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Article

Short-Term Effects of Poultry Litter and Cattle Manure on Soil’s Chemical Properties and Bacterial Community

by
Gustavo Souza Lima Sant’Anna
1,
Lucas Amoroso Lopes de Carvalho
2,
Maura Santos Reis de Andrade da Silva
1,
João Vitor da Silva Gonçalves
1,
Daniel Guariz Pinheiro
2,
Everaldo Zonta
3 and
Irene da Silva Coelho
1,*
1
Department of Veterinary Microbiology and Immunology, Veterinary Institute, Federal Rural University of Rio de Janeiro, Seropédica 23897-970, RJ, Brazil
2
Department of Agricultural and Environmental Biotechnology, School of Agricultural and Veterinary Sciences (Jaboticabal Campus), Paulista State University, Jaboticabal 14884-900, SP, Brazil
3
Department of Soils, Institute of Agronomy, Federal Rural University of Rio de Janeiro, Seropédica 23897-970, RJ, Brazil
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(7), 1382; https://doi.org/10.3390/agronomy14071382
Submission received: 29 May 2024 / Revised: 19 June 2024 / Accepted: 25 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Recycling of Organic Wastes in Agriculture: Serving for Sustainable Agriculture)
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Abstract

:
The expansion of animal husbandry for food production has necessitated effective management of livestock waste, including cattle manure and poultry litter. Using these byproducts as organic fertilizers in agriculture represents a sustainable approach to their disposal. While these residues offer known benefits for soil health and crop productivity, further studies are needed to explore the effect of different manure microbiota on soil composition. The objective of this study was to address this gap and contribute to the advancement of this area. A randomized block design experiment was set up in the field with three replications per treatment, including the application of cattle manure, poultry litter, and a control treatment without fertilizer. After a 60 day period, the chemical characteristics and bacterial population composition of the soil were analyzed using 16S rRNA gene sequencing. Organic carbon, phosphorus (P), aluminum (Al), and the pH level played pivotal roles in changing the structure of the soil’s bacterial community. Notably, the incorporation of poultry litter induced more pronounced changes in both the chemical properties and bacterial community composition compared with cattle manure. Bacterial groups were enriched in the soils treated with poultry litter, which may indicate enhanced soil fertility. This association may stem from both the chemical alterations resulting from poultry litter application and the direct transfer of microorganisms from this organic fertilizer to the soil.

1. Introduction

In 2023, global production of chicken meat and beef reached 103 million and 59 million metric tons, respectively. During the same year, Brazil emerged as one of the world’s main meat producers, contributing approximately 15 million metric tons of chicken meat and 11 million tons of beef. These statistics positioned Brazil as the second largest producer worldwide in both sectors [1].
However, the escalating demand for meat, driven by economic and population growth, has resulted in substantial environmental concerns [2]. In addition to greenhouse gas emissions due to deforestation for pasture expansion, livestock farming increases waste generation, including cattle manure and poultry litter, which often carry pollutant loads [3,4,5]. If left unaddressed, these waste streams pose significant environmental liabilities.
A sustainable and promising alternative is to use this waste as organic fertilizer, given its rich nutritional composition which includes essential elements that improve the soil and increase plant production, reducing dependence on chemical fertilizers [6]. Organic fertilizers offer macro- and micronutrients and increase organic matter, thereby improving soil fertility and preserving its biodiversity [7,8]. They are also excellent sources of nutrients, increasing microbial diversity and richness [9,10]. Furthermore, microorganisms incorporated into the soil can influence nutrient cycling, affecting their availability to plants and, consequently, crop productivity [11]. This approach, however, has some drawbacks, including increased heavy metal levels [12], incorporation of pathogenic bacteria [13], and the spread of antimicrobial resistance genes in soil [14].
Therefore, investigations into soil microbiota alterations after the application of these residues are essential to understand the potential benefits and risks to soil health. However, data on changes in bacterial composition following the incorporation of animal production residues in tropical soils are limited. Therefore, gathering information from different soils, climates, and production systems is crucial to deepening the understanding of this dynamic. This necessity is further underscored by the scarcity of comparative studies on microbial structures between different waste sources [15]. Additionally, the immediate impact of these fertilizers on the incorporation or enrichment of microbial groups needs to be better understood [16], as these effects can directly influence crops subsequent to this practice. Considering the critical importance of understanding the effects of organic fertilizers on soil, the objective of this study was to evaluate the short-term impact of incorporating cattle manure and poultry litter on the chemical characteristics and bacterial community of soil.

2. Materials and Methods

Cattle manure (CAM-F) was sourced from a corral within the dairy cattle farming sector of UFRRJ, while poultry litter (POL-F) was obtained from a chicken egg production unit. Both fertilizers were used in their fresh states without undergoing a composting process.
Determination of the Ca, Mg, Na, K, P, Mn, Ni, Fe, Cd, Cr, Cu, Pb, and Zn contents followed the methodology proposed in [17]. For this, 0.5 g of the compound samples were weighed, ground, and sieved. Subsequently, they were digested in nitric acid on a heated plate. The P content was determined by colorimetry, while Na and K were analyzed using flame spectrophotometry. Furthermore, Ca, Mg, Cd, Cr, Cu, Pb, Mn, Ni, Fe, and Zn were quantified using atomic absorption spectrometry (Table 1).
The experiment was conducted on a farm located at the coordinates 22°49′19.79″ S and 43°44′16.43″ W at Reta dos 800 in Piranema, a rural area of Seropédica in Rio de Janeiro, Brazil. The soil was classified as Ultisol [18], corresponding to Argissolo Vermelho-Amarelo in the Brazilian Soil Classification System [19]. According to the Köppen classification system, the climate in the region is Aw (Figure 1) [20]. The experimental site comprised a bahiagrass (Paspalum notatum) pasture in a state of degradation. Prior to commencing the experiment, the soil was plowed and received 2.5 Mg ha−1 of dolomitic limestone with a relative total neutralizing power (rTNP) of 85.6%, 40% CaO, and 10% MgO. This limestone was incorporated to a depth of 20 cm using a mini-tractor equipped with a rotating hoe on 25 April 2019. The experiment officially began 60 days after the liming process. Figure 1 shows climatic data, including precipitation and maximum and minimum temperatures, spanning from the beginning to the end of the experiment.
The randomized block design included three replications per treatment, each characterized using soil with CAM-F (CAM-S) or POL-F (POL-S), and a control treatment (CTL-S) without fertilizers, totaling nine experimental plots of 4 m2 each (2 × 2 m). Fertilizers were applied at a dose of 100 kg ha−1 of N by incorporating 8.1 Mg ha−1 of CAM-F and 3.5 Mg ha−1 of POL-F at a depth of 20 cm, using a mini-tractor with a rotating hoe.
A total of five simple samples were collected 60 days post fertilization from the 0–10 cm layer of each experimental plot, resulting in three composite samples per treatment. For the determination of exchangeable nutrient contents, the samples were analyzed as follows. Ca, Mg, and Al were extracted with a KCl solution and quantified via titration. P, K, and Na were extracted with Mehlich 1 solution, with P quantified by colorimetry and K and Na quantified by flame photometry. H + Al was evaluated using a 0.025 mol L−1 calcium acetate solution. The soil’s pH was determined in water, and the cation exchange capacity (CTC) and base saturation (V) were calculated. The soil organic carbon (SOC) was determined by the wet oxidation method using potassium dichromate, and the total nitrogen was determined through Kjeldahl digestion [21].
A DNA PowerSoil extraction kit (MO Bio Laboratories Inc., Carlsbad, CA, USA) was used for the extraction of both soil and fertilizer DNA, following the manufacturer’s protocol. Subsequently, the extracted samples were stored at −20 °C, and the quantity and quality of DNA were evaluated using a spectrophotometer (NanoDrop 1000, Thermo Fisher Scientific Inc., Waltham, MA, USA). The overall integrity of the total DNA was further assessed through electrophoresis on 0.8% agarose gel [22].
For sequencing purposes, a library of amplicons targeting the V3–V4 variable region of the 16S rRNA gene was amplified using Bakt_341F (CCTACGGGNGGCWGCAG) and Bakt_805R (GACTACHVGGGTATCTAATCC) primers. Sequencing was conducted using a paired-end 2 × 250 system on the MiSeq platform (Illumina Inc., San Diego, CA, USA) at Macrogen Inc. (Seoul, South Korea).
The quality of the sequenced data was initially evaluated using FastQC software v.0.11.9 [23]. The “fastx_info” and “fastq_eestas2” functions of USEARCH software v.11.0.667 were used with the libraries [24], displaying the distribution of qualities, sequence sizes, and expected errors. The “search_oligodb” function of the same software was used to detect the presence and position of primer pair sequences referring to the V3–V4 region of the 16S rRNA gene (341F ‘5-CCTACGGGNGGCWGCAG-3’ and 805R ‘5-GACTACHVGGGTATCTAATCC-3’). Subsequently, the primers were removed with Atropos software v.1.1.31 [25], which filtered sequences whose primers were not present (“--discard-untrimmed”).
To ensure better quality, the final portion of the sequences was trimmed using Fastp software v.0.23.2 [26] to remove up to 20 (“--max_len1 230”) and 30 (“--max_len2 220”) low-quality final bases from the forward and reverse libraries, respectively. Additionally, complete reads with a total average Phred score (Q) less than Q20 were removed (“--average_qual 20”). Finally, the library pairs were merged by overlapping using Flash software v.1.2.11 [27], accepting a minimum overlap of 10 bases (“--min-overlap 10”).
Merged reads with between 390 and 430 bases were subjected to the “DADA2” pipeline [28] using the “dada2” package v.1.22.0 of the R statistical software v.4.1.2 [29]. Initially, the reads were filtered with the “filterAndTrim” function, considering an expected error of four (“maxEE = 4”). Subsequently, the probability of errors in the bases was estimated (“learnErrors”), and the sequences were corrected according to the obtained model (“dada”). Amplicon variant sequences (ASVs) were designated in each sample, investigated, and filtered for the presence of possible chimeric sequences (“removeBimeraDenovo”).
All ASVs were taxonomically annotated against the SILVA v.138 reference sequence database [30], with additional support from the RDP v.18 [31] and GTDB v.202 [32] databases to identify potential contaminants. ASVs not assigned to bacteria or archaea, as well as those assigned to chloroplasts or mitochondria, were filtered out. Furthermore, ASVs prevalent in only a single replicate were excluded from the analysis. The ASV counts and taxonomic annotations were exported in “phyloseq” format using the R “phyloseq” package v.1.38.0 [33]. Subsequently, the data were transformed into compositional data (“method = ‘total’”) by the “phyloseq_standardize_otu_abundance” function of the R “metagMisc” package v.0.04 [34].
Sampling effectiveness was inferred by rarefaction curves obtained through “amp_rarecurve” analysis of the R “ampvis2” package v.2.7.17 [35]. Alpha diversity was estimated by assessing the observed richness and diversity measures (Shannon and Gini-Simpson indices) obtained using the “alpha” function from the R package “microbiome” v.1.16.0 [36]. The obtained measures were statistically compared using the Kruskal–Wallis test (p value ≤ 0.1), and the post hoc Fisher’s LSD test was used for pairwise comparison and grouping of the means (p value ≤ 0.1). Both were implemented using the “kruskal” function from the R package “agricolae” v.1.3.5 [37]. A beta diversity analysis was performed by calculating the Bray–Curtis distances with the “distance” function of the “phyloseq” package [33]. The distances were statistically compared between the groups with the “adonis” function from the same package using permutational multivariate analysis of variance (PERMANOVA), considering a p value of 0.05. Furthermore, we used the distances to carry out a canonical analysis of principal coordinates (CAP), where the physical-chemical parameters measured were added to the distances between the compositions and compared for their significance using the “ordinate” function of “phyloseq” with the same significance threshold (p value ≤ 0.05). A graphical representation was obtained with the “plot_ordination” function from the “phyloseq” package. The assessment of differentially abundant taxa involved identifying those present in significantly greater amounts in one treatment compared with the other. This analysis used the “DESeq2” approach v.1.34.0 [38], which compares means based on a negative binomial model and uses the Wald test (adjusted p value < 0.05). Graphical representations of these analyses were prepared using the R “ggplot2” package v.3.3.6 [39].
Additionally, the chemical properties of the soil and the microbiota were correlated while considering the taxonomic level of the family. To filter out low-abundance taxa, we only considered families that corresponded to at least 1% of the abundance of the samples. Pearson coefficients were determined between each taxon and soil property using the “corr.test” function of the R “psych” package v.2.3.9 [40], where we considered a p value of 0.05. A heatmap was created using the “ggplot2” package [39], utilizing the “geom_tile” function to plot correlation values and “geom_text” to indicate significant correlations.
Data on chemical characteristics were initially subjected to analysis of variance using the F test. Then, the Shapiro–Wilk test verified the normality of residuals, and the Bartlett test verified the homoscedasticity. If the F test of the analysis of variance showed significant differences (p < 0.05), then the treatment means were compared using the Tukey test at 10% probability.

3. Results

In Table 2, it is observed that POL-S presented higher levels of Ca and P contents compared with CTL-S, along with a higher soil organic C (SOC) content than CAM-S. However, CAM-S and CTL-S presented higher Al concentrations in contrast to POL-S. Differences in bacterial communities were observed in the statistical comparison of Bray–Curtis distances using the PERMANOVA test (p value: 0.004). When relating this information to the environmental variables through the canonical analysis of principal coordinates (CAP), the treatment tended to separate, with POL-S being distinct from the others. However, the inclusion of environmental variables did not result in statistically significant sample separation. However, Figure 2 illustrates that CTL-S and CAM-S were associated with Al and H + Al, whereas POL-S showed correlations with Ca, Mg, SB, V, and CEC. Regarding alpha diversity, there was no significant difference in the observed richness of ASVs between treatments. However, POL-S exhibited lower Shannon and Gini–Simpson indices compared with those of CTL-S and CAM-S (Table 3).
The CAM-F sample showed a higher abundance of Proteobacteria (49.91%), Actinobacteria (22.56%), and Bacteroidota (13.62%), and the POL-F sample had greater abundances of Firmicutes (50.13%) and Actinobacteria (43.40%). The prevalent phyla in CLT-S and CAM-S were Proteobacteria (25.24 and 27.13%, respectively) and Actinobacteriota (17.22 and 25.17%, respectively), while there was a prevalence of Proteobacteria (30.58%) and Verrucomicrobiota (18.89%) in POL-S (Figure 3).
The most prevalent family in CAM-F was Xanthomonadaceae (Proteobacteria) (14.73%), and POL-F presented the families Staphylococcaceae, Bacillaceae (both Firmicutes) (19.26 and 17.42%, respectively), and Brevibacteriaceae (Actinobacteriota) (15.17%). The three families found in POL-F corresponded to 52% of the ASVs. CTL-S and CAM-S showed a prevalence of the families Bacillaceae (Firmicutes) (10.31 and 11.21%, respectively), Xanthobacteraceae (Proteobacteria) (7.08 and 7.61%, respectively), and Chthoniobacteraceae (Verrucomicrobiota) (7.86 and 5.52%, respectively), while POL-S had the families Chthoniobacteraceae (Verrucomicrobiota) (15.59%), Sphingomonadaceae (Proteobacteria) (11.86%), and Chitinophagaceae (Bacteroidota) (7.02%) (Figure 4).
Among the identified ASVs, 704 were detected across all treatments, with 405 found in both CAM-S and CTL-S, 326 in POL-S and CTL-S, and 284 in POL-S and CAM-S (Figure 5). Additionally, only 66 ASVs were found exclusively in CTL-S, 110 were found in CAM-S, and 187 were in POL-S, 24 ASVs were found both in CAM-F and CAM-S, and nine were found between POL-F and POL-S.
Among the 24 ASVs found in CAM-F and the nine ASVs detected in POL-F, which were also present in soils incorporated with the respective fertilizers, the representation of the 20 most abundant bacterial families was limited. Specifically, only three were classified as Planococcaceae, two as Sphingomonadaceae, and one as Micrococcaceae in both CAM-F and CAM-S. POL-F presented two ASVs classified as Bacillaceae, one as Planococcaceae, and one as Nocardioidaceae. These values represent a small portion of the 3673 ASVs found throughout the study.
A comparison between CAM-S and POL-S revealed 15 bacterial families with significantly different abundances between treatments (Figure 6). Among these, the families Chitinophagaceae and Sphingomonadaceae were more abundant in POL-S, while the families Streptomycetaceae, Planococcaceae, and Bacillaceae were more prevalent in CAM-S. A comparison between CAM-S and CTL-S showed significant differences only in the families Micrococcaceae and Ktedonobacteraceae, with Micrococcaceae being most abundant in CAM-S and Ktedonobacteraceae being most abundant in CTL-S. Among the 11 families that exhibited significant differences between POL-S and CTL-S, Micrococcaceae and Sphingomonadaceae were found to be more abundant in POL-S, while Planococcaceae, Bacillaceae, and Ktedonobacteraceae were more prevalent in CTL-S.
Among the 20 most abundant bacterial families observed in the analyzed soils, 17 exhibited correlation with the chemical characteristics, with 12 of them showing either positive or negative correlations with the SOC. Moreover, among the families correlated with chemical characteristics, 10 were significantly different between treatments (Figure 7). This means that 50% of the 20 families most abundant in the soil showed significant differences correlated with some chemical characteristic of the soil between treatments.
The families Chthoniobacteraceae, Sphingomonadaceae, Chitinophagaceae, Pyrinomonadaceae, and Acidobacteriaceae (subgroup 1) were most abundant in POL-F, showing a positive correlation with P and SOC and a negative correlation with Al. The families Planococcaceae, Bacillaceae, Micromonosporaceae, and Acidothermaceae were less abundant in POL-S, generally showing a positive correlation with Al and a negative correlation with P and SOC. The family Ktedonobacteraceae was more abundant in CTL-S than in the other treatments, presenting a positive correlation with H + Al and a negative correlation with pH, Ca, P, and SOC.

4. Discussion

This study suggests that the soil bacterial community 60 days after incorporation of POL-F, compared with soils with CAM-F and CTL-S, is likely influenced by factors such as the SOC, P, and Al contents and the pH level (Figure 2). However, POL-S and POL-F presented different bacterial compositions (Figure 2 and Figure 3). The low number of ASVs found both in POL-F and POL-S indicates a low transfer of bacteria from the litter to the soil (Figure 5). These findings imply that POL-F changed the soil bacterial community by introducing new species and altering the characteristics of the soil.
In our study, we did not observe significant differences in the composition or alpha diversity (Table 3) of the soil community between samples without organic fertilizers (CTL) and those with cattle manure (CAM). Many bacteria present in manure originate from the intestinal microbiome of cattle and may be less competitive in the soil environment. This could be due to the fact that soil is more aerobic compared with the intestinal tract, leading to the disappearance of oxygen-sensitive genera [41]. Therefore, depending on the microbial composition of the manure, the survival of certain taxa may be impaired in the soil, which explains the similarity between the CAM and CTL soil samples.
This study aimed to determine whether the increase in microbial biomass and soil diversity after manure application is due to the survival of microbes introduced with the manure or activation of the soil microbiome. The data revealed that manure application increased the microbial biomass and diversity, in addition to activating many underrepresented taxa in the soil. However, manure microorganisms had low survival rates in the soil, indicating the soil’s ability to act as an effective buffer against the entry of new microorganisms [42]. Thus, it is important to conduct more studies in this area, considering the differences inherent in waste production and soil conditions that can lead to varying compositions of the soil microbiota.
Regarding poultry litter (POL), previous studies have reported that long-term fertilization with poultry litter alters the edaphic microbial community through changes in soil chemical attributes [9,43]. Our study showed that changes in soil diversity already occurred 60 days after POL application (Table 3).
In this study, the disparity in the C:N ratio between CAM-F and POL-F likely contributed to the observed differences in soil bacterial compositions. Specifically, POL-F led to a relative increase in the abundance of the family Chthoniobacteraceae (phylum: Verrucomicrobiota), which has been positively correlated with SOC [44] and negatively correlated with the pH level [45]. Previous studies have also reported a positive correlation with soil N levels [45]. This family, which has only one cultivated representative (Chthoniobacter flavus), harbors denitrification genes and is involved in the decomposition of organic substrates [46]. N, normally associated with organic matter, requires mineralization to become available in soil, a process that largely depends on specific functional microorganism groups, including decomposers and ammonia oxidizers, whose abundance and activity depend on the C:N ratio of the soil [47]. Additionally, the genus RB41 represented 100% of the sequences in the family Pyrinomonadaceae (phylum: Acidobacteriota), known for its diverse metabolic processes, including the degradation of residues rich in lignocellulose and chitin and soil C cycling [48,49].
The family Sphingomonadaceae (phylum: Proteobacteria), which was also abundant in the soil after POL-F incorporation, was previously negatively correlated with Al [50]. These bacteria participate in the N cycle [51], degrade pollutants, and inhibit pathogens [52]. The family Chitinophagaceae (Bacteroidota), which was positively correlated with P and negatively correlated with Al in this study, tends to be relatively more prevalent in soils with higher pH levels [53]. The presence of these bacterial groups in soils incorporated with POL-F could be related to the common practice of using CaO, which is frequently used in POL-F incorporation and aims to reduce the presence of pathogenic bacteria such as Salmonella and Clostridium [54]. POL-F tends to increase soil’s pH level due to its alkaline nature, also reducing the exchangeable acidity of the soil and, consequently, P availability [55].
Finally, the genus Edaphobacter was present in 70% of the sequences in the family Acidobacteriaceae (subgroup 1), which also belongs to the phylum Acidobacteriota. This family is associated with higher nutrient contents in the soil at pH levels close to neutral, being an indicator of soil fertility [56]. Therefore, in our study, the greater occurrence of these microorganisms in POL-S may indicate improved soil fertility with the use of POL-F.
Conversely, some bacterial families were relatively less abundant with POL-F incorporation. The family Planococcaceae (phylum: Firmicutes) may have been negatively affected by competition for resources with the family Sphingomonadaceae [57]. These bacteria are important for reducing nitrate and degrading organic pollutants in the soil [58,59]. The family Bacillaceae (phylum: Firmicutes) was also positively correlated with Al and negatively correlated with P, corroborating [60]. The genus Bacillus, which corresponded to 78% of the sequences in this family, is involved in N cycling [61] and P solubilization [62]. The family Micromonosporaceae (phylum: Actinobacteriota) showed a positive correlation with Al and H + Al and a negative correlation with Ca, SB, and V. These bacteria play important roles in soil ecology by decomposing complex organic substrates. Finally, the family Acidothermaceae (phylum: Actinobacteriota) showed a positive correlation with Al, being represented by thermophilic acidophilic bacteria that have thermostable enzymes capable of degrading cellulose. This family is currently represented only by the genus Acidothermus [63].
Therefore, despite the increased bacterial abundance indicating soil fertility improvement, it is crucial to acknowledge that families potentially important to the soil were negatively affected by the use of POL-F. Some of the functions of these bacteria include participating in specific N and P cycling processes, degrading complex carbohydrates, and bioremediation. The reduced abundance of these families can be justified by changes in the soil characteristics. POL-F reduced the microbial groups adapted to soils with lower nutrient contents and acidic pH levels. Therefore, POL-F increased nutrient incorporation and the soil’s pH due to the increased presence of microorganisms adapted to these new soil conditions, decreasing bacterial groups adapted to less fertile soils.
The Ktedonobacteraceae family (phylum: Chloroflexi) includes aerobic filamentous bacteria, which were more abundant in CTL-S than in the other treatments, and has only the species Ktedonobacter racemifer described for it [64]. These bacteria were positively correlated with H + Al and negatively correlated with P, Ca, and the pH level. This outcome corroborates a previous study which reported increased relative abundance with lower soil pH levels [65].
Families such as Oxalobacteraceae and Micrococcaceae showed different relative abundance between treatments but without correlation with the chemical characteristics of the soil or a significant number of ASVs present in either the fertilizer or the incorporated soil. The increased or decreased relative abundance of these microorganisms in the soil with fertilizer incorporation may be associated with other factors, including the presence of specific organic compounds or heavy metals.
The results of this highlight the impact of animal waste incorporation, particularly poultry litter (POL-F), on soil bacteria. However, optimizing soil management for subsequent crops demands further studies into the correlation between the bacterial composition of organic fertilizers and their short- and long-term impacts on soil microbiota. It is essential to ensure that the functions of these microorganisms are sustained over time.

5. Conclusions

The introduction of poultry litter (POL-F) significantly changed the soil bacterial community, whereas the incorporation of cattle manure (CAM-F) demonstrated a negligible effect compared with the control (CTL-S). This shift does not appear to be primarily linked to the direct transfer of bacteria from the fertilizer to the soil. Rather, it seems to be driven by changes in the chemical characteristics of the soil subsequent to the incorporation of POL-F.

Author Contributions

Conceptualization, E.Z., I.d.S.C. and G.S.L.S.; methodology, G.S.L.S. and J.V.d.S.G.; formal analysis, G.S.L.S., L.A.L.d.C., M.S.R.d.A.d.S. and D.G.P.; writing—original draft preparation, G.S.L.S. and M.S.R.d.A.d.S.; writing—review and editing, L.A.L.d.C., M.S.R.d.A.d.S., J.V.d.S.G., D.G.P., E.Z. and I.d.S.C.; supervision, E.Z. and I.d.S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (309002/2018-4), the Coordenaçao de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (finance code 001), and the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) (Process SEI: E-26/205.892/2022).

Data Availability Statement

The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.

Acknowledgments

We thank the Programa de Pós-graduação em Ciência, Tecnologia e Inovação em Agropecuária (PPGCTIA) of the UFRRJ for their support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Monthly climate data from the automatic meteorological station of the national meteorology institute (INMET) during the experiment’s execution period (INMET, 2024). * Data from 25 April to 26 August 2019.
Figure 1. Monthly climate data from the automatic meteorological station of the national meteorology institute (INMET) during the experiment’s execution period (INMET, 2024). * Data from 25 April to 26 August 2019.
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Figure 2. Structure of bacterial communities in soils that received cattle manure (CAM-S) or poultry litter (POL-S) and in the control (CTL-S) without residues application. Distance-based constrained analysis of principal coordinates (CAP) using the Bray–Curtis distance between the composition of amplicon sequence variants (ASVs) in samples was performed. Dots indicate the sample scores for the first two axes, while the vectors indicate the chemical attributes of the soil. H + Al = potential acidity; SB = sum of bases; and V = base saturation.
Figure 2. Structure of bacterial communities in soils that received cattle manure (CAM-S) or poultry litter (POL-S) and in the control (CTL-S) without residues application. Distance-based constrained analysis of principal coordinates (CAP) using the Bray–Curtis distance between the composition of amplicon sequence variants (ASVs) in samples was performed. Dots indicate the sample scores for the first two axes, while the vectors indicate the chemical attributes of the soil. H + Al = potential acidity; SB = sum of bases; and V = base saturation.
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Figure 3. Relative abundance of bacterial groups at the phylum level, showing the 24 most abundant taxa in samples of cattle manure (CAM-F), poultry litter (POL-F), and soils 60 days post fertilization with cattle manure (CAM-S) and poultry litter (POL-S) along with a control without application of residue (CTL-S). The less abundant phyla are grouped under the category “Others”.
Figure 3. Relative abundance of bacterial groups at the phylum level, showing the 24 most abundant taxa in samples of cattle manure (CAM-F), poultry litter (POL-F), and soils 60 days post fertilization with cattle manure (CAM-S) and poultry litter (POL-S) along with a control without application of residue (CTL-S). The less abundant phyla are grouped under the category “Others”.
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Figure 4. Relative abundance of bacterial groups at the family level, showing the 24 most abundant taxa in samples of cattle manure (CAM-F), poultry litter (POL-F), and soils 60 days post fertilization with cattle manure (CAM-S) and poultry litter (POL-S) as well as a control without application of residue (CTL-S). The less abundant families are grouped under the category “Others”.
Figure 4. Relative abundance of bacterial groups at the family level, showing the 24 most abundant taxa in samples of cattle manure (CAM-F), poultry litter (POL-F), and soils 60 days post fertilization with cattle manure (CAM-S) and poultry litter (POL-S) as well as a control without application of residue (CTL-S). The less abundant families are grouped under the category “Others”.
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Figure 5. Venn diagram showing ASVs found in cattle manure (CAM-F), poultry litter (POL-F), and in soil samples 60 days after fertilization with cattle manure (CAM-S) or poultry litter (POL-S) as well as a control (CTL-S).
Figure 5. Venn diagram showing ASVs found in cattle manure (CAM-F), poultry litter (POL-F), and in soil samples 60 days after fertilization with cattle manure (CAM-S) or poultry litter (POL-S) as well as a control (CTL-S).
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Figure 6. Differentially abundant taxa at family level in soil samples after 60 days of application of cattle manure (CAM-S) or poultry litter (POL-S) and a control soil (CTL-S) which did not receive application of these residues. (a) Comparison between CAM-S and POL-S, (b) comparison between CAM-S and CTL-S, (c) comparison between POL-S and CTL-S. Means were compared using a negative binomial model with a Wald test (adjusted p value < 0.05).
Figure 6. Differentially abundant taxa at family level in soil samples after 60 days of application of cattle manure (CAM-S) or poultry litter (POL-S) and a control soil (CTL-S) which did not receive application of these residues. (a) Comparison between CAM-S and POL-S, (b) comparison between CAM-S and CTL-S, (c) comparison between POL-S and CTL-S. Means were compared using a negative binomial model with a Wald test (adjusted p value < 0.05).
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Figure 7. Correlation between chemical characteristics of the soil and the 20 most abundant bacterial families after 60 days of application of cattle manure (CAM-S) or poultry litter (POL-S) and in the control (CTL-S). Pearson’s coefficient was used (p value: 0.05). Families that represented at least 1% of the sample abundance were considered. H + Al = potential acidity; SB = sum of bases; CEC = cation exchange capacity; and V = base saturation. Different numbers of asterisks (*) represent different levels of significance.
Figure 7. Correlation between chemical characteristics of the soil and the 20 most abundant bacterial families after 60 days of application of cattle manure (CAM-S) or poultry litter (POL-S) and in the control (CTL-S). Pearson’s coefficient was used (p value: 0.05). Families that represented at least 1% of the sample abundance were considered. H + Al = potential acidity; SB = sum of bases; CEC = cation exchange capacity; and V = base saturation. Different numbers of asterisks (*) represent different levels of significance.
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Table 1. Chemical composition of cattle manure (CAM-F) and poultry litter (POL-F) incorporated into soil.
Table 1. Chemical composition of cattle manure (CAM-F) and poultry litter (POL-F) incorporated into soil.
NCPKNaCaMgFeMnZnCuAlNiPbCrCd
-------------------- g kg−1 ----------------------------------------- mg kg−1 ---------------------
CAM-F12.32270.1513.47.215.94.22.6251.25917.8012.83.400
POL-F28.63250.5417.650.561.36.61.9463.6471.491.6127.75.812.600
Table 2. Soil chemical composition 60 days after the application of cattle manure (CAM-S) or poultry litter (POL-S) and in the control soil (CTL-S) which did not receive residue application.
Table 2. Soil chemical composition 60 days after the application of cattle manure (CAM-S) or poultry litter (POL-S) and in the control soil (CTL-S) which did not receive residue application.
pH *CaMgNaKAlH + AlSBCECVPKNSOC
1:2.5cmolc dm−3%mg dm−3%
CTL-S4.901.26 b1.490.030.160.05 a4.372.787.1438.093.00 b63.000.133.47 ab
CAM-S5.251.90 ab1.710.050.280.05 a4.013.667.6847.718.33 ab108.670.193.43 b
POL-S5.362.03 a1.710.050.230.02 b3.723.787.5150.5617.33 a89.000.164.86 a
* pH in water. SB = sum of bases; CEC = cation exchange capacity; V = base saturation; and SOC = soil organic carbon. Different letters denote significant differences according to Tukey test at 10% significance level (p < 0.10).
Table 3. The variability of alpha diversity (richness, Shannon index, and Gini–Simpson index) in soil samples after 60 days of application of cattle manure (CAM-S), poultry litter (POL-S), and control soil (CTL-S) which did not receive application of these residues.
Table 3. The variability of alpha diversity (richness, Shannon index, and Gini–Simpson index) in soil samples after 60 days of application of cattle manure (CAM-S), poultry litter (POL-S), and control soil (CTL-S) which did not receive application of these residues.
MeanSDp Value
Richness0.8752
CAM-S893 ns74
CTL-S876 ns47
POL-S872 ns103
Shannon0.0608
CAM-S6.18 a0.15
CTL-S6.21 a0.08
POL-S5.84 b0.28
Gini–Simpson0.0608
CAM-S0.996 a0.001
CTL-S0.996 a0.001
POL-S0.988 b0.006
Differences between treatments were statistically evaluated using a Kruskal–Wallis test (p value ≤ 0.1) followed by Fisher’s LSD post hoc test for multiple comparisons and grouping of means (p value ≤ 0.1). The difference between treatments is represented by different letters, while “ns” means there are no significant differences between treatments.
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Sant’Anna, G.S.L.; de Carvalho, L.A.L.; da Silva, M.S.R.d.A.; Gonçalves, J.V.d.S.; Pinheiro, D.G.; Zonta, E.; Coelho, I.d.S. Short-Term Effects of Poultry Litter and Cattle Manure on Soil’s Chemical Properties and Bacterial Community. Agronomy 2024, 14, 1382. https://doi.org/10.3390/agronomy14071382

AMA Style

Sant’Anna GSL, de Carvalho LAL, da Silva MSRdA, Gonçalves JVdS, Pinheiro DG, Zonta E, Coelho IdS. Short-Term Effects of Poultry Litter and Cattle Manure on Soil’s Chemical Properties and Bacterial Community. Agronomy. 2024; 14(7):1382. https://doi.org/10.3390/agronomy14071382

Chicago/Turabian Style

Sant’Anna, Gustavo Souza Lima, Lucas Amoroso Lopes de Carvalho, Maura Santos Reis de Andrade da Silva, João Vitor da Silva Gonçalves, Daniel Guariz Pinheiro, Everaldo Zonta, and Irene da Silva Coelho. 2024. "Short-Term Effects of Poultry Litter and Cattle Manure on Soil’s Chemical Properties and Bacterial Community" Agronomy 14, no. 7: 1382. https://doi.org/10.3390/agronomy14071382

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