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

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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

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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

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Shifts in global cereal net trade to compensate

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Increases in global cereal prices

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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

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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!