Original Article
Influence of Delayed Sowing on Physiological Traits and Canopy Dynamics in Jute (Corchorus olitorius L.): A Narrative Review with Emphasis on LAI, LAR and SLA
Year: 2026 | Month: June | Volume 13 | Issue 1
Debashis Ghosh1
Sananda Mondal1
Debasis Panda2 and Deb Prasad Ray2*
1.Asseng, S. et al. 2015. Rising temperatures reduce global wheat production. Nature Climate Change, 5(2): 143–147.
View at Google Scholar2.Basu, G., Bandyopadhyay, P.K., Biswas, S., Roy, A. and Mahapatra, B.S. 2012. Effect of sowing date on growth and fibre yield of jute (Corchorus olitorius L.). Indian Journal of Agronomy, 57(3): 259–263.
View at Google Scholar3.Biscoe, P.V. and Gallagher, J.N. 1977. Weather, dry matter production and yield. In: Environmental Effects on Crop Physiology . London: Academic Press, pp. 75–100.
View at Google Scholar4.Board, J.E. and Harville, B.G. 1992. Explanations for greater light interception in narrow vs wide row soybean. Crop Science , 32: 198–202.
View at Google Scholar5.Boote, K.J., Jones, J.W. and Pickering, N.B. 2013. Potential uses and limitations of crop models. Agronomy Journal, 105(8): 2557–2566.
View at Google Scholar6.Campbell, G.S. and Norman, J.M. 1998. An Introduction to Environmental Biophysics. New York: Springer.
View at Google Scholar7.Chenu, K., Chapman, S.C. and Hammer, G.L. 2013. Largescale characterization of drought pattern: A continentwide modelling approach applied to the Australian wheatbelt. Environmental Modelling & Software, 41: 98–114.
View at Google Scholar8.Craufurd, P.Q. and Wheeler, T.R. 2009. Climate change and flowering time of annual crops. Journal of Experimental Botany , 60: 2529–2539.
View at Google Scholar9.Evans, J.R. and Poorter, H. 2001. Photosynthetic acclimation of plants to growth irradiance: The relative importance of specific leaf area and nitrogen partitioning in maximizing carbon gain. Plant, Cell & Environment , 24(8): 755–767.
View at Google Scholar10.Falster, D.S. and Westoby, M. 2003. Leaf size and angle vary widely across species: What consequences for light interception? New Phytologist, 158(3): 509–525.
View at Google Scholar11.Falster, D.S., Brännström, Å., Dieckmann, U. and Westoby, M. 2018. Plant Form–Functional Ecology and Evolution. Cambridge: Cambridge University Press.
View at Google Scholar12.Ferreira, L.G., Góes, R.M., Silva, T.S.F. and Castro, J.F. 2019. Remote sensing for crop monitoring. International Journal of Applied Earth Observation and Geoinformation, 80: 1–10.
View at Google Scholar13.Galmés, J., Flexas, J., Savé, R. and Medrano, H., 2007. Water relations and stomatal characteristics of Mediterranean plants with different growth forms and leaf habits: Responses to water stress and recovery. Plant and Soil , 290(1–2): 139–155.
View at Google Scholar14.Gardner, F.P., Pearce, R.B. and Mitchell, R.L., 1985. Physiology of Crop Plants. Ames: Iowa State University Press.
View at Google Scholar15.Goudriaan, J. and van Laar, H.H. 1994. Modelling Potential Crop Growth Processes. Dordrecht: Kluwer Academic Publishers.
View at Google Scholar16.Hammer, G.L. et al. 2019. Crop design for specific adaptation in variable environments. Field Crops Research, 234: 115–128.
View at Google Scholar17.Hay, R.K.M. and Walker, A.J. 1989. An Introduction to the Physiology of Crop Yield. London: Longman.
View at Google Scholar19.ICAR-CRIJAF, 2020. Annual Report 2019–20. Barrackpore: ICAR-Central Research Institute for Jute and Allied Fibres.
View at Google Scholar20.Jones, J.W. et al. 2003. The DSSAT cropping system model. European Journal of Agronomy, 18(3–4): 235–265.
View at Google Scholar21.Kiniry, J.R. et al. 2001. Radiation-use efficiency in biomass accumulation. Field Crops Research, 68: 199–214.
View at Google Scholar22.Koch, K.E. 2004. Sucrose metabolism: Regulatory mechanisms and pivotal roles in sugar sensing and plant development. Current Opinion in Plant Biology, 7(3): 235–246.
View at Google Scholar23.Lambers, H., Chapin, F.S. and Pons, T.L. 2008. Plant Physiological Ecology . 2nd ed. New York: Springer.
View at Google Scholar24.Li, Y., Shipley, B. and Lechowicz, M.J. 2018. Are plant ecological strategies consistent across environments? Evidence from a multispecies test. New Phytologist , 218(1): 272–283.
View at Google Scholar25.Loomis, R.S. and Williams, W.A., 1969. Productivity and the morphology of crop stands. Annual Review of Plant Physiology , 20: 289–312.
View at Google Scholar26.Marcelis, L.F.M., 1996. Sink strength as a determinant of dry matter partitioning in the whole plant. Journal of Experimental Botany, 47(Special Issue): 1281–1291.
View at Google Scholar27.Messina, C.D. et al. 2018. Crop modelling strategies for precision agriculture. Agronomy Journal, 110(6): 1–12.
View at Google Scholar28.Monsi, M. and Saeki, T. 1953. Über den Lichtfaktor in den Pflanzengesellschaften. Japanese Journal of Botany, 14: 22–52.
View at Google Scholar29.Monteith, J.L. 1977. Climate and efficiency of crop production in Britain. Philosophical Transactions of the Royal Society B, 281: 277–294.
View at Google Scholar30.Muchow, R.C. and Sinclair, T.R. 1991. Water deficit effects on maize yields modeled under current and climate change scenarios. Agronomy Journal , 83: 105–112.
View at Google Scholar31.Nguyen, H.T., Bousser, J., Roumet, C. and Garnier, E. 2019. Biomass allocation in relation to plant size, light, and nutrient availability. Functional Ecology , 33(9): 1645–1655.
View at Google Scholar32.Niinemets, Ü. and Valladares, F. 2006. Tolerance to shade, drought, and waterlogging of temperate northern hemisphere trees and shrubs. Ecological Monographs, 76(4): 521–547.
View at Google Scholar33.Niinemets, Ü. 2010. A review of light interception in plant canopies: Physical models and underlying concepts. Annals of Botany , 105(3): 369–389.
View at Google Scholar34.Paul, M.J. and Foyer, C.H. 2001. Sink regulation of photosynthesis. Journal of Experimental Botany, 52(360): 1383–1400.
View at Google Scholar35.Poorter, H. and Garnier, E. 1996. Plant growth analysis: An evaluation of experimental design and computational methods. Journal of Experimental Botany, 47(305): 1343– 1351.
View at Google Scholar36.Poorter, H., Niinemets, Ü., Poorter, L., Wright, I.J. and Villar, R. 2012. Biomass allocation to leaves, stems and roots: Meta-analyses of interspecific variation and environmental control. New Phytologist , 193(1): 30–50.
View at Google Scholar37.Poorter, H., Niinemets, Ü., Poorter, L., Wright, I.J. and Villar, R. 2009. Causes and consequences of variation in leaf mass per area (LMA): A meta-analysis. New Phytologist , 182(3): 565–588.
View at Google Scholar38.Rahman, M.S., Amin, M.R. and Hassan, M.M. 2021. Phenological responses of jute to sowing time and climate variability. Journal of Crop Science and Biotechnology, 24(2): 101–113.
View at Google Scholar39.Ray, D.P. and Ghosh, R.K. 2018. Perspective of Jute in a New Realm beyond Sacking, Economic Affairs, 63(4): 981-986.
View at Google Scholar40.Reich, P.B., Walters, M.B. and Ellsworth, D.S. 2003. From tropics to tundra: Global convergence in plant functioning. Proceedings of the National Academy of Sciences , 94(25): 13730–13734.
View at Google Scholar41.Reymond, M. et al. 2003. Combining QTL analysis and ecophysiological modelling to study genetic variability of rice. Field Crops Research, 87(2–3): 85–104.
View at Google Scholar42.Ritchie, J.T. and Otter, S. 1985. Description and performance of CERES models. In: ARCP Wheat Simulation Modeling Workshop Proceedings, pp. 159–175.
View at Google Scholar43.Roberts, E.H., Summerfield, R.J. and Cooper, J.P. 1988. Photoperiod and flowering in crop plants. Advances in Agronomy , 41: 1–61.
View at Google Scholar44.Ross, J. 1981. The Radiation Regime and Architecture of Plant Stands . The Hague: Dr W. Junk Publishers.
View at Google Scholar45.Rotter, R.P. et al. 2015. Crop–climate models need an overhaul. Nature Climate Change, 5(9): 768–774.
View at Google Scholar46.Sarlikioti, V., De Visser, P.H.B., Buck-Sorlin, G.H. and Marcelis, L.F.M. 2011. How plant architecture affects light absorption and photosynthesis in tomato: Towards an ideotype for plant architecture using a functional– structural plant model. Annals of Botany, 108(6): 1065–1073.
View at Google Scholar47.Shipley, B. 2002. Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference . Cambridge: Cambridge University Press.
View at Google Scholar48.Shipley, B., Meziane, D. and Wright, I.J. 2006. The evolutionary ecology of leaf economics. Functional Plant Biology , 33(12): 1151–1161.
View at Google Scholar49.Sinclair, T.R. and Muchow, R.C. 1999. Radiation use efficiency. Advances in Agronomy, 65: 215–265.
View at Google Scholar50.Sinoquet, H., Le Roux, X. and Adam, B. 2001. Analysis of the spatial distribution of leaf area index (LAI) at tree and canopy scales in a broad-leaved forest. Plant, Cell & Environment, 24(2): 195–211.
View at Google Scholar51.Slewinski, T.L. 2012. Diverse functional roles of monosaccharide transporters in vascular plants: A physiological perspective. Molecular Plant , 5(3): 787–797.
View at Google Scholar52.Squire, G.R. 1990. The Physiology of Tropical Crop Production. Wallingford: CAB International.
View at Google Scholar53.Thornton, P.K. et al. 2017. Response of agricultural systems to climate change. Annual Review of Environment and Resources, 42: 205–226.
View at Google Scholar54.Tollenaar, M. and Aguilera, A., 1992. Radiation use efficiency of maize under stress conditions. Crop Science, 32: 221–227.
View at Google Scholar55.Tollenaar, M. and Wu, J. 1999. Yield improvement in temperate maize is attributable to greater stress tolerance. Crop Science, 39(6): 1597–1604.
View at Google Scholar56.Valladares, F. and Niinemets, Ü. 2008. Shade tolerance, a key plant feature of complex nature and consequences. Annual Review of Ecology, Evolution, and Systematics, 39: 237–257.
View at Google Scholar57.Valladares, F., Wright, S.J., Lasso, E., Kitajima, K. and Pearcy, R.W. 2007. Plastic phenotypic response to light of 16 congeneric shrubs from a tropical dry forest. Ecology , 88(11): 1–11.
View at Google Scholar58.Vile, D., Garnier, E., Shipley, B. and Laurent, G. 2005. Specific leaf area and dry matter content estimate thickness in laminar leaves. Annals of Botany , 96(6): 1129–1136.
View at Google Scholar59.Vos, J. et al. 2010. Functional–structural plant models: A growing paradigm for crop science. Trends in Plant Science, 15(6): 304–312.
View at Google Scholar60.Watson, D.J. 1947. Comparative physiological studies on growth of field crops. Annals of Botany , 11: 41–76.
View at Google Scholar61.Weiner, J. 2017. Applying plant ecological knowledge to increase agricultural sustainability. Journal of Ecology , 105(4): 865–870.
View at Google Scholar62.Westoby, M. and Wright, I.J. 2006. Land-plant ecology on the basis of functional traits. Trends in Ecology & Evolution, 21(5): 261–268.
View at Google Scholar63.Wright, I.J. and Westoby, M. 2002. Leaves at low versus high rainfall: Coordination of structure, lifespan and physiology. New Phytologist , 155(3): 403–416.
View at Google Scholar64.Wright, I.J., Reich, P.B., Westoby, M. et al. 2004. The worldwide leaf economics spectrum. Nature, 428(6985): 821–827.
View at Google Scholar65.Zhang, C. et al. 2020. Machine learning for crop yield prediction and climate adaptation. Computers and Electronics in Agriculture, 175: 105592.
View at Google Scholar66.Zhang, Y., Fincke, B.G., Zhang, K. et al. 2017. Effects of canopy structure and light interception on stem growth and yield formation in soybean (Glycine max L.). Field Crops Research, 212: 170–180.
View at Google Scholar67.Ziska, L.H., Gebhard, D.E. and Frenkel, C. 2016. Temperature effects on biomass allocation patterns in annual crops. Agricultural and Forest Meteorology , 226–227: 165–173.
View at Google Scholar



