Climate change is a major concern in wheat agroecosystems as it can affect productivity and crop water use. This study used the AquaCrop model to evaluate climate change impacts on the wheat yield, crop water use and water footprint of wheat production in the Middle-Manyame sub-catchment of Zimbabwe. Climate scenarios were based on simulations from the NCC-NorESM1-M, CCCma-CanESM2 and MOHC-HadGEM2-ES General Climate Models downscaled using three Regional Climate Models (RCA4, RegCM4 and CRCM5) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). The results showed that, compared to the baseline climate (1980–2010), yield may increase by 22.60, 29.47 27.80, and 53.85% for the RCP4.5 2040 s, RCP4.5 2080 s, RCP8.5 2040 s and RCP8.5 2080 s scenarios, respectively. Crop water use may decrease by 1.68, 1.25, 3.7 and 6.47%, respectively, under the four scenarios, respectively. Consequently, the blue water footprint may decrease by 19, 23, 24 and 38%, respectively, under the four scenarios. Sensitivity analysis attributed the increase in yields and the decrease in crop water use to the CO2 fertilization effect, which had a dominant effect over high-temperature effects. The results suggest that future wheat yields could be enhanced while crop water use is reduced because of climate change. However, the realization of these benefits requires farmers to adapt to climate change by adopting recommended agronomic practices and farm input rates that are consistent with those used in the modelling approach of this study.

  • Influence of climate variables consumptive water footprint (WF) of wheat was analysed first time for Zimbabwe.

  • A beneficial increase in wheat yields and decrease in the blue WF was predicted over Middle-Manyame sub-catchment.

  • Increase in yield is due to the [CO2] fertilization effect.

  • The study confirms the local occurrence of the ‘evaporation paradox’.

  • Realization of the beneficial effects on adaptation to climate change.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Uncertainties in modelling projections of climate change impacts on agroecosystems are mainly derived from issues of scale (Steffen & Canadell 2005; Coppola et al. 2011; Saini et al. 2015). Under climate change, the most important factors influencing the productivity of agroecosystems are elevated atmospheric carbon dioxide [CO2] concentration, enhanced air temperature and change in precipitation. These factors are regulated by key processes such as cloud cover and vegetation, which depend on very fine spatial scales. Although Regional Climate Models (RCMs) have been developed to provide high-resolution simulations on climatological timescales, they cannot accurately simulate climate systems with spatial resolution finer than 50 km (0.44° gridbox length), which is the required scale for proficient decision-making in agroecosystems (Jones et al. 2011; Šeparović et al. 2013; Teichmann et al. 2020).

Furthermore, climate change impacts on agroecosystem components such as yield and crop water are usually derived from crop growth models (Singh & Kalra 2016). These models have the advantage of being able to incorporate the effects of changing [CO2], temperature and precipitation on crop production (Asseng et al. 2015; Lobell & Asseng 2017). However, crop simulation models were designed to be applied to small areas, typically fields and catchments with homogeneous environmental conditions (Abraha & Savage 2006; Walker & Schulze 2006). Processes like the carbon fertilization effect (CFE) depend on the presence and magnitude of localized environmental factors, such as temperature, altitude and soil type.

To boost confidence in projections of climate change impacts on agroecosystems, it is apparent that crop growth models should be applied to small homogenous areas using climate data, which have been downscaled to spatial scales that are typically less than 50 km (Teichmann et al. 2020). The demand for such lower spatial scale has resulted in the creation of the Coordinated Output for Regional Evaluations (CORE) platform within the Coordinated Regional Climate Downscaling Experiment (CORDEX) community (Gutowski et al. 2016). This CORDEX–CORE dataset can provide projections at a spatial scale of approximately 25 km (0.22°), which is appropriate for applications to agroecosystem assessments.

Despite the presence of the CORDEX–CORE platform, the majority of climate change impact studies targeting African agroecosystems depend on coarse-scale multi-model ensembles of RCMs. This generates a lot of uncertainty regarding localized climate change impacts on agroecosystems in many African countries, including Zimbabwe. The majority of climate change impact studies in Zimbabwe are based on either GCM or RCM data with coarse spatial resolution (Hulme et al. 2001; Christensen et al. 2007; Shongwe et al. 2015; Pinto et al. 2016; Tang et al. 2019; Sibanda et al. 2020; Maviza & Ahmed 2021).

The purpose of this study was to apply the CORDEX–CORE dataset and use the AquaCrop model to explore the localized impacts of future climate change on wheat yields, crop water use and the consumptive water footprint (green and blue water footprint) in the Middle-Manyame sub-catchment (MMSC) of Zimbabwe. Wheat was selected as a case study crop because most local climate change impact studies in the country have focused on maize because of its importance as the prime staple of the country (Muchena & Iglesias 1995; Makadho 1996; Mano & Nhemachena 2007; Lebel et al. 2015; Makuvaro et al. 2018). Very few studies have explored climate change impacts on wheat, which is the nation's second most important staple, providing over 50% of the population's dietary calories (Mutambara et al. 2013). In Zimbabwe, wheat production is carried out under full irrigation during the cold and winter season. The heavy reliance of wheat production on irrigation in the predominantly semi-arid climate of Zimbabwe substantiates research that explores the efficient utilization of water resources.

Study area

The MMSC is located between 17.00° and 17.50 °S and 29.74° and 30.45 °E, in Zimbabwe, and has a total area of 4,245 km2 (Latham 2001) (Figure 1). The sub-catchment was selected for the study because it forms part of the Manyame Catchment, which is the national epicentre of intensive irrigated wheat production (Derman & Manzungu 2016). Within the sub-catchment, considerable rainfall activity (750–900 mm) occurs during summer (October to April) with little or no rain during winter (May to September). The mean annual temperature ranges from 18 to 19 °C with lower minimum and maximum temperatures, which are conducive for wheat production, which are experienced in winter. The soil type is largely homogenous, with Fersialitic soils (i.e. Cambisol or Paleustalf when using the FAO or USDA Soil Taxonomy systems, respectively) covering approximately 90% of the sub-catchment (Thompson 1965).

Figure 1

Location, topography and soils of MMSC.

Figure 1

Location, topography and soils of MMSC.

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Four sample sites within the sub-catchment were selected and used to collect baseline data relevant to the study. The sites were Angwa farm, Eldorado farm, Ayrshire farm and Chitomborwizi farm located within/near the urban towns of Mutorashanga, Banket, Trelawney and Chinhoyi. Soil physico-chemical and hydraulic properties for each site was experimentally derived, while long-term climate data (1980–2010) for each site, hereafter referred to as the ‘baseline period’, were collected from nearby weather stations managed by the Meteorological Services Department. Any missing data from the four stations were augmented by data collected from NASA's climate data repository Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) (Ruane et al. 2015).

Future climate scenarios

Future climate data for the periods 2031–2060 and 2071–2100, hereafter referred to as the ‘2040s’ and ‘2080s’, were derived from the NCC-NorESM1-M, CCCma-CanESM2 and MOHC-HadGEM2-ES GCMs. Each GCM was downscaled using three RCMs (RCA4, CRCM5 and RegCM4) under two climatic forcing scenarios, Representative Concentration Pathways (RCP4.5 and RCP8.5), which are medium–low- and high-emission scenarios with a radiative forcing of 4.5 and 8.5 wm−2 at the end of the 21st century, respectively. All RCM data were downloaded from the ESGF node (https://esgf-index1.ceda.ac.uk/search/esgf-ceda/) from the CORDEX–CORE domain, which has a horizontal grid spacing of 0.22°, with a 20-min time step. Daily outputs from the three RCMs (maximum and minimum temperatures, relative humidity, precipitation, sunshine hours and wind speed) were obtained from the CORDEX–CORE project. The RCMs used were the RCA4 (Rossby Centre Regional Atmospheric Model), REgCM4 (RCM system, version 4) and the CRCM5 (Canadian RCM, version 5). These models have been applied satisfactorily in climate change studies in Zimbabwe (Laprise et al. 2013; Diallo et al. 2015; Sibanda et al. 2020).

Bias correction

RCM data may have considerable bias which, if not checked, may cause inaccurate results (Teutschbein & Seibert 2012). The simple linear scaling method was used as a bias correction tool and applied to all the RCM dataset. The average difference between monthly observed time series (1980–2010) and RCM historical time series of RCM for each station was used to make additive corrections to all temperature parameters (WTMEAN, WTMAX and WTMIN). The ratio between monthly observed time series (1980–2010) and RCM historical for each station was used to make multiplicative corrections for the rest of the meteorological parameters (WP, WH, WSUN and WS)

Climate data analysis

The daily meteorological parameters for each year were averaged over the winter wheat-growing season that is traditionally taken as the period between 1 May and 31 September (Table 1).

Table 1

Description of the meteorological parameters

Meteorological variableSymbolUnits
Daily precipitation WP mm 
Daily maximum temperature WTMAX °C 
Daily minimum temperature WTMIN °C 
Daily mean temperature WTMEAN °C 
Sunshine hours WSUN hours 
Relative humidity WH 
Wind speed (at 2 m height) WWS m s−1 
Meteorological variableSymbolUnits
Daily precipitation WP mm 
Daily maximum temperature WTMAX °C 
Daily minimum temperature WTMIN °C 
Daily mean temperature WTMEAN °C 
Sunshine hours WSUN hours 
Relative humidity WH 
Wind speed (at 2 m height) WWS m s−1 
The climate normal for each meteorological parameter over the baseline and future periods was calculated using methods recommended by the World Meteorological Organization (Arguez & Vose 2011). A standard climate normal is the average of a climate parameter over 30 years:
formula
(1)
where Tn is the 30-year climate normal for the meteorological parameter T, Ti is the mean of meteorological variable T for the year i, and x is an integer between 1 and 29. The climate normal for each parameter was calculated for the baseline year and compared with the normal for the years 2040 and 2080 under RCP4.5 and RCP8.5 using Student's t-test.

AquaCrop model simulations

The Food and Agricultural Organization's AquaCrop model (version 6) was used to project future wheat yields and crop water use in MMSC. AquaCrop is an empirical process-based crop growth model that can simulate biomass and yield response of field and vegetable crops to water under varying management and environmental conditions. The model's sub-routines are described fully in Raes et al. (2018). It has been used to simulate accurately wheat production and crop water use in many studies globally (Paredes et al. 2015; Thaler et al. 2017; Nouri et al. 2019; Rosa et al. 2020). The validation of the model for wheat production in MMSC, including the wheat cultivars used, is reported in a forthcoming publication (Govere et al. 2022). The model determines the soil water balance at the root zone, which can be used to estimate the transpiration at the root zone by simulating the daily blue and green soil water balances following the methods described by Zhuo & Hoekstra (2017) below:
formula
(2)
formula
(3)
where Sg(t) and Sb(t) are the green and blue soil water contents at the end of day t, respectively; PR(t) (mm) is the precipitation on day t; IRR(t) (mm) is the amount of irrigation water applied; CR(t) (mm) is the capillary rise from groundwater; E(t) (mm) is the evapotranspiration from the field (excluding crop transpiration); T(t) (mm) the crop transpiration (mm); RO(t) (mm) the daily surface runoff and DP(t) (mm) is the deep percolation.

In the study, the model was run 150 times (one baseline (1980–2010) and four scenarios (RCP4.5 2040 s; RCP4.5 2080 s; RCP8.5 2040 s and RCP8.5 2080 s)) under ideal agronomic management practices and farm input rates; recommended wheat planting date of 15 May, no soil fertility stress, adequate pest and weed control and irrigation set to trigger automatically when 80% of the total available water in the soil has been depleted to bring the root zone total soil moisture back to field capacity. Three outputs were analysed from the simulation runs and crop yield at harvest and crop water use, which were used to calculate the consumptive water footprint (WFblue and WFgreen).

Water footprints

The WF calculations were based on methods developed by Mekonnen & Hoekstra (2011). The WFgreen and WFblue were calculated as follows:
formula
(4)
formula
(5)
where ETgreen and ETblue are the green and blue water evapotranspiration (mm) partitioned from the total ET based on equations (2) and (3); Ya is the simulated crop yield (ton ha−1); a and b are planting and maturity dates, respectively; the factor 10 converts water depths (in mm) into water volumes per land surface (in m3 ha−1).

Data analysis

The simulated wheat yield, crop water use and water footprints under climate change scenarios were expressed as percentage changes from the baseline period. Additionally, wheat yields were also expressed as cumulative distribution functions (CDFs). The Student's t-test was used to calculate if there was a significant difference between the outputs for different future scenarios compared to the baseline. The Kolmogorov–Smirnov (K–S) test was used to determine if there was a significant difference between the CDF of the baseline period compared to the future periods. It is defined by the K–S statistic:
formula
(6)
where E and I are the CDFs, n is the length of the CDFs and zi denotes the ith data value of the sorted joined sample. K–S is a high value (max = 1.0) when there are no differences in the CDFs and a low value (min = 0.0) when the CDFs totally differ. A K–S value below 0.05 indicates a significant difference in the CDFs.

Sensitivity analysis

The climatological sensitivity and response of wheat yield and crop water use to all meteorological parameters was analysed using the simulation modelling technique outlined by Degener (2015). Two simulation runs are carried out in AquaCrop using climate model data to determine future outputs of wheat yield and crop water use. In the first simulation run, all meteorological variables were changed annually according to the used climate model data. In the second simulation, all meteorological variables were changed annually according to the used climate model data except for the meteorological parameter under sensitivity analysis; it was kept constant at the baseline value. The difference in outputs between the two simulation runs indicates the sensitivity of wheat yield and crop water use to the meteorological parameters. To quantify the sensitivity of wheat yield and crop water use to meteorological parameters, the difference in outputs from the two simulation runs was divided by the variation of the meteorological parameter under sensitivity analysis.
formula
(7)
where S is the sensitivity; Δ is the actual and percentage difference in crop water use or yields between the two simulation runs using (i) climate model data and (ii) similar climate model data but a particular meteorological parameter, p, kept constant at baseline conditions; Δp is the variation in the meteorological parameter between its baseline value and its value for a particular future scenario. For [CO2], the sensitivity (S) is numerically equal to CFE (i.e. percent increase in yield per 1 ppm increase in carbon dioxide concentration), which is used by McGrath & Lobell (2013) and Fischer (2009). Similarly, the sensitivity (S) for temperature would be numerically equal to the temperature effect (TE) reported in studies like Zhao et al. (2017).

Future climate change in MMSC

The results of the future climate for MMSC under RCP4.5 and RCP8.5 scenarios are illustrated in Figure 2. Compared to a baseline (1980–2010) WP value of 13 mm, the study predicts a non-significant (p > 0.05) decrease to 9, 7, 10 and 4 mm under RCP4.5 2040 s, RCP4.5 2080 s, RCP8.5 2040 s and RCP8.5 2080 s scenarios, respectively. This represents an overall decline of WP of between 24.18 and 69.48% (0.01–0.09 mm) for the entire catchment regardless of the time slice, pointing towards an increasingly drier winter.

Figure 2

Differences in climate variables between the baseline and RCP scenarios for MMSC.

Figure 2

Differences in climate variables between the baseline and RCP scenarios for MMSC.

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Compared to the baseline scenario, all climate models used in the study project a significantly (p < 0.05) hotter shift in the thermal regime for all temperature parameters (WTMAX, WTMIN, and WTMEAN). The highest gains in the temperature are expected for WTMIN compared to WTMAX and WTMEAN.

WTMIN will likely increase by between 2 and 7 °C, while WTMAX and WTMEAN are anticipated to increase by between 0.1–3 and 0.9–4 °C, respectively, for all scenarios and time slices. The average WSUN increased significantly (p < 0.05) by 16.39, 16.73, 16.15 and 16.90% for RCP4.5 2040 s, RCP4.5 2080 s, RCP8.5 2040 s and RCP8.5 2080 s scenarios, respectively. WWS increased significantly (p < 0.05) by 28.94, 43.83, 33.83 and 31.82% for the same period. WH registered the greatest increase (125–137%) for RCP4.5 2040 s, RCP4.5 2080 s, RCP8.5 2040 s and RCP8.5 2080 s scenarios, respectively. The results on WH can be explained by the rise in temperature parameters (WTMEAN, WTMAX and WTMIN) since warmer temperatures are correlated with higher humidity (Coffel et al. 2018).

Climate change and wheat yields

Wheat yields for MMSC under baseline and climate change scenarios were simulated using the AquaCrop model, and the results are summarized in Figure 3.

Figure 3

(a) Boxplot of simulated wheat yields under different climate change scenarios. (b) Cumulative density function of simulated wheat yields under different climate change scenarios.

Figure 3

(a) Boxplot of simulated wheat yields under different climate change scenarios. (b) Cumulative density function of simulated wheat yields under different climate change scenarios.

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For the baseline period (1980–2010), the average yield ranged between 6.2 and 8.7 t ha−1, with an average value of 7.9 t ha−1 and 95th percentile of 8.3 t ha−1. These are realistic values since high yields above 8 t ha−1 have been reported for well fertilized and non-water-limited wheat systems in the country (Macrobert & Savage 1998; Havazvidi 2012). Under the RCP4.5 scenario, the average wheat yields increased significantly (K–S < 0.05) to 9.78 t ha−1 (95th percentile = 10.1 t ha−1) and 10.33 t ha−1 (95th percentile = 10.36 t ha−1) for the 2040 and 2080 s time slices, respectively. This increase represents a 22.6% and 29.47% increment in yields under the RCP4.5 (2040 s) and RCP4.5 (2080 s) scenarios, respectively. For the RCP8.5 scenario, the average wheat yield was 10.23 t ha−1 (95th percentile = 11.1 t ha−1) and 12.28 t ha−1 (95th percentile = 12.9 t ha−1) for the 2040 and 2080 s, respectively, representing a significant (K–S < 0.05) average wheat yield increase of 27.8 and 53.85%, respectively.

Crop water use

The simulated crop water use for the baseline period (1980–2010) averaged 730 mm with a 95th percentile value of 745 mm (Figure 6). The simulated values of crop water use tally with values reported by Rahman Talaat & Zawe (2016) (549–989 mm) and Govere et al. (2020) (684–746 mm).

The simulation runs in AquaCrop showed no contribution of any effective precipitation to satisfying the crop water use demand. Compared to the baseline period, there was a general decline in the crop water use which was non-significant under the RCP4.5 scenario but significant for the RCP8.5 scenario. Crop water use for the RCP4.5 (40 s), RCP4.5 (80 s), RCP8.5 (40 s) and RCP8.5 (80 s) periods was projected to 718, 721, 707 and 683 mm, respectively. This represents a percentage decreases of −1.68, −1.25, −3.7 and −6.47%, respectively (red bars in Figure 4).

Figure 4

Projected crop water use under baseline and future emission scenarios (red bars illustrates the percentage decrease in CWU between future and baseline periods). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.038.

Figure 4

Projected crop water use under baseline and future emission scenarios (red bars illustrates the percentage decrease in CWU between future and baseline periods). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2022.038.

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Water footprint

The calculated water footprints are shown in Figure 5. Due to the absence of any effective precipitation, the WFgreen was absent and only the WFblue was observed. For the baseline period, the WFblue averaged 906 m3 t−1 which ranged between 877 and 974 m3 t−1 for the four sites within MMSC. Compared to the baseline values, the WFblue decreased to 718, 721, 707 and 683 m3 t−1 for the RCP4.5 2050 s, RCP4.5 2080 s, RCP8.5 2050 s and RCP8.5 2080 s scenarios, respectively.

Figure 5

Calculated water footprints for wheat production in MMSC under climate change scenarios.

Figure 5

Calculated water footprints for wheat production in MMSC under climate change scenarios.

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Figure 6

(a) Yield sensitivity to temperature changes. (b) Crop water use sensitivity to ambient [CO2] levels. (c) Yield sensitivity to ambient [CO2] levels. (d) Comparison of the carbon fertilization effect with the high TE.

Figure 6

(a) Yield sensitivity to temperature changes. (b) Crop water use sensitivity to ambient [CO2] levels. (c) Yield sensitivity to ambient [CO2] levels. (d) Comparison of the carbon fertilization effect with the high TE.

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This represented significant (p < 0.05) declines of −19, −23, −24 and −38% under the respective future scenarios.

Sensitivity analysis

The results showed that out of the eight meteorological parameters, only WTMEAN and [CO2] significantly (p < 0.05) altered the outputs from the simulations. The effect of [CO2] and temperature on yields and Crop Water Use (CWU) is illustrated in Figure 6. Simulations that incorporated increments in [CO2] levels resulted in higher yields compared to the baseline (i.e. maintaining [CO2] at baseline levels of 390 ppm). The incremental effect of [CO2] on wheat yields was 2.19, 2.91, 2.28 and 4.65 t ha−1 for the RCP4.5 2040s, RCP4.5 2080s, RCP8.5 2040s and RCP8.5 2080s scenarios, respectively, compared to simulations using baseline [CO2] values. This represents increases of 27.72, 36.83, 28.86, and 58.86%, respectively, for the future scenarios. Dividing the increase in yields by the variation in [CO2] for each scenario resulted in sensitivity values of 0.03, 0.22, 0.015 and 0.02 t ha−1 per ppm of [CO2] or 0.058, 0.069, 0.056 and 0.074% yield increase per ppm [CO2] for the RCP4.5 2050s, RCP4.5 2080s, RCP8.5 2050s and RCP8.5 2080s scenarios, respectively.

Simulation runs without WTMEAN increments (i.e. maintaining temperatures at baseline levels but increasing [CO2]) resulted in higher yields, implying that temperature rises lowered yields. The decrease in yields because of the TE was −0.39, −0.56, −0.47 and −0.35 t ha−1 for the RCP4.5 2040s, RCP4.5 2080s, RCP8.5 2040s and RCP8.5 2080s scenarios. This represents a decrease of 4.93, 7.08, 5.94 and 4.43% for the different scenarios, respectively. Dividing the decrease in yields by the variation in WTMEAN for each scenario resulted in the TE of −0.13, −0.19, −0.17 and −0.12 t ha−1 per °C or −4.97, −2.07, −3.99 and −1.35% decline in yield per °C.

For crop water only, [CO2] had an effect on simulation outputs. Simulation runs without incorporating increases in [CO2] (i.e. maintaining CO2 at baseline levels of 390 ppm) resulted in high crop water use compared to the baseline. The decremental effect of [CO2] on crop water use was −13.20, −9.12, −23.67 and −46.89 mm for the RCP4.5 2040 s, RCP4.5 2080s, RCP8.5 2040s and RCP8.5 2080s scenarios. This represents decreases of −1.68, −1.25, −3.7 and −6.47%, respectively, for the future scenarios. The sensitivity (S) of crop water use to [CO2] was −0.16, −0.69, −0.16 and −0.19 mm per ppm for the RCP4.5 2040s, RCP4.5 2080s, RCP8.5 2040s and RCP8.5 2080s scenarios.

Future climate change in MMSC

The results on WP are consistent with most studies on climate change in Zimbabwe, which predict a decline in precipitation over the country; however, the magnitude of the decrease noted in this study is higher. For instance, Hulme et al. (2001) and Christensen et al. (2007) project a 5–18% decline in mean annual rainfall for Zimbabwe by the year 2080 compared to 1961–1990 normal. The results of this study do, however, contradict the findings of Davis & Hirji (2014) who predicted a decrease in mean annual rainfall for the Manyame catchment under A2 and B2 emission scenarios. The variation in the magnitude of precipitation decline between this study and others could be attributed to the different spatial resolution of climate data used. The current study simulated precipitation over the winter period using CORDEX–CORE data (25 km resolution), while the values represented in some studies represent mean annual values derived from GCM and RCM data (≥50 km resolution). The results of the study suggest that the spatial resolution of the climate dataset used might not affect the direction of the climate shift but the magnitude of the shift, especially where precipitation is concerned. Knowledge about the different results produced by the use of climate data with different spatial resolution is important to Vulnerability, Impacts, Adaptation and Climate Services (VIACS) communities and other climate information users.

The study noted that all temperature parameters (WTMAX, WTMIN and WTMEAN) would likely increase in the future for the sub-catchment. The magnitude of change was within the temperature brackets anticipated by most climate change studies with differences in values probably due to variations in time slices and the resolution of the climate data used. For the 2050s period, Mujere & Mazvimavi (2012) projected a 3 °C maximum temperature increase for the Mazowe catchment which is adjacent to MMSC. Unganai (1996) predicts an annual increase in mean national temperatures of between 2 and 4 °C. Hulme et al. (2001) and Christensen et al. (2007) projected an annual mean temperature over Zimbabwe to increase by about 3 °C for the 2050s, compared to the 1961–1990 normal. Wheat is a temperate crop requiring low temperature thresholds (15–20 °C) for vernalization, and the projected temperature increments predicted in this study exceed these thresholds implying negative impacts on productivity (Hatfield et al. 2011). High temperatures in low latitudes can cause heat stress in wheat, which is a major environmental factor that limits yield in wheat with a 2 °C increase above a mean temperature of 23 °C decreasing wheat yield by ∼10% (Gornall et al. 2010).

The increase in solar radiation and wind speed noted in this study corresponds with findings by Fant et al. (2016) who project modest gains in wind speed and solar radiation over much of southern Africa due to global warming. There is a possibility that high winds might cause reduction of wheat yields due to lodging (Araghi et al. 2022). Solar radiation is one of the main drivers of wheat productivity, and its increase over the study area might indicate an increase in wheat productivity. However, studies have shown that the particular mix of solar radiation and temperature may work in tandem either increase or decrease yields (Ahmed et al. 2011).

Wheat yield and crop water use

The results suggest substantial gains in yields under future scenarios, implying that climate change might be beneficial to wheat productivity in MMSC. This concurs with most studies which show that for many areas around the world, wheat yields might increase under climate change (Fitzgerald et al. 2016; Carreras et al. 2020). However, most studies show that the nutritional value of wheat will decrease. The results imply that the impacts of climate change on wheat (second most important national cereal) in Zimbabwe will be more favourable in contrast to the most important national cereal, maize, which is predicted to undergo a decline in productivity due to high temperatures and severe declines in summer precipitation (Muchena 1994; Makadho 1996; Mano & Nhemachena 2007; Lebel et al. 2015; Makuvaro et al. 2018). This raises the possibility of crop switching from maize to wheat production as a climate change adaptation strategy. Crop switching has been proposed as a viable climate change adaptation strategy in countries like Ethiopia and Latin America (Seo & Mendelsohn 2007; Tessema et al. 2019). Costinot et al. (2014) suggest that about 33% of the potential damage from climate change in the agricultural sector can be avoided by effective crop switching. For Zimbabwe, crop switching from maize to wheat might require further investigations on the socio-economic and environmental adjustments required for such a transition since maize is mainly produced by smallholder farmers who lack irrigation infrastructure.

It is important to note that the yield simulations in this study were carried out under ideal crop management practices and optimum farm input application rates which differ substantially from real-life practices of some wheat-farming systems in Zimbabwe where low yields are endemic due to late planting, inadequate nutrient and water management, and poor pest control (Masiyandima et al. 2011). The implications of this study show that the realized beneficial effects of climate change will obviously dependent on and be modified by famers’ actual crop management practices. Better farming practices like timely planting, adequate fertilization and pest control have been shown not only to increase food security but also help farmers adapt to climate change (Shahbaz et al. 2022).

Generally, the results of the study corroborate the outcomes of free-air carbon dioxide experiments (FACE) conducted in semi-arid regions while simultaneously contradicting the findings of most region-wide modelling studies that cover Zimbabwe. O'Leary et al. (2015) noted that a 40% increase in wheat yields in a FACE conducted under the high semi-arid temperatures (+2 °C of the ambient mean) of Australia and elevated [CO2] of 550 μmol mol−1 (i.e. 550 ppm). Wheat yield increments of 24.8% were also reported by Ynag et al. (2007) in a FACE conducted in a semi-arid region of China under high temperatures and elevated [CO2] (550 μmol mol−1). In contrast, Fischer (2009) modelled yield declines of between 63 and 44% in the 2050s for eastern and southern Africa, respectively, under the [CO2] fertilization effect. Using the HadCM3 model, Parry et al. (2004) estimated the future global wheat yields at a national level using yield transfer functions and determined wheat reductions of between 3 and 10% for Zimbabwe during 2080s under A1FI, A2, B1and B2 scenarios. Other studies that have projected a decline in wheat yields over Zimbabwe are reported by Konar (2016) and Deryng et al. (2014). This does not mean that all modelling studies exclusively project declines in wheat yields since increments have been simulated elsewhere in the world for sites in China (Tao et al. 2014), Australia (Nicholls 1997; Ludwig & Asseng 2006) and Italy (Sabella et al. 2020).

The contrast in results between this modelling study and other regional modelling studies can be attributed to the variation in the spatial resolution of climate models. This study CORDEX–CORE datasets with a finer resolution while most regional studies have relied solely on either GCMs or RCMs with a large spatial resolution. Substantial heterogeneity exists at the regional scale due to variations in topography, soil type and other biophysical parameters, which cannot be captured by GCMs or RCMs due to their large grid size (grid-cell resolutions of approximately 250 and 600 km for GCMs) (Chokkavarapu & Mandla 2019). In addition, the use of crop models in regional studies may compound the situation since process-based models were developed for finer spatial scales with homogeneous environmental conditions and their use over large spatial scales with multiple heterogeneities in environmental conditions might lead to errors (Abraha & Savage 2006; Walker & Schulze 2006).

This counter-intuitive effect of reduction in crop water requirements for MMSC despite temperature increments noted in this study confirms the local occurrence of the ‘evaporation paradox’ in Zimbabwe, a phenomenon which has been documented globally and is caused by the diurnal temperature range (DTR; the difference between daily maximum temperature and daily minimum temperature). Temperature increments may not cause higher evapotranspiration in cases where there is a decline of the DRT (Liu et al. 2013; Zhang & Cai 2013). The results contradict the findings of Fant et al. (2013) who modelled an increase in wheat water demand for southern Zimbabwe using climate data from a 68 GCM ensemble based on CMIP-3 scenarios. The results, however, agree with the findings of Zhang & Cai (2013) who used five GCMs and noted that wheat irrigation requirements for Zimbabwe and Africa as a whole decreased despite the anticipated rise in temperature. In a global study, Liu et al. (2013) noted that crop water use would both increase (12.5–11.4%) and decrease (−45.% to –25.8%) for the 2030 and 2090s, respectively.

Water footprints

These simulated values agree with the findings of Mekonnen & Hoekstra (2011) who determined a WFblue of 864 m3 t−1 for wheat in Zimbabwe between 1991 and 2000. The difference in values could be attributed to the fact that the global study by Mekonnen & Hoekstra (2011) used course data from a large spatial scale with high heterogeneity with respect to environmental conditions. Govere et al. (2020) used national average wheat yields and determined a national average WFblue of 1 555 m3 t−1.

The decline in the WF for the various time slices can be attributed to the simultaneous increase in wheat yields and decrease in crop water use. Overall, the results can be interpreted to mean that climate change will likely result in an increase in water-use efficiency within the sub-catchment.

For comparison, there are no local studies that have assessed the impact of future climate change on the WF of wheat productivity in Zimbabwe. Previously, the authors assessed the impact of climate change on the WF of wheat productivity in Zimbabwe, but this was a retrospective analysis focusing on historical climate change for the period 1980–2010 (Govere et al. 2020). At a global level, our results differ from those of other workers who used a variety of GCMs and crop models. Fader et al. (2011) showed that by the 2070s, under A2 emission scenarios of future climate change and increasing atmospheric CO2 concentrations, the WF for temperate cereals like wheat and barley will decrease globally. However, Deryng et al. (2016) projected that the global WF of cereals would decline by 10–27% during the 2080s. FACEs conducted by O'Leary et al. (2015) showed an increase in the water use efficiency (i.e. the inverse of WFblue) of 36% under elevated temperature (+2 °C of ambient mean) and [CO2].

Sensitivity analysis

The results of the sensitivity analysis provide strong evidence of the CFE; that high [CO2] levels increase the rate of carbon assimilation through accelerated photosynthesis, with a doubling of CO2 concentration anticipated to increase yields of C3 crops like wheat by 30% as reported by Hatfield et al. (2011). The fact that the magnitude of the CFE was not constant for each time slice but varied according to the emission scenario, suggesting that there is an optimum [CO2] which maximizes the [CO2] fertilization effect, which in this case is the RCP4.5 (2080 s) concentration of 500 ppm. Broberg et al. (2019) noted optimum maximum grain yield stimulation around 600 ppm. The [CO2] sensitivity values reported in this study are within the bracket of CFE values reported by Müller et al. (2010), Fischer (2009) and McGrath & Lobell (2013). Fischer (2009) found an average CFE value of 0.024% increase in yield per 1 ppm increase in [CO2] for sub-Saharan Africa, while Müller et al. (2010) report a value of 0.099% per ppm. McGrath & Lobell (2013) report on an average CFE value of 0.028% per ppm for Africa.

The results of the study noted a TE for wheat which is within documented ranges. For instance, Zhao et al. (2017) estimated a global decline in wheat yield of −7.8 to −4.1% per °C, while Asseng et al. (2015) reported a mean global value of 6% per °C. From the results, it is evident that the CFE was greater than the TE, with the overall effect of both meteorological parameters on wheat yields resulting in gains of 1.8, 2.35, 1.81 and 4.3 t ha−1 for the RCP4.5 2050s, RCP4.5 2080s, RCP8.5 2050s and RCP8.5 2080s scenarios. This represents an overall increase of 22.78, 29.74, 26.91 and 54.43%, respectively. This contradicts findings by Challinor et al. (2014) who used a global meta-analysis and observed that yield decline to a unit increase in temperature (°C) was 4.90%, while yield increments for a unit increase in atmospheric CO2 (ppm) was 0.06%, suggesting that at a global level, TE overrides the CFE.

For crop water use, simulation runs without incorporating increases in [CO2] (i.e. maintaining CO2 at baseline levels of 390 ppm) resulted in high crop water use compared to the baseline. This implies that rising [CO2] levels lowered crop water use. High atmospheric CO2 levels decrease crop water use by reducing the need for large stomata thus reducing evapotranspiration (Hatfield & Dold 2019).

Climate change impacts on winter wheat yields, crop water use and the consumptive water footprint were determined for the first time in Zimbabwe, MMSC, using the CORDEX–CORE dataset and the Food and Agricultural Organizations’ AquaCrop model. The results of the study show that the winter wheat season for MMSC will get warmer, drier and more humid. All temperature parameters (minimum, maximum and mean) increased significantly (p < 0.05) when compared to the baseline period (1980–2010). Projected sunshine hours and average wind speed also showed signs of increase.

Climate shifts in the catchment increased wheat yields and decreased crop water use by maximum magnitudes of 58 and 7% for the worst-case RCP8.5 (2080s) scenario. The increase in yield for MMSC can be attributed to the unbalanced and antagonistic effect of the [CO2] fertilization effect and the TE. Sensitivity analysis quantified the [CO2] effect at 2.19, 2.91, 2.28 and 4.65 t ha−1 per ppm for the RCP4.5 2040s, RCP4.5 2080s, RCP8.5 2040s and RCP8.5 2080s scenarios, respectively, which was greater than the TE of −0.13, −0.19, −0.17 and −0.12 t ha−1 per °C for each respective scenario. Similarly, increments in [CO2] decreased crop water use by −13.20, −9.12, −23.67 and −46.89 mm for the RCP4.5 2040s, RCP4.5 2080s, RCP8.5 2040s and RCP8.5 2080s scenarios, respectively. Overall, the WFblue decreased by −19, −23, −24 and −38% under the respective future scenarios due to the simulated declines in wheat yields and crop water use.

This study is significant because it makes a contribution to the discourse on the beneficial responses of wheat to [CO2] under semi-arid conditions, which are largely under-reported. The study differs from previous works in that it is the first detailed study that has explored localized climate change impacts on wheat productivity in Zimbabwe. Projections in this study show that [CO2] fertilization can help to mitigate negative influences of climate change on wheat productivity and crop water use. Under climate change scenarios, [CO2] fertilization could enhance wheat yields and save more water within the sub-catchment.

Since the impacts of climate change on wheat within the catchment are favourable compared to other major crops like maize, this study proposes crop switching from maize to wheat as a climate change adaptation strategy. Globally, crop switching is expected to substantially reduce the damage from climate change in agriculture. These findings are relevant for adaptation planning in the context of smallholder farms who are most vulnerable to climate change since they mainly rely on maize production for both subsistence and livelihood.

The results of the study imply that good agronomic recommended agronomic practices and farm input rates can be valuable climate change adaptation strategies that can boost yields and increase water use efficiency. Generally, it is perceived that climate change adaptation strategies need to be novel or developed outside of the box. The results of this study can be used by local agronomists and water managers that good agronomic practices can be used to simultaneously boost food and water security while adapting to a changing climate.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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