China’s food demand, supply and trade in 2030: simulations with Chinagro II model Michiel A. Keyzer and Wim C.M. van Veen Centre for World Food Studies (SOW-VU), Amsterdam Final CATSEI briefing DG-AGRI, Brussels, 21 February

Overview (1)Introduction (2) Chinagro II (3) Outcomes from simulation (4) Summary of findings

(1) Introduction: transition worldwide Since 2005 world prices are volatile, picking up in 2010 During raw materials (F5: food, feed, fuel, fibers, and fertilizers) foreseen to be scarcer than before

An illustration of China’s influence: Fertilizers: P-price spike in October 2008 following China’s export ban on P-rock A central question is, therefore, can China’s ambitions be met? And what are the pressures this imposes on others? Particularly, following from rising meat-feed demand, and biofuel

(2) Chinagro II model: main characteristics General equilibrium model representing consumer and producer behavior, government policies and markets Focus on agriculture (trends non-agriculture largely given via scenarios) Spatial detail: agricultural supply by county (2885!), because of: –Large distances in China –Wide income spread especially from East to West –High spatial concentration of environmental discharges Commodity detail: –17 tradable commodities (explicit trade flows across regions and from/to abroad) –Commodity balances kept in quantity terms (as opposed to monetary units of base year), so as to trace better the underlying land needs and flows of raw materials, nutrients etc. In every county 8 farm types/production activities competing for manpower, land and stable capacity, Treatment of demand more aggregated –8 regions with 3 rural and 3 urban classes  Chinagro is the most detailed model of Chinese agriculture available

CATSEI: from Chinagro I to Chinagro II Database update from to –with associated change in county list from 2433 to 2885 and associated new county maps –use of NSBC household surveys –splitting agricultural production data into yields and areas/livestock numbers –as in Chinagro I, Chinagro II fully replicates base year data, after multiple plausibility checks Scenario revisions for 2020, 2030 –e.g. adapting to current views about future scarcities and technical progress –including two biofuel scenarios Changes in model specification –Chinagro I: world prices given by scenario labor, land and stable capacity allocation across farm-types –Chinagro II price adjustment on world market via import and export functions, estimated on basis of simulations with GTAP-model also land and stable capacity balances by crop/livestock type within each farm types, and with it explicit yields –allowing for yield improvement by crop/county/farmtype –with possibility of abandoning crops and allowing for new crops detailed nutrient accounts (N,P,K) for crops and manure

Chinagro II: data requirements Baseyear 2005: Detailed agricultural accounts in quantity as well as value terms, specifying outputs and inputs, at county level and including locals goods such as manure, crop residuals Accounts non-agricultural commodity aggregated, with input use by agriculture represented at county level, but production at regional level only Consumption in quantity and value by commodity and income class, at regional level Market prices, at regional level (from data collected by province) Foreign trade volumes and prices Consistency imposed: regional supply-demand balances, price margins from international to regional level and from regional to county level, value added accounting farmers Simulation years 2010, 2020 and 2030: Specification of (exogenous) scenario drivers

(3) Outcomes from scenario simulation Base scenario for drivers referring to an expected central tendency (not “business as usual”) Four policy variants -Liberal -Biofuel -Irrigation -Low growth The model produces very detailed accounts Here its outcomes are to presented in -bar charts: for aggregate outcomes at national level -maps: for outcomes at regional and county level

Base scenario : main drivers (1) (Relatively) high non-agricultural growth sustained at 6 - 7% annually Moderate population growth to 1,436 million by 2030 Urbanization moves on to slightly above 60% by 2030 Loss of crop land of 6.5 million ha by 2030, especially for rainfed land Further intensification of livestock sector –Higher feed efficiency but less based on residuals Continued improvement of input efficiency and yields in cropping

Base scenario : main drivers (2) Government policy: Continued trade liberalization (reducing both tariffs and non- tariff barriers) Farm taxes abolished and grain price support introduced (as fixed subsidy rate, i.e. not as fixed floor price) No significant biofuel use of output from cropland World agricultural prices: Based on OECD-FAO projections in Agricultural Outlook , but with several upward adjustment (grains, feed, meat) Still modest in terms of assumed rise in meat and biofuel demand worldwide

Outcomes Baserun (1)

Outcomes Baserun (2a)

Outcomes Baserun (2b)

Outcomes Baserun (3a)

Outcomes Baserun (3b) Household consumption: 2005 and 2030

Outcomes Baserun (4) surplus = inflow-outflow

Baserun, the regional dimension: annual growth maize, by county, period , % Steady growth maize output throughout the country

Annual growth pork, by county, period , % Several hotspots of fast rising production pork

Annual growth of crop value added by county, period , % Rainfed cropping performs better than irrigated

Annual growth of farm value added by county, period , % Highest growth rates where livestock is relatively important

Environmental challenges in base run: net surplus of P-oxide on farmland, by county, 2030, in kg/ha Excessive application of P more common than deficits

Environmental challenges in base run: net surplus of K-oxide on farmland, by county, 2030, in kg/ha Soil mining of K dominates

Policy variants Liberal: all tariffs on foreign trade are strongly reduced after 2010 and abolished after 2020 Margbio: 10 million ton biofuel by 2020 (not cereal- based), with use of marginal land Irrigup: enhanced irrigation (location-specific), with the same total seasonal crop land Lowgrow: lower growth of non-agricultural incomes, with less rural-to-urban migration and less technical progress in agriculture

Outcomes policy variants (1a)

Outcomes policy variants (1b)

Outcomes policy variants (2)

Outcomes policy variants (3)

Liberal run: crop value added, growth difference from baserun, % Feed shrinks somewhat, Fruits&vegetables expand

Liberal: sugar output, growth difference from baserun, % Sugar shrinks, except where livestock shrinks more

Liberal: rural meat consumption, growth difference from baserun, % Rural consumers gain from lower prices

Intensified biofuel, also on marginal land: farm value added, growth difference from baserun, % Marginal lands and feed producing areas gain, while livestock producing areas lose

Enhanced Irrigation: crop value added, growth difference from baserun, % Counties with predominantly rainfed crops gain, especially if no rainfed land is converted

Enhanced Irrigation: rural consumption, difference from baserun, 2030, kcal/cap/day Rural consumers gain from lower market prices

Low Growth: farm value added, growth difference from baserun, % All lose except counties where higher labor supply compensates for lower prices

(4) Chinagro II findings: summary China’s agricultural transition: : era of raw material scarcity worldwide, in minerals (e.g. P), and in agriculture, particularly if OECD persists on biofuels In China, low growth would cause severe income problems in rural areas, primarily via reduced rural-to-urban migration, lower meat demand, and lower meat prices Trade: China will need significant feed imports from the world market, but its claims are not unmanageable particularly because it keeps its biofuel ambitions limited Impacts China’s ambitions on world prices are moderate because China adapts to the prices Since it has lots of grassland (and foreign exchange) available, whether it is to import dairy becomes a policy choice Its textile sector will more than its farmers remain vulnerable for volatility in cotton price It can for 50-60% balance its agricultural trade account with exports of fruits and vegetables but it will face severe competition also from EU Agreeing on and meeting SPS-requirements in China-EU relations will be beneficial for both parties, particularly for fruits&vegetables, dairy and meat

Social: There is great social diversity across rural areas, which only a spatially explicit equilibrium model like Chinagro can highlight while maintaining consistency with what happens at national and international level Livestock contributes significantly to rural revenue and employment Labor becomes critical input in rural areas, but this may be beneficial as it helps containing rural-urban income disparity, particularly if supplemented with improved rural credit to finance both investments in e.g. mechanization and the delivery of social services Environment: There also is great spatial diversity in nutrient imbalances but overall, one sees excess application of N and P and deficient K that have to be addressed P, K are precious resources that deserve recycling © SOW-VU 2010 Chinagro II findings: summary (end)

Prices: not all like China’s crops