Modeling nutrient losses in an Oxisol under different management systems and rainfall events

ABSTRACT

This study aimed to model nutrient losses in an Oxisol under varied soil properties and vegetation cover. The experimental area was located at the Paraná Rural Development Institute, in Santa Tereza do Oeste, Paraná, Brazil. The treatments consisted of six cover crops in summer, six cover crops in winter, and management systems no-tillage (NT) traditional (control), NT with gypsum, and NT with scarification. Nutrient data were collected at each natural rainfall through sample collection from collection troughs. The design was completely randomized, and the data were adjusted using an exponential function of two variables, as a function of soil density, macroporosity, straw dry mass, green cover, and rainfall. Greater losses of Mg and Ca was related to higher soil density and lower macroporosity. Lower losses of Mg, Ca, K, and P were associated with lower straw dry mass and higher green cover. K and P losses were stable for all soil density values, but they decreased with an increase in macroporosity.

Key words
water erosion; models; optimization

INTRODUCTION

Grain production requires an increasing use of technology and improvements in soil management from rural producers. In 2018, the National Supply Company (CONAB) estimated that the 2017-2018 harvests in Brazil produced 111,558.6 thousand tons of soybeans, ranking second highest in the global grain production. Soybean production in Paraná ranks second highest of Brazilian states, with an estimated production of 18,307.1 thousand tons (CONAB 2018).

Despite high production in Brazil, aspects of soil management (such as water erosion) are concerning for future grain production. Water erosion drags soil particles by surface runoff, causing loss of water, soil, and nutrients. Causes of water erosion can be natural or human (Marioti et al. 2013), and the soil properties influence the propensity for erosion. The consequences of erosion include decrease in soil fertility, reduction in productive potential, increase in fertilizer costs, the impracticability of production areas, siltation, and decrease in the availability of water for crops (Marioti et al. 2013; Candido et al. 2014). Changes to the soil characteristics, which usually occur during agricultural practices, should therefore be mitigated by environmental management (Ouyang et al. 2018).

Several studies related to soil, water, and nutrient losses have contributed significantly to our understanding of erosion and strategies for soil conservation practices, generally using patterns under simulated or natural rainfall (Miguel et al. 2014). Consequently, a variety of soil erosion models have been developed and presented as mathematical expressions that characterize individual processes (Pandey et al. 2016).

Research has shown that the main method of nutrient loss is water runoff (Oliveira et al. 2015, Adviento-Borbe et al. 2018). Thus, soil impoverishment is linked to sediment transport and soil slope (Mendonça et al. 2015). Bramorski et al. (2015) evaluated nitrogen losses resulting from water erosion and found that the loss of nutrients negatively affects the quality of surface and groundwater. Dechen et al. (2015) analyzed uncovered soils and estimated that 16% P₂O₅ and 8% KCl were lost compared to the recommended fertilizer rate for the crop. Huo et al. (2021) reported that the factors positively impacting soil nutrient loss were runoff (44–48%), maximum rainfall intensity over a 30-min period (18–29%), rainfall depth (20–27%), and soil loss (10–14%). Li et al. (2023) revealed that the total rainfall and rainfall duration were the primary factors that determined the levels of runoff, soil erosion, and the associated nutrient losses.

It was hypothesized that nutrient losses were influenced by crop rotation and management and that high nutrient losses occurred due to high rainfall intensities in compacted soils. We therefore suggest there is a gap in our knowledge related to losses under different management systems. In this study, we aimed to model nutrient losses in an Oxisol. We explored the effect of different management systems that vary the physical attributes of the soil and vegetation cover.

MATERIAL AND METHODS

Study area

The study area is located at the Paraná Rural Development Institute (IDR-Paraná), a regional center in Santa Tereza do Oeste, Paraná, Brazil (53°29’37”W, 24°50’42”S, and 607 m a.s.l.). According to the Köppen classification, the region has a humid subtropical climate with a mean annual rainfall of 1,840 mm. The local relief gently undulates, and the slope varies from 0.21 to 5.41%. The experiment was carried out during the 2017/2018 harvest, and the soybean crop was grown on crambe straw by the practice of no-till.

The soil of the experimental area was classified as Oxisol (Soil Survey Staff 2014), with 60% clay, 4% sand, and 36% silt in the top 20 cm. Other soil properties were pH in CaCl₂ of 5, 24.8 g·dm^-3 of soil organic carbon (SOC), 25.3 mg·dm^-3 of P (Mehlich-1), 0.50 cmol_c·dm^-3 of K, 4.90 cmol_c·dm^-3 of Ca, 2.50 cmol_c·dm^-3 of Mg, and 52% of soil base saturation.

Treatments and experimental design

Our experimental design consisted of 15 plots, in which vegetation was planted after soybean cultivation, six in summer, six cover crops in winter, and management systems no-tillage (NT) traditional (control), NT with gypsum, and NT with scarification (Table 1).

Table 1
Fifteen first treatments of the experiment.

Cover crops/summer
Millet - Pennisetum americanum
Pigeon pea - Cajanus cajan
Sunn hemp - Crotalaria juncea
Sunn hemp - Crotalaria spectabilis
Pigeon pea dwarf - Cajanus cajan
Velvet bean - Mucuna pruriens
Cover crops/winter
White oat - Avena sativa
Black oat - Avena strigosa
Rye - Secale cereale
Black oat + forage turnip - Raphanus sativus
Black oat + white lupine - Lupinus albus
Black oat + group pea - Pisum arvense
Use and management systems
NT system with scarification
NT with application of 3 t·ha-1 of gypsum on surface
Traditional NT (control)

• NT: no-tillage.

A compressed range was determined for all treatments, except T5, to increase the data available for curve fitting. Thus, 29 treatments were included in our fitting analysis. For runoff collection, 29 polyvinyl chloride collector gutters were built (Fig. 1): one for each treatment, with areas of 9 m² (3 × 3 m) delimited by grass separators. The gutters were placed along slope so that water could drain into 25 L containers via 3/4” corrugated hoses. Natural rainfall events throughout the soybean rainfall cycle were analyzed.

Figure 1
Model of collector gutters and their measurements.

Determination of physical properties

Undisturbed soil samples were collected before sowing and after harvest at three depths (0–0.1, 0.1–0.2, and 0.2–0.3 m). Soil density (SD), macroporosity (Ma), microporosity (Mi), and total porosity (TP) were analyzed. Trenches 30-cm wide, 40-cm long, and 30-cm deep were opened in each plot. The samples were capillary saturated and subjected to the tension of 6 kPa on a sand column (Reinert and Reichert 2006). Finally, the samples were dried at 105 °C to a constant weight to determine SD. The TP was calculated from the SD and particle density (PD) values (TP = 1- (SD/PD)). Mi was calculated based on the volume at 6 kPa tension, while Ma is the difference between TP and Mi. The Mi had little variation and was not presented.

Nutrient collection

We collected nutrients on 20 occasions between Aug. 11, 2017, and Oct. 2, 2018 (Fig. 2), recording the time of day for collection in each case. We stored the collected material in containers and then homogenized and quantified the material using graduated cylinders. We then removed 500 mL of material from the polyethylene terephthalate (PET) bottles. To quantify nutrient loss, the concentrations of P, K, Ca, and Mg were analyzed in each sample and determined according to the methodology recommended by Embrapa (1997).

Figure 2
Accumulated precipitation during the days of collection.

Straw collection and green cover

Plant residue was collected every 15 days. We used a quadrangular frame measuring 30 × 30 cm, with two repetitions per plot. The material was collected in Kraft paper bags. The straw samples were weighed after being placed in an oven at 65°C until they reached a constant mass.

To collect green cover, two random points were selected per plot, and a quadrangular frame of 1 m² was placed on top of the soybean leaf cover. Photos of this area were digitally cropped and then processed by Canopeo (Mathworks, Inc., Natick, MA, United States of America), which calculates the percentage of leaf area (Patrignani and Ochsner 2015).

Statistical analysis

Measures of central tendency were calculated in R (R Core Team 2019), while Microsoft Excel was used for curve fitting for straw and green cover.

Mathematical modeling

Models of nutrient loss were adjusted as a function of SD, Ma, SDM, green cover (Cov), and rainfall intensity (Int). The Statistica software (Version 7.0, StatSoft Inc., Tulsa, United States of America) was used to calculate the fit parameters. The Levenberg–Marquardt method was implemented to optimize the nonlinear least squares fit.

The loss of nutrients (nutrients loss) was adjusted using an exponential function of two variables (Eq. 1):

$$\mathit{\text{ Nutrients loss }}\mathit{(}Int\mathit{,}f\mathit{)}\mathit{=}a\mathit{ }exp\mathit{ }\mathit{(}b\mathit{\cdot }Int\mathit{)}\mathit{+}cf\mathit{+}\mathit{ }d$$ (1)

where: Int: the maximum rainfall intensity for each precipitation event; f: a physical parameter known through field observations to be considered in the modeling (SD, Ma, Cov, and SDM); a, b, c and d: fitting constants.

For SDM, the least squares method was used to estimate the values for each collection day through a third-degree polynomial function (Eq. 2):

$$Sdm\left(t\right)=a{t}^3+b{t}^2+ct+d$$ (2)

where: t: the number of days after sowing.

A third-degree polynomial was used because it showed a good coefficient of determination that captured a representative trend as a function of time (Fig. 3).

Figure 3
Example of straw dry mass (Sdm) submodel throughout the soybean cycle. The observed data is represented by the points, while the trendline is the result of the equation shown in the article.

Green cover is presented as a percentage, whose values were estimated for each day of collection. The coverage was 0% on the day of sowing and on the day of complete drying of the leaves (135 days after sowing). Mathematically, it is represented as Eq. 3:

$$Cov\left(0\right)=Cov\left(135\right)=0$$ (3)

where: Cov(t): the percentage of green cover over time, t.

To ensure that no estimate exceeds 100%, the following is defined (Eq. 4):

$$0\le \mathrm{Cov}\left(t\right)\le 100$$ (4)

We found that the combination of two different polynomial degrees better represented the data (Fig. 4). Therefore, we chose to use a function defined by parts, as follows (Eq. 5):

Figure 4
Example of submodel for green cover using piecewise defined function.

$${\mathrm{Cov}}_{\tau }\left(t\right)=\left\{\begin{array}{l}\underset{𝔦=0}{\overset{n_1}{\Sigma }} a_i{t}^{𝔦},\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}t<40\\ \underset{𝔦=0}{\overset{n_2}{\Sigma }} b_i{t}^{𝔦},\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}t\ge 40\end{array}\right.$$ (5)

where: τ: the treatment number; n₁: the degree of the polynomial referring to the model before the 40th day; a_i and b_i: the parameters adjusted to the data; n₂: the degree of the polynomial referring to the model after the 40th day.

RESULTS AND DISCUSSION

Descriptive statistics

Table 2 presents the descriptive statistics of nutrient losses for all treatments and collection days. The averages represent the approximate losses for each treatment and the rain collection.

Table 2
Descriptive statistics of nutrient losses.

	Mg (kg·ha-1)	Ca (kg·ha-1)	K (kg·ha-1)	P (kg·ha-1)
Min.	4.09 · 10-6	0.00061	0.00629	0.0002613
Q1	1.96 · 10-3	0.00713	0.0629	0.001919
Q2	3.81 · 10-3	0.01542	0.1401	0.004095
Average	9.47 · 10-3	0.04369	0.5459	0.1026
Q3	9.32 · 10-3	0.04565	0.3321	0.008597
Max.	7.08 · 10-2	0.57980	11.19	0.2356
S	1.69 · 10-4	0.06996	1.526011	0.022775
S2	1.37 · 102	0.00489	2.32871	0.00052
CV (%)	137,336	160.15250	279.5493	221.9226
Ass.	100,300	1.21221	0.79777	12.97559
K	0.122	0.16119	0.36064	0.19506
ANOVA (p-value)	0.9925	0.7196	1.0000	0.9998

• Min.: minimum; Q1: first quartile; Q2: second quartile or median; Q3: third quartile; Max.: maximum; S: standard deviation; S2: variance; CV: coefficient of variation; Ass.: asymmetry; K: kurtosis; ANOVA: analysis of variance.

The coefficient of variation was high for all nutrients, showing its heterogeneity (Table 2). The p-values of the analysis of variance was > 0.05, so no significant differences were identified in the loss of nutrients, which can be explained by the good soil structure present in the experimental area.

Models

Mg loss

The models generated for Mg loss are presented in Table 3. The function dependent on SD and Ma presented values close to R², as well as in the function that considered straw dry mass and green cover. This shows the extent to which both parties are associated.

Table 3
Mg, Ca, K, and P loss models as a function of maximum rainfall intensity per precipitation event (mm·h^-1), soil density (g·cm^-3), macroporosity (%), straw dry mass (t·ha^-1) and cover vegetable (%).

Equations	R2
Mg loss(Int,SD) = 32.4067 exp(3.92·10-5 Int) + 0.01342 Ds – 32.421	0.4145
Mg loss(Int,Ma) = 30.0883 exp(4.22·10-5 Int) – 3.8·10-4 Macro – 30.082	0.4153
Mg loss(Int,Sdm) = 38.4991 exp(3.42·10-5 Int) + 8.4·10-4 Sdm – 38.5	0.5409
Mg loss(Int,Cov) = 36.5216 exp(3.6·10-5 Int) + -2.2·10-5 Cov – 36.52	0.5422
Ca loss(Int,SD) = 81,9488 exp(7.96·10-5 Int) + 0.088132 Ds – 82.045	0.3626
Ca loss(Int,Ma) = 79.3407 exp(8.22·10-5 Int) – 0.00333 Macro – 79.295	0.3660
Ca loss(Int,Sdm) = 90.7954 exp(7.23·10-5 Int) + 0.004049 Msp – 90.802	0.4716
Ca loss(Int,Cov) = 79.3833 exp(8.25·10-5 Int) – 1.8·10-3 Cov – 79.369	0.4776
K loss(Int,SD) = 1501.67 exp(5.19·10-5 Int) – 0.00695 Ds – 1501.7	0.1077
K loss(Int,Ma) = 1450.35 exp(5.37·10-5 Int) – 0.01452 Macro – 1450.2	0.1079
K loss(Int,Sdm) = 1325.66 exp(5.85·10-5 Int) + 0.209262 Sdm – 1326.0	0.1676
K loss(Int,Cov) = -2.1242 exp(-0.06199 Int)-0.00131 Cov + 2.16698	0.1721
P loss(Int,SD) = 21.9236 exp(5.01·10-4 Int) – 0.00445 Ds – 21.916	0.0957
P loss(Int,Ma) = 23.1815 exp(4.73·10-5 Int) – 3.5·10-4 Macro – 23.175	0.0962
P loss(Int,Sdm) = 31.3146 exp(3.9·10-5 Int) + 0.002738 Sdm – 31.318	0.1765
P loss(Int,Cov) = 31.0742 exp(3.93·10-5 Int) – 2.7·10-4 Cov – 31.071	0.1702

• Int: maximum rainfall intensity per precipitation event; SD: soil density; Ma: macroporosity; Sdm: straw dry mass; Cov: green cover.

Mg losses increased with increasing SD (Fig. 5). Bertol et al. (2017) evaluated nutrient losses due to water erosion and found that Mg is highly sensitive to the effects of soil management and the type of crop in the surface soil layer. Mg is an essential element for chlorophyll production, and prolonged Mg loss can result in soil degradation (Wang et al. 2018). Therefore, it is necessary to pay attention to soil management strategies that interfere with soil compaction, because SD increase can lead to nutrient loss.

Figure 5
Magnesium loss affected by (a) soil density, (b) macroporosity, (c) straw dry mass, and (d) green cover.

We also observed reduction in Mg loss with increasing Ma (Fig. 5). Therefore, the greater the pore volume in the soil profile, the lower the Mg loss. We also observed an increase in Mg loss with increasing straw dry mass. This can be verified by adjustment according to green coverage. The same behavior of the graphs generated, combined with values close to R², shows the integrated effect of straw and mulch on Mg loss.

As highlighted by Bertol et al. (2003), variations in waterborne Mg concentrations due to erosion processes are significantly shaped by the soil management systems adopted. The amount of Mg lost through water erosion was positively correlated with the degree of soil fertilization and increased soil tillage intensity. Through a comprehensive assessment of Mg loss via water runoff under simulated rainfall conditions, researchers have shown that substantial Mg concentrations in eroded water are associated with conventional tillage practices.

Ca loss

The models generated for Ca loss are listed in Table 3. They show a clear relationship between Ca loss and Ma, dry mass of straw, and green cover. The opposite signs of the coefficients for both relations show that they are inversely proportional.

Figure 6 illustrates the shifts in losses relative to the assessed variables. The results revealed that the most substantial nutrient losses coincided with increased SD and decreased Ma values. The susceptibility of Ca to soil management strategies and crop selection were observed particularly in the uppermost soil layers (Bertol et al. 2017; Borgmann et al. 2021). Ca shows high susceptibility to erosional sediment conveyance, owing to its affinity for absorption onto soil colloids (Oliveira et al. 2015).

Figure 6
Calcium loss affected by (a) soil density, (b) macroporosity, (c) straw dry mass, and (d) green cover.

The study conducted by Bertol et al. (2003) revealed that Ca was considerably lost through water erosion, possibly owing to the elevated solubility of the element in water; this is further compounded by its propensity for elevated concentrations within the soil matrix. Wang et al. (2019) reported that soil acidification potentially increased surface Ca loss through runoff mechanisms.

Analysis of Ca loss as a function of the dry mass of the straw and the green cover showed opposite relationships, in which there was an increase in Ca loss with a decrease in straw and an increase in green cover. We found that the green coverage data were dispersed, which can be explained by the homogeneity of the green cover data on certain collection dates.

K loss

The models generated for K showed unusual behavior, since the coefficient in the P(Int, Cov) equation was negative. This is represented by the exponential curve in Table 3. This result is physically problematic, because it means that K losses at rainfall intensities greater than those proposed in this work will be limited by the horizontal asymptote. K losses were stable with variations in SD (Fig. 7), and there was reduction in K loss with increase in Ma. Since K is a mobile element, it is very susceptible to leaching (Kaufmann et al. 2019), which can interfere with laminar flow losses. Leaching, defined as the loss of nutrients by water transport in the soil profile, is facilitated in soils with greater Ma.

Figure 7
Potassium loss affected by (a) soil density, (b) macroporosity, (c) straw dry mass, and (d) green cover.

Regarding nutrient loss as a function of straw dry mass, the highest losses were associated with straw dry mass. This contradicts the results of Leite et al. (2018), who evaluated nutrient loss in the context of natural rainfall. The authors found that the lowest K losses occurred in systems with greater conservation of vegetation cover. This can be explained by the combination of straw and green cover in our study. We also observed lower K losses with higher green cover. This differs to the results from Bosch et al. (2015), who found that erosion-induced K loss did not differ between treatments with exposed soil and vegetation cover.

Based on the study conducted by Bertol et al. (2011) on the overall concentration of K within the runoff from simulated rainfall of varying intensities, a relationship was observed between rainfall intensity and K concentration in the runoff. Another study by Bertol et al. (2003) indicated substantial K losses, irrespective of the management scheme employed. This outcome was attributed to the abundance of K within the soil and its high-water solubility.

Peri et al. (2021) aimed to determine soil erosion rates from the exposed roots of four species of shrubs and dwarf bushes, and they discovered that nitrogen was the primary nutrient lost to erosion, followed by K and P. As part of soil management investigations, Wang et al. (2011) evaluated the impacts of anionic polyacrylamide application and found that its implementation reduced the proportion of K loss through surface runoff. Moreover, in a study on K runoff, Munodawafa et al. (2007) showed that K was more significantly lost under conventional tillage than under mulch ripping and tied ridging practices.

P loss

The models generated for P were similar with SD (Fig. 8), since P has low mobility in soil (Kaufmann et al. 2019). We observed a slight decrease in P loss with increasing Ma. The model of P loss as a function of the dry mass of straw and the green cover showed similar behavior as for K: P loss increased with increasing dry mass of the straw and with decreasing green cover. Modeling P losses is relevant for assessing soil use and management, as P losses in runoff water can increase the risk of eutrophication (Farias et al. 2018; Leite et al. 2018; Wang et al. 2018).

Figure 8
Phosphorus loss affected by (a) soil density, (b) macroporosity, (c) straw dry mass, and (d) green cover.

Bertol et al. (2003) investigated nutrient losses caused by water erosion and found that P concentrations within the runoff were consistently low across all treatments and rainfall sessions applied; a phenomenon potentially attributed to the scanty presence of this element in the soil and its strong colloidal adsorption. To unravel the influence of management systems on the physical quality of a Oxisol, Marques et al. (2010) revealed the sway of management on erosion control, demonstrated by high soil and nutrient losses. Specifically, P and organic matter exhibited pronounced losses in plots subjected to conventional plowing.

Bertol et al. (2010) carried out a study on P loss in a surface runoff generated by simulated rainfall on a Oxisol, regardless of the fertilizer source. This revealed that high-intensity rainfall events resulted in increased concentrations and quantities of P within the runoff. In another study, Bertol et al. (2004) noted that total P losses were more under no-till management practices than under treatments involving scarification with harrowing or plowing with harrowing.

Yang et al. (2023) observed that nutrient transport rates significantly varied across different land uses. Specifically, agricultural lands have lower nutrient transport rates than forested areas. This variation can be attributed to the fact that agricultural lands are predominantly situated on flat terrain (with slopes of less than 2%), which result in minimal soil loss. The precise mechanisms through which dynamic erosion factors contribute to P loss remain unknown (Cheng et al. 2021). Notably, the runoff of P in areas subjected to fertilization is closely associated with the eutrophication of water bodies, suggesting that it has broader environmental implications (Costa et al. 2010).

CONCLUSION

The mathematical model proposed in this study confirmed the hypothesis that soil management strategies can affect soil nutrients. Higher Mg and Ca losses were related to higher SD and lower Ma. Lower Mg, Ca, K, and P losses were associated with lower straw dry mass and higher green cover. K and P losses were stable for all values of SD, but decreased with increasing Ma.

ACKNOWLEDGMENTS

Thanks are offered to IDR-Paraná for support and for making the study area available.

• How to cite: Chang, P., Secco, D., Marins, A. C., Rizzi, R. L., Bassegio, D. and Savioli, M. R. (2024). Modeling nutrient losses in an Oxisol under different management systems and rainfall events. Bragantia, 83, e20230191. https://doi.org/10.1590/1678-4499.20230191

• FUNDING

  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
  Finance Code 001

DATA AVAILABILITY STATEMENT

All dataset were generated and analyzed in the current study.

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Edited by

Section Editor: Wellingthon Guimarães Júnnyor https://orcid.org/0000-0002-9463-1457

Publication Dates

• Publication in this collection
  18 Dec 2023
• Date of issue
  2024

History

• Received
  27 Mar 2023
• Accepted
  30 Oct 2023