Thermal stress in an Agroforestry System: a case study of the use of a thermal sensor and unmanned aerial vehicle
DOI:
https://doi.org/10.18011/bioeng.2025.v19.1278Keywords:
thermal condition index, remote sensing, multifunctionality, solar radiationAbstract
An agroforestry system (AFS) is a multifaceted agricultural practice that integrates the cultivation of shade-providing trees and understory crops. These systems have been shown to play a crucial role in mitigating extreme temperatures, reducing soil evapotranspiration, and shielding crops from wind damage. Effective management practices, including pruning, ensure optimal light distribution and temperature regulation in the AFS. The employment of unmanned aerial vehicles (UAVs) has led to significant advancements in the evaluation of crop traits, including the assessment of heat stress. However, research addressing thermal stress and temperature variations across AFS areas is limited. It is evident that thermal stress exerts a substantial influence on the physiological and genetic processes within plants. The present study investigated the relationship between temperature, insolation, and thermal stress in a 0.5-hectare AFS at Universidade Federal de São Carlos, Brazil. A Unmanned Aerial Vehicle (UAV) equipped with a thermal sensor was utilized to capture thermal and RGB images in four regions. These images were then analyzed with software such as Agisoft Metashape and QGIS to calculate the Thermal Condition Index (TCI). This index enabled the quantification of plant stress. The findings indicated that the midday period experienced the highest levels of stress, particularly in open pasture areas. The correlation between insolation exposure and higher surface temperatures (up to 40.56 °C) was significant in these areas. In contrast, tree-covered areas demonstrated a lower level of stress. TCI variations have been shown to align with temperature trends, underscoring the significance of microclimatic data for optimizing AFS management practices, including pruning and crop selection.
Downloads
References
Adefarati, T. & Bansal, R. C. (2019). Energizing Renewable Energy Systems and Distribution Generation. In Taşcıkaraoğlu, A. & Erdinc, O. (Eds.) Pathways to a Smarter Power System, (pp. 29–65). Academic Press. https://doi.org/10.1016/b978-0-08-102592-5.00002-8.
Alves, R. F. & Putti, F. F. (2022). Use of images for early identification of water stress. Revista Brasileira de Engenharia de Biossistemas, 16. https://doi.org/10.18011/bioeng.2022.v16.1114.
Armson, D., Stringer, P., & Ennos, A. R. (2012). The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban Forestry & Urban Greening, 11(3), 245–255. https://doi.org/10.1016/j.ufug.2012.05.002.
D’Acunha, B., Dalmagro, H. J., Zanella de Arruda, P. H., Biudes, M. S., Lathuillière, M. J., Uribe, M., Couto, E. G., Brando, P. M., Vourlitis, G., & Johnson, M. S. (2024). Changes in evapotranspiration, transpiration and evaporation across natural and managed landscapes in the Amazon, Cerrado and Pantanal biomes. Agricultural and Forest Meteorology, 346, 109875. https://doi.org/10.1016/j.agrformet.2023.109875.
Feron, S., Cordero, R. R., Damiani, A., Llanillo, P. J., Jorquera, J., Sepulveda, E., Asencio, V., Laroze, D., Labbe, F., Carrasco, J., & Torres, G. (2019). Observations and Projections of Heat Waves in South America. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-44614-4.
Jose, S., Gillespie, A. R., & Pallardy, S. G. (2004). Interspecific interactions in temperate agroforestry. Agroforestry Systems, 61-62(1-3), 237–255. https://doi.org/10.1023/b:agfo.0000029002.85273.9b.
Kim, J., Abdou Khouakhi, Corstanje, R., & Alice S.A. Johnston. (2024). Greater local cooling effects of trees across globally distributed urban green spaces. Science of the Total Environment, 911, 168494–168494. https://doi.org/10.1016/j.scitotenv.2023.168494.
Kogan, F. N. (1997). Global Drought Watch from Space. Bulletin of the American Meteorological Society, 78(4), 621–636. https://doi.org/10.1175/1520-0477(1997)078%3C0621:gdwfs%3E2.0.co;2.
Kumar, B. M., Kunhamu, T. K., Bhardwaj, A., & Santhoshkumar, A. V. (2024). Subcanopy light availability, crop yields, and managerial implications: a systematic review of the shaded cropping systems in the tropics. Agroforestry Systems, 98, 2785-2810. https://doi.org/10.1007/s10457-024-00957-0.
Liang, Y., Kou, W., Lai, H., Wang, J., Wang, Q., Xu, W., Wang, H., & Lu, N. (2022). Improved estimation of aboveground biomass in rubber plantations by fusing spectral and textural information from UAV-based RGB imagery. Ecological Indicators, 142, 109286. https://doi.org/10.1016/j.ecolind.2022.109286.
Luiza, M., Castiglioni, P. P., Macedo, R., Patrícia Tholon, & Antônio Aparecido Carpanezzi. (2016). Reducing competition in agroforestry by pruning native trees. Revista Árvore, 40(3), 509–518. https://doi.org/10.1590/0100-67622016000300014.
Mikó, E., Donyina, G. A., Baccouri, W., Tóth, V., Flórián, K., Gyalai, I. M., Yüksel, G., Köteles, D., Srivastava, V., & Wanjala, G. (2025). One health agriculture: Heat stress mitigation dilemma in agriculture. One Health, 20, 100966. https://doi.org/10.1016/j.onehlt.2025.100966.
Multifunctionality. (2001). In OECD eBooks. Organization for Economic Cooperation and Development. OECD Publishing, Paris. https://doi.org/10.1787/9789264192171-en.
Rosisca, J.R., Caramori, P.H., Morais, H., Aguiar, M., Mitsuo, G., & Caramori, D. C. (2022). Thermal environment in an agroforestry system of coffee and rubber tree in Southern Brazil. Agrometeoros, 30. https://doi.org/10.31062/agrom.v30.e026934.
Ren, J., Yang, J., Wu, F., Sun, W., Xiao, X., & Xia, J. (Cecilia). (2023). Regional thermal environment changes: Integration of satellite data and land use/land cover. IScience, 26(2), 105820. https://doi.org/10.1016/j.isci.2022.105820.
Ryu, Y., Berry, J. A., & Baldocchi, D. D. (2019). What is global photosynthesis? History, uncertainties and opportunities. Remote Sensing of Environment, 223, 95–114. https://doi.org/10.1016/j.rse.2019.01.016.
Santos, Pinheiro, J., Luana, M., Ferraz, R., Vieira, M., Batista, C., Jaramillo, D. M., Bezerra, A. C., & Oliveira, J. (2024). Can Unmanned Aerial Vehicle Images Be Used to Estimate Forage Production Parameters in Agroforestry Systems in the Caatinga? Applied Sciences, 14(11), 4896–4896. https://doi.org/10.3390/app14114896.
Sgarbossa, J., Elli, E. F., Schwerz, F., Nardini, C., Miguel Knapp, F., Schmidt, D., Dal’Col Lúcio, A., & Caron, B. O. (2020). Bean–soybean succession under full sun and in agroforestry systems: Impacts on radiation use efficiency, growth and yield. Journal of Agronomy and Crop Science, 207(2), 362–377. https://doi.org/10.1111/jac.12450.
Singh, A., Singh, P., & Gill, R. I. S. (2024). Agroforestry could be one of the viable options to deal with terminal heat stress in wheat causing yield loss in Indo-Gangetic Plains. Environment, Development and Sustainability, 27, 19631-19673. https://doi.org/10.1007/s10668-024-04731-1.
Thomsen, S. J., Sanjok Poudel, Fike, J. H., & Pent, G. J. (2023). Heifer performance and body temperatures in open pasture versus silvopasture in mid-Atlantic USA. Agroforestry Systems, 98(1), 47–59. https://doi.org/10.1007/s10457-023-00889-1.
Urban, O., Klem, K., Ač, A., Kateřina Havránková, Holišová, P., Navrátil, M., Zitová, M., Klára Kozlová, Radek Pokorný, M. Šprtová, Tomášková, I., Vladimír Špunda, & Grace, J. R. (2012). Impact of clear and cloudy sky conditions on the vertical distribution of photosynthetic CO2uptake within a spruce canopy. Functional Ecology. 26(1), 46–55. https://doi.org/10.1111/j.1365-2435.2011.01934.x.
Viana, L. A., Zambolim, L., Sousa, T. V., & Tomaz, D. C. (2018). Potential use of thermal camera coupled in UAV for culture monitoring. Revista Brasileira de Engenharia de Biossistemas, 12(3), 286. https://doi.org/10.18011/bioeng2018v12n3p286-298.
Xie, C., & Yang, C. (2020). A review on plant high-throughput phenotyping traits using UAV-based sensors. Computers and Electronics in Agriculture, 178, 105731. https://doi.org/10.1016/j.compag.2020.105731.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
By publishing in this journal, authors agree to the following terms:
a) Authors retain copyright and grant the journal the right of first publication. The work is simultaneously licensed under the Creative Commons Attribution License, which permits sharing and adaptation of the work with appropriate credit to the authors and the journal.
b) Authors may enter into separate, additional agreements for non-exclusive distribution of the published version of the work (e.g., posting to an institutional repository or inclusion in a book), provided that proper credit is given to the original publication in this journal.
