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Stochastic dominance was used to determine the risk characteristics of phosphate fertilization of millet, sorghum and maize with commercial NPK fertilizer, rock phosphate and partially acidulated rock phosphate in Burkina Faso. On‐farm‐trial data from 1989, 1990 and 1991 in three rainfall zones was used. The analysis shows that among the four treatments tested, commercial NPK fertilizer has the most desirable risk characteristics. It is acceptable to risk averse decision makers for all three crops in all rainfall zones. The no‐fertilizer control is dominated by the fertilizer treatments. The rock phosphate treatments have higher yields and in certain cases higher returns than the no fertilizer control, but those benefits are less sure than for the soluble commercial fertilizer. The distributions of cash returns to rock phosphate treatments are rarely significantly different from those of the control. Rock phosphate treatments never dominate the commercial fertilizer treatment. If farmers have a choice between commercial fertilizer, rock phosphate and partially acidulated rock phosphate, at current prices most of those who use fertilizer would choose the soluble commercial product. If the availability of commercial fertilizer were limited (e.g. by lack of hard currency), some farmers would use rock phosphate—especially the partially acidulated product. Stochastic dominance permitted a timely and detailed analysis of risk inherent in phosphate fertilizer alternatives. Because on‐farm‐trails involve a modest number of alternatives, pairwise stochastic dominance comparisons are feasible. The stochastic dominance analysis permits researchers to communicate to extension staff and policymakers not only the degree of risk, but also something about the characteristics of the crop response that contribute to risk. The key to effective use of stochastic dominance is careful study of the distributions and understanding why a technology is dominated or is potentially acceptable to risk averse decisionmakers.

The present study aims to determine fertilizer (N-P-K) recommendations for maize (Zea mays L.) on Acrisols (south Benin) and Ferric and Plintic Luvisols (centre Benin). Two years (2011 and 2012) experiment was conducted at Dogbo and Allada districts (southern) and Dassa (centre Benin). Six onfarm experiments were carried out to validate fertilizer rates simulated by the DSSAT model. The experimental design in each field was a completely randomized bloc with four replications and ten N-P-K rates: 0-0-0 (control), 44-15-17.5 (standard fertilizer recommendation for maize), 80-30-40, 80-15-40, 80-30-25, 80-30-0, 69-30-40, 92-30-40, 69-15-25 and 46-15-25 kg ha -1 . Treatments 44-15-17.5 and 46-15-25 showed the lowest grain and stover yields. The observed maize grain yields were highly correlated with the estimated grain yields (R 2 values varied between 80 and 91% for growing season 2011 and between 68 and 94% for growing season of 2012). The NRSME values varied between 12.54 and 22.56% (for growing season of 2011) and between 13.09 and 24.13% (for growing season of 2012). The economic analysis for the past 32 years including the current experiment showed that N-P-K rates 80-30-25 (at Dogbo), 80-15-40 (at Allada) and 80-30-0 (at Dassa) were the best fertilizer recommendations as they presented the highest grain yields and the best return to investment per hectare. Nevertheless, 80-30-25 is advised for Dassa considering that sustainable maize production will require regular inputs of potassium. The 2 years of field experiments were not sufficient to derive biophysically optimal fertilizer recommendation rates for each site.

This chapter describes the general agricultural context of Burkina Faso, the characteristics of the agroecological zones, the soil types, and the main cropping. It addresses fertilizer use optimization in Burkina Faso and factors that affect profitability of fertilizer use. Computer-run and paper-based decision tools are introduced for optimizing fertilizer use giving choices expected to maximize profit to fertilizer use. Also, a tool for adjusting fertilizer rates according to practices such as manure use and according to soil test information is provided. A comparison is made of current fertilizer rate recommendations with the rates that are expected to maximize net returns per hectare due to fertilizer use, called in this chapter the economically optimal rates of nutrient application.

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