The Global Yield Gap Atlas Climate Zones (GYGA-CZs) and Technology Extrapolation Domains (GYGA-TEDs)

The goal of the Global Yield Gap Atlas (GYGA) project is to estimate the yield gap for major food crops in all crop-producing countries based on locally observed data. Unlike past efforts to estimate Yg that rely on gridded weather data as described above, GYGA seeks to use a "bottom-up" approach with location-specific observed weather data. To aggregate results from location-specific observed data to larger spatial areas, the GYGA approach utilizes a hierarchal climate zonation scheme based on a matrix of climate zones (see Wart et al., 2013; van Bussel et al., 2015) as described below.

We expanded the CZ spatial framework to facilitate technology transfer in crop production systems by delineating technology extrapolation domains (GYGA-TEDs) that are defined by a unique combination of CZ and soil water storage capacity to support crop growth. Within a GYGA-TED it is expected that crop and soil management technology options would perform similarly because biophysical conditions governing crop and cropping system response are sufficiently homogeneous.  Hence, the extrapolation domain for field research evaluating crop and soil management options, or comparison of different cropping systems, conducted at a given location can be can spatially delineated by the GYGA-TED in which the study was conducted.

The power of this approach contributes to the effectiveness of agricultural R & D in three ways:

  1. to identify field research locations with greatest potential for impact in terms of crop production area with similar climate and soils;
  2. to identify regions with greatest potential impact for scaling up adoption of new technologies;
  3. to improve both ex-ante and ex-post quantitative assessment of impact from potential or actual adoption of new crop and soil management technologies or alternative crops and cropping systems.

 

Data sources and delineation of GYGA-CZs

The GYGA-CZ scheme is constructed from three categorical variables:

  1. growing degree days (GDD)
  2. temperature seasonality
  3. an annual aridity index (AI)

Grid cell size for the underpinning weather data was 5' grid (roughly 100 km2 at the equator).

The GDD were calculated as in Licker et al. (2010) with:

 

in which  is the temperature (°C) for each time step and is  the base temperature (0 °C for our calculations).  Licker et al. (2010) used mean monthly temperatures for the period 1961-1990 from the CRU CL v. 2.0 dataset at 10' grid (http://www.cru.uea.ac.uk/cru/data/hrg/tmc/, (New et al., 2002)) and downscaled it to a 5' grid.

Temperature seasonality was taken from WorldClim (http://www.worldclim.org/current , data for current conditions (~1950-2000), Bioclim4 at 5' grid, (Hijmans et al., 2005)), calculated as the standard deviation of the 12 mean monthly temperatures × 100 (note that mean monthly temperatures are in °C × 10).

The annual aridity index values were taken from CGIAR-CSI (http://www.cgiar-csi.org/data/global-aridity-and-pet-database, at 30'' grid, (Trabucco et al., 2008; Zomer et al., 2008)), calculated as:


in which MAP is the mean annual precipitation (mm × 100) and MAE the mean annual potential evapotranspiration (mm × 100). We aggregated these AI values to a 5' grid. in which MAP is the mean annual precipitation (mm) and MAE the mean annual potential evapotranspiration (mm). We aggregated these AI values to a 5' grid, taking the spatial average of the 100 cells at 30 arcsecond resolution within each 5 arcminute gridcell. Next, we multiplied the spatially averaged AI with 10000.

Following Mueller et al. (2012), only terrestrial surface covered by at least one of the major food crops (maize, rice, wheat, sorghum, millet, barley, soybean, cassava, potato, yam, sweet potato, banana and plantain, groundnut, common bean and other pulses, sugar beets, sugarcane) was considered in this zonation scheme. To avoid inclusion of areas with negligible crop production, only grid cells with sum of the harvested area of major food crops > 0.5% of the grid cell area were accounted for, based on HarvestChoice SPAM crop distribution maps (You et al., 2006; You et al., 2009), which update geospatial crop distribution data of Monfreda et al. (2008).

The resulting range in values for GDD and aridity index were divided into 10 intervals, each with 10% of grid cells with harvested area of the major food crops, and combined in a grid matrix with 3 ranges of temperature seasonality to give a total of 300 classes. Of these, only 265 occur in regions where major food crops are grown.

This classification of the variables resulted in the following ranges:

GDD (°Cd)

GYGA-CZ Value

0 - 2670

1000

2671 - 3169

2000

3170 - 3791

3000

3792 - 4829

4000

4830 - 5949

5000

5950 - 7111

6000

7112 - 8564

7000

8565 - 9311

8000

9312 - 9850

9000

> 9851

10000

 

AI (-)

GYGA-CZ Value

0 - 2695

0

2696 - 3893

100

3894 - 4791

200

4792 - 5689

300

5690 - 6588

400

6589 - 7785

500

7786 - 8685

600

8686 - 10181

700

10182 - 12876

800

> 12877

900

 

Temperature seasonality

GYGA-CZ Value

0 - 3832

1

3833 - 8355

2

> 8356

3

 

Data sources and delineation of GYGA-TEDs

Each GYGA-TED is a unique combination of a GYGA-CZ and water storage capacity in the rootable soil depth, the latter defining the root zone plant-available water holding capacity (RZPAWHC). GYGA-TEDs were created for Africa and US.

Africa GYGA-TED

Plant-available soil water holding capacity in the root zone was taken from the Africa Soil Information Service (AfSIS; http://www.isric.org/data/afsis-gyga-functional-soil-information-sub-saharan-africa-rz-pawhc-ssa-version-01-0 version April 2015, af_agg_ERZD_TAWCpF23mm__M_1km.tif, with a resolution of 1 × 1 km). RZPAWHC is determined by evaluation and spatial interpretation of the AfSIS soil profile database:

  1. the so-called "legacy" soil point data (Africa Soil Profiles database v1.2; www.isric.org/data/africa-soil-profiles-database-version-01-2; 18,500 points) and
  2. all AfSIS sentinel site soil point data (approx. 9,600 points, including spectral data and 10% wet chemistry reference data), which were provided by AfSIS for this collaboration,
  3. SoilGrids1km layers (www.soilgrids.isric.org) produced at ISRIC using global models; updated and fine-tuned fitting a continental model, with finer resolution satellite data and above mentioned soil data, resulting in AfrSoilGrids250m (http://www.isric.org/data/AfSoilGrids250m).

As a component of the GYGA-TED spatial framework, RZPAWHC values are classified into nine 25 mm classes, with =< 50 mm and >250 mm as lower and upper classes, respectively. 

This classification of the variables resulted in the following ranges:

RZWHC (mm)

RZWHC Value

0 - 50

100000

50 - 75

200000

75 - 100

300000

100 - 125

400000

125 - 150

500000

150 - 175

600000

175 - 200

700000

200 - 225

800000

225 - 250

900000

> 250

1000000

 

US GYGA-TED

Plant-available soil water holding capacity in the root zone was taken from gSSURGO database (Soil Survey Staff; Resolution of 250 × 250 m). There are two classifications, both based on the RZPAWHC values. For the fine TEDs the RZPAWHC values are classified into thirteen 25 mm classes, with 0-25 mm and >300 mm as lower and upper classes, respectively. For the coarse TEDs the RZPAWHC values are classified into seven 50 mm classes, with 0-50 mm as the lower class and >300 mm as the upper class. 

This classification of the variables resulted in the following ranges:

Fine TEDs:

RZWHC (mm)

RZWHC Value

0 – 25

100000

25 – 50

200000

50 – 75

300000

75 – 100

400000

100 - 125

500000

125 – 150

600000

150 – 175

700000

175 – 200

800000

200 – 225

900000

225 – 250

1000000

250 – 275

1100000

275 – 300

1200000

> 300

1300000

Coarse TEDs:

RZWHC (mm)

RZWHC Value

0 – 50

100000

50 - 100

200000

100 - 150

300000

150 - 200

400000

200 - 250

500000

250 – 300

600000

> 300

700000

 

Some small areas within climate zones were considered irrelevant for technology transfer and removed from the original US GYGA-CZs scheme. Inclusions were removed when (i) they covered an area < 350,000 ha, (ii) the surrounding climate zone was, at least, 5 times larger, and (iii) standard deviation for terrain elevation was <10% (USDA-FSA-APFO, 2016). This rule aims to discard small inclusions attributable to an artefact of climate zones computations while keeping small CZ that portray microclimates caused by changes in temperature and precipitation due to complex topography. These refined climate zones were combined with the PAWR map to create the TED maps.

 

Values of the GYGA-TEDs

Value for each cell indicates the unique combination of soil type and climate for that cell. The value of the GYGA-CZs is constructed by the sum of the three GYGA-CZ variables and for the GYGA-TEDs and the value of the RHWHC variable is added to the GYGA-CZ value. A few examples:

  • value = 101001 =

GYGA-RZWHC value

100000

 

GYGA-CZ Value GDD

1000 +

 

GYGA-CZ Value AI

0 +

 

GYGA-CZ Value Temperature seasonality

        1

  • value = 806801 =

GYGA-RZWHC value

800000

 

GYGA-CZ Value GDD

6000 +

 

GYGA-CZ Value AI

800 +

 

GYGA-CZ Value Temperature seasonality

        1

  • value = 1010402 =

GYGA-RZWHC value

1000000

 

GYGA-CZ Value GDD

10000 +

 

GYGA-CZ Value AI

400 +

 

GYGA-CZ Value Temperature seasonality

        2

 

References

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978.

Licker, R., Johnston, M., Foley, J.A., Barford, C., Kucharik, C.J., Monfreda, C. & Ramankutty, N. (2010) Mind the gap: how do climate and agricultural management explain the ‘yield gap' of croplands around the world? Global Ecology and Biogeography, 19, 769-782.

Monfreda, C., Ramankutty, N. & Foley, J.A. (2008) Farming the planet: 2. geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles, 22

Mueller, N.D., Gerber, J.S., Johnston, M., Ray, D.K., Ramankutty, N. & Foley, J.A. (2012) Closing yield gaps through nutrient and water management.  490, 254-257.

Soil Survey Staff, National Value Added Look Up (valu) Table Database for the Gridded Soil Survey Geographic (gSSURGO) Database for the United States of America and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS., (2016)

New, M., Lister, D., Hulme, M. & Makin, I. (2002) A high-resolution data set of surface climate over global land areas. Climate Research, 21, 1-25.

Trabucco, A., Zomer, R.J., Bossio, D.A., van Straaten, O. & Verchot, L.V. (2008) Climate change mitigation through afforestation/reforestation: A global analysis of hydrologic impacts with four case studies. Agriculture, Ecosystems and Environment, 126, 81-97.

van Bussel, L.G.J., Grassini, P., Van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., Saito, K., Cassman, K.G. & van Ittersum, M.K. (2015) From field to atlas: Upscaling of location-specific yield gap estimates. Field Crops Research, 177, 98-108.

Wart, J.v., Bussel, L.G.J.v., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H.L., Gerber, J., Mueller, N.D., Claessens, L.F.G., Ittersum, M.K.v. & Cassman, K.G. (2013) Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research, 143, 44-55.

You, L., Wood, S. & Wood-Sichra, U. (2006) Generating global crop maps: from census to grid. In: IAAE (International Association of Agricultural Economists), Annual Conference, Gold Coast, Australia.

You, L., Crespo, S., Guo, Z., Koo, J., Sebastian, K., Tenorio, M.T., Wood, S. & Wood-Sichra, U. (2009) Spatial Production Allocation Model (SPAM) 2000 Version 3 Release 6, http://mapspam.info/. In:

Zomer, R.J., Trabucco, A., Bossio, D.A. & Verchot, L.V. (2008) Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture, Ecosystems and Environment, 126, 67-80.

 

TEDs in central and eastern USA TEDs in central and eastern USA

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Download the technology extrapolation domains for the USA

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Download files for the GYGA climate zonation can be found on the the methods page on site selection and upscaling

 

Climate zonation used in the GYGA project