Evidence map›Paper›PMID 41315378›Full record

ArticleScientific reports2025

Edaphic homologous zones and digital tools as a basis for sustainable soil management in potato growing areas in Colombia.

William Alfonso Leon-Rueda, William Fernando Cárdenas-Urrego, Roger Camilo Niño-Medina, Joaquín Guillermo Ramirez-Gil

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

William Alfonso Leon-RuedaFederación Colombiana de Productores de Papa-FEDEPAPA, Fondo Nacional de Fomento de La Papa-FNFP, Bogotá, Colombia.
William Fernando Cárdenas-UrregoLaboratorio de Agrocomputacion y Analisis Epidemiológico, Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia.
Roger Camilo Niño-MedinaFederación Colombiana de Productores de Papa-FEDEPAPA, Fondo Nacional de Fomento de La Papa-FNFP, Bogotá, Colombia.
Joaquín Guillermo Ramirez-GilLaboratorio de Agrocomputacion y Analisis Epidemiológico, Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia. jgramireg@unal.edu.co.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial heterogeneity in soil physicochemical properties is central to sustainable land management and productivity, yet remains poorly quantified across many potato landscapes. This study quantified and mapped the spatial variability of key soil attributes across Colombia's main potato-growing departments (Antioquia, Cundinamarca, Cauca, Boyacá, Santander, Nariño, among others), and delineated edaphic homogeneous zones to guide site-specific management. We compiled and quality-controlled 3,137 georeferenced soil samples and performed exploratory multivariate analysis. Unsupervised K-means clustering was then applied to generate edaphic zones, which were characterized by pH, organic matter, texture proxies, macro- and micronutrients (with emphasis on phosphorus and iron). A multi-focus modeling approach integrating soil variables and a genetic algorithm was applied to optimize potato recommendations within an open-access platform.Three clusters emerged with clear contrasts in nutrient status and dispersion, both within and among clusters. Using crop nutritional requirements as benchmarks, 59% of the mapped area was classified as highly suitable for potato cultivation. Linking soil information with climatic covariates and available yield records revealed multiple associations between productivity and edaphic climatic conditions, underscoring the importance of genotype by environment management interactions. The outputs support an evidence-based fertilization recommendation system and a suitability (aptitude) model, both integrated into a digital decision-support platform for Colombia's potato sector. The proposed framework provides a reproducible pathway from raw soil data to actionable zoning and management guidance capable of scaling to new regions, incorporating additional variables (e.g., spectral signatures), and updating recommendations as new data accrue. The aligning agronomic decisions with spatial soil variability, the approach enables more efficient input use, reduced environmental burdens, and improved resilience of potato production systems. Findings generalize to Andean tuber systems with comparable edaphoclimatic mosaics regionally.

Indexed as

Crop ProductionCrops, AgriculturalSoilSolanum tuberosumAgricultureColombiaSpatial AnalysisSustainable DevelopmentSoilClustering processData scienceManagement zoneMultiapproach modelsSpatial variability

Identifiers

PMID41315378
PMCPMC12663417

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.