Normalized differential vegetative index as indicator of remote water stress in vineyard (Vitis vinifera L. cv. Cabernet Sauvignon)
DOI:
https://doi.org/10.34188/bjaerv8n2-022Palavras-chave:
midday stem water potential, NDVI, precision viticulture, regulated deficit irrigation, remotely piloted aircraft systemsResumo
Regulated deficit irrigation (RDI) is a technique of controlling irrigation in the vineyards to obtain benefits such as the reduction of water used, control of vegetative growth and berry quality. However, if the magnitude and duration of the water deficit are not monitored, stress may be applied, potentially leading to negative effects on the crop. To successfully carry out the management of the RDI, a series of proximal methodologies have been used that have facilitated decision-making based on indicators of the water status of plants and soil, however, considering the advances in precision agriculture, it is necessary to validate multispectral indicators that can be used as tools for farm-level water management. This study aimed to evaluate whether the normalized differential vegetative index (NDVI), measured remotely with a multispectral camera coupled to a drone, is related to the midday stem water potential (Ψs), in Vitis vinifera L. cv. Cabernet Sauvignon, during the phenological stage of fruit set, under RDI, to be used as an indicator of remote water stress. The main results of the study showed that: 1) in pre-fruit set states, there is no relationship between NDVI and Ψs; 2) once set is initiated, NDVI is significantly related to Ψs. Therefore, this indicator could be proposed as a remote tool for predicting the water status of the vineyard, specifically in this phenological state, facilitating the control of the RDI.
Referências
Allen, R., Pereira, L., Raes, D., and Smith, M. 2006. Evapotranspiración del cultivo: Guías para la determinación de los requerimientos de agua de los cultivos. Estudio FAO Riego y Drenaje 56. FAO. https://www.fao.org/3/x0490s/x0490s00.htm
Bianchi, D., Modina, D., Cavallaro, L., Spadaccini, R., Carnevali, P., and Brancadoro, L. 2021. Vineyard water stress evaluation using a multispectral index: a case study in the Chianti area. Acta horticulturae, 1314, 39–46. https://doi.org/10.17660/actahortic.2021.1314.6
Borgogno-Mondino, E., Novello, V., Lessio, A., Tarricone, L., and de Palma, L. 2018. Intra-vineyard variability description through satellite-derived spectral indices as related to soil and vine. Acta Horticulturae. https://doi.org/10.17660/ActaHortic.2018.1197.8
Bellvert, J., Zarco-Tejada, P. J., Marsal, J., Girona, J., González-Dugo, V., and Fereres, E. 2016. Vineyard irrigation scheduling based on airborne thermal imagery and water potential thresholds: Vineyard irrigation using airborne thermal imagery. Australian Journal of Grape and Wine Research, 22(2), 307–315. https://doi.org/10.1111/ajgw.12173
Coombe, B. 1995. Growth stages of the grapevine. (1), 100-110. Australian journal of Grape and Wine research.
Diago, M., Tardaguila, J., Barrio, I., and Fernández-Novales, J. 2022. Combination of multispectral imagery, environmental data and thermography for on-the-go monitoring of the grapevine water status in commercial vineyards. https://doi.org/https://doi.org/10.1016/j.eja.2022.126586
Gago, J., Douthe, C., Coopman, R. E., Gallego, P. P., Ribas-Carbo, M., Flexas, J., Escalona, J., and Medrano, H. 2015. UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9–19. https://doi.org/10.1016/j.agwat.2015.01.020
Huang, S., Tang, L., Hupy, J. P., Wang, Y., and Shao, G. 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1
Lukasova, V., Lang, M., and Skvarenina, J. 2014. Seasonal changes in NDVI in relation to phenological phases, LAI and PAI of beech forests. 20, 248-262. Balt. For.
Matese, A., and Di Gennaro, S. 2015. Technology in precision viticulture: A state of the art review. International Journal of Wine Research. https://doi.org/10.2147/IJWR.S69405
Mirás-Avalos, J., and Araujo, E. 2021. Optimization of vineyard water management: challenges, strategies, and perspectives. Water. https://doi.org/10.3390/w13060746
Munitz, S., Netzer, Y., and Schwartz, A. 2017. Sustained and regulated deficit irrigation of field-grown Merlot grapevines: Sustained and regulated deficit irrigation. Australian Journal of Grape and Wine Research, 23(1), 87–94. https://doi.org/10.1111/ajgw.12241
Pádua, L., Vanko, J., Hruška, J., Adão, T., Sousa, J. J., Peres, E., and Morais, R. 2017. UAS, sensors, and data processing in agroforestry: a review towards practical applications. International Journal of Remote Sensing, 38(8–10), 2349–2391. https://doi.org/10.1080/01431161.2017.1297548
Pôças, I., Rodrigues, A., Gonçalves, S., Costa, P. M., Gonçalves, I., Ls, P., and Cunha, M. 2015. Predicting grapevine water status based on hyperspectral reflectance vegetation indices. Remote Sens, 7, 16460–16479.
Romero, M., Luo, Y., Su, B., and Fuentes, S. 2018. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture, 147, 109–117. https://doi.org/10.1016/j.compag.2018.02.013
Romero, P., Navarro, J. M., and Ordaz, P. B. 2022. Towards a sustainable viticulture: The combination of deficit irrigation strategies and agroecological practices in Mediterranean vineyards. A review and update. Agricultural Water Management, 259(107216), 107216. https://doi.org/10.1016/j.agwat.2021.107216
Tang, Z., Jin, Y., Alsina, M., McElrone, A., Bambach, N., and Kustas, W. 2022. Vine water status mapping with multispectral UAV imagery and machine learning. Irrigation Science. https://doi.org/10.1007/s00271-022-00788-w
Virnodkar, S., Pachghare, V., Patil, V., and Kumar, S. 2020. Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture. https://doi.org/10.1007/s11119-020-09711-9
Zúñiga, C., Khot, L., Sankaran, S., and Jacoby, P. 2017. High Resolution Multispectral and Thermal Remote Sensing-Based Water Stress Assessment in Subsurface Irrigated Grapevines. Remote Sensing. https://doi.org/10.3390/rs9090961
Downloads
Publicado
Como Citar
Edição
Seção
Licença
- O conteúdo dos artigos é de responsabilidade exclusiva dos autores.
- É permitida a reprodução total ou parcial do conteúdo dos artigos, desde que citada a fonte.
- Artigos com plágio serão recusados, e o autor do plágio perderá o direito de publicar nesta revista.
- Os nomes e endereços informados nesta revista serão utilizados exclusivamente para os serviços prestados por esta publicação e não estão disponíveis para outros fins ou a terceiros.
- Depois de enviar os artigos, os autores cedem os direitos autorais de seus artigos ao BJAER. Se você se arrepender da submissão, o autor tem o direito de solicitar ao BJAER que não publique seu artigo. Porém, essa solicitação deve ocorrer até dois meses antes da divulgação do número que o artigo será publicado.
- O BJAER usa a licença Creative Commons CC BY. Informações sobre esta licença podem ser encontradas em: https://creativecommons.org/licenses/by/3.0/br/
Brazilian Journal of Animal and Environmental Research (BJAER) se reserva o direito de efetuar nos originais alterações de ordem normativa, ortográfica e gramatical, com vistas a manter o padrão culto da língua, respeitando, porém, o estilo dos autores. Os trabalhos finais não serão enviadas aos autores.
