Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis

Autores
Comolli, Luis Raúl; Fassola, Hugo Enrique
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Introduction: Conventional agricultural intensification has increased global food production but has also accelerated deforestation, soil degradation, and biodiversity loss. Agroforestry offers a sustainable alternative by integrating trees into farming systems to enhance ecosystem functions. However, predicting how reciprocal interactions between a focal crop species and multiple associated tree species shape long-term productivity under adaptive management remains a major scientific challenge. Methods: We developed an empirically calibrated Agent-Based Model (ABM) based on a decade of measurements from a 25-year old multispecies agroforestry experiment integrating Ilex paraguariensis (yerba mate) with nine tree species. The model simulates species-specific growth, canopy shading, harvest, pruning, soil organic matter (SOM) feedbacks, and management interventions. It represents 778 interacting perennial individuals and enables quantitative exploration of reciprocal inter-species feedbacks under fixed and adaptive management strategies. Because the model is deterministic, statistical replication of simulation runs is not applicable. Results: Simulations reproduce key field-observed patterns with quantitative agreement. Starting from degraded soil conditions, both management strategies show an initial fertility decline followed by recovery driven by endogenous SOM accumulation. Adaptive management yields a ~56% higher net productivity than fixed management, shortens the recovery time of soil fertility from ~260 weeks to ~88 weeks, and produces nearly threefold higher total biomass. Across species, the model reproduces observed relative Ilex paraguariensis yield differences, correctly predicting that Toona, Cañafístola, Petiribi, Anchico, and Kiri support higher harvest yields than the control (no trees), consistent with experimental field observations over a 10-year period. This quantitative agreement strengthens the model’s validity in capturing beneficial inter-species synergies.Conclusion: The simulations reveal that the focal crop responds to tree-mediated shade and nutrient inputs while actively reorganizing soil fertility gradients through biomass extraction and residue return, thereby reshaping tree regrowth and competitive structure. Together, these dynamics define a mechanistically transparent and predictive framework linking empirical field data with long-term system forecasting.
EEA Montecarlo
Fil: Comolli, Luis. Investigador independiente; Suiza
Fil: Fassola, Hugo Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina
Fuente
Frontiers in Agronomy 8 : 1657465. (March 2026)
Materia
Biodiversidad
Sostenibilidad
Materia Orgánica del Suelo
Sistemas Agroforestales
Productividad
Biodiversity
Sustainability
Ilex paraguariensis
Mate
Soil Organic Matter
Agroforestry Systems
Productivity
Yerba Mate
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/26486
Acceder
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oai_identifier_str oai:localhost:20.500.12123/26486
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysisComolli, Luis RaúlFassola, Hugo EnriqueBiodiversidadSostenibilidadMateria Orgánica del SueloSistemas AgroforestalesProductividadBiodiversitySustainabilityIlex paraguariensisMateSoil Organic MatterAgroforestry SystemsProductivityYerba MateIntroduction: Conventional agricultural intensification has increased global food production but has also accelerated deforestation, soil degradation, and biodiversity loss. Agroforestry offers a sustainable alternative by integrating trees into farming systems to enhance ecosystem functions. However, predicting how reciprocal interactions between a focal crop species and multiple associated tree species shape long-term productivity under adaptive management remains a major scientific challenge. Methods: We developed an empirically calibrated Agent-Based Model (ABM) based on a decade of measurements from a 25-year old multispecies agroforestry experiment integrating Ilex paraguariensis (yerba mate) with nine tree species. The model simulates species-specific growth, canopy shading, harvest, pruning, soil organic matter (SOM) feedbacks, and management interventions. It represents 778 interacting perennial individuals and enables quantitative exploration of reciprocal inter-species feedbacks under fixed and adaptive management strategies. Because the model is deterministic, statistical replication of simulation runs is not applicable. Results: Simulations reproduce key field-observed patterns with quantitative agreement. Starting from degraded soil conditions, both management strategies show an initial fertility decline followed by recovery driven by endogenous SOM accumulation. Adaptive management yields a ~56% higher net productivity than fixed management, shortens the recovery time of soil fertility from ~260 weeks to ~88 weeks, and produces nearly threefold higher total biomass. Across species, the model reproduces observed relative Ilex paraguariensis yield differences, correctly predicting that Toona, Cañafístola, Petiribi, Anchico, and Kiri support higher harvest yields than the control (no trees), consistent with experimental field observations over a 10-year period. This quantitative agreement strengthens the model’s validity in capturing beneficial inter-species synergies.Conclusion: The simulations reveal that the focal crop responds to tree-mediated shade and nutrient inputs while actively reorganizing soil fertility gradients through biomass extraction and residue return, thereby reshaping tree regrowth and competitive structure. Together, these dynamics define a mechanistically transparent and predictive framework linking empirical field data with long-term system forecasting.EEA MontecarloFil: Comolli, Luis. Investigador independiente; SuizaFil: Fassola, Hugo Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; ArgentinaFrontiers Media2026-06-04T11:22:28Z2026-06-04T11:22:28Z2026-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/26486https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1657465/full2673-3218https://doi.org/10.3389/fagro.2026.1657465Frontiers in Agronomy 8 : 1657465. (March 2026)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-10-01T10:25:24Zoai:localhost:20.500.12123/26486instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-10-01 10:25:25.926INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
title Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
spellingShingle Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
Comolli, Luis Raúl
Biodiversidad
Sostenibilidad
Materia Orgánica del Suelo
Sistemas Agroforestales
Productividad
Biodiversity
Sustainability
Ilex paraguariensis
Mate
Soil Organic Matter
Agroforestry Systems
Productivity
Yerba Mate
title_short Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
title_full Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
title_fullStr Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
title_full_unstemmed Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
title_sort Inter-species feedbacks drive emergent productivity in agroforestry systems - an agent-based analysis
dc.creator.none.fl_str_mv Comolli, Luis Raúl
Fassola, Hugo Enrique
author Comolli, Luis Raúl
author_facet Comolli, Luis Raúl
Fassola, Hugo Enrique
author_role author
author2 Fassola, Hugo Enrique
author2_role author
dc.subject.none.fl_str_mv Biodiversidad
Sostenibilidad
Materia Orgánica del Suelo
Sistemas Agroforestales
Productividad
Biodiversity
Sustainability
Ilex paraguariensis
Mate
Soil Organic Matter
Agroforestry Systems
Productivity
Yerba Mate
topic Biodiversidad
Sostenibilidad
Materia Orgánica del Suelo
Sistemas Agroforestales
Productividad
Biodiversity
Sustainability
Ilex paraguariensis
Mate
Soil Organic Matter
Agroforestry Systems
Productivity
Yerba Mate
dc.description.none.fl_txt_mv Introduction: Conventional agricultural intensification has increased global food production but has also accelerated deforestation, soil degradation, and biodiversity loss. Agroforestry offers a sustainable alternative by integrating trees into farming systems to enhance ecosystem functions. However, predicting how reciprocal interactions between a focal crop species and multiple associated tree species shape long-term productivity under adaptive management remains a major scientific challenge. Methods: We developed an empirically calibrated Agent-Based Model (ABM) based on a decade of measurements from a 25-year old multispecies agroforestry experiment integrating Ilex paraguariensis (yerba mate) with nine tree species. The model simulates species-specific growth, canopy shading, harvest, pruning, soil organic matter (SOM) feedbacks, and management interventions. It represents 778 interacting perennial individuals and enables quantitative exploration of reciprocal inter-species feedbacks under fixed and adaptive management strategies. Because the model is deterministic, statistical replication of simulation runs is not applicable. Results: Simulations reproduce key field-observed patterns with quantitative agreement. Starting from degraded soil conditions, both management strategies show an initial fertility decline followed by recovery driven by endogenous SOM accumulation. Adaptive management yields a ~56% higher net productivity than fixed management, shortens the recovery time of soil fertility from ~260 weeks to ~88 weeks, and produces nearly threefold higher total biomass. Across species, the model reproduces observed relative Ilex paraguariensis yield differences, correctly predicting that Toona, Cañafístola, Petiribi, Anchico, and Kiri support higher harvest yields than the control (no trees), consistent with experimental field observations over a 10-year period. This quantitative agreement strengthens the model’s validity in capturing beneficial inter-species synergies.Conclusion: The simulations reveal that the focal crop responds to tree-mediated shade and nutrient inputs while actively reorganizing soil fertility gradients through biomass extraction and residue return, thereby reshaping tree regrowth and competitive structure. Together, these dynamics define a mechanistically transparent and predictive framework linking empirical field data with long-term system forecasting.
EEA Montecarlo
Fil: Comolli, Luis. Investigador independiente; Suiza
Fil: Fassola, Hugo Enrique. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina
description Introduction: Conventional agricultural intensification has increased global food production but has also accelerated deforestation, soil degradation, and biodiversity loss. Agroforestry offers a sustainable alternative by integrating trees into farming systems to enhance ecosystem functions. However, predicting how reciprocal interactions between a focal crop species and multiple associated tree species shape long-term productivity under adaptive management remains a major scientific challenge. Methods: We developed an empirically calibrated Agent-Based Model (ABM) based on a decade of measurements from a 25-year old multispecies agroforestry experiment integrating Ilex paraguariensis (yerba mate) with nine tree species. The model simulates species-specific growth, canopy shading, harvest, pruning, soil organic matter (SOM) feedbacks, and management interventions. It represents 778 interacting perennial individuals and enables quantitative exploration of reciprocal inter-species feedbacks under fixed and adaptive management strategies. Because the model is deterministic, statistical replication of simulation runs is not applicable. Results: Simulations reproduce key field-observed patterns with quantitative agreement. Starting from degraded soil conditions, both management strategies show an initial fertility decline followed by recovery driven by endogenous SOM accumulation. Adaptive management yields a ~56% higher net productivity than fixed management, shortens the recovery time of soil fertility from ~260 weeks to ~88 weeks, and produces nearly threefold higher total biomass. Across species, the model reproduces observed relative Ilex paraguariensis yield differences, correctly predicting that Toona, Cañafístola, Petiribi, Anchico, and Kiri support higher harvest yields than the control (no trees), consistent with experimental field observations over a 10-year period. This quantitative agreement strengthens the model’s validity in capturing beneficial inter-species synergies.Conclusion: The simulations reveal that the focal crop responds to tree-mediated shade and nutrient inputs while actively reorganizing soil fertility gradients through biomass extraction and residue return, thereby reshaping tree regrowth and competitive structure. Together, these dynamics define a mechanistically transparent and predictive framework linking empirical field data with long-term system forecasting.
publishDate 2026
dc.date.none.fl_str_mv 2026-06-04T11:22:28Z
2026-06-04T11:22:28Z
2026-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/26486
https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1657465/full
2673-3218
https://doi.org/10.3389/fagro.2026.1657465
url http://hdl.handle.net/20.500.12123/26486
https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2026.1657465/full
https://doi.org/10.3389/fagro.2026.1657465
identifier_str_mv 2673-3218
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv Frontiers in Agronomy 8 : 1657465. (March 2026)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.854657

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