Introduction

Maize (Zea mays L.) has the highest global production of all cereals with 1,037,791,518 tons grain produced (FAOSTAT 2014). From 1990 to 2005 in South, East, Central and West Africa countries, maize made up about 56% of the cultivated area (FAOSTAT 2007). About 50% of the population of Africa depends for their subsistance on maize as a staple food and source of carbohydrates, protein, iron, vitamin B and minerals (Zeller et al. 2006). Maize is becoming a cash crop (FAOSTAT 2013), which contributes to the improvement of farmers’ livelihood. Based on these statistics, supporting maize production will ensure successful food security and will improve the economic growth of West African countries (Toléba-Séidou et al. 2015).

In Benin, maize is the principal staple food crop. It is the most consumed cereal ahead of rice and sorghum and plays major role for food security. Maize is also used for animal feed and constitutes farmers’ principal source of income (Toléba-Séidou et al. 2015). Therefore, maize contributes for 6.54% to the agricultural Gross Domestic Product (GDP) (Adégbola and Arouna 2003). Maize is a strategic crop in Benin’s economy as it provides employment in rural area (Saïdou et al. 2012). In general, maize cropping systems are heterogenous in the different agroecological zones (Diallo et al. 2012). Due to climate variability, short growing cycle maize varieties of 3 months are widely grown with attainable yield of 6 t ha−1 on station. The most limiting factors for maize cultivation in Benin are the erratic rainfall pattern and the low soil fertility (Saïdou et al. 2012; Balogoun et al. 2013; Igué et al. 2013). The main causes of the low soil fertility are low organic matter content, the low use of fertilizer, poor soil fertility management practices and monocropping (Saïdou et al. 2012; Balogoun et al. 2013). Typical farmer maize yields are low about 800 kg ha−1 (Saïdou et al. 2003) and generally without fertilizer application.

Maize cultivation under soil conditions in Benin requires high quantity of nutrients (N and P). There is therefore a need to develop adequate fertilizer recommendations in order to achieve the level of productivity that could meet the needs of the increasing population in the rural area. This implies an intensification of the production by addressing the main constraints including farmers’ fertilization practices. In Benin, fertilizer use, as in many other countries of West Africa, has been promoted to intensify crop production. Different crop fertilization practices have been proposed by research and extension services. Many fertilizer types were used for maize production such as: urea, diammonium phosphate (DAP) and various NPK forms (Adégbidi et al. 2000; Acakpo 2004). Furthermore, to be efficient in terms of crop yield improvement under farmer conditions, high yielding varieties must be used. The same fertilizer rates are recommended for all agroecological zones within the country. Such practices do not take into account soil types and the specificity of farmers’ cropping systems and farm ecology. These recommended standard fertilizer rates are old. Therefore, there is a need to update this fertilizer recommendation for maize production regarding each agroecological zone of Benin, soil types, and the economic profitability for the farmer.

The best way to do this is through the establishment of long term field trials which are expensive and time consuming (Dzotsi 2002; Dzotsi et al. 2003). Alternatively, agricultural simulation models are one way to predict yield components in various agroecosystem to save time and reduce field trials. Agricultural simulation models are originally developed, calibrated and validated under different agroecological conditions, and their application in other specific conditions does guarantee reliabilibty (Miao et al. 2006; Thorp et al. 2007, 2008; DeJonge et al. 2007). The present research was carried out in the framework of the IFDC-Africa fertilizer research program in West Africa. The objectives of the study were to: (1) validate the effect of fertilizer rates simulated by the DSSAT model in the context of the agroecological zones in the South and Central Benin, and (2) propose an updated and profitable N–P–K rates for maize production for the South and Central Benin by using the CERES-Maize model in DSSAT.

Materials and methods

Description of the study area

The study covered two agroecological zones (AEZ) of the nine in Benin. The transitional Sudano-Guinean AEZ has rainy season from mid-April to October, where yam, cotton, maize, cassava and cashew trees are predominant in the crop rotation systems. Ferric and Plintic Luvisols (FAO 2006) are the dominant soil types. The Sudano-Guinean on “Terre de barre” AEZ located in the southern Benin has a sub-equatorial rainy season. The cropping systems are based mainly on slash and burn agriculture, maize and cassava are predominant crops in the cropping systems and soil types are Acrisols.

On-farm trials were used to validate fertilizer doses simulated by DSSAT model during two growing seasons (2011 and 2012) under farmers’ conditions for maize production. Combining DSSAT and geographical information system (GIS), a fertilizer recommendation map for the south and central Benin was drawn using soil data base of the area (at 1:100,000 scale) established by Igué (2000) and Weller (2002). In the Sudano-Guinean on “Terre de barre” AEZ, Sékou and Attogon (municipality of Allada, Atlantique Department) and Dévé and Ayomi (municipality of Dogbo, Couffo Department) were selected villages for the on-farm experiments. In the transitional Sudano-Guinean AEZ (Central Benin), Gomé, Minifi and Dovi-Somè (all in the municipality of Dassa-Zoumé) in the Collines Department were selected. Villages and farmers were jointly identified with the local extension service. In total, six farmers’ fields were selected to conduct the experiment. The municipality of Dogbo lies between latitude 6°47′56″N and longitude 1°50′35″E (58 msl) while the municipality of Allada lies between latitude 6°39′52″N and longitude 2°09′30″E. Dassa municipality lies between latitude 7°50.4′N and 2°10′E.

Field experiments and simulation studies

Two year on-farm experiments were conducted during the rainy season (from April to June). In each AEZ, farmers’ fields were selected based on the result of the previous crops. Fields were chosen where no fertilizer was applied before. In each farmer’s field, a randomized complete block design with 4 replications and 10 treatments was carried out. Plots’ size of 8 m × 5.6 m (44.8 m2) was used. All experimental plots were farmer-managed. The maize variety used was EVDT 97 STRW (90 days growing cycle and attainable yield of 6 t ha−1) planted at the beginning of April of each year at a spacing of 80 cm × 40 cm (two seeds per hole leading to a planting density of 62,500 plants ha−1). The same maize seed and fertilizer were used by all of the farmers’ selected. Planting and weeding operations were left up to the farmers after providing them with general guidelines. The nitrogen source (N) was urea (46% N); phosphorus (P) was from triple super phosphate (TSP, 46% P2O5) and potassium (K) was from potassium chlorite (KCl, 60% K2O).

Four levels of N (0, 40, 80 and 120 kg ha−1), three levels of P (0, 30 and 60 kg ha−1) and three levels of K (0, 40 and 80 kg ha−1) leading to 36 combinations of N, P and K simulated were tested. These 36 combinations were put on the fertilizer recommendation maps of the south and central Benin (Ezui et al. 2011; Igué et al. 2013). The simulations were performed on the scale of 1:100,000 for both AEZ. From these, two simulated fertilizer rates (80–30–40 and 80–30–0) were selected for the two AEZ. In addition to these two simulated rates, the control (0–0–0) and the standard fertilizer recommendation dose (44–15–17.5) and six more N–P–K combinations were considered: 80–15–40, 80–30–25, 69–30–40, 92–30–40, 69–15–25 and 46–15–25.

In total, ten fertilizer (N–P–K combination) rates were validated during the on-farm experiment. Thus, the treatments were the following fertilizer 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. The standard fertilizer recommendation for maize consists of 150 kg ha−1 NPK 14–23–14 and 50 kg ha−1 urea (Dugué 2010).

Composite soil samples were collected at 0–20 cm depth after plowing and before fertilizer application. Fertilizer application was done by researcher team. Phosphorus and potassium were applied just before sowing maize. Urea was applied 15 days after sowing (DAS) and 45 DAS (after the second weeding period) about 5 cm from the plant. Maize was harvested at physiological maturity. Maize stover was cut at soil surface for biomass yield after leaving the two border lines and two border seed holes. Cobs and stover were weighed with hand scale and samples of each part taken were weighed with an electronic scale and dry matter determined after drying at 60 °C for 72 h in the oven at laboratory. Soil chemical analyzes were performed at the Laboratory of Soil Science, Water and Environment of Benin National Research Institute (LSSEE/INRAB).

Soil samples were analysed for pH(water) (using a glass electrode in 1:2.5 v/v soil solution), organic carbon (Walkley and Black method), total nitrogen (Kjeldahl digestion method in a mixture of H2SO4, selenium followed by distillation and titration), available phosphorus (Bray 1 method) and exchangeable potassium (1 N ammonium acetate at pH 7 method, after which K+ was determined by flame photometer).

The statistical analyses were performed using SAS v. 9.2 packages. Observed maize grain and stover yield of each growing season and within an AEZ were subjected to a one-way analysis of variance (ANOVA). The Student Newman–Keuls test was performed for means separation at a significance levels of P < 0.05.

Decision Support System for Agrotechnology Transfer (DSSAT v 4.5) was used for the simulations. The model requires minimum of input data including: name and geographical position of the field (longitude, latitude and altitude), previous crops grown on the field, crop management informations (tillage, planting date, planting method, sowing density and fertilizer application dates). Plant genetic coefficients were determined through GLUE program of DSSAT (He et al. 2010). The genetic coefficients used for the maize cultivar are presented in Table 1. Soil analytical characterisitics used were: pH(water), organic carbon, available phosphorus (P-Bray 1), total nitrogen and exchangeable potassium. Weather data from 1981 to 2010 were used for the initial fertilizer dose simulation and daily data of 2011 and 2012 were used for the on-farm validation of the fertilizer recommendation. These data included precipitation, minimum and maximum temperatures and solar radiation. They were collected from ASECNA (Agence pour la Sécurité de la Navigation Aérienne en Afrique et à Madagascar) synoptic station of Cotonou, Bohicon and Savè close to the research area. Field results were used to determine genetic coefficients of maize (Table 1) and these model inputs were integrated to provide a framework for simulating and analyzing the outputs. The calibration procedure of the CERES-Maize model consisted of making initial estimate of the genetic coefficient and running the model interactively, so that simulated values match as closely as possible the measured data. The thermal time was computed using algorithm developed by Jones and Kiniry (1986) which assumes development rate increases as a linear function of temperature between the base temperatures (8 °C) and an optimal temperature of 34 °C. Based on the phenological data collected, genetic coefficients (P1, P5 and PHINT) were calculated from daily temperature data collected for the study area. These coefficients were fine-tuned to attain appreciable agreement between simulated and observed values for anthesis and physiological stages. The genetic coefficients for G2 and G3 were determined by iteration of model simulations based on data collected under limited growth stress condition. Iterations were repeated until there was an appreciable agreement between simulated and observed value for the yield data using a 1:1 line for each season of production. Biophysical and economic analyses were also performed in order to determine a series of cost-effective options.

Table 1 Genetic coefficients used for modeling the EVDT 97 STRW maize variety in CERES-maize model at the three sites
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Correlation coefficients (Singh and Wilkens 2001) were determined to assess gaps between simulated yields and those observed from the field, Root Mean Square Error (RMSE) (Du Toit et al. 2001) and Normalize Root Mean Square Error (NRMSE) (Loague and Green 1991; Jamieson et al. 1991) were used to assess the performance of the model. The financial analysis was done by integrating as input in the model production cost and maize price collected in the study area. Maize price in the market during the harvest period was FCFA kg−1 200, 225 and 175 (US $ 1 = FCFA 550) respectively at Dogbo, Allada and Dassa. The price of maize seed was FCFA kg−1 450 and the price of fertilizer was FCFA kg−1 717, 1393 and 703 respectively for N, P and K. The cost of labour was also assessed and introduced in the model for the economic analysis. The cost of clearing in the area were FCFA ha−1 25,000; 22,500 and 20,000 respectively at Dogbo, Allada and Dassa; ploughing FCFA ha−1 40,000, 50,000 and 62,500 respectively at Dogbo, Allada and Dassa; sowing: FCFA ha−1 10,000 (at Dogbo) and 7500 (at Allada and Dassa); weeding: FCFA ha−1 33,750 (at Dogbo) and 17,500 (at Allada and Dassa) and harvesting: FCFA ha−1 37,500; 11,250 and 10,000 respectively at Dogbo, Allada and Dassa.

After obtaining the distribution of economic returns, strategic analysis was done to compare fertilizer rates in economic terms, taking into account weather and price-related risks from 1980 to 2012. This analysis allowed the evaluation of the long-term rainfall effect on the simulated yields (Jones et al. 2003). It was done by examining the mean–variance plots of gross margins or net returns per hectare, or using the mean-Gini stochastic dominance. This analysis leads to the choice of the best and efficient fertilizer option. In more detail, the mean-Gini stochastic dominance as developed by Fosu et al. (2012) assumes that:

$${\text{for}}\;{\text{two}}\;{\text{risky}}\;{\text{prospects}}\;{\text{A}}\;{\text{and}}\;{\text{B}},\;{\text{A}}\;{\text{dominate}}\;{\text{B}}\;{\text{if}}\;{\text{E}}\left( {\text{A}} \right) > {\text{E}}\left( {\text{B}} \right)$$
$${\text{or}}\;{\text{if}}\;{\text{E}}\left( {\text{A}} \right)\;{-}\;{\text{F}}\left( {\text{A}} \right) > {\text{E}}\left( {\text{B}} \right){-}{\text{F}}\left( {\text{B}} \right)$$

where E(·) is the mean, and F(·) the Gini coefficient of distributions A and B. F is half the value of Gini’s mean difference. It is a measure of the spread of a probability distribution.

The most economically superior fertilizer rates were then selected by this process.

Results

Soil chemical parameters in each agroecological zone

Soil chemical analysis of the different farms investigated before planting the maize revealed the following properties: pH(water) of 6.51, 6.58 and 6.4 (respectively for Dogbo, Allada and Dassa); organic C of 4.45, 8.08 and 3.99 g kg−1 (respectively for Dogbo, Allada and Dassa); total N of 0.74, 0.64 and 0.42 g kg−1 (respectively for Dogbo, Allada and Dassa); available P of 82.75, 53.29 and 82.75 mg kg−1 (respectively for Dogbo, Allada and Dassa) and exhangeable K 1.05, 1.81 and 1.44 cmol kg−1. In general, the soils of the study area are slightly acid and low in organic matter (C/N ratio of the acrisols varying between 14.06 to 22.42 and that of the Ferric and Plintic Luvisols is 25.95). The consequence of this high C/N ratio is a low level of total N which seems to be with P the most limiting nutrients.

Calibration and validation of the model: observed versus simulated maize grain and stover yields in each agroecological zone

In general, the observed maize grain and stover yields of the different N–P–K combinations, except for the fertilizer rate 46–15–25 (in 2011), were significantly different compared to the standard fertilizer recommendation (44–15–17.5) at Dogbo (Table 2). A yield increase of 1.4 compared with the standard recommendation was observed. During this growing season, no significant differences were noticed among the N–P–K fertilizer rates but all the treatments had significantly increased yields by a factor of 1.5–2 respectively compared to control (0–0–0). The stover yields followed the same trend as the grain yields. In the cropping season 2012, the N–P–K fertilizer rates studied showed significant effect on both grain and stover yields compared to the control. The lowest values were found on the control field while the highest with 80–30–25, 92–30–40 and 80–15–40 at Dogbo, Allada and Dassa respectively. The standard fertilizer recommendation and the N–P–K rate 46–15–25 showed lowest stover yields compared to the other treatments. Thus, maize grain and stover yields were increased by 1.4–1.6, 1.3–2 and 1.1–1.4 respectively in Dogbo, Allada and Dassa.

Table 2 Average (± SE) value of the observed maize grain yield and stover mass regarding the different sites and N–P–K combinations in the growing season of 2011 and 2012
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Data simulated by DSSAT-CERES model were compared with the real data obtained in 2011 and 2012 in the field, in order to determine the suitability of making site specific fertilizer recommendations. In general, maize grain yields simulated by the model were close to that measured in the field (Table 3).

Table 3 Observed and simulated maize grain yields (kg ha−1) for 2011 and 2012 growing seasons regarding N–P–K nutrient combinations at Dogbo, Allada and Dassa sites in Benin
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Performance of the model

Results of the t test for paired sample analysis, showed significant (P < 0.05 and P < 0.001) difference between mean value of observed and simulated maize grain yields in Dogbo and Dassa during both growing seasons (2011 and 2012). The model has slightly underestimated maize grain yields at Dassa (growing season of 2011) and Dogbo (growing season of 2012) while data predicted by the model fit well with that of Allada during the growing season of 2012 (Table 4). Furthermore, it was noticed that, the observed maize grain yields were highly correlated with estimated values by the model. The R2 values varied between 80 and 91% (for the growing season of 2011) and 68 and 94% (for the growing season of 2012). The NRSME values between the observed and simulated maize grain yields varied between 12.54 and 22.56% (for the growing season of 2011) and between 13.09 and 24.13% (growing season of 2012).

Table 4 Comparison between the observed and simulated maize yield parameters (kg ha−1) in 2 years (2011 and 2012) at Dogbo and Allada (Sudano-Guinean zone on terre de barre) and Dassa (transitional Sudano-Guinean zone)
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Seasonal and biophysical analysis

A seasonal analysis of 32 years (1980–2012) was done based on the observed maize grain yields for the different N–P–K combinations (Fig. 1). In general, it was observed from the field data that, maize grain yields are related to the variation of the N rates. With an increase of N rate of 12 kg ha−1, 21.1 kg ha−1 of maize grain yield was obtained.

Fig. 1
figure 1

Maize yield as affected by different N–P–K fertilizer rates for 32 years (1980–2012) seasonal and biophysical analysis using 2011 and 2012 growing season grain yields at Dogbo, Allada and Dassa in Benin. 1 = 0–0–0; 2 = 44–15–17.5; 3 = 80–30–40; 4 = 80–15–40; 5 = 80–30–25; 6 = 80–30–0; 7 = 69–30–40; 8 = 92–30–40; 9 = 69–15–25; 10 = 46–15–25

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From Fig. 1, it is also observed that at 75% cumulative probability, at Dogbo, the maximum average maize grain yields of 750, 1750, 2300 and 2500 kg ha−1 were obtained when respectively 0–0–0, 46–15–25, 69–30–40 and 80–30–40 fertilizer rates, were applied. At Allada, the average maize grain yields of 750, 1825, 2200 and 2250 kg ha−1 were obtained when respectively, 0–0–0, 46–15–25, 69–30–40 and 92–30–40 fertilizer rates, were applied. Finally, at Dassa, 1500, 2250, 2300 and 2650 kg ha−1 of maize grain yields were obtained when respectively, 0–0–0, 44–15–17.5, 69–30–40 and 92–30–40 fertilizer rates, were applied.

Economic and strategic analysis

In order to determine fertilizer N–P–K rates to be proposed for maize cultivation, an economic analysis was done (Table 5) based on mean-Gini dominance analysis. This economic strategic analysis for the past 32 years showed that fertilizer rates 80–30–25, 80–15–40 and 80–30–0 respectively for the sites of Dogbo, Allada and Dassa, were the economically superior fertilizer recommendations as they presented the highest return to investment per hectare and the highest efficiency. The model suggested no application of K to the soil at the Dassa site (dominated by Ferric and Plintic Luvisols). To avoid this long-term unsustainable option the fertilizer rate 80–30–25 (with a net return to investment per hectare of FCFA 309708.7 against 315,749.6 for fertilizer rate 80–30–0) would be economically sound and viable for soil fertility management. There was a similarity between fertilizer rates determined from the seasonal and biophysical analysis and the economic analysis for the Dogbo site.

Table 5 Mean-Gini dominance of seasonal partial budget analysis for the different rates of N–P–K fertilizer at Dogbo, Allada and Dassa in Benin
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Discussion

Soil fertility and maize productivity in south and central Benin

The soil analysis showed low soil fertility for the Ferric and Plintic Luvisols (central) and the Acrisols (south) as is typical for most Sub-Saharan African soils. The main characteristic of both soils is their low organic matter level which was also mentioned by several studies (Sanchez et al. 1989; Giller 2002; Saïdou et al. 2003). The high mineralisation rate of the organic matter (Pieri 1989) creates a lack of nitrogen in these soils. The result of our study clearly shows that maize grain and stover yields increased proportionally with an increase in N, P, and K rates. This confirms the results of Brassard (2007) and Singh et al. (2001). These authors also found that nitrogen is the most limiting nutrient for cereal production in the Sub-Saharan Africa’s soils. As mentioned by previous studies, most of the Africa’s soils have low P levels (Koné et al. 2009, 2010) due to the nature and the type of the clays they contain (kaolinite for most of the Acrisols). This shows the importance of the supply of N and P to improve maize production in this part of Africa. This could explain the rate of N applied (80 kg ha−1) suggested by DSSAT model to optimize maize production in these three sites over 30 years simulation.

Performance of DSSAT model in the maize yield simulation in the south and central Benin

The maize grain and the stover yields simulated by DSSAT model fit well with data observed in the field during the two growing seasons (2011 and 2012) for all of the experimental sites. In the Dogbo and Dassa sites, the R2 values between the observed and simulated results were closed to 100% showing a good performance of the model. There is a strong correlation between the simulated and the observed yields (R2 varying between 80 and 91% for the growing season of 2011 and 68 and 94% for the growing season of 2012). These results confirm those of Singh et al. (1999), Dzotsi et al. (2003) in Togo (R2 = 83%), Atakora et al. (2014) in the Guinea savannah zone of Ghana (R2 = 91.7%) and Tetteh and Nurudeen (2015) in the Sudan Savannah agro-ecology in Ghana (R2 between 75 and 99%) who found good agreement between the observed maize grain yield and the simulated. The general observation is that the model is very sensitive to fertilizer rates especially N as mentioned by Tetteh and Nurudeen (2015) and Atakora et al. (2014). It is suggested that for this soil, organic matter improvement should be included in the strategy of soil fertility replenishment.

The value of the standardized mean prediction error (NRMSE) between the observed and simulated results varied between 12.54 and 22.56% for the 2011 growing season and between 13.09 and 24.13% for the 2012 growing season. This means that DSSAT model performed well in simulating maize grain yields as the NRMSE values calculated were within the acceptable range (Jamieson et al. 1991; Loague and Green 1991). Our findings showed that the model has performed well, compared to data found by Nurudeen (2011) with NRMSE and R2 values respectively of 26.1 and 91.5% between the maize grain yields observed and that simulated by the model. This proves that with the correct inputs of soil and varietal characteristics a decision support tool like DSSAT can be used to extrapolate fertilizer recommendation data within a large agroecological zone presenting similar climatic characteristics and soil types. The results are also consistent with study carried out by Ritchie and Alagarswamy (2003) and Soler et al. (2007) who found that the CERES-Maize was able to accurately predict the phenology and maize grain yield for a wide range of environmental conditions.

Seasonal and biophysical analysis of the efficiency of the N–P–K fertilizer rates on maize grain yield in the south and central Benin

Fertilizer rates 80–30–25, 80–15–40 and 80–30–0 presented the best return to investment per hectare and the best efficiency. On the Dassa site, the level of K found presents a risk in the long term. These N–P–K fertilizer rates are far from current standard fertilizer recommendations and do not allow a maize crop to satisfy its nutrient requirements. The fertilizer dose 80–30–0 generated by the model suggested no application of K at the Dassa site which is not sustainable as it will contribute to K mining in these soils (the quantity of K taken up by the plant is not returned to the soil). This N–P–K option should be followed by proper crop residue management and organic manure supply. But considering the Gini coefficient, the net return to investment per hectare calculated for N–P–K fertilizer dose 80–30–25 is sound as far as K application is concerned. The model suggests a uniform rate of N (80 kg ha−1) for both soil types. This high quantity of N suggested by the model denotes the low level of N in most of the Benin even in West Africa’s soils.

During the simulation process, the model did not consider the highest level of N (92 kg ha−1) tested as it is provided low net return per hectare due to the relatively low maize grain yields simulated. Furthermore, one can also consider that the DSSAT model has been rational in the economy of N utilisation by suggesting a reduced quantity. This observation confirms the findings of Fosu et al. (2012) who stated that a supply of high rate of N leads to N leaching and possible contamination of water and luxury consumption by the plant while reducing the net return. Despite that the Dogbo and Allada sites are located in the same soil type, almost twice the amount of P was suggested for the Dogbo site while for Allada site the model suggested an additional application of K. These results reflected land use types which considerably affect fertilizer use efficiency in the farmers’ fields (Saïdou et al. 2012).

The lack of difference in maize grain yields found between fertilizer rates 80–30–40, 80–15–40, 80–30–25 and 80–30–0 suggested that whatever the rate of P and K, the simulated net returns per hectare were similar when N rate does not vary. This can be explained by the fact that the version 4.5 of DSSAT model is not sensitive to the rates of K during the simulation process. The model gave a good prediction of N rate to be applied.

We admit that the 2 years field experiments were not sufficient to derive biophysically optimal fertilizer recommendation rates for each of the sites. In consideration, choice of model-based stochastic approaches combined with economic analyses have been made in the present study. Our results suggest that for intensive maize cultivation the most economically superior N–P–K fertilizer rates are 80–30–25 and 80–15–40 (respectively for Acrisols of Dogbo and Allada in the south) and 80–30–25 (for Ferric and Plintic Luvisols of the Centre). These N–P–K fertilizer rates provide the best net return to investment per hectare.

Conclusion

It appears from the present study that maize grain yields increase with an evolution of the N rates in all of the experimental sites. Apart from the control plot, maize yields predicted were very good (R2 values more or less close to 100%) compared to the field results. In the case of intensive maize cultivation, N–P–K options 80–30–25 and 80–15–40 (for Acrisols) and 80–30–0 including crop residue management (for Ferric and Plintic Luvisols) were the most economic and efficient fertilizer rates that gave maximum return to investment for farmers. In order to avoid K mining in the Ferric and Plintic Luvisols as suggested by DSSAT model, an N–P–K fertilizer rate 80–30–25 was suggested. The way forward is to rerun the model considering different maize cultivars with different growing cycles, combining organic manure with different rates of mineral fertilizer and strategies to improve crop water use efficiency.