Methodologies for Agricultural Gypsum Application Recommendations in No-Tillage Systems on Tropical Sandy Soils ^†

Abstract

This study aimed to evaluate which of five commonly used methodologies for agricultural gypsum application in no-tillage systems under tropical agricultural conditions is the most efficient in sandy soils. The evaluation focused on soil chemical and physical properties, nutrient movement through the soil profile, and crop productivity. Methods: Soil layers of 0–0.20 m and 0.20–0.40 m were analyzed. Data were subjected to analysis of variance, and means were compared using Student’s t-test (p < 0.05). Principal component analysis (PCA) and multivariate analysis were also performed. Results: An increase in calcium (Ca), potassium (K), and sulfur (S) contents was observed at the analyzed soil depths, along with a reduction in subsurface aluminum saturation (m%). Gypsum application also increased the dry matter yield of cover crops. Conclusions: For sandy soils under no-tillage systems with high nutrient export, aiming to improve soil fertility and aggregation, the gypsum application methodologies proposed by Caires and Guimarães or Raij and collaborators are recommended. According to multivariate analysis, these methodologies showed the best correlation with base saturation (V%), a key indicator of soil fertility. Additionally, the Caires and Guimarães method demonstrated a stronger correlation with maize productivity. However, none of the evaluated methodologies increased soybean yields.

1. Introduction

The increasing global demand for food has intensified soil exploitation, often resulting in issues associated with monocropping practices, such as soil fertility degradation and reduced structural quality [1]. In this context, it is essential to adopt agricultural practices that preserve soil fertility while employing sustainable management methods to ensure the health and longevity of soils used in agricultural production [2].

One such practice is the no-tillage system (NTS), characterized by sowing without soil disturbance—except for the planting furrow—crop rotation, and maintaining crop residues on the soil surface. This technique minimizes the impact on the soil, aiming to preserve ecological balance and the sustainability of agricultural systems. It is considered an integrated management strategy rather than merely a planting technique. NTS was introduced as an alternative to conventional soil preparation methods, such as plowing and harrowing, which repeatedly disrupt the soil, compromising its physical structure and long-term productivity potential [3].

In Brazil and other tropical regions, where a significant portion of agriculture is conducted on highly weathered, acidic, and low-fertility soils, such as sandy soils, this issue becomes even more critical. These soils have low water and nutrient retention capacity and low organic matter (OM) content, requiring correction techniques to ensure agricultural productivity. In this scenario, agricultural gypsum has emerged as an efficient strategy. A byproduct of the phosphate fertilizer industry, gypsum is applied as a subsoil conditioner, neutralizing toxic aluminum (Al) in the soil and displacing it to deeper layers [4].

Agricultural gypsum (CaSO₄·2H₂O) is a by-product generated during the production of phosphoric acid by the phosphate fertilizer industry, which uses phosphate rock and sulfuric acid. For every ton of P₂O₅ produced, approximately 4.5 tons of gypsum is generated, accumulating around industrial facilities. Composed of 17.7% sulfur (S), 30.9% calcium oxide (CaO), 0.2% fluoride (F), and 0.7% P₂O₅ [5], its agricultural application repurposes this waste, promoting sustainability and reducing the environmental impact associated with large-scale disposal.

The application of agricultural gypsum not only mitigates Al toxicity in arable and subsoil layers—where liming may be less effective—but also provides calcium (Ca) and sulfur (S) to plants. These are essential elements for both the structural stability of the soil and the physiological stability of plants. Furthermore, these nutrients play a crucial role in protein synthesis and strengthening plant defense mechanisms. However, one of the main challenges associated with agricultural gypsum use lies in the varying methodologies for dose recommendation, which often underestimates or overestimate the amounts required to achieve significant improvements in soil fertility and agricultural productivity [6].

In the current context of climate change, agricultural gypsum, a by-product of the phosphate fertilizer industry, emerges as an effective alternative to improve soil profile conditions, promoting deeper root growth and consequently mitigating abiotic stresses such as water deficit. The calcium (Ca) present in gypsum plays an essential role in plant cell structure, contributing to the development of cell walls and nutritional balance, which are critical factors for crop productivity [7,8,9]. In tropical soils, which often exhibit high concentrations of toxic aluminum (Al), gypsum has proven to be a viable option for improving fertility and reducing environmental impacts. Furthermore, it is an affordable input, with an average cost of 20–25 USD/ton [10,11,12].

Another factor enhancing the effectiveness of agricultural gypsum is the use of cover crops, such as sunn hemp (Crotalaria spp.), whose straw helps retain soil moisture and improve fertility through organic matter accumulation. Gypsum has also shown positive effects in supplying Ca and sulfur (S) to key agricultural crops such as maize and soybean, promoting root development, increasing productivity, and reducing toxic Al levels [13,14]. For maize, gypsum contributes to an increase in the nutrient absorption surface area, resulting in greater growth and grain yield. For soybean, gypsum application has proven beneficial in areas prone to water deficit, promoting greater efficiency in accessing deep soil water [15,16,17,18].

However, the recommended gypsum application rate remains a subject of debate, as various methodologies exist depending on soil characteristics such as clay content, calcium levels, aluminum neutralization, and cation exchange capacity (CEC). Determining the most suitable methodology is crucial, especially in sandy soils, which require specific management practices. To establish which of the recommendation methodologies available in the literature is relevant for refining agricultural production, as well as for the efficient and sustainable use of this byproduct.

Significant differences exist in the efficiency of the five gypsum recommendation methodologies in NTS regarding improvements in soil fertility, nutrient movement through the soil profile, and agricultural productivity in sandy soils. Thus, this study aims to evaluate which of these five commonly used methodologies in tropical agricultural systems is the most efficient under sandy soil conditions, considering soil chemical and physical properties, nutrient movement through the soil profile, and crop productivity.

2. Materials and Methods

2.1. Characterization of the Experimental Area

The experiment was conducted from 2018 to 2020 at the Advanced Research Center for Technological Research in Rubber and Agroforestry Systems, part of the Agronomic Institute of Campinas (IAC), of the São Paulo Agribusiness Technology Agency (APTA), located in the municipality of Votuporanga, state of São Paulo, Brazil, at geographical coordinates 20°28′ S latitude and 50°04′ W longitude. The area features gentle topography and an altitude of 467 m (Figure 1).

The climate at the location is characterized as tropical with dry winters (Aw [19]), with an average annual temperature of 24 °C, an average maximum temperature of 30 °C, and an average minimum temperature of 18 °C. The average annual rainfall is 1448.7 mm. During the experimental period, data on precipitation and average temperature were collected (Figure 2).

The soil of the experimental area was classified as an Ultisol with a sandy texture [20]. Before the installation of the experiment, granulometric [21] and chemical [22] characterization of the soil was performed at depths of 0 to 0.20 m and 0.20 to 0.40 m (

Table 1. Physical and chemical characterization of the experimental area, performed at depths of 0 to 0.20 m and 0.20 to 0.40 m, before the installation of the experiment.

Depth (m)	P (resin)	S (S-SO4)	OM	pH	K	Ca	Mg	H + Al	V	m	Sand	Clay
	mg dm−3	g dm−3		CaCl2	--------mmolc dm−3 --------				--- % ---		---- g kg −1 ----
0–0.20	34	3	15	4.3	2.8	10	6	31	38	18	815	81
0.20–0.40	9	4	13	4.1	2.7	3	4	28	34	25	783	75

Note: Phosphorus (P), sulfur (S-SO₄), soil organic matter (OM), calcium (Ca), potassium (K), magnesium (Mg), potential acidity (H + Al), base saturation (V%), aluminum saturation (m%).

).

2.2. Experimental Conduct

The experimental design used was a randomized block design with four replications, evaluating the five main recommendations for agricultural gypsum application in tropical soils:

T1: Caires and Guimarães (2018) [23]: (0.6 × CEC_e − Ca content in [cmol_c dm⁻³]) × 6.4 = (kg ha⁻¹);

T2: Sousa, Lobato and Rein (2005) [24]: 50 × clay (%) = (kg ha⁻¹);

T3: Vitti et al. (2008) [25]: (V2-V1) × CEC/500 = (Mg ha⁻¹);

T4: Demattê (1986) [26]: Soils with less than 15% clay in the 0.20–0.40 m layer—apply 700 kg ha⁻¹ of gypsum;

T5: Raij et al. (1996) [27]: 6 × g kg⁻¹ of clay (kg ha⁻¹) or 60 × % of clay (kg ha⁻¹);

T6: Control treatment (no gypsum application).

The agricultural gypsum (Nutrigesso, Ribeirão Preto, São Paulo State, Brazil) applied contained 18% calcium oxide (CaO), 16% sulfur (S), 0.5% P₂O₅, 0.6% fluoride (F), and approximately 35% moisture (according to the information provided by the manufacturer). The doses applied in each treatment were calculated according to the proposal of each methodology and according to the soil analysis of each plot (

Table 2. Doses calculated for the application of agricultural gypsum following the methodologies for an Ultisol in Votuporanga, São Paulo state, Brazil.

Treatment	Year of Application
	2017	2018	2019
	---------------- kg ha−1 ----------------
T1	4500	3360	4876
T1	4500	2640	4224
T1	4500	4320	4224
T1	4500	5800	3032
T2	700	720	700
T2	700	720	700
T2	700	720	700
T2	700	720	700
T3	3080	6920	2156
T3	3080	5240	572
T3	3080	2760	4793
T3	3080	5080	2768
T4	0	2000	1500
T4	0	1520	1500
T4	0	0	1500
T4	0	1520	1500
T5	584	600	584
T5	584	600	584
T5	584	600	584
T5	584	600	584
T6	0	0	0
T6	0	0	0
T6	0	0	0
T6	0	0	0

Note: T1: Caires and Guimarães (2016) [23], T2: Sousa, Lobato and Rein (2005) [24], T3: Vitti et al. (2008) [25], T4: Demattê (1986) [26], T5: Raij et al., (1996) [27], T6: standard (without agricultural gypsum application).

).

Gypsum was applied on the surface, without incorporation into the soil, in November 2017, and reapplications occurred in August 2018 and September 2019.

For the installation and evaluation of the experiment, mechanized corn sowing was carried out in an NTS on Crotalaria juncea straw on 24 November 2017. The cultivar used was Dow AgroSciences 2B587 PowerCore™ (Dow AgroSciences, Jacareí, São Paulo State, Brazil) with a spacing of 0.80 m between rows and 6 seeds per meter, with a sowing fertilizer application at a rate of 315 kg ha⁻¹ of the 08-28-16 (Mosaic, Uberaba, Minas Gerais State, Brazil) formulated fertilizer (1.7% Ca and 3.6% S (S-SO₄)). On 11 December 2017, the first topdressing fertilization was performed, applying the 20-00-17 (N-P-K) (Mosaic, Uberaba, Minas Gerais State, Brazil), formulated fertilizer at a rate of 270 kg ha⁻¹ (Figure 3).

Urochloa brizantha cv. Marandu (Facholi Sementes, Santo Anastácio, São Paulo State, Brazil) was sown on 14 December 2017. A seed rate of 10 kg ha⁻¹, with a cultural value of 50%, was used, mixed with a single superphosphate fertilizer (Mosaic, Uberaba, Minas Gerais State, Brazil) at a rate of 26.4 kg ha⁻¹ of P. Two rows of forage grass were sown between the corn rows, with a spacing of 0.25 m from the corn and 0.30 m between the Marandu grass rows.

On 18 December 2017, the second topdressing fertilization was performed. Ammonium sulfate (20% N and 22% S-SO₄) (Mosaic, Uberaba, Minas Gerais State, Brazil) was used as the source at a rate of 250 kg ha⁻¹. Corn (Agranda, Ribeirão Preto, São Paulo State, Brazil) harvesting was carried out on 27 March 2018. Seven days after the desiccation of Marandu grass (glyphosate (Bayer, São José dos Campos, São Paulo State, Brazil) at a rate of 1560 kg ha⁻¹), on 24 November 2018, U. brizantha in the area was managed with a tractor-mounted mower to facilitate soybean sowing, as an average production of 18,840 kg ha⁻¹ of green matter was observed.

Mechanized soybean sowing was also carried out in an NTS on U. brizantha straw on 27 November 2018, using the cultivar HO Paranaíba IPRO (HO Genética, Goiânia, Goiás State, Brazil) (maturity group: 7.4) with a spacing of 0.50 m and a final population of 14 seeds per meter. Sowing fertilization was applied at a rate of 300 kg ha⁻¹ using the 04-09-17 (N-P-K) formulated fertilizer. Soybean was harvested on 17 April 2019, and C. juncea was sown across the entire experimental area.

C. juncea was harvested for seed production on 9 September 2019, and the following day it was managed using a tractor-mounted mower. On 12 November 2019, a corn and U. brizantha cv. Marandu intercropping system was sown. Corn was sown mechanically, while the forage grass was broadcasted manually, 10 kg ha⁻¹ of seed, together with the corn, the cultivar Forseed FS533 PWU (Forseed, São José do Rio Preto, São Paulo State, Brazil) was sown with a spacing of 0.50 m between rows and 4 seeds per meter, using a sowing fertilizer application rate of 250 kg ha⁻¹ of the 08-12-13 (N-P-K) (Mosaic, Uberaba, Minas Gerais State, Brazil) formulated fertilizer. On 27 November 2019, the first topdressing fertilization was applied, using the 20-00-17 formulated fertilizer at a rate of 250 kg ha⁻¹. On 2 December 2019, the second topdressing fertilization was performed, with ammonium sulfate (20% N and 22% S-SO₄) as the source, applied at a rate of 250 kg ha⁻¹. Corn was harvested on 9 March 2020, and the grass was left in the area for subsequent dry matter evaluation (Figure 3).

2.3. Evaluations Conducted

To determine the soil’s chemical attributes, disturbed soil samples were collected from the 0–0.20 m and 0.20–0.40 m layers, with ten simple samples taken per plot at the end of each cultivation cycle. Chemical determinations, including pH, exchangeable bases (Ca, magnesium—Mg, and K), extractable Al and H⁺, available P, OM, and S, were performed following the methodology of [22].

For the determination of physical and structural soil properties, undisturbed soil samples were collected using a volumetric ring at depths of 0–0.05, 0.05–0.20, and 0.20–0.40 m at the end of each cultivation cycle. Physical analysis (macroporosity, microporosity, and bulk density) followed the methodology of [28], while structural analysis (mean weight diameter and soil aggregates) was performed according to the method proposed by [29].

For corn yield analysis, the following parameters were evaluated: height of the first ear insertion (measured from the base of the plant at soil level to the insertion point of the first ear, using a measuring tape) and plant height (measured from the base of the plant at soil level to the top of the tassel, also using a measuring tape), on 10 plants per plot; plant count in the two central rows of the plots and the calculation of the final stand. After harvesting and threshing the ears collected in the two central rows, the mass of 100 grains and grain yield (adjusted to 13% moisture on a wet basis and extrapolated to kg ha⁻¹) were determined. For soybeans, analyses of the height of the first pod insertion (considering the measurement from the soil level to the first pod using a measuring tape), plant height (measuring the distance between the soil level and the end of the main stem using a measuring tape), final plant stand, mass of 100 grains, and grain yield (adjusted to 12% moisture on a wet basis and extrapolated to kg ha⁻¹) were performed.

The total dry matter productivity of the cover crop preceding soybeans was evaluated using the metal square method, measuring 0.5 × 0.5 m (0.25 m²), randomly placed at 3 points in the central useful area of the plots. After cutting the plants close to the soil, the plant material was chopped and placed in an oven for drying at 60 °C for 72 h to determine the dry weight per hectare.

2.4. Statistical Analysis

For the obtained results, analysis of variance was performed, and means were compared using the Student’s t-test (p < 0.05). Principal component analysis (PCA) was conducted using R software version 4.0.1 [30], with the ‘FactoMiner’, ‘Shiny’, ‘FactoInvestigate’, and ‘ggplot2’ packages. For better discussion and interpretation of the results, a multivariate comparison analysis was performed within the R software 4.0.1.

3. Results

At the depth of 0–0.20 m, the first component (PC1) accounted for 41.62% of the total variance, while the second component (PC2) explained 16.11% of the total variance (

Table 3. Principal component analysis for soil chemical attributes at depths of 0 to 0.20 m and 0.20 to 0.40 m, and maize productivity (PROD) in the 2017/2018 cropping season.

PCA	0–0.20 m		0.20–0.40 m
	PC1	PC2	PC1	PC2
Eigenvalue	5.41	2.09	6.93	1.68
Explained variance (%)	41.62	16.11	53.32	12.95
Cumulative variance (%)	41.62	57.73	53.32	66.26
Variables	Correlation
P	0.07	0.69	−0.14	0.25
OM	0.53	−0.25	0.27	0.26
pH	0.92	−0.28	0.96	0.05
K	0.23	0.04	0.50	−0.62
Ca	0.83	0.15	0.83	0.44
Mg	0.58	0.61	0.80	−0.32
H + Al	−0.80	0.52	−0.96	−0.11
Al	−0.82	0.19	−0.87	−0.06
S	−0.19	−0.15	−0.21	0.87
CEC	0.30	0.85	−0.71	−0.06
V%	0.96	0.03	0.97	0.12
m%	−0.83	−0.15	−0.96	−0.12
PROD	0.41	−0.02	0.50	−0.27

Phosphorus (P), organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), hydrogen + aluminum (H + Al), sulfur (S), cation exchange capacity (CEC), base saturation (V%), aluminum saturation (m%), and crop productivity (PROD).

). At the depth of 0.20–0.40 m, PC1 explained 53.32% of the total variance, while PC2 accounted for 12.95% of the total variance. The estimation of the appropriate number of axes to be interpreted suggests that only these axes carry important information and, as a result, the description will be limited to them.

At the depth of 0–0.20 m, dimension 1 contrasts individuals such as T1 and T4 (to the right of the graph, characterized by a strongly positive coordinate on the axis) with individuals such as T3 and T4 (to the left of the graph, characterized by a strongly negative coordinate) (Figure 4a). The group formed by T1 and T4 shares high values for the variables OM and pH and low, inversely proportional values for the variable H + Al. The group containing T3 and T4 shares high values for the variables Al, H + Al, and m%, and low values for V%, Ca, and pH, with these results being directly related. The variable V% is highly correlated with this dimension (correlation of 0.92) and could therefore be summarized by dimension 1.

At the depth of 0.20–0.40 m, dimension 1 contrasts individuals such as T1, T2, T5, and T6 (to the right of the graph, characterized by a strongly positive coordinate) with individuals such as T2, T3, T4, and T5 (to the left of the graph, characterized by a strongly negative coordinate) (Figure 4b).

The group in which individuals T1, T2, T5, and T6 are located, characterized by a positive coordinate on the axis, shares high values for pH, V%, Mg, Ca, K, and PROD, which are directly related to each other. Meanwhile, the low values of H + Al, m%, Al, and CEC show a high inverse correlation with the other attributes mentioned. The group in which individuals T2, T3, T4, and T5 are located (characterized by a negative coordinate on the axis) shares high values for H + Al, CEC, m%, and Al, and low values for Ca, V%, and pH. Thus, it can be observed that the variables pH, H + Al, V%, and m% are highly correlated with this dimension (correlations of 0.92, 0.92, 0.94, and 0.92, respectively). Therefore, these variables could be summarized by dimension 1.

At the depth of 0−0.20 m, for the chemical attributes, PC1 accounted for 40.40% of the total variance, while PC2 explained 27.13% of the total variance (

Table 4. Principal component analysis for soil chemical attributes at depths of 0 to 0.20 m and 0.20 to 0.40 m, and maize productivity (PRODM) and forage dry matter (PRODF) in the 2019/2020 cropping season.

PCA	0–0.20 m		0.20–0.40 m
	PC1	PC2	PC1	PC2
Eigenvalue	5.66	3.80	5.35	2.85
Explained variance (%)	40.40	27.13	38.19	20.33
Cumulative variance (%)	40.40	67.53	38.19	58.52
Variables	Correlation
P	0.69	0.37	0.35	0.03
OM	0.60	0.72	−0.09	0.89
pH	0.60	−0.61	0.81	−0.01
K	0.33	0.81	−0.01	0.64
Ca	0.91	−0.16	0.64	0.41
Mg	0.44	0.64	0.48	0.05
H + Al	−0.28	0.88	−0.87	0.42
Al	−0.83	0.40	−0.93	−0.05
S	0.49	−0.12	0.12	0.46
CEC	0.57	0.74	−0.59	0.71
V%	0.92	−0.29	0.95	0.07
m%	−0.93	0.19	−0.95	−0.22
PRODM	0.13	−0.26	0.33	0.34
PRODF	−0.47	−0.02	−0.14	−0.64

Phosphorus (P); organic matter (OM); potassium (K); calcium (Ca); magnesium (Mg); hydrogen + aluminum (H + Al); sulfur (S); cation exchange capacity (CEC); base saturation (V%); aluminum saturation (m%); maize productivity (PRODM); forage productivity (PRODF).

). At the depth of 0.20−0.40 m, PC1 accounted for 38.19% and PC2 for 20.33% of the total variance. The estimation of the appropriate number of axes to be interpreted suggests that only these axes carry important information and, as a result, the description will be limited to them.

At the depth of 0–0.20 m, dimension 1 contrasts individuals such as T1, T2, and T5 (to the right of the graph (Figure 5a)), characterized by a strongly positive coordinate, with individuals such as T1, T3, and T4 (to the left of the graph, characterized by a strongly negative coordinate). The group in which individuals T1, T2, and T5 are located is characterized by a positive coordinate on the axis and shares high values for V%, Ca, pH, and S, and low values for m%, Al, and PRODF. The group of individuals T1, T3, and T4 has a negative coordinate and shares high values for m%, Al, and PRODF, and low values for Ca, V%, P, pH, S, and OM. Dimension 2 particularly distinguishes individuals such as T6, which shows high values for K, OM, CEC, Mg, and P.

At the depth of 0.20–0.40 m, dimension 1 contrasts individuals such as T1, T2, and T4 (to the right of the graph, characterized by a strongly positive coordinate on the axis) with individuals such as T1 and T3 (to the left of the graph, characterized by a negative coordinate on the axis) (Figure 5b). The group in which individuals T1 and T2 are located, characterized by a positive coordinate on the axis, shares high values for P, S, and Ca. The group in which individual T4 is located, characterized by a positive coordinate on the axis, shares high values for pH and V%, and low values for the variables H + Al, m%, CEC, and Al. The group in which individuals T1 and T3 are located (characterized by a negative coordinate on the axis) shares high values for Al, m%, and H + Al, and low values for Mg, V%, P, and PRODM.

Thus, the variables V% and m% are highly correlated with this dimension (respective correlations of 0.91 and 0.90); therefore, these variables could be summarized by dimension 1. Dimension 2 contrasts individuals such as T1 and T2 (at the top of the graph, characterized by a strongly positive coordinate on the axis) with individuals such as T4 (at the bottom of the graph, characterized by a strongly negative coordinate on the axis). The group in which individuals T1 and T2 are located shares high values for P, S, and Ca. Meanwhile, the group in which individual T4 is classified (characterized by a negative coordinate on the axis) shares high values for PRODF and low values for K and CEC.

Considering the physical and chemical attributes and their respective productivities, at the depth of 0–0.20 m, PC1 accounted for 25.54% of the total variance (

Table 5. Principal component analysis for physical and chemical soil attributes at depths of 0 to 0.20 m and 0.20 to 0.40 m, and maize productivity (PRODM) and forage dry matter (PRODF) in the 2019/2020 cropping season.

PCA	0–0.20 m		0.20–0.40 m
	PC1	PC2	PC1	PC2
Eigenvalue	2.04	1.73	2.02	1.56
Explained variance (%)	25.54	21.67	25.23	19.54
Cumulative variance (%)	25.54	47.21	25.23	44.77
Variable	Correlation
TP	−0.03	0.36	0.36	0.62
SD	−0.29	−0.69	−0.01	−0.81
WAD	−0.14	0.60	0.28	0.08
AG > 2	−0.04	0.54	−0.25	0.58
OM	0.80	−0.22	0.65	0.13
Ca	0.50	−0.12	0.73	0.09
PRODM	0.41	0.63	0.58	0.00
PRODF	−0.76	0.10	−0.67	0.39

Total porosity (TP); soil bulk density (SD); weighted average diameter (WAD); aggregates larger than 2 mm (AG > 2); organic matter (OM); calcium (Ca); maize productivity (PRODM); forage productivity (PRODF).

), while PC2 explained 21.67% of the total variance. At the depth of 0.20–0.40 m, PC1 accounted for 25.23% and PC2 for 19.54% of the total variance. The estimation of the appropriate number of axes to be interpreted suggests that only these axes carry important information and, as a result, the description will be limited to them.

At the depth of 0–0.20 m, dimension 1 contrasts individuals such as T2, T5, and T6 (to the right of the graph, characterized by a strongly positive coordinate on the axis) with individuals such as T4 (to the left of the graph, characterized by a strongly negative coordinate on the axis) (Figure 6a). The group in which individuals T2, T5, and T6 are located (characterized by a positive coordinate on the axis) shares high values for PRODM and Ca, and low values for PRODF. The group in which individual T4 is located (characterized by a negative coordinate on the axis) shares high values for WAD and low values for OM.

Dimension 2 contrasts individuals such as T4 (at the top of the graph, characterized by a strongly positive coordinate on the axis) with individuals such as T5 and T6 (at the bottom of the graph, characterized by a strongly negative coordinate on the axis). The group in which individual T4 is located (characterized by a positive coordinate on the axis) shares high values for WAD and low values for OM. The group in which individuals T5 and T6 are classified (characterized by a negative coordinate on the axis) shares low values for AG > 2 and PRODM.

At the depth of 0.20–0.40 m, dimension 1 contrasts individuals such as T2 and T5 (to the right of the graph, characterized by a strongly positive coordinate on the axis) with individuals such as T3, T4, and T5 (to the left of the graph, characterized by a strongly negative coordinate on the axis) (Figure 6b). The group in which individuals T2 and T5 are located (characterized by a positive coordinate on the axis) shares high values for Ca, OM, and PRODM, and low values for PRODF. The group in which individuals T3, T4, and T5 are classified (characterized by a negative coordinate on the axis) shares low values for OM.

Dimension 2 particularly distinguishes individuals such as T1 (at the top of the graph, characterized by a strongly positive coordinate on the axis). These individuals form a group that shares high values for AG > 2 and PT.

In the evaluated years, at depths of 0–0.20 m and 0.20–0.40 m, treatments T1, T2, and T5 stood out, as they resulted in better correlations with exchangeable bases and V%, while the other methodologies showed a stronger correlation with soil acidity-related attributes. For soil physical attributes, treatments T2 and T5 showed better correlation with aggregates larger than 2 mm.

Overall, it can be observed that the application of agricultural gypsum plays an important role in agriculture. The methodologies of T1 and T5 showed satisfactory results with the multivariate analysis of the cultivation years. What stands out in general is the increase in nutrients in the soil profile (especially K, Ca, and S), the decrease in the soluble Al rate (m%), and the increase in base saturation (V%), which enabled higher crop productivity, particularly maize.

3.1. Soil Physics

Regarding the soil’s physical attributes, three depths were analyzed: 0–0.05 m, 0.05–0.20 m, and 0.20–0.40 m in 2020, the final year of the experiment (

Table 6. Soil physical attributes at depths of 0 to 0.05 m, 0.05 to 0.20 m, and 0.20 to 0.40 m, based on gypsum recommendation methodologies. Votuporanga, São Paulo State, Brazil, 2020.

Treatments	TP	DS		WAD		AG > 2
	m3 m−3	kg dm−3		mm		%
	0–0.05 m
T1	43.95	1.50		5.02	ab	33.21	a
T2	46.49	1.47		4.48	bc	20.08	b
T3	46.75	1.46		4.23	c	29.99	ab
T4	47.24	1.42		5.32	a	28.85	ab
T5	44.93	1.53		4.80	abc	27.14	ab
T6	34.38	1.59		4.42	bc	29.91	ab
Pr > Fc	0.08	0.80		0.02 *		0.03 *
LSD	13.09	0.18		0.74		11.63
CV%	20.72	7.90		11.35		24.27
	0.05–0.20 m
T1	33.45	1.76		4.83	a	7.07
T2	36.75	1.69		4.71	ab	5.68
T3	35.05	1.74		4.65	ab	4.81
T4	36.07	1.72		4.74	ab	5.08
T5	34.74	1.71		4.63	ab	5.38
T6	35.83	1.75		4.46	b	4.29
Pr > Fc	0.60	0.70		0.01 *		0.20
LSD	9.89	0.13		0.33		6.04
CV%	19.03	5.48		5.14		23.06
	0.20–0.40 m
T1	43.53	1.66	b	4.53	b	10.54	a
T2	37.85	1.69	ab	4.51	b	1.41	b
T3	34.93	1.80	a	4.67	ab	2.27	b
T4	41.67	1.74	ab	4.47	b	4.65	ab
T5	34.93	1.77	ab	4.98	a	2.62	b
T6	38.69	1.77	ab	4.62	ab	4.16	b
Pr > Fc	0.45 ns	0.02 *		0.03 *		0.001 *
LSD	9.71	0.13		0.39		6.38
CV%	18.30	5.17		6.06		29.77

Note: T1: Caires and Guimarães (2016) [23], T2: Sousa, Lobato and Rein (2005) [24], T3: Vitti et al. (2008) [25], T4: Demattê (1986) [26], T5: Raij et al., (1996) [27], T6: standard (without agricultural gyp-sum application). Total porosity (TP); soil density (SD); weighted average diameter (WAD); aggregates larger than 2 mm (AG > 2); (*) significant at 5% probability; (^ns) not significant; LSD: least significant difference; CV: coefficient of variation. Means followed by the same letter do not differ from each other by the Student’s t-test (p < 0.05).

). In the 0–0.05 m layer, the highest values of WAD were observed in T4 compared to T3, and for aggregates larger than 2 mm (AG > 2), T1 showed higher values than T2 in this layer. In the 0.05–0.20 m layer, T1 presented higher WAD compared to T6. At the greatest depth, 0.20–0.40 m, differences were observed for soil density (DS), with T3 being superior to T1. For WAD, T5 was higher than T1, T2, and T4, while for AG > 2, treatment T1 was superior to T2, T3, T5, and T6.

3.2. Productivity and Main Biometric and Productive Components of Crops

Regarding productivity, it was observed that in the 2017/2018 growing season for maize, T5 resulted in a greater height of the first ear insertion (

Table 7. Productivity and agronomic characteristics of maize and soybean in a no-tillage system area during the 2017/2018, 2018/2019, and 2019/2020 growing seasons.

Treatments	2017/2018 Growing Seasons
	Maize
	Height of First Ear Insertion		Plant Height		Final Stand	Grain Yield
	------------------ m ------------------				Plants ha−1	kg ha−1
T1	1.05	b	2.10		68,750	8746	a
T2	1.03	b	1.91		68,750	5969	b
T3	1.01	b	2.06		71,975	7045	ab
T4	1.04	b	2.11		68,750	7459	ab
T5	1.15	a	2.07		68,750	7300	ab
T6	1.05	b	2.04		67,187	7959	ab
Pr > Fc	0.04 *		0.12 ns		0.38 ns	0.02 *
LSD	0.98		0.20		5818	2124
CV%	6.59		6.89		5.87	20.52
	Safra 2018/2019
	Soybean
	First pod insertion height		Plant height		Final stand	Grain yield
	------------------ m ------------------				plants ha−1	kg ha−1
T1	0.05		0.77	ab	131,667	4621
T2	0.05		0.77	ab	110,833	4382
T3	0.05		0.83	ab	137,500	5150
T4	0.04		0.85	a	122,500	4530
T5	0.04		0.76	b	129,166	4806
T6	0.06		0.78	ab	139,166	4734
Pr > Fc	0.36 ns		0.05 *		0.31 ns	0.43 ns
LSD	0.02		0.08		38,346	893
CV%	18.17		6.87		16.19	12.96
	2019/2020 growing seasons
	Maize
	Height of first ear insertion		Plant height		Final stand	Grain yield
	------------------ m ------------------				plants ha−1	kg ha−1
T1	1.09	ab	2.26	ab	79,167	7348
T2	1.03	b	2.22	ab	85,833	7198
T3	1.08	ab	2.28	ab	81,667	7750
T4	1.19	a	2.32	a	77,500	7482
T5	1.06	ab	2.19	b	80,833	7012
T6	1.13	ab	2.28	ab	79,167	6927
Pr > Fc	0.02 *		0.03 *		0.7 ns	0.97 ns
LSD	0.13		0.12		11155	2221
CV%	8.18		3.56		9.17	20.22

Note: T1: Caires and Guimarães (2016) [23], T2: Sousa, Lobato and Rein (2005) [24], T3: Vitti et al. (2008) [25], T4: Demattê (1986) [26], T5: Raij et al., (1996) [27], T6: standard (without agricultural gyp-sum application). (*) significant at 5% probability; (^ns) not significant; LSD: least significant difference; CV: coefficient of variation. Means followed by the same letter do not differ from each other by the Student’s t-test (p < 0.05).

), but the highest grain yield was achieved with T1, with a productive increase of 787 kg ha⁻¹ compared to T6, representing an approximate increase of 32%, or about 13 bags ha⁻¹ of maize. This trend continued in the 2019/2020 season, where T3 showed the best performance, with a yield increase of 823 kg ha⁻¹ compared to the control treatment (T6), representing an 11% increase, approximately 14 bags ha⁻¹. Although no statistically significant differences were observed among the treatments, the gypsum application treatments exhibited higher productivity compared to the control. For soybeans, no significant differences among treatments were observed.

In the 2018/2019 off-season, T5 provided the highest dry matter productivity of crotalaria when compared to T1, T2, and T4 (

Table 8. Dry matter productivity of crotalaria and forage in no-till areas during the 2018/2019 and 2019/2020 growing seasons, respectively.

Treatments	Crotalária—Dry Matter Productivity (kg ha−1)	Forage—Dry Matter Productivity (kg ha−1)
T1	1983 b	4607 ab
T2	2070 b	4518 ab
T3	2284 ab	4622 ab
T4	1898 b	5996 a
T5	2952 a	3325 b
T6	2440 ab	3180 b
Pr > Fc	0.04 *	0.01 *
LSD	675	1978.58
CV%	19.71	30.01

Note: T1: Caires and Guimarães (2016) [23], T2: Sousa, Lobato and Rein (2005) [24], T3: Vitti et al. (2008) [25], T4: Demattê (1986) [26], T5: Raij et al., (1996) [27], T6: standard (without agricultural gyp-sum application). (*) significant at 5% probability; LSD: least significant difference; CV: coefficient of variation. Means followed by the same letter do not differ from each other by the Student’s t-test (p < 0.05).

). For forage dry matter productivity, it was observed that treatments with gypsum yielded the best results. Treatments T1, T2, T3, and T4 provided the highest averages, with special emphasis on treatment T4.

4. Discussion

The increase in Ca content at the depth of 0.20–0.40 m under treatment T1 is most likely due to the fact that this treatment incorporates the CEC of the soil into its base calculation. With a higher gypsum dose combined with rainfall in sandy soil, this likely facilitated the leaching of Ca into the subsurface layer. According to [16], the application of gypsum to the soil surface has been recommended to increase Ca concentration in the subsurface [31]. This occurs through leaching, which significantly raises its content in deeper soil layers (Figure 4b, Figure 5b and Figure 6b). [32] Using a dose of 1.7 Mg ha⁻¹, we observed that gypsum application increased the concentration of Ca²⁺ throughout the soil profile. In a study by [33], with four gypsum doses (1.5, 3.0, 4.5, and 6.0 Mg ha⁻¹) and evaluation six months after application, an increase in Ca levels in soil depth was observed. This result is consistent with previous findings, as it was observed that after three years, the calcium content at depth increased in treatments T1, T2, and T5 (Figure 4).

The higher Mg levels observed during the first evaluation year (2018) can be explained by the increased Ca²⁺ content in subsequent years due to the annual gypsum application. This practice promotes the displacement of Mg²⁺ from exchange sites, allowing it to be leached into the soil profile either as an ionic pair (MgSO₄⁰) with SO₄²⁻ or in the form of Mg²⁺ ions. This occurs because Ca²⁺ replaces Mg²⁺ on the soil’s negative charges, and sulfate forms ionic pairs with Mg²⁺, enhancing its leaching as MgSO₄ [34,35,36,37].

The K, being highly mobile in soil, is easily leached in soils with low CEC, such as sandy soils. However, in 2019, factors such as the accumulation of crop residue provided by the NTS combined with drought conditions may have contributed to the increased K levels in the 0–0.40 m soil layer. This outcome is common in areas under NTS, where a gradual rise in K levels in the 0–0.20 m layer is observed, even with gypsum application [11]. In a study using a gypsum dose of 1.7 Mg ha⁻¹, an increase in K⁺ levels was observed in soil layers below 0.40 m. Conversely, other studies evaluating the soil profile three months after gypsum application found that agricultural gypsum did not influence K⁺ levels throughout the soil profile [32,33,38]. In this context, the application of gypsum combined with the use of NTS in sandy soils presents itself as an alternative for the sustainable use of these soils, considering their significant role in food production in tropical climates. The increasing global population drives the growing demand for food. However, achieving high productivity in sandy soils remains challenging due to their low cation retention, high permeability, and susceptibility to leaching and erosion [39,40]. This underscores the need for strategies like those evaluated in this study to improve soil profile construction and fertility.

This lack of effect can be explained by the low formation of the ionic pair K₂SO₄ [36], as the concentration of K⁺ is low compared to the concentrations of Ca²⁺ and Mg²⁺ in the soil and in the solution. Therefore, the SO₄²⁻ provided by gypsum will not bind to K⁺ due to competition between the cations [41]. In general, K levels in 2020, after three years of gypsum application, were lower than those obtained in the initial analysis (Table 2), confirming the theory that K⁺ was leached to deeper soil layers, as observed by other authors after the application of agricultural gypsum [12,41,42,43,44].

Compared to the initial soil analysis (Table 1), the final assessment conducted in 2020 reveals that the annual practice of gypsum application increased S levels in both analyzed depths. Several authors state that gypsum application promotes increases in Ca²⁺ and SO₄²⁻ concentrations throughout the soil profile [25,45,46,47,48]. Additionally, according to [42,43], gypsum can enhance Ca and sulfur (SO₄²⁻) levels in the subsoil, increase P concentration, and decrease Mg in the surface soil layer [49]. The leaching of S from the surface to deeper soil layers occurs due to the high mobility of sulfate when it forms a neutral ionic pair with Ca²⁺ [46,50]. It is important to note that, in this study, the treatments receiving agricultural gypsum application provided a greater amount of S to the plants, resulting in higher productivity for grasses, as observed in maize during the first year (T1) and in forage dry matter production (T4). Thus, gypsum can be used as an alternative and cost-effective source of S for crops, in addition to the other beneficial effects on cultivated soils mentioned here. The results observed in T6 (without gypsum application) reflect the impact of long-term NTS, demonstrating that combining agricultural system management techniques contributes to the development of fertile, healthy, and balanced soils.

Although gypsum does not correct active acidity, it reduces the concentration of exchangeable Al³⁺ in the soil. This means it is not applied with the purpose of increasing soil pH, as it is a neutral salt [46,51]. However, it is often used as a complement to lime, as it reduces Al³⁺ phytotoxicity and provides Ca²⁺ and SO₄²⁻ in deeper soil layers [46]. The increase in Ca levels may result in ionic exchange with Al and H on CEC sites, which can also lead to a pH increase. In the soil, gypsum reacts with Al³⁺ to form aluminum sulfate (Al₂(SO₄)₃), which is less soluble [36,52], thereby converting it into less toxic forms. Additionally, it increases Ca²⁺ and SO₄²⁻ levels in the subsoil [53,54]. Overall, this study found that the lower concentration of toxic Al at depth reduced the chemical barrier to root development in crops [55], resulting in greater nutrient absorption, better plant development, and consequently, an increase in productivity.

It is important to consider the increase in OM on the soil surface caused by NTS, as its decomposition is one of the primary processes contributing H⁺ ions to the soil. This occurs through the formation of organic compounds saturated with H⁺, increasing soil acidity, which varies according to the quantity and quality of the OM [56]. Moreover, [57] observed that in sandy soils with low plant residue input throughout the year—such as those in the Brazilian Cerrado—these practices result in nutrient supply through residue decomposition. These cover residues promote the mobilization of cations, making them available for subsequent crops. The increase in Ca²⁺ and S-SO₄²⁻ levels caused by gypsum application alters some soil characteristics [58,59], with sulfur complexing free Al and reducing its toxicity [60]. This reaction involves gypsum precipitating Al³⁺, converting it into a less toxic form (AlSO₄⁺). Simultaneously, Ca²⁺ and S levels increase in the subsoil, as observed in 2019.

Agricultural gypsum application can provide calcium at depth, reducing Al saturation in subsurface layers [61]. However, this study’s results for Al differ, showing a more significant decrease in m% at the surface (Figure 4a, Figure 5a and Figure 6a). Even so, surface gypsum application has been recommended as an alternative solution to this issue. The leaching of Ca increases its concentration and reduces Al saturation in the soil subsurface after a certain period, which varies depending on soil type, gypsum dose, and climatic and management conditions of production systems [16].

Given the higher OM levels in NTS, the quantity of complexed Al becomes highly significant [36]. This is because the interaction between Al and OM is one of the most influential reactions affecting the properties of acidic soils, as the soil’s organic fraction has a high capacity for complex cations due to the greater input of crop residues in the system. High doses of gypsum have the potential to leach nutrients that would otherwise be absorbed by plants, such as K⁺ and Mg²⁺, among others. This occurs due to the high concentration of Ca²⁺ supplied by gypsum application.

In addition, high doses have the potential to assist in raising soil pH and reducing acidity [62]. However, the leaching of these nutrients into subsurface layers is not problematic if the plant’s root system is well developed to access them at depth. This effect is promoted by agricultural gypsum, as its application helps reduce toxic Al, a chemical barrier to root growth. Consequently, roots will develop more easily, aiding in the uptake of these leached nutrients. It was observed that the increase in nutrients at depth can be beneficial for plants, provided they have the means to absorb them—namely, an extensive root system at greater depths. In this way, Ca and P in deeper soil layers can promote greater root development, as plants tend to seek these nutrients at depth [9]. In this scenario, with lower concentrations of toxic Al in the soil, roots are likely to develop more extensively, enabling greater access to water and nutrient uptake, which ultimately enhances productivity, as demonstrated in this study. Regarding soil physical properties (Table 5), it can be stated that the higher the DS, the greater the compaction, the more disaggregated the structure, the lower the PT, and consequently, the greater the restrictions on root system growth and plant development. By accumulating crop residues on the soil surface, NTS promotes carbon buildup, enhancing water adsorption and infiltration, and increasing soil sorptivity, TP, and macroaggregation [63]. These benefits are more pronounced with crop rotation or succession systems. However, in the present study, the responses over three years were minimal due to the sandy texture of the soil, which has a low amount of cementing agents in the short term.

There are few studies on the effects of soil amendments and conditioners, such as agricultural gypsum, on aggregate quality [64,65]. However, it is known that gypsum application increases Ca²⁺ concentration in the soil, and together with OM, these are considered cementing agents that can enhance aggregation and stabilization of soil aggregates by forming clay-Ca²⁺-OM complexes in the colloidal fraction of the soil [66,67] over the medium and long term. Similarly, [68] observed changes in soil physical properties 50 months after gypsum application, such as a reduction in bulk density in the top 0.15 m. In the present study, the treatments with higher cumulative gypsum doses over three years showed improvements in soil structure, highlighting that the effects of gypsum application may become more pronounced in the medium and long term across the soil profile (Table 5). Multivariate analysis showed that T1 contributed more effectively to soil aggregate structure and total porosity, as the increase in Ca content at depth was associated with the higher dose provided by this recommendation methodology.

The lack of effect of gypsum application on soybean productivity (Table 6), according to [6], can be attributed to the fact that soybean is a leguminous crop. The most satisfactory results from gypsum application have been observed in grasses, which have fibrous root systems. This type of root system promotes a greater exploration of the soil profile, consequently improving water and nutrient absorption by the root system in deeper layers.

Although there are benefits to the chemical properties of the soil provided by gypsum application, several studies conducted on clayey soils did not observe an increase in soybean productivity with the surface application of gypsum in areas under NTS [43,53,61,69]. However, in addition to the improvements in soil attributes caused by crop rotation in this system, where the current crop can take advantage of the nutrition from the previous crop, there is also an improvement in the water use efficiency of maize [70]. This highlights the long-term effect of gypsum application, promoting deeper root growth and better nutrient absorption, leading to improved plant development. It is worth noting that in all seasons, the grain yields obtained were higher than the national average: for the 2017/2018 season, the national average for maize was 5169 kg ha⁻¹ [71], while the average for the studied area was 7412 kg ha⁻¹; for the 2018/2019 season, the national average for soybeans was 3206 kg ha⁻¹ [72], compared to 4704 kg ha⁻¹; and for the 2019/20 season, the national average for maize was 6065 kg ha⁻¹ [73], compared to 7286 kg ha⁻¹ obtained in the present study.

In this study, the application of agricultural gypsum yielded positive results, particularly in grasses, mainly due to the supply of Ca and S. Moreover, further research is needed to assess the long-term effects of gypsum in such systems. Another point worth highlighting is that in sandy soils, the need for gypsum varies continuously due to the soil’s structure [40], which necessitates regular doses over the years, as observed in this study. Therefore, it is essential to consider the cost–benefit balance of gypsum application, along with regular evaluations through chemical and physical soil analyses. As a relatively inexpensive fertilizer, gypsum application is recommended for acidic and infertile soils, such as tropical soils, due to the significant benefits discussed here.

Contrary to existing theoretical reviews that extensively discuss methodologies for gypsum application, our research focuses on practically evaluating which of the five gypsum recommendation methodologies shows the best correlation with soil chemical and physical attributes, as well as crop productivity in tropical sandy soils under no-tillage systems. There is a significant challenge in choosing the most appropriate methodology, as the available recommendations do not account for the specific characteristics of each site, such as soil type and climatic conditions. This knowledge gap is even more evident in tropical regions, like the study area, where variables can differ significantly from other contexts. Therefore, our study contributes by filling this gap, providing a practical, data-driven analysis to help choose the most efficient gypsum application methodology to improve soil fertility and crop productivity.

5. Conclusions

In sandy soils and tropical conditions under a no-tillage system with high nutrient exportation, it is recommended to use the gypsum application methodologies proposed by Caires and Guimarães or Raij and collaborators to improve soil fertility and aggregation. These methodologies for calculating gypsum requirements by Caires and Guimarães or Raij and collaborators showed the best correlation with the V%—an attribute that is most correlated with soil fertility. Additionally, the methodology by Caires and Guimarães showed a better correlation with maize productivity. However, neither methodology resulted in increased productivity in soybean crops. Overall, it was observed that the application of agricultural gypsum contributed to soil fertility by increasing CEC and V%, which is reflected in the productivity of grasses. Furthermore, further studies are needed to explore the long-term effects, as well as the microbiological responses to these different gypsum application recommendation methodologies in various soils and climatic conditions. Additionally, due to its accessibility and low cost, the use of agricultural gypsum in tropical sandy soils remains a valuable tool for improving fertility in highly weathered soils.