Overall framework: environmental impacts of alternative cropping systems

Get Complete Project Material File(s) Now! »

Crop yields and residue biomass

Crop yields were determined every year from 1998 to 2014 based on grain collected by the combine harvester. The biomass of aboveground (AG) residues of the main crops returned to soil was estimated using the harvest index of each crop which was 0.54, 0.60, 0.31 and 0.46 for wheat, pea, rapeseed and maize respectively (Dubrulle et al., 2004). The AG biomass of catch crops and cover crops totally returned to soil was not measured but estimated using crop growth allometric equations (see Appendix 1.A). It was assessed using a relationship based on the thermal time, i.e. the cumulative temperature above the crop base temperature. In the case of alfalfa as main crop, the exported cut was measured at mowing time and the cuttings left on the soil were estimated using regional references. When alfalfa was grown as cover crop, we used relationships depending on the date of cutting and regrowth: one for the period of establishment after seeding at the end of summer, one for autumn regrowth and the last for spring and summer regrowth.
Belowground (BG) biomass was not quantified in the experiment. BG biomass of main crops was assumed to be independent of AG biomass and calculated using references from Dubrulle et al. (2004): it was 2.33, 2.33, 0.98 and 2.24 t DM ha-1 for wheat, rapeseed, pea and maize respectively. In the case of catch crops and cover crops, we assumed that their BG biomass was proportional to their AG biomass because they derived from younger, unripe plants. The BG/AG ratio was set at 1.6 for fescue (Vertès et al., 2002), 0.6 for alfalfa (Thiébeau et al., 2011) and 0.7 for catch crops (Constantin et al., 2010). The conversion of dry mass to C content was made by assuming a 42% and 38% carbon content in the AG and BG residues, respectively (Justes et al., 2009).

Soil sampling and analysis

The soil sampling strategy was designed to calculate SOC stocks on an equivalent soil mass (ESM) basis (Ellert & Bettany, 1995) over a depth at least equal to the deepest tillage event. The ploughing depth was ca. 30 cm before 1998 and shallower afterwards, about 25 cm. The SOC measurements were carried out at the experimental site on both blocks in 1998 and 2014, and only in block 2 in 2000, 2003 and 2011. In February 1998, twenty soil samples were taken in each plot over 30 cm depth. In May 1998, March 2000, March 2003 and March 2011, six soil samples per plot were taken in block 2 down to 30 cm in plots where wheat was grown. In April 2014, six soil cores per plot were taken in both blocks down to 60 cm using a hydraulic gauge of 6 cm diameter. A single soil layer (0-30 cm) was analysed for samples taken in February 1998, March 2000, March 2003 and March 2011. The soil cores were divided in 3 layers in May 1998 (0-10, 10-20 and 20-30 cm), and 5 layers in April 2014 (0-10, 10-25, 25-30, 30-35 and 35-60 cm). Soil was homogenized, coarse residues (> 2 mm) and visible roots were removed by hand picking. Soil samples were oven dried for 48h at 35°C and sieved (2 mm). A soil subsample of 20 g was finely ground in a ball mill (PM 400, Retsch, Germany) and an aliquot taken for carbon analysis. The Dumas method was used for carbon analysis using an elemental analyzer (EURO EA, Eurovector, Italy). The CaCO3 content was measured by acid decarbonation (NF ISO 10693). Inorganic C represented on average 0.08 g C kg-1 (Table 2.1) and was subtracted from total C to obtain the organic C.
Bulk density was measured for three layers (0-10, 10-20 and 20-30 cm) simultaneously with soil sampling in 1998, 2000, 2003 and 2011 using a steel cylinder of 98 cm3 inserted vertically in the soil. Soil was weighed after drying during 48 h at 105 °C. The same method was used to determine the bulk density on the 0-5 cm layer in 2014. A second method was used in 2014 to measure the bulk density every 5 cm in the layers between 5 and 40 cm with a gamma-densitometer (LPC-INRA, Angers, France).
Coarse particulate organic matter (cPOM) was determined in soil samples taken in 2003 and 2014 by particle size separation. A sample of 50 g of 2 mm sieved and air dried soil was dispersed under water on a 200 µm sieve. Coarser particles (200-2000 µm) were washed out in a bucket, floating particles (cPOM) collected and oven dried at 60°C before being crushed and analysed for C concentration.

Calculations of soil mass and SOC stock

SOC stocks were calculated on ESM basis at different depths, particularly over the old ploughing depth, using measurement of bulk densities and organic C concentrations. To facilitate calculations, the soil was discretized into elementary layers of 1 mm thickness. The soil mass at a fixed depth z (in mm), M(z) (in t ha-1), can be calculated as the sum of soil masses of z elementary layers, as follows:
where ρ(k) is the bulk density of the elementary layer k (g cm-3), k varying from 1 to 600 mm. A reference soil mass MR (in t ha-1) was considered, corresponding to the old ploughing depth of the CON system (30 cm) which was estimated in 1998 at 4300 t ha-1. For the subsequent years, the z value corresponding to MR was determined by fulfilling the equation: M(z) = MR. We also considered three other soil mass references in order to analyse the SOC evolution in the soil profile: L1 (ca. 0-10 cm) and L2 (ca. 10-20 cm) with a fixed mass of 1300 t ha-1 for each layer, L3 (ca. 20-30 cm) with a fixed mass of 1700 t ha-1 of soil, L4 (ca. 30-40 cm) with a fixed mass of 1400 t ha-1 and L5 (ca. 40-60 cm) with a fixed mass of 2800 t ha-1. The cumulative SOC stock QC(z) (in t ha-1) in the layer 0-z is:
( ) = 0.01 ∑ =1 ( ). ( ) (2.2)
where C(k) is the SOC concentration in the elementary layer k (g kg-1 dry soil). Since the measured values of bulk densities and SOC concentrations refer to macro-layers (L1 to L5), ρ(k) and C(k) were supposed to be equal to their respective values in these macro-layers.

Statistical analysis

Statistical analyses were performed using the R software (R Core Team, 2010). Since the number of true replicates in the experiment was low (two randomized blocks), each of the two subplots (not randomized) was considered as replicate thus producing four pseudo replicates. This choice resulted from the weakness of the experimental design, which forces us to be conservative with our results as explained by (Hurlbert, 1984). (Henneron et al., 2014) and (Pelosi et al., 2015), analysing the soil organisms on the same site, have described the rationale supporting this choice: i) the entire experiment had the same crop management before 1998, ii) soil sampling was done in large plots (0.56 ha) and samples were taken far enough from each other to be considered as independent, and iii) the pre-existing topographic and pedological gradients were controlled by blocking. Indeed, our measurements relative to SOC concentrations and stocks made in 1998 show that the intra-plot variability (between subplots) was as important as the inter-plot variability (within blocks), as indicated by the comparison of variances (F=1.83, p<0.05). Furthermore, our objective was to compare not only SOC stocks at given dates but also the temporal variations of SOC stocks between cropping systems. These variations, calculated as the difference between final and initial SOC stocks measured in each subplot, can be considered as true replicates, if we assume that possible interactions between the effect of cropping systems and the initial SOC stocks were of second order of magnitude.
Analyses of variance (ANOVA) were performed on measurements made in 1998, 2000 and 2014 to test the effect of cropping system on SOC stocks for all layers L1, L2, L3 and L1-3, and only on L1-3 for 1999, 2001, 2003 and 2011. A separate ANOVA was done to compare the SOC concentrations and stocks of 1998 and 2014 for each treatment and the change in SOC stocks between 1998 and 2014 for each treatment. The assumptions of ANOVA were checked by visually examining the residuals against predicted values and using the Shapiro-Wilk and Levene’s tests. The existence of significant effects (p<0.05) was followed by a post-hoc comparison test of means with the SNK.test from the agricolae package (De Mendiburu, 2014). When normality and homoscedasticity were not respected, a Kruskall-Wallis test was applied followed by means comparison using the kruskal.test from the agricolae package (De Mendiburu, 2014).

READ  Carbon analysis and supplementary data 

AMG model

The simulation of SOC stocks evolution was made over the 1998-2014 period using the AMG model (Andriulo et al., 1999; Saffih-Hdadi & Mary, 2008). AMG is a simple soil simulation model with an annual time step, which considers three compartments of organic matter: crop residues, active and stable humified organic matter. AMG was successfully evaluated to simulate SOC evolution in Argentina (Andriulo et al., 1999; Milesi Delaye et al., 2013) and in 9 long term experiments (Saffih-Hdadi & Mary, 2008). The model uses the following equations:
where QC is the SOC stock (t ha-1), CS is the stable carbon stock (t C ha-1), CA is the active carbon stock (t C ha-1), mi is the annual carbon input of organic residue i (t ha-1 yr-1), hi is the humification coefficient of the residue i and k is the mineralization rate of the soil active fraction (yr-1). In the case where carbon input rate is constant every year, equations (3-4) can be integrated as:
where C0 is the initial SOC stock (t ha-1). The second term represents the residual amount of old carbon initially present and the third term is the humified carbon formed since the initial time. The mineralization rate k is dependent on pedoclimatic conditions and calculated as follows:
where k0 is the potential mineralization rate (yr-1), A the clay content (g kg-1) and T the temperature (°C). The functions and parameters are described in Saffih-Hdadi and Mary (2008).

Table of contents :

Overall framework: environmental impacts of alternative cropping systems
1.1 General context
1.2 Alternative cropping systems – definition
Alternative practices and ecosystem services
1.3 Carbon and nitrogen cycles in agro-ecosystems
Soil organic matter turnover in agro-ecosystems Soil organic matter compartments Soil organic matter decomposition
1.4 Carbon and nitrogen impacts of cropping systems on the long term
1.5 How do alternative cropping systems impact C and N cycles?
Crop fertilization
Reduced tillage
1.6 Research questions and purposes of the thesis
1.7 Organisation of the document
1.8 Experiments
1.9 Methodological approach
Alternative arable cropping systems: a key to increase soil organic carbon storage? Results from a 16 year field experiment
2.1 Introduction
2.2 Materials and methods
Cropping systems
Crop yields and residue biomass
Soil sampling and analysis
Calculations of soil mass and SOC stock
Statistical analysis
Simulation of SOC stocks evolution AMG model Modelling steps
2.3 Results
Bulk densities
SOC concentrations in 1998 and 2014
cPOM concentration in 2014
SOC stocks in 1998 and 2014
Simulating the evolution of SOC stocks in the old ploughed layer
Simulation of SOC evolution in block 1 and elementary layers of block 2
2.4 Discussion
SOC storage in relation with cropping systems
SOC distribution over the old ploughed layer in CA
Simulation of SOC storage
2.5 Conclusion
2.6 Acknowledgements
Similar mineralization rates of soil organic carbon and nitrogen in different alternative arable cropping systems
3.1 Introduction
3.2 Materials and methods
Cropping systems
Soil sampling and analysis
C, N and microbial biomass
C and N mineralization measurements
Simulation of C and N mineralization kinetics
Statistical analysis
3.3 Results
Mineralization of C and N in disturbed soils
Mineralization of C and N in undisturbed soils
3.4 Discussion
Effect of cropping system on mineralization rates
3.5 Conclusion
3.6 Acknowledgements
Can alternative cropping systems mitigate nitrogen losses and improve GHG balance? Results from a 19-yr experiment in Northern France
4.1 Introduction
4.2 Material and methods
Cropping systems and management
Measurements Crop yields and N uptake Soil water and mineral N contents SOC and SON stocks N2O emissions Biological N fixation N surplus N leaching Gaseous N losses GHG balance
4.3 Results
Annual N surplus
SON storage
N leaching
N surplus and gaseous N losses
N2O emissions
Global GHG balance
4.4 Discussion
N surplus, an ambiguous indicator
SON storage
N leaching
Gaseous N emissions
The GHG balance, and ultimate environmental indicator
4.5 Conclusion
4.6 Acknowledgements
Long term modelling of crop production and nitrogen fate in organic cropping systems
5.1 Introduction
5.2 Material and methods
Climate and soil characteristics
STICS model improvement
Experimental data used for modelling
Simulation strategy
Model assessment
Statistical analysis
5.3 Results
Evaluation of STICS for soil water and mineral N
Organic C and N balances
N surplus
Nitrogen fate
N mineralization
5.4 Discussion
Simulation of soil N surplus
Drivers of N leaching in organic systems
Gaseous N losses affected by the fertilization
Long term evolution of soil organic N pools
5.5 Conclusion
5.6 Acknowledgements
General conclusions and perspectives
6.1 Main results and achievements
Reducing nitrogen losses in alternative cropping systems
The greenhouse gas balance: an absolute indicator
6.2 Interest and drawbacks of the methodological approach
6.3 Perspectives and advices
Modelling environmental impacts: research perspectives
Which alternative cropping systems for the least environmental impacts?
Which advices for policies maker?


Related Posts