Climate change impacts on crop productivity in Africa and South Asia

The projected climate impacts by crop type, aggregated into sub-regions for Africa (southern, central, east, west, Sahel and northern) and S. Asia (southern and south east) are shown in figure 1. The data relate to the projected mean yield variations for each crop type, for all crop models, all GCM models and all time slices. Where published, the confidence intervals are shown. The primary data are then summarized in table 1. The significance of the mean yield reduction, compared to zero change, was tested using Student's t-test (see supplementary information (available at stacks.iop.org/ERL/7/034032/mmedia) for the countries included and statistical methods employed). Overall, a mean yield reduction of −8% was identified in both Africa and S. Asia, with significant reductions projected for wheat (−12%), maize (−7%), sorghum (−3%) and millet (−9%). However, when the data were broken down by time slice, only projected changes for the 2050s and beyond were significant. It is important to note that projected yield data were not necessarily available for all crops in all regions, so the lack of a significant response may in part be due to the absence, or limited number of studies for certain crops and/or regions. Furthermore, the results include all reported yield projections, for all time slices, for all GCM combinations (whether single or ensemble) and for all crop modelling approaches (whether based on simple or more complex biophysical approaches). This highlights the magnitude of variability that exists when all possible causes of uncertainty are included. Subsequent analyses then disaggregate the meta-database (by time slice, by climate model, and by crop model) to highlight which factors might be contributing most to the projected variations.

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For Africa, most of the evidence reviewed projected a yield reduction of up to −40%, across all crop types and sub-regions, although there was a large magnitude of variation in the reported impact (figure 1(a)). Only a small number (n = 9) of studies accounting for around 30 observations reported yield increases, mainly for maize grown in East, West and Northern Africa. Most evidence in the scientific literature relates to maize (n = 106). Rice, sugarcane and yams account for only 6 of 162 observations for Africa despite the fact that these crops collectively account for 27% of the total cropped area in Africa (FAO 2010). The estimated mean yield change for West Africa (−12.5%) correlates closely with a median yield loss of 11% reported by other research based on data from 16 published studies (Roudier et al 2011). Central Africa has by far the fewest estimates of climate change impacts on crop productivity, highlighting the point that major gaps in climate impact knowledge still exist for particular crops and regions, despite the apparent large evidence base within the scientific literature.

For S. Asia, a similar negative projected impact is shown, but with wider variations among studies (figure 1(b)). The majority of data observations (75%) relate to South Asia (India, Nepal, Pakistan and Sri Lanka) compared to South East Asia (Bangladesh and Bhutan). Most evidence, not surprisingly, relates to rice (n = 38) grown in India and some important subsistence crops, such as millet and cassava, for which the climate impact data is incredibly sparse. For sugarcane too the lack of evidence is surprising given that it is the third most important crop (after paddy rice and wheat) in terms of value of production in S. Asia (FAO 2010). Robust, scientific evidence on the impacts of climate change on cane yield, as well as for wheat and maize, is thus still very limited.

The projected impacts are also crop and region specific. For example, in Africa, the crops with significant projected yield reductions include wheat (−17%), maize (−5%), sorghum (−15%) and millet (−10%). However, in S. Asia, of the crops reported, only maize (−16%) and sorghum (−11%) are projected to have significant yield reductions (table 1). Despite a relatively large dataset (n = 43) the projected mean yield change for rice is not significantly different from zero, with some sources (40%) projecting an increase; others (60%) a decrease; but for several studies, the range straddles the 'no effect' line. The variation in projections for rice is also smaller for Africa than for S. Asia, although this largely reflects a smaller number of studies available for meta-analysis. The frequency distributions of the projected mean yield change for all crops (figure 2) shows nearly half of all observations falling within the −15% (−20% to −10%) and −5% (−10% to 0%) bins, although the data for Africa extend across a wider range.

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The systematic review also considered projected yield changes over time, based on the reported time slices used for GCM downscaling and crop modelling. Figure 3 shows, for example, the 'box and whisker' plot for the projected yield changes (%) for all crops and regions. The 'box' defines the upper (25%) and lower (75%) quartiles; the line shown in the middle of the box represents the median and the 'whiskers' indicate the 10th (lower) and 90th (upper) deciles. Any outliers in the dataset are shown as individual points. Overall the median projected yield change is negative in all time slices and with smaller decreases projected for the 2020s and 2030s compared to the 2050s and 2080s. However, in all cases the interdecile range (10%–90%) spans the zero variation line and some of the studies reviewed even project a large increase in mean yield. As the climate signal increases, the general trend is that the mean yield productivity decreases with time. However, for most crops and regions, there were either too few studies or no consistent message regarding changes in yield impact over time. Only projections beyond the 2050s and 2080s for maize and sorghum, respectively, were found to be significantly different from zero.

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Projections of crop productivity under climate change are subject to uncertainty that can arise from several sources (Challinor et al 2009). Increasing the number of climate models that are used for crop simulations reduced the median range and outliers about the mean change in yield (figure 4). Thus, variation in crop projections was reduced by increasing the number of climate models in an ensemble. However, whether such reduction in uncertainty due to ensemble size is simply a confounding of errors or is physically meaningful is not certain (Smith and Stern 2011).

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Increasing the complexity of crop simulation also had a strong impact on yield variation. Figure 5 shows the projected yield changes for studies based on either statistical models (n = 62), simple simulation models such as the FAO agro-ecological zoning approach (n = 33), or more complex simulation using biophysical crop models (e.g. CERES, Cropsyst, InfoCrop). Here the variation around the mean increases with model complexity—however, this could be attributed to a number of reasons, including either the larger number of observations using complex simulation modelling approaches (n = 154), and/or the combined influence of other 'effect modifiers'. In this context, the choice of climate modelling approach could account for some of the uncertainty and the large observed range in yield projections (figure 4). This suggests other factors are accounting for the wide spread in modelled yield changes, including the assumptions used in the crop modelling. Others (Lobell and Burke 2010, Lobell et al 2011) suggest that such imperfect knowledge of crop modelling inputs is indeed a major constraint since many simulation models were developed for use in temperate systems but lack sufficient data for accurate calibration and validation in developing countries such in as Africa where field data is often sparse or non-existent. Others cite the lack of site specific parametrization of the crop management options and cultivars (Müller et al 2011), and the risk of model overtuning (Challinor et al 2007). Recent research (White et al 2011) stressed that the widely varying protocols used by such assessments could also be a potential source of bias that would constrain cross-study syntheses.

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The impact of climate change on crop yield will be a balance between yield increases due to elevated CO₂ concentrations (Drake et al 1997), any negative effects due to warmer mean temperatures mediated through changes in phenology (Craufurd and Wheeler 2009) and extreme temperature (Wheeler et al 2000) and any rainfall limitations to growth, all of which depend on the specific combination of climate projection and the modelled crop response. In the simulations of our meta-analysis, 33.7% and 23.4% of C3 and C4 simulations showed a positive change in yield to climate change, and 66.3% and 76.6% showed a negative change, respectively. A Chi-squared test on that data (pooled for Africa and Asia) showed no significant difference in the number of positive and negative responses between C3 and C4 crops (p = 0.07). This is surprising given expectations of greater benefits to crop yield of elevated CO₂ concentrations for C3 crops compared with those with a C4 photosynthetic pathway and suggest a low sensitivity of crop simulations to elevated CO₂ compared with other weather variables.

The results presented here, like all climate impact assessments need to be interpreted with caution. Systematic reviews are most suited to replicated, controlled (e.g. clinical) trials where the effect of an intervention can be clearly identified. However, the impact of climate change on crop yield is generally estimated from a combination of biophysical crop growth models and climate models, a process which itself is prone to error by the simplification of real-world complexity (Müller et al 2011). As a result, some of the variation in yield projection is likely to be due to the differences between the different crop modelling approaches used and the alternative general circulation models (GCM) selected by the researchers. Systematic reviews are also dependent on good primary data. In this study, we were limited to assessing the modelled outputs from a wide range of climate change impact studies, all of which inevitably contain so-called 'effect modifiers' or reasons for heterogeneity. These include, for example, different assumed emission scenarios and ensembles, different agro-ecological conditions, and varying assumptions regarding crop varieties, agricultural systems (e.g. rainfed or irrigated) and levels of mechanization and crop husbandry. The spatial scale of enquiry, ranging from detailed local case studies to broad scale regional assessments is also likely to explain some of the reported variation. The modelled yield changes thus inevitably integrate both the projected impacts of climate change and the varying combined effects of many other factors implicit in each of the reported studies. However, notwithstanding these limitations, this study has allowed the results from all available modelling studies to be synthesized in a systematic and unbiased manner, and for general but very important conclusions to be drawn.

Climate change will clearly threaten farming livelihoods of the rural poor in Africa and S. Asia, particularly where soils and climate are already marginal for production and where limited access to agricultural knowledge and technology will hamper their ability to adapt (CCAFS 2009, Lobell et al 2008). Some autonomous adaptations, such as shifting planting dates, modifying crop rotations or the uptake of pre-existing crop varieties will help offset some negative impacts of climate change. However, it is reported that the greatest benefits in food insecure regions are likely to arise from more expensive adaptation measures including the development of new crop varieties and uptake of new technologies including, for example, the expansion of irrigation infrastructure (Lobell et al 2008). These will require substantial investments by farmers, governments and development agencies. It is thus vital that any policy decisions to support their implementation, particularly aid investments, are informed by a synthesis of the best available evidence, and not distorted by single studies.

Prioritization of farm level adaptations to climate change will also need to account for the different crops grown within a target region, local farmer attitudes to risk and the time horizons over which investments are made (Lobell et al 2008). An important distinction here is between food or subsistence crops grown for local consumption (e.g. cassava, millet) and commodity crops such as sugarcane grown for export. Most of the food crops in this study are grown under rainfed conditions and will be highly susceptible to future changes in soil moisture availability. In contrast, commodity crops are more typically dependent on irrigation to maintain yields, which should help buffer the impacts of changes in rainfall and temperature. However, research shows that even irrigated commodity cropping in Africa will be at risk (Knox et al 2010). In many African countries, commodity cropping is a major employer in rural communities, so climate change could affect local populations involved in both subsistence and commercial crop production, albeit in differing ways.

Our findings provide policy makers and practitioners with a robust assessment of the likely impacts on crop yield, and the areas and crops where attention should be focused. Research (Müller et al 2011) shows that there is still no comprehensive continent-wide assessment for all major cropping systems in Africa. This study helps to address that research gap by providing the first systematic review of climate impacts on crop productivity in two major food insecure regions, by highlighting important differences in impact between individual crops, continental regions and geographical sub-regions. The analysis has also helped to identify where major gaps in knowledge still exist on the impacts of climate change on yield. For example, figure 2(a) in the supplementary information (available at stacks.iop.org/ERL/7/034032/mmedia) shows the spatial distribution of maize production in Africa derived from crop statistics (FAOSTAT 2012) in relation to the reported number of yield observations for maize. It highlights where there is a growing evidence base, for example, in Kenya, Zimbabwe and Cameroon, and conversely the numerous countries where there is still very limited climate impact evidence despite the importance of maize to agricultural production in those areas (e.g. Nigeria, Tanzania, Malawi). Whether maize yield changes are generally more positive where there are more studies, requires further study (figure 2(b)). Despite the yield change uncertainty and lack of robust data for large parts of Africa and S. Asia, this systematic review provides useful evidence for informing national governments, regional decision makers, and international organizations and in guiding international policy makers and research agendas. Whilst research effort moves towards identifying appropriate adaptation options and responses, for Africa (at least in the short to medium term), it is important that the evidence base on climate change impact on crop productivity continues in earnest.