3.1. Logistics Costs of the Fertilizer Production Chain
An important parameter of the mathematical model is the logistics costs incurred in the fertilizer production chain. From the results obtained in each scenario, it was possible to identify the contribution of each of the four components that constitute the total logistics cost considered in the mathematical modeling.
Figure 4 summarizes, in a stratified way, the composition of logistics costs in each scenario analyzed.
The optimal results obtained from the mathematical model show that the port costs have a high representation compared to the total logistics costs of the transport network considered. In Scenarios 1, 2 and 4, where there were restrictions equal to the import volumes, port costs totaled around USD 356.85 million. On the other hand, in C3, when there were no restrictions on the imported volumes, port costs dropped to around USD 281.71 million, which indicates a change in the allocation of the flows to ports that have a lower associated cost. In C5, which considers the expansion in fertilizer demand, there is an increase of 22.2% in port costs, reaching around USD 435 million. This shows that the Brazilian dependency on imports will cause port costs to rise considerably with the increase in the demand for fertilizers.
When the representation of the port costs was analyzed, Scenarios 1, 3 and 4 indicated that these costs correspond to between 26 and 26.5% of the total logistics costs. In C2, a scenario where taxes are set to zero, there was an increase in the representation of port costs (29.3%). This increase is associated with a reduction in the total logistics costs (USD 1.21 billion), where the optimum result shows better allocation of flows when taxes are set to zero. In C5, in addition to a rise in the total port cost, there is an increase in representation compared to the scenarios with import restrictions, providing further evidence of the Brazilian dependency on imports.
Regarding the optics of the costs of taxes, it was verified that they do not constitute a large portion of the total logistics costs, representing around 0.29% in C1, 0.18% in C3, 0.28% in C4 and 0.21% in C5. This shows that the strategy used in the fertilizer sector to reduce logistics costs is associated with the use of routes that do not have taxation associated with ICMS. Comparing the total logistics costs of C1 and of C2, for instance, a reduction of 12.5% is observed, decreasing from USD 1.37 billion to 1.21 billion. In other words, the taxation of the fertilizer logistics chain has a great impact on the design of the transport network, which justifies considering that this type of cost is also a logistics cost.
It is also interesting to highlight that the tax issue considerably modifies the stratification of transport costs, with the costs associated with outbound transport being the main component of the total cost (57.4%). Compared to the other scenarios, this cost represents between 32.6 and 34.7% of the total logistics costs. Regarding the inbound costs, the opposite is true; in other words, they have a much lower representation (13.3%) in relation to the other scenarios (between 34.8 and 35.7%). This shows the large impact of taxation on the fertilizer logistics chain.
The results also show a great reduction in total cost in C3 when there are no restrictions on fertilizer import. In this scenario, a reduction of almost 26% was observed in the total volume of fertilizers transported to meet the demand of NPK. In this sense, it is highlighted that the preference for the transport of more concentrated fertilizers has a great impact on the logistics costs, with a reduction of 22.5% in the total cost per demand.
In Scenarios 4 and 5, when increases in capacity and new infrastructures for intermodality are considered, there is a reduction in the logistics costs to meet the demand. In C4, the reduction is very low, around 0.1% in relation to C1. On the other hand, in C5, the cost is reduced to USD 80.42/t, almost 10% less in relation to C1. This situation shows that the expansion of intermodality tends to generate logistics gains only in the future with growth in the demand for fertilizers.
3.3. Transport Flows of Fertilizer
An important decision variable that resulted from the mathematical model refers to the location of the mixers. With the lack of official data on this link in the chain, this section aims to present the optimal locations of mixer factories for the analyzed scenario, as well as an estimate of their production capacities.
Figure 7 and
Figure 8 graphically demonstrate all inbound and outbound transport flows, respectively, as well as the locations of the mixers obtained from the results of each scenario. It is worth highlighting that when there are no transport flows leaving from or arriving at a certain point, this indicates a proprietary supply flow.
Regarding the locations of the mixers, port regions are found to be ideal in all analyzed scenarios, which can be explained by their close proximity to the sources of fertilizers in import flows. The locations of national factories are also determining factors in the locations of the mixers, with emphasis on the mesoregions Sul Goiano and Triângulo Mineiro. Intermodal output terminals are also proven to be ideal places for the location of the factories.
A decisive issue regarding the location of mixing factories concerns taxation. With the imposed tax rules, the mathematical model aims to always allocate a mixer to states that do not have fertilizer importing ports, such as Goiás, Minas Gerais, Mato Grosso do Sul, Mato Grosso, Tocantins and Piauí. With a minimum restriction of mixing capacity of 300 thousand tons, states that present low consumption of fertilizers are rarely considered ideal places for the location of mixers, as is the case for regions in the north and northeast.
Regarding exemption from taxation, C2 presents the greatest alteration in the profile of the mixer location. The results show that there is an even greater concentration of mixers in port regions, negating the need for their location inland for ICMS taxation. Only Goiás, Minas Gerais and Mato Grosso can be considered candidates for the location of mixers because of the proximity of national factories, yet they present large reductions in capacity compared to the other scenarios.
Regarding port regions, the port of Paranaguá was identified as the main mixing hub in the country, totaling almost 10 million tons (an increase of 83% in relation to C1), followed by Rio Grande (5.2 million, an increase of 14%), Santos (5 million, an increase of 31%), São Francisco do Sul (2.2 million, an increase of 92%) and São Luís (2 million, an increase of 102%). On the other hand, for the inland mesoregions, the southeast of Mato Grosso was the main mixing region, with its capacity reduced from almost 2.9 million to 300 thousand tons. The south of Goiás presented a reduction of 55% in mixing capacity (920 thousand tons in C2). The north of Mato Grosso, southwest of Mato Grosso do Sul, east of Goiás, northeast of Mato Grosso and west of Paraná, which were important mixing regions in C1, had their capacities set to zero in C2. When the regions close to the factories were analyzed, Ribeirão Preto and Triângulo Mineiro were the mesoregions that exhibited the greatest increase in mixing capacity with tax exemption, with increases of 317% and 38%, respectively.
When C4 is analyzed, which incorporates an increase in capacity and new intermodal infrastructures, there are no considerable changes in the aspect of the location of mixing factories. The mixing centers are very similar to those observed in C1, with little variation in capacity. The most significant change is observed in the state of Tocantins, where in C1, there is a higher concentration of mixing capacity in the eastern Tocantins mesoregion. In C4, a higher concentration is observed in the western Tocantins mesoregion, especially because of the presence of the new corridor for rail transport for fertilizers from São Luís (MA) to Palmeirante (TO).
In C5, considering the growth in the demand for fertilizers, there are considerable changes in the design of mixing factories, especially in Mato Grosso (the main fertilizer consumer center). Of the five mesoregions that compose the state, only central–south Mato Grosso was not a candidate for mixing in any of the scenarios. Considering the other four mesoregions, the southeast of Mato Grosso was the main mixing region in C1, with a capacity of almost 2.9 million tons. The results obtained for C5 demonstrate a reduction of 65% in the mixing volume of this region, corresponding to just over 1 million tons.
The mesoregion north of Mato Grosso absorbs a large part of this volume with the loss of capacity in the southeast of Mato Grosso. In C5, this region develops a capacity close to 3.7 million tons, which represents an increase of 208% in relation to C1. This change reflects the increase in the capacity of the waterway flow from Santarém (PA) to Itaituba (PA), as well as the possibility of the expansion of fertilizer imports by the ports of the Northern Arc, according to the results of the mathematical model. Another mesoregion affected by C5 is the northeast of Mato Grosso, which presents an increase of 37% in capacity in relation to C1, increasing from 767 thousand to just over 1 million tons. In addition to the previously mentioned factors, this region will also have a large expansion in demand for fertilizers in 2028.
This expansion in demand, combined with the establishment of new infrastructures with multimodalities, must also cause alterations in the mixing capacities of other localities. The new railway corridor from Santos (SP) to Rio Verde (GO) must further consolidate the mesoregion of the south of Goiás as an ideal fertilizer mixing center. In C5, the region presents an increase of 8% in mixing capacity, which represents a volume close to 2.2 million tons.
The alternatives proposed in C5 also increase the mixing capacities for the ports of the Northern Arc in general. Belém (PA), for instance, presents an increase of 31% in relation to C1, whereas Santarém (PA) represents an important new mixing location. On the other hand, the port of São Luís has almost 1.3 million tons of mixing capacity, which represents an increase of 28% in relation to C1.
The volumes moved in each transport flow are important variables in the mathematical model that help in understanding the main inbound and outbound flows of the fertilizer logistics network. The supply flows of the mixer will be analyzed to understand the dynamics of fertilizer origin, while the supply flows at the destination will be analyzed to understand the area of influence of the mixers.
From the results obtained by the mathematical model referring to inbound flows, it is possible to infer the handled volumes originating in importing ports or national factories. Regarding the characteristics imposed on the model, the import volumes are equal in Scenarios 1, 2 and 4. Conversely, the factories present variations in all scenarios in relation to fertilizer supply.
The results of the base scenario (C1) indicate that the import flows represent around 80.2% of the entire volume that supplies the mixers, with the other 19.8% corresponding to the flows originating in the national factories. In C3, with no restrictions on import capacity, these flows represent 84.2% of the total inbound volume. In this scenario, in relation to C1, there is a considerable alteration in the import flows of Rio Grande do Sul, which leads to a greater concentration of volume in Porto Alegre (RS) than in Rio Grande (RS). As such, the volumes allocated from the factory of Rio Grande (RS) present a reduction of 93%, falling from 1.3 million to 100 thousand tons. On the other hand, C3 demonstrates an increase in imports of 21% in Santos (SP), which also favors greater production capacity in Cubatão, increasing from almost 1.1 million in C1 to 1.5 million tons in C3.
Regarding the optics of the scenario with tax exemption (C2), there is a reduction of around 3% in the volumes allocated to the factories compared to C1. In general, tax exemption, especially for fertilizer imports, disfavors the flows of national factories, especially those located in port regions. The factories located in Paranaguá (PR), for example, had a reduction in flows from 835 thousand tons to 268 thousand tons (−68%). In Cubatão (SP), the reduction was 36% (from 1.1 million to 695 thousand tons), whereas Rio Grande (RS) presented a less dramatic drop, from 1.3 to 1.2 million tons (−8%).
When the increases in capacities and new intermodal infrastructures are analyzed (C4), there are no major alterations in the profile of the mixer supply. On the other hand, with the expansion in demand projected in C5, the inbound flows originating from the factories demonstrate a steep decline, representing only 6.3% of all mixer supply flows. Thus, Brazil becomes even more dependent on imports considering these modifications, which indicates a need to increase national capacities to logistically benefit the fertilizer chain.
Regarding Scenario 5, inland factories suffer the most with the increase in the transport capacity of the intermodality. Catalão (GO), for instance, a major producer of fertilizers, presents a reduction of 64% in inbound flows compared to C1. A large part of this reduction is compensated for by the new railway route from Santos (SP) to Rio Verde (GO), which leads to much competition for supply in the state of Goiás. Conversely, the factories located in Uberaba (MG) present a reduction of 67% in the flows of fertilizer organization, decreasing from 2.2 million to almost 1 million tons, when Scenarios 5 and 1 are compared.
Regarding fertilizer import flows, in Scenarios 1, 2 and 4, when the parameters are not altered, there is a concentration of volume in the ports of the south and southeast, with emphasis on Paranaguá (PR), Rio Grande (RS), Santos (SP) and São Francisco do Sul (SC), which together represent almost 82% of the total national import of fertilizers. On the other hand, the ports of the north/northeast contribute close to 18% of fertilizer imports, with São Luís (MA), Salvador (BA) and Belém (PA) as the main ports regarding import volume.
An analysis was conducted on changes in the fertilizer import profile. In C3, with unrestricted import capacity, the contribution of the ports in the north/northeast increases to 30% in relation to scenarios C1, C2 and C4 (these scenarios displayed the same import capacity restrictions for fertilizers by port, so they are grouped together in the figure). Conversely, in C5, considering the expansion in demand and the new intermodal infrastructures, the mathematical model also tends to expand the inbound flows for the ports in the north/northeast region, representing 28% of the total volume. In
Figure 9, these changes are presented in each scenario analyzed.
Upon evaluating C3, the ports in the north/northeast region that show the highest growth in the representation of imports occur in Belém (PA), which contributes 5.9% of the total volume of imported fertilizers, compared to 2.6% in C1 and Santarém (PA), whose import contribution is 14.4%, compared to 1.6% in C1. In other words, the results obtained from the mathematical model indicate that the northern and northeastern ports present competitive logistical costs, suggesting significant potential for the development of this region for fertilizer imports.
When analyzing the ports of the south and southeast, the import profile changes considerably. The results of C3 demonstrate a drop to 11.4% in the import contribution of the port of Paranaguá (PR), which is currently the main importer of fertilizers. In C1, the port represented 33.3% of the total volume imported by Brazil. The port of Rio Grande (RS) is another port for which the model indicates a reduction in import contribution, reaching 3.1% in C3, compared to 16.2% in C2. On the other hand, the opportunities for the ports of the south and southeast are evidenced by Santos (SP), Porto Alegre (RS) and Vitória (ES), whose contributions in C3 are 24.8%, 12.3% and 7.6%, respectively, compared to 15.9%, 2.1% and 7% in C1.
When C5 is analyzed, the ports of the north/northeast region show a slight drop in the contribution to imports compared to C3; nonetheless, they also indicate a large growth opportunity with the expansion of agriculture and intermodal infrastructures. The new import flow originating in the municipality of Santana (AP) brings a lot of competitiveness to the supply of mixers, representing 6.2% of the volume imported by Brazil. As in C3, Belém (PA), Barcarena (PA) and Santarém (PA) also present an increase in the volume of imported fertilizer contributed, which reflects the rise in the capacity of the waterway route to Itaituba (PA).
Considering the new railway logistics corridor from São Luís (MA) to Palmeirante (TO), the port of São Luís (MA) presents an increase in its imported fertilizer contribution of 5.8%, compared to 3.4% observed in C3. The port of Salvador (BA) does not present great alterations in import volume in the analyzed scenarios.
As identified in this work, the fertilizer logistics chain exhibits high dependency on the road modality, with a low rate of utilization of intermodality in the transport network. An important characteristic of the fertilizer sector is that the intermodal infrastructures have not been designed for fertilizer operations. In this sense, the need for a flow from the port to an input terminal represents a disadvantage for the competitiveness of railway and waterway fertilizer logistics. Thus, with the results obtained, it was also possible to evaluate how each scenario will impact the fertilizer transport matrix in Brazil, as detailed in
Table 3.
The optimal results obtained by the model indicate that 91% of the transport flows were allocated to the road mode, 8% to the railway mode and 1% to the waterway mode in C1. In other words, only 9% of all fertilizer flows involved transport via multimodality. Compared to C2, tax exemption in the logistics chain drastically reduces the allocation of flows to the intermodal routes. The waterway flows were practically zero, while the rail flows represented only 2% of the total volume. Thus, the gains obtained by tax exemption make it practically impossible to transport fertilizers through other modalities.
Regarding C4, the expansion of capacities and the new intermodal infrastructures are enough to increase the contribution of multimodality in fertilizer transport, and only the rail flows present an increase (albeit small) in the transport matrix. Compared to C1, there is an increase of around 500 thousand tons considering the routes from Santos (SP) to Rio Verde (GO) and from São Luís (MA) to Palmeirante (TO).
On the other hand, considering the expansion in demand for fertilizers, the use of the railway and waterway routes increases more significantly. The data of C5 demonstrate that multimodality must contribute 14% of the transport of fertilizers in Brazil, compared to 9% observed in C1. This increase reflects greater use of the railway from Santos (SP) to Rio Verde (GO) and from São Luís (MA) to Palmeirante (TO). Still, the waterway route from Santarém (PA) to Itaituba (PA) boosts the use of this modality for fertilizers. The only reason the share of intermodality does not increase further is because of the lower use of the railway from Santos (SP) to Rondonópolis (MT) in this scenario.
3.4. Location of Fertilizer Mixer Factories: Capacity and Resilience
The mathematical model results also show changes in the positions of mixing factories in each scenario. Considering the tax issues of the fertilizer sector in Brazil,
Figure 10 indicates a tendency for the optimal location of mixing factories to be closer to demand sites rather than to import ports, meaning that outbound flows have a shorter average transport distance compared to the supply distance for mixers.
The expansion of fertilizer demand and intermodal infrastructures (C5) leads not only to the previously presented cost reductions but also to a decrease in the average transport distance in the logistics chain by 10 km compared to C1. The increased intermodality in fertilizer transportation also results in mixing factories moving even closer to demand sites, replacing long-distance inbound road flows with rail or waterway transport.
It is notable that the most divergent scenario is the one that ignores regional tax differences (C2), where there is a strong tendency for mixing factories to be located closer to import ports. Additionally, there is a reduction in the average transport distance by 52 km, showing that the tax rules for the fertilizer sector in Brazil create inefficient transport flows from a logistics standpoint. However, tax exemption for this sector would discourage investments in intermodal infrastructures in the country, given the logistical challenges of moving mixed fertilizers through other transport modes.
Besides bringing factories closer to fertilizer import ports, tax exemption would also lead to greater concentrations than in other scenarios.
Figure 11 shows that in C2, the four major optimal regions for mixer location account for over 60% of the total volume demand in Brazil. In other scenarios, the analysis shows that this concentration is close to 45%, indicating a wider distribution of factories across the national territory.
It is important to note that the higher the concentration of mixing factories, the greater the installed capacity of the factory tends to be. Therefore, tax exemption would help some port regions already overburdened with the import and mixing of fertilizers, such as the port of Paranaguá (PR). In contrast, expanding port capacity for fertilizer unloading operations (C3) results in a lower concentration of mixing factories. It is important for relieving traditional ports and reducing logistical costs in the fertilizer chain.
The final analysis focuses on identifying the resilience of optimal locations for fertilizer mixing factories. We define location resilience as the frequency with which a region is recommended as an optimal location in the evaluated scenarios. This study analyzed five distinct scenarios, each varying in its specific characteristics. A region that emerges as an optimal location in all five scenarios is considered highly resilient to the tested changes. In contrast, regions with only one optimal location indication are classified as having low resilience, demonstrating limited viability under specific conditions.
Location resilience analysis serves as a measure of locational risk. Regions showing high resilience to the tested scenarios present a low risk of needing relocation to achieve the optimal solution.
Figure 12 illustrates the number of optimal recommendations for each region across the five evaluated scenarios. Ten regions showed just one optimal location recommendation, indicating low resilience. One region had two recommendations, six regions had three recommendations, sixteen regions had four recommendations and fifteen regions received five recommendations, showing high resilience. The regions of high resilience, receiving five recommendations, are highlighted in blue in the figure. These regions are characterized by proximity to the country’s main fertilizer import ports or significant railway terminals.
Furthermore,
Figure 12 presents the coefficient of variation of the optimal capacity of fertilizer mixing factories in each region, considering the five evaluated scenarios. This coefficient indicates the stability of the fertilizer mixing capacity in these regions. For example, the regions of Araguari (MG), Paracatu (MG) and the port of Santos (SP) showed high location resilience with five optimal recommendations and a coefficient of variation in mixing capacity below 20%. In contrast, the regions of Guaíra (SP), Paranaguá (PR), Porto Alegre (RS), Rondonópolis (MT) and Vitória (ES) demonstrated high resilience but with a coefficient of variation in mixing capacity above 50%.