Economic Evaluation of Nitrogen Fertilization Levels in Beef Cattle Production: Implications for Sustainable Tropical Pasture Management

Abstract

Understanding economic scenarios is crucial in all production chains. Tropical pastures are Brazil’s primary food source for beef cattle production, and current pasture management is not ideal due to land degradation. An economic evaluation assists farmers with improving pasture management using novel techniques, such as nitrogen (N) fertilization, which is straightforward and practical. The economic effects of different N fertilizer levels in beef cattle production were evaluated. This study was conducted over three years (2014/2015, 2015/2016, and 2016/2017) using four concentrations of urea fertilizer (0, 90, 180, and 270 kg N/ha). A principal component analysis and sensitivity analysis were performed using financial data. A financial pattern was observed, with increases in some variables, such as cost-effective operating and cost-total operating from those measuring costs and gross revenue, operating profit, and net income from those estimating revenues. Treatment with 180 kg N/ha fertilizer resulted in increased profitability, payback, internal rate of return, and net present value (at 6% and 12% tax) of 17.76%, 2.79 years, 35.79%, and USD 5926.03 and USD 1854.35, respectively. For this study, the main costs associated with profitability were supplementation, animal purchases, and sale prices. The best treatment to achieve excellent grazing pressure in tropical areas with oxisol is 180 kg/ha per year.

1. Introduction

Brazilian beef cattle production is among the largest industries in the world. The numbers show the importance of this system: Brazil has around 188 million cattle, which produces 10.32 million tons of carcasses [1]. These high figures are responsible for Brazil’s positive gross domestic product (GDP). The main Brazilian export product is in natura or fresh beef. In 2020, Brazil exported close to 2.24 million tons of fresh beef [1]. Combined with other products, total Brazilian exports are close to 2.69 million tons of carcass equivalents, valued at approximately USD 8.5 million.

The present study features data collected between 2014 and 2017, before the global spread of COVID-19, which was considered a pandemic on the 11 March 2020 [2]. The present economic evaluation considered the relevant parameters and values before this public health crisis, which promoted effects on farming production, agro-livestock, and marketing [3]. The chain values were severely affected due to marketing network interruption and changes in consumer expenses [4].

The central production system of Brazilian beef cattle is based on the use of pastures but with low technology. According to Agastin et al. [5], this system mainly uses pastures, which tend to have a higher profitability than feedlots. Therefore, it is justifiable to say that Brazilian production still has room for improvement using different technological packages. Of the strategies to improve pasture management, using appropriate stocking rates during different seasons is a primary and fundamental technology that has had the greatest impact on profitability [6]. Nitrogen (N) fertilization is one of many techniques used to increase the stocking rates. However, the efficiency of N fertilization in grazed pasture systems is affected by different factors, including environmental and management practices [7]. Therefore, simply increasing N fertilizer levels will not result in higher forage production and possibly will not increase the stocking rates.

Beef producers have introduced recent technologies to the system to maintain a positive GDP and to improve farm profitability. However, as defined in economics studies, an increase in N fertilizer levels has been explained by using the theory of a production function, which represents the relationship between inputs (N fertilizer) and the maximum amount produced within a given period with a given level of technology [8]. Furthermore, nutritional supplementation with concentrate can be strategically administered within these beef cattle production systems to tackle issues related to the quantity and quality of pasture-based feed, consequently ensuring efficient growth [9].

Therefore, considering the classic sustainable economic concept of a production function, we hypothesize that financial outcomes and indices change with the increase in N fertilizer levels in the growing beef cattle production system that employs tropical forage as its primary food source and intensive supplementation during the finishing phase. This research study evaluated the economic effects of using tropical pastures as the main sustainable food source in beef cattle production. Principal component analysis was used to investigate how each cost and economic result were explicitly associated.

2. Materials and Methods

2.1. Experiment and Management

The experimental procedure was developed by the Forage Crops and Grasslands Section, Animal Science Department of São Paulo State University, “Júlio de Mesquita Filho” (Unesp) (Jaboticabal, SP, Brazil), in tropical areas with oxisol. The regional climate in northeast São Paulo state is subtropical humid, with dry winters and wet summers. Pastures were seeded with Uruchloa brizantha cv. Marandu in 2001 and subsequently managed using a continuous grazing system with a variable stocking rate. Sward height was 25 cm, considering a 95% light interception efficiency. The study was conducted over three years (2014/2015, 2015/2016, and 2016/2017) and included four treatments using urea fertilizer (0, 90, 180, and 270 kg N/ha); after applying them in three fertilization stages during the wet season for three replications, in a total of 12 paddocks (experimental units), the results were evaluated. To assess animal performance, young Nellore bulls (Bos taurus indicus) with different initial body weights (BW: 309.5, 251.5, and 244 kg in the first, second, and third experimental years) were selected. The bulls were weighed every 28 days (without fasting) during the testing period to adjust the stocking rate to the pasture height. The experimental design was completely randomized, with four treatments (0, 90, 180, and 270 kg N ha⁻¹) using urea fertilizer, applied in a split design during three fertilizations per growing season and three replicates per treatment. The paddock areas were 1.3, 1, 0.7, and 0.5 ha for the 0, 90, 180, and 270 kg N ha⁻¹ treatments, respectively. The experimental area included a reserve area of 3 ha for the spare animals. The remaining animals were used to maintain a pre-determined grazing height using put-and-take methodology [10]. There was an increase in the forage accumulation rate under fertilization with different doses of nitrogen, with a linear and positive effect, described by the following equation: Y = 0.2211x + 31.36. The average accumulation rates were 31.36, 51.26, 71.16, and 91.06 kg DM/ha/day for doses of 0, 90, 180, and 270 kg N/ha, respectively. As per Delevatti et al. [11], which served as the foundation for this study, the ethics committee reviewed and approved all experimental procedures (certificate number 12703/15).

Due to pasture management during the first and second years (2014/2015 and 2015/2016), two phases (growing and finishing) were evaluated for each treatment while constantly adjusting the stocking rate to preserve ideal conditions for forage allowance. To maintain the pasture perennially, only the growing phase was implemented in the last experimental year (2016/17), due to weather conditions. The growing phase—when animals are growing up to nearly 360 kg BW—was conducted during the wet season, when the stocking rate can be higher depending on grazing management. The finishing phase, when animals finish growing at up to almost 500 kg BW, was implemented during the dry season, when the stocking rate is generally lower, to preserve ideal conditions for forage regrowth.

2.2. Dataset Description

Data were analyzed following year combinations from 2014/2015 to 2016/2017. The first year was chosen because of the stocking rate, sward height adjustments, and fertilizer practice during the wet season, close to December. Hence, we had three experimental years: 1 (2014/2015), 2 (2015/2016), and 3 (2016/2017). During these periods, we used the same supplements, that is, a mineral mixture, for all treatments. Annual weather differences may have affected production and the nutritive value of forage, which is the main food source.

Animal performance data and productive indices used for the economic analyses were recorded for each experimental year and adjusted for paddock area (1.0 ha) to facilitate the comparison. We used the initial and final body weight (kg), stocking rate (animal unit (AU)/ha), and supplementation costs. Supplementation costs include the mineral mixture used during the growing phase and supplements [1.5% BW] used during the finishing phase, which were formulated using different ingredients, such as dried distillers grain, cotton seed meal, corn meal, urea, and a mineral mixture. Nitrogen fertilizer levels (0, 90, 180, and 270 kg N/ha) were the main research variable.

2.3. Economic Evaluation

We evaluated the cash flow in each phase (growing and finishing) for all the experimental years. These evaluations considered cost-effective operating (CEO), cost-total operating (CTO) [12], gross revenue (GR), operating profit (OP), and net income (NI), all measured in USD. Another critical analysis performed to compound the CTO was linear depreciation (LD), which considered new value (NV), scrap value (SV), lifetime of the building, equipment, and everyone involved in beef cattle production (LT), and use days of the building and equipment (UD). The LD of each item was calculated as [(NV − SV/LT)/365] × UD [13].

Other analyses were conducted to evaluate each phase economically. Economic indices were calculated; the first was profitability, calculated as [OP/GR]. The second one, payback, measured in years, was calculated using average capital stocktaking by area in use divided by [GR–CEO]; payback is the time taken to repay all investments made in the system. The average capital stocktaking by area in use corresponds to the actual use of buildings, equipment, and all items involved in beef cattle production for each treatment. Due to the experimental conditions, all these items had their costs considered to compound the initial investment, which followed the use proportion for each treatment evaluated. Another economic variable used was the internal rate of return (IRR), calculated as 100/payback [14]. We also calculated the benefit-to-cost ratio (B: C) as [(GR − CEO)/CEO]. Further, we computed the break-even points (BEP) of the entire period and a daily one, measured in kg BW. The BEP of the whole period was calculated as [CTO/(revenue pay per kg–average CEO)] and the daily BEP as [BEP period/length of days]. The net present value (NPV) was calculated considering ten years and three different taxes (6%, 12%, and 18%) per the equation described in Aguiar and Resende [14] [NPV = ∑Results − (Investment/(1 − taxes)¹⁰)].

The CEO of each experimental year was calculated based on the purchase of Nellore bulls, fertilizer, fertilization practice costs, purchase of supplements, supplementation practice, and management costs (animal markers, medicine, labor, and fuels). The CTO was calculated as CEO + LD. The GR of each experimental year was calculated by selling animals to maintain an accurate stocking rate during the next phase (dry season—finishing phase) when the stocking rate was less than that of the growing phase. According to historical price quotations, the animals were sold using the BW to determine the price by head or kilogram [15]; additionally, animals were sold to slaughterhouses. The price per kilogram was based on historical price quotations using the BW and carcass yield of 50% [15].

The input prices were the average prices during 2014–2017 and were obtained from Centro de Estudos Avançados em Economia Aplicada (CEPEA) [15], and the price quotations were from the same region of animal production (northeast São Paulo State) during the period as mentioned earlier. The values were calculated in USD according to the exchange rate for each year (2014: BRL 2.35; 2015: BRL 3.34; 2016: BRL 3.48; 2017: BRL 3.17).

2.4. Multivariable Analysis

Principal component analysis (PCA) was conducted using economic results (CEO, CTO, GR, OP, and NI), economic indices (profitability, payback, IRR, and NPV of 6%, 12%, and 18%, respectively), and main costs (animal, fertilization, and supplementation). The data were standardized before the analysis. Subsequently, factor analysis was performed with the extraction factors’ effect using principal components calculated from the correlation matrix of the variables [16,17].

The first factor extracted from the previously mentioned matrix was the linear combination of the original variables, which gave the highest total variability of the data used. The other factor was the second linear function of the actual variables, which explained the total variability from the residual variables. The factors obtained in the analysis, which will be used in the PCA, are independent, do not include unity, and are standardized variables (normal distribution with mean = 0 and variance = 1). The coefficient obtained from the linear functions for each variable was used to understand significance based on its sign (positive or negative) and value. Only values above 0.70 were considered significant and responsible for the effects in the analysis. Multivariable analyses were performed using Software STATISTICA v.12 (StatSoft, Inc., Tulsa, OK, USA) [18].

2.5. Sensitivity Analysis

The sensitivity analysis was performed considering that all values presented in this study were obtained from a market without pandemic effects. According to Hossain et al. [19], these analyses can investigate the projected fluctuations based on major factors regarding production cost involvement. For this specific study, two factors were altered: the initial animal purchase price and the animal price when finished and sold. The economic sensitivity analysis measured the variable profitability, which was calculated considering a range of animal purchase and sale prices of ±25%. This method aimed to measure the economic risk by considering the product prices (purchase and sale) as the main sources of instability [20] to represent anomaly conditions on the market due to a pandemic situation, as occurred in 2020.

3. Results

The weather data showed that 2015 had more rainfall (1711.9 mm) than the other observed years (mean 1047.2 mm). For 2015/2016, the weather conditions (temperature (range 31.9 to 20.6 °C), rainfall (range 449.4 to 201.0 mm), and rainy days (range 18 to 14 days)) during the fertilizer period, conducted between December 2015 and February 2016, were appropriate for forage growth, allowing a good N fertilizer value for forage.

The increased N fertilizer levels caused a systematic increase in all the economic results for the observed years and the total (

Table 1. Economic results (CEO, CTO, GR, OP, NI, and CTOm) obtained from each treatment (0, 90, 180, and 270 kg N/ha) during different years of evaluation (2014/2015, 2015/2016, and 2016/2017) and total among experimental years.

Treatment (kg N/ha)	Year	Variable (USD/ha) *
		CEO	CTO	GR	OP	NI	CTOm 1
0	2014/15	1919.66	2048.47	2905.72	857.25	986.06	1.04
	2015/16	2571.31	2708.68	2929.12	220.44	357.81	1.35
	2016/17 ⸸	1536.65	1611.78	1743.69	131.91	207.04	1.45
	Total	6027.62	6368.93	7578.52	1209.59	1550.90	1.28
90	2014/15	2403.20	2551.80	3530.19	978.39	1126.99	1.07
	2015/16	2898.76	3054.23	3374.70	320.46	475.94	1.34
	2016/17 ⸸	2509.69	2616.81	2873.43	256.61	363.74	1.43
	Total	7811.65	8222.85	9778.32	1555.47	1966.67	1.28
180	2014/15	3180.42	3364.81	4985.03	1620.23	1804.62	1.02
	2015/16	3347.54	3518.70	3835.23	316.53	487.69	1.37
	2016/17 ⸸	2734.51	2847.64	3007.07	159.46	272.56	1.48
	Total	9262.47	9731.15	11,827.34	2096.18	2564.87	1.29
270	2014/15	3709.14	3915.15	5554.95	1639.80	1845.81	1.06
	2015/16	3774.76	3959.69	4277.45	317.76	502.69	1.39
	2016/17 ⸸	3237.31	3367.11	3539.58	172.47	302.27	1.49
	Total	10,721.21	11,241.95	13,371.98	2130.03	2650.77	1.31

* CEO: cost-effective operating; CTO: cost-total operating; GR: gross revenue; OP: operating profit; NI: net income; CTOm: CTO mean; ¹ (USD kg/BW). ^⸸ 2016/2017: only developed growing phase.

). The finishing phase was not conducted during the 2016/2017 period. Therefore, results were only obtained for the growing phase for every treatment evaluated. The CEO increased by 29.60%, 18.57%, and 15.75% for treatments between 0 and 90 kg N/ha, 90 and 180 kg N/ha, and 180 and 270 kg N/ha, respectively. For treatments from 0 to 180, and 270 kg N/ha, the CEO increased by 53.67% and 77.87%, respectively. Following the same pattern, the CTO values increased similarly: 29.11%, 18.34%, and 15.53% for treatments 0 and 90 kg N/ha, 90 and 180 kg N/ha, and 180 and 270 kg N/ha, respectively. Compared to treatment with 0 kg N/ha, the CTO results increased by 52.79% and 78.55% for treatments of 180 and 270 kg N/ha.

The GR increased following the increase in N fertilizer levels (Table 1). For treatments of 0, 90, 180, and 270 kg N/ha, the revenue was USD 7578.52, USD 9778.32, USD 11,827.34, and USD 13,371.98, respectively. Except for the treatment with no N fertilizer (0 kg N/ha), GR was better during the 2015/2016 period (USD 1929.12). GR was better in the 2014/2015 period with the other treatments: USD 3503.19, USD 4985.03, and USD 5554.95, for treatments of 90, 180, and 270 kg N/ha, respectively. Note that there was no finishing phase with animals in the final period, 2016/2017.

The OP values were higher during the 2014/2015 period (Table 1) than in the other formative years. The total OPs were USD 1209.59, USD 1555.47, USD 2096.18, and USD 2130.03 for treatments of 0, 90, 180, and 270 kg N/ha, respectively. Like the OP, the NI values had the same pattern for the different treatments: USD 1550.90, USD 1966.67, USD 2564.87, and USD 2650.77, for nitrogen fertilizer levels of 0, 90, 180, and 270 kg N/ha, respectively.

Regarding the cost-total operating mean (CTOm) measured in USD.kg/BW, the values indicated that an increase in the technology package (caused by the increase in N fertilizer levels) resulted in a higher CTOm (Table 1). The result was similar between treatments 0 and 90 kg N/ha, mainly due to the head number purchased during each phase (growing and finishing). The increase of CTOm was 0.78% and 2.34% for treatments between 0 and 180 kg N/ha, and 0 and 270 kg N/ha, respectively. Comparing the treatments of 180 and 270 kg N/ha, the increase was 1.55%.

Some economic indices are presented in

Table 2. Economic indices (profitability, payback, IRR, B: C, and BEP of the period and daily) for total of all experimental years corresponding to each treatment (0, 90, 180, and 270 kg N/ha).

Variable *	Treatment (kg N/ha)
	0	90	180	270
Profitability (%)	15.92	15.92	17.76	15.96
Payback (years)	4.30	4.25	2.79	2.30
IRR (%)	23.26	23.55	35.79	43.38
B:C ratio	0.26	0.25	0.28	0.25
BEPp (kg BW)	168.38	164.18	169.26	169.94
BEPd (kg BW)	1.06	1.03	1.06	1.07

* IRR: internal rate of return; B:C ratio: benefit to cost ratio; BEPp: break-even point of period measured in kg of body weight (BW); BEPd: break-even point daily measured in kg of BW.

. The profitability was better with a treatment of 180 kg N/ha (an increase of 17.76%) than the other treatments (increases of 15.92% for 0 and 90 kg N/ha and 15.96% for 270 kg N/ha). Another economic index analyzed was payback. The results showed minor differences between treatments of 180 and 270 kg N/ha (2.79 and 2.30 years, respectively) and between treatments of 0 and 90 kg N/ha (4.30 and 4.25 years, respectively). Thus, better payback was obtained with 270 kg N/ha, with a value 1.87 times smaller than the worst value obtained with 0 kg N/ha. Similarly, the IRR was better with the treatment of 270 kg N/ha (43.38%), followed by those of 180, 90, and 0 kg N/ha (35.79%, 23.55%, and 23.26%, respectively).

The B: C ratios were very similar for the evaluated N fertilizer levels (Table 2): 0.26, 0.25, 0.28, and 0.25 for treatments of 0, 90, 180, and 270 kg N/ha, respectively. This standard occurred at the break-even point of the period (BEPp) and the daily one (BEPd). Smaller BEP values were considered better for the treatment of 90 kg N/ha (164.18 kg BW and 1.03 kg BW, for BEPp and BEPd, respectively). Higher BEP values were obtained for treating 270 kg N/ha (169.94 kg BW and 1.07 kg BW, respectively, for BEPp and BEPd).

The NPV analysis (

Table 3. Net present value (NPV), in USD, of each treatment (0, 90, 180, and 270 kg N/ha), according to economic results for three different taxes (6%, 12%, and 18%) with ten years of investment.

Taxes (%) *	Treatment (kg N/ha)
	0	90	180	270
6	3663.30	4955.60	5926.03	3557.90
12	1089.26	1672.97	1854.35	−544.03
18	−643.67	−539.05	−853.94	−3251.03

* Taxes were determined following the Brazilian government savings rates at the time of the study (SELIC tax = 6%). The exchange rate used was a mean value for the entire study period (USD 1.00 = BRL 3.33).

) showed better results with 180 kg N/ha for taxes of 6% and 12% (USD 5926.03 and USD 1854.35, respectively). When using the higher tax (18%) to compute NPV, no treatment resulted in a positive value. The NPVs were −USD 643.67, −USD 539.05, −USD 853.94, and −USD 3251.03, for 0, 90, 180, and 270 kg N/ha, respectively. This demonstrated that none of the treatments would be economically attractive for practice by commercial farmers.

The PCA showed all correlations among economic and results variables and indices (Figure 1). Factor 1, accounting for 58.33% of the total data variability, displayed positive correlations with specific inputs (CEO, CTO, GR, OP, NI, and profitability). Simultaneously, these inputs showed negative correlations with other variables (NPV 12%, NPV 18%, and supplementation). In contrast, the latter set of variables (NPV 12%, NPV 18%, and supplementation) demonstrated positive correlations internally. Factor 2 accounted for 27.34% of the total data variability and positively correlated with three inputs (NPV 6%, animal, and fertilization).

The sensitivity analyses (Figure 2) performed using a range of ±25% in both price quotations (initial animal purchase or price when sold, individually measured) indicated that the sold price had a higher effect on profitability. When the simulated values were −25% of the real sold value, the measured profitability from every treatment was negative. However, when the same variation, but with an increase in the animal purchase price, was imputed (+25%), the profitability for these treatments ranged between 1.46% and 3.52% (treatments 270 and 180 kg N/ha, respectively). Other relevant information about these analyses is the lesser range of each treatment after using the simulated prices. The treatment 180 kg N/ha was the one that presented lesser variation in profitability; that was 3.52% and 31.99% (when the range (+25% and −25%) was applied to the animal purchase price) and −9.16% and 33.9% (when the range (−25% and +25%) was used on the sold price).

4. Discussion

The Food and Agriculture Organization of the United Nations (FAO) takes a multidimensional approach to defining sustainable agriculture as farming activities that meet the needs of present and future generations sustainably [21]. To meet this target, the activities must be technologically adequate, economically viable, socially acceptable, and not cause environmental degradation [21]. Within this framework, our objective was to analyze the economic sustainability of the beef cattle production system based on pastures using nitrogen fertilization.

The weather conditions associated with market prices, when the latter are mainly determined by the cattle cycle, led to the 2014/2015 period having better conditions with corn prices that were lower than their previous ten-year average. The prices of finished bulls were higher than the average of the previous ten-year average and the calf costs. This situation corroborates Zen and Barros [22], who mentioned that the owner had control over farm costs, but the equilibrium between supply and demand determined the market prices. In this study, another crucial factor that resulted in higher OP and NI for the treatments (0, 90, 180, and 270 kg N/ha) was the duration of the finishing phase. During 2014/2015, 84 days were needed to finish animals, and during 2015/2016, 112 days were required. This delay caused higher costs and a decrease in OP and NI.

Following the rules of a production function [8], considering as input the increase in N fertilizer levels in beef cattle production systems developed using forage as the main food source, the economic results (CEO, CTO, GR, OP, NI, and CTOm in Table 1) showed a pattern of growth that appeared optimistic according to the production function. However, the effect of the production scale was included in the costs [23]. This effect was described by Zen and Barros [22], using graphics to convey the different costs of traditional beef cattle farms in other countries and on different production scales. The costs for both calves and finished bulls in Brazil are lower than those in Argentina. This allows Brazilian beef to be competitive internationally.

Increasing N fertilizer levels can be considered a management technique with a technology package. Other related methods for forage management are stocking rate, sward height, and supplementation. Although some Brazilian farmers are averse to new technologies [24], the technology mentioned above is being used more frequently in beef cattle farms, increasing costs and generating higher revenues [22]. Considering that beef cattle production in Brazil is trilateral and includes Nellore–Cerrado–Brachiaria cattle [25], management needs to be continuous to obtain optimal results from foraging until the animal is finished. This corroborates Oliveira Silva et al. [26]. These authors reported how pasture management improved economic and environmental outcomes.

Brazilian beef cattle systems have drastically changed production practices since the 1990s when they needed higher efficiency for ideal economic results, resulting in good values for financial indices. Efficiency is fundamental when observing Brazilian pasture occupation and herd size. According to ABIEC [27], pasture occupation decreased from 191 million hectares in 1990 to 163 million in 2019, a 14.7% reduction. However, herd size, measured in kg BW/ha/year, increased by 42.6%, which is associated with increased productivity. The productivity was 123 kg BW/ha/year in 2019, compared to 48.9 kg BW/ha/year in 1990. These numbers show the importance of ideal management of beef cattle farms.

The economic indices (Table 2) obtained for treatments with higher N fertilizer levels are associated with those reported by Corrêa et al. [28]. System intensification, caused by factors such as fertilization and animal supplementation, increases system complexity and causes changes in the cost structure [28]. These changes cause higher risks but can also improve profitability if the system management is ideal. Regarding intensification in Brazilian beef cattle production, Carvalho and Zen [29] described the heterogeneity in these systems’ economies. The authors reported two kinds of systems: one with high quality and using economic tools, and another with low quality and productivity and without strategic economic management. According to Carvalho and Zen [29], planning associated with cost management could improve the use of resources and, consequently, productivity, sustainability, economy, and the social aspects of beef cattle production. The higher profitability obtained with the treatment of 180 kg N/ha (17.76%) positively correlated with N utilization in grasslands assessed by other studies in the same area; see Delevatti et al. [11].

The NPV values corroborated the profitability (Table 3) and showed that the 180 kg N/ha treatment with taxes of 6% and 12% was superior compared to other treatments. NPV, when studied by other authors, has yielded negative values. Usually, this result is obtained during grassland degradation periods [26]. Therefore, considering that our treatments produced ideal pasture management, mainly due to sward height and variable stocking rate management, the NPV results with a traditional interest rate (6%) were positive. Considering that only 14.06% of beef cattle slaughtered in Brazil are from feedlots [27], one can understand the importance of pastures in this chain. However, technological packages need to be used to improve results. According to Zen and Barros [22], Brazilian conditions result in a higher potential for beef cattle production than in the rest of the world. These conditions are because of the weather and costs that are usually lower than those in other countries.

From the PCA performed using data from this study (Figure 1), the main costs affecting profitability were supplementation and animal purchases. Considering the increase in fertilizer levels, fertilization costs also played a major role in profitability but less than the two factors mentioned above. Brazilian beef cattle production presented some similarities in the costs of these systems. Pini et al. [30] reported that a farm developing rearing and finishing phases attributed 61.17% of production costs to animal purchases, a value close to that observed in this study, which is an essential implication for increasing profitability. Even if animal supplementation is applied only during the finishing phase, the practice costs are high and affect the systems’ profitability. Animal supplementation needs to be used during the finishing phase and be undertaken in the dry season because tropical pastures will not supply the animals’ requirements for ideal gains. Therefore, supplementation is an essential tool for managing beef cattle production, which causes an increase in average daily gains [31,32], allowing animals to be slaughtered at the end of the dry season. Animal supplementation during the dry season and fertilization during the wet season are crucial intensification methods contributing to livestock sustainability. Therefore, their costs can be considered as costs that increase future profitability [28]. By understanding economic farming results, farmers can choose the best-suited method to increase their beef cattle indices and, consequently, financial results and farm profitability [33].

The sensitivity analyses can measure the economic risk by the techno-economic results through product price as the primary source of instability [20]. The present study performed an evaluation ranging from the initial animal purchase price to the final price when sold, individually. The current world pandemic scenario has influenced both these prices recently, mainly in the Brazilian beef chain. The profitability results measured in the range of ±25% for animal purchase or sale prices, used as different scenarios to represent best or worst market conditions, could support decision-making processes [34] in beef cattle production. In this way, the treatment of 180 kg N/ha resulted in lesser profitability variation (Figure 2), with lesser economic risk for the farmer during the decision-making process.

5. Conclusions

Considering all findings, increasing N fertilizer levels changed the economic results and indices in tropical pastures with continuous grazing and accurate grass management with oxisol. Therefore, as suggested in the study by Delevatti et al. [11], the 180 kg/ha nitrogen dose is recommended to increase forage yield and quality. In this economic evaluation study, the dose of 180 kg/ha also achieved the best economic indicators with a higher profitability return for better system sustainability. Both the PCA and sensitivity analysis results determined that the main costs associated with profitability were supplementation, animal purchases, and cattle prices when sold. Thus, the supplement formulation, animal purchases, and sales on the market can determine the final economic results of beef cattle systems using tropical grasses as a main food source.