Tillage, Manure, and Biochar Short-Term Effects on Soil Characteristics in Forage Systems

Abstract

Manure, a globally used soil amendment, can contribute to excessive N and P runoff, leading to water pollution. Biochar (BC) shows promise in mitigating nutrient loss by retaining soil nutrients. However, there is limited research exploring the combined effects of tillage practices, biochar, manure, forage crops, and soil types on soil nutrient characteristics in a single field study. Our objectives are to determine if, in North Central Texas, differing soil types, soil amendments, forage crops, and tillage practices affect soil nutrients when applied short term, and whether correlations exist among soil nutrient characteristics as affected by soil amendments, tillage practices, and the presence of forage crops. The study encompasses three field sites with five factors, including soil types, manure rates, biochar rates, tillage practices, and forage crop types. Soil samples were assayed for pH, electrical conductivity (EC), macronutrients, and micronutrients. Data analyses involved variance analysis, Fisher’s tests, and Pearson’s correlations using R in Rstudio (the IDE). Microplots treated with manure (average 2.16 ppm) retained 60% greater average nitrate levels at the end of the growing season than those treated with a synthetic fertilizer (average 1.35 ppm) (p ≤ 0.05). Moderate and strong correlations were observed between EC and S (r (106) = 0.43, p < 0.001 in loamy sand soil; r (106) = 0.80, p < 0.001 in clay loam soil) and between nitrate and Zn, (r (106) = 0.36, p < 0.001 in loamy sand soil; r (106) = 0.44, p < 0.001 in sandy loam soil) across different soil types. Soil type (texture) emerged as the primary influencing factor on plant-available soil nutrients and characteristics, followed by manure application and tillage practices. The impact of BC and forage crop type varied depending on other experimental factors. Understanding the influence of soil type, amendment application, and tillage on soil nutrient characteristics can guide sustainable forage production practices and soil nutrient management strategies.

1. Introduction

Atmospheric CO₂ concentration is on the rise due to human activities, leading to significant impacts on global climate, agriculture, and human health [1]. Biochar (BC) has emerged as a potential solution to mitigate anthropogenic CO₂ emissions, with studies suggesting that widespread biochar use in cropping operations could mitigate up to 12% of the current anthropogenic CO₂ emissions [2]. Given the increasing global population and the need for agricultural production, awareness of long-term soil health and carbon sequestration is useful.

While previous studies have examined the individual effects of biochar on soil properties or crop yield, there is a lack of research that investigates the correlation between soil nutrient characteristics in different soil types and the combined impacts of manure, biochar, and forage crop management practices in a single field trial [1,2,3,4]. Independently of any environmental benefits, the short-term negative effects of biochar application on forage field soil nutrients are of particular concern.

As the application of manure as a soil nutrient replenishment in agriculture is practiced worldwide, understanding its interaction with biochar, tillage practices, and forage crops could be important in understanding how biochar can bind manure nutrients in the soil [1,4,5,6]. This is particularly important when dairy manure is applied to forage fields because it contains nitrogen and phosphorus [7]. Continuous liquid manure dispersal results in soil P oversaturation and high runoff probability [7]. Phosphorus has a low water solubility and the potential to runoff during rainfall. Water runoff, then, often becomes a pollutant and flows into the surrounding surface water, leading to eutrophication and many other environmental issues [8].

In the soil, immediate contact between BC and manure binds nutrients more readily than BC alone without manure [7]. Biochar coupled with a manure amendment may retain more nutrients in the soil than if applied individually because BC can retain nutrients in its pores and slowly release them into the soil for an extended period [4]. This is because the nutrients are not fully leached from the soil via infiltration. Furthermore, biochar amendments, in conjunction with no-till practices, can improve soil aggregate properties by acting as a bonding agent, minimizing soil erosion potential, and improving soil quality [9,10]. A combination of no-till practices with manure and biochar application could be of particular importance in regions with coarse-textured soil, subject to nutrient loss due to soil texture and nutrient infiltration.

At present, there is little published research on the field-scale application of biochar in conjunction with no-tillage practices in forage systems. There have been greenhouse experiments looking at BC and manure effects on soil, a field study examining the effects of biochar and manure on a prairie, BC effects on soil aggregation under no- till for maize, biochar use for soil ecosystem services, and many more studies looking at how BC and manure affect soil [4,9,10,11]. However, the comparison of different tillage practices, along with the combined effects of biochar and manure, on forage crops and soil nutrient characteristics has not been thoroughly investigated in a single study.

This study aims to bridge these knowledge gaps by examining the effects of no- tillage versus conventional tillage practices, varying manure and biochar application rates, and different soil types on soil nutrient characteristics. The short-term effects of biochar, in conjunction with multiple farming practices, on soil characteristics are necessary to be studied for producers. Oftentimes, growers need to be able to determine if they can afford to potentially sacrifice their nutrients being bound in the soil short term, to help their soil health in the long term.

We hypothesized that varying BC and manure amendments, with tillage and no-tillage practices, would affect soil nutrient correlations within different soil types growing various forage crops. We further hypothesized that BC and manure amendments would correlate positively with soil nutrient characteristics, depending on tillage practices. Our objectives for this study are to determine if, in North Central Texas, differing soil amendments, crops, and tillage practices affect soil nutrients when applied short term, and whether correlations exist between soil nutrient characteristics as affected by soil amendments, tillage practices, and the presence of forage crops. By studying the interactions among biochar, manure, tillage practices, and soil nutrient characteristics, we hope this research will contribute to the understanding of sustainable agricultural practices and nutrient management strategies.

2. Materials and Methods

2.1. Field Experimental Design

Our experiment was conducted from March to November 2021 (a 7-month period) in three field sites, each containing different soil textures. Two field sites were located at Texas A&M AgriLife Center in Stephenville, TX, USA, and a third field site was located at Texas A&M AgriLife Blackland Research and Extension Center, Temple, TX, USA. This was a five-factorial experiment with three replications. Each replicate contained treatment combinations of the following factors: (1) soil type (loamy sand, sandy loam, or clay loam/sandy clay loam); (2) manure application rate (0 or 10 Mg Dry Matter (DM)/ha); (3) BC application rate (0, 5, or 10 Mg DM/ha); (4) tillage practices (conventional tillage or no-till); and (5) forage crop type (bermudagrass (Cynodon dactylon), sorghum-sudangrass (Sorghum drummondii, Super Sugar hybrid), or maize (Zea mays, Brevant, Corteva B13T77SX)).

Each of the three replicates had 36 microplots; therefore, there were 108 microplots in each field site. Microplots were 3 × 3 m² and there were 1.5 m alleys between the microplots. Experimental units were plots representing combinations of all possible treatment combinations (Figure 1). There were three blocks for all plant types in each repetition, to account for field variability. Soil, tillage, and crop treatments were applied as strip plots. Crops were planted in a split-block design, with whole plots in randomized complete blocks. Entire plot treatments were blocked by either no-tillage or conventional tillage practices. Manure and BC were completely randomized. The microplot layout for the loamy sand field is shown in Figure 1 to demonstrate the experimental design.

2.2. Independent Variables (Individual Treatments within Factors)

2.2.1. Soil Type

Each field site consisted of different soil texture types: sandy clay loam/clay loam soil (CL), loamy sand (LS), and sandy loam soil (SL) (Table S1, and Figures S1–S3). Soil type identification was carried out through a particle size analysis using a hydrometer method on three soil samples collected from each field [12,13]. Soil types were determined by calculating the averages of particle sizes. The CL field classified as both sandy clay loam and clay loam, as the percent sand differed by less than 1% between these texture types. The SL and LS field sites were situated at the Texas A&M AgriLife Center in Stephenville, while the CL site was located at the Texas A&M AgriLife Blackland Research & Extension in Temple, TX, USA. The LS soil study site is at 32°15′08.7″ N, 98°11′45.9″ W, the SL site at 32°15′10.2″ N, 98°11′33.8″ W, in Stephenville, TX (Alfisols; fine, mixed, active, thermic Udic Paleustalfs), and the CL site at 31°02′48.0″ N, 97°21′04.1″ W in Temple, TX (Mollisol; fine-silty, carbonatic, thermic Udorthentic Haplustolls soils) [14,15]. These soil types were chosen because sandy loam and loamy sand soils are common in the Cross Timbers ecological region of Texas. Clay loam and sandy clay loam soils are common in the Texas Blackland Prairie region.

In March 2021, two pre-trial soil samples were collected from each repetition of the field sites at 0 to 10 cm depths for chemical analysis [16,17,18,19]. The obtained results were averaged per site to determine the initial soil chemical characteristics. The field sites have historically been tilled for previous research projects and were tilled prior to amendment application for this study. Therefore, soil sampling was conducted based on depth rather than horizon. Prior to application, a sample of dairy manure was also analyzed to assess its chemical properties. The chemical characteristics of both the initial soil samples and the manure sample can be found in

Table 1. Initial soil and manure plant-available soil characteristic averages in research field sites.

Chemical Characteristics	Loamy Sand Field	Sandy Loam Field	Sandy Clay Loam/Clay Loam Field	Dairy Manure
pH	6.47	6.00	7.37	5.80
Electrical Conductivity (µmho/cm)	103.67	137.50	240.50	6530.00
NO3-N (ppm)	7.00	3.83	7.50	>400.00
P (ppm)	21.33	5.00	4.33	1015.00
K (ppm)	191.67	181.67	369.50	1070.00
Ca (ppm)	671.33	1218.50	17,188.33	2668.00
Na (ppm)	5.33	19.83	10.83	380.00
Mg (ppm)	148.33	243.83	160.00	2052.00
S (ppm)	2.67	8.17	7.83	130.00
Fe (ppm)	7.90	15.05	18.48	N/A
Zn (ppm)	0.37	0.18	0.15	N/A
Mn (ppm)	2.86	5.13	3.26	N/A
Cu (ppm)	0.59	0.29	0.24	N/A
Organic matter	0.48%	0.85%	3.16%	71.35%

.

2.2.2. Manure Application Rate

The second factor was application of dry dairy manure obtained from Tarleton State University Dairy (0 or 10 Mg DM/ha) (Table 1 and Table S2). Manure was screened from dairy stall flush spillways for collection, sand was removed, and then left to dry in the sun for two weeks before field application. This was applied based on weight and dispersed over each microplot based on individual manure treatment and then spread evenly over plots. Commercial fertilizer (10-20-10, and 8% sulfur) from American Plant Food Corporation (2021) was applied per Texas A&M AgriLife Extension soil analysis adjustments for each crop species; it was corrected for manure-N in those plots, such that all plots were isonitrogenous. In microplots where high manure was applied, a lower amount of fertilizer (255 g/microplot) was added (Table S3). Conversely, microplots without manure received a higher amount of fertilizer (855 g/microplot).

Manure application was based on P, rather than N additions. Sanderson and Jones (1997) reported annual compost application rates for manure as fertilizer should not exceed 44.8 Mg/ha−1 in the southern U.S.A., when applied to coastal bermudagrass (Cyondon dactylon (L). Pers) [20]. This maximum rate was based on average amount of P in manure and is recommended to prevent soil P accumulation and NO3-N leaching in soil. Bermuda grass has the least amount of P requirements of the three crops, so this was used as the maximum to reduce environmental risks. In this study, 10 Mg DM/ha⁻¹ is considered the “high” rate because the wet weight of the dairy manure was 45 Mg/ha⁻¹ before drying prior to field application. Synthetic fertilizer was applied at low rates to microplots receiving manure to “top up” N. With synthetic fertilizer additions, all plots, regardless of manure application, were isonitrogenous.

2.2.3. Biochar Application Rate

The third factor was BC at 0 (no), 5 (low), or 10 Mg DM/ha (high) rates. The BC used was trade-named “Pristine BC” and was prepared from yellow pine wood using a continuous flow pyrolysis system at 550 °C for 10 min (Waste to Energy, Inc., South Slocomb, AL, USA). The BC was spread manually based on the weight (0 kg/microplot for no application; 4.5 kg/microplot for low rates; 9 kg/microplot for high rates). The BC properties are presented in

Table 2. Chemical characteristics of Pristine BC used in the research field sites.

Elemental Analysis (wt. %)				O/C	H/C	Proximate Analysis (%, Dry Basis)
C	H	O	N			FC	VC	Ash
83.40	0.67	6.16	<0.10	0.055	0.096	80.54	9.68	9.78
Mineral composition (wt. %)						BET surface area (m2/g)		pHpzc
P	K	Ca	Mg	Na	Fe
0.11	0.34	1.50	0.38	0.01	0.19	260.50		9.77

.

2.2.4. Tillage Practices

The fourth factor was conventional tillage or no-till seedbed preparations. These tillage practices were chosen to be examined to determine how BC and manure amendments may have different impacts based on differing amendment incorporation within the studies’ soil types. Fields were initially tilled using a 50 hp tractor (John Deere 5310; John Deere, Waterloo, IA, USA) with a 1.8 m tiller (Land Pride RGR1274 three-point tiller; Land Pride, Salina, KS, USA) at a 5.1 to 7.6 cm soil depth before manure and BC amendments were added. Following the addition of amendments, the tilled plots were cultivated again using the same tractor, at the same depth, to incorporate the amendments. No-till plots were not tilled after amendments (manure, fertilizer, and biochar) were broadcast.

2.2.5. Forage Crop Type

The fifth factor was forage crop system: Jigg’s bermudagrass (Cynodon dactylon), sorghum sudangrass, (Sorghum drummondii, Super Sugar hybrid), and silage maize (Zea mays, Brevant by Corteva B13T77SX). These crop types were chosen, as they are commonly grown in North Central Texas as forage crops. To create the bermudagrass plots, each microplot received 12 sprigs planted evenly spaced apart, which were 100% covered within 6 weeks. Maize and sorghum were seeded within each microplot, with one plant every 15 cm, with 90 cm between rows. Fields were irrigated with 2.54 cm of water per week during the growing season, except when rainfall reached or exceeded this amount (Table S4).

2.2.6. Sample Intervals

Soil sampling was conducted in October and November 2021, prior to the harvest of the forage crops. Within each microplot, three holes were dug randomly within the interior of the microplot to reduce edge effects, using a shovel. The holes were dug 10 cm from the plants, so soil tests would reflect soil chemical characteristics with more significance to the plants’ roots. However, for the bermudagrass microplots, the holes were randomly dug in various parts due to its spreading nature. Once dug, the top 0–10 cm layer of soil from each of the three holes was collected and combined in a bucket, thoroughly mixed to achieve homogeneity, and then transferred to sample bags for subsequent chemical analyses. Once the sample bags were filled, the remaining soil was placed back into the holes they were dug from. The CL field only had soil samples taken from the bermudagrass microplots because the sorghum-sudangrass and maize plants were not established in this field in 2021.

2.3. Nutrient and Soil Type Determination

Soil samples from 0 to 10 cm depths were assayed for chemical and nutrient characteristics by Texas A&M AgriLife Extension Services-Soil, Water, and Forage Testing Laboratory at the College Station using the standard methods practiced in this lab [16]. Data from the assays included pH, electrical conductivity (EC), nitrate (NO₃-N), P, K, Ca, Mg, S, Na, Fe, Zn, Mn, and Cu values. Phosphorus, K, Ca, Mg, Na, and S were extracted using Mehlich III extractant and were determined by ICP [17,18]. Soil samples were also tested for permanganate oxidizable carbon [19] and total carbon and total nitrogen using a CN828 elemental analysis by combustion (LECO Corporation, St. Joseph, MI, USA) at the Texas A&M AgriLife Center at Stephenville.

2.4. Statistical Analysis

Data were analyzed using R and R Studio (R-4.2.2) (R Core Team, 2022). Independent variables consisted of soil type, manure application rate, BC application rate, tillage practices, and forage crop type. Dependent variables consisted of pH, EC, NO₃-N, P, S, and Zn. These response variables were chosen for statistical analyses because NO₃-N, P, S, and Zn were already considered deficient in the initial soil testing, and because pH and EC can be used as indicators of nutrient availability [20,21].

If the data were not normally distributed or lacked homogeneity, they were transformed to correct this. The pH data did not require transformation. However, for the data to be distributed normally, EC data had to have outliers removed from the sandy loam and loamy fields; the square root of the data was used for NO₃-N; the natural log of the data was used for P; the log of the data was used for P; the natural log of the data was used for S, and outliers were removed from SL and LS fields; the log of the data was used for Zn. An analysis of variance (ANOVA) was then used to evaluate whether interaction combinations, affected soil nutrient content or characteristics by order, and then individual factor (if no interactions were detected; p > 0.05). Factors were tested for 5-way, 4-way, 3-way, and 2-way interactions, and the results are discussed by the highest significant interaction order (Table S5).

In cases of significant interactions, Fisher’s LSD method was used to determine the effects of individual treatments within each of the five factors on soil characteristics, to determine where the differences in the ANOVA interactions occurred. The Fisher’s LSD results are displayed in tables, with uppercase letters used to group columns and lowercase letters used to group rows. Results with p > 0.05 are reported in the text, while differences were considered significant at p ≤ 0.05.

Pearson’s correlations were employed to investigate the relationship between soil types and nutrient characteristics resulting from amendments, tillage practices, and the presence of forage crops, addressing the second objective of this study. Data were transformed to achieve normal distributions and identify interactions among soil characteristics. Strong correlations were defined as Pearson’s coefficient values (r) ranging from ±0.50 to ±1, while moderate correlations fell within the range of ±0.30 to ±0.49. Weak correlations (below ±0.29) were not discussed. Results with p > 0.05 were not reported in the text, as differences were considered significant at p ≤ 0.05. Correlations previously addressed in earlier sections were not reported or discussed again to avoid repetition of information.

3. Results and Discussion

3.1. pH

The ANOVA test revealed the highest order of interaction for factors affecting pH was soil type × tillage × manure (F_2,168 = 3.25, p = 0.041), while biochar affected soil pH as a simple effect (F_2,168 = 14.51, p < 0.001) (Table S5). The LSD tests indicating the differences in these treatment combinations are found in

Table 3. Biochar (BC) with Fisher’s LSD test displaying the mean ± standard error for pH; ANOVA one-way interaction with F_2,168 = 14.51, p < 0.001.

Biochar Rate	pH
High BC	6.715 ± 0.058 a *
Low BC	6.565 ± 0.069 ab
No BC	6.446 ± 0.070 b

* Values within each column followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

and

Table 4. Soil type × tillage × manure; Fisher’s LSD test displaying the mean ± standard error for pH; ANOVA three-way interaction with F_2,168 = 3.25, p = 0.041.

Manure Application	Tillage	Sandy Clay Loam	Sandy Loam	Loamy Sand
Manure	No-till	7.73 ± 0.008 a A *	6.43 ± 0.051 b B	6.48 ± 0.082 b A
Manure	Till	7.71 ± 0.017 a A	6.77 ± 0.051 b A	6.62 ± 0.077 b A
No manure	No-till	7.71 ± 0.016 a A	6.20 ± 0.050 b C	5.92 ± 0.051 c B
No manure	Till	7.72 ± 0.027 a A	6.47 ± 0.079 b B	6.45 ± 0.098 b A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

, respectively.

Comparing the final soil pH values in Table 3 with the initial soil chemical tests in Table 1, the pH of each soil type slightly increased after the completion of the growing season. However, only the SL soil type exhibited an increase in pH (p < 0.05). The initial pH of the SL field site was 6.0, and it concluded the study with a pH of 6.47, indicating a 7.8% increase.

Data from LSD Table 4 indicate tillage practices and manure application did not have a significant influence on CL soil pH, and that CL pH was different than pH in SL and LS soils. As the clay content increased, so did the pH (Table 4), except for two exceptions observed in the LS soil. Clayey soils generally tend to be more alkaline than sandy soils due to their better moisture retention and have reduced leaching [22]. The abundance of Ca in the CL soil (17,188 ppm) is also a good indication of why the field is slightly alkaline, as calcium carbonate is a liming agent (Table 1) [23].

Two exceptions to the general trend of pH increase with the clay content were observed in LS and SL soils under the specific treatment combinations of manure × no-till and no-manure × till (Table 4). In LS soil with manure × no-till treatments, it is likely that the pH was maintained at its initial level (Table 1) due to the presence of untilled manure acting as a physical barrier against salt leaching caused by irrigation. No-manure × tillage treatments in LS soil may not have had a difference for the same treatments in SL soil, because there was a clay pan beneath the LS soil that could have been partially incorporated from tillage. The increased clay content likely diminished the effects of the salts from the surface of the LS soil and allowed for a clay content closer to that of SL soil than the LS soil previously had in the upper portion of the soil.

No manure × no-till practices treatments produced the lowest pH in SL and LS soils (Table 4). Manure × tillage practices likely did not affect pH since the pH was initially slightly alkaline (Table 2), and there was a higher clay content in the CL soil (Table 3). The pH serves as a measure of the buffering capacity, and as it approaches neutrality (pH 7), the impact of manure and tillage decreases, as neutral soils are better equipped to buffer ionic charges [20]. The application of solid cattle manure moves acidic and alkaline soil pH towards neutrality [24,25]. Although the field site soils were already close to neutral, the additional organic matter (OM) in manure can still act as a pH buffer, releasing H⁺ in response to alkaline materials and accepting H⁺ in response to acidic materials.

Although BC affected the pH, the difference was very slight from an ecological perspective (Table 3). No BC and low BC rates had similar pH values, and low BC and high BC rates were also undifferentiated. However, a difference was observed between no BC and high BC treatments. Adding high and low rates of BC may change the pH differently (Table 2 and Table 3). Nonetheless, the pH difference between the lowest mean (6.482) and highest mean (6.730) was only 0.247, suggesting that crop growth is unlikely to be affected by pH changes resulting from BC management practices (Table 3). Biochar can increase the soil cation exchange capacity (CEC) and pH [26]. However, it is worth considering that the pH increase from BC could have been influenced by the alkaline pH of the BC itself (Table 2), as the samples were homogenized, making it difficult to separate the pH contribution of the soil and BC.

Pearson correlation analysis was conducted to examine the relationship between EC, NO₃-N, P, S, and Zn with pH as the fixed variable, categorized by soil type (Figure 2). The figure illustrates the positive or negative correlations between pH and the analyzed soil chemical properties. The results indicate there was a moderate, negative correlation between EC and pH, r (34) = −0.42, p = 0.015 in CL soil. This could be the result of BC practices having a significant influence on differences in soil pH (Table 3). However, EC can also be affected by pH, since the attraction of H⁺ ions from pH levels can impact the soil’s nutrient exchange capacity [26]. A greater deviation from neutral pH reduces the ability for ion exchange, which likely explains the moderate negative correlation between NO₃-N (r (34) = −0.55, p < 0.001) and pH, and the strong, negative S (r (34) = −0.47, p = 0.003) with pH in the CL field site.

A strong, positive correlation was found between pH and Zn, r (94) = 0.53, p < 0.001, in the SL soil (Figure 2). This correlation can be attributed to the initial acidity of the LS and SL soils (Table 2), where higher H⁺ cation concentration limits the binding capability of Zn²⁺ ions. Zinc and pH are likely correlated in the SL soil because this soil was initially more acidic (initial pH was 6.47 in LS and 6.00 in the SL soil) (Table 2). The more H⁺ cations there were, the less capability Zn²⁺ had to bind to the soil [22]. With an increasing pH, there was more opportunity for retention in the soil, especially when OM was added.

There was also a moderate, positive correlation between EC and pH, r (94) = 0.40, p < 0.001, in SL soils. If there is a pH increase in the soil, the acidic SL soil becomes more neutral. As the pH of acidic SL soil approaches neutrality, its ion exchange capacity enhances, resulting in an increase in EC. This finding contrasts with the EC correlation observed in the more alkaline CL soil.

3.2. Electrical Conductivity

The highest interaction orders affecting EC were for soil type × tillage × biochar × manure (F_4,159 = 6.62, p < 0.001), and soil × tillage × crop type (F_2,159 = 3.70, p = 0.027) (Table S5). These interactions are displayed with Fisher’s LSD multiple mean separations by soil type × tillage × biochar × manure in

Table 5. Sandy clay loam/ clay loam soil type × tillage × biochar (BC) × manure interaction; Fisher’s LSD test displaying the mean ± standard error for electrical conductivity; ANOVA four-way interaction with F_4,159 = 6.62, p < 0.001.

Manure Application	Tillage	No BC	Low BC	High BC
Manure	No-till	169 ± 18.4 a A *	165 ± 14.5 a B	205 ± 12.9 a A
Manure	Till	197 ± 16.8 a A	161 ± 9.2 a B	176 ± 12.9 a A
No manure	No-till	215 ± 18.3 a A	203 ± 6.0 a A	173 ± 22.5 a A
No manure	Till	165 ± 5.2 b A	220 ± 10.5 a A	229 ± 23.2 a A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

,

Table 6. Loamy soil type × tillage × biochar (BC) × manure; Fisher’s LSD test displaying the mean ± standard error for electrical conductivity; ANOVA four-way interaction with F_4,159 = 6.62, p < 0.001.

Manure Application	Tillage	No BC	Low BC	High BC
Manure	No-till	95 ± 3.3 a AB *	93 ± 3.5 a A	89 ± 4.1 a B
Manure	Till	108 ± 5.4 a A	104 ± 4.0 a A	109 ± 6.6 a A
No manure	No-till	93 ± 4.3 a B	98 ± 4.2 a A	87 ± 3.2 a B
No manure	Till	95 ± 5.13 a AB	96 ± 5.3 a A	97 ± 4.6 a AB

* Values within each column (upper case) and each line (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

and

Table 7. Sandy loam soil type × tillage × biochar (BC) × manure; Fisher’s LSD test displaying the mean ± standard error for electrical conductivity; ANOVA four-way interaction with F_4,159 = 6.62, p < 0.001.

Manure Application	Tillage	No BC	Low BC	High BC
Manure	No-till	121 ± 4.3 a AB *	114 ± 7.3 a B	113 ± 6.0 a B
Manure	Till	126 ± 6.5 a A	134 ± 8.1 a A	137 ± 5.9 a A
No manure	No-till	106 ± 7.3 a B	110 ± 6.3 a B	111 ± 6.7 a B
No manure	Till	114 ± 7.3 a AB	124 ± 3.8 a AB	121 ± 7.3 a AB

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

(separated by soil type to facilitate interpretation) and soil × tillage × crop type in

Table 8. Soil type × tillage × crop type; Fisher’s LSD test displaying the mean ± standard error for electrical conductivity; ANOVA three-way interaction with F_2,159 = 3.70, p = 0.027.

Crop Type	Tillage	Sandy Clay Loam	Sandy Loam	Loamy Sand
Bermudagrass	No-till	188 ± 7.4 a A *	118 ± 4.5 b BC	88 ± 2.4 c C
Bermudagrass	Till	191 ± 8.0 a A	117 ± 3.9 b BC	101 ± 3.5 c AB
Maize	No-till		106 ± 3.2 a C	95 ± 2.8 b ABC
Maize	Till		136 ± 5.1 a A	101 ± 4.1 b AB
Sorghum	No-till		114 ± 5.3 a C	93 ± 2.7 b BC
Sorghum	Till		126 ± 4.7 a AB	103 ± 3.9 b A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

.

Differences (p ≤ 0.05) in EC capacity were observed among soil types. The CL soil exhibited the highest average EC, followed by the SL soil and LS soil, likely due to the increased clay content influencing EC (Table 5, Table 6 and Table 7) [27]. This trend was consistent with the initial soil chemical analyses (Table 1). However, there was a decrease in the average EC for each soil type after harvest compared to the initial testing. The CL field site experienced a 21.1% decrease in EC, with the SL field site showing a 12.3% decrease and the LS field site exhibiting a 6.9% decrease when comparing the initial soil test results to the final results (Table 1, Table 5, Table 6 and Table 7). This decline may be attributed to irrigation and rainfall washing soluble salts from the soil, as well as the removal of plant matter during weeding, which led to a reduced soil moisture retention.

The soil type × tillage × biochar × manure interactions indicate EC changed mostly due to tillage and manure treatments (not BC rates) because separated means within rows are the same, with one exception in Table 5 (Table 5, Table 6 and Table 7). The uppercase letters in the columns of Table 5, Table 6 and Table 7 indicate differences, suggesting biochar does have an influence on EC, but its effect depends on the specific tillage and manure treatments within the microplots. The one exception for this is the combination of no manure, no BC, and tillage in the CL soil, which had a lower mean EC than any other combination of those factors in CL (Table 5). Apart from this exception, BC and manure were unaffected. Either BC or manure can raise EC, but they may interact if both amendments are added. When low BC was added with manure, there was less EC compared to the microplots having synthetic fertilizer instead of manure (Table 5).

Tilled microplots showed higher EC compared to no-till microplots in LS and SL sites. Manure application amplified EC in these coarse-textured soils, but tillage raised conductivity regardless of manure rate. The SL and LS soils that had the highest EC, regardless of BC rate, were tilled soils with manure amendments. The grouped differences in the no-BC and high-BC columns were consistent between Table 6 and Table 7, indicating that an increased EC with a high BC is associated with tillage in SL and LS soils. In all soil types, the addition of manure for increased EC had a greater effect if manure was tilled in, i.e., incorporated into the rhizosphere, compared to no-tilled soil (Table 5, Table 6 and Table 7).

Tilled microplots in SL and LS field sites exhibited greater EC than their no-till counterparts. This can be attributed to the presence of a clay pan at different depths in these fields. Tillage likely incorporated clay from the lower soil layers into the upper tested portion, contributing to an increased EC. Additionally, the absence of pre-study irrigation may have led to the accumulation of soluble salts, which were subsequently incorporated during tillage. However, the CL field site deviated from this trend, possibly due to its high shrink–swell capacity. The natural swelling and cracking of the field soil could resemble the effects of tilling, resulting in a different EC pattern.

Biochar can be used as a universal sorbent to absorb liquids and gases, but only as much as its adsorption area allows [28]. In CL soil, the addition of low BC with manure resulted in sufficient adsorption area to bind manure nutrients, but the low BC rate may not have significantly affected cation exchange capacity (CEC) due to the limited bound nutrients (Table 5). Conversely, in SL and LS soils, the combination of manure and high BC in no-till fields may have lowered EC because the amendments created a slight physical barrier over the soil against rain and irrigation (Table 6 and Table 7), when water infiltration can enhance EC up to a certain point [27].

Table 8, along with Table 5, Table 6 and Table 7 demonstrates that EC increased with higher clay content. Tillage, however, did not affect CL soil as it did in coarser soils. The main difference observed in SL and LS soils, regardless of crop type, was that tilled soils tended to have higher EC than no-till soils. The maize and sorghum-sudangrass samples were not taken from the CL field site due to failed plant establishment. The LSD multiple mean separation groups for this table showed high variability among crop types in SL and LS soils, but the general trend in Table 8 is that EC increased with clay content and tillage.

Pearson correlation coefficients are computed to assess the linear relationship among EC and other soil chemical analyses and separated by soils in Figure 3. In the LS soil, moderate positive correlations were observed between EC and S (r (106) = 0.43, p < 0.001) as well as Zn (r (106) = 0.41, p < 0.001). Clay loam soils exhibited strong positive correlations between EC and S (r (106) = 0.80, p < 0.001). Since EC and clay content increased together (Table 5, Table 6 and Table 7), it makes sense that S also corresponded to this. Sulfur, being prone to leaching, can bind more effectively to soils high in clay content and EC, since they exhibit reduced leaching potential and S would have greater ability to adsorb to clay particles [21]. The positive correlation between Zn and EC was a result of increased organic matter and fertilizing. There was almost no Zn in the soils when the study began (Table 1) and Zn was primarily added to the soil with manure, which increased EC for the most part (Table 5, Table 6 and Table 7).

3.3. Nitrate

The highest interaction order for factors affecting plant-available N (NO₃-N) was from tillage × crop type (F_2,168 = 4.69, p = 0.010), and tillage × soil type (F_2,168 = 3.36, p = 0.037). Manure also has a simple effect (F_1,168 = 26.16, p < 0.001) (Table S5). Interactions were separated using Fisher’s LSD multiple mean separation with tillage × crop type (

Table 9. Tillage × crop type; Fisher’s LSD test displaying the mean ± standard error for NO₃-N; ANOVA two-way interaction with F_2,168 = 4.69, p = 0.010.

Tillage	Bermuda Grass	Sorghum	Maize
Till	2.59 ± 0.010 a A *	1.08 ± 0.004 b B	1.39 ± 0.006 b A
No-till	2.37 ± 0.123 a A	1.59 ± 0.006 b A	0.96 ± 0.005 b A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

), tillage × soil type (

Table 10. Soil type × tillage; Fisher’s LSD test displaying the mean ± standard error for NO₃-N; ANOVA two-way interaction with F_2,168 = 3.36, p = 0.037.

Tillage	Sandy Clay Loam	Sandy Loam	Loamy Sand
Till	4.80 ± 0.034 a A *	1.61 ± 0.004 b A	1.21 ± 0.004 b A
No-till	5.62 ± 0.015 a A	1.85 ± 0.004 b A	0.79 ± 0.004 c B

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

), and manure × NO₃-N (

Table 11. Manure × NO₃-N; Fisher’s LSD test displaying the mean ± standard error for NO₃-N; ANOVA one-way interaction with F_1,168 = 326.16, p < 0.001.

Manure Application	NO3-N
Manure	2.16 ± 0.003 a *
No manure	1.35 ± 0.003 b

* Values within each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

).

After the growing season, all soils decreased in nitrate levels compared to the initial tests (Table 1). Sandy loam soils showed a decrease in nitrate from 3.83 ppm to 1.73 ppm, indicating a 55% decrease. Clay loam soils also experienced a significant decrease in nitrate, dropping from 7.50 ppm to 5.21 ppm, resulting in a 26% decrease. Loamy sand soils exhibited the most substantial decline, with nitrate levels decreasing from 7 ppm to 1 ppm, representing an 86% decrease.

There was no difference in soil plant-available N between tillage and no-till practices for microplots with maize and bermudagrass (Table 9). Microplots with bermudagrass ended the season with more nitrate than sorghum and maize microplots in both till and no-till soils (Table 9). This is likely because bermudagrass requires less nitrate for growth than maize or sorghum-sudangrass [29]. Bermudagrass also produces groundcover, which may reduce runoff and slow soil moisture infiltration, thereby preventing some leaching [21,29]. In tilled plots, sorghum-sudangrass also had less available nitrate than in no-till plots. This could have occurred because sorghum-sudangrass benefitted more when the nitrate provided by the fertilizers was incorporated in the soil from the beginning, as opposed to waiting for the nitrate to reach the roots by leaching after irrigation and rainfall.

Nitrate availability in no-till soils showed an increasing trend with higher clay content, consistent with previous findings (Table 10) [22]. Among the tilled soils, CL had the highest nitrate levels, likely due to its higher clay content compared to SL and LS soils. Nitrate content did not differ significantly between tilled SL and LS soils, as both underwent tillage. Tillage facilitated nutrient retention from manure and biochar amendments in LS soils, resulting in higher nitrate levels compared to no-till LS soils. This highlights the importance of incorporating amendments through tillage, as no-till practices may lead to amendments remaining on the soil surface.

Manure application resulted in significantly higher soil nitrate levels compared to synthetic fertilizer (Table 11). Soils treated with manure had 60% more nitrate compared to those treated with commercial fertilizer. Since both synthetic fertilizers and manure initially contained the same amount of nitrate, the higher nitrate levels in manure-treated soils can be attributed to the OM content, which likely increased soil CEC. This higher CEC facilitated nutrient retention, reducing the potential for nitrate leaching and runoff loss through the soil profile [22].

A Pearson correlation test was performed to evaluate the relationship between NO₃-N and other soil chemical analysis results, separated by soil type (Figure 4). The results show that NO₃-N has a strong negative correlation to pH r (82) = −0.55, p = 0.001 in CL soil. When soils have pH > 7, N starts to undergo many reactions, and some forms can be lost to volatilization [20]. The negative relationship between pH and EC (Figure 4) may also play a role. A low EC indicates less water-holding capacity for the soil because there is decreased ability for that soil to conduct electrical currents [22,27]. The dark nature of CL soils, coupled with a decreased water content in the pores, can result in increased temperature fluctuations and accelerated nutrient transformations. Soils that contain limited water and are subject to temperature fluctuations may require more management for nitrate.

A moderate, positive correlation was observed between NO₃-N and Zn in both LS soil, r (106) = 0.36, p < 0.001, and SL soil, r (106) = 0.44, p < 0.001. This positive relationship between zinc and NO₃-N in LS and SL soils can be attributed to the significant increase in average nutrient levels resulting from manure additions (Table 1 and Table 11). While the primary goal of fertilizer amendments was to enhance N availability for crops, the inclusion of Zn from manure provided an additional advantage compared to non-manure treatments. Manure amendments had a positive impact on both nitrate and zinc in coarse-textured soils.

3.4. Phosphorus

The greatest orders for factors affecting plant-available P (Mehlich III) were from soil type × manure (

Table 12. Soil type × manure; Fisher’s LSD test displaying the mean ± standard error for P; ANOVA two-way interaction with F_2,168 = 14.78, p < 0.001.

Manure Application	Sandy Clay Loam	Sandy Loam	Loamy Sand
No manure	11.82 ± 1.14 c B *	30.57 ± 1.07 b A	44.26 ± 1.05 a A
Manure	21.76 ± 1.19 b A	22.65 ± 1.05 b B	31.19 ± 1.04 a B

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

) and soil type × crop type (

Table 13. Soil type × crop type interaction; Fisher’s LSD test displaying the mean ± standard error for P; ANOVA two-way interaction with F_2,168 = 3.16, p = 0.045.

Crop Type	Sandy Clay Loam	Sandy Loam	Loamy Sand
Maize		23.10 ± 1.08 b B	40.04 ± 1.07 a A
Sorghum		31.50 ± 1.07 b A	36.23 ± 1.06 a A
Bermudagrass	16.12 ± 1.13 c A *	25.03 ± 1.08 b B	35.16 ± 1.07 a A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

and Table S5). Regardless of the presence of manure amendments or commercial fertilizer, all soil types exhibited an increase in Mehlich III soil P by the end of the growing season compared to the initial tests (Table 1 and Table 12). Sandy loam soils showed a 511% increase in plant-available P with commercial fertilizer and a 353% increase with manure amendments (Table 2 and Table 12). Loamy sand soils had a 108% increase with commercial fertilizer and a 46% increase with manure amendments compared to initial tests. Clay loam soils experienced a 173% increase with commercial fertilizer and a 403% increase with manure amendments compared to the initial tests.

Loamy sand and sandy loam soils held more plant-available P if commercial fertilizer was applied rather than manure (Table 12). Considering all types of soil, when commercial fertilizer was applied, plant-available P decreased as the soil clay content increased. However, the addition of manure mitigated this P loss in CL soils. In microplots without manure, SL soils had Mehlich III soil P at 31 ppm and 12 ppm in CL soils, resulting in a 61% P decrease as the clay content rose (Table 12). However, when manure was applied, SL soils had 23 ppm available P, and CL soils had 22 ppm, indicating a 4% P loss.

A possible cause for the 4% decline in available soil-P compared to the 61% decrease as the clay content increased from SL to CL is that coarse-textured soils allow for more infiltration than fine-textured soils (Table 12) [30]. Therefore, the fine-textured CL soil is more prone to P loss through runoff when fertilizer pellets release their chemicals, especially considering the presence of large cracks during drought. The contribution of pellets to this study may have been compromised. The addition of manure to no-till plots resulted in a thin mat on the soil surface. Because OM has more chelating properties than commercial fertilizers, it can reduce P loss in fine-textured soils, especially when acting as a physical barrier to runoff.

The potential relationship of soil texture to the plant-available P shown in Table 12 could be explained by the overall clay content. The surfaces of the clay particles tend to exchange Ca and Mg, which can be released and react with P [30]. The CL field site in this study exhibited a notably high relative Ca level compared to other soils (Table 1) [24]. Organic matter competes for P adsorption [30]. Consequently, P from manure, which contains large amounts of OM, may not be adsorbed as strongly as P from inorganic sources, such as the APF commercial fertilizer used on the no-manure plots.

Due to crop failure (drought), soil samples for maize and sorghum-sudangrass microplots were not collected at this site, resulting in Table 13 not fully depicting the impact of crop type on P availability. Nevertheless, crop type did not influence available P in LS soils. In SL soils, microplots planted with sorghum-sudangrass retained more available P compared to those planted with maize and bermudagrass (Table 13). This could be attributed to the initial high P levels, indicating that P was not likely the limiting factor for growth (Table 2). Sorghum-sudangrass requires less P for growth compared to maize or bermudagrass, potentially leading to higher available P as sorghum-sudangrass may have lower demand for the nutrient [29].

In Figure 5, a strong positive correlation between P and Zn (r (34) = 0.64, p < 0.001) was observed in the CL soil. This correlation is consistent with the historical trend of P and Zn correlation [22]. The presence of this correlation in the CL soil, but not in SL and LS soils, may be attributed to the increased clay content; clay loam has a greater ability to retain Zn and P. Clay can help to adsorp nutrients, such as zinc and phosphorus, to clay particles and retain them in the soil for longer durations.

3.5. Sulfur

The highest interaction orders for factors affecting plant-available S (Mehlich III; SO₄²⁻) were soil type × tillage × biochar × manure (F_4,162 = 5.80, p < 0.001) and soil type × crop type (F_2,162 = 3.65, p = 0.028) (Table S5). These interactions were separated by Fisher’s LSD multiple-mean separation with soil type × tillage × biochar × manure in

Table 14. Sandy clay loam/ clay loam soil type × tillage × biochar (BC) × manure interaction; Fisher’s LSD test displaying the mean ± standard error for S; ANOVA four-way interaction with F_4,162 = 5.80, p < 0.001.

Manure Application	Tillage	No BC	Low BC	High BC
Manure	No-till	12.81 ± 1.22 a A *	9.49 ± 1.39 a B	14.73 ± 1.23 a B
Manure	Till	11.02 ± 1.02 a A	10.70 ± 1.07 a B	10.07 ± 1.18 a B
No manure	No-till	25.28 ± 1.05 a A	18.92 ± 1.04 a A	14.15 ± 1.36 a B
No manure	Till	10.07 ± 1.39 b A	26.58 ± 1.37 ab A	34.81 ± 1.28 a A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

,

Table 15. Loamy sand soil type × tillage × biochar (BC) × manure interaction; Fisher’s LSD test displaying the mean ± standard d error for S; ANOVA four-way interaction with F_4,162 = 5.80, p < 0.001.

Manure Application	Tillage	No BC	Low BC	High BC
Manure	No-till	7.61 ± 1.10 a A *	7.24 ± 1.07 a AB	6.96 ± 1.05 a B
Manure	Till	8.33 ± 1.04 a A	8.41 ± 1.05 a A	9.12 ± 1.05 a A
No manure	No-till	7.46 ± 1.09 a A	8.50 ± 1.09 a A	7.61 ± 1.09 a AB
No manure	Till	8.93 ± 1.13 a A	6.82 ± 1.05 b B	7.69 ± 1.08 ab AB

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

and

Table 16. Sandy loam soil type × tillage × biochar (BC) × manure interaction; Fisher’s LSD test displaying the mean ± standard error for S; ANOVA four-way interaction with F_4,162 = 5.80, p < 0.001.

Manure Application	Tillage	No BC	Low BC	High BC
Manure	No-till	16.44 ± 1.09 a A *	15.03 ± 1.10 a A	14.01 ± 1.09 a A
Manure	Till	12.81 ± 1.09 a B	16.28 ± 1.10 a A	13.74 ± 1.09 a A
No manure	No-till	15.64 ± 1.04 a AB	14.01 ± 1.07 a A	14.59 ± 1.08 a A
No manure	Till	14.01 ± 1.12 a AB	14.88 ± 1.09 a A	13.07 ± 1.11 a A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

and soil type × crop type in

Table 17. Sulphur soil × crop type interaction; Fisher’s LSD test displaying the mean ± standard error for S; ANOVA four-way interaction with F_2,168 = 3.65, p = 0.028.

Crop Type	Sandy Clay Loam	Sandy Loam	Loamy Sand
Sorghum		16.12 ± 1.03 a A	8.17 ± 1.04 b A
Maize		15.49 ± 1.04 a A	7.46 ± 1.03 b A
Bermudagrass	15.03 ± 1.10 a A *	12.81 ± 1.05 a B	7.85 ± 1.03 b A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

. Table 14, Table 15 and Table 16 are separated by soil type for ease of interpretation.

Plant-available soil SO₄²⁻ was influenced primarily by tillage and manure treatments, except when the CL and LS soils had no manure amendment and were tilled (Table 14 and Table 15). Because the LSD groupings in the columns of Table 14, Table 15 and Table 16 varied, the presence of BC affected SO₄²⁻ availability, but its influence depended on tillage and manure practices. If BC was not present in loamy sand and clay loam soils, the LSD values indicated no differences in SO₄²⁻ availability among different manure applications and tillage practices (Table 14 and Table 15). However, with the addition of BC, differences in LSD values for tillage and manure rates emerged.

In the CL soil with tilled × no-manure × high biochar amendments, there was greater availability of SO₄²⁻ compared to other combinations of manure and tillage treatments (Table 14, fifth column). In CL soils with no-manure × tilled treatments, increasing the application rate of BC resulted in increased SO₄²⁻ availability (Table 14, fifth row). Specifically, in CL tilled × no-manure soils, there was a 164% increase in available S when comparing no BC treatments (10.1 ppm) to low BC treatments (26.6 ppm). There was also a 31% increase in SO₄²⁻ availability from low BC (26.6 ppm) to high BC rates (34.8 ppm), and an overall 246% increase in SO₄²⁻ availability when comparing CL soils with no BC, no-manure, and tilled treatments (10.1 ppm) to those with high BC (34.8 ppm).

The rise in SO₄²⁻ with increased BC rate application for the tilled × no-manure treatments in CL soils can be explained by multiple factors (Table 14, fifth row). Moisture infiltration through the soil profile can readily leach SO₄²⁻ [22]; therefore, BC amendments could have reduced leaching when soils were tilled due to its ability to increase the water-holding capacity in the soil [28]. Biochar can also be used as a sorbent for SO₄²⁻. Tillage without adding an OM amendment for its chelating properties can decrease EC and CEC, hindering the ability of the clay particles to adsorp SO₄²⁻ (Table 5) [22]. Breaking up clayey soil by tillage can also cause decreased porosity and lower Mehlich III S.

Sulfur availability decreased when manure and BC were added in place of commercial fertilizer, regardless of tillage practices (Table 14). This table suggests that the BC effect on manure is different from that of commercial fertilizer. It is probable that manure and BC interactions in a clayey soil could transform SO₄²⁻ from manure into forms unavailable to plants, which were not tested in this study. The manure amendment contained SO₄²⁻, but it also contained organic matter and a greater variety of nutrients compared to commercial fertilizer (Table 1). Clayey soil, biochar, and OM facilitate nutrient interactions and reactions that could have led to sulfur transformation [22,28].

In Table 15, in loamy sand soils with tilled × no-manure treatments, BC application rate created differences in SO₄²⁻ availability. Adding low BC to these soils led to a lower available SO₄²⁻- compared to no BC amendments. However, when high BC was tilled into no-manure LS soils, the available SO₄²⁻ was not significantly different from low or no BC additions. This reaction probably occurred because BC can adsorb SO₄²⁻ and BC has a high water-holding capacity [28]. When low rates of BC were incorporated in the tilled LS soil, BC likely adsorbed much of the nutrients from the commercial fertilizer because the initially available soil SO₄²⁻ was low (2.67 ppm) (Table 2). However, in the coarse-textured LS soil, BC probably could not have had enough water-holding capacity to both mitigate the tendency of SO₄²⁻ to leach and also keep the plant-available S adsorped to their surfaces when BC was applied in low rates. When high BC amendments were added to this soil, they were better able to retain adsorbed fertilizer nutrients and water than low BC amendments.

In Table 15, loamy sand soil with high BC × manure × tillage yielded the least amount of plant-available S when compared with other manure and tillage treatments. This is probably because when BC and manure amendments were applied to the fields, if not tilled, the manure created a mat at the surface, with BC sandwiched between the manure and the soil. When irrigation, or a rainfall event occurred, nutrients from the manure encountered BC before infiltrating the soil. The BC likely adsorbed some nutrients before they could reach the soil. This particular combination of treatments probably had the least plant-available S when comparing tillage and fertilizer practices with high BC rates in the LS soil because the other combinations did not involve the likelihood of SO₄²⁻ being adsorbed to the BC before it reached the soil.

In Table 16, manure × tillage had the least available S in the “No BC” column. This is likely because, although sulfur was added through manure, the soil structure was destroyed due to tillage and the readily available S in the manure was readily able to leach through the profile. The APF fertilizer also contained SO₄²⁻, so its release may have been more extended than the dairy manure. However, when the manure formed a barrier over the undisrupted soil in the no-till × manure treatments, the SO₄²⁻ was able to be best utilized out of the no-BC microplots in SL soils (Table 16). The no-manure treatments for the “No BC” column are comparable to this because, although the synthetic fertilizer added some plant available S, it did not add as much as the manure applications did.

Post-season soil samples for maize and sorghum-sudangrass microplots were not taken in the CL soil because the plants did not establish, so that data are left blank in Table 17. The comparison among soil types in the bermudagrass plots, however, indicated that, as the clay content increased, the plant-available sulfur increased. This is a normal nutrient reaction because SO₄²⁻ adsorption has a positive interaction with the percent clay in the soil [22].

Crop type did not affect available sulfur in the LS soil (Table 17). However, in the SL soil, bermudagrass (15.03 ppm SO₄²⁻) showed lower available sulfur compared to sorghum (12.81 ppm SO₄²⁻) and maize (7.85 ppm SO₄²⁻) (Table 17). This difference may have occurred in the SL soil and not in LS soil because plant-available sulfur is more likely to remain near the surface in soils with a higher percent clay, due to the adsorption capacity of clay to SO₄²⁻, and because coarser-textured soils more easily lose SO₄²⁻ to leaching [16]. When SO₄²⁻ stayed closer to the surface, instead of leaching, bermudagrass had a better ability to utilize the SO₄²⁻ compared to maize and sorghum-sudansgrass. Bermudagrass roots come from stolons (surface-level), vis-á-vis deeper, fibrous roots of maize and sorghum-sudangrass [22,31].

3.6. Zinc

The highest interaction orders for factors affecting plant-available Zn (Zn²⁺) were from tillage × manure (F_1,168 = 9.57, p = 0.002), soil type × manure (F_2,168 = 37.86, p < 0.001), and soil type × crop type (F_2,168 = 3.86, p = 0.023) (Table S5). These interaction means were separated by Fisher’s LSD multiple-mean separation with tillage × manure in

Table 18. Zinc tillage × manure application interaction; Fisher’s LSD test displaying the mean ± standard error for Zn; ANOVA two-way interaction with F_1,168 = 9.57, p = 0.002.

Tillage	Manure	No Manure
Till	1.09 ± 1.07 a A *	0.29 ± 1.08 b A
No-till	0.73 ± 1.07 a B	0.29 ± 1.09 b A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

, soil type × manure in

Table 19. Zinc soil type × manure application; Fisher’s LSD test displaying the mean ± standard error for Zn; ANOVA two-way interaction with F_2,168 = 37.86, p < 0.001.

Manure Application	Sandy Clay Loam	Sandy Loam	Loamy Sand
Manure	1.45 ± 1.18 a A *	0.93 ± 1.04 b A	0.73 ± 1.09 c A
No manure	0.13 ± 1.13 c B	0.29 ± 1.05 b B	0.39 ± 1.10 a B

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

, and soil type × crop type in

Table 20. Zinc soil type × crop type interaction; Fisher’s LSD test displaying the mean ± standard error for Zn; ANOVA two-way interaction with F_2,168 = 3.86, p = 0.023.

Crop Type	Sandy Clay Loam	Sandy Loam	Loamy Sand
Maize		0.43 ± 1.12 a B	0.54 ± 1.09 a A
Sorghum		0.66 ± 1.16 a A	0.51 ± 1.07 b A
Bermudagrass	0.43 ± 1.26 a A *	0.50 ± 1.18 a AB	0.54 ± 1.08 a A

* Values within each column (upper case) and each row (lower case) followed by the same letter do not differ (p ≤ 0.05) according to Fisher’s LSD test.

.

Manure application increased Zn²⁺ by 276% in tilled soils and by 152% in no-till soils compared to no-manure applications (Table 18). Manure application increased plant-available Zn regardless of tillage practices. However, tilling the soil with manure applications likely helps to retain Zn more than no-till practices because Zn binds strongly to soil particles [22].

The greater the clay content, the more Zn²⁺ from the manure application was retained in the soil, whereas the opposite was true when no manure was added. This pattern of coarse-textured soils having more Zn when manure is not added is likely because it reflects the initial quantities of plant-available Zn in the soils (Table 1). The initial chemical analyses of the soils indicated that, when plant-available Zn increased, the clay content decreased (Table 1). This was the same pattern as in the case of plant-available P because there is a positive correlation with these nutrient interactions in soil [22]. P and Zn levels in soil typically rise and fall together. Soils with a higher clay content exhibited reduced availability of P and Zn, likely due to elevated levels of calcium compared to coarser-textured soils. An increased calcium supply enhances root oxidizing ability, thereby decreasing Zn solubility and absorption by plants [22]. When manure was added, Zn was added (Table 19), increasing the nutrient levels in post-season samples vis-á-vis pre-season assays [18].

Manure application increased available zinc in all soil types (Table 19). The loamy sand soil had an initial Zn²⁺ content of 0.37 ppm, the SL soil had 0.18 ppm, and the CL soil had 0.15 ppm (Table 1). These levels indicate a severe deficiency in Zn²⁺, as the critical level is considered to be 1.0 ppm [23]. The inclusion of manure amendments and tillage practices slightly increased the availability of Zn in all soil types, elevating it to just above the critical level (Table 18 and Table 19). Even then, it is barely above the critical level. This is significant because zinc is essential for the synthesis of tryptophan, a compound necessary for growth hormone production [22]. Insufficient growth hormone production can lead to shortened internodes and smaller leaves. Furthermore, plant roots must uptake Zn for chlorophyll synthesis, enzyme activation, and cell membrane integrity.

Zn availability did not change across crop types in LS soil (Table 20). Bermudagrass plots did not exhibit variations in available Zn²⁺ among soil types (Table 20). Sorghum-sudangrass microplots in the LS soil showed lower plant-available Zn compared to the SL soil, where the highest Zn availability was observed. This difference might be attributed to the lower phosphorus requirement of sorghum-sudangrass crops compared to maize or bermudagrass [29]. However, the significantly higher Zn levels in sorghum-sudangrass SL plots compared to other averages in Table 20 may suggest a potential human error during soil sampling. Sorghum-sudangrass plots in the SL field also exhibited slightly higher available P, which can be attributed to the known relationship between Zn and P (Table 13) [22,32]. In soils with low P and Zn, adding P without Zn can reduce crop yields, and adding Zn without P can have similar effects [22]. However, positive interactions and increased yields occur when both nutrients are adequately supplied or abundant.

4. Conclusions

While amendments, crop type, and tillage practices changed the nutrient characteristics of the soils in many ways, soil type was the most influential factor on plant-available soil nutrients and characteristics. When considering nutrient manipulation with different amendments, crops, or tillage practices, soil type is most important to examine first, because the other treatments have varying effects dependent on soil characteristics.

Following soil type, manure application and tillage practices were the main factors influencing plant-available nutrients. Combining manure and tillage generally increased nutrient levels and EC in most soils, with few exceptions. The impact of biochar and forage crop type varied depending on experimental factors. Notably, in the initial year after BC application, no detrimental effects were observed on adequately fertilized and irrigated warm-season forage crops, suggesting that BC applied for long-term soil nutrient retention or environmental mitigation does not compromise short-term nutrient availability.

Percent clay had a positive correlation with EC, nitrate, and plant-available P and Zn among soil types. Conversely, coarse textures had the opposite impact on EC, nitrate, and Zn²⁺ due to a lower clay content and because greater infiltration may have fostered leaching into the soil profile. Moderate to strong correlations were found among EC and S, NO₃-N, and Zn across different soil types, indicating interrelationships among these chemical properties. Targeting one of these properties for soil amendment will affect the others.

Future research should continue this study long-term to account for subsequent season differences in tilled and no-till soils, with particular attention to the prolonged effects of BC and the decreasing influence of manure as the latter decomposes. In particular, the waning soil-nutrient benefits of adding manure might be prolonged when BC is added and incorporated to soils with a low clay content. Biochar presence may change soil characteristics for decades, if not centuries, but manure benefits may dissipate more quickly. One question that remains unanswered is whether the combined effect of BC and manure amendment prolong the benefits of the latter.

Another issue that merits attention is the soil sampling methodology. In this study, soils were sampled by homogenizing O, A, and other horizons to a 10 cm depth prior to the assay, potentially confounding the assessment of tillage effects. Further research in subsequent growing seasons is necessary to evaluate the medium-to-long-term effects of the studied factors. Long-term assessments hold particular significance for environmental and soil nutrient availability studies, especially when examining BC, manure, and tillage management interactions in warm-season forages.