Rising Temperatures and Maize Sector Performance in Zimbabwe: Evidence from 1961–2022

Variable	Description	Unit of measurement	Source
Temp	Annual mean land-surface air temperature anomaly relative to the 1951–1980 climatological mean	°C	FAOSTAT (based on NASA–GISS GISTEMP)
Prdxn	Maize production	‘000 tonnes	FAOSTAT
Yield	Maize yield	kg/ha	FAOSTAT
Imports	Maize imports	‘000 tonnes	FAO
Exports	Maize exports	‘000 tonnes	FAO

[i] Source: Annual temperature-change data were obtained from the FAOSTAT Temperature Change dataset. The indicator measures changes in annual mean land-surface air temperature relative to the 1951–1980 climatological mean and is based on NASA-GISS GISTEMP data.

Table 2.

Descriptive statistics of variables used in the study

Variable	Obs	Mean	Median	Std. dev.	Min	Max
Temp	62	0.317	0.2	0.458	−0.476	1.336
Prdxn	62	1 399.8	1 362.7	588.4	361.9	2 833.3
Yield	62	1 210.9	1 186.5	488.82	286.7	2 311
Imports	62	183.4	27.8	271.18	0	1 208.1
Exports	62	207.7	180.3	245.65	0.003	1 279.9

[i] Source: own calculations using STATA based on FAOSTAT/FAO data.

Temperature changes in Zimbabwe
Source: own work based on FAOSTAT data.

Production, export, import, and yield of maize in Zimbabwe
Source: own work based on the FAOSTAT.

Table 3.

Correlation matrix of temperature and maize variables

Variables	Temp	Imports	Exports	Yield	Prdxn
Temp	1.0000
Imports	0.3665^*	1.0000
Exports	−0.1664	−0.5018^*	1.0000
Yield	−0.5771^*	−0.5674^*	0.4561^*	1.0000
Prdxn	−0.4563^*	−0.4171^*	0.4054^*	0.8231^*	1.0000

* Indicates statistical significance at the 5% level.

Source: own calculations using STATA.

Table 4.

OLS estimates with heteroskedasticity-robust standard errors for maize-sector performance in Zimbabwe

Variables	Prdxn	Yield	Exports	Imports
Temp	−585.77^*** (151.38)	−615.29^*** (104.98)	–89.15 (60.83)	216.78^** (87.84)
Cons	1 585.51^*** (528.14)	1 405.98^*** (402.51)	236.01^*** (74.92)	114.69 (92.41)
Observations	62	62	62	62
F-stat	14.97	34.35	2.15	6.09
Prob > F	0.00	0.00	0.15	0.02
R-squared	0.21	0.33	0.03	0.14

Note: standard errors in parentheses.

*** p < 0.01,

** p < 0.05,

* p < 0.1.

Source: own calculations using STATA robust regression estimation.

Table 5.

First-difference OLS estimates with heteroskedasticity-robust standard errors

Variables	Δ prdxn	Δ yield	Δ export	Δ import
Δ temp	−845.971^*** (135.192)	−573.193^*** (107.279)	−9.378 (47.201)	32.059 (80.114)
Cons	−0.364 (68.494)	−5.688 (53.869)	−4.19 (21.462)	1.875 (39.12)
Observations	61	61	61	61
F-stat	39.16	28.55	0.04	0.16
Prob > F	0.00	0.00	0.85	0.69
R-squared	0.481	0.408	0.001	0.004

Note: standard errors in parentheses.

*** p < 0.01,

** p < 0.05,

* p < 0.1.

Source: own calculations using STATA first-difference robust regression estimation.

References

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Articles in this issue

DOI: https://doi.org/10.17306/j.jard.2026.3.00028r1 | Journal eISSN: 1899-5772 | Journal ISSN: 1899-5241

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Language: English

Page range: 313 - 321

Accepted on: Aug 24, 2026

Published on: Sep 30, 2026

Published by: Poznań University of Life Science

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

temperature,

maize,

yield,

climate,

food,

Zimbabwe

Related subjects:

Business and economics,

Political economics,

Political economics, other,

Business management,

Business management, other,

Life sciences,

Life sciences, other,

Social sciences,

Political science,

Political science, other

© 2026 Deborah Rusununguko Chinamatira, Sylwester Kozak, published by Poznań University of Life Science
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 81 (2026): Issue 3 (September 2026)

Rising Temperatures and Maize Sector Performance in Zimbabwe: Evidence from 1961–2022

Figures & Tables

Table 1.

Table 2.

Fig. 1.

Fig. 2.

Table 3.

Table 4.

Table 5.

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