Spatial Planning of Agricultural Production under Environmental Risks and Uncertainties

1. CwU 2007December 10-12, 2007 IIASA, Laxenburg Austria Spatial Planning of Agricultural Production under Environmental Risks and Uncertainties G. Fischer, T. Ermolieva International Institute for Applied Systems Analysis, Laxenburg, Austria

2. Background This research is under the umbrella of two EU-sponsored projects CHINAGRO: Decision Support System for China's Agricultural Sustainable Development (EU-ICA4-CT-2001-10085), 2002-2005. CATSEI: Chinese Agricultural Transition: Trade, Social and Environmental Impact (EU FP6 Project 44255), 2007-2009. Broad range of factors determining spatio-temporal heterogeneity of demand and supply of agricultural products: • Demographic change • Urbanization • Overall economic growth • Availability of farmland; irrigated land • Technological progress in agriculture • Trade policies • Conditions on international markets

3. Research question • Growing demands for meat, intensification trends, concentration of production according to “increasing returns” principle. • Main risks: - environmental pollution (manure combined with chemical fertilizers) - livestock related diseases and epidemics - market risks - demand uncertainties and instabilities • A continuation of current intensification trend would bring in high risks for the future. • Risk perspective suggests rationales for spatial diversification and co-existence of large- and small-scale producers. • Long-term planning needs to base on sustainability principles: increasing returns in combination with enforced policies relying on risk indicators.

4. Co-existence of heterogeneous producers: a risk-hedging strategy Absence of risks:Two producers with production costs c1<c2<b minimize solution Risk exposure:a1 and a2 are random variables (shocks to production) minimize minimize where bE max{0, d – a1x1 – a2x2} is the expected import cost if demand exceeds the supply. Ermoliev, Y., Wets, R. (Eds.) Numerical Techniques for Stochastic Optimization. Computational Mathematics, Springer Verlag, Berlin, 1988.

5. The less-efficient producer 2 stabilizes the aggregate production and the market in the presence of contingencies affecting the “most cost-effective” producer 1. Co-existence of heterogeneous producers If Producer 1 is at risk:0 < E a1 < 1,a2= 1. Positive optimal decisions exist if: i.e., less efficient producer 2 is active unconditionally: c2 – b < 0 The cost efficient producer 1 is active if: c1 – bEa1 < 0 Market share of the Producer 2 (risk-free producer with higher production costs): Take derivative Optimal production share of Producer 2 is defined by the quantile of the distribution function describing contingencies of the Producer 1, i.e., a1 , and the ratio c2 / b.

6. Challenges of spatial livestock production planning under risks and uncertainties • Long horizons of problems related to production and risks. • Spatially explicit framework: 2434 counties. • Aggregate or insufficient data for estimation of spatially “disperse” agricultural risks, indicators and constraints; compound risks. • Need for spatially-explicit stochastic LS production planning model and data upscaling/downscaling, harmonization procedures. • Production allocation and intensification levels are projected • from the base year for: • - Pigs, poultry, sheep, goat, meat cattle, milk cows) and • - Management system (grazing, industrial, specialized, traditional.

7. IIASA model for livestock production planning • Model structure and inputs • Base year distribution of production activities/resources at county level • Alternative demographic projections and • Economic scenarios • Model derives estimates of: - Demand for cereals and livestock products - Spatial allocation and intensity levels of crop and livestock production; • - Environmental pressure from agricultural production - Health and environmental risk indicators • Incorporates/compares: Alternative production allocation criteria; Procedures: Rebalancing/dowscaling & stochastic optimization

8. Livestock production allocation under risks and uncertainties

9. -prior, reflects alternative “behavioral” allocation principles can be represented as Sequential rebalancing procedure Demand for product i; production in location k Aggregate constraint on meat production at location k - expected initial allocation of demand to location i and system k Butmay not satisfy the constraint Derive relative imbalanceand update may not satisfy the constraint Calculateand update

10. e.g., by using a Bayesian type of rule for updating the prior distribution, . The procedure converges to the optimal solution maximizing the cross-entropy function Sequential rebalancing procedure The procedure can be viewed as a redistribution of required supply increase di by applying sequentially adjusted : , For Hitchcock-Koopmans transportation model the proof is in: Bregman, L.M. “Proof of the Convergence of Sheleikhovskii’s Method for a Problem with Transportation Constraints”, Journal of Computational Mathematics and Mathematical Physics, Vol. 7, No. 1, pp191-204, 1967 (Zhournal Vychislitel’noi Matematiki, USSR, Leningrad, 1967). For more general constraints and using duality theorem the proof is in: Fischer, G., Ermolieva, T., Ermoliev, Y., and van Velthuizen, H., “Sequential downscaling methods for Estimation from Aggregate Data” In K. Marti, Y. Ermoliev, M. Makovskii, G. Pflug (Eds.) Coping with Uncertainty: Modeling and Policy Issue, Springer Verlag, Berlin, New York, 2006.

11. Alternative production allocation scenarios • Demand Driven Scenario: Production increase in locations is proportional to demand potential (people, rural/urban, income) 2. Sustainable Scenario: trade-off between development and risks. • Economic, social, environmental risk and sustainability indicators • and constraints reflect location-specific conditions and limitations • such as water and land scarcity, livestock density, urbanization level. • Allocation of livestock beyond specified constraints may lead to • disastrous consequences related to water and air pollution, • hazards of livestock disease outbreaks, threats to • human health, which may incur high costs. • The indicators and constraints are treated within priors or as explicit • constraints/goals. • Individual “weights” of indicators/constraints reflect the critical trade- offs, limitations and goals in locations.

12. Resource Constraints: Intensity of cultivated and orchard land (percent of total land in county) in 2000.

13. Population distribution(persons per square kilometer)

14. Meat demand by income Meat demand by sector Meat demand by type Pigs by type of production system

15. Hot-spots of high intensity of confined livestock (livestock biomass in kg/ha cultivated land) 2000 2030

16. Hot-spots of manure nutrients from confined livestock, (kg nitrogen/ha cultivated land), year 2030: a. Demand-driving b. Risk-adjusted 2000: a. 2000 33: Zhejiang 44: Guandong

17. Hot spots offertilizer consumption (kg nitrogen/ha cultivated land) 2000 2030

18. Nutrient balance calculations. • County-specific nutrient balances compare nutrients from livestock • manure and fertilizers with the requirements and uptake capacities of crops. • Thus, calculated total nutrients losses include: • nutrient losses from livestock housing, from manure storage facilities as well • as total liquid manure (largely unused), • losses stemming from non-effective manure and fertilizers, • losses due to over-supply of nutrients from fertilizers and manure to crops, • non-effective manure nutrients produced by pastoral livestock systems.

19. Nutrient (nitrogen) losses per unit area, kg/ha 2030 2000 B A

20. Frequency distribution of: a. Number of counties, and b. Population, with regard to the intensity of nitrogen losses per unit of land area.

21. Two scenarios are compared with respect to number of people in China’s regions exposed to different categories of environmental risks Figure 3. Relative distribution of population according to classes of severity of environmental pressure from livestock, 2030: (a) demand driven scenario, (b) environmentally friendly scenario.