Water demand of black pepper (Piper nigrum L.) determined by NDVI
DOI:
https://doi.org/10.15517/zxczt264Keywords:
Remote sensing, crop coefficient, precision agriculture, irrigationAbstract
Introduction. Water scarcity compromises agricultural productivity and requires precise management tools. Normalized Difference Vegetation Index (NDVI) emerges as an effective alternative for estimating crop coefficient and monitoring crop water needs. Objective. To estimate the water demand of black pepper during the flowering phase in the Northeast region of Pará, by means of satellite images to obtain the NDVI and, from it, the estimation of the crop coefficient. Materials and methods. The study was conducted from 2016 to 2018 in the Avelinos community, Santa Luzia do Pará, Brazil, in an area cultivated with black pepper that was 1, 2, and 3 years old. Coordinates were obtained by GPS and Landsat 8 images were used to calculate NDVI. The crop coefficient (Kc) was estimated using the SAFER algorithm based on NDVI, and the crop evapotranspiration (ETc) was calculated by multiplying Kc by the reference evapotranspiration. Results. In areas with 1 and 2 years of cultivation, Kc ranged from 0.22 to 1.00, while in the area with 3 years of cultivation, values ranged from 0.11 to 0.33, which indicates low vegetative vigor and water stress. The estimated water demand for the flowering phase was 2.50, 2.62, and 1.30 L m² day-1 for areas with 1, 2, and 3 years of cultivation, respectively. Conclusions. NDVI-based estimates effectively captured crop water requirements and vegetative vigor across different cultivation ages, revealing that the oldest area exhibits lower water demand coupled with elevated water stress. Given the absence of stage-specific values for black pepper, these estimated parameters provide a fundamental basis for refining irrigation management in Northeastern Pará.
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References
Albornoz, V. M., Ñanco, L. J., & Sáez, J. L. (2019). Outlining robust rectangular management zones based on the column generation algorithm. Computers and Electronics in Agriculture, 161, 194-201. https://doi.org/10.1016/j.compag.2019.01.045 DOI: https://doi.org/10.1016/j.compag.2019.01.045
Albuquerque, P. E. P., & Maeno, P. (2007). Requerimento de água das culturas para fins de dimensionamento e manejo de sistemas de irrigação localizada. Embrapa Milho e Sorgo. https://www.infoteca.cnptia.embrapa.br/infoteca/bitstream/doc/486183/1/Doc65.pdf
Allen, R. G. (2016). REF-ET: Reference evapotranspiration calculator (Version 4.1.22) [Computer software]. University Idaho. https://ref-et.software.informer.com/
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements (FAO Irrigation and Drainage Paper No. 56). Food and Agriculture Organization of the United Nations.
Allen, R. G., Walter, I. A., Elliott, R., Howell, T. A., Itenfisu, D., & Jensen, M. (Eds.). (2005). Watershed management and operations management. In Standardized reference evapotranspiration equation (pp. 1-11). American Society of Civil Engineers. https://ascelibrary.org/doi/book/10.1061/9780784408056 DOI: https://doi.org/10.1061/9780784408056
Alves, E. S., Lima, D. F., Barretos, J. A. S., Santos, D. P., & Santos, M. A. L. (2017). Determination of the cultivation coefficient for radish cultivation through drainage lysimetry. Irriga, 22, 194-203. https://doi.org/10.15809/irriga.2017v22n1p194-203 DOI: https://doi.org/10.15809/irriga.2017v22n1p194-203
Ambrozim, C. S., Medici, L. O., Cruz, E. S. D., Abreu, J. F. G., & Carvalho, D. F. D. (2021). Physiological response of black pepper (Piper nigrum L.) to deficient irrigation. Revista Ciência Agronômica, 53, Article e20207348. https://doi.org/10.5935/1806-6690.20220002 DOI: https://doi.org/10.5935/1806-6690.20220002
Bastiaanssen, W. G. (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229(1-2), 87-100. https://doi.org/10.1016/S0022-1694(99)00202-4 DOI: https://doi.org/10.1016/S0022-1694(99)00202-4
Bernardi, A. D. C., Bettiol, G. M., Grego, C. R., Andrade, R. G., Rabello, L. M., & Inamasu, R. Y. (2015). Precision agriculture tools as an aid to soil fertility management. Cadernos de Ciência & Tecnologia, 32, 211-227.
Çetin, Ö., Fayrap, A., & Yolcu, R. (2024). Sustainability and modernization of agricultural irrigation: a comparative evaluation of two irrigation schemes. Irrigation and Drainage, 73, 284-293. https://doi.org/10.1002/ird.2878 DOI: https://doi.org/10.1002/ird.2878
Chávez, P. S. (1988). An improved dark object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of the Environment, 24, 459-479. https://doi.org/10.1016/0034-4257(88)90019-3 DOI: https://doi.org/10.1016/0034-4257(88)90019-3
Dibaba, W. T., Demissie, T. A., & Miegel, K. (2020). Hydrological watershed response to combined land use/land cover and climate change in the Ethiopian highlands: Finchaa watershed. Water, 12, Article 1801. https://doi.org/10.3390/w12061801 DOI: https://doi.org/10.3390/w12061801
Dingre, S. K., & Gorantiwar, S. D. (2020). Determination of water requirement values and sugarcane cultivation coefficient by the field water balance method in the semiarid region. Agricultural Water Management, 232, Article 106042. https://doi.org/10.1016/j.agwat.2020.106042 DOI: https://doi.org/10.1016/j.agwat.2020.106042
Doorenbos, J., & Pruitt, W. O. (1977). Guidelines for predicting crop water requirements (FAO Irrigation and Drainage Paper 24). Food and Agriculture Organization of the United Nations. https://cabidigitallibrary.org/doi/full/10.5555/19771935325
Feng, W., Zhang, H. Y., Zhang, Y. S., Qi, S. L., Heng, Y. R., Guo, B. B., Ma, D. Y., & Guo, T. C. (2016). Remote sensing of nitrogen concentration in canopy leaves in winter wheat using water-resistant vegetation indices from in-situ hyperspectral data. Field Crops Research, 198, 238-246. https://doi.org/10.1016/j.fcr.2016.08.023 DOI: https://doi.org/10.1016/j.fcr.2016.08.023
Freden, S. C., Mercanti, E. P., & Friedman, D. B. (Eds.). (1974). Third Earth Resources Technology Satellite Symposium: Volume III, discipline summary reports (NASA-SP-357). National Aeronautics and Space Administration. https://ntrs.nasa.gov/api/citations/19740025760/downloads/19740025760.pdf
Gupta, A., Rico-Medina, A., & Caño-Delgado, A. I. (2020). The physiology of plant responses to drought. Science, 368, 266-269. https://doi.org/10.1126/science.aaz7614 DOI: https://doi.org/10.1126/science.aaz7614
Hu, Y., & Dong, Y. (2018). An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 347-359. https://doi.org/10.1016/j.isprsjprs.2018.10.008 DOI: https://doi.org/10.1016/j.isprsjprs.2018.10.008
Larrea-Gomez, M., Peña, A., Martinez-Vargas, J. D., Ochoa, I., & Ramirez-Guerrero, T. (2024). Modeling detecting plant diseases in precision agriculture: an NDVI analysis for early and accurate diagnosis. In M. Tabares, P. Vallejo, B. Suarez, M. Suarez, O. Ruiz, & J. Aguilar (Eds.), Advances in computing (CCIS 2023, Vol. 1924). Springer. https://doi.org/10.1007/978-3-031-47372-2_24 DOI: https://doi.org/10.1007/978-3-031-47372-2_24
Lasaponara, R., Abate, N., & Masini, N. (2024). Early identification of vegetation pest diseases using the Sentinel 2 NDVI 2016-2023 time series: the case of Toumeyella parvicorvis in Castel Porziano (Italy). IEEE Geoscience and Remote Sensing Letters, 21, Article 2502305. https://doi.org/10.1109/LGRS.2024.3386218 DOI: https://doi.org/10.1109/LGRS.2024.3386218
Maia, F. C. O., Bufon, V. B., & Leão, T. P. (2023). Vegetation indices as a tool for mapping sugarcane management zones. Precision Agriculture, 24, 213-234. https://doi.org/10.1007/s11119-022-09939-7 DOI: https://doi.org/10.1007/s11119-022-09939-7
Malek, K., Adam, J. C., Stöckle, C. O., & Peters, R. T. (2018). Climate change reduces the availability of water for agriculture, decreasing losses from non-evaporative irrigation. Journal of Hydrology, 561, 444-460. https://doi.org/10.1016/j.jhydrol.2017.11.046 DOI: https://doi.org/10.1016/j.jhydrol.2017.11.046
Marcial-Pablo, M. de J., Ontiveros-Capurata, R. E., Jiménez-Jiménez, S. I., & Ojeda-Bustamante, W. (2021). Estimation of the corn crop coefficient based on spectral indices of vegetation and vegetation cover fraction derived from UAV-based multispectral images. Agronomy, 11, Article 668. https://doi.org/10.3390/agronomy11040668 DOI: https://doi.org/10.3390/agronomy11040668
Passioura, J. B., & Angus, J. F. (2010). Chapter 2 - Improving productivity of crops in water-limited environments. Advances in Agronomy, 106, 37-75. https://doi.org/10.1016/S0065-2113(10)06002-5 DOI: https://doi.org/10.1016/S0065-2113(10)06002-5
Pereira, L. S., Paredes, P., Melton, F., Johnson, L., Wang, T., López-Urrea, R., Cancela, J. J., & Allen, R. G. (2020). Prediction of crop coefficients from land cover fraction and height: Background and validation using soil data and remote sensing. Agricultural Water Management, 241, Article 106197. https://doi.org/10.1016/j.agwat.2020.106197 DOI: https://doi.org/10.1016/j.agwat.2020.106197
Puteh, S., Rodzali, N. F. M., Razman, M. A. M., Ibrahim, Z. Z., Shapiee, M. N. A., & Razman, M. A. M. (2020). Features extraction of capsicum frutescens (C.F) NDVI values using image processing. Mekatronika: Journal of Intelligent Manufacturing and Mechatronics, 2(1), 38-46. https://doi.org/10.15282/mekatronika.v2i1.6727 DOI: https://doi.org/10.15282/mekatronika.v2i1.6727
Qin, Y., Mueller, N. D., Siebert, S., Jackson, R. B., AghaKouchak, A., Zimmerman, J. B., Tong, D., Hong, C., & Davis, S. J. (2019). Flexibility and intensity of global water use. Nature Sustainability, 2, 515-523. https://doi.org/10.1038/s41893-019-0294-2 DOI: https://doi.org/10.1038/s41893-019-0294-2
Rallo, G., Paço, T. A., Paredes, P., Puig-Sirera, À., Massai, R., Provenzano, G., & Pereira, L. S. (2021). Updated single and double crop coefficients for tree and grapevine crops. Agricultural Water Management, 250, Article 106645. https://doi.org/10.1016/j.agwat.2020.106645 DOI: https://doi.org/10.1016/j.agwat.2020.106645
Rering, C. C., Franco, J. G., Yeater, K. M., & Mallinger, R. E. (2020). Drought stress alters floral volatiles and reduces floral rewards, pollinator activity, and seed set in a global plant. Ecosphere, 11(9), Article e03254. https://doi.org/10.1002/ecs2.3254 DOI: https://doi.org/10.1002/ecs2.3254
Safre, A. L. S., Nassar, A., Torres-Rua, A., Aboutalebi, M., Saad, J. C. C., Manzione, R. L., Teixeira, A. H. de C., Prueger, J. H., McKee, L. G., Alfieri, J. G., Hipps, L. E., Nieto, H., White, W. A., Alsina, M. del M., Sanchez, L., Kustas, W. P., Dokoozlian, N., Gao, F., & Anderson, M. C. (2022). Performance of Sentinel-2 SAFER ET model for daily and seasonal estimation of grapevine water consumption. Irrigation Science, 40, 635-654. https://doi.org/10.1007/s00271-022-00810-1 DOI: https://doi.org/10.1007/s00271-022-00810-1
Santos, H. G., Jacomine, P. K. T., Anjos, L. H. C., Oliveira, V. A., Lumbreras, J. F, Coelho, M. R., Almeida, J. A., Araújo Filho, J. C., Oliveira, J. B., & Cunha, T. J. F. (2018). Sistema brasileiro de classificação de solos (5ª. ed.). Embrapa.
Schneider, A. D., Howell, T. A., Moustafa, A. T. A., Evett, S. R., & Abou-Zeid, W. (1998). A simplified weighing lysimeter for monolithic or reconstructed soils. Applied Engineering in Agriculture, 14, 267-273. https://doi.org/10.13031/2013.19388 DOI: https://doi.org/10.13031/2013.19388
Scott, R. L., Knowles, J. F., Nelson, J. A., Gentine, P., Li, X., Barron-Gafford, G., Bryant, R., & Biederman, J. A. (2021). Impacts of water availability on evapotranspiration partitioning. Agricultural and Forestry Meteorology, 297, Article 108251. https://doi.org/10.1016/j.agrformet.2020.108251 DOI: https://doi.org/10.1016/j.agrformet.2020.108251
Shao, G., Han, W., Zhang, H., Liu, S., Wang, Y., Zhang, L., & Cui, X. (2021). Mapping of the corn crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices. Agricultural Water Management, 252, Article 106906. https://doi.org/10.1016/j.agwat.2021.106906 DOI: https://doi.org/10.1016/j.agwat.2021.106906
Teixeira, A. H. C. (2010). Determination of regional real evapotranspiration of irrigated and natural vegetation in the São Francisco River basin (Brazil) using remote sensing: a Penman Monteith equation. Remote Sensing, 2, 1287-1319. https://doi.org/10.3390/rs0251287 DOI: https://doi.org/10.3390/rs0251287
Teixeira, A. H. D. C., Hernandez, F. B., Leivas, J. F., Nuñez, D. N., & Momesso, R. F. (2017). Surface temperature estimated with Landsat 8 images and geostatistical tools in the northwestern São Paulo state. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX (Vol. 10421, pp. 75-83). SPIE. https://doi.org/10.1117/12.2277588 DOI: https://doi.org/10.1117/12.2277588
Teixeira, A. H. D. C., Leivas, J. F., Andrade, R. G., Hernandez, F. B., & Momesso, F. R. (2015). Modelling radiation and energy balances with Landsat 8 images under different thermohydrological conditions in the Brazilian semi-arid region. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII (Vol. 9637, pp. 112-125). SPIE. https://doi.org/10.1117/12.2195044 DOI: https://doi.org/10.1117/12.2195044
United States Geological Survey. (2016). Landsat data access. Retrieved May 31, 2018, from https://www.usgs.gov/landsat-missions/landsat-data-access
Volk, J. M., Huntington, J. L., Melton, F. S., Allen, R., Anderson, M., Fisher, J. B., Kilic, A., Ruhoff, A., Senay, G. B., Minor, B., Morton, C., Ott, T., Johnson, L., Comini, B. A., Carrara, W., Doherty, C. T., Dunkerly, C., Friedrichs, M., Guzman, A., … Yang, Y. (2024). Evaluating the accuracy of OpenET satellite-based evapotranspiration data to support water resources and land management applications. Nature Water, 2, 193-205. https://doi.org/10.1038/s44221-023-00181-7 DOI: https://doi.org/10.1038/s44221-023-00181-7
Yadav, A., Sharma, N., Upreti, H., & Singhal, G. D. (2022). Technical-economic analysis of irrigation systems for efficient use of water in the context of climate change. Current Science, 122, 664-673. https://doi.org/10.18520/cs/v122/i6/664-673 DOI: https://doi.org/10.18520/cs/v122/i6/664-673
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The research data consists of satellite images acquired from the Explore platform database of the United States Geological Survey, easily accessible on the institution's website.
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