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Transcript of Siwa ifpri water&mktprojections_wmoc_washdc_june2015_final
Imposing constraints on water in market projectionsAccounting for environment in market models
Siwa Msangi IFPRI
World Market Outlook ConferenceWashington DC, USA8-9 June, 2015
The linkage between water and food
Next to land, water is one of the most importantresources supporting agriculture• It’s not always managed (as in rainfed agriculture)
– but remains essential to growth• In biophysical modelling of crop growth – water is a
key yield-limiting factor (along with nitrogen) and isalways made explicit
• In economic modelling of agriculture – water tendsto only appear in activity-based farm models (if atall) – esp if irrigation is involved
Various approaches and key challenges to modeling water and agriculture linkages
Page 3
Water in Ag farm or market models
Water fits more ‘naturally’ into farm-level ag models• It can be accounted for as one of the inputs to
production (w/in an explicit yield or prodn function)• Even if yield is fixed and input use is accounted for
by Leontief coeffs – water can be a constraint• Even w/o explicit pricing of water (which is rare in
agriculture) – shadow values can be derived• W/in a multi-market framework – the yield-water
relationship becomes more reduced-form and therepresentation of water tends to become lessexplicit – except for a few cases where wateraccounting modules have been added
Examples of mkt models with waterType of model Name (institution) Key features
Global PE IMPACT (IFPRI) Inter-sectoral water alloc model determines avail for ag. Supported by hydrological balance model and calculation of yield-water relationship
Watersim (IWMI) Approach largely based on IMPACT – different way of representing basin efficiencies and water alloc rules
GLOBIOM (IIASA) Disagg prodn systems into rainfed/irrig, includes irrig tech & costs, EPIC captures water-balance & crop cons use (no non-ag use)
Global CGE GTAP-W Numerous variants of GTAP-based models incorporating water • Calzadilla, Tol, Redahnz – now a formalized
GTAP-W database• Mirage variant
Regional PE CAPRI (IPTS/U Bonn) Exploratory effort to incorporate water (starting with 2 NUTS regions – Andalusia and mid-Pyrenees) – w/irrig & water use modules
Model examples with water (cont)Type of model Name (institution) Key features
Regional PE USMP/REAP (USDA) Regional math programming model which can take resource constraints like water directly into account
Various ag sector models (ASME – Egypt)
Math programming models with explicit constraints and yield-water relationships
SWAP model (UnivCalifornia)
Uses explicit prodn function – linked to statewide hydrological model to obtain surface & GW availability
Country-levelPE-GE analysis
Terry Roe & Xinshen Diao (various papers with co-authors 2000-2005)
Combines a top-to-bottom and bottom-up linkage b/w country-level CGE model for Morroco to illustrate:• The impact of trade policy changes
on farm-level water use • The impact of changes in property
rights & water mgmt on mkts, prices and other sectors at country-level
Key challenges to capturing waterThe question of ‘quantities vs prices’ applies to how one chosesto capture water in ag farm or mkt models• Primal vs dual – many find it preferable to capture the
physical aspects of water use in ag (kg per m3) rather thanthe cost side (given rare pricing of water)
• The biophysical requirements of crop growth wrt water arefairly well-known and can be captured in agronomicmodelling approaches (i.e. yld penalties)
• There is a spatial as well as a temporal dimension to water –it matters when in the crop cycle the deficit happens, andwhere water is located relative to the crops that need it (forirrigation)
• The water dimensions of livestock are typically not well-captured in any modeling (biophysical or economic)
• Nothing on aquaculture either (esp for inland systems) - data• Water quality dimensions are also rarely addressed
Key challenges (cont.)
As in all aspects of modelling agriculture – data isalways a challenge (more so for some regions)• Getting a good handle of how much irrigated area
there really is – can be challenging for some regions• Sometimes the definition of what really is irrigated is
not straightforward (e.g. the Fadama in Nigeria)• Often times there are discrepancies between various
sources of data (OECD, FAO Aquastat, Kassel/UFrankfurt)
• This is a key piece of data to have if doing basin-level water modelling in order to determine thebalance between ET, runoff, precip, deep percolation
Quantitative Experiment: Groundwater Depletion in India & Implications for Global Food Balance
Page 9
Simple quantitative experiment
• In this experiment, we simulate what would happen if the groundwater availability in northern India (Gujarat, Rajasthan, Haryana, Uttar Pradesh, Madhya Pradesh, Bihar) were to decrease dramatically over 2010-2020
• Essentially halving the water available for irrigation (since GW supplies ~50% irrigated area)
• Simulated over the corresponding IMPACT basins (Indus, Ganges, Mahi-Tapti & Luni basins)
• Observe the impact on food production, prices, consumption and malnutrition in India & the world
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Ganges
Mahi-Tapti
Luni
Indus
Key Indian basins targeted in scenario
Global cereal production changes
Page 12
Shifts in global cereal net trade to compensate
Page 13
Increases in global cereal prices
Page 14
Page 15
A more widespread problem in India would be even more dramatic ( “Too Big to Fail” !)
Similar effects would be observed if the North China Plain were subjected to such a scenario
This is purely illustrative of the importance of India to the global food balance – and the implications of it falling into deficit due to environmental impacts
This underscores the importance of water for food
SummaryThe impact of groundwater declines in northern India have a sizable impact on food production, trade and security in both India and the world
Page 15
Conclusions
• Water is one of the key constraints to ag –but not widely captured in ag mkt model
• Need to take the first step – separate harvested area b/w irrigated & rainfed– If rainfed – enters as an exog factor– if irrigated – is part of on-farm mgmt decisions
• Tend to prefer the biophysical approach to capture the impacts on yield
• Ag water can be valued but is rarely priced –therefore a mkt-based alloc is more of a “scenario” rather than the baseline case
Thank you!