Quantifying Agroforestry Yield Buffering Potential Under Climate Change in the Smallholder Maize Farming Systems of Ethiopia

Abstract

Agroforestry is a promising adaptation measure for climate change, especially for low external inputs smallholder maize farming systems. However, due to its long-term nature and heterogeneity across farms and landscapes, it is difficult to quantitatively evaluate its contribution in building the resilience of farming systems to climate change over large areas. In this study, we developed an approach to simulate and emulate the shading, micro-climate regulation and biomass effects of multi-purpose trees agroforestry system on maize yields using APSIM, taking Ethiopia as a case study. Applying the model to simulate climate change impacts showed that at national level, maize yield will increase by 7.5 and 3.1 % by 2050 under RCP2.6 and RCP8.5, respectively. This projected increase in national-level maize yield is driven by maize yield increases in six administrative zones whereas yield losses are expected in other five zones (mean of −6.8% for RCP2.6 and −11.7% for RCP8.5), with yields in the other four zones remaining stable overtime. Applying the emulated agroforestry leads to increase in maize yield under current and future climatic conditions compared to maize monocultures, particularly in regions for which yield losses under climate change are expected. A 10% agroforestry shade will reduce maize yield losses by 6.9% (RCP2.6) and 4.2 % (RCP8.5) while 20% shade will reduce maize yield losses by 11.5% (RCP2.6) and 11% (RCP8.5) for projected loss zones. Overall, our results show quantitatively that agroforestry buffers yield losses for areas projected to have yield losses under climate change in Ethiopia, and therefore should be part of building climate-resilient agricultural systems.

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

“…Based on statistical metrics, the evaluation of the performance showed that fourteen GCMs (Table 10) have sufficient performance when evaluated with observations from Harare Metropolitan gauging stations (d > 0.7, r > 0.7 and R 2 > 0.5), with the exception of MRI-CGCM3, observed to have the lowest determination coefficient of 0.47. This may suggest that the general circulation model could have other specific years that were not properly simulated [63]; however, the analysis shows that most GCMs displayed good simulation. Above all, the GCMs have an rRMSE below 20%, which is reasonably acceptable [116,117,129].…”

“…However, uncertainties in GCMs primarily exist on biases of raw outputs, resulting in either over or underestimation of climate variables due to erroneous assumptions in the model's development [60,61]. As such, many studies have embarked on the use of multi-modelling techniques to minimize the uncertainty of future predictions in order to obtain plausible future projections [62][63][64][65][66].…”

“…This evaluation was processed by applying the interpolated GCMs average rainfall data from six available grid points within the Harare Metropolitan Province in parallel with observed average precipitation from the Harare Meteorological stations (Table 3) using the standard statistical metrics [114]. The evaluation of the GCMs performance was assessed using the standard metrics to outweigh GCMs that are not representative: the coefficient of determination (R 2 ), relative root mean square error (rRMSE) (%), correlation coefficient (r), and index of agreement (d) [63,115,116]. With values ranging between 0 and 1, the lower the values of the rRMSE, the better the model's performance, while the higher the value for R 2 , the better the goodness of fit of the model [115][116][117].…”

“…With values ranging between 0 and 1, the lower the values of the rRMSE, the better the model's performance, while the higher the value for R 2 , the better the goodness of fit of the model [115][116][117]. For the index of agreement (d), the closer values are to 1, the better they document the increasing goodness of the fit of the model, ascertaining that there is good agreement between the simulated and observed annual precipitation [63,118,119]. Separate runs of the GCMs ensemble averages from 2019 to 2034 and 2035 to 2050 were used for the assessment of climate variability and its impact on future soil erosion risk under RCP4.5 and RCP8.5 climate scenarios.…”

“…Estimations of future climate change scenarios from single GCMs relay limited information required for the direct calculation of the R factor [120,121]. Therefore, the application of multi-GCM ensemble averages decreases individual model errors and provides more robust predictions for future climate change [61,63,64,122,123]. Accordingly, empirical relations were used between monthly and annual precipitation in order to analyse GCM outputs relative to R factor changes [120].…”