Abstract
This study evaluates the impacts of climate change and an adaptation strategy on agriculture in the Wami River sub-basin in Tanzania. This study uses the Agricultural Model Improvement and Inter-comparison Project (AgMIP) framework that integrates climate, crops and economic models and data using a novel multi-model approach for impact assessment of agricultural systems under current and future conditions. This study uses five Global Circulation Models (GCMs) from the fifth phase of the Coupled Model Inter-comparison Project (CMIP5), two crop simulation models, and one economic impact assessment model. In this study, a representative agricultural pathways (RAP) that characterises future conditions following ‘business-as-usual’ trends was developed and used to model future agricultural systems in the Wami River sub-basin. Results show that by mid-century, the maximum and minimum temperatures will increase by 1.8–4.1 °C and 1.4–4.6 °C, respectively. Rainfall is predicted to be variable with some places projected to increase by 12%, while in other areas it is projected to decrease by 14–28%. Maize yields under these conditions are projected to decrease by 5.3–40.7%. Results show that under current conditions, 50–60% of farm households are vulnerable to losses due to climate change. The impacts of climate change on poverty and per capita income are also projected to be negative. Under the current production system, poverty rates were projected to increase by 0.8–15.3% and per-capita income to drop by 1.3–7.5%. Future socio-economic conditions and prices offset the negative impacts of climate change. Under future conditions, the proportion of households vulnerable to loss is estimated to range from 25 to 50%. Per-capita income and poverty rates are expected to improve under the future climate change conditions. Poverty rates would decrease between 1.9 and 11.2% and income per-capita would increase between 2.6 and 18.5%. The proposed future adaptation package will further improve household livelihoods. This integrated assessment of climate change projections using the improved methods and tools developed by AgMIP has contributed to a better understanding of climate change and adaptation impacts in a holistic manner.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antle JM (2011) Parsimonious Multi-dimensional impact assessment. Am J Agr Econ 93(5):1292–1311
Antle JM, Valdivia RO (2011) TOA-MD 5.0: Tradeoff analysis model for multi-dimensional impact assessment. http://tradeoffs.oregonstate.edu
Antle JM, Stoorvogel JJ, Valdivia RO (2014) New parsimonious simulation methods and tools to assess future food and environmental security of farm populations. Philos Trans R Soc Lond B Biol Sci 369(1639):20120280. https://doi.org/10.1098/rstb.2012.0280
Antle J, Roberto OV, Ken B, Jerry H, Sander J, Jim J, Cheryl P, Cynthia R, Alex R, Peter T (2015) AgMIP’s trans-disciplinary approach to regional integrated assessment of climate impact, vulnerability and adaptation of agricultural systems. In: Rosenzweig C, Hillel D (eds) Handbook of climate change and agroecosystems: the agricultural model intercomparison and improvement project (AgMIP). ICP series on climate change impacts, adaptation, and mitigation, vol 3. Imperial College Press. https://doi.org/10.1142/9781783265640_0002
Bonnett DG, Price RM (2002) Statistical inference for a linear function of medians: confidence intervals, hypothesis testing, and sample size requirements. Psychol Methods 7:370–383
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265
Kilembe C, Thomas TS, Waithaka M, Kyotalimye M, Tumbo S (2013) Tanzania. In: Waithaka M, Nelson GC, Thomas TS, Kyotalimye M (eds) East African agriculture and climate change: a comprehensive analysis. IFPRI, Washington, DC, pp 313–343
Knutti R, Sedlácek J (2012) Robustness and uncertainties in the new CMIP5 climate model projections. Nature Clim Change. https://doi.org/10.1038/nclimate1716
Leenaars JGB (2013) Africa soil profiles database, ISRIC report 2013/03. Wageningen, ISRIC-World Soil Information
McCown RL, Hammer GL, Hargreaves JNG, Holzworth DP, Freebairn DM (1996) APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agric Syst 50(3):255–271
Matari EE, Chang’a, LB, Chikojo G, Hyera T (2008) Climate change scenarios development for second national communication—Tanzania. TMA Res J 1, 8–18
Meridian Institute (2013) Chairs’ summary, high level dialogue, harnessing innovation for African agriculture and food systems: meeting the challenges and designing for the 21st century. Addis Ababa, Ethiopia
MLHSSD (2009) National land use framework plan 2009–2029. Volumes I-III, Dar-es-Salaam, Tanzania
Mourice SK, Rweyemamu CL, Tumbo SD, Amuri N (2014) Maize cultivar specific parameters for decision support system for agrotechnology transfer (DSSAT) application in Tanzania. Am J Plant Sci 5:821–833
Mwandosya MJ, Nyenzi BS, Luhanga ML (1998) The Assessment of vulnerability and adaptation to climate change impacts in Tanzania. Centre for Energy, Environmental Science and Technology, Dar-es-Salaam, Tanzania
NBS (2012) Tanzania national panel survey report wave 2, 2010–2011, living standards measurements survey. National Bureau of Statistics, Dar-es-Salaam, Tanzania
Nelson GC, van der Mensbrugghe D, Ahammad H et al (2013) Agriculture and climate change in global scenarios: why don’t the models agree. Agric Econ 45:85–101
Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (2007) Contribution of working group Ii to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK
Riahi K, Krey V, Rao S, Chirkov V, Fischer G, Kolp P, Kindermann G, Nakicenovic N, Rafai P (2011) RCP-8.5: Exploring the consequence of high emission trajectories. Clim Change. https://doi.org/10.1007/s10584-011-0149-y
Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich MG, Schubert SD, Takacs L, Kim GK, Bloom S (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24(14):3624–3648
Rosenzweig C et al (2013) The agricultural model inter-comparison and improvement project (AgMIP): protocols and pilot studies. Agric For Meteorol 170:166–182
Ruane AC, Winter JM, McDermid SP, Hudson NI (2015) AgMIP climate datasets and scenarios for integrated assessment. In: Rosenzweig C, Hillel D (eds) Handbook of climate change and agroecosystems: the agricultural model intercomparison and improvement project (AgMIP), ICP series on climate change impacts, adaptation, and mitigation, vol 3. Imperial College Press, pp 45–78, https://doi.org/10.1142/9781783265640_0003
Ruane AC, Goldberg R, Chryssanthacopoulos J (2015b) AgMIP climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agr Forest Meteorol 200:233–248. https://doi.org/10.1016/j.agrformet.2014.09.016
Thomson AM, Calvin KV, Smith SJ et al. (2011) RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim Change. https://doi.org/10.1007/s10584-011-0151-4
Tumbo SD, Kahimba FC, Mbilinyi BP, Rwehumbiza FB, Mahoo HF, Mbungu WB, Enfors E (2012) Impact of projected climate change on agricultural production in semi-arid areas of Tanzania: a case of Same district. Afri Crop Sci J 20(2):453–463
United Nations (2013) World population prospects: the 2012 revision, highlights and advance tables. Working Paper No. ESA/P/WP.228
United Republic of Tanzania (2012) National climate change strategy. United Republic of Tanzania, Vice President’s Office, Division of Environment. Dar es salaam, Tanzania
United Republic of Tanzania (2013a) 2012 population and housing census. population distribution by administrative areas. National Bureau of Statistics, Dar-es-Salaam, Tanzania
United Republic of Tanzania (2013b) 2012 population and housing census: population distribution by administrative units; key findings. Dar es salaam, Tanzania
Valdivia RO, Antle JM, Rosenzweig C et al. (2015) Representative agricultural pathways and scenarios for regional integrated assessment of climate change impact, vulnerability and adaptation. In: Rosenzweig C, Hillel D (eds) Handbook of climate change and agroecosystems: the agricultural model intercomparison and improvement project (AgMIP). ICP series on climate change impacts, adaptation, and mitigation, vol 3. Imperial College Press, pp 101–156
Valdivia RO, Antle JM, Stoorvogel JJ (2017) Designing and evaluating sustainable development pathways for semi-subsistence crop-livestock systems: lessons from Kenya. Agric Econ (In press)
White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate change on crop production. Field Crops Res 124:357–368
Acknowledgements
This work was sponsored by IDRC-AARC regional collaborative research project entitled “Enhancing Climate Change Adaptation in Agriculture and Water Resources in the Great Horn of Africa” led by the Soil–Water Management Research Programme at Sokoine University of Agriculture and supported by the UKaid grant GB-1-202108 to the Agricultural Model Inter-comparison and Improvement Project (AgMIP) and collaborators. The authors would like to acknowledge the contribution by Dr. Ayub J. Churi for assisting with data analysis and Tatu S. Mnimbo for assisting with the editing of the manuscript. The results reflect the findings of the authors and not necessarily the views of the sponsors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tumbo, S.D. et al. (2020). Integrated Assessment of Climate Change Impacts and Adaptation in Agriculture: The Case Study of the Wami River Sub-basin, Tanzania. In: Matondo, J.I., Alemaw, B.F., Sandwidi, W.J.P. (eds) Climate Variability and Change in Africa . Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-030-31543-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-31543-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31542-9
Online ISBN: 978-3-030-31543-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)