globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2317
论文题名:
Threat to future global food security from climate change and ozone air pollution
作者: Amos P. K. Tai
刊名: Nature Climate Change
ISSN: 1758-1235X
EISSN: 1758-7355
出版年: 2014-07-27
卷: Volume:4, 页码:Pages:817;821 (2014)
语种: 英语
英文关键词: Environmental health ; Agri-ecology ; Climate-change adaptation ; Atmospheric chemistry
英文摘要:

Future food production is highly vulnerable to both climate change and air pollution with implications for global food security1, 2, 3, 4. Climate change adaptation and ozone regulation have been identified as important strategies to safeguard food production5, 6, but little is known about how climate and ozone pollution interact to affect agriculture, nor the relative effectiveness of these two strategies for different crops and regions. Here we present an integrated analysis of the individual and combined effects of 2000–2050 climate change and ozone trends on the production of four major crops (wheat, rice, maize and soybean) worldwide based on historical observations and model projections, specifically accounting for ozone–temperature co-variation. The projections exclude the effect of rising CO2, which has complex and potentially offsetting impacts on global food supply7, 8, 9, 10. We show that warming reduces global crop production by >10% by 2050 with a potential to substantially worsen global malnutrition in all scenarios considered. Ozone trends either exacerbate or offset a substantial fraction of climate impacts depending on the scenario, suggesting the importance of air quality management in agricultural planning. Furthermore, we find that depending on region some crops are primarily sensitive to either ozone (for example, wheat) or heat (for example, maize) alone, providing a measure of relative benefits of climate adaptation versus ozone regulation for food security in different regions.

Global demand for food is expected to increase by at least 50% from 2010 to 2050 mainly as a result of population growth and a shift towards a more ‘westernized diet in developing regions11. Assuming that agricultural production is able to meet the growing demand through a combination of economic growth and agricultural advancements, undernourishment rates in developing countries are projected to decline substantially11. Future production is, however, sensitive to both climate change and air pollution. Temperature extremes are highly damaging to various major crops1, 2, 5. Surface ozone, formed through the photochemistry of precursor gases mainly arising from human activities, is phytotoxic and detrimental to crop yields4, 12, 13. Climate adaptation and ozone regulation have thus been identified as important measures to tackle food insecurity, but their relative benefits for different crops and regions remain largely uncertain.

In this study, we quantify the individual and combined effects of 2000–2050 mean temperature and ozone pollution trends on the global production of wheat, rice, maize and soybean and then on undernourishment rates in developing countries as a necessary input to policy formulation for food security. Figure 1 illustrates a roadmap for our methodology and summarizes our results. First, we use the Community Earth System Model (CESM) to simulate present-day (2000) and derive future (2050) projections of hourly temperature and ozone concentration consistent with the representative concentration pathways (RCPs) represented in the Intergovernmental Panel on Climate Change Fifth Assessment Report14, 15 (AR5). Our future ozone projections not only follow trends in anthropogenic emissions of precursor gases but also include the effects of climate and land use changes; these confounding factors are known to significantly impact future ozone projections16, 17 but are not considered in previous crop impact studies. We consider two scenarios: RCP4.5, representing an intermediate pathway with a global reduction in surface ozone due to pollution control measures worldwide (except in South Asia)14; and RCP8.5, representing a more ‘pessimistic, energy-intensive pathway with a worldwide increase in ozone except in the US and around Japan18 (Supplementary Fig. 1). The two scenarios represent a range of policy options regarding ozone regulation. Both scenarios project a global increase in surface temperature (Supplementary Fig. 1), with similar effects on crop production as discussed below. Previous historical crop–temperature impact analyses5, 19 suggest a substantial potential for crop-level adaptation to avoid losses from warming, but they do not consider the concurrent impacts of changing ozone levels that may offset the benefits of adaptation12. We therefore exclude adaptation in our projections, and focus on the potential of ozone regulation to combat the warming impacts. Other environmental factors such as water scarcity and land degradation may influence future food production but are outside the scope of this study.

Figure 1: Methodology and results.
Methodology and results.

Using CESM, we derive future (2050) projections for ozone exposure indices and agro-climatic variables, which are used to estimate subsequent effects on total annual crop productivity based on statistical crop–ozone and crop–climate relationships. Effects are expressed as the sum of ΔP in equation (1) per unit harvested area multiplied by equivalent food energy for all four major crops (wheat, rice, maize, soybean). af, Changes following 2000–2050 RCP4.5 (ac) and RCP8.5 (df) anthropogenic emissions and land use scenario. a and d represent effects of ozone trends alone, b and e effects of climate change alone, and c and f represent combined effects. In the purple rectangles below af are global total effects ( > over all grid cells, where A is harvested area). Here we use current production as the baseline (g = 1) with global total of 7.09 × 1015 kcal yr−1; see Supplementary Table 5 for results based on 2050 projections. g, Shift in distribution of per capita dietary energy supply (DES) in developing countries (by 2000 definition) following 2000–2050 ozone and temperature changes (for RCP8.5 as an example). Shaded in colour is the proportion of population consuming below the minimum dietary energy requirement (MDER).

We use CESM to simulate present-day and project future surface ozone and climate in 2050. Our configuration employs coupled atmosphere and land components along with fixed data ocean and cryosphere consistent with current and future climates, at a latitude-by-longitude resolution of 1.9° × 2.5°. Anthropogenic emissions of greenhouse gases and ozone precursors follow the RCP4.5 and RCP8.5 scenarios represented in the Intergovernmental Panel on Climate Change Fifth Assessment Report. See Supplementary Methods for details of these simulations, data sources, and definitions of various metrics used below.

The influence of ozone pollution on crop production is parameterized using the statistical relationships of relative yield for various crops with four ozone exposure indices (AOT40, SUM06, W126, and M7 or M12):

where Y is the yield, Y0 is the maximum potential yield with zero ozone exposure, M is any one of the four ozone exposure metrics, and f(M) represents a function of M. We use the exact forms of f(M) obtained from an ensemble of statistical studies in the literature (Supplementary Table 1). The scaling factor, γp, for pollution effect in equation (1) is then

where M2000 and M2050 in equation (3) refer to M evaluated in year 2000 and 2050, respectively.

The influence of climate change on crop production is parameterized using statistical relationships of crop yield with GDD, which is the summation (over the growing season) of daily mean temperature in excess of a minimum temperature threshold, essentially capturing the beneficial effect of warmth; and killing degree day5 KDD, which is the summation of daily maximum temperature in excess of an optimal growth temperature and captures the adverse effect of temperature extremes. A rise in both the mean temperature and frequency of temperature extremes can increase KDD. We find relationships for each 1.9° × 2.5° grid cell and each crop using a multiple linear regression model on 1961–2010 annual crop yield and meteorological data

where Y is the annual crop yield from FAOSTAT, GDD and KDD are annual values calculated from National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis 1 meteorological data, m denotes 5-year moving averages for detrending the data, and βGDD and βKDD are the observed sensitivities of crop yield to GDD and KDD. Equation (4) is constrained such that βGDD ≥ 0 and βKDD ≤ 0, which helps separate between the beneficial effect of warmth and adverse effect of temperature extremes, and remove collinearity when GDD and KDD are too strongly correlated. Historical data for 1961–2010 are available from FAOSTAT only at national level, so we derive finer resolution (1.9° × 2.5°) historical maps of crop yield by applying a data fusion technique on the fine-resolution map of crop yield for year 2000 (ref. 25). As the observed sensitivities βGDD and βKDD may arise in part from ozone damage at high GDD and KDD instead of warming per se, we estimate the true sensitivities, and , as

where lnY/∂M is the sensitivity of crop yield to ozone exposure estimated as

with f′(M) in equation (6) being the first derivative of f(M) in equation (2) with respect to M. In equation (5), D is either of the two agro-climatic variables GDD and KDD, and dM/dD is the observed sensitivities of ozone exposure indices to GDD or KDD estimated from simple linear regression using 1993–2010 hourly ozone observations in the US and Europe. The scaling factor, γc, for climate effect is

where ΔGDD and ΔKDD are 2000–2050 average changes in GDD and KDD. See the Supplementary Methods for further technical details of equations (4)–(7), and Supplementary Figs 3–5 for maps of βD and the spatial correlation between and growing season temperature.

Globally averaged values for production growth factor g in equation (1) for year 2050 are 1.46, 1.37, 1.95 and 2.35 for wheat, rice, maize and soybean, respectively11. Following FAO methodology26 (Supplementary Methods), the distribution of per capita food consumption or dietary energy supply (DES) (kilocalories per person per day) for the population in developing countries can be modelled as a lognormal distribution f(x) (x representing DES) with parameters related to the actual arithmetic mean DES (xm). Undernourishment rate (ru) is defined to be the fraction of population with a DES below the minimum dietary energy requirement. Any change in xm can result in a shift in the distribution f(x) and affect ru as in Fig. 1g. The change in mean DES (Δxm) is estimated by

where ΔE is the change in total global crop production (Fig. 1 and Supplementary Table 5) in terms of food equivalent energy (kcal yr−1). In equation (8), a is the fraction of global crop production consumed by developing countries27, b is the fraction consumed as food (as opposed to non-food use)27, and N is total population in developing countries. The analysis assumes that: population has flexible dietary habits; there is little barrier for international trade. Violation of these assumptions would probably further increase ru.

  1. Lobell, D. B. et al. Prioritizing climate change adaptation needs for food security in 2030. Science 319, 607610 (2008).
  2. Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240244 (2009).
  3. Avnery, S., Mauzerall, D. L., Liu, J. F. & Horowitz, L. W. Global crop yield reductions due to surface ozone exposure: 2 year 2030 potential crop production losses and economic damage under two scenarios of O3 pollution. Atmos. Environ. 45, 22972309 (2011).
  4. Avnery, S., Mauzerall, D. L., Liu, J. F. & Horowitz, L. W. Global crop yield reductions due to surface ozone exposure: 1 year 2000 crop production losses and economic damage. Atmos. Environ. 45, 22842296 (2011). URL:
http://www.nature.com/nclimate/journal/v4/n9/full/nclimate2317.html
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5059
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Amos P. K. Tai. Threat to future global food security from climate change and ozone air pollution[J]. Nature Climate Change,2014-07-27,Volume:4:Pages:817;821 (2014).
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