Soil & Tillage Research 156 (2016) 54–62

Least limiting water range and soybean yield in a long-term, no-till,integrated crop-livestock system under different grazing intensities

Diego Cecagnoa,*, Sérgio Ely Valadão Gigante de Andrade Costaa, Ibanor Anghinonia,Taise Robinson Kunrathb, Amanda Posselt Martinsa, José Miguel Reichertc,Paulo Ivonir Gubianic, Fabricio Balerinia, Jessé Rodrigo Finka,Paulo Cesar de Faccio Carvalhob

aDepartment of Soil Science, Federal University of Rio Grande do Sul, P.O. Box 15100, 91540-000 Porto Alegre, RS, BrazilbDepartment of Forage Plants and Agrometeorology, Federal University of Rio Grande do Sul, Porto Alegre, RS, BrazilcDepartment of Soil Science, Federal University of Santa Maria, Santa Maria, RS, Brazil

A R T I C L E I N F O

Article history:Received 4 May 2015Received in revised form 14 September 2015Accepted 4 October 2015

Keywords:RainfallSoil compactionRelative yield

A B S T R A C T

Crop-livestock integrated systems possess some uniqueness in soil and plant hydro-physical propertiesand processes. To obtain a better understanding of these systems, it is necessary to evaluate them withindices that take into account several attributes. Our study aimed to evaluate the efficiency of the leastlimiting water range in determining the influence of grazing intensities on soybean yield in an Oxisolmanaged in a long no-till, integrated soybean-beef cattle system. We evaluated an 11 year trial located insouthern Brazil, with soybean summer cropping and black oat + Italian ryegrass winter grazing anddifferent winter grazing intensities, namely intensive, moderate and no grazing. Intensive grazing onlyresults in the most superficial soil layer compaction. Long-term moderate grazing, on the other hand,leads to intermediate compaction, not negatively affecting surface or subsurface soil physical properties.The least limiting water range is an inadequate indicator of soil physical quality in integrated soybean-beef cattle system, provided no direct relations with soybean yields. Under normal rainfall conditions,soybean yield depend mainly on rainfall amount and distribution, rather than on soil quality.

ã 2015 Elsevier B.V. All rights reserved.

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1. Introduction

Food insecurity affects an estimated of 825 (Lobell et al., 2008)to 850 million people (Borlaug, 2007) in the world, and 60 millionpeople in South America alone (Borlaug, 2007). Improvements infood production systems are essential for meeting the everincreasing food demand (Lal, 2009).

The state of Rio Grande do Sul, located in the Braziliansubtropical region, contributes significantly to Brazilian meat andgrain production (CONAB, 2014). Despite the widespread use of no-tillage (NT) systems in Brazil (Boddey et al., 2010), more diversifiedfood production systems, such as integrated crop-livestocksystems (ICLSs), bring more flexibility to the producer (Sulc andTracy, 2007). Advances in the understanding of ICLSs operationinclude a holistic perception of the system, in which the animal

* Corresponding author at: Bento Gonçalves Avenue 7712, Soil ScienceDepartment, Federal University of Rio Grande do Sul, P.O. Box 15100, 91540-000Porto Alegre, RS, Brazil.

E-mail address: dcecagno@hotmail.com (D. Cecagno).

http://dx.doi.org/10.1016/j.still.2015.10.0050167-1987/ã 2015 Elsevier B.V. All rights reserved.

acts as a catalyst in the soil–plant–animal–machine–atmosphere(SPAMA) system, modifying rates and flows of systemic processes(Anghinoni et al., 2013).

Two converging issues have been reinforced topics on the long-term impacts of ICLSs in the SPAMA system: soil compaction andwater availability. Isolated approaches seem inadequate tounderstand the synergy resulting from feedbacks (Ryschawyet al., 2012) and ecosystem functions in ICLSs (Hendricksonet al., 2008). The definition of a critical degree of soil compactionrequires caution because of the difficulty in understandingcompaction-crop yield relationships (Collares et al., 2008) andthe lack of a “universal best soil physical condition” to plantproduction (Letey, 1985).

The least limiting water range (LLWR—Silva et al., 1994), aphysical-water indicator generated from the non limiting waterrange concept (Letey, 1985), was proposed as a tool for evaluatingthe “physical fertility” of the soil. Soil bulk density (BD), anestimator of the LLWR, defines the conditions for plant growth,which may be favorable or critical (Silva and Kay, 1997). However,soil physical quality assessment through the LLWR has beenchallenged because there is not always a relationship between

D. Cecagno et al. / Soil & Tillage Research 156 (2016) 54–62 55

LLWR and crop yield (Gubiani et al., 2013), causing the LLWR to bean inaccurate agronomic index for compaction management.

Soil physical quality assessment through the LLWR in ICLSsdepends on sward height, and on the gap between animal removaland soybean sowing in the field (Petean et al., 2010). Grazingimpacts on soil organization under ICLSs conditions is not yet fullyunderstood (Logsdon and Karlen, 2004), although moderategrazing intensities seem appropriate for environmental conditionsand ICLSs production system, enabling organic carbon accumula-tion (Assmann et al., 2014) and not limiting soil exploration by theroots of intercrops. Grazing cycle affects the yield of subsequentcrops, with post-grazing grain yields under moderate-grazingconditions being higher than observed in non-grazed areas(Moraes et al., 2014).

The concept of available water (AW) used in constructing theLLWR has been examined because of the following limitations:field capacity (FC) and permanent wilting point (PWP) (Ruiz et al.,2003), and hysteresis phenomena occurring during water absorp-tion by roots (Reid et al., 1984). Changes in soil physical andchemical properties in ICLSs, with increased resistance to waterloss by evaporation, result from partial consolidation of the soilsurface during drying (Veiga et al., 2010) and operational(Reszkowska et al., 2011) and structural changes (Martinez andZinck, 2004) due to trampling, explaining possible benefits of soilcompaction (Bouwman and Arts, 2000).

Our study aimed to evaluate the efficiency of the LLWR indetermining the influence of grazing and its intensity on soybeanyield in an Oxisol managed for 11 years in no-till, integratedsoybean-beef cattle system.

2. Materials and methods

This study was started in May 2001 and conducted in SãoMiguel das Missões county in the state of Rio Grande do Sul,southern Brazilian. The soil is classified as Rhodic Hapludox. Thesite elevation is 465 m, and the climate is characterized as humidsubtropical warm (Cfa) according to the Köppen classification(Kottek et al., 2006), with an average annual temperature of 19 �Cand an average annual rainfall of 1850 mm (CEMETRS, 2013).Before starting the experiment, the area had been managed since1993 under a no-tillage system with black oat pasture (Avenastrigosa Schreb) during winter, and a summer soybean crop(Glycine max (L.) Merrill). The area was first used for animal grazingduring the winter of 2000. In the fall of 2001 after soybean harvest,the experiment was started with the establishment of grazing on amixture of black oat plus ryegrass (Lolium multiflorum Lam.).

The treatments included different sward heights, namely 10, 20,30 and 40 cm, distributed in a randomized block experimentaldesign with three replicates. Intense and moderate grazingintensities were used, corresponding to grazing sward heights of10 and 20 cm, respectively, in addition to non-grazed plots (NG)used as a reference. The choice of these two grazing intensities wasbased on observations over the years using this protocol, that is,high (10 cm) and moderate (20 cm) intensities chosen to representinadequate and adequate management, respectively, for main-taining the energy flow balance in this food production system(Anghinoni et al., 2013). Grazing cycles prior to the beginning ofthis study were assessed to characterize the influence of grazingintensity under the initial assessment conditions (Kunrath, 2014).

Neutered male steers (crossbred Angus, Hereford and Nellore)approximately 12-months old entered the pasture system weigh-ing approximately 200 kg to simulate a cattle fattening or finishingsystem. During the grazing cycle cattle feeding was forage-based,and they were only furnished with mineral salt. A continuousgrazing system was adopted (with a minimum of three remainingsteers = test steers), and grazing began when the forage height

reached approximately 20 cm (approximately 1.5 Mg of dry matterha�1). Therefore, each grazing cycle was carried out from the firsthalf of July to the first half of November. Pasture heights werecontrolled every 14 days by the Sward stick method (Barthram,1986), which consists of a graduated stick measuring system with a“marker” that slides up and down until the first forage leaf blade isreached. In each plot, approximately 100 randomized readings(points) were conducted. The average pasture height resulted frommanaging the grazing intensity (stocks) by adding or removingsteers from each plot as required.

The samplings were conducted in all treatments only in the firstblock due to the plot size (averaging 1.8 ha) and the necessity ofother monitoring assessment (the current study is part of a broaderstudy regarding also soil moisture and plant physiologicalparameters). Soil samples were collected on November 2�, 2011,at depths of 0–5, 5–10, 10–20, 20–30 and 30–50 cm in the 10, 20,30 and 40 cm treatments, one day after removal of the animalsfrom the experimental area, and in the NG reference area.

Trenches were dug after animals’ removal from the field, andfour undisturbed soil samples were collected per soil layer(totaling 100 samples), using soil core rings with 0.057 m diameterand 0.04 m height. Subsequently, the samples were wrapped inplastic wrap, packed in styrofoam boxes and transported to thelaboratory, where soil penetration resistance (PR, MPa), BD(g cm�3) and volumetric water content (QV, m3m�3) weredetermined.

2.1. Soil analyses

To determine the LLWR, soil samples were saturated andequilibrated to water tensions (Cs) of 0.001, 0.006 and 0.01 MPa insand columns (Reinert and Reichert, 2006) and to 0.03, 0.1, 0.5 and1.5 MPa in Richards’ chambers (Klute, 1986). For each tension, 14samples were collected from 0 to 5, 5–10, 10–20, 20–30 and 30–50 cm soil layers, from the areas of different grazing intensitiesincluding the 10, 20, 30 and 40 cm heights and the NG area, andgrouped to produce a wide range of BD values. The samples wereremoved from tension equipment after drainage, weighed, andsubjected to PR testing.

Soil samples PR was measured at a constant penetration speedof 10 mm min�1 with a bench top, electronic penetrometer with ametal rod with basal area of 129 mm2, base diameter of 12 mm, andangle of 30� (Reinert et al., 2007). The BD and Q were calculatedafter the soil mass drying at 105 �C for 24 h. The PR, BD and Q datawere fitted to Busscher’s model (1990): PR = aBDbQc, where a,band c are the fitting coefficients.

Soil water retention curve (WRC) was obtained from the samesamples used to measure soil PR. Thus, water content of soilsamples was measured without changing soil structure during thepenetration test. The WRC model was fitted to the Q, C and BDdata, Q = exp(d + eBD)Cf, where d, e and f are the fittingparameters. The LLWR was determined as described by Leãoet al. (2005). For the upper limit, a Q value of 0.01 MPa (QFC) wasused for C or for an air-filled porosity of 10% (QAFP). For the lowerlimit of the PR, a Q value of 2 MPa (QPR) was used, and for thepermanent wilting point moisture (QPWP), 1.5 MPa was used. TheLLWR was calculated as the difference between the upper andlower limits of water contents for the considered physicalparameters. The upper limit is the lowest value of Q consideredfor the FC or for AFP of 10%, and the lower limit is the highest valueof Q for a PR of 2.0 MPa or for PWP. The penetration resistance andwater retention models were fitted using PROC REG (SAS Institute,1999).

Soil particle size analysis was performed with the pipettemethod after soil dispersion with 1 mol L�1 NaOH. The H2O2

treatment was not performed to remove organic matter from soil.

56 D. Cecagno et al. / Soil & Tillage Research 156 (2016) 54–62

Clay fraction (Ø < 0.002 mm) was collected by sedimentationaccording to Stokes law, flocculated with 0.5 mol L�1 HCl andwashed twice with an ethanol/water (1:1) solution.

Selective dissolution of iron oxides was conducted in fine air-dried soil. Iron from the pedogenic iron oxides was extracted withdithionite–citrate–bicarbonate at 80 �C (Fed; Mehra and Jackson,1960), and the fraction of low-crystallinity oxides was extractedwith ammonium oxalate (0.2 mol L�1) at pH 3.0 in the dark (Feo;Schwertmann, 1964). Iron concentrations were determined byatomic absorption spectrometry (AAS).

Clay mineralogical composition was determined by X-raydiffraction (XRD) in the iron-free clay fraction and in the fractionwith concentrated iron oxides, in samples from 0 to 5 and30–50 cm soil layers of the control plot. Iron-free fraction wasobtained by treating the total clay fraction with dithionite–citrate–bicarbonate at 80 �C. Iron oxides were concentrated with hot5 mol L�1 NaOH (Kämpf and Schwertmann, 1982). The analyseswere conducted in the iron-free clay fraction (from 4� to 40� 2u)and in the fraction with concentrated iron oxides (18–60� 2u), bothin nonoriented thin sections with 5% halite and 0.02� 2uincrements, and reading time of 0.4 s in a Bruker D2 Advancedevice.

2.2. Weather monitoring

A Nexus weather station, model 35.1075.1 was installed inNovember 2011, within the experimental area. The stationprovided data of rainfall distribution throughout the soybeancycle (Fig.1). Rainfall data from previous years were taken from theClimate Atlas of Rio Grande do Sul (CEMETRS, 2013).

2.3. Soybean yields

Soybeans were planted on September 16�, 2011 and harvestedon March 31�, 2012, totaling 136 cycle days. Grain yield wasevaluated by manually harvesting 4.5 m2 plot�1 (5 rows of2 � 0.45 m) in all treatments, after physiological maturity. Thesoybean water content was adjusted to 130 g kg�1. Relative yieldswere calculated considering as 100% the highest yield among thetreatments. Thus, the other treatments were calculated propor-tionally.

0

10

20

30

40

50

60

1º 2º 3º 1º 2º 3º 1º

November December Ja

Pre

cipi

tati

on (m

m)

Decendia

Seed

ing

Fig. 1. Rainfall in 2011/2012 soybean

2.4. BD/BDcLLWR ratio and soybean yield

A ratio between soil bulk density (BD) and soil critical bulkdensity (BD/BDcLLWR) was calculated and a relationship betweenthis ratio and relative soybean yield was performed. An approachbetween soybean yield and rainfall distribution in the 2002/2003,2004/2005, 2005/2006, 2006/2007 and 2011/2012 crop seasonwas also performed, using some data of Conte et al. (2011).

2.5. Statistical analysis

Data were analyzed as a completely randomized design,considering the sample units collected at random (independent)within each treatment as replicates (pseudoreplicates) (Ferreiraet al., 2012), since only one experimental block was used. Soil BDdata were subjected to analysis of variance (ANOVA) and tocontrast analysis at 0.05 significance level.

3. Results

3.1. Soil physical and mineralogical properties and least limiting waterrange

Similar mineralogical composition: the iron-free clay fractionand the fraction with concentrated iron oxides, was observed in theevaluated two (0–5 and 30–50 cm) layers: (Fig. 2). Kaolinite, quartzand rutile were identified in the iron-free clay fraction, and 2:1 or2:1:1 (interstratified) minerals were identified only in 30–50 cmlayer. The oxides goethite, hematite, maghemite, rutile and quartzwere presented in the fraction with concentrated iron oxides.

The soil was clayey to very clayey, with slightly increasing claycontent with depth (Table 1). The Fed contents ranged from 59.7 to80.3 g kg�1. Significant differences were not observed betweendifferent depths and grazing intensities. Low Feo values, comparedwith Fed values, are explained by the predominance of crystallineiron oxides, as observed in XRD (Fig. 2).

Soil BD and PR showed significant variation in their values, asrequired for the parameterization of the WRC and soil retentioncurve (SRC). Thus, BD was used as independent variable in WRCand SRC and in the least limiting water range (LLWR). The followingequations were obtained from models fitted to all (n = 98) available

2º 3º 1º 2º 3º 1º 2º 3º

nuary February March

l / month

Har

vest

crop in the experimental area.

Fig. 2. X-ray difractogram of the iron-free clay fraction and iron oxides fraction of two layers from control treatment. Kt: Kaolinite; 2:1: 2:1 minerals; Qz: quartz; Ru: rutile;Hl: halite; Gt: goethite; Hm: hematite; Mh: maghemite.

Table 1Soil physical and mineralogical properties under different grazing intensities in the experimental area.

Grazing intensity Soil layer (cm) Clay (g kg�1) Textural class Fed (g kg�1) Feo (g kg�1)

Intensive grazing 0–5 538 Clayey 59.7 2.05–10 557 Clayey 60.6 1.7

10–20 572 Clayey 62.4 1.820–30 601 Very clayey 69.0 1.730–50 603 Very clayey 63.0 1.9

Moderate grazing 0–5 534 Clayey 71.1 2.25–10 569 Clayey 66.2 2.0

10–20 582 Clayey 76.1 2.120–30 595 Clayey 72.0 1.930–50 625 Very clayey 74.0 2.0

No grazing 0–5 570 Clayey 66.0 2.85–10 604 Very clayey 76.7 2.9

10–20 701 Very clayey 70.9 2.620–30 608 Very clayey 76.3 2.930–50 564 Clayey 80.3 2.8

Fed = Fe of pedogenic iron oxides; Feo = Fe of low crystallinity iron oxides.

0.00

0.10

0.20

0.30

0.40

0.50

1.00 1.10 1.20 1.30 1.40 1.50 1.60

Wat

er c

onte

nt (

m3

m-3

)

Soil bulk density (BD) (Mg m-3)

θPA

θCC

θRP

θPMP

Critical BD = 1.41

θAFP

θFC

θPR

θPWPLLWR0.30

0.20

0.10

1.10 1.20 1.30 1.40 1.500.00

0.00

Fig. 3. Variation in water content and soil bulk density in critical levels of fieldcapacity (QFC), permanent wilting point (QPWP), penetration resistance (QPR) andair-filled porosity (QAFP) as a function of soil bulk density (BD) in 0–50 cm layer inan Oxisol under no-till integrated soybean-beef cattle system with different grazingintensities.

D. Cecagno et al. / Soil & Tillage Research 156 (2016) 54–62 57

grazing intensity data, including data from the no-grazing area.

Q ¼ expð�0:5877 � 0:6882BDÞC�0:1016 R2 ¼ 0:98PR ¼ ð0:3338ÞQ�0:7971BD2:7067 R2 ¼ 0:98

where Q and C units are m3m�3 and MPa, respectively; and the PRand BD units are MPa and Mg m�3, respectively.

The coefficient of determination (R2 = 1 � residual sum ofsquares/corrected total sum of squares) was 0.98 for both theWRC and SRC models, which were significant in t-tests (P < 0.01).

The LLWR is represented by the highlighted area in Fig. 3; theupper limit of the range was defined by moisture at field capacity(QFC), and its lower limit was defined by moisture at permanentwilting point (QPWP) or the moisture at penetration resistance(QPR).

The BDcLLWR, which is the BD value at which the LLWR is equalto zero (Reichert et al., 2009), defined by inserting the equationsthat determine its upper and lower limits, was 1.41 Mg m�3 (Fig. 3),where afterwards there is a limiting physical condition for plantgrowth (Letey, 1985). In our study, the 1.41 value corresponded tothe intersection of QFCwith QPR, with the LLWR amplitude rangingfrom 0 to 0.17 cm3 cm�3. The LLWR decreased with increasing BD,

58 D. Cecagno et al. / Soil & Tillage Research 156 (2016) 54–62

and even the 1.25 Mg m�3 value was limited by QPWP and,afterwards, by QPR (Fig. 3).

Soil BD in the profile (0–50 cm) was influenced by grazingintensity (Fig. 4). In the surface layer (0–5 cm), BD increased withgrazing intensity. BD under moderate grazing did not differ fromthat under intense grazing or from the no-grazing pasture.However, the latter two differed from each other, with BD higherunder intense grazing and exceeding BDcLLWR. In the 5–10 cm layer,BD was higher than BDcLLWR under intense grazing, but there wereno significant differences in BD among treatments. Below the10 cm depth, there was reversed behavior of BD, where treatmentwithout animals showed higher values than treatments withmoderate and intense grazing pressures. BD increased because ofgrazing pressure was higher in the surface most layers (0–10 cm)than in subsurface layer (10–20 cm) (Fig. 4).

4.1. Relationship of soil BD with critical density based on LLWR,relative soybean yield, and temporal distribution of rainfall

Soil BD values concentrate predominately (90%) in the range of1.30–1.38 Mg m�3 down to a depth of 20 cm, regardless of adoptedmanagement. Therefore, BD was usually less than BDcLLWR, and theLLWR decreased with increase in BD.

The decline in the observed in relation to the expected yieldexceeded 90%, regardless of grazing pressure because the totalrainfall for soybean crop was only 320 mm (50% of crop demand),with just 130 mm occurring during the most critical periods(Fig. 1). Numerically lower yields were also observed as thegrazing intensity increased from the NG to moderate andintensive levels (Table 2). Contrasting with the LLWR behavior,soybean yield behavior was not explained by BD/BDcLLWR ratiobecause the high values of this ratio were close to or even higherthan the LLWR critical value. Yield in NG, although low, exceededthe yield in other treatments, showing a BD/BDCLLWR ratio of 0.97and 0.98 in the 0–5 and 5–10 cm soil layers, respectively.Regardless of assessed layer or management system, BD was apoor indicator of soil physical quality, irrespective of water deficitduring the crop cycle (Fig. 5).

In the five assessed treatments, BD/BDcLLWR ratio was alwaysbelow 1.0, with the exception of intense grazing treatment in the2004/2005 crop, which showed a relative yield of 100%. This yieldvalue is inconsistent with the LLWR-based assumption that thehigher the BD/BDCLLWR ratio, the lower the crop yields.

0

10

20

30

40

50

1.10 1.20 1.30 1.40 1.50

Soil

dept

h (c

m)

Soil bulk dens ity (Mg m-3)

Intensive gr azing Mod erate gr azing No gr azing

LSD 5% = 0.030 3

0.00 1.20 1.30

Fig. 4. Soil bulk density in profile of an Oxisol under no-till integrated soybean-beefcattle system with different grazing intensities, after the 2011 grazing season andprior to 2011/2012 soybean season. Horizontal bar represents the least significantdifference by t test at 5% of significance.

5. Discussion

5.1. Soil physical and mineralogical properties and least limiting waterrange

The main minerals occurred in our experimental area (goethite,hematite, maghemite, rutile and quartz) were also observed bySilva et al. (2008) in a typical Oxisol of this region. This is indicativeof low variation in mineralogical properties of these Oxisols and, asshown in Fig. 2, the soil mineral composition should not haveinfluenced the physical results of our study, both in surface andsubsurface soil layers.

Our study showed that soil QAFP did not limit the LLWR (Fig. 3).In Brazil, where crops are mainly cultivated in well-drained soils,soil PR was an attribute most limiting for crop performance, ascompared to AFP (Lapen et al., 2004). The BDcLLWR of 1.41 Mg m�3

was consistent with the value calculated using the equation(BDcLLWR = �0.00078 clay + 1.83803, where clay is in g kg�1)proposed by Reichert et al. (2009): calculated BDcLLWR variesfrom 1.29 to 1.42 Mg m�3, for soil with clay content ranging from534 to 701 g kg�1 (range observed in our experiment). In soils withsimilar texture but different mineralogy, under continuous androtational grazing systems, Leão et al. (2004) obtained a similarBDcLLWR value (1.43 Mg m�3). However, these authors used acritical PR value of 3.0 MPa, while we used 2.0 MPa. When using thesame limits applied in our study and similar soil texture, Kaiseret al. (2009) reported the very-close value of 1.40 Mg m�3.

Soil sampling timing to obtain BD was an important aspect to beconsidered in ICLSs where animals create a unique condition aftergrazing (Moreira et al., 2012). After soybean sowing, the soilcompaction was diminished due to the sowing machine operation,and all grazing intensities presented similar values of bulk densityin the 0–10 cm layer (1.31 Mg m�3) (Conte et al., 2011). Kunz et al.(2013) observed in a clayey Oxisol no significant changes in soil BDwith rotational grazing, but Collares et al. (2011) observed soilphysical degradation with animal trampling in a different high-clay soil Oxisol. This soil condition after grazing, combined withtemporal and spatial variability in BD and consequently in LLWR(Tormena et al., 2007), highlighted the difficulty of adopting soil BDas a soil physical fertility indicator.

Critical or limiting soil PR for crop development is acontroversial topic, given the species specificity and processesoccurring in the root zone (Gregory et al., 2000). For instance, Kleinand Camara (2007) highlighted the inadequacy of using 2 MPa forsoybean crop. In clayey soils, such as the Oxisol evaluated in ourstudy, the soil moisture content where the PR was considered thelimiting attribute and the most important factor to determine theLLWR amplitude (Imhoff et al., 2001). An increase in PR criticallimit results with increased soil drying with no mechanicalconstraints to plants, because higher incidence of biopores wasobserved in conservationist systems (Betioli et al., 2012).Moreover, according to Gubiani et al. (2013), the difficulty ofrelating PR to plant growth requires determining the range ofwater contents over which the effect of mechanical stress ispredominant.

Assuming that the LLWR assesses soil physical quality throughBD, the behavior of this attribute in the soil profile as a function ofthe grazing intensities was used to measure the impacts of theadopted grazing in the ICLS system (Fig. 4). Intensive grazing in anOxisol managed as ICLS may cause higher proportion of BD valuesto exceed the BDcLLWR (Petean et al., 2010). Greenwood andMcKenzie (2001) observed, regardless of climate conditions oradopted grazing system, an increased BD with an increase ingrazing intensity is caused by trampling and not by defoliation.

Conversely, the same impact of grazing on BD in surface layerswas responsible for an inverse response in subsurface layers

Table 2Ratio of soil bulk density (BD) and critical BD (BD/BDcLLWR) determined by least limiting water range and soybean yield in an Oxisol under a no-till integrated soybean-beefcattle system with different grazing intensities.

Grazing intensity Soil layer (cm) Yield (Mg ha�1) Relative yield (%)

0–5 5–10 10–20 20–30 30–50 Mean

Intensive grazing 1.02 1.01 0.95 0.88 0.89 0.95 0.23 58Moderate grazing 0.99 0.96 0.93 0.92 0.92 0.94 0.28 71No grazing 0.97 0.98 1.00 1.01 1.01 0.99 0.39 100Average 0.99 0.98 0.96 0.94 0.94 0.95 0.30

0

20

40

60

80

100

120

0.7 0.8 0.9 1.0 1.1

Rel

ativ

e so

ybea

n gr

ain

yiel

d (%

)

BD/BDcLLWR

Nor mal 2002 /03

Estiagem mod erada 2004 /05

Estiagem mod erada 2005 /06

Nor mal 2006 /07

Estiagem severa 2011 /12

0.00 0.70 0.80 0.90 1.00 1.10 1.20

Normal rainfall 200 2/03

Moderat e drought 20 04/05

Moderat e drought 20 05/06

Normal rainfall 200 6/07

Severe drough t 2011 /12

Fig. 5. Relative soybean grain yield as a function of soil bulk density (BD) andcritical BD (BD/BDcLLWR) determined by least limiting water range in the 0–30 cmsoil layer in an Oxisol under no-till integrated soybean-beef cattle system withdifferent grazing intensities. With data from Conte et al. (2011).

D. Cecagno et al. / Soil & Tillage Research 156 (2016) 54–62 59

deeper than 20 cm. Through hydraulic and mechanical processesassociated with grazing, a new level of soil organization wasgenerated, with a different geometry and pore continuity. In thisnew arrangement, soil resistance to transfer loads is highervertically (biopores) than horizontally (Alaoui et al., 2011). This“umbrella” effect, according the authors, did not allow transferringof loads applied to subsurface layers. This new organizational levelwas defined by the configuration of “remaining pores” (Richardet al., 2001), which resulted from the destruction of structuralpores by animal and machine traffic. Tensions at deeper depthsmay result in reorientation of soil particles, reducing theproportion of elongated pores and, consequently, restrictingvertical flow. Consequently, water infiltration and redistributionto deep layers was small, thus root development was limited to theupper soil (up to 20 cm depth).

0

200

400

600

800

1000

1200

1400

2002/03 2004/05 2005/06 2006/07 201

Pre

cipi

tati

on (m

m)

Soybean season

Fig. 6. Soybean grain yield and rainfall in different soybean crop seasons in an Oxisol unDAS = days after soybean seeding.

Compaction depth varies according to machines and tiresconfigurations, soil conditions (moisture and compaction degree),number of times the soil was worked, history of pressures appliedto soil, and land management (pasture, forest or agriculture)(Reichert et al., 2007). Despite the greater impact of animaltrampling on soil surface compared with subsurface layers (Fig. 4),it was essential to also evaluate the soil profile at deeper layers tooptimize adopted production system, particularly regarding waterexploration by crops (Servadio et al., 2005).

For the production system examined in our study, most of themachine traffic occurred during the summer (November–Febru-ary) because only the operations of oat seeding and N topdressingare conducted during the grazing cycle. However, surface andsubsurface compaction must be addressed as separate processes,because subsurface compaction is a function of transfer of loadsapplied to soil surface and persists longer than the surfacecompaction (Hamza and Anderson, 2005). The importance of theabovementioned processes is increased when considering the highcost and short-lasting effects of mechanical procedures used forsubsurface soil decompaction (chiseling and subsoiling), and theseprocedures may actually increase soil susceptibility to recompac-tion (Abreu et al., 2004; Hamza and Anderson, 2005).

5.2. Relationship of soil BD with critical density based on LLWR,relative soybean yield, and temporal distribution of rainfall

The negative relationship between BD and LLWR was critical toassessing soil water behavior because LLWR amplitude influencesplant development (Tormena et al., 2007). Given the waterconditions that soybean crop developed in the evaluated growingseason (2011/2012) (Table 2), soybean yields observed in thecurrent study deserve an in-depth discussion. There were extremewater deficits which led to lower yields, when compared with thehistorical yields of the experiment (Carvalho et al., 2011).Depending on the cultivar characteristics, soybean requiresapproximately 450–700 mm of water throughout its cycle (Doganet al., 2007), with highest demand during flowering and grain-

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1/12

Yie

ld (M

g ha

-1)

> 90 DA S

61 - 90 DAS

31 - 60 DAS

0 - 30 DA S2.0

3.0

1.5

2.5

3.5

4.0

1.0

0.5

0.0

Prior to see ding

Climatological normal

Soybean yield

der no-till integrated soybean-beef cattle system with different grazing intensities.

60 D. Cecagno et al. / Soil & Tillage Research 156 (2016) 54–62

filling stages (Meckel et al., 1984). However, in this period only250 mm of rainfall occurred in our experimental area, which is justhalf of the minimal amount needed. Under water stress conditions,Specht et al. (1999) observed soybean yield reductions ofapproximately 40%, a value much less than found in our study.

As highlighted by Martins et al. (2014), soybean yield in theprotocol under study depended more on rainfall distribution(Fig. 6), than on chemical and physical soil properties such as BD.Large fluctuation in yield, despite little fluctuation in cumulativerainfall during crop cycle (such as during the 2004/2005 and 2005/2006 cycles), was due to rainfall distribution throughout the cycle.In general, cumulative rainfall volume in 30 days prior to sowingdid not have a significant effect on yields. The periods thatcontributed most to greater yield were the first 30 days aftersowing (DAS), when plant stand is established, and later than60 DAS, which includes the flowering stage.

The LLWR limitations as an indicator of soil physical quality(Fig. 5) originated from hydraulic processes occurring at the soil-rootinterface (Lipiec et al., 2012). Low BD/BDcLLWR ratio, combined withthe relative soybean yield throughout the crop cycles, with andwithout water deficit (Fig. 5), reinforces the inefficiency of using thistool for long-term assessments of different ICLS managements(soybean-beef cattle) effects on system productivity.

The AW concept used for LLWR should be replaced byextractable water (EW) concept, as suggested by Ritchie (1981)and reinforced by Carlesso (1995). The EW is defined by theparticular soil-crop combination and quantified as the differencebetween QV after drainage following a complete wetting and QV

after plants have completed extraction from the soil profile.Uncertainties related to the determination of AW result from thedifficulty in relating soil water retention energy with the rate ofwater release to plants (Narjary et al., 2012).

Small reductions in water and nutrient availability may hamperplant development, especially in commercial crops (Boyle et al.,1991) because of their higher sensitivity to changes in soil andatmospheric water conditions (Boyer, 1982). Even under irrigatedagriculture, chemical fertility and appropriate crop management,this author observed commercial crop yields were approximately20% of the full crop potential.

Rainfall distribution and regime of water “pulses” during droughtdetermine water availability, thus shaping the structure andoperation of ecosystems (Schwinning et al., 2004). Productivityresponse varies with water supply regularity (Harper et al., 2005).Therefore, in food production systems, water availability andefficiency in the exploration of “green water” override other factors.These areas of concern were in line with the statement by Davies andGowing (1999) that climate fluctuations will have substantialimpacts on global agriculture, with an increasing frequency andintensity of dry periods (Gutschick and Bassirad, 2003).

The complexity of maintaining high yields under water stressconditions may involve processes not directly related to droughttolerance or water relationships (Richards,1996). In root apex, cellsin different regions grow at different rates, showing differentdegrees of sensitivity to stresses such as a reduction in cell waterpotential (Sharp et al., 2004). At the root cell level, under low tomoderate conditions of decreased soil water potential, reactiveoxygen species increase the elasticity of cell walls (Dumville andFry, 2003), whereas under extreme conditions these chemicalsmay damage membrane integrity (Sharp et al., 2004). Whengreater soil volume is explored there is decrease in the impact ofsoil drying on water absorption, because of competition amongnearby roots, especially in compacted soils (Davies, 2006).

ICLS is an open system relying on many agents (soil, plant,atmosphere, machinery, animal), thus depending on numerousmodifications of different natures and periods. Therefore, com-prehending the systems impacts on animal or plant yields requires

long-term assessments, that allow understanding the influence ofabiotic factors, such as rainfall distribution, and soil properties thatmay, over time, gain or lose importance as indicators of systemequilibrium.

The relationship between yield and the BD/BDcLLWR ratio foundin the present experimental ICLS (Fig. 5) corroborates the inquiriesof Gubiani et al. (2013) regarding the insufficiency of LLWR as ahydraulic soil indicator. Advances involving the inclusion of flowassessments (hydraulic conductivity) in the LLWR methodologywere suggested by Groenevelt et al. (2001), giving rise to the term“integral water capacity”. Relating water flows to mobile watercontent (free water) through the dissipation of momentum(Germann and Di Pietro, 1996), based on the theory of kinematicwaves (Lighthill and Whitham,1955), would be another interestingapproach to better estimate processes governing water extractionby plants.

The time factor should not be overlooked when assessingproduction systems with larger numbers of components, such as inICLS. Long-term assessments showed the dependence of crops onrainfall distribution, which may even override the state of soilcompaction (Fernández et al., 2007). Approaches such as ofMcGregor et al. (1999), who monitored soybean yields over14 years in no-tillage and tillage systems and included timevariable in fitting equation relating observed differences, couldalso be used in ICLSs. McGregor et al. (2006) were able to suggestchanges in crop succession/rotation system under no-tillage thatgenerated soybean yields, surpassing conventional tillage systembut showed significant decline under drought conditions.

6. Conclusions

Intensive grazing results in soil surface compaction, but not indeeper layers. Long-term moderate grazing, on the other hand,leads to intermediate compaction, not affecting surface orsubsurface soil physical conditions.

Animal trampling avoids the soil compaction on subsurfacelayers, which occurs in non grazed areas.

Approaching all components that participate in integrated foodproduction systems is crucial since, beyond soil-plant relations,environmental issues and machinery impacts interfere with plantgrowth and development.

The least limiting water range is an inadequate indicator of soilphysical quality in integrated soybean-beef cattle system, with nodirect relation with soybean yield. Under rainfall conditions,soybean yields rely mainly on rainfall amount and distribution.

This research opens doors for a new insight on soil scienceevaluation of food production systems, in which not only soilparameters are linked to plant behavior. Physiological indicatorsmay be more efficient to represent plant perception of the soil–plant–animal–machinery–atmosphere system.

Acknowledgements

We would like to thank Adao Luis Ramos dos Santos for thesupport provided in the laboratorial analysis and field activities aswell as the Cabanha Cerro Coroado for providing the experimentalarea, animals, machinery and field support. We also thank theNational Council for the Development of Science and Technology(CNPq) and the Coordination for the Improvement of HigherEducation Personnel (CAPES) for financial and scholarship support.

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