Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice
Abstract
:1. Introduction
2. Results
2.1. Model Calibration and Performance Evaluation
2.1.1. Phenology
2.1.2. Grain Yield and Aboveground Dry Matter
2.1.3. Nitrogen Uptake
2.1.4. Nitrogen Use Efficiencies
2.2. Simulations for Optimum Nitrogen Fertilization Rate
2.3. Optimization of Nitrogen Fertilization Rate for Different Sowing Windows
3. Discussion
4. Materials and Methods
4.1. Study Area
4.2. Field Experiments and Management
4.3. Data Collection and Computations
4.4. Model Configuration and Simulations
4.5. Statistics and Economics
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- FAO. FAOSTAT (Online). 2020. Available online: http://www.fao.org/faostat/en/#home (accessed on 18 August 2021).
- Sporchia, F.; Thomsen, M.; Caro, D. Drivers and Trade-Offs of Multiple Environmental Stressors from Global Rice. Sustain. Prod. Consum. 2021, 26, 16–32. [Google Scholar] [CrossRef]
- Hussain, T.; Hussain, N.; Ahmed, M.; Nualsri, C.; Duangpan, S. Responses of Lowland Rice Genotypes under Terminal Water Stress and Identification of Drought Tolerance to Stabilize Rice Productivity in Southern Thailand. Plants 2021, 10, 2565. [Google Scholar] [CrossRef] [PubMed]
- Hussain, T.; Hussain, N.; Ahmed, M.; Nualsri, C.; Duangpan, S. Impact of Nitrogen Application Rates on Upland Rice Performance, Planted under Varying Sowing Times. Sustainability 2022, 14, 1997. [Google Scholar] [CrossRef]
- Hussain, T.; Anothai, J.; Nualsri, C.; Soonsuwon, W. Evaluating Performance of Sixteen Upland Rice Genotypes under Field Conditions for Further Breeding Process. J. Agric. Sci. 2018, 10, 10–5539. [Google Scholar] [CrossRef]
- Mansour, E.; Desoky, E.S.M.; Ali, M.M.A.; Abdul-Hamid, M.I.; Ullah, H.; Attia, A.; Datta, A. Identifying Drought-Tolerant Genotypes of Faba Bean and Their Agro-Physiological Responses to Different Water Regimes in an Arid Mediterranean Environment. Agric. Water Manag. 2021, 247, 106754. [Google Scholar] [CrossRef]
- Thakur, A.K.; Rath, S.; Mandal, K.G. Differential Responses of System of Rice Intensification (SRI) and Conventional Flooded-Rice Management Methods to Applications of Nitrogen Fertilizer. Plant Soil 2013, 370, 59–71. [Google Scholar] [CrossRef]
- Mboyerwa, P.A.; Kibret, K.; Mtakwa, P.; Aschalew, A. Rice Yield and Nitrogen Use Efficiency With System of Rice Intensification and Conventional Management Practices in Mkindo Irrigation Scheme, Tanzania. Front. Sustain. Food Syst. 2022, 6, 802267. [Google Scholar] [CrossRef]
- Hameed, F.; Xu, J.; Rahim, S.F.; Wei, Q.; Rehman Khalil, A.U.; Liao, Q. Optimizing Nitrogen Options for Improving Nitrogen Use Efficiency of Rice under Different Water Regimes. Agronomy 2019, 9, 39. [Google Scholar] [CrossRef]
- Limsakul, A.; Singhruck, P. Long-Term Trends and Variability of Total and Extreme Precipitation in Thailand. Atmos. Res. 2016, 169, 301–317. [Google Scholar] [CrossRef]
- DRRD Soil and Fertilizer Management in Rice Fields, Fertilizer Recommendations for Southern Region. Rice Knowledge Bank. 2017. Available online: http://www.knowledgebank.irri.org/ (accessed on 22 December 2021).
- Norsuwan, T.; Utasuk, K.; Panyasai, T.; Sangchyoswat, C. Optimization of Nitrogen Fertilizer Application in Lowland Rice Production System of Agricultural Resource System Research Station Using Tailored Farm-Plot Database. Chiang Mai Univ. J. Nat. Sci. 2020, 19, 333–349. [Google Scholar] [CrossRef]
- Xu, K.; Sun, Z.; Huo, Z.; Dai, Q.; Zhang, H.; Liu, J.; Song, Y.; Yang, D.; Wei, H.; Wu, A.; et al. Effects of Seeding Date and Variety Type on Yield, Growth Stage and Utilization of Temperature and Sunshine in Rice. Sci. Agric. Sin 2013, 46, 4222–4233. [Google Scholar] [CrossRef]
- Zhang, R.P.; Zhou, N.N.; Ashen, R.G.; Zhou, L.; Feng, T.Y.; Zhang, K.Y.; Liao, X.H.; Aer, L.S.; Shu, J.C.; He, X.W.; et al. Effect of Sowing Date on the Growth Characteristics and Yield of Growth-Constrained Direct-Seeding Rice. Plants 2023, 12, 1899. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.L.; Zhang, Y.P.; Xiang, J.; Wang, L.; Chen, H.Z.; Zhang, Y.K.; Zhang, W.Q.; Zhu, F. Response of Indica Rice Spikelet Differentiation and Degeneration to Air Temperature and Solar Radiation of Different Sowing Dates. Chin. J. Appl. Ecol. 2017, 28, 3571–3580. [Google Scholar] [CrossRef]
- Wu, Y.; Qiu, X.; Zhang, K.; Chen, Z.; Pang, A.; Tian, Y.; Cao, W.; Liu, X.; Zhu, Y. A Rice Model System for Determining Suitable Sowing and Transplanting Dates. Agronomy 2020, 10, 604. [Google Scholar] [CrossRef]
- Amiri, E.; Razavipour, T.; Farid, A.; Bannayan, M. Effects of Crop Density and Irrigation Management on Water Productivity of Rice Production in Northern Iran: Field and Modeling Approach. Commun. Soil Sci. Plant Anal. 2011, 42, 2085–2099. [Google Scholar] [CrossRef]
- Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.A.; Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT Cropping System Model. Eur. J. Agron. 2003, 18, 235–265. [Google Scholar] [CrossRef]
- Hussain, T.; Gollany, H.T.; Mulla, D.J.; Ben, Z.; Tahir, M.; Ata-Ul-Karim, S.T.; Liu, K.; Maqbool, S.; Hussain, N.; Duangpan, S. Assessment and Application of EPIC in Simulating Upland Rice Productivity, Soil Water, and Nitrogen Dynamics under Different Nitrogen Applications and Planting Windows. Agronomy 2023, 13, 2379. [Google Scholar] [CrossRef]
- Li, T.; Angeles, O.; Marcaida, M.; Manalo, E.; Manalili, M.P.; Radanielson, A.; Mohanty, S. From ORYZA2000 to ORYZA (v3): An Improved Simulation Model for Rice in Drought and Nitrogen-Deficient Environments. Agric. For. Meteorol. 2017, 237–238, 246–256. [Google Scholar] [CrossRef]
- Stöckle, C.O.; Donatelli, M.; Nelson, R. CropSyst, a Cropping Systems Simulation Model. Eur. J. Agron. 2003, 18, 289–307. [Google Scholar] [CrossRef]
- Zhang, X.; Lee, J.-H.; Abawi, Y.; Kim, Y.; McClymont, D.; Kim, H.-D. Testing the Simulation Capability of APSIM-ORYZA under Different Levels of Nitrogen Fertiliser and Transplanting Time Regimes in Korea. Aust. J. Exp. Agric. 2007, 47, 1446–1454. [Google Scholar] [CrossRef]
- Choi, S.K.; Jeong, J.; Kim, M.K. Simulating the Effects of Agricultural Management on Water Quality Dynamics in Rice Paddies for Sustainable Rice Production-Model Development and Validation. Water 2017, 9, 869. [Google Scholar] [CrossRef]
- Tang, L.; Zhu, Y.; Hannaway, D.; Meng, Y.; Liu, L.; Chen, L.; Cao, W. RiceGrow: A Rice Growth and Productivity Model. NJAS Wagening. J. Life Sci. 2009, 57, 83–92. [Google Scholar] [CrossRef]
- Ahmad, S.; Ahmad, A.; Soler, C.M.T.; Ali, H.; Zia-Ul-Haq, M.; Anothai, J.; Hussain, A.; Hoogenboom, G.; Hasanuzzaman, M. Application of the CSM-CERES-Rice Model for Evaluation of Plant Density and Nitrogen Management of Fine Transplanted Rice for an Irrigated Semiarid Environment. Precis. Agric. 2012, 13, 200–218. [Google Scholar] [CrossRef]
- Hussain, T.; Anothai, J.; Nualsri, C.; Ata-Ul-Karim, S.T.; Duangpan, S.; Hussain, N.; Ali, A. Assessment of CSM–CERES–Rice as a Decision Support Tool in the Identification of High-Yielding Drought-Tolerant Upland Rice Genotypes. Agronomy 2023, 13, 432. [Google Scholar] [CrossRef]
- Liu, K.; Harrison, M.T.; Yan, H.; Liu, D.L.; Meinke, H.; Hoogenboom, G.; Wang, B.; Peng, B.; Guan, K.; Jaegermeyr, J.; et al. Silver Lining to a Climate Crisis in Multiple Prospects for Alleviating Crop Waterlogging under Future Climates. Nat. Commun. 2023, 14, 765. [Google Scholar] [CrossRef] [PubMed]
- La, N.; Lamers, M.; Nguyen, V.V.; Streck, T. Modelling the Fate of Pesticides in Paddy Rice–Fish Pond Farming Systems in Northern Vietnam. Pest Manag. Sci. 2014, 70, 70–79. [Google Scholar] [CrossRef]
- Luo, Y.; Spurlock, F.; Gill, S.; Goh, K.S. Modeling Complexity in Simulating Pesticide Fate in a Rice Paddy. Water Res. 2012, 46, 6300–6308. [Google Scholar] [CrossRef]
- Jha, P.K.; Ines, A.V.M.; Han, E.; Cruz, R.; Vara Prasad, P.V. A Comparison of Multiple Calibration and Ensembling Methods for Estimating Genetic Coefficients of CERES-Rice to Simulate Phenology and Yields. F. Crops Res. 2022, 284, 108560. [Google Scholar] [CrossRef]
- Li, S.; Fleisher, D.; Timlin, D.; Reddy, V.R.; Wang, Z.; McClung, A. Evaluation of Different Crop Models for Simulating Rice Development and Yield in the U.S. Mississippi Delta. Agronomy 2020, 10, 1905. [Google Scholar] [CrossRef]
- Hussain, T.; Anothai, J.; Nualsri, C.; Soonsuwon, W. Application of CSM-CERES-Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield. Indian J. Agric. Res. 2018, 52, 140–145. [Google Scholar] [CrossRef]
- Vilayvong, S.; Banterng, P.; Patanothai, A.; Pannangpetch, K. Evaluation of CSM-CERES-Rice in simulating the response of lowland rice cultivars to nitrogen application. Aust. J. Crop Sci. 2012, 6, 1534–1541. [Google Scholar]
- Osman, R.; Ata-ul-karim, S.T.; Naveed, M.; Ishaque, W. Multi-Model Ensembles for Assessing the Impact of Future Climate Change on Rainfed Wheat Productivity under Various Cultivars and Nitrogen Levels. Eur. J. Agron. 2022, 139, 126554. [Google Scholar] [CrossRef]
- Zhang, J.; Miao, Y.; Batchelor, W.D.; Lu, J.; Wang, H. Improving High-Latitude Rice Nitrogen Management with the CERES-Rice Crop Model. Agronomy 2018, 8, 263. [Google Scholar] [CrossRef]
- Park, S.; Chun, J.A.; Kim, D.; Sitthikone, M. Climate Risk Management for the Rainfed Rice Yield in Lao PDR Using APCC MME Seasonal Forecasts. Agric. Water Manag. 2022, 274, 107976. [Google Scholar] [CrossRef]
- Nicolas, F.; Migliaccio, K.W.; Hoogenboom, G.; Rathinasabapathi, B.R.; Eisenstadt, W.R. Assessing the Potential Impact of Climate Change on Rice Yield in the Artibonite Valley of Haiti Using the Csm-Ceres-Rice Model. Trans. ASABE 2020, 63, 1385–1400. [Google Scholar] [CrossRef]
- Ansari, A.; Lin, Y.P.; Lur, H.S. Evaluating and Adapting Climate Change Impacts on Rice Production in Indonesia: A Case Study of the Keduang Subwatershed, Central Java. Environments 2021, 8, 117. [Google Scholar] [CrossRef]
- Alejo, L.A. Assessing the Impacts of Climate Change on Aerobic Rice Production Using the Dssat-Ceres-Rice Model. J. Water Clim. Change 2021, 12, 696–708. [Google Scholar] [CrossRef]
- Sun, Q.; Zhao, Y.; Zhang, Y.; Che, X.; Yang, Z.; Song, Y.; Zheng, X. Improvement of the CERES-Rice Model Using Controlled Experiments and a Meta-Analysis. Theor. Appl. Climatol. 2020, 141, 1271–1284. [Google Scholar] [CrossRef]
- Nasir, I.R.; Rasul, F.; Ahmad, A.; Asghar, H.N.; Hoogenboom, G. Climate Change Impacts and Adaptations for Fine, Coarse, and Hybrid Rice Using CERES-Rice. Environ. Sci. Pollut. Res. 2020, 27, 9454–9464. [Google Scholar] [CrossRef]
- Thind, H.S.; Singh, Y.; Sharma, S.; Goyal, D.; Singh, V.; Singh, B. Optimal Rate and Schedule of Nitrogen Fertilizer Application for Enhanced Yield and Nitrogen Use Efficiency in Dry-Seeded Rice in North-Western India. Arch. Agron. Soil Sci. 2018, 64, 196–207. [Google Scholar] [CrossRef]
- Fausti, S.; Erickson, B.J.; Clay, D.E.; Carlson, C.G. Deriving and Using an Equation to Calculate Economic Optimum Fertilizer and Seeding Rates. In Practical Mathematics for Precision Farming; ASA, CSSA, and SSSA Books: Madison WI, USA, 2017; pp. 181–189. ISBN 9780891183624. [Google Scholar]
- Puntel, L.A.; Sawyer, J.E.; Castellano, M.J.; Moore, K.J. Evaluation of Statistical and Process-Based Models as Nitrogen Recommendation Tools in Maize Production Systems; Iowa State University: Ames, IA, USA, 2018. [Google Scholar]
- Phakamas, N. Performance of the CSM-CERES-Rice Model in Evaluating Growth and Yield of Rice in the Farm Level. Int. J. Agric. Technol. 2015, 11, 1285–1295. [Google Scholar]
- Kadiyala, M.D.M.; Jones, J.W.; Mylavarapu, R.S.; Li, Y.C.; Reddy, M.D. Identifying Irrigation and Nitrogen Best Management Practices for Aerobic Rice-Maize Cropping System for Semi-Arid Tropics Using CERES-Rice and Maize Models. Agric. Water Manag. 2015, 149, 23–32. [Google Scholar] [CrossRef]
- Lu, X.; Wang, H.; Xiang, Y.; Wang, Q.; Su, T.; Gong, R.; Zhang, H.; Zhu, L.; Li, E.; Abdelghany, A.E. Determination of Nitrogen Application Ratio and Sowing Time for Improving the Future Yield of Double-Harvest Rice in Nanchang Based on the DSSAT-CERES-Rice Model. Agronomy 2022, 12, 3199. [Google Scholar] [CrossRef]
- Hussain, T.; Hussain, N.; Ahmed, M.; Tahir, M.; Duangpan, S. Synchronizing Nitrogen Fertilization and Planting Date to Improve Resource Use Efficiency, Productivity, and Profitability of Upland Rice. Front. Plant Sci. 2022, 13, 895811. [Google Scholar] [CrossRef]
- Aslam, M.A.; Ahmed, M.; Hassan, F.-U.; Afzal, O.; Mehmood, M.Z.; Qadir, G.; Asif, M.; Komal, S.; Hussain, T. Impact of Temperature Fluctuations on Plant Morphological and Physiological Traits. In Building Climate Resilience in Agriculture; Springer: Cham, Switzerland, 2022; pp. 25–52. [Google Scholar]
- GRiSP. Rice Almanac: Source Book for One of the Most Economic Activities on Earth; CABI Publishing: Wallingford, UK, 2013; ISBN 978-9712203008. [Google Scholar]
- Zhang, J.; Tong, T.; Potcho, P.M.; Huang, S.; Ma, L.; Tang, X. Nitrogen Effects on Yield, Quality and Physiological Characteristics of Giant Rice. Agronomy 2020, 10, 1816. [Google Scholar] [CrossRef]
- Chen, Z.; Wang, Q.; Ma, J.; Zou, P.; Jiang, L. Impact of Controlled-Release Urea on Rice Yield, Nitrogen Use Efficiency and Soil Fertility in a Single Rice Cropping System. Sci. Rep. 2020, 10, 10432. [Google Scholar] [CrossRef]
- Jahan, A.; Islam, A.; Sarkar, M.I.U.; Iqbal, M.; Ahmed, M.N.; Islam, M.R. Nitrogen Response of Two High Yielding Rice Varieties as Influenced by Nitrogen Levels and Growing Seasons. Geol. Ecol. Landsc. 2022, 6, 24–31. [Google Scholar] [CrossRef]
- Mirakhori, M.; Mirshekari, B.; Amiri, E.; Paknejad, F.; Yarnia, M. Evaluation of CERES-Rice Model in Simulation of Rice Growth under Constraint Irrigation and Nitrogen Fertilizer Conditions. Ambient Sci. 2017, 03–04. [Google Scholar] [CrossRef]
- Ullah, H.; Santiago-Arenas, R.; Ferdous, Z.; Attia, A.; Datta, A. Improving Water Use Efficiency, Nitrogen Use Efficiency, and Radiation Use Efficiency in Field Crops under Drought Stress: A Review, 1st ed.; Elsevier Inc.: Amsterdam, The Netherlands, 2019; Volume 156, ISBN 9780128175989. [Google Scholar]
- Santiago-Arenas, R.; Dhakal, S.; Ullah, H.; Agarwal, A.; Datta, A. Seeding, Nitrogen and Irrigation Management Optimize Rice Water and Nitrogen Use Efficiency. Nutr. Cycl. Agroecosyst. 2021, 120, 325–341. [Google Scholar] [CrossRef]
- Sung, J.; Kim, W.; Oh, T.K.; So, Y.S. Nitrogen (N) Use Efficiency and Yield in Rice under Varying Types and Rates of N Source: Chemical Fertilizer, Livestock Manure Compost and Food Waste-Livestock Manure Compost. Appl. Biol. Chem. 2023, 66, 4. [Google Scholar] [CrossRef]
- Alou, I.N.; van der Laan, M.; Annandale, J.G.; Steyn, J.M. Water and Nitrogen (N) Use Efficiency of Upland Rice (Oryza sativa L. × Oryza glaberrima Steud) under Varying N Application Rates. Nitrogen 2020, 1, 151–166. [Google Scholar] [CrossRef]
- TMD Climatological Center, Thai Meteorological Department Report. 2022. Available online: http://climate.tmd.go.th/ (accessed on 20 February 2023).
- Takahashi, H.G.; Yasunari, T. A Climatological Monsoon Break in Rainfall over Indochina—A Singularity in the Seasonal March of the Asian Summer Monsoon. J. Clim. 2006, 19, 1545–1556. [Google Scholar] [CrossRef]
- He, J.; Jones, J.W.; Graham, W.D.; Dukes, M.D. Influence of Likelihood Function Choice for Estimating Crop Model Parameters Using the Generalized Likelihood Uncertainty Estimation Method. Agric. Syst. 2010, 103, 256–264. [Google Scholar] [CrossRef]
- Buddhaboon, C.; Jintrawet, A.; Hoogenboom, G. Methodology to Estimate Rice Genetic Coefficients for the CSM-CERES-Rice Model Using GENCALC and GLUE Genetic Coefficient Estimators. J. Agric. Sci. 2018, 156, 482–492. [Google Scholar] [CrossRef]
- Willmott, C.J.; Ackleson, S.G.; Davis, R.E.; Feddema, J.J.; Klink, K.M.; Legates, D.R.; O’donnell, J.; Rowe, C.M. Statistics for the Evaluation and Comparison of Models. J. Geophys. Res. Ocean. 1985, 90, 8995–9005. [Google Scholar] [CrossRef]
- Loague, K.; Green, R.E. Statistical and Graphical Methods for Evaluating Solute Transport Models: Overview and Application. J. Contam. Hydrol. 1991, 7, 51–73. [Google Scholar] [CrossRef]
Growing Season | Sowing Window | EONFR | Grain Yield | Gross Return | Profit | MNR |
---|---|---|---|---|---|---|
kg ha−1 | kg ha−1 | USD ha−1 | USD ha−1 | USD ha−1 | ||
2018–2019 | SD1 | 140 | 3935.0 | 7004.0 | 3859.0 | 6937.1 |
SD2 | 170 | 5226.0 | 9302.3 | 5437.9 | 9220.7 | |
SD3 | 130 | 3580.0 | 6372.4 | 3602.1 | 6310.0 | |
2019–2020 | SD1 | 140 | 3391.0 | 6036.0 | 2662.9 | 5968.8 |
SD2 | 170 | 4378.0 | 7792.8 | 4252.4 | 7711.2 | |
SD3 | 130 | 2840.0 | 5055.2 | 2438.6 | 4992.8 |
Growing Season | Sowing Window | EONFR | Grain N Contents | Total Crop N Uptake | NUE | NUtE |
---|---|---|---|---|---|---|
kg ha−1 | kg ha−1 | kg ha−1 | kg ha−1 kg−1 N | kg ha−1 kg−1 N | ||
2018–2019 | SD1 | 140 | 36.0 | 75.0 | 28.1 | 52.5 |
SD2 | 170 | 63.0 | 114.0 | 30.7 | 45.8 | |
SD3 | 130 | 32.0 | 63.0 | 27.5 | 56.8 | |
2019–2020 | SD1 | 140 | 33.0 | 64.0 | 24.2 | 53.0 |
SD2 | 170 | 51.0 | 91.0 | 25.8 | 48.1 | |
SD3 | 130 | 30.0 | 57.0 | 21.9 | 49.8 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hussain, T.; Mulla, D.J.; Hussain, N.; Qin, R.; Tahir, M.; Liu, K.; Harrison, M.T.; Sinutok, S.; Duangpan, S. Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice. Plants 2023, 12, 3685. https://doi.org/10.3390/plants12213685
Hussain T, Mulla DJ, Hussain N, Qin R, Tahir M, Liu K, Harrison MT, Sinutok S, Duangpan S. Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice. Plants. 2023; 12(21):3685. https://doi.org/10.3390/plants12213685
Chicago/Turabian StyleHussain, Tajamul, David J. Mulla, Nurda Hussain, Ruijun Qin, Muhammad Tahir, Ke Liu, Matthew T. Harrison, Sutinee Sinutok, and Saowapa Duangpan. 2023. "Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice" Plants 12, no. 21: 3685. https://doi.org/10.3390/plants12213685
APA StyleHussain, T., Mulla, D. J., Hussain, N., Qin, R., Tahir, M., Liu, K., Harrison, M. T., Sinutok, S., & Duangpan, S. (2023). Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice. Plants, 12(21), 3685. https://doi.org/10.3390/plants12213685