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Valuing Provisioning Ecosystem Services in Agriculture: The Impact of Climate Change on Food Production in the United Kingdom

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Abstract

This paper provides an estimate of the contribution of the ecosystem to the provisioning services generated by agriculture. This is achieved by valuing the changes in productivity generated by a marginal alteration in ecosystem inputs. As an example, we consider the variation in rainfall and temperature projected by the recent UK Climate Impacts Programme. The analysis implements a spatially explicit, econometric model of agricultural land use based on the methodology recently developed by Fezzi and Bateman (Am J Agric Econ 93:1168–1188, 2011). Land use area and livestock stocking rates are then employed to calculate farm gross margin estimates of the value of changes in provisioning ecosystem services. Findings suggest that the variation in ecosystem inputs induced by climate change will have substantial influence on agricultural productivity. Interestingly, within the UK context climate change generates mainly positive effects, although losses are forecasted for those southern areas most vulnerable to heat-stress and drought.

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Notes

  1. Note that this multivariate Tobit specification does not take into account the role of the virtual prices when goods are not consumed (Lee and Pitt 1986). However this should not be a major issue in this analysis since, as shown in the next section, we do not model prices explicitly but just control for their effect via yearly dummy variables.

  2. However, note that we assume “other land” to have no relevance for Scotland because of the lack of information on this category.

  3. As described on the EDINA website, grid-square land use estimates can sometimes overestimate or underestimate the amount of agricultural land within an area, since their collection is based on the location of the main farm house. For example, when a farm’s agricultural land belongs to more than one parish, all the land use is assigned to that parish in which the main farm is registered. For this reason the recorded areas of land use and the numbers of livestock can sometimes significantly overestimate the real values. For instance, it is not uncommon for the recorded value of rough grazing to exceed the total amount of land within a grid square (400 ha). We correct this feature by rescaling the sum of the different agricultural land use areas assigned to each grid square to match with the total agricultural land derived from the agricultural land classification (ALC) system published by DEFRA and the Welsh Assembly (data available at: http://www.naturalengland.org.uk/). We discard from the analysis those cells for which this correction would be too substantial, defined as those cells where the difference between the sum of the agricultural land use areas from the JAC and the total agricultural area according to the ALC is higher than the total grid cell size of 400 ha or (b) the ratio of these two agricultural areas is greater than 4 or smaller than 1/4. This roughly corresponds to 7 % of the observations.

  4. The UKCIP baseline uses monthly data available from the Met Office website (www.metoffice.gov.uk) to calculate averages for the period 1961–1990.

  5. The multinomial logit model is specified including the same explanatory variables of the structural model and estimated with Ordinary Least Squares (OLS) as a system of log odds of shares, following Zellner and Lee (1965). Since this model is undefined when there are zero values in the dependent variables, in each observation we set to a very small value (0.1 ha) all uses with no allocated land, as proposed by Wu and Segerson (1995).

  6. Note that, due to the availability of data for the most recent period, this comparison is conducted for the whole of England and Wales in 2004 rather than for all of Great Britain. Since only 5 % of the 2004 data are used to estimate the model, this consists of mainly out-of-sample forecasting. Therefore it is an appropriate yardstick to compare models performances avoiding the risk of preferring an over-fitting specification.

  7. For detailed discussion see http://edina.ac.uk/agcensus/description.html (accessed 14 September 2011).

  8. FGM for 2004 taken from Fezzi et al. (2010) as follows: “cereals” \(=\) \({\pounds }\)290/ha, “root crops” \(=\) \({\pounds }\)2,425/ha, “oilseed rape” \(=\) \({\pounds }\)310/ha, “dairy”\(=\) \({\pounds }\)576/head, “beef” \(=\) \({\pounds }\)69/head, “sheep” \(=\) \({\pounds }\)9.3/head, “other land” assumed to have the same FGM/ha of cereals. The “Appendix provides” an analysis of the impacts of variation in these prices.

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Acknowledgments

This research was supported by the UK National Ecosystem Assessment and the Social and Environmental Economic Research (SEER) into Multi-Objective Land Use Decision Making project; funded by the UK Economic and Social Research Council, reference RES-060-25-0063.

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Authors and Affiliations

  1. CSERGE, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

    Carlo Fezzi, Ian Bateman, Paul Munday, Antara Sen & Amii Harwood

  2. Department of Land Economy, University of Cambridge, CB39EP, Cambridge, UK

    Tom Askew & Unai Pascual

  3. Basque Centre for Climate Change (BC3), Alameda Urquijo, 4-4, 48008, Bilbao, Basque Country

    Unai Pascual

  4. Ikerbasque Basque Foundation for Science, Alameda Urquijo, 36-5, 48011, Bilbao, Basque Country

    Unai Pascual

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  1. Carlo Fezzi
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  2. Ian Bateman
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  4. Paul Munday
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  5. Unai Pascual
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  7. Amii Harwood
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Corresponding author

Correspondence to Carlo Fezzi.

Appendix: The Impact of Variation in Prices on Projections of Climate Change Impacts

Appendix: The Impact of Variation in Prices on Projections of Climate Change Impacts

Figure 4 holds real prices constant at 2004 levels for its analysis of the impacts of climate change across three periods. However, agriculture is characterised by price instability and this has been particularly evident during the past decade. To illustrate the substantial impacts of price instability in Fig. 5 we consider a single climate change period (2004–2040) and take prices from three recent years; 2004 (Fezzi et al. 2010), 2006 (Nix 2006) and 2009 (Nix 2009). Allowance is made for intervening inflation from 2004 to the latter 2 years, bringing all three to 2004 real value equivalents. These are then applied to the changes in land use and livestock intensities projected for 2004–2040 under the low emission scenario.

Fig. 5
figure 5

The impact of climate change (low emissions scenario) on FGM between 2004 and 2040 evaluated using three recent sets of real prices

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Analysis of the spatial patterns illustrated in Fig. 5 reveals that these are similar to those of Fig. 4 with the south and eastern lowlands of England faring worst from climate change while other more upland areas benefit from such change. Further comparison of these figures shows that even the absolute shifts in FGM induced by price changes are of a similar magnitude to those arising from climate change. This suggests that price volatility might be at least as important to the financial prosperity of UK agriculture (if not more given that the above analysis only considered price variation over a relatively short period). Note that this comparison does not take into account possible land use changes arising from changes in prices. However, these are likely to be minor if farmers’ expectations of future prices do not differ substantially from present prices.

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Fezzi, C., Bateman, I., Askew, T. et al. Valuing Provisioning Ecosystem Services in Agriculture: The Impact of Climate Change on Food Production in the United Kingdom. Environ Resource Econ 57, 197–214 (2014). https://doi.org/10.1007/s10640-013-9663-x

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