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Heat shocks, maize yields, and child height in Tanzania

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Abstract

This paper advances previous literature that has posited a climate-nutrition link without identifying a specific pathway via agriculture. We measure the specific effects of exposure to extreme heat on maize yields in Tanzania, and then test whether prenatal heat-induced yield losses predict subsequent child growth outcomes. In the first stage we find that substituting one full day (24 h) exposure to 39 °C for a day at 29 degrees reduces predicted yield for the entire growing season by 6–11%. In the second stage we find that in utero exposure to growing degree days greater than 29 °C predicts lower postnatal HAZ scores for Tanzanian boys 0–5 years of age, but not girls. Consistent with a maternal malnutrition mechanism, we also find a negative association between maize yields and women’s body mass. Insofar as climate change is likely to increase the incidence of heat shocks in much of sub-Saharan Africa, our results suggest a significant risk of adverse nutritional impacts.

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Fig. 1

Source: NASA/Goddard Institute for Space Studies surface temperature data

Fig. 2
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Data availability

All data and replication files will be available on request from the authors.

Notes

  1. Growing degree days (as detailed above) measure the cumulative heat exposure of crops during the growing season, defined as the number of days in the growing season in which mean daily temperature is within the useful range for maize growth.

  2. Using a threshold of 30C (instead of 29C) in a multi-country study of maize yields in Africa, Lobell et al. (2011) also estimate a reduction of 1% of yield per GDD above the kink.

  3. All regressions for HAZ exclude children resident in the major urban hub of Dar es Salaam, where domestic yield shocks will be less relevant given the availability of imported cereals.

  4. Applying Eq. (6), we multiply the point estimate for the effect of log maize yield on HAZ times the effect of a single GDD > 29C on yield to obtain the effect of a single GDD > 29C on HAZ, and then scale that product by the mean number of such days: 0.14 ×  (− 0.6) × 7.6 = − 0.64 for boys.

  5. Our data do not distinguish mothers from other women. To at least partially address this measurement issue, we limit the sample of women here to those between the ages of 16 and 45.

  6. In addition, these studies raise the possibility that the data used here reflect a degree of selection bias, as children must have survived in utero and infancy to appear in our data, although Alderman et al. (2011) generally expect such bias to be small.

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Acknowledgements

Authors are grateful to Kyle Emerick (Tufts University) and Avery Cohn (ex-Tufts University) as well as Kalle Hirvonen (IFPRI) for their guidance on the relevant literature and techniques on the measurement of weather variability. We thank Wahid Quabili (IFPRI) for his support during the weather data processing. This research was supported by a grant from the Bill and Melinda Gates Foundation to IFPRI in support of the Advancing Research on Nutrition and Agriculture (ARENA) project.

Funding

The work presented here was supported by a project led at IFPRI on Advancing Research in Nutrition and Agriculture (ARENA) funded by the Bill & Melinda Gates Foundation as OPP1177007 through the International Food Policy Research Institute (project number 301052.001.001.515.01.01).

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Authors and Affiliations

  1. Fletcher School, Tufts University, Medford, MA, 01742, USA

    S. Block

  2. International Food Policy Research Institute, Washington, DC, 20005, USA

    B. Haile, L. You & D. Headey

  3. Macro Agriculture Research Institute, College of Economics and Management, Huazhong Agricultural University, Wuhan, 430070, Hubei, China

    L. You

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Contributions

LY constructed the datasets. SB lead the statistical analysis with support from BH and DH. All authors participated in writing.

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Correspondence to S. Block.

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Appendices

Appendix A

(see Table

Table 7 Distribution of Tanzania National Panel Survey sample by survey round, area of residence, and share of maize growers
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7; Figs. 

Fig. 4
figure 4

Spatial distribution of Tanzania’s National Panel Survey GPS data

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4,

Fig. 5
figure 5

The distribution of long-season maize yields in Tanzania across all rounds

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5).

1.1 Additional details about the weather data

The Climate Research Unit Time Series Grid Version 3.23 (CRU TS v. 3.23) at the University of East Anglia provides a monthly 0.5 degree spatial resolution gridded weather product from 1901 to 2014. Among the available climate variables, are maximum (max) and minimum (min) temperature, which reflects average daily max and min temperature for the month in °C. In addition, total monthly rainfall is reported in millimeters. Data from over 4000 weather stations are used to assign the temperature and rainfall grid values [1].

The CRU temperature and rainfall data from 1981 to 2014 were downloaded and processed as.netcdf files [2]. Once data were downloaded, a point shapefile of the household clusters for Tanzania were used to generate the value of each point for each monthly temperature and rainfall pixel it intersects with. Points that fall into a pixel with missing data were moved to the nearest pixel with data.

The output is a.csv table of every date and the temperature and rainfall values of the points for each household coordinate point for every month.

The final data was converted to a Stata file. Five columns were added as the first five columns of the original survey data table: (1) FID- the ID of the point shapefile, which can be linked back to the shapefile created to map. (2) cru_temp_min -The daily average minimum temperature for the month in °C. (3) cru_temp_max -The daily average maximum temperature for the month in °C. (4) cru_rain_mm -The total monthly rainfall in millimeters. 5) date- time is in the following format: YYYY_M or YYYY_MM.

1.2 Daily rainfall data

CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) was downloaded from http://chg.geog.ucsb.edu/data/chirps/ as daily 0.05 degree resolution grids for all of Africa. The years 2000-present were selected.

A point shapefile of households were used to generate the value of each point for each daily rainfall pixel it intersects with. The output is a.csv table of every date and the rainfall values of the points for each household coordinate point.

Some of the coordinates in Tanzania do not intersect with the rainfall data for any day. These coordinates are near water and the rainfall pixels do not have data when the majority of pixel contains water. To compensate, the points were moved to the nearest pixel and given the value of the nearest pixel to which they were moved.

The final data was converted to a Stata file with three columns added to the first three columns of the original survey data table: (1) fid—the id of the point shapefile, which can be linked back to the shapefile created to map. (2) CHIRPS_daily_mm—The daily rainfall values in milimeters. A value of 0 simply means no rainfall for that day. (3) date—time is in the following format: YYYY.MM.DD.

If interested python script for data generation, minus the movement of the coordinates to the nearest pixel, can be found at: https://github.com/timpjohns/python-pandas/blob/master/CHIRPS_extraction_daily.py. Please contact for any questions.

1.3 Daily temperature data

The Noah 2.7.1 model in the Global Land Data Assimilation System (GLDAS) has several simulated land surface parameters. The data are in 0.25 degree resolution and range from February 24, 2000 to present. The temporal resolution is 3-h. The simulation was created by: “combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency’s AGRicultural METeorological modeling system (AGRMET)”(39).

The data are located on the OPENDAP NASA web server as GRIB and netcdf files. 22 land surface parameters are available, our interest for now was just the “near surface air temperature” parameter in Kelvins.

Once data were downloaded. A point shapefile for Tanzania were used to generate the value of each point for each 3-hourly temperature pixel it intersects with. The output is a.csv table of every date and the temperature values of the points for each household coordinate point for every 3-h.

Several of the household coordinates do not intersect with the temperature data for any day. These coordinates are near water and the temperature pixels do not have data when the majority of pixel contains water. To compensate, the points were moved to the nearest pixel and given the value of the nearest pixel to which they were moved.

The final data was converted to a Stata file. There are 8 temperature points of the 3-hourly data for each day. We take the minimum and the maximum among the eight data points as daily minimum and maximum temperatures, the average of the eight data points is the daily temperature. Five columns were added as the first three columns of the original survey data table: (1) fid- the id of the point shapefile, which can be linked back to the shapefile created to map. (2) dailyMin- The daily minimum temperature values in Kelvins. (3) dailyMax- The daily maximum temperature values in Kelvins. (4) dailyTemp- The daily average temperature values in Kelvins. (5) day- time is in the following format: YYYYDDD. The last column is “Data”, where 0 is when the household coordinate was moved to nearest pixel.

Appendix B

2.1 Heterogeneity of heat shock effects on maize yields

The yield impacts of temperature shocks may be more severe for lower productivity farmers because of lower levels of inputs, lower quality inputs (e.g. soil) or poorer management practices. To explore this possibility we employ quantile regressions, which allows us to explore the full distribution of yield data. Table 3 presents simultaneous quantile regressions at the 25th, 50th, and 75th percentiles (q25, q50, and q75, respectively), and tests the difference between q75 and q25. We find a statistically significant yield discontinuity at all three points along the yield distribution, controlling for rainfall and region in columns (1–3). Households at the 25th percentile of the yield distribution appear to suffer greater effect of high heat than more productive households (e.g., those at the 75th percentile), though the difference (column 4) is not statistically significant. Adding controls for household characteristics (columns 5–7) somewhat increases the difference in point estimates between the 25th percentile and 75th percentile households, though the difference remains statistically insignificant. The point estimates suggest, however, that households at the 25th percentile of yield lose 0.7% of maize yield for each GDD > 29C, as compared with a loss of 0.4% for households at the 75th percentile. Figure 4 illustrates these differences as a function of additional days at given temperatures over the relevant range.

(See Table

Table 8 Quantile regressions of log maize yield at the 25th, 50th, 75th percentiles of the yield distribution with interquartile differences
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8).

(See Fig. 

Fig. 6
figure 6

Effect of an additional degree day of exposure to temperatures on predicted maize yield at the 25th, 50th, and 75th Percentiles of the Maize Yield Distribution

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6).

These differences across quantiles relate directly to the role of cross-sectional geographic effects as the source of identifying variation to the extent that lower-yielding versus higher-yielding households may live in different places that vary by exposure to high heat. Figure 5 explores this by comparing the cumulative distribution functions of degree days > 29 °C across quartiles of the yield distribution for data pooled across three survey rounds. The differences across quartiles with respect to heat exposure are striking. As reflected in both the cumulative density functions and the kernel densities, the lower-yielding households (q25) have much greater exposure to extreme heat than the higher-yielding households (q75). Thus lower-yielding households both face greater exposure to high heat and suffer greater losses in yield for each hot day. Our reliance on geographic variation, however, fails to preclude the existence of potential unobserved confounding factors and thus limits a causal interpretation of these results.

(See Fig. 

Fig. 7
figure 7

Cumulative distribution functions of exposure to degree days > 29 °C, by quartile of the yield distribution (q25 (q75) indicates the 25th (75th) percentile of the yield distribution, median is q50

Full size image

7).

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Block, S., Haile, B., You, L. et al. Heat shocks, maize yields, and child height in Tanzania. Food Sec. 14, 93–109 (2022). https://doi.org/10.1007/s12571-021-01211-6

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