Elsevier

Ecological Modelling

Volume 322, 24 February 2016, Pages 82-91
Ecological Modelling

A system dynamics model for managing regional N inputs from human activities

https://doi.org/10.1016/j.ecolmodel.2015.12.001Get rights and content

Highlights

  • A system dynamics model (NANI-SD) coupling regional anthropogenic N inputs and socioeconomic drivers is developed for the first time.

  • Human dietary adjustment and livestock control may be key to the local N input management, while measures focused on population control and limiting crop production may fail to achieve their goal.

  • It is important to incorporate the balance of N flows between a specific region and its adjacent regions in local N management or it may lead to policy that protects local environment by transferring the environmental cost to outside the region.

  • When N recycling rate reaches a critical value (“saturation point”), further increase in internal N recycle rate has no effect on net anthropogenic N inputs

Abstract

Human activities are the main drivers of alterations of regional N cycles. With increasing population and economic development, human-induced N inputs are expected to continue to increase in the future, especially in many regions of developing countries. Because N sources vary substantially at different temporal and spatial scales and stages of economic development, it is of great importance for environmental managers to be able to simulate the dynamics of N inputs to a specific region of interest. Based on the concept of net anthropogenic N inputs (NANI), a quasi-mass-balance method, a system dynamics model simulating regional N inputs (NANI-SD) is developed and presented here for the first time. The NANI-SD model evaluates how much new N from anthropogenic activities is introduced to the whole basin, providing a simple but effective way to examine human influences on regional N cycles. Our application of the NANI-SD model to the Lake Dianchi basin in China shows that human-induced N inputs will continue to increase under current trends of development. Scenarios focused on lowering population growth rate and banning crop production were not effective in achieving long-term reductions in N inputs because their impacts were compensated by the increases in croplands and food imports, respectively. However, adjusting diet patterns and limiting livestock numbers within the basin were shown to be highly effective in controlling regional N inputs without compromising environmental sustainability of food imported regions. There was a significant trade-off between N self-sufficiency and N inputs to the region, posing the issue of “pollution transfer” as the regions of livestock production providing animal products to the Lake Dianchi basin could suffer from locally intensified levels of N pollution introduced while producing those animal N products. The positive relationship between NANI and the proportion of animal-based protein in food indicates that reducing meat consumption could be an effective way of controlling local N inputs without sacrificing food sovereignty. NANI to the basin could also be reduced by recycling N in human and livestock wastes, but its capacity to reduce NANI is limited and projected to diminish with time.

Introduction

Nitrogen (N), and especially its reactive forms (Nr) have long been recognized as a critical factor in controlling primary production in terrestrial, marine and aquatic ecosystems of the biosphere (Gruber and Galloway, 2008). The N fluxes between living organisms and physical environment, also referred to as the N cycle, are one of the most fundamental nutrient cycles on Earth. Before the invention of Haber–Bosch process in the early 1900s, N inputs to terrestrial ecosystem were dominated by natural sources, mainly from biological N fixation (Galloway et al., 2004). However, by the mid-1970s, anthropogenic N input sources had grown to match these natural N input sources and have exceeded them since then (Howarth, 2004). A recent research article showed that the anthropogenic perturbation of the N cycle exceeded the “safe operating boundary” of the Earth by a factor of 3.5 (Rockstrom et al., 2009). The enrichment of N in the environment and its adverse effects on human health, water quality and biodiversity have been extensively reported and have become increasingly important issues (Cui et al., 2013, Erisman et al., 2013, Howarth et al., 2000, Smith and Schindler, 2009, Townsend et al., 2003, Vitousek et al., 1997).

Globally, food production and energy production are the two main drivers of anthropogenic N inputs (Galloway et al., 2004). In the modern food production system, synthetic fertilizer application is the main source of N inputs to crop systems which ultimately improve and increase crop and livestock production. Currently, synthetic fertilizer is the largest source of Nr from human actions, and its quantity and relative contribution are expected to further increase in the future (Galloway et al., 2004). In addition to crop production, animal production is a substantial driver for N inputs (Bouwman et al., 2013). Reducing the proportion of meat in diet, especially ruminant meat, has been considered an important approach to mitigate N pollution (Kastner et al., 2012, Lassaletta et al., 2013, Ma et al., 2013).

In the energy production system, Nr can be released as a product of fossil fuel combustion in which the high temperature of combustion breaks the N2 bonds of atmospheric nitrogen. In some developed areas such as the northeastern US, NOy deposition resulting from energy production is a very significant contribution, making up 29% net anthropogenic N inputs (Hong et al., 2011). When considering relatively small spatial scales such as a country or a catchment, trade patterns may be a dominant factor shaping regional N cycles (Gao et al., 2014a, Lassaletta et al., 2013). Because of its many chemical forms and the variability of its sources, quantification and management of N have been a great challenge.

To quantify the human influence on N inputs in a region, Howarth et al. (1996) introduced a quasi-mass-balance method, the net anthropogenic N inputs (NANI), to estimate human-induced N inputs to a specific region. In the NANI calculations, only those processes that bring new N to the region are considered N inputs, simplifying the calculation and allowing flexibility in the data required. The NANI approach has been widely applied to many watersheds of different sizes and climatic conditions (Boyer et al., 2006, Chen et al., 2014, Howarth et al., 1996, Swaney et al., 2012), demonstrating that it is an effective tool for characterizing human-induced N inputs to the landscape and their impacts on riverine N export (Hong et al., 2013). Riverine N exports account for 21–25% NANI in 106 U.S. watersheds (Hong et al., 2013) and the percentage varies depending on land use patterns and climate (Howarth et al., 2012, Zhang et al., 2014). However, its applications so far have been mainly limited to examining historic trends or assessing current conditions. Since N sources are expected to be altered substantially in the foreseeable future (Han and Allan, 2012), it is important to be able to project changes in NANI and its drivers, especially in response to changes in policy, providing useful information for decision making.

Based on the NANI concept, in this study we developed a regional model for calculating NANI using a system dynamics (SD) approach, hereafter referred to as NANI-SD. The system dynamics model approach was first developed by Forrester in 1958 for use in industrial management (Forrester, 1958). The SD model is preferred when large, dynamic and complex simulations are needed, which is very common in both environmental and socioeconomic systems. Because of its excellent capacity for simulating feedback relationships within and among systems especially when the socioeconomic system is involved, the SD model is widely used in studies of environmental carrying capacity, water resources and water environment management (Liu et al., 2015). In this study, the model establishes a connection between the anthropogenic drivers and NANI, allowing the users to simulate alterations and interactions of N production, N consumption, N trade, and NANI calculation in a region of interest. We demonstrate the use of this system dynamics approach to examine key drivers of regional alteration of N inputs, such as population, diet choice and trade patterns, evaluated under different policy scenarios. We used Lake Dianchi basin in China as a case study in which NANI has been recently estimated (Gao et al., 2014b). Lake Dianchi basin currently suffers from severe N pollution, and developing tools for predicting the potential future of NANI in response to different environmental policies and identifying key variables controlling NANI have a high priority in this region.

Section snippets

Case study area

Lake Dianchi is the largest freshwater lake in the Yunnan-Guizhou Plateau and the sixth largest in China, and has been given the name of “Pearl of the Plateau”. The basin is located in central Kunming City, the capital of Yunnan province in southwestern China (24°29–25°28′ N, 102°29–103°01′ E; Fig. 1), covering a total area of 2920 km2 with average altitude of 1900 m. Land cover in the basin is dominated by forest (47.3% in 2008), followed by cropland (19.9%), urban (16.5%), water (10.8%), and

Current anthropogenic N inputs to the Lake Dianchi basin

In 2010, net anthropogenic N input to the Lake Dianchi basin was 11,800 kg-N/km2/yr, which is much higher than most regions of China and the world (Han et al., 2014, Zhang et al., 2014). Synthetic fertilizer and food import are the most important contributions, accounting for 63% and 33%, respectively. The other two components of NANI, agricultural N fixation and N deposition, are negligible in this region, making up less than 5% of the total NANI. Crop and flower productions are the main

Tradeoff between N self-sufficiency and N pollution

NANI is closely related to local food production, which can alter N inputs to a region through fertilization, agricultural N fixation, and food and feed imports. Since N self-sufficiency is dependent on local food production and consumption, it should also be a factor directly influencing NANI. However, results from the two scenarios relevant to local food production (S3 and S4 in Fig. 8) indicate that the response of NANI to food self-sufficiency is complex. To gain further insights into how N

Conclusion

Based on a system dynamics approach and the net anthropogenic N input (NANI) concept, a NANI-SD model was developed to simulate the relationship between NANI and its drivers. A case study in China using the NANI-SD model suggests that regional N inputs are controlled by many interactive factors, and the performance of a certain policy generally combines the net effects from multiple influences of the policy, as successfully captured by our model. Overlooking the systematic linkage of the

Acknowledgement

This work was supported by the Major Science and Technology Program for Water Pollution Control and Treatment of China (2013ZX07102).

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