Off-season corn responses to nitrogen fertilization in Brazil: A systematic review

ABSTRACT

Characterizing yield response patterns of off-season corn (Zea mays L.) to nitrogen (N) fertilization is a key step toward improving fertilizer use and mitigating the environmental footprint of N in grain production systems. Off-season corn cultivation areas in Brazil possess high edaphoclimatic diversity, and consequently, different environmental drivers may influence N responses. The present systematic review aimed to integrate and synthesize available evidence on the response patterns of off-season corn to N from field experiments in Brazil. A peer-reviewed literature search was conducted in the Scopus, Web of Science, and ScienceDirect databases. Filtering processes were employed to select non-duplicated studies that reported yield and N rate data. Ultimately, 27 studies were eligible, resulting in 303 observations. The N response curves showed wide variability across studies, with no regional differences in yield under no or low N fertilization (< 30 kg ha^-1 of N) (overall estimated mean = 5416 kg ha^-1). The quadratic-plateau model, fitted to the literature data, identified a relative yield increase up to ~78 kg ha^-1 of N. The uncertainty associated with the estimate was high, with a 95 % confidence interval ranging from 52 to 119 kg ha^-1 of N. Across simulated maximum-yield and price-ratio scenarios, the economic optimum N rate ranged from 24 to 65 kg ha^-1 of N. Under high water availability conditions (>832 mm), starter N supply and soil organic matter (SOM) content were the key variables in yield fluctuations. This synthesis provides a national-scale benchmark for agronomic and economic optimum N rates in Brazilian off-season corn and supports the need for environment-specific soil and fertilizer management strategies to achieve higher yields.

Keywords
Zea mays L.; optimum nitrogen rate; nitrogen fertilizers; fertilizer management; grain yield

INTRODUCTION

Brazil ranks among the four largest consumers of fertilizers containing nitrogen (N), phosphorus (P), and potassium (K), accounting for approximately 10 % of global demand. Of this total, N represents about 29 % (International Fertilizer Association, 2025). Despite this high demand, estimates indicate that only 4-10 % of the N fertilizer consumed in the country is produced domestically, with the remainder imported (USDA, 2022; Nunes and Pires, 2025). This dependency exposes the agricultural sector to potential risks associated with geopolitical instability and supply shortages, with direct implications for food security (SAE-PR, 2021).

Corn (Zea mays L.) is the most widely cultivated cereal in Brazil and plays a strategic role in supplying national and international markets (Erenstein et al., 2022; Conab, 2025). In tropical regions of Brazil, the absence of a harsh winter allows corn to be sown in both the main season (spring-summer) and the off-season (autumn-winter). Generally, yield potential is higher in the first crop, as higher temperatures and greater water availability favor it. However, since the 2011/2012 growing season, with the expansion and predominance of soybean [Glycine max (L.) Merrill] during the spring-summer period, the area dedicated to off-season corn has surpassed that of the first crop (Conab, 2024). Since then, the off-season, or "safrinha", has established itself as the main corn production season in Brazil, currently covering about 17 million ha and producing approximately 110 million megagrams (Mg). This value contrasts sharply with the first crop (summer corn), which occupies only about 4 million ha and produces approximately 26 million Mg (Conab, 2025).

Nitrogen is the most demanded nutrient by the corn crop (Silva et al., 2018). The relevance of N stems from its key role in the formation of cellular biomolecules (e.g., proteins, nucleic acids, chlorophyll, co-enzymes, phytohormones, and secondary metabolites) (Hawkesford et al., 2023). In Brazil, as pre-plant soil N analysis is not routinely performed, N fertilization recommendations are primarily based on yield goals, the previous crop, and soil organic matter content (Sousa and Lobato, 2004; Pauletti and Motta, 2019; Cantarella et al., 2022). Although each region of the country has technical bulletins that guide producers on fertilizer management, these materials require significant time and resources for experimental validation and updates. Consequently, these materials may not always fully account for recent advances, such as the expansion and specificities of off-season corn cultivation.

The high dependency on imported N fertilizers, combined with significant N losses to the environment in tropical systems, poses major challenges to nutrient management (Minato et al., 2020; Cassim et al., 2022). In double-cropping systems within the same growing season (e.g., soybean-off-season corn), these challenges are intensified by high N export rates (Duarte et al., 2019; Esper Neto et al., 2021). In this context, it is necessary to improve N fertilization recommendations to reduce the nutrient's environmental footprint and prevent the depletion of soil N stocks. However, the wide variability in production environments, management systems, and experimental methodologies limits the ability of individual studies to generate broadly applicable guidelines. Thus, approaches that integrate multiple lines of evidence emerge as valuable strategies for identifying general response patterns and guiding more refined recommendations.

The present systematic review was conducted to integrate and synthesize available evidence on the responses of off-season corn to N fertilization in Brazil. The specific objectives of the study were to (i) examine the yield-N rate response patterns for off-season corn across Brazilian regions, including regional variation in yield potential under zero or minimal N input as an indirect indicator of soil N supply capacity; (ii) identify, from existing literature, the agronomic optimum N rate, the uncertainty associated with its estimation, and the economic optimum N rate under contrasting yield and price-ratio scenarios; and (iii) rank additional factors described in the literature that govern off-season corn responses to N management using a conditional inference tree.

MATERIALS AND METHODS

Data collection

A peer-reviewed literature search was conducted in November 2025 using the Scopus, Web of Science, and ScienceDirect databases. The search string applied to titles, abstracts, and keywords included a combination of the following terms: (corn OR maize OR milho) AND ("second season" OR "second-season" OR "off season" OR "off-season" OR safrinha OR "segunda safra" OR "segunda-safra") AND (nitrogen OR nitrogênio OR "adubação nitrogenada" OR "dose de N" OR "N rate"). Due to word count restrictions on ScienceDirect, the search query was limited to English terms.

The primary search yielded 297 articles. Since these records could contain duplicates and/or data unrelated to the study scope, filtering procedures previously adopted by Gomez et al. (2024) and Volpato et al. (2025) for the construction of literature-based databases were applied. In the first stage, 100 duplicate articles were removed (Figure 1). In the second stage, an abstract screening was conducted to exclude studies that did not address the review topic, resulting in the removal of 156 studies. Both initial stages were performed using the R software with the revtools package (Westgate, 2019).

Finally, each article was individually verified to select studies derived from field experiments. These studies were required to report off-season corn yield responses to at least three topdressed N rates evaluated in Brazil. At the end of this final stage, 27 articles were eligible for the review (Figure 1). From these articles, 303 grain yield observations (standardized to 13 % moisture content) as a function of topdressed N rates were collected. Additionally, when reported, information regarding the previous crop, soil clay content, soil organic matter (SOM) (0.00-0.20 m soil layer), total water supply (precipitation + irrigation, when applicable), N source, and growing season was obtained. For data reported only in graphical form, the WebPlotDigitizer tool was used for extraction.

Figure 1
Sankey diagram illustrating the steps performed in the article screening process. The “n” represents the number of articles at each filtering stage after selection or removal from the database.

In experiments in which grain yield responded to N rates, the agronomic optimum N rate (AONR) and the economic optimum N rate (EONR) were determined from the fitted response models. The AONR was assumed as the N rate required to achieve maximum grain yield, whereas the EONR was assumed as the N rate that maximizes on-farm partial profit, considering fertilizer cost relative to grain value.

For quadratic models, AONR was defined as the N rate corresponding to the maximum point of the fitted curve, whereas for linear-plateau models, it was defined as the minimum N rate required to reach the estimated maximum yield. For linear models, AONR was assumed to be the highest N rate applied. Only one response curve was best described by a cubic model; in this case, AONR was not calculated.

The EONR was estimated according to the fitted model type. For linear and linear-plateau models, EONR was defined as the highest N rate applied and the join point, respectively. For quadratic models, EONR was calculated by equating the first derivative of the fitted response function to a price ratio (PR) of 5.9 (Equation 1).:

Eq. 1 $\mathrm{EONR}=\frac{{\mathrm{\beta }}_1-\mathrm{PR}}{-2{\mathrm{\beta }}_2}$

in which: EONR is the economic optimum N rate, and PR represents the price ratio, expressed as the kilograms of corn grain required to purchase 1 kg of N fertilizer. The PR was determined using the median of the annual mean corn grain and N prices from the 2020-2025 period, obtained from the Centro de Estudos Avançados em Economia Aplicada and Companhia Nacional de Abastecimento databases, respectively (Cepea, 2026; Conab, 2026). Urea was adopted as the reference N fertilizer source.

DATA ANALYSIS

Modeling yield response to N rates

To investigate the relationship between relative grain yield (response variable) and topdressed N rate (predictor variable), a quadratic-plateau model was fitted. This model was chosen because it captures the nonlinear yield response to increasing N rates and provides parameters with clear agronomic interpretation for developing N recommendations, as documented in previous studies (Correndo et al., 2021; Baum et al., 2025). This model can be expressed according to equation 2.

Eq. 2 ${\mathrm{y}}_{\mathrm{i}}=\{\begin{array}{c}{\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{x}}_{\mathrm{i}}-{\mathrm{\beta }}_2{\mathrm{x}}_{\mathrm{i}}^2,\mathrm{if}{\mathrm{x}}_{\mathrm{i}}<\mathrm{AONR}\\ {\mathrm{\beta }}_0+{\mathrm{\beta }}_1\mathrm{AONR}-{\mathrm{\beta }}_2{\mathrm{AONR}}^2,\mathrm{if}{\mathrm{x}}_{\mathrm{i}}\ge \mathrm{AONR}\end{array}$

in which: for the i^th observation, y represents the relative corn grain yield (%); x represents the N rate (kg ha^-1); β₀ is the intercept; β₁ is the linear coefficient; and β₂ is the quadratic coefficient. The AONR was obtained analytically by differentiating the quadratic function with respect to x, setting the first derivative equal to zero, and solving for x ($\left(AONR=\frac{{\beta }_1}{-2{\beta }_{\mathit{2}}}\right)$.

Only observations with less than 30 kg ha^-1 of N applied as a subsurface band at sowing were considered in this analysis. This threshold was adopted to ensure greater comparability across studies, as starter N applications exceeding 30 kg ha^-1 could advance part of the fertilizer response, thereby introducing bias into the estimated effect of topdressed rates.

Relative yield for each experiment was determined by dividing the observed yield (at a given N rate) by the maximum observed yield within each experiment (100 %). For the purposes of this review, studies conducted in different locations and/or growing seasons were treated as independent experiments. Furthermore, each independent combination of treatments that resulted in a distinct yield-N rate response curve was considered a separate experiment. Thus, when a single study evaluated N rates across multiple factors, such as different hybrids or cropping systems, and reported separate response curves for each level, each combination was recorded as an individual experiment.

The uncertainty associated with the AONR estimated by the quadratic-plateau model was quantified using bootstrap resampling. For each dataset, 500 samples were generated with replacement, and the quadratic-plateau model was fitted to each. From the 500 estimates of the plateau, the 0.025 and 0.975 quantiles were calculated to obtain the 95 % confidence interval (95 % CI). Relative yield modeling and 95 % CI generation via bootstrap were performed in R software using the soiltestcorr package (Correndo et al., 2023).

The EONR was estimated using the fitted quadratic-plateau model. Because the model was fitted to relative yield data, the price ratio (PR) was converted to the relative-yield scale according to the yield range assumed for each scenario. Yield scenarios were defined by dividing the observed yield distribution into terciles, and the corresponding yield ranges were considered in the EONR calculations. In addition, three PR scenarios were defined using the 25th, 50th, and 75th percentiles of PR values observed from mean annual values from 2020 to 2025, representing contrasting but plausible economic conditions.

Comparative analysis of yield under no or low N fertilizer input

In this review, yield under no or low N fertilizer input was used as a proxy for the soil N supply capacity. Observations were included when no N was applied as topdressing and less than 30 kg ha^-1 of N was applied at sowing. This criterion was adopted to retain enough observations for analysis while still representing conditions with minimal fertilizer N input.

Differences in yield across regions were evaluated using a linear mixed-effects model, fitted with the lme4 R package (Bates et al., 2015). Region was considered a fixed effect. Study and experiment (nested within study) were included as random effects to account for the hierarchical structure of the data. Subsequently, estimated marginal means (EMMs) for each region were calculated using the emmeans package (Lenth et al., 2025). Degrees of freedom were calculated using the Kenward-Roger method, and pairwise comparisons between regions were adjusted using Tukey’s HSD test (α = 0.05).

To further explore the factors associated with yield in the no or low N fertilizer input observations, a linear mixed-effects model was fitted following a similar procedure, with grain yield as the response variable, but with soil organic matter, clay content, and total water supply as fixed effects, and study and experiment (nested within study) as random effects. Previous crop category was not included in the analysis because observations were highly unbalanced between legume (n = 23) and non-legume (n = 11) groups. The significance of fixed effects was assessed by Type III analysis of variance using the car package (Fox et al., 2026), and effects were considered significant at α = 0.05. Model assumptions were checked by visual inspection of residual plots.

Conditional inference tree

To explore the influence of additional N management factors on off-season corn yield variation, a conditional inference tree was employed. This approach is widely used for integrating data from multiple experiments, as it allows the identification of patterns using statistically rigorous criteria (Mourtzinis et al., 2018; Hansel et al., 2019; Carcedo and Ciampitti, 2023). The method is based on successive hierarchical binary splits, where the significance of each split is evaluated by a p-value (α = 0.05).

The tree was constructed using the partykit package in R (Hothorn and Zeileis, 2015), with yield as the response variable. Explanatory variables included clay content, water supply during the crop cycle, starter N supply (≥30 kg ha^-1; yes or no), SOM, inoculation (yes or no), and intercropping (yes or no). To ensure that only splits supported by sufficient data were considered, a minimum of 20 observations was required for both inner and terminal nodes. All statistical analyses and figures were created using R software (R Core Team, 2020).

RESULTS

Database description

Approximately 55 % of the collected observations originated from the Central-West region of Brazil, encompassing the states of Goiás (GO), Mato Grosso (MT), Mato Grosso do Sul (MS), and the Distrito Federal (DF) (Figure 2). The database covers diverse experimental contexts, including varying clay content, SOM, water supply, and yield levels (Table 1). The topdressed N rates studied ranged from 0 to 300 kg ha^-1, with approximately 62 % of the studies using urea (46 % N) as the N source (Figure 3). Most studies evaluated the effect of N topdressing in combination with an additional factor, such as the use of inoculants with associative microorganisms, sole cropping versus intercropping with grasses, or the application of starter N (Table 1). Approximately 90 % of the observations were from non-inoculated sole cropping systems. Around 60 % of the observations followed soybean as the previous crop.

Figure 2
Spatial distribution of the analyzed field experiments, highlighting the regions of origin of the compiled data. On the map of Brazil shown in the upper-right, the dashed lines delimit the main off-season corn-producing region, which accounts for approximately 80 % of the off-season corn area, according to Conab (2025).

Table 1
Description of the field studies selected for the review

No.	Authors	City, State	Year	Main characteristics	Number of extracted observations
1	Alves et al. (2021)	Seropédica, RJ	2005, 2006, 2007	Herbaspirillum seropedicae strain ZAE94 as inoculant under different N rates	15
		Sete Lagoas, MG	2005, 2006, 2007
		Planaltina, DF	2005, 2007, 2008
2	Batista et al. (2011)	Palmital, SP	2008	Intercropping with grasses and N rates	4
		Campos Novos Paulista, SP
		Florínea, SP
		Pedrinhas Paulista, SP
3	Silva et al. (2008)	Campo Grande, MS	2006	Nitrogen rates and foliar Zn application	4
4	Simão et al. (2020)	Rio Verde, GO	2015	Nitrogen rates associated with starter N supply or lack thereof	64
		Sinop, MT
		Vilhena, RO
5	Zuffo et al. (2023)	Chapadão do Sul, MS	2019	Nitrogen fertilization responses of sole or intercropped corn	10
6	Altarugio et al. (2019)	Uberlândia, MG	2016	Nitrogen rates, application timing, and N splitting	5
7	Faria et al. (2019)	Uberlândia, MG Pedro Afonso, TO	2016	Plant density and N rates	12
8	Balbinot Junior et al. (2023)	Londrina, PR	2020, 2021	Corn-ruzigrass intercropping and N rates	12
9	Souza et al. (2025)	Jaboticabal, SP	2016	Enhanced-efficiency N fertilizers	4
10	Carvalho et al. (2023)	Lagoa da Confusão, TO	2022	Nitrogen rates	12
		Palmas, TO
11	Albert et al. (2023)	Jataí, GO	2019, 2020	Starter fertilization and N rates	25
		Rio Verde, GO	2019
		Montividiu, GO	2020
12	Silva et al. (2022)	Ipameri, GO	2018	Starter fertilization and N rates	5
13	Sapucay et al. (2020)	Londrina, PR	2018, 2019	Nitrogen fertilization responses of sole or intercropped corn	14
14	Batista et al. (2019)	Dois Vizinhos, PR	2015	Hybrids, plant density, and N rates	5
15	Costa et al. (2015)	Quirinópolis, GO	2014	Azospirillum brasilense inoculation and N rates	15
16	Casagrande and Fornasieri Filho (2002)	Jaboticabal, SP	1999	Starter fertilization and N rates	4
17	Gazola et al. (2014)	Londrina, PR	2012	Foliar amino acid application and N rates	4
18	Breda et al. (2016)	Seropédica, RJ	2012	Herbaspirillum seropedicae inoculation and N fertilization	10
19	Souza et al. (2011)	Selvíria, MS	2007, 2008	Nitrogen rates, application timing, and nitrogen sources	10
20	Resende et al. (2013)	Lavras, MG	NR*	Trichoderma harzianum inoculation and N fertilization	8
21	Fuentes et al. (2018)	Dourados, MS	2015	Nitrogen rates	5
22	Mar et al. (2003)	Dourados, MS	1998	Nitrogen rates and application timing	24
23	Soratto et al. (2010)	Chapadão do Céu, GO	2006	Nitrogen sources and rates	4
24	Torres et al. (2014)	Dourados, MS	2009	Influence of cover crops and N rates	4
25	Zoz et al. (2019)	Maripá, PR	2009	Plant density, row spacing, and nitrogen fertilization	4
26	Batista et al. (2020)	Dois Vizinhos, PR	2015	Plant density and N rates	4
27	Silva et al. (2020)	Selvíria, MS	NR	Influence of cover crops and N rates	16

• ^*
  NR: not reported.

Figure 3
Boxplots illustrating the variability of soil organic matter (SOM), clay contents, and water supply among the included studies (panels a, b, and c). Panels d, e, f, and g show the number of observations across the levels of intercropping, inoculation, fertilizers, and previous crop, respectively. In panel f, AMN is ammonium nitrate. Panels h and i present histograms demonstrating the distribution of nitrogen (N) rates and observed grain yields, respectively. Observations recorded as “NA” indicate situations in which the corresponding variable could not be defined, either because the study did not report the information or because the authors published only average values across the levels of the evaluated factor. The “n” represents the number of observations collected for each property.

Variability in response curves and optimum N rates

Of the total published experiments receiving less than 30 kg ha^-1 of N as starter N, twenty-one were fitted to the linear model, two to the linear-plateau model, seven to the quadratic model, one to the cubic model, and eight showed no significant yield response to topdressed N rates (Figure 4a). At the regional level, in the Central-West, for example, 61 % of the experiments showed a linear response, 26 % had a quadratic response, 4 % had a cubic response, and 9 % had no response.

Figure 4
Proportion of (a) off-season corn yield response patterns across experiments within each Brazilian region as a function of topdressed nitrogen (N) rates, and agronomic optimum N rate (AONR) and economic optimum N rate (EONR) distribution based on topdressing N rates, regardless of the rate applied as starter N. (b) The “n” represents the number of experiments.

Figure 4b shows the distribution of AONR and EONR based on topdressing N rates, regardless of the rate applied as starter N. For AONR, the median was 120 kg ha^-1, with an interquartile range from 90 to 148 kg ha^-1, and values ranging from 60 to 300 kg ha^-1. For EONR, the median was 94 kg ha^-1, with an interquartile range from 69 to 120 kg ha^-1, and values ranging from 9 to 300 kg ha^-1.

Corn yield under no or low N fertilizer input

No differences were observed among the estimated marginal means for yield under no or low fertilizer N input across the regions of the country (Figure 5). The overall estimated mean was 5416 kg ha^-1. The South region, represented by studies conducted in Paraná (PR), showed the highest variability, with an interquartile range [25-75 %] of 3647-9015 kg ha^-1. In this dataset, the mixed-effects model detected no significant association of corn yield under no or low N fertilizer input with SOM (p=0.50), clay content (p=0.50), or total water supply (p=0.61).

Figure 5
Corn yield under no or low nitrogen fertilizer (<30 kg ha^-1 of N) inputs. The solid line indicates the median, and the triangles indicate the estimated marginal means for each region. Estimated marginal means followed by the same letter do not differ statistically by Tukey HSD test (p>0.05). The “n” represents the number of observed experimental units.

Estimation of the agronomic optimum N rate, its associated uncertainty, and economic optimum N rate

The fitted relationship between relative grain yield and topdressed N rates is presented in figure 6. In the quadratic-plateau model, relative yield increases quadratically with increasing topdressed N rates until it reaches a stabilization point, characterizing the plateau, beyond which additional N increments do not result in significant yield gains. In the fitted model, the plateau (95 %) was reached at a rate of ~78 kg ha⁻¹ of N (pseudo-R² = 0.32). The 95 % confidence interval (95 % CI) for this estimate ranged from 52 to 119 kg ha⁻¹ of N, reflecting the uncertainty in the AONR for off-season corn estimation.

Figure 6
Relative grain yield as a function of topdressed nitrogen (N) rates for observations with less than 30 kg ha⁻¹ of N applied as a subsurface band at sowing (starter N) (n= 177). The fitted model was <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mover><mi>Y</mi><mo accent="true">̂</mo></mover><annotation encoding="application/x-tex">\widehat{Y}</annotation></semantics></math></inline-formula>= 78.55 + 0.441 - 0.00284x² when x < 77.64, reaching a plateau at 95.67 % (pseudo-R² = 0.32; p-value < 0.05). The vertical dashed lines indicate the estimated agronomic optimum nitrogen rate (AONR) and its 95 % confidence interval (52-119 kg ha⁻¹ of N) obtained by bootstrap (n= 500). The empirical distribution is presented in the inset panel (the area within the dashed lines represents the 95 % confidence interval).

The EONR estimates obtained from the quadratic-plateau model fitted to the relative yield data, for the different grain yield and PR scenarios, are presented in table 2. These values represent estimates derived from the yield distribution observed in the dataset and from the PR scenarios defined based on the variation observed between 2020 and 2025 and are not accompanied by a formal measure of uncertainty. Considering the median PR of the period (5.9 kg of corn grain per kg of N), the estimated EONR ranged from 24 to 64 kg ha^-1 of N across the yield range observed in the dataset, from 2149 to 10295 kg ha^-1 of grain.

Table 2
Estimated EONR range according to the combination of different corn grain yield and price ratio scenarios.

Corn grain yield scenario	Price ratio	Estimated EONR range
kg ha-1	kg corn grain per kg N	kg ha-1
2149-5940	5.3	32-58
	5.9	28-57
	6.4	24-55
5941-7437	5.3	58-61
	5.9	57-60
	6.4	55-59
7438-10295	5.3	61-65
	5.9	60-64
	6.4	59-63

Influence of additional sources of variation on off-season corn yield responses to N management

Based on the variables addressed in this review, the conditional inference tree model identified seasonal water supply throughout the off-season corn cycle as the primary factor determining yield differences (Figure 7). When water supply exceeded 832 mm, yield was favored when at least 30 kg ha⁻¹ of starter N was applied (mean of 8837 kg ha⁻¹) (Node 9, Figure 7). In the absence of starter N, lower yields (mean of 7596 kg ha⁻¹) were observed when SOM content was ≤2.5 % (Node 7, Figure 7), while values above this threshold resulted in an increase in average yield (8229 kg ha⁻¹) (Node 8, Figure 7). Conversely, when water supply was ≤832 mm, the most relevant variable to explain yield differences was soil clay content. Under this condition, observations with clay content >58 % showed higher yields (mean of 6669 kg ha⁻¹) (Node 4, Figure 7) compared with observations with clay content ≤58 % (mean of 6008 kg ha⁻¹) (Node 3, Figure 7).

Figure 7
Conditional inference tree explaining the variation in off-season corn grain yield (kg ha^-1) among published studies. Starter N = supply of at least 30 kg ha^-1 of N as a subsurface band.

DISCUSSION

This study provides an integrated synthesis of the primary evidence available regarding off-season corn yield responses to nitrogen (N) fertilization in Brazil. The results indicate that variability in N response patterns is closely linked to the yield potential of the environments, which dictates plant growth and the subsequent N demand (Lemaire et al., 2008). Furthermore, this variability can be partially attributed to the specific characteristics of each production system, such as hybrid-specific differences in soil exploration and N uptake capacity (Presterl et al., 2002; Correndo et al., 2021). Despite edaphoclimatic contrasts, the average yield under no or low N fertilizer inputs was similar across the evaluated regions. This indirect indicator, reflecting the biological N buffering capacity of the system (Morris et al., 2018), suggests that basal N availability played a limited role in the observed differences.

In this context, the predominance of linear relationships, particularly in the Central-West, is consistent with high-yield potential environments driven by modern hybrids with an average N extraction of 13 kg of N per Mg of dry biomass (Silva et al., 2018). Additionally, methodological factors may have contributed to this pattern. Considering the upward trend in the optimal nitrogen rate for corn observed in other global regions (Baum et al., 2025), it is likely that, in many of these studies, the tested rates were insufficient to achieve the maximum yield response. Consequently, a potentially biologically curvilinear response is fitted as linear.

The quadratic-plateau model revealed that, under conditions where less than 30 kg ha^-1 of N is supplied as starter fertilizer, the maximum yield response rate for off-season corn was 78 kg ha^-1, with a 95 % confidence interval of 52 to 119 kg ha^-1. In the absence of local scientific data, these values can serve as a benchmark to evaluate how specific regions deviate from this pattern, indicating a potential need to adjust (upward or downward) existing regional recommendations. Following a legume crop, for example, the state of Paraná recommends N rates of 10-20 and 61-80 kg ha^-1 at sowing and topdressing, respectively (Pauletti and Motta, 2019). These values are consistent with the estimated AONR range, especially considering the fertility conditions and legume cropping history typical of that region.

The estimated EONR remained below the point estimate of the AONR across all evaluated scenarios, reinforcing that the N rate required to maximize yield is not necessarily the same as that required to maximize profitability. At a given price ratio, EONR increased with the yield scenario, whereas within each yield scenario it decreased as the price ratio increased. These results show that the EONR was sensitive to both yield level and fertilizer-to-grain price relationship. Because the EONR values were derived from scenario-based calculations and lacked formal uncertainty estimates, they should be interpreted as plausible ranges rather than as a single recommendation.

When analyzing the influence of additional sources of variation, water supply emerged as the primary factor governing yield differences, a result aligned with Nóia and Sentelhas (2020). Values exceeding 832 mm were infrequent (~27 % of observations). Although supplies of ≤832 mm may still meet the crop's water demand (Pereira Filho, 2015), poor temporal distribution can result in yield penalties (Bergamaschi et al., 2006). Beyond reducing biomass formation, water stress episodes cause N extraction by corn to become independent of the N fertilizer rate (Rimski-Korsakov et al., 2009). Thus, the higher average yield obtained in soils with >58 % clay is likely due to soil water potential characteristics, which depend on water retention properties determined by soil texture (Grassini et al., 2011).

Under conditions where water supply exceeded 832 mm, starter N application favored higher yields. Quinn et al. (2020) demonstrated in a meta-analysis that subsurface banded starter fertilization increased corn yield by 5.2 % in the United States, contradicting the notion that this strategy is only viable under conditions of poor root growth and nutrient availability. In this study, we hypothesize that this effect stems primarily from the P and K input in the furrow alongside and below the seed, as most starter fertilizers used in Brazil are N-P₂O₅-K₂O formulations. Finally, in the absence of starter N, soils with SOM exceeding 2.5 % resulted in higher yields, reflecting a greater capacity to supply and buffer mineralized N throughout the crop cycle (Espe et al., 2015). Results from studies using the ¹⁵N isotopic technique (Oliveira et al., 2018; Cassim et al., 2026) demonstrated that only 33-37 % of the N absorbed by plants was derived from topdressed fertilizer, while the majority originated from SOM. These findings reinforce the importance of management strategies that increase and conserve SOM levels.

The results of this systematic review present significant implications for nitrogen (N) fertilization management in Brazilian off-season corn. By synthesizing yield-N rate response patterns from diverse studies, this study contributes to refining N recommendations by providing an evidence-based indicative range. This approach can reduce technical uncertainty and align recommendations more closely with actual field requirements. Furthermore, the identification of key factors conditioning N responses, obtained through a conditional inference tree, highlights the pivotal roles of soil, climate, and management attributes in crop performance, reinforcing the notion that off-season corn yield is not solely governed by N fertilization.

While the present study captures a substantial portion of the data on off-season corn response to N management, it is recognized that the search restriction to peer-reviewed articles may have limited the collection of potentially relevant data from other publication types, such as technical bulletins or books. Future efforts should examine the existence of potential N-response clusters by integrating literature data with data generated under on-farm conditions. The development of a comprehensive database would allow for a better capture of the actual variability of off-season corn production environments. Moreover, incorporating the influence of other management-related factors, such as sowing date, into future analyses could yield more precise N recommendations tailored to the specific conditions of each environment.

CONCLUSION

Off-season corn responses to N in Brazil exhibit wide variability across environments, primarily resulting from differences in management and yield potential (N demand). Based on the reviewed studies, for observations with <30 kg ha^-1 of N at sowing as starter N, the agronomic optimum N rate was estimated at 78 kg ha^-1 (95 % CI: 52-119 kg ha^-1). In contrast, across the simulated maximum yield and price ratio scenarios, the estimated economic optimum N rate ranged from 24 to 65 kg ha^-1. These findings highlight a clear gap between optimal N rates and underscore the need for environment-specific, economically informed N recommendations for off-season corn.

Finally, among the variables evaluated, water supply, clay content, starter N supply, and SOM emerged as the main factors associated with yield differences in off-season corn. In this sense, practices that increase water availability to the crop (such as synchronizing the sowing date with periods of higher precipitation and/or the use of irrigation); ensure the supply of at least 30 kg ha^-1 of N applied as a subsurface band at sowing; and favor the increase of SOM levels (such as the adoption of no-till) tend to favor the achievement of higher off-season corn yields.

How to cite:

Suguiura LH, Cassim BMAR, Brignoli FM, Yamashita LTN, Quintilhano HA, Inoue TT, Batista MA. Off-season corn responses to nitrogen fertilization in Brazil: A systematic review. Rev Bras Cienc Solo. 2026;50:e0260008. https://doi.org/10.36783/18069657rbcs20260008

FUNDING INFORMATION

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Bruno Cassim acknowledges the postdoctoral fellowship provided by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (grant No. 2025/11112-1). Marcelo Batista acknowledges the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for a research productivity grant (grant No. 312764/2023-5).

DATA AVAILABILITY

The data will be provided upon request.

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*

Corresponding author: e-mail: [e-mail]

Editor:

José Miguel Reichert https://orcid.org/0000-0001-9943-2898 and Clístenes Willians Araújo do Nascimento https://orcid.org/0000-0002-5103-5524

Publication Dates

• Publication in this collection
  02 Nov 2026
• Date of issue
  2026

History

• Received
  15 Jan 2026
• Accepted
  06 Apr 2026