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Travail de Fin d'Études
Du Avril 2012 au Septembre 2012
María Suárez Bonet
3ème année promotion 2012
Parcours Fluides : Energie, Transport, Environnement, Santé
Géochimie des Sols et des Eaux Aix en Provence
France
École Centrale Marseille Marseille FRANCE
Calibration and testing of a pedogenesis model: emphasis on clay migration
Tuteur école : Fabien Anselmet
Tuteur entreprise: Sophie Cornu
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RÉSUMÉ L’objectif principal de mon stage dans l’unité de recherche Géochimie des Sols et des Eaux (GSE) était de calibrer une nouvelle version de SoilGen, un model de pédogénèse qui simule le lessivage, et de tester sa sensibilité à des changements de pratiques agricoles et d’utilisation du sol. Le lessivage est un processus considéré comme très important dans l’évolution des sols. Par contre, pour le moment il y a un manque de modélisation du processus. En outre, les activités humaines ont des répercussions importantes dans les sols. L’agriculture et le changement climatique sont quelques exemples. Si on veut assurer que les sols continuent à remplir ses fonctions, il est essentiel pour pouvoir modéliser et prédire leur évolution. ABSTRACT The main objective of this internship in the laboratory of Soil and Water Geochesmitry (GSE) was to calibrate a new version of SoilGen, a pedogenesis model that simulates clay migration, and to test its sensibility to changes in agricultural practices and landuse. Indeed, while clay migration is considered to have a leading role in soils evolution, there is an evident lack of modelisation of the process. Furthermore, human activities have important impacts in soil. Agriculture or climate change are some examples. This makes it essential to be able to model and predict soils evolution if we want to ensure that they will continue to fulfill their functions. KEY WORDS Clay migration - SoilGen - Calibration - Climate - Agriculture
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In the context of my internship I would like to be grateful to all the staff of the laboratory of Soil and Water Geochesmitry (GSE). They were always helpful and assisted me in the resolution of my task all along my internship. I am especially grateful to my tutor in the laboratory, Sophie Cornu. I learned a lot from her, not only in what refers to the theoretical basis needed to develop my tasks, but also in what refers to getting to know the world of scientific research. Finally, I am also especially grateful to Peter Finke, developer of the model SoilGen. His assistance was essential during the development of my job.
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TABLE OF CONTENTS PART 1
1. THE LABORATORY OF SOIL AND WATER GEOCHEMISTRY (GSE) ......................................... 5
2. PRESENTATION OF THE MISSION ...................................................................................... 7
2.1. Context ............................................................................................................................... 7
2.2. The missions ....................................................................................................................... 8
PART 2
1. PRESENTATION OF SOILGEN ............................................................................................. 9
1.1. Main processes in SoilGen ................................................................................................. 9
1.2. Processes related clay migration ..................................................................................... 10
2. CALIBRATION OF SOILGEN FOR LESSIVAGE ..................................................................... 14
2.1. Starting point .................................................................................................................... 14
2.2. Calibration approach: ....................................................................................................... 15
2.3. Results of the calibration ................................................................................................. 17
2.3.A. Calibration of h(θmacro) .............................................................................................. 18
2.3.B. Calibration of n .......................................................................................................... 18
2.3.C. Calibration of Ps,max and B .......................................................................................... 19
3. SOILGEN’S SENSIBILITY TO CHANGES IN AGRICULTURAL PRACTICES AND LANDUSE ......... 23
3.1. Description of the experimental sites .............................................................................. 23
3.1.A. Feucherolles .............................................................................................................. 24
3.1.B. Mons ......................................................................................................................... 26
3.1.C. Boigneville ................................................................................................................. 26
3.2. Climate data ..................................................................................................................... 27
3.2.A.Temperature .............................................................................................................. 27
3.2.B. Annual precipitation.................................................................................................. 30
3.2.C. Potential evapotranspiration .................................................................................... 32
3.2.D. Rainwater composition ............................................................................................. 34
3.3. The plant module and organic matter ............................................................................. 35
3.3.A. Dynamic of the vegetal cover depending on the type of plant ................................ 35
3.3.B. C-input to the soil depending on the type of plant ................................................... 37
3.4. Bioturbation ..................................................................................................................... 41
3.5. Parent material................................................................................................................. 42
4. CONCLUSIONS AND OUTLOOKS ...................................................................................... 44
BIBLIOGRAPHIE ................................................................................................................. 45
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PART 1: INTRODUCTION
1. THE LABORATORY OF SOIL AND WATER GEOCHEMISTRY (GSE) The laboratory of Soil and Water Geochesmitry (GSE) is a research unity part of the French National Institute for Agricultural Research (INRA). INRA’s principal role in agricultural research is not only limited to France. Indeed, it is the main agricultural research institute in Europe and the second one in the world. Research carried out in this institute has as objective high quality and healthy foods, the development of competitive and at the same time sustainable agriculture with a constant regard in its effects on the environment. Issues addressed by INRA are at a national and international level. Most of the research employees are scientists and engineers. INRA cooperates closely with universities, giving lectures mostly in master degrees and with internships. INRA consists of several research unities all around France, organized in 14 research divisions:
- Nutrition, Chemical Food Safety and Consumer Behavior - Plant Biology - Science and Process Engineering of Agricultural Products - Forest, Grassland and Freshwater Ecology - Environment and Agronomy - Animal Genetics - Plant Breeding and Genetics - Applied Mathematics and Informatics - Microbiology and the Food Chain - Animal Physiology and Livestock Systems - Animal Health - Plant Health and Environment - Science for Action and Development - Social Sciences, Agriculture and Food, Rural Development and Environment
The laboratory of Soil and Water Geochesmitry (GSE) is part of the division "Environment and Agronomy" and contributes to the Pole "Adaptation to Global Change" of INRA’s research center Provence-Alpes-Cote d'Azur.
GSE is located in the techno-environmental cluster Technopôle de l’environnement Arbois-Mediteranée, in Aix en Provence. It was founded in 2001 and nowadays it employs 9 people among scientists and technicians, secretary staff and PhD students. Their research activities are mainly focused on
biogeochemical processes in agrosystems and ecosystems and their thermodynamic and
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kinetic modeling with the aim of predicting and assessing on the impact of human activities and climate change on soil quality. The research approaches include isotopic tracing of soil carbon at a molecular level and continuous analysis of geochemical properties of soil and water. During my internship, I was able to see how a research unity works from the interior and to have a first contact with the scientific research world.
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2. PRESENTATION OF THE MISSION
2.1. Context It is now widely accepted by the scientific community that soils are a non-renewable resource over a human period. The European Commission recognized this in a communication on soil protection (European Commission, 2002). Moreover, human activities such as agriculture, pollution, climate change, etc, can have an important impact on soil evolution. Thus, soil protection is essential in order to ensure it can continue to fulfill their functions. Although agriculture may be considered as a forcing variable on soil evolution, its impact on soil evolution at a time scale of 10 to 100 years has rarely been studied and is poorly known (Tugel et al., 2005). In addition, climate warm up due to human activities has as consequence a water shortage in many parts of the world. This obliges to adapt agricultural practices to the future climate conditions and so it is crucial to understand and to be able to predict the impact of agriculture on soil evolution in order to ensure we use soils in a sustainable way. In this context, the need of complete models of pedogenesis is clear. Among the multiple processes that have an impact in pedogenesis, Samouelian and Cornu (2008) identified two that were poorly modeled: bioturbation and clay migration. In soil science, bioturbation is the process of redistribution of soil phases caused by animals and plants. Clay migration is
understood as the substantial vertical translocation of fine particles (smaller than 2m) from a horizon, called eluviated horizon, to another deeper horizon, called illuviated horizon. Clay migration is often referred to as lessivage. It is considered to be one of the major pedogenetic processes. Actually, it is generally accepted that it is the main pedogenetic process suffered by Luvisols, which represent 20 % of the soils in Europe. This type of soil is widely used for agricultural purposes. Kühn (2003) and Fedoroff (1997) are some of the authors that claim that agricultural practices could have a very important impact on Luvisol’s evolution. Thus, understanding the process of clay migration in Luvisol appears to be essential in the research of sustainable agricultural practices and in the measurement of the impact of human activities on soils evolution. As a response to all this, the project AGRIPED (funded by the French National Agency for Research in 2010 – ANRBlanc) was initiated by the Laboratory of Geochemistry of Soil and Environment (GSE) with the collaboration of several other French research laboratories in Environment. GSE is leading the project. The main purposes of the project are to quantify the importance of clay migration, improve knowledge of the process and quantify the impact that agriculture has on the process. The project has many approaches based on field characterization, lab experiments and modeling. Modeling will be performed at two time scales: a process scale (a rain event) and a long term scale (thousands of years) that will study the effects of clay migration in soils evolution. My internship in the Laboratory of Soil and Water Geochemistry (GSE) was focused on the modeling part of the project.
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2.2. The missions Modeling approaches of soil development and evolution on time scales ranging from tenths of years to centuries are scarce in the literature. SoilGen is one of the only models that allow such a modeling approach for soil developed on calcareous loess. However in its first version (Finke and Huston, 2008), clay migration, a key process for the development of the modeled soils is only represented as a switch. In a second version of the model, Finke (2011) added an approach to the process inspired from Jarvis et al. (1999). The model was then calibrated for soils in Belgium (Finke, 2011). In order to use the model to study clay migration for the project AGRIPED, parameters related to clay migration have to be calibrated for soils in France. This was the first part of my job during my internship in the Laboratory of Soil and Water Geochemistry (GSE). To verify the calibration is adequate, simulations will be run for three French experimental sites with a known land use history and for a clay migration lab-experiment. The second part of my job was to validate the calibrated version of SoilGen on the three French experimental sites chosen in the AGRIPED project. However, due to computing time required for the calibration, it was not possible for me to run SoiGen on the site during the duration of my stay at GSE. During the whole duration of my internship, I worked in collaboration with P. Finke, developer of the model SoilGen. At the beginning I spent two days to Ghent, where P. Finke works, to be initiated in the program. Later, P. Finke came two times to Aix en Provence to work with us and assist us in the use of SoilGen.
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PART 2: THE MISSION
1. PRESENTATION OF SOILGEN SoilGen is a very complete model developed by Finke and Huston (2008) and Finke (2012) to simulate soil evolution in calcareous loess. SoilGen simulates water and solute transport using Richards’ equation for unsaturated water flow, the Convection-Dispersion Equation for solute transport and the heat flow equation for dynamic simulation of soil temperature. The model also takes into account type of plant present in the soil and simulates the associated C-cycle. A detailed description can be found in Finke and Hutson (2008). In it’s first version, SoilGen only simulated lessivage via a on-off switch depending of the calcareous content of the soil. in 2012, Finke developed a new version of SoilGen improving the lessivage modelisation based on Jarvis et al. (1999) formalism of colloid transport. The main processes added to SoilGen were physical and chemical weathering and clay migration (Finke, 2011). Many soil’s properties are dependent on clay distribution in soil. Thus, clay migration from the surface to deeper parts has an important effect on soil’s properties. In SoilGen, the soil profile is represented with depth as a number of compartments. Usually 30 compartments are used with a compartment height of 15 cm. Follows a brief description of the main processes simulated by SoilGen.
1.1. Main processes in SoilGen Water Flow: The flow of water going through soil is simulated by Richards’ equation for transient vertical flow:
With soil water pressure head (Pa10), θ the volumetric water content (m3 of water/m3 of soil), the differential water capacity , the hydraulic conductivity (m 10-3/d), H the hydraulic head (Pa10) and U(z,t) a sink term to take into account water lost at depth z and time t due to transpiration. Upper boundary condition is given by evaporation and infiltration. Lower boundary condition is zero flux in the case of an annual precipitation deficit and free drainage in case of an annual precipitation surplus. Heat flow: The heat flow equation used is given by Hutson (2003a) and Tillotson et al. (1980):
Where T is temperature (ºC), Kt(θ) is the thermal conductivity (J/m, s, ºC), and β the volumetric heat capacity (J/m3, ºC). The upper boundary condition is given by a sinusoidal daily air temperature fluctuation obtained with daily averages and amplitudes of temperature,
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which are input to the model. The lower boundary condition that ensures a zero heat flux in the bottom of the profile. Flow of soluble matter To describe the flow of each of the soluble compounds, the equation used is the convection-dispersion equation.
Where C is the solute concentration (kg/m3), D(θ,q) is the dispersion coefficient (mm2/d), q is the water flux (mm/d) and is a source or sink term (kg/m3, d) that represents plants’ uptake or release. Possible upper boundary conditions are infiltration, evaporation and zero flux. Lower boundary condition is dependent on that of water flow: zero concentration when zero flux and constant concentration for free drainage. Flow of CO2 The flow of CO2 is considered to be diffusive. The equation used to simulate is:
With ε the air filled porosity, c the CO2 concentration (partial pressure) in the air in the soil, D(T)gas is the gas diffusion coefficient in soil (m2/s )and P(z,t) is the CO2 production in each soil compartment. Plant related processes Plants have an important influence in soils development for several reasons. Root distribution, water uptake, ion uptake or release or C-deposition are some examples. SoilGen considers four types of vegetation: grass/scrub, coniferous forest, deciduous forest and agriculture. The cycling of C uses concepts of the Roth 26.3 model (Jenkinson and Coleman, 1994) Bioturbation Bioturbation, understood as the movement of matter in the soil due to the presence of living organisms (Gobat et al., 1998), is modeled in SoilGen by a vertical mixing of soil. As bioturbation decreases with depth, the proportion of soil subject to vertical mixing for each compartment decreases with depth.
1.2. Processes related clay migration Physical weathering
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Physical weathering provokes a reduction in soil’s grain size that has as a result the production of clay that can be subject of clay migration. The main cause of particle splitting is strain due to temperature gradients and due to the growth of ice. Thus, the splitting of a particle is considered to follow a Bernouilli probabilistic distribution:
Ps = Where Ps,max represents the maximal split probability and B is a threshold temperature gradient dT/dt above which the splitting probability becomes maximal. Figure2.1 shows the distribution of the splitting probability.
Figure 2.1 – Splitting probability distribution
Clay migration Clay particles are those inferior to 2 μm. The process contains three phases: particle release, particle transport and particle retention. In SoilGen particle release if described by two mechanisms: 1) the detachment of part of the clay particles present in soil’s surface by rainwater drops. This is modeled following Jarvis et al (1999) approach. 2) dispersion due to the low ionic capacity of the water (REF). Detached particles are brought to a dispersed state and begin to travel through soil’s profile in the water solution. Their precipitation in deeper layers can be due to flocculation as a consequence of higher solute concentrations or due to filtering processes (De Novio et al, 2004). Clay transport in SoilGen is modeled by the Convection-Dispersion Equation and clay precipitation by a filtering mechanisms derived from Jarvis et al. (1999). Mass balance of dispersible particles at soil’s surface is:
Ps,max if dT/dt > B
if dT/dt ⦤ B
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dAs/dt = -D+P
Where As represents the mass of dispersible particles at soil’s surface (g/m2), D the splash detachment rate (g/m2, h) and P the replenishment rate (g/m2, h). Initially, As is set to:
As = DCS · 0.01 · ρ
With DCS, dispersible clay in the upper 1 mm (%), set to its maximal value DCmax; 0.01 a factor for unit conversion and ρ the dry soil bilk density (kg/m3). DCmax is function of the cation exchange capacity, CEC (mmol+/kg of soil), the clay content and the organic cabon, OC, content (Brubaker et al, 1992). The splash detachment rate D is obtained at each rainfall event by the equation:
D = kd · E · R · (1-sc) · DCS
Where kd is soil’s detachability coefficient (g/J), E is the kinetic energy of a rainfall (J/m2, mm), R is the rainfall intensity (mm/h) and sc is an dimensionless factor to take into consideration the amount of soil covered by ground vegetation or the humus profile. E is function of the rainfall R (Brown and Foster, 1987). As for the replenishment rate, it is obtained as said in Jarvis et al. (1999):
P = kr · (1-DCS/DCmax)
With kr (g/m2, h) the replenishment rate coefficient. With all this, we have finished with the mass balance at soil’s surface. To calculate the fraction of clay in a transportable dispersed state (fDC) in each compartment of soil, SoilGen uses:
fDC = [1-(SC/CSC)] · θmacro· fVC
Where SC is the total electrolyte concentration (mmol/dm3 of water), CSC is the critical concentration at which soil clay mixtures stay flocculated, θmacro is the volumetric water fraction present in macropores (m3 of water/m3 of soil) and fVC is the fraction of soil volume taken by clay. θmacro is estimated from the water retention characteristic at a pressure head h(θmacro) (hPa) close to the saturation pressure. The product θmacro· fVC is a measurement of the clay fraction that is in contact with rapidly flowing water, which occurs in macropores. Finally, the simulation of filtering (g/m3,h) is done with the equation (Jarvis et al., 1999):
F = fref · νref n · ν1-n · c · θ
With fref a reference filter coefficient (1/m), νref (m/h) the pore water velocity at which fref is measured, ν current pore water velocity (m/h), c the clay particle concentration (g/m3 of water) and n an empirical factor between 0 and 1. The values of fref and νref are obtained from Jarvis et al. (1999). Current pore water velocity is simulated by SoilGen. In all, there are 6 parameters needed to simulate clay migration:
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B (ºC/h): threshold temperature gradient dT/dt above which splitting probability is maximal
Ps,max (-): maximal splitting probability
kd (g/J): detachability coefficient
kr (g/m2, h): replenishment rate coefficient
h(θmacro) (hPa): pressure head close to saturation
n (-): filtering factor
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2. CALIBRATION OF SOILGEN FOR LESSIVAGE The six parameters introduced in SoilGen to simulate clay migration have to be calibrated. To do this, six Luvisol profiles developed on Loess were selected from the French soil database DoneSol, all of them located in the geographic domain that goes from the Paris Basin to the North of France.
2.1. Starting point Before the calibration, a sensitivity analysis of the parameters has to be done. This was already done when I started with my internship. For the sensibility analysis, 9 simulation scenarios were run. Dissimilarity between simulated and observed values for clay percentage and the sensitivity of the simulations for each parameter for each soil profile were calculated. Dissimilarity was obtained by (Gower, 1971):
With Claymax and Claymin the maximal and minimal clay contents (%) in the profile studied, ClayMk the measured clay content and ClaySk the simulated clay content in layer k. The results for the sensitivity analysis are shown in figures 2.2 and 2.3.
Figure 2.2 – Dissimilarity for each of the simulated scenarios for the 6 DoneSol profiles
0 5 10 15 20
Kd (g.J-1)
0.1
0.3
0.5
0.7
0.9
1.1
1.3
1.5
1.7
Diss
imila
rity
34392
34410
34538
73303
84987
84993
0 0.05 0.1 0.15 0.2 0.25 0.3
Kr (g.m-2.h-1)
0 1 2 3 4
B (°C.h-1)
0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80
n
0.0 0.5 10-6 1.0 10-6 1.5 10-6 2.0 10-6 2.5 10-6 3.0 10-6
0.1
0.3
0.5
0.7
0.9
1.1
1.3
1.5
1.7
Diss
imila
rity
-100 -80 -60 -40 -20 0
hm (hPa)
0.1
0.3
0.5
0.7
0.9
1.1
1.3
1.5
1.7
Diss
imila
rity
Soi l profi le
PS,max
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Figure 2.3 - Estimated means and standard deviations of elementary effects of each parameter on dissimilarity.
Labeled symbols indicate the most influential parameters
2.2. Calibration approach: Calibration is the process by which the optimal values for the parameters of the model are determined. In our case, the optimum set of values is that for which the dissimilarity with the measured profile is minimum and the conservation of the total profile clay content is maximal. Among the several DoneSol chosen profiles, the calibration is done with the soil with the highest dissimilarity. The order in which parameters are calibrated is given by the sensitivity analysis: the most sensitive parameter is the first to be calibrated, then the second most sensitive parameter and so on, finishing with the least sensitive parameter. While a parameter is being calibrated, the other ones remain at a fixed value. The initial value and the calibration range for each parameter are chosen regarding the parameter value giving the lowest dissimilarity among the initial simulations. The calibration of a parameter begins with several runs for different values of the calibration range. In our case, we decided to take 4 values from the calibration range, taking always the limit values. Once the results of the simulations are obtained, the difference between the dissimilarity of the best and second best simulations has to be evaluated. In our case, if it is less than 5 % the parameter is assigned the value that corresponds to the best run. If it is not, the procedure is repeated assigning the parameter values around the optimum found, until a difference between dissimilarities lower than 5% is reached. Once a parameter is calibrated, it is fixed to the value obtained and the next parameter begins being calibrated. The high computational cost of the runs in SoilGen makes it unfeasible to use statistical methods of calibrations, for example the Monte Carlo method.
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Calibration of Psmax and B As described above (figure 1), the two weathering parameters, Psmax and B, are linked and were thus calibrated together. In order to accelerate the calibration process, we tried to reduce the calibration ranges of the two parameters. As not all the dT/dt gradient do exist in the reality, we reduced the ranges of Psmax and B according to the dT/dt encountered in the simulations along time. The procedure followed is described below. Each couple of values of Psmax and B defines a probability function for physical weathering. Drawing 100 couples of values of Psmax and B randomly inside their corresponding ranges, we obtained 100 splitting probability distributions with which an average physical weathering distribution was obtained. Combining the average physical weathering distribution and our soils dT/dt distribution we obtained a weighted average splitting probability distribution. We then compared this distribution to each of the 100 initial splitting probability distributions and selected those initial probability distributions that most resembled the weighted average distribution. Repeating this procedure several times should lead to the reduction of the initial calibration ranges. Figure 2.4 shows the different curves used.
Figure 2.4 - Fine lines: 100 splitting probability distributions. Thick blue line: average splitting distribution. Thick black line: dT/dt distribution. Thick red line: weighted average splitting probability distribution
Once this was done, the pairs of values of each parameter to run the calculations had to be chosen. Figure 2.5 shows how we did this. If the calibration ranges of each parameter are (Ps,max 0 , Ps,max 1) and (B0 , B1), the corners of the biggest square in the left of the figure represent the coordinates of the pairs of values (Ps,max , B) corresponding to the extreme values of the ranges. The black dots in the centre of each small square represent the
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coordinates of the pairs of values that were to be simulated. Once finished the nine first simulations, if the difference between the unscaled dissimilarity of the two best simulations is less than 5%, Psmax and B are fixed to the values corresponding to the center of the square with the lowest dissimilarity (in our example, the one corresponding to the green small square). If not, this square is subdivided in other nine squares whose centers give the coordinates of the pairs of values for the following simulations. The process finishes when the difference between the unscaled dissimilarity of the two best simulations is less than 5%.
Figure 2.5 – Left : Square representing the initial pairs of values (coordinates of the black dots) that were simulated.
The different colors of the small squares make reference to the results of the simulations, green for the best simulation and red for the worst ones. Right : Square corresponding to the best of the previous simulations. The subrange for the following simulations is given by the corners of the initial green square in the left. The pairs of
values for the following simulations were obtained by dividing the green square in other nine smaller squares. Once again, the black dots represent the coordinates of the pairs of values of Ps,max and B.
To be coherent with the calibration of the rest of the parameters, the calibration of Psmax and B finishes when for a set of 9 calibrations, the difference between the dissimilarities of the two best runs is less than 5%.
2.3. Results of the calibration As a conclusion of the sensitivity analysis, the DoneSol profile with the highest dissimilarity is the profile 73303 (figure 2.3), so this is the profile with which the calibration will be done. Also, of the six parameters, only four need to be calibrated: h(θmacro), n, B and Ps,max (figure 2.4). The model is barely sensible to variations in kd and kr, so they do not need to be calibrated. The order of calibration of the parameters is (figure 2.4):
1. h(θmacro) 2. n 3. B and Ps,max
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2.3.A. Calibration of h(θmacro) The results of the calibration of h(θmacro) are given in figure 2.6.
Figure 2.6 – Calibration of h(θmacro)
The graph shows that for increasing values of the parameter, the unscaled dissimilarity tends to decrease. It can be noticed that for values of h(θmacro) between -2 hPa and 0 hPa the dissimilarity has exactly the same value. This is because, in order to avoid numerical instabilities, the maximal value of h(θmacro) acceptable by SoilGen is -2 hPa. For values between -2 hPa and 0 hPa, the program considers h(θmacro) to be -2 hPa. Since this is also the value for which the unscaled dissimilarity is minimal, we decide to fix h(θmacro) to -2 hPa. The unscaled dissimilarity is 14,51.
2.3.B. Calibration of n Figure 2.7 shows the results of the calibration process of n.
0
5
10
15
20
25
30
-45 -40 -35 -30 -25 -20 -15 -10 -5 0
Un
sca
led
dis
sim
ila
rity
h(θmacro) (hPa)
0
5
10
15
20
25
0 0,2 0,4 0,6 0,8 1 1,2
Un
sacl
ed
dis
sim
ila
rity
n
19
Figure 2.7 – Calibration of n
The simulations for the values of n in the initial range (0,5 ; 1) showed a clear decrease of dissimilarity when n decreased. Therefore, the range was widened to take into account lower values of n. The final results gave 0,35 as the best value for n. The unscaled dissimilarity was in this case 12,01.
2.3.C. Calibration of Ps,max and B In order to reduce the calibration ranges of Ps,max and B, the values of dT/dt in the soil and over time were obtained from one of our simulations. Figure 2.8 shows the correspondent histogram. It is clear that small values of dT/dt are predominant in our soils.
Figure 2.8 – Histogram of de gradient of temperature in calibrated soils
With this and following the procedure of reduction of the initial calibration ranges explained above, we obtained in the case of B a reduction of the calibration range from (0,2 ; 4,5) to (0,2 ; 2,2), all values in °C.h-1. The results for Ps,max led to the initial range. The results of the successive sets of calibration can be seen in figure 2.9.
Figure 2.9 – Results of the calibration of B and Psmax. The numbers inside each small square represent the value of
the dissimilarity obtained for the corresponding simulation. The colour of the squares indicate the quality of the simulation in comparison to the rest of the simulations from the set: green for the best simulation, red for the worst
ones.
1
10
100
1000
10000
100000
1000000
0,0
05
0
,15
0
,29
5
0,4
4
0,5
85
0
,73
0
,87
5
1,0
2
1,1
65
1
,31
1
,45
5
1,6
1
,74
5
1,8
9
2,0
35
2
,18
2
,32
5
2,4
7
2,6
15
2
,76
2
,90
5
3,0
5
3,1
95
3
,34
3
,48
5
3,6
3
3,7
75
3
,92
4
,06
5
4,2
1
4,3
55
Fre
qu
en
cy
dT/dt
20
After four sets of simulations, we did not reach the chosen threshold of less of 5 % of dissimilarity and the average dissimilarity for the successive runs did not seem to decrease. We summarized the results of sets 2 to 4 on figure 2.10 (see next page) in order to find a pattern of evolution of the dissimilarity. No spatial pattern of the dissimilarity values appears on this representation of the results. Considering ths variations in dissimilarity along three axes of symmetry (a horizontal line -line A-, a vertical line -line B- and a diagonal -line C, figure 2.11 in next page), no trend of dissimilarity decreases was observed whatever the axe considered. The dissimilarity values seems to have an erratic distribution within the studied range of set 2, with however a minimum value always found for the same values. One of the simulations from the second set, the one highlighted in black in figure 2.9, crashed because for the corresponding combination of B and Ps,max, porosity becomes too low. We do not know what is the exact reason for this, but it results in a modification of the SoilGen program. For the following sets of simulations (sets 3 and 4), this modified version of SoilGen was used, but the G5 runs was never reruned and was used successively as run G5e and G5e-5. This run always remained the one having the minimum value of dissimilarity. This may be due to a modification introduced in SoilGen. To verify this hypothesis, the run G5 should be reruned with the modified version of SoilGen. There was no time to do this before the handing over of this report. The values of Ps,max and B for which dissimilarity was lowest at the moment of writing this report were:
B = 0,533 °C.h-1
Ps,max =1,38 E-6 The dissimilarity obtained for this case was 11,68. This are not definitive values, further simulations and analyses have to be done. In sum, the results of the calibration process are shown in table 2.1.
Parameter Final value Unscaled
dissimilarity
h(θmacro) (hPa) -2 14,51
n 0,35 12,01
B (°C.h-1) 0,533* 11,68
PS,max 1,38 E-6* * Not definitive values. We have not yet arrived to convergence, additional simulations have to be done.
Table 2.1 – Final values of the parameters after the calibration with the unscaled dissimilarity obtained at the end of
the calibration of the corresponding parameter
21
Figure 2.10 – Analysis of the second, third and fourth sets of simulations to try to find a pattern of evolution for dissimilarity
L-B
L-C
L-A G5e_1G5e_2G5e_3
G5e_4G5e_5G5e_6
G5e_7G5e_8G5e_9
Ps max
G5g G5h G5i
G1 G2 G3
G6
G5a G5b G5c
G5d G5fB
G9G8G7
G4
22
Figure 2.11 - variations in dissimilarity over three lines of symmetry: a horizontal line (line A), a vertical line (line B) and a diagonal (line C)
23
3. SOILGEN’S SENSIBILITY TO CHANGES IN AGRICULTURAL PRACTICES AND LANDUSE As it has already been said, one of the main objectives of simulating soils evolution is to be able to develop sustainable agricultural practices. This is why it is important to verify the model sensibility to changes in agricultural practices and land use once the parameters have been calibrated. This will be done by running simulations for the three experimental sites selected for the project AGRIPED: Feucherolles, Mons and Boigneville. Table 2.2 shows a compilation of the three sites and their plots. Once the simulations are done, we will be able to determine SoilGen’s sensibility to the type of landuse (agriculture, meadow or forest), to plowing (conventional plowing, reduced plowing or no plowing) and to the use of manure.
Conventional plowing Reduced plowing
No plowing
No manure Feucherolles: Témoin 303
Mons: ES23 Boigneville: L0
Mons: ES11
Boigneville: L1
Boigneville: L2
Manure Feucherolles: Fumier 203 x x
Meadow x x Mons: Prairie
Forest x x Feucherolles: Fôret Table 2.2 - Compilation of the sites and the plots
Duration of the calibration process made it impossible to do the simulations of the sites, but the input files have been created, leaving all ready to start the simulations once the calibration will be finished. Preparation of the input files needs to solve a number of questions that will be discussed after the description of the experimental sites. Main types of input data are related to the type of vegetation and the C it produces, to the climate, to the use of fertilizers and to the parent material.
3.1. Description of the experimental sites As it has already been said, we are studying clay migration on Luvisols as Luvisols are othotype of soils experiencing clay migration. Therefore the soil of the chosen experimental sites had to be Luvisols. Two agricultural practices are supposed to have an influence in clay migration: plowing and organic matter input (Projet Agriped, scientific document; 2010). Plowing has an impact in soil’s structure and consequently in water flux through soil. This implies that particle transfer is affected and therefore clay migration. Tillage reduction experiment started 40 years ago in Boigneville and 10 years ago in Mons. The comparison between the simulations of these two sites will inform on SoilGen’s sensibility to plowing simulated here as a change in intensity and depth of mixing and change in soil hydrolic properties. Organic matter plays a role in the stabilization of soil structure. Particle dispersion is directly related to soil stability. Thus, input of organic matter to the soil will influence clay migration. In the experimental site of Feucherolles, the two agricultural plots only differ in the input or not of manure. This will allow studying the SoilGen’s sensibility to input of organic matter.
24
Finally, the three experimental sites have a long-term recorded agricultural history with a great amount of data available. Feucherolles and Mons are two OREs (French observatories for research on environment) and Boigneville is an experimental site of ARVALIS. ARVALIS-Institut of plant is an organization focused on applied research that produces technical-economic and agronomic references applicable on productive systems. All of this makes the three selected sites ideal for the project. The three studied site are located in the Paris Basin or in the North of France where the main part of Luvisols present in France is found (Figure 2.10).
Figure 2.10 - Map of Luvisols in France (in red, after the Soil atlas of Europe, European Commission;
2005) and location of the three experimental sites (black dots).
Figure 2.11 (see next page) presents a description of the three experimental sites: history of their changes in landuse, consequences in terms of gradient along the plot sequences and other plot characteristics.
3.1.A. Feucherolles The experimental site is located in Feucherolles, in northern France, West of Paris. There are three plots, the first one (Forêt) is in the Flambertines forest, of oaks and mulberry trees, and the other two are agricultural plots located in the limit between the community of Orgeval and the community of Feucherolles, only a couple of km apart from the forested plot. Both agricultural plots experienced a crop rotation of wheat and corn between 1998 (start of the experiment) and 2011 (sampling year). In the case of the plot 203, manure from unweaned bovines was applied every two years while in plot 303, manure was never applied. In both cases, during the period of the experiment, no inputs of potassium or phosphate were applied, while an annual input of nitrogen in the form of the chemical product Solution 39 took place. Data for this site was obtained from the study Essai Qualiagro (Mercier et al., 2010).
25
Figure 2.11 – Description of the three experimental sites: history of their changes in landuse, consequences in terms of gradient along the plot sequences and other plot characteristics
26
3.1.B. Mons The three plots of this experimental site are located in the community of Mons, at the North of France, close to the frontier with Belgium. The three plots were agriculture plots since the beginning of agriculture until 1939, when one of the plots, Prairie, was changed into a grassland. The two remaining agricultural plots differ in the depth of plowing between the years 2001 (start of the experiment) and 2011 (sampling year), for the plot ES 11 it was 10 cm and for the plot ES 23 it is 25 cm. None of them has had a manure input but potassium, phosphor and magnesium have been applied in the form of chemical fertilizer every year between 1990 and 2011. In both cases, there was crop rotation over four years: wheat/corn/wheat/sugar beet.
3.1.C. Boigneville
Boigneville is a community located in the South of Paris, in the Paris Basin. The experimental dispositive began to operate in 1971. In this case, the three plots have an agricultural use. They differ among them in the depth of plowing, as in the case of Mons. No manure was applied any year and there was no crop rotation as it was a monoculture of wheat. There was an input of phosphate, sulfate, potassium and nitrogen over several years in the form of chemical fertilizers. Reconstruction of the past landuse for the three sites SoilGen simulates soil development over 15000 years. This means that we need to specify the type of vegetation of each plot over the past 15000 years. For years before the beginning of agriculture (around 2000 years before present) natural vegetation has been reconstructed using climate data from the past (Harvis et al., 2003 and Verbruggen et al., 1996). In what refers to the exact date at which agriculture started, there is not information about when this happened in the regions where our sites are. It is known that agriculture in the European region began in the roman period, this is around 2000 years ago, but it is impossible to know whether the land of our sites was cultivated at that time or not. The most ancient information about soil occupation in France is given by the Cassini map, developed by César-François Cassini and his son Jean-Dominique Cassini during the XVIII century. This map is available in the official French website Geoportail (www.geoportail.gouv.fr). With the geo-localization of each of the plots of the three sites, it was checked that at the time when the map was done, the landuses of our sites where the same as they are nowadays. In conclusion, we know that there have been at least 300 years of agriculture for non-forest plots while forest plots have probably never been cultivated. This means that there have been a minimum of 300 years and a maximum of 2000 years of agriculture. In order to test the impact of this incertitude in the number of years of agriculture simulations for the maximum and the minimum number of years will be done for the agricultural plots of Feucherolles. It has already been discussed the importance of plowing depth when modeling clay migration. Agricultural practices have changed a lot over the past century, mainly due to mechanization of work. The tractor had an enormous impact, not only because it increased yield and facilitated the work of soil, but also because plowing depth was substantially increased, with consequent effects on clay migration. We thus chose a scenario of tillage history for the different plot that were reported. Table 2.3 shows changes over time in the depth of tillage and % of soil mixing for the different plots. SoilGen is able to simulate changes in depth of
27
plowing and in the proportion of soil mixing with time, but the effects have not yet been tested. Therefore, extra simulations will be done for Mons to check the model sensibility to this parameter. In one case, depth of tillage and % of mixing will be considered constant through time from the beginning of agriculture onwards. In the other, depth labour and % of soil mixing will be set to values chosen in table 2.3 (see next page), taking into account changes over time.
3.2. Climate data Climate has an essential role in soils evolution. It is determinant in heat flow, water flow and physical weathering among many other of the processes simulated by SoilGen. Thus, the program needs several climate data as input in order to do the simulations. It needs for each simulated year the following climate inputs: - Average temperature of January and July - Total amount of precipitations - Total potential evapotranspiration. When not available, non-specified years will get interpolated data. Reconstruction of paleo-climate is done by an auxiliary application of SoilGen by introducing data for a current standard year (Finke and Hutson, 2008). In our study, we are mostly interested in the years during which the experimental dispositive were in place, so for recent years we use data measured in the experimental sites. Once done the paleo-climate reconstruction, reconstructed data have to be compared with current available data of the experimental sites. To compare both types of data we calculated the average of the reconstructed data between 2400 years BP and 60 years BP (last year of the reconstructed period) with the average of real data. Temperature, precipitation and potential evapotranspiration are stable over the last part of the reconstructed period, so a period of time between 2400 years BP and 60 years BP is considered to be an adequate period of time to do the average. Results are shown in table 2.4.
Site Variable Average of
reconstructed data Average of
measured data Difference Difference (%)
Feucherolles
Jan T (ºC) 4,70 4,32 0,38 8,42
Jul T (ºC) 18,78 14,87 3,92 23,28
Precipitations (mm) 564,56 568,68 4,12 0,73
ETP (mm) 773,00 836,18 63,17 7,85
Mons
Jan T (ºC) -0,75 3,77 4,52 299,72
Jul T (ºC) 17,19 18,01 0,82 4,64
Precipitations (mm) 608,33 606,26 2,07 0,34
ETP (mm) 796,35 799,55 3,20 0,40 Table 2.4 – Comparison of reconstructed climate data and measured data for Feucherolles and Mons
3.2.A.Temperature Temperature data is reconstructed by SoilGen using a combination of 1) Davis et al. (2003) temperature anomaly data for central-western Europe for the past 12000 years, 2)Vostok Ice
28
Table 2.3 - Changes over time in the depth of tillage and % of soil mixing
Site Plot 2000BP - 1960 1961 - 1970 1971 - 1990 1991 - 2000 2001 - 2011
Depth of plowing (cm)
Feucherolles Temoin
20 40
40 30 30 Fumier
Mons 11
40 30 10
23 25
Boigneville
L0 28 28 28
L1 10 10 10
L2 0 0 0
% of soil mixing
Feucherolles Temoin
25 50
50 50 50 Fumier
Mons 11
50 50 25
23 50
Boigneville
L0 50 50 50
L1 25 25 25
L2 0 0 0
29
Core Data set (Petit et al., 1999) for years between 15000 BP and 12000BP and 3) a current standard year. Davis et al. (2003) data are based on pollen data. Feucherolles: Figure 2.12 presents the reconstructed January and July average temperatures (ºC) for Feucherolles.
Figure 2.12 - Reconstructed January and July average temperatures (ºC) for Feucherolles
Measured data for Feucherolles is shown in figure 2.13.
Figure 2.13- Real recent temperature (ºC) data for Feucherolles
-10
-5
0
5
10
15
20
25
60 2060 4060 6060 8060 10060 12060 14060
T (º
C)
Years BP
Reconstructed temperatures
JanT
JulT
-10
-5
0
5
10
15
20
25
0 5 10 15
T (º
C)
Years BP
Recent temperatures
JanT
JulT
30
Mons: Figure 2.14 presents the reconstructed January and July average temperatures (ºC) for Mons.
Figure 2.14 - Reconstructed January and July average temperatures (ºC) for Mons
Measured data for Mons is shown in figure 2.15.
Figure 2.15- Real recent temperature (ºC) data for Mons
The difference between reconstructed and measured January temperature for Mons (table 2.5) is very high if we consider it in %, but absolute difference is of only 4,52 ºC, which is not high considering that data we are comparing data over the past 3000 years.
3.2.B. Annual precipitation To reconstruct annual precipitation, SoilGen uses precipitation data anomalies in Europe for the last 12000, provided by Davis (unpublised). For the period of time between 15000 BP and
-10,0
-5,0
0,0
5,0
10,0
15,0
20,0
60 2060 4060 6060 8060 10060 12060 14060
T (º
C)
Years BP
Reconstructed temperatures
JanT
JulT
-10
-5
0
5
10
15
20
25
0 5 10 15 20 25
T (º
C)
Years BP
Recent temperatures
JanT
JulT
31
12000 BP precipitation is obtained by assuming minimal values for precipitation during the Lateglacial and Younger Dryas period, as reported by Liu et al. (1995) and guessing values for Alleröd, Middle Dryas and Bölling periods (Finke and Hutson, 2008). Feucherolles: The results for Feucherolles are shown in figures 2.16. and 2.17.
Figure 2.16 - Reconstructed precipitation (mm) for Feucherolles
Figure 2.17- Real recent annual precipitation (mm) for Feucherolles
Mons: The results for Mons are shown in figures 2.18. and 2.19.
100
200
300
400
500
600
700
800
900
60 2060 4060 6060 8060 10060 12060 14060
P (
mm
)
Years BP
Reconstructed annual precipitation
100
200
300
400
500
600
700
800
900
0 2 4 6 8 10 12 14
P (
mm
)
Years BP
Recent annual precipitation
32
Figure 2.18 - Reconstructed precipitation (mm) for Mons
Figure 2.19- Real recent annual precipitation (mm) for Mons
3.2.C. Potential evapotranspiration In what refers to potential evapotranspiration, SoilGen does the reconstruction over time using the method of Hargreaves and Samani (1985) using current monthly average temperatures and daily temperature ranges. For recent years, we used available data of temperature to calculate potential evapotranspiration by Hargreaves method. Hargreaves model uses the following equation to estimate daily potential evapotranspiration (Hargreaves and Samani, 1985).
ETP = 0.0023 Ra (TD)1/2 (TC+17,8) Where Ra is the extraterrestrial radiation expressed in equivalent evapotranspiration units (mm/day), TD is the maximum daily temperature minus minimum daily temperature and TC is the average daily temperature (°C). Adding up daily values over each year we obtained the potential evapotranspiration for recent years.
100,0
200,0
300,0
400,0
500,0
600,0
700,0
800,0
900,0
60 2060 4060 6060 8060 10060 12060 14060
P (
mm
)
Years BP
Reconstructed annual precipitation
100
200
300
400
500
600
700
800
900
0 5 10 15 20 25
P (
mm
)
Years BP
Recent annual precipitations
33
Feucherolles:
Figure 2.20 - Reconstructed annual potential evapotranspiration (mm) for Feucherolles
Figure 2.21- Real recent annual potential evapotranspiration (mm) for Feucherolles
690
740
790
840
890
60 2060 4060 6060 8060 10060 12060 14060
ETP
(m
m)
Years BP
Reconstructed annual ETP
690
740
790
840
890
940
0 2 4 6 8 10 12 14
ETP
(m
m)
Years BP
Recent annual ETP
34
Mons:
Figure 2.22 - Reconstructed annual potential evapotranspiration (mm) for Mons
Figure 2.23 - Real recent annual potential evapotranspiration (mm) for Mons
Data for Boigneville was not yet treated at the time of writing the report. We can conclude that for Feucherolles and Mons, reconstructed data fit well to measured data.
3.2.D. Rainwater composition Rainwater contributes to the chemistry of soil by adding cations and anions that will interact with soil’s components. SoilGen applies the same rainwater composition over time. This means that, although we know that rainwater composition has changed over the past 15000 years, we have to choose a composition that will be applied each year. The main change in rainwater composition took place due to the industrial revolution, which provoked an acidification of rainwater. Nevertheless, this has an effect only on the past 300 years, while simulations go
670,0
720,0
770,0
820,0
870,0
60 2060 4060 6060 8060 10060 12060 14060
ETP
(m
m)
Years BP
Reconstructed annual ETP
670
720
770
820
870
0 5 10 15 20 25
ETP
(m
m)
Years BP
Recent annual ETP
35
over 15000 years. Thus, we need a pre-industrialized rainwater composition. A coastal region free of industries and were wind regularly cleans the environment from pollution can be considered as a pre-industrialized scene in what refers to rainwater composition, but it would also be too salty. Taking an average composition between this and one of an interior region is considered to be an adequate solution. Previous simulations done by Finke and Hutson (2008) for soils in Belgium had already considered this approach, so for our simulations we decided to take the rainwater composition chosen for the simulations of Belgium.
3.3. The plant module and organic matter Vegetal cover has an essential role in the soil’s C-cycle and hydrological regime, both simulated by SoilGen. Thus, we need to introduce in the model information about the dynamics of the vegetal cover and about the restitution of C to the soil, which are function of the type of plant present in the soil. In the case of deciduous forest, coniferous forest and grassland, vegetal cover is stable over the years, so we take the values that the program gives by default when these modes of occupation are present. In the case of agriculture, it depends on the type of crop and the rotation of crops. In the case of agriculture, vegetal cover and C-cycle depends on the type of crop. This leads to the following questions: - Dynamic of the vegetal cover depending on the type of plant - C input to the soil depending on the type of plant
3.3.A. Dynamic of the vegetal cover depending on the type of plant Different crops have different dynamics of their vegetal cover. For the crops that concern our sites, representative dates for the crop cycle are shown in table 2.5.
Sowing Germination Emergence Root Mat Cover Mat Harvest
Corn 04-may 14-may 29-may 15-jun 05-aug 25-oct
Wheat 28-oct 05-nov 25-nov 15-apr 30-jul 24-aug
Sugar beet 20-mar 04-apr 12-apr 19-may 03-jun 25-oct Table 2.5 - Representative dates for each crop
For a model year, SoilGen uses these dates for hydrological calculations in the following way: - Germination: start of root growth - Emergence: start of soil cover increase, needed to calculate transpiration - Root maturity: maximum root density reached; depending on soil water conditions this can implies that transpiration uptake is maximal - Cover maturity: maximum soil cover reached, which implies that potential transpiration is maximal - Harvest: transpiration and associated water uptake stop Since SoilGen is able to take into account only one model year for the dynamics of the vegetal cover, in the cases of crop rotation we have decided to make a weighted average of dates according to the frequency of each crop in the rotation. From table 2.5 can be seen that wheat cycle does not take place between the limits of a regular year (January-December). The type of wheat used is winter wheat, sown before winter
36
because the crop can start growing as soon as soil temperatures are suitable and because young plants can stand frost considerably well. During winter, the amount of biomass production and of soil water uptake is considerably low. This means that the dates of germination and emergence of winter wheat can be shifted towards spring, consequences for plant water and nutrient uptakes are minimal. Bearing this in mind, we decided that in the case of wheat it is acceptable to consider that the dates of germination, emergence, root maturity and cover maturity correspond to those of sugar beet. Feucherolles For Feucherolles (wheat/corn), the calculation of the dates of germination, emergence, root maturity and cover maturity was done using the equation:
Where J stands for Julian day number. Figure 2.24 shows how calculations were done. The date of harvest was harmonized with the date of maximum C going back to the soil in the form of crop residues, obtained further on in the text (see table 2….)
Figure 2.24 - Estimation of the crop cycle for Feucherolles
The final results are in table 2.6.
Sowing Germination Emergence Root Mat Cover Mat Harvest
13-apr 25-apr 6-may 4-jun 7-jul 29-aug Table 2.6 - Crop cycle of Feucherolles
Mons The same approach was used for Mons (wheat/corn/wheat/sugar beet) taking into consideration that the frequency of wheat crops is two times that of corn and sugar beet.
37
Figure 2.25 - Estimation of the crop cycle for Mons
From figure 2.25 we obtained the following dates:
Sowing Germination Emergence Root Mat Cover Mat Harvest
4-apr 16-apr 22-apr 29-may 17-jun 29-aug Table 2.7 - Crop cycle of Mons
In Boigneville (wheat) there was no rotation of crops, so there was no need for recalculations.
3.3.B. C-input to the soil depending on the type of plant The total input of C to the soil depends on the type of plant, but also the distribution of the C-input over the year. This leads to two other issues: - Total amount of C going back for different types of crops - How take into account crop rotation in the C-input distribution over a model year 3.3.B.i) Total amount of C going back for different types of crops For each experimental site, data for annual yield is available, but we need the total amount of C input to the soil. The whole amount of C going into the soil from the plants can be calculated using the annual yield of each crop. For a given crop, the net primary production (NPP) is given by the following expression.
38
Thus, annual C input to the soil consists of the sum of aboveground inputs and belowground inputs. Balesdent et al. (2012) and Jones et al. (2009) suggest the following procedure to calculate annual C input:
Inputs aboveground =
Inputs belowground =
With: Y: Annual yield
HI: Harvest Index =
BI: Belowground Input Index=
Which gives:
Annual C input =
=(C-input factor) Y
In the case of wheat, a small grain cereal, HI is set equal to 0.45, while for corn it takes the value of 0,55. For both crops, the BI index is set equal to 0.45, with a contribution of 0.32 from the root biomass production and of 0.13 from the rhizodeposition. The exported matter for the sugar beet crop includes part of the roots. This implies that the restitution of carbon to the soil is a lot lower for sugar beet than for other crops for which only the grains are exported. Thus, the carbon input factor for the sugar beet needs another approach. The carbon going into the soil for sugar beet crops with an annual yield of about 80 tones/ha is 324 g/m2
(Soltner, 1996), which means that the C-input factor is about 0,041. 80 tones/ha is a high yield for sugar root. Table 2.8 shows the resulting C-input factor.
Crop Corn Wheat Sugar beet
C-input factor 1,636 2,222 0,041 Table 2.8 – C- input factor for different crops
3.2.B.ii) Distribution of C-input to the soil over the year taking into account crop rotation SoilGen needs, for a model year, the monthly distribution of inputs (% per month) of plants residues for the four types of vegetation it simulates in order to calculate the amount of C that goes back into the soil each year. This is simulated using the approach of RothC-26.3 (Coleman and Jenkinson, 2005). In the case of grassland, deciduous forest and coniferous forest for such distribution we used that given by default by SoilGen (Finke and Huston, 2008) as vegetation is considered stable over time. In the case of agriculture, it is dependent on the crop. Annual distribution of C going back to the soil is not homogene over a year. The 80% of the whole annual C going into the soil due to plants residues does it during the month of harvest; the rest goes into the soil in the period between the sowing and the month of harvest with a linear increase with time (J. Balesdent, 2012). Figure 2.26 represents this distribution.
39
Figure 2.26 - Annual distribution of C going back to the soil for a given crop
Since different plants have different dates of sowing and harvest (table 2.6), they also have a different C-input distribution. Considering the dates of the sowing and harvest, the percentage of C going into the soil each month can be calculated for the different crops. The results are shown in table 2.9.
Month Corn Wheat Sugar beet
1 0 1,33 0
2 0 1,78 0
3 0 2,22 0
4 0 2,67 0,95
5 1,33 3,11 1,91
6 2,67 3,56 2,86
7 4 4 3,81
8 5,33 80 4,77
9 6,67 0 5,71
10 80 0 80
11 0 0,44 0
12 0 0,89 0 Table 2.9 - Annual distribution of C-input to the soil for each crop in %
In order to take into account crop rotation, three factors have to be taken into account: 1) differences in the total amount of C going back to the soil 2) differences in the distribution of C-input over the year (table 2.9) and 3) frequency of each crop. Average annual yield was calculated from data from the experimental sites. With this and with the C-input factor, the average C-input to the soil for each crop was obtained. Then, using values for annual distribution of C-input from table 2.9, we obtained the C-input for each month for each crop, weighted in function of the crop’s frequency in the rotation. We added up the values of all the crops present in the rotation and the % of C going back to the soil was obtained. For the plot Temoin 303 in Feucherolles, the results are highlighted in blue in table 2.11.
40
Crop Average Yield (T/ha, year)
C-input Factor Average C-input
(T/ha, year)
Corn 8,58 1,64 14,03
Wheat 7,10 2,22 15,79
Table 2.10 - Calculation of the average annual C-input weighted for the rotation for the plot Feucherolles Temoin 303 in T/ha
Month Amount of C-input each month for
each crop (T/ha) Total amount of C-input (T/ha)
C-input distribution taking into account
the rotation (%) Corn (0,5) Wheat (0,5)
1 0,00 0,21 0,21 0,71
2 0,00 0,28 0,28 0,94
3 0,00 0,35 0,35 1,18
4 0,00 0,42 0,42 1,41
5 0,19 0,49 0,68 2,27
6 0,37 0,56 0,94 3,14
7 0,56 0,63 1,19 4,00
8 0,75 12,63 13,38 44,87
9 0,94 0,00 0,94 3,14
10 11,22 0,00 11,22 37,64
11 0,00 0,07 0,07 0,24
12 0,00 0,14 0,14 0,47
Table 2.11- Calculation of the annual distribution of C-input to the soil for the plot Feucherolles Temoin 303 in %
For the plot ES11 in Mons the results are highlighted in blue in table 2.13.
Crop Average
Yield (T/ha)
C-input Factor
Average C-input weighted for the
rotation (T/ha, year)
Corn 8,00 1,64 3,27
Wheat 8,60 2,22 9,56
Sugar beet 80,64 0,04 0,82 Table 2.12 - Calculation of the average annual C-input weighted for the rotation for the plot Mons ES11 in T/ha
Month Amount of C-input each month for each crop
(T/ha) Total
amount of C-input (T/ha)
C-input distribution taking into account the
rotation (%) Corn (0,25) Wheat (0,5) Sugar beet (0,25)
1 0,00 0,13 0,00 0,13 0,93
2 0,00 0,17 0,00 0,17 1,25
3 0,00 0,21 0,00 0,21 1,56
4 0,00 0,25 0,01 0,26 1,92
5 0,04 0,30 0,02 0,36 2,61
6 0,09 0,34 0,02 0,45 3,30
7 0,13 0,38 0,03 0,54 3,99
8 0,17 7,65 0,04 7,86 57,59
9 0,22 0,00 0,05 0,26 1,94
10 2,62 0,00 0,65 3,27 23,97
11 0,00 0,04 0,00 0,04 0,31
12 0,00 0,08 0,00 0,08 0,62 Table 2.13: Calculation of the annual distribution of C-input to the soil for Mons ES 11 in %
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3.4. Bioturbation Bioturbation is the movement of matter in the soil due to the presence of living organisms (Gobat et al., 1998). This has several important consequences on soil structure: mechanical loosening of the soil, oxygenation of the deepest layers, redistribution of organic matter, brought up of buried elements to the surface and neutralization of pH. In all, it is clear that bioturbation has an essential role in clay distribution in the soil. SoilGen takes into account bioturbation effects on soils distribution by introducing as input the mass of soil subject to bioturbation. Although there are many agents that act in bioturbation, under temperate climate, earthworms are considered as the main actor (Gobat, 1998 and Persson et al., 2007), and mainly anecic worms when vertical movement is considered (Persson et al., 2007). There is a high incertitude on the way of estimating bioturbation from the number of worms. In SoilGen, bioturbation is based on Gobat (1998), who estimates bioturbation from the amount of castings found in the surface. There are two approaches to calculating bioturbation: counting the number of cast (Gobat, 1998) or considering after Bouche (1981) that anecic species consume about 200 times their biomass of dry matter per year. Persson et al. (2007) compared both approaches for Sweden and concluded that the order of magnitude were generally the same even if the former tends to underestimate bioturbation (table 2.14).
Table 2.14 - Comparison of approximate values of bioturbation for non-agricultural plots used in SoilGen with values according to Personn et al. (2007)
However, no data for agriculture support the bioturbation value used in SoilGen. In addition, tillage, according to Capowiez et al. (2009) has an important impact on the number of worms and consequently on bioturbation. As in all of our experimental plots under agriculture sites the number of worms and their biomass was measured, bioturbation was estimated using Bouche (1981) estimation. The results are shown in table 2.15.
Mons Feuch Boign
ES 11 ES 23 Tem Fum L0 L1 L2
Anecic worms
(number/m2) 49,5 32,1 14 168 12 37 39
Biomass
(g/m2) 9,9 6,42 2,49 20,65 18,47 53,1 63,03
Bioturbation
(t/ha,y) 19,8 12,84 4,98 41,3 36,94 106,2 126,06
Table 2.15 - Estimation of bioturbation for agricultural plots
Bioturbation (T/ha,y)
SoilGen Persson
Coniferous
forest 7 0-20
Decidous
forest 11,4 5-13
Meadow 16 20
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In general, soils under agriculture have less bioturbation than meadow plots. Bioturbation in meadow estimated by Persson et al. (2007) is around 20 t/ha, year. Values of bioturbation obtained for Boigneville are considerably high, mainly in plots L1 and L2. The number of anecic worms is not high in none of Boigneville’s plots. However, biomass is considerably high. This may be due to an error when obtaining worms’ biomass in the experimental site.
3.5. Parent material
Soils evolve from a parent material, considered as the original rock from which horizons are formed over time due essentially to physical and chemical weathering. Soils inherit important characteristics from their parent material. SoilGen needs as input the characteristics of the parent material as initial situation 15000 years ago. In our study, soils were developed from loess deposit. The question arose if the soil is developed only from a unique loess deposit or from several or from the loess and the substratum as different types of parent material were described at the base of soils in Feucherolles and Boigneville. To answer this question, we calculated the relative proportion of the particle fractions superior to 2 μm that are considered as inert to pedogenesis in this type of soil. Their proportion should thus be stable throughout the soil profile if only one parent material is involved in the soil profile development. In Feucherolles (figure 2.27) there not a clear change in the proportion of particles of different classes, so only one parent material is involved.
Figure 2.27 - distribution of the proportion of each particle class at several depths for the profile Temoin
303 from Feucherolles
0
10
20
30
40
50
60
70
2/20 µm 20/200 µm 200/2000 µm
Proportion of each particle
class
Particle class
303 Tem 10-15 cm
303 Tem 27-29 cm
303 Tem 31-35 cm
303 Tem 45-50 cm
303 Tem 55-60 cm
303 Tem 65-70 cm
303 Tem 75-85 cm
303 Tem 105-115 cm
303 Tem 125-135 cm
303 Tem 155-165 cm
43
In the case of Mons (figure 2.28), there is a clear change in the proportion of particles of different classes at around the depth of 80 cm. This has to be taken into account when introducing input data in SoilGen by considering the parent material is divided in two at the depth of 80 cm indicating a change in the loess nature. In each of the parts will be introduced the data of the corresponding parent material. A parent material from the same region as Mons was analysed by Joret and Malterre (1947). It enables to verify that the deepest parent material does correspond with data available from the region and gives more information about its structure and characteristics.
Figure 2.28 - distribution of the proportion of each particle class at several depths for the profile ES 11
from Mons. In the legend are highlighted the two depths that mark the limit between both parent materials.
Experimental data from Boigneville was not ready at the time of writing this report.
0
10
20
30
40
50
60
70
80
2-20µm 20-200µm 200-2000µm
Proportion of each particle
class
Particle class
ES 11 5-10 cm
ES 11 12-15 cm
ES 11 18-20 cm
ES 11 25-30 cm
ES 11 33-37 cm
ES 11 42-48 cm
ES 11 65-75 cm
ES 11 85-95 cm
ES 11 105-115 cm
ES 11 125-135 cm
ES 11 145-155 cm
Loess PM after Joret and Malterre (1947)
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4. CONCLUSIONS AND OUTLOOKS SoilGen is a very complex model that takes into account many processes for the simulation of soils evolution. This complexity makes it a powerful predicting tool, but at the same time leads to a large amounts of difficulties when making use of it. This report has shown the difficulties found in our case and the solutions applied. For the continuation of the job I have done, several things should be considered. First, the calibration process has to be finished. To do this, the simulation runs with the pre-crashed model (see section 2.3.C from part 2) should be redone with the current version of SoilGen. During the calibration process, we noticed that decalcification is going too rapidly in the simulations, so the calcite dissolution constant has to be calibrated once the calibration of clay migration is finished. Once the calibration process is finished, SoilGen’s sensibility to changes in agricultural practices and landuse will be studied. During the preparation of the inputs files, we noticed that the influence of several additional factors should be considered. The incertitude on the date of agriculture beginning led to program additional runs for the agricultural plots of Feucherolles. For all of these plots, two simulations will be done: one considering that agriculture begins 2000 years ago and another one considering that it begins 300 years ago. The effects of varying plowing depth over time have not yet been tested for SoilGen. Consequently, additional runs will be done for the agricultural plots of the experimental site of Mons considering in one case that plowing depth is constant over time and that it varies in the other case. In the long term, there are two main lines of actions. We found that the mechanisms of bioturbation and ways to estimate it are poorly studied for the moment. Thus, further analysis of bioturbation and its role in clay migration should be done. Finally, there is still a lack of knowledge on the processes that play in clay migration. Future research will definitely aim this uncertainty.
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