globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2228
论文题名:
Adaptation potential of European agriculture in response to climate change
作者: Frances C. Moore
刊名: Nature Climate Change
ISSN: 1758-1308X
EISSN: 1758-7428
出版年: 2014-05-18
卷: Volume:4, 页码:Pages:610;614 (2014)
语种: 英语
英文关键词: Climate-change impacts ; Agriculture ; Climate-change adaptation ; Environmental economics
英文摘要:

Projecting the impacts of climate change on agriculture requires knowing or assuming how farmers will adapt. However, empirical estimates of the effectiveness of this private adaptation are scarce and the sensitivity of impact assessments to adaptation assumptions is not well understood1, 2. Here we assess the potential effectiveness of private farmer adaptation in Europe by jointly estimating both short-run and long-run response functions using time-series and cross-sectional variation in subnational yield and profit data. The difference between the impacts of climate change projected using the short-run (limited adaptation) and long-run (substantial adaptation) response curves can be interpreted as the private adaptation potential. We find high adaptation potential for maize to future warming but large negative effects and only limited adaptation potential for wheat and barley. Overall, agricultural profits could increase slightly under climate change if farmers adapt but could decrease in many areas if there is no adaptation. Decomposing the variance in 2040 projected yields and farm profits using an ensemble of 13 climate model runs, we find that the rate at which farmers will adapt to rising temperatures is an important source of uncertainty.

Determining the overall effectiveness of adaptation solutions in agriculture is challenging because it is impossible to accurately enumerate and model all economically feasible options. Further, the rate at which farmers will adopt these options in response to climate change remains uncertain1, 2, 3. As a result, the sensitivity of existing impact projections to assumptions of private farmer adaptation is not well understood.

One promising approach to assess the potential of private adaptation in agriculture is to use past observations to simultaneously estimate two relationships between farm profits or yields and climate variables. The first is the long-term, equilibrium relationship based on cross-sectional variation in climate. Under the assumption that farmers have adjusted over the long-run to take full advantage of the climate they face, this response function captures the impacts from climate change if farmers are able to fully adapt using the set of available technologies4. The other is a short-term relationship based on interannual weather variation. As these weather shocks are transient and partially unanticipated, farmers can mitigate their effects only with a much more limited set of management options. Therefore, this response function gives the impacts from climate change if farmers are unable to implement long-run adaptations and instead respond to climate change as though it were simply unusual weather. Climate change impact projections made using these two response functions can be used to characterize the spread in impact projections resulting from uncertainty over how quickly farmers will adopt adaptive technologies and management practices already in use elsewhere5, 6.

Here we apply this approach to data from Europe, and then estimate the impact of future temperature and precipitation changes on yields and farm profits with and without adaptation. We estimate equation (1) separately for each dependent variable (farm profits and the yields of five major crops) using balanced panel data sets (Methods). Figure 1 shows the results graphically.

Figure 1: Short- and long-run temperature response curves.
Short- and long-run temperature response curves.

Graphical depiction of the long-run (red solid lines) and short-run (black dotted lines) relationship between farm profits or yields and growing season temperature (°C) estimated using equation (1) (first specification, column one in Supplementary Table 3). The range of the x axis corresponds to the range of growing season temperature in each panel data set. The three examples of short-run relationships are plotted centred at the 25th, 50th and 75th percentiles of growing-season temperature. Curves are shifted along the y axis so that the maximum value over the plotting range is 1. As the dependent variable is logged, movement along the y axis represents a percentage change in the outcome variable.

We use equation (1) to jointly estimate both the long-run effect of different climates and the short-run effect of annual deviations from this climate6. The economic model and relevant assumptions are described in the Supplementary Information. Equation (1) is used for each of six dependent variables: farm profits and the yields of five major crops. The farm profits/yields (V) for subnational region i, in country j in year t are estimated in the preferred model as:

where is a vector that includes growing season temperature and precipitation and is the 30-year climatological average for the years preceding year t. Bold denotes vectors of coefficients. We control for unobserved, possibly nonlinear time trends at the country level using a country-by-year linear and quadratic time trend, unobserved time-constant variation between countries using a country fixed effect, and other observed within-country variation using a suite of controls. These controls include a vector of soil-quality variables (organic carbon content, water-holding capacity, erodibility, and soil type), altitude and altitude squared, subsidies received per hectare, irrigated area per hectare and a crop-price index.

Our source of economic and yield data is the EU Farm Accountancy Data Network (FADN) survey between 1989 and 2009 (ref. 26). The data we use are aggregated from the farm to the regional (subnational) level using weights based on the three-way stratified sampling methodology used by FADN. Therefore, the representativeness of these aggregated values depends on the soundness of the sampling and weighting schemes used by the EU (ref. 26). Farm profits are defined as the total value of farm production minus all costs plus subsidies received minus taxes paid. They are normalized by the total agricultural area used in the year to give farm profits per hectare. Yields are calculated as the crop produced in the year divided by the area of crop planted. The five crops considered in this paper constitute around 25% of the value produced by European farms, with the remainder coming from meat and dairy production and other grain, fruit and vegetable crops. Weather data are monthly averages averaged over a growing season defined by the observed planting and harvest date for each region using the SAGE crop calendar data set27, 28, 29. For the profits per hectare regression we use a standard March–September growing season definition. We create balanced data sets by retaining only those observations with data for the whole period 1989–2009. This prevents possible confounding of the estimation by gradual entry of eastern European countries into the data set after the mid-1990s. Additional details on the construction of the data set and control variables are given in the Supplementary Information.

We estimate equation (1) using ordinary least-squares regression, weighting by the square root of farm area to reduce heteroskedasticity and to make results more representative of the average growing area. Standard errors were estimated using 500 block-bootstraps, blocking at the country by 2-year level to account for heteroskedasticity, within-country spatial autocorrelation, and temporal autocorrelation at one-year lag. The estimated response functions can be used to calculate the expected damages from climate change with (equation (2)) and without (equation (3)) adaptation. These responses are shown diagrammatically in Supplementary Fig. 1 and, given a shift in climate from to can be estimated for each observation as:

where the βs are the parameter estimates obtained from the equation (1) regression. These response curves are combined with climate model projections for the period 2030–2049 under the A1B scenario using a 13-member ensemble from the ENSEMBLES project7. The projections from this ensemble and the equations for decomposing ensemble projection uncertainty are described in the Supplementary Information. The ensemble we use pertains to a single emission scenario but scenario uncertainty constitutes only a small fraction of total uncertainty in climate projections at regional levels by 2040 and therefore is unlikely to affect our conclusions30.

  1. Schneider, S. H., Easterling, W. E. & Mearns, L. O. Adaptation: Sensitivity to natural variability, agent assumptions and dynamic climate changes. Clim. Change 45, 203221 (2000).
  2. White, J. W., Hoogenboom, G., Kimball, B. A. & Wall, G. W. Methodologies for simulating impacts of climate change on crop production. F. Crop. Res. 124, 357368 (2011).
  3. Menzel, A., Von Vopelius, J., Estrella, N., Schleip, C. & Dose, V. Farmers’ annual activities are not tracking the speed of climate change. Clim. Res. 32, 201207 (2006).
  4. Mendelsohn, R., Nordhaus, W. D. & Shaw, D. The impact of global warming on agriculture: A ricardian analysis. Am. Econ. Rev. 84, 753771 (1994).
  5. Schlenker, W. & Roberts, D. L. Nonlinear temperature effects indicate severe damages to US corn yields under climate change. Proc. Natl Acad. Sci. USA 106, 1559415598 (2009).
  6. Kelly, D., Kolstad, C. & Mitchell, G. Adjustment costs from environmental change. J. Environ. Econ. Manage. 50, 468495 (2005).
  7. Johns, T. C. et al. Climate change under aggressive mitigation: The ensembles multi-model experiment. Clim. Dynam. 37, 19752003 (2011).
  8. Reidsma, P., Ewert, F. & Lansink, A. O. Analysis of Farm Performance in Europe under different climatic and management conditions to improve understanding of adaptive capacity conditions. Climatic Change 84, 403422 (2007).
  9. Hawkins, E. et al. Increasing influence of heat stress on French maize yields from the 1960s to the 2030s. Glob. Change Biol. 19, 937947 (2013).
  10. Supit, I. et al. Assessing climate change effects on European crop yields using the crop growth monitoring system and a weather generator. Agric. For. Meteorol. 164, 96111 (2012).
  11. Peltonen-Sainio, P. et al. Coincidence of variation in yield and climate in Europe. Agric. Ecosyst. Environ. 139, 483489 (2010).
  12. Olesen, J. E. et al. Impacts and adaptation of European crop production systems to climate change. Eur. J. Agron. 34, 96112 (2011).
  13. Supit, I. et al. Recent changes in the climatic yield potential of various crops in Europe. Agric. Syst. 103, 683694 (2010).
URL: http://www.nature.com/nclimate/journal/v4/n7/full/nclimate2228.html
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5130
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

Files in This Item:
File Name/ File Size Content Type Version Access License
nclimate2228.pdf(585KB)期刊论文作者接受稿开放获取View Download

Recommended Citation:
Frances C. Moore. Adaptation potential of European agriculture in response to climate change[J]. Nature Climate Change,2014-05-18,Volume:4:Pages:610;614 (2014).
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Frances C. Moore]'s Articles
百度学术
Similar articles in Baidu Scholar
[Frances C. Moore]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Frances C. Moore]‘s Articles
Related Copyright Policies
Null
收藏/分享
文件名: nclimate2228.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.