154
that, on average, Tanzania will have future rainfall totals approximately equivalent
to today, seasons may shift and the predictability of precipitation will decline
(International Center for Tropical Agriculture and World Bank 2017). There is
already a need to help farmers to prepare for today’s variable precipitation. And
coping with today’s conditions will help farmers adjust better to changes in the
future.
Increasing the rate of adoption of improved agricultural technologies can help
build resilience to weather-related risks. For example, Kimaro et al. (2015) examined the resilience of productivity across four seasons within conventional and conservation agriculture in the highlands of central Tanzania and found higher yields
and lower interannual variation across all permutations of conservation agriculture
in comparison to the control. Furthermore, rainwater use efficiency (RUE) and soil
moisture retention were found to be higher in conservation farming and intercropping practices in Tanzania versus traditional practices (Kizito et al. 2016). These
results suggest that improved technologies can increase resilience, especially in
areas with lower than average rainfall and persistent drought.
However, the adoption of improved technology may affect more than just the
resilience of the farming system. It may also affect the system’s productivity, including both yields of edible and non-edible crop products and incomes (Charles et al.
2013). Furthermore, it may change the environmental sustainability of the production system; for instance, the climate change mitigation potential, by either sequestering carbon in biomass and/or soils and/or reducing greenhouse gas emissions
(Kaonga and Bayliss-Smith 2009). The multidimensionalities of impacts with agricultural change are fundamental to climate-smart agriculture (CSA), which aims to
achieve three goals simultaneously: sustainably increase production, improve resilience and mitigate climate change.
Despite multiple goals, rarely are CSA practices evaluated in ways that cross
more than one of these three objectives (Rosenstock et al. this volume). This is
important for development practitioners because it limits the evidence with which
to evaluate potential trade-offs and increases the likelihood of unintended consequences with development programming (Lamanna et al. 2016). Comprehensive
information that addresses multiple objectives is needed to evaluate changes in agricultural systems. That, however, is easier said than done, because research is typically undertaken for specific purposes without these three factors in mind, and the
costs of multi-indicator measurements may be prohibitive.
We present data from three previously unpublished experiments in two regions
of Tanzania: two near Dodoma and one near Tabora. Dodoma has a semiarid climate
with a unimodal rainfall regime (7 to 8-month dry period) and mean annual precipitation of 560 millimetres (mm) (Kimaro et al. 2009). Tabora is subhumid with mean
annual precipitation of 928 mm (Nyadzi et al. 2003). The experiments in both sites
use pigeonpea -based intercropping systems. Here, we present examples of how
scientists can investigate CSA in multidimensional assessments.
A. A. Kimaro et al.
that, on average, Tanzania will have future rainfall totals approximately equivalent
to today, seasons may shift and the predictability of precipitation will decline
(International Center for Tropical Agriculture and World Bank 2017). There is
already a need to help farmers to prepare for today’s variable precipitation. And
coping with today’s conditions will help farmers adjust better to changes in the
future.
Increasing the rate of adoption of improved agricultural technologies can help
build resilience to weather-related risks. For example, Kimaro et al. (2015) examined the resilience of productivity across four seasons within conventional and conservation agriculture in the highlands of central Tanzania and found higher yields
and lower interannual variation across all permutations of conservation agriculture
in comparison to the control. Furthermore, rainwater use efficiency (RUE) and soil
moisture retention were found to be higher in conservation farming and intercropping practices in Tanzania versus traditional practices (Kizito et al. 2016). These
results suggest that improved technologies can increase resilience, especially in
areas with lower than average rainfall and persistent drought.
However, the adoption of improved technology may affect more than just the
resilience of the farming system. It may also affect the system’s productivity, including both yields of edible and non-edible crop products and incomes (Charles et al.
2013). Furthermore, it may change the environmental sustainability of the production system; for instance, the climate change mitigation potential, by either sequestering carbon in biomass and/or soils and/or reducing greenhouse gas emissions
(Kaonga and Bayliss-Smith 2009). The multidimensionalities of impacts with agricultural change are fundamental to climate-smart agriculture (CSA), which aims to
achieve three goals simultaneously: sustainably increase production, improve resilience and mitigate climate change.
Despite multiple goals, rarely are CSA practices evaluated in ways that cross
more than one of these three objectives (Rosenstock et al. this volume). This is
important for development practitioners because it limits the evidence with which
to evaluate potential trade-offs and increases the likelihood of unintended consequences with development programming (Lamanna et al. 2016). Comprehensive
information that addresses multiple objectives is needed to evaluate changes in agricultural systems. That, however, is easier said than done, because research is typically undertaken for specific purposes without these three factors in mind, and the
costs of multi-indicator measurements may be prohibitive.
We present data from three previously unpublished experiments in two regions
of Tanzania: two near Dodoma and one near Tabora. Dodoma has a semiarid climate
with a unimodal rainfall regime (7 to 8-month dry period) and mean annual precipitation of 560 millimetres (mm) (Kimaro et al. 2009). Tabora is subhumid with mean
annual precipitation of 928 mm (Nyadzi et al. 2003). The experiments in both sites
use pigeonpea -based intercropping systems. Here, we present examples of how
scientists can investigate CSA in multidimensional assessments.
A. A. Kimaro et al.