Agroforestry's potential to buffer climate change impacts on maize yields in Dodoma, Tanzania

doi:10.21203/rs.3.rs-4945305/v1

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Agroforestry's potential to buffer climate change impacts on maize yields in Dodoma, Tanzania

https://doi.org/10.21203/rs.3.rs-4945305/v1

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Agricultural productivity in the semi-arid region of central Tanzania is low and projected to further decrease due to climate change, exacerbating the already high levels of food insecurity among the population of Tanzania. Agroforestry has been suggested as an adaptation strategy due to its multiple biophysical and socio-economic benefits, but its effectiveness is site- and context specific. Therefore, the objective of this study was to investigate the impact of climate change and agroforestry on maize yields in Dodoma, Tanzania. The process-based crop model APSIMX is used to quantify climate change impacts on maize yields at six sites in Dodoma considering different shared socio-economic pathways and periods. In addition, the effects of different levels of shading, temperature reductions from shading and the addition of biomass from trees on maize yields were analyzed. Four different indicators (climate change impact, agroforestry impact, agroforestry adaptation effect and climate change impact with adaptation) were calculated to provide a comprehensive evaluation. The simulation results indicate that climate change is likely to decrease maize yields by 23–33%, but agroforestry is found to partially mitigate this decline. The benefits of implementing agroforestry will increase in the future, providing a positive adaptation effect. A shading level of 20% was found to be most effective at reducing yield losses. However, under the high-emission scenario, the effectiveness of agroforestry in mitigating climate change impacts is simulated to decrease over time. These results highlight the urgency of climate change adaptation and demonstrate the possible benefits but also the limitations of agroforestry as an adaptation strategy under present and future climate conditions.

climate change

agroforestry

adaptation

maize yields

Tanzania

Climate change is likely to decrease maize yields by 23 % to 33 % in Dodoma, Tanzania
Agroforestry is likely to buffer but not reverse these yield trends
A shading level of 20 % was found to provide a greater adaptation effect than a 10% shade
The effectiveness of agroforestry in mitigating climate change impact decreases under high-emission scenarios and in the mid-term future

Climate variability and change are among the core reasons for yield losses and food insecurity in Tanzania, with the semi-arid regions of central Tanzania being particularly vulnerable (Mdemu, 2021; Randell et al., 2022; UN.ECA, 2023). Currently, approximately 59% of Tanzania's total population is food insecure, almost half of whom are severely threatened (FAO, 2023). High temperatures and variable rainfall have contributed to yield losses and food insecurity in the past (Afifi et al., 2014; Mkonda & He, 2018; Tarnavsky et al., 2018). Further declines in crop yields are expected under climate change if no appropriate adaptation measures are taken (Rowhani et al., 2011; Tumbo et al., 2020). For instance, a coupled climate-crop modeling study showed that maize yields are likely to decrease by 5–25% in central Tanzania toward the end of the century (Msongaleli et al., 2015). As the majority of households practice small-scale rainfed agriculture primarily for subsistence and income generation (National Bureau of Statistics, 2017), climate change is likely to affect indicators related to food security and poverty (Ahmed et al., 2011; Gebre & Rahut, 2021).

Consequently, adaptation of semi-arid agroecosystems to current and future climate conditions is needed. In that context, the promotion of agroforestry was proposed (Rosenstock et al., 2019; Skole et al., 2021; Terasaki Hart et al., 2023). Agroforestry refers to “land-use systems and technologies where woody perennials [...] are deliberately used on the same land management unit as agricultural crops and/or animals, in some form of spatial arrangement or temporal sequence" (Lundgren & Raintree, 1983; Nair et al., 2021). Potential biophysical and socio-economic benefits of agroforestry systems include the creation of microclimates with lower mean air temperatures (Villani et al., 2022), less soil erosion and improved soil fertility (Macedo et al., 2024), enhanced biodiversity (Zemp et al., 2023), the provision of income through food and wood production (Quandt, 2020) and synergies with climate change mitigation through carbon sequestration (P. Smith et al., 2020; Zomer et al., 2016). However, negative impacts are also possible when trees and crops compete for water and nutrients or when trees diminish the amount of light accessible to crops, thereby affecting their photosynthetic activity (Sanou et al., 2012; Sida, Baudron, Hadgu, et al., 2018).

A meta-analysis of studies comparing agroforestry and non-agroforestry practices in sub-Saharan Africa revealed that, compared with controls, agroforestry significantly increased crop yield, total soil nitrogen, soil organic carbon, and available phosphorus (Kuyah et al., 2019). It also reduces surface runoff and soil loss and improves infiltration rates and soil moisture content, making it a viable strategy for adapting to climate change (ibid.). A review of ecosystem services provided by parklands in the Sahel of West Africa showed that these agroforestry systems are capable of buffering climate risks and sustaining agricultural production (Bayala et al., 2014). An increase in vegetation cover is associated with an increase in the total carbon content, highlighting the potential of agroforestry for carbon sequestration and for reducing greenhouse gases (Bayala et al., 2020). Similarly, Faidherbia albida (F. albida) trees, which are common in parklands, can buffer wheat against climate extremes in the Central Rift Valley of Ethiopia (Sida, Baudron, Kim, et al., 2018). In the Dodoma region of Tanzania, a high tree cover canopy was associated with a reduction in land surface temperature (Villani et al., 2022). In addition, a survey of 900 small-scale farmers in the Dodoma and Morogoro regions in Tanzania revealed that adopters of climate-smart strategies, such as agroforestry, are more food secure (Brüssow et al., 2017).

Although agroforestry is promoted as a potential climate change adaptation strategy, studies modeling tree‒crop interactions are relatively rare. Major challenges in modeling agroforestry systems include the diverse arrangements of trees and crops, the interactions between different system components, and the long lifespan of the tree component (Luedeling et al., 2016). According to a study on the climate change mitigation potential of alley cropping (walnut with wheat) in southern France, agroforestry systems are able to reduce the expected increases in heat, drought, and nitrogen stress by 20–35% compared to monoculture (Reyes et al., 2021). Agroforestry was also found to be beneficial for coffee production in southeast Brazil, where 50% shade cover could maintain 75% of the area suitable for its production by 2050 (Gomes et al., 2020). In a virtual experiment on the effect of F. albida on millet growth in Senegal, Sow et al., (2024) reported that shading levels above 20% had adverse effects on aboveground biomass and yield. However, such levels of shading are unlikely to occur under F. albida, as it remains leafless during the growing season. Overall, the presence of F. albida was found to significantly increase millet yields due to higher organic nitrogen contents in the topsoil (ibid.). Similarly, Chemura et al. (2021) demonstrated that 20% shading in Ethiopia could reduce maize yield losses from climate change by 11%. An analysis of the profitability of gliricidia-maize agroforestry systems in Dodoma, Tanzania, based on simulated yields, concluded that these systems can contribute to increased income and food security (Swamila et al., 2021).

Despite this evidence, knowledge gaps remain concerning the effectiveness of agroforestry systems in buffering climate change impacts in the semi-arid Dodoma region in central Tanzania. Therefore, this paper seeks to answer the following research question: “What is the impact of climate change on maize yields in Dodoma, Tanzania, and how can agroforestry help to mitigate these impacts?" To answer this research question, the Agricultural Production System sIMulator (APSIM; Keating 2003, Holzworth 2014, Holzworth 2018) was used to first quantify the impact of climate change on maize yields in Dodoma and second to investigate the adaptation potential of agroforestry under current and future climate conditions.

2.1 Study area

The Dodoma region, situated in central Tanzania, East Africa (Fig. 1a), covers an area of 41.310 km² and includes the country´s capital. Most of the region has a hot semi-arid climate, with a long dry season spanning from late April to early December and a short rainy season from late December to early April (Mayaya et al., 2015; Villani et al., 2022, see SP-Fig. 1). The yearly average precipitation reaches 570 mm, with 85% of the rainfall occurring during the rainy season, while the average maximum and minimum temperatures are 31°C and 18°C, respectively (ibid.)

The primary land cover types in Dodoma are grassland, shrubland, and cropland. Only 8.5% of the area is covered by trees (Fig. 1b). Although Dodoma holds the largest share of land dedicated to agricultural activities within Tanzania, productivity is low, with maize yields falling 30% behind the national average (National Bureau of Statistics, 2021). The reasons for low productivity are heavy land degradation and frequently occurring droughts (Moore et al., 2020a). Mono- or mixed cropping are the dominant farming practices, and maize is the most widely planted crop. Other important food crops include sorghum, millet, cassava, sweet potato, groundnuts, beans and cowpea. The key cash crops are sunflower and sesame (National Bureau of Statistics, 2021). The average farm size in Dodoma is 3 ha, and the farm is managed manually mainly by using hand hoes, swords and, to a much lesser extent, animals (draft animals, ox plowing) and tractors (ibid.). Inorganic fertilizers are almost absent from Dodoma (< 1%), while 17.5% of the farmers use organic fertilizers during the long rainy season (ibid.). The predominant soil types in Dodoma are luvisols, cambisols, and lixisols (Poggio et al., 2021).

To represent Dodoma, six sites were randomly selected from each of the seven districts in Dodoma, except for Dodoma urban (Fig. 1b). To ensure validity, the selected locations were checked visually for agricultural production based on images from Google Maps (2023). The geographic coordinates of the sites and a summary of their main agro-climatic conditions can be found in the supplementary information (SI-Table 1 and SI-Fig. 1).

2.2 Agroforestry systems in Dodoma and selection of tree species

In Dodoma, approximately 10% of the farmers actively plant trees or practice agroforestry (Jha et al., 2021). Common agroforestry systems include home gardens, alley plantings, mixed intercropping, scattered trees on farmland and boundary plantings (Nnko et al., 2022; Yasu, 1999). Tree species found in agroforestry systems in Dodoma are diverse and include mango, eucalyptus, gliricidia sepium and grevillea robusta, among others (Table 1). A survey conducted in Dodoma revealed large variation in tree species, with the majority of observed species being present only in single instances (Volkamer & Steuernagel, unpublished). For the purpose of this study, we decided to mimic the effects of the non-legume tree species Grevillea robusta, which is commonly found in Tanzanian agroforestry systems, although not native (Charles et al., 2013; von Hellermann, 2016; Yasu, 1999). It is a deep-rooted and fast-growing member of the Proteacae family, which makes it suitable for use in agroforestry systems because competition for water and nutrients is expected to be low, and its timber is suitable for flooring and the manufacture of plywood and light furniture (Ong et al., 2000). Table 1 summarizes the tree species commonly utilized in agroforestry systems and on farms in semi-arid regions of Tanzania, including Dodoma, and their primary uses.

Table 1

Tree species of Tanzanian semi-arid agroforestry systems and their uses
Species	Primary uses¹
Species	Food	Fodder	Medicine	Timber	Fuel	Shade	Windbreak	Erosion control	N fixation
Acacia spp. (Kimaro et al., 2012; Maghembe & Redhead, 1982; Moore et al., 2020a; URT, 2006)		X		X	X	X			X
Adansonia digitata (Msalilwa et al., 2020; Volkamer & Steuernagel, unpublished)	X	X	X
Azadirachta indica (Moore et al., 2020a; URT, 2006; Volkamer & Steuernagel, unpublished)		X	X		X	X	X	X
Cordia monoica, Cordia sinensis² (Moore et al., 2020a; Volkamer & Steuernagel, unpublished)	X	X	X		X
Eucalyptus spp. (Maghembe & Redhead, 1982; URT, 2006)			X	X	X	X	X
Faidherbia albida (syn. Acacia albida) (Chamshama et al., 1998; Charles et al., 2013)		X		X	X	X			X
Gliricidia sepium (Akinnifesi et al., 2010; Swamila et al., 2020)		X			X	X			X
Grevillea robusta (Charles et al., 2013; Yasu, 1999)		X		X	X	X	X	X
Leuceana spp. (Chamshama et al., 1998; Maghembe & Redhead, 1982; URT, 2006)		X			X	X			X
Mangifera indica (Charles et al., 2013; Volkamer & Steuernagel, unpublished)	X				X		X	X
Senna spp. (Kimaro et al., 2012; Moore et al., 2020a; URT, 2006)		X	X	X	X		X	X
¹ Based on Charles et al., 2013; Mbuya et al., 1994; Moore et al., 2020b
² Authors` identification based on Mbuya et al., 1994

Constraints to agroforestry implementation include barriers to knowledge about suitable trees and their management, lack of awareness, deficits in tenure security, high implementation costs and little access to input and output markets for agroforestry products (Muthee et al., 2022). Farmers who are part of a project, who have access to seedlings, who are able to rent land and who perceive the land to be relatively fertile are more likely to adopt agroforestry (Jha et al., 2021). To encourage agroforestry and afforestation, the Tanzanian government has previously provided free seedlings to households (Yasu, 1999). This initiative is backed by non-governmental organizations and foundations that motivate local farmers to restore vegetation on their land by utilizing farmer-managed natural regeneration and rainwater harvesting methods (Moore et al., 2020a).

2.3 Model selection and evaluation

The process-based crop model APSIM Next Generation (APSIMX, v2022.11.7125.0) was used to simulate climate change and agroforestry impacts on maize yields across Dodoma (Holzworth et al., 2018). Among several models available to simulate agroforestry systems (e.g. Hi-sAFe, EPIC, and WaNuLCAS), we decided for APSIMX, as it covers the largest number of ecosystem properties relevant for climate change and agroforestry assessments (Kraft et al., 2021). The framework for simulating agroforestry using APSIMX was introduced by Huth et al. (2002) and was further developed by Smethurst et al. (2017), who showed that APSIMX is capable of accurately simulating crop yields and water content in a gliricidia-maize agroforestry systems in Kenya and Malawi. Since then, APSIMX has been applied in several other agroforestry studies (Chemura et al., 2021; A. M. Dilla et al., 2020), which showed that the model can be reliably employed to simulate maize productivity in agroforestry systems with up to 50% shading (A. Dilla et al., 2018).

APSIMX has also been extensively used to simulate maize yields and the response of maize growth to climate change factors, showing overall good performance in diverse model intercomparison studies (Bassu et al., 2014; Kimball et al., 2023). For this study, the maize cultivar “Katumani” was chosen because it is commonly used in Dodoma and has already been calibrated for the use in APSIM (Gurney et al., 2002; Patrick, 2017). The performance of APSIMX in simulating maize yields without the effect of trees (see baseline simulation, section 2.4) was assessed by comparing simulated yields to observed ones. Maize yield observations at the administrative level of Dodoma for the years 2009–2019 were provided by the Tanzanian Ministry of Agriculture (Laudien et al., 2020) and were complemented with values for the years 2003 and 2008, as published by the Tanzanian government (URT, 2012). In sum, maize yield observations included 13 data points covering the years 2003 and 2008 to 2019. To assess the model performance, several statistical metrics were calculated, such as Root Mean Square Error (RMSE), normalized Root Mean Square Error (nRMSE), Mean Absolute Error (MAE), model efficiency (EF), Willmott´s index of agreement (d) and percent bias (pbias).

2.4 Baseline simulations

First, a baseline simulation was set up to represent the most common farming practice in Dodoma, namely, maize mono-cropping, under recent past climate conditions. The baseline simulation covered 1988 to 2019, with the analysis focusing on the 30 years from 1990 to 2019. The simulation began one year earlier to ensure that the sowing rule was met. Climate data, including solar radiation and minimum and maximum temperatures, for the six study sites and for the years 1988 to 2019 were obtained from the W5E5v2.0 dataset at a spatial resolution of 0.5° (Lange et al., 2021). Daily precipitation data were taken from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is available at a fine spatial resolution of 0.05°. (Funk et al., 2015). A high spatial resolution is especially important for precipitation data because it better captures local variability and more accurately represents extreme weather events.

Site-specific physical and chemical soil properties were derived from SoilGrids (Poggio et al., 2021), which provides soil characteristics at six soil layers, reaching a depth of 2 m and is available at a spatial resolution of 250 × 250 m. Parameters U and Cona, which describe first-stage and second-stage evaporation, respectively, were set to 6 mm and 3 mm day^− 0.5 (Cichota et al., 2021). The initial plant available water (PAW) was set to 5%, corresponding to approximately 120 mm of PAW in the profile, following assumptions made by Msongaleli et al. (2017) for the area. A detailed overview of the physical and chemical soil properties of the six study sites can be found in the supplementary information (SI-Table 2 and SI-Table 3).

Surface organic matter was assumed to be 500 kg in accordance with Mourice et al. (2015), who modelled maize cropping systems in the region based on household survey information. The C:N ratio of that residue was assumed to be 60, as reported from a field experiment in central east Tanzania by Nishigaki et al. (2017). Sowing depth (7 mm), row spacing (75 cm), and plant density (3.3 plants/ha) were adopted from Lana et al. (2018), who studied the effectiveness of dry-soil planting of maize as an adaptation strategy to climate change in Dodoma. The sowing window was scheduled for a two-month period beginning on January 4th, reflecting the start of the growing season with a 50% probability (Yonah et al., 2006). The exact timing of sowing was triggered when 20 mm of rainfall was received within 10 days and at least 30 mm of extractable water was available (ibid). Since many farms lack financial resources for fertilizer purchase (Harou et al., 2022) and there is little overall availability of organic fertilizers due to low livestock density (Jackson & Mtengeti, 2005), a minor fertilization of 500 kg/ha manure with an N content of 1.27% in dry matter (Sileshi et al., 2017) was applied at sowing. Irrigation was not considered in the simulations. Initial water, surface organic matter and soil nutrients were reset every growing season to prevent carry-over effects between years and ensure similar conditions across all periods.

2.5 Climate change simulations

To assess the impact of climate change on maize yields in Dodoma, bias-adjusted climate data from the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b, Lange & Büchner, 2021) were obtained for the years 2015 to 2065. Of these years, two 30-year periods centered around 2030 (2016–2045, near-term future) and 2050 (2036–2065, mid-term future) were analyzed. The ISIMIP3b data are bias adjusted with the observational dataset W5E5v2.0 and statistically downscaled to a resolution of 0.5° × 0.5° using the downscaling method ISIMIP3BASD v2.5.0 (Lange, 2019). Of the 5 primary GCMs present in ISIMIP3b, MRI-ESM2-0 (MRI), IPSL-CM6A-LR (IPSL) and MPI-ESM1.2-HR (MPI) were selected because the first two reportedly simulate temperatures well across East Africa (Hersi et al., 2023; Makula & Zhou, 2022), and the latter shows good performance in simulating precipitation in the same area (Ayugi et al., 2021). Climate data from these models were extracted for two distinct Shared Socioeconomic Pathways (SSPs), namely, SSP 1-2.6 and SSP 5-8.5, which represent low- and high-emission scenarios, respectively.

2.6 Agroforestry simulations

When modeling crop yield responses to tree presence, their nutrient, water and light uptake, in addition to altered microclimate and biomass supply, are most relevant (Luedeling et al., 2016). Thus, the agroforestry simulation focused on tree shading, manipulation of the microclimate in response to shading and the addition of biomass from trees, largely following the approach described in Chemura et al. (2021).

Tree shading was implemented in the simulations by reducing incoming solar radiation in the weather files by 10% and 20%, as described by Chemura et al. (2021). While the 10% level of shade represents a case of light shading (e.g. trees planted at field boundary), the 20% shade level is comparable to the mean intensity of shade imposed by Grevillea robusta on maize reported from a field trial in semi-arid Kenya (Ong et al., 2000). The effect of shading on the microclimate was implemented by calculating the cooling effect of reduced radiation on temperature. The daily minimum and maximum temperatures of the baseline climate dataset (see section 2.4) were set into relation to solar radiation in a random forest regression model for each study site. In a second step, the obtained regressions were applied to climate datasets with reduced radiation to obtain temperatures reduced by shading for both the baseline and future climates. Model evaluations of each regression are shown in the supplementary information (SI-Fig. 2 and SI-Fig. 3).

Annual leaf litterfall and nutrient input from Grevillea robusta were derived from an experiment in Ethiopia in which litterfall traps were randomly positioned beneath the subcanopy for 12 months (Kassa et al., 2022). An annual litterfall of 325 kg/ha at a tree density of 18.67 trees/ha was found, leading to an N addition of 1.84 kg/ha. Based on these findings, the litter biomass from Grevillea robusta was assumed to be 325 kg/ha, amounting to 1.85 kg N/ha for 10% shade, and 650 kg litterfall/ha, amounting to 3.71 kg N/ha for 20% shade. To account for this biomass input, an additional manure fertilization at sowing with 0.0057% N in dry matter was implemented. The contribution of litterfall to phosphorus (P) supply was assumed to be minimal and was therefore not included in the simulation.

2.7 Estimation of climate change impacts, agroforestry and adaptation

The impacts of climate change and agroforestry on maize yields in Dodoma were quantified by calculating four different indicators: climate change impact, agroforestry impact, agroforestry adaptation effect and climate change impact with adaptation (Fig. 2). These indicators are calculated as outlined below with the following characters: ȳ refers to the long-term mean yield calculated as the mean of a 30-year simulation, and the subscript i refers to a specific combination of management and climate. i is further specified in some cases by i_BC, a simulation employing baseline climate, or i_CC, a simulation using climate projections. Management is specified by i_BM for a simulation with baseline management or i_AF for a simulation with agroforestry management. An asterisk (*) refers to a combination of those.

The impact of climate change on yields is quantified by the climate change impact (Eq. 1, Fig. 2a). It is evaluated only under baseline management and defined as the percentage difference in the long-term mean yield between the baseline and climate change simulations (Lobell, 2014). A negative climate change impact implies yield losses relative to the baseline climate.

Climate change impact\(\:=\:\frac{{{\stackrel{-}{y}}_{i}}_{CC*BM}-{{\stackrel{-}{y}}_{i}}_{BC*BM}}{{{\stackrel{-}{y}}_{i}}_{BC*BM}}\:\times\:\:100\)(1)

The effect of agroforestry on yields is depicted by the agroforestry impact (Eq. 2, Fig. 2b). It compares the long-term mean yield between the baseline and agroforestry management under the same climate. A positive agroforestry impact indicates yield gains due to agroforestry relative to the baseline management.

Agroforestry impact \(\:=\:{AFI}_{{i}_{AF}}=\:\frac{{{\stackrel{-}{y}}_{i}}_{AF}-{{\stackrel{-}{y}}_{i}}_{BM}}{{{\stackrel{-}{y}}_{i}}_{BM}}\:\times\:100\)(2)

Adaptation through agroforestry is quantified by the agroforestry adaptation effect (Eq. 3, Fig. 2c). This is the percentage difference between the agroforestry impact under baseline and projected climates relative to the yield under baseline climate with the respective agroforestry management practices. Positive values denote a greater agroforestry impact under climate change than under the baseline climate, which implies climate change adaptation (Lobell, 2014)

Agroforestry adaptation effect \(\:=\:\frac{{AFI}_{{i}_{AF*CC}}-{AFI}_{{i}_{AF*BC}}}{{{\stackrel{-}{y}}_{i}}_{AF*BC}}\times\:100\)(3)

Yield changes under climate change with agroforestry are quantified by the climate change impact with adaptation (Eq. 4, Fig. 2d). Here, long-term mean yields under climate change and agroforestry are compared to the baseline long-term mean yield. A value of 0 implies full preservation of baseline yields under climate change due to agroforestry. Negative values indicate climate change-induced yield losses despite the adoption of agroforestry, while positive values point toward yield gains.

Climate change impact with adaptation = \(\:\frac{{{\stackrel{-}{y}}_{i}}_{CC*AF}-{{\stackrel{-}{y}}_{i}}_{BC*BM}}{{{\stackrel{-}{y}}_{i}}_{BC*BM}}\) \(\:\times\:\:100\) (4)

3.1 Evaluation of model performance

The performance of APSIMX in simulating maize yields without agroforestry (baseline simulations) in Dodoma, Tanzania, is deemed acceptable, with a d of 0.77 and an EF of 0.14. The RMSE was 247 kg/ha, with an nRMSE of 8.8%, suggesting a present but acceptable deviation between the simulated and observed annual mean yields. A graphical comparison of the simulated and observed values revealed high correspondence in some years but deviations above the RMSE value for three years (Fig. 3). The mean error, as expressed by MAE, was 183 kg/ha. Overall, the model's tendency to either overestimate or underestimate yield was low, with a pbias of 0.1%. Based on these performance metrics, it is concluded that the baseline simulation satisfactorily predicted the annual mean maize yield of Dodoma.

3.2 Simulated climate change impact on maize yields

Simulation results suggest that climate change is likely to have a negative impact on maize yields in Dodoma. When averaged across all three GCMs, the baseline long-term mean yield of 763 kg/ha is projected to decrease by ~-23%, regardless of the considered SSP and period, except for SSP 5-8.5 in 2050 (Fig. 4). In the high-emission scenario and mid-term future, the negative impacts of climate change on maize yields are expected to increase to − 33%. However, the magnitude of climate change impacts and dynamics between SSPs and periods differ among GCMs. Crop yield simulations based on climate data from IPSL and MRI indicate a growing negative impact of climate change from 2030 to 2050, whereas MPI suggests the opposite, particularly under SSP 1.2-6. An increase of adverse effects from SSP 1-2.6 to SSP 5-8.5 was found for crop yield simulations based on IPSL and, to some extent, also for MRI and MPI.

3.3 Simulated agroforestry impact on maize yields

The adoption of agroforestry is expected to increase annual mean yields in Dodoma under baseline climate conditions (see Fig. 5). Specifically, long-term yields are projected to increase by 15%, reaching 874 kg/ha with 10% shade and by 14%, reaching 872 kg/ha with 20% shade. This indicates a positive effect of agroforestry. However, under climate change scenarios, a decrease in maize yields is expected to occur, even with the implementation of agroforestry. In all agroforestry climate change simulations, there is a consistent decrease in the long-term mean yields compared to those under the baseline conditions. This downward trend persists even with increased shading. Nevertheless, a positive agroforestry impact persists under climate change (Fig. 5). The mean across all climate change simulations suggests yield gains attributable to agroforestry of at least 25% and up to 42% relative to the baseline management. The benefits of agroforestry are particularly notable with 20% shade, as evidenced by an additional yield increase of 8% under SSP 1-2.6 and 4–6% under SSP 5-8.5 depending on the period.

A comparison of SSP 1-2.6 and SSP 5-8.5 reveals that the simulated agroforestry impact is considerably greater under the high-emission scenario and in the mid-term future. In both SSPs, the impact of agroforestry increases from 2030 to 2050. An exception is simulated for MPI SSP 1-2.6. Here, the agroforestry impact stagnated between 2030 and 2050 under 10% shade and declined until 2050 under 20% shade.

In summary, agroforestry is likely to have a positive impact on maize yields under both the baseline climate and climate change. A higher shade level of 20% did not result in a greater agroforestry impact under baseline climate conditions; however, it did result in an increased agroforestry impact under climate change. The agroforestry impact increased from 2030 to 2050 and from SSP 1-2.6 to SSP 5-8.5 in the mid-term future.

3.4 Simulated agroforestry adaptation effect on maize yields

Simulation results indicate a positive adaptation effect for all agroforestry climate change simulations (Fig. 6). This finding points to a greater agroforestry impact under climate change relative to the baseline climate. The mean across climate change simulations suggested adaptation effects of 4–13%. Adaptation effects are generally greater with 20% shade. Only the simulations based on IPSL exhibited similar effects at both shading levels under SSP 5-8.5 in 2050, as previously observed for the agroforestry impact. Adaptation effects are likely to increase toward 2050 in all GCMs. SSPs affect adaptation effects differently in relation to period and shade level. With 10% shade, the adaptation effects are the same regardless of the SSP in 2030, while they increase with SSP 5-8.5 in 2050. At 20% shading, the adaptation effects are slightly lower in SSP 5-8.5 than in SSP 1-2.6 during both periods. In summary, agroforestry has a positive adaptation effect. Adaptation effects increase with time under both shading regimes and SSPs. A 20% shade level was found to provide a greater adaptation effect than a 10% shade level.

3.5 Simulated climate change impact with agroforestry adaptation

The climate change impact with adaptation ranges from − 19–4% under 10% shade and from − 19–10% under 20% shade (Fig. 7). In general, the effectiveness of agroforestry in reducing climate change impacts was greater at the 20% shading level. An exception are simulations from IPSL 5-8.5 in 2050, as yields were similar under both shading regimes, as described above. The remaining climate change impact becomes more negative under the high-emission scenario and in the mid-term future. A reduction in climate change impacts through agroforestry was simulated at all sites even in the absence of adaptation effects due to the positive agroforestry impact. In summary, the simulations reveal that agroforestry has the potential to at least partially reduce climate change impacts and maintain yields. However, the effectiveness of agroforestry in sustaining yields and mitigating climate change impacts declines under the high-emission scenario.

4.1 Climate change impacts on maize yields

Simulation results have shown that climate change is likely to have a negative impact on maize yields, ranging from − 23% in 2030 under SSP 1-2.6 to -33% in 2050 under SSP 5-8.5 compared to the baseline period. Our findings are in line, though slightly more pessimistic, with other modeling studies that assessed climate change impacts on maize yields in semi-arid central Tanzania. For instance, Tumbo et al. (2020) found that maize yields in the Wami River sub-basin in Tanzania are likely to decrease by 5.3–40.7% in a “business-as-usual” scenario by mid-century. Another study on climate change impacts on maize yields in the neighboring Singida region projected yield losses of 16% in 2020–2060 and 20% in 2061–2099 compared to the 1980–2012 baseline (Volk et al., 2021). Likewise, Msongaleli et al. (2015) found that maize yields are likely to decrease by 5–25% in Singida and Dodoma toward the end of the century. Our results also show that climate change impacts increase notably under a high-emission scenario and in the mid-term future. This finding is in accordance with Volk et al. (2021), who reported a significant increase in climate change impacts on maize yields under a high-emission scenario. A decrease in maize yields under climate change likely results from temperature increases, as described in several studies (Volk et al., 2021; Waha et al., 2013). As temperatures increase, maize plants may experience several adverse effects, such as heat and drought stress and a shortened grain filling period due to accelerated growth (Mohammed et al., 2022).

4.2 Possible yield gains through agroforestry

Simulation results indicate a positive impact of agroforestry on maize yields, with average gains of ~ 14%. Our finding is in contrast to the literature, which suggests a neutral effect of non-legume trees on maize yields. More specifically, field trials of non-leguminous agroforestry in sub-Saharan Africa report maize yield declines in the presence of shade and competition from trees (Baier et al., 2023; Broadhead et al., 2003; Ndoli et al., 2017; Odhiambo et al., 2001). Gains in maize yield were reported only when maize was grown in combination with legume trees (A. M. Dilla et al., 2019; Mungai et al., 2001). Other studies show neutral effects of legume (Okorio & Maghembe, 1994) and non-legume trees (Tadesse et al., 2021).

These diverging findings on agroforestry impacts may be explained by the relatively low levels of shade in the present study. In field trials, a greater reduction in daily solar radiation was observed (A. M. Dilla et al., 2019; Ndoli et al., 2017). While this paper highlights the positive effects of 10 and 20% shade, it does not neglect possible negative effects at higher shading levels. Furthermore, neglected competition for water between trees and maize might have resulted in an overestimated positive agroforestry impact. While a positive tree effect on water availability through reduced evapotranspiration (Siriri et al., 2013) was introduced into the model and resulted in reduced water stress, the negative effect of tree water uptake was not implemented. However, the extent to which competition for water would have decreased agroforestry impact is unclear. For instance, a field trial from semi-arid Kenia showed a decline in agroforestry maize yield only when rainfall was less than 150 mm (Mungai et al., 2001), a condition not applicable in Dodoma. In addition, N stress was generally greater than water stress in the simulations and was thus the most yield-limiting factor. It can be concluded that competition for water would probably not have decreased the agroforestry impact notably.

4.3 Adaptation potential of agroforestry

Agroforestry exhibited positive adaptation effects for the maize crop component at most of the investigated sites, ranging from 4–9% with 10% shade and 8–13% with 20% shade. Simulated values of adaptation effects are among the estimates of adaptation provided in other studies (Carr et al., 2022; Lobell, 2014). Furthermore, they are within the range of agroforestry adaptation effects for maize yields ranging from − 39–30%, as reported for Ethiopia. Both the absence of adaptation and high adaptation effects were found depending on the region (Chemura et al., 2021).

The study's findings on agroforestry adaptation effects reveal an increasing impact of tree effects on maize yields in Dodoma under climate change. Specifically, shade and cooling appear to be crucial here, as yield losses are projected to be lower at a shading level of 20% under climate change. Negative effects from shade are either absent or outweighed by the benefits of temperature and evaporation reduction. Conversely, positive impacts of biomass and contained N from trees likely persisted but did not trigger adaptation effects, as N stress often does not confer adaptation benefits under sub-Saharan conditions (Kassie et al., 2015; Rurinda et al., 2015).

In general, agroforestry-induced adaptation was greatest under the high-emission scenario and in the mid-term future. This highlights the increasing benefits of trees for maize productivity with progressing climate change. Constraints to agroforestry adaptation for maize yields were also apparent, with two of the selected study sites showing no adaptation effects. This finding aligns with previous studies on site-specific levels of adaptation, as reported for Ethiopia (Chemura et al., 2021). The absence of adaptation effects was particularly notable at two low-yielding sites, highlighting the need for caution in making generalized recommendations.

4.4 Limitations and scope for further research

The major limitation of this study stems from an incomplete and static implementation of agroforestry in the simulations, which did not encompass all tree effects on soil properties and water resources. Only the positive effect of trees on soil water through evaporation reduction from cooling was modeled, although other agroforestry effects on soils, such as an increase in cation exchange capacity (Gindaba et al., 2005), gains in soil infiltration and water retention (Kuyah et al., 2019), are likely profitable for agroecosystems (Carsan et al., 2014; Dollinger & Jose, 2018) and gain importance under climate change (Allen et al., 2011). Tree water demand might induce competition for crop water availability (Odhiambo et al., 2001; D. M. Smith et al., 1999), but tree hydraulic lift can contribute to tree water supply (David et al., 2013). However, its contribution under climate change is unclear (Alagele et al., 2021). Consequently, our agroforestry model setup did not include yield effects of the specified dynamics in soil properties or soil water balance.

The simulated tree effects were further assumed to provide a static quantity of shade and litterfall every year. However, trees are likely to be negatively affected by climate change themselves (Watts et al., 2022). Diminishing success rate of tree plantings under harsher climate conditions might consequently also reduce the exploitation of agroforestry adaptation effects.

Uncertainties in simulation results are also inherent to climate change projections. Despite the use of three GCMs here, higher certainty could be achieved through the use of more GCMs and ensemble means (Ma et al., 2021). Furthermore, future crop yield simulations were carried out assuming a constant CO₂ concentration of 350 ppm instead of increasing CO₂ concentrations. However, as maize is a C4 crop, the yield benefit effect of increased CO₂ is expected to be marginal (Ainsworth & Long, 2021). In addition to GCMs, crop models have also demonstrated divergence in their results regarding the impact of climate change due to differences in internal processes and algorithms. The use of multiple crop models could have reduced these uncertainties (Martre et al., 2015).

Consequently, further research should focus on higher levels of shade in agroforestry. Investigations of agroforestry should further emphasize competition for water and reaction of trees to climate change. Finally, obtaining more comprehensive observational data on tree species and crop-tree interactions would be highly beneficial for enhancing process understanding and refining model calibration.

The objective of this paper was to assess the impact of climate change on maize yields in Dodoma, Tanzania, and to evaluate the potential of agroforestry as a strategy to mitigate possible negative effects of climate change. In conclusion, this paper confirms that maize yield losses of approximately 20–30% are to be expected in semi-arid Tanzania by mid-century if no appropriate adaptation measures are taken. The study also highlights the growing leverage of shade and cooling from trees in the face of climate change and highlights the contribution of agroforestry as a climate change adaptation strategy in maize cropping systems. Decomposing the effect of climate change, agroforestry and adaptation, our results reveal yield increases with agroforestry regardless of climate change and a net positive effect in future climate periods (adaptation effect). Both effects could aid in securing future maize yields in Dodoma and beyond. However, the study also demonstrates that agroforestry can buffer yield losses but may not completely reverse them. In addition, the effectiveness of agroforestry in mitigating climate change is site-specific, calling for caution and further research on low-yielding conditions. Finally, agroforestry exhibited a reduced ability to secure yields under a high-emission scenario. This highlights the need to effectively mitigate climate change, as not all yield losses can be prevented by adaptation. The study also emphasizes the importance of a timely implementation of adaptation measures, given that establishing agroforestry systems is a long-term task, yet its benefits are relevant even in the face of current climate conditions.

Author Contribution

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by J.S. The first draft of the manuscript was written by J.S. A substantial revision of the manuscript was made by A.H. All authors commented the manuscript and approved the final manuscript version.

Data Availability

The data that support the findings of this study are available from the authors upon request.

Afifi, T., Liwenga, E., & Kwezi, L. (2014). Rainfall-induced crop failure, food insecurity and out-migration in Same-Kilimanjaro, Tanzania. Climate and Development, 6(1), 53–60. https://doi.org/10.1080/17565529.2013.826128
Ahmed, S. A., Diffenbaugh, N. S., Hertel, T. W., Lobell, D. B., Ramankutty, N., Rios, A. R., & Rowhani, P. (2011). Climate volatility and poverty vulnerability in Tanzania. Global Environmental Change, 21(1), 46–55. https://doi.org/10.1016/j.gloenvcha.2010.10.003
Ainsworth, E. A., & Long, S. P. (2021). 30 years of free-air carbon dioxide enrichment (FACE): What have we learned about future crop productivity and its potential for adaptation? Global Change Biology, 27(1), 27–49. https://doi.org/10.1111/gcb.15375
Akinnifesi, F. K., Ajayi, O. C., Sileshi, G. W., Chirwa, P. W., & Chianu, J. (2010). Fertiliser trees for sustainable food security in the maize-based production systems of East and Southern Africa. A review. Agronomy for Sustainable Development, 30(3), 615–629. https://doi.org/10.1051/agro/2009058
Alagele, S. M., Jose, S., Anderson, S. H., & Udawatta, R. P. (2021). Hydraulic lift: Processes, methods, and practical implications for society. Agroforestry Systems, 95(4), 641–657. https://doi.org/10.1007/s10457-021-00614-w
Allen, D. E., Singh, B. P., & Dalal, R. C. (2011). Soil Health Indicators Under Climate Change: A Review of Current Knowledge. In B. P. Singh, A. L. Cowie, & K. Y. Chan (Eds.), Soil Health and Climate Change (Vol. 29, pp. 25–45). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-20256-8_2
Ayugi, B., Zhihong, J., Zhu, H., Ngoma, H., Babaousmail, H., Rizwan, K., & Dike, V. (2021). Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa. International Journal of Climatology, 41(15), 6474–6496. https://doi.org/10.1002/joc.7207
Baier, C., Gross, A., Thevs, N., & Glaser, B. (2023). Effects of agroforestry on grain yield of maize (Zea mays L.)—A global meta-analysis. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1167686
Bassu, S., Brisson, N., Durand, J.-L., Boote, K., Lizaso, J., Jones, J. W., Rosenzweig, C., Ruane, A. C., Adam, M., Baron, C., Basso, B., Biernath, C., Boogaard, H., Conijn, S., Corbeels, M., Deryng, D., De Sanctis, G., Gayler, S., Grassini, P., … Waha, K. (2014). How do various maize crop models vary in their responses to climate change factors? Global Change Biology, 20(7), 2301–2320. https://doi.org/10.1111/gcb.12520
Bayala, J., Sanou, J., Bazié, H. R., Coe, R., Kalinganire, A., & Sinclair, F. L. (2020). Regenerated trees in farmers’ fields increase soil carbon across the Sahel. Agroforestry Systems, 94(2), 401–415. https://doi.org/10.1007/s10457-019-00403-6
Bayala, J., Sanou, J., Teklehaimanot, Z., Kalinganire, A., & Ouédraogo, S. (2014). Parklands for buffering climate risk and sustaining agricultural production in the Sahel of West Africa. Current Opinion in Environmental Sustainability, 6, 28–34. https://doi.org/10.1016/j.cosust.2013.10.004
Broadhead, J. S., Black, C. R., & Ong, C. K. (2003). Tree leafing phenology and crop productivity in semi-arid agroforestry systems in Kenya. Agroforestry Systems, 58(2), 137–148. https://doi.org/10.1023/A:1026091921043
Brüssow, K., Faße, A., & Grote, U. (2017). Implications of climate-smart strategy adoption by farm households for food security in Tanzania. Food Security, 9(6), 1203–1218. https://doi.org/10.1007/s12571-017-0694-y
Carr, T. W., Mkuhlani, S., Segnon, A. C., Ali, Z., Zougmoré, R., Dangour, A. D., Green, R., & Scheelbeek, P. (2022). Climate change impacts and adaptation strategies for crops in West Africa: A systematic review. Environmental Research Letters, 17(5), 053001. https://doi.org/10.1088/1748-9326/ac61c8
Carsan, S., Stroebel, A., Dawson, I., Kindt, R., Mbow, C., Mowo, J., & Jamnadass, R. (2014). Can agroforestry option values improve the functioning of drivers of agricultural intensification in Africa? Current Opinion in Environmental Sustainability, 6, 35–40. https://doi.org/10.1016/j.cosust.2013.10.007
Chamshama, S. A. O., Mugasha, A. G., Kløvstad, A., Haveraaen, O., & Maliondo, S. M. S. (1998). Growth and yield of maize alley cropped with Leucaena leucocephala and Faidherbia albida in Morogoro, Tanzania. Agroforestry Systems, 40(3), 215–225. https://doi.org/10.1023/A:1006025005441
Charles, R. L., Munishi, P. K. T., & Nzunda, E. F. (2013). Agroforestry as Adaptation Strategy under Climate Change in Mwanga District, Kilimanjaro, Tanzania. International Journal of Environmental Protection, 3(11), 29–38. https://www.researchgate.net/profile/Emmanuel-Nzunda/publication/261245498_Agroforestry_as_Adaptation_Strategy_under_Climate_Change_in_Mwanga_District_ Kilimanjaro_Tanzania/links/02e7e533aa351bb68f000000/Agroforestry-as-Adaptation-Strategy-under-Climate-Change-in-Mwanga-District-Kilimanjaro-Tanzania.pdf
Chemura, A., Yalew, A. W., & Gornott, C. (2021). Quantifying Agroforestry Yield Buffering Potential Under Climate Change in the Smallholder Maize Farming Systems of Ethiopia. Frontiers in Agronomy, 3, 609536. https://doi.org/10.3389/fagro.2021.609536
Cichota, R., Vogeler, I., Sharp, J., Verburg, K., Huth, N., Holzworth, D., Dalgliesh, N., & Snow, V. (2021). A protocol to build soil descriptions for APSIM simulations. Methods X, 8. https://doi.org/10.1016/j.mex.2021.101566
David, T. S., Pinto, C. A., Nadezhdina, N., Kurz-Besson, C., Henriques, M. O., Quilhó, T., Cermak, J., Chaves, M. M., Pereira, J. S., & David, J. S. (2013). Root functioning, tree water use and hydraulic redistribution in Quercus suber trees: A modeling approach based on root sap flow. Forest Ecology and Management, 307, 136–146. https://doi.org/10.1016/j.foreco.2013.07.012
Dilla, A. M., Smethurst, P. J., Barry, K., Parsons, D., & Denboba, M. A. (2019). Tree pruning, zone and fertiliser interactions determine maize productivity in the Faidherbia albida (Delile) A. Chev parkland agroforestry system of Ethiopia. Agroforestry Systems, 93(5), 1897–1907. https://doi.org/10.1007/s10457-018-0304-9
Dilla, A. M., Smethurst, P. J., Huth, N. I., & Barry, K. M. (2020). Plot-Scale Agroforestry Modeling Explores Tree Pruning and Fertilizer Interactions for Maize Production in a Faidherbia Parkland. Forests, 11(11), Article 11. https://doi.org/10.3390/f11111175
Dilla, A., Smethurst, P. J., Barry, K., Parsons, D., & Denboba, M. (2018). Potential of the APSIM model to simulate impacts of shading on maize productivity. Agroforestry Systems, 92(6), 1699–1709. https://doi.org/10.1007/s10457-017-0119-0
Dollinger, J., & Jose, S. (2018). Agroforestry for soil health. Agroforestry Systems, 92(2), 213–219. https://doi.org/10.1007/s10457-018-0223-9
FAO. (2023). The State of Food Security and Nutrition in the World 2023. FAO; IFAD ; UNICEF ; WFP ; WHO ; https://openknowledge.fao.org/handle/20.500.14283/cc3017en
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2(1), 150066. https://doi.org/10.1038/sdata.2015.66
Gebre, G. G., & Rahut, D. B. (2021). Prevalence of household food insecurity in East Africa: Linking food access with climate vulnerability. Climate Risk Management, 33, 100333. https://doi.org/10.1016/j.crm.2021.100333
Gindaba, J., Rozanov, A., & Negash, L. (2005). Trees on farms and their contribution to soil fertility parameters in Badessa, eastern Ethiopia. Biology and Fertility of Soils, 42(1), 66–71. https://doi.org/10.1007/s00374-005-0859-2
Gomes, L. C., Bianchi, F. J. J. A., Cardoso, I. M., Fernandes, R. B. A., Filho, E. I. F., & Schulte, R. P. O. (2020). Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil. Agriculture, Ecosystems & Environment, 294, 106858. https://doi.org/10.1016/j.agee.2020.106858
Gurney, A. L., Taylor, A., Mbwaga, A., Scholes, J. D., & Press, M. C. (2002). Do maize cultivars demonstrate tolerance to the parasitic weed Striga asiatica? Weed Research, 42(4), 299–306. https://doi.org/10.1046/j.1365-3180.2002.00287.x
Harou, A. P., Madajewicz, M., Michelson, H., Palm, C. A., Amuri, N., Magomba, C., Semoka, J. M., Tschirhart, K., & Weil, R. (2022). The joint effects of information and financing constraints on technology adoption: Evidence from a field experiment in rural Tanzania. Journal of Development Economics, 155, 102707. https://doi.org/10.1016/j.jdeveco.2021.102707
Hersi, N. A. M., Mulungu, D. M. M., & Nobert, J. (2023). Prediction of future climate in semi-arid catchment under CMIP6 scenarios: A case study of Bahi (Manyoni) catchment in Internal Drainage basin (IDB), Tanzania. Physics and Chemistry of the Earth, Parts A/B/C, 129, 103309. https://doi.org/10.1016/j.pce.2022.103309
Holzworth, D., Huth, N. I., Fainges, J., Brown, H., Zurcher, E., Cichota, R., Verrall, S., Herrmann, N. I., Zheng, B., & Snow, V. (2018). APSIM Next Generation: Overcoming challenges in modernising a farming systems model. Environmental Modelling & Software, 103, 43–51. https://doi.org/10.1016/j.envsoft.2018.02.002
Jackson, H. L., & Mtengeti, E. J. (2005). Assessment of animal manure production, management and utilization in Southern Highlands of Tanzania. 17.
Jha, S., Kaechele, H., & Sieber, S. (2021). Factors influencing the adoption of agroforestry by smallholder farmer households in Tanzania: Case studies from Morogoro and Dodoma. Land Use Policy, 103, 105308. https://doi.org/10.1016/j.landusepol.2021.105308
Kassa, G., Bekele, T., Demissew, S., & Abebe, T. (2022). Leaves litterfall and nutrient inputs from four multipurpose tree/shrub species of homegarden agroforestry systems. Environmental Systems Research, 11(1), 29. https://doi.org/10.1186/s40068-022-00278-0
Kassie, B. T., Asseng, S., Rotter, R. P., Hengsdijk, H., Ruane, A. C., & Van Ittersum, M. K. (2015). Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Climatic Change, 129(1), 145–158. https://doi.org/10.1007/s10584-014-1322-x
Kimaro, A. A., Sileshi, G. W., Mpanda, M., Swai, E., Nyoka, B. I., Majule, A. E., Perfect, J., & Kundhlande, G. (2012). Evidence-Based Scaling-Up of Evergreen Agriculture for Increasing Crop Productivity, Fodder Supply and Resilience of the Maize-Mixed and Agro-Pastoral Farming Systems in Tanzania and Malawi. Africa RISING research program final technical report for the jumpstrat projects. https://cgspace.cgiar.org/bitstream/handle/10568/69125/ar_esa_evergreen.pdf
Kimball, B. A., Thorp, K. R., Boote, K. J., Stockle, C., Suyker, A. E., Evett, S. R., Brauer, D. K., Coyle, G. G., Copeland, K. S., Marek, G. W., Colaizzi, P. D., Acutis, M., Alimagham, S., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Bertuzzi, P., Constantin, J., … Zhou, W. (2023). Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models. Agricultural and Forest Meteorology, 333, 109396. https://doi.org/10.1016/j.agrformet.2023.109396
Kraft, P., Rezaei, E. E., Breuer, L., Ewert, F., Große-Stoltenberg, A., Kleinebecker, T., Seserman, D.-M., & Nendel, C. (2021). Modelling Agroforestry’s Contributions to People—A Review of Available Models. Agronomy, 11(11), Article 11. https://doi.org/10.3390/agronomy11112106
Kuyah, S., Whitney, C. W., Jonsson, M., Sileshi, G. W., Öborn, I., Muthuri, C. W., & Luedeling, E. (2019). Agroforestry delivers a win-win solution for ecosystem services in sub-Saharan Africa. A meta-analysis. Agronomy for Sustainable Development, 39(5), 47. https://doi.org/10.1007/s13593-019-0589-8
Lana, M. A., Vasconcelos, A. C. F., Gornott, C., Schaffert, A., Bonatti, M., Volk, J., Graef, F., Kersebaum, K. C., & Sieber, S. (2018). Is dry soil planting an adaptation strategy for maize cultivation in semi-arid Tanzania? Food Security, 10(4), 897–910. https://doi.org/10.1007/s12571-017-0742-7
Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geoscientific Model Development, 12(7), 3055–3070. https://doi.org/10.5194/gmd-12-3055-2019
Lange, S., & Büchner, M. (2021). ISIMIP3b bias-adjusted atmospheric climate input data (v1.1). ISIMIP Repository. https://data.isimip.org/10.48364/data.isimip.org
Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Schmied, H. M., Hersbach, H., Buontempo, C., & Cagnazzo, C. (2021). WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0). https://doi.org/10.48364/ISIMIP.342217
Laudien, R., Schauberger, B., Makowski, D., & Gornott, C. (2020). Robustly forecasting maize yields in Tanzania based on climatic predictors. Scientific Reports, 10(1), 19650. https://doi.org/10.1038/s41598-020-76315-8
Lobell, D. B. (2014). Climate change adaptation in crop production: Beware of illusions. Global Food Security, 3(2), 72–76. https://doi.org/10.1016/j.gfs.2014.05.002
Luedeling, E., Smethurst, P. J., Baudron, F., Bayala, J., Huth, N. I., van Noordwijk, M., Ong, C. K., Mulia, R., Lusiana, B., Muthuri, C., & Sinclair, F. L. (2016). Field-scale modeling of tree–crop interactions: Challenges and development needs. Agricultural Systems, 142, 51–69. https://doi.org/10.1016/j.agsy.2015.11.005
Lundgren, B. O., & Raintree, J. B. (1983). Sustained Agroforestry. https://pdf.usaid.gov/pdf_docs/PNABC621.pdf
Ma, D., Jing, Q., Xu, Y.-P., Cannon, A. J., Dong, T., Semenov, M. A., & Qian, B. (2021). Using ensemble-mean climate scenarios for future crop yield projections: A stochastic weather generator approach. Climate Research, 83, 161–171. https://doi.org/10.3354/cr01646
Macedo, R. S., Moro, L., dos Santos Sousa, C., de Almeida Alves Carneiro, K., Campos, M. C. C., de Bakker, A. P., & Beirigo, R. M. (2024). Agroforestry can improve soil fertility and aggregate-associated carbon in highland soils in the Brazilian northeast. Agroforestry Systems, 98(5), 1167–1179. https://doi.org/10.1007/s10457-023-00875-7
Maghembe, J. A., & Redhead, J. F. (1982). Agroforestry: Preliminary results of intercropping acacia, eucalyptus, and leucaena with maize and beans. In C. L. Keswani & B. J. Ndunguru (Eds.), Intercropping: Proceedings of the Second Symposium on Intercropping in Semi-Arid Areas, held at Morogoro, Tanzania, 4–7 August 1980 (pp. 43–49). Symposium on Intercropping in Semi-Arid Areas, Ottawa. International Development Research Centre.
Makula, E. K., & Zhou, B. (2022). Coupled Model Intercomparison Project phase 6 evaluation and projection of East African precipitation. International Journal of Climatology, 42(4), 2398–2412. https://doi.org/10.1002/joc.7373
Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., Boote, K. J., Ruane, A. C., Thorburn, P. J., Cammarano, D., Hatfield, J. L., Rosenzweig, C., Aggarwal, P. K., Angulo, C., Basso, B., Bertuzzi, P., Biernath, C., Brisson, N., Challinor, A. J., … Wolf, J. (2015). Multimodel ensembles of wheat growth: Many models are better than one. Global Change Biology, 21(2), 911–925. https://doi.org/10.1111/gcb.12768
Mayaya, H. K., Opata, G., & Kipkorir, E. C. (2015). Understanding Climate Change and Manifestation of its Driven Impacts in the Semi Arid Areas of Dodoma Region, Tanzania. Ethiopian Journal of Environmental Studies and Management, 8(4), Article 4. https://doi.org/10.4314/ejesm.v8i4.2
Mbuya, L. P., Msanga, H. P., Ruffo, C K, Birnie, A., & Tengnas, B. (1994). Useful Trees and Shrubs for Tanzania: Identification, Propagation, and Management for Agricultural and Pastoral Communities. Regional Soil Conservation Unit, Swedish International Development Authority.
Mdemu, M. V. (2021). Community’s Vulnerability to Drought-Driven Water Scarcity and Food Insecurity in Central and Northern Semi-arid Areas of Tanzania. Frontiers in Climate, 3. https://doi.org/10.3389/fclim.2021.737655
Mkonda, M. Y., & He, X. (2018). Climate variability and crop yields synergies in Tanzania’s semiarid agroecological zone. Ecosystem Health and Sustainability, 4(3), 59–72. https://doi.org/10.1080/20964129.2018.1459868
Mohammed, A., Yimer, E., Gessese, B., & Feleke, E. (2022). Predicting Maize (Zea mays) productivity under projected climate change with management options in Amhara region, Ethiopia. Environmental and Sustainability Indicators, 15, 100185. https://doi.org/10.1016/j.indic.2022.100185
Moore, E., van Dijk, T., Asenga, A., Bongers, F., Sambalino, F., Veenendaal, E., & Lohbeck, M. (2020a). Species Selection and Management Under Farmer Managed Natural Regeneration in Dodoma, Tanzania. Frontiers in Forests and Global Change, 3, 563364. https://doi.org/10.3389/ffgc.2020.563364
Moore, E., van Dijk, T., Asenga, A., Bongers, F., Sambalino, F., Veenendaal, E., & Lohbeck, M. (2020b). Species Selection and Management Under Farmer Managed Natural Regeneration in Dodoma, Tanzania. Frontiers in Forests and Global Change, 3. https://doi.org/10.3389/ffgc.2020.563364
Mourice, S. K., Tumbo, S. D., Nyambilila, A., & Rweyemamu, C. L. (2015). Modeling potential rain-fed maize productivity and yield gaps in the Wami River sub-basin, Tanzania. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, 65(2), 132–140. https://doi.org/10.1080/09064710.2014.976252
Msalilwa, U. L., Ndakidemi, P. A., Makule, E. E., & Munishi, L. K. (2020). Demography of baobab (Adansonia digitata L.) population in different land uses in the semi-arid areas of Tanzania. Global Ecology and Conservation, 24, e01372. https://doi.org/10.1016/j.gecco.2020.e01372
Msongaleli, B. M., Rwehumbiza, F., Tumbo, S. D., & Kihupi, N. (2015). Impacts of Climate Variability and Change on Rainfed Sorghum and Maize: Implications for Food Security Policy in Tanzania. Journal of Agricultural Science, 7(5), p124. https://doi.org/10.5539/jas.v7n5p124
Msongaleli, B. M., Tumbo, S. D., Kihupi, N. I., & Rwehumbiza, F. B. (2017). Performance of Sorghum Varieties under Variable Rainfall in Central Tanzania. International Scholarly Research Notices, 2017(1), 2506946. https://doi.org/10.1155/2017/2506946
Mungai, D. N., Stigter, C. J., Coulson, C. L., Ng’ Ang’ A, J. K., Netondo, G. W. S., & Umaya, G. O. (2001). Understanding yields in alley cropping maize (Zea mays L.) and Cassia siamea Lam. Under semi-arid conditions in Machakos, Eastern Kenya. Journal of Environmental Sciences, 13(3), 291–298.
Muthee, K., Duguma, L., Majale, C., Mucheru-Muna, M., Wainaina, P., & Minang, P. (2022). A quantitative appraisal of selected agroforestry studies in the Sub-Saharan Africa. Heliyon, 8(9), e10670. https://doi.org/10.1016/j.heliyon.2022.e10670
Nair, P. K. R., Kumar, B. M., & Nair, V. D. (2021). Definition and Concepts of Agroforestry. In P. K. R. Nair, B. M. Kumar, & V. D. Nair (Eds.), An Introduction to Agroforestry: Four Decades of Scientific Developments (pp. 21–28). Springer International Publishing. https://doi.org/10.1007/978-3-030-75358-0_2
National Bureau of Statistics. (2017). National Panel Survey 2014–2015, Wave 4. World Bank, Development Data Group. https://doi.org/10.48529/Y3QJ-D018
National Bureau of Statistics. (2021). 2019/20 National Sample Census of Agriculture: Key Finding Report for Crops, Livestock and Fish Farming. Dodoma, Tanzania. https://www.nbs.go.tz/nbs/takwimu/Agriculture/2019-20_Agri_Census_Main_Report.pdf
Ndoli, A., Baudron, F., Schut, A. G. T., Mukuralinda, A., & Giller, K. E. (2017). Disentangling the positive and negative effects of trees on maize performance in smallholdings of Northern Rwanda. Field Crops Research, 213, 1–11. https://doi.org/10.1016/j.fcr.2017.07.020
Nishigaki, T., Sugihara, S., Kilasara, M., & Funakawa, S. (2017). Soil nitrogen dynamics under different quality and application methods of crop residues in maize croplands with contrasting soil textures in Tanzania. Soil Science and Plant Nutrition, 63(3), 288–299. https://doi.org/10.1080/00380768.2017.1332454
Nnko, L. E., Kashaigili, J. J., Monela, G. C., Munishi, P., Lyimo, P. J., & Nyemo, C. (2022). The Extent of Adoption of Agroforestry Systems and Practices and Conservation Implications in Kilombero District, Tanzania. East African Journal of Forestry and Agroforestry, 5(1), Article 1. https://doi.org/10.37284/eajfa.5.1.766
Odhiambo, H. O., Ong, C. K., Deans, J. D., Wilson, J., Khan, A. A. H., & Sprent, J. I. (2001). Roots, soil water and crop yield: Tree crop interactions in a semi-arid agroforestry system in Kenya. Plant and Soil, 235(2), 221–233. https://doi.org/10.1023/A:1011959805622
Okorio, J., & Maghembe, J. A. (1994). The growth and yield of Acacia albida intercropped with maize (Zea mays) and beans (Phaseolus vulgaris) at Morogoro, Tanzania. Forest Ecology and Management, 64(2), 183–190. https://doi.org/10.1016/0378-1127(94)90292-5
Ong, C. K., Black, C. R., Wallace, J. S., Khan, A. A. H., Lott, J. E., Jackson, N. A., Howard, S. B., & Smith, D. M. (2000). Productivity, microclimate and water use in Grevillea robusta-based agroforestry systems on hillslopes in semi-arid Kenya. Agriculture, Ecosystems & Environment, 80(1), 121–141. https://doi.org/10.1016/S0167-8809(00)00144-4
Patrick, A. N. (2017). Determinants of derived demand for improved maize seeds in rural mainland Tanzania [Sokoine University of Agriculture]. https://www.suaire.sua.ac.tz/handle/123456789/2181
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021
Quandt, A. (2020). Contribution of agroforestry trees for climate change adaptation: Narratives from smallholder farmers in Isiolo, Kenya. Agroforestry Systems, 94(6), 2125–2136. https://doi.org/10.1007/s10457-020-00535-0
Randell, H., Gray, C., & Shayo, E. H. (2022). Climatic conditions and household food security: Evidence from Tanzania. Food Policy, 112, 102362. https://doi.org/10.1016/j.foodpol.2022.102362
Reyes, F., Gosme, M., Wolz, K. J., Lecomte, I., & Dupraz, C. (2021). Alley Cropping Mitigates the Impacts of Climate Change on a Wheat Crop in a Mediterranean Environment: A Biophysical Model-Based Assessment. Agriculture, 11(4), 356. https://doi.org/10.3390/agriculture11040356
Rosenstock, T. S., Dawson, I. K., Aynekulu, E., Chomba, S., Degrande, A., Fornace, K., Jamnadass, R., Kimaro, A., Kindt, R., Lamanna, C., Malesu, M., Mausch, K., McMullin, S., Murage, P., Namoi, N., Njenga, M., Nyoka, I., Paez Valencia, A. M., Sola, P., … Steward, P. (2019). A Planetary Health Perspective on Agroforestry in Sub-Saharan Africa. One Earth, 1(3), 330–344. https://doi.org/10.1016/j.oneear.2019.10.017
Rowhani, P., Lobell, D. B., Linderman, M., & Ramankutty, N. (2011). Climate variability and crop production in Tanzania. Agricultural and Forest Meteorology, 151(4), 449–460. https://doi.org/10.1016/j.agrformet.2010.12.002
Rurinda, J., van Wijk, M. T., Mapfumo, P., Descheemaeker, K., Supit, I., & Giller, K. E. (2015). Climate change and maize yield in southern Africa: What can farm management do? Global Change Biology, 21(12), 4588–4601. https://doi.org/10.1111/gcb.13061
Sanou, J., Bayala, J., Teklehaimanot, Z., & Bazié, P. (2012). Effect of shading by baobab (Adansonia digitata) and néré (Parkia biglobosa) on yields of millet (Pennisetum glaucum) and taro (Colocasia esculenta) in parkland systems in Burkina Faso, West Africa. Agroforestry Systems, 85(3), 431–441. https://doi.org/10.1007/s10457-011-9405-4
Sida, T. S., Baudron, F., Hadgu, K., Derero, A., & Giller, K. E. (2018). Crop vs. tree: Can agronomic management reduce trade-offs in tree-crop interactions? Agriculture, Ecosystems & Environment, 260, 36–46. https://doi.org/10.1016/j.agee.2018.03.011
Sida, T. S., Baudron, F., Kim, H., & Giller, K. E. (2018). Climate-smart agroforestry: Faidherbia albida trees buffer wheat against climatic extremes in the Central Rift Valley of Ethiopia. Agricultural and Forest Meteorology, 248, 339–347. https://doi.org/10.1016/j.agrformet.2017.10.013
Sileshi, G. W., Nhamo, N., Mafongoya, P. L., & Tanimu, J. (2017). Stoichiometry of animal manure and implications for nutrient cycling and agriculture in sub-Saharan Africa. Nutrient Cycling in Agroecosystems, 107(1), 91–105. https://doi.org/10.1007/s10705-016-9817-7
Siriri, D., Wilson, J., Coe, R., Tenywa, M. M., Bekunda, M. A., Ong, C. K., & Black, C. R. (2013). Trees improve water storage and reduce soil evaporation in agroforestry systems on bench terraces in SW Uganda. Agroforestry Systems, 87(1), 45–58. https://doi.org/10.1007/s10457-012-9520-x
Skole, D. L., Mbow, C., Mugabowindekwe, M., Brandt, M. S., & Samek, J. H. (2021). Trees outside of forests as natural climate solutions. Nature Climate Change, 11(12), 1013–1016. https://doi.org/10.1038/s41558-021-01230-3
Smith, D. M., Jackson, N. A., Roberts, J. M., & Ong, C. K. (1999). Root distributions in a Grevillea robusta-maize agroforestry system in semi-arid Kenya. Plant and Soil, 211(2), 191–205. https://doi.org/10.1023/A:1004635414462
Smith, P., Calvin, K., Nkem, J., Campbell, D., Cherubini, F., Grassi, G., Korotkov, V., Le Hoang, A., Lwasa, S., McElwee, P., Nkonya, E., Saigusa, N., Soussana, J.-F., Taboada, M. A., Manning, F. C., Nampanzira, D., Arias-Navarro, C., Vizzarri, M., House, J., … Arneth, A. (2020). Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification? Global Change Biology, 26(3), 1532–1575. https://doi.org/10.1111/gcb.14878
Sow, S., Senghor, Y., Sadio, K., Vezy, R., Roupsard, O., Affholder, F., N’dienor, M., Clermont-Dauphin, C., Gaglo, E. K., Ba, S., Tounkara, A., Balde, A. B., Agbohessou, Y., Seghieri, J., Sall, S. N., Couedel, A., Leroux, L., Jourdan, C., Diaite, D. S., & Falconnier, G. N. (2024). Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth. Field Crops Research, 306, 109206. https://doi.org/10.1016/j.fcr.2023.109206
Swamila, M., Philip, D., Akyoo, A. M., Sieber, S., Bekunda, M., & Kimaro, A. A. (2020). Gliricidia Agroforestry Technology Adoption Potential in Selected Dryland Areas of Dodoma Region, Tanzania. Agriculture, 10(7), Article 7. https://doi.org/10.3390/agriculture10070306
Swamila, M., Philip, D., Akyoo, A., Manda, J., Mwinuka, L., Smethurst, P. J., Sieber, S., & Kimaro, A. (2021). Profitability of Gliricidia-Maize System in Selected Dryland Areas of Dodoma Region, Tanzania. https://doi.org/10.18452/23859
Tadesse, S., Gebretsadik, W., Muthuri, C., Derero, A., Hadgu, K., Said, H., & Dilla, A. (2021). Crop productivity and tree growth in intercropped agroforestry systems in semi-arid and sub-humid regions of Ethiopia. Agroforestry Systems, 95(3), 487–498. https://doi.org/10.1007/s10457-021-00596-9
Tarnavsky, E., Chavez, E., & Boogaard, H. (2018). Agro-meteorological risks to maize production in Tanzania: Sensitivity of an adapted Water Requirements Satisfaction Index (WRSI) model to rainfall. International Journal of Applied Earth Observation and Geoinformation, 73, 77–87. https://doi.org/10.1016/j.jag.2018.04.008
Terasaki Hart, D. E., Yeo, S., Almaraz, M., Beillouin, D., Cardinael, R., Garcia, E., Kay, S., Lovell, S. T., Rosenstock, T. S., Sprenkle-Hyppolite, S., Stolle, F., Suber, M., Thapa, B., Wood, S., & Cook-Patton, S. C. (2023). Priority science can accelerate agroforestry as a natural climate solution. Nature Climate Change, 13(11), 1179–1190. https://doi.org/10.1038/s41558-023-01810-5
Tumbo, S. D., Mutabazi, K. D., Mourice, S. K., Msongaleli, B. M., Wambura, F. J., Mzirai, O. B., Kadigi, I. L., Kahimba, F. C., Mlonganile, P., Ngongolo, H. K., Sangalugembe, C., Rao, K. P. C., & Valdivia, R. O. (2020). Integrated Assessment of Climate Change Impacts and Adaptation in Agriculture: The Case Study of the Wami River Sub-basin, Tanzania. In J. I. Matondo, B. F. Alemaw, & W. J. P. Sandwidi (Eds.), Climate Variability and Change in Africa: Perspectives, Experiences and Sustainability (pp. 115–136). Springer International Publishing. https://doi.org/10.1007/978-3-030-31543-6_10
UN.ECA. (2023). African regional overview of food security and nutrition 2023 | United Nations Economic Commission for Africa. United National Economic Comission for Africa. https://www.uneca.org/african-regional-overview-of-food-security-and-nutrition-2023
URT, (United Republic of Tanzania). (2006). National Sample Census 2002/2003. Volume Va Regional Report: Dodoma Region. https://www.nbs.go.tz/nbs/takwimu/Agriculture/DODOMA%20REGION%20REPORT.pdf
URT, (United Republic of Tanzania). (2012). Sample Census of Agriculture 2007/2008. Volume Va: Regional Report: Dodoma Region. https://www.nbs.go.tz/nbs/takwimu/Agr2007-08/Dodoma_Region_Report.zip
Villani, L., Castelli, G., Sambalino, F., Almeida Oliveira, L. A., & Bresci, E. (2022). Reprint of: Influence of trees on landscape temperature in semi-arid agro-ecosystems of East Africa. Biosystems Engineering, 223, 209–223. https://doi.org/10.1016/j.biosystemseng.2022.10.004
Volk, J., Gornott, C., Sieber, S., & Lana, M. A. (2021). Can Tanzania’s adaptation measures prevent future maize yield decline? A simulation study from Singida region. Regional Environmental Change, 21(4), 94. https://doi.org/10.1007/s10113-021-01812-z
von Hellermann, P. (2016). Tree Symbolism and Conservation in the South Pare Mountains, Tanzania. Conservation and Society, 14(4), 368. https://doi.org/10.4103/0972-4923.197615
Waha, K., Müller, C., & Rolinski, S. (2013). Separate and combined effects of temperature and precipitation change on maize yields in sub-Saharan Africa for mid- to late-21st century. Global and Planetary Change, 106, 1–12. https://doi.org/10.1016/j.gloplacha.2013.02.009
Watts, M., Hutton, C., Mata Guel, E. O., Suckall, N., & Peh, K. S.-H. (2022). Impacts of climate change on tropical agroforestry systems: A systematic review for identifying future research priorities. Frontiers in Forests and Global Change, 5. https://doi.org/10.3389/ffgc.2022.880621
Yasu, H. (1999). The Diffusion Process of planting (Grevillea robusta) among Rural Households in North-Central Tanzania. African Study Monographs, 20(3), 119–145. https://doi.org/10.14989/68186
Yonah, I. B. B., Oteng’i, S. B. B., & Lukorito, C. B. (2006). Assessment of the Growing Season over the Unimodal Rainfall Regime Region of Tanzania. Tanzania Journal of Agricultural Sciences, 7(1), Article 1. https://www.ajol.info/index.php/tjags/article/view/109811
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.-E., Xu, P., Ramoino, F., & Arino, O. (2022). ESA WorldCover 10 m 2021 v200 (Version v200) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7254221
Zemp, D. C., Guerrero-Ramirez, N., Brambach, F., Darras, K., Grass, I., Potapov, A., Röll, A., Arimond, I., Ballauff, J., Behling, H., Berkelmann, D., Biagioni, S., Buchori, D., Craven, D., Daniel, R., Gailing, O., Ellsäßer, F., Fardiansah, R., Hennings, N., … Kreft, H. (2023). Tree islands enhance biodiversity and functioning in oil palm landscapes. Nature, 618(7964), 316–321. https://doi.org/10.1038/s41586-023-06086-5
Zomer, R. J., Neufeldt, H., Xu, J., Ahrends, A., Bossio, D., Trabucco, A., van Noordwijk, M., & Wang, M. (2016). Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets. Scientific Reports, 6(1), 29987. https://doi.org/10.1038/srep29987

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Agroforestry's potential to buffer climate change impacts on maize yields in Dodoma, Tanzania

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Abstract

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Highlights

1 Introduction

2 Methods and material

2.1 Study area

2.2 Agroforestry systems in Dodoma and selection of tree species

2.3 Model selection and evaluation

2.4 Baseline simulations

2.5 Climate change simulations

2.6 Agroforestry simulations

2.7 Estimation of climate change impacts, agroforestry and adaptation

3 Results

3.1 Evaluation of model performance

3.2 Simulated climate change impact on maize yields

3.3 Simulated agroforestry impact on maize yields

3.4 Simulated agroforestry adaptation effect on maize yields

3.5 Simulated climate change impact with agroforestry adaptation

4 Discussion

4.1 Climate change impacts on maize yields

4.2 Possible yield gains through agroforestry

4.3 Adaptation potential of agroforestry

4.4 Limitations and scope for further research

5 Conclusion

Declarations

Author Contribution

Data Availability

References

Additional Declarations

Supplementary Files

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