Climate Zones
Approach
The goal of the Potential Yield Atlas (Atlas) 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, Atlas 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 Atlas approach utilizes a hierarchical climate zonation scheme based on a matrix of climate zones (see Van 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 (Atlas-TEDs) that are defined by a unique combination of CZ and soil water storage capacity to support crop growth. Within a Atlas-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 Atlas-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 Atlas-CZs
The Atlas-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 values were calculated as follows (see Licker et al.2010):
Climate Zone Formula 1
in which Ti is the temperature (°C) for each time step and is Tb 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:
Climate Zone Formula 2
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, 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)
Atlas-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 (-)
Atlas-CZ Value
0 - 2695
000
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
Atlas-CZ Value
0 - 3832
01
3833 - 8355
02
> 8356
03
Values of the Atlas-CZs
Value for each cell indicates the unique combination climate for that cell. The value of the Atlas-CZs is constructed by the sum of the three Atlas-CZ variables. A few examples:

Atlas-CZ value 6801 (6-8-01)

Atlas-CZ Value GDD

6000 +

Atlas-CZ Value AI

800 +

Atlas-CZ Value Temperature seasonality

01
Atlas-CZ value 10402 (10-4-02)

Atlas-CZ Value GDD

10000 +

Atlas-CZ Value AI

400 +

Atlas-CZ Value Temperature seasonality

02
References
  1. Andrade JF, Rattalino Edreira JI, Farrow A, van Loon MP, Craufurd PQ, Rurinda J, Zingore S, Chamberlin J, Claessens L, Adewopo J, van Ittersum MK, Cassman KG, Grassini P (2019) A spatial framework for ex-ante impact assessment of agricultural technologies. Global Food Security 20, 72-81
  2. Rattalino Edreira JI, Cassman KG, Hochman Z, van Ittersum MK, van Bussel L, Claessens L, Grassini P (2018) Beyond the plot: Technology extrapolation domains for scaling out agronomic science. Environmental Research Letters 13, 054027
  3. 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.
  4. 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.
  5. 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
  6. 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.
  7. 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)
  8. 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.
  9. 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.
  10. 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.
  11. Van Wart, J., Van Bussel, L.G.J., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H.L., Gerber, J., Mueller, N.D., Claessens, L.F.G., Van Ittersum, M.K. & Cassman, K.G. (2013) Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research, 143, 44-55.
  12. 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.
  13. 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/.
  14. 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.
Climate zones in Sub-Saharan Africa
Climate Zone