Introduction
⌅Soil quality is a primary indicator of sustainable agricultural management (Doran, 2002Doran JW, 2002. Soil health and global sustainability: translating science into practice. Agric Ecosyst Environ. 88:119–127. https://doi.org/10.1016/s0167-8809(01)00246-8
) since it encompasses the ability of a soil to support crop growth without harming environmental quality (Karlen et al., 1997Karlen
DL, Mausbach MJ, Doran JW, Cline RG, Harris RF, Schuman GE, 1997. Soil
Quality: A Concept, Definition, and Framework for Evaluation (A Guest
Editorial). Soil Sci Soc Am J. 61:4–10. https://doi.org/10.2136/sssaj1997.03615995006100010001x
). A number of soil quality and health indicators are easy to find in the literature (Andrews & Carroll, 2001Andrews
SS, Carroll CR, 2001. Designing a soil quality assessment tool for
sustainable agroecosystem management. Ecol Appl. 11:1573–1585. https://doi.org/10.2307/3061079
; Bünemann et al., 2018Bünemann
EK, Bongiorno G, Bai Z, Creamer RE, Deyn GD, Goede R, de Fleskens L,
Geissen V, Kuyper TW, Mäder P, Pulleman M, Sukkel W, van Groenigen JW,
Brussaard L, 2018. Soil quality – A critical review. Soil Biol Biochem.
120:105–125. https://doi.org/10.1016/j.soilbio.2018.01.030
) and the ideal indicator should be informative, sensitive, effective, and relevant (Lehmann et al., 2020Lehmann
J, Bossio DA, Kögel-Knabner I, Rillig MC, 2020. The concept and future
prospects of soil health. Nat Rev Earth Environ. 1:544–553. https://doi.org/10.1038/s43017-020-0080-8
).
One of the main indicators is soil organic carbon (SOC), due to its
importance and the role it plays in many soil functions and ecosystems
services (Nuneset al., 2021Nunes
MR, Veum KS, Parker PA, Holan SH, Karlen DL, Amsili JP, Es HM, Wills
SA, Seybold CA, Moorman TB, 2021. The soil health assessment protocol
and evaluation applied to soil organic carbon. Soil Sci Soc Am J.
85:1196–1213. https://doi.org/10.1002/saj2.20244
).
However, SOC changes usually take time to occur and, thereby, increases
and/or decreases are observed several years after any management change
(Smith, 2004Smith P, 2004. How long before a change in soil organic carbon can be detected? Global Change Biol. 10:1878–1883. https://doi.org/10.1111/j.1365-2486.2004.00854.x
).
Accordingly, it is interesting to evaluate and test other more rapid
indicators that provide insight into soil carbon (C) changes in the
short-term (Plaza-Bonilla et al., 2014Plaza-Bonilla
D, Álvaro-Fuentes J, Cantero-Martínez C, 2014. Identifying soil organic
carbon fractions sensitive to agricultural management practices. Soil
Tillage Res. 139: 19–22. https://doi.org/10.1016/j.still.2014.01.006
), such as soil C fractions or soil biological properties (e.g., microbial biomass or enzyme activities) (Lehmannet al., 2020Lehmann
J, Bossio DA, Kögel-Knabner I, Rillig MC, 2020. The concept and future
prospects of soil health. Nat Rev Earth Environ. 1:544–553. https://doi.org/10.1038/s43017-020-0080-8
).
Identifying selected soil quality indicators would allow optimal
management practices to be adopted, especially in semiarid irrigated
maize production areas in which rapid changes in certain soil properties
have been observed after a shift in management practices (Álvaro-Fuentes et al., 2021Álvaro-Fuentes
J, Franco-Luesma S, Lafuente V, Sen P, Usón A, Cantero-Martínez C,
Arrúe JL, 2021. Stover management modifies soil organic carbon dynamics
in the short-term under semiarid continuous maize. Soil Tillage Res.
213:105143. https://doi.org/10.1016/j.still.2021.105143
).
In
Mediterranean areas, irrigated maize is intensively managed with the
associated elevated use of agriculture inputs (pesticides, fertilisers,
tillage) and the continuous growth of maize as the main cropping system,
with no crop diversification. Increased fertiliser use, particularly of
nitrogen (N) fertilisers, is linked to high production costs and severe
environmental problems, including greenhouse gas emissions (Franco-Luesma et al., 2020Franco-Luesma
S, Cavero J, Plaza-Bonilla D, Cantero-Martínez C, Tortosa G, Bedmar EJ,
Álvaro-Fuentes J, 2020. Irrigation and tillage effects on soil nitrous
oxide emissions in maize monoculture. Agron J. 112:56–71. https://doi.org/10.1002/agj2.20057
) and nitrate pollution (Berenguer et al., 2008Berenguer
P, Santiveri F, Boixadera J, Lloveras J, 2008. Fertilisation of
irrigated maize with pig slurry combined with mineral nitrogen. Eur J
Agron. 28:635–645. https://doi.org/10.1016/j.eja.2008.01.010
).
However, the effect of N fertilisation on SOC and associated C pools is
not clear since it has been associated to both positive and negative
effects on soil organic matter mineralisation (Mahal et al., 2019Mahal
NK, Osterholz WR, Miguez FE, Poffenbarger HJ, Sawyer JE, Olk DC,
Archontoulis SV, Castellano MJ, 2019. Nitrogen Fertilizer Suppresses
Mineralization of Soil Organic Matter in Maize Agroecosystems. Frontiers
Ecol Evol. 7:59. https://doi.org/10.3389/fevo.2019.00059
).
Likewise, the expected positive effect of N fertilisation on soil C
inputs and, thus, on SOC storage is dependent on attaining the agronomic
optimum N fertilisation rate (Poffenbarger et al., 2017Poffenbarger
HJ, Barker DW, Helmers MJ, Miguez FE, Olk DC, Sawyer JE, Six J,
Castellano MJ, 2017. Maximum soil organic carbon storage in Midwest U.S.
cropping systems when crops are optimally nitrogen-fertilized. Plos
One. 12:e0172293. https://doi.org/10.1371/journal.pone.0172293
). According to Poffenbarger et al. (2017)Poffenbarger
HJ, Barker DW, Helmers MJ, Miguez FE, Olk DC, Sawyer JE, Six J,
Castellano MJ, 2017. Maximum soil organic carbon storage in Midwest U.S.
cropping systems when crops are optimally nitrogen-fertilized. Plos
One. 12:e0172293. https://doi.org/10.1371/journal.pone.0172293
,
N rates above the agronomic optimum N fertilisation rate do not
increase crop residue inputs, but may favour soil organic matter
mineralisation due to the increase in soil mineral N.
One option
which is gaining ground as a method for reducing the use of N
fertilisers in irrigated maize systems in Mediterranean Spain is crop
diversification, introducing N-fixing crops (Gabriel & Quemada, 2011Gabriel
JL, Quemada M, 2011. Replacing bare fallow with cover crops in a maize
cropping system: yield, N uptake and fertiliser fate. Eur J Agron.
34:133-143. https://doi.org/10.1016/j.eja.2010.11.006
).
In this area, the majority of studies have been oriented at evaluating
what agronomic impact the introduction of legumes as cover crops has, as
a substitute for the typical bare fallow between maize seasons (Salmerón et al., 2010Salmerón
M, Cavero J, Quílez D, Isla R, 2010. Winter Cover Crops Affect
Monoculture Maize Yield and Nitrogen Leaching under Irrigated
Mediterranean Conditions. Agron J. 102:1700. https://doi.org/10.2134/agronj2010.0180
; Gabriel et al., 2012Gabriel
JL, Muñoz-Carpena R, Quemada M, 2012. The role of cover crops in
irrigated systems: Water balance, nitrate leaching and soil mineral
nitrogen accumulation. Agric Ecosyst Environ. 155:50–61. https://doi.org/10.1016/j.agee.2012.03.021.
).
However, less attention has been paid to evaluating the performance of
double-annual cropping systems in which the system is intensified
through the growth of two crops within the same year, involving the
associated increased production and profitability per land unit (Maresma et al., 2019Maresma
A, Martínez-Casasnovas JA, Santiveri F, Lloveras J, 2019. Nitrogen
management in double-annual cropping system (barley-maize) under
irrigated Mediterranean enviroments. Eur J Agron. 103:98-107. https://doi.org/10.1016/j.eja.2018.12.002
).
Besides the agronomic performance, the impact of cropping system
changes on soil quality parameters has also been evaluated but only in
terms of cover crops for which positive impacts on SOC and C fractions (García-González et al., 2018García-González
I, Hontoria C, Gabriel JL, Alonso-Ayuso M, Quemada M, 2018. Cover crops
to mitigate soil degradation and enhance soil functionality in
irrigated land. Geoderma. 322:81–88. https://doi.org/10.1016/j.geoderma.2018.02.024
), arbuscular mycorrhizal fungi (Hontoria et al., 2019Hontoria
C, García-González I, Quemada M, Roldán A, Alguacil MM, 2019. The cover
crop determines the AMF community composition in soil and in roots of
maize after a ten-year continuous crop rotation. Sci Total Environ.
660:913–922. https://doi.org/10.1016/j.scitotenv.2019.01.095
) and soil microorganisms (Muñoz et al., 2007Muñoz
A, López-Piñeiro A, Ramírez M, 2007. Soil quality attributes of
conservation management regimes in a semi-arid region of south western
Spain. Soil Tillage Res. 95:255–265. https://doi.org/10.1016/j.still.2007.01.009
)
have been reported. However, the impact of cropping intensification on
other soil properties related to soil quality, such as soil enzyme
activities, and in other systems besides cover crops (e.g., cropping
intensification through double-annual cropping systems) is less studied
in these irrigated maize systems (Maresma et al., 2019Maresma
A, Martínez-Casasnovas JA, Santiveri F, Lloveras J, 2019. Nitrogen
management in double-annual cropping system (barley-maize) under
irrigated Mediterranean enviroments. Eur J Agron. 103:98-107. https://doi.org/10.1016/j.eja.2018.12.002
; Zugasti-López et al., 2024Zugasti-López
I, Cavero J, Clavería I, Álvaro-Fuentes J, Isla R, 2024. Alternatives
to maize monocropping in Mediterranean irrigated conditions to reduce
greenhouse gas emissions. Sci Total Environ. 912:169030. https://doi.org/10.1016/j.scitotenv.2023.169030
).
Accordingly, this study had a double objective that consisted of: (i)
assessing the effects of N fertilisation on soil quality under different
cropping systems (monocropping vs. double-annual cropping systems)
under irrigated maize conditions; and (ii) identifying soil parameters
related to soil quality that respond quickly to short-term management
changes in Mediterranean irrigated maize systems. Our main hypothesis
was that the expected positive effect of N fertilisation on crop residue
production would promote a short-term increase in soil parameter values
related to soil quality.
Material and methods
⌅Site characteristics and experimental design
⌅The experiment was set up in Zaragoza, Spain (41º42′N, 0º49′W, 225 m altitude) in a Typic Xerofluvent soil (Soil Survey Staff, 2015Soil
Survey Staff, 2015. Illustrated guide to soil taxonomy. U.S. Department
of Agriculture, Natural Resources Conservation Service, National Soil
Survey Center, Lincoln, Nebraska.
). The air
temperature, annual mean precipitation and annual reference
evapotranspiration (ETo) of the experimental site are 14.1 ºC, 298 mm,
and 1243 mm, respectively. The soil properties at the start of the
experiment are shown in Table 1.
The experiment was established in a 1 ha flood-irrigated field,
historically managed (past 25 years) as irrigated maize and wheat (Triticum aestivum L.) monocultures. The alternation of the two monocultures over the
25-year period varied, but periods of at least 5 years were maintained.
Prior to establishing the experiment, the field had been cultivated with
a maize monoculture for four years. Crop residues had been kept in the
field and incorporated through mouldboard ploughing (after wheat) or
subsoiling (after maize). Fertilisation management consisted of the use
of high N inputs, always in the form of mineral fertilisers. The typical
N rates applied to the crops were about 350 and 200 kg N ha-1 for maize and wheat, respectively. The use of these elevated N rates is
common in irrigated Mediterranean areas, especially for summer crops (Franco-Luesma et al., 2022Franco-Luesma
S, Lafuente V, Alonso-Ayuso M, Bielsa A, Kouchami-Sardoo I, Arrúe JL,
Álvaro-Fuentes J, 2022. Maize diversification and nitrogen fertilization
effects on soil nitrous oxide emissions in irrigated mediterranean
conditions. Frontiers Environ Sci. 10: 914851 https://doi.org/10.3389/fenvs.2022.914851
).
| Soil property | Soil depth (cm) | |
|---|---|---|
| 0-10 | 10-30 | |
| pH (H2O, 1.25) | 7.90 | 8.05 |
| EC 1:5 (dS m-1) | 0.33 | 0.25 |
| CaCO3 eq. (%) | 33.2 | 33.1 |
| Particle size distribution (g kg-1) | ||
| Sand (2000-50 µm) | 196 | 194 |
| Silt (50-2 µm) | 616 | 616 |
| Clay (<2 µm) | 188 | 190 |
In October 2018, the field was divided into three parts (0.33 ha each) and a different cropping system was established in each part: the traditional maize monoculture (MM), and two alternative double-annual cropping systems (pea-maize, PM; and barley-maize, BM). These two alternative cropping systems were selected to integrate cropping diversification together with cropping intensification. Thus, in the PM and BM systems, both crops (pea/maize and barley/maize) were successively grown within the same year. In all three cropping systems, three N fertilisation rates were compared (i.e., control or unfertilised, 0N; medium nitrogen rate, MN; and high nitrogen rate, HN) in plots of 6 x 25 m (150 m2), using an experimental design of strip plots with three repetitions. There was, therefore, a total of twenty-seven 150 m2 plots: 3 systems x 3 N rates x 3 repetitions.
In terms of crop operations, long-cycle maize (FAO 700) was planted both years in the MM system, on 15 April, 2019, and 29 April, 2020, and harvested on 30 September, 2019, and 6 October, 2020, respectively. However, in the PM and BM systems, short-cycle maize (FAO 400) was planted on 25 June, 2019, and 17 June, 2020, and harvested on 10 December, 2019, and 24 November, 2020, respectively. In all three systems, maize was planted at an intensity of 89,500 plants ha-1. In the MM system, the period between maize crops consisted of bare fallow with subsoiler ploughing in March and rotary tilling in April to prepare the maize seedbed. In the two alternative systems (PM and BM), pea and barley were sown on 26 October, 2018 (start of the experiment), and on 15 January, 2020, (after the maize was harvested) and harvested on 29 May, 2019, and 8 June, 2020, respectively, before the planting of maize. Disk harrowing followed by rotary tilling were performed before the pea and barley were sown. In 2020, after the harvesting of the pea and barley the seedbed was prepared for maize using rotary tilling. In 2019, the maize was planted directly over the pea and barley residue. The three N fertilisation rates (0N, MN and HN) were 0, 200 and 400 kg N ha-1 and 0, 125 and 250 kg N ha-1 for the maize and barley phases, respectively. In both of these crops, N fertilisation was split into two applications: pre-sowing as 8-15-15 fertiliser compound; and top-dressing as calcium ammonium nitrate (CAN 27%). In the pea phase of the PM system, only an 8-10 PK fertiliser compound was applied at pre-sowing.
All three cropping systems were flood
irrigated. The total amount of water applied differed between the
cropping systems and years, ranging from 743 mm (MM system in 2020) to
1,027 mm (PM and BM systems in 2019). The irrigation requirements were
calculated based on precipitation and crop evapotranspiration as
reported in Franco-Luesma et al. (2022)Franco-Luesma
S, Lafuente V, Alonso-Ayuso M, Bielsa A, Kouchami-Sardoo I, Arrúe JL,
Álvaro-Fuentes J, 2022. Maize diversification and nitrogen fertilization
effects on soil nitrous oxide emissions in irrigated mediterranean
conditions. Frontiers Environ Sci. 10: 914851 https://doi.org/10.3389/fenvs.2022.914851
.
Crop residue biomass and soil analyses
⌅The aboveground crop residue biomass was measured at crop maturity for all three crops (maize, pea and barley) and during the two cropping seasons (2018-2019 and 2019-2020). For maize, in two areas per plot, all the plants included in 2-m-long rows were sampled. For pea and barley, also in two areas per plot, all the plants included in 0.25 and 0.2 m2 were sampled, respectively. For all the samples, the grain was separated from the rest of the plant, oven-dried at 60ºC for 48 h and weighed.
In July 2021, soil sampling was performed at two soil depths: 0-10 and 10-30 cm. In each plot, samples were taken at two different points 10 m apart and mixed to obtain a composite soil sample per depth and plot. A total of 108 soil samples were therefore collected (27 plots, 2 soil depths and 2 observations per plot and depth). The soil samples were collected using a flat spade and carefully stored in air-tight containers for aggregate separation and plastic bags for the other measurements. During the field sampling and transportation to the laboratory, the plastic bags were stored in a cool-box. Once in the laboratory, the soil samples for aggregate separation were passed through an 8-mm sieve and air dried. The soil samples in plastic bags were split into two subsamples. A first subsample for enzyme activities was kept in the freezer until analysis and a second subsample for C analyses was air dried and ground to pass through a 2-mm sieve.
Soil water-stable macroaggregates (> 250 µm) were isolated following an adapted method from Elliot (1986)Elliott
ET, 1986. Aggregate structure and carbon nitrogen, and phosphorus in
native and cultivated soils. Soil Sci Soc Am J 50:627–633.
in which a 100 g air-dried soil sample (< 8 mm) was submerged in
deionised water for 5 min and then manually sieved through a 250 µm
sieve for 2 min with a frequency of 25 movements per min. The
water-stable macroaggregate (WSM) content was calculated as the
relationship between the mass of aggregate retained in the sieve and the
initial soil (100 g). A subsample of macroaggregates was used to
measure the aggregate C concentration. The particulate organic matter
carbon (POM-C) was isolated following Cambardella & Elliot (1992)Cambardella
CA, Elliot ET, 1992. Particulate soil organic-matter changes across a
grassland cultivation sequence. Soil Sci Soc Am J. 56:777–783. https://doi.org/10.2136/sssaj1992.03615995005600030017x.
,
where 20 g of soil was dispersed in sodium hexametaphosphate solution
and later passed through a 53 μm sieve. The material that passed through
that sieve was oven dried (50ºC) and the C concentration determined.
Total SOC, aggregate C and C from the < 53 μm fraction were
determined by dry combustion in a LECO RC-612 analyser (Leco Corp., St.
Joseph, MI). The POM-C was calculated by subtracting the C in the <
53 μm fraction from the total SOC. The permanganate-oxidisable organic C
(POxC) was measured according to Weil et al. (2003)Weil
RR, Islam KR, Stine MA, Gruver JB, Samson-Liebig SE, 2003. Estimating
active carbon for soil quality assessment: a simplified method for
laboratory and field use. Am J Altern Agr. 18:3–17. https://doi.org/10.1079/AJAA200228
in which absorbance was measured at 550 nm in soil mixed with a 0.2 M KMnO4 solution. Soil microbial biomass C was measured using the substrate-induced respiration method (Anderson & Domsch 1978Anderson
JPE, Domsch KH, 1978. A physiological method for the quantitative
measurement of microbial biomass in soils. Soil Biol Biochem.
10:215–221. https://doi.org/10.1016/0038-0717(78)90099-8
), where CO2 production was quantified over a 24 h period in glucose-amended soils
using a µ-Trac 4200 system (SY-LAB, GmbH P.O. Box 47, A-3002 Pukersdorf,
Austria). Dehydrogenase and ß-glucosidase enzyme activities were
analysed via iodonitrotetrazolium chloride determination (Von Mersi & Schinner 1991Von
Mersi W, Schinner F, 1991. An Improved and Accurate Method for
Determining the Dehydrogenase Activity of Soils with
Iodonitrotetrazolium Chloride. Biol. Fert. Soils. 11: 216–220. https://doi.org/10.1007/BF00335770
) and p-nitrophenol determination (Tabatabai 1982Tabatabai
MA, 1982. Soil enzymes. In: Page, A.L., Miller, R.H., Keeney, D.R.
(Eds.), Methods of Soil Analysis. Part 2, Second ed. Agronomical
Monograph No. 9, American Society of Agronomy and Soil Science of
America, Madison WI, pp. 501–538.
), respectively.
Statistical analyses
⌅The
main effects (N fertilisation and soil depth) and the results of their
interactions on the different soil variables studied were evaluated
using analyses of variance (ANOVA). The experimental design consisted of
a strip plot design in which the N fertilisation was replicated three
times but the cropping system was not replicated. The lack of
replication in the cropping system prevented it being evaluated as a
main factor and only the interaction between this factor and the other
two factors (N fertilisation and soil depth) could be tested (Federer and King, 2007Federer
WT, King F, 2007. Variations on split plot and split block experiment
designs. Wiley Series in Probability and Statistics. John Wiley &
Sons.
). The data was tested to meet ANOVA assumptions,
homogeneity of variances and normality, using the Levene and
Shapiro-Wilk tests, respectively. The post-hoc test Fisher’s Least
Significant Difference (LSD) was used when significant differences were
found at the 0.05 level. To assess possible relationships among the
measured variables the Pearson correlation analysis was used. The
statistical analyses were performed using R software (R Core Team, 2017R Core Team, 2017. R: A Language and Environment for Statistical Computing. https://www.r-project.org/
).
Results
⌅The N fertilisation rate impacted the aboveground crop residue biomass but only in the two diversified cropping systems (Table 2). In the MM systems, the total crop residue biomass produced in the two growing seasons (2018-2019 and 2019-2020) was similar among the different N fertilisation rates. However, in the PM rotation the two fertilised treatments showed greater crop residue biomass than the unfertilised treatment (Table 2). In the BM rotation, residue production decreased in the order HN>MN>0N. Additionally, in the two intensified systems (PM and BM), the proportions of maize residue biomass in the two double cropping systems were 62 and 66% for the maize-barley and maize-pea systems, respectively (Table 2).
| Cropping system | Crop | N fertilization rate | ||
|---|---|---|---|---|
| 0N | MN | HN | ||
| MM | 2019 - Maize | 16.27 (1.31) | 15.11 (4.27) | 17.34 (2.81) |
| 2020 - Maize | 1.98 (1.03) | 2.75 (0.75) | 5.45 (2.13) | |
| Total 2 years | 18.25 (2.04) | 17.86 (3.71) | 22.79 (4.86) | |
| PM | 2019 - Pea | 5.63 (0.79) | 7.22 (1.16) | 6.38 (0.87) |
| 2019 - Maize | 6.07 (2.86) | 7.48 (2.85) | 6.77 (1.00) | |
| 2020 - Pea | 2.13 (0.37) | 1.70 (0.44) | 1.74 (0.79) | |
| 2020 - Maize | 5.59 (1.47) | 6.98 (1.47) | 9.50 (1.30) | |
| Total 2 years | 19.42 (3.65) b‡ | 23.38 (5.82) a | 24.39 (3.21) a | |
| BM | 2019 - Barley | 4.55 (1.61) | 4.98 (1.21) | 6.95 (1.50) |
| 2019 - Maize | 5.49 (2.56) | 7.90 (0.49) | 9.21 (3.22) | |
| 2020 - Barley | 1.51 (0.41) | 4.15 (2.25) | 4.30 (1.70) | |
| 2020 - Maize | 4.30 (0.33) | 5.79 (0.60) | 8.78 (2.01) | |
| Total 2 years | 15.85 (4.27) c | 22.82 (2.01) b | 29.24 (3.09) a | |
‡ Within a cropping system, values followed by different letters indicate
significant differences in total above-ground crop residue biomass for
the 2 years among N fertilization rates at 0.05 level.
The analysis of variance of soil properties showed significant differences for the two main factors considered, soil depth and fertilisation N rate (Table 3). The soil depth affected all the soil properties studied, except for WSM. In all cases, the greatest values were always observed in the first 10 cm soil depth. The maximum difference between the two soil depths (0-10 and 10-30) was observed for the macroaggregate C in which the C content of the topsoil was 40% greater than at 10-30 cm depth. In the other soil properties, the difference between the two soil depths ranged between 10 and 24% (Table 3).
| Treatments | WSM | Macro-C | SOC | POM-C | POxC | MBC | Dehydrogenase | Glucosidase |
|---|---|---|---|---|---|---|---|---|
| (g g-1 soil) | (g C kg-1 macroaggregate) | (g C kg-1 soil) | (g C kg-1 soil) | (mg C kg-1 soil) | (mg C kg-1 soil) | (μmol INTF g-1 dry soil h-1) | (μmol pNP g-1 dry soil h-1) | |
| Soil depth (cm) | ||||||||
| 0-10 | 15.0 | 23.8 a‡ | 11.1 a | 3.4 a | 442 a | 812 a | 0.16 a | 1.15 a |
| 10-30 | 15.0 | 16.8 b | 10.1 b | 3.0 b | 399 b | 684 b | 0.13 b | 1.02 b |
| Fertilization N rate | ||||||||
| 0N | 14.6 | 16.7 b | 10.0 b | 2.5 b | 398 | 665 b | 0.14 | 0.90 b |
| MN | 15.5 | 19.3 b | 10.9 a | 3.6 a | 430 | 790 a | 0.15 | 1.14 a |
| HN | 14.8 | 24.9 a | 10.9 a | 3.5 a | 434 | 788 a | 0.14 | 1.22 a |
| ANOVA (p values) | ||||||||
| Soil depth (Depth) | ns | <0.001 | <0.001 | <0.01 | <0.001 | <0.001 | <0.001 | <0.01 |
| Fertilization N rate (Fert) | ns | <0.01 | <0.01 | <0.05 | ns | <0.01 | ns | <0.01 |
| System x Depth | <0.05 | ns | ns | ns | ns | ns | ns | ns |
| System x Fert | ns | ns | ns | ns | ns | <0.05 | ns | <0.05 |
| Depth x Fert | ns | ns | ns | <0.05 | ns | ns | ns | ns |
| System x Depth x Fert | ns | ns | ns | ns | ns | ns | ns | ns |
ns, non-significant
‡ Values followed by different letters are significantly different at 0.05 level.
The N fertilisation rate affected macroaggregate C, SOC, POM-C, MBC, and the ß-glucosidase enzyme activity. These five soil properties were also affected by the soil depth, as were POxC and the dehydrogenase enzyme activity (Table 3). The unfertilised treatment (0N) showed the lowest SOC, POM-C, MBC and ß-glucosidase activity. However, in the case of macroaggregate C, the 0N and MN levels presented lower contents than with HN (Table 3). The WSM, POxC and dehydrogenase activity did not differ between N fertilisation levels. When the effect of the N fertilisation rate was analysed across cropping systems and for each soil layer, different trends were observed depending on the soil variable considered (Figs. 1 and 2). At the 0-10 cm soil depth, WSM, POxC and MBC were affected by the N fertilisation rate but only in the BM system, where the 0N rate always presented the lowest values (together with MBC in the case of BM) (Fig. 1). The POM-C and ß-glucosidase activity was affected by the N fertilisation rate in the two diversified systems (PM and BM). As before, in these two cropping systems the lowest POM-C and ß-glucosidase activity values were observed for the 0N rate (Fig. 1). The exception was the PM system, where both soil properties presented similar values for the 0N and the MN rates. The total SOC content showed differences in the MM monoculture and the BM rotation. In MM, the HN rate led to a 10% greater SOC content compared to 0N (Fig. 1). However, in the BM rotation, the SOC content in 0N was 17 and 22% lower than the MN and HN, respectively (Fig. 1). In the MM and PM systems, the C concentration of soil macroaggregates was significantly greater under HN than under 0N, this difference being almost two-fold in the case of PM (Fig. 1). The dehydrogenase activity was the only soil variable studied which did not present significant differences among N rates for any cropping system (Fig. 1). At the 10-30 cm soil depth, ß-glucosidase activity was the only soil variable to show significant differences between N fertilisation rates (Fig. 2). In particular, the differences in ß-glucosidase activity were only observed in the BM system, where the 0N rate showed the lowest enzyme activity with values close to 0.8 μmol pNP g-1 dry soil h-1 (Fig. 1).
The WSM was affected by the interaction between the cropping system and soil depth (Table 3). Compared to the PM and BM systems, in the MM system the WSM at 10-30 cm soil depth was greater than at 0-10 cm (data not shown). Likewise, the POM-C was affected by the interaction between the N fertilisation rate and soil depth. The POM-C in the 0N treatment was similar between the two soil depths, unlike MN and HN in which greater POM-C was observed at 0-10 cm depth compared to 10-30 cm (data not shown) (Table 3).
The eight soil parameters studied together with the aboveground crop residue biomass showed significant positive Pearson correlation coefficients except for the relationship between aboveground crop residues and the macroaggregate C concentration (Table 4). There was no correlation between macroaggregate C and the following parameters: WSM, MBC, POM-C and the two enzyme activities (dehydrogenase and ß-glucosidase), nor in any of the relationships of the aboveground crop residue biomass with soil parameters, except for the relationship between crop residues and ß-glucosidase (Table 4). The highest coefficients were found in the relationship between SOC and POM-C (0.88) and between SOC and the ß-glucosidase enzyme activity (0.88). In contrast, the lowest significant coefficients were found in the relationships between aboveground crop residue biomass and ß-glucosidase, and between SOC and macroaggregate C, where the coefficients only reached 0.28 and 0.36, respectively (Table 4).
| AbRes | WSM | Macro-C | SOC | POM-C | POxC | MBC | Dhns | Gds | |
|---|---|---|---|---|---|---|---|---|---|
| AbRes | 1.00 | ||||||||
| WSM | 0.25 | 1.00 | |||||||
| Macro-C | -0.07 | 0.07 | 1.00 | ||||||
| SOC | 0.23 | 0.63* | 0.36* | 1.00 | |||||
| POM-C | 0.27 | 0.65* | 0.28* | 0.88* | 1.00 | ||||
| POxC | 0.15 | 0.43* | 0.46* | 0.73* | 0.53* | 1.00 | |||
| MBC | 0.26 | 0.67* | 0.26 | 0.79* | 0.78* | 0.50* | 1.00 | ||
| Dehydrogenase | 0.20 | 0.37* | 0.18 | 0.59* | 0.55* | 0.41* | 0.57* | 1.00 | |
| Glucosidase | 0.28* | 0.57* | 0.32 | 0.80* | 0.75* | 0.70* | 0.74* | 0.53* | 1.00 |
Discussion
⌅This
study, performed under irrigated Mediterranean conditions, demonstrates
the short-term effect of N fertilisation in different cropping systems
on soil properties related to soil quality. In general, it was observed
that unfertilised treatments always tended to show lower soil property
values compared to the two fertilised treatments (MN and HN). According
to Geisseler and Scow (2014)Geisseler
D, Scow KM, 2014. Long-term effects of mineral fertilizers on soil
microorganisms – A review. Soil Biol Biochem. 75:54–63. https://doi.org/10.1016/j.soilbio.2014.03.023
,
mineral N fertilisation increases SOC levels and, concomitantly, soil
microbial biomass, with SOC being a major factor controlling microbial
growth and activity. In our experiment, the significant increase between
the unfertilised and fertilised plots was not only observed for SOC and
microbial biomass but also for other soil properties related to the C
cycle (POM-C, Macro-C) and microbial activity (ß-glucosidase).
Interestingly, in our study, the positive effect of N fertilisation on
SOC, C fractions and related microbial activity properties relied on the
cropping system established. Hence, the BM cropping system showed the
greatest impact of N fertilisation on soil properties, followed by PM
and, lastly, the monoculture (MM). In other words, cropping
intensification, by reducing the fallow period, boosted the positive
effect of N fertilisation on SOC, C fractions and microbial activity. It
has been reported that reduced fallow duration improves the overall
soil condition (Álvaro-Fuentes et al., 2008Álvaro-Fuentes
J, Arrúe JL, Gracia R, López MV, 2008. Tillage and cropping
intensification effects on soil aggregation: Temporal dynamics and
controlling factors under semiarid conditions. Geoderma. 145:390–396. https://doi.org/10.1016/j.geoderma.2008.04.005
; Rosenweig et al., 2018Rosenzweig
ST, Fonte SJ, Schipanski ME, 2018. Intensifying rotations increases
soil carbon, fungi, and aggregation in semi-arid agroecosystems. Agric
Ecosyst Environ. 258:14–22. https://doi.org/10.1016/j.agee.2018.01.016.
; Gabriel et al., 2021Gabriel
JL, García-González I, Quemada M, Martin-Lammerding D, Alonso-Ayuso M,
Hontoria C, 2021. Cover crops reduce soil resistance to penetration by
preserving soil surface water content. Geoderma. 386:114911. https://doi.org/10.1016/j.geoderma.2020.114911
).
Therefore, in our experiment, the reduction of the bare fallow period
in the PM and BM systems should have improved the overall soil
performance. In an irrigated experiment in central Spain, after 10
years, the reduction of the fallow period through the introduction of
barley and vetch cover crops between summer maize and sunflower crops
improved several soil properties such as reduced penetration resistance
and increased SOC and water-stable aggregates (García-González et al., 2018García-González
I, Hontoria C, Gabriel JL, Alonso-Ayuso M, Quemada M, 2018. Cover crops
to mitigate soil degradation and enhance soil functionality in
irrigated land. Geoderma. 322:81–88. https://doi.org/10.1016/j.geoderma.2018.02.024
; Gabriel et al., 2021Gabriel
JL, García-González I, Quemada M, Martin-Lammerding D, Alonso-Ayuso M,
Hontoria C, 2021. Cover crops reduce soil resistance to penetration by
preserving soil surface water content. Geoderma. 386:114911. https://doi.org/10.1016/j.geoderma.2020.114911
).
In that experiment, after two years, SOC and water-stable aggregates in
the barley-maize system increased about 29 and 41% compared to the
maize monoculture (García-González et al. 2018García-González
I, Hontoria C, Gabriel JL, Alonso-Ayuso M, Quemada M, 2018. Cover crops
to mitigate soil degradation and enhance soil functionality in
irrigated land. Geoderma. 322:81–88. https://doi.org/10.1016/j.geoderma.2018.02.024
).
In our experiment, the difference between the monoculture and the two
intensified cropping systems was lower for SOC (15%) but greater for the
water-stable macroaggregates (74%).
It is well established that crop residues and carbon inputs control SOC accrual in agroecosystems (Virto et al., 2012Virto
I, Barré P, Burlot A, Chenu C, 2011. Carbon input differences as the
main factor explaining the variability in soil organic C storage in
no-tilled compared to inversion tilled agrosystems. Biogeochemistry.
108:17–26. https://doi.org/10.1007/s10533-011-9600-4
; Fujisaki et al., 2018Fujisaki
K, Chevallier T, Chapuis-Lardy L, Albrecht A, Razafimbelo T, Masse D,
Ndour YB, Chotte JL, 2018. Soil carbon stock changes in tropical
croplands are mainly driven by carbon inputs: A synthesis. Agric Ecosyst
Environ. 259:147–158. https://doi.org/10.1016/j.agee.2017.12.008
).
In our study, the PM and BM rotations implied two crops per year
(pea/barley and maize) whereas in the MM only one crop (maize) was grown
per year followed by a 6-month fallow period between the two
consecutive maize seasons. Replacing this fallow period with a crop (pea
or barley) in the two rotations (PM and BM, respectively) resulted in
15% greater aboveground crop residues which returned to the soil
surface. However, according to the results of the correlation analysis,
the differences in aboveground crop residues between cropping systems
was not enough to explain the variability observed in the soil
parameters studied. Therefore, in this irrigated Mediterranean system,
other variables such as belowground crop residues or root exudates could
be more important drivers of the changes found in the soil parameters
studied. It has been observed that root C presents greater mean
residence times and, consequently, greater stabilisation in the soil
than aboveground crop C (Rasse et al., 2005Rasse
DP, Rumpel C, Dignac M-F, 2005. Is soil carbon mostly root carbon?
Mechanisms for a specific stabilisation. Plant Soil. 269: 341-356 https://doi.org/10.1007/s11104-004-0907-y
).
Besides the intensification of the cropping systems, N fertilisation also favours crop residue production and SOC increase (Halvorson et al., 2011Halvorson
AD, Jantalia CP, 2011. Nitrogen Fertilization Effects on Irrigated
No-Till Corn Production and Soil Carbon and Nitrogen. Agron J. 103:1423. https://doi.org/10.2134/agronj2011.0102
; Poffenbarger et al., 2017Poffenbarger
HJ, Barker DW, Helmers MJ, Miguez FE, Olk DC, Sawyer JE, Six J,
Castellano MJ, 2017. Maximum soil organic carbon storage in Midwest U.S.
cropping systems when crops are optimally nitrogen-fertilized. Plos
One. 12:e0172293. https://doi.org/10.1371/journal.pone.0172293
).
However, in an irrigated maize monoculture experiment, also in NE
Spain, it was observed that under conventional tillage, N fertilisation
did not affect SOC and the related C fractions, but this was positively
affected under no-tillage (Pareja-Sánchez et al., 2020Pareja-Sánchez
E, Cantero-Martínez C, Álvaro-Fuentes J, Plaza-Bonilla D, 2020. Soil
organic carbon sequestration when converting a rainfed cropping system
to irrigated corn under different tillage systems and N fertilizer
rates. Soil Sci Soc Am J. 84: 1219–1232. https://doi.org/10.1002/saj2.20116
).
These latter authors concluded that intensive tillage limited crop
growth and, in turn, residue production with the concomitant constraint
in SOC build-up (Pareja-Sánchez et al., 2020Pareja-Sánchez
E, Cantero-Martínez C, Álvaro-Fuentes J, Plaza-Bonilla D, 2020. Soil
organic carbon sequestration when converting a rainfed cropping system
to irrigated corn under different tillage systems and N fertilizer
rates. Soil Sci Soc Am J. 84: 1219–1232. https://doi.org/10.1002/saj2.20116
).
Furthermore, in our study, also under conventional tillage conditions,
in the MM system the crop residues did not respond to N fertilisation.
This would explain the lack of response to N fertilisation found in the
majority of soil properties in the MM system. However, in the two
diversified systems (PM and BM) aboveground crop residues did respond to
N fertilisation and, concomitantly, different soil properties were
affected by N fertilisation.
Active SOC pool measurements are an
effective way of detecting early changes in SOC trends in response to
land use and management changes (Cotrufo et al., 2019Cotrufo
MF, Ranalli MG, Haddix ML, Six J, Lugato E, 2019. Soil carbon storage
informed by particulate and mineral-associated organic matter. Nat
Geosci. 12:989–994. https://doi.org/10.1038/s41561-019-0484-6
).
In our study, two active C pools were measured, POM-C and POxC. In the
two diversified systems, the differences in POM-C between the fertilised
and the unfertilised treatments were higher than for POxC and, indeed,
significant differences in POM-C were even obtained in PM where no
differences were found in POxC. Also, in NE Spain, but under rainfed
conditions, it was concluded that POM-C was a better predictor of total
SOC changes than POxC for a number of management practices and soil
depths (Plaza-Bonilla et al., 2014Plaza-Bonilla
D, Álvaro-Fuentes J, Cantero-Martínez C, 2014. Identifying soil organic
carbon fractions sensitive to agricultural management practices. Soil
Tillage Res. 139: 19–22. https://doi.org/10.1016/j.still.2014.01.006
).
This is related to the nature of each active C fraction since the POxC
pool is associated with smaller and heavier POM-C and therefore with a
higher degree of stabilisation (Culman et al., 2012Culman
SW, Snapp SS, Freeman MA, Schipanski ME, Beniston J, Lal R, Drinkwater
LE, Franzluebbers AJ, Glover JD, Grandy AS, Lee J, Six J, Maul JE,
Mirksy SB, Spargo JT, Wander MM, 2012. Permanganate Oxidizable Carbon
Reflects a Processed Soil Fraction that is Sensitive to Management. Soil
Sci Soc Am J. 76:494–504. https://doi.org/10.2136/sssaj2011.0286
).
ß-glucosidase has been recognised as an interesting indicator of short-term soil quality changes (Ndiaye et al., 2009Ndiaye
EL, Sandeno JM, McGrath D, Dick RP, 2000. Integrative biological
indicators for detecting change in soil quality. Am J Altern Agric.
15:26–36. https://doi.org/10.1017/S0889189300008432
) and the stability of this enzyme activity between seasons is also an advantage as a soil quality indicator (Knight & Dick 2004Knight
TR, Dick RP, 2004. Differentiating microbial and stabilized
β-glucosidase activity relative to soil quality. Soil Biol. Biochem.
36:2089–2096. https://doi.org/10.1016/j.soilbio.2004.06.007
).
In our experiment, the ß-glucosidase activity was the only soil
parameter which showed differences at both soil depths and it was also
the only parameter that positively correlated with aboveground crop
residues, showing its ability as a fast-response parameter to reflect
changes in soil and crop management.
Conclusions
⌅Our results demonstrate that under Mediterranean irrigated maize conditions, N fertilisation has a positive effect on a number of soil parameters related to soil quality in the short-term. Furthermore, this positive effect of N fertilisation on soil quality may vary depending on the cropping system considered. Indeed, when intensified cropping systems are implemented, N fertilisation increases its positive effect on soil properties related to soil quality. In this sense, in Mediterranean irrigated maize systems the combination of N fertilisation and cropping intensification is a promising strategy for attaining beneficial soil quality effects only two years after the start of the experiment.
Acknowledgments
⌅We acknowledge the field and laboratory assistance of Fernando Gómez and Leticia Pérez. This research was supported by the EU Horizon 2020 Programme for Research & Innovation project “Crop diversification and low-input farming across Europe: from practitioners’ engagement and ecosystem services to increased revenues and value chain organisation (DIVERFARMING)” (grant agreement no. 728003) and partially by the Spanish State Agency for Research (AEI) (Grant AGL2017-84529-C3-1-R) and the European Union (FEDER funds).
Competing interests
⌅The authors have declared that no competing interests exist.
Authors’ contributions
⌅Victoria Lafuente: Data acquisition, formal analysis, Writing—review. Ana Bielsa: Data acquisition, formal analysis. María Alonso-Ayuso: Writing—review; editing. Samuel Franco-Luesma: Conceptualization, data acquisition, writing—original draft, data curation, methodology, formal analysis, investigation. Carmen Castañeda: Conceptualization, methodology, investigation; Laura B. Martínez-García: Data curation, formal analysis, investigation. José L. Arrúe: Writing—review; editing. Jorge Álvaro-Fuentes: Conceptualization, methodology, investigation, writing—review; editing, supervision, project administration, resources, funding acquisition.
| Funding agencies/institutions | Project / Grant |
|---|---|
| European Union, HORIZON 2020 | DIVERFARMING, grant no. 728003 |
| Spanish State Agency for Research (AEI) | PID2021-126343OB-C31 |
| EU FEDER funds | Non-applicable |