Balancing the economic, social and environmental dimensions of agro-ecosystems: An integrated modeling approach
Introduction
Agriculture contributes 24% of global GDP and provides employment to 1.3 billion people or 22% of the world's population (Smith et al., 2007). It is a critical sector of the world economy. Meanwhile, agriculture is arguably the most important managed ecosystem in the world. As the ways in which agro-ecosystems are managed and evaluated are heavily dependent on human values, the economic and social components of agro-ecosystems have been overemphasized in the past. This has caused malfunctioning (dis-services) of agro-ecosystems like land degradation, greenhouse gases emission, loss of bio-diversity, nitrate leaching to water bodies and depletion of groundwater (Conway, 1985, Dale and Polasky, 2007).
There is an increasing need to view agro-ecosystems and to identify the remedial management practices in a holistic way (Pacini et al., 2004). Since the publication of the Brundtland report, the concept of sustainability has received increasing attention in agricultural research. There would appear to be some consensus that sustainability has three basic features: environmental soundness, economic viability and social acceptability (Dumanski and Pieri, 2000). Pannell and Schilizzi (1999) argue that sustainability indicators are a practical and reasonable vehicle for attempting to deal with the multifaceted nature of the ambiguous term ‘sustainability’. As understanding of the complex relationship between agriculture and environment increases, many indicators of agricultural sustainability, environmental sustainability and the effect of agriculture on natural resources and the environment have been developed (Wei et al., 2007c). However links between sustainability indicators and agricultural management practices on one hand, and economic policies on another hand, are not well defined. As a consequence, farmers, policy makers and administrators do not have enough information to alter management systems according to environmental needs (Ahuja, 2003).
Research of agro-ecosystems requires the use of models—the question is what kind? Models of agricultural systems have been developed and have evolved since the 1960s. Prior to the mid-1980s most of the modeling work focused on individual processes of agricultural systems (e.g. Saeki, 1960, Monteith, 1965). Then some multi-process models which describe the processes within an agro-ecosystem appeared including RZWQM (Ahuja et al., 2000), EPIC (Williams, 1995) and DNDC (Li et al., 1992). When a multi-process model is found not to represent spatial heterogeneity at a regional scale, some spatially referenced models like AGNPS (Young et al., 1987), SWAT (Arnold et al., 1990) and WNMM (Li et al., 2007) have been developed. These models allow users to evaluate alternative practices and scenarios in large agro-ecosystems. However, they do not provide answers to the questions of how farmers’ management behaviour could be changed to introduce new management practices (Wei et al., 2005). To overcome this, a rapidly growing number of research projects are integrating economic and biophysical processes into models (Janssen and van Ittersum, 2007). Some of the more notable integrated models are ECECMOD (Vatn et al., 1999), FASSET (Berntsen et al., 2003) and SAM (Belcher et al., 2004). Most of these integrated models have focused on component parts of the system rather than the agro-ecosystem as an entire unit. Furthermore, many of the studies that advocate a systems approach lack a holistic interpretation of the sustainability of the agro-ecosystem. The environmental impacts that are actually modeled are often limited in number and aggregate in depiction (e.g. only total pesticide use and nitrogen losses are assessed). The omission of many environmental aspects can lead to serious errors in a multi-objective policy-making process and conflicts between different government programs or regulations.
Given this backdrop, the purpose of this paper is to provide an integrated-modeling policy analysis tool for improving the sustainability of agro-ecosystems, in which a holistic impact assessment system is adopted. The intensively cropped ecosystem of the North China Plain is taken as the case study area.
Section snippets
The study area
The North China Plain (NCP) is in north-east China with an area of 350,000 km2 (Fig. 1). It is the largest agricultural zone in China containing 34% of the nation's population, 30% of the irrigated land and 40% of the total grain production. The NCP lies in a semi-arid to semi-humid continental monsoon zone. The average cultivated area per person is 0.095 ha and the average water resource per person is less than 500 m3. Irrigation is applied intensively and extensively and agricultural water use
Base scenario
The status of the wheat–maize agro-ecosystem on the NCP under the biophysical, social and economic context in 2003 is given in Fig. 5. It was found that there were high indices for food self-sufficiency, nutrient balance in soil and greenhouse gases emission in Fengqiu County in the base scenario. The indicator value for farm gross margin was 0.73. There was the moderate sustainability in nitrogen use efficiency, nitrate leaching and irrigation water use efficiency. Finally, the indicator value
Discussion and conclusions
Estimating the effects of a policy measure requires the identification of the causal links between the implementation of the measure and its impact on human activities and the environment. The conceptual framework proposed in this study based on the driving force–pressure–state–impact–response approach can be used to examine those links. An integrated biophysical and economic model can capture the stochastic, interconnective, nonlinear interactions and spatial and temporal differentiation of
Acknowledgements
The authors are indebted to the Australian Centre of International Agricultural Research (ACIAR) (project no: LWR/2003/039) and the Australia-China Special Fund (project no: CH06136) for their financial support.
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