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Article

Nutrient Accumulation in Cover Crops under Contrasting Water Regimes in the Brazilian Cerrado

by
Alberto do Nascimento Silva
1,
Walter Quadros Ribeiro Junior
2,
Maria Lucrecia Gerosa Ramos
1,*,
Cristiane Andrea de Lima
1,
Adilson Jayme-Oliveira
3,
Antonio Marcos Miranda Silva
4 and
Arminda Moreira de Carvalho
2
1
Faculdade de Agronomia e Medicina Veterinária, Universidade de Brasília, Brasília 70910970, Brazil
2
Embrapa Cerrados, Empresa Brasileira de Pesquisa Agropecuária, Planaltina 73310970, Brazil
3
Instituto Federal de Brasília, Campus Planaltina, Brasília 73380900, Brazil
4
Departamento de Ciência do Solo, Universidade de São Paulo—ESALQ, Av. Pádua Dias, 11, Piracicaba 13418900, Brazil
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(10), 1617; https://doi.org/10.3390/atmos13101617
Submission received: 17 August 2022 / Revised: 15 September 2022 / Accepted: 23 September 2022 / Published: 3 October 2022
(This article belongs to the Special Issue Agrometeorology)
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Versions Notes

Abstract

:
Brazilian Cerrado has a dry period, and the inclusion of new species for diversification in the production system needs to be drought-tolerant. This work aimed to evaluate biomass and nutrient accumulation in species with potential as cover crops and grain crops under different water levels. Irrigation treatments were obtained through an irrigation bar with sprinklers with increasing water flows to create a continuous gradient. The experimental design randomized complete blocks in split plots with four replications. The main plots were composed of four water regimes (167 mm, 268 mm, 381 mm and 432 mm), and the subplots were formed by the following cover crops: Amaranthus cruenthus, Chenopodium quinoa and Pennisetum glaucum, the latter already used for this purpose. Amaranthus cruenthus and P. glaucum recorded the highest dry biomass (10.16 and 9.75 Mg ha−1, respectively). Dry biomass production and the cellulose contents decreased with the reduction of water availability for all species. A. cruenthus was the species that most accumulated P (37.42 kg ha−1), K (416.92 kg ha−1), Mg (30.88 kg ha−1), S (43.53 kg ha−1), Fe (2.22 kg ha−1), B (0.124 kg ha−1) and Zn (0.240 kg ha−1). Amaranthus cruenthus produced the highest yield. Under high and low water availability conditions, A. cruenthus presents potential as a cover crop and grain cash crop, in addition to accumulating more nutrients; P. glaucum has potential as a cover crop and C. quinoa only for grain production.

1. Introduction

The Cerrado is one of the largest and most important biomes in Brazil, with an area of 2,036,448 km2, and represents about 24% of the national territory [1]. Agriculture in the Cerrado region is characterized by two cultivation periods: the main crop season (period with the highest rainfall) from October to January and a second crop “off-season” from February to May. As a result, water shortages are expected to impact up to two-thirds of humanity between 2010 and 2050, and subsistence farmers worldwide would benefit from nutrition and drought-tolerant cover crops [2].
Winter cultivation in Cerrado, between May and September, presents low rainfall, and irrigation is used. Currently, one of the major challenges in the Cerrado region is to obtain species with a high potential for grain production in the second crop and simultaneously produce enough biomass for coverage and protect the soil during the off-season, since water availability for the plants in these periods is reduced [3]. The use of cover crop in an agricultural system is mainly beneficial for soil and water conservation [4]. Cover crops such as Vicia villosa improved the soil moisture preceding the soybean growing season in very fine sandy loam soil [5]. On the other hand, Hunter et al. [6] observed that cover crops, such as clover and radish, neither ameliorated nor exacerbated drought stress tolerance in the following cash crop: maize; in the same work, the authors obtained a negative effect of rye on the subsequent crop. Cover crops, in addition to soil protection against degradation agents such as erosion and compaction, can restore considerable amounts of nutrients since they absorb nutrients from the soil subsurface layers and release them on the soil surface by decomposition of plant residues [7,8,9].
The use of species tolerant to water stress with slower decomposition rate favors soil coverage and a gradual release of nutrients for subsequent crops. Crop residue accumulation and nutrient release into soil depend on their quantity and quality, which influences the processes of plant decomposition [7,10]. Cellulose, hemicelluloses, lignin contents and C/N ratio are important indicators of crop residue quality for maintaining soil covered due to slower decomposition [11]. Moreover, the decomposition rates of plant residues are negatively related to the number of compounds rich in aromatic rings and that is difficult to break down, such as lignin [12].
Pennisetum glaucum (L.) R. Brown is a traditional crop in West Africa and Asia, with exceptional adaptation to abiotic stresses [13]. It is one of the most cultivated cover crop species in the Cerrado region due to its greater tolerance to drought, high biomass production and efficient nutrient cycling [8,14,15], and it is adapted to semi-arid regions [16].
Some species characterized as pseudocereals are potential alternatives as cover crops because of their adaptation to the Cerrado region [14]. Among them, Amaranthus cruenthus is a widely cultivated species that produces grains; its leaves are also used for human and animal consumption [17]. In addition, A. cruenthus has pivoting roots with abundant lateral roots, which favor the absorption of water and nutrients [18], and is adapted to arid regions or places with prolonged drought periods.
Chenopodium quinoa (Willd) is a pseudocereal species from the Andes region and is considered an exceptional crop for its potential to contribute to food security [19]. The species is well-adapted to abiotic stresses, such as water stress, low temperatures, salinity and nutrient-poor soils [20,21]. In addition, this species has well-adapted to the Cerrado region due to the amount of biomass and grain production and is an alternative for soil protection in the no-tillage system [22].
Cover crops may have a secondary purpose, grain production, which would promote economic sustainability, providing income from the commercialization of the grains. The pseudocereals are species with high potential for grain production, and studies have reported A. cruenthus productivity ranging from 990 to 3692 kg ha−1 [23,24]. Some C. quinoa genotypes produce up to 8.34 t ha−1 with a water regime of 389 mm during the crop cycle [25]. Additionally, the grains of these two species present high nutritional value; are rich in macronutrients and micronutrients, including vitamins and minerals, high protein and essential amino acids; and are considered functional foods [19,26].
Therefore, we have tested the hypothesis that, in addition to Pennisetum glaucum, A. cruenthus and C. quinoa consist of alternative species, as they have potential as cover crops, nutrient accumulation and grain yield, even in conditions of low water availability. The objective of this work was to evaluate biomass production and nutrient accumulation in species with potential as cover crops and grain production under different water levels.

2. Materials and Methods

The experiment was conducted at the Embrapa Cerrados in Planaltina, DF, Brazil, located at the geographic coordinates: 15°35′30″ S and 47°42′30″ W. The climate of the region is characterized as Aw, according to the Köppen classification, with two well-defined seasons (dry and rainy). Summer is warm and humid, with dry spells during the rainy season, called veranicos. It presents average annual rainfall of 1400 mm and an average temperature of 21.3 °C [27]. The monthly average temperature and rainfall data during the experiment are presented in Figure 1.
The soil of the experimental area is classified as clayey Oxisol (Typic Haplustox) [28] and presents the following chemical composition 0–20 cm layer: pH (H2O) = 5.77 and Ca (cmolc dm−3) = 3.34, Mg (cmolc dm−3) =1.41, K (mg dm−3) = 207.55, H + Al (cmolc dm−3) = 4.52, P (mg dm−3) = 48.56, S (mg kg−1) = 19.71 and organic matter (g kg−1) = 26.0.
The history of the experimental area over the last nine years is presented in Table 1. Before being cultivated with soybeans in the 2005/2006 crop season, the area was under native Cerrado vegetation.
The experimental design was randomized blocks with split plots and four replications. The main plots were composed of four water regimes (167 mm, 268 mm, 381 mm and 432 mm), and the subplots were formed by the following cover crops: Amaranthus cruenthus; Chenopodium quinoa “Genotype derived from BRS Piabiru” and Pennisetum glaucum. The plots measured 24 m × 3.2 m, and the subplots measured 8 m × 3.2 m. Each plot was composed of 8 lines, spaced at 0.40 m.
Cover crops were sown in the first week of May 2015. Seeds were sown manually under a no-tillage system. The seeding density was 200 seeds m−1 for A. cruenthus, 150 seeds m−1 for C. quinoa and 58 seeds m−1 for P. glaucum. The high seeding rate was applied to compensate germination failures due to the small seed size. Twenty days after emergence (DAE), thinning was performed, obtaining 10 plants m−1 for A. cruenthus and 20 plants m−1 for C. quinoa and P. glaucum.
NPK fertilization at planting was used with the formulation 04-30-16 at 400 kg ha−1. Thirty days after seedling emergence, nitrogen topdressing was applied at a dose of 100 kg N ha−1 as urea. To avoid the competition of invasive plants, manual weeding was performed.
The water regimes were obtained using a sprinkler irrigator bar 40 m wide, connected to a spool with adjustable speed and ten sprinklers were installed on each side of the bar. During the 35 of germination, irrigation was uniform, and ten irrigations were performed, totaling 135 mm. After this period, the line source methodology was adapted [29], using sprinklers with decreasing sizes from the central area to the end of the experimental area. The sprinklers overlapped and promoted a decreasing gradient of water. For each side of the irrigation bar, 4 plots were delimited, with a linear distance between them, representing the water regimes (WR). In this phase, 13 irrigations were performed. The accumulated depths of the uniform plus variable irrigations were 167, 268, 381 and 432 mm for the four WRs. Two rows of collectors parallel to the irrigation line were installed to measure the volume of water applied to each irrigation. Irrigations were carried out according to the irrigation monitoring program in the Cerrado [30], using wheat crop as a reference, the agrometeorological indicators of the region, the soil type and the date of germination.

2.1. Production of Dry Biomass and Structural Components (Lignin, Cellulose, Hemicellulose and Lignin/N)

For dry biomass production, a sample was collected from each plot during the flowering of cover crops, with an area of 3 m2, in the four central lines, with 2.5 m in length. The collected material was kept at 65 °C for 72 h until reaching a constant weight.
From dry biomass samples, three subsamples were collected to determine lignin, cellulose and hemicellulose contents by the sequential method [31], through of the analysis of fiber neutral detergent (FND) and fiber acid detergent (FAD), modified by Komarek [32], using an Ankom fiber apparatus (Ankom Technology Corp., Fairport, NY, USA). Lignin analysis was determined by digestion of FAD residue with 72% sulfuric acid, with the extracts of cellulose and hemicellulose, producing lignin inorganic matter as a residue. Hemicellulose and cellulose were quantified by the difference between the FND and FAD residues and between the FAD and lignin residues, respectively. The difference between the acid digestion residue and after burning at 600 °C for four hours was determined the lignin content.

Accumulation of Macro and Micronutrients in the Shoot

The concentrations of phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), sulfur (S), copper (Cu), manganese (Mn), zinc (Zn), boron (B) and iron (Fe) in the dry biomass were determined using an inductively coupled plasma optical emission spectrophotometer (ICP-OES, Thermo Cientific, 7000, Waltham, MA USA). The nitrogen (N) concentration in the plants was determined by the Kjeldahl method. The accumulation of macro and micronutrients in plants was calculated by the product between the concentration of each element in the plant tissue and the amount of dry biomass produced. The results were expressed in kg ha−1.

2.2. Grain Productivity

Grain yield was obtained by mechanical harvesting of plants in an area of 7.2 m2 plot−1. A subsample was oven dried at 65 °C until constant weight to determine the moisture of the grains. Productivity was corrected to 13% humidity, and the results were expressed in kg ha−1.

2.3. Statistical Analysis

Data were subjected to analysis of variance (ANOVA) and the comparison of means was performed by the Tukey’s test was used, at 5% of probability, using statistical software SAS [33]. The statistical model was adjusted using the SAS PROC MIXED through the restricted maximum likelihood method (reml). The variation sources were water regimes (plots), cover crops (subplots) and their interactions. For the variables in percentages (cellulose, hemicellulose and lignin), data were transformed into square root of arcsine (x/100). These transformations were necessary to obtain data residue normality.
Data were also submitted to the redundancy analysis (RDA) in the CANOCO® statistical program [34] after being transformed into Log C + 1 and meeting the criterion of the gradient length lower than 3 of the distended correspondence analysis (DCA) [35]. The variables (P, K, S, Al, Fe, Cu, Ca, Mn, Mg, B and Zn) were analyzed as explanatory variables, and the contents of lignin, cellulose, hemicellulose and dry biomass were analyzed as response variables. The Monte Carlo permutation test (permutations = 999) was carried out to determine which explanatory variables were most significant (p ≤ 0.05) in the model.

3. Results

3.1. Production of Dry Biomass and Structural Components (Lignin, Cellulose, Hemicellulose and Lignin/N)

Amaranthus cruenthus and P. glaucum presented the highest dry biomass (BS) production, with 10.16 and 9.75 Mg ha−1 (p < 0.05), respectively (Table 2), and C. quinoa was the species with the lowest BS (7.31 Mg ha−1), 28% smaller than the best species (A. cruenthus). Penisetum glaucum was the species with the lowest concentration of lignin (28% lower than C. quinoa) and higher concentration of cellulose and hemicellulose: 12 and 45% higher cellulose and hemicellulose content, respectively, compared with C. quinoa. The cellulose, hemicellulose and lignin concentrations in A. cruenthus and C. quinoa were statistically similar.
Regarding the water regime (WR), the lowest BS production occurred in the lower water regime (167 mm), which differed from the other regimes (p < 0.05). Comparing the highest water regime (432 mm) with the lowest one (167 mm), there was a 31% reduction in BS production. Increased water availability enhanced the cellulose and reduced the hemicellulose contents. The lignin contents were not influenced by WRs. No significant difference was obtained for the lignin/N ratio for cover crops and water regimes.

3.2. Accumulation of Macro and Micronutrients in the Shoot

A significant effect of cover crops and water regimes was obtained in the accumulation of macro and micronutrients. The interaction between water regimes and cover crops was significant (p < 0.05) only for P, Ca, B and Zn (Table 3). In general, A. cruenthus was the species that accumulated more nutrients. Under higher water availability (432 mm), this species accumulated up to 37.42 kg ha−1 of P; P. glaucum reached up to 30.88 kg ha−1 (381 mm), and C. quinoa reached 24.29 kg ha−1, 33% lower than A. cruenthus, with the same water amount of applied water (432 mm).
The reduction of water availability resulted in a lower accumulation of P in the shoot for all species. C. quinoa was the most sensitive to a lower water regime, with a reduction of 70% in the P content. For A. cruenthus and P. glaucum, these reductions were 62% and 66%, respectively.
A. cruenthus was the species with the highest Ca content in all WRs (p < 0.05), with values up to 162.36 kg ha−1. P. glaucum and C. quinoa accumulated similar amounts of Ca, except for WR 432 mm, which presented greater accumulation in C. quinoa. The increase in water availability positively influenced the accumulation of this nutrient in A. cruenthus and C. quinoa. As with the P content, the Ca accumulation was more sensitive to lower water availability in C. quinoa, with a reduction of 62% for this species and 51% and 41% for A. cruenthus and P. glaucum, respectively.
The B content was increased by 66.33% and 59.42% for A. cruenthus and C. quinoa, respectively, when comparing the highest and lowest water regime. P. glaucum accumulated the same amount of B, regardless of the applied water. Among the species, in general, a greater accumulation of B was observed in A. cruenthus.
The three species showed similar contents of Zn in the 167, 268 and 381 mm water regimes (Table 3). In WR 432 mm, A. cruenthus and P. glaucum presented the highest and lowest accumulation of Zn (p < 0.05), respectively. Unlike that observed for P and Ca, A. cruenthus showed the lowest Zn concentrations at the lowest water regime. The reduction of Zn content was equivalent to 72% for this species, 57% for C. quinoa and 47% for P. glaucum, when comparing the highest and the lowest water regime. P. glaucum was the species less affected by the reduction of water availability in Ca and Zn contents.
N content was similar among the species (Table 4). A. cruenthus accumulated most K (416.92 kg ha−1), Mg (30.88 kg ha−1), S (43.53 kg ha−1) and Fe (2.22 kg ha−1). For Cu, this species presented similar values to P. glaucum and C.quinoa; for Mn, this species presented similar content to C. quinoa. P. glaucum had the lowest levels of K and Mg (p < 0.05) and showed similar contents of S, Fe and Mn compared to C. quinoa. Although it presented lower values than A. cruenthus, it is worth mentioning the high capacity of C. quinoa to accumulate K (367.89 kg ha−1). There was a significant reduction in the concentration of these nutrients in the plant biomass with the decrease in water availability (p < 0.05), except for Fe, which was not influenced by the water regimes.

3.3. Grain Productivity and Redundancy Analysis (RDA)

The highest grain yields of A. cruenthus and C. quinoa were between 3549.45 kg ha−1 and 3488.86 kg ha−1, respectively, in WR 432/381 (p < 0.05) (Table 5). A. cruenthus produced higher grain production than C. quinoa under the two intermediate regimes and did not differ under the extreme ones (168 and 432 mm). However, A. cruenthus presented a reduction of 71% when comparing the regime with the highest productivity (381 mm) with the lowest (167 mm). For C. quinoa, the reduction was 80%. P. glaucum did not produce grains.
In the redundancy analysis, two groups were formed for A. cruenthus species (Figure 2A). These groups presented different relations with the dynamics of the explanatory variables. The group was most closely related to the explanatory variables were WR 381 and 432 mm (Figure 2B). In contrast, the 167 and 268 mm water regimes for A. cruenthus species presented the lowest relation with the explanatory variables. However, there was a partial overlap in C. quinoa and P. glaucum, especially in the intermediary water regimes (268 and 381 mm) (Figure 2C). Among the explanatory variables of the model, P, Cu, Mn, S and Mg were the significant variables, according to the Monte Carlo permutation test (Table 6).
In the separation of WRs, the most contrasting water regime, which was not related to the dynamics of the explanatory variables, was 167 mm (Figure 2B). WR 381 mm was the most closely related to the dynamics of the explanatory and response variables (Figure 2C). The length of the response vectors in the ordering diagram (Figure 2A–C) reflected their contribution to the model. The relationship between the variables is expressed in the diagram by the angle formed between them. Thus, dry biomass showed greater contribution among the variables, followed by the lignin and cellulose contents. A possible no correlation between dry biomass and the lignin content was observed, expressed in the diagram as an approximate angle of 90° between these vectors. The lignin content was correlated with C. quinoa species in the 381-mm water regime (Figure 2C), whereas the BS was less correlated with all water regimes for this species (Figure 2C).

4. Discussion

4.1. Dry Biomass Production and Structural Components (Lignin, Cellulose and Hemicelluloses)

In the Cerrado region, the residual crop and cover crops on the soil surface are between 2.38 to 10.64 ton ha−1 [36]. Biomass production for the legumes ranged from 1420 (velvet bean) to 4807 kg ha–1 (sesbania) in North Carolina in a clayed soil with an adequate level of P and K [37]. In the present work, A. cruenthus (10.16 Mg ha−1) and P. glaucum (9.75 Mg ha−1) presented BS production close to the highest amount of crop residues on the soil surface. Penisetum glaucum is commonly used cover crop in the Cerrado region due to its rapid growth and establishment and high BS production. In addition, this plant is tolerant to water stress, which makes it promising for use in the Cerrado [14], especially due to climate change, with precipitation instability and extreme temperatures [14]. Therefore, A. cruenthus is useful as a cover crop in the Cerrado region in dry and rainy years.
A. cruenthus is a crop recently introduced in the Cerrado region. The BS values obtained in the present study were much higher than those obtained by other authors [24]. This difference in productivity may be related to climatic conditions, water availability provided by the different regimes, genotype used and fertilization. Fertilization is carried out to produce grains, constituting an unusual practice in the cultivation of cover crops, which use only residual fertilization of the main crop. This practice must be changed considering the benefits of cover crops. Due to the high production of dry biomass obtained in this work, sufficient for soil protection, this species can be considered an alternative as a cover crop in the Cerrado, resulting in greater diversification of the production system.
The maintenance of plant biomass on the soil surface depends mainly on the biomass decomposition rate, which is controlled by its quality, which is directly related to its organic components (cellulose, hemicelluloses and lignin). Alternatively, they must improve soil fertility by cycling and releasing nutrients, gradually supplying the needs of succession crops [9].
Cellulose is more labile and generally decomposes faster than lignin due to its composition and chemical structure, consisting of C less recalcitrant, such as alkyls and O-alkyls [38,39]. Thus, higher decomposition rates are observed in plants with higher cellulose content and lower lignin content [15]. In the present work, A. cruenthus and P. glaucum presented higher biomass production, whereas P. glaucum showed higher cellulose content (30.41%) and lower lignin (3.64%). Pacheco et al. [15] evaluated biomass production and nutrient cycling by cover crops in the Cerrado and found higher decomposition rates in P. glaucum and U. ruziziensis. The authors attributed the results to the lower concentrations of lignin of these species and concluded that it altered the rate of decomposition of the cover crops. Thus, P. glaucum presents a greater potential for nutrient release in the soil, as it may present faster decomposition than A. cruenthus. However, A. cruenthus presents greater potential to protect the soil due to its higher lignin contents than P. glaucum. Besides, A. cruenthus can be used as soil protection, especially in degraded soils, as it has slow decomposition compared with P. glaucum. In addition, the nutrient release could be used not only in the next crop but also in the long-term or with subsequent perennial crops.

4.2. Accumulation of Macro and Micronutrients in the Shoot

The accumulation of nutrients by plants is influenced by the species/cultivar, type of soil, environmental conditions, nutrient characteristics, and water availability, which mainly affect ion-root contact processes [40]. N, Ca, Mg, S, B, Cu, Fe, and Zn are absorbed by roots through mass flow; diffusion is the process for absorption of P and K, and root trapping for Mn. Diffusion also contributes to Ca absorption, while water stress reduces plant growth and the diffusion and mobility of nutrients in soil [41].
The accumulation of macro and micronutrients was related to the increase in water availability. This may be associated with the movement of these nutrients in the soil by mass flow and diffusion. Water stress reduces the mobility of P and its transfer from soil to root and subsequent transport to the stem, as this nutrient moves predominantly by diffusion through the difference in soil concentration [42,43]. Drought tolerance and plant water use efficiency can be enhanced by adequate phosphorus nutrition [44].
Dry biomass production of the three species in the most stressed water regime (168 mm) showed that C. quinoa was the species with lower phosphorus accumulation. This fact may have contributed to the lower drought tolerance and lower biomass production of this species, considering that P is the main limiting nutrient in the Cerrado region. Among the mechanisms proposed to justify the effect of phosphorus on root growth are the increase in root growth, stomatal conductance, leaf area and photosynthesis, and increased cell membrane stability [45,46].
Potassium is another nutrient that, despite being more mobile than P in the soil, under water stress conditions, has reduced absorption and translocation by the roots [47]. Water stress dramatically affects the opening and closing of the stomata and, consequently, the photosynthetic activity [25,46]. The osmotic adjustment or regulation, cell elongation, root growth promotion, stomatal regulation as well as reactive oxygen detoxification are pathways of potassium activation towards greater tolerance to drought by plants [47].
Nutrient uptake is also directly related to the development of the roots, as their growth is influenced by water availability in the soil. Therefore, plants with greater specific root length and a high proportion of fine roots [48] absorb nutrients more efficiently.
Amaranthuscruenthus was the species that most accumulated P (37.42 kg ha−1), K (416.92 kg ha−1), Mg (30.88 kg ha−1), S (43.53 kg ha−1) and Fe (2.22 kg ha−1) in the shoot. Cover crops with great nutrient accumulation can release nutrients to the main crop in rotation, succession, or consortium [49,50]. P. glaucum showed the lowest concentration of nutrients; however, it presented higher accumulation of K and Ca than those obtained by Pacheco et al. [15], who found 155 kg ha−1 of K and 28 kg ha−1 of Ca for this species. A. cruenthus and C. quinoa presented high levels of P and K. These nutrients play an important role in the preservation and transfer of energy in metabolism, osmotic and stomatal regulation in plants, which are fundamental of water stress tolerance [47].

4.3. Grain Productivity and Redundancy Analysis (RDA)

No grain yield was observed in P. glaucum, probably due to 76 days with temperatures below 15 °C (Figure 1) during the crop cycle. Low temperatures (13 to 16 °C) may favor the appearance of sterile plants in this species. Jayme-Oliveira et al. [14] evaluated the performance of cover crops in the Cerrado between May and October and obtained 115 h of temperatures below 16 °C, which may have prevented the production of grains in P. glaucum.
A. cruenthus (3866.89 kg ha−1) and C. quinoa (3488.86 kg ha−1) showed high productivity. Da Silva et al. [25] studied several C. quinoa genotypes and obtained high yields in the Brazilian Cerrado under four water regimes and found yields between 8570 and 2580 kg ha−1, comparing the highest (480 mm) and the lowest water regimes (150 mm). For A. cruenthus, yields ranging from 990 to 3692 kg ha−1 are reported [24]. In the present study, A. cruenthus showed higher productivity than those obtained by [51], but for C. quinoa under irrigation, productivity was higher, mainly in WR 432 mm. However, when cultivated under severe stress (268 and 167 mm), significant reductions in productivity were observed for both A. cruenthus (71%) and C. quinoa (80%). In severe water deficit, intense morphological and physiological changes are observed in plants [25,52], directly affecting grain yield. Jayme-Oliveira et al. [14] evaluated the growth and development of A. cruenthus and C. quinoa under different water regimes and observed reductions in leaf area, leaf number and height of A. cruenthus and C. quinoa under water stress.
According to the RDA, P was one of the significant variables in the model, followed by Cu, S, Mn and Mg. The P was correlated with the A. cruenthus species in the WR 381 mm. Likewise, this result was observed in the univariate analysis, in which A. cruenthus was the species with the highest accumulation of P in the dry biomass (38.81 kg ha−1).

5. Conclusions

Amaranthus cruenthus produced higher nutrient accumulation than P. glaucum and C. quinoa, and higher biomass than C. quinoa. In addition, in general, this species produced a higher yield than C. quinoa, especially in lower water regimes. A. cruenthus can be recommended as a dual purpose crop for the target region. Future research should test it in the productive system in Cerrado farm conditions with several options as subsequent annual or perennial crops. C. quinoa can be indicated for grain production but not as cover crop.

Author Contributions

Conceptualization, W.Q.R.J. and M.L.G.R.; methodology, A.J.-O., A.d.N.S., M.L.G.R., W.Q.R.J., C.A.d.L. and A.M.d.C.; validation, A.M.M.S., A.J.-O. and C.A.d.L.; formal analysis, A.M.d.C., A.d.N.S., M.L.G.R., W.Q.R.J., C.A.d.L. and A.M.M.S.; writing—original draft preparation, W.Q.R.J., M.L.G.R.; A.d.N.S., C.A.d.L., A.J.-O. and A.M.d.C.; and funding acquisition, W.Q.R.J., M.L.G.R. and A.M.d.C. All authors have read and agreed to the published version of the manuscript.

Funding

Project funding was provided by the Brazilian Agricultural Research Corporation and Coordination for the Improvement of Higher-Level Personnel (“CAPES/EMBRAPA Call for Proposals”—15/2014, project number 76).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

To Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the scientific productivity fellowships granted to the third and seventh authors. Furthermore, to Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the Master’s granted to the first author.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Atmosphere 13 01617 g001 550
Figure 1. Average precipitation and temperature of the experimental area from May to November 2015. Data were obtained from an automatic station located next to the experimental area.
Figure 1. Average precipitation and temperature of the experimental area from May to November 2015. Data were obtained from an automatic station located next to the experimental area.
Atmosphere 13 01617 g001
Atmosphere 13 01617 g002 550
Figure 2. Order diagram of the redundancy analysis (RDA) with the explanatory variables (P, K, S, Al, Fe, Cu, Ca, Mn, Mg, B and Zn) and the variable responses (lignin, cellulose and dry biomass contents) based on the Monte Carlo permutation test (permutation = 999) distributed according to plant species (A; n = 16), water regime (B; n = 12) and both (C; n = 4). Asterisk denotes significance (p < 0,05), according to Table 6.
Figure 2. Order diagram of the redundancy analysis (RDA) with the explanatory variables (P, K, S, Al, Fe, Cu, Ca, Mn, Mg, B and Zn) and the variable responses (lignin, cellulose and dry biomass contents) based on the Monte Carlo permutation test (permutation = 999) distributed according to plant species (A; n = 16), water regime (B; n = 12) and both (C; n = 4). Asterisk denotes significance (p < 0,05), according to Table 6.
Atmosphere 13 01617 g002
Table
Table 1. Description of the cultivation history of the studied area between 2005 and 2015.
Table 1. Description of the cultivation history of the studied area between 2005 and 2015.
Crop SeasonPeriod
WinterSummer
2005/2006FallowSoybean
2006/2007FallowSoybean
2007/2008FallowSoybean
2008/2009FallowSoybean
2009/2010FallowSoybean
2010/2011FallowSoybean
2011/2012Soybean under different water regimesFallow
2012/2013Wheat under different water regimesSoybean
2013/2014A. cruenthus, P. glaucum e C. C. quinoa under different water regimesCrotalaria juncea
2014/2015A. cruenthus, P. glaucum e C. C. quinoa under different water regimesZea mays
Table
Table 2. Dry biomass (BS) and contents of cellulose, hemicellulose, lignin and lignin/N ratio in the cover crops (A. cruenthus, P. glaucum and C. quinoa) and in the water regimes (167, 268, 381 and 432 mm).
Table 2. Dry biomass (BS) and contents of cellulose, hemicellulose, lignin and lignin/N ratio in the cover crops (A. cruenthus, P. glaucum and C. quinoa) and in the water regimes (167, 268, 381 and 432 mm).
TreatmentDry Biomass (Mg ha−1)Cellulose (%)Hemicellulose (%)Lignin (%)Lignin/N
Cover crop
C. quinoa7.31 b26.75 b13.22 b4.28 a0.22 a
P. glaucum9.75 a30.41 a24.39 a3.64 b0.22 a
A. cruenthus10.16 a27.36 b13.56 b4.35 a0.24 a
Water regime (mm)
1676.84 b26.20 b 18.19 a3.68 a0.20 a
2689.47 a28.43 a 17.03 ab4.40 a0.26 a
3819.94 a29.43 a 17.28 ab4.44 a0.24 a
43210.04 a28.63 a 15.74 b3.85 a0.21 a
Means followed by the same letter in each column do not differ statistically by Tukey’s test at 5% probability.
Table
Table 3. Interaction between the water regime and cover crops in the accumulation of phosphorus (P), calcium (Ca), boron (B) and zinc (Zn) in the shoot biomass of the three cover crop species.
Table 3. Interaction between the water regime and cover crops in the accumulation of phosphorus (P), calcium (Ca), boron (B) and zinc (Zn) in the shoot biomass of the three cover crop species.
Water Regime (mm)Cover Crop
A. cruenthusP. glaucumC. quinoa
P (kg ha−1)
16714.40 Ac10.32 Bc7.11 Cc
26831.04 Ab20.60 Ab15.36 Bb
38138.81 Aa30.88 Ba21.46 Cab
43237.42 Aab26.39 Bab24.29 Ba
Ca (kg ha−1)
16779.24 Ac23.94 Ba28.47 Bb
268141.09 Ab36.05 Ba51.18 Bab
381141.03 Ab40.65 Ba60.81 Ba
432162.36 Aa33.48 Ca75.99 Ba
B (kg ha−1)
1670.042 Ac0.016 Ba0.028 Ab
2680.088 Ab0.020 Ca0.042 Bb
3810.110 Aa0.026 Ca0.065 Ba
4320.124 Aa0.021 Ca0.069 Ba
Zn (kg ha−1)
1670.064 Ac0.067 Ab0.070 Ab
2680.150 Ab0.102 Aab0.128 Aa
3810.179 Ab0.125 Aa0.157 Aa
4320.240 Aa0.121 Ca0.162 Ba
Means followed by the same small letter in each column, and capital letter in a row, do not differ statistically by Tukey´s test at 5% probability.
Table
Table 4. Isolated effect of cover crops and water regimes on the accumulation of macro- and micronutrients in the shoot biomass of the three cover crops.
Table 4. Isolated effect of cover crops and water regimes on the accumulation of macro- and micronutrients in the shoot biomass of the three cover crops.
TreatmentNKMgSFeCuMn
Cover crop
kg ha−1
A. cruenthus182.68 a416.92 a30.88 a43.53 a2.22 a0.03 ab0.17 a
P. glaucum165.51 a200.37 c12.87 c21.02 b1.46 b0.04 a0.10 b
C. quinoa144.88 a367.89 b18.63 b23.22 b1.65 b0.02 b0.13 ab
Water regime (mm)
167141.91 b222.84 b13.69 b18.03 c1.82 a0.02 c0.11 b
268168.19 a345.30 a21.49 a27.69 b1.85 a0.03 b0.14 ab
381166.52 a380.11 a23.42 a34.28 a1.67 a0.03 b0.13 ab
432180.80 a365.32 a24.56 a37.02 a1.78 a0.04 a0.16 a
Means followed by the same letter in each column do not differ statistically by Tukey´s test at 5% probability.
Table
Table 5. Interaction between water regimes and cover crops in grain yield under four water regimes.
Table 5. Interaction between water regimes and cover crops in grain yield under four water regimes.
Water Regime (mm)Cover Crop
C. quinoaA. cruenthus
kg ha−1
167691.58 Ac1018.43 Ac
2681904.31 Bb2875.02 Ab
3812882.90 Ba3866.89 Aa
4323488.86 Aa3549.45 Aa
Means followed by the same small letter in each column, and capital letter in a row, do not differ statistically by Tukey´s test at 5% probability.
Table
Table 6. Summary of conditional effects between plant nutrients based on Monte Carlo permutation test.
Table 6. Summary of conditional effects between plant nutrients based on Monte Carlo permutation test.
Chemical AttributeLambda (λ)Contribution (%)F-Testp-Value
P0.223712.630.0020***
Cu0.08134.410.0080***
Mn0.07125.680.0040***
S0.0473.490.0320**
Mg0.0473.670.0320**
Al0.0472.810.0760ns
Zn0.0251.570.2060ns
K0.0231.560.2120ns
Fe0.0231.600.2160ns
Ca0.0231.430.2360ns
B0.0131.260.3040ns
Levels of significance: *** (p ≤ 0.01), ** (p ≤ 0.05) and ns (non-significant).
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Silva, A.d.N.; Ribeiro Junior, W.Q.; Ramos, M.L.G.; de Lima, C.A.; Jayme-Oliveira, A.; Silva, A.M.M.; de Carvalho, A.M. Nutrient Accumulation in Cover Crops under Contrasting Water Regimes in the Brazilian Cerrado. Atmosphere 2022, 13, 1617. https://doi.org/10.3390/atmos13101617

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Silva AdN, Ribeiro Junior WQ, Ramos MLG, de Lima CA, Jayme-Oliveira A, Silva AMM, de Carvalho AM. Nutrient Accumulation in Cover Crops under Contrasting Water Regimes in the Brazilian Cerrado. Atmosphere. 2022; 13(10):1617. https://doi.org/10.3390/atmos13101617

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Silva, Alberto do Nascimento, Walter Quadros Ribeiro Junior, Maria Lucrecia Gerosa Ramos, Cristiane Andrea de Lima, Adilson Jayme-Oliveira, Antonio Marcos Miranda Silva, and Arminda Moreira de Carvalho. 2022. "Nutrient Accumulation in Cover Crops under Contrasting Water Regimes in the Brazilian Cerrado" Atmosphere 13, no. 10: 1617. https://doi.org/10.3390/atmos13101617

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