Soil microbiological attributes under the cultivation of Pennisetum purpureum genotypes¹

ABSTRACT

This study aimed to evaluate the biological quality of soil under the influence of different genotypes of elephant grass in the agreste region, which has a climate that marks the transition between a humid climate with a dry season and the semi-arid climate of the north-eastern hinterland. The study was conducted at the Experimental Farm of the Universidade Federal Rural de Pernambuco, Garanhuns, PE, Brazil. The treatments comprised a combination of two elephant grass cultivars (Elefante B and Mott), two irrigation regimes (with and without irrigation), and two climatic periods (dry and rainy). Biological indicators, microbial biomass carbon, soil basal respiration, metabolic quotient, enzymatic activity of soil β-glucosidase, acid and alkaline phosphatase, arylsulfatase, urease, and the hydrolytic determination of fluorescein diacetate were evaluated. The Mott genotype showed superior results, attributed to the biological indicators studied at different times and irrigation management, even during periods of drought, and Mott grass had significant effects microbial activities. This genotype constitutes one of the alternatives for soil quality in semiarid regions, with advantageous biomass and soil microbial activity, thus presenting the greatest complexity in biological attributes with microorganisms tolerant to climate change.

Keywords
enzymatic activity; bioindicators; soil quality; soil water content; temporal variability

INTRODUCTION

Understanding the soil biology and ecology is important for ecosystem restoration and sustainability. In all ecosystems, soil microbes play important roles in organic matter breakdown, nutrient cycling, and nutrient availability to plants (Li et al., 2022). Quantification of biomass carbon, basal respiration, its relationship with the metabolic quotient, and enzymatic activity, have been used as potential microbiological indicators, as they indicate changes in soil microbial biomass and community composition in this environment and are sensitive to small variations, allowing for a quick assessment of soil quality (Rodrigues et al., 2022).

As an important indicator of soil fertility, enzymatic activity is fundamental for maintaining the availability of nutrients, including arylsulfatase, β-glucosidase, urease, acid, and alkaline phosphatase, which are involved in the transformation of organic compounds and as available inorganic sources for plants (Piotrowska-Długosz et al., 2021).

Studies found that soil enzymes in various vegetation and grassland have different sensitivity to moisture and heat, while the sensitivity of the same soil enzyme was also different under vegetation types (Li et al., 2022). However, little information is available on the effect of forage grasses on the diversity and activity of soil microbiota.

The use of elephant grass (Pennisetum purpureum Schum.), a grass that is highly efficient in fixing atmospheric CO₂, could contribute significantly as an alternative source of nutrients to the soil-plant system with biological processes, as it acts as an easily obtainable source of energy and carbon, containing several substances that help the population development of microorganisms from composting processes (Adesemuyi et al., 2020; Santos et al., 2021). Owing to its well-developed root system, it can efficiently contribute to increasing soil organic matter content or carbon sequestration in the soil (Nguyen et al., 2021).

In the semi-arid region of Northeast Brazil, which is characterised by high temperatures associated with irregular precipitation and low rainfall (Silva et al., 2019), the cultivation of grass adapts well to the climatic conditions of the region; however, the seasonality of forage production has restricted its use, which can be supplied by the use of irrigation (Silva et al., 2022). Irrigation during the dry season has been suggested as an option to minimise the seasonal effects on forage production (Souza et al., 2020).

Therefore, considering the importance of biological attributes for the processes that occur in the soil, studies on the quantity and activity of microbial biomass can provide subsidies for the rational use of natural resources and promote the development of soil conservation practices. However, studies on the biological attributes of soil in plantations of P. purpureum genotypes subjected to different irrigation management regimes in semi-arid regions remain scarce.

Thus, this study aimed to evaluate the biological indicators of soil under the influence of different genotypes of elephant grass in the agreste region of Pernambuco, a region whose climate marks the transition between a humid climate with a dry season and the semi-arid climate of the north-eastern hinterland, under different irrigation regimes.

MATERIAL AND METHODS

Soil samples were collected in an experimental area of elephant grass planting, belonging to the Experimental Farm of the Federal Rural University of Pernambuco, located at latitude 08°58’28” S, longitude 36°27’11” W, and altitude of 736 m, in the Municipality of Garanhuns, located in the Southern Agreste of Pernambuco, Northeast Brazil (Alvares et al., 2013). The climate, according to the Köppen–Geiger classification, is tropical mesothermal (Cs’a) with dry and rainy seasons in summer and winter, respectively. The average annual precipitation is 660 mm, concentrated between May to August, and an average annual temperature of 21.7 ºC (Climate Data, 2022).

The collections were performed out in areas of cultivation of elephant grass genotypes Elefante B and Mott, with the presence or absence of irrigation twice annually, covering the rainy period with collection in July and the dry period with collection in December, according to rainfall and temperature data for the study period (INMET, 2018) in 2017 (Table 1).

Table 1
Accumulated data of maximum and minimum temperatures and precipitation of the Experimental Farm by quarter in the 2017, Garanhuns, Pernambuco, Brazil

Quarterly	Temperature (ºC)		Precipitation (mm)
	Maximum	Minimum
January - April	28.3	19.2	55.0
May – July	21.5	15.9	300.2
August – October	25.6	17.2	149.7
November - December	31.1	19.9	5.5

Irrigation was managed by dripping at an average flow of 1.5 L h^-1 using a spring located close to the experimental area as the water source. During winter, irrigation was managed in a variable irrigation shift, considering precipitation. During summer, irrigation was performed daily for 2 h. The study was conducted on treatments selected from a field trial arranged in a randomised block design, using two elephant grass genotypes, Elefante B and Mott, with and without irrigation.

Soil for analysis was collected from the rhizosphere, and within each treatment plot, the composite samples were homogenised from 10 subsamples of all plots of the Elefante B and Mott genotypes under different irrigation treatments, and the samples were analysed in triplicate. Soil samples were sieved through a 2 mm mesh and subsequently stored at 4 ºC.

Microbial biomass carbon (MBC) was determined using 20 g of the second soil sample (Islam & Weil, 1998). Then, the carbon was extracted with potassium sulphate (0.5 mol L⁻¹), followed by oxidation with potassium dichromate (0.066 mol L⁻¹) in the presence of sulfuric acid (H₂SO₄) and phosphoric acid (H₃PO₄) in a heating plate. After cooling, the solution was titrated with ferrous ammonium sulphate (0.033 mol L^-1) in the presence of a diphenylamine indicator (1%) (Vance et al., 1987). The amount of MBC was determined by the difference between organic carbon extracted from fumigated and non-fumigated soil samples using a correction factor (Kec) of 0.35 and the results were expressed as mg g^-1 of carbon on the ground.

Soil basal respiration (SBR) was quantified by incubating 20 g of soil in a hermetically sealed container for three days at 28 °C. The CO₂ released from the samples was captured by the NaOH solution (0.5 mol L⁻¹), which was titrated with HCl (0.5 mol L⁻¹) using phenolphthalein (1%) as an indicator (Anderson & Domsch, 1993). The difference of consumed volume of HCl between the treatment and the control in titration was used to calculate the quantity of CO₂ evolution from soil microbes, 1 ml 0.1 M consumed NaOH was equivalent to 2.2 mg CO₂ the results were expressed in mg carbon – CO₂. The metabolic quotient (qCO₂) was calculated using the SBR/MBC ratio, and the results were expressed in mg carbon – CO₂ (Anderson & Domsch, 1993).

β-Glucosidase activity was determined according to the methodology described by Eivazi & Tabatabai (1988) with some modifications. Using spectrophotometry, 1 g of soil was incubated with 4 mL of buffer (pH 6) and 1 mL of ρ-nitrophenyl-β-D-glucoside (0.05 mol L^-1) for 1 h at 37 °C. Following the addition of 1 mL of CaCl₂ (0.5 mol L^-1) and 4 mL of THAM buffer (pH 12), when the solution turned a yellowish colour, the reading was performed in a spectrophotometer at 410 nm.

Acid and alkaline phosphatase activities were determined according to the method of Eivazi & Tabatabai (1977), with some modifications. In 1 g of soil, 4 mL of modified universal buffer (MUB) solution at pH 6.5 for acid phosphatase and pH 11 for alkaline phosphatase, and 1 mL p-nitrophenyl phosphate (15 mM) were diluted in the MUB with a pH corresponding to the desired enzyme. The samples were incubated at 37 ºC for 1 h. After incubation, p-nitrophenol extraction from the soil was initiated by the addition of 1 mL of CaCl₂ (0.5 M) and 4 mL of NaOH (0.5 M), followed by stirring and subsequent filtering of the suspension on Whatman No. 1 filter paper. The reading was taken using spectrophotometry at 400 nm.

Arylsulfatase activity was measured following the methods proposed by Tabatabai & Bremner (1970), with some modifications. Initially, 1 g of soil was incubated in potassium p-nitrophenyl sulphate (50 mM) with 4 mL of acetate buffer (0.5 mol L^-1) at pH 5.8 for 1 h at 37 ºC. After incubation, 1 mL of calcium chloride solution (0.5 mol L^-1) and 4 mL of sodium hydroxide solution (0.5 mol L^-1) were added, followed by agitation and immediate filtering through Whatman No. 1 filter paper.

Urease activity was determined according to the methodology described by Kandeler & Gerber (1988) with some modifications. In 5 g of soil, 2.5 mL of urea was added per soil sample and incubated in a water bath for 2 h at 37 °C. Following the addition of 50 mL of KCl and stirring for 30 min at 140 rpm, on a shaking table, subsequently, 0.5 mL of the filtered solution was added to 4.5 mL of distilled water, 2.5 mL of dichloroisocyanuric acid solution and 1 mL of dichloride, gently vortexed, and after 30 min, the reading was performed using a spectrophotometer at 690 nm.

Enzymatic activity was determined in μg PNG g^-1 soil h^-1 for β-glucosidase and acid phosphatase. Alkaline phosphatase was expressed in μg PNP g^-1 soil h^-1, arylsulfatase was expressed in μg PNS g^-1 soil h^-1, urease in μg NH4-N g^-1 soil h^-1, and fluorescein diacetate (FDA) was expressed in μg hydrolysates g⁻¹ soil.

The total enzymatic activity of the soil was estimated by hydrolysis of fluorescein diacetate (Dick et al., 1997), where 2.5 g of soil sample was incubated at 37 °C together with a fluorescein solution in sodium phosphate buffer (60 mmol L⁻¹ at pH 7.0) for 24 h, under stirring at 50 rpm. The reaction was then interrupted with acetone (50%) and centrifuged for 5 min at 3,000 x g, the supernatant was filtered, and FDA was measured at 490 nm.

Data from biological variables were evaluated by analysis of variance using R statistical software (R Development Core Team, 2018). Statistical differences between the means were compared using Tukey’s test at a 5% significance level. Principal component analysis was performed using the Past 4.03 software to present the ordering of microbiological attributes.

RESULSTS AND DISCUSSION

In this study, MBC increased significantly during the period of reduced rainfall; however, the magnitude of this effect differed according to the elephant grass genotype (Table 2). The results showed that irrigation had a significant effect on the carbon content of the microbial biomass in the soil under the Elefante B genotype; however, in Mott grass, there was no significant difference between the irrigation treatments.

Table 2
Biological analysis of the soil under the cultivation of Pennisetum purpureum, Elefante B and Mott genotypes, with and without irrigation systems, in different periods of the year: rainy (July/2017) and dry (December/2017)

Elephant grass genotypes	Rainy
	Irrigated			Non-irrigated
	MBC	SBR	qCO2	MBC	SBR	qCO2
Elefante B	13.280Aaa	7.285Bbb	2.754Aaa	8.1365Bba	12.274Baa	2.007Aaa
Mott	11.522Aab	11.658Abb	3.415Aaa	15.456Aab	18.325Aaa	3.003Aaa
	Dry
Elefante B	15.834Baa	15.687Baa	3.302Aaa	7.619Bba	8.020Bbb	2.337Aaa
Mott	20.690Aaa	22.858Aaa	3.683Aaa	22.858Aaa	19.334Aaa	3.222Aaa

• MBC: Microbial biomass carbon (mg g^-1 of carbon in the soil); SBR: Soil basal respiration (mg carbon – CO₂); qCO₂: Metabolic quotient (mg carbon – CO₂); capital letters compare the elephant grass genotypes in each period of the year. Lowercase letters compare irrigation systems in each period of the year. Exponential letters compare genotypes between the periods of the year. Similar letters do not differ according to Tukey’s test at the 5% level.

Plant diversity influences soil microbial biomass (Bargali et al., 2018) and, according to Böhme & Böhme (2006), the root system of the plant can contribute to the microbial carbon content due to rhizodeposition, which results from the abundance of the root system that provides greater availability of organic substrate for the microbiota. Therefore, each genotype of the same species can differ in the quantity and quality of exudates supplied to the soil (Bargali et al., 2018).

Water availability affects soil microbial communities in natural and agricultural ecosystems at the levels of microbial growth, biomass, and biogeochemical cycles (Morugán-Coronado et al., 2019). However, water resources will be insufficient to support sustainable agriculture in semi-arid agroecosystems, and, as shown in the experiment, the Mott elephant grass genotype can obtain significant results in soil MBC in this place, both without and with the presence of irrigation.

Regarding soil basal respiration, considering the sampling time, higher respiration values were found in the Mott genotype in both irrigation management treatments when compared to the Elephant B genotype, and irrigation in the dry period contributed to an increase in the breathing rate.

Similar results were reported by González-Ubierna & Lai (2019), corroborating the present study. They stated that irrigation practices contribute to increasing the soil respiration rate when applied in summer, suggesting that this result is related to the availability of water in the soil, as a soil with low water content tends to reduce microbial respiration, and water stress causes microorganisms to decrease their metabolic activity.

Kabiri et al. (2016) reported lower qCO2 values in arid and semi-arid soils; there was a decrease in qCO2, mainly due to the extent that microbial biomass becomes more efficient in the use of ecosystem resources; however, the increase in values indicates that there is a greater expenditure of energy to maintain the microbial community, and microorganisms tend to consume more substrate to survive (Ghosh et al., 2020). However, the results of this study did not show significant differences in qCO2 among the evaluated treatments; thus, respiration and microbial biomass exhibited the same metabolic efficiency.

Regarding the Elephant B genotype, different periods of the year influenced the enzymatic activity of the soil, β-glucosidase, alkaline, and acid phosphatase, and the soil collected in the rainy season showed greater activity than that observed in the dry season (Table 3), indicating a strong correlation (Figure 1). Arylsulfatase, urease, and FDA activities showed greater activity during the dry season, and Figure 1 shows a correlation between arylsulfatase and urease activities.

Table 3
Enzymatic activity of soil in the cultivation of Pennisetum purpureum, Elefante B and Mott genotypes, cultivated under different irrigation systems, in the rainy (July/2017) and dry (December/2017) seasons

β-Glucosidase (μg PNG g-1 solo h-1)
Elephant grass genotypes	Rainy		Dry
	Irrigated	Non-irrigated	Irrigated	Non-irrigated
Elefante B	21.710Aaa	17.280Aba	10.897Aab	5.900Abb
Mott	17.796Baa	11.903Baa	8.997Aab	5.347Abb
Acid phosphatese (μg PNP g-1 solo h-1)
Elefante B	17.797Aaa	11.833Aba	7.547Bab	2.270Abb
Mott	4.903Bab	2.577Bbb	10.480Aaa	4.330Aba
Alkaline phosphatase (μg PNP g-1 solo h-1)
Elefante B	13.380Aaa	8.826Aba	8.570Aab	2.687Abb
Mott	10.037Aaa	8.763Aba	7.690Aab	2.127Abb
Arylsulfatase (μg PNS g-1 solo h-1)
Elefante B	19.726Aab	12.366Abb	37.693Baa	18.116Bba
Mott	20.216Aab	13.263Abb	42.763Aaa	24.396Aba
Urease (μg NH4-N g-1 solo h-1)
Elefante B	14.880Bbb	17.226Bab	26.500Baa	24.300Bba
Mott	18.353Abb	21.586Aab	29.950Aaa	26.043Aba
FDA (μg hydrolysates g−1 de solo)
Elefante B	28.063Aab	27.356Baa	45.176Baa	23.730Bbb
Mott	29.286Abb	37.733Aaa	51.750Aaa	28.350Abb

• Capital letters compare the elephant grass genotypes for each period of the year. Lowercase letters compare irrigation systems in each period of the year. Exponential letters compare genotypes between the periods of the year. Similar letters do not differ according to Tukey’s test at the 5% level.

Figure 1
Principal component analysis of the enzymatic activities of soil in the cultivation of Pennisetum purpureum, Elefante B and Mott genotypes, cultivated under different irrigation systems, in the rainy (July/2017) and dry (December/2017) seasons. Mott, no irrigation, dry period = ●; Mott, with irrigation, dry period = ○; Mott, no irrigation, rain period = ∆; Mott, with irrigation, rainy period = ▲; Elefante B, no irrigation, dry period = ■; Elefante B, with irrigation, dry period = □; Elefante B, no irrigation, rain period = +; Elefante B, with irrigation, rainy season = X.

Irrigation contributed to greater enzymatic activities of β-glucosidase, arylsulfatase, acid, and alkaline phosphatase, regardless of the collection period and genotype. However, in terms of urease and FDA activities in Mott grass, irrigation during the rainy season resulted in lower enzymatic activity.

It was verified that in the enzymatic activity of alkaline phosphatase and arylsulfatase, the genotypes did not differ significantly, whereas in relation to the other enzymes, the genotypes showed significant differences, where the most representative Mott grass had high levels of enzymatic activity in the soil.

The elephant grass genotypes showed high dispersion in relation to the point of the axes (Figure 2), indicating that they were statistically different environments in terms of biological attributes according to the average test applied (Tables 1 and 2). When correlating irrigation management (Figure 3), the genotypes showed high correlations, thus showing that water contributes to better quality of microorganism activities. Notably, Mott grass showed high correlations under both irrigation treatments. The Mott genotype has a good ability to withstand different climatic conditions while maintaining a stable production level, thus releasing exudates throughout its production cycle and improving soil quality (Rupollo et al., 2012).

Figure 2
Principal component analysis of Elefante B = ○ and Mott = ● genotypes grown under different irrigation systems in the rainy (July/2017) and dry (December/2017) seasons.

Figure 3
Principal component analysis of Mott and Elefante B genotypes grown under different irrigation systems. A = Mott and B = Elefante B; ■ = With irrigation and □ = Without irrigation.

As shown in Figure 4, the climatic seasons exert a greater influence on the biological attributes of the genotypes, in which it is possible to perceive the existence of two distinct groupings: the dry period (warm months) on the left and the rainy period (cold months) on the right, considering that the rainy period is more favourable to the biological attributes in both genotypes and the dry period, and the level of soil quality is more intense in Mott grass.

Figure 4
Principal component analysis of the Mott and Elefante B genotypes in the rainy (July/2017) and dry (December/2017) seasons. A = Mott and B = Elefante B; ▲ = dry period and ∆ = rainy period.

Enzymatic activity in soil is considered a potential indicator of fertility because of its rapid reaction to changes caused by management and environmental variations (Puissant et al., 2018). Enzymes are strongly influenced by soil physicochemical properties such as pH, temperature, moisture, texture, mineralogy, carbon availability, and composition (Dotaniya et al., 2019).

The results of this study indicate that climatic conditions caused by different seasonal periods and soil moisture directly influence enzymatic activity. According to Li et al. (2018), changes in soil environmental conditions, such as moisture and temperature, are correlated with changes in enzymatic activities, microbial communities, and consequently, nutrient cycling.

The area subjected to low water availability showed a reduction in β-glucosidase activity, which suggests less decomposition of organic matter because the activity of this enzyme is correlated with the organic matter content in the soil. According to Taketani et al. (2015), the restriction of water content may be related to the reduction in activity, since microbial activity tends to decrease in dry soil, and consequently, enzymatic activity is slowed down.

The results suggest that the activities of acid and alkaline phosphatases were influenced by soil humidity, with better activity in the rainy period and in the presence of irrigation. According to Baldrian et al. (2008), the activity of acid and alkaline phosphatases is sensitive to seasonal changes, showing greater activity in the rainy season and lower activity in very dry soils, which is considered common because of the reduction in the metabolism of microorganisms and nutrient transport.

Arylsulfatase activity can be used as a direct indicator of the presence of fungi in the soil. Fungi are the only microorganisms present in the microbial biomass that have sulphate esters as substrates for the activity of this enzyme (Bandick & Dick, 1999). During the dry period with irrigation, higher activity levels of this enzyme were observed, which can be attributed to favourable humidity and temperature conditions for these microorganisms.

Urease is produced by plants and microorganisms, mainly bacteria, and is directly related to the nitrogen cycle, which is responsible for hydrolysing urea and releasing CO₂ and ammonia into the soil (Alizadeh et al., 2017). Weitao et al. (2018) observed a negative correlation between urease activity and moisture content, and saw that the urease concentration was lower at higher soil moisture indices, which corroborates our results.

One method to evaluate the biological changes occurring in the soil is to determine the total enzymatic activity in the soil, as evaluated using FDA. The amount of hydrolysed fluorescein is related to the greater amount of enzymes released by microorganisms, which may be directly associated with the abundance of soil organic matter (Barbieri et al., 2019). It was verified that the collection performed in the dry period the FDA was superior to that in the rainy period, and these results confirm the data of Pereira et al. (2004) and Barbieri et al. (2019), in which higher FDA hydrolysis values were found in soils in the dry period.

CONCLUSIONS

Elephant grass genotypes show a good ability to establish the biological attributes of the soil. Among the genotypes studied, Mott grass stood out the most, constituting one of the alternatives for soil quality in the semi-arid region, favouring biomass and soil microbial activity, and presenting greater complexity in biological attributes with microorganisms tolerant to climate change. It is feasible to plant Mott grass during periods of high and low rainfall because it resists variations in irrigation.

Studies on bioindicators have shown that soil microorganisms, owing to their characteristics such as abundance and biochemical and metabolic activity, in addition to providing faster responses to changes in the environment, have a high potential for use in assessing soil quality.

ACKNOWLEDGEMENTS, FINANCIAL SUPPORT AND FULL DISCLOSURE

All authors declare that there is no conflict of financial interest or personal relationship that could have influenced the work reported in this article. Was extracted from the dissertation, from the Graduate Program in Agricultural Production/UFRPE, by the first author, Euzanyr Gomes da Silva, scholarship holder Fundação Fundação de Amparo a Ciência e Tecnologia de Pernambuco – Process PBPG-0083-5.01/17.1.

• 1
  The article was extracted from the dissertation of the first author.

REFERENCES

• Adesemuyi MF, Adebayo MA, Akinola AO, Olasehinde EF, Adewole KA & Lajide L (2020) Preparation and characterisation of biochars from elephant grass and their utilisation for aqueous nitrate removal: Effect of pyrolysis temperature. Journal of Environmental Chemical Engineering, 8:104507.
• Alizadeh H, Kandula DR, Hampton JG, Stewart A, Leung DW, Edwards Y & Smith C (2017) Urease producing microorganisms under dairy pasture management in soils across New Zealand. Geoderma Regional, 11:78-85.
• Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM & Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22:711-728.
• Anderson TH & Domsch AK (1993) The metabolic quotient for CO₂ (qCO₂) as a specific activity parameter to assess the effects of environmental conditions, such as pH, on the microbial biomass of forest soils. Soil Biology and Biochemistry, 25:393-395.
• Baldrian P, Trögl J, Frouz J, Šnajdr J, Valášková V, Merhautová V, Cajthaml T & Herinková J (2008) Enzyme activities and microbial biomass in topsoil layer during spontaneous succession in spoil heaps after brown coal mining. Soil Biology and Biochemistry, 40:2107-2115.
• Bandick AK & Dick RP (1999) Field management effects on soil enzyme activities. Soil Biology and Biochemistry, 31:1471-1479.
• Barbieri M, Dossim MF, Dalla Nora D, Santos WB, Bevilacqua CB, Andrade N, Boeni M, Deuschle D, Jacques RJS & Antoniolli ZI (2019) Ensaio sobre a bioatividade do solo sob plantio direto em sucessão e rotação de culturas de inverno e verão. Revista de Ciências Agrárias, 42:122-134.
• Bargali K, Manral V, Padalia K, Bargali SS & Upadhyay VP (2018) Effect of vegetation type and season on microbial biomass carbon in Central Himalayan Forest soils, India. Catena, 171:125-135.
• Böhme L & Böhme F (2006) Soil microbiological and biochemical properties affected by plant growth and different long-term fertilisation. European Journal of Soil Biology, 42:01-12.
• Climate Data (2022) Climate Data. Available at:<https://pt.climate-data.org/search/?q=Garanhuns>. Accessed on: June 01^rst, 2022.
  » https://pt.climate-data.org/search/?q=Garanhuns
• Dick RP, Breakwell DP & Turco RF (1997) Soil enzyme activities and biodiversity measurements as integrative microbiological indicators. In: Doran JW & Jones AJ (Eds.) Methods for Assessing Soil Quality. Madison, SSSA Special Publications. p.247-271.
• Dotaniya ML, Aparna K, Dotaniya CK, Singh M & Regar KL (2019) Role of soil enzymes in sustainable crop production. In: Kuddus M (Ed.) Enzymes in Food Biotechnology. Oxford, Academic Press. p.569-589.
• Eivazi F & Tabatabai MA (1977) Phosphatases in soils. Soil Biology and Biochemistry, 9:167-172.
• Eivazi F & Tabatabai MA (1988) Glucosidases and galactosidases in soils. Soil Biology and Biochemistry, 20:601-606.
• Ghosh A, Singh AB, Kumar RV, Manna MC, Bhattacharyya R, Rahman MM, Sharma P, Rajput PS & Misra S (2020) Soil enzymes and microbial elemental stoichiometry as bio-indicators of soil quality in diverse cropping systems and nutrient management practices of Indian Vertisols. Applied Soil Ecology, 145:103304.
• González-Ubierna S & Lai R (2019) Modelling the effects of climate factors on soil respiration across Mediterranean ecosystems. Journal of Arid Environments, 165:46-54.
• INMET - Instituto Nacional de Meteorologia (2018) Banco de dados meteorológicos para ensino e pesquisa. Available at: <https://portal.inmet.gov.br/dadoshistoricos> Accessed on: July 01^rst, 2018.
  » https://portal.inmet.gov.br/dadoshistoricos
• Islam KR & Weil RR (1998) Microwave irradiation of soil for routine measurement of microbial biomass carbon. Biology and Fertility of Soils, 27:408-416.
• Kabiri V, Raiesi F & Ghazavi MA (2016) Tillage effects on soil microbial biomass, SOM mineralization and enzyme activity in a semi-arid Calcixerepts. Agriculture, Ecosystems & Environment, 232:73-84.
• Kandeler E & Gerber H (1988) Short-term assay of soil urease activity using colorimetric determination of ammonium. Biology and Fertily Soils, 6:68-72.
• Li G, Kim S, Han SH, Chang H, Du D & Son Y (2018) Precipitation affects soil microbial and extracellular enzymatic responses to warming. Soil Biology and Biochemistry, 120:212-221.
• Li Q, Yang J, He G, Liu X & Zhang D (2022) Characteristics of soil C:N:P stoichiometry and enzyme activities in different grassland types in Qilian Mountain nature reserve-Tibetan Plateau. Plos One, 17:e0271399.
• Morugán-Coronado A, García-Orenes F, McMillan M & Pereg L (2019) The effect of moisture on soil microbial properties and nitrogen cyclers in Mediterranean sweet orange orchards under organic and inorganic fertilization. Science of The Total Environment, 655:158-167.
• Nguyen BT, Le LB, Pham LP, Nguyen HT, Tran TD & Van Thai N (2021) The effects of biochar on the biomass yield of elephant grass (Pennisetum Purpureum Schumach) and properties of acidic soils. Industrial Crops and Products, 161:113224.
• Pereira SV, Martinez CR, Porto ER, Oliveira BRB & Maia LC (2004) Atividade microbiana em solo do Semi-Árido sob cultivo de Atriplex nummularia Pesquisa Agropecuária Brasileira, 39:757-762.
• Piotrowska-Długosz A, Kobierski M & Długosz J (2021) Enzymatic activity and physicochemical properties of soil profiles of Luvisols. Materials, 14:6364.
• Puissant J, Jassey VE, Mills RT, Robroek BJ, Gavazov K, De Danieli S, Spiegelberger T, Griffiths R, Buttler A, Brun J-J & Cécillon L (2018) Seasonality alters drivers of soil enzyme activity in subalpine grassland soil undergoing climate change. Soil Biology and Biochemistry, 124:266-274.
• R Core Team (2018) R: A Language and environment for statistical computing. Version 3.5.2. Available at: <http://www.r-project.org> Accessed on: June 15^th, 2018
  » http://www.r-project.org
• Rodrigues PG, Rodrigues HJB, Ruivo MD, Gomes DJC & da Costa ACL (2022) Variações nos atributos microbiológicos do solo na Floresta Amazônica oriental, sob condição natural, estresse hídrico e em sistemas. Acta Geográfica, 16:56-76.
• Rupollo CZ, Bergoli LM, Bronzatti R, Londero AL, Maixner AR & Fernandes SBV (2012) Desempenho de cultivares de Capim Elefante no segundo ano de implantação sob condição de irrigação. In: XX Seminário de Iniciação Científica, Ijuí. Proceedings, UNIJUÍ. p.25-27.
• Santos CM, Nascimento WB, Nascimento BP, Schwab S, Baldani JI & Vidal MS (2021) Temporal assessment of root and shoot colonization of elephant grass (Pennisetum purpureum Schum.) host seedlings by Gluconacetobacter diazotrophicus strain LP343. Microbiological Research, 244:126651.
• Silva EO, Medeiros EV, Duda GP, Junior MAL, Brossard M, Oliveira JB, Santos UJ & Hammecker C (2019) Seasonal effect of land use type on soil absolute and specific enzyme activities in a Brazilian semi-arid region. Catena, 172:397-407.
• Silva JON, Santos JPAS, Salvador KRS, Leite RMC, Aviz RO, Silva NSG, Amaral EM & Leite ML de MV (2022) O uso da irrigação com água salina pode reduzir o déficit de forragem no Semiárido brasileiro? Research, Society and Development, 11:e45611528357.
• Souza R, Hartzell S, Feng X, Antonino ACD, Souza ES, Menezes RSC & Porporato A (2020) Optimal management of cattle grazing in a seasonally dry tropical forest ecosystem under rainfall fluctuations. Journal of Hydrology, 588:125102.
• Tabatabai MA & Bremner JM (1970) Arylsulfatase activity of soils 1. Soil Science Society of America Journal, 34:225-229.
• Taketani RG, Kavamura VN, Mendes R & Melo IS (2015) Functional congruence of rhizosphere microbial communities associated to leguminous tree from Brazilian semiarid region. Environmental Microbiology Reports, 7:95-101.
• Vance ED, Brookes PC & Jenkinson DS (1987) An extraction method for measuring soil microbial biomass C. Soil Biology and Biochemistry, 19:703-707.
• Weitao L, Meng W, Ming L, Jiang C, Xiaofen C, Kuzyakov Y, Rinklebe J & Zhongpei L (2018) Responses of soil enzyme activities and microbial community composition to moisture regimes in paddy soils under long-term fertilization practices. Pedosphere, 28:323-331.

Publication Dates

• Publication in this collection
  08 July 2024
• Date of issue
  2024

History

• Received
  23 June 2021
• Accepted
  06 Mar 2024