Supplemental Material for Aggarwal et al. “How much does climate change add to the challenge

of feeding the planet this century”

1. Comparison with previous analysis and historical data Largest and only global meta-analysis of climate change impacts on agriculture primarily dealt

with quantification of adaptation benefits and impact responses with temperature and

precipitation (1). The study analyzed 1700 published data points but did not segregate analysis

by timeslice. Although direct comparison of the study with current research is difficult as

primary objective of both studies is different, impact responses with common indicators are

comparable. Comparison with other regional meta-analysis is erroneous due to bias from region-

specific conditions (2) (3). Unlike previous research, we found more equitable impacts between

tropic and temperate regions after adaptation. We also, found minor regional differences in

impacts (especially after inclusion of adaptation). Another recently published study compared

different methods with temperature increase (4), although direct comparison cannot be made as

impacts are not segregated by timeslice, approximate idea can be inferred with temperature

changes.

Comparing assessments with observed data is difficult, due to underlying uncertainty of current

assessment methodologies in replication observed effects (5). Still, numerous studies have

documented observed climatic impacts on crop production (6) (7). Since the metaanalysis used

in this research is varied and include global analysis from diverse regions, direct comparison

with field experiments may not be possible nor advisable. Instead, comparison with regionally

aggregated studies can be attempted and yield responses of different crops are in agreement with

results from this research (8). Since this research is first in identifying impact trends with time

and no other study to compare, indirect comparison can be made to changing climate projections

with time in Figure 1.4 of IPCC AR5, which shows change in mean temperature with time by

different assessment reports (9).

2. Caveats

Inclusion of diverse studies and global datasets had greatly increased the sample size in this

research which is statistically robust, but cause potential for biases and limitations. Most

apparent bias based on number of entries per study, geographical spread of data and agricultural

importance has been corrected. Inclusion of assessments with different methods and procedures

helps in preventing skewing of data towards a fundamental method. However, due to specific

assumptions in different studies, their comparison is often difficult. Meta-analysis technique

using weighting studies by variance, and bootstrapped regressions helped in eliminating a part of

this uncertainty. Another important limitation was this research focused on assessing impacts of

climate change, without consideration of extreme events which can destabilize food systems and

depress yields greatly in the future (10).

Supplementary Table 1: Assumptions in baseline and projection periods

Time Periods Baseline Midpoint Projection Midpoint Year gap

Broad

Timeslice

2010s 1975 2015 40 Near -2020s

2020s 1975 2025 50 Near -2020s

2030s 1975 2035 60 Near -2020s

2040s 1975 2045 70 Medium- 2050s

2050s 1975 2055 80 Medium- 2050s

2060s 1975 2065 90 Medium- 2050s

2070s 1975 2075 100 Far- 2080s

2080s 1975 2085 110 Far- 2080s

2090s 1975 2095 120 Far- 2080s

2100s 1975 2105 130 Far- 2080s

Supplementary Table 2: Geographical Representation in the analysis

Country Cereal Production (Metric Tonnes)

% of Total World production- 2014 Data Points

India 295,360,000 16.3 536

United States of America 442,849,090 24.4 455

China 559,315,083 30.8 422

Canada 51,301,401 2.8 234

Australia 38,423,006 2.1 230

Supplementary Table 3: Literature consulted for Current and Future Yield Rate Analysis

1. Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield Trends Are Insufficient to Double Global Crop Production by 2050. PLoS One 8, e66428 (2013).

2. Alexandratos, N., & Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision (No. 12-03, p. 4). Rome, FAO: ESA Working paper.

3. FAO (2016). The state of food and agriculture . Food & Agriculture Organization of the UN (FAO)

4. Fischer R.A., Byerlee D. and Edmeades G.O. 2014. Crop yields and global food security: will yield increase continue to feed the world? ACIAR Monograph No. 158. Australian Centre for International Agricultural Research: Canberra. xxii + 634 pp

5. Nelson G.C., Rosegrant M.W., Palazzo A., Gray I., Ingerstoll C., Robertson R. 2010. Food security, farming, and climate change to 2050: scenarios, results, policy options. Research Monograph. International Food Policy Research Institute, Washington, DC. doi:10:2499/9780896291867.

6. Conforti, P. (2011). Looking ahead in world food and agriculture: perspectives to 2050. Food and Agriculture Organization of the United Nations (FAO).

7. Linehan V., Thorpe S., Gunning-Trant C., Heyhoe E., Harle K., Hormis M. et al. 2013. Global food production and prices to 2050: scenario analysis under policy assumptions. Conference paper 13.6. Australian Bureau of Agricultural and Resource Economics and Sciences: Canberra.

8. Tweetin S.R. and Thompson L. 2009. Long-term global agricultural output supply–demand balance, and real farm and food prices. Farm Policy Journal 61, 1–15.

9. Van Dijk, M., & Meijerink, G. W. (2014). A review of global food security scenario and assessment studies: results, gaps and research priorities. Global Food Security, 3(3), 227-238.

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15. Valin, H., Sands, R. D., van der Mensbrugghe, D., Nelson, G. C., Ahammad, H., Blanc, E., … Willenbockel, D. (2014). The future of food demand: understanding differences in global economic models. Agricultural Economics, 45(1), 51–67. https://doi.org/10.1111/agec.12089

16. Müller, C., & Robertson, R. D. (2014). Projecting future crop productivity for global economic modeling. Agricultural Economics, 45(1), 37–50. https://doi.org/10.1111/agec.12088

17. Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260–20264. https://doi.org/10.1073/pnas.1116437108

18. Davis, K. F., Gephart, J. A., Emery, K. A., Leach, A. M., Galloway, J. N., & D’Odorico, P. (2016). Meeting future food demand with current agricultural resources. Global Environmental Change, 39, 125–132. https://doi.org/10.1016/j.gloenvcha.2016.05.004

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Supplementary Table 4: Data break-up with different categories

Total Data Points 27208

Number of Studies 158

Timeslice

2020s 9022

2050s 9133

2080s 9053

Spatial Scale

Global 25159

Regional 1017

National 424

Site 608

Crop Type

Major Cereals (Maize, Rice and Wheat) 26481

Other Crops 727

Publication Year

Pre-2000 4579

Post-2000 22629

Adaptation

Without Adaptation 1509

With Adaptation 25699

CO2 Fertilization

With CO2 26150

Without CO2 1058

Supplementary Table 5: Data break-up with adaptation types

Adaptation Type Data Points

Conservation Tillage 4

Dynamic irrigation and nutrient application 7476

Dynamic Irrigation application 2189

Dynamic nutrient application 297

Planting Date, Cultivar, Fertilizer, Irrigation 6307

Fertilizer 26

Cultivar 49

Cultivar, Technology 1

Irrigation 85

Planting Date 78

Planting Date , Fertilizer and Irrigation 60

Planting Date, Cultivar 5168

Planting Date, Cultivar, Fertilizer 7

Planting Date, Cultivar, Irrigation 3913

Planting Date, Irrigation 15

Technology 24

Supplementary Figure 1: Change in Modelling approach with time

Supplementary Figure 2: Average Impacts (with adaptation) for 2050s and 2080s

Supplementary Figure 3: Country-level average impacts with adaptation from meta-analysis

(blue) and paired adaptation (green) along latitude. Confidence bands for rice and wheat not

shown due to very few data points in paired study.

Supplementary Figure 4: Climate Change Impact on Food gap, with current supply rates. The

figure shows Food gap (difference in demand and supply of the crop) and impacts of climate

change after adaptation. Current supply rates are shown by color mapping (highest green to

lowest red) and crop area by size of the point (4: > 1 million ha, 3: 1 to 10,00,000 ha, 2: 10 to

1,00,000 ha and 1: <1,00,000 ha). Three contour lines shows Net impacts as a percentage of

food gap at -20, -10 and -5 % levels. Please note, axis of three crops are unequal and

independently expanded to accommodate all country labels.

Supplementary Figure 5: Boxplot of country-wise impacts derived by simple averaging of the

data and weighing procedure used in the meta-analysis. All crops and timeslices combined. P

value indicates statistically significant difference between the groups, based on Kruskal-Wallis

test.

Supplementary Figure 6: Global variation in estimates due to carbon fertilization effects.

Distribution of data under studies including carbon fertilization and excluding carbon

fertilization is shown, outliers are removed for clarity. Please note very less number of data

points are available for studies which estimated impacts with adaptation, and without fertilization

effects (n= 56, covering 8 countries) therefore country-level estimates are not shown.

Supplementary Figure 7: Scatterplot of average impacts of climate change with different

adaptation types along latitude for rice and wheat. Results are derived from multiple data points

across different time-slices and are relative to a baseline of 1960-90 (See SI for details). Each dot

represents centroid of a single country (See SI for details). Solid lines show best fits of with

latitude. Shaded bands indicate 95 % confidence interval. Adaptation type of dynamic irrigation

for wheat has very few data points and country level estimates are thereby not shown.

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