CHINAGRO-II

A general equilibrium model to assess China’s agricultural prospects and challenges up to 2030, covering national, regional and county level.

© Chinagro project team | A lake of greenhouses in a Shandong village

© Chinagro project team | A lake of greenhouses in a Shandong village

The CHINAGRO Model was developed to help identify the most effective policies to tackle China’s agricultural challenges. It can be used to analyze potential policy impacts on different parts of China and is the most detailed model of Chinese agriculture currently available. The model provides informative analysis down to county level and helps researchers analyze consumer and producer behavior, government policies, and markets.

Drivers

The model was developed to analyze and test policy options for managing China’s agricultural challenges. These challenges resulted from the fast growth of consumption in China, meat in particular, which increased China’s dependence on international markets and triggered intensification and concentration of domestic production.

FAST FACTS

  • Developed to assess policy options to tackle China’s agricultural challenges

  • Is the most detailed model of Chinese agriculture currently available

  • Chinagro-II is a 17-commodity, 8-region general equilibrium welfare model

  • The model describes the price-based interaction between the supply behavior of farmers, the demand behavior of consumers, and the trade flows connecting them

Model design and process

The CHINAGRO model has been developed over a series of projects since 2001. It was developed by IIASA researchers and other seven research partner institutions.

Chinagro-II is a 17-commodity, 8-region general equilibrium welfare model. Farm supply is represented at county level (2,885, virtually all), and accommodates for every county outputs of 28 activities and 9 land use types and livestock systems. Consumption is depicted at a regional level, separately for the urban and the rural population, each divided into three income groups, and domestic trade is interregional.

The model describes the price-based interaction between the supply behavior of farmers, the demand behavior of consumers, and the trade flows connecting them. Farmers maximize their revenue by optimally allocating labor and equipment to cropping and livestock systems, at exogenously specified land resources, stable capacities, and levels of technology, while taking the buying and selling prices in the county as given. In addition to purchased inputs, local inputs such as crop residuals, grass, organic manure, and household waste contribute to the production process. Consumers maximize their utility, at given prices, by optimally allocating their expenditures according to a utility function that is quasi-linear, that is, linear with a unit coefficient in part of non-food consumption and obeying a linear expenditure system in food commodities and the remainder of non-food consumption. Trade between regions in China and with the rest of the world is cost minimizing at given world prices and import and export tariff rates. The impact of China’s imports and exports on the world market is assessed by coupling Chinagro-II and the GTAP-model of world trade. Through its significant geographic detail, the model can incorporate location-specific information on climate, resources, and technology while its equilibrium structure enables it to represent coordination flows among the various agents and describe market clearing at different levels.

Schematic of the CHINAGRO II model

Outputs

  • The Chinagro-II model has produced a comprehensive quantitative assessment of future developments of China’s agricultural economy, under alternative scenarios about exogenous driving forces.
  • Alongside the economic and trade impacts on and from the development of China’s agriculture sector, Chinagro-II has also explored its social and environmental implications.
  • The simulations of Chinagro-II show that China’s trade with world food and feed markets will have to expand.

© Chinagro project team | Flat land in Puding County, Guizhou Province

Li S, Li X, Sun L, Cao G-Y, Fischer G, & Tramberend S (2018). An Estimation of the Extent of Cropland Abandonment in Mountainous Regions of China. Land Degradation & Development DOI:10.1002/ldr.2924. (In Press)

Tian Z, Niu Y, Fan D, Sun L, Fischer G, Zhong H, Deng J, & Tubiello FN (2018). Maintaining rice production while mitigating methane and nitrous oxide emissions from paddy fields in China: Evaluating tradeoffs by using coupled agricultural systems models. Agricultural Systems 159: 175-186. DOI:10.1016/j.agsy.2017.04.006.

Zhong H, Sun L, Fischer G, Tian Z, van Velthuizen H, & Liang Z (2017). Mission Impossible? Maintaining regional grain production level and recovering local groundwater table by cropping system adaptation across the North China Plain. Agricultural Water Management 193: 1-12. DOI:10.1016/j.agwat.2017.07.014.

Yang X, Tian Z, Sun L, Chen B, Tubiello F, & Xu Y (2017). The impacts of increased heat stress events on wheat yield under climate change in China. Climatic Change 140 (3): 605-620. DOI:10.1007/s10584-016-1866-z.

Yan J, Yang Z, Li Z, Li X, Xin L, & Sun L (2016). Drivers of cropland abandonment in mountainous areas: A household decision model on farming scale in Southwest China. Land Use Policy 57: 459-469. DOI:10.1016/j.landusepol.2016.06.014.

Wang X, Li X, Fischer G, Sun L, Tan M, Xin l, & Liang Z (2015). Impact of the changing area sown to winter wheat on crop water footprint in the North China Plain. Ecological Indicators 57: 100-109. DOI:10.1016/j.ecolind.2015.04.023.

Tian Z, Liang Z, Sun L, Zhong H, Qiu H, Fischer G, & Zhao S (2015). Agriculture under climate change in China: mitigate the risks by grasping the emerging opportunities. Human and Ecological Risk Assessment 21 (5): 1259-1276. DOI:10.1080/10807039.2014.955392.

Fan D, Ding Q, Tian Z, Sun L, & Fischer G (2015). Simulating the adaptive measures of soybean production to climate change in China: based on cross-scale model coupling. In: Emerging Economies, Risk and Development, and Intelligent Technology: Proceedings of the 5th International Conference on Risk Analysis and Crisis Response, June 1-3, 2015, Tangier, Morocco.

Tian Z, Zhong H, Sun L, Fischer G, van Velthuizen HT, & Liang Z (2014). Improving performance of Agro-Ecological Zone (AEZ) modeling by cross-scale model coupling: An application to japonica rice production in Northeast China. Ecological Modelling 290: 155-164. DOI:10.1016/j.ecolmodel.2013.11.020.

Feng K, Hubacek K, Pfister S, Yu Y, & Sun L (2014). Virtual scarce water in China. Environmental Science & Technology 48 (14): 7704-7713. DOI:10.1021/es500502q.


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Last edited: 21 December 2017

CONTACT DETAILS

Laixiang Sun

Senior Research Scholar

Water

T +43(0) 2236 807 543

CONTACT DETAILS

Günther Fischer

Senior Research Scholar

Water

T +43(0) 2236 807 292

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313