Combined effect of intercropping and minimum tillage on soil carbon sequestration and organic matter pools in the semiarid region of Brazil.
Arid and semiarid regions cover around 41% of the land surface of the Earth and are home to more than 38% of the global population (MEA 2005; Qiu et al. 2012). In Brazil, the semiarid region comprises a total area of 980133.079 k[m.sup.2], representing 11.5% of the country and 11.8% of the population (Medeiros et al. 2012). The Brazilian semiarid region is characterised by high temperatures, low rainfall, slightly weathered soils and small production of phytomass. In addition to unfavourable conditions of soil and climate, farming is completely extractive and overgrazing by livestock is considerable. Agriculture is developed at the expense of indiscriminate deforestation, burning and inadequate periods of fallow (Maia et al. 2006). As a result of these conditions, the productivity of crops such as beans and maize (the main annual crops) in the semiarid region remains well below the national average. For example, in the semiarid region, beans and maize have an average productivity of 327.0 and 440.0 kg ha ' respectively, whereas the averages for Brazil are 1028 and 5250 kg [ha.sup.-1] respectively (municipal agricultural production database--IBGE, https://sidra.ibge.gov. br/pesquisa/pam/tabelas). In addition, although the productivity of these crops has increased by 150-180% in the last 25 years in Brazil as a whole, in the semiarid region there has been almost no increase in beans, with maize increasing by only 18%. These data show that agriculture continues to stagnate in the semiarid region of Brazil, as well as in other semiarid regions worldwide (El Tahir et al. 2009; Rathore et al. 2014; Campiglia et al. 2015; Camilli et al. 2016).
In this context, the identification of suitable cropping systems that make the best use of available resources and provide higher yields is important if the diverse needs of farming communities and environmental sustainability in arid and semiarid regions are to be catered for (Rathore et al. 2014). Thus, practices such as intercropping, crop rotation, organic fertiliser and conservation tillage systems have been assessed for application under semiarid conditions around the world (Xavier et al. 2006; Maia et al. 2007; Rathorc et al. 2014; Ojiem et al. 2014). Intercropping, which is the simultaneous cultivation of two or more crop species on the same area of land (Campiglia et al. 2015), has a particular importance in agroecosystems to increase yield stability and decrease weed pressure (Corre-Hellou et al. 2011). Studies on intercropping have found significant yield advantages of intercropping compared with simple cropping, with a land-equivalent ratio of up to 1.34 (Ghaley et al. 2005). Moreover, intercropping systems may have additional benefits such as the higher input of biomass (Riihlemann and Schmidtke 2015) and nitrogen (N) when legumes are part of the intercropping system (Ojiem et al. 2014; Vrignon-Brenas et al. 2016).
Likewise, the conservation tillage system has been increasing in countries like USA, Brazil, Argentina and Australia; however, this increase has been much smaller amongst poor smallholder farmers growing rainfed crops in semiarid regions (Kurothe et al. 2014). Conservation agriculture has been proposed as an important strategy to improve food security by increasing productivity and reducing resource degradation (FAO 2012), since it can increase soil organic carbon (SOC) and soil aggregation in the surface layer (Plaza-Bonilla et al. 2013; Kurothe et al. 2014), and can also provide substantial water conservation, energy and ecological benefits (Morris et al. 2010; Kurothe et al. 2014).
Despite all the benefits attributed to intercropping and conservation tillage, there is still a lack of research that is devoted to assessing the combined effect of these two practices on soil, especially on soil organic matter (SOM) under semiarid conditions. Nevertheless, Guimaraes et al. (2013) showed that integrated coconut with leguminous cover crops, reduced soil tillage and mulching significantly increased SOC content, as well as other soil properties such as cation exchange capacity and phosphorus (P) content when compared with native vegetation and conventional systems. Likewise, Kurothe et al. (2014) found that intercropping systems under notillage for 11 years, enhanced the water stable macroaggregate (>0.25 mm) by 28%, and SOC content by 24% when compared with conventional tillage.
SOM is an essential component of soils, and it has been widely used as soil quality indicator due to its ability to interact with several physical, chemical and biological soil properties (Xavier et al. 2006; Plaza-Bonilla et al. 2013), as well as its high sensitivity to changes in management systems (Maia et al. 2013). The correct management of SOM is critical in maintaining the economic and environmental aspects of food production, especially in arid and semiarid zones. However, management of SOM in tropical, semiarid regions such as those in Brazil, is not simple because these regions are characterised by low and irregular rainfall, which limits plant growth and consequently the input of organic residues to the soil. In addition, the regions are subject to high temperatures, which can result in a relative increment in the rate of decomposition and mineralisation, reducing the levels of soil organic matter when compared with soils from cold regions (Bayer and Mielniczuk 2008). In semiarid regions, this depletion can occur within a few years when conventional tillage systems are adopted. For example, Maia et al. (2007) found that conventional system reduced soil carbon (C) stock by 40.0% in just 5 years compared with the native vegetation in the Brazilian semiarid region. El Tahir et al. (2009) also noted a loss ranging from 26.0% to 49.0% of SOC concentration after just three cropping seasons in different land management systems in the Sudan.
Thus, motivated by this scenario, a non-governmental organisation (NGO) called ESPLAR and a group of family farmers located at semiarid region of Ceara state, Brazil, began in the early 90s in accordance with sustainable practices. It was established as a participatory project that involved using intercropping systems with cotton, bean, maize and sesame, and using conservative practices such as minimum tillage, soil cover, foliar fertilisation and control of insects with natural products and manure fertilisation. This experiment started with 4 farmers in 1997 and currently involves around 500, and since its beginning has been the focus of several studies that seek to evaluate different components related to the sustainability of systems (Sousa 2006; Lima et al. 2007; Silva 2010). As a result, this group of family farmers obtain certifications of organic production and fair trade, which are important in the process of economic development.
In the current study, our aim was to evaluate the effects of two intercropping systems associated with minimum soil tillage on SOC and SOM pools compared with the native vegetation. This paper reports the results of field experiments that tested the hypothesis that the adoption of minimum soil tillage combined with intercropping system can contribute to maintaining the soil quality of agricultural areas when compared with native vegetation areas, suggesting that these combined management practices are a sustainable alternative to crop production in semiarid regions.
Materials and methods
Location and general description of the area
The study was carried out in the town of Taua, located in the south-west of the State of Ceara, Brazil. It is located in the geographical micro-region of Sertao dos Inhamuns, at 6[degrees]00'S and 40[degrees]18'W, at an elevation of between 400 and 500 m at its highest points.
The predominant climate in the region according to the Gaussen classification is type 4aTh, (tropical hot, severe drought in the winter), having 7-8 dry months, with a xerothermic index between 200 and 150, type BSwh (hot, semiarid climate, the rainy season starting only in the autumn). The mean annual precipitation is 650 mm. The landscape of the region is flat (BRASIL 1973), and the predominant natural vegetation is hyperxerophyl 'Caatinga', a tropical thorn bush forest, mainly composed of open bushes of short, thorny individuals with leaves that fall completely in the dry season (BRASIL 1973).
This study is part of an agricultural-ecological proposal developed in 1997 by ESPLAR (Centre for Research and Assistance), an NGO that works with family farmers on Ceara state. The farms under study employ management practices based on agroecology with the technical assistance of ESPLAR. Food crops (cotton, maize, bean and sesame) are grown intercropped with pigeon pea, by planting the cotton or bean in strips alternating with rows of the other crops, and, depending on the resources of each farmer, fertilised with cattle manure. In addition, planting is on level ground, leaf fertilisation is carried out with a biofertiliser based on fermented manure, and pest management employs mechanical and biological control techniques (ESPLAR 1998).
Two farmers were selected from participating farmers who had adopted agroecological management in their areas. These farms were selected because they presented the greatest homogeneity among the studied areas (native vegetation and agricultural systems), and because these farmers were well organised and had the best knowledge about the agricultural practices adopted. The experimental area of Farm 1 (3298 [m.sup.2]) has a eutrophic RedYellow Ultisol soil type, and is managed in an intercropping system with bean, sesame and pigeon pea. The experimental area of Farm 2 (2000 [m.sup.2]) has an Entisol Fluvent in transition to an Aridsol soil type, and the intercropping system includes cotton, maize, bean, sesame and pigeon pea. The crops were repeated every year without irrigation in both farms. A contiguous area of 20 years secondary native vegetation (Caatinga), 200 m from the experimental areas was used as reference. The agricultural areas of the two farms were converted from slashing and burn of the native vegetation (Caatinga) and had been in use 4 years at the time of soil sampling. Soil tillage in both areas consisted of minimum tillage using a shallow plough (up to 30 cm) in the first year and manual tools in the following years.
Soil sampling was carried out in the fourth year of cropping. Soil samples were collected from three pits (1 m x 1 m x 0.8 m deep) at each site (native vegetation and intercropping systems), with soil samples being collected at depths of 0-5, 5-10, 10-20, 20-30 and 30-50 cm for chemical and biological analysis. Soil bulk density was measured using a volumetric (100 [cm.sup.3]) ring method for each soil layer. The physical and chemical characteristics of the soils are presented in Table 1.
Plant sampling and analysis
Plant sampling for each crop within the intercropping was performed in an enclosed area of 1 [m.sup.2]. All the aboveground part and the root system (up to 50 cm soil layer) was collected, the material was washed, dried in an oven at 65[degrees]C until constant weight and then ground. The calcium (Ca), magnesium (Mg), P and potassium (K) contents were determined in the extract obtained after nitropercloric digestion (2:1) and the reading obtained by atomic absorption, with the exception of K, which was determined by flame photometry (Malavolta et al. 1989). The C and N contents were determined by dry combustion using a Perkin Elmer analyser CHNS/0-2400 (Waltham, MA, USA).
Soil chemical analysis
The total nitrogen (TN) was quantified by submitting the soil samples to sulfuric digestion dosed by Kjeldahl distillation (Bremner and Mulvaney 1982). The nitrate (N-N[O.sub.3.sup.-]) and ammonium (N-N[H.sub.4.sup.+]) fractions were extracted with a 1 M KC1 solution at a soil to extractant ratio of 1:10. The N-N[H.sub.4.sup.+] was determined after distillation (EMBRAPA 1999), and the N-N[O.sub.3] was determined by colourimetry (Yang et al. 1998).
The soil samples were ground and passed through a 0.21 -mm sieve prior to determination of total SOC, which was quantified by wet combustion with a mixture of potassium dichromate and sulfuric acid (Yeomans and Bremner 1988). For each soil layer, the SOC stock was calculated by multiplying the concentration of C (g [kg.sup.-1]) by the soil bulk density (g [cm.sup.-3]) and thickness of the layer (cm). To calculate the SOC stock in the 0-50 cm layer, the equivalent soil mass (ESM) approach was adopted, following the procedure described by Sisti et al. (2004). This approach eliminates the effect of the soil management on bulk density. Thus, the soil under native vegetation was used as reference for the ESM.
The chemical fraction of the humic substances was measured following the technique of differential solubility in both alkaline and acid medium. Fulvic (FAF) and humic acids (HAF) and the humin (HUM) were separated as per the International Humic Substances Society (Swift 1996). The extractant used was 0.1 M NaOH, at a soil to extractant ratio of 1:10. Fractionation of the humic substances was carried out both with and without pretreatment with a 0.1 M HC1 solution, so as to eliminate carbonates from the samples (Swift 1996). The C content of the fractions was determined by wet oxidation (Yeomans and Bremner 1988).
Soil biological analysis
Microbial biomass (CMic) was quantified by the irradiationextraction method, using a microwave oven (Consul, frequency 2450 MHz, energy 900W for 180 s) (Ferreira et al. 1999). The extractor used was 0.5 M [K.sub.2]S[O.sub.4], and the C contained in the extracts was quantified by wet oxidation (Yeomans and Bremner 1988). A conversion factor of 0.33 was used to convert the C flow to CMic (Sparling and West 1988). The proportion of Cmic t0 SOC, or microbial quotient (qMic), was calculated for microbial biomass C, to reflect the inputs of C and conversion of the organic substrate (Sparling 1992).
The mineralisable C ([C.sub.min]) was quantified according to methodology proposed by Gregorich and Zech (1990), where soil samples of 100 g, previously incubated for a period of 7 days at 60% field capacity, were placed into hermetic flasks. Vessels containing 20 mL of 0.5 M NaOH were placed into the flasks, and the absorption of carbon dioxide (C[O.sub.2]) by the NaOH solution was quantified by titration with a 0.5 M HC1 solution. The C-C[O.sub.2] was assessed at 2, 5, 10, 15, 20, 25 and 30 days.
Initially, the data from each farm were submitted to analysis of variance (ANOVA), considering a completely randomised design. A joint analysis of experiments was then performed since the ratio of maximum-to-minimum experimental error mean square for most variables was less than or equal to 7 (Pimentel-Gomes 1987). TheFtestat 10% probability was used for ANOVA for the comparisons between the land use systems in each farm, and the comparisons of the depths were performed using the Tukey test at 10% probability. The analyses were performed using Statistica software 7.0 (TIBCO Software, Palo Alto, CA, USA).
According to the data shown in Table 2, the intercropping system in Farm 2 resulted in an annual input of biomass (dry matter) of 35.9 Mg [ha.sup.-1], whereas in Farm 1, the input was 13.7 Mg [ha.sup.-1]. Sesame accounted for 40.3% and the pigeon pea for 39.2% of biomass input contribution in Farm 1, whereas in the Farm 2 the main crops responsible were pigeon pea, maize and sesame with 31.3%, 27.1% and 22.7% of total input respectively. On average for the two farms, the root system was responsible for 15% of supply and the aboveground components represented 85%. It should be noted that these values are the net input, which means that biomass from crop harvest has been deducted.
In terms of C and N input, a total of 17.9 and 0.348 Mg [ha.sup.-1] [year.sup.-1] of C and N respectively was obtained on Farm 2. On Farm 1 the input was lower, at 6.4 and 0.206 Mg [ha.sup.-1] [year.sup.-1] for C and N respectively. In this context, the presence of a legume had an essential role, since the pigeon pea accounted for 54.8% and 67.6% of the C and N input in Farm 1, and 33.7% and 53.0% in Farm 2. In addition to the substantial input of the C and N, intercropping systems also provided a high input of other macronutrients, especially K and Ca (Table 2).
The levels of SOC and N fractions (NF) for the intercropping areas and the areas of Caatinga vegetation are presented in Table 3. The intercropping systems did not result in reduced levels of SOC and NF, as these were influenced by soil depth only, with the greatest levels detected in the surface layers.
For SOC stocks, the area of Caatinga (native vegetation) on Farm I presented a total stock (i.e. in the 0-50 cm layer) that was just 1.7% higher than the intercropping system, with distribution of C across the layers being similar for both areas. In contrast, on Farm 2, the intercropping system resulted in a total stock of 45.6 Mg C [ha.sup.-1] whereas the Caatinga presented a stock of 39.7 Mg C [ha.sup.-1] (Fig. 1). This difference was mainly due to the greater values for the C stocks in the 10-20 and 30-50 cm layers under the intercropping system. For soil N, there was a high dominance of the organic fraction, as the proportion of mineral N (N-N[O.sub.3.sup.-] + N-N[H.sub.4.sup.+]) varied from just 0.2% to 1.2% on Farm 1 and from 0.2% to 0.9% on Farm 2 (Table 3). These values may be considered low since, in general, forms of mineral N contribute from 2% to 5% of TN (Havlin et al. 2005).
The [C.sub.MIC] presented results similar to the SOC and NF, since the only effect was from depth. The mean values varied from 0.02 g [kg.sup.-1] (0-5 cm) to 0.01 g [kg.sup.-1] (30-50 cm) for Farm 1 and from 0.02 g kg 1 (0-5 cm) to 0.01 g [kg.sup.-1] (30-50 cm) for Farm 2. The qMic ([C.sub.MIC]: SOC proportion) was influenced by area, where the values for Farm 2 varied from 0.18 (0-5 cm) to 0.46 (10-20 cm) compared with 0.15 (0-5 cm) to 0.29 (30-50 cm) for Farm 1.
The determination of [C.sub.min], also known as C[O.sub.2] evolution, portrays a SOM fraction that is more quickly oxidised by the action of microorganisms (Thomazini et al. 2015). No differences in [C.sub.min] were found on Farm 1 (Table 4), whereas on Farm 2, the [C.sub.min] content presented significant interaction between depth and the areas under intercropping and Caatinga vegetation. At depths of 0-5, 5-10 and 20-30 cm, the values for [C.sub.min] were greater in the area under Caatinga vegetation than in the area under intercropping.
In the two study areas, HUM was greater than HAF, which was greater than FAF. On Farm 1, the FAF content was not altered by pretreatment with HCl (Table 5). The HAF content increased with pretreatment, with that of the HUM fraction being consequently reduced. This result indicated that in this area, the HAF is stabilised by the presence of Ca and Mg carbonates. The HUM fraction content was slightly greater in the area of Caatinga, indicating that there was a larger proportion of stable organic matter components under Caatinga than under intercropping.
On Farm 2 (Table 6), the humic substances content increased in the samples without pretreatment with HCl, demonstrating that, on this farm, Ca and Mg carbonates were acting to stabilise these fractions. Based on the results of pre-treated samples, the Caatinga area presented the highest values for all the humic substances; however, statistical differences were found only for FAF and HUM fractions at the 0-5 cm depth (Table 6).
Input of biomass, C and nutrients
According to Maia et al. (2007) in a region close to the present study, the native vegetation of Caatinga generates an annual contribution of aboveground biomass of 3.7 Mg [ha.sup.-1], even without taking into account the contribution of belowground input, which was much lower than that observed in intercropping systems. This therefore indicates the potential of such systems to promote biomass input to soil, and therefore promote nutrient cycling. It is important to emphasise that the results for biomass input are data of total input, that is, they do not take in account the fraction removed by grazing, which is a usual practice in the study region. Even so, the potential of intercropping systems to provide organic material is evident.
Soil organic C and N
Several studies have documented that land use change from native vegetation to arable agriculture or pastures is responsible for a rapid decrease of SOM in temperate or tropical environments (Ogle et al. 2005; Anderson-Teixeira et al. 2009; Maia et al. 2013). According to Lai (2008), this depletion is ~30-50% over 50-100 years after conversion to agricultural land use in a temperate climate, and 50-75% over 10-20 years in a tropical climate, and these losses are strongly related to conventional tillage systems. In semiarid regions, the SOC loss can be even more rapid, as reported by El Tahir et al. (2009), who observed a reduction between 26.0% and 49.0% in soil C content in just 3 years, even with the adoption of intercropping systems. In addition, Maia et al. (2007) noted a decrease of 40.0% in the C stock in a traditional farming system after 5 years compared with native vegetation.
However, our findings show the opposite results, and the maintenance of, or even an increase in the stocks of C in the soil under the intercropping systems when compared with native vegetation, is probably due to the low soil disturbance in these systems combined with the input of crop residues provided by the intercropping systems. In addition, the organic fertilisation, which was not quantified in this study but is conducted each year according to the manure cattle availability, certainly contributed to preserving soil C stocks. It is worth noting that this beneficial effect generated by the intercropping systems occurred even with these areas presenting lower clay contents than native vegetation areas. That is, even in the face of soil texture difference, which is due to the natural variation of the environment, the intercropping systems were able to compensate the reduced role of clay in the formation of organo-mineral complexes and protection of SOM (Krull et al. 2003; Lutzow et al. 2006).
It has been widely documented that the lower the disturbance of the soil, the greater the potential for the accumulation of C and N (Ogle et al. 2005; Panettieri et al. 2014). Furthermore, some studies (Maia et al. 2007; Guimaraes et al. 2013) have shown that in the semiarid region, cultivated systems may result in larger amounts of residue input than the native vegetation (Caatinga). Therefore, our results demonstrated that the intercropping and reduced soil disturbance, since the soil tillage is based on the use of manual tools, did not promote substantial C loss from the system, agreeing with the notion that less intense systems, associated with a continuous input of organic residue, can maintain SOC levels and contribute to soil quality even in semiarid regions.
For N, our results show that there was little mineralisation, possibly because the sampling took place in the dry season in the region, which reduces the potential for mineralisation of SOM due to low microbial activity.
Carbon in the labile soil compartments
The [C.sub.MIC] content did not differ in the areas of intercropping or Caatinga for Farm 1 or Farm 2. Soil samples to determine the microbial biomass were collected in the dry season. The microbial biomass tends to be stimulated in periods of good water availability and mild temperatures, whereas in dry periods with high temperatures, the opposite effect may occur. The low plant cover in the dry period leaves the soil susceptible to great variations in temperature and water, and also limits the availability of organic residues and root exudates to the microorganisms (Yemadje et al. 2016), resulting in a low microbial population. In addition, plant residue and tillage practices have a direct effect on soil microbiota (Vargas and Scholles 1998) and also on the rate of decomposition of the residue and nutrient immobilisation and mineralisation. For the intercropping systems in this study, however, there was no change in the soil microbial biomass compared with the natural systems.
There were no significant differences in qMic between treatments, and the values were low when compared with other studies conducted in the State of Ceara. For example, Xavier et al. (2006) found values that varied from 0.61% to 1.37% under different organic systems with acerola in sandy soils of the mountain region of Ceara. Whereas Maia et al. (2007), studying organic agroforestry systems in the semiarid region of Ceara, found values between 1.2% and 2.5%. The low values observed in the present study may be related to a water shortage during the period of soil sampling, suggesting a reduction in microbial activity during that time. In addition, the differences in soil type and management, in sampling periods and in the analytical methods used, can result in a wide range of variation for qMic in the soil (Balota et al. 1998).
The [C.sub.min] results are probably associated with the greater availability of substrate and nutrients to the microorganisms at these depths for soils under natural vegetation. The [C.sub.min]: SOC ratio was also greater under Caatinga (3.91) than under intercropping (1.69). A greater value for the [C.sub.min]: SOC ratio in the area of Caatinga can be attributed to the proportion of labile constituents, compared with the more stable form of organic C in the soil of this ecosystem. This result, together with the [C.sub.min] : SOC data, indicated that even with lower organic residue input, there was greater cycling of C in the area under native vegetation compared with intercropping on Farm 2, which was probably due to a more stable environment. Conversely, this could indicate lower C and nutrient losses under the agricultural system.
Carbon in the stable soil compartment
Similar results for the humic substances were reported in studies of tropical soils by Leite (2002) and Maia et al. (2007). These results suggest a high interaction of the mineral fraction in tropical soils with organic matter. Furthermore, the greater HUM content may be related to its insolubility and resistance to biodegradation, caused by the formation of stable metallic complexes and/or clay-humic complexes, or the Ca and Mg carbonates (Longo and Espindola 2000). A greater HUM content results in long cycling times for C and nutrients, especially for N.
In addition, given the soil quality improvement indicated above, it is important to highlight that these intercropping systems have also promoted a substantial increase in crop yields when compared with the study area average. According to Sousa (2006), the cotton yield was higher than the municipal average, ranging from 68% to 300%, and maize between 10% and 18%. An average income 44.0% greater than a traditional cultivation system was obtained on Farm 2. Similarly, Silva (2010) observed that intercropping systems in several sites of semiarid in the Ceara state presented a more efficient use of land than single production systems.
Therefore, the present work sought to scientifically evaluate some environmental aspects of experience in a participatory context, which involved small farmers, an NGO and the academy in the search for sustainable production systems in the adverse conditions of semiarid regions of Brazil. It should be recognised that further studies need to be carried out in order to confirm the results obtained and to increase knowledge about the intercropping systems. Ideally studies should be conducted with a greater diversity of treatments (different crops, arrangements and time periods) and greater number of repetitions and local control, with the purpose of minimising soil spatial variability and producing even more robust results.
It is widely known that conventional agricultural systems, which are based on intensive soil tillage and single cropping, lead to reduction in the soil C stocks, contributing to C[O.sub.2] emissions and global warming, and resulting in soil degradation. Our findings demonstrate that the intercropping system has a high potential to increase biomass input in small farms in the Brazilian semiarid region. Moreover, when combined with minimum soil tillage it was effective in maintaining and sometimes increasing the stocks of SOC and some SOM fractions such as microbial C and humic substances.
These systems can therefore represent an alternative form of sustainable soil management and agricultural systems in a region that traditionally adopts conventional methods of land use, which have invariably resulted in soil degradation. However, more studies are needed to consider other crops, other management systems and longer periods of use.
Conflicts of interest
The authors declare no conflicts of interest.
The authors wish to thank the National Council for Scientific and Technological Development (CNPq) which provided the scholarships and funding the project. We also thank the ESPLAR and the farmers who gave all the necessary support for the development of this work.
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Handling Editor: Christian Walter
Stoecio Malta Ferreira Maia (iD) (A,E), Adriana Tamie Otutumi (B), Eduardo de Sa Mendonga (C), Julio Cesar Lima Neves (D), and Tedgenes Senna de Oliveira (D)
(A) Federal Institute of Education, Science and Technology of Alagoas, Rua Lourival Alfredo, 176, 57160-000, Marechal Deodoro, Alagoas, Brazil.
(B) Soil Science Department, Federal University of Ceara, Fortaleza, Ceara, Brazil.
(C) Department of Plant Production, Federal University of Espirito Santo, 29500-000, Alegre, Espfrito Santo, Brazil.
(D) Soil Science Department, Federal University of Vifosa, Avenida P. H. Rolfs, s/n, 36570-000, Viposa, Minas Gerais, Brazil.
(C) Corresponding author. Email: email@example.com
Caption: Fig. 1. Mean values of soil organic carbon (SOC) stocks ([+ or -]2 standard deviation) in the intercropping and Caatinga areas of (a) Farm 1 and (b) Farm 2. The SOC stocks for the 0-50 cm layer were corrected by the equivalent soil mass approach.
Table 1. Chemical and physical characteristics of studied soils CEC, cation exchange capacity; SEC, sum of exchangeable cations Soil characteristics Farm System Depth Bulk density (cm) (g [cm.sup.-3]) Intercropping 0-5 1.54 5-10 1.52 10-20 1.56 20-30 1.68 30-50 1.65 Farm 1 Caatinga 0-5 1.61 5-10 1.60 10-20 1.59 20-30 1.57 30-50 1.58 Intercropping 0-5 1.55 5-10 1.53 10-20 1.56 20-30 1.58 30-50 1.56 Farm 2 Caatinga 0-5 1.46 5-10 1.45 10-20 1.48 20-30 1.77 30-50 1.80 Soil characteristics Farm System Depth pH [H.sub.2]O CEC ([cmol.sub.c] (cm) (1:2.5) [kg.sup.-1]) Intercropping 0-5 7.20 11.7 5-10 7.17 9.80 10-20 7.13 9.10 20-30 6.93 9.00 30-50 6.73 8.60 Farm 1 Caatinga 0-5 6.97 11.3 5-10 6.57 10.7 10-20 6.67 10.7 20-30 6.73 12.8 30-50 6.77 16.4 Intercropping 0-5 6.93 7.60 5-10 7.03 6.70 10-20 7.23 5.87 20-30 7.50 5.77 30-50 7.67 5.90 Farm 2 Caatinga 0-5 5.60 7.67 5-10 5.20 4.80 10-20 5.83 4.67 20-30 6.37 5.40 30-50 7.03 5.23 Soil characteristics Farm System Depth SEC ([cmol.sub.c] Sand (g (cm) [kg.sup.-1]) [kg.sup.-1]) Intercropping 0-5 11.5 710.0 5-10 9.30 673.4 10-20 8.37 623.3 20-30 8.30 550.0 30-50 7.77 560.0 Farm 1 Caatinga 0-5 10.8 640.0 5-10 8.77 610.0 10-20 9.67 553.3 20-30 11.9 490.0 30-50 15.4 540.0 Intercropping 0-5 7.07 600.0 5-10 6.47 683.3 10-20 5.73 696.7 20-30 5.63 696.6 30-50 5.83 690.0 Farm 2 Caatinga 0-5 5.00 563.3 5-10 3.10 603.4 10-20 3.63 610.0 20-30 4.73 573.3 30-50 5.07 563.4 Soil characteristics Farm System Depth Silt (g Clay (g (cm) [kg.sup.-1]) [kg.sup.-1]) Intercropping 0-5 186.6 103.3 5-10 196.7 130.0 10-20 200.0 176.6 20-30 210.0 240.0 30-50 243.3 196.7 Farm 1 Caatinga 0-5 233.3 126.6 5-10 236.7 153.3 10-20 260.0 186.7 20-30 253.3 256.7 30-50 200.0 260.0 Intercropping 0-5 323.3 76.6 5-10 226.7 90.0 10-20 230.0 73.3 20-30 223.3 80.0 30-50 236.7 73.3 Farm 2 Caatinga 0-5 326.6 110.0 5-10 320.0 76.6 10-20 313.3 76.7 20-30 290.0 136.7 30-50 286.6 150.0 Table 2. Input of biomass (dry matter), carbon and nutrients in the intercropping areas Dry matter K Ca Farm 1 Sesame Roots 818.0 13.4 4.6 Aboveground 4732.8 153.1 56.2 Bean Roots 281.4 7.1 2.9 Aboveground 2524.2 122.6 79.9 Pigeon pea Roots 1060.8 12.3 4.7 Aboveground 4323.1 67.3 46.5 Total 13740.5 375.8 194.8 Farm 2 Sesame Roots 900.2 12.9 5.0 Aboveground 7270.7 171.7 84.6 Bean Roots 399.2 5.5 3.3 Aboveground 3092.0 53.2 64.7 Pigeon pea Roots 1529.2 14.9 6.4 Aboveground 9728.3 121.1 81.2 Cotton Roots 660.4 8.0 2.2 Aboveground 2629.7 36.9 66.3 Maize Roots 1661.0 12.5 7.1 Aboveground 8085.3 122.3 40.8 Total 35956.0 559.2 361.7 Mg (kg P C N [ha.sup.-1]) Farm 1 Sesame Roots 1.8 0.9 312.9 0.3 Aboveground 14.05 7.5 1709.2 2.8 Bean Roots 1.02 0.6 137.6 3.4 Aboveground 13.5 6.4 1341.3 60.2 Pigeon pea Roots 1.5 2.5 527.2 13.5 Aboveground 9.1 10.9 2437.2 126.2 Total 40.9 28.9 6465.4 206.5 Farm 2 Sesame Roots 2.6 1.1 411.3 0.5 Aboveground 35.1 9.8 3778.6 57.4 Bean Roots 1.7 0.9 141.8 3.5 Aboveground 13.5 5.3 1252.5 52.6 Pigeon pea Roots 2.6 3.5 777.3 14.8 Aboveground 22.9 25.4 5275.3 169.8 Cotton Roots 1.3 1.3 273.8 3.3 Aboveground 10.3 5.8 899.5 30.3 Maize Roots 3.2 2.2 570.5 4.7 Aboveground 26.8 12.8 4559.9 11.5 Total 120.1 68.3 17940.6 348.5 Table 3. Soil organic carbon (SOC), organic nitrogen (N), nitrate (N-N[O.sub.3.sup.-]) and ammonium (N-[NH.sub.4.sup.+]) in the intercropping systems and Caatinga vegetation areas in Ceara State, north-east Brazil The comparisons were performed within each farm, and means followed by the same small letters in the columns, and capital letters in rows are not different at P < 0.10 Farm 1 Variable Depth. Intercropping Caatinga (cm) SOC 0-5 15.0 Aa 11.2Aa (g [kg.sup.-1]) 5-10 9.2 Aab 8.3 Aa 10-20 7.6 Abe 7.5 Aa 20-30 5.8 Abe 6.2 Aa 30-50 3.3 Ac 5.7 Aa Organic N 0-5 1176.1 Aa 690.7 Ba (mg [kg.sup.-1]) 5-10 588.1 Aab 588.2 Aa 10-20 597.4 Aab 522.7 Aa 20-30 401.4 Ab 438.7 Aa 30-50 261.4 Ab 130.7 Aa N-N[O.sub.3.sup.-] 0-5 0.36 Aa 0.04 Ba (mg [kg.sup.-1]) 5-10 0.11 Ab 0.04 Ba 10-20 0.05 Ab 0.05 Aa 20-30 0.04 Ab 0.06 Aa 30-50 0.04 Ab 0.04 Aa N-[NH.sub.4.sup.+] 0-5 3.11 Aa 3.11 Aa (mg [kg.sup.-1]) 5-10 3.50 Aa 1.17 Ba 10-20 1.94 Aa 1.55 Aa 20-30 1.55 Aa 1.94 Aa 30-50 1.94 Aa 1.55 Aa Farm 2 Variable Depth. Intercropping Caatinga (cm) SOC 0-5 8.6 Ba 16.0 Aa (g [kg.sup.-1]) 5-10 7.6 Aa 7.6 Ab 10-20 5.2 Aa 4.0 Ab 20-30 4.0 Aa 4.9 Ab 30-50 3.8 Aa 2.3 Ab Organic N 0-5 535.2 Ba 1008.0 Aa (mg [kg.sup.-1]) 5-10 553.8 Aa 550.6 Aab 10-20 360.9 Aa 634.6 Aab 20-30 220.9 Aa 261.3 Aab 30-50 202.2 Aa 177.3 Aab N-N[O.sub.3.sup.-] 0-5 0.054 Aa 0.047 Aa (mg [kg.sup.-1]) 5-10 0.067 Aa 0.027 Aa 10-20 0.027 Aa 0.005 Aa 20-30 0.010 Aa 0.069 Aa 30-50 0.030 Aa 0.006 Aa N-[NH.sub.4.sup.+] 0-5 2.33 Ba 5.05 Aa (mg [kg.sup.-1]) 5-10 1.17 Aa 2.33 Ab 10-20 2.33 Aa 0.78 Ab 20-30 0.78 Aa 2.33 Ab 30-50 1.17 Aa 0.39 Ab Table 4. Microbial biomass C ([C.sub.MIC]), mineralisable C ([C.sub.min]), and their proportion in relation to total soil organic carbon (SOC) in the intercropping systems and Caatinga vegetation areas in Ceara State, north-east Brazil The comparisons were performed within each farm, and means followed by the same small letters in the columns, and capital letters in rows are not different at P< 0.10 Farm 1 Intercropping Caatinga Variable Depth g [kg. %SOC g [kg. %SOC (cm) sup.-1] sup.-1] [C.sub.MIC] 0-5 0.019 Aa 0.15 Aa 0.024 Aa 0.23 Aa 5-10 0.014 Aa 0.15 Aa 0.014 Aab 0.17 Aa 10-20 0.016 Aa 0.21 Aa 0.015 Aab 0.20 Aa 20-30 0.012 Aa 0.23 Aa 0.016 Aab 0.25 Aa 30-50 0.009 Aa 0.29 Aa 0.010 Ab 0.21 Aa [C.sub.min] 0-5 0.21 Aa 1.71 Aa 0.27 Aa 2.36 Aa 5-10 0.17 Aab 1.56 Aa 0.19 Aab 2.03 Aa 10-20 0.11 Ab 1.28 Aa 0.13 Ab 1.79 Aa 20-30 0.08 Ab 1.59 Aa 0.14 Ab 1.89 Aa 30-50 0.07 Ab 1.86 Aa 0.14 Ab 4.72 Aa Farm 2 Intercropping Caatinga Variable Depth g [kg. %SOC g [kg. %SOC (cm) sup.-1] sup.-1] [C.sub.MIC] 0-5 0.016 Aa 0.21 Aa 0.024 Aa 0.15 Aab 5-10 0.013 Aa 0.18 Aa 0.009 Ab 0.15 Aab 10-20 0.012 Aa 0.26 Aa 0.010 Ab 0.46 Aa 20-30 0.008 Aa 0.20 Aa 0.009 Ab 0.14 Aab 30-50 0.010 Aa 0.27 Aa 0.008 Ab 0.36 Aab [C.sub.min] 0-5 0.21 Ba 2.39 Aa 0.38 Aa 3.28 Aa 5-10 0.11 Bab 1.33 Ba 0.23 Ab 4.52 Aa 10-20 0.06 Ab 1.23 Aa 0.10 Ac 2.17 Aa 20-30 0.06 Bb 1.46 Ba 0.15 Abe 4.64 Aa 30-50 0.09 Ab 2.03 Ba 0.12 Ac 4.95 Aa Table 5. Carbon content in the fulvic acid fraction (FAF), humic acid fraction (HAF) and humin fraction (HUM), with and without pretreatment with HCI, and their proportion in relation to soil organic carbon (SOC) in the Farm 1 (intercropping systems and Caatinga vegetation) in Ceara State, north-east Brazil The comparisons were performed within each farm, and means followed by the same small letters in the columns, and capital letters in rows are not different at P<0.10 With pretreatment Intercropping Caatinga Depth g [kg. %SOC g [kg. %SOC (cm) sup.-1] sup.-1] FAF 0-5 1.0 Aa 6.9 Aa 0.9 Aa 7.2 Aa 5-10 0.7 Aab 6.6 Aa 0.7 Aab 7.8 Aa 10-20 0.6 Aab 6.1 Aa 0.6 Aab 7.9 Aa 20-30 0.4 Ab 8.4 Aa 0.5 Aab 5.9 Aa 30-50 0.2 Ab 5.6 Aa 0.3 Ab 7.9 Aa HAF 0-5 2.8 Aa 20.1 Aa 2.7 Aa 21.5 Aa 5-10 2.7 Aa 25.4 Aa 2.3 Aab 24.7 Aa 10-20 2.4 Aa 25.2 Aa 2.0 Aab 26.5 Aa 20-30 1.3 Aab 26.2 Aa 1.6 Aab 20.3 Aa 30-50 0.6 Ab 15.7 Aa 0.8 Ab 21.2 Aa HUM 0-5 9.3 Aa 69.5 Aa 8.4 Aa 67.7 Aa 5-10 6.2 Aab 59.1 Aa 6.5 Aab 71.5 Aa 10-20 5.1 Abe 57.1 Aa 4.7 Aab 60.9 Aa 20-30 3.5 Abe 68.3 Aa 4.8 Aab 62.5 Aa 30-50 2.1 Ac 56.5 Aa 3.0 Ab 78.9 Aa Without pretreatment Intercropping Caatinga Depth g [kg. %SOC g [kg. %SOC (cm) sup.-1] sup.-1] FAF 0-5 1.5 Aa 8.6 Aa 0.8 Aa 7.1 Aa 5-10 0.7 Aa 7.6 Aa 0.6 Aa 8.0 Aa 10-20 0.6 Aa 7.9 Aa 0.5 Aa 6.2 Ba 20-30 0.4 Aa 7.66 Ba 0.5 Aa 8.0 Aa 30-50 0.2 Ba 5.95 Ba 0.4 Aa 6.2 Aa HAF 0-5 0.8 Aa 7.0 Aa 1.0 Aa 7.8 Aa 5-10 1.5 Aa 16.2 Aa 0.4 Aa 4.2 Aa 10-20 0.7 Aa 9.7 Aa 0.7 Aa 9.1 Aa 20-30 0.5 Aa 9.6 Aa 0.6 Aa 9.8 Aa 30-50 0.5 Aa 15.0 Aa 0.5 Aa 6.3 Aa HUM 0-5 11.3 Aa 74.1 Aa 8.9 Aa 80.2 Aa 5-10 7.8 Aab 87.7 Aa 7.5 Aa 92.3 Aa 10-20 4.7 Abe 60.6 Aa 7.4 Aa 101.2 Aa 20-30 4.4 Abe 76.6 Aa 7.6 Aa 122.2 Aa 30-50 1.9 Ac 56.2 Aa 5.9 Aa 125.5 Aa Table 6. Carbon content in the fulvic acid fraction (FAF), humic acid fraction (HAF) and humin fraction (HUM), with and without pretreatment with HC1, and their proportion in relation to total organic carbon (SOC) in the Farm 2 (intercropping systems and Caatinga vegetation) in Ceara State, north-east Brazil The comparisons were performed within each farm, and means followed by the same small letters in the columns, and capital letters in rows are not different at P < 0.10 With pretreatment Intercropping Caatinga Depth g [kg. %SOC g [kg. %SOC (cm) sup.-1] sup.-1] FAF 0-5 0.7 Bab 7.9 Ba 1.5 Aa 12.6 Aab 5-10 1.0 Aa 11.0 Aa 0.9 Abe 17.1 Aab 10-20 0.3 Ab 6.2 Ba 1.3 Aab 24.6 Aa 20-30 0.2 Ab 4.5 Aa 0.4 Acd 9.5 Ab 30-50 0.2 Ab 4.3 Ba 0.2 Ad 9.1 Ab HAF 0-5 2.4 Aa 28.7 Aa 3.0 Aa 22.7 Aa 5-10 2.2 Aa 27.3 Aa 1.6 Aab 23.1 Aa 10-20 1.7 Aa 33.2 Aa 1.8 Aab 22.4 Aa 20-30 1.3 Aa 29.2 Aa 0.8 Ab 24.4 Aa 30-50 1.3 Aa 31.5 Aa 0.3 Ab 13.2 Ba HUM 0-5 5.1 Ba 60.9 Aa 8.1 Aa 61.8 Aa 5-10 4.1 Aa 49.8 Aab 3.5 Ab 51.9 Aa 10-20 2.5 Aa 48.7 Aab 3.4 Ab 45.7 Aa 20-30 1.2 Aa 27.7 Ab 2.0 Ab 47.8 Aa 30-50 1.7 Aa 41.2 Aab 0.9 Ab 36.9 Aa Without pretreatment Intercropping Caatinga Depth g [kg. %SOC g [kg. %SOC (cm) sup.-1] sup.-1] FAF 0-5 3.8 Aa 40.1 Aa 1.8 Ba 12.2 Aa 5-10 1.3 Ab 17.5 Aa 1.6 Aa 30.6 Aa 10-20 0.9 Bb 19.3 Ba 1.5 Aa 54.5 Aa 20-30 0.7 Ab 16.6 Aa 1.3 Aa 34.9 Aa 30-50 0.5 Ab 13.5 Aa 1.0 Aa 45.9 Aa HAF 0-5 3.2 Aa 32.5 Aa 1.6 Ba 10.3 Aa 5-10 1.1 Ab 14.2 Aa 0.9 Aa 11.7 Aa 10-20 0.8 Ab 15.0 Aa 0.6 Aa 22.1 Aa 20-30 0.9 Ab 21.1 Aa 0.5 Aa 13.3 Aa 30-50 1.0 Ab 24.8 Aa 0.3 Aa 12.1 Aa HUM 0-5 7.3 Ba 83.6 Aa 11.3 Aa 70.3 Ba 5-10 6.4 Aa 85.1 Aa 6.3 Aab 109.9 Aa 10-20 4.7 Aa 88.5 Aa 2.9 Ab 80.1 Aa 20-30 3.4 Aa 83.9 Aa 3.1 Ab 69.3 Aa 30-50 4.4 Aa 113.8 Aa 1.9 Ab 82.9 Aa
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|Author:||Maia, Stoecio Malta Ferreira; Otutumi, Adriana Tamie; de Sa Mendonga, Eduardo; Neves, Julio Cesar Li|
|Date:||May 1, 2019|
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