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Field Crops Research 143 (2013) 44–55

Contents lists available at SciVerse ScienceDirect

Field Crops Research

jou rna l h om epage: www.elsev ier .com/ locate / fc r

se of agro-climatic zones to upscale simulated crop yield potential

ustin van Warta,∗, Lenny G.J. van Busselb, Joost Wolfb, Rachel Lickerc, Patricio Grassinia,ndrew Nelsond, Hendrik Boogaarde, James Gerber f, Nathaniel D. Mueller f, Lieven Claessensg,artin K. van Ittersumb, Kenneth G. Cassmana

Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0915, USAPlant Production Systems Group, Wageningen University, P.O. Box 430, 6700 AK, Wageningen, The NetherlandsWoodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08544, USAInternational Rice Research Institute (IRRI), Los Banos 4031, PhilippinesAlterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA, Wageningen, The NetherlandsInstitute on the Environment (IonE), University of Minnesota, 325 Learning and Environmental Sciences, 1954 Buford Avenue, Saint Paul, MN 55108, USAInternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT), P.O. Box 39063, 00623 Nairobi, Kenya

r t i c l e i n f o

rticle history:eceived 23 October 2012eceived in revised form7 November 2012ccepted 30 November 2012

eywords:groecological zonelimate zoneield potentialater-limited yield

ield gapxtrapolation domainlobal food security

a b s t r a c t

Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential(Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped foodproduction capacity. Reliable location-specific estimates of yield gaps, either derived from research plotsor simulation models, are available only for a limited number of locations and crops due to cost and timerequired for field studies or for obtaining data on long-term weather, crop rotations and managementpractices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemesfor suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimatingyield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluatedfor climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficientCZ scheme should strike an effective balance between zone size and number of zones required to cover alarge portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemeswith less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain(GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index,temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable

balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones.While CZ schemes derived from two climate-related categorical variables require a similar number ofzones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-EDfor most weather variables that are sensitive drivers of crop production. Some CZ schemes are crop-specific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with croprotations rather than single crop species.

. Introduction

Growing demand for food in coming decades will require sub-tantial increase in crop production (Godfray et al., 2010). Givenisadvantages and limitations of massive expansion of existingropland, such as loss of biodiversity and increasing GHG emis-ions, it is of critical importance to know where and how best to

ncrease crop yield on existing cropland area (Foley et al., 2005;ilman et al., 2002). Yield gap (Yg) analysis, an evaluation of theifference between crop yield potential and actual farmers’ yields

∗ Corresponding author.E-mail address: justin.vanwart@gmail.com (J. van Wart).

378-4290/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.fcr.2012.11.023

© 2012 Elsevier B.V. All rights reserved.

(Lobell et al., 2009), provides a quantitative estimate of possibleincrease in food production capacity for a given location, which isa critical component of strategic food security planning at regional,national and global scales. For irrigated cropping systems, yieldpotential (Yp) is defined as the yield of crop cultivar when grownwithout limitations from water, nutrients, pests and diseases; inrainfed cropping system, water-limited yield potential (Yw) is alsodetermined by water supply amount and distribution during thecropping season (van Ittersum et al., 2013). At a given location, Ygis the difference between Yp or Yw and actual yield.

Both Yp and Yw are site-specific because they are determinedby weather, management, length of growing season, and soil prop-erties that affect root-zone water storage capacity (the latter forYw only). Both can be estimated from research plots, in which

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he crop is grown without limitations, or by simulation using cropodels (Lobell et al., 2009). In a recent comparison of these two

ptions across a range of cropping systems and environments, vanttersum et al. (2013) concluded that use of crop simulation with aong-term weather database provides a more robust estimate of Ypnd Yw than research plots because simulation better accounts forhe impact of variation in temperature, solar radiation, and rainfallver time. But use of crop models requires reliable location-specificata on sowing date, cultivar maturity, plant population, soils andeather and such data are not generally available for most locations

Ramirez-Villegas and Challinor, 2012). Obtaining these data at aarge number of locations is time-consuming, costly, and often sim-ly not feasible. Therefore, an upscaling method is needed to extendoverage of estimates of Yp and Yw based on location-specific infor-ation to an appropriate extrapolation domain using a protocol

hat minimizes the number of location-specific simulations. Ideally,xtrapolation domains would be small enough to minimize varia-ion in climate and crop management practices within the domain,nd large enough to minimize data collection requirements to esti-ate Yg at regional and national scales. Likewise, relevance of a

onation scheme for simulation of Yp and Yw is determined by theuality, resolution, extent and choice of variables used to delineateoundaries.

Previous studies have distinguished geographical space by cli-ate and soil classification schemes as a basis for extrapolating

nd applying agricultural information and research to broaderpatial scales (Wood and Pardey, 1998; Padbury et al., 2002). Aegion can be divided into agro-climatic zones (CZs) based onomogeneity in weather variables that have greatest influencen crop growth and yield, while agro-ecological zones (AEZs) areefined as geographic regions having similar climate and soils forgriculture (FAO, 1978). Such zonation schemes have been usedo identify yield variability and limiting factors for crop growthCaldiz et al., 2002; Williams et al., 2008), to regionalize opti-

al crop management recommendations (Seppelt, 2000), compareield trends (Gallup and Sachs, 2000), to determine suitable loca-ions for new crop production technologies (Geerts et al., 2006;raya et al., 2010), and to analyze impacts of climate change ongriculture (Fischer et al., 2005). Table 1 includes a description ofreviously published zonation schemes used to evaluate extrapola-ion domains for agricultural technologies and in yield gap analysis.ur review focuses on CZ schemes and the climatic componentsf AEZ schemes with the goal of identifying an appropriate CZcheme for upscaling location-specific estimates of Yp or Yw toegional and national levels. To our knowledge, no such review haseen previously published with this goal in mind. Specific objec-ives of this review are to: (1) evaluate zonation schemes based onhe degree of variability in weather variables within zones, and (2)valuate the usefulness and limitations of these zonation schemesor upscaling location-specific estimates of Yp and Yw to nationalevels.

. Agro-climatic and agro-ecological zonation schemes

Zonation schemes essentially fall into two categories: matrixnd cluster. In this section differences between matrix and clus-er methodologies are explained, and six global matrix and clusteronation schemes useful for extrapolation of estimates of Yp or Ywre described.

.1. Matrix methodologies

Perhaps the best known and earliest example of a matrix zona-ion scheme is described by Köppen (1900). Köppen developed alimate classification system based on multiple variables related

search 143 (2013) 44–55 45

to temperature and precipitation, and used his system to identifythe type of vegetation, including some crops, that could grow ineach zone. In a matrix zonation, each variable used to delineatezones is divided into classes or class-ranges. Class cutoff values foreach variable can be based on expert opinion or frequency distri-butions of the variable’s range of values. Zones are formed by thematrix “cells” of intersecting classes. For example, a matrix zonecell might be a geographic area in which mean annual temperatureis between 20 and 25 ◦C and mean annual precipitation is between300 and 400 mm.

Matrix zonation schemes are advantageous in that the range ofinput parameters for all zones is known and specifically definedby the researchers. The size of the zones in a matrix zonationresults from the number of input variables used and the degreeof specificity in classes for each variable, i.e. more class variablesand more sub-divisions within each variable result in a larger num-ber of zones with smaller area. Thus, matrix methodology allowsfor high degree of control over the number the resulting zonesas determined by intended use of the zonation scheme. Robustmatrix schemes for uscaling Yp and Yw would use the most sensi-tive weather variables for simulation of crop yields under irrigatedand rainfed conditions.

2.2. Cluster methodologies

Cluster methodologies [also referred to as statistical stratifica-tion (Hazeu et al., 2011)] relies on multivariate statistical analysesto separate cells into a researcher-specified number of distinctzones. Clustering essentially involves assigning grid-cell valuesderived from mathematical or statistical modeling of categori-cal variables. Grouping or “clustering” grid-cells based on thesederived values is accomplished using a variety of techniques suchas assigning a certain value or range of values as a class orcluster, minimizing the sum of the difference between grid-cellswithin clusters, or more sophisticated Iterative Self-OrganizingData Analysis (ISODATA). In the latter, the number of cluster cen-ters is specified, randomly placed, and then clusters are dividedor merged based on standard deviation of grid-cells assigned toeach cluster (Tou and González, 1974). The process continues untilreassignment of grid cells no longer improves cluster standarddeviation. Due to the statistical nature of “clustering,” subjectiv-ity is avoided in selection of class ranges for each variable. Thoughclass ranges may be more objective in clustering compared tomatrix methodology, size of zones is partially dependent on num-ber of zones specified by the researcher, which may introducesubjectivity. Unlike matrix zonation, the number of zones is notdetermined by the number of weather variables that determinethe zonation. Therefore, a relatively large number of variables canbe considered without necessarily reducing the size of the resultingzones.

One of the better known examples of a cluster zonation wascreated through climate-based modeling of natural vegetation ongrid-cells, which were then grouped into regions based on domi-nant plant types (Prentice et al., 1992). Cluster methodologies alsohave been used to determine the applicability of farm managementresearch in different regions (Seppelt, 2000), to study potentialimpacts of climate change on ecosystems and the environment(Metzger et al., 2008), and to identify potential new productionareas for bio-energy crops (EEA, 2007).

2.3. Zonation schemes that can be used in estimation of yieldpotential

2.3.1. The Global Agro-Ecological Zone modeling frameworkThe Global Agro-Ecological Zone modeling framework (GAEZ)

was developed to spatially analyze agricultural systems and

46 J. van Wart et al. / Field Crops Research 143 (2013) 44–55

Table 1Previously published global zonation schemes (AEZ).

AEZ scheme Number of zones Type of AEZ Variables considered, methodology Reference

FAOa 14 Matrix Mean growing period temperature and length of growing period,determined by annual precipitation, potential evapotranspiration andthe time required to evapotranspire 100 mm of water from the soilprofile

FAO (1978)

CGIAR-TACb 9 Matrix Mean annual and growing period temperature, and length of growingperiod (determined the same as in the FAO zonation scheme)

Sivakumar and Valentin (1997)

Prentice 17 Cluster Soil texture based water-storage capacity, monthly precipitation,sunshine hours, potential evapotranspiration, growing degree days,minimum temperature, mean temperature. These variables were usedin a model which calculated most likely vegetation type for theenvironment of this gridcell and cells were grouped based onvegetation type.

Prentice et al. (1992)

Pappadakis 74 Matrix Precipitation and temperature are used in calculations of a variety ofseasonal statistics. Ranges of variables for each zone are based on croprequirements.

Papadakis (1966)

Köppen-Geiger 31 Matrix Mean annual temperature, minimum and maximum temperature ofwarmest and coolest months, accumulated annual precipitation,precipitation of driest month, lowest and highest monthlyprecipitation for summer and winter half years, and a drynessthreshold based on seasonality of precipitation

Kottek et al. (2006)

Holdridge 100 Matrix Mean annual temperature, mean annual precipitation, elevation(evaporative demand and frost were also considered in determiningclimate ranges of zones).

Holdridge (1947)

GAEZ-LGPc 16 Matrix Temperature, precipitation, potential evapotranspiration and soilcharacteristics are used to calculate length of growing season.

Fischer et al. (2012)

HCAEZd 21 Matrix Mean temperatures, elevation, and GAEZ-LGP are used to definethermal regimes and temperature seasonality.

Wood et al. (2010)

SAGEe 100 Matrix Growing degree days (GDD;∑

Tmean–crop-specific basetemperature) and soil moisture index (actual evapotranspirationdivided by potential evapotranspiration).

Licker et al. (2010)

GLIf 25 Matrix Harvested area of target crop, crop-specific GDD and soil moistureindex (actual evapotranspiration divided by potentialevapotranspiration).

Mueller et al. (2012)

GEnSg 115 Cluster 4 variables (GDD with base temperature of 0 ◦C, an aridity index,evapotranspiration seasonality, temperature seasonality) used iniso-cluster analysis to “cluster” grid-cells into zones of similarity.

Metzger et al. (in press)

a Food and Agricultural Organization.b Consultative Group on International Agricultural Research – Technical Advisory Committee.c Global Agro-Ecological Zone Length of Growing Period.d HarvestChoice Agro-ecological Zone.e Center for Sustainability and the Global Environment.

e(b2oGgiaaadocwftab1act2n

f Global Land Initiative.g Global Environmental Stratification.

valuate the impacts of agricultural policies at a global scaleFischer, 2009). Delineation of AEZs within GAEZ are determinedy monthly weather data with a resolution of 10′ (roughly0 km × 20 km at the equator, or 400 km2). The weather data werebtained from the Climate Research Unit (New et al., 2002) and thelobal Precipitation Climatology Centre (Rudolf et al., 2005). Cate-orical variables used, or derived, from these data to define an AEZnclude: (a) accumulated temperature sums for mean daily temper-ture above a base temperature [growing degree days (GDD)], (b)nnual temperature profiles, based on mean annual temperaturend within-year temperature trends, (c) delineation of continuous,iscontinuous, sporadic and no permafrost zones, (d) quantificationf soil water balance and actual evapotranspiration for a referencerop, (e) length of growing period (LGP), defined as the sum of dayshen mean daily temperature exceeds 5 ◦C and evapotranspiration

or the reference crop exceeds half of potential evapotranspira-ion, (f) multiple cropping classification, which indicates whethernnual single, double or triple cropping is possible in a given zone,ased on the LGP and assuming a growth duration per crop of20 days (Fischer et al., 2012). This GAEZ framework has beendapted to assess the potential production of all major bio-fuel

rops (Fischer and Schrattenholzer, 2001), to analyze the poten-ial impact of accelerated biofuel production on food security to050, and to evaluate the resulting social, environmental and eco-omic impacts (Fischer et al., 2009). Additional assessments have

used a GAEZ framework to evaluate scenarios of future land useand production of major crops at a global scale (Fischer et al., 2002,2006). Of the various AEZ schemes used in the GAEZ framework, weselected the one based on LGP in which LGP is derived from temper-ature, precipitation, and soil water holding capacity as categoricalvariables. The GAEZ-LGP was selected because it utilizes the mostagronomically relevant categorical variables and has the smallest,and presumably most climatically homogenous zones, within theGAEZ-family of AEZ schemes (Figs. 1a–5a).

2.3.2. Center for Sustainability and Global Environment zonationscheme

The Center for Sustainability and the Global Environment (SAGE)zonation scheme was generated using global, gridded data for twovariables known to be important drivers for crop developmentand crop growth (Licker et al., 2010): growing degree-days (GDD)and a crop soil moisture index, the latter calculated as the ratioof actual to potential evapotranspiration following the approachof Prentice et al. (1992) and Ramankutty et al. (2002). Calcula-tions utilized a 33-y monthly averaged weather database fromthe Climate Research Unit (New et al., 2002) with a 10′ resolu-

tion. Soil texture data used to estimate the soil moisture indexwere taken from the International Soil Reference and Informa-tion Center with a 5′ resolution (Batjes, 2006). By downscalingthe weather data from a 10′ to a 5′ resolution, calculations were

J. van Wart et al. / Field Crops Research 143 (2013) 44–55 47

Fig. 1. Zonation of Africa for (a) Global Agro-Ecological Zone for length of growing season (GAEZ-LGP), (b) Center for Sustainability and the Global Environment (SAGE)zonation scheme (crop-specific, derived using GDD with base temperature of 8 ◦C as used for maize), (c) HarvestChoice Agroecological Zone (HCAEZ, d) Global LandscapesI d for

E

c1vtddtmF

nitiative (crop-specific, derived using GDD with base temperature of 8 ◦C as usextrapolation Domain (GYGA-ED).

arried out on a 5′ grid basis (approximately 10 km × 10 km, or00 km2 at the equator). The global ranges of the two categoricalariables were each divided into ten classes, which were then usedo develop a matrix of 100 unique combinations of growing degree-ay and soil moisture conditions. Separate zonation schemes were

eveloped for each of 18 crop species using crop-specific baseemperatures for calculation of growing degree-days (e.g., 8 ◦C for

aize, 5 ◦C for rice). The zonation scheme for maize is shown inigs. 1b–5b.

maize), (e) Global Environmental Stratification (GEnS), (f) Global Yield Gap Atlas

This zonation scheme was developed to determine within-zonemaximum yield achieved for a specific crop within each of the 100zones. If the zonal-maximum yield was larger than observed yieldsfor a particular region within the zone the authors considered this aYg and identified the region as having an opportunity for increasing

yields (Licker et al., 2010). The SAGE zonation was also employedby Johnston et al. (2011) to examine opportunities to expand globalbiofuel production through agricultural intensification in regionswith similar growing conditions.

48 J. van Wart et al. / Field Crops Research 143 (2013) 44–55

Fig. 2. Zonation of Asia for (a) Global Agro-Ecological Zone for length of growing season (GAEZ-LGP), (b) Center for Sustainability and the Global Environment (SAGE) zonations ◦ r maiz( , (e) GD

2

iHs

ac

cheme (crop-specific, derived using GDD with base temperature of 8 C as used focrop-specific, derived using GDD with base temperature of 8 ◦C as used for maize)omain (GYGA-ED).

.3.3. Modifications of GAEZ and SAGE zonation schemesAspects of both the SAGE and GAEZ have been utilized or mod-

fied to develop improved AEZ schemes for yield gap analysis. ThearvestChoice1 AEZ scheme (HCAEZ), developed for analysis in

ub-Saharan Africa, is an example (Wood et al., 2000, 2010). It is

1 HarvestChoice is a large collaborative effort to provide knowledge productsimed at guiding investments to improve well-fare through more profitable agri-ulture in Sub-Saharan Africa led by scientists from the University of Minnesota and

e), (c) HarvestChoice Agroecological Zone (HCAEZ, d) Global Landscapes Initiativelobal Environmental Stratification (GEnS), (f) Global Yield Gap Atlas Extrapolation

a matrix with 21 zones based on GAEZ-LGP and thermal regimeclasses for the tropics, sub-tropics, temperate, and boreal zonesdistinguished by highland and lowland regions. Essentially, HCAEZ

is a combination, or intersection, of several distinct and indepen-dent zonation schemes used in the GAEZ framework. Although ituses data of more recent orgin, the HCAEZ resembles an earlier

the International Food Policy Research Institute (IFPRI). Several zonation schemeshave been used at HarvestChoice, based on the same underlying methodology.

J. van Wart et al. / Field Crops Research 143 (2013) 44–55 49

Fig. 3. Zonation of Europe for (a) Global Agro-Ecological Zone for length of growing season (GAEZ-LGP), (b) Center for Sustainability and the Global Environment (SAGE)z s useI d for

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ststs

onation scheme (crop-specific, derived using GDD with base temperature of 8 ◦C anitiative (crop-specific, derived using GDD with base temperature of 8 ◦C as usextrapolation Domain (GYGA-ED).

EZ scheme developed by the Technical Advisory Committee (TAC)f the Consultative Group on International Agricultural ResearchCGIAR) (TAC/CGIAR, 1992; Sivakumar and Valentin, 1997).

The SAGE zonation scheme was modified by the Global Land-capes Initiative (GLI) group at the University of Minnesota, keeping

he classification based on crop-specific GDD but replacing the cropoil moisture index by annual total precipitation. Another modifica-ion was that only terrestrial surface covered by harvested area for apecific crop was considered based on geospatial crop distribution

d for maize), (c) HarvestChoice Agroecological Zone (HCAEZ, d) Global Landscapesmaize), (e) Global Environmental Stratification (GEnS), (f) Global Yield Gap Atlas

maps of Monfreda et al. (2008). Climate zones were developed foreach crop by dividing GDD and precipitation each into ten classes,the intersection of which formed a matrix of 100 individual CZs.Instead of using equal ranges for the classes, zones were deter-mined using an algorithm such that 1% of the global harvested area

of that specific crop was in each zone, a methodology known asthe ‘equal-area approach’ (Figs. 1d–5d). This revision of the SAGEzonation scheme formed the basis of the yield gap estimates inFoley et al. (2011) and Mueller et al. (2012).

50 J. van Wart et al. / Field Crops Research 143 (2013) 44–55

Fig. 4. Zonation of North America for (a) Global Agro-Ecological Zone for length of growing season (GAEZ-LGP), (b) Center for Sustainability and the Global Environment( e of 8 ◦

L ◦C as uA

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SAGE) zonation scheme (crop-specific, derived using GDD with base temperaturandscapes Initiative (crop-specific, derived using GDD with base temperature of 8tlas Extrapolation Domain (GYGA-ED).

.3.4. The Global Environmental Stratification methodologyGEnS)

The Global Environmental Stratification (GEnS) by Metzger et al.in press) is the first cluster methodology aiming at establishing

global, climate-explicit zonation system. GEnS was developedithin the Group on Earth Observations Biodiversity Observationetwork (GEOBON, Scholes et al., 2008) and will be available tossist further research on global ecosystems. This cluster zonation

C as used for maize), (c) HarvestChoice Agroecological Zone (HCAEZ, d) Globalsed for maize), (e) Global Environmental Stratification (GEnS), (f) Global Yield Gap

uses monthly gridded climate data from the WorldClim database(Hijmans et al., 2005) and annual aridity and potential evapo-transpiration seasonality derived from the CGIAR Consortium forSpatial Information (CGIAR-CSI, Trabucco et al., 2008; Zomer et al.,

2008), with 30′ ′ resolution (approximately 1 km2 at the equa-tor). GEnS was constructed in three stages. In the first stage, 42categorical variables were screened to remove those that wereauto-correlated. Among the variables with high auto-correlation,

J. van Wart et al. / Field Crops Research 143 (2013) 44–55 51

Fig. 5. Zonation of South America for (a) Global Agro-Ecological Zone for length of growing season (GAEZ-LGP), (b) Center for Sustainability and the Global Environment(SAGE) zonation scheme (crop-specific, derived using GDD with base temperature of 8 ◦C as used for maize), (c) HarvestChoice Agroecological Zone (HCAEZ, d) GlobalLandscapes Initiative (crop-specific, derived using GDD with base temperature of 8 ◦C as used for maize), (e) Global Environmental Stratification (GEnS), (f) Global Yield GapAtlas Extrapolation Domain (GYGA-ED).

rtvpbracsazep2

esearchers selected the most sensitive parameters and eliminatedhe others to prevent over-weighting the zonation by co-linearariables. In the second step, statistical clustering analysis waserformed on remaining variables: annual cumulative GDD usingase temperature = 0 ◦C, temperature and potential evapotranspi-ation seasonalities (month to month variation), and an annualridity index (calculated as the ratio of mean annual total pre-ipitation to mean annual total potential evapotranspiration). Thetatistical clustering was carried out using principle componentnalysis and iterative self-organizing data analyses, resulting in 125

ones (Figs. 1e–5e). A climatic stratification of Europe (Metzgert al., 2005) has been used in modeling efforts to quantify croproduction potential and yield gaps in Europe (Hazeu et al.,009).

2.3.5. The Global Yield Gap Atlas Extrapolation Domain(GYGA-ED)

The goal of the Global Yield Gap Atlas (GYGA) project(www.yieldgap.org) is to estimate the yield gap for major foodcrops in all crop-producing countries based on locally observeddata. Unlike past efforts to estimate Yg that rely on griddedweather data as described above, GYGA seeks to use a “bottom-up” approach with location-specific observed weather data. Toextrapolate results from location-specific observed data, the GYGAapproach utilizes a hybrid zonation scheme, called the GYGA

Extrapolation Domain (GYGA-ED), which combines componentsof other zonation schemes as reviewed in this paper. The chal-lenge of using a bottom-up approach is the time, expense andaccess to acquire observed weather data as well as associated

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ocation-specific information about crop rotations, soil propertiesnd farm management, which are required for robust estimates ofp and Yw (van Ittersum et al., 2013). Therefore, the GYGA approachtrives for a zonation scheme that balances need to minimize theumber of location-specific sites requiring weather, soils, and cropanagement data with the goal of minimizing climatic heterogene-

ty within the CZs.GYGA-ED is constructed from three categorical variables also

sed by the GEnS: (1) GDD with base temperature of 0 ◦C and (2)emperature seasonality (quantified as the standard deviation of

onthly average temperatures), and (3) an aridity index (annualotal precipitation divided by annual total potential evapotranspi-ation). Grid cell size for the underpinning weather data was theame as for GLI based on the SAGE framework (5′ grid, or roughly00 km2 at the equator). Both GDD and temperature seasona-

ity were calculated using climate data from WorldClim (Hijmanst al., 2005); the aridity index values were taken from CGIAR-CSITrabucco et al., 2008; Zomer et al., 2008). Following Mueller et al.2012), only terrestrial surface covered by at least one of the majorood crops (maize, rice, wheat, sorghum, millet, barley, soybean,assava, potato, yam, sweet potato, banana and plantain, ground-ut, common bean and other pulses, sugarbeets, sugarcane) wasonsidered in this zonation scheme. To avoid inclusion of areas withegligible crop production, only grid cells with sum of the harvestedrea of major food crops > 0.5% of the grid cell area were accountedor, based on HarvestChoice SPAM crop distribution maps (Yout al., 2006, 2009), which update geospatial crop distribution dataf Monfreda et al. (2008). The resulting range in values for GDD andridity index were divided into 10 intervals, each with 10% of gridells with harvested area of the major food crops, and combinedn a grid matrix with 3 ranges of temperature seasonality to give aotal of 300 AEZ classes. Of these, only 265 occur in regions where

ajor food crops are grown.

. Comparison of the agro-climatic and agro-ecologicalonation schemes

Zonation schemes vary widely in defining the size and bound-ries of regions with similar climate (Figs. 1–5). For example, eachf the schemes recognizes the significance of the Sahara desert, buthey differ by as much as 2◦ or 3◦ (roughly 250–350 km) in loca-ion of the southern border in some areas. Differences among theonation schemes are considered in the following sections accord-ng to relevance for assessing performance of crops and croppingystems within a zone, and in the degree of homogeneity of thenderpinning weather variables.

.1. Key variables used within the zonation schemes

All global zonation schemes analyzed in the present study aressociated with temperature and water availability but they dif-er in selection of specific weather variables to delineate zonesTable 1). For example, to account for thermal conditions, GDD isalculated within the SAGE and GLI schemes using crop-specific baseemperatures resulting in a different set of CZs for each crop whileEnS and GYGA-ED use a single, non-crop-specific base tempera-

ure (0 ◦C) to calculate GDD, which gives a single set of CZs for allrops. Creating a different zonation scheme for each crop, however,imits opportunities to analyze Yg for crop rotations and much ofhe world’s cropland produces more than one major food crop. Forxample, crop-specific schemes make it difficult to reconcile per-

ormance of crops within a specific cropping system (e.g. double orriple rice or rice-wheat cropping systems in Asia). In addition toDD, GEnS and GYGA-ED include a measure of temperature varia-

ion during the year based on temperature seasonality.

search 143 (2013) 44–55

Different indexes have been used to quantify the degree of waterlimitation. Water supply in the GLI zonation is calculated as totalannual rainfall. However, this approach does not account for thedegree of water limitation to crop growth, which varies depend-ing on the balance between crop water demand, hereafter calledpotential evapotranspiration, and water supply. In contrast, GAEZ-LGP, HCAEZ, and SAGE try to account for both water supply anddemand using actual and potential evapotranspiration. Specifically,the number of days in which actual evapotranspiration is greaterthan 50% of potential evapotranspiration are used by GAEZ-LGPand HCAEZ to determine when crop growth is possible due to lackof water stress. SAGE considers the ratio of actual evapotranspi-ration to potential evapotranspiration as a soil moisture index.Estimation of actual evapotranspiration is derived from data onsoil texture, bulk density, and depth of root zone (which definesplant-available water-holding capacity), temperature, precipita-tion, and leaf area. The soil components of this estimate are derivedfrom spatially explicit global databases and require a number ofassumptions in order to calculate hydraulic conductivity. Finally,GEnS and GYGA-ED consider an aridity index calculated as theratio of annual total precipitation to annual total potential evapo-transpiration. While not as sophisticated as the GAEZ-LGP or SAGEschemes, this aridity index is derived directly from variables in theweather database and does not require soil data and the associateduncertainties of assumptions used to estimate soil water holdingcapacity.

One of the most influential differences among zonation schemesis whether they define zones over total terrestrial area or only thefraction of that area in which crops are grown. For example, GEnS,GAEZ-LGP, HCAEZ and SAGE all consider total terrestrial area inconstructing their zonation schemes. In contrast, GLI considers onlyharvested area of individual major food crop species to give sep-arate zonation schemes for each crop while GYGA-ED considersone scheme based on harvested area of all major food crops. As aresult the area over which zones are defined is therefore signifi-cantly reduced for those AEZ schemes that only consider harvestedcrop area (Figs. 1–5).

3.2. Climatic variability within the zones

Climate homogeneity for a given zonation scheme was evalu-ated by calculating frequency distributions of the range of grid-cellvalues found within each zone for mean annual temperature,cumulative annual water deficit (precipitation less evapotrans-piration), temperature seasonality, and precipitation seasonality(month to month coefficient of variation in precipitation) based onWorldClim data at 5′ resolution (Hijmans et al., 2005). In addition tocalculating ranges of these variables for each zone in a given zona-tion scheme, ranges of mean annual temperature and cumulativeannual water deficit were calculated only for those cells in whichwheat is grown based on spatial crop distribution of Portmannet al. (2010), in order to minimize bias for those zonation schemesthat are not crop-specific. The geospatial distribution of Portmannet al. (2010) was chosen for use in this analysis over the SPAM orMonfreda et al. (2008) data because these two datasets were usedin the derivation of one or more of the zonation schemes examined.However, it should be noted that climate data used for this analysisare the same as those used in the GEnS, GYGA-ED, and HCAEZ.

3.2.1. Temperature variabilityZone size was largest in GAEZ-LGP and HCAEZ (Table 2). Large

zone area with schemes that consider complete terrestrial cover-

age results in a wide range of within-zone temperature as indicatedby the cumulative frequency distribution of mean annual tem-perature (Fig. 6a). For example, 50% of the GAEZ and HCAEZzones have a range of mean annual temperature > 29 ◦C and 24 ◦C,

J. van Wart et al. / Field Crops Research 143 (2013) 44–55 53

Table 2AEZ scheme coverage of global, China and USA rainfed wheat and maize based on data from Portmann et al. (2010). Values in parenthesis indicate (±SD) of the mean.

AEZ scheme Number of zones Average zone area (Mkm2) Rainfed maize area perzone (Mha)

Number of zones to cover 80% ofrainfed maize harvested area

Global China USA

GAEZ-LGPa 16 20.2 (18.2) 7.5 (7.2) 7 6 4HCAEZb 21 15.3 (28.0) 5.8 (8.2) 6 3 2SAGEc 100 2.7 (4.7) 1.2 (2.1) 28 11 5GLId 100 2.9 (2.0) 1.2 (0.7) 66 37 25GEnSe 125 2.6 (2.5) 1.0 (1.7) 30 13 5GYGA-EDf 265 0.3 (0.3) 0.4 (0.7) 49 21 9

a Global Agro-Ecological Zone Length of Growing Period.b HarvestChoice Agro-ecological Zone.c Center for Sustainability and the Global Environment.d Global Land Initiative.

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e Global Environmental Stratification.f Global Yield Gap Atlas Extrapolation Domain.

espectively. In contrast, zonation schemes with smaller zoneize have considerably less within-zone temperature variability.or example, the range of mean annual temperature for 50% ofhe GLI and GEnS zones is >4 ◦C. When only cropped terrestrialrea is evaluated (whether for a specific crop or multiple crops),ithin-zone temperature variability decreases substantially. The

lustering methodology of Metzger et al. (in press) also resultedn zones with small ranges in temperature variability despiteonsidering total terrestrial area within zones. Apparently the largeumber of categorical variables considered in the GEnS cluster-

ng scheme results in relatively homogeneous temperature regimeespite complete terrestrial coverage. When only wheat harvestedrea is considered in all zonation schemes, the frequency distribu-ion narrows substantially (Fig. 6b).

.2.2. Water availabilitySimilar to temperature variability within zones, schemes with

he largest zone area (GAEZ-LGP and HCAEZ) have greatest rangef cumulative water deficit (Fig. 6c). Likewise, crop-area zonationchemes, such as GYGA-ED and GLI have greatest homogeneityithin zones. Considering only harvested wheat area within zona-

ion schemes that have complete terrestrial coverage decreases theithin-zone range of water deficit of the zonal schemes somewhat,

ut the range is still relatively large (Fig. 6d).

.2.3. Temperature and precipitation seasonalityThe GYGA-ED, which considers three ranges of temperature

easonality as categorical variables, and the GEnS scheme, forhich temperature seasonality is an explicit input parameter, have

mallest range in temperature seasonality within zones. While theCAEZ, which also accounts for temperature seasonality, has lesseterogeneity for this variable than zonation schemes that do notxplicitly consider it, its large zone size results in a greater rangehan for GYGA-ED. The GAEZ-LGP has the largest within-zone rangef temperature seasonality because its delineation is based more onater availability and many of its zones have relatively large north

o south extension, capturing a wide range of temperature regimes.ange of precipitation seasonality was also smallest in the GYGA-D scheme even though this parameter is not explicitly consideredn its derivation.

.3. Balancing number of zones and within-zone climaticeterogeneity

An appropriate zonation scheme for extrapolating point-basedstimates of yield potential while limiting requirements for dataollection is one which optimizes the trade-off between achievinglimatic homogeneity within zones and minimizing the number of

zones necessary to capture large portions of harvested area of targetcrop. While zonation schemes with few zones and large zone area,such as GAEZ-LGP and HCAEZ, require <10 zones to cover 80% ofglobal rainfed maize harvested area (Table 2), they have large vari-ability in weather variables that influence crop growth and yield(Fig. 6). Among schemes with at least 100 zones and smaller zonesize, those schemes that use the clustering methodology (GEnS)or a three-parameter matrix (GYGA-ED) appear to have the bestbalance between number of zones for 80% coverage of harvestedarea (Table 2) and homogeneity in weather variables within zones(Fig. 6). While the crop-specific GLI zonation scheme has relativelyhomogeneous weather within its zones, it requires the largest num-ber of zones to achieve 80% coverage of rainfed maize area, and itrequires a separate zonation scheme for each crop species. In con-trast, the SAGE scheme requires the smallest number of zones for80% coverage of rainfed maize area but has high degree of variabil-ity in weather variables within its zones despite use of crop-specificbase temperatures used to derive GDD.

4. Discussion

The GAEZ-LGP and HCAEZ schemes are simply too coarse foruse in estimating and extrapolating yield gap analyses becauseclimate heterogeneity within zones is too large. Both SAGE andGLI schemes are crop-specific and use a two-parameter zonationmatrix. Of the two, the GLI approach gives much greater homo-geneity of weather variables within zones, but it requires thelargest number of zones to cover crop area. Both schemes requireseparate zonation schemes for each crop which would make itcumbersome to estimate Yp, Yw, and yield gaps in regions wheremore than one crop was grown in rotation. Both GYGA-ED andGEnS approaches are not crop specific and achieve relatively lowwithin-zone heterogeneity in key weather variables. Whereas GEnSrequires fewer zones to achieve 80% coverage of rainfed maize areaand has slightly less heterogeneity in mean temperature, GYGA-ED has substantially less within-zone heterogeneity in cumulativewater deficit and in seasonality of temperature and precipitation.Both methods appear to be well-suited for up-scaling yield gapanalysis.

Several conclusions follow from this evaluation. Climate zonesused as extrapolation domains for yield gap analysis of current pro-duction should focus on areas where crops are grown to minimizewithin-zone weather variability. While the cluster methodologyalso appears efficient at limiting the number of zones required to

cover crop area and minimizing within-zone heterogeneity, theyare less intuitive than matrix zonation schemes because of thesophisticated mathematics and large number of weather variablesconsidered. However, for matrix-based zonation schemes it has not

54 J. van Wart et al. / Field Crops Research 143 (2013) 44–55

Fig. 6. Frequency distribution of within-zone range of mean annual temperature, annual water deficit (precipitation less evapotranspiration), temperature seasonality andmonth to month coefficient of variation in precipitation based on WorldClim data at 5′ resolution (Hijmans et al., 2005) for 6 climate zonation schemes. All terrestrial areacovered by the zones are considered (panels a, c, e, f); mean annual temperatures and annual water deficit was also calculated considering only where zones overlap wheatharvested area (b and d). The latter evaluation eliminates bias of generic zonation schemes that evaluate all terrestrial area (GAEZ-LGP, GEnS, SAGE, HCAEZ) and all majorc

bb(aadp

ua

rops (GYGA-ED).

een tested how to best determine the range-boundaries, whethery equal distributions (Licker et al., 2010), frequency distributionsGYGA-ED), or another set of criteria such as quantity of harvestedrea within zones (GLI). Beneficial future work would be validationnd comparison of zonation schemes using weather data fromifferent weather stations within a zone or by performing and com-

aring yield gap analysis for several sites within a zone.

All zonation schemes are limited by choice and quality of thenderpinning data used to derive them. This includes availabilitynd distribution of high-quality, location specific weather station

data. Using any zonation scheme to estimate Yp, Yw and yield gapsat larger scales also requires data on soils and management varia-tion within zones (van Ittersum et al., 2013), and quality of thosedata will also affect the accuracy and uncertainty in such large scaleestimates (van Wart et al., 2013).

Acknowledgments

The authors would like to thank the Bill and Malinda Gates Foun-dation whose support of the Global Yield Gap Atlas project made

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his research possible. Secondly, we thank Dr. Marc J. Metzger fromhe University of Edinburgh for providing us with the data on GEnS.

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