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Analysis of farm performance in Europe under differentclimatic and management conditions to improveunderstanding of adaptive capacityPytrik Reidsma & Frank Ewert & Alfons Oude LansinkReceived: 7 June 2005 /Accepted: 10 January 2007 / Published online: 7 March 2007# Springer Science + Business Media B.V. 2007Abstract The aim of this paper is to improve understanding of the adaptive capacity ofEuropean agriculture to climate change. Extensive data on farm characteristics of individualfarms from the Farm Accountancy Data Network (FADN) have been combined with climaticandsocio-economicdatatoanalyzetheinfluenceofclimateand managementon cropyieldsandincome and to identify factors that determine adaptive capacity. A multilevel analysis wasperformedtoaccountforregionaldifferencesinthestudiedrelationships.Ourresultssuggestthatsocio-economicconditionsandfarmcharacteristicsshouldbeconsideredwhenanalyzingeffectsof climate conditions on farm yields and income. Next to climate, input intensity, economic sizeandthetypeoflandusewereidentifiedasimportantfactorsinfluencingspatialvariabilityincropyields and income. Generally, crop yields and income are increasing with farm size and farmintensity. However, effects differed among crops and high crop yields were not always related tohigh incomes, suggesting that impacts of climate and management differ by impact variable. Asfarm characteristics influence climate impacts on crop yields and income, they are goodindicators of adaptive capacity at farm level and should be considered in impact assessmentmodels. Different farm types with different management strategies will adapt differently.1 IntroductionClimate change is expected to affect agriculture very differently in different parts of theworld (Parry et al. 2004). Many studies have analyzed the influence of climate and climateClimatic Change (2007) 84:403–422DOI 10.1007/s10584-007-9242-7P. Reidsma (*):F. EwertDepartment of Plant Sciences, Group Plant Production Systems, Wageningen University,P.O. Box 430, 6700 AK Wageningen, The Netherlandse-mail: firstname.lastname@example.com. ReidsmaNetherlands Environmental Assessment Agency (RIVM/MNP),P.O. Box 1, 3720 BA Bilthoven, The NetherlandsA. Oude LansinkDepartment of Social Sciences, Business Economics, Wageningen University,P.O. Box 8130, 6700 EW Wageningen, The Netherlands
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change on agriculture, and the problem of agricultural vulnerability is increasinglyrecognized (e.g. Mendelsohn et al. 1994; Antle et al. 2004; Parry et al. 2004). The extentto which systems are vulnerable depends on the actual exposure to climate change, theirsensitivity and their adaptive capacity (IPCC 2001). Exposure and sensitivity determine thepotential impacts, which include all impacts that occur given the projected climate changewithout considering adaptation. The actual impact is the impact that remains after allowingfor adaptation. The adaptive capacity refers to the ability to cope with climate changeincluding climate variability and extremes in order to (a) moderate potential damages,(b) take advantage of emerging opportunities, and/or (c) cope with its consequences. Mostquantitative studies that address the vulnerability of agricultural systems have focussed onexposure and sensitivity, while adaptive capacity is often highly simplified. Realisticadaptation processes are not well understood and therefore hard to quantify (Smit et al.2001).The impact of climate change on society is frequently determined by assessing impactson ecosystem services (Metzger 2005; Reid et al. 2005). Because ecosystem services form adirect link between ecosystems and society, the concept is especially useful for illustratingthe need to employ mitigation or adaptation measures to prevent or alleviate impacts(Metzger 2005). The main ecosystem services provided by the agricultural sector are foodproduction, farmers' income and environmental sustainability. Impacts of climate changeon food production are generally assessed with crop models (Gitay et al. 2001). Studieshave been performed on different levels of organization: crops (Tubiello and Ewert 2002),cropping systems (e.g. Tubiello et al. 2000), regional (Iglesias et al. 2000; Saarikko 2000;Trnka et al. 2004), continental (Harrison et al. 1995; Downing et al. 2000; Reilly 2002) andglobal (IMAGE Team 2001; Parry et al. 2004).In crop modelling studies, farmers' responses to climate change are purely hypotheticaland either no adaptation or optimal adaptation is assumed. Easterling et al. (2003) made afirst attempt to model agronomic adaptation more realistically proposing a logistic growthfunction to describe the adaptation process over time. How agricultural adaptive capacityvaries spatially has not been assessed to date, however. Mendelsohn and Dinar (1999)suggest that climatic conditions have relatively smaller impact on farmers' income (netincome/farm value) than on crop yields as simulated by crop models. Their cross-sectionalanalysis implicitly includes adaptive capacity. Adaptation strategies adopted could beagronomic strategies to increase crop yields as well as economic strategies such as changesin crops and inputs. Agro-economic models (Kaiser et al. 1993; Antle et al. 2004) canassess optimal economic adaptation strategies, but do not consider the capacity to adaptthese. In addition, biophysical relationships are often underrepresented.In Europe, concerns in agriculture are mainly related to farmer livelihood and the landavailable for farming (Schröter et al. 2005) and less to food production. A Europeanvulnerability assessment showed that farmer livelihood is especially vulnerable in theMediterranean region (Metzger et al. 2006). This projection was based on calculationssuggesting that intensification of production will reduce the need for agricultural land inless favoured areas (Ewert et al. 2005; Rounsevell et al. 2005). Although the impact ofclimate change in Europe was projected to be small on average, regions with lessfavourable climatic conditions and hence lower crop yields would have difficulties tosustain farmer livelihood. Projected impacts on European agricultural land use were lesssevere when the global food market and regional land supply curves were included in themodelling framework (van Meijl et al. 2006). Assumptions related to different drivers havea large influence on climate change impact projections. Farm-level responses are usually404Climatic Change (2007) 84:403–422
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not considered and spatial variability in farm performance and adaptive capacity is not wellunderstood.In this paper we analyzed the impact of farm characteristics and climatic and socio-economic conditions on crop yields and farmers' income across the EU15. The influence ofclimate is assessed using a Ricardian approach, similar to that employed by Mendelsohn et al.(1994). By including farm-level information (e.g. farm size, intensity) and socio-economicconditions in the analysis, we captured factors that influence farm-level adaptive capacity.We investigated both crop yields and income variables and the relationships between theseto understand farm performance and adaptation.Emphasis is on spatial variability in farm performance considering data from threedifferent years (1990, 1995 and 2000). Since data were available at different scales amultilevel statistical approach was used. Results of this study can improve the modelling ofagricultural adaptation to climate change.2 Methodology2.1 Conceptual basis for analyzing farm performance and adaptive capacityChanges in climatic conditions will affect crop growth and yield at the field level throughbiophysical relationships and these impacts are commonly assessed with crop models. Thedynamic nature of climate effects is well understood for potential, water and nitrogenlimited growth and yield (e.g. van Ittersum et al. 2003). Actual yields, however, are alsoaffected by other factors such as pests and diseases not considered in crop models and farmmanagement will largely influence the obtained actual yield. Therefore, climate changeimpacts on crop yields also depend on factors determining farm performance. Potentialimpacts can be assessed with crop models, but for projections of actual impacts the adaptivecapacity of farmers should be taken into account.We found it important to distinguish between two groups of factors related to (1) farmcharacteristics and (2) regional conditions such as biophysical, socio-economic and policyfactors (Fig. 1). Both factor groups represent different levels of organization (farm andregion). We account for possible interactions between farm characteristics and regionalconditions on farm performance through a multilevel analysis (see Section 2.3). Farmcharacteristics may also change as a result of regional impacts on farm performance, which,however, is not further addressed in this paper. As different crops respond differently to Regional conditions Regional conditionsBiophysical(climate, soil, …) (climate, soil, …)Socio-economic(welfare, technology, prices,…)(welfare, technology, prices,…)Policy(subsidies, regulations,…)(subsidies, regulations,…)Farm(er) characteristics Farm(er) characteristicsIntensityEconomic sizeAgricultural areaCrop diversityObjectives….….Farm performance(ecosystem services)(ecosystem services)Crop yieldsCrop yieldsFarmers' incomeFarmers' incomeBiophysicalSocio-economicPolicyIntensityEconomic sizeAgricultural areaCrop diversityObjectivesFarm performanceFig. 1 The investigated relation-ships (represented by the blockarrows). Potential impacts of cli-mate conditions are influenced byother regional conditions andfarm characteristics, which deter-mine adaptive capacityClimatic Change (2007) 84:403–422 405
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climatic conditions, yields of five important crops (wheat, grain maize, barley, potato andsugar beet), were analyzed.Farm management decisions have to be economically viable in order to ensure the farm'ssustainability. We considered the economic performance of farms by including farmers'income in the analysis and explicitly studied relationships between income and crop yields.Farmers' income is represented by farm net value added per hectare (fnv/ha) and farm netvalue added/annual work unit (fnv/awu). Fnv/ha measures economic performance per unitof land and a relationship to crop yield can be expected. Fnv/awu is a measure that enablescomparison of farmers' income directly to GDP per capita and can therefore relate farmperformance to general socio-economic performance. By directly measuring revenues, weaccount for the direct impacts of climate on yields of different crops as well as the indirectsubstitution of different inputs, introduction of different activities, and other potentialadaptations to different climates (Mendelsohn et al. 1994).Farm characteristics that explain farm performance are related to determinants ofadaptive capacity: awareness, technological ability and financial ability (Schröter et al.2003; Metzger et al. 2006). Adaptive capacity is difficult to quantify explicitly fromobservations on farm performance however. Information about potential impacts, i.e.impact without adaptation, is not available as observed farm performance implicitlyincludes adaptation to present climatic and other conditions. We assume that adaptation isrelated to farm performance and farms that perform well are also well adapted.2.2 Data sources and data processingThe Farm Accountancy Data Network (source: FADN-CCE-DG Agri and LEI) providesextensive data on farm characteristics of individual farms throughout the EU151. Data havebeen collected annually since 1989. They have been used as an instrument to evaluate theincome of agricultural holdings and the impacts of the Common Agricultural Policy.Information about the exact geographic location of the sample farms is not available forprivacy reasons; only the region in which farms are located is known. In total, 100 HARMregions2are distinguished (see Fig. 3) with 51,843 sample farms.FADN considers the following land-using production types: specialist field crops,specialist permanent crops, specialist grazing livestock, mixed cropping and mixed crops/livestock. At approximately 40% of all farms, i.e. 20,936 farms, crop production is the mainactivity, i.e. when more than 66% of the total standard gross margin3(economic size) wasobtained from the sale of field crop products and/or when the arable area was more than66% of the total utilized agricultural area. Only these farms were included in the analysis ofeffects on farmers' income.For each farm, data were available on outputs representing farm performance: cropyields and farm net valued added. Crop yields of five important crops (wheat, grain maize,barley, potato and sugar beet) were calculated by dividing production (in tons fresh matter)by crop area (in ha). Farm characteristics considered to explain farm performance representdifferent determinants of adaptive capacity: awareness, technological ability and financial1The EU15 comprises the 15 member countries of the European Union before the extension in 2004.2HARM is the abbreviation for the harmonized division created by the Dutch Agricultural EconomicsResearch Institute (LEI). It gives the opportunity to compare the different regional divisions of the EU15used by Eurostat (NUTS2) and FADN.3The standard Gross Margin (SGM) of a crop or livestock item is defined as the value of output from onehectare or from one animal less the cost of variable inputs required to produce that output.406 Climatic Change (2007) 84:403–422
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ability (Schröter et al. 2003; Metzger et al. 2006). Awareness is reflected in the land use(arable land, permanent cropping land, grassland, area of each crop grown). Arable farmershave more skills in crop production than livestock farmers and therefore obtain higheryields and probably less yield variability. A farmer growing a specific crop in a large area isexpected to put more effort in obtaining a high crop yield. Technological ability isrepresented by the input intensity (irrigated area, input costs of fertilizer and crop protectionproducts, whether the farm is conventional or organic). It is expected that farms with a highinput intensity aim for a high output intensity. Financial ability is reflected by the economicsize and/or the size of the farm in hectares. A larger farm is a priori expected to have morecapital available for investments in new technologies. Altitude class and location in a less-favoured area (LFA) were used as proxies for the biophysical characteristics of the land.More variables were available, but variables needed to be selected to reduce multi-collinearity (see Sections 2.3.2 and 3.2). Data from three years (1990, 1995 and 2000) wereconsidered but results presented refer mainly to the year 2000 as little or no differenceswere found among years.Climatic effects were analyzed using data from the ATEAM project4based on New et al.(2002). Averages from the 30-year period 1971–2000 are assumed to be representative forthe climatic conditions that influence spatial variability in farm performance.5Meantemperature and precipitation of all months were obtained with a resolution of 100×100. Asmonthly climate variables are often correlated, average variables were created to notconfound the results. Monthly mean temperatures of the first six months (January–June)have been averaged, resulting in the mean monthly temperature of the first half of the year.Also precipitation data was averaged to obtain the mean monthly precipitation for the first6 months of the year that can be considered as the main growing period for Europe. Allclimatic data were averaged to HARM regions.Data on regional socio-economic variables, such as GDP per capita and populationdensity were obtained from Eurostat (2004). Population density can serve as a proxy for thepressure on the land. When land becomes scarce, rental rates increase, which is assumed toincrease production intensity (Van Meijl et al. 2006). Data were available at NUTS26leveland transformed to HARM regions.A macro-scale adaptive capacity index has been developed at NUTS2 regional level forthe EU15 (Schröter et al. 2003; Metzger et al. 2006). This adaptive capacity index serves asa proxy for the socio-economic conditions that influence farmers' decisions; it sets theregional context in which individuals adapt. The index is based on twelve indicators, whichare aggregated by application of fuzzy set theory. The indicators comprise: female activityrate & income inequality (equality), literacy rate & enrolment ratio (knowledge), R&Dexpenditure & number of patents (technology), number of telephone lines & number ofdoctors (infrastructure), GDP per capita & age dependency ratio (flexibility), world tradeshare & budget surplus (economic power). In Table 1 a description is given of all variablesused in the analysis.6Nomenclature des Units Territoriales Statistiques 2: regions or provinces within a country as distinguishedby Eurostat.5Spatial variability in crop yields and income is mainly determined by long-term climate variability.Temporally, variability in crop yields and income is relatively smaller than climate variability (results notshown). Using yearly climate data disturbs the impact of long-term spatial variability in climatic conditions.4ATEAM (Advanced Terrestrial Ecosystem Analysis and Modelling), http://www.pik-potsdam.de/ateam/ateam.html.Climatic Change (2007) 84:403–422407
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2.3 Statistical analysis2.3.1 Multilevel modellingThe effect of climate and management on farm performance is analyzed by fitting amultilevel (or generalized linear mixed model; GLMM) model to the data. A multilevelmodel expands the general linear model (GLM) so that the data are permitted to exhibitcorrelated and non-constant variability (e.g. Snijders and Bosker 1999; McCulloch andSearle 2001). Multilevel modelling originates from the social sciences and has morerecently also been applied to geographic studies (e.g. Polsky and Easterling 2001; Pan et al.2004). A multilevel model can handle complex situations in which experimental units areTable 1 Data description and sourcesVariable DefinitionSourceaMeanbS.D.bDependentCrop yieldFnv/awuFnv/haFarm characteristicsIrr_perc*Fert/ha*Prot/ha*Org*Actual crop yield (tons/ha)Farm net value addedd/annual work units (€)Farm net value added/hectare (€)111c26,60990650,4781,761Irrigated percentage of utilized agricultural area (%)Costs of fertilizers and soil improvers per hectare (€)Costs of crop protection products per hectare (€)1=conventional, 2=organic, 3=converting/partiallyorganicUtilized agricultural area (ha)Economic sizee(ESU)Annual work units (AWUf)Permanent cropping area/utilized agricultural area (–)Grass/uaa* Grassland area/utilized agricultural area (–)Crop_pr* Crop area/total arable area (–)Biophysical conditionsAlt* Altitude: 1=<300 m, 2=300–600 m, 3=>600 mLfa*1=not in lfag, 2=in lfa not mountain, 3=in lfa mountain 1Tmean* Mean monthly temperature (°C) of first half yearPmean* Mean monthly precipitation (mm) of first half yearSocio-economic conditionsAc* Macro-scale adaptive capacity index (–)Gdp/capGross domestic product per capita (€)Pop_dens Population density (people per km2)11111531119113112971.01 0.17UaaEc_size*LabourPerm/uaa*11111182701.90.0380.0441941544.10.0920.099c1 18.104.22.1680.80.82.517222330.540.1214,1451585,181151*Independent variables included in multilevel modelsa1: FADN, 2: ATEAM, 3: Eurostat (1=farm level; 2,3=HARM level).bStatistics based on 2000 data, for cropping systems only.cDiffers per crop considered.dCorresponds to the payment for fixed factors of production (land, labour and capital), whether they areexternal or family factors. As a result, holdings can be compared irrespective of the family/non-family natureof the factors of production employed. Fnv=total output−total intermediate consumption+balance currentsubsidies and taxes−depreciation.eThe economic size is determined on the basis of the overall standard gross margin of the holding. It is givenin European Size Units (ESU); one ESU corresponds to a standard gross margin of €1,200.fOne Annual Work Unit (AWU) is equivalent to one person working full-time on the holding.gLfa = Less-favoured area.408Climatic Change (2007) 84:403–422
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nested in a hierarchy. In a multilevel model, responses from a subject are thought to be thesum of the so-called fixed and random effects. If a variable, such as fertilizer use, affectswheat yield, it is fixed. Random effects contribute only to the covariance of the data.Intercepts and slopes of variables may vary per region and this covariance is modelledusing random effects. Hence, multi-level modelling accounts for regional differences whenanalyzing within region effects of farm characteristics on yields and income. In Fig. 2 this isdepicted graphically.Fitting a multilevel model to the data comprises a few steps. Firstly, the model isformulated with fixed effects only as in a GLM, to compare against models includingdifferent forms of HARM-level variation.XIn Eq. 1, yijis the dependent variable, b0jis the intercept estimate, bqjis the coefficientestimate of the variable xqj, i indexes the farm, j indexes the HARM region and the residualrij∼N(0, σ2). In this model, b0jand bqjare the same for all HARM regions. The model givessimilar results as a GLM. The goodness of fit is measured in different ways though. Amultilevel model is based on (restricted) maximum likelihood methods, versus theminimization of squared error in GLM. The preferred GLM is the model with the highestR2, while the preferred multilevel model is selected using likelihood ratio tests. The preferredmultilevel model is the model with the lowest information criteria, such as −2 log likelihood(deviance) or Aikaike's Information Criterion (AIC). A single deviance or AIC has no usefulinterpretation, it is only the difference between the values of different models that matters.In a second model, the proposition that the average of the dependent variable variesbetween regions is being tested by including a random intercept. This model combinesEqs. 1 and 2.yij¼ b0jþq¼1...Qbqjxqijþ rijð1Þb0j¼ b0þ mjð2Þwhere μjis the regional level residual from the average intercept estimate. To test whetherthe overall model fit is improved, two models can be compared by subtracting thedeviances. This is the χ2, and the associated d.f. is the difference in the number ofFertilizer useFertilizer use Fertilizer use Fertilizer useFertilizer useFertilizer useWheat yieldWheat yieldaaabbb0000j 0j0j 0jq0q0 q0 qjqj==dxdxdy dyWheat yieldWheat yieldWheat yieldWheat yieldβqjβββFig. 2 Graphicalexampleofamultilevelmodelwitha random intercept b0jand b random intercept b0jand slopes bqj.Each solid line represents the effect of fertilizer use on wheat yield in a specific region j, whilst the dotted linerepresents the mean (fixed) relationship across all regions (bq0). In a simple regression model, the mean relationship isa line through all the data points, while in a multilevel model it's the average of the relationships per region. SeeSection 2.3.1 for further explanationClimatic Change (2007) 84:403–422409
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parameters. A random intercept model allows for a better representation of the influence offarm-level variables on the dependent variables, as regional differences are being capturedin the random intercept. Since the focus is on the explanation of variables within regions,regional differences in climatic or socio-economic conditions which are not captured by theselected variables, do not confound the results. The influence of variables can also differbetween regions. We therefore tested the random coefficients model, in which also theslopes vary between regions. This model combines Eqs. 1, 2 and 3.bqj¼ bq0þ uqjð3Þwhere uqjis the regional level residual from the average coefficient estimate. All statisticalanalyses were performed with the data of the years 1990, 1995 and 2000 separately. Sinceresults were consistent across years only results from 2000 are presented (see Section 3).2.3.2 Selection of variablesCrop yields (wheat, grain maize, barley, potato and sugar beet) and income variables (farmnet value added/annual work unit, farm net value added/ha) were the dependent variables indifferent models. These and the independent variables are presented in Table 1. For theclimate variables, linear and quadratic terms were included to capture their potentialnonlinear effects on crop yields and income variables. For crop yield models all samplefarms in the database were analyzed, for income models only farms where crop productionwas dominating were considered (see Section 2.2).The two-way relationship between the dependent variables and fertilizer and cropprotection use violates a basic assumption of independence and therefore can lead toendogeneity. Farmers' decisions about the rate of fertilizer and crop protection applicationsdepend on its marginal effects on the net value added, which is determined by the marginaleffect on crop yields, the prices of crops, and the prices of fertilizers and crop protectionproducts. Non-linearity of the relationship between these input costs and dependentvariables has been tested by curve estimation in SPSS 11. To test for the impact oferroneously treating endogenous variables as exogenous, we used instrumental variables(IV) to estimate the effect of fert/ha and prot/ha on the dependent variables. Usinginstrumental variables allows for removing the error terms in fert/ha and prot/ha thatconfound with the errors in the equations of crop yields and farm income. All variables inthe database that could possibly influence application of fert/ha and prot/ha were includedas instrumental variables in the IV regression (e.g. land improvement costs, costs onmachinery and equipment, percentages of various crops, annual working units7). The IVregression was performed with a multilevel model. Endogeneity of fert/ha and prot/ha wastested by the Hausman test (Hausman 1978). The test statistic is?whereeb is the parameter vector resulting from the model based on IV estimates for thevalues.eV andbV are the variance-covariance matrices ofeb andbb, respectively. This test hasM ¼ eb ?bb?0eV ?bV??eb ?bb??ð4Þpossible endogenous variables andbb is the parameter vector of the model with the observeda χ2distribution with N degrees of freedom (N is the number of parameters). The null7A full list of variables used in the instrumental variables regression can be obtained from the correspondingauthor.410 Climatic Change (2007) 84:403–422
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hypothesis is that the two estimators do not differ. If the null hypothesis is rejected,exogeneity of the variables under investigation is rejected. The Hausman test can result innegative test values. One way to deal with this is to apply the test on the parameters testedfor endogeneity only (Ooms and Peerlings 2005).Beforefittingamultilevelmodel,thepossibleinfluenceofmulticollinearitymustbeexamined.Climate, socio-economic and management variables all have, to some extent, a north–southgradient in the European Union. A high multicollinearity causes coefficient estimates to beunreliable and confounding in interpreting the model results. An advantage of a full multilevelmodelincomparisonwithGLMsisthatmulticollinearityonlyneedstobeexaminedperlevel.Asthe influence of management variables is analyzed per region (as random effects account forregional differences), a possible correlation of input use (at individual farm level) with climaticvariables (at regional level) won't influence the results.The linear mixed model procedure in SPSS 11 does not include collinearity diagnostics.We therefore applied a linear regression model to the data to examine these. We based theselection of variables on the partial correlation matrix and on the linear regression modelwith wheat yield as dependent variable. Firstly insignificant variables were removed;secondly variables with a variance inflation factor (VIF) of 10 or higher were removed fromthe analysis (Allison 1999). The process of excluding variables was continued until allcondition indices (CI) were below 30 and all variables contributed to the output. CI greaterthan 30 indicate that multicollinearity is a serious concern; multicollinearity is not presentwhen all condition indices equal one.3 Results3.1 Spatial variability in yield and income variablesInFig. 3 the spatial variability of wheat yield, maize yield, farm net value added/annual workunit (fnv/awu) and farm net value added/hectare (fnv/ha) between and within HARM regionsin 2000 is presented. The coefficient of variation (CV) gives an indication of the spatialvariability within a region due to management and/or biophysical factors. Spatial distributionsof yields were different for wheat and maize. Wheat yields were generally highest innorthwest Europe, while the highest maize yields were obtained in Spain and Greece. Spatialvariability within regions was generally higher in regions with lower yields. The variabilityamong regions of fnv/awu was similar to that of wheat yields, but different to the spatialvariability of fnv/ha which was especially high for some Mediterranean regions.3.2 Selection of variables affecting crop yield and incomeThe instrumental variables regression model could account for 81.2% of the variation infert/ha and 83.1% of prot/ha. Results of the Hausman test indicated that fertilizer use andcrop protection use were exogenous to crop yields (p>0.05), but endogenous to fnv/ha andfnv/awu (p<0.001). Hence the observed values were used in the crop yield models, whilethe estimates based on the IV model were used in the income models.In a partial correlation matrix (Table 2) we identified variables that were correlated, andvariables that were correlated to the dependent variables in which we were interested. Thecorrelation between crop protection use (prot/ha) and wheat yield for example wassignificantly positive with an r2=0.467, suggesting that prot/ha may be a good predictor ofwheat yield and should be included in the multilevel model.Climatic Change (2007) 84:403–422 411
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For each model it was tested whether including quadratic terms improved modelperformance. Models that include mean temperature (tmean), as well as the macro-scaleadaptive capacity (ac) showed Variance Inflation Factors of nearly 2 and Condition Indiceshigher than 30, which indicates that coefficient estimates were not reliable. For each modelFig. 3 Spatial variability of crop yields (tons/ha) and income variables (€) in 2000 between and withinHARM regions for a average wheat yield, b CVof wheat yield, c average maize yield, d CVof maize yield,e average of farm net value added/annual work unit (fnv/awu), f CVof fnv/awu, g average of farm net valueadded/hectare (fnv/awu) and h CV of fnv/ha. Only values for regions where more than 15 farms grow thecrop considered are presented412 Climatic Change (2007) 84:403–422
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Table 2 Partial correlation matrix of selected variables in 2000 for farms with crop production as the main farming activitywheatmaizepotatosugarbeetbarleyfnv/awufnv/hairr_perc fert/haprot/ha perm/uaagrass/ uaauaaec_size labourtmeanpmean acgdp/capmaize0.261potato0.3970.411sugarbeet0.2110.1730.242barley0.6880.1970.2770.190fnv/awu0.1380.0330.2210.0340.264fnv//ha−0.0120.1010.0860.0690.0200.267irr_perc−0.0970.1520.0160.209 −0.003 −0.0280.170fert/ha0.2490.2590.1280.1080.355 −0.0330.3560.280prot/ha0.4670.1360.2860.1250.5390.0150.3780.2760.522perm/uaa −0.183 −0.064 −0.142 −0.110−0.183 −0.0660.1600.0180.0570.104grass/uaa0.031 −0.131 −0.1370.0040.018 −0.027 −0.105 −0.117−0.106 −0.125 −0.117uaa0.006 −0.0480.009 −0.0940.0240.106 −0.081 −0.070−0.074 −0.040 −0.0940.097ec_size0.076 −0.0330.088 −0.0910.0810.134 −0.041 −0.038−0.0330.046 −0.0760.0500.928labour−0.043 −0.038 −0.005 −0.084 −0.0130.0080.023 −0.004−0.0010.029 −0.0010.0570.8170.866tmean−0.196 −0.076 −0.0120.040 −0.133 −0.0800.1280.2720.0640.0010.231 −0.145 −0.144 −0.160 −0.079pmean−0.122 −0.013 −0.2600.018 −0.008 −0.0410.021 −0.1120.0560.0120.0530.082 −0.129 −0.103 −0.062 −0.104ac0.396 −0.0650.104 −0.2380.2850.105 −0.150 −0.422−0.112 −0.021 −0.1830.1700.1340.1640.060 −0.687 0.120gdp/cap0.2870.0060.095 −0.0180.1830.099 −0.097 −0.334−0.0670.006 −0.1290.068 −0.0350.009 −0.081 −0.621 0.2030.764pop_dens0.2110.0640.167 −0.0290.1530.0270.032 −0.1300.0320.114 −0.0070.070 −0.0300.026 −0.010 −0.025 0.1230.270 0.272Pearson's correlation coefficients (r2) in bold are significant. Names of crops refer to actual yields. Other variables are described in Section 2.2 and Table 1.Climatic Change (2007) 84:403–422413
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either climate variables or the ac have been included. Gdp/cap was highly correlated withac and was excluded from further analysis. Both variables can represent the socio-economicconditions influencing farmers' decision making; however, ac is more comprehensive and abetter indicator of the regional context in which individuals adapt. Although populationdensity (pop_dens) had a significant positive effect on wheat and maize yields and fnv/awu,its effect was not significant in multilevel models and was excluded from further analysis.On the individual farm level, the size of the farm in hectares (uaa) and labour units(labour) were highly correlated with the economic size of the farm (ec_size). Only ec_sizewas included in the multilevel models. As the share of arable land (ar/uaa), permanentcropping land (perm/uaa) and grassland (grass/uaa) in total uaa almost add up to one, theycan not all be included in the model. Consequently, ar/uaa is excluded from the model.Thus, a negative effect of the other land use types implies a positive effect of ar/uaa.3.3 The influence of climate and management on crop yieldsThe multilevel model with wheat yield as dependent variable clearly improved whenrandom intercepts and slopes were introduced. The deviance decreased from 61,744 for amodel with fixed effects only, to 57,104 (p<0.001) when a random intercept was included,to 55,735 (p<0.001) when random slopes were included. The covariance parameters of therandom effects were significant for all variables, indicating significance of between-regionvariation. Thus, for estimating parameters of fixed effects it is better to use the model withrandom intercept and slopes; this also holds for all other crop yield models.Table 3 presents the fixed effects of multilevel models with random intercept and slopes.The coefficient estimates refer to models with climate variables included. However, sincewe were also interested in the effects of ac, coefficient estimates for ac (i.e. without climatevariables) are shown.Wheat yield was significantly related to all variables included in the model, except forirrigated percentage (irr_perc). The parameter estimates of the linear and quadratic terms ofmean temperature (tmean) and precipitation (pmean) suggests that relationships with wheatyield were concave in these variables. Variables representing input intensity (fertilizer use,fert/ha; crop protection use, prot/ha; conventional/organic farming, org) and financialability (economic size, ec_size) all influenced wheat yields significantly positive. The typeof land use also influenced wheat yield significantly: the percentage of wheat area(crop_pr) had a positive effect and the percentage of permanent cropping area (perm/uaa)and grassland area (grass/uaa) had a negative effect, indicating a positive effect for thepercentage of arable land (ar/uaa). The influence of irr_perc was not significant, which wasprobably due to the fact that wheat is usually not irrigated. Effects of factors representinggrowing conditions were highly significant. Farms on higher altitudes (alt) and farms in lessfavoured areas (lfa) had, ceteris paribus, lower wheat yields compared to farms under morefavourable conditions. These results suggest that climatic conditions influence wheat yields,but that farm characteristics can increase or diminish this influence.Relationships for maize yields were less clear than for wheat. Effects of tmean were onlysignificant at p<0.10, while the effect of pmean was not significant. Variation in pmeanacross Europe was relatively small and availability of water depends also on other factorssuch as soil water holding capacity and depth and potential evapo-transpiration. In regionswith a low water availability irrigation is applied to maize.Including quadratic terms of climate variables didn't improve model performance(in terms of AIC). For some farm characteristics such as irr_perc, fert/ha and perm/uaasignificant effects were evident. The maize growing area (crop_pr) was significant at p<0.10,414Climatic Change (2007) 84:403–422
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Table 3 Fixed effects of multilevel models of 2000 with random intercept and slopes, with crop yields and income variables as dependent variablesVariables yijβq0WheatyieldMaize yieldPotatoyieldSugar beetyieldBarleyyieldFnv/awuFnv/haIntercept (β0)0.50a9.75***21.76***−30.64a3.76***3,3728ab−755aFert/ha0.0020***0.0037***0.0105**0.0113*0.0025***−52.02***2.72***Prot/ha0.0043***0.00020.0098**0.0129***0.0043***−17.18ab(+)7.02*Irr_perc−0.0008a0.0098**0.01320.0418a(+)b−0.0039†2,285a0.96Org=2−1.52***−1.42ab−4.96**−15.07*−1.02***−5,482622***Org=3−0.79**−1.36−3.75†−4.49−0.73***−1,391282abEc_size0.0014***0.0011a(−)b(+)0.0240***0.0011a(−)0.0014***247***−0.016aPerm/uaa−2.58***−1.95***−6.07***−13.97†−1.92***−15,055***713†Grass/uaa−0.59***−0.25a−1.57ab−4.99*−0.24**−27,936***30.3a(−)Crop_prc0.26**0.38†2.28*−1.25a−0.31***Alt=2−0.15*−0.18a1.16*0.33a0.03ab−2,425*39.3Alt=3−0.30**−0.55*0.27a−0.09ab(+)−0.11a−1,786a(+)161*Lfa=2−0.32***−0.38**−1.95*−3.08**−0.22***−2,298*−0.72ab(+)Lfa=3−0.47***−0.77***−3.82***−2.41ab−0.22**−2,150†399***Tmean0.43***−0.18†1.45*6.15*0.37***2,515†43.1ab(−)Pmean0.13**−0.0022a−0.056a2.21†0.006a−94.6a−35.3a(+)Tmean2−0.0449***−0.105*−0.435**−0.037***−273**−0.68ab(+)Pmean2−0.0008**−0.017†0.22a(−)Acd8.33***2.16a3.98ab8.46a6.65***44,729**−797a***p<0.001; **p<0.01; *p<0.05;†p<0.10.aSignificant (p<0.05) in models with fixed effects only. If the sign changes, this is indicated between brackets.bSignificant (p<0.05) in models with fixed effects and random intercept. If the sign changes, this is indicated between brackets.c'Crop' is the crop concerned in the column.dThe models presented exclude the adaptive capacity index ac. When the ac is included instead of the climate variables, these are the coefficient estimates and significancelevels. Coefficient estimates of other variables are similar.Climatic Change (2007) 84:403–422415
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but highly significant in models with fixed effects only, suggesting that maize yields were,ceteris paribus, higher in regions were more maize was grown. Effects on yield were alsoobserved for ec_size but were only significantly positive in a model without random slopes.This means that within regions, farms with a large economic size generally obtain highermaize yields. In models with random slopes other variables can account for this however. Thenegative effect in the fixed effects model suggests higher yields in regions with mainlysmaller farms. The correlation between prot/ha and maize yield (Table 2) was not confirmedin the multilevel model. Maize yields were lower on organic farms (org), at higher altitudes(alt) and in less favoured areas (lfa).Results for barley were similar to the ones for wheat for most variables which was also truefor potato and sugar beet. Although these root crops are often irrigated, there was nosignificant relationship between irr_perc and yield. This result is explained by the fact that inregions with insufficient precipitation these crops are always irrigated, whereas in regionswith sufficient precipitation no irrigation takes place. Hence, variation among farms isinsufficient to identify a significant effect. Tmean had a non-linear influence on barley, potatoand sugar beet yields, whereas the influence of pmean was not significant. The effect of ac oncrop yield was positive for all crops, although not always significant in models with randomeffects. This suggests some influence of the regional context for farm-level adaptation.3.4 The influence of climate and management on income variables3.4.1 Variability in farmers' incomeMultilevel models with farm net value added/annual work unit (fnv/awu) and farm net valueadded/hectare (fnv/ha) as dependent variable, clearly improved with random intercept andslopes. Applying a random coefficients model to the data can thus give better insight in theeffect of specific variables on farmers' income. Fnv/awu was significantly positive relatedto ec_size and ac and negative to fert/ha, perm/uaa and grass/uaa. The relation with tmeanwas concave; there was no significant relation with pmean. For fnv/ha, effects of fert/ha andprot/ha were significantly positive. Although not always significant, organic farming,altitude and a less favoured area location generally had a positive effect on fnv/ha, whereasthey had a negative effect on fnv/awu.The positive effect of variables representing input intensity on fnv/ha was not evident forfnv/awu. On the other hand, variables that did not influence fnv/ha, like ec_size and ac, hadan effect on fnv/awu. Results show that intensification leads to higher fnv/ha, but also thatfnv/awu is, ceteris paribus, higher on larger farms and on farms with a lower intensity.Enlargement thus seems to be a better adaptation strategy than intensification. However, itis evident that farmers' income is influenced by most farm characteristics considered.Fnv/ha was not related to climate variables, whereas tmean had a non-linear concave effectand pmean a negative effect on fnv/awu. This was surprising, as especially fnv/ha, which shouldreflect the productivity of the land, was expected to be influenced by climatic variables. Ap-parently, the relationship between crop productivity and farmers' income is not straightforward,as also evident from the change in signs in models without random effects and the (non-significant) negative effect of ac on fnv/ha, which was positive for crop yields and fnv/awu.3.4.2 Relationship between crop yields and farmers' incomeThere was a highly significant relationship at the regional level between yields of mostcrops and fnv/awu [wheat, r2=0.685; barley, r2=0.638; sugar beet, r2=0.407; potato, r2=416Climatic Change (2007) 84:403–422
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0.348; maize, r2=0.209 (only significant at the p<0.10 level)]. These correlations were alsosignificant at the farm level, but less pronounced (Table 2). Although a causal relation canbe assumed, this relation seems to be confounded by other factors. Income was highlydistorted by government support programs; the highest subsidies were received in the sameregions where the highest wheat yields were observed (e.g. northern France, England, EastGermany). Fnv represents the sum of revenues from outputs (O) − variable input costs (I) +subsidies − taxes. The average O – I was negative in these regions, but due to subsidies theaverage fnv became positive. Although average fnv/ha was still low, the large farm sizesresulted in high fnv/awu.Thus, fnv/ha was not related to crop yields and was especially high in manyMediterranean countries with typically lower crop yields and smaller farms (note, however,that Table 2 shows a small positive within region correlation between fnv/ha and yields ofsome crops). This suggests that maximizing crop yields is not always an efficient economicstrategy. Clearly, differences in fnv/awu in Europe were mainly determined by farm size andsubsidies, while climatic conditions played a minor role.3.5 Separating between climatic and management effectsResults from a multilevel analysis cannot directly differentiate between climate andmanagement effects. However, the influence of farm characteristics can be identified bycomparing the influence of tmean estimated by a multilevel model including climaticconditions and farm characteristics with the influence estimated by a model only includingclimatic conditions (Kaufmann and Snell 1997). An example is provided for wheat yield(Fig. 4a). Omitted-variable bias in the model only including climatic variables causesoverestimation of the direct effect of tmean, as the effect of farm characteristics is forcedinto the parameter estimates of the climatic variables. As a result, the reduction in yieldwhen climate conditions move away from the optimum are much more severe in the modelincluding only climate variables compared to the model with all variables included. Thissuggests that current wheat management in relation to the variables included in the modelamplifies the effect of climatic conditions in less favourable areas. The exacerbated climateeffect in less favourable areas can be explained by (1) less- favourable socio-economic-15-10-5068 10 12 14tmean (ºC) Δ Δ maize yield (tons/ha)model with all variablesmodel including only climate variablesb-15-10-50-4 -202468 10 1214tmean (ºC) wheat yield (tons/ha)model with all variablesmodel including only climate variablesaFig. 4 The effect of tmean (°C) on a wheat yield (tons/ha) and b maize yield (tons/ha), based on the fullmultilevel model, including climate variables and farm characteristics (thick line) and a model only includingclimate variables. A value of zero represents no reduction in yield and is the physiological optimum for thatvariable. The model only including climate variables indicates the total impact of tmean, while the fullmultilevel model indicates the impact that can directly attributed to tmean. The difference between both linesindicates the amplifying effect of farm characteristics on the impact of tmean on crop yieldsClimatic Change (2007) 84:403–422417
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conditions (lower ac) influencing management and/or (2) planned adaptation as thereduction in marginal product lowers the optimal use of purchased inputs for wheatproduction (Kaufmann and Snell 1997). Adaptation is not focused on wheat production, buton income, and hence inputs are reduced.Formaize,theeffectsofclimaticconditionswerenotsignificant(Table3). Nevertheless, wecan also draw the relationship between tmean (including the quadratic term) and maize yield.Fig. 4b shows that effects of climatic conditions were smaller than for wheat yield, especiallywhen farm characteristics were considered. Average maize yields were relatively similar allover the EU15; only in Portugal and southern Italy yields were much lower (where tmeanwas around 13°C and farms were generally smaller and less intensive). As there is (almost)no reduction in marginal product, the use of inputs is close to optimal. Only in regions whereac is specifically low, sub-optimal management decreases maize yields.4 Discussion4.1 Methodology of analysisThe FADN database provides information on a range of farm characteristics for individualfarms across the EU15. Extent and detail of this database is unique and a good basis foranalysis of relationships determining adaptive capacity of farms in Europe.No data are provided on absolute amounts of inputs and we used economic variables onproduction costs as proxy indicators for input intensity. The amount of money spent on inputsis not necessarily directly related to the quantities used on the farm. However, prices offertilizers and crop protection products are very similar throughout the EU15, and costs can,therefore,serveasaproxyforquantities.Moreover,ourmethodologyofmultilevelmodellingwith random effects reduced the potential disturbing effect of regional differences in prices offertilizersandpesticides.Andersen etal.(2004) showed input costs to be clearly related withnitrogen surplus. To correct for endogeneity between input costs and outputs, we usedinstrumental variables to estimate fertilizer and crop protection use.FADN data refer to individual farms, but information about the exact location of the farmsis not accessible for privacy reasons. Farms are located within a HARM region, and only fewvariables are provided to characterize their specific location. The altitude class (alt) andwhether or not a farm belongs to a Less Favoured Area (lfa) give some information on thebiophysical conditions. Other factors such as soil characteristics that are known to influencecrop yields were not included in the analysis. However, recent studies suggest that soilcharacteristics explained only little of the spatial variability in wheat yields across Europe(Bakker et al. 2005) and significant effects on farmers' income were not observed in otherregions (Liu et al. 2004). It can be assumed that farms are randomly distributed throughouteach region, minimizing the influence of local conditions. The exogeneity of fertilizer andcrop protection use in relation to crop yields and the many significant variables that werefound to explain variability in yields and income support this assumption.Climatic conditions can be represented in different ways. Temperature and precipitationare often represented by several variables including various months or seasons. Althoughclimate variability may have different effects for different months, multicollinearity caninflate the standard errors, which complicates the identification of significant effects onindividual variables. Polsky and Easterling (2001) accounted for this and excluded variablesto minimize multicollinearity. We prevented this problem by including a minimum set ofrepresentative variables, i.e. one for temperature and one for precipitation.418Climatic Change (2007) 84:403–422
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4.2 Factors determining farm performance and adaptive capacitySpatial variability of both crop yields and farmers' income across Europe was high andlargely explained by a set of selected climatic and socio-economic including managementfactors. This is consistent with recent investigations in which more than 80% of thevariability in regional wheat yields across Europe could be explained by climatic and socio-economic factors (Bakker et al. 2005). However, our results also indicate that spatial yieldvariability across Europe and the importance of factors explaining this variability differsamong crops. Maize yields are expected to decrease in southern Europe due to climatechange (Wolf and van Diepen 1995), but the present results indicate that climate has only asmall influence on maize yields. Management can decrease but also increase the effect ofclimatic conditions (as presented in Fig. 4), suggesting that farm management will beimportant for adaptation to climate change.Variability in farmers' income (fnv/awu and fnv/ha) was mainly related to farmcharacteristics and less to climatic conditions suggesting that farmers in Europe havelargely adapted to the local climate. This contrasts with other studies in which, also basedon Ricardian analysis, significant influences of climate variability on farmers' income havebeen reported, as for the United States (Mendelsohn et al. 1994; Polsky and Easterling2001), India and Brazil (Mendelsohn and Dinar 1999), China (Liu et al. 2004) andCameroon (Molua 2002). The relationship between climate variables and farmers' incomecan be highly distorted by government support programs, as in the European Union and theUnited States. However, our data also suggest that farmers have adapted in other ways andnot only through subsidies. In regions with relatively low crop yields, farmers seem to growmore profitable crops to increase fnv/ha. This is supported by the fact that fnv/ha is, ceterisparibus, higher in less favourable areas and on higher altitudes. Also, revenues from outputper ha and revenues from output–input costs per ha, excluding subsidies from fnv/ha, werehigher on organic farms, on higher altitudes and in less favoured areas. Although subsidiescomprised a large part of fnv on many European farms, they were higher in more favourableareas, which implies they should amplify the climate effect instead of decreasing it. In morefavourable areas, farm size has been increased to profit from the high crop yields ofrelatively unprofitable crops, which increased fnv/awu.Few recent attempts have been made for integrated assessment of climate effects onagriculture considering both biophysical and socio-economic factors (e.g. Parry et al. 2004).We know of no studies that explicitly analyzed factors that influence agricultural adaptivecapacity to climate change. Characteristics like farm size, area sown with a specific crop,access to technology, education, tenancy status, attitude towards risk and contact withextension agents are the main factors that affect technology adoption (Caswell et al. 2001;Sheikh et al. 2003). The first three characteristics have also been identified in this research,while the others represent farmers' characteristics that can only be identified by detailedsurveys.Optimization models that assess the vulnerability of agriculture (e.g. Kaiser et al. 1993;Antle et al. 2004) might be useful for identifying efficient adaptation strategies. But moreinsight in farmers' behaviour is needed to be able to predict how climate change willinfluence economic vulnerability. In this study we showed factors that influence theadaptive capacity of farmers. We assume that adaptation is related to farm performance andfarms that perform well are also well adapted. It should be noted however that responses tospatial variability in climate conditions indicate long-term adaptation to climate conditions;see Reidsma et al. (in review) for analysis of temporal variability. As mentioned in Section3.5, maximizing crop yields is not the only objective of farmers and adaptation may beClimatic Change (2007) 84:403–422419
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focussed on other objectives. Sterk et al. (2006) showed that farmers do not search foroptimal strategies; rather they adapt their management gradually over the years. Modelsshould describe what individuals do rather than asserting how individuals should makedecisions. Even with extensive datasets the complexity remains difficult to unravelhowever. Factors related to farmers' objectives and perceptions require detailed surveys,which are difficult to be performed across Europe. Results from the present study providehelpful information about factors determining adaptive capacity in agriculture at anaggregated level which may be further substantiated as more detailed information aboutfarmers behaviour becomes available.5 ConclusionFrom our analysis of farm performance in Europe under different climatic and managementconditions we conclude that next to climate, input intensity, economic size and the land usetype are important factors influencing spatial variability in crop yields and income. Ingeneral, crop yields and income are increasing with farm size and farm intensity.Nevertheless, effects differed among crops and high crop yields were not always relatedto high incomes. This suggests that impacts of climate and management also differ byimpact variable. Climate influences crop yields, but has no direct influence on farmers'income.As farm characteristics influence the impact of climate variability on crop yields andincome, they are good indicators of adaptive capacity at farm level. Therefore, they shouldbe considered in models attempting to assess climate change impact on agriculture.Acknowledgements We thank the Netherlands Agricultural Economic Institute LEI, and especiallyBoudewijn Koole, for providing the FADN data. Climatic data and projections of the macro-scale adaptivecapacity index were provided by the ATEAM project. We thank the EU-funded SEAMLESS project (Systemfor Environmental and Agricultural Modelling; Linking European Science and Society) for funding for FE.We gratefully acknowledge comments from Rik Leemans and Bas Eickhout and three independent reviewersfor their helpful comments on the manuscript.ReferencesAllison PD (1999) Logistic regression using the SAS system – theory and application. SAS Institute, Cary, NCAndersen E, Elbersen B, Godeschalk F (2004) Farming and the environment in the European Union – usingagricultural statistics to provide farm management indicators. Paper presented at the OECD expertmeeting on farm management indicators. New Zealand, March 2004Antle JM, Capalbo SM, Elliott ET, Paustian KH (2004) Adaptation, spatial heterogeneity, and thevulnerability of agricultural systems to climate change and CO2fertilization: an integrated assessmentapproach. Clim Change 64:289–315Bakker M, Govers G, Ewert F, Rounsevell MDA, Jones R (2005) Variability in regional wheat yields as afunction of climate, soil and economic variables: assessing the risk of confounding. Agric EcosystEnviron 110:195–209Caswell M, Fuglie K, Ingram C, Jans S, Kascak C (2001) Adoption of agricultural production practices:lessons learned from the U.S. department of agricultural area studies project. Resource EconomicsDivision, Economic Research Service, U.S. Department of Agriculture, Washington, DCDowning TE, Harrison PA, Butterfield RE, Lonsdale KG (2000) Climate change, climatic variability andagriculture in Europe: an integrated assessment. Research Report No. 21. Environmental ChangeInstitute, University of Oxford, Oxford, UK, p 445Easterling WE, Chhetri N, Niu XZ (2003) Improving the realism of modeling agronomic adaptation toclimate change: simulating technological substitution. Clim Change 60:149–173420Climatic Change (2007) 84:403–422
Page 19
Eurostat (2004) http://epp.eurostat.cec.eu.intEwert F, Rounsevell MDA, Reginster I, Metzger MJ, Leemans R (2005) Future scenarios of Europeanagricultural land use. I: estimating changes in crop productivity. Agric Ecosyst Environ 107:101–116Gitay H, Brown SD, Easterling W, Jallow B (2001) Ecosystems and their goods and services. In: McCarthyJJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate change 2001. Impacts, adaptation, andvulnerability. Cambridge University Press, Cambridge, pp 237–315Harrison SP, Butterfield RE, Downing T (1995) Climate change and agriculture in Europe. Assessment ofimpacts and adaptations, environmental change unit. University of Oxford, Oxford, p 411Hausman JA (1978) Specification tests in econometrics. Econometrica 46:1251–1271Iglesias A, Rosenzweig C, Pereira D (2000) Agricultural impacts of climate change in Spain: developingtools for a spatial analysis. Global Environ Change 10:69–80IMAGE Team (2001) The IMAGE 2.2 implementation of the SRES scenarios: a comprehensive analysis ofemissions, climate change and impacts in the 21st century. National Institute of Public Health and theEnvironment, BilthovenIPCC (2001) Climate change 2001. Impacts, adaptation and vulnerability. Cambridge University Press,CambridgeKaiser HM, Riha SJ, Wilks DS, Rossiter DG, Sampath R (1993) A farm-level analysis of economic andagronomic impacts of gradual climate warming. Am J Agric Econ 75:387–398Kaufmann RK, Snell SE (1997) A biophysical model of corn yield: integrating climatic and socialdeterminants. Am J Agric Econ 79:178–190Liu H, Li XB, Fischer G, Sun LX (2004) Study on the impacts of climate change on China's agriculture.Clim Change 65:125–148McCulloch CE, Searle SR (2001) Generalized, linear and mixed models. Wiley, New YorkMendelsohn R, Dinar A (1999) Climate change, agriculture and developing countries: does adaptationmatter? World Bank Res Obs 14:277–293Mendelsohn R, Nordhaus WD, Shaw D (1994) The impact of global warming on agriculture – a Ricardiananalysis. Am Econ Rev 84:753–771Metzger MJ (2005) European vulnerability to global change. A spatially explicit and quantitative assessment.PhD thesis, Wageningen University, Wageningen, p 192Metzger MJ, Rounsevell MDA, Acosta-Michlik L, Leemans R, Schroter D (2006) The vulnerability ofecosystem services to land use change. Agric Ecosyst Environ 114:69–85Molua EL (2002) Climate variability, vulnerability and effectiveness of farm-level adaptation options: thechallenges and implications for food security in Southwestern Cameroon. Environ Dev Econ 7:529–545New M, Lister D, Hulme M, Maken I (2002) A high-resolution data set of surface climate over global landareas. Clim Res 21:1–25Ooms DL, Peerlings JHM (2005) Effects of EU dairy policy reform for Dutch dairy farming: a primalapproach using GMM estimation. Eur Rev Agric Econ 32:517–537Pan WKY, Walsh SJ, Bilsborrow RE, Frizzelle BG, Erlien CM, Baquero F (2004) Farm-level models ofspatial patterns of land use and land cover dynamics in the Ecuadorian Amazon. Agric Ecosyst Environ101:117–134Parry ML, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global foodproduction under SRES emissions and socio-economic scenarios. Global Environ Change 14:53–67Polsky C, Easterling WE (2001) Adaptation to climate variability and change in the US great plains: a multi-scale analysis of Ricardian climate sensitivities. Agric Ecosyst Environ 85:133–144Reid WV, Mooney HA, Cropper A, Capistrano D, Carpenter SR, Chopra K, Dasgupta P, Dietz T, DuraiappahAK, Hassan R, Kasperson R, Leemans R, May RM, McMichael AJ, Pingali P, Samper C, Scholes R,Watson RT, Zakri AH, Shidong Z, Ash NJ, Bennet E, Kumar P, Lee MJ, Raudsepp-Hearne C, Simons H,Thonell J, Zurek NB (2005) Millenium ecosystem assessment synthesis report. Island Press,Washington, DCReidsma P, Ewert F, Oude Lansink A, Leemans R (2007) Vulnerability and adaptive capacity of Europeanfarmers. A multi-level analysis of yield and income responses to climate variability. Global EnvironChange (in review)Reilly J (2002) Agriculture: the potential consequences of climate variability and change for the UnitedStates. Cambridge University Press, Cambridge, UKRounsevell MDA, Ewert F, Reginster I, Leemans R, Carter TR (2005) Future scenarios of Europeanagriculturallanduse II.Projectingchangesincroplandandgrassland.AgricEcosystEnviron107:117–135Saarikko RA (2000) Applying a site based crop model to estimate regional yields under current and changedclimates. Ecol Model 131:191–206Schröter D, Acosta-Michlik L, Reidsma P, Metzger M, Klein RJT (2003) Modelling the vulnerability of eco-social systems to global change: Human adaptive capacity to changes in ecosystem service provision.Climatic Change (2007) 84:403–422421
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Paper presented at the fifth open meeting on the human dimensions of global change, Montreal, 16–18October 2003Schröter D, Cramer W, Leemans R, Prentice IC, Araújo MB, Arnell NW, Bondeau A, Bugmann H, CarterTR, Gracia CA, de la Vega-Leinert AC, Erhard M, Ewert F, Glendining M, House JI, Kankaanpää S, KleinRJT, Lavorel S, Lindner M, Metzger MJ, Meyer J, Mitchell TD, Reginster I, Rounsevell M, Sabaté S,Sitch S, Smith B, Smith J, Smith P, Sykes MT, Thonicke K, Thuiller W, Tuck G, Zaehle S, Zierl B (2005)Ecosystem service supply and vulnerability to global change in Europe. Science 310:1333–1337Sheikh AD, Rehman T, Yates CM (2003) Logit models for identifying the factors that influence the uptake ofnew 'no-tillage' technologies by farmers in the rice–wheat and the cotton–wheat farming systems ofPakistan's Punjab. Agric Syst 75:79–95Smit B, Pilifosova OV, Burton I, Challenger B, Huq S, Klein RJT, Yohe G, Adger N, Downing T, Harvey E,Kane S, Parry ML, Skinner M, Smith J, Wandel J (2001) Adaptation to climate change in the context ofsustainable development and equity. In: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS(eds) Climate change 2001. Impacts, adaptation, and vulnerability. Cambridge University Press,Cambridge, UK, pp 876–912Snijders TAB, Bosker RJ (1999) Multilevel analysis: an introduction to basic and advanced multilevelmodeling. Sage, New YorkSterk B, van Ittersum MK, Leeuwis C, Rossing WAH, van Keulen H, van de Ven GWJ (2006) Findingniches for whole-farm design models – contradictio in terminis? Agric Syst 87:211–228Trnka M, Dubrovsky M, Zalud Z (2004) Climate change impacts and adaptation strategies in spring barleyproduction in the Czech Republic. Clim Change 64:227–255Tubiello FN, Ewert F (2002) Simulating the effects of elevated CO2on crops: approaches and applicationsfor climate change. Eur J Agron 18:57–74Tubiello FN, Donatelli M, Rosenzweig C, Stockle CO (2000) Effects of climate change and elevated CO2oncropping systems: model predictions at two Italian locations. Eur J Agron 13:179–189van Ittersum MK, Leffelaar PA, van Keulen H, Kropff MJ, Bastiaans L, Goudriaan J (2003) On approachesand applications of the Wageningen crop models. Eur J Agron 18:201–234van Meijl H, van Rheenen T, Tabeau A, Eickhout B (2006) The impact of different policy environments onagricultural land use in Europe. Agric Ecosyst Environ 114:21–38Wolf J, van Diepen CA (1995) Effects of climate change on grain maize potential in the Europeancommunity. Clim Change 29:299–331422Climatic Change (2007) 84:403–422

subset: commoncrawl


text:
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Puppies of any breed may be affected, but tracheal hypoplasia is most common in brachycephalic breeds such as English Bulldogs.
What are the symptoms?
Affected puppies will generally be showing symptoms by two months of age. Breathing will be difficult; they will cough and wheeze, and have a low activity level.
What are the risks?
Puppies with tracheal hypoplasia will not only have a difficult time breathing, but are also likely to develop chronic respiratory infections. Mildly affected puppies will survive, but will live a life of restricted activity and will likely develop periodic infections of the airways.
What is the management?
Antibiotics are generally administered to treat bacterial infections, if present. Medications to dilate the airways (bronchodilators) may be beneficial in some dogs. Affected dogs should not be used in a breeding program.
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The minimalist vibe in this airy, wood-slabbed space—which was named in a nod to Chilean-born Cornejo's distaste for overembellishment—lets the designer's high-end, otherworldly pieces do all the talking. Asymmetrical dresses, layered blouses, and well-draped jumpsuits in dusty shades of ecru and cobalt offer a geometrical vision of the future that the fashion-forward can't resist. The steep prices of Cornejo's creations seem worthwhile with one look at her mix of avant-garde silhouettes.
Zero + Maria Cornejo (CLOSED)
|Venue name:
|Zero + Maria Cornejo (CLOSED)
807 Greenwich St
New York
|Cross street:
|at Jane St
|Opening hours:
|Mon–Sun noon–7pm
|Subway: A, C, E to 14th St; L to Eighth Ave
|Do you own this business?


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Thread: Symbolic Of Our Season?
-01-15-2008 #1moloch41 Guest
Symbolic Of Our Season?
I found this pic on MSNBC about a year back- it was taken after some trouble in the Palestinian territories. I swear- I did not Photoshop this pic in any way- it came right off MSNBC's webpage as you see it. But as I look at it now- wouldn't it be a good photographic analogy of our season? I know I had that look after walking out of the Sports Bar after games a bunch of times this season!!!
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In recognition of National Child Abuse Prevention Day tomorrow, April 18, the Exchange Club of San Ramon Valley will be having a two-hour event in the Danville area. From 10 a.m.-noon, club members will pass out awareness information at Lunardi's in Danville, at the farmers market and at the Safeway in Blackhawk.
Personnel from the San Ramon Fire Protection District and the Danville Police Department will be there; children can be fingerprinted at the event.
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Ani Nenkova
Associate Professor
Computer and Information Science (CIS)
Honors and Awards: NSF CAREER Award - 2010
Research Expertise: Computational linguistics | Artificial Intelligence
Ani works in the area of natural language processing, focusing on automatic summarization, text generation, discourse and prosody. She is addressing the increasing need for intelligent summarization systems brought on by the proliferation of textual information in electronic form. Ani's research has addressed challenges in summarization, including identifying important content to include in the summary, automatic revision of human written text to better fit in the new context of the summary, and automatic identification of inputs that current systems handle poorly.Member of:
- Temporal Bayesian Fusion for Affect Sensing: Combining Video, Audio, and Lexical Modalities, Savran, A. | Cao, H. | Nenkova, A. | Verma, R., IEEE Transactions on Cybernetics, 2015
- Lexical use in emotional autobiographical narratives of persons with schizophrenia and healthy controls, Hong, K. | Nenkova, A. | March, M.E. | Parker, A.P. | Verma, R. | Kohler, C.G., Psychiatry Research, 2015
- Acoustic and lexical representations for affect prediction in spontaneous conversations, Cao, H. | Savran, A. | Verma, R. | Nenkova, A., Computer Speech and Language, 2015
- Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech, Cao, H. | Verma, R. | Nenkova, A., Computer Speech and Language, 2015
- CREMA-D: Crowd-sourced emotional multimodal actors dataset, Cao, H. | Cooper, D.G. | Keutmann, M.K. | Gur, R.C. | Nenkova, A. | Verma, R., IEEE Transactions on Affective Computing, 2014


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IBM Watson found a job as a risk expert! IBM Watson goes to work in financial services as a risk expert. One of the largest Financial Services institutes and IBM now partner to enhance and simplify the consumer banking experience with faster,...
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Last year IBM acquired OpenPages as a strategic move into the area of Governance, Risk and Compliance. The lasest announcement to acquire Algorithmics (quantitative risk management) shows the continuous commitment of IBM in the GRC market. GRC software will...
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Oh, The Places You'll Go
Reptile breeders have become ad-hoc geneticists with the flurry of boa and python morphs appearing over the last decade....May 8, 20090
Tuataras Endangered? Give Capitalist Breeders a Shot!
A suggestion that professional, for-profit, reptile breeders be given a chance to help in captive breeding efforts of endangered...March 20, 20096


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Acronyms that contain the term eads
What does eads mean? This page is about the various possible meanings of the acronym, abbreviation, shorthand or slang term: eads.
We've found a total of 4 shorthands for eads:
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Eads Municipal Airport, Eads, Colorado USA
Eads, CO
Eads, TN
EADS Technology Licensing
Still can't find the acronym definition you were looking for? Use our Power Search technology to look for more unique definitions from across the web!
"eads." Abbreviations.com. STANDS4 LLC, 2016. Web. 11 Feb. 2016. <http://www.abbreviations.com/eads>.


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One fine body…
Choose to have your panel with antique brass grommets or a 3 rod packet with back tabs, for a full pleated appearance. The 1" side hems and 5" bottom hems create a neatly tailored look. Choose from a variety of drapery rod styles such as Custom 1 inch Wrought Iron or Adjustable 1 inch Metal.
Sample swatches are for color representation only, they will not show the double lining.
Got Kids or Pets? Click Here for important information to maximize safety.