis and interpolation functions.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMaps\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.4.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eProvides support for map visualization in R.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003elubridate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.9.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSimplifies date and time handling and manipulation.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRaster\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.6\\u0026ndash;26\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eProcesses and analyzes spatial data in raster format.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSf\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.0\\u0026ndash;16\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHandles spatial data in simple features format.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSp\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.1-4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDefines classes and methods for spatial data.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLeaflet\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.2.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eEnables the creation of interactive maps in R.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eleaflegend\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.2.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAdds custom legends to Leaflet maps.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003erworldxtra\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eProvides additional high-resolution geospatial data for maps.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eggspatial\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.1.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eImproves the visualization of spatial data within ggplot2.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGstat\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSupports spatial statistics and geostatistics.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eutf8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.2.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eManages and encodes UTF-8 character strings in R.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCar\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.1-2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eProvides advanced statistical analysis and regression tools.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlotly\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.10.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eGenerates interactive graphics in R.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePng\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.1-8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eReads and writes PNG image files.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003efontawesome\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.5.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAllows the use of FontAwesome icons in Shiny applications.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ermarkdown\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTools for dynamic report generation in various formats.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ehtmlwidgets\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.6.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eIntegrates HTML widgets into R.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eKnitr\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.46\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAutomates report generation in R.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOfficer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.6.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eCreates and edits Microsoft Word and PowerPoint documents.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eofficedown\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eExtends officer for advanced Word report generation.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003epandoc\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.2.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDocument conversion tool between different formats.\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.2. Application Structure and Components\\u003c/h2\\u003e\\u003cp\\u003eAgrorac's structure follows a typical web application design, divided into a frontend (UI) and a backend (server logic). The UI defines the application's appearance and user interaction using Shiny functions like fluidPage(), sidebarLayout(), sidebarPanel(), and mainPanel(), which organize interactive elements such as sliders, text boxes, dropdowns, and checkboxes. The backend, implemented in R, manages data processing, analysis, and dynamic updates of output elements based on user input, facilitated by input and output objects.\\u003c/p\\u003e\\u003cp\\u003eThe application's source code is organized into two main R files: app.R, which contains the UI and server logic, and FuncionesR which holds auxiliary functions for geospatial, climatic, and productive data processing. Data are stored in dedicated folders, including Climatolog\\u0026iacute;a, DEM, Municipios_Colombia_shp, Proyecciones, Sistemas_Productivos, and Suelos_Antioquia, and are loaded into the application's internal data collection at startup, ensuring rapid and efficient access. AGRORAC does not use a traditional database engine due to the nature and organization of the information it processes and displays.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.2.3. Deployment\\u003c/h2\\u003e\\u003cp\\u003eThe application is deployed on Shinyapps.io with a standard license. Furthermore, a Dockerization process has been implemented to facilitate deployment on corporate servers for production environments, enhancing scalability and responsiveness for multiple simultaneous users.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3. Data Sources and Management\\u003c/h2\\u003e\\u003cp\\u003eAgrorac integrates various types of geospatial and agricultural data, pre-processed and structured for efficient analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.1. Geospatial Base Data\\u003c/h2\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eColombia's Political Division: A digital file in .SHP format representing the current political division of Colombia at department and municipality scales, sourced from the official IGAC geoportal (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://geoportal.igac.gov.co/contenido/datos-abiertos-cartografia-y-geografia\\u003c/span\\u003e\\u003cspan address=\\\"https://geoportal.igac.gov.co/contenido/datos-abiertos-cartografia-y-geografia\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). This vector data, with Magna-Sirgas projection, is loaded once.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eDigital Elevation Model (DEM): A raster file (GeoTiff) of Colombia's digital elevation model, originally from the Shuttle Radar Topography Mission (SRTM) with 30m precision. To optimize computational time for real-time processing, the original DEM was resampled with an aggregation factor of 10 in both coordinates, resulting in a lower resolution (382.19m x 382.19m) but significantly smaller file size (68.9 MB from 7.64 GB). This resampled DEM (SRTM_600.tif) is critical for calculating reference evapotranspiration (ET\\u003csub\\u003e0\\u003c/sub\\u003e).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.2. Climatic Data\\u003c/h2\\u003e\\u003cp\\u003eAll climatic raster files (GeoTiff format) use geographical coordinates (WGS84).\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eClimatology Raster: Monthly multiannual averages (30 years, 1991\\u0026ndash;2020) for precipitation, maximum temperature, mean temperature, and minimum temperature are provided by IDEAM (Instituto de Hidrolog\\u0026iacute;a, Meteorolog\\u0026iacute;a y Estudios Ambientales de Colombia). These 12 monthly files (e.g., CLIMA_ENE_PREC.tif) are loaded once into the application unless IDEAM updates the official climatology.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eClimate Projections Raster: Monthly climate projections for precipitation, maximum temperature, mean temperature, and minimum temperature are also provided by IDEAM, with a forecasting horizon of up to six subsequent months. These files (e.g., PROYECCION AAAA-MM.tif) are updated monthly in the application.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.3. Soil Data\\u003c/h2\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eSoil Raster: This dataset contains information from soil surveys conducted at a departmental scale, obtained from the official IGAC website, and presented at a 1:100,000 scale. The study involved analyzing specific soil profiles to extract textural and practical root depth data. Typical values for the sand, silt, and clay soils were generated, assuming a 2.5% organic matter content. Using the equations described by Saxton \\u0026amp; Rawls (2006), soil hydraulic parameters such as water storage capacity (mm) were calculated. Four GeoTiff raster files (Agua aprovechable (mm), fracci\\u0026oacute;n de arcilla (%), fracci\\u0026oacute;n de arena (%), fracci\\u0026oacute;n de limo (%)) store this processed soil property data. Currently, this dataset only covers the department of Antioquia because funding was specifically allocated for this region; expansion to other regions will depend on the availability of additional resources.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eUser-Defined Soil Data: The application also allows users to input specific soil physics data (e.g., sand, clay, organic matter, salinity, gravel, compaction, horizon A depth) to estimate hydraulic parameters like field capacity, permanent wilting point, saturation, and available water, and to generate a customized water retention curve.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.4. Productive Systems Data\\u003c/h2\\u003e\\u003cp\\u003eAgroclimatic Technical Datasheets (ATDs) contain comprehensive information for each crop or productive system. These are stored as .xlsx files in the Sistemas_Productivos folder. For the current version of the Agrorac application, the tool is fundamentally designed to manage a diverse array of preloaded productive systems. Users can select from an extensive list of available crops to conduct their agroclimatic risk analyses. This list, provided for context and not explicitly detailed within the given sources, includes: Hass avocado in productive phase (Antioquia), Hass avocado in vegetative phase (Antioquia), Sugarcane for panela (Northeast Antioquia), Bush bean, Climbing bean, Tahiti lime (Meta - Piedemonte), Maize (Yond\\u0026oacute; - Antioquia), Cashew in establishment phase (Vichada), Cashew in productive phase (Vichada), Potato (Cundinamarca), Plantain (Quind\\u0026iacute;o), Soybean (Altillanura), and Carrot (Southwest Antioquia). This intrinsic capability to integrate various species and geographical territories is a cornerstone of Agrorac's architectural design and aligns with the application's long-term sustainability strategy, enabling future adaptation to additional species or territories as resources become available. Each ATD specifies:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eIdentification: Species, variety, geographical context, altitudinal range, and full cycle duration.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003ePhenology: Description of phenological stages with their duration in months.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eCrop Coefficient (Kc): Kc values for initial, vegetative, reproductive, and harvest stages.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eVulnerability: It includes detailed lists of exposed elements (plant parts, cultural activities) and associated risks for specific climate threats (excess/deficit) across phenological stages.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cem\\u003eCo-creation Methodology for Vulnerability and Risk in Agroclimatic Technical Datasheets\\u003c/em\\u003e:\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eThrough co-creation sessions of Agroclimatic Technical Datasheets (ATDs), researchers, technical specialists, agronomists, extension agents, and producers collaboratively define the crop timeline, covering planting dates, harvest dates, and phenological stages, identify exposed elements (leaves, flowers, fruits, agronomic operations, etc.), and characterize the risks associated with water deficit or surplus, predetermined via crop coefficients (Kc), as well as temperatures exceeding the species-specific optimal thresholds. Subsequently, each ATD is integrated into Agrorac to enable spatial detection of agroclimatic threats and precise identification of the associated risks.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.5. Analytical Framework and Key Functionalities\\u003c/h2\\u003e\\u003cp\\u003eAgrorac provides a suite of functionalities for data visualization, analysis, and risk assessment through its specialized modules.\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cem\\u003eArea of Study Definition\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eUsers can define their area of interest through three methods:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eBy Municipality: Selecting a department and a specific municipality from dropdown menus in Colombia.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eBy User-Defined Area (Shapefile): Uploading a custom shapefile (requiring .shp, .shx, and .dbf files).\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eBy Point Selection: Clicking a point on an interactive map, which generates a square study area of 2 km side centered on the selected point. Once selected, the area is visualized using static (ggplot2) and interactive (leaflet) maps.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cem\\u003eSoil Properties Analysis\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe \\\"Soil Module\\\" offers functionalities for understanding soil characteristics:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eOfficial Soil Data Visualization: Users can visualize spatial information for soil properties (e.g., available water, clay, sand, silt fractions) for areas within the Antioquia department, based on preloaded official raster data.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eHydro-Physical Parameter Estimation: For specific plots or farms, users can input detailed soil physics data (e.g., textural class, sand, clay, organic matter, salinity, gravel, compaction, depth of A horizon). The application then calculates and displays estimated hydro-physical parameters such as field capacity, permanent wilting point, saturation, and available water, along with a water retention curve.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cem\\u003eClimatic and Evapotranspiration Visualization\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe \\\"Climate Module\\\" allows users to consult and visualize climatic data:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eClimatology: Displays monthly multiannual averages (1991\\u0026ndash;2020) for precipitation, maximum, mean, and minimum temperatures. Users select the variable, month, and coordinate system, generating maps (static and interactive), descriptive statistics (mean, median, standard deviation, min, Q1, Q3, max), and frequency analysis (histograms).\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eClimate Projections: Visualizes monthly climate projections from IDEAM for precipitation and temperatures up to six months in advance. Similar to climatology, outputs include maps, descriptive statistics, and frequency analysis.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eReference Evapotranspiration (ET\\u003csub\\u003e0\\u003c/sub\\u003e): Calculates and visualizes ETo for the selected study area, based on the resampled DEM. This includes maps and descriptive statistics.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cem\\u003eProductive System Characterization\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe \\\"Productive Systems Module\\\" allows for the management and visualization of crop-specific data:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eLoading Productive Systems: Users can select a preloaded system.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eVulnerability Visualization: For a loaded system, an interactive matrix displays vulnerabilities and associated risks (excess or deficit) for specific exposed elements (e.g., roots, leaves, cultural operations) across the crop cycle, enabling the identification of critical periods.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eWater Requirement (Kc) Visualization: The module calculates and graphically displays the crop coefficient (Kc) curve over the productive cycle, showing the crop's water requirements at different phenological stages.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cem\\u003eAgroclimatic Risk Assessment\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis core functionality integrates all previously loaded data to perform a comprehensive risk analysis:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eInput Parameters: Users select the planting date or phase start, the desired soil water retention capacity (qualitative: high/low; or quantitative: based on Antioquia soil study or user-input physics data), and the study area.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eRisk Calculation: The system estimates the optimal precipitation range (minimum and maximum precipitation required for the crop, Ppmin and Ppmax), based on the crop's Kc and the soil's water retention capacity. It then compares this optimal range with the actual precipitation offer (either from climatology or IDEAM projections).\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eWater Deficit: Occurs when precipitation is below Ppmin.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eWater Excess: Occurs when precipitation is above Ppmax.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eOutput: The analysis generates:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eMaps of optimal precipitation range.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eMaps of water threat, thematicized by excess (blue), deficit (red), or no threat.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eA bar chart showing the percentage distribution of threat across the study area.\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eA detailed table of latent agroclimatic risks, identifying exposed elements, detected threats, and the percentage of the area under risk.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cem\\u003eReport Generation\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eA key feature of Agrorac is the ability to generate a detailed report of the agroclimatic risk analysis in Microsoft Word format. This report includes the threat map, risk table, and all relevant findings, facilitating documentation and communication of results to stakeholders.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.3.6. User Interface Design\\u003c/h2\\u003e\\u003cp\\u003eThe UI is designed to be clear, simple, and intuitive, adhering to principles that minimize complexity for end-users. Navigation elements, such as menus and buttons, are easily recognizable and accessible, allowing for a logical and natural flow through the interface. Customization with HTML and CSS is possible for enhanced visual appearance, and additional Shiny packages are integrated to extend interactivity and aesthetic appeal.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eTo demonstrate the functionality of AGRORAC, we designed two hypothetical case studies in Colombia. The first focused on the banana agrosystem in the municipality of Montenegro (Quind\\u0026iacute;o Department). The second was set in the municipality of Mosquera (Cundinamarca Department). In the first case, a technical assistant (AGRORAC user) preemptively assessed the probability of agroclimatic risk. This involves evaluating the likelihood that weather and soil conditions will negatively impact crop production for a banana cultivar, Dominico Hart\\u0026oacute;n (Musa AAB Simmonds), planted on 1 March 2025.\\u003c/p\\u003e\\n\\u003cp\\u003eTo begin, the user enters the Agrorac URL (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://agroclimatica.agrosavia.co/\\u003c/span\\u003e\\u003c/span\\u003e) (Fig.\\u0026nbsp;2a) and selects the plot location in the Study Area module. This was done by choosing a georeferenced point (4.544276 N, \\u0026minus;\\u0026thinsp;75.785327 W) (Fig.\\u0026nbsp;2b). Alternatively, the user can upload a shapefile, a digital map file format, or select the entire municipality (Fig.\\u0026nbsp;2c).\\u003c/p\\u003e\\n\\u003cp\\u003eNext, in the Soil module, the user entered known edaphic (soil-related) parameters (Fig.\\u0026nbsp;2d). These included loam texture, 8% organic matter, and a 40 cm A-horizon depth (the topsoil layer). Agrorac then calculated soil metrics based on this information. These included the permanent wilting point (the soil moisture level at which plants can no longer obtain water, in %), field capacity (the amount of soil moisture or water content held in the soil after excess water has drained, in %), saturation (the point at which soil pores are filled with water, in %), and plant-available water (the portion of water in soil that can be readily absorbed by plant roots, in mm) for the soil profile (Fig.\\u0026nbsp;2e). Subsequently, in the Climate module, the user requested the precipitation forecast for September 2025, corresponding to the flower differentiation stage, which occurs seven months after establishment (Fig.\\u0026nbsp;2f). The user obtained a forecast of 153\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.63 mm. Next, in the Productive Systems module, they loaded the Agroclimatic Technical Datasheet (ATD), a summary of crop-specific environmental requirements and sensitivities, for banana cv. Dominico Harton (Fig.\\u0026nbsp;2g). This action enabled Agrorac to provide further crop-specific information on general characteristics, developmental-stage vulnerability (the crop\\u0026apos;s sensitivity to climate at each growth stage), and water requirement, as determined by the crop coefficient (Kc, a factor used to estimate the water needs of a crop).\\u003c/p\\u003e\\n\\u003cp\\u003eIn the Risk Analysis module, the user selected month 7 for review. The results included maps showing the optimal rainfall range for plant growth, a September rainfall forecast, a map highlighting areas at risk of excess water, and a list of primary risks associated with that growth stage (Fig. 2h). The Figure indicates that the location surveyed faces an excess water hazard, with projected precipitation levels exceeding optimal thresholds for flower development.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the second case, an agronomist advises on a specific cultivar of potatoes. To preemptively assess agroclimatic risks for the tuber-filling stage of a crop planted on July 15, 2025, across several farms in Mosquera (Cundinamarca Department), he uses the Study Area module, selecting \\u0026ldquo;by municipality\\u0026rdquo; (Mosquera)\\u0026mdash;although uploading a shapefile or drawing the area are alternatives. In the Soil module, he enters a sandy-loam texture, 2.3% organic matter, and a 30 cm A-horizon depth. AGRORAC then estimates relevant soil parameters (permanent wilting point, field capacity, saturation, plant-available water), producing a soil profile with low moisture-retention capacity.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the Climate module, he requested the precipitation projection for November 2025 (five months after crop establishment, corresponding to the tuber-filling phase\\u0026mdash;a key growth stage), obtaining approximately 97.1 mm. In the Productive Systems module, he loaded the potato cv. Diacol capiro entry is preconfigured in the Agroclimatic Technical Datasheet (ATD), a document specific to Cundinamarca that outlines crop-specific climate requirements.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the Risk Analysis module, he entered the sowing date and set month 5 as the analysis period. The system then generated spatial maps showing the optimum precipitation range for potato growth and the November rainfall forecast. Next, it produced a water-deficit/excess threat map, indicating risk areas for too little or too much rain across the municipality, and highlighted crop-specific risks during the tuber-filling stage.\\u003c/p\\u003e\\n\\u003cp\\u003eIn this scenario, the water threat map for the municipality of Mosquera revealed a distinct pattern: approximately 1.4% of the territory exhibited risk of excess water (precipitation exceeding the optimal threshold for potato growth), primarily concentrated in the western sector of the municipality (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea). In contrast, 98.6% of the evaluated area remained within optimal hydric thresholds defined by the Agroclimatic Technical Datasheet, showing no signs of either deficit or excess (green zones). No portion of the study area displayed water-deficit threat (red zones, indicating rainfall below the optimal level) during this critical tuber-filling stage (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb).\\u003c/p\\u003e\\n\\u003cp\\u003eBoth case studies presented here correspond to risk analyses for a single phenological window (a defined crop growth stage); however, to explore multiple scenarios. By varying the study site, sowing date, edaphic characteristics (soil-related properties such as texture, organic matter, A-horizon depth), and choosing between historical climatology (past weather data) or future projections, one can generate spatially explicit (location-based) risk diagnostics tailored to any phenological phase and local context, thus enabling truly personalized agronomic planning. (Fig. 4).\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 4. Cashew producers, extensionists, instructors from the National Apprenticeship Service (SENA), and professionals from the Colombian Corporation for Agricultural Research (AGROSAVIA) defining vulnerability of the agrosystem in Puerto Carre\\u0026ntilde;o (Vichada) (a). Results of the elicitation process on the plantain production system in Chinchin\\u0026aacute; (Caldas) (b).\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eCrop models are indispensable tools for anticipating actions in diverse agronomic, climatic, and production scenarios. However, calibration can become a significant bottleneck, especially in countries like Colombia, which lack sufficient resources for experimentation. This challenge can limit the precision of model predictions and hinder the adoption of decision support systems (DSS). Seidel et al (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) demonstrate that methodological heterogeneity and the lack of high-resolution field data can produce discrepancies among models and increase uncertainty in their outputs. Crop-model calibration involves adjusting parameters (e.g., phenological coefficients, growth rates, and responses to water and nutrient availability) so that simulations reproduce observed field data as closely as possible. In crop modeling, a range of calibration approaches exists, from manual empirical methods to automatic optimization algorithms, and many studies lack high-resolution datasets (such as detailed temporal yield series, precise phenological event dates, and local-station climate records). This combination of disparate methods and insufficient data yields widely divergent predictions among models that, in theory, describe the same crop under similar conditions, thereby significantly amplifying uncertainty in yield projections (Seidel et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eSimilarly, Wallach et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) emphasize that the absence of standardized calibration protocols, clear procedures specifying which parameters to calibrate, which datasets to employ, which validation criteria to use, and how to report results, leads to inconsistent parameter estimates and impedes comparability across studies. This lack of uniformity obstructs rigorous cross-study comparisons and hinders the cumulative refinement of crop models.\\u003c/p\\u003e\\u003cp\\u003eAra et al. (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) further note that much of the agricultural DSS landscape suffers from a \\u0026ldquo;technology push\\u0026rdquo; approach, in which advances in technological knowledge, whether substantial improvements to existing products or the development of entirely new technologies, drive innovation and bring these developments to market without explicit or structured demand from end users. Such tools also frequently omit clear quantification of uncertainty, diminishing both their tactical and strategic utility. Meanwhile, Iakovidis et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), through Q-methodology, reveal that DSS effectiveness and adoption also hinge on socio-technical factors: affordable cost, ongoing training, intuitive interfaces, and active stakeholder participation.\\u003c/p\\u003e\\u003cp\\u003eIn response to these challenges, Agrorac adopts a co-creation approach grounded in the conceptual framework of (Jakku \\u0026amp; Thorburn, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e), which identifies three critical elements for a successful DSS: (1) technological frames: Agrorac is developed through collaborative workshops that align the assumptions and objectives of researchers, technicians, and producers by collectively defining the components of Agroclimatic Technical Datasheets; (2) interpretative flexibility: the datasheets are designed to adapt to multiple use contexts, allowing each stakeholder to interpret and apply results based on their own experience; and (3) boundary objects: the datasheets serve as shared tools that translate scientific knowledge into practical parameters (planting dates, water requirements, identified vulnerabilities, and risk thresholds).\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eAgrorac represents a progressive step forward in strengthening agroclimatic risk management in Colombia\\u0026rsquo;s equatorial agroecosystems, which are characterized by a high dependence on rainfed agriculture, limited irrigation infrastructure, pronounced diurnal thermal variation, and short periods of intense climatic variability. These conditions make Agrorac\\u0026rsquo;s capacity to deliver anticipatory knowledge particularly valuable in supporting agronomic decision-making. Its usability and accessibility oriented design allows technicians and field assistants in rural areas with limited technological infrastructure to incorporate climate and soil information into their planning processes.\\u003c/p\\u003e\\u003cp\\u003eAgrorac has potential to complement conventional crop models, especially in contexts where precise model calibration still faces technical and data-related barriers. The tool enables the integration of multiple sources of information: climate, soil, phenology, and crop requirements, into a single operational platform, generating spatially explicit diagnostics that have shown practical utility in case studies. Moreover, its co-creation approach in the development of Agroclimatic Technical Datasheets (ATDs), which incorporate both scientific and local knowledge, enhances the contextual relevance of its outputs. While it is acknowledged that the tool\\u0026rsquo;s accuracy depends on the quality of the input data and climate projections used, Agrorac constitutes a foundation upon which to continue building a more robust tool for climate-smart agricultural landscape management in Colombia. To achieve this, it will be necessary to improve its ability to assess output certainty, leverage available global and regional climate and soil databases, and expand the number of Agroclimatic Technical Datasheets covering the country\\u0026rsquo;s main cropping systems.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003ch2\\u003eCompeting interests:\\u003c/h2\\u003e\\u003cp\\u003eThe authors have no relevant financial or non-financial interests to disclose\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding:\\u003c/h2\\u003e\\u003cp\\u003eThis work was supported by the Orinoquia Biocarbono Project (World Bank) (Consultancy 065 of 2022) and the project \\u003cem\\u003eAgroantioquia Exporta 4.0 Phase 3 \\u0026ndash; Code 1002653: Automated Agroclimatic Technical Datasheet for Hass Avocado in Antioquia.\\u0026rdquo;\\u003c/em\\u003e\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eA.M.C.M: Conceptualization, Methodology, Data curation, Validation, Writing original draft, Writing review \\u0026amp; editing. G.A.G.V: Conceptualization, Methodology, Software, Data curation, Validation, Writing original draft, Writing review \\u0026amp; editing. L.F.G.G: Conceptualization, Methodology, Validation, Writing, review \\u0026amp; editing.D.L.C.M: Methodology, Writing original draft. J.H.B.R. Methodology, Writing review \\u0026amp; editing. A.J.P.Q: Conceptualization, Methodology, Validation, Writing original draft, Writing review \\u0026amp; editing. All authors read and approved the final manuscript. All authors contributed to the study conception, design, and reviewed the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThe authors acknowledge the Ministry of Agriculture and Rural Development of Colombia for its support. Special recognition is extended to Dr. N\\u0026eacute;stor Miguel Ria\\u0026ntilde;o Herrera for his contribution to the initial conceptualization of the Agrorac information system. The authors also acknowledge the valuable contributions of Bernardo Mej\\u0026iacute;a, Lucas Cano, Mauricio Londo\\u0026ntilde;o, and Paula Andrea Aguilar, and the support of Claudia Patricia Rend\\u0026oacute;n in the logistics and content development of the co-creation workshops for the Agroclimatic Technical Datasheets.\\u003c/p\\u003e\\u003ch2\\u003eData availability\\u003c/h2\\u003e\\u003cp\\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to they are protected under the copyright of AGROSAVIA but are available on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAllen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. FAO, Rome, Italy, p 300\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAra I, Turner L, Harrison MT, Monjardino M, deVoil P, Rodriguez D (2021) Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review. Agric Water Manage 257(August):107161. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.agwat.2021.107161\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.agwat.2021.107161\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBrouwer C, Goffeau A, Heibloem M (1985) Irrigation Water Management: Training Manual No. 1 - Introduction to Irrigation. 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Environ Model Softw 180(July):106147. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.envsoft.2024.106147\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.envsoft.2024.106147\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":true,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7457303/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7457303/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis paper introduces AGRORAC, a robust and user-friendly web tool designed to assess future climate risks in equatorial agrosystems, such as excess or deficient water and extreme air temperatures. The tool uses climate prediction data from the Colombian meteorological service (IDEAM), soil data on water storage capacity, and agroecosystem susceptibility to extreme rainfall and temperature events. The primary programming language used was R version 4.4.0, in conjunction with the Shiny framework (version 1.8.1.1). Agrorac enables data visualization, analysis, and risk assessment by integrating climate scenarios with agroecosystem vulnerabilities related to crops phenological stages. Case studies on banana crops (Dominico Hart\\u0026oacute;n, in Montenegro \\u0026ndash; Quind\\u0026iacute;o) and potato crops (Diacol Capiro, in Mosquera \\u0026ndash; Cundinamarca) demonstrate Agrorac\\u0026rsquo;s capability to generate detailed spatial maps and customized risk lists based on crop development stages. These functionalities enhance spatial resolution beyond that of traditional agroclimatic bulletins, providing useful information for precise, localized agronomic planning.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Agrorac: A web-based tool focused on knowledge management for identifying agroclimatic risks in equatorial agrosystems\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-18 14:10:41\",\"doi\":\"10.21203/rs.3.rs-7457303/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d187ca4b-2e6f-41c5-b88a-661390a194f3\",\"owner\":[],\"postedDate\":\"October 18th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-27T19:53:22+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-18 14:10:41\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7457303\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7457303\",\"identity\":\"rs-7457303\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}