Functional groups of leaf phenology are key to build climate-resilience in cocoa agroforestry systems

Agroforestry has the potential to enhance climate change adaptation. While benefits from agroforestry systems consisting of cash crops and shade trees are usually attributed to the (shade) trees, the trees can also have negative impacts due to resource competition with crops. Our hypothesis is that leaf phenology and height of shade trees determine their seasonal effect on crops. We test this hypothesis by categorizing shade tree species into functional groups based on leaf phenology, shade tree canopy height and shade tree light (wet and dry season) interception as well as the effects. To this end, leaf phenology and the effects on microclimate (temperature, air humidity, intercepted photoactive radiation (PAR)), soil water, stomatal conductance and cocoa yield were monitored monthly during wet and dry seasons over a two-year period on smallholder cocoa plantations in the northern cocoa belt of Ghana. Seven leaf phenological groups were identified. In the wet season, highest buffering effect of microclimate was recorded under the trees brevi-deciduous before dry season. During dry season, high PAR and lowest reduction in soil moisture were observed under the trees in the group of completely deciduous during dry season. The evergreen groups also showed less reduction in soil water than the brevi-deciduous groups. In the wet season, shade tree effects on cocoa tree yields in their sub canopy compared to the respective control of outer canopy with full sun ranged from positive (+10 %) to negative (-15 %) for the deciduous groups, while yield reductions for the evergreen groups ranged from −20 % to −33 %. While there were negative yield impacts for all phenological groups in the dry season, the trees in completely deciduous during dry season group recorded least penalties (-12 %) and the trees with evergreen upper canopy the highest (-35 %). The function of shade trees in enhancing climate resilience is therefore strongly dependent on their leaf phenological characteristics. Our study demonstrates how the key trait leaf phenology can be applied to successful design of climate-resilient agroforestry systems.