Soil quality decay rate and plant traits in green manure following monocropping farming systems at tropical conditions

doi:10.21203/rs.3.rs-1594828/v1

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Soil quality decay rate and plant traits in green manure following monocropping farming systems at tropical conditions

https://doi.org/10.21203/rs.3.rs-1594828/v1

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Aims

Soil quality index shed light on soil health and its capacity to sustain high primary production. It also can assist decision-making in farming systems by integrating this valuable product into soil management planning. However, the currently existing models are based on rather local data, and thus, there is a lack of predictive tools to monitor soil quality.

Methods

We characterized soil chemical properties, and plant biomass production during 6 years in field conditions at a tropical soil.

Results

Our findings suggest that: 1) a consecutive green manure practice without any input of fertilizers after 6 years reduced soil quality in tropical savanna climate conditions; and 2) there are three groups of green manure considering soil quality reduction, G1 (low soil quality reduction) that comprises C. ochroleuca and N. wightii, G2 (medium soil quality reduction) that comprises B. decumbens, P. glaucum, and C. juncea, and G3 (high soil quality reduction) that comprises M. pruriens, C. ensiformis, C. spectabilis, D. lablab, and S. aterrimum.

Conclusions

This study highlights the importance to consider predictive models as a tool to be used in soil management. Our study also provides a deeper view about the use of green manure, and the importance to avoid the use of green manure with traits that decrease soil quality.

Green manure

Leguminous plant species

Long-term field experiment

Sandy soil

Soil quality index

The importance of the green manure farming system as a key management practice to improve soil quality and some ecosystem services, such as soil organic matter inputs and nutrient cycling at scales ranging from regional to global have been widely described (He et al. 2020; Khan et al. 2020). Among them, soil chemical properties and net primary production (e.g., shoot dry biomass and root density) are especially relevant because of their environmental and economic importance (Fernández et al. 2020).

Soil chemical properties influence plant growth and biomass production (Cardone et al. 2020; Yang et al. 2020), increasing net primary production and recycling soil nutrients (Chen et al. 2020; Wang et al. 2020; Li et al. 2021). In green manure farming systems, two main effects are widely reported: (i) Soil surface protection when the plant species act as cover crops; and (ii) soil organic matter improvement when plant materials are fully incorporated into soil profile (Gabriel et al. 2021; Torres et al. 2021). From an economic point of view, the use of green manure practice provides important benefits to smallholder farmers by reducing costs with fertilizers and other soil conditioners (Zhou et al. 2020).

In fact, this practice reduces in 69% the overall costs with only organic fertilizers in certain regions such as the Brazilian Northeast (Nascimento et al. 2021). Within this context, it is important to have accurate estimations of soil quality at tropical ecosystem, not only for smallholder farmers to integrate them into soil management planning, but also to comply with the low carbon agriculture requirements proposed by the Brazilian government (Stabile et al. 2020; Vinholis et al. 2021). Empirical models can contribute to these two tasks by providing quantitative understanding of the impact of several plant species cultivated as green manure on soil ecosystem, allowing to integrate the management of different plant species in existing management practices at the tropics (Sharma et al. 2021). In Brazil, different soil quality models at local and regional scales have been developed so far, mainly considering both soil chemical and biological database (Santos et al. 2021).

Forstall-Sosa et al. (2020) developed a model for predicting soil quality as a function of abundance of Carabidae, Formicidae and Termitidae, shoot dry biomass, soil pH and Olsen’s available P in green manure farming systems of Brazilian Northeast. Later, Kormann et al. (2021) developed similar model as a function of rainfall, soil pH, and abundance of Lumbricidae, Spirobolida and Staphylinidae for agroforestry systems and Mixed Ombrophilous Forest in Brazilian Southern. However, such models require a very trained taxonomist to classify an entire soil biota at Family level (Heydari et al. 2020). On the other hand, there is a lack of models enabling accurate enough prediction of soil quality. The main reason behind this problem is the difficulty to obtain constant quantities of data over long-term experiments, especially if the model considers possible changes in meteorological conditions (Andrade et al. 2020).

In this study, we have used data from a green manure farming system because this experiment is monitored for changes in soil physical, chemical, and biological properties using permanent plots established in tropical environment since 2013. Previous works have shown that cover crops used as green manure are key for improving soil organic carbon, shoot dry biomass production, and soil biota diversity and abundance (Souza et al. 2019; Melo et al. 2019; Forstall-Sosa et al. 2020; Nascimento et al. 2021). However, we need to understand the role of green manure practice on soil quality overtime. Thus, we hypothesized that (i) the green manure practice can improve soil fertility over the years of its use. It has been reported that plant species with high root biomass production over a temporal scale may improve soil quality by promoting some chemical properties (Laurindo et al. 2021); and (ii) soil quality index will follow certain plant patterns (e.g., plant species elicits distinct traits) and, therefore, the variability among plots on soil quality will be a function of the cumulative effects of the green manure utilization (Souza et al. 2019; Sánchez-González et al. 2019).

The main aim of this study was to access the influence of green manure practice on soil chemical properties, soil quality, and its decay rate. We also have developed predictive models for plant traits and soil chemical properties considering a 6-year field study with green manure, considering shoot dry biomass, root density, Olsen’s available P, K⁺, Ca²⁺, and soil organic carbon (SOC) stock as explanatory variables. The predictive models were fitted for all the studied plant species (Brachiaria decumbens Stapf. cv. Basilisk, Canavalia ensiformis (L.) DC, Crotalaria juncea (L.), Crotalaria ochroleuca (G.) Don, Crotalaria spectabilis Roth, Dolichos lablab (L.), Mucuna pruriens (L.) DC, Neonotonia wightii (Wight & Arn.) J.A. Lackey, Pennisetum glaucum (L.), and Stizolobium aterrimum Piper & Tracy).

Field experiment

The field experiment was conducted in six sites at Areia, Paraiba, Brazil. The climatic condition from the field experiment is classified as tropical wet and dry climate (As-type climate following Köppen-Geiger climate classification), average annual precipitation, and mean air temperature of 1300 mm and + 22.5°C, respectively (Barbosa et al. 2021). The soil type of the six sites was classified as a Regosol with sandy loam texture (WRB 2015).

Experimental design

Our field study obtained data from 2014 to 2019 from six sites located at Areia, Paraiba, Brazil. Sampling collection was carried out from July to December from each studied year. The studied sites were selected following similar soil and climate type (Fig. 1).

The field experiment into each site followed a randomized block design, within five blocks. Each treatment followed a monocropping system (Table 1), using the same treatments in the same plots during each studied year. Each studied treatment consisted of plots with 24 m² (6 × 4 m), and the plots were spaced 0.5 m between them. In each year, the soil was prepared through manual preparation, and all studied plant species were sown at a seedling rate of 400 seeds m^− 2 at 2-cm depth. During the field experiment, we monitored the emergency time for all plant species. The flowering stage ranged from 53 days (e.g., Crotalaria ochroleuca) to 180 days (e.g., Brachiaria decumbens). Thus, the root and shoot dry biomass collection ranged from 53 days to 180 days following the life history of each studied plant species. After finishing the flowering stage, the plant species were incorporated into the soil profile at 20-cm depth. We have collected soil samples to analyse soil chemical properties before (2 days) and after (60 days) soil incorporation for each year.

Table 1

Flowering stage from each plant species, and biomass data collection of the studied plant species.
Plant species	Flowering (days)	Shoot and root collection (days)
Brachiaria decumbens Stapf.	150–180	180
Canavalia ensiformis (L.) DC	62–70	70
Crotalaria juncea (L.)	53–56	56
Crotalaria ochroleuca (G.) Don	52–58	58
Crotalaria spectabilis Roth	72–78	78
Dolichos lablab (L.)	87–95	95
Mucuna pruriens (L.) DC	121–124	124
Neonotonia wightii (Wight & Arn.) J.A. Lackey	69–75	75
Pennisetum glaucum (L.)	60–66	66
Stizolobium aterrimum Piper & Tracy	119–124	124

Studied plant species

Brachiaria decumbens Stapf is a native plant species from Uganda (e.g., tropical east Africa). It was introduced into Northeast Brazil because its specific traits (e.g., high phenotypic plasticity) for grazing uses. It requires fertile and well-drained soil (Namazzi et al. 2020). It can produce in environments with temperature, and annual precipitation near to + 30°C and 1000 mm, respectively. This plant species grows up 1.0 m in the clump-shaped, and its dry biomass production is about 8 t ha^− 1 (Forstall-Sosa et al. 2020). Pennisetum glaucum (L.) is a native plant species from Tropical Africa. It was introduced worldwide because its traits (fast growth, extensive root system, and water use efficiency), its ability to improve soil chemical properties (e.g., Ca²⁺, Mg²⁺, and K⁺) and its nutritional value as a food resource (Leite et al. 2019). It requires low fertile soils, and high temperatures (Moussa et al. 2021). Its dry biomass production is about 0.8 t ha^− 1 (Dias-Martins et al. 2018; Soares et al. 2021).

Canavalia ensiformis (L.) DC is a native plant species from Central America (Souza et al. 2019). It has been related with improvement on soil quality through its root traits that facilitates access to mineral nutrients. It grows well in environments with high temperature (more than + 30ºC) and drought conditions (less than 800 mm) within the tropics (Santos et al. 2021). Its dry biomass production is about 8 t ha^− 1 (Melo et al. 2019). Crotalaria spp. are native plant species from Eastern and Southern tropical Africa (Moreno et al. 2021). They are both annual and perennial (Wasongaa et al. 2021). Crotalaria spp. have a deeper and branched root system, that contributes to break up soil compacted layers and some studies have reported their ability to reduce abundance of fitonematoides (Cruz et al. 2020; Moreno et al. 2021; Lanna et al. 2021). Dolichos lablab (L.) is a native plant species from India. It grows well in arid and semi-arid regions of the world (Singh and Abhilash 2019), because its drought tolerance, and use as a green manure. It has been related reducing soil from erosion and improving soil quality (Naeem et al. 2020). Mucuna pruriens (L.) DC is native plant species from Southern China. It can growth in acid soils with low fertility (Lepcha and Sathyanarayana 2021), temperatures near to 25ºC, and precipitation ranging from 1.000 to 2.500 mm. It is used as a green manure because its biomass production (it is about 4 t ha^− 1) with N-rich compounds (Makhaye et al. 2021). Neonotonia wightii (Wight & Arn.) J.A. Lackey is native plant species from East Africa (Mengistu et al. 2018). It produces a satisfactory root dry biomass (about 1.73 t ha^− 1), possibly associated with its regrowth traits. It grows well environments with temperature, and precipitation near to + 25°C, and 1.200 mm, respectively (Ferreira et al. 2019). Stizolobium aterrimum Piper & Tracy is a native plant species from Africa (Santos et al. 2017). It has been used as green manure worldwide (Santos et al. 2017; Mendes et al. 2021), because its high rusticity, growth traits, and high plasticity to different soil and climate types (Barros et al. 2020; Mendes 2021). Its dry biomass production produce is about 8 t ha^− 1 (Forstall-Sosa et al. 2020).

Soil sampling

Soil sampling was carried out 90 days after each studied plant species was incorporated to soil profile. Initially, we collected with a soil auger five soil samples nested per plot to characterize the soil chemical properties. Soil samples were then placed into labelled plastic bags, air-dried, and passed in a 2-mm sieve.

Soil chemical properties

To chemically characterize the soil from each plot, we analysed soil pH, Olsen’s available P, soil exchangeable cations (Ca²⁺, Mg²⁺, Al³⁺, K⁺, Na⁺), sum of bases, cation exchange capacity, soil organic carbon, SOC stock, and base saturation. Soil pH was measured in a suspension of soil and distilled water (1:2.5, v:v) (Black 1965). Olsen’s available P was determined colorimetrically using a spectrophotometer at 882 nm by extraction with sodium bicarbonate for 30 min (Olsen et al. 1954). All exchangeable cations (Ca²⁺, Mg²⁺, Al³⁺, K⁺, Na⁺) were determined by the extraction method using potassium chloride for Ca²⁺, Mg²⁺, and Al³⁺, and Mehlich-1 for K⁺ and Na⁺ (IITA 1979). Sum of bases was characterized based on the following equation: SB (cmol_c kg^− 1) = K⁺ + Na⁺ + Ca²⁺ + Mg²⁺, while cation exchange capacity was determined using the following equation: C.E.C (cmol_c kg^− 1) = SB + H⁺+Al³⁺ (Black 1965). Soil organic carbon was determined following protocols described by Okalebo et al. (1993), while soil organic carbon stock was estimated by the equation: SOC stock = SOC × BD × 20 × 100, where SOC stock (t C ha^− 1) is the soil organic carbon stock, SOC (g kg^− 1) is the soil organic carbon content, BD (g cm^− 3) is the soil bulk density, 20 (cm) is the thickness of the given soil horizon, and 100 is used for unit conversion (Xiong et al. 2020). Finally, base saturation was calculated by the following equation: V (%) = (SB/C.E.C) × 100 (Black 1965).

Shoot dry biomass and root density

Shoot dry biomass production from each studied plot was recorded over the 6 studied years, and this variable was estimated as described by Forstall-Sosa et al. (2020). Initially, we selected ten plants per plot with homogenous characteristics of plant height and diameter near soil surface. Subsequently, all plants were harvested at 5-cm above the soil surface, and the shoot dry biomass was used to estimated plant biomass production in t ha^− 1. For root density, we collect soil monoliths (20 × 20 × 20 cm) as described by Souza and Santos (2018), where ten soil monoliths in each studied plot were collected and wrapped with plastic film. All the monoliths were transported with minimal disturbance until analysis. During our analysis, and to estimate root density, we collected roots from the soil monoliths, and washed them using a 0.5-mm nylon mesh bag. Root density (g cm^− 3) was determined after drying the samples for 48 h at 65°C.

Soil quality index

An explanatory principal component analysis was performed to explore all variability among sites, plant species, and years with respect to explore the effects of the studied plant species on plant and soil traits. We developed a soil quality index (SQI) using the same PCS-LSF-SQIw approach as described by Forstall-Sosa et al. (2020) and Nascimento et al. (2021). In our SQI, we combined the normalized values from shoot dry biomass, root density, and soil chemical properties (Eq. 1). High values of SQI indicated a high-class soil that provides a high plant biomass production, and soil structure without negative effects to soil ecosystem.

Eq. 1 SQI = (53.94 × N – Root) + (27.37 × N – P) + (5.43 × N – Shoot) + (3.17 × N – Ca²⁺) + (2.15 × N –K⁺) + (1.73 × N – SOC stock) + 6.22

Where: N - R = Normalized values of root density (g cm^− 3), N - P = normalized values of Olsen’s available P (mg kg^− 1), N - S = normalized values of shoot dry biomass (t ha^− 1), N - Ca = normalized values of exchangeable Ca (cmol_c kg^− 1), N - K = normalized values of exchangeable K (mg kg^− 1), and N - SCS = normalized values of SOC stock (t C ha^− 1). All normalized values were obtained dividing the mean of each component by their scores obtained in a PCA analysis.

Soil quality decay rate

To determine the soil quality decay rate (k, year^− 1), we adapted the protocol described by Olsen (1963). We adapted the equation for organic residue decay rate: (k (year^− 1) = ln [Χ/ Χ₀]/time. Where, Χ in the standard equation is final mass (g), and Χ₀ is the initial mass (g). For our study, we used SQI, SQI₂₀₁₃, and SQ_k instead Χ, Χ₀ and k, respectively. We used the SQI for each studied year from 2014 to 2019. They were divided by the SQI₂₀₁₃. We wanted to understand how does each plant studies contributed to soil quality decay rate over the 6 studied years using an equation that could show to us the soil quality dynamics following the compounds dynamics as proposed by Michaelis-Menten law (Touhami et al. 2022).

Statistical analysis

All data was analysed with using R statistical software (R Core Team 2018). Next, we arcsin square root transformed all dependent variables to meet assumption of normal distribution. We created predictive models using the step function in the stats package to verify the effect of individual or combined variables (soil chemical properties, shoot dry biomass, and root density) on each specific studied variable. We also used treatment (e.g., the studied plant species used as green manure) and the years of their cultivation as fixed effect as described by Rosenfield and Müller (2020). Plots and blocks were included as a random effect in each model.

An explanatory analysis of variance was performed to explore all data variability among the six studied sites over the 6 years on soil abiotic traits, plant dry biomass production (shoot and root), soil quality index, and soil quality decay rate. At this explanatory analysis we have considered plant species (df = 9), years (df = 5), sites (df = 5), and their interaction, respectively as sources of variation in a three-way ANOVA following a randomized block design. We did not find any significative differences for sites (F_5,1436 = 2.31, p = 0.45), sites × plant species (F_45,1436 = 0.98, p = 0.59), sites × years (F_25,1436 = 1.56, p = 0.51), and sites × plant species × years (F_225,1436 = 2.01, p = 0.48) on all studied variables. Thus, we performed a two-way ANOVA considering plant species, years, and their interaction as source of variation, and the observed results are showed in the further subsections.

Green manure influence on soil abiotic traits over 6 consecutive years in a Tropical Regosols

The two-way ANOVA showed a significant influence of green manure practice on exchangeable Ca content (F_45,1436 = 22.81, p < 0.001), exchangeable K content (F_45,1436 = 19.28, p < 0.001), Olsen’s available P (F_45,1436 = 37.18, p < 0.001), and SOC stock (F_45,1436 = 32.56, p < 0.001). Considering that before to start the experiment the initial content of Ca²⁺, K⁺, Olsen’s available P, and SOC stock was 1.23 cmol_c kg^− 1, 27.90 cmol_c kg^− 1, 16.99 mg kg^− 1, and 7.00 t C ha^− 1, respectively. We observed that all plant species improved the soil Ca²⁺ content (except M. pruriens that showed 2.4% less exchangeable Ca in 2019 when compared to its initial content), soil K⁺ content (except C. spectabilis and N. wightii that showed 24.73 and 1.43% less soil K⁺ content, respectively), and SOC stock. However, all green manure practice decreased the Olsen’s available P. In 2019, we observed the highest increment of Ca²⁺ on plots where B. decumbens, C. ensiformis, and C. juncea were cultivated with 62.60, 46.34, and 38.21% more Ca²⁺, respectively. For exchangeable K, its highest content was observed on plots where B. decumbens (+ 32.61%), C. ensiformis (+ 29.03%), C. ochroleuca (+ 24.37%), D. lablab (+ 21.86%), P. glaucum (+ 37.99%), and S. aterrimum (+ 22.22%) were cultivated. Finally, we observed the highest increment of SOC stock on plots where C. juncea (+ 49.71%), C. ochroleuca (+ 70.0%), C. spectabilis (+ 49.85%), M. pruriens (+ 75.57%), P. glaucum (+ 74.14%), and S. aterrimum (+ 84.84%) were cultivated (Table 2).

Table 2

Mean values of soil abiotic traits as affected by green manure practice over 6 consecutive years in a Tropical Regosols, Areia, Paraiba, Brazil.
Green manure	2014	2015	2016	2017	2018	2019
Green manure	Exchangeable Ca (cmol_c kg^− 1)
B. decumbens	1.30 (0.01) b	1.40 (0.02) b	1.50 (0.01) b	1.54 (0.01) a¹	1.63 (0.03) a	2.00 (0.01) a
C. ensiformis	1.24 (0.02) c	1.30 (0.01) c	1.36 (0.03) c	1.50 (0.01) a	1.57 (0.01) a	1.80 (0.03) a
C. juncea	1.26 (0.01) c	1.31 (0.01) c	1.37 (0.03) c	1.43 (0.03) b	1.47 (0.01) b	1.70 (0.01) a
C. ochroleuca	1.33 (0.02) b	1.39 (0.02) b	1.42 (0.01) b	1.41 (0.03) b	1.37 (0.03) c	1.30 (0.03) c
C. spectabilis	1.39 (0.01) a	1.54 (0.01) a	1.59 (0.03) a	1.55 (0.01) a	1.54 (0.03) a	1.50 (0.01) b
D. lablab	1.25 (0.01) c	1.27 (0.02) c	1.31 (0.03) c	1.42 (0.01) b	1.49 (0.01) b	1.60 (0.03) b
M. pruriens	1.41 (0.02) a	1.55 (0.01) a	1.60 (0.01) a	1.45 (0.01) b	1.40 (0.03) b	1.20 (0.01) d
N. wightii	1.27 (0.02) c	1.32 (0.02) c	1.38 (0.03) c	1.44 (0.01) b	1.49 (0.01) b	1.60 (0.03) b
P. glaucum	1.28 (0.01) c	1.33 (0.01) c	1.35 (0.01) c	1.40 (0.03) b	1.45 (0.03) b	1.50 (0.03) b
S. aterrimum	1.43 (0.02) a	1.59 (0.02) a	1.62 (0.01) a	1.63 (0.01) a	1.56 (0.01) a	1.40 (0.01) c
	Exchangeable K (cmol_c kg^− 1)
B. decumbens	29.70 (0.02) a	31.90 (0.03) b	32.40 (0.01) b	34.50 (0.01) b	35.32 (0.01) b	37.00 (0.03) a
C. ensiformis	28.80 (0.03) b	31.12 (0.03) b	32.56 (0.03) b	34.27 (0.01) b	38.93 (0.02) b	36.00 (0.02) a
C. juncea	28.01 (0.02) b	28.19 (0.01) c	28.25 (0.03) c	28.50 (0.02) c	29.50 (0.02) b	28.00 (0.03) b
C. ochroleuca	31.45 (0.02) a	36.78 (0.01) a	40.17 (0.01) a	41.34 (0.02) a	42.72 (0.01) a	34.70 (0.02) a
C. spectabilis	26.57 (0.02) c	23.45 (0.02) d	22.56 (0.02) d	18.34 (0.02) d	16.82 (0.03) d	21.00 (0.02) c
D. lablab	25.46 (0.03) c	28.98 (0.02) c	32.50 (0.02) b	35.60 (0.01) b	26.19 (0.03) c	34.00 (0.01) a
M. pruriens	29.98 (0.03) a	30.98 (0.02) b	31.90 (0.01) b	32.92 (0.03) b	32.92 (0.02) b	32.50 (0.01) b
N. wightii	27.50 (0.01) b	27.60 (0.01) c	27.20 (0.02) c	27.00 (0.01) c	26.86 (0.02) c	27.50 (0.01) c
P. glaucum	32.56 (0.03) a	34.56 (0.01) a	37.98 (0.01) a	39.91 (0.03) a	40.13 (0.01) a	38.50 (0.01) a
S. aterrimum	29.91 (0.03) a	31.17 (0.02) b	32.98 (0.02) b	33.41 (0.03) b	36.19 (0.03) b	34.10 (0.02) a
	Olsen’s available P (mg kg^− 1)
B. decumbens	15.95 (0.23) a	14.91 (0.27) a	13.31 (0.21) a	11.25 (0.17) a	10.66 (0.10) b	10.83 (0.05) b
C. ensiformis	14.60 (0.12) b	13.20 (0.09) b	11.10 (0.08) c	10.70 (0.06) b	10.57 (0.05) b	14.32 (0.02) a
C. juncea	15.50 (0.04) a	14.30 (0.03) a	13.20 (0.03) a	11.60 (0.03) a	10.59 (0.02) b	10.02 (0.02) b
C. ochroleuca	14.90 (0.31) b	13.60 (0.23) b	12.70 (0.20) b	11.10 (0.18) a	11.61 (0.09) a	9.77 (0.04) b
C. spectabilis	13.50 (0.27) b	12.40 (0.14) c	11.00 (0.11) c	10.25 (0.08) b	10.33 (0.06) b	12.42 (0.03) a
D. lablab	14.91 (0.02) b	12.75 (0.03) c	11.57 (0.02) c	10.17 (0.03) b	10.57 (0.02) b	11.61 (0.02) a
M. pruriens	15.15 (0.19) a	13.14 (0.18) b	12.54 (0.15) b	11.12 (0.10) a	10.74 (0.05) b	9.86 (0.03) b
N. wightii	14.90 (0.36) b	13.55 (0.09) b	12.23 (0.04) b	11.17 (0.03) a	10.11 (0.02) b	8.08 (0.02) b
P. glaucum	15.35 (0.28) a	13.78 (0.24) b	12.10 (0.10) b	11.21 (0.07) a	10.31 (0.03) b	8.78 (0.02) b
S. aterrimum	15.00 (0.19) a	13.13 (0.15) b	11.70 (0.10) c	10.00 (0.05) b	9.57 (0.02) c	6.74 (0.01) c
	SOC stock (t C ha^− 1)
B. decumbens	4.90 (0.50) c	3.80 (0.09) c	3.81 (0.02) c	5.30 (0.05) c	6.75 (0.09) c	8.48 (0.03) b
C. ensiformis	3.00 (0.47) c	3.40 (0.18) c	3.56 (0.11) c	4.00 (0.07) c	6.01 (0.11) c	9.61 (0.09) b
C. juncea	7.70 (0.61) b	8.10 (0.09) b	8.30 (0.45) b	9.60 (0.21) b	10.54 (0.08) b	10.48 (0.04) a
C. ochroleuca	11.50 (0.58) a	12.90 (0.37) a	13.60 (0.32) a	13.70 (0.18) a	14.73 (0.21) a	11.90 (0.03) a
C. spectabilis	10.10 (0.49) a	12.10 (0.39) a	13.50 (0.27) a	13.20 (0.18) a	12.80 (0.12) a	10.49 (0.09) a
D. lablab	4.00 (0.23) c	2.00 (0.09) c	2.10 (0.09) c	3.70 (0.20) c	5.39 (0.09) c	8.85 (0.08) b
M. pruriens	12.00 (0.12) a	14.00 (0.28) a	14.70 (0.28) a	15.00 (0.15) a	15.98 (0.23) a	12.29 (0.11) a
N. wightii	7.60 (0.48) b	8.20 (0.21) b	8.50 (0.32) b	8.60 (0.19) b	8.71 (0.10) b	9.60 (0.09) b
P. glaucum	4.20 (0.28) c	3.50 (0.10) c	3.70 (0.27) c	5.50 (0.21) c	7.63 (0.13) c	12.19 (0.10) a
S. aterrimum	12.10 (0.31) a	14.10 (0.27) a	14.70 (0.29) a	15.00 (0.21) a	16.00 (0.09) a	12.94 (0.04) a
Standard error in parentheses.
¹Within each column (studied year), same small letters represent no significant differences by Bonferroni’s test (p < 0.05).

Plant species performance (shoot dry biomass production and root density) used as green manure over 6 years in a Tropical Regosols

The two-way ANOVA showed significant differences among green manure practice on shoot dry biomass (F_45,1436 = 32.97, p < 0.001), and root density (F_45,1436 = 11.56, p < 0.001). Considering that before to start the experiment the shoot dry biomass and root density (measured by sampling above-, and belowground native weed material) were 1.59 t ha^− 1, and 1.76 g cm^− 3, respectively. We observed that all plant species showed higher shoot dry biomass when compared to the results from 2013. However, all plant species showed lower root density when compared to the initial values. In 2019, we observed the highest dry biomass production on plots where B. decumbens, C. juncea, and D. lablab were cultivated. For root density, its highest content was observed on plots where N. wightii was cultivated (Table 3).

Table 3

Mean values of shoot dry biomass and root density from plots where plant species were cultivated over 6 consecutive years in a Tropical Regosols, Areia, Paraiba, Brazil.
Green manure	2014
Green manure	Shoot dry biomass (t ha^− 1)
B. decumbens	4.00 (0.17) c	6.00 (0.21) b	7.10 (0.25) b	6.20 (0.32) c	4.31 (0.28) b	5.81 (0.20) a¹
C. ensiformis	3.60 (0.11) d	4.20 (0.17) c	4.10 (0.15) c	3.40 (0.18) d	2.50 (0.17) c	1.64 (0.16) d
C. juncea	6.00 (0.08) b	7.70 (0.10) b	8.20 (0.11) b	7.00 (0.10) b	4.25 (0.09) b	6.11 (0.12) a
C. ochroleuca	8.00 (0.20) a	9.70 (0.18) a	10.80 (0.21) a	9.80 (0.19) a	6.40 (0.21) a	4.51 (0.20) b
C. spectabilis	4.44 (0.18) c	5.25 (0.17) b	5.40 (0.19) c	4.30 (0.15) d	3.00 (0.17) c	2.91 (0.19) c
D. lablab	6.34 (0.16) b	9.78 (0.15) a	11.50 (0.18) a	10.74 (0.17) a	6.33 (0.19) a	5.42 (0.19) a
M. pruriens	2.62 (0.21) e	3.90 (0.19) d	4.78 (0.21) c	4.51 (0.20) d	3.12 (0.20) c	2.15 (0.22) c
N. wightii	3.84 (0.13) d	4.12 (0.15) c	4.65 (0.18) c	3.65 (0.17) d	2.41 (0.16) c	1.79 (0.19) d
P. glaucum	2.53 (0.17) e	3.63 (0.19) d	4.44 (0.21) c	3.99 (0.18) d	2.51 (0.20) c	1.66 (0.21) d
S. aterrimum	4.37(0.20) c	4.37 (0.20) c	7.14 (0.25) b	8.44 (0.22) b	7.22 (0.19) a	4.20 (0.18) b
	Root density (g cm^− 3)
B. decumbens	1.23 (0.01) b	1.01 (0.01) a	0.80 (0.02) b	0.60 (0.01) b	0.68 (0.03) b	0.64 (0.01) b
C. ensiformis	1.20 (0.02) b	0.81 (0.01) b	0.59 (0.04) b	0.38 (0.01) b	0.21 (0.01) b	0.17 (0.03) b
C. juncea	1.40 (0.01) a	1.10 (0.01) a	0.81 (0.01) b	0.58 (0.04) b	0.23 (0.01) b	0.02 (0.01) b
C. ochroleuca	1.34 (0.04) a	1.00 (0.01) a	0.71 (0.01) b	0.42 (0.05) b	0.22 (0.01) b	0.07 (0.02) b
C. spectabilis	1.39 (0.02) a	1.18 (0.04) a	0.80 (0.01) b	0.56 (0.05) b	0.23 (0.02) b	0.12 (0.01) b
D. lablab	1.37 (0.01) a	1.18 (0.03) a	0.81 (0.03) b	0.54 (0.01) b	0.29 (0.03) b	0.21 (0.03) b
M. pruriens	1.20 (0.02) b	0.80 (0.04) b	0.41 (0.03) b	0.20 (0.01) b	0.19 (0.01) b	0.27 (0.01) b
N. wightii	0.80 (0.01) c	0.97 (0.02) b	1.12 (0.01) a	1.15 (0.01) a	1.28 (0.04) a	1.54 (0.04) a
P. glaucum	1.05 (0.01) b	0.48 (0.01) b	0.27 (0.01) b	0.23 (0.01) b	0.18 (0.05) b	0.04 (0.03) b
S. aterrimum	1.26 (0.01) b	0.83 (0.01) b	0.50 (0.04) b	0.33 (0.02) b	0.26 (0.01) b	0.20 (0.01) b
Standard error in parentheses.
¹Within each column (studied year), same small letters represent no significant differences by Bonferroni’s test (p < 0.05)

Redundancy analyses

Redundancy analyses (RDA) illustrated that shoot dry biomass was positively correlated to SOC stock, and K⁺. Conversely, the root density was positively correlated to Na⁺, Ca²⁺, Mg²⁺ and Olsen’s available P. All variables into the RDA together explain about 74% of data variance (Monte Carlo permutation test with 999 permutation, p < 0.001) considering the database from the 6 studied years. Exchangeable Al was negatively correlated to the main factors driving soil quality (e.g., root density, Olsen’s available P, shoot dry biomass, Ca²⁺, K⁺, and SOC stock) (Fig. 2).

Multivariate predictive models: a useful tool to help farmers for decision made when choosing a plant species to use as green manure

Based on the results obtained in the stepwise procedure to understand the multivariate factors that improved shoot dry biomass, root density, and soil chemical properties (e.g., Olsen’s available P, exchangeable K, exchangeable Ca, and SOC stock), we have built six predictive models considering multiple regressions. The proposed models showed the following functions: i) shoot dry biomass was influenced by soil pH, K⁺, and Olsen’s available P; ii) root density was influenced by Ca²⁺, Mg²⁺, K⁺, Olsen’s available P, H⁺+Al³⁺, and C.E.C; iii) Olsen’s available P was influenced by root density, Ca²⁺, Mg²⁺, and H⁺+Al³⁺; iv) K⁺ was influenced by shoot dry biomass, root density, Ca²⁺, Mg²⁺, H⁺+Al³⁺, and C.E.C.; v) Ca²⁺ was influenced by soil pH, Mg²⁺, and Olsen’s available P; and vi) SOC stock was influenced by shoot dry biomass, Mg²⁺, Olsen’s available P, and SOC (Table 4).

Table 4

Predictive models to estimate biotic, and abiotic traits based on biomass production (shoot and root), and soil chemical properties using a database obtained in our field study, Areia, Paraiba, Brazil.
Predictive model	F-value	R²	p-value
Shoot dry biomass (t ha^− 1) = 2.44 – (0.54 × Soil pH) + (0.01 × K⁺) + (0.02 × Olsen’s available P)	12.79	0.72	< 0.001
Root density (g cm^− 3) = 186.47 – (6602.47 × Ca²⁺) – (6624.21 × Mg²⁺) – (22.46 × K⁺) – (10.29 × Olsen’s available P) – (6636.32 × H⁺+Al³⁺) + (6620.77 × C.E.C)	5.74	0.83	< 0.001
Olsen’s available P (mg kg^− 1) = 6.18 – (0.01 × Root density) + (4.05 × Ca²⁺) + (1.68 × Mg²⁺) – (1.60 × H⁺+Al³⁺)	14.93	0.94	< 0.001
K⁺ (mg kg^− 1) = -8.14 + (1.88 × Shoot dry biomass) – (0.01 × Root density) – (0.03 × Ca²⁺) – (0.03 × Mg²⁺) – (0.03 × H⁺+Al³⁺) + (0.03 × C.E.C.)	248.8	0.92	< 0.001
Ca²⁺ (cmol_c kg^− 1) = -2.28 + (0.70 × Soil pH) – (0.28 × Mg²⁺) + (0.03 × Olsen’s available P)	36.81	0.47	< 0.001
SOC stock (t C ha^− 1) = 0.14 – (0.37 × Shoot dry biomass) + (0.19 × Mg²⁺) + (0.02 × Olsen’s available P) + (1.36 × SOC)	663.9	0.95	< 0.001
Soil pH (H₂O, 1:2.5, v:v); K⁺, Ca²⁺, Mg²⁺, H⁺ + Al³⁺, and C.E.C: Cation exchange capacity (cmol_c kg^− 1); Olsen’s available P (mg kg^− 1); SOC (g kg^− 1)

Soil quality index

For soil quality, the two-way ANOVA showed significant differences among green manure practice (F_45,1436 = 26.56, p < 0.001). Considering that before to start the experiment the soil quality index was about 651.08. We observed that all plant species decreased the soil quality index overtime when compared to the results from 2013. By the end we observed the highest values of SQI on plots where C. ochroleuca (534.9) and N. wightii (628.65) were cultivated, but these results are lower 17.84 and 3.44% when compared with the SQI from 2013 (Table 5).

Table 5

Mean values of soil quality index from plots where plant species were cultivated over 6 consecutive years in a Tropical Regosols, Areia, Paraiba, Brazil.
Green manure	2014	2015	2016	2017	2018	2019
B. decumbens	606.39 (8.31) a¹	580.29 (9.14) a	532.36 (7.84) a	467.80 (9.33) a	428.71 (8.36) b	273.92 (6.55) b
C. ensiformis	560.56 (9.38) b	510.36 (9.23) b	443.95 (9.23) b	422.84 (7.99) b	418.96 (8.23) b	141.14 (8.29) c
C. juncea	615.31 (10.22) a	576.53 (6.55) a	533.83 (9.81) a	474.36 (8.35) a	417.54 (7.12) b	299.8 (8.43) b
C. ochroleuca	620.66 (7.28) a	589.61 (8.89) a	563.76 (8.56) a	501.68 (8.71) a	491.24 (6.48) a	534.9 (9.91) a
C. spectabilis	553.13 (9.21) b	513.32 (5.37) b	456.13 (2.71) b	406.68 (4.35) b	380.51 (7.58) b	84.24 (2.31) c
D. lablab	587.16 (8.58) b	540.35 (4.32) a	505.19 (3.98) a	458.28 (5.49) a	415.18 (6.49) b	101.01 (3.89) c
M. pruriens	589.18 (10.92) b	525.33 (5.39) b	495.99 (3.33) b	446.76 (3.98) b	430.05 (5.51) b	133.24 (4.01) c
N. wightii	553.84 (9.33) b	485.53 (8.91) b	449.74 (8.49) b	516.88 (2.12) a	588.14 (4.59) a	628.65 (6.79) a
P. glaucum	577.72 (8.76) b	513.08 (4.29) b	465.83 (7.33) b	443.71 (8.33) b	420.35 (5.31) b	340.60 (2.89) b
S. aterrimum	597.46 (9.13) a	530.19 (3.78) a	492.74 (2.79) b	445.52 (6.33) b	430.67 (5.93) b	107.50 (1.79) c
¹Within studied plant species, same letters represent no significant differences by Bonferroni’s test t (p < 0.05)

Soil quality decay rate

The soil quality decay rate (SQ_k) showed that all the studied plant species have reduced the soil quality over the 6 studied years. After finishing our study, we found three clear and dissimilar groups (p < 0.001) considering SQ_k. The following groups were (i) low SQ_k with C. ochroleuca and N. wightii; (ii) intermediate SQ_k with B. decumbens, C juncea, P. glaucum; and (iii) high SQ_k with C. ensiformis, C. spectabilis, D. lablab, M pruriens, and S. aterrimum (Fig. 3).

Our results emphasize the green manure influence on soil chemical properties through their plant biomass production and further incorporation into soil profile in a Tropical ecosystem over 6 consecutive years. Essentially, we wanted to understand how the biomass incorporation into soil profile created a positive effect on soil ecosystem by improving soil properties without any input of fertilizers or soil conditioners. Our hypothesis about soil fertility improvement by green manure practice was not supported for Olsen’s available P, while for Ca²⁺, K⁺, and SOC stock it was supported. Our results also supported the Liebig's Law of the Minimum states that biomass production is proportional to the amount of the most limiting nutrient (Touhami et al. 2022). In our study, the limiting nutrient was P since its content decreased overtime on plots where all plant species were cultivated.

Green manure system based on leguminous plant species has been described as a management practice that would promote positive changes on SOC stock (103% more SOC stock), and soil nutrient contents (23.5, 17, and 31.5% more Ca²⁺, Mg²⁺, and K⁺, respectively) (Ashworth et al. 2019; Demir and Işık 2019; Melo et al. 2019). In our study, we observed SOC stock increases overtime, but even the leguminous plant species increasing both SOC stock, Ca²⁺, and K⁺ they were not able to improve soil P content (e.g., It was one factor that influenced both shoot dry biomass and root density as described in our predictive models). Many studies have reported soil P decline after consecutive monocropping systems (Liu et al. 2021; Xu et al. 2021), and in our study the green manure system considering monostands of Fabaceae and Poaceae plants showed the same phenomenon.

Considering that the Olsen’s available P content was not enough to supply plant P requirement, all studied plant species were unable to complete a normal life cycle and to produce their maximum sustainable yield in terms of shoots (aboveground biomass) and roots (belowground biomass) (Fontana et al. 2021; Fan et al. 2021). We have expected an increase on shoot dry biomass (e.g., as the soil ability to provide plant habitat) and soil quality after 6 consecutive years of green manure practice. Our results also highlighted the concept of soil quality that defines the soil capacity of a specific kind of soil function, within natural or managed ecosystem boundaries, to sustain plant productivity (Seaton et al. 2021; Ma et al. 2021).

It is important to described that we based our findings here considering that root density, Olsen’s available P, shoot dry biomass, Ca²⁺, K⁺, and SOC stock were the soil chemical properties that most contributed to soil quality changes and data variance (Nascimento et al. 2021). We also determined other soil abiotic traits (ESM_1), predictive models (ESM_2), and polynomial regression to estimate soil quality decay rate (ESM_3). We observed that there is a reduction in soil quality for all studied plant species, possibly by the 6 consecutive years using the green manure practice in a monocropping system. These findings support the ecological collapse hypothesis that describes soil function loss because of an event occurring on a short time scale, such as our treatments (Martins et al. 2021; Zhang et al. 2021).

The root density was a factor that explained about 54% on soil quality index. Thus, the belowground biomass production can be considered more important than the aboveground biomass production (e.g., since the shoot dry biomass just explained about 5% on soil quality index). We found that N. wightii showed after the 3rd year the highest root density values and it have contributed to soil quality index, and to reduce soil quality decay rate overtime. These results agree with previous studies done by Parhizkar et al. (2021), Ma et al. (2021), and Mortensen et al. (2021), which reported that soil ecosystems with high rootability and constant nutrient-rich organic amendments may increase soil chemical properties.

The green manure practice that this tropical soil type has been put through over the last 6 years has led to a soil quality decline, especially on plots where C. ensiformis, C. spectabilis, D. lablab, M pruriens, and S. aterrimum were cultivated. This soil quality decline has also been observed in agricultural fields (Soto et al. 2021; Thomaz and Antoneli 2021), degraded areas (Huang et al. 2021; Yeilagi et al. 2021), where values of soil quality index are lower and rarely reach 100 and 50, respectively. The way that we applied the green manure practice (monostands) must also be responsible for the soil P reduction (Wang et al. 2021; Freund et al. 2021). Such a decrease in soil nutrient contents limits the soil quality (e.g., since Olsen’s available P explained about 24% of soil quality variance).

These results can be justified to the frequency of green manure practice following a monostand for each studied plant species without nutrient inputs (via mineral or organic fertilizers), which have led to the drastically reduction of the natural low P content. There was greater variation among green manure species over the years with respect to influences on soil parameters, plant traits, and soil quality. It is well established that green manure exhibit positive influence on soil fertility which tends to accumulate its effects overtime (Chen et al. 2021; Adetunji et al. 2021). This may be related to the capacity of some plant species used as green manure to improve SOC, and nutrient contents through their incorporation into soil profile (Torres et al. 2021; Crespo et al. 2021). Our data on SOC stock, Ca²⁺ and K⁺ (See also ESM_1) corroborates our first hypothesis that green manure practice improve soil chemical properties. However, all plant species were not very efficient at improving Olsen’s available P. Our results emphasize that even the green manure practice needs additional inputs (chemical or organic fertilizers), since the soil P content is an important factor for the plant nutrition that aim to improve plant traits (Wang et al. 2021; Zhou et al. 2021).

For all the species tested here, we observed significantly soil quality index variability among plant species, which is consistent with previous studies (Gunasekaran et al. 2021; Santos et al. 2021). A variation from 84.24 to 628.65 in soil quality index has been associated with the following traits: root density, Olsen’s available P, shoot dry biomass, Ca²⁺, K⁺, and SOC stock (e.g., each traits have been influenced 53.94, 27.37, 5.43, 3.17, 2.15, and 1.73% of soil quality index variance, respectively), suggesting that soil quality index cannot be solely predicted based on above- or belowground biomass production (Gunasekaran et al. 2021; Islam et al. 2021). Observing the soil quality decay rate when associated with each plant species corroborates our second hypothesis that plant species elicits distinct traits after at least 3 consecutive years of its cultivation as described by Souza et al. (2015). This tended to be more evident on the SQ_k in 2019 compared with 2016. The 6th year was more affected than the 3rd year when soil P content were significantly reduced. These results corroborate other findings that soil quality responses may depend on several soil and plant traits (Yan et al. 2021; Zhang et al. 2021), and we cannot exclude the Liebig’s law (Ledari et al. 2021; Wu et al. 2021). The mechanisms responsible for the asymmetric effects of green manure on tropical soil traits are not fully understood (Ding et al. 2021; Peralta-Antonio et al. 2021). The asymmetry in soil traits over the studied years (Amede et al. 2021), or variations in the same year (Abera and Gerkabo 2020) has already been widely reported in the literature (Ma et al. 2021).

It is suggested, however, that the variation in plant traits such as allocation of nutrients, nutrient uptake, root traits (e.g., morphology, thickness, and development of root hair), nutritional requirements, and growth forms (annual vs. perennial), may account for some of these involved mechanisms (Freschet et al. 2020; Han et al. 2021). Plant species with high root density (e.g., N. wightii) can improve rootability (Restovich et al. 2019), and rhizodeposition around the rhizosphere by the allocation of C-rich compounds (Zhang et al. 2021; Kelly et al. 2022). Plant roots may influence the input of SOC into soil profile (Rossi et al. 2020). We found that N. wightii provided the highest values for soil quality index in 2019. In our plots, we observed that N. wightii developed a dense perennial root system during the six years of our experiment, in addition this plant species showed an extraordinary regrowth capacity (Acosta et al. 2020; Forstall-Sosa et al. 2020).

Our results highlighted the importance to take care with green manure practice following a monocropping system (Haruna et al. 2020). It may act as a driver for reducing the soil P content, especially if we are considering the effect of the green manure practice over 6 consecutive years. It has been widely reported that sandy soils with high organic matter inputs tends to increase soil P loss by vertical mobilization (Koutika et al. 2020; Kalkhajeh et al. 2021). This suggest that mechanisms related to the capacity of green manure to increase soil P leaching and soil quality decay rate are complex and depend on several factors such as root traits, soil P content, soil texture, plant species diversity, SOC stock, and time (Tran et al. 2021; Vandermoere et al. 2021; Zhang et al. 2021).

Our data provided strong evidence that a careful selection of plant species for green manure practice is important to ensure positive and negative outcomes for this plant-soil interaction with respect to soil P content and soil quality. Another important practical implication of our study is that generalization about the green manure practice cannot be made based on the effects from a single year or experiment. This highlights the importance of assessing plant and soil traits overtime to select the best plant species when developing policies for soil management that will promote both agroecosystems, and environment.

Author Contributions: We declare that all the authors made substantial contributions to the conceptualization, methodology, software, validation, formal analysis, investigation, resources, writing – original draft preparation, writing – review and editing, visualization, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding: This work was partly funded by the FAPESQ and CAPES.

Acknowledgments: We thank the GEBIOS (Soil Biology Research Group) for practical support. We thank the Post-graduate Program of Crop Production of the Federal University of Acre for facilitating the post-doc studies of the second author. Tancredo Souza is supported by a Research fellowship from FAPESQ-Brazil. Lucas Jónatan Rodrigues da Silva, and Lídia Klestadt Laurindo benefited from a scholarship provided by FAPESP and CNPq, respectively.

Conflicts of Interest: The authors declare that they have no conflict of interest.

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Soil quality decay rate and plant traits in green manure following monocropping farming systems at tropical conditions

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Introduction

Material And Methods

Field experiment

Experimental design

Studied plant species

Soil sampling

Soil chemical properties

Shoot dry biomass and root density

Soil quality index

Soil quality decay rate

Statistical analysis

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Green manure influence on soil abiotic traits over 6 consecutive years in a Tropical Regosols

Redundancy analyses

Soil quality index

Soil quality decay rate

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