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Less negative impacts of climate change on crop yields in West Africa in the new CMIP6 climate simulations ensemble [1]

['Benjamin Sultan', 'Espace-Dev', 'University Montpellier', 'Ird', 'University Guyane', 'University Reunion', 'University Antilles', 'University Avignon', 'Maison De La Télédétection', 'Montpellier Cedex']

Date: 2023-12

Food insecurity is among one of the greatest risks posed by climate change in Africa, where 90 to 95% of African food production is rainfed and a large proportion of the population already faces chronic hunger and malnutrition. Although, several studies have found robust evidence of future crop yield losses under climate change scenarios, there is wide variation among crops and regions as well as large modeling uncertainties. A large part of this uncertainty stems from climate projections, as climate models may differ in simulating future changes in precipitation and temperature, which could lead to different future crop production scenarios. This work examines the impacts of climate change on crop yields of maize, millet and sorghum in West Africa using climate change projections from the Coupled Model Intercomparison Project 5th Phase (CMIP5) and from the new generation of climate models from the Coupled Model Intercomparison Project 6th Phase (CMIP6). We use the SIMPLACE crop modeling framework to simulate historical and future crop yields, and bootstrap techniques to evaluate projected changes in crop productivity between the CMIP5 and CMIP6 ensembles. Using the new generation of climate models CMIP6, we find that the negative crop yield projections shown by CMIP5 simulations are largely reduced, with even large increases in crop yields when the effect of atmospheric CO 2 concentration is considered in the crop model. These differences in crop yield impacts between the CMIP5 and CMIP6 simulations are mainly due to different climate projections of temperature and precipitation in West Africa; CMIP6 projections being significantly wetter and cooler by mid-century and to a lesser extent by the end of the century. Such results highlight the large uncertainties that remain in assessing the impacts of climate change in the region and the consequent difficulty for end-users to anticipate adaptation strategies.

Funding: This work was supported by AFD and IRD (67000 euros to BS) within the CECC project ( http://www.projet-cecc.org/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2023 Sultan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This work examines the impacts of climate change on crop yields of maize, millet and sorghum in West Africa using the CMIP5 climate models and the new generation of CMIP6 climate models. We use the SIMPLACE crop modeling framework to simulate historical and future crop yields and bootstrap techniques to evaluate projected changes in crop productivity between the CMIP5 and CMIP6 ensembles.

The Intergovernmental Panel on Climate Change’s Sixth Assessment Report (AR6) warned that although Africa’s contribution to historical greenhouse gas emissions is among the lowest of historical greenhouse gas emissions, the continent is particularly vulnerable to human-induced climate change [ 1 ]. Food insecurity is among one of the major risks posed by climate change in Africa, as 90 to 95% of African food production is rainfed [ 2 ], and a large proportion of the population already faces chronic hunger and malnutrition [ 3 ]. Several studies have found robust evidence that climate change is already negatively affecting crop production in Africa [ 4 – 7 ]. The last IPCC report conducted a meta-analysis of crop yields projections under climate change across 35 studies and 1040 locations and cases, showing average crop yield losses with increasing global warming for staple crops in Africa [ 1 ]. Other meta-analyses of the literature [ 8 – 10 ] have shown that projected impacts on yield in several African countries are mainly negative (−10% to −6%), but there is wide variation among crops and regions, as well as large modeling uncertainties, making it difficult to provide a robust assessment of future yield changes at the regional scale. These uncertainties in estimating future yield changes are driven by adaptation responses such as changes in planting dates, varieties, irrigation, and other management practices. For example, Carr et al. [ 8 ] showed that adaptation strategies could increase crop yields under climate change from −4% to +19% depending on the adaptation option, relative to a no-adaptation scenario. In addition, uncertainty in future crop yield projections also comes from crop responses to increasing atmospheric CO 2 concentrations, which could mitigate climate-induced losses but with considerable variations across crop models [ 1 , 11 ]. A large part of this uncertainty also comes from climate projections where climate models might differ in simulating future rainfall in regions such as West Africa [ 12 ] or East Africa [ 13 ], with large differences between Global Climate Models (GCMs) and high-resolution regional climate models projections [ 13 ] and large biases of climate models for current conditions when compared to observations [ 14 ]. Furthermore, even if a robust warming is still simulated throughout the twenty-first century, the release of the Coupled Model Intercomparison Project 6th Phase (CMIP6; [ 15 ]) has shown important changes in future climate projections when compared to the previous one from the Coupled Model Intercomparison Project 5th Phase (CMIP5; [ 16 ]). For some regions such as the western Sahel, mean summer precipitation is projected to decrease drastically in CMIP5 projections while CMIP6 shows an increase over nearly 40% of the land area [ 13 ]. Changes in temperature projections have also been shown between CMIP5 and CMIP6 climate models [ 17 ]. It is likely that such changes in terms of climate change projections will drive changes in future crop yield projections, as precipitation and temperature are among the key drivers of crop simulations. Indeed, a recent study compared future global changes in crop production of major crops (i.e. maize, rice, soybean and wheat) from nine crop models forced by 45 CMIP5 and 34 CMIP6 climate projections. The authors found substantial differences in the total variance of projected changes in crop productivity between the CMIP5 and CMIP6 ensembles [ 18 ]. Global crop yield projections changes in CMIP5 and CMIP6 were also investigated in the study by Jägermeyr et al. [ 19 ] which showed greater yield losses for maize, soybean and rice and more crop yield gains for wheat using CMIP6 simulations and the more recent ensemble of crop models from the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison [ 19 ]. However, to the best of our knowledge, there are currently no studies evaluating changes in projected yields of major staple food crops such as millet and sorghum in Africa from CMIP5 to CMIP6.

2 Material and methods

2. 1 Climate simulations We used daily outputs of global climate models from Coupled Model Intercomparison Project 5th Phase (CMIP5; [16]) and the Coupled Model Intercomparison Project 6th Phase (CMIP6; [15]). The climate data required for crop modeling included the daily near-surface minimum and maximum air temperature, precipitation, global radiation, and wind speed. These data were extracted in netcdf format in the CICLAD platform (https://mesocentre.ipsl.fr/), and we selected only climate models with complete daily fields available at the time we performed the crop simulations (S1 Table). The CMIP5 models cover the period 1950–2099, including 29 GCMs for the historical period 1950–2005 and for 2006–2099 period, 29 models for the RCP8.5 projections, 27 GCMs for the RCP4.5 projections and 20 GCMs for the RCP2.6 projections. Similarly, CMIP6 models cover the historical period 1979–2014 and the future scenarios (SSP126, SSP245 and SSP585) for 2015–2100, including 18 GCMs for maximum temperature, 16 GCMs for minimum temperature and 19 GCMs for precipitation. At the time of the crop simulations, only five CMIP6 models were available with the daily fields of global radiation and wind speed required for crop modeling. The data were first rescaled at 0.5° spatial horizontal resolution and then bias corrected using the CDF-t method, following the protocol described by Famien et al. [14], using the EWEMBI forcing data as the reference dataset. This bias-correction method is widely used in Africa and globally both as a statistical downscaling model and as a bias-correction method [20–22]. We considered three different time periods: the reference period (1975–2004 for CMIP5 and CMIP6) and future horizons in the short term (2035–2064) and long term (2065–2094) future horizons.

2.2 Climate indices In order to investigate the key drivers of yield changes in the future, we computed a set of annual climate indices that are known to have an impact of crop yields in West Africa. These indices are average minimum and maximum temperatures during crop growth and accumulated rainfall from planting to maturity. Planting and maturity dates are provided by the SIMPLACE crop model. We also used a more sophisticated index, the annual growing degree days (GDD), as defined by Lobell et al. [23]. The growing degree days (GDD) are used to estimate the growth and development of various crops during the growing season. The concept is that development occurs only when the temperature exceeds the base temperature (T base ). It was estimated using daily minimum and maximum temperature data at each site: where t is an individual time step (hour) within the growing season, T t is the average temperature during that time step (determined by interpolating between minimum and maximum temperature with a sin curve), and N is the number of hours between planting and maturity [23]. Here, the GDD was calculated using T base = 8°C and T opt = 30°C as defined by Lobell et al. [23] for maize in Africa. Similar values of T base and T opt were used for millet and sorghum. Indeed, Kiniry and Bonhomme [24] indicated that a T base ranging from 7°C to 9°C could be used for several crops such as maize, sorghum, pearl millet, rice, soybean, and sunflower. The T opt is more variable from one crop to another, ranging from 26° to 32°C depending on the crop but it can also vary across different genotypes of the same crop. We thus chose to have the same T opt of 30°C for the three crops since it has been widely used for maize [23] but also used for modeling crop phenology of sorghum (see for instance [25]).

2.3 The SIMPLACE crop modeling framework The SIMPLACE crop modeling framework (www.simplace.net) was used in this study. It combines the LINTUL5 crop growth model [26], a biophysical model that simulates plant growth, biomass, and yield as a function of climate, soil properties, and crop management using experimentally derived algorithms. LINTUL5 simulates plant growth under potential, water and nitrogen (N), phosphorus (P) and potassium (K) limitation. Plant growth is simulated in LINTUL5 as a function of intercepted radiation and radiation use efficiency. Plant development times are simulated using daily temperature sums (thermal time) and crop thermal time requirements from emergence to anthesis and from anthesis to maturity, respectively. LINTUL5 has been widely used in various studies at field, national and continental scales. A modified version of the soil water balance Slimwater [27,28] model simulates the soil water balance and crop water uptake using the FAO Penman-Monteith equation with the reference crop and dual crop coefficient method [29]. The change in soil water content is simulated on a daily time scale using the SlimwaterModified in several variable soil layers estimated from the volumes of soil evaporation, crop water uptake, surface runoff and seepage below the root zone. The daily demand and uptake of N, P and K demand, nitrogen stress and movement in the soil profile together with leaching of soil mineral nitrogen (Nitrate-N and Ammonium-N) are simulated by the NPKDemandSlimNitrogen model. The SlimwaterModified module simulates the turnover and leaching of nitrate and ammonium related to soil water dynamics with input data related to soil water content and soil water fluxes on the daily time scale. The SoilCN model provides the daily total mineral N. The dead roots are transferred to the “root litter pool” in the SoilCN model, which also simulates soil organic carbon and soil nitrogen dynamics using multiple litter pools and soil layers as well as three soil organic matter pools (see [30]). Simulated hourly canopy temperature [31] is used as an input to the heat stress model [32] when the hourly temperature is above a critical threshold temperature around the flowering period. Daily air temperature is used to drive all other processes. Stress indices are calculated daily for the water and nutrient limitation and range from 0.0 to 1.0.

2.4 Soil data Soil data were obtained from the Harmonized World Soil Database (HWSD; https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). The original data had a resolution of 30 arcseconds by 30 arcseconds. Physical and chemical characteristics of topsoil (0–30 cm) and subsoil (30–100 cm) were selected for clay, silt, sand, bulk density, organic carbon, and available water capacity. Data were aggregated to the 0.5° horizontal resolution grid of the climate data by selecting the soil class with the largest area in each grid cell. The parameters such as soil water at field capacity, wilting point and saturation were calculated using the Pedotransfer functions (https://cran.r-project.org/web/packages/medfate/index.html), and the Van Genuchten parameters were determined from texture class.

2.5 Simulation setup The SIMPLACE model has previously been used to assess climate change impacts in the Sudan savanna of West Africa [33,34]. Simulations were performed using CMIP5 and CMIP6 climate inputs at a spatial horizontal resolution of 0.5° for maize, pearl millet and sorghum. For each crop, short (90 days) and long (120 days) varieties were considered. In addition, a photoperiod-sensitive variety of pearl millet and sorghum was also simulated using a photoperiod-sensitive phase that depends on both temperature and astronomical day length [35]. This allows us to sample the diversity of crop responses to climate change, which may vary widely from one crop to another. Indeed, Sultan et al. [36] showed that short (90 days) millet and sorghum cultivars are more affected by climate change than longer duration and photoperiod-sensitive cultivars. Defrance et al. [37] compared simulations of climate change impacts on crop yields of millet, sorghum and maize in West Africa and found that maize is much more sensitive to water stress variations than millet and sorghum. A total of eight different varieties (Mais90, Mais120, Mil90, Mil120, MilPP, Sor90, Sor120, SorPP) were simulated under current fertilizer use (N limitation) and intensified fertilizer (no NPK limitation). Crop growth parameters were derived from [33], who used experimental datasets of local varieties in Africa for calibration. Since these crops are typically grown without irrigation, simulations were conducted under rainfed conditions. Simulations were run for each year from 1975 to 2094, and each emission scenario and simulated crop yields were then averaged over the three time horizons: the baseline (1975–2004) and the two future periods 2035–2064 and 2065–2094. We did not consider any varietal adaptation between baseline or climate change scenarios. Rising atmospheric [CO 2 ] has the potential to increase crop water productivity (ratio of crop yield to total crop water use) by enhancing photosynthesis and reducing leaf-level transpiration, but the amplitude of this effect is still uncertain in crop models [11]. Therefore, we replicated the simulations under ambient historical [CO 2 ] (no change in CO 2 concentration) and elevated [CO 2 ]. There was no effect of elevated [CO 2 ] on radiation use efficiency (RUE) for all crops since maize, pearl millet and sorghum are C 4 crops. The CO 2 concentration for each period and each scenario is shown in S2 Table. To evaluate the performance of the crop model in simulating historical yield anomalies, we conducted an additional simulation using the reanalyzed EWEMBI datasets as climate inputs over the 1979–2013 period. The simulated historical yields were compared with observed yield data from the FAO dataset. The FAO dataset is provided at the country level for each crop without variety differentiation. Then, simulated yields were aggregated across varieties at the country level for comparison to the FAO dataset following the method of Porwollik et al. [38]. The correlation between observed and predicted crop yield time series was obtained after removing a linear trend in both time series. Indeed, FAO yield time series over the 1979–2013 period show increasing yields over time (Burkina-Faso, Senegal), while others show yield losses (Chad, Niger). Although climate variability may influence these trends, non-climatic factors such as land degradation, management and economic crises are likely to be the main drivers. Since these non-climatic factors are not included in the crop model, it is necessary to remove trends in crop yields. Thus, we removed a linear trend in both observed and simulated yields, as suggested by Sultan et al. [36]. In this study, a set of crop-specific masks were used for yield analysis. This type of mask is often used to identify pixels where specific land crops are present. The resulting land use datasets represent the harvested area of several different crops in the world around the year 2000 [39]. Three different crops in West Africa (maize, millet, and sorghum) were selected for use in this study. In order to avoid extreme values, we have set a threshold for the harvested area and we took when the average number of hectares harvested per land area of a grid was above 100 per hectare. We then interpolated the crop mask dataset, originally reported at a spatial resolution of 5 minutes by 5 minutes in latitude by longitude (approximately 10 km by 10 km), to a horizontal spatial resolution of 0.5° to have the same spatial resolution as the crop yield simulations.

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[1] Url: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000263

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