615
sions. It is urged that increased use of agricultural inputs (i.e., agricultural intensification) will simultaneously increase GHG emissions. The GHG emissions reported
to occur from most SSA areas are mainly estimates from models or simulated conditions in Europe and USA (Rosenstock et al. 2019). Few studies have estimated
GHG emissions from fields in SSA. It is reported that N rates lower than 150 kg N/
ha have lower emissions of nitrous oxides (Palm et al. 2017).
4.3 Climate Smart Agriculture and Weather Forecast
The most important climate change impact in SSA agriculture is increased interannual and inter-season variability in climate, including increased severity of heat
stress, drought, and floods (Burian et al. 2019; Trenberth et al. 2014). To enhance
agriculture adaptation to such climate change impact and enhance resilience to climate change, studies on adaptive weather forecast for agricultural intensification are
critical. Integration of weather forecast to address inter-annual and inter-season
variability in climate is one of the areas that has been least studied (Thornton et al.
2014). Studies to enhance prediction of rainfall incidences are important for decisions on use of inputs based on weather forecast. Time of planting must be updated
based on onset of rainfall in a particular season and in a particular year. The quantities of fertilizer and type of seed variety to be used can be properly guided by the
updated climate information for agriculture.
The analysis by Heisse and Morimoto (2019) showed some concern that use of
mineral fertilizers in countries like Tanzania might not bring sustainable development
due to fossil fuel and energy consumption in manufacturing of fertilizers, environmental degradation due to N runoff, and transaction costs in distribution and an uneven
wealthy distribution due to subsidies. Amuri (2015) argued that environmental degradation due to depletion of soil fertility leads to the need to open up new farms to
increase yield by expanding farmland, which in turn leads to increased vulnerability
to wind and water erosion, and low productivity. Reports show that from 2000 to
2005, deforestation in Africa was 4 million ha, estimated to be 55% of global forest
loss (Lupala et al. 2014). Thus, more research to integrate environmental externalities,
weather variability, and prediction and agronomic performance of technologies is
needed. The agronomic technology should ensure efficient utilization of inputs (fertilizer, pesticides) in the face of a given weather condition and potential for environmental degradation (both due to excessive or deficiencies of essential nutrients).
Heisse and Morimoto (2019) also highlighted the potential negative impact of
policies that support subsidies and distribution of mineral fertilizers as increasingly
risky due to dependency on imports of fertilizers and hence burden to balance of
trade. This should also be analyzed against the risk of increased nutrient mining
degradation, reduction of food and fiber yields, increased risk of food insecurity,
and increased food importation that might exacerbate poverty. Thus, a smart agricultural research that will develop an adoptive technology to the agro-ecological
conditions of SSA is needed.
Knowledge Gaps and Research Priorities
sions. It is urged that increased use of agricultural inputs (i.e., agricultural intensification) will simultaneously increase GHG emissions. The GHG emissions reported
to occur from most SSA areas are mainly estimates from models or simulated conditions in Europe and USA (Rosenstock et al. 2019). Few studies have estimated
GHG emissions from fields in SSA. It is reported that N rates lower than 150 kg N/
ha have lower emissions of nitrous oxides (Palm et al. 2017).
4.3 Climate Smart Agriculture and Weather Forecast
The most important climate change impact in SSA agriculture is increased interannual and inter-season variability in climate, including increased severity of heat
stress, drought, and floods (Burian et al. 2019; Trenberth et al. 2014). To enhance
agriculture adaptation to such climate change impact and enhance resilience to climate change, studies on adaptive weather forecast for agricultural intensification are
critical. Integration of weather forecast to address inter-annual and inter-season
variability in climate is one of the areas that has been least studied (Thornton et al.
2014). Studies to enhance prediction of rainfall incidences are important for decisions on use of inputs based on weather forecast. Time of planting must be updated
based on onset of rainfall in a particular season and in a particular year. The quantities of fertilizer and type of seed variety to be used can be properly guided by the
updated climate information for agriculture.
The analysis by Heisse and Morimoto (2019) showed some concern that use of
mineral fertilizers in countries like Tanzania might not bring sustainable development
due to fossil fuel and energy consumption in manufacturing of fertilizers, environmental degradation due to N runoff, and transaction costs in distribution and an uneven
wealthy distribution due to subsidies. Amuri (2015) argued that environmental degradation due to depletion of soil fertility leads to the need to open up new farms to
increase yield by expanding farmland, which in turn leads to increased vulnerability
to wind and water erosion, and low productivity. Reports show that from 2000 to
2005, deforestation in Africa was 4 million ha, estimated to be 55% of global forest
loss (Lupala et al. 2014). Thus, more research to integrate environmental externalities,
weather variability, and prediction and agronomic performance of technologies is
needed. The agronomic technology should ensure efficient utilization of inputs (fertilizer, pesticides) in the face of a given weather condition and potential for environmental degradation (both due to excessive or deficiencies of essential nutrients).
Heisse and Morimoto (2019) also highlighted the potential negative impact of
policies that support subsidies and distribution of mineral fertilizers as increasingly
risky due to dependency on imports of fertilizers and hence burden to balance of
trade. This should also be analyzed against the risk of increased nutrient mining
degradation, reduction of food and fiber yields, increased risk of food insecurity,
and increased food importation that might exacerbate poverty. Thus, a smart agricultural research that will develop an adoptive technology to the agro-ecological
conditions of SSA is needed.
Knowledge Gaps and Research Priorities