Agricultural Systems 173 (2019) 364–377

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Agricultural Systems
journal homepage: www.elsevier.com/locate/agsy

The role of soil hydraulic properties in crop water use eﬃciency: A processbased analysis for some Brazilian scenarios

T⁎

Everton Alves Rodrigues Pinheiroa,b, , Quirijn de Jong van Liera, Jirka Šimůnekb
ab

CENA/University of São Paulo, P.O. Box 96, 13416-900 Piracicaba, SP, Brazil
Department of Environmental Sciences, Univ. of California, Riverside, CA 92521, USA

A R T I C LE I N FO

A B S T R A C T

Keywords:
Agrosystem analysis
Agro-hydrological simulations
Root water uptake
Water productivity

The need for improvements in the water use eﬃciency by agricultural ecosystems requires a holistic assessmentof the hydraulic functioning of cropped soils, taking into consideration the most relevant interactions andfeedbacks that control the soil water budget. We implemented a mechanistic approach to isolate the eﬀects ofsoil hydraulic properties (K-θ-h) of layered soils on water balance components and land and water productivity,
adopting comprehensive scenarios of soil water availability and requirements. The agro-hydrological simulations were performed using the SWAP model integrated with the WOFOST crop growth module. The simulated
scenarios included the rainfed crop growth of maize and soybean in three climate zones, evaluating the current
climate scenarios as well as two future scenarios, a wetter and a drier one, totaling 108 scenarios simulated for
30 years each. Simulations were performed for six soils, grouped pairwise (3 × 2), where each pair represented
the same soil group with two diﬀerent long-term land uses: natural forest (proxy of a no-tillage system) andconventional agricultural use. The K-θ-h relationships were obtained simultaneously by inverse modeling for thefull range of soil water contents commonly found in the domain of crop available water. The agro-hydrologicalsimulations showed that the soil hydraulic properties aﬀect dynamically water balance components and land
productivity by relating soil hydraulic functioning to climate patterns and crop water requirements. In general,maize productivity was more sensitive to soil hydraulic properties under future climate scenarios than soybean.
While land productivities of maize and soybean increased under the wetter climate scenario, water productivityof both crops was consistently reduced by both future climate scenarios. The K-θ-h of soils under conventional
agricultural use over-performed their counterparts under long-term natural forest use, especially regarding land
productivity during growing seasons with pronounced dry spells. Depending on the length and timing of drought
stress during the growing season, the yield response is determined by soil-speciﬁc conditions strictly related towater availability. The long-term average revealed that the sampled loamy sand soils have more favorable
hydraulic properties for crop growth; moreover, the reduced unproductive water losses, especially runoﬀ, increased the dynamic water storage of those soils.

1. Introduction
In a crop planning system, climate determines the suitability of aregion for crops adaptation while weather and soil characteristics determine the yield level (Araya et al., 2010; Bodner et al., 2015). Soils
play a decisive role in the soil-plant-atmosphere system by allowing
extraction of water and nutrients by roots, while providing physicalsupport for plant growth (Bonfante et al., 2017). In this context, soil
hydraulic properties (K-θ-h) together with hydraulic gradients control
water ﬂow toward roots, a highly dynamic process that determines thepartitioning of surface energy ﬂuxes by driving crop growth and dry
matter accumulation. However, unlike climatic features, such⁎properties do not obey a general pattern. Spatial variability of soil
hydraulic properties, such as hydraulic conductivity and retention
characteristics, of surface and subsurface layers add complexity to theprediction of soil water redistribution and availability in time and space(Vereecken et al., 2014; Bonfante and Bouma, 2015; De Jong van Lierand Wendroth, 2016).
Given strong interactions between weather variables and soil
available water, predicted climate change scenarios might pose a severe
threat to food security by reducing water availability for crop production (Sadras et al., 2010; Challinor et al., 2016; Zhao et al., 2017).
Water scarcity is a common reality in many regions around the globe,
implicitly in arid and semi-arid regions (Bodner et al., 2015) andCorresponding author at: CENA/University of São Paulo, P.O. Box 96, 13416-900 Piracicaba, SP, Brazil.
E-mail addresses: pinheiroear@usp.br (E.A.R. Pinheiro), qdjvlier@usp.br (Q. de Jong van Lier), jsimunek@ucr.edu (J. Šimůnek).

https://doi.org/10.1016/j.agsy.2019.03.019
Received 18 December 2018; Received in revised form 20 February 2019; Accepted 23 March 20190308-521X/ © 2019 Elsevier Ltd. All rights reserved.

Agricultural Systems 173 (2019) 364–377

E.A.R. Pinheiro, et al.

where t denotes time (d), z is the vertical coordinate taken positive
upwards (m), θ is the volumetric water content (m3 m−3), h is thepressure head (m), K(h) is the hydraulic conductivity (m d−1), and S(h)represents water uptake by plant roots (m d−1). The numerical solutionof Eq. (1) requires the knowledge of unsaturated soil hydraulic properties (K-θ-h), which may be described using the analytical functions
deﬁned by the van Genuchten-Mualem model (Mualem, 1976; Van
Genuchten, 1980):

occasionally in sub-humid and other climate types. Projected alterations of climate patterns are expected to change the spatial extent ofsuch regions as well as the randomness of weather behavior (Xie et al.,2015; Zhao et al., 2017). In terms of crop productivity, adjustments orcompensation measures may be necessary to increase the water resources use eﬃciency (Elliot et al., 2014; Jägermeyr et al., 2017;
Battisti et al., 2017). In regions with a surplus of water, reduced soil
moisture may be compensated by a net increase in irrigation (Kanget al., 2009). However, simply increasing the irrigated area would be
insuﬃcient to compensate for the negative impacts of climate change
on crop productivity (Elliot et al., 2014). Consequently, regions that arelikely to suﬀer water stress due to changes in weather patterns would
beneﬁt from a ﬁne-tuned combination between soil and crop in order to
optimize the use of water resources by agricultural systems (Kang et al.,2009; Molden et al., 2010; Bonfante et al., 2017).
The current need for improvements in the water use eﬃciency byagricultural ecosystems requires a holistic assessment of the hydraulicfunctioning of cropped soils, taking into consideration the most relevant interactions and feedbacks that control the soil water budget.
However, most of the attempts to do so are restricted to empiricalformulations that deﬁne the so-called “soil-water holding capacity,”
based on the concepts of ﬁeld capacity and the wilting point as static
upper and lower limits of soil available water, respectively. Althoughlargely used worldwide, this approach is ﬂawed when it is utilized torepresent the nuances of the transient soil-moisture dynamics and climate seasonality (Romano et al., 2011; De Jong van Lier, 2017;
Pinheiro et al., 2018). On the other hand, the analysis of the water use
in crop production under current and hypothetical future climate scenarios can be carried out using robust, process-based models supported
by experimental data, while considering vertical and horizontal soil
heterogeneity, diﬀerent crop types, and agricultural management. The
state-of-the-art vadose zone hydrological models (based on the Richards
equation), such as Hydrus (Šimůnek et al., 2016) and SWAP (Kroes
et al., 2017), allow to simulate the spatial and temporal water redistribution in soils, making intensive use of existing soil physicalknowledge (De Jong van Lier et al., 2015; Bonfante and Bouma, 2015;
Jones et al., 2016). When coupled with a crop growth module, thesemodels dynamically link soil-plant-atmosphere processes and can beused to predict how changes in the dynamic boundary conditions (e.g.,
climate, soil type, and land use) interact and control the state variables
related to the use and management of water resources in agriculture.
Based on the ability of physically-based simulations of dynamically
predicting the soil water availability for crop transpiration, the role ofsoil hydraulic properties on hydrology-related processes can be assessedby simulation models. Therefore, the main objectives of this study area) to implement agro-hydrological simulations of crop growth on soilsof diﬀerent texture and historical land use in three climate zones ofSoutheast Brazil, b) to investigate the hydraulic functioning responsesof cropped soils to future eﬀects of climate change by the assessment ofland and water productivity, and c) to test whether the same soil group
under diﬀerent land uses behave diﬀerently in terms of dynamic water
availability by assessing the eﬀect of their hydraulic properties on waterbalance components, and land and water productivity.

⎤
⎡
z
⎥
⎢
Rz
Tact = TP ∑ ⎢φZ wZ z⎥
z=1 ⎢∑ wz R z ⎥
⎥
⎢
z=1
⎦
⎣

2. Material and methods

2.2. Crop growth module

2.1. Agro-hydrological simulations

The SWAP model simulates the accumulated dry matter and grain
yield for annual ﬁeld crops using the integrated WOFOST crop growthmodule (Van Diepen et al., 1989). Taking into consideration environmental conditions, the module dynamically simulates crop growth byquantifying potential and actual photosynthesis, maintenance respiration, and a dry matter increase and its partitioning into roots, stems,
leaves, and storage organs (Kroes et al., 2017). The partitioning of assimilates over diﬀerent plant organs depends on the development stage(DVS) of the crop, where DVS = 0 corresponds to emergence, DVS = 1
to anthesis, and DVS = 2 to maturity. The DVS is determined by the(2)

K = Ks Θ λ [1 − (1 − Θn /(n − 1) )1 − (1/ n) ]2(3)where Θ = (θ − θr)/(θs − θr) is the eﬀective saturation, θr is the residual water content (m3 m−3), θs is the saturated water content(m3 m−3), K is the unsaturated hydraulic conductivity (m d−1), Ks is thesaturated hydraulic conductivity (m d−1), and α (m−1), n (−), and λ(−) are shape parameters.
The root water uptake term S(h) in Eq. (1) corresponds to the actual
transpiration (Tact, m d−1). It is calculated in the SWAP model according to a modiﬁed version of the transpiration reduction function
proposed by Feddes et al. (1978). Tact is calculated by multiplying thepotential transpiration Tp (m d−1) by a semi-empirical factor φz, evaluated for each soil layer (Z) and weighted by the layer thickness wz (m)and the relative root length density (Rz):(4)

The empirical reduction factor φz (varying from 0 to 1) is deﬁned byfour typical pressure head values (0 > h1 > h2 > h3 > h4). At
wilting point (h < h4) and in the anaerobic phase (h > h1), φz = 0; inthe falling rate phase (h4 < h < h3), φz = (h-h4)/(h3-h4); in the constant (optimum) rate, delimited by h3 and h2, φz = 1; in the wet phase(h2 < h < h1), φz = (h−h1)/(h2−h1). The SWAP model allows h3 tovary as a function of potential transpiration rate (h3h for high Tp and h3l
for low Tp). A compilation of h3l, h3h and h4 values for several crops andcrop groups is listed in Taylor and Ashcroft (1972). For our simulations,the adopted critical h values were based on Taylor and Ashcroft (1972)
with modiﬁcations performed by Schwantes (2017).
We adopted a soil proﬁle 1 m deep for all scenarios, and at thisdepth the bottom boundary condition was set as free drainage, as thegroundwater level for the studied sites is very deep, at least 5 m below
the soil surface (De Jong van Lier, 2017). The 1 m deep soil proﬁle wasdiscretized in 0.05 m increments, where hydraulic properties for the top
0.30 m were determined in samples taken between 0 and 0.15 m, andthe bottom 0.70 m between 0.30 and 0.45 m. The upper boundary
condition was described in terms of potential evapotranspiration (ETp)and precipitation. ETp was further partitioned into potential transpiration (Tp) and potential evaporation (Ep) according to the leaf area index(LAI) development. The following sections describe input parameters
for the crop, soil, and climate necessary for running the SWAP model
coupled with the WOFOST module (Van Diepen et al., 1989).

The agro-hydrological simulations were performed using the SWAP
model (Kroes et al., 2017). SWAP is a 1-D agro-hydrological model thatnumerically solves the Richards equation with a root water extraction
sink term describing water ﬂow in the soil-plant-atmosphere environment:

∂θ
∂ ⎡
∂h
=
+ 1⎞ ⎤ − S (h)K (h) ⎛
∂t
∂z ⎢
∂z
⎝
⎠⎥
⎣
⎦

Θ = [1 + |αh|n ](1/ n) − 1

(1)
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Agricultural Systems 173 (2019) 364–377

E.A.R. Pinheiro, et al.

Fig. 1. Geographical locations and aerial images of the sampling sites.

functions deﬁned by Mualem (1976) and Van Genuchten (1980),
Table 2. For each site, the undisturbed soil samples were collected fromtwo depths (between 0 and 0.15 m and between 0.3 and 0.45 m). Although these sampling depths may not represent the entire root zone,
for a majority of crops they are expected to contain most active roots
responsible for water uptake (De Willigen and Van Noordwijk, 1987;
Gao et al., 2010; Moraes et al., 2018).

accumulated number of degree-days.
The growth of maize and soybean, two important crops in Brazilian
agriculture, were simulated in this study. Crop-speciﬁc input parameters for maize and soybean were obtained from the parametrizationand validation of WOFOST performed for Brazilian conditions by
Schwantes (2017), using rainfed ﬁeld experiments during 42 growing
seasons of maize and 19 of soybean (at diﬀerent locations) between
2006 and 2015. Meteorological conditions of the experimental sites
selected by Schwantes (2017) are similar to all three climate zones usedin this study. For the future scenarios with modiﬁed climate variables,
the number of degree-days to anthesis and maturity and leaf lifespanwere adapted in order to preserve the current crop cycle length and thepotential yield level.

2.2.2. Climate zones with current and future climate scenarios
Each soil from the previous section was assumed to occur in threemain climate zones of the São Paulo state, a densely populated region
with intensive agriculture. The selection of these climate zones followed
the Köppen climate map for Brazil, with updates performed by Alvares
et al. (2014). The main climate zones of the São Paulo state are Aw(30.8%), Cfa (33.4%), and Cwa (17.4%). Basic daily meteorological
data used in this study (precipitation, maximum and minimum temperature, solar radiation, relative humidity, and wind speed) were extracted from a robust gridded dataset produced by Xavier et al. (2015)
for the entire Brazilian territory. Thirty years of daily meteorological
data were extracted from a high-resolution grid (0.25° × 0.25°) within
the domains of each climate zone. The extracted meteorological data
(Fig. 2) were implemented into the agro-hydrological simulations as
representative of the current climate conditions and used as baselines
for generating future climate scenarios.
Future climate scenarios were created using general circulation
models (GCM) from the Fifth Coupled Model Intercomparison Project(CMIP5) and the 30-year present-day climate data for each climate zone
as baselines. The protocol of data generation was based on the
Agricultural Model Intercomparison and Improvement Project (AgMIP,
www.agmip.org, Rosenzweig et al., 2013). The AgMIP project addresses the challenge of future climate alterations by adopting the ensemble approach, whereby numerous downscaled future climate

2.2.1. Soil data and hydraulic properties
Six sampling sites under diﬀerent land use types covering a broad
spectrum of soil textures typical of humid subtropical climate (theKöppen climate zones Aw, Cfa, and Cwa) in southeast Brazil were selected for this study (Fig. 1 and Table 1). Sampling sites can be grouped
according to their locations, with each group of sites (S1, S2), (S3, S4),and (S5, S6) representing two diﬀerent long-term land uses on the
nearby and same soil groups: Native forest, a proxy of a long-term notillage system (odd subscripts), and conventional agricultural use (evensubscripts).
Soil hydraulic properties (K-θ-h) were obtained by Pinheiro et al.
(2019) for the entire range of crop available water (from near-saturation to wilting point). As described in Pinheiro et al. (2019), undisturbed soils samples were subjected to an evaporation experimentwith soil water contents, θ(t), measured over time by a non-invasive
technique (gamma-ray attenuation). The speciﬁc experimental
boundary conditions and θ(t) were implemented in an inverse modelingprotocol to derive K-θ-h relationships according to the analytical366

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E.A.R. Pinheiro, et al.

Table 1
Particle size distribution, soil texture class, and long-term land use at six sampling sites.
Great soil groupa

Ferralsol

Site

0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00

S1
S2

Acrisol

S3
S4

Arenosol

S5
S6

aParticle size fraction (kg kg−1)

Soil proﬁle depth m

Sand

Silt

Clay

0.481
0.509
0.427
0.388
0.233
0.278
0.202
0.168
0.847
0.850
0.856
0.845

0.113
0.061
0.092
0.081
0.076
0.055
0.045
0.038
0.039
0.024
0.031
0.029

0.406
0.430
0.481
0.531
0.691
0.667
0.753
0.794
0.114
0.126
0.113
0.126

Soil texture class

Land use

Sandy clay

Native Forest

Clay

PastureClay

Native Forest

Clay

Annual crops

Loamy sand

Native Forest

Loamy sand

Sugarcane

IUSS Working Group WRB (2015).

Table 2
Soil hydraulic parameters for surface soils and subsoils according to the van Genuchten-Mualem analytical functions (Mualem, 1976; Van Genuchten, 1980). The
values between parentheses are the respective standard errors.
Site

S1
S2
S3
S4
S5
S6

Soil proﬁle depth

θr

θs
3

−3

m

m m

0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00
0.00–0.30
0.30–1.00

0.042 (0.0060)
0.046 (0.0053)
0.030 (0.0068)
0.028 (0.0095)
0.048 (0.0066)
0.054 (0.0107)
0.053 (0.0154)
0.063 (0.0147)
0.040 (0.0029)
0.039 (0.0028)
0.034 (0.0022)
0.031 (0.0041)

αm0.350 (0.0013)
0.344 (0.0010)
0.362 (0.0010)
0.350 (0.0010)
0.410 (0.0011)
0.415 (0.0020)
0.427 (0.0012)
0.419 (0.0010)
0.357 (0.0054)
0.368 (0.0040)
0.290 (0.0015)
0.306 (0.0023)

Κs

n
−1

1.080 (0.0050)
1.400 (0.0290)
0.888 (0.0330)
1.608 (0.0610)
1.482 (0.0600)
1.129 (0.0670)
1.205 (0.0440)
0.783 (0.0210)
4.272 (0.3800)
2.752 (0.1830)
2.211 (0.0850)
2.361 (0.1410)

λ
−1

−

md

−

1.242 (0.0094)
1.217 (0.0066)
1.208 (0.0076)
1.197 (0.0100)
1.183 (0.0060)
1.201 (0.0107)
1.192 (0.0126)
1.180 (0.0109)
1.428 (0.0148)
1.455 (0.0135)
1.471 (0.0141)
1.397 (0.0180)

0.094 (0.0110)
0.301 (0.0220)
0.096 (0.0100)
0.102 (0.0130)
0.233 (0.0240)
0.293 (0.0460)
0.217 (0.0320)
0.054 (0.0060)
0.502 (0.1040)
1.586 (0.2420)
0.515 (0.0590)
0.316 (0.0550)

0.000 (−)
−0.002 (0.0585)
0.000 (−)
−0.010 (0.0934)
0.000 (−)
0.000 (−)
0.261 (0.1443)−0.012 (0.1236)
0.000 (−)
0.000 (−)
0.005 (0.0548)
0.013 (0.0828)

Table 3
Annual average changes in temperature and rainfall simulated by two selected
general circulation models (GCMs) for three climate zones, considering the
RCP8.5 scenario for the mid-century (2040–2069).
Climate zone

GCM1 (INM-CM4)a
Tmax

Tmin

°C
Aw
Cfa
Cwa

+1.37
+1.43
+1.25

+1.85
+1.49
+1.46

GCM2 (GFDL-ESM2M)bRainfall

Tmax

%

°C

−0.75
−3.07
+0.20

+2.24
+2.08
+2.08

Tmin

Rainfall
%

+1.99
+1.71
+1.71

−19.5
−19.1
−15.8

a,b
For more information on GCM1 and GCM2 see Volodin et al. (2010) andDunne et al. (2012), respectively.

scenarios are generated using diﬀerent GCMs and CO2 emission pathways and baseline local daily weather data. For this study, we selected
two GCM models with moderate annual changes in rainfall and maximum and minimum temperatures for the studied sites (Table 3). We
selected the mid-century (2040–2069) scenario with CO2 emissions
according to the trajectory RCP8.5 (representative concentration
pathway, Moss et al., 2010). As we were particularly interested in assessing comprehensive future scenarios of soil water availability andatmosphere water demand, the mid-century time window (2040–2069)
together with RCP8.5 allowed us to obtain those scenarios.

Fig. 2. Solar radiation (top), mean temperatures (bottom) (solid and dashedlines), and long-term (30 years) mean monthly rainfall (bars) together withstandard deviations (bottom) for three considered climate zones (Aw, Cfa, andCwa).

2.3. Simulated scenarios
The simulations considered crop growth of maize and soybean at six
367

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E.A.R. Pinheiro, et al.

some values of soil water contents (θ) and unsaturated hydraulic conductivities (K) for the surface layer of the sampled soils. The available
water capacity (AWC) for the soil proﬁle depth of 1 m, subdivided into
two layers (L1 and L2), is shown as well, in which the speciﬁc hydraulicproperties of both surface (L1 = 0.30 m) and subsoil layers(L2 = 0.70 m) were considered according to Table 2. Recalling Table 1,
group of sites (S1, S2), (S3, S4) and (S5, S6) in Table 4 represents the
nearby and same soil group under diﬀerent historical land uses, S1, S3,
and S5 under native forest and S2, S4, and S6 under conventional
agricultural use.
A closer look at Table 4 reveals that S5 is the only soil under (native)
forest use that has near saturation (h = −0.1 m) θ larger than its
counterpart under agricultural use. Regarding K values, all soils under
forest use have lower K when compared with their counterparts forpressure heads h < −1.0 m, with more noticeable diﬀerences between
S5 and S6. Regarding the K values of Table 4, Ferralsols (S1 and S2) andAcrisols (S3 and S4) have very similar values of K that are higher thanthose of the Arenosols (S5 and S6) as the pressure head drops below−10 m. Regarding AWC, there is only a slight diﬀerence between land
uses of the same soil group, with Arenosols, especially S6, having thelowest values. However, it does not necessarily mean that soil S6 is themost limited one. In fact, soil available water is a highly dynamic
process (Romano et al., 2011; De Jong van Lier, 2017) that is usually
underrepresented when ﬁxed quantities such as AWC are used, especially for humid climate zones, where a considerable fraction of cropwater uptake occurs at water contents higher than ﬁeld capacity (DeJong van Lier, 2017).
Although the general analysis of values given in Table 4, along withsoil texture-based classiﬁcations (Twarakavi et al., 2010), is important
to assess the static behavior of a soil, it does not provide information to
unfold dynamic processes in the vadose zone (e.g., water redistribution,root water uptake, and crop growth). For that, a mechanistic approachis required that combines the dynamics of soil, plant, and climate(Bonfante and Bouma, 2015, Bonfante et al., 2019; De Jong van Lieret al., 2015; Jones et al., 2016). Process-based simulations for rainfed
cropped soils under diﬀerent climate conditions, and for diﬀerent soil
types and land uses are presented and discussed in the following
paragraph.

soil types (Tables 1 and 2) for three climatic zones with three climate
scenarios each (current climate and two future climate scenarios Table 3). All simulations were performed under rainfed conditions. The
sowing date for soybean was in the second half of September and formaize in the ﬁrst half of October, in agreement with the local practice.
Thus, a total of 108 scenarios (6 soils × 2 crops × 3 climatic zones × 3
climate scenarios) were simulated for 30 years each.
2.4. Land and water productivity
Land and water productivity for maize and soybean were obtained
from the SWAP/WOFOST simulations of water balance and biomass, inwhich land productivity (LP) was considered as the actual yield (ﬁnalgrain mass, kg ha−1). The agricultural water productivity (WP) or water
use eﬃciency is the ratio between the amount (mass) of agricultural
yield per a volume (or value) of water used or diverted for this production. The most common units for expressing water productivity are
kg m−3 or ton m−3, i.e., the mass of agricultural yield per a volume ofwater used (Geerts and Raes, 2009; Bastiaanssen and Steduto, 2017).
Expressing WP in kg m−3, land productivity (LP) in kg ha−1, and evapotranspiration (ET) in mm, water productivity during the crop
growing season can be calculated as

WP =

LP
10ET

(5)

where the factor 10 in the denominator stands for the conversion between m3 ha−1 and mm (1 mm = 10 m3 ha−1).
2.5. Statistical analysis
The statistical analysis was performed with the software R 3.5.0 (RDevelopment Core Team, 2018). The water balance components(transpiration, evaporation, interception loss, runoﬀ, and bottom ﬂux)
and the land and water productivity did not satisfy the presumption of
normal distribution and homogeneity of variances required for theparametric analysis. Therefore, the comparison of means of water balance components, land and water productivity was tested by implementing the Kruskal-Wallis one-way analysis of variance (Kruskal
and Wallis, 1952) with signiﬁcance levels of p < 0.05. This test assesses
the diﬀerences among independent groups of data without presuming
any speciﬁc hypothesis on the distribution. The source of variations(climate scenarios and soils) were tested individually for each climate
zone. The Bonferroni correction for p-values adjustment for multiple
testing was adopted.

3.1. Agro-hydrological simulations for rainfed cropping season
In order to obtain more comprehensive scenarios of water availability, our simulations considered climate change eﬀects on rainfall(generated by the GCMs), allowing a more detailed assessment of thesoil hydraulic properties eﬀects on soil hydrology-related processes.
The GCM1 predicts that on an annual scale, the rainfall amount is expected to be very close to the current average for all three climate
zones, while the GCM2 predicts a reduction of up to 19.5% (Table 3). A
closer look at the main cropping season window, which starts in thesecond half of September and ends in the early beginning of March,
reveals that changes in average rainfall amounts in diﬀerent months are
diﬀerent from the annual pattern. During this period of the year, theGCM1 projects an average rainfall increase of up to 20% over thecurrent values, whereas the GCM2 predicts a 5% rainfall reduction. In
this sense, the two chosen GCMs assured that the soil water balance andrelated processes driven by soil hydraulic properties were simulated formore demanding atmospheric conditions (higher temperatures predicted by both GCMs) and under wetter and drier climate scenarios.
Fig. 3 shows a general overview of 30-year averages of water balance components (rainfall, actual transpiration (Tact), actual evaporation (Eact), runoﬀ, interception loss (IL), and bottom ﬂux (BFlux)) for themain growing seasons of maize and soybean obtained in 108 simulated
scenarios, involving six soil textures (S1 through S6), three climatic
zones (Aw, Cfa, and Cwa), and three climate scenarios (current and twoGCMs).

3. Results and discussion
The implemented analysis focused on the eﬀects of soil hydraulicproperties on water balance components and land and water productivity, adopting comprehensive scenarios of soil water availability
and rainfall distributions. The values of all six soil hydraulic parameters(θr, θs, α, n, Ks, and λ) required by the analytical functions of the van
Genuchten-Mualem model (Mualem, 1976; Van Genuchten, 1980),
given in Table 2, are representative of the full range of soil watercontents commonly found in the domain of crop available water(Pinheiro et al., 2019). This is important, because a common practice inmany vadose zone hydrological studies is to adopt the Mualem (1976)
capillary bundle model (Eq. (3)) together with independent measurements of the saturated hydraulic conductivity, Ks, and assign a genericpore connectivity value (λ) of 0.5, the best average ﬁt found by Mualem(1976). However, this practice may introduce large uncertainties in thesimulations of soil hydrology-related processes (Vereecken et al., 2010;
De Jong van Lier et al., 2015), especially in the dry range.
Describing the K-θ-h relations by combining the parameters given in
Table 2 with Eqs. (2) and (3) and assuming characteristic pressure head(h) values commonly related to plant available water, Table 4 shows368

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Table 4
Water contents and hydraulic conductivities for some values of pressure heads in the range of crop available water for the surface soil layer of the six analyzed soils
along with available water capacity (AWC) (values calculated from soil hydraulic parameters in Table 2).
Site

Texture class

AWCa

Pressure head (m)
−0.1

−1.0

−3.3

−10

−150

−0.1

θ (m3 m−3)
S1
S2
S3
S4
S5
S6

Sandy clay
Clay loam
Clay
Clay
Loamy sandLoamy sand

0.346
0.359
0.404
0.422
0.333
0.282

−1.0

−3.3

−10

−150

K (m d−1)
0.309
0.328
0.360
0.381
0.204
0.196

0.260
0.285
0.313
0.332
0.141
0.133

0.213
0.238
0.268
0.283
0.103
0.093

−2

0.132
0.150
0.183
0.191
0.060
0.051

1.7·101.5·10−2
2.2·10−2
2.5·10−2
6.4·10−2
1.4·10−1mm−3

1.3·101.5·10−3
1.2·10−3
1.7·10−3
6.1·10−4
3.5·10−3−4

1.2·101.6·10−4
1.1·10−4
1.6·10−4
2.3·10−5
1.4·10−4−6

9.1·101.3·10−5
9.0·10−6
1.2·10−5
9.9·10−7
5.7·10−6−8

1.2·102.1·10−8
1.6·10−8
1.8·10−8
4.3·10−10
2.0·10−9

165
168
185
180
165
152

a
AWC is the sum of available water capacity of surface (L1) and subsoil (L2) layers. It was estimated by the diﬀerence in the water contents at pressure heads
routinely assumed as proxies of ﬁeld capacity and wilting point (h = −1 m and h = −150 m, respectively).

Fig. 3. Simulated 30-year averages of soil water balance components (WBC) for the growing season of maize and soybean, three climate zones (Aw, Cfa, and Cwa),
six soil textures (S1 through S6), and current and future (GCM1 and GCM2) climate scenarios.
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Table 5
Variations (%) in land productivity (LP) and water productivity (WP) for two GCM scenarios relative to current climate for maize and soybean in three climate zones(Aw, Cfa, and Cwa) in Brazil.
GCM

Maize

Soybean

Aw

GCM1
GCM2

Cfa

Cwa

Aw

Cfa

Cwa

LP

WP

LP

WP

LP

WP

LP

WP

LP

WP

LP

WP

+11
−18

−6
−20

+5
−10

−7
−16

+4
−9

−8
−15

+14
−12

−2
−13

+7
−5

−10
−14

+5
−1

−9
−9

Fig. 4. Climate as a source of variations in land productivity (LP) of maize and soybean (for three climate zones (Aw, Cfa, and Cwa), six soil textures (S1 through S6),and current and future (GCM1 and GCM2) climate scenarios. Solid circles inside each boxplot represent the mean value. LP of the same soil followed by the same
letter do not diﬀer from each other statistically at p < 0.05. NS: not signiﬁcant.

LPM of all soils under the GCM2 scenario diﬀered signiﬁcantly fromthose under the GCM1 scenario for all three climate zones. LPM of soils
S2 and S4 under current climate did not diﬀer statistically from thoseunder two GCM scenarios for the Aw and Cwa climatic zones. For thezone Cfa, LPM under current climate diﬀered signiﬁcantly only for soil
S3, with the GCM2 scenario producing the lowest mean. Regarding theoccurrence of drought stress, this indicates that according to selectedclimate scenarios, soils cropped with maize in the Cfa climatic zone are
expected to suﬀer the lowest impact on LP under climate change, whilea drier climate in the other two climate zones will have a signiﬁcantnegative impact for the majority of soils.
LPS in the Aw zone showed higher sensitivity to the climate scenarios than in the other two zones, with soils S5 and S6 producing
larger LPS under the wetter scenario, whereas the other soils maintaining the current LPS level similar to the two GCMs. For the Cfa zone,
only soils S3 and S5 showed signiﬁcant diﬀerences between LPS, while
for the Cwa zone, none of the soils diﬀered statistically (p > 0.05),
indicating that in these two zones, a drier or wetter climate will havethe least impact on soil hydrological behavior when cropped withsoybean.
Regarding water productivity of maize (WPM), current climate allowed signiﬁcantly higher mean values (p < 0.05) for all soils andclimate zones than those under a drier climate scenario (GCM2). The
same behavior was observed for water productivity of soybean (WPS)
grown in the Cfa and Cwa zones. For the Aw zone, none of the climate

3.1.1. Climate data as a source of variations in land and water productivity
The future climate scenarios predicted by the GCM1 increased land
productivity of maize (LPM) and soybean (LPS) for all three climate
zones, whereas those predicted by the GCM2 reduced them. Meanwhile,water productivity (WP) of both crops was consistently reduced for
future climate scenarios predicted by both GCMs, independently of theclimate zone (Table 5). As will be discussed in more detail below, lower
WP levels for both wetter and drier climate scenarios are explained
mainly by an unproductive increase in the evapotranspiration rate anda decrease in LP due to drought stresses, respectively.
Simulated long-term averages of land productivity of both maize
and soybean for three climate scenarios, reported in Fig. 4, are within
the ranges observed in other studies carried out in the same climate
zones as in our simulation scenarios (Battisti et al., 2017; Schwantes,2017). Testing of the statistical signiﬁcance of numerical diﬀerences in
LP and WP, while assuming climate scenarios as a source of variations,showed that diﬀerences are signiﬁcant for most soils under diﬀerent
scenarios (p < 0.05). Regarding LP (Fig. 4), soils cropped with maize
showed higher sensitivity to the climate scenarios than those cropped
with soybean. Note that maize and soybean are among the major crops
at the global scale (which also include wheat and rice) and that maize is
expected to endure the largest loss in yield due to a temperature increase, which may be related to direct and indirect temperature impacts(e.g., shorter crop season, heat stress, and reduced soil water availability) (Challinor et al., 2016; Zhao et al., 2017).
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Fig. 5. Climate as a source of variations in water productivity (WP) of maize and soybean (for three climate zones (Aw, Cfa, and Cwa), six soil textures (S1 throughS6), and current and future (GCM1 and GCM2) climate scenarios. Solid circles inside each boxplot represent the mean value. WP of the same soil followed by thesame letter do not diﬀer from each other statistically at p < 0.05. NS: not signiﬁcant.

LP achieved for wetter scenarios may not be translated into gains in WP.
In fact, our results indicate that for future climate scenarios, WP values
for all six soils were signiﬁcantly reduced (p < 0.05) when cropped
with soybean in the Cfa and Cwa climate zones and when cropped withmaize in the Cwa climate zone. From the perspective of crop- and ﬁeldspeciﬁc strategies, soil hydraulic properties are inherently linked to the
management of the water stress and the enhancement of water resources use eﬃciency (Kang et al., 2009; Bonfante and Bouma, 2015),
and for this reason, ﬁeld-adaptation strategies must consider the soil
hydraulic behavior in the context of local climate patterns (here represented by the climate zones) and crop water requirements.

scenarios signiﬁcantly impacted WPS values (p > 0.05) (Fig. 5).
Comparing WPM for current climate and the wetter scenario(GCM1), insigniﬁcant diﬀerences were observed for all six soils in the
Aw zone, and signiﬁcant diﬀerences were observed only for soils S2 andS4 in the Cfa zone, with higher WPM values for the current climate
scenario. All soils in the Cwa zone produced signiﬁcantly higher WPM
values under the current climate than under future scenarios. Both Cfa
and Cwa zones showed higher WPS values under the current climate
and signiﬁcant diﬀerences for all soils.
While a temperature increase is usually associated with negative(Zhao et al., 2017) eﬀects on crop yield, as long as new cultivars aredeveloped to improve drought tolerance, positive eﬀects may also be
expected (Battisti et al., 2017). On the other hand, Kang et al. (2009)
pointed out that for some scenarios, crop yield is more sensitive toprecipitation than temperature. Either way, the yield response is a result of interacting processes between plant and soil at the ﬁeld scale(Bonfante and Bouma, 2015) and many factors are involved in crop
growth. Despite the eﬀorts of farmers and crop breeders, some studies
indicate that crop yield increases have been recently slowing down oreven disappearing for some major crops worldwide, with climatechange playing a signiﬁcant role in this process, requiring adaptation
strategies to isolate the eﬀects of individual factors on crop yield(Brisson et al., 2010; Ray et al., 2012; Challinor et al., 2016; Zhao et al.,2017).
Although there is some evidence that soil macroporosity may be
modiﬁed as a result of climate change (Hirmas et al., 2018), our attempt to isolate the eﬀects of soil hydraulic properties assumed that theK-θ-h relations will remain unaltered under climate change scenarios.
Accordingly, the simulation results show that the impact of climatechange on LP and WP will vary depending on the K-θ-h relations of eachsoil, with some soils being more resilient than others, depending on the
climate zone and crop water requirements. Considering the overall
performance of the six soils in terms of LP, regardless of the climatezone and crop, only soils S2 and S4 will not endure signiﬁcant impacts
on LP (p > 0.05), while the response of the other soils will be determined by crop and climate zones.
As discussed in previous studies (e.g., Molden et al., 2010), gains in

3.1.2. Soils as a source of variations in water balance components, and land
and water productivity
Within the same climatic zone and climate scenario, actual transpiration (Tact), Fig. 6, and interception loss (IL) did not diﬀer amongsoils (p > 0.05) in both cropping scenarios with maize and soybean.
Since IL mostly depend on the rainfall intensity, the evaporation rate,
and the aboveground biomass storage capacity, its mean values for soils
with the same climate and crop scenarios were fairly similar, ranging
from 16 ± 8 ( ± standard deviation) to 50 ± 10 mm for maize, andfrom 37 ± 10 to 87 ± 12 mm for soybean, with the lowest values forsoils in the Aw zone under the GCM2 scenario (drier climate) and thehighest values recorded for soils in the Cwa zone under the GCM1
scenario (wetter climate).
Although mean values of Tact did not diﬀer statistically between
soils, a closer look reveals some important numerical diﬀerences(Fig. 6). Tact of soil S6 was higher than Tact calculated for other soils inalmost all scenarios. The largest diﬀerence was observed between S6and S1, up to 11% for maize and 4% for soybean. The only exception
was recorded in the Aw zone under the GCM2 scenario, where Tact ofmaize for soils S2 and S4 was about 1% higher than for S6, and similarly, for soybean grown in S4, Tact was also about 1% higher. Regarding land use, scenarios with maize and hydraulic properties of thesoils taken as proxies of no-tillage systems resulted in lower Tact values
for all climate scenarios and climatic zones than when using hydraulicproperties of their counterparts under conventional agricultural use;371

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Fig. 6. Soil as a source of variations in actual transpiration of maize and soybean, grouped into three climatic zones (Aw, Cfa, and Cwa) and three climate scenarios(current and future GCM1 and GCM2), for six soil textures (S1 through S6). Solid circles inside each boxplot represent the mean value. NS: not signiﬁcant at
p < 0.05.

soils under natural forest use (S1, S3, and S5) produced higher average
BFlux (99 ± 74 mm) than their counterparts under agricultural use(88 ± 77 mm), although no signiﬁcant diﬀerences were found for anyof the simulated scenarios. As all soils were subjected to the same
weather patterns and initial pressure head conditions, and no impedinglayers were present, the BFlux was only driven by soil hydraulic properties, and especially the hydraulic conductivity. It is generally assumed
that soils under natural forest or long-term no-tillage systems develop
higher macroporosity (Price et al., 2010; Luo et al., 2010; Bodner et al.,2015) that likely enhances deep drainage for near saturated conditions,with further negative impacts on water productivity (Sadras et al.,2010).
As expected, runoﬀ for soils S5 and S6 was lower and signiﬁcantly
diﬀerent (p < 0.05) from the other soils in all simulated scenarios. On
the other hand, soils S1 and S2 (medium-textured) produced the highest
runoﬀ means (Fig. 8). The reason for these results is related to the Ks
values of the surface layer shown in Table 2 (lowest for soils S1 and S2,
and highest for soils S5 and S6).
No statistical diﬀerences in runoﬀ between similar soils under different land uses were detected. In general, runoﬀ was more sensitive tosoil hydraulic properties within the Cfa climate zone where statistical
diﬀerences were registered between Arenosol soils and the other soils.
Additionally, soil S2 (medium-textured) diﬀered signiﬁcantly from soils
S3 and S4 (both ﬁne-textured) under the wetter scenario (GCM1) andfrom soil S3 under the current and drier climate scenarios. In a broader
context, both soil hydraulic properties and land management, together
with weather patterns, are important factors that drive the runoﬀ capacity of soils (Parajuli et al., 2016). Soils with reduced saturated hydraulic conductivities are prone to more intense runoﬀ events, which
besides reducing the soil water storage (less inﬁltration) also increase
the risk of soil erosion in cropped ﬁelds, which can also contribute to adiﬀuse nutrient loading into surface reservoirs at the catchment scale.
Land productivity is one of the most important state variables that is
aﬀected by soil water balance. Signiﬁcant diﬀerences (p < 0.05) in LP
between diﬀerent soils were noticed only in the Cwa climate zone forboth maize and soybean under the GCM1 climate scenario and maize

when cropped with soybean, this numerical diﬀerence was more consistent in the Aw zone. Larger values of Tact for soils under agricultural
use may be related to a moderate reduction in macroporosity(Gonzalez-Sosa et al., 2010; Luo et al., 2010; Yu et al., 2015), which
may favor water retention and unsaturated hydraulic conductivity(Bodner et al., 2015), allowing longer periods of water supply to transpiring leaves, with a generally positive eﬀect on carbon assimilation
rates for leaves and root development.
While lowest actual soil evaporation (Eact) for all scenarios wasobserved for soil S6, signiﬁcant diﬀerences (p < 0.05) were revealed
only between S3 and S6 when cropped with maize in the Cfa climatezone under the GCM2 climate scenario (Fig. 7).
In general, the evaporation rate is determined exclusively by soil
hydraulic properties, environmental conditions, and the land cover.
Larger evaporation rates reduce crop available water, possibly triggering the onset of the drought stress in earlier stages of the crop cycle.
As soil water evaporation does not contribute to the carbon assimilation
process, a lower evaporation rate relative to ET results in higher waterproductivity of a cropped ﬁeld (Molden et al., 2010; Sadras et al.,2010). Diﬀerences in evaporation among diﬀerent textural classes aremainly explained by the air-entry pressure head, which coarser textured
soils reach faster during the evaporation process, allowing faster air
entry into their pore system (Or et al., 2013).
Regarding the bottom ﬂux (BFlux), although the global average ofthis component was consistently higher (~ 35%) for the Arenosols(soils S5 and S6), signiﬁcant diﬀerences (p < 0.05) were found only
between soils S2 and S5 when cropped with maize within the Aw climate zone under the drier climate scenario (GCM2), where BFlux in soil
S5 (79 ± 51 mm) was more than twice as high as in soil S2(34 ± 39 mm). Considering only the textural classes, soils S5 and S6
are expected to have higher BFlux values due to their high sand content(> 80%), resulting in a higher macroporosity and a narrower pore-size
distribution (higher Ks, Table 2). On the other hand, soils S1 and S2
attained the lowest BFlux means in all scenarios, despite being coarser
than soils S3 and S4. When the same soil group under diﬀerent land
uses was compared, it could be noticed that the hydraulic properties of372

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Fig. 7. Soil as a source of variations of actual evaporation of maize and soybean, grouped into three climatic zones (Aw, Cfa, and Cwa) and three climate scenarios(current and future GCM1 and GCM2), for six soil textures (S1 through S6). Solid circles inside each boxplot represent the mean value. Soils followed by the same
letters under the same climate scenario do not diﬀer from each other statistically at p < 0.05. NS: not signiﬁcant.

textured soils. Although the rainfall amount during their cropping
season was similar to our scenarios, the rainfall pattern was very distinct. Rainfall was highly concentrated at the beginning of the crop
season, followed by erratic events during the remaining growing
season, which may have produced very distinct drought stress events,
which may explain reduced land productivity in all their simulated
scenarios.

under the current climate scenario (Fig. 9). In these cases, soil S6
showed higher LP than soil S2 when cropped with maize and higher
than soils S2 and S4 when cropped with soybean. In general, the highest
LP was observed for maize in soil S6 in all three climate zones (~10%higher), followed by soil S5 for soybean scenarios. Contrary to our
ﬁndings, Jalota et al. (2010) simulated maize yields within sub-humid
regions in India and found higher yields for crops grown on ﬁner

Fig. 8. Soil as a source of variations in runoﬀ, grouped into three climatic zones (Aw, Cfa, and Cwa) and three diﬀerent climate scenarios (current and future GCM1
and GCM2), for six soil textures (S1 through S6). Solid circles inside each boxplot represent the mean value. Soils followed by the same letters under the same climate
scenario do not diﬀer from each other statistically at p < 0.05. NS: not signiﬁcant.
373

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Fig. 9. Soil as a source of variations in land productivity, grouped into three climatic zones (Aw, Cfa, and Cwa) and three diﬀerent climate scenarios (current andfuture GCM1 and GCM2), for six soil textures (S1 through S6). Solid circles inside each boxplot represent the mean value. Soil types followed by the same lettersunder the same climate scenario do not diﬀer from each other statistically at p < 0.05. NS: not signiﬁcant.

Fig. 10. Pressure heads at three depths (Z = 7, 15, and 40 cm) during one speciﬁc cropping season (1985/1986) of maize grown within the Aw climatic zone under
the current climate scenario (rainfall is shown on the right axis). S1 through S6 represent diﬀerent soils as described in Tables 1 and 2.

signiﬁcant drought stress. We selected one speciﬁc year from the current climate scenario for soils cropped with maize within the Aw zone.
In this simulated agricultural year (1985/1986), potential land

Although diﬀerences in average LP between soils were not statistically signiﬁcant for most scenarios, a closer look at pressure head timeseries may highlight larger numerical diﬀerences for certain years with374

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productivity was 9401 kg ha−1, while rainfall during the cropping
season was 335 mm, far below the historical average (460 mm) for the
Aw climate zone. Soil hydraulic properties aﬀected land productivity
very diﬀerently, with the soils under long-term natural forest use (S1,S3 and S5) showing very low relative productivity (S1 = 0.31,
S3 = 0.24, and S5 = 0.31), while their counterpart soils under agricultural use attaining expressively higher values (S2 = 0.53, S4 = 0.55,
and S6 = 0.52). Fig. 10 shows the temporal variation of pressure heads
as predicted for the cropping season at three depths (7 cm, 15 cm, and40 cm), together with the limiting thresholds (h2, h3, and h4) that deﬁne
the stress response function for root water uptake according to Feddes
et al. (1978) as used in the SWAP scenarios.
According to Fig. 10, the ﬁrst 22 days were crucial for the cumulative dry grain biomass at the end of the crop cycle, where maizegrown on soils S1, S3, and S5 endured stronger drought stress in thethree analyzed depths than when grown on their counterparts, especially in the topsoil layer. Right after emergence, the root system isconstrained within the ﬁrst few centimeters of the soil proﬁle, and thehydraulic conditions of the surface layer will determine whether any
drought stress may result in an irreversible biomass reduction. Accordingly, the dry spell at the start of the crop cycle negatively aﬀected
the leaf area index (LAI) and root biomass, impacting irreversibly thecrop productivity. For soils S1, S3, and S5, the relative LAI at day 22
after emergence was 0.07, 0.05, and 0.07, respectively. On the other
hand, maize grown on soils S2, S4, and S6 attained higher relative LAI
values, ranging from 0.16 for S6 to 0.41 for S2. Regarding relative root
biomass, it ranged from 0.06 to 0.08 for soils under long-term naturalforest use, while for soils under agricultural use it was between 0.18
and 0.43. Higher LAI and root biomass allowed maize grown on soils
under current agricultural use to transpire up to 2.9 times more thanmaize grown on soils under long-term natural forest use, which explains
the occurrence of the drought stress later in the growing season for soils
S2, S4, and S6 (Fig. 10). It is common sense that soils with larger water
holding capacity and favorable hydraulic properties produce bettercrop performance during the drought stress (Bodner et al., 2015;
Bonfante and Bouma, 2015; Battisti et al., 2017; Pinheiro et al., 2018),
and our ﬁndings add to this knowledge by showing that depending on
the length and timing (e.g., early or late season) of the drought stress,
the yield response is determined by soil-speciﬁc conditions strictly related to water availability.
Water productivity (WP) is a function of climate, crop, agricultural
management, and soil available water. Therefore, a large range of WP
may be expected globally, regionally, and even at the ﬁeld scale (Zwart
and Bastiaanssen, 2004; Brauman et al., 2013). Based on this concept,
an increase in water productivity is directly related either to an increasein land productivity or to water savings (Sinclair, 2018). Fig. 11 shows
the WP values for maize and soybean. Only for soils cropped withsoybean in the Cwa zone under the GCM1 scenario (drier climate), WP
diﬀered signiﬁcantly between soils (S5 > S2).
Simulated WP averages for maize and soybean are within the rangeof values commonly found in the literature. The range of
1.1–2.7 kg m−3 was reported for maize (Zwart and Bastiaanssen, 2004;
Bastiaanssen and Steduto, 2017), whereas a narrower range of
0.6–1.0 kg m−3 was reported for soybean (Sadras et al., 2010). Higher
WP for maize is mostly related to its C4 photosynthesis pathway. Different performance of soil S6 regarding Tact (higher), Eact (lower),
Runoﬀ (lower), and LP (higher) was shown above. These components
are integrated into WP, explaining the higher means attained for soil S6(Fig. 11), followed closely by soil S5 in some scenarios. As highlighted
by Molden et al. (2010) and Bodner et al. (2015), understanding thewater balance pattern of cropping systems is essential for the optimization of growth factors of rainfed crops. For example, higher soil
available water is directly related to nutrient use eﬃciency around themost demanding stages of the crop growing season (Sadras et al., 2010).

3.1.3. Long-term land use: native forest soils versus soils under agriculturaluse
Focusing exclusively on soil hydraulic properties, and based on thelong-term average, land use did not aﬀect signiﬁcantly (p > 0.05)
water balance components, nor land and water productivity of soils of
the same pedological group. Although some consistent numerical differences could be noticed (specially for LP of maize grown in the Aw
zone under the drier climate scenario), major eﬀects were restricted toyears with signiﬁcant drought stress at critical stages of the crop development, where soils under current agricultural use over-performed
their counterparts under long-term natural forest use. These results arein line with other studies that observed lower yields of maize grownunder no-tillage (Jalota et al., 2010; Irmak et al., 2019) and an insigniﬁcant eﬀect (Parajuli et al., 2016) of the tillage practice on maize andsoybean yields. Extensive literature covers the inﬂuence of tillage
practices on soil hydraulic properties (Strudley et al., 2008). The degreeof disturbance is usually related to the soil organic matter content,
macroporosity, and pore connectivity (Price et al., 2010; Bodner et al.,2015), which may aﬀect water inﬁltrability near saturation. Although
we did not determine the soil organic matter content, it is likely thatsampled soils under long-term natural forest use have higher OM content than those under agricultural use. This would be reﬂected in higher
Ks values, especially in the subsoil Ks, which is much higher in soils S1,
S3, and S5 than in the other soils under current agricultural use(Table 2). The soil organic matter content is usually related to better
hydraulic functioning of the bulk soil, and especially to better water
retention properties. However, this positive eﬀect may be limited andhighly dependent on the OM content and soil texture (Rawls et al.,
2003; Minasny and McBratney, 2018). Yu et al. (2015) did not ﬁnd any
signiﬁcant impact of diﬀerent land cover systems (including forest) on
the pore size range that determines water redistribution and related
processes in the unsaturated soil (meso- and micro-porosity). Similarly,
Siltecho et al. (2015) found a negligible inﬂuence of diﬀerent land uses
(forest, rubber plantation, and grass) on soil hydraulic properties.
Many studies have attempted to assess soil “physical quality” byusing static indexes or by comparing isolated hydraulic properties
based solely on water retention characteristics. Although implicitly
these approaches do not consider the dynamic process of water ﬂow inthe root zone, they are widely used to pinpoint the “best” soil management practices related to water availability for crops. In terms of
hydraulic functioning, soils have a high degree of complexity, which ispartially reﬂected by the correlation of properties, such as total porosity, pore size distribution, and hydraulic conductivity (Šimůnek et al.,1998), and as a result, changes in one property will impact all otherproperties simultaneously, restricting general conclusions on soil hydraulic functioning based on individual property measurements. Furthermore, cropping systems are very dynamic and subject to complex
interactions of several components (Jones et al., 2016). As our results
show, soil hydraulic properties eﬀects on root water uptake and biomass accumulation may vary greatly between years and soils, depending on rainfall distribution, the vapor pressure deﬁcit, and drought
stresses, together with plant water requirements. Therefore, as discussed in Bonfante et al. (2019) and in Pinheiro et al. (2019), processbased simulation models are the most adequate tools to analyze thehydraulic qualities and limitations of soils under dynamic boundary
conditions.
4. Conclusions
The agro-hydrological simulations described in this study that include comprehensive scenarios of soil-plant-atmosphere systems underrainfed conditions and future climate scenarios allow us to conclude
that:
1. Land productivities of maize and soybean ﬁelds increase under thewetter future climate scenario, while they are impacted negatively
375

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Fig. 11. Soil as a source of variations for water productivity, grouped into three climatic zones (Aw, Cfa, and Cwa) and three diﬀerent climate scenarios (current andfuture GCM1 and GCM2), for six soil textures (S1 through S6). Solid circles inside each boxplot represent the mean value. Soil types followed by the same lettersunder the same climate scenario do not diﬀer from each other statistically at p < 0.05. NS: not signiﬁcant.

by the drier climate scenario for all evaluated soils. On the other
hand, water productivity of both crops was consistently reduced byboth future climate scenarios.
2. The hydraulic functioning of soils is dynamically related to climate
zones and crop water requirements. In general, maize was moresensitive to soil hydraulic properties under future climate scenarios
than soybean.
3. Hydraulic properties of soils under conventional agricultural use
over-performed their counterparts under natural forest use, especially regarding land productivity during growing seasons withpronounced drought stresses. In general, depending on the lengthand timing (e.g., early or late season) of the drought stress, the yield
response is determined by soil-speciﬁc conditions strictly related towater availability.
4. The long-term average revealed that the Arenosols, particularly the
one under current agricultural use, provided a higher dynamic water
storage and more favorable hydraulic properties for crop growth.
This positive behavior for these soils is mainly explained by their
reduced unproductive water losses, especially by runoﬀ.

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Acknowledgments
This research was supported by the São Paulo Research Foundation(FAPESP) (grant numbers 2016/16128-4 and 2017/17793-4).
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