Agricultural Development under Risks and Uncertainties

1. CSM’0620th Workshop on Complex Systems Modeling August 28-30, 2006 IIASA, Laxenburg, Austria Agricultural Development underRisks and Uncertainties G. Fischer, T. Ermolieva, Y. Ermoliev, H. van Velthuizen International Institute for Applied Systems Analysis, Laxenburg, Austria.

2. Livestock (background) • Urbanization, expected large growth of per capita incomes, and the ongoing demographic transition in China is bringing about major changes in consumption and production of livestock products. • To meet the growing meat demand, China as many other countries, is rapidly moving from traditional natural resource based management to intensified peri-urban and urban production systems. • The choice of options how to expand livestock production determines the vulnerability towards disease risk. • Environmental impacts through nutrient burden from concentrated pig and poultry systems, where insufficient land is available for manure disposal and recycling, can cause land and water pollution.

3. China development scenario Composition of Rural Population by age Rural/Urban Population Rural / Urban household incomes

4. Estimation of meat demand (125 countries, 1975-1997) Source: SOW-VU, 2002.

5. Meat demand by income Meat demand by sector Meat demand by type Per-capita consumption, meat and eggs

6. Livestock intensification • The increasing meat demand can only be met through rapid introduction of intensified livestock systems. Pig stocks in intensified systems are estimated to increase 3 to 3.5 times, broilers 4.4 to 5 times, and layers 2 to 2.4 times. • With high population and animal densities, in a mixture of still large numbers of backyard producers and a rapidly growing specialized meat sector, disease risks are a great concern. • Due to further intensification of agricultural production in both crop and livestock sectors, we estimate that with current rates of efficiency the environmental pressures stemming from nutrient concentration and overload would increase by at least one-third. • It is of high importance to improve fertilizer use efficiency and balance of nutrients, and to plan for environmentally adequate ways of livestock manure treatment and recycling.

7. Changes in production structure Source: Somwaru 2003

8. Livestock production intensification • Increasing intensification and specialization of livestock production facilities close to markets in urban areas • How far can (and should) China expand its production and increase its intensification? • According to Ricardo, … “intensification is beneficial and trading nations will gain by specialization in goods of comparative advantage.” • Assertion is true if risks are not taken into account. • Main risks: - environmental pollution (manure combined with chemical fertilizers) - livestock related diseases and epidemics - market risks - demand uncertainties and instabilities • Intensification should take into account various risks • The need for co-existence of large- and small-scale (efficient) producers.

9. Co-existence of heterogeneous producers 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.

10. 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.

11. Spatial planning of livestock production facilities in China: Main challenges • Contingencies (analytical form of pdfs) are often not known • Spatially explicit framework: 2434 China counties • 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) • Production level to be restricted with respect to location-specific economic & environmental constraints and indicators aggregating information on current environmental conditions, livestock composition, management characteristics, availability of land, etc. • Aggregate or insufficient data for estimation of the risks, indicators and constraints • Need for spatial data estimation procedures: - Upscaling / Downscaling • - Data harmonization procedures

12. Data for livestock production planning • Survey statistics on livestock at county level: China 1997 year census • Statistics on livestock products by province: statistical year books • Crop production statistics at province and county level • Information on fertilizer use • Land and population statistics • Expert estimates and survey data on livestock production systems with different management characteristics, availability of feeds, etc. • Expert estimates, available statistics and simulated data on the level of veterinary services, agriculture related pollution, health control, etc. • Market conditions and risks: demand, prices, stability, etc. • …

13. Population distribution(persons per square kilometer)

14. Intensity of cultivated land (in percent)

15. Geographical distribution of pig stocks (in 1000)

16. Hot-spots of high intensity of confined livestock (livestock biomass in kg / ha cultivated land) projected for 2030 Hot-spots offertilizer consumption (in kg of nitrogen / ha cultivated land) projected for 2030

17. Change of cultivated land(including orchards) Nitrogen from manure of pigs and poultry in relation to stock of land for crop cultivation and orchards (kg N per ha)

18. Disease risk in China • In extensive systems, all animals are exposed to diseases, they favour disease spread (high number of stepping stones), and present a very low proportion of susceptible animals. The working hypothesis is that the distribution of these extensive production units determines the distribution of disease persistence, and the spatial context for disease spread. • Intensification implies producing disease-free production systems, characterised by an increasing proportion of susceptible animals in the herds. The probability of an outbreak decreases as a function of intensification, mostly because the production conditions are more and more isolated from sources of infection. However, the impact of outbreaks also increases as a function of intensification. • Cases reports of disease transmission from extensive to intensive production units are found frequently in the available literature (e.g. Tong & Tong 1995). • The co-existence of highly contrasted production systems is considered as the main risk factor, and serves as a framework to determine risk categories. • Quantifying these patterns is difficult because of the absence of detailed epidemiological data.

19. Livestock production allocation under risks and uncertainties

20. -prior can be represented as Sequential rebalancing procedure Demand for product i 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

21. 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.

22. Numerical experiments • An intensification scenario: production is allocated proportionally to demand increase. • A scenario that combines the demand driven preference structure of the first scenario with information on population densities and its vulnerability to environmental risks. • Environmental risks combine three factors: a. density of confined livestock, b. human population density, c. availability of cultivated land. • 2434 counties were classified as follows: • Percentage of land under cultivation (< 10 percent, 10 to 50 percent, > 50 percent), • Livestock-to-cultivated land ratio i.e., total live weight of confined livestock per ha available cultivated (< 300 kg/ha, 300 to 600 kg/ha, and > 600kg/ha), • Population density (< 100 persons per square kilometer, 100 to 1000 persons, and > 1000)

23. The resulting 27 combined classes were further reduced to: A: No confined livestock, counties in scarcely populated areas (desert or mountain/plateau) and with very little confined livestock B: No environmental pressure, i.e., counties with substantial crop production but with little confined livestock; C: Slight environmental pressure counties with low environmental pressure from confined livestock production; D: Moderate environmental pressure, i.e., counties with moderate environmental pressure from confined livestock production; E: Environmental pressure, i.e., counties with substantial urbanization and environmental pressure from confined livestock production; F: High Environmental pressure, i.e., counties with substantial urbanization and high environmental pressure from livestock production, and G: Extreme environmental pressure i.e., counties with high degree of urbanization coinciding with high environmental pressure from confined livestock production.

24. Environmental pressures from confined livestock production, 2000.

25. Distribution of population (year 2000) by severity of livestock related environmental pressure (a) (b) Figure 2. (a) Absolute (million people) and (b) relative (share of total population) distribution of population according to classes of severity of environmental pressure from livestock, 2000. The labels on the horizontal axis indicate China regions: N, NE, E, C, S, SW, NW stand for North, North-East, East, Center, South, South-West, North-West, respectively.

26. 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) “intensification” scenario, (b) environmentally more friendly scenario.

27. Conclusions • This paper addresses some important aspects of agricultural production planning under risks, uncertainties and incomplete information. • We illustrate the need for co-existence and cooperation of various agricultural producers with diversified risks, which enhances stability of agricultural markets. • We show that production expansion can not merely follow historical intensification trends which in many cases would lead to environmental and health problems and violate threshold constraints. • In many real-world applications, the distribution functions of contingencies are not tractable analytically due to the complexity of interacting factors and spatial relationships. • To derive estimates of contingencies, production planning relies on modeling of the dependencies and interactions among the processes as well as on appropriate downscaling and upscaling algorithms for estimating variables on required scales using all available information. • For many practical situations the assumption of “average flows” may be rather strong, which calls for more rigorous probabilistic treatments. http://dx.doi.org/10.1007/s11518-006-5018-2 Paper published in "Journal of Systems Science and Systems Engineering“, available at:

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