Using the DSSAT Model to Support Decision Making Regarding Fertilizer Microdosing for Maize Production in the Sub-humid Region of Benin

Fertilizer microdosing is being widely promoted across sub-Saharan Africa, yet all recommendations regarding this technology are derived from short-term studies. Such studies are insufficient to properly assess the production risk caused by climatic variability. To address this issue while avoiding costly long-term experiments, a common and well accepted strategy is to combine results from short-term experiments with validated dynamic crop models. However, there have been few documented attempts so far to model fertilizer microdosing under sub-humid tropical conditions. The objective was therefore to evaluate the potential of the DSSAT model for simulating maize response to fertilizer microdosing, and to use the validated model to assess the effects of inter-annual rainfall variability on maize productivity and economic risk. The model was calibrated and validated against data from a 2-year on-station experiment (2014 and 2015) with 2 levels of hill-placed manure and five mineral fertilization options including broadcast and fertilizer microdosing. Model simulations were in good agreement with the observed grain and biomass yields for conventional broadcast fertilization, with relative RMSE and d-values of 12% and 0.96 for grain and 8% and 0.97 for biomass, respectively. For fertilizer microdosing, the N stress coefficient needed to be adjusted to avoid occurrence of large N stresses during simulation. After optimization, the model adequately reproduced grain yields for fertilizer microdosing, with relative RMSE of 10%. Considering the long-term scenario analysis, the use of the validated model showed that the application of 2 g of NPK15−15−15 fertilizer + 1 g urea per hill (equivalent to 23.8 kg N ha−1, 4.1 kg P ha−1 and 7.8 kg K ha−1) improved both the minimum guaranteed yield and the long-term average without increasing inter-annual variability and the economic risk compared to unfertilized plots. Even though combining microdosing with manure (1–3 t ha−1) was economically slightly riskier than microdosing alone, this risk remained low since a value-cost ratio of 2 could be achieved in almost 100% of the years. Furthermore, combined application consistently reduced the inter-annual yield variability. Considering this as well as the other benefits of manure for soil health, combining microdosing with small quantities of manure would be recommended to increase the sustainability of the system

Using the DSSAT Model to Support Decision Making Regarding Fertilizer Microdosing for Maize Production in the Sub-humid Region of Benin

Fertilizer microdosing is being widely promoted across sub-Saharan Africa, yet all recommendations regarding this technology are derived from short-term studies. Such studies are insufficient to properly assess the production risk caused by climatic variability. To address this issue while avoiding costly long-term experiments, a common and well accepted strategy is to combine results from short-term experiments with validated dynamic crop models. However, there have been few documented attempts so far to model fertilizer microdosing under sub-humid tropical conditions. The objective was therefore to evaluate the potential of the DSSAT model for simulating maize response to fertilizer microdosing, and to use the validated model to assess the effects of inter-annual rainfall variability on maize productivity and economic risk. The model was calibrated and validated against data from a 2-year on-station experiment (2014 and 2015) with 2 levels of hill-placedmanure and fivemineral fertilization options including broadcast and fertilizer microdosing. Model simulations were in good agreement with the observed grain and biomass yields for conventional broadcast fertilization, with relative RMSE and d-values of 12% and 0.96 for grain and 8% and 0.97 for biomass, respectively. For fertilizer microdosing, the N stress coefficient needed to be adjusted to avoid occurrence of large N stresses during simulation. After optimization, the model adequately reproduced grain yields for fertilizer microdosing, with relative RMSE of 10%. Considering the long-term scenario analysis, the use of the validated model showed that the application of 2 g of NPK15−15−15 fertilizer + 1 g urea per hill (equivalent to 23.8 kg N ha−1, 4.1 kg P ha−1 and 7.8 kg K ha−1) improved both the minimum guaranteed yield and the long-term average without increasing inter-annual variability and the economic risk compared to unfertilized plots. Even though combining microdosing with manure (1–3 t ha−1) was economically slightly riskier than microdosing alone, this risk remained low since a value-cost ratio of Tovihoudji et al. Simulating Maize Response to Microdosing 2 could be achieved in almost 100% of the years. Furthermore, combined application consistently reduced the inter-annual yield variability. Considering this as well as the other benefits of manure for soil health, combining microdosing with small quantities of manure would be recommended to increase the sustainability of the system

Chapter 3

Benin covers a land area of 114,763 square kilometers and occupies a long stretch of land perpendicular to the coast of the Gulf of Guinea in West Africa. It is bordered on the north by Burkina Faso and the Republic of Niger, on the east by the Federal Republic of Nigeria, and on the west by the Republic of Togo. With a 124-kilometer coastline, it stretches north to south some 672 kilometers and east to west 324 kilometers at its widest point. Most of the country experiences transitional tropical conditions, with less rainfall than in other areas at the same latitude—a climate known as the Benin variant, marked by a dry season from November to early April and a rainy season from mid-April to October. Climate change, as a worldwide concern, implies generally warmer temperatures as well as changes in precipitation patterns, with more extreme weather events and shifting seasons. Agriculture is especially vulnerable, and climate change will thus disproportionately affect the poor, who depend on agriculture for their livelihoods and who have a lower capacity to adapt. The populatio