Harnessing spectral reflectance as novel approach to estimate photosynthetic capacity in nutrient-deficient immature rubber saplings
Article Main
Abstract
Measuring photosynthetic capacity using gas exchange systems such as the LI-COR 6400XT is time-consuming and limited by stomatal dynamics, particularly in large-scale field applications. Spectral reflectance-based tools, such as the PolyPen 400 UV-VIS, offer a rapid and non-destructive alternative. The present study aimed to develop predictive models for estimating photosynthetic parameters from spectral reflectance data, using immature rubber trees (Hevea brasiliensis) grown under nutrient omission treatments. Five treatments Treatment A: NPK (5.29-8.01-5.34 g plants-1 month-1); Treatment B: -K (5.29-8.01-0 g plants-1 month-1); Treatment C: -P (5.29-0-5.34 g plants-1 month-1); Treatment D: -N (0-8.01-5.34 g plants-1 month-1); Treatment E: Control (-NPK 0-0-0 g plants-1) were applied in a completely randomized design with five replications. Photosynthetic parameters—maximum carboxylation rate (Vcmₐₓ), maximum electron transport rate (Jmₐₓ), and triose phosphate utilization (TPU)—were measured using the LI-COR 6400XT, while spectral reflectance was recorded using the PolyPen 400 UV-VIS during the second and third leaf flushes. Multiple linear regression models were developed and validated using root mean square error (RMSE). Key wavelengths for estimating photosynthetic parameters were identified in the ultraviolet and near-infrared regions: Vcmₐₓ (R324, R329, R334, R349, R379, R384, R394), Jmₐₓ (R339, R785), and TPU (R324, R339, R404, R785). Model accuracies under self-validation were 90% (Vcmₐₓ), 55% (Jmₐₓ), and 75% (TPU), and under cross-validation were 84%, 47%, and 64%, respectively. These results demonstrated the potential of reflectance-based models as efficient tools for estimating photosynthetic capacity in immature rubber trees, particularly in resource-limited environments.
Article Details
Article Details
Keywords
Immature rubber, Leaf optical properties, Leaf spectral reflectance, Macronutrient omissions, Photosynthetic capacity, Stepwise general linear model
References
Ainsworth, E. A., Serbin, S. P., Skoneczka, J. A. & Townsend, P. A. (2014). Using leaf optical properties to detect ozone effects on foliar biochemistry. Photosynthesis Research, 119(1), 65–76. https://doi.org/10.1007/s11120-013-9837-y
Ali, A. A., Nugroho, B., Moyano, F. E., Brambach, F., Jenkins, M. W., Pangle, R., Stiegler, C., Blei, E., Cahyo, A. N., Olchev, A., Irawan, B., Ariani, R., June, T., Tarigan, S., Corre, M. D., Veldkamp, E. & Knohl, A. (2021). Using a bottom-up approach to scale leaf photosynthetic traits of oil palm, rubber, and two coexisting tropical woody species. Forests, 12(3), 359. https://doi.org/10.3390/f12030359
Anasrullah, A., Sajjaphan, K., Rattanapichai, W., Kasemsap, P., Nouvellon, Y., Chura, D., Chayawat, C. & Chotiphan, R. (2023). The dynamics of immature rubber photosynthetic capacities under macronutrient deficiencies. Trends in Sciences, 20(5), 4527. https://doi.org/10.48048/tis.2023.4527
Azizan, F. A., Kiloes, A. M., Astuti, I. S. & Abdul Aziz, A. (2021). Application of optical remote sensing in rubber plantations: A systematic review. Remote Sensing, 13(3), 429. https://doi.org/10.3390/rs13030429
Baulkwill, W. & Webster, C. (1989). Rubber. John Wiley & Sons Inc., New York.
Bray, R. H. & Kurtz, L. T. (1945). Determination of total, organic and available forms of phosphorus in soils. Soil Science, 59(1), 39–45. https://doi.org/10.1097/00010694-194501000-00006
Correia, M. A. R., Maranhão, D. D. C., Flores, R. A., da Silva, S. F., de Araujo, M. A. & de Lima Leite, R. L. (2017). Growth, nutrition and production of dry matter of rubber tree (Hevea brasiliensis) in function of K fertilization. Australian Journal of Crop Science, 11(1), 95–101. https://doi.org/10.21475/ajcs.2017.11.01.268
Croft, H., Chen, J. M., Luo, X., Bartlett, P., Chen, B. & Staebler, R. M. (2017). Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology, 23(9), 3513–3524. https://doi.org/10.1111/gcb.13599
De Bang, T. C., Husted, S., Laursen, K. H., Persson, D. P. & Schjoerring, J. K. (2021). The molecular–physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants. New Phytologist, 229(5), 2446–2469. https://doi.org/10.1111/nph.17074
El-Hendawy, S., Al-Suhaibani, N., Alotaibi, M., Hassan, W., Elsayed, S., Tahir, M. U. & Schmidhalter, U. (2019). Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis. Scientific Reports, 9(1), 16473. https://doi.org/10.1038/s41598-019-52802-5
Faithfull, N. T. (2002). Methods in agricultural chemical analysis: A practical handbook. CABI Publishing., Wallingford, UK. https://doi.org/10.1079/9780851996080.0000
Fu, P., Meacham-Hensold, K., Guan, K. & Bernacchi, C. J. (2019). Hyperspectral leaf reflectance as a proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms. Frontiers in Plant Science, 10, 730. https://doi.org/10.3389/fpls.2019.00730
Ge, Y., Atefi, A., Zhang, H., Miao, C., Ramamurthy, R. K., Sigmon, B. & Schnable, J. C. (2019). High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: A case study with a maize diversity panel. Plant Methods, 15, 12. https://doi.org/10.1186/s13007-019-0450-8
Gill, A. K., Gaur, S., Sneller, C. & Drewry, D. T. (2024). Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat. Frontiers in Plant Science, 15, 1426077. https://doi.org/10.3389/fpls.2024.1426077
Guo, J., Liu, X., Ge, W., Zhao, L., Fan, W., Zhang, X., Lu, Q., Xing, X. & Zhou, Z. (2024). Tracking photosynthetic phenology using spectral indices at the leaf and canopy scales in temperate evergreen and deciduous trees. Agricultural and Forest Meteorology, 344, 109809. https://doi.org/10.1016/j.agrformet.2023.109809
Guo, R., Zhao, M. Z., Yang, Z. X., Wang, G. J., Yin, H. & Li, J. D. (2017). Simulation of soybean canopy nutrient contents by hyperspectral remote sensing. Applied Ecology and Environmental Research, 15(4), 1185–1198.https://doi.org/10.15666/aeer/1504_11851198
Huang, I. Y., James, K., Thamthanakoon, N., Pinitjitsamut, P., Rattanamanee, N., Pinitjitsamut, M., Yamklin, S. & Lowenberg-DeBoer, J. (2023). Economic outcomes of rubber-based agroforestry systems: A systematic review and narrative synthesis. Agroforestry Systems, 97(3), 335–354. https://doi.org/10.1007/s10457-022-00734-x
Inoue, Y., Peñuelas, J., Miyata, A. & Mano, M. (2008). Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO₂ flux measurements in rice. Remote Sensing of Environment, 112(1), 156–172. https://doi.org/10.1016/j.rse.2007.04.011
Jackson, K. W. & Chen, G. (1996). Atomic absorption, atomic emission, and flame emission spectrometry. Analytical Chemistry, 68(12), 231–256. https://doi.org/10.1021/a1960012l
Jackson, P. E., Krol, J., Heckenberg, A. L., Mientijes, M. & Staal, W. (1991). Determination of total nitrogen in food, environmental and other samples by ion chromatography after Kjeldahl digestion. Journal of Chromatography A, 546(C), 405–410. https://doi.org/10.1016/S0021-9673(01)93039-0
Joseph, J. & Ray, J. G. (2024). A critical analysis of soil fertility parameters of rubber plantations with long-term fertilizer use in the Western Ghats of South India from a global sustainability perspective. Journal of Rubber Research, 27(3), 459–475. https://doi.org/10.1007/s42464-024-00262-6
Joseph, M. (2024). Natural rubber (Hevea brasiliensis Muell. Arg.). In G. V. Thomas & V. Krishnakumar (Eds.), Soil health management for Plantation Crops (pp. 121–142). Springer, Singapore. https://doi.org/10.1007/978-981-97-0092-9_7
Junker, L. V. & Ensminger, I. (2016). Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves. Tree Physiology, 36(6), 694–711. https://doi.org/10.1093/treephys/tpv148
Kositsup, B., Montpied, P., Kasemsap, P., Thaler, P., Améglio, T. & Dreyer, E. (2009). Photosynthetic capacity and temperature responses of photosynthesis of rubber trees (Hevea brasiliensis Müll. Arg.) acclimate to changes in ambient temperatures. Trees, 23(3), 357–365. https://doi.org/10.1007/s00468-008-0284-x
Li, Y., Zhao, Y., Bao, X., Xie, H., Lü, X., Fu, Y., Tang, S., Ge, C. & Liang, C. (2024). Soil total and available C:N:P stoichiometry among different parent material soil profiles in rubber plantations of Hainan Island, China. Geoderma Regional, 36, e00765. https://doi.org/10.1016/j.geodrs.2024.e00765
Long, S. P. & Bernacchi, C. J. (2003). Gas exchange measurements: What can they tell us about the underlying limitations to photosynthesis? Procedures and sources of error. Journal of Experimental Botany, 54(392), 2393–2401. https://doi.org/10.1093/jxb/erg262
Min, S., Huang, J., Waibel, H., Yang, X. & Cadisch, G. (2019). Rubber boom, land use change and the implications for carbon balances in Xishuangbanna, Southwest China. Ecological Economics, 156, 57–67. https://doi.org/10.1016/j.ecolecon.2018.09.009
Minardi, S., Harieni, S., Anasrullah, A. & Purwanto, H. (2017). Soil fertility status, nutrient uptake, and maize (Zea mays L.) yield following organic matter and P fertilizer application on Andisol. In IOP Conference Series: Materials Science and Engineering 193, p. 012054. https://doi.org/10.1088/1757-899X/193/1/012054
Mulvaney, R. L., Yaremych, S. A., Khan, S. A., Swiader, J. M. & Horgan, B. P. (2004). Use of diffusion to determine soil cation-exchange capacity by ammonium saturation. Communications in Soil Science and Plant Analysis, 35(1–2), 51–67. https://doi.org/10.1081/CSS-120027634
Pratiwi, L. F. L., Rosyid, A. H. Al & Kafiya, M. (2020). Strategi penghidupan berkelanjutan rumah tangga tani melalui pengelolaan usaha tani lahan marginal pesisir pantai Kabupaten Bantul DIY [in Indonesian]. Agribusiness Journal, 3, 232–236. https://doi.org/10.15408/aj.v14i1.16304
Qian, X., Zhang, Y., Liu, L. & Du, S. (2019). Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate. International Journal of Remote Sensing, 40(14), 5411–5428. https://doi.org/10.1080/01431161.2019.1579940
Sari, S., Achmar, M. & Zahrosa, D. B. (2020). Strategi optimalisasi penggunaan lahan marginal untuk pengembangan komoditas tanaman pangan [in Indonesian]. CERMIN: Jurnal Penelitian, 4(2), 281–288. https://doi.org/10.36841/cermin_unars.v4i2.771
Shanti, R. & Nirmala, R. (2018). Evaluation of post-mining land for rubber and oil palm plantations in Kutai Kartanegara, Indonesia. Journal of Agriculture and Environment for International Development, 112(1), 25–42. https://doi.org/10.12895/jaeid.20181.689
Sharkey, T. D., Bernacchi, C. J., Farquhar, G. D. & Singsaas, E. L. (2007). Fitting photosynthetic carbon dioxide response curves for C₃ leaves. Plant, Cell and Environment, 30(9), 1035–1040. https://doi.org/10.1111/j.1365-3040.2007.01710.x
Song, G., Wang, Q. & Jin, J. (2023). Estimation of leaf photosynthetic capacity parameters using spectral indices developed from fractional-order derivatives. Computers and Electronics in Agriculture, 212, 108068. https://doi.org/10.1016/j.compag.2023.108068
Sterling, A. & Di Rienzo, J. A. (2022). Prediction of South American leaf blight and disease-induced photosynthetic changes in rubber tree using machine learning techniques on leaf hyperspectral reflectance. Plants, 11(3), 329. https://doi.org/10.3390/plants11030329
Thenkabail, P. S., Lyon, J. G. & Huete, A. (2018). Biophysical and biochemical characterization and plant species studies (2nd ed.). CRC Press, Florida.
Thenkabail, P. S., Lyon, J. G. & Huete, A. (2019). Advanced applications in remote sensing of agricultural crops and natural vegetation (2nd ed.). CRC Press, Florida.
Walkley, A. & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29-38.
Wong, C. Y., D’Odorico, P., Bhathena, Y., Arain, M. A. & Ensminger, I. (2019). Carotenoid-based vegetation indices for accurate monitoring of the phenology of photosynthesis at the leaf scale in deciduous and evergreen trees. Remote Sensing of Environment, 233, 111407. https://doi.org/10.1016/j.rse.2019.111407
Xue, C., Zan, M., Zhou, Y., Chen, Z., Kong, J., Yang, S., Zhai, L. & Zhou, J. (2024). Response of solar-induced chlorophyll fluorescence-based spatial and temporal evolution of vegetation in Xinjiang to multiscale drought. Frontiers in Plant Science, 15, 1418396. https://doi.org/10.3389/fpls.2024.1418396
Yang, Z., Tian, J., Zhang, L., Zan, O., Yan, X. & Feng, K. (2024). Spectral detection of leaf carbon and nitrogen as a proxy for remote assessment of photosynthetic capacity for wheat and maize under nitrogen stress. Computers and Electronics in Agriculture, 224, 109174. https://doi.org/10.1016/j.compag.2024.109174
Zhang, Y., Guanter, L., Berry, J. A., Joiner, J., van der Tol, C., Huete, A. & Köhler, P. (2014). Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Global Change Biology, 20(12), 3727–3742. https://doi.org/10.1111/gcb.12664
Zhou, X., Huang, W., Zhang, J., Kong, W., Casa, R. & Huang, Y. (2019). A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status. International Journal of Applied Earth Observation and Geoinformation, 76, 128–142. https://doi.org/10.1016/j.jag.2018.10.012
Zhou, Z., Jabloun, M., Plauborg, F. & Andersen, M. N. (2018). Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato. Computers and Electronics in Agriculture, 144, 154–163. https://doi.org/10.1016/j.compag.2017.12.005
Ali, A. A., Nugroho, B., Moyano, F. E., Brambach, F., Jenkins, M. W., Pangle, R., Stiegler, C., Blei, E., Cahyo, A. N., Olchev, A., Irawan, B., Ariani, R., June, T., Tarigan, S., Corre, M. D., Veldkamp, E. & Knohl, A. (2021). Using a bottom-up approach to scale leaf photosynthetic traits of oil palm, rubber, and two coexisting tropical woody species. Forests, 12(3), 359. https://doi.org/10.3390/f12030359
Anasrullah, A., Sajjaphan, K., Rattanapichai, W., Kasemsap, P., Nouvellon, Y., Chura, D., Chayawat, C. & Chotiphan, R. (2023). The dynamics of immature rubber photosynthetic capacities under macronutrient deficiencies. Trends in Sciences, 20(5), 4527. https://doi.org/10.48048/tis.2023.4527
Azizan, F. A., Kiloes, A. M., Astuti, I. S. & Abdul Aziz, A. (2021). Application of optical remote sensing in rubber plantations: A systematic review. Remote Sensing, 13(3), 429. https://doi.org/10.3390/rs13030429
Baulkwill, W. & Webster, C. (1989). Rubber. John Wiley & Sons Inc., New York.
Bray, R. H. & Kurtz, L. T. (1945). Determination of total, organic and available forms of phosphorus in soils. Soil Science, 59(1), 39–45. https://doi.org/10.1097/00010694-194501000-00006
Correia, M. A. R., Maranhão, D. D. C., Flores, R. A., da Silva, S. F., de Araujo, M. A. & de Lima Leite, R. L. (2017). Growth, nutrition and production of dry matter of rubber tree (Hevea brasiliensis) in function of K fertilization. Australian Journal of Crop Science, 11(1), 95–101. https://doi.org/10.21475/ajcs.2017.11.01.268
Croft, H., Chen, J. M., Luo, X., Bartlett, P., Chen, B. & Staebler, R. M. (2017). Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology, 23(9), 3513–3524. https://doi.org/10.1111/gcb.13599
De Bang, T. C., Husted, S., Laursen, K. H., Persson, D. P. & Schjoerring, J. K. (2021). The molecular–physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants. New Phytologist, 229(5), 2446–2469. https://doi.org/10.1111/nph.17074
El-Hendawy, S., Al-Suhaibani, N., Alotaibi, M., Hassan, W., Elsayed, S., Tahir, M. U. & Schmidhalter, U. (2019). Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis. Scientific Reports, 9(1), 16473. https://doi.org/10.1038/s41598-019-52802-5
Faithfull, N. T. (2002). Methods in agricultural chemical analysis: A practical handbook. CABI Publishing., Wallingford, UK. https://doi.org/10.1079/9780851996080.0000
Fu, P., Meacham-Hensold, K., Guan, K. & Bernacchi, C. J. (2019). Hyperspectral leaf reflectance as a proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms. Frontiers in Plant Science, 10, 730. https://doi.org/10.3389/fpls.2019.00730
Ge, Y., Atefi, A., Zhang, H., Miao, C., Ramamurthy, R. K., Sigmon, B. & Schnable, J. C. (2019). High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: A case study with a maize diversity panel. Plant Methods, 15, 12. https://doi.org/10.1186/s13007-019-0450-8
Gill, A. K., Gaur, S., Sneller, C. & Drewry, D. T. (2024). Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat. Frontiers in Plant Science, 15, 1426077. https://doi.org/10.3389/fpls.2024.1426077
Guo, J., Liu, X., Ge, W., Zhao, L., Fan, W., Zhang, X., Lu, Q., Xing, X. & Zhou, Z. (2024). Tracking photosynthetic phenology using spectral indices at the leaf and canopy scales in temperate evergreen and deciduous trees. Agricultural and Forest Meteorology, 344, 109809. https://doi.org/10.1016/j.agrformet.2023.109809
Guo, R., Zhao, M. Z., Yang, Z. X., Wang, G. J., Yin, H. & Li, J. D. (2017). Simulation of soybean canopy nutrient contents by hyperspectral remote sensing. Applied Ecology and Environmental Research, 15(4), 1185–1198.https://doi.org/10.15666/aeer/1504_11851198
Huang, I. Y., James, K., Thamthanakoon, N., Pinitjitsamut, P., Rattanamanee, N., Pinitjitsamut, M., Yamklin, S. & Lowenberg-DeBoer, J. (2023). Economic outcomes of rubber-based agroforestry systems: A systematic review and narrative synthesis. Agroforestry Systems, 97(3), 335–354. https://doi.org/10.1007/s10457-022-00734-x
Inoue, Y., Peñuelas, J., Miyata, A. & Mano, M. (2008). Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO₂ flux measurements in rice. Remote Sensing of Environment, 112(1), 156–172. https://doi.org/10.1016/j.rse.2007.04.011
Jackson, K. W. & Chen, G. (1996). Atomic absorption, atomic emission, and flame emission spectrometry. Analytical Chemistry, 68(12), 231–256. https://doi.org/10.1021/a1960012l
Jackson, P. E., Krol, J., Heckenberg, A. L., Mientijes, M. & Staal, W. (1991). Determination of total nitrogen in food, environmental and other samples by ion chromatography after Kjeldahl digestion. Journal of Chromatography A, 546(C), 405–410. https://doi.org/10.1016/S0021-9673(01)93039-0
Joseph, J. & Ray, J. G. (2024). A critical analysis of soil fertility parameters of rubber plantations with long-term fertilizer use in the Western Ghats of South India from a global sustainability perspective. Journal of Rubber Research, 27(3), 459–475. https://doi.org/10.1007/s42464-024-00262-6
Joseph, M. (2024). Natural rubber (Hevea brasiliensis Muell. Arg.). In G. V. Thomas & V. Krishnakumar (Eds.), Soil health management for Plantation Crops (pp. 121–142). Springer, Singapore. https://doi.org/10.1007/978-981-97-0092-9_7
Junker, L. V. & Ensminger, I. (2016). Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves. Tree Physiology, 36(6), 694–711. https://doi.org/10.1093/treephys/tpv148
Kositsup, B., Montpied, P., Kasemsap, P., Thaler, P., Améglio, T. & Dreyer, E. (2009). Photosynthetic capacity and temperature responses of photosynthesis of rubber trees (Hevea brasiliensis Müll. Arg.) acclimate to changes in ambient temperatures. Trees, 23(3), 357–365. https://doi.org/10.1007/s00468-008-0284-x
Li, Y., Zhao, Y., Bao, X., Xie, H., Lü, X., Fu, Y., Tang, S., Ge, C. & Liang, C. (2024). Soil total and available C:N:P stoichiometry among different parent material soil profiles in rubber plantations of Hainan Island, China. Geoderma Regional, 36, e00765. https://doi.org/10.1016/j.geodrs.2024.e00765
Long, S. P. & Bernacchi, C. J. (2003). Gas exchange measurements: What can they tell us about the underlying limitations to photosynthesis? Procedures and sources of error. Journal of Experimental Botany, 54(392), 2393–2401. https://doi.org/10.1093/jxb/erg262
Min, S., Huang, J., Waibel, H., Yang, X. & Cadisch, G. (2019). Rubber boom, land use change and the implications for carbon balances in Xishuangbanna, Southwest China. Ecological Economics, 156, 57–67. https://doi.org/10.1016/j.ecolecon.2018.09.009
Minardi, S., Harieni, S., Anasrullah, A. & Purwanto, H. (2017). Soil fertility status, nutrient uptake, and maize (Zea mays L.) yield following organic matter and P fertilizer application on Andisol. In IOP Conference Series: Materials Science and Engineering 193, p. 012054. https://doi.org/10.1088/1757-899X/193/1/012054
Mulvaney, R. L., Yaremych, S. A., Khan, S. A., Swiader, J. M. & Horgan, B. P. (2004). Use of diffusion to determine soil cation-exchange capacity by ammonium saturation. Communications in Soil Science and Plant Analysis, 35(1–2), 51–67. https://doi.org/10.1081/CSS-120027634
Pratiwi, L. F. L., Rosyid, A. H. Al & Kafiya, M. (2020). Strategi penghidupan berkelanjutan rumah tangga tani melalui pengelolaan usaha tani lahan marginal pesisir pantai Kabupaten Bantul DIY [in Indonesian]. Agribusiness Journal, 3, 232–236. https://doi.org/10.15408/aj.v14i1.16304
Qian, X., Zhang, Y., Liu, L. & Du, S. (2019). Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate. International Journal of Remote Sensing, 40(14), 5411–5428. https://doi.org/10.1080/01431161.2019.1579940
Sari, S., Achmar, M. & Zahrosa, D. B. (2020). Strategi optimalisasi penggunaan lahan marginal untuk pengembangan komoditas tanaman pangan [in Indonesian]. CERMIN: Jurnal Penelitian, 4(2), 281–288. https://doi.org/10.36841/cermin_unars.v4i2.771
Shanti, R. & Nirmala, R. (2018). Evaluation of post-mining land for rubber and oil palm plantations in Kutai Kartanegara, Indonesia. Journal of Agriculture and Environment for International Development, 112(1), 25–42. https://doi.org/10.12895/jaeid.20181.689
Sharkey, T. D., Bernacchi, C. J., Farquhar, G. D. & Singsaas, E. L. (2007). Fitting photosynthetic carbon dioxide response curves for C₃ leaves. Plant, Cell and Environment, 30(9), 1035–1040. https://doi.org/10.1111/j.1365-3040.2007.01710.x
Song, G., Wang, Q. & Jin, J. (2023). Estimation of leaf photosynthetic capacity parameters using spectral indices developed from fractional-order derivatives. Computers and Electronics in Agriculture, 212, 108068. https://doi.org/10.1016/j.compag.2023.108068
Sterling, A. & Di Rienzo, J. A. (2022). Prediction of South American leaf blight and disease-induced photosynthetic changes in rubber tree using machine learning techniques on leaf hyperspectral reflectance. Plants, 11(3), 329. https://doi.org/10.3390/plants11030329
Thenkabail, P. S., Lyon, J. G. & Huete, A. (2018). Biophysical and biochemical characterization and plant species studies (2nd ed.). CRC Press, Florida.
Thenkabail, P. S., Lyon, J. G. & Huete, A. (2019). Advanced applications in remote sensing of agricultural crops and natural vegetation (2nd ed.). CRC Press, Florida.
Walkley, A. & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29-38.
Wong, C. Y., D’Odorico, P., Bhathena, Y., Arain, M. A. & Ensminger, I. (2019). Carotenoid-based vegetation indices for accurate monitoring of the phenology of photosynthesis at the leaf scale in deciduous and evergreen trees. Remote Sensing of Environment, 233, 111407. https://doi.org/10.1016/j.rse.2019.111407
Xue, C., Zan, M., Zhou, Y., Chen, Z., Kong, J., Yang, S., Zhai, L. & Zhou, J. (2024). Response of solar-induced chlorophyll fluorescence-based spatial and temporal evolution of vegetation in Xinjiang to multiscale drought. Frontiers in Plant Science, 15, 1418396. https://doi.org/10.3389/fpls.2024.1418396
Yang, Z., Tian, J., Zhang, L., Zan, O., Yan, X. & Feng, K. (2024). Spectral detection of leaf carbon and nitrogen as a proxy for remote assessment of photosynthetic capacity for wheat and maize under nitrogen stress. Computers and Electronics in Agriculture, 224, 109174. https://doi.org/10.1016/j.compag.2024.109174
Zhang, Y., Guanter, L., Berry, J. A., Joiner, J., van der Tol, C., Huete, A. & Köhler, P. (2014). Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Global Change Biology, 20(12), 3727–3742. https://doi.org/10.1111/gcb.12664
Zhou, X., Huang, W., Zhang, J., Kong, W., Casa, R. & Huang, Y. (2019). A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status. International Journal of Applied Earth Observation and Geoinformation, 76, 128–142. https://doi.org/10.1016/j.jag.2018.10.012
Zhou, Z., Jabloun, M., Plauborg, F. & Andersen, M. N. (2018). Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato. Computers and Electronics in Agriculture, 144, 154–163. https://doi.org/10.1016/j.compag.2017.12.005
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How to Cite
Harnessing spectral reflectance as novel approach to estimate photosynthetic capacity in nutrient-deficient immature rubber saplings. (2025). Journal of Applied and Natural Science, 17(4), 1856-1864. https://doi.org/10.31018/jans.v17i4.7004
