Processing of high-resolution temporal climate data for daily simulations of a complex agro-ecosystem

Autores/as

DOI:

https://doi.org/10.12795/rea.2021.i42.10

Palabras clave:

Imputación, Conversión de resoluciones temporales, Lenguaje R, Olivar, ALMaSS

Resumen

Los servicios ecosistémicos, como por ejemplo el control natural de plagas, son herramientas que se deberán promover en las futuras metodologías agrícolas. En este artículo nos enfocamos en el procesamiento de datos climáticos, necesarios para el funcionamiento de un sistema de modelos que hace simulaciones computacionales diarias de la interacción entre una plaga y su predador, en un paisaje dinámico, el olivar. Completamos las series horarias de datos climáticos y las convertimos a series diarias, usando lenguaje R. La metodología utilizada produce datos climáticos aceptables para el funcionamiento del sistema y permite segregar periodos específicos del día manteniendo una resolución temporal diaria. Esperamos que este artículo pueda ser útil en casos similares.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Afrifa-Yamoah, E., Mueller, U., Taylor, S. & Fisher, A. (2020). Missing data imputation of high‐resolution temporal climate time series data. Meteorological Applications, 27. https://doi.org/10.1002/met.1873.

Agência Portuguesa do Ambiente (2016). Poluição Provocada Por Nitratos De Origem Agrícola – Diretiva 91/676/CEE, de 12 de dezembro – Relatório 2012-2015.

Associação de Municípios da Terra Quente Transmontana (2021, 15 May). Caracterização. https://www.amtqt.pt/pages/298

Barreira, R., Paz, M.C., Amaro, L., Sousa, J.P., Benhadi-Marín, J., Rasko, M., Alves da Silva, A., Alves, J., Chuhutin, A., Topping, C.J. & Santos S.A.P. (2020). Developing an Agent-Based Model for Haplodrassus rufipes (Araneae: Gnaphosidae), a Generalist Predator Species of Olive Tree Pests: Conceptual Model Outline. https://doi.org/10.3390/IECPS2020-08745

Benhadi-Marín, J. (2019). Diversity Patterns of Araneae along a Gradient of Farming Practices in Olive Groves: Linking Landscape Pattern, Management Practices, and Species Interactions. [Ph.D. Thesis, University of Coimbra]. http://hdl.handle.net/10316/87551

Brito C., Dinis, L.-T., Moutinho-Pereira, J. & Correia, C.M. (2019). Drought stress effects and olive tree acclimation under a changing climate. Plants, 8, 232. https://doi.org/10.3390/plants8070232

Corral, J. & Calegari, D. (2011). Towards an Agent-Based Methodology for Developing Agro-Ecosystem Simulations. In G. Barthe, A. Pardo & G. Schneider (eds.) SEFM 2011: Software Engineering and Formal Methods (pp 431-446). Springer. https://doi.org/10.1007/978-3-642-24690-6_30

Daane, K. M. & Johnson, M. W. (2010). Olive fruit fly: managing an ancient pest in modern times. Annual Review of Entomology, 55, 155–169. https://doi.org/10.1146/annurev.ento.54.110807.090553

Diamond, J., Moatar, F., Cohen, M., Poirel, A., Martinet, C., Maire, A. & Pinay, G. (2021). Metabolic regime shifts and ecosystem state changes are decoupled in a large river. Limnology and Oceanography. https://doi.org/10.1002/lno.11789.

Direção Geral de Agricultura e Desenvolvimento Rural. (2021, 15 May). Zonas Vulneráveis. https://www.dgadr.gov.pt/rec-hid/diretiva-nitratos/zonas-vulneraveis

Dinis, A.M. (2014). Role of edaphic arthropods on the biological control of the olive fruit fly (Bactrocera oleae). [Ph.D. Thesis, Polytechnic Institute of Bragança]. http://hdl.handle.net/10198/11593

Dunic, J., Brown, C., Connolly, R., Turschwell, M. & Côté, I. (2021). Long‐term declines and recovery of meadow area across the world’s seagrass bioregions. Global Change Biology. https://doi.org/10.1111/gcb.15684.

Engler, M. & Krone, O. (2021). Movement patterns of the White‐tailed Sea Eagle (Haliaeetus albicilla): post‐fledging behaviour, natal dispersal onset and the role of the natal environment. Ibis. https://doi.org/10.1111/ibi.12967.

European Environment Agency (2019). Climate change adaptation in the agriculture sector in Europe. EEA Report No 4/2019. https://www.eea.europa.eu/publications/cc-adaptation-agriculture.

Gonçalves, M.F. (2011). Control of the olive fly, Bactrocera oleae (Rossi), in the context of a sustainable production of olives. [Ph.D. Thesis, University of Trás-os-Montes and Alto Douro].

Habitats Directive. (1992). Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Official Journal of the European Communities, 22/07/1992, L 206, 0007–0050.

Instituto Nacional de Estatística. (2019). Estatísticas do Ambiente.

Instituto Português do Mar e da Atmosfera. (2021a, 15 May). Normais Climatológicas. https://www.ipma.pt/pt/oclima/normais.clima/

Instituto Português do Mar e da Atmosfera. (2021b, 05 July). Séries Longas. https://www.ipma.pt/pt/oclima/series.longas/

Leite, I. (2021, 07 June). Rede urbana e sistema urbano. https://www.slideshare.net/seculoXXI/rede-e-sistema-urbanos-em-portugal2

Little, R.J.A. & Rubin, D.B. (2002). Statistical analysis with missing data, 2nd ed. Wiley, Hoboken. https://doi.org/10.1002/9781119013563

Luo, Y., Cai, X., Zhang, Y., Xu, J. & Yuan, X. (2018). Multivariate time series imputation with generative adversarial networks. 32nd Conference on Neural Information Processing Systems. Montréal, Canada.

Miranda, P., Coelho, M.F., Tomé, A., Valente, M., Carvalho, A., Pires, C., Pires, H.O., Pires, V. & Ramalho, C. (2002). 20th Century Portuguese Climate and Climate Scenarios. In F.D. Santos, K. Forbes, & R. Moita (eds.) Climate Change in Portugal: Scenarios, Impacts and Adaptation Measures (SIAM Project) (p. 23-83). Gradiva.

Moritz, S. & Bartz-Beielstein, T. (2017) imputeTS: Time series missing value imputation in R. The R Journal, 9:1, 207–2018. https://doi.org/10.32614/RJ-2017-009

National Pesticide Information Center (2020) Pesticide movement in the environment. http://npic.orst.edu/outreach/movement-infographic.png

National Weather Service. (2021, 07 July). Climate Time Series: Seasonal Variability. https://training.weather.gov/pds/climate/pcu2/statistics/Stats/part1/CTS_SeaVar.htm

Nitrates Directive. (1991). Directive 91/676/EEC of 12 December 1991 concerning the protection of waters against pollution caused by nitrates from agricultural sources. Official Journal of the European Communities, 31/12/1991, L375, 1–13.

Paz, M.C., Santos S.A.P., Barreira, R., Rasko, Duan, X., Alves, J., Alves da Silva, A., Mina, R., Topping, C.J. & Sousa, J.P. (2021) Developing a subpopulation-based model for the olive fruit fly Bactrocera oleae (Diptera: Tephritidae): conceptual model outline. The 1st International Electronic Conference on Agronomy. https://doi.org/10.3390/IECAG2021-09680

Qin, Y., Ren, G., Zhang, P. Wu, L. & Wen, K. (2021). An imputation method for the climatic data with strong seasonality and spatial correlation. Theoretical and Applied Climatology, 144. https://doi.org/10.1007/s00704-021-03537-9.

Sofo, A., Manfreda, S., Fiorentino, M., Dichio, B. & Xiloyannis, C. (2008) The olive tree: a paradigm for drought tolerance in Mediterranean climates. Hydrology and Earth System Sciences, 12, 293–301. https://doi.org/10.5194/hess-12-293-2008

Topping, C.J., Hansen, T.S., Jensen, T.S., Jepsen, J.U., Nikolajsen, F. & Odderskær, P. (2003). ALMaSS, an agent-based model for animals in temperate European landscapes. Ecological Modelling, 167, 65–82. https://doi.org/10.1016/S0304- 3800(03)00173-X

Topping, C.J., Dalby, L. & Valdez, J.W. (2019). Landscape-scale simulations as a tool in multi-criteria decision making for agri-environment schemes. Agricultural Systems, 176, https://doi.org/10.1016/j.agsy.2019.102671.

R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/.

Roldán, R.A., Brito, A. & Tinelli, F. (2019) Pests and diseases of the olive tree, EIP-AGRI Focus Group. European Comission. https://ec.europa.eu/eip/agriculture/sites/default/files/fg33_mp_effectsofintensification_2019_en.pdf

Tzanakakis, M.E. (2003). Seasonal development and dormancy of insects and mites feeding on olive: a review. Netherlands Journal of Zoology, 52, 87–224. http://dx.doi.org/10.1163/156854203764817670

Van Buuren, S. (2012). Flexible imputation of missing data, 2nd ed. Chapman and Hall/CRC, Boca Raton. https://doi.org/10.1201/b11826

Villa, M., Santos, S.A.P., Aguiar, C. & Pereira, J. (2020a). Plants Biodiversity in Olive Orchards and Surrounding Landscapes from a Conservation Biological Control Approach. The 1st International Electronic Conference on Agronomy. https://doi.org/10.3390/IECPS2020-08604.

Villa, M., Santos, S.A.P., Pascual, S. & Pereira, J. (2020b). Do non-crop areas and landscape structure influence dispersal and population densities of male olive moth? Bulletin of Entomological Research, 111, 1–9. https://doi.org/10.1017/S0007485320000310.

Water Framework Directive (2000). Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for community action in the field of water policy. Official Journal of the European Communities, 22/12/2000, L 327, 0001–0073.

World Meteorological Organization (2017). WMO Guidelines on the Calculation of Climate Normals. WMO-No. 1203. https://library.wmo.int/doc_num.php?explnum_id=4166

Ziółkowska, E., Topping, C.J., Bednarska, A.J. & Laskowski, R. (2021). Supporting non-target arthropods in agroecosystems: Modelling effects of insecticides and landscape structure on carabids in agricultural landscapes. Science of the Total Environment, 774, 145746. https://doi.org/10.1016/j.scitotenv.2021.145746.

Publicado

2021-07-23

Cómo citar

Paz, M. C., A.P.-Santos, S., & Barreira, R. (2021). Processing of high-resolution temporal climate data for daily simulations of a complex agro-ecosystem. Revista De Estudios Andaluces, (42), 202–219. https://doi.org/10.12795/rea.2021.i42.10
Recibido 2021-06-16
Aceptado 2021-07-10
Publicado 2021-07-23