The oil palm cadastre in Colombia Received for publication: October 1, 2021. Accepted for publication: August 9, 2022. Doi: 10.15446/agron.colomb.v40n2.98801 1 Centro de Investigación en Palma de Aceite – Cenipalma, Bogotá (Colombia). * Corresponding author: vrincon@cenipalma.org Agronomía Colombiana 40(2), 258-269, 2022 ABSTRACT RESUMEN This article describes the process of constructing a model of the geographic information management for the cultivation of oil palm in Colombia. Due to the need to collect, store, update, and analyze data from planted areas in the country, it was necessary to rely on the soft systems model to propose an information system structure that would respond to the needs of accounting for planted areas and to be able to integrate such information with other strategic data for the oil palm sector. This research developed a database model on which the geo- graphic data related to the Colombian planted area of palm oil has been stored for over ten years. The geographic model has allowed creating new information at various territorial scales, integrated with phytosanitary data important for regional crop management. The integration of a web-based platform has positioned the oil palm cadastre as a consultation service for users working in various roles in the oil palm industry, as a reliable geographical bank of information, available to other oil palm project agribusinesses. Este artículo describe el proceso de construcción de un modelo de gestión de la información geográfica para el cultivo de palma de aceite en Colombia. Debido a la necesidad de capturar, alma- cenar, actualizar y analizar datos de las áreas sembradas en el país, fue necesario soportarse en el modelo de sistemas blandos para plantear una estructura de sistema de información que respondiera a las necesidades de inventario de áreas sembradas, y que estuviera en capacidad de integrar dicha información con otros datos estratégicos para el sector palmicultor. Esta investigación permitió construir un modelo de base de datos sobre el cual, durante más de diez años, se han almacenado de manera continua los datos geográficos relacionados con el área sembrada en palma de aceite en el país. El modelo geográfico ha permitido generar nueva información a diferentes escalas territoriales y la integración con datos de índole fitosanitaria, de gran importancia para el manejo regional de los cultivos. La integración de una plataforma tecnológica web ha logrado posicionar el Catastro Palmero como servicio de consulta para usuarios de diversos roles dentro del gremio palmicultor y como información geográfica base de confianza para soportar otros proyectos de agronegocio de la agroindustria palmera. Key words: cadastral administration, land administration domain model, monoculture, oil crops. Palabras clave: administración catastral, modelo para el ámbito administrativo del territorio, monocultivo, cultivos oleaginosos. The oil palm cadastre in Colombia Catastro de la palma de aceite en Colombia Victor Orlando Rincón-Romero1, *, Angie Molina-Villarreal1, Andrea Zabala-Quimbayo1, Osmar Ricardo Barrera-Agudelo1, and Jorge Luis Torres-León1 Introduction The oil palm crop in Colombia has achieved a significant presence, accounting for approximately 10% of the coun- try’s total planted area and 30% of the agro-industrial crops (DANE, 2016; Fedepalma, 2019). This presence calls for the development and permanent update of an inventory of the country’s planted areas, since this information could be the cornerstone for adequate planning and ordering of the territories. From its inception, the Federación Nacional de Cultiva- dores de Palma - Fedepalma has worked to consolidate and develop a database that provides information on aspects relevant to Colombian agribusiness that includes the delimitation, area, and spatial representation of planted areas that can assist the palmiculture sector in adequate decision-making. Therefore, as a first approach to achieving these objectives, Fedepalma, through the Oil Palm Sector Statistical Information System (SISPA), has periodically compiled data of country wide seed sales as an alternative to an estimation of the planted area. This method leads to certain inaccuracies in the calculation determining the ef- fectively planted area requiring corrections, mainly related to discarded seeds. Fully aware of this condition, in 2007, Fedepalma, in an agreement with Corporación Colombia Internacional https://doi.org/10.15446/agron.colomb.v40n2.98801 mailto:vrincon@cenipalma.org 259Rincón-Romero, Molina-Villarreal, Zabala-Quimbayo, Barrera-Agudelo, and Torres-León: The oil palm cadastre in Colombia (CCI), completed the first georeferencing study on the area planted with oil palm in the country, creating polygons for around 276,000 ha of the 301,000 ha estimated to be planted. Additionally, in 2009 the Centro de Investigación en Palma de Aceite - Cenipalma, as part of a project funded by the Servicio Nacional de Aprendizaje (SENA), made the first adjustment to the cartographic base developed by the CCI and georeferenced additional areas of the crop that added up to a total 310,000 ha in comparison to the 350,000 ha estimated for that year. Simultaneously, Cenipalma, as part of several regional phy- tosanitary information management projects, developed geographic databases that used polygons to represent the planted areas in the areas of the Colombian eastern plains, the Magdalena Medio (Central region), and San Andres de Tumaco (Pacific coast). In 2012, this first stage of data collection required focusing efforts on the planning and design of an information management system that would ensure the storage, administration, and consultation of data from the crop plots, i.e., from a specific cadastre of the areas with palm oil. The cadastre (a public register showing the details of ownership and value of land) corresponds to a land inventory (Kaufmann & Steudler, 1998) and under the new trends of the multipurpose cadastre, the inventory transcends the delimitation of properties and is supported by subsystems that carry out inventories of land uses (Wil- liamson et al., 2014; Ponvert et al., 2015). So, the subsystem called Oil Palm Cadastre (OPC) was conceived, its corner- stone was the “land management paradigm” (Williamson et al., 2014) in which high-quality territorial information was an essential component, using the property cadastre as a methodological framework. According to Ponvert et al. (2015), most national cadastre information systems store general data that often cannot meet the particular needs of every user. For this reason, it is convenient to propose and create specific subsystems that allow managing information at a larger scale, leading to the creation of cadastre subsystems mainly in rural areas (which, by tradition in the countries, has been character- ized for having less detailed sales). In the last decade this need has been framed as Green Cadastres (Zysk et al., 2020). Some examples include the land parcel information system to record crops and manage agricultural subsidies in the European Community (Inan et al., 2010), the Specialized Property and oil palm cadastres Property cadastre polygon Oil palm cadastre polygon Legend Spatial reference system: EPSG 4326 Source: *GIS oil palm cadastre *Google satellite image 0 250 500 m 73.18°W 73.16°W 73.18°W 73.16°W 4. 36 °N 4. 36 °N FIGURE 1. Difference between minimum units of the proper ty cadastre and crop cadastral subsystems. 260 Agron. Colomb. 40(2) 2022 Sugar Cane Registry in Cuba (Samuel & Reyes, 2007; Rojas-Martínez et al., 2014), the Forest Cadastre in Greece (Drosos, 2014), the vineyards cadastre in Romania (Hutanu & Moca, 2010; Hutanu et al., 2016), and the inventory of tea areas in Turkey (Ozcelik & Nisanci, 2016), etc. From these initiatives, and taking into account that the cadastre of the property was historically proposed to deter- mine the value of such properties for tax purposes and/or to create a register for the legitimacy of the owners (Alcázar, 2000; Hopfer, 2003), the difference between these subsys- tems and the cadastre of the property was defined as the fact that the delimitation of the polygons specifically marks the minimum constituent unit of the cadastral subsystem (Samuel & Reyes, 2007; Ozcelik & Nisanci, 2016) and is not carried out considering the property’s boundaries (Fig. 1). Therefore, the cadastral subsystems are a complement to this type of cadastre, with the spatial representation as an integrating component (Ponvert et al., 2015). Therefore, it was important to create a system to store the georeferenced inventory of areas planted with oil palm at a plot scale in Colombia. The primary purpose of this system was to support decision-making in the oil palm industry and to provide updated information about planted areas. The system was developed as a technological tool under the guidelines of a cadastre information system (Çaǧdaş & Stubkjær, 2011); therefore, this paper describes the OPC conceptualization and the development of a continuously updated system. This tool was proposed as a benchmark for consultation about oil palm plantations in Colombia. Materials and methods The system was planned based on “soft systems” theory (Çaǧdaş & Stubkjær, 2011) to define the elements and actors of the OPC and to define the technical aspects that allowed implementing the OPC based on the system’s conceptual framework. System conceptualization The OPC system is based on the uncertainty about the ac- tual planted areas and their characteristics and geographic distribution. Therefore, the purpose of elucidating them is to create a theoretical framework through practice (Roux & Barry, 2009). For this reason, the OPC is conceived as a system and, as such, it has the following characteristics (Wastell, 2012): • Purpose: to provide georeferenced information on areas planted with oil palm as well as associated information in order to make decisions related to the oil palm sector; • Transformation: a collection of geographical data for areas planted with oil palm and data related to the characteristics and management of the crops in order to create information for research, extension, and decision-making; • Feedback cycles: the purpose is to identify the entire planted area of the country; therefore, measurements ensure the fulfillment of its purpose. However, this is understood as a soft system under systems theory, considering that its purpose as a registry storage system transcends the inclusion of the complexity of human decision-making processes in all its components (Çaǧdaş & Stubkjær, 2011). From its inception, the OPC was conceived as a tool whose main actors are Fedepalma, Cenipalma, and palm-growers, interrelated through the collection, storage, and consulta- tion of data on the areas planted with oil palm and the information products generated from them. A first activity carried out was the definition of the con- stituent elements of OPC, to break down each of the ele- ments that participate in the definition of the areas with oil palm cultivation. Such a definition is considered a hierarchy determined by spatial units mainly associated with the functionalities and interests of the actors; each unit was assigned characteristics and in turn topological relationships. Driving the conception of the system towards the opera- tional component, the aspects in which the OPC is trans- formed from an inventory of areas to functional data were conceived that can be integrated with different sources to consolidate multipurpose characteristics of the inventory. Technical aspects Geographical data sources were classified into three ca- tegories: products of photointerpretation, a direct survey with GPS devices, or third-party information available at the plantations. Therefore, a data processing scheme had to be defined to monitor the technical operation of the geographical database construct (Fig. 2). These data structuring aspects were designed considering a detailed revision, correction, and adjustment for the geographical information, based on the Colombian Technical Standard NTC 5043 (ICONTEC, 2010): 261Rincón-Romero, Molina-Villarreal, Zabala-Quimbayo, Barrera-Agudelo, and Torres-León: The oil palm cadastre in Colombia • Totality The list of plots per plantation provided at the beginning of the project was used as a source, understanding that the total polygons must be equal to the number of plots reported for the plantation; • Logical consistency The following elements were evaluated: » Domain consistency, for the name, area, palms, and material attributes. » Topological consistency that evaluates compliance with topological rules such as overlapping, invalid geometries, gaps, and non-repeating geometries; • Position accuracy The data provided by the plantations were reviewed and the actual location of the polygons was validated through satellite images. Results and discussion Elements of the oil palm cadastre The oil palm crop in Colombia is distributed throughout more than 120 municipalities. The geographic delimi- tation of what is known in the palm-growing sector as palm-growing areas from an administrative and logistics perspective, were named as follow: north, central, east, and southwest (Fedepalma, 2019) (Fig. 3). These were establis- hed based on the surrounding regions that are part of the same geographical region. Similarly, for operationality and accessibility, a group of sub-zones was established within each zone, defined mainly as plantation aggregates. The boundaries of the sub-zones may or may not coincide with the boundaries of municipalities. These sub-zones contai- ned plantations in various rural properties used mainly to grow oil palm, and they are legally owned by one or several people/companies. Depending on their size, these plantations cover one or several plots (plot being the minimum spatial and adminis- trative division within a plantation) that are usually defined based on homogeneous characteristics of its constituent ele- ments (cultivar, planting date, type of soil, etc.), geographi- cal accidents, or infrastructure works developed inside the plantation. Therefore, it was important to emphasize that an oil palm plantation was a group of plots, which in turn had diverse geographical elements and/or characteristics inherent to them (palms, roads, channels, type of cover, etc.). However, there is no absolute relationship between the property and the plantation, or the property and the plot, because the latter may conform to the entire property, a fraction of it, or the sum of several properties (Sagris & Devos, 2008). Below are the definitions of the elements that comprise the OPC. • Plot: minimum spatial unit planted with oil palm within a plantation that generally has similar characteristics regarding the planting year and cultivar; • Plantation: set of plots —not necessarily adjacent— grouped under the property and administered by one or several persons (natural or legal); • Sub-zone: Geographical sub-division of the oil palm zones that allow for adequately monitoring the logistics and extension. The boundaries of one sub-zone may or may not coincide with those of the municipalities; Previous inputs Acquisition Processing Storage publication List of plantations (coordinates) Plantation polygons Photointerpretation - restitution GPS surveys Third-party geodata structuring Elements Totality Logical consistency Position accuracy Thematic accuracy Geo-services FIGURE 2. Data processing scheme. 262 Agron. Colomb. 40(2) 2022 • Zone: Geographical division created by Fedepalma to adequately manage the palm productive areas in Co- lombia. It covers one or more municipalities in one or more departments. Features of the OPC The cadastre of the property generally refers to the spatial and legal features (Çaǧdaş& Stubkjær, 2011). In the case of the OPC, the physical feature of the property refers to identifying and representing geographical elements (plots, plantations, sub-zones, and zones). The agronomic feature refers explicitly to the specification of the cultivar, species, age, phytosanitary status, etc., to describe crop’s particularities found in the smallest mappable unit (plot). Topological relationships Considering that the fundamental elements of the OPC have geographical representation, topological relations- hips that establish the hierarchical connections between them had to be defined; i.e., the zone (the element with the highest hierarchy) is formed by various sub-zones that in turn are formed by a certain number of oil palm plantations that cover one or several plots. Figure 4 is a graphical representation of the hierarchical distribution of the elements of the OPC. Furthermore, defining the relationship of the elements of the OPC with the cadastre of the property allowed inte- grating new actors to the s ystem. The territorial entities East Southwest Central North East Southwest Central North 0 100 200 km 75°W80°W 70°W 80°W 75°W 70°W 5° N 10 °N -5 °N 0° 5° N 10 °N -5 °N 0° Colombian oil palm zones Central North East Southwest Legend Spatial reference system: EPSG 4326 Source: GIS oil palm cadastre FIGURE 3. Distribution of oil palm zones in Colombia. 263Rincón-Romero, Molina-Villarreal, Zabala-Quimbayo, Barrera-Agudelo, and Torres-León: The oil palm cadastre in Colombia and institutions related to the ordering of the territories considered the information products created by the OPC as inputs. Integrated to the property, the inputs are funda- mental for planning and proposing strategies and policies in the territories. Multi-purpose structure In a first instance, the OPC was conceived as a solution to quantify the total planted area in Colombia. However, having the geographical conceptualization and represen- tation of the spatial objects allowed integrating another type of strategic information for representation. Therefore, the OPC is currently conceived under a multi-purpose approach that facilitates the integration of agronomic in- formation derived fro crop activities that enhance decision- making criteria. Therefore, the OPC elements were redefined, considering the possibility of representing phytosanitary and produc- tive variables and the management of crops at a regional scale. Defining plot boundaries Considering cadastres of property, an accuracy to the cen- timeter is essential for urban areas. However, in Colombia, rural cartography has been developed at a maximum scale of 1:10,000, substantially reducing accuracy requirements. However, the material definition of property boundaries in rural and urban spaces is indicative and accurate, e.g., fences of rural properties. However, oil palm plots are ge- nerally known for having no explicit physical delimitation. Therefore, the conceptualization included a definition of the plot boundaries, understood as the projection to the ground of the canopies of the palms planted on the edge of the plot. In practice, under the above definition, the boundary would change as the palm grows; hence, the above concept was complemented with the projection to the ground of the canopies of adult palms, which in practical terms can be extrapolated to half the planting distance between palms. After defining the concept of plot boundary, it was neces- sary to specify the minimum accuracy for surveying it. Hence, it was decided that the maximum tolerable error to define the perimeter of a plot would be 10 m, thus tolerating the variation found in the definition of the boundary based on the canopy projection. This allowed including tools such as low-accuracy GPS devices and satellite images with a 5-m resolution and optimized the area surveying works without affecting data quality. As a result, and considering the guidelines set by Tobler (1987), a scale of 1:20,000 was established for the OPC. Considering that the OPC development aims to provide an approximate quantification of the country’s planted area and coordination of the multi-purpose approach, two fundamental components were defined that established the system’s objectives and scope. First, approximation to the physical aspect (quantification and geographical represen- tation), and the second inclusion of the agronomic aspect (crop characterization). Physical aspect With the first approach, the areas planted with oil palm crops were identified via photointerpretation of satellite images without leading to a definitive demarcation of the plots and plantations (Kelly et al., 1999; Rodríguez et al., 1999; Johansen et al., 2009). The sensors used as data sources were Sentinel 2 and Landsat 8, considering the free access to their data, the multi-temporality, and characte- ristics for crop discrimination (Sarvia et al., 2020; Jayanth et al., 2022). A future alternative would be support in the delimitation of the plots from satellite image processing techniques, such as fully convoluted networks (KC et al., 2021; Taravat et al., 2021). However, these techniques are Plot Plantation Sub-zone Zone FIGURE 4. Hierarchical distribution of elements of the OPC. 264 Agron. Colomb. 40(2) 2022 still in the research stage and in order to guarantee the quality of the data, human photointerpretation was inclu- ded in the processing. The following properties of a plot were established to define the rules of photointerpretation (Stone, 1955): shape, size, structure, texture, and color. • Shape: the plots planted with oil palm are generally known for having regular shapes; however, there are also irregular shapes or shapes defined by administra- tive considerations that are not related to geographical boundaries (Fig. 5); • Size: generally, an area with oil palm may vary between 1 and 20 ha without identifiable discontinuity like roads or water bodies. However, there may be unusual cases where there are plantations with a larger area; • Structure: the structure inside an oil palm plot must include the presence of staggered formations or planting patterns that form equilateral triangles between palms. However, there may be cases where the plantations do not meet this rule, such as terraced plantations. The presence of irrigation channel infrastructure within the crop may be an indicator of new or young plantations; • Texture and color: In this case, two closely related properties were addressed, since both are mainly sub- ject to variation in planting age. In high-resolution spatial images, it is possible to distinguish the staggered formation. However, images with a spatial resolution equal to or higher than 5 m required defining a series of keys that facilitate identifying the crops (Fig. 6). However, the similarity between oil palm crops’ ref lec- tance with other crops like banana and forests implies field verification. To establish orderly photointerpretation, a grid of 5 x 5 km2 was defined to cover all the palm areas. Each grid was assigned tracking information to develop the process This series of data traced the area update process. A revision was established for each square, manually employing lateral and vertical displacement, where each of the areas identified as oil palms were restored. Since the process of updating areas planted with oil palm is carried out every six months, management of the grid data was established as an additional component in the OPC database, making it possible to keep track of the revisions made in the same quadrant over time and identify potential areas for updating. Definition of the shape of plots Regular delimited plot Legend Regular plot without specific limits Irregular plot without specific limits Irregular delimited plot Spatial reference system: EPSG 4326 Source: *GIS oil palm cadastre *Google satellite image 0 250 500 m 73.69°W73.70°W 73.70°W 73.69°W 73.70°W 73.69°W 73.70°W 73.69°W 6. 97 °N 6. 98 °N 6. 97 °N 6. 98 °N FIGURE 5. Definition of the shape of plots in oil palm crops. 265Rincón-Romero, Molina-Villarreal, Zabala-Quimbayo, Barrera-Agudelo, and Torres-León: The oil palm cadastre in Colombia Agronomic feature This corresponded to characterization of the minimum agronomic administration unit of the plantations, com- monly called a plot. In the absence of a standard for the definition of plot limits, each plantation was autonomous to define its plots, and it was necessary to define the plot as the minimum grouping of palms to which production data, phytosanitary, etc., were recorded. In most cases, plots contained palms from the same planting year, the same cultivar, and had physical limits. In this way, the agronomic feature defined the geometry of the plots in accordance with the administration of the plantation and complemented the data on the following: number of palms, planting density, cultivar(s), planting year and net area. This feature required greater efforts and dedication, as it demanded establishing direct contact with the owners, managers, or directors of the plantation through palm clusters (oil palm mills). The bonds of trust between clusters and palm growers allowed access to the plantations and access to truthful data. The contact with the commercial liaison between the clus- ters and the palm growers, the Technical, Environmental and Social Assistance Units (UAATAS) adopted in 2008 (Fedepalma, 2009) enhanced the palm-grower contact strategy because the clusters were receptive to consider- ing the importance of georeferencing planted areas at a plot scale. Below is the process scheme designed for the correct management and collection of plantation cadastral information (Fig. 7). Finally, the National Oil Palm Cadastre Database is inte- grated in two stages: • Inclusion of geographical data (physical feature), with semi-annual update; • Association of the agronomic information with each plot (agronomic features), which is a permanent process. With the inclusion of technical considerations, a new ac- tor became evident in the OPC system that had not been contemplated in the initial formulation. Hence, the palm tree clusters appeared as a determining factor in the process of formation and as a consumer of the information. Thus, finally, the formalization of the processes oriented to man- agement and processing of the data finally consolidated the model of the Oil Palm Cadastre System (Fig. 8). New crops (1 to 3 years) Young crops (5 to 10 years) Adult crops (Over 10 years old) Crop age reflectance New crops Young crops Adult crops Legend Spatial reference system: EPSG 4326 Source: *GIS oil palm cadastre *Sentinel 2 Image - T18NYL (2021-02-21) 0 250 500 m 72.90°W 72.88°W 72.90°W 72.88°W 4. 54 °N 4. 56 °N 4. 54 °N 4. 56 °N FIGURE 6. Interpretation of palm age in combination with false color infrared in LandSat 8 image. 266 Agron. Colomb. 40(2) 2022 Information technologies artifacts A spatial database was designed and implemented in Mi- crosoft SQL Server Database Management System to store spatial and alphanumeric data. This allowed elimination of redundancy, allowing capability for data processing, analysis, visualization and exchange (Dawidowicz et al., 2020). The spatial database on the SQL Server was inte- grated into the ArcServer platform to develop geoservices aimed at publishing the OPC information on the internet: https://geopalma.cenipalma.org The main developed geoservice consolidates the values of planted areas at a municipality scale and was an example of the interaction between the OPC and territorial units such as municipalities (Fig. 9). Progress in the consolidation of information As mentioned above, the work of the Corporación Co- lombia Internacional (CCI) in 2007 is considered the foundation of the OPC. Throughout 10 years, about 100% of the estimated area planted with oil palm in the country FIGURE 7. Data management scheme of plantation cadastral information. Yes No Yes No Socialization of the project with the oil palm grower Have geographic information? Topological and thematic quality check Field validation Structured geographic layer of plots Field survey Integrating plot attributes Structuring (logical consistency) Does it exist in an image bank? OPC Database Photogrammetric restitution Actors Components Technical scheme Fedepalma Cenipalma Plot Plantation Sub-zone Zone Elements Physical Agronomic M ut at io ns Multipurpose Fuentes de datos Phytosanitary Productive Environmental Social … Municipality Department Data processing Property cadastre Land parcel Oil palm growers Local government Institutions Oil palm clusters Information products Data management Update Definition Definition Interaction Planning, strategies and policies Pr op er ty Integration Re se ar ch C on so lid at io n Analysis, strategies and policies Analysis, strategies and policies Inquiry Planting Aspects Update Definition Definition Interaction Planning, strategies and policies Pr op er ty Integration Re se ar ch C on so lid at io n Analysis, strategies and policies Analysis, strategies and policies Inquiry Planting Aspects FIGURE 8. Oil palm cadastre system model. 267Rincón-Romero, Molina-Villarreal, Zabala-Quimbayo, Barrera-Agudelo, and Torres-León: The oil palm cadastre in Colombia has been georeferenced. This physical feature has had continuous revision (a review of the areas is carried out every two years) parallel to the expansion of the crop, beginning from about 276,000 ha georeferenced in 2007 to 625,066 ha in 2020, exceeding the area reported by the SISPA. Regarding the progress of the agronomic feature, though it is slower it increased from zero hectares in 2007 to 160,000 ha in 2017. Thus, the speed of this (agronomic) feature increased during the last three years, mainly due to the active participation and strong interest of palm growers during OPC development (Fig. 10). As of 2020, there were a total of 625,066 ha in physical terms, of which 345,600 ha are fully characterized physically and agronomically. Progress in the above features depended on the conditions of each region, with sub-zones such as South Cesar, South Bolívar, Acacías or Fundación mostly able to consolidate their agronomic aspect due to the integration and partici- pation of the oil palm clusters of the sub-zones. The physical feature has enabled accurately identification of the extent of oil palm trees in each of the palm-growing areas (Fig. 11). To date, the eastern zone has about 46% of the country’s total. One of the most interesting results has been the possibil- ity of consolidating data at a territorial unit scale, such as departments (Fig. 12). This data was previously estimated without the OPC. As of 2017, the department with the 389,828 635,066 512,976 516,960 540,687 559,582 O il pa lm a re a (h a) - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 Agronomic aspect SISPAPhysical aspect 2016 2017 2018 2019 2020 507,066 566,432 579,846 93,085 163,412 215,695 258,453 345,600 507,066 566,432 579,846 93,085 163,412 215,695 258,453 345,600 O il pa lm a ar ea ( ha ) Whitout agronomic aspect Agronomic aspect Total area 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 Central zone North zone East zone Southwest zone 150,348 147,169 62,137 56,014 65,362 121,169 22,866 1,618 21,248 297,518 150,348 147,169 118,151 62,137 56,014 186,531 65,362 121,169 FIGURE 9. Geoservice developed for consultation of data for the oil palm cadastre (in Spanish; https://geopalma.cenipalma.org/M%C3%B3dulos/ Catastro-Palmero/Tablero-Catastro/geoserviciocatastrofisico). FIGURE 10. Evolution of the oil palm cadastre. FIGURE 11. Distribution of planted area of oil palm per zone. 268 Agron. Colomb. 40(2) 2022 largest planted area of the country is Meta, followed by Santander and Cesar, reiterating that the Eastern and Cen- tral palm growing areas have the country’s most extensive planted areas. FIGURE 12. Planted area of oil palm by department. Conclusions From a soft systems perspective, the OPC system model allowed including the complex interactions between ac- tors. This was translated into a significant advance in the consolidation of the system’s information. Incorporating the palm clusters and growers as active actors accelerated data collection and guaranteed continuous feedback for the project’s results. The OPC’s conceptualization was made “along the way,” allowing adjustments that were mainly due to the inclusion of new actors in the process. With this, soft systems theory is confirmed as an alternative to conceptualize cadastral systems and subsystems. The creation of a record of planted areas by delineating each of their perimeters is an effort that has involved many years and resources. However, this effort has been worthwhile, due to the complexity of the crop and its setting. From this experience, we highlight the integration of the system to the pre-established processes of the extension program (UAA- TAS), without which, it would not have been possible to consolidate the agronomic features, and the system would have been limited to only the physical features. After several years of work, the OPC is considered to have reached maturity, consolidating processes that guarantee the quality of the data, which are increasingly closer to representing the country’s total planted areas. Therefore, upcoming efforts should focus on the system’s communica- tion phase. Although with some progress, this phase must be mainly aimed at the use of the information produced, in order to transcend the processes of the palm-sector and to connect with the governmental territorial organizations. It is worth mentioning that the oil palm agroindustry is an essential part of the development of the regions where it exists. Acknowledgments The authors thank the palm promotion fund, Fedepalma, Cenipalma, the palm clusters and the palm growers for being the main actors of the OPC and the cornerstone of its development. The authors especially thank Professor Rodrigo Castellanos (RIP), who promoted the development of the cadastre beyond the property registry in his lessons. Finally, the authors thank their families, who are the main drivers of their work. Conflict of interest statement The authors declare that there is no conf lict of interest regarding the publication of this article. Author’s contributions VRR wrote the initial draft. VRR and AMV formulated de overarching research goals and aims. VRR, AMV and AZQ designed the methodology. OBA and VRR implemented the computer code and supporting software. JTL managed and coordinated the project and reviewed the manuscript. All authors have read and approved the final version of the manuscript. Literature cited Alcázar, M.-G. (2000). El catastro y su evolución hasta el siglo XVI. Catastro, (39), 51–64. Çaǧdaş, V., & Stubkjær, E. (2011). Design research for cadastral systems. Computers, Environment and Urban Systems, 35(1), 77–87. https://doi.org/10.1016/j.compenvurbsys.2010.07.003 DANE. (2016). Encuesta nacional agropecuaria ENA-2016. Depar- tamento Administrativo Nacional de Estadística Colombia. http://w w w.dane.gov.co/index.php/estadisticas-por-tema/ agropecuario/encuesta-nacional-agropecuaria-ena Dawidowicz, A., Kulawiak, M., Zysk, E., & Kocur-Bera, K. (2020). System architecture of an INSPIRE-compliant green cadastre system for the EU Member State of Poland. Remote Sensing Applications: Society and Environment, 20, Article 100362. https://doi.org/10.1016/j.rsase.2020.100362 Drosos, V. C. (2014). CADASTRE (forest maps) and spatial land uses planning, strategic tool for sustainable development [Proc. SPIE 9229]. 2nd International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014). Conference 92291F. https://doi.org/10.1117/12.2069662 Meta 34% Santander 16%Cesar 13% Casanare 11% Magdalena 9% Bolívar 6% Norte de Santander 5% Nariño 4% Vichada 2% Others 0% https://doi.org/10.1016/j.compenvurbsys.2010.07.003 http://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena http://www.dane.gov.co/index.php/estadisticas-por-tema/agropecuario/encuesta-nacional-agropecuaria-ena https://doi.org/10.1016/j.rsase.2020.100362 https://doi.org/10.1117/12.2069662 269Rincón-Romero, Molina-Villarreal, Zabala-Quimbayo, Barrera-Agudelo, and Torres-León: The oil palm cadastre in Colombia Fedepalma. (2009). Unidades de Asistencia y Auditoría Técnica, Ambiental y Social (UAATAS): Una estrategia para el manejo sanitario y para incrementar la productividad y competitividad palmera. Palmas, 30(3), 5–6. Fedepalma. (2019). Anuario estadístico 2019. La agroindustria de la palma de aceite en Colombia y en el mundo 2014-2018. https://publicaciones.fedepalma.org/index.php/anuario/issue/ view/1556 Hopfer, A. (2003). Cadastre as a compact tool for proper land use – via taxation and physical planning. FIG Working Week, April 13-17 2003, Paris, France. http://www.eurocadastre.org/pdf/ documents/hopfer.pdf Hutanu, C., & Moca, V. (2010). Use of cadastral plan in digital form and the databases informations system for the national vine- yard landsurvey. Lucrări Ştiinţifice, 53(2), 96–101. Hutanu, C., Radu, O., & Padure, D. (2016). Updating of cadastral database with a view to the creation of vineyards and fruit trees. Lucrări Ştiinţifice, 59(1), 289–294. ICONTEC. (2010). NTC 5043. Conceptos básicos de calidad de los datos geográficos. ICONTEC. Inan, H. I., Sagris, V., Devos, W., Milenov, P., van Oosterom, P., & Zevenbergen, J. (2010). Data model for the collaboration between land administration systems and agricultural land parcel identif ication systems. Journal of Environmental Management, 91(12), 2440–2454. https://doi.org/10.1016/j. jenvman.2010.06.030 Jayanth, J., Aravind, R., & Amulya, C. M. (2022). Classification of crops and crop rotation using remote sensing and GIS-based approach: A case study of Doddakawalande Hobli, Nanjan- gudu Taluk. Journal of the Indian Society of Remote Sensing, 50(2), 197–215. https://doi.org/10.1007/s12524-020-01296-0 Johansen, K., Phinn, S., Witte, C., Philip, S., & Newton, L. (2009). Mapping banana plantations from object-oriented classifica- tion of SPOT-5 imagery. Photogrammetric Engineering and Remote Sensing, (9), 1069–1081. https://doi.org/10.14358/ PERS.75.9.1069 Kaufmann, J., & Steudler, D. (1998). Cadastre 2014: A vision for future cadastral system. FIG XXI International Congress, Brighton, United Kingdom. https://fig.net/resources/publications/fig- pub/cadastre2014/translation/c2014-english.pdf Kc, K., Zhao, K., Romanko, M., & Khanal, S. (2021). Assessment of the spatial and temporal patterns of cover crops using remote sensing. Remote Sensing, 13(14), Article 2689. https://doi. org/10.3390/rs13142689 Kelly, M., Estes, J. E., & Knight, K. A. (1999). Image interpretation keys for validation of global land-cover data sets. Photogram- metric Engineering and Remote Sensing, 65(9), 1041–1050. Ozcelik, A. E., & Nisanci, R. (2016). Land use patterns for driving environmental management of tea agricultural croplands. Computers and Electronics in Agriculture, 122, 41–54. https:// doi.org/10.1016/j.compag.2016.01.013 Ponvert Delisles, D. R., Samuel Kelly, F., & Reyes, I. (2015). Las técnicas geomáticas aplicadas en la agricultura: el catastro agrícola en Cuba. Revista de Ciencias Espaciales, 8(2), 11–28. https://doi.org/10.5377/ce.v8i2.2067 Rodríguez Pérez, J. R., González Vázquez, X. P., & Arias Sánchez, P. (1999). Cartografía de usos del suelo por fotointerpretación mediante sistemas de información geográfica (SIG): análisis comparativo de los sistemas ráster y vectorial. Actas del XI Congreso Internacional de Ingeniería Gráfica (Vol. 3., pp. 1513–1523). Universidad Pública de Navarra; Universidad de La Rioja. Rojas-Martínez, O., Rodriguez-Fajardo, A., Vázquez-Acuña, C., Pablos-Reyes, P., & Borrero-Reynaldo, Y. (2014). Implement- ación de un sistema de información geográfica para las deci- siones en la producción cañera de Santiago de Cuba. Ciencia en su PC, (2), 42–55. Roux, L., & Barry, M. (2009). Paradigms and cadastral research. Spatial data serving people: Land governance and the envi- ronment – building the capacity. Proceedings 7th FIG Regional Conference Spatial Data Serving People: Land Governance and the Environment – Building the Capacity. Hanoi, Vietnam. http://www.fig.net/resources/proceedings/fig_proceedings/ vietnam/ppt/ts02d/ts02d_roux_barry_ppt_3704.pdf Sagris, V., & Devos, W. (2008). LPIS core conceptual model: Method- ology for feature catalogue and application schema [EUR23764 EN]. European Communities; Institute for the Protection and Security of the Citizen. https://doi.org/10.2788/80091 Samuel Kelly, F., & Reyes, I. (2007). Catastro especializado en la agricultura cañera en Cuba: antecendentes, creación y relación con el Catastro Nacional. Mapping, 129, 39–42. Sarvia, F., De Petris, S., & Borgogno-Mondino, E. (2020). Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions. GIScience and Remote Sensing, 57(6), 770–784. https://doi.org/10.1080/1548 1603.2020.1798600 Stone, K. H. (1956). Air photo interpretation procedures. Photogra- metric Engineering, 22, 123–132. Taravat, A., Wagner, M. P., Bonifacio, R., & Petit, D. (2021). Advanced fully convolutional networks for agricultural field boundary detection. Remote Sensing, 13(4), 722. https://doi.org/10.3390/ rs13040722 Tobler, W. (1987). Measuring spatial resolution. Proceedings of Land Resources Information Systems Conference (pp. 12–16). Beijing. Wastell, D. (2012). Systems Thinking: an introductory essay. https:// pdf4pro.com/amp/view/systems-thinking-an-introductory- essay-a817f.html Williamson, I., Enemark, S., Wallace, J., & Rajabijard, A. (2014). Administración de la tierra para un desarrollo sostenible. ESRI Press Academic. Zysk, E., Dawidowicz, A., Nowak, M., Figurska, M., Źróbek, S., Źróbek, R., & Burandt, J. (2020). Organizational aspects of the concept of a green cadastre for rural areas. Land Use Policy, 91, Ar ticle 104373. https://doi.org/10.1016/j. landusepol.2019.104373 https://publicaciones.fedepalma.org/index.php/anuario/issue/view/1556 https://publicaciones.fedepalma.org/index.php/anuario/issue/view/1556 https://doi.org/10.1016/j.jenvman.2010.06.030 https://doi.org/10.1016/j.jenvman.2010.06.030 https://doi.org/10.1007/s12524-020-01296-0 https://doi.org/10.14358/PERS.75.9.1069 https://doi.org/10.14358/PERS.75.9.1069 https://doi.org/10.3390/rs13142689 https://doi.org/10.3390/rs13142689 https://doi.org/10.1016/j.compag.2016.01.013 https://doi.org/10.1016/j.compag.2016.01.013 https://doi.org/10.5377/ce.v8i2.2067 http://www.fig.net/resources/proceedings/fig_proceedings/vietnam/ppt/ts02d/ts02d_roux_barry_ppt_3704.pdf http://www.fig.net/resources/proceedings/fig_proceedings/vietnam/ppt/ts02d/ts02d_roux_barry_ppt_3704.pdf https://doi.org/10.2788/80091 https://doi.org/10.1080/15481603.2020.1798600 https://doi.org/10.1080/15481603.2020.1798600 https://doi.org/10.3390/rs13040722 https://doi.org/10.3390/rs13040722 https://pdf4pro.com/amp/view/systems-thinking-an-introductory-essay-a817f.html https://pdf4pro.com/amp/view/systems-thinking-an-introductory-essay-a817f.html https://pdf4pro.com/amp/view/systems-thinking-an-introductory-essay-a817f.html https://doi.org/10.1016/j.landusepol.2019.104373 https://doi.org/10.1016/j.landusepol.2019.104373