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A Brief Review of Tools to Promote Transdisciplinary Collaboration for Addressing Climate Change Challenges in Agriculture by Model Coupling

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Digital Ecosystem for Innovation in Agriculture

Part of the book series: Studies in Big Data ((SBD,volume 121))

Abstract

Climate Change threatens agriculture, and agriculture can also play an essential role in climate change mitigation and adaptation through increasing agricultural efficiencies and greening the energy sector by making room for sustainable renewable bioenergy crops. Efforts for climate change mitigation and adaptation with a focus on agriculture must come from transdisciplinary collaboration, which is often not easy and require one to come out of one’s comfort zone. However, at the same time, plenty of tools can facilitate and make such collaboration easy, especially for those who are not modellers or modelling with a focus on a specific aspect of climate change and agriculture. This chapter summarizes such tools and their application in accelerating transdisciplinary collaboration for more sustainable and climate-resilient agriculture.

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References

  • 2015 water-use withdrawals by category | U.S. Geological Survey. https://www.usgs.gov/media/images/2015-water-use-withdrawals-category. Accessed 24 October 2022.

  • Abrahamsen, P., & Hansen, S. (2000). Daisy: An open soil-crop-atmosphere system model. Environmental Modelling & Software, 15, 313–330. https://doi.org/10.1016/S1364-8152(00)00003-7

    Article  Google Scholar 

  • Ainsworth, E. A., & Long, S. (2005). What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytologist, 165, 351–372.

    Article  Google Scholar 

  • Asseng, S., Zhu, Y., Basso, B., et al. (2014). Simulation modeling: Applications in cropping systems. In N. K. Van Alfen (Ed.), Encyclopedia of agriculture and food systems (pp. 102–112). Academic Press.

    Chapter  Google Scholar 

  • Auzmendi, I., & Hanan, J. (2018). Using L-studio to visualize data and modify plant architecture for agronomic purposes: Visualization and modification of plant architecture with L-studio. In 2018 6th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA) (pp. 43–49)

    Google Scholar 

  • Bajželj, B., & Richards, K. (2014). The Positive feedback loop between the impacts of climate change and agricultural expansion and relocation. Land, 3, 898–916. https://doi.org/10.3390/land3030898

    Article  Google Scholar 

  • Baker, R. E., Pena, J.-M., Jayamohan, J., & Jérusalem, A. (2018). Mechanistic models versus machine learning, a fight worth fighting for the biological community? Biology Letters, 14, 20170660.

    Article  Google Scholar 

  • Beaulieu, E., Lucas, Y., Viville, D., et al. (2016). Hydrological and vegetation response to climate change in a forested mountainous catchment. Model Earth Syst Environ, 2, 1–15. https://doi.org/10.1007/s40808-016-0244-1

    Article  Google Scholar 

  • Blöschl, G., Bierkens, M. F. P., Chambel, A., et al. (2019). Twenty-three unsolved problems in hydrology (UPH)—a community perspective. Hydrological Sciences Journal, 64, 1141–1158. https://doi.org/10.1080/02626667.2019.1620507

    Article  Google Scholar 

  • Brisson, N., Mary, B., Ripoche, D., et al. (1998). STICS: A generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie, 18, 311–346. https://doi.org/10.1051/agro:19980501

    Article  Google Scholar 

  • Chenu, K., Porter, J. R., Martre, P., et al. (2017). Contribution of crop models to adaptation in wheat. Trends in Plant Science, 22, 472–490. https://doi.org/10.1016/j.tplants.2017.02.003

    Article  Google Scholar 

  • Churkina, G., Running, S. W., Schloss, A. L., & ThE. Participants OF. ThE. Potsdam NpP. Model Intercomparison. (1999). Comparing global models of terrestrial net primary productivity (NPP): The importance of water availability. Global Change Biology, 5, 46–55. https://doi.org/10.1046/j.1365-2486.1999.00006.x

  • David O, Markstrom, S. L., Rojas, K. W., et al. (2016). The object modeling system. In: Agricultural system models in field research and technology transfer (pp. 317–330). CRC Press.

    Google Scholar 

  • De Clercq, M., Vats, A., & Biel, A. (2018). Agriculture 4.0: The future of farming technology. In Proceedings of the World Government Summit, Dubai, UAE (pp. 11–13).

    Google Scholar 

  • De Souza, A. P., Burgess, S. J., Doran, L., et al. (2022). Soybean photosynthesis and crop yield are improved by accelerating recovery from photoprotection. Science, 377, 851–854. https://doi.org/10.1126/science.adc9831

    Article  Google Scholar 

  • Dokoohaki, H., Gheysari, M., Mousavi, S.-F., Zand-Parsa, S., Miguez, F. E., Archontoulis, S. V., & Hoogenboom, G. (2016). Coupling and testing a new soil water module in DSSAT CERES-Maize model for maize production under semi-arid condition. Agricultural Water Management, 163, 90–99. https://doi.org/10.1016/j.agwat.2015.09.002

    Article  Google Scholar 

  • Druckenmiller, H. (2022). Accounting for ecosystem service values in climate policy. Nature Clinical Practice Endocrinology & Metabolism, 12, 596–598. https://doi.org/10.1038/s41558-022-01362-0

    Article  Google Scholar 

  • Dunn, S. M., & Mackay, R. (1995). Spatial variation in evapotranspiration and the influence of land use on catchment hydrology. Journal of Hydrology, 171, 49–73. https://doi.org/10.1016/0022-1694(95)02733-6

    Article  Google Scholar 

  • Evenson, R. E., & Gollin, D. (2003). Assessing the impact of the green revolution, 1960 to 2000. Science, 300, 758–762. https://doi.org/10.1126/science.1078710

    Article  Google Scholar 

  • Gazestani, V. H., & Lewis, N. E. (2019). From genotype to phenotype: Augmenting deep learning with networks and systems biology. Current Opinion in Systems Biology, 15, 68–73. https://doi.org/10.1016/j.coisb.2019.04.001

    Article  Google Scholar 

  • Gennari, J. H., Neal, M. L., Galdzicki, M., & Cook, D. L. (2011). Multiple ontologies in action: Composite annotations for biosimulation models. Journal of Biomedical Informatics, 44, 146–154. https://doi.org/10.1016/j.jbi.2010.06.007

    Article  Google Scholar 

  • Gilhespy, S. L., Anthony, S., Cardenas, L., et al. (2014). First 20 years of DNDC (DeNitrification DeComposition): Model evolution. Ecological Modelling, 292, 51–62. https://doi.org/10.1016/j.ecolmodel.2014.09.004

    Article  Google Scholar 

  • Goodale, T., Allen, G., Lanfermann, G., et al. (2003). The Cactus framework and toolkit: Design and applications. In J. M. L. M. Palma, A. A. Sousa, J. Dongarra, & V. Hernández (Eds.), High performance computing for computational science—VECPAR 2002 (pp. 197–227). Springer.

    Chapter  Google Scholar 

  • Gregersen, J. B., Gijsbers, P. J. A., & Westen, S. J. P. (2007). OpenMI: Open modelling interface. Journal of Hydroinformatics, 9, 175–191. https://doi.org/10.2166/hydro.2007.023

    Article  Google Scholar 

  • Guo, Y. (2006). Plant modeling and its applications to agriculture. In: 2006 second international symposium on plant growth modeling and applications (pp. 135–141).

    Google Scholar 

  • Han, M., Zhao, C., Šimůnek, J., & Feng, G. (2015). Evaluating the impact of groundwater on cotton growth and root zone water balance using Hydrus-1D coupled with a crop growth model. Agricultural Water Management, 160, 64–75. https://doi.org/10.1016/j.agwat.2015.06.028

    Article  Google Scholar 

  • He, R., Hu, J., He, Y., & Fang, H. (2010). Structural plant modelling based on real 3d structural parameters, resulting simulation system and rule-based language xl. Presented at XVIIth World Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR), Canadian Society for Bioengineering (CSBE/SCGAB) Québec City, Canada, June 13–17 2010.

    Google Scholar 

  • He, Y., Jaiswal, D., Liang, X., et al. (2022). Perennial biomass crops on marginal land improve both regional climate and agricultural productivity. GCB Bioenergy. https://doi.org/10.1111/gcbb.12937

  • Hejazi, M., Edmonds, J., Clarke, L., et al. (2014). Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework. Technological Forecasting and Social Change, 81, 205–226. https://doi.org/10.1016/j.techfore.2013.05.006

    Article  Google Scholar 

  • Houska, T., Multsch, S., Kraft, P., et al. (2014). Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model. Biogeosciences, 11, 2069–2082. https://doi.org/10.5194/bg-11-2069-2014

    Article  Google Scholar 

  • Izaurralde JRW and RC (2010) The APEX model. In: Watershed models (pp. 461–506). CRC Press.

    Google Scholar 

  • Jaiswal, D., De Souza, A. P., Larsen, S., et al. (2019). Reply to: Brazilian ethanol expansion subject to limitations. Nature Clinical Practice Endocrinology & Metabolism, 9, 211–212. https://doi.org/10.1038/s41558-019-0423-y

    Article  Google Scholar 

  • Jaiswal, D., De Souza, A. P., Larsen, S., et al. (2017). Brazilian sugarcane ethanol as an expandable green alternative to crude oil use. Nature Clim Change, 7, 788–792. https://doi.org/10.1038/nclimate3410

    Article  Google Scholar 

  • Jiao, Y., Lei, H., Yang, D., et al. (2017). Impact of vegetation dynamics on hydrological processes in a semi-arid basin by using a land surface-hydrology coupled model. Journal of Hydrology, 551, 116–131. https://doi.org/10.1016/j.jhydrol.2017.05.060

    Article  Google Scholar 

  • Jones, J. W., Hoogenboom, G., Porter, C. H., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265. https://doi.org/10.1016/S1161-0301(02)00107-7

    Article  Google Scholar 

  • Kannan, K., Wang, Y., Lang, M., et al. (2019). Combining gene network, metabolic and leaf-level models shows means to future-proof soybean photosynthesis under rising CO2 in silico Plants, 1, diz008. https://doi.org/10.1093/insilicoplants/diz008

  • Keating, B. A., Carberry, P. S., Hammer, G. L., et al. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, 267–288. https://doi.org/10.1016/S1161-0301(02)00108-9

    Article  Google Scholar 

  • Kellner, J., Multsch, S., Houska, T., et al. (2017). A coupled hydrological-plant growth model for simulating the effect of elevated CO2 on a temperate grassland. Agricultural and Forest Meteorology, 246, 42–50. https://doi.org/10.1016/j.agrformet.2017.05.017

    Article  Google Scholar 

  • Kolbert, E. (2021). Creating a better leaf could tinkering with photosynthesis prevent a global food crisis? In The New Yorker. https://www.newyorker.com/magazine/2021/12/13/creating-a-better-leaf. Accessed 14 September 2022.

  • Kraft, P., Vaché, K. B., Frede, H.-G., & Breuer, L. (2011). CMF: A hydrological programming language extension for integrated catchment models. Environmental Modelling & Software, 26, 828–830. https://doi.org/10.1016/j.envsoft.2010.12.009

    Article  Google Scholar 

  • Lang, M. (2019). yggdrasil: A Python package for integrating computational models across languages and scales. in silico Plants, 1. https://doi.org/10.1093/insilicoplants/diz001

  • Lenka, N., Lenka, N., Sejian, V., & Mohanty, M. (2015). Contribution of agriculture sector to climate change. In Climate change impact on livestock: Adaptation and mitigation (pp. 37–48).

    Google Scholar 

  • Liang, H., Hu, K., Batchelor, W. D., et al. (2016). An integrated soil-crop system model for water and nitrogen management in North China. Science and Reports, 6, 25755. https://doi.org/10.1038/srep25755

    Article  Google Scholar 

  • Lobell, D. B., Roberts, M.J., Schlenker, W., et al. (2014). Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest, 344, 5.

    Google Scholar 

  • Lochocki, E. B., Rohde, S., Jaiswal, D., et al. (2022). BioCro II: A software package for modular crop growth simulations. In silico Plants, 4, diac003. https://doi.org/10.1093/insilicoplants/diac003

  • Long, S. P., Karp, A., Buckeridge, M. S., et al. (2015). Feedstocks for biofuels and bioenergy. Bioenergy & Sustainability: Bridging the Gaps 302–347

    Google Scholar 

  • Lopez-Jimenez, J., Vande Wouwer, A., & Quijano, N. (2022). Dynamic modeling of crop-soil systems to design monitoring and automatic irrigation processes: A review with worked examples. Water, 14, 889. https://doi.org/10.3390/w14060889

    Article  Google Scholar 

  • Lynch, J. (2021). Agriculture’s contribution to climate change and role in mitigation is distinct from predominantly fossil CO2-Emitting Sectors. Frontiers in Sustainable Food Systems, 4, 9.

    Article  Google Scholar 

  • Ma, L., Hoogenboom, G., Saseendran, S. A., et al. (2009). Effects of estimating soil hydraulic properties and root growth factor on soil water balance and crop production. Agronomy Journal, 101, 572–583. https://doi.org/10.2134/agronj2008.0206x

    Article  Google Scholar 

  • Manfreda, S., Smettem, K., Iacobellis, V., et al. (2010). Coupled ecological–hydrological processes. Ecohydrology, 3, 131–132. https://doi.org/10.1002/eco.131

    Article  Google Scholar 

  • Marshall-Colon, A., Long, S. P., Allen, D. K., et al. (2017). Crops In Silico: generating virtual crops using an integrative and multi-scale modeling platform. Frontiers in Plant Science, 8, 786. https://doi.org/10.3389/fpls.2017.00786

    Article  Google Scholar 

  • Miguez, F. E., Maughan, M., Bollero, G. A., & Long, S. P. (2012). Modeling spatial and dynamic variation in growth, yield, and yield stability of the bioenergy crops Miscanthus × giganteus and P anicum virgatum across the conterminous United States. Gcb Bioenergy, 4, 509–520.

    Article  Google Scholar 

  • Moore, R. V., & Tindall, C. I. (2005). An overview of the open modelling interface and environment (the OpenMI). Environmental Science & Policy, 8, 279–286. https://doi.org/10.1016/j.envsci.2005.03.009

    Article  Google Scholar 

  • Multsch, S., Kraft, P., Frede, H. G., & Breuer, L. (2011). Development and application of the generic Plant growth Modeling Framework (PMF). In: Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2011.

    Google Scholar 

  • Murray, S. J., Foster, P. N., & Prentice, I. C. (2011). Evaluation of global continental hydrology as simulated by the Land-surface Processes and eXchanges Dynamic Global Vegetation Model. Hydrology and Earth System Sciences, 15, 91–105. https://doi.org/10.5194/hess-15-91-2011

    Article  Google Scholar 

  • Ort, D., & Long., (2014). Limits on yields in the corn belt. Science, 344, 484–485.

    Article  Google Scholar 

  • Peña-Haro, S., Zhou, J., Zhang, G., et al. (2012). A multi-approach framework to couple independent models for simulating the interaction between crop growth and unsaturated-saturated flow processes. In: R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.), International Environmental Modelling and Software Society (iEMSs) 2012, International Congress on Environmental Modelling and Software, Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty, Sixth Biennial Meeting, Leipzig, Germany, July 2012, pp. 1224–1231.

    Google Scholar 

  • Peng, C. (2000). From static biogeographical model to dynamic global vegetation model: A global perspective on modelling vegetation dynamics. Ecological Modelling, 135, 33–54. https://doi.org/10.1016/S0304-3800(00)00348-3

    Article  Google Scholar 

  • Peng, H., Zhao, C., Feng, Z., et al. (2014). Canopy interception by a spruce forest in the upper reach of Heihe River basin, Northwestern China. Hydrological Processes, 28, 1734–1741. https://doi.org/10.1002/hyp.9713

    Article  Google Scholar 

  • Perkel, J. M. (2015). Programming: Pick up Python. Nature, 518, 125–126. https://doi.org/10.1038/518125a

    Article  Google Scholar 

  • Porter, J. R., Xie, L., Challinor, A. J., et al. (2014). Food security and food production systems In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485–533.

    Google Scholar 

  • Pradal, C., Dufour-Kowalski, S., Boudon, F., et al. (2008). OpenAlea: A visual programming and component-based software platform for plant modelling. Functional Plant Biol, 35, 751–760. https://doi.org/10.1071/FP08084

    Article  Google Scholar 

  • Pradal, C., Fournier, C., Valduriez, P., & Cohen-Boulakia, S. (2015) OpenAlea: scientific workflows combining data analysis and simulation. In Proceedings of the 27th International Conference on Scientific and Statistical Database Management (pp. 1–6). Association for Computing Machinery, New York, NY, USA.

    Google Scholar 

  • Prusinkiewicz, P., Karwowski, R., & Lane, B., et al. (2007). The L+C plant-modelling language. In J. Vos, L. F. M. Marcelis, & P. H. B. De Visser (Eds.), Functional-structural plant modelling in crop production (pp. 27–42). Springer.

    Chapter  Google Scholar 

  • Qin, Y., Mueller, N. D., Siebert, S., et al. (2019). Flexibility and intensity of global water use. Nature Sustainability, 2, 515–523. https://doi.org/10.1038/s41893-019-0294-2

  • Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PLoS ONE, 8, e66428.

    Article  Google Scholar 

  • Ray, D. K., West, P. C., Clark, M., et al. (2019). Climate change has likely already affected global food production. PLoS ONE, 14, e0217148. https://doi.org/10.1371/journal.pone.0217148

    Article  Google Scholar 

  • Schlesinger, W. H., & Jasechko, S. (2014). Transpiration in the global water cycle. Agricultural and Forest Meteorology, 189–190, 115–117. https://doi.org/10.1016/j.agrformet.2014.01.011

    Article  Google Scholar 

  • Shameer, S., Wang, Y., Bota, P., et al. (2022). A hybrid kinetic and constraint-based model of leaf metabolism allows predictions of metabolic fluxes in different environments. The Plant Journal, 109, 295–313. https://doi.org/10.1111/tpj.15551

  • Shelia, V., Šimůnek, J., Boote, K., & Hoogenbooom, G. (2018). Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system. Journal of Hydrology and Hydromechanics, 66, 232–245. https://doi.org/10.1515/johh-2017-0055

    Article  Google Scholar 

  • Siad, S. M., Iacobellis, V., Zdruli, P., et al. (2019). A review of coupled hydrologic and crop growth models. Agricultural Water Management, 224, 105746. https://doi.org/10.1016/j.agwat.2019.105746

    Article  Google Scholar 

  • Simunek, J. J., Šejna, M., Saito, H., et al. (2013). The Hydrus-1D software package for simulating the movement of water, heat, and multiple solutes in variably saturated media, Version 4.17, HYDRUS Software Series 3, Department of Environmental Sciences. USA 342

    Google Scholar 

  • Steduto, P., Hsiao, T. C., Raes, D., & Fereres, E. (2009). AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and Underlying Principles. Agronomy Journal, 101, 426–437. https://doi.org/10.2134/agronj2008.0139s

    Article  Google Scholar 

  • Stöckle, C. O., Donatelli, M., & Nelson, R. (2003). CropSyst, a cropping systems simulation model. European Journal of Agronomy, 18, 289–307. https://doi.org/10.1016/S1161-0301(02)00109-0

    Article  Google Scholar 

  • Subak, S. (1999). Global environmental costs of beef production. Ecological Economics, 13.

    Google Scholar 

  • Tonnang, H. E., Sokame, B. M., Abdel-Rahman, E. M., Dubois, T. (2022). Measuring and modelling crop yield losses due to invasive insect pests under climate change. Current Opinion in Insect Science, 100873.

    Google Scholar 

  • Van Diepen, C. A., Wolf, J., van Keulen H, Rappoldt C (1989) WOFOST: a simulation model of crop production. Soil Use and Management, 5, 16–24. https://doi.org/10.1111/j.1475-2743.1989.tb00755.x

  • Viana, C. M., Freire, D., Abrantes, P., et al. (2022). Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review. Science of the Total Environment, 806, 150718.

    Article  Google Scholar 

  • Wang, J., Huang, G., Zhan, H., et al. (2014). Evaluation of soil water dynamics and crop yield under furrow irrigation with a two-dimensional flow and crop growth coupled model. Agricultural Water Management, 141, 10–22. https://doi.org/10.1016/j.agwat.2014.04.007

    Article  Google Scholar 

  • Wang, K., Sun, S., Li, Y., et al. (2022). Response of regional agricultural water use to the change of climate and plantation structure in the typical agricultural region of China. Journal of Water and Climate Change, 13, 1370–1388. https://doi.org/10.2166/wcc.2022.416

    Article  Google Scholar 

  • Wang, X., Liu, G., Yang, J., et al. (2017). Evaluating the effects of irrigation water salinity on water movement, crop yield and water use efficiency by means of a coupled hydrologic/crop growth model. Agricultural Water Management, 185, 13–26. https://doi.org/10.1016/j.agwat.2017.01.012

    Article  Google Scholar 

  • Wilde, M., Hategan, M., Wozniak, J. M., et al. (2011). Swift: A language for distributed parallel scripting. Parallel Computing, 37, 633–652.

    Article  Google Scholar 

  • Yang, W., Pallas, B., Durand, J.-B., et al. (2016). The impact of long-term water stress on tree architecture and production is related to changes in transitions between vegetative and reproductive growth in the ‘Granny Smith’ apple cultivar. Tree Physiology, 36, 1369–1381. https://doi.org/10.1093/treephys/tpw068

    Article  Google Scholar 

  • Zhang, G., Zhou, J., & Zhou, Q., et al. (2012). Integrated eco-hydrological modelling by a combination of coupled-model and algorithm using OMS3. International Congress on Environmental Modelling and Software.

    Google Scholar 

  • Zhang, J., Petersen, S. D., Radivojevic, T., et al. (2020). Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nature Communications, 11, 4880. https://doi.org/10.1038/s41467-020-17910-1

    Article  Google Scholar 

  • Zhou, J., Cheng, G., Li, X., et al. (2012). Numerical modeling of wheat irrigation using coupled HYDRUS and WOFOST models. Soil Science Society of America Journal, 76, 648–662. https://doi.org/10.2136/sssaj2010.0467

    Article  Google Scholar 

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  1. Environmental Sciences and Sustainable Engineering Centre, IIT Palakkad, Palakkad, Kerala, 678557, India

    Sruthi Surendran & Deepak Jaiswal

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  1. Sruthi Surendran
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  1. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Sanjay Chaudhary

  2. CIFOR-ICRAF India, Asia Continental Program, New Delhi, Delhi, India

    Chandrashekhar M. Biradar

  3. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Srikrishnan Divakaran

  4. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Mehul S. Raval

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Surendran, S., Jaiswal, D. (2023). A Brief Review of Tools to Promote Transdisciplinary Collaboration for Addressing Climate Change Challenges in Agriculture by Model Coupling. In: Chaudhary, S., Biradar, C.M., Divakaran, S., Raval, M.S. (eds) Digital Ecosystem for Innovation in Agriculture. Studies in Big Data, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-99-0577-5_1

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