RPAS para precisión de la evapotranspiración en arrozales y reducir el consumo de agua

Autores/as

DOI:

https://doi.org/10.15517/am.2024.56529

Palabras clave:

arrozales, balance de energía, drone, riego con secas controladas

Resumen

Introducción. La estimación de la evapotranspiración del cultivo (ETc) permite conocer los requerimientos de agua del cultivo, que ayudan a proponer técnicas de riego con ahorro de agua. Objetivo. Usar el sistema de aeronave pilotada remotamente (RPAs) para mayor precisión de la evapotranspiración en arrozales a fin de reducir el consumo de agua. Materiales y métodos. Para el estudio la distribución de parcelas siguió un diseño de bloques completamente al azar con estructura factorial de dos experimentos: riego inundado (E1) y riego con secas controladas (E2), y tres variedades de arroz (IR43, IR71706, Sahod Ulan 12); se llevó a cabo en el Área Experimental de Riego (AER) de la Universidad Nacional Agraria La Molina, Perú. Entre enero y febrero del 2019 se realizaron ocho vuelos, de un RPAs, distribuidos entre las etapas de macollamiento y punto de algodón. Resultados. El análisis combinado de tratamientos con análisis de varianza y prueba de Duncan con p < 0,05, reveló diferencia significativa de la ETc, entre E1 y E2. Sin embargo, no se encontró diferencia significancia entre las variedades de arroz. Se obtuvo valores máximos de ETc y rendimiento para E1 de 4,50 mm/d y 10 389 kg/ha, y para E2 de 3,7 mm/d y 9710 kg/ha, respectivamente. Conclusiones. El uso de un sistema de aeronave pilotada remotamente permitió mejorar la resolución temporal y espacial de las imágenes multiespectrales y térmicas para obtener mayor precisión en la evapotranspiración del cultivo (ETc) bajo dos regímenes de riego. En el riego con secas controladas se obtuvo una reducción del 24 % de la ETc lo que permitió un ahorro de agua de 855 m3/ha.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Abebe, T., Alamerew, S., Tulu, L. (2017). Genetic variability, heritability and genetic advance for yield and its related traits in rainfed lowland rice (Oryza sativa L.) genotypes at Fogera and Pawe, Ethiopia. Advances in Crop Science and Technology, 5, Article 272. https://doi.org/10.4172/2329-8863.1000272

Acharya, B., & Sharma, V. (2021). Comparison of satellite driven surface energy balance models in estimating crop evapotranspiration in semi-arid to arid inter-mountain region. Remote Sensing, 13(9), Article 1822. https://doi.org/10.3390/rs13091822

Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. Journal of Irrigation and Drainage Engineering, 133(4), 380–394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)

Anupoju, V., & Kambhammettu, B. (2020). Role of deficit irrigation strategies on ET partition and crop water productivity of rice in semi-arid tropics of south India. Irrigation Science, 38, 415–430. https://doi.org/10.1007/s00271-020-00684-1

Bian, J., Zhang, Z., Chen, J., Chen, H., Cui, C., Li, X., Chen, S., Fu, Q. (2019). Simplified evaluation of cotton water stress using high resolution unmanned aerial vehicle thermal imagery. Remote Sensing, 11(3), Article 267. https://doi.org/10.3390/rs11030267

Castañeda-Ibáñez, C. R., Flores-Magdaleno, H., Martínez-Menes, M., Esparza-Govea, S., Fernández-Reynoso, D., Prado-Hernández, V., Pascual-Ramírez, F. (2018). Estimación de la evapotranspiración mediante un balance de energía utilizando sensores remotos. Ecosistemas y Recursos Agropecuarios, 5(15), 537–545. https://doi.org/10.19136/era.a5n15.1647

Chandel, A. K., Molaei, B., Khot, L. R., Peters, R. T., & Stöckle, C. O. (2022). High resolution geospatial evapotranspiration mapping of irrigated field crops using multispectral and thermal infrared imagery with METRIC energy balance model. Journal of the. Drone, 4(3), Article 52. https://doi.org/10.3390/drones4030052

Cheng, H., Shu, K., Zhu, T., Wang, L., Liu, X., Cai, W., Qi, Z., & Feng, S. (2022). Effects of alternate wetting and drying irrigation on yield, water and nitrogen use, and greenhouse gas emissions in rice paddy fields. Journal of Cleaner Production, 349, Article 131487. https://doi.org/10.1016/j.jclepro.2022.131487

Chu, G., Chen,T., Chen, S., Xu, C., Wang, D., & Zhang, X. (2018). The effect of alternate wetting and severe drying irrigation on grain yield and water use efficiency of Indica-japonica hybrid rice (Oryza sativa L.). Food and Energy Security, 7(2), Article e00133. https://doi.org/10.1002/fes3.133

Djaman, K., Rudnick, D. R., Moukoumbi, Y. D., Sow, A., & Irmak, S. (2019). Actual evapotranspiration and crop coefficients of irrigated lowland rice (Oryza sativa L.) under semiarid climate. Italian Journal of Agronomy, 14(1), 19–25. https://doi.org/10.4081/ija.2019.1059

Durán Gómez, M. R., Ramos Fernández, L., Altamirano Gutiérrez, L., & Arapa Quispe, J. (2021). Thermal imaging and thermocouple sensors for estimating water stress index of rice cultivation under drip irrigation. Idesia (Arica), 39(1), 109–118. https://dx.doi.org/10.4067/S0718-34292021000100109

Fan, L., Gao, Y., Brück, H., & Bernhofer, C. (2009). Investigating the relationship between NDVI and IAF in semi-arid grassland in Inner Mongolia using in-situ measurements. Theoretical and Applied Climatology, 95(1–2), 151–156. https://dx.doi.org/10.1007/s00704-007-0369-2

Fonseca Salazar, S., Verano Zelada, C., & Mariluz Silva, J. P. (2012, octubre 26). Huella hídrica del cultivo del arroz. Huella hídirca del arroz en el Perú. Oficina del Sistema Nacional de Información de Recursos Hídricos. https://hdl.handle.net/20.500.12543/546

Food and Agriculture Organization. (n.d.). FAOSTAT. Crop and livestock products. Retrieved February 23, 2022, from http://www.fao.org/faostat/en/#data/QC

Folhes, M. T., Rennó, C. D., & Soares, J. V. (2009). Remote sensing for irrigation water management in the semi-arid Northeast of Brazil. Agricultural Water Management, 96(10), 1398–1408. https://doi.org/10.1016/j.agwat.2009.04.021

Hardin, P. J., & Jensen, R. R. (2011). Small-scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities. GIScience & Remote Sensing, 48(1), 99–111. https://doi.org/10.2747/1548-1603.48.1.99

Heros Aguilar, E. C., Lozano-Isla, F., & Casas Diaz, A. V. (2023). Tecnologías para la producción de arroz: recomendaciones para el Perú basadas en investigaciones científicas. South Sustainability, 4(1), Article e069. https://doi.org/10.21142/SS-0401-2023-e069

Hussain, Z., Wang, Z., Wang, J., Yang, H., Arfan, M., Hassan, D., Wang, W., Azam, M. I., & Faisal, M. (2022). A comparative appraisal of classical and holistic water scarcity indicators. Water Resources Management, 36(3), 931–950. https://doi.org/10.1007/s11269-022-03061-z

Ishfaq, M., Farooq, M., Zulfiqar, U., Hussain, S., Akbar, Ahmad, N., Nawaz, A., & Ahmad Anjum, S. (2020). Alternate wetting and drying: A water-saving and ecofriendly rice production system. Agricultural Water Management, 241, Article 106363. https://doi.org/10.1016/j.agwat.2020.106363

Jiang, Y., Carrijo, D., Huang, S., Chen, J., Balaine, N., Zhang, W., van Groenigen, K. J., & Linquist, B. (2019). Water management to mitigate the global warming potential of rice systems: A global meta-analysis. Field Crops Research, 234, 47–54. https://doi.org/10.1016/j.fcr.2019.02.010

Kumar, A., & Rajitha, G. (2019). Alternate Wetting and Drying (AWD) irrigation - A smart water saving technology for rice: A review. International Journal of Current Microbiology and Applied Sciences, 8(3), 2561–2571. https://doi.org/10.20546/ijcmas.2019.803.304

Lee, Y., & Kim, S. (2016). The modified SEBAL for mapping daily spatial evapotranspiration of South Korea using three flux towers and terra MODIS data. Remote Sensing, 8(12), Article 983. https://doi.org/10.3390/rs8120983

Liu, X., Xu, J., Yang, S., & Zhang, J. (2018). Rice evapotranspiration at the field and canopy scales under water-saving irrigation. Meteorology and Atmospheric Physics, 130, 227–240. https://doi.org/10.1007/s00703-017-0507-z

Machaca-Pillaca, R., Pino-Vargas, E., Ramos-Fernández, L., Quille-Mamani, J., & Torres-Rua, A. (2022). Estimación de la evapotranspiración con fines de riego en tiempo real de un olivar a partir de imágenes de un drone en zonas áridas, caso La Yarada, Tacna, Perú. Idesia (Arica), 40(2), 55–65. https://doi.org/10.4067/S0718-34292022000200055

Maresma, A., Chamberlain, L., Tagarakis, A., Kharel, T., Godwin, G., Czymmek, K., Shields, E., Ketterings, Q. M. (2020) Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing. Computers and Electronics in Agriculture, 169, Article 105236. https://doi.org/10.1016/j.compag.2020.105236

Mendoza-Pérez, C., Ramírez-Ayala, C., Ojeda-Butamante, W., Flores-Magdaleno, H. (2017). Estimation of leaf area index and yield of greenhouse-grown poblano pepper. Ingeniería Agrícola y Biosistemas, 9(1), 37–50. https://doi.org/10.5154/r.inagbi.2017.04.009

Montibeller, Á. G. (2017). Estimating energy fluxes and evapotranspiration ofcorn and soybean with an unmanned aircraft system in Ames, Iowa [Master of Arts thesis, University of Northern Iowa]. UNI Scholar Works. https://scholarworks.uni.edu/etd/416

Neira Huamán, E., Ramos Fernández, L., & Razuri Ramírez, L. R. (2020). Coeficiente del cultivo (Kc) del arroz a partir de lisímetro de drenaje en La Molina, Lima-Perú. Idesia (Chile), 38(2), 49–55. https://doi.org/10.4067/s0718-34292020000200049

Nassar, A., Torres-Rua, A., Kustas, W., Alfieri, J., Hipps, L., Prueger, J., Nieto, H., Alsina, M. M., White, W., McKee, L., Coopmans, C., Sanchez, L., & Dokoozlian, N. (2021). Assessing daily evapotranspiration methodologies from one-time-of-day sUAS and EC information in the GRAPEX project. Remote Sensing, 13(15), Article 2887. https://doi.org/10.3390/rs13152887

Nhamo, L., Magidi, J., Nyamugama, A., Clulow, A. D., Sibanda, M., Chimonyo, V. G. P., & Mabhaudhi, T. (2020). Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10(7), Article 256. https://doi.org/10.3390/agriculture10070256

Niu, H., Hollenbeck, D., Zhao, T., Wang, D., & Chen, Y. (2020). Evapotranspiration estimation with small UAVs in precision agriculture. Sensors, 20(22), Article 6427. https://doi.org/10.3390/s20226427

Ortega-Farías, S., Ortega-Salazar, S., Poblete, T.; Kilic, A., Allen, R., Poblete-Echeverría, C., Ahumada-Orellana, L., Zuñiga, M., Sepúlveda, D. (2016). Estimation of energy balance components over a drip-irrigated olive orchard using thermal and multispectral cameras placed on a helicopter-based unmanned aerial vehicle (UAV). Remote Sensing, 8(8), Article 638. https://doi.org/10.3390/rs8080638

Ouda, S., & Zohry, A. E. H. (2022). Water-smart practices to manage water scarcity. In S. Ouda, & A. El-Hafeez Zohry (Eds.), Climate-smart agriculture (pp. 3–26). Springer, Cham. https://doi.org/10.1007/978-3-030-93111-7_1

Porras-Jorge, R., Ramos-Fernández, L., Ojeda-Bustamante, W., & Ontiveros-Capurata, R. (2020). Performance assessment of the AquaCrop model to estimate rice yields under alternate wetting and drying irrigation in the coast of Peru. Scientia Agropecuaria, 11(3), 309–321. https://doi.org/10.17268/sci.agropecu.2020.03.03

Quille-Mamani, J., Ramos-Fernández, L., & Ontiveros-Capurata, R. (2021). Estimación de la evapotranspiración del cultivo de arroz en Perú mediante el algoritmo METRIC e imágenes VANT. Revista de Teledetección, (58), 23–38. https://doi.org/10.4995/raet.2021.13699

Roy, D., Hossain, M. B., Bin Hafiz Pranto, M. R., & Islam, M. T. (2022). Drought management by integrated approaches in T. Aman rice season to escalate rice productivity in drought prone regions of Bangladesh. In G. m. Terekul Islam, S. Shampa, & A. I. A. Chowdhury (Eds.), Water management: A view from multidisciplinary perspectives (pp. 351-364). Springer, Cham. https://doi.org/10.1007/978-3-030-95722-3_17

Saha, S., Ahmmed R., & Jahan, N. (2022). Actual evapotranspiration estimation using remote sensing: Comparison of Sebal and Metric models. In G. M Tarekul Islam, Shampa, & A. I. Amin Chowdhury (Eds.), Water management: A view from multidisciplinary perspectives (pp. 365–383). Springer, Cham. https://doi.org/10.1007/978-3-030-95722-3_18

Sawadogo, A., Kouadio, L., Traoré, F., Zwart, S. J., Hessels, T., & Gündoğdu, K. S. (2020). Spatiotemporal assessment of irrigation performance of the Kou Valley irrigation scheme in Burkina Faso using satellite remote sensing-derived indicators. ISPRS International Journal of Geo-Information, 9(8), Article 484. https://doi.org/10.3390/ijgi9080484

Shabir, K., Kumar, R., & Maryam, M.(2023). Evapotranspirationin contest of climate change: Evapotranspiration. In M. Kumar, R. Kumar, & V. P. Singh (Eds.), Advances in water management under climate change (1st ed., Capter 15, pp. 274–290). CRC Press. https://doi.org/10.1201/9781003351672

Sisheber, B., Marshall, M., Mengistu, D., & Nelson, A. (2022). Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS data fusion. International Journal of Applied Earth Observation and Geoinformation, 106, Article 102670. https://doi.org/10.1016/j.jag.2021.102670

Suwanlertcharoen, T., Chaturabul, T., Supriyasilp, T., & Pongput, K. (2023). Estimation of Actual Evapotranspiration Using Satellite-Based Surface Energy Balance Derived from Landsat Imagery in Northern Thailand. Water, 15(3), Article 450. https://doi.org/10.3390/w15030450

Taherparvar, M., & Pirmoradian, N. (2018). Estimation of rice evapotranspiration using reflective images of Landsat satellite in sefidrood irrigation and drainage network. Rice Science, 25(2), 111–116. https://doi.org/10.1016/j.rsci.2018.02.003

Tsouni, A., Kontoes, C., Koutsoyiannis, D., Elias, P., & Mamassis, N. (2008). Estimation of actual evapotranspiration by remote sensing: Application in Thessaly plain, Greece. Sensors, 8(6), 3586–3600. https://doi.org/10.3390/s8063586

Villar Barraza, H. D., Ramos Fernández, L., & Alminagorta Cabezas, O. (2021). Evaluación del estrés hídrico del cultivo de arroz (IR 71706) a través del uso de termografía calibrada del área del dosel en Lima, Perú. Idesia (Arica), 39(4), 59–70. https://dx.doi.org/10.4067/S0718-34292021000400059

Wei, G., Cao, J., Xie, H., Xie, H., Yang, Y., Wu, C., Cui, Y., & Luo, Y. (2022). Spatial-temporal variation in paddy evapotranspiration in subtropical climate regions based on the SEBAL model: A case study of the Ganfu Plain irrigation system, southern China. Remote Sensing, 14(5), Article 1201. https://doi.org/10.3390/rs14051201

Zobeidi, T., Yaghoubi, J., & Yazdanpanah, M. (2022). Farmers’ incremental adaptation to water scarcity: An application of the model of private proactive adaptation to climate change (MPPACC). Agricultural Water Management, 264, article 107528. https://doi.org/10.1016/j.agwat.2022.107528

Zhou, S., Sun, H., Bi, J., Zhang, J., Riya, S., & Hosomi, M. (2020). Effect of water-saving irrigation on the N2O dynamics and the contribution of exogenous and endogenous nitrogen to N2O production in paddy soil using 15N tracing. Soil & Tillage Research, 200, Article 104610. https://doi.org/10.1016/j.still.2020.104610

Publicado

2024-04-11

Cómo citar

Quispe-Tito, D. J. ., Ramos-Fernández, L., Pino-Vargas, E. ., Quille-Mamani, J., & Torres-Rua, A. (2024). RPAS para precisión de la evapotranspiración en arrozales y reducir el consumo de agua. Agronomía Mesoamericana, 35, 56529. https://doi.org/10.15517/am.2024.56529

Artículos más leídos del mismo autor/a