Climate Exchange

281 / 288

[ ] 282
I: Agriculture
Climate science and services to support decision-making
1.
Climate services for agricultural production in Guinea Bissau
1. Quoted in the 2006 National Action Plan for Adaptation, 2006
2. Rui Nené Djata Mane, Manuel Indi; Analise da Fileira do Arroz, 2003
3. Project GBS/87/013/B/01/16
Supporting decision-making in the sugar industry with integrated seasonal climate
forecasting
1. Stone, R.C., Hammer, G.L., and Marcussen, T. (1996) ‘Prediction of global rainfall
probabilities using phases of the Southern Oscillation Index’. Nature 384, 252-255.
2. Stone, R.C. et al (1996) ibid.
Everingham, Y.L., Muchow, R.C., Stone, R.C., Inman-Bamber, G., Singels, A., and
Bezuidenhout, C.N. (2002) ‘Enhanced risk management and decision-making
capability across the sugarcane industry value chain based on seasonal climate
forecasts’. Agric. Systems 74, 3, 459-477.
Fawcett, R.J.B. and Stone, R.C. (2010) ‘A comparison of two seasonal rainfall
forecasting systems for Australia.’ Australian Meteorological and Oceanographic
Journal, 60, 1, 3-11.
Further reading:
Hammer, G.L. (2000) ‘A general systems approach to applying seasonal climate
forecasts’. In Applications of Seasonal Climate Forecasting in Agricultural and Natural
Ecosystems: The Australian Experience (eds. G.L. Hammer, N, Nicholls, and C.
Mitchell), pp51-66. Dordrecht, The Netherlands: Kluwer Academic Publishers.
Stone, R. C., and Meinke, H. (2005) ‘Operational seasonal forecasting of crop
performance’. Philosophical Transactions of the Royal Society, B 360, 2109-2124.
Seasonal climate prediction in Chile: the Agroclimate Outlook
1. Barret, B.S., J.F. Carrasco and A.P. Testino, 2012: ‘Madden-Julian Oscillation
(MJO) modulation of atmospheric circulation and Chilean precipitation’. Journal of
Climate, 25, 1678-1688
2. Montecinos, A. and P. Aceituno, 2003: ‘Seasonality of the ENSO related rainfall
variability in central Chile and associated circulation anomalies’. Journal of Climate,
16, 281-296.
Quintana, J. and P. Aceituno, 2012: Changes in the rainfall regime along the
extratropical west coast of South America (Chile): 30 – 43°S. Atmósfera 25 (1), 1-22.
3. CEPAL (Comisión Económica para América Latina y El Caribe), 2009: La
Economía del Cambio Climático en Chile: Síntesis, 88 pp.
4. Martinez, R. and A. Mascarenhas, 2009: ‘Climate risk management in western South
America: implementing a successful information system’, Bulletin WMO: Weather,
Climate, Water. Vol. 58(3), 188-196.
5.
6.
7.
8.
Climate services and agriculture in the Caribbean
1. Toba N (2009) ‘Potential Economic Impacts of Climate Change in the Caribbean
Community’. Latin America and Caribbean Region Sustainable Development Working
Paper No. 32 In: Assessing the Potential Consequences of Climate Destabilization in
Latin America, W Vergara (ed.). World Bank, pp 35-47.
2. Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones,
R.K. Kolli, W.-T. Kwon, R. Laprise, V. Magaña Rueda, L. Mearns, C.G. Menéndez, J.
Räisänen, A. Rinke, A. Sarr and P. Whetton, 2007: ‘Regional Climate Projections’. In:
Climate Change 2007: The Physical Science Basis. Contribution of Working Group I
to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
[Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and
H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA.
3. Organisation of Eastern Caribbean States (OECS). 2004 Grenada: Macro-Socio-
Economic Assessment of the damages caused by Hurricane Ivan. September 7, 2004.
OECS.
4. Farrell, D., Trotman, A. & Cox, C. 2010, Drought Early Warning and Risk
Reduction: A Case Study of the Drought of 2009-2010, UNISDR, Geneva,
Switzerland.
5. ECLAC, 2005. Guyana Socio-Economic Assessment of the Damages and Losses
Caused by the January-February 2005 Flooding. Economic Commission for Latin
America and Caribbean.
6. ECLAC, 2006. Guyana: The impact on Sustainable Livelihoods Caused By the
December 2005 – February 2006. Flooding Economic Commission for Latin America
and Caribbean.
7. CAMI:
8. CIMH, 2012.
9. McKee, T.B.; N.J. Doesken; and J. Kleist. 1993. The relationship of drought
frequency and duration to time scales. Preprints, 8th Conference on Applied
Climatology, pp. 179–184. January 17–22, Anaheim, California.
Gibbs, W.J.; and J.V. Maher. 1967. ‘Rainfall deciles as drought indicators’. Bureau of
Meteorology Bulletin No. 48, Commonwealth of Australia, Melbourne.
10. The Climate Predictability Tool is a product of the International Research Institute
for Climate and Society (IRI)
11. http://63.175.159.26/~cimh/cami/national_bulletin.html
Further reading
World Bank, 2007. World Development Indicators Online. World Bank, Washington, DC.
MOSAICC: an interdisciplinary system of models to evaluate the impact of climate
change on agriculture
1. Confiño, A. S., San-Martín, D. and Gutiérrez, J. M. (2007). ‘A web portal for
regional projection of weather forecast using GRID middleware’, Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), 4489 LNCS(82-89.
2. Hsiao, T. C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D. and Fereres, E.
(2009) ‘Aquacrop-The FAO crop model to simulate yield response to water: III.
Parameterization and testing for maize’, Agronomy Journal, 101(3), 448-459.
Raes, D., Steduto, P., Hsiao, T. C. and Fereres, E. (2009) ‘Aquacrop-The FAO
crop model to simulate yield response to water: II. Main algorithms and software
description’, Agronomy Journal, 101(3), 438-447.
Steduto, P., Hsiao, T. C., Raes, D. and Fereres, E. (2009) ‘Aquacrop-the FAO crop
model to simulate yield response to water: I. concepts and underlying principles’,
Agronomy Journal, 101(3), 426-437.
3. Aerts, J. C. J. H., Kriek, M. and Schepel, M. (1999) STREAM (Spatial tools for
river basins and environment and analysis of management options): set up and
requirements, Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and
Atmosphere, 24(6), 591-595.
4. Notebaert, B., Verstraeten, G., Ward, P., Renssen, H. and Van Rompaey, A. (2011)
‘Modeling the sensitivity of sediment and water runoff dynamics to Holocene climate
and land use changes at the catchment scale’, Geomorphology, 126(1–2), 18-31.
5. Bouwer, L. M., Aerts, J. C. J. H., Droogers, P. and Dolman, A. J. (2006). ‘Detecting
the long-term impacts from climate variability and increasing water consumption
on runoff in the Krishna river basin (India)’, Hydrology and Earth System Sciences,
10(5), 703-713.
6. Winsemius, H. C., Savenije, H. H. G., Gerrits, A. M. J., Zapreeva, E. A. and Klees,
R. (2006) ‘Comparison of two model approaches in the Zambezi river basin with
regard to model reliability and identifiability’, Hydrology and Earth System Sciences,
10(3), 339-352.
7. Ward, P., Renssen, H., Aerts, J. and Verburg, P. (2011) ‘Sensitivity of discharge and
flood frequency to twenty-first century and late Holocene changes in climate and land
use (River Meuse, northwest Europe)’, Climatic Change, 106(2), 179-202.
Ward, P. J., Renssen, H., Aerts, J. C. J. H., Van Balen, R. T. and Vandenberghe, J.
(2008) ‘Strong increases in flood frequency and discharge of the River Meuse over
the late Holocene: Impacts of long-term anthropogenic land use change and climate
variability’, Hydrology and Earth System Sciences, 12(1), 159-175.
8. Thornthwaite, C. W. (1948) ‘An Approach toward a Rational Classification of
Climate’, Geographical Review, 38(1), 55-94.
Thornthwaite, C. W., Mather, J. R., Carter, D. B. and Drexel Institute of, T. (1957)
Instructions and tables for computing potential evapotranspiration and the water
balance, Centerton, N.J.: Drexel Institute of Technology, Laboratory of Climatology.
9. Löfgren, H., Harris, R. L. and Robinson, S. (2001) A standard computable general
equilibrium (CGE) model in GAMS, International Food Policy Research Institute
(IFPRI).
Thurlow, J. and van Seventer, D. E. (2002) A standard computable general equilibrium
model for South Africa, International Food Policy Research Institute (IFPRI).
Climate change adaptation methodologies in the Bay of Bengal fishing
communities
1. See Around Us project, 2012
2. Dwivedi, S.N. 1993. “Long-Term Variability in the Food Chains, Biomass Yield,
and Ocenaography of the Bay of Bengal Ecosystem,” in Kenneth Sherman, et al.
(eds.), Large Marine Ecosystems: Stress, Mitigation, and Sustainability (Washington,
D.C.: American Association for the Advancement of Science, 1993) pp. 43-52.
3. Allison, E.H., Perry, A.L., Adger, W.N. et al. (2009) Vulnerability of national
economies to the impacts of climate change on fisheries. Fish and Fisheries. DOI:
10.1111/j.1467-2979.2008.00310.x.
4. Vivekanandan, E (2011) Marine Fisheries Policy Brief-3; Climate change and Indian
Marine Fisheries. CMFRI Special Publication, 105. pp. 1-97.
Vivekanandan, E (2010) Impact of Climate Change in the Indian Marine Fisheries
and the Potential Adaptation Options. In: Coastal Fishery Resources of India -
Conservation and sustainable utilisation.
Vivekanandan, E (2009) Climate change, marine ecosystems and fisheries. Sagara
Sangamam Souvenir - 2009. pp. 120-134.
Improved livelihoods and building resilience in the semi-arid tropics: science-led,
knowledge-based watershed management
1. Wani SP, Rockstrom, J, Benkateswarlu B and Singh AK. 2011. ‘New Paradigm
to Unlock the Potential of Rainfed Agriculture in the Semi-arid Tropics. World Soil
Resources and Food Security’. Eds. Rattan Lal and BA Stewart. Advances in Soil
Science. CRC Press. 419-469.
2. Rockström, J., L. Gordon., C. Folke et al. 1999. ‘Linkages among water vapor
flows, food production, and terrestrial ecosystem services’. Conservation Ecology
3(2):5.
3. Wani SP, Pathak P, Jangawad LS, Eswaran H and Singh P. 2003. ‘Improved
management of Vertisols in the semi-arid tropics for increased productivity and soil
carbon sequestration’. Soil Use and Management 19:217–222.
4. Kesava Rao AVR and Suhas P Wani. 2011. ‘Evapotranspiration paradox at a semi-arid
Notes and References