Sustainable agriculture is recognized as a potentially viable means to provide future food demands of world population. It helps to balance agricultural productivity, economic stability, natural resources utilization and environmental impacts. Soil resource management is one aspect of sustainable agriculture (Lal and Stewart, 1995) that is needed to overcome limitations to productivity while maintaining or enhancing environmental quality.
Soil performs many functions to give us clean air and water, to provide enough crop and food and to maintain diverse wildlife and sustainable ecosystems. Scientists grouped these functions in different ways. A summary of these functions is: Nutrient cycling, water relations, biodiversity and habitat, filtering and buffering and physical stability and support (Karlen et al., 1997). It is essential to maintain and enhance these soil functions in natural and managed systems to protect environmental quality, system productivity and biodiversity. Soil itself keeps a unique balance among its physical, chemical and biological factors. However human activities are affecting soil functions. A number of management strategies include grazing systems, forest management, tillage, fertilization, crop rotation, water management, liming and cover crops have been developed to control the soil and its functions and hence to improve its productivity and health (quality) (Wienhold et al., 2004). Soil quality or health is an approach for relating soil functions and management goals (Wienhold et al., 2005a). Soil quality was defined as "the capacity of a specific kind of soil to function, within natural and managed boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation" (Karlen et al., 1997).
Field management strategies
Land management practices can influence soil quality, negatively or positively (figure 1). The knowledge of how management practices affect soil quality will help us to develop new management systems to improve the quality of soil. Therefore, to find the effects of each management system on soil functions and soil quality we need to monitor changes in the soil properties. Hence soil quality serves as a tool for monitoring soil function changes and system sustainability (Wienhold et al., 2005a). The goal of management systems is usually producing food, forage and fiber needed by society (productivity). Of course in agriculture, management systems should focus on improvement of soil properties (physical, chemical and biological properties) which have a paramount impact on soil production (food and fiber) ability.
Fig. 1. Overview of the state of soil degradation in the world (Philippe Rekacewicz, 1997). About 43% of Earths vegetation lands have been degraded due to a number of factors, many or most related to human activities including deforestation, overexploitation for fuel wood, overgrazing, agricultural activities and industrialization.
Assessment of soil quality needs understanding of the spatial and temporal variability of soil physical, chemical, and biological properties (fig. 2) that affect the intended objective function of a soil (Corwin et al., 2006). The first step in assessing soil quality is to determine the management goals and to identify the soil functions that involved in achieving them. As mentioned before, in agriculture (our case) we focus more on food production, thus functions such as providing a suitable plant substrate and serving as a nutrient reservoir are important. The next step is determining and selecting soil properties influencing those functions, to be measured depending on location and soil type (Wienhold et al., 2005b). These selected soil properties will provide a minimum data set (MDS) (Gregorich et al., 1994, Doran and Safely, 1997, Andrews et al., 2004) and they might be aggregated to provide an overall index of soil quality (Burns et al., 2006).
Fig. 2. Schematic presentation of the soil quality definition and its functions, along with examples of indicator properties that can be used to measure the quality of a soil.
Source: Lesley Dampier, Faculty of Land and Food Systems, the University of British Columbia
A one-time measure of these soil properties can help us to ascertain the properties that are outside an acceptable range. When soil properties show an unacceptable value, suitable management practices can be used to improve them and restore the associated soil function. In case we have multiple management systems on a single soil type, this one-time measurement of soil properties can be used to determine the effect of different managements on soil quality (comparative soil quality assessment). Another effective way of assessing soil quality is dynamic assessment (Larson et al., 1994) which means "assessing management effects on soil properties after collection of data from a system over time". To ascertain changes in soil quality under a given land use and management system it is important to monitor changes in the key soil quality indicators (SQI) over time. This monitoring can determine if quality of a soil is improving, stable or declining. Collection of repeat indicator measurements allows determination of how spatial and temporal variability of soil properties can influence soil quality. It is with this knowledge that it can be adequately determined if the soil quality is changing due to natural variation or anthropogenic activities.
Effective soil resource management requires spatial information about key soil properties which affect soil functions. Determination of clay content is one of the most important requirements that will help farmers to manage their soil resources effectively (Triantafilis and Lesch, 2005). Clay content greatly affects soil physical and chemical properties including hydraulic properties and cation exchange capacity and hence influences soil fertility and productivity. Clay content in lower depths of a soil also affects soil permeability (Triantafilis and Lesch, 2005). Then, it would be of great benefit if we were able to map the spatial variation of clay content in 3 dimensions.
Research is needed to develop an effective methodology or framework for soil quality assessment under emerging regulatory requirements. This allowing the scale of application of soil quality assessment methods to be determined (Bone et al., 2010). Bone et al. (2010) also believe that there is an ongoing need to establish linkages between soil indicators, effectively allowing further development of pedotransfer functions.
Overall Aims of the project
To quantify the effects of organic matter application and intensive tillage and traffic on soil quality.
To quantify the effect of cover crops, residue management and tillage operations on soil structure and soil quality indicators.
To determine which indicators are the most important in soil quality assessment.
To develop a practical multi-sensor approach for detailed 3D mapping of Clay in fields as one of the most important soil quality indicators in soil.
My project consists of 3 sections:
First section:
The effects of organic matter application and intensive tillage and traffic on some soil structural properties
Introduction
Modern mechanized agriculture is characterized by large inputs of mechanical energy to soil from traffic and tillage operations. When applied as kinetic energy by power-harrows, mechanical energy fractures the soil structural elements and in addition disperses soil colloids (e.g. Watts et al., 1996). Dispersed clay may cement on soil surfaces (on the topsoil or on inner surfaces of aggregates) and hence affect soil friability (e.g. Schjønning et al., 2012). Traffic by heavy machinery will induce compaction as well as shear failure. The reaction of soil to compaction is known to be influenced by organic carbon (OC). Soane et al. (1980), review results indicated that organic carbon decreased the maximum impact of compaction at the most sensitive water content. More recently, Schjønning et al. (2007) showed that management-derived increases in OC boosted the resilience of soil to compaction. The purpose of this study was to evaluate the effects of mechanical inputs to soil with different levels of organic carbon.
Material and methods
The field experiment was carried out in Denmark at Research Centre Foulum. The soil is a coarse sandy loam (Typic Hapludult) with 82 g clay (<2 µm), 116 g silt (2-20 µm) and 776 g sand (20 µm - 2 mm) â„ kg soil. The basic field experiment, initiated 13 years prior to sampling, includes four adjoining fields in a cash-crop rotation including winter wheat (Triticum aestivum L.). Different organic matter management strategies were applied: fertilization with slurried manure, mineral fertilizer and straw incorporation (treatment ORG), or fertilization with only mineral fertilizers and with all crop residues removed (treatment MIN). The main plots with MIN and ORG treatments were replicated three times in a randomized block design. The soil was rotovated (treatment ROT), compacted (treatment PAC) or left undisturbed (treatment REF) as split-plot treatments in the main plots with organic matter management. The mechanical treatments took place immediately after each mouldboard ploughing operation in a two-year period prior to sampling. Treatment PAC included wheel-by-wheel traffic by a small tractor with narrow tyres, while treatment ROT involved a Howard RotoLabour tine cultivator that operated to a depth of ca. 0.1 m in the soil and with a high ratio between rotovation speed and tractor driving speed. The six combinations of treatments are labelled MIN-REF, MIN-ROT, MIN-PAC, ORG-REF, ORG-ROT and ORG-PAC.
In the spring of two consecutive years 13-14 years after start of the experiment, we sampled cubes of soil from the 6-13 cm layer in the field grown with winter wheat. The cubes were taken to the laboratory and stored at 2oC until analyses could take place. In the field, a drop-shatter test was performed as described by Schjønning et al. (2002), and soil fragmentation quantified as the mean weight diameter (MWD) of the aggregate size distribution (figures 3).
Fig. 3. Determination of aggregate size distribution after a drop shatter test for soil taken from 10-20 cm depth (Schjønning et al., 2002).
In the laboratory, subsamples from the soil cubes were taken to a Yoder-type measurement of wet aggregate stability and a measurement of clay dispersibility as described by Schjønning et al. (2002).Total organic carbon was analysed by a LECO carbon analyzer following tests for carbonates. The fumigation-extraction method (Vance et al., 1987) as described in detail by Schjønning et al. (2007), was used to measure microbial biomass. Hot-water extractable Polysaccharide-C was determined by following in principle the method of Ball et al. (1996).
Results
The organic treatment with no mechanical energy input (ORG-REF) gave rise to the highest friability (least MWD; Figure 4). Soil compaction (PAC) reduced soil friability in the MIN as well as in the ORG treatments, but most pronounced for the MIN soil. Rotovation (ROT) increased the MWD and hence decreased soil friability for the MIN as well as the ORG soil but significantly only for the latter.
Fig. 4.Effects of different management systems on the Mean Weight Diameter (MWD) determined from the size distribution of aggregates following a drop shatter test. Bars labeled by identical letters are not significantly different (P=0.05).
Thirteen years of amendment with animal slurry and plant residues (ORG) increased aggregate stability (trend with P~0.12) and decreased clay dispersion compared to non-organic soil (MIN) (Table 1). Both mechanical treatments (PAC and ROT) increased significantly the clay dispersion as compared to the REF treatment, meaning that clay dispersibility is highly sensitive to mechanical energy input, which is in accordance with Watts et al. (1996). Also the stability of macroaggregates to mechanical breakdown was reduced by the mechanical treatments, but only significant for the ROT treatment. We interpret this as a puddling effect of the kinetic energy applied in the rotovation process; apparently this kind of energy is more injurious for soil aggregate stability.
Long-term application of organic matter increased significantly organic C and polysaccharide-C in both whole soil and macroaggregates as compared to MIN treatment.
Microbial C was not significantly affected by organic matter application as compared to non-organic treatment (MIN).
Generally, the mechanical treatments did not affect C fractions in table 2. An exception was the effect of compaction treatment (PAC) on aggregate organic C, which had a significant effect as compared to REF (if we compare it at significant level of 10 percent).
Table 1.Treatment effects on some soil properties including water stable aggregates, clay dispersion, organic C, aggregate organic C, soil polysaccharide, aggregate polysaccharide and microbial C. Numbers followed by identical letters are not significantly different (P=0.05).
Soil properties
Organic Treatments
Mechanical Treatments
MIN
ORG
REF
ROT
PAC
Water stable aggregates mg aggr. g-1 soil
538a
593a
589a
541b
566ab
Clay Dispersion mg clay g-1 soil
5.27a
4.40b
4.55b
4.90a
5.06a
Soil Organic C, g.kg-1 soil
16.0b
17.0a
16.5a
16.5a
16.6a
Aggregate Organic C,
g.kg-1 aggregate
17.1b
18.7a
17.6B
17.8AB
18.3A
Soil polysaccharide C,
mg.g-1 soil
0.169b
0.186a
0.179a
0.178a
0.175a
Aggregate polysaccharide C,
mg.g-1 soil
0.164b
0.178a
0.169a
0.173a
0.172a
Microbial C, mg.g-1
0.206a
0.224a
0.204a
0.227a
0.214a
Table 2.Geometric mean values of tensile strength, Y and specific rupture energy, Esp. Values followed by the same letter for a given aggregate size and pressure are not significantly different at the P<0.05 level.
Aggregate
Size, mm
Organic Treatments
Mechanical Treatments
MIN
ORG
REF
ROT
PAC
Y, kpa
1-2
81.4a
87.9a
82a
85.6a
86.2a
2-4
54.0a
56.3a
57.6a
63.0a
51.6a
4-8
37.3a
38.4a
37.0b
41.8a
35.1b
8-16
23.3a
24.1a
22.9a
24.2a
24.0a
Mean
44.2a
46.2a
44.5a
44.2a
44.0a
Esp. Jkg-1
1-2
1.39a
1.53a
1.38a
1.54a
1.46a
2-4
0.71a
0.79a
0.78a
0.77a
0.69a
4-8
0.35a
0.40a
0.37b
0.44a
0.33b
8-16
0.25a
0.30a
0.25a
0.29a
0.28a
Mean
0.62a
0.69a
0.64a
0.70a
0.63a
There was no effect of organic matter treatments on the aggregate tensile strength and rupture energy of aggregates. However there was a tendency (trend with P~0.092) of higher aggregate rupture energy in organic treated soil compare to mineral treated soil for aggregate size4-8 mm.
The effect of mechanical treatments on aggregate tensile strength and rupture energy was significantly different only for aggregate size 4-8 mm and the ROT-treated soil had significantly higher tensile strength and rupture energy among mechanical treatments.
Conclusions
The friability of the organic soil was less affected by soil compaction than the soil dressed only with mineral fertilizers
Thirteen-fourteen years of amendment with organic manure and incorporation of straw increased macro-aggregate stability and decreased clay dispersion
Soil compaction and rotovation decreased macro-aggregate stability and increased clay dispersion
Long-term application of soil organic matter enhanced all carbon fractions of soil although not statistically different for microbial C
Rotovation had the capacity to enhance aggregate tensile strength in all aggregate size classes.
Our results indicate that soil organic matter may help soils cope with the detrimental effects of traffic and tillage
Second section:
The effect of different tillage and cover crops on soil quality
Optimized use of cover crops can improve soil structure and carbon retention and thereby reduce the need for intensive tillage. Using conservation tillage such as direct drilling (no tillage) is universally accepted as a way of protecting soil against structural degradation and erosion (Reeves et al., 2005, Hargrove, 1991). Decreasing soil disturbance and utilising cover crops and crop rotation will reverse the process of soil degradation and improve soil quality (Motta et al., 2007). Using cover crop enables soil to compensate for lack of crop residues and C input under long-term conditions and hence, increase crop yield (Motta et al., 2007).
This study examines the effect of different tillage treatments and cover crop on soil physical, chemical and biological properties of a sandy loam soil in a long-term field trial.
Material and methods
The field experiment was carried out on a long term tillage and rotation trial at Foulum research center, Denmark. The soil is a Mollic Luvisol according to the WRB (FAO) system (Krogh and Greve, 1999) and in the 0-25 cm depth has 9% clay (<2 µm), 13% silt (2-20 µm), 44% fine sand (20-200 µm) and 31% coarse sand (200-2000 µm) and 3.1% organic matter (Munkholm et al., 2008)
The experiment was a split-plot in three replications with two factors: tillage as main plot and cover crop as subplots (figure 5). The tillage systems included in this study were direct drilling (D), harrowing to a depth of 8-10 cm (H), and ploughing to a depth of 20 cm (P). A tine drill was used in the H and D treatments and a traditional mouldboard was used in the P treatment. Each tillage plot consisted of two 3 m wide tillage widths of 72.2 m length. The gross area of each sub-plot was 13.7*3 m. Paired subplots with fodder radish as cover crop (+CC) or left without cover crop (-CC) were used for this study. The main crop was spring barley (Hordeum Vulgar L.) (Petersen et al., 2011). Fodder radish was sown every year during the experimental period (2007-2011) in spring barley by surface broadcasting of seeds, two weeks before harvesting of spring barley.
Fig.5.Field plan for experimental design (page 1).
Fig. 5.Field plan for experimental design ( page 2).
Soil measurements:
In the spring of 2012 we carried out an extensive sampling and in field measurement campaign (900 samples and measurements according to table 3) to investigate the effect of prescribed management strategies on soil quality indicators including: total soil organic C, microbial biomass C, hot water extractable C, soil fragmentation, tensile strength, aggregate stability, clay dispersibility, unsaturated hydraulic conductivity, penetration resistance and visual assessment of soil structure. The samples were taken to the laboratory and stored at 2oC until analyses could take place.
In the field, a drop-shatter test was performed as described by Schjønning et al. (2002), and soil fragmentation quantified as the mean weight diameter (MWD) of the aggregate size distribution (figure 3).
Unsaturated hydraulic conductivity measured using a tension infiltrometer (figure 6).
Table 3. Sampling plan, according that sampling carried out.
Parameter
Sampling
Depth (cm)
Sampling points
per plot
(18 plots)
Samples
Per
point
Number
Of
samples
Bulk density
4-8,12-16,18-27
2
3
324
Retention curve
4-8,12-16,18-27
2
3
Air permeability
4-8,12-16,18-27
2
3
Clay Dispersibility
0-10,10-20
2
2
144
Aggregate stability
0-10,10-20
2
2
Microbial biomass
0-10,10-20
2
2
OM%,
0-10,10-20
2
2
36
Soil pH
0-10,10-20
2
2
Total N
0-10,10-20
2
2
Available P
0-10,10-20
2
2
Available K
0-10,10-20
2
2
Soil friability
(tensile strength)
0-10,10-20
2
1
72
Visual evaluation
0-10
2
1
36
MWD
10-20
2
2
72
Infiltration rate
0-40
2
1
36
Soil penetration
resistance
0-40
10
180
Sum
900
Fig. 6.Measuring unsaturated hydraulic conductivity at -4 hPa in field (Sauer et al., 1990)
Assessing topsoil structural quality in field performed using a visual method according to Ball et al. (2007) (figure 7).
Fig. 7.Assessing topsoil structural quality in field using a visual method, before (left) and after (right) breaking dawn sample
For CT scanning 18 topsoil samples (H=20 cm, Ø=20 cm) (Fig.5) were scanned using a medical CT scanner (Aarhus University Hospital) at 120 KeV with a voxel size: 0.43Ã-0.43Ã-0.60 mm. layer. A volume of interest (VOI=6760 cm3) was cropped in the CT scan image. Scanned greyscale data of VOI were segmented using global thresholding method (Otsu Algorithm) to separate solid and pore phases (figure 8).
Fig. 8.Soil sampling by pushing the core into the soil and CT scanning using a Medical CT scanner.
Penetration resistance
Soil penetration resistance was measured to a depth of 60 cm using an automated cone penetrometer (Olsen, 1988). The measurements were performed at field capacity soil water content. Ten measurements were performed in each plot (figure 9).
Fig. 9.Measuring soil penetration resistance using an automated cone penetrometer.
In the laboratory, the fumigation-extraction method (Vance et al., 1987) as described in detail by Schjønning et al. (2007), was used to measure microbial biomass.
We are busy in the laboratory to measure other indicators.
Statistical analysis
The unsaturated hydraulic conductivity best fitted by a lognormal distribution and transformed to achieve normality. The other data were best fitted by a normal distribution. Averages were calculated for each plot and used in the calculation of mean and standard error. The averages were also used as input in mixed models to test for treatment effects. We used the Mixed procedure of the statistical software SAS version 9.2 (SAS Institute Inc., 2009)
Results so far
Drop shatter
The effect of tillage treatments on soil friability was significant (figure 7). Ploughing (P) has the least MWD (best friability) amongst other tillage treatments. There was no significant difference between D and H treatments in this experiment. There was no significant difference in soil friability for the cover crop effects. The interaction between cover crop and tillage treatments was significant, meaning that cover crop has a positive effect on direct drilling (D) and yields significantly lower MWD (better friability).
Visual soil structure assessment
In general there was no significant difference between tillage and cover crop treatments in visual soil assessment, but a tendency for ploughing to be lower (better soil quality) was found (p~0.067). The interaction between tillage and cover crop was almost significant (p~0.097). Statistical analysis shows a good interaction between cover crop and ploughing, meaning that ploughing plus cover crop can enhance soil quality.
Unsaturated hydraulic conductivity
The effect of treatments on unsaturated hydraulic conductivity of soil (Kunsat.) was not significant. However lower Kunsat in harrowing treatment (H) compare to other treatments is highlighted (51.3 cm day-1 for H, 58.2 for D and 94.7 for P).'
Fig. 10.Effects of different management systems on the Mean Weight Diameter (MWD) determined from the size distribution of aggregates following a drop shatter test. Bars labelled by identical letters are not significantly different (P=0.05).
Table 4. Treatment effects on unsaturated hydraulic conductivity and Visual soil evaluation of soil quality. Numbers followed by identical letters are not significantly different (P=0.05).
Soil property
Tillage treatments
Cover crop treatments
D
H
P
+CC
-CC
Kunsat.at -4kpa, cm.day-1
58.2a
51.3a
94.7a
65.8a
65.5a
Visual evaluation scores
2.29a
2.06a
1.82a
2.08a
2.01a
X-ray CT scanning
The results have not analysed yet. Examples of 3D images are shown in Figure 10.
P-CC
P+CC
H-CC
H+CC
D-CC
D+CC
Figure 11: X-ray CT scanned image of soil pores.
In red, the connected pore and in purple, the unconnected pores
Conclusion so far
Drop shatter
Ploughing (P) had the least MWD (best friability) amongst other tillage treatments.
Significant interaction between cover crop and tillage treatments, i.e., cover crop had a positive effect on direct drilling (D).
Visual evaluation
No significant different between tillage and cover crop treatments. A tendency for a better structural quality for P than for D (lower scores) (p~0.067).
The interaction between tillage and cover crop was almost significant (p~0.097), indicating a positive effect of ploughing plus cover crop.
Unsaturated hydraulic conductivity
The effect of treatments on unsaturated hydraulic conductivity of soil was not significant. However, there was a tendency to lower Kunsat for H and D than for P.
Conclusion
We conclude that P improved soil quality compared to H and D, especially when combined with cover crop. We also conclude that D may benefit from cover crop to yield better soil friability and hence soil quality.
Third section:
3D-Mapping of clay content in a soil profile
Effective soil resource management requires spatial information about key soil properties which affect soil functions. Determination of clay content is one of the most important requirements that will help farmers to manage their soil resources effectively (Triantafilis and Lesch, 2005). Clay content greatly affects soil physical and chemical properties including hydraulic properties and cation exchange capacity and hence influences soil fertility and productivity. Clay content in lower depths of a soil also affects soil permeability (Triantafilis and Lesch, 2005).
Intensive grid sampling is generally accepted as the most appropriate ways of mapping soil attributes in detail. Regular sampling campaigns are labor intensive and time consuming and thus impractical at farm scale. Therefore, it is recommended to find other, more rapid and cost effective ways of obtaining information for detailed soil mapping (Serrano et al., 2010).
Soil sensors like (DUALEM 21S) provide rapid and cheap information for mapping soil clay contents using Electromagnetic induction (EMI) technique. Electromagnetic induction (EMI) instruments measures soil electrical conductivity as an average for a specific soil depth (Saey et al., 2011) by inducing an electric current in the soil. This electrical conductivity called apparent electrical conductivity (ECa) and influenced by a combination of soil properties including soil water, soluble salts, clay content (Llewellyn et al., 2008) and clay mineralogy (Triantafilis et al., 2002).
"DUALEM (DUAL-geometry Electromagnetic) multi-separation geo-conductivity sensor simultaneously measure terrain-conductivity to four or six distinct depths of exploration" (DUALEM-21S User's Manual, 2007).
In this study the spatial relationship between clay content and EMI data to a depth of 2.5 meters will be investigated.
Material and methods
The DUALEM 21S soil sensor (DUALEM Inc., Milton, Canada) was employed in a field of about 5 ha size in Vindum, Viborg, in Denmark. Soil ECa data obtained with a high spatial resolution. The sensor pulled behind a small tractor at about 15 km h-1 crossing the field at 4 m apart parallel lines. Ordinary point kriging was used as an interpolation method to obtain estimates of the ECa at unsampled locations.
For calibration of sensor data we will use 15 representative samples in the field to a depth of 2.5 meters. Latin Hypercube sampling was used to find the proper place of points for measuring EC and sampling 15 representative points for clay determination. These points will be used as validation points. DUALEM data were inverted to find ECa in separate depths using an excel sheet from DUALEM company.
Results and discussion
Preliminary results showed that there is more than 50 percent correlation (R2=52.53) between clay content and DUALEM 21S data.
We think high spatial variability in the field is the reason for this low correlation. We will try to find the correlation between clay content and EMI measurement in the subsoil.
Fig. 12. Correlation between clay content and ECa from Dualem 21S in top soil (0-75 cm).
Plan for remaining study
Lab analysis of physical, chemical and biological soil properties mentioned in method part, from the field samplings
So far, I have finished measuring clay dispersibility, aggregate stability, microbial biomass, MWD, infiltration rate, tensile strength and soil penetration resistance. Other measurements - including retention curve, air permeability, gas diffusivity and bulk density will be finished within a couple of months An overview of the measurements on the soil cores is listed below.
Table 5.Plan for measurement of some physical soil properties in the lab
Soil property
soil matric potential hPa or cm
-10
-30
-100
-300
-1000
Small cores
Water content
x
x
x
x
x
Air permeability
x
x
x
Gas diffusivity
x
Large cores
Water content
x
x
x
Air permeability
x
x
Other sample have been sent to Germany for measuring soil pH, total N, available P, available K and we still waiting for results.
Data Inversion and data gathering for clay mapping
Regarding the clay mapping part of my work we have planned a collaborative work with Geophysics department of Aarhus University to work more on data inversion. This will enable me to get a better estimation of soil Electrical conductivity in different depths of soil and based on this data inversion we hope to get a better prediction of clay content.
In addition to this I would be using an existing data from Near-infra red spectroscopy to predict clay content based on models and wave lengths gathered from scanned samples in the same field we are working now. This work is done in cooperation with other PhD students linked to the CARBODYNE project
More field works including a new survey with DUALEM 21S and sampling to measure the clay content in different depths might be necessary to get a better over view of the field properties.
International stay at USDA for a 5-month period, working on soil quality modeling and assessment.
I am going to work with Douglas Karlen to spend some time on an overall analysis of management effects on soil quality based on some results from my PhD project and some existing datasets in USDA. "Doug Karlen is a Supervisory Soil Scientist and Research Leader with the USDA-Agricultural Research Service (ARS) at the National Laboratory for Agriculture and the Environment (NLAE) in Ames, IA. He also leads the ARS Renewable Energy Assessment Project (REAP), a multi-location team focused on sustainable harvest of crop residues"(IOWA State University, 2012). During this stay I wish to conduct Estimation and evaluation of a soil quality index and/or a management index (threshold) calculated from a minimum data set (MDS) using Danish and international datasets.
Another possibility could be comparing some of soil quality indices in different field management systems in Denmark and USA to explore the relationships (pedo-transfer functions) between soil properties (indicators) obtained after long term application of different treatments and different field management systems.
Writing papers and dissemination
I have planned to write 4 papers during my PhD period. First manuscript (provisional title): The effects of organic matter application and intensive tillage and traffic on soil quality (Soil & tillage research). Regarding this paper I have finished all data analysis. I have also written some parts of the paper according to provided results. This paper will be submitted by the end of this year.
Second manuscript (provisional title): 3D mapping of clay content in the field scale, using soil sensors (Computers and Electronics in Agriculture).
Third manuscript (provisional title): The effects of cover crops on important soil physical properties (Soil Use and Management).
Fourth manuscript (provisional title): Comparing some of soil quality indices in different field management systems in Denmark and USA (European Journal of Soil Science).
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Disseminations so far
I presented a poster in NJF-448 seminar in Finland, Helsinki, 6-8 March 2012.
A poster also submitted to ASA congress in USA, this poster was accepted and will be presented on 22nd of October 2012 in Cincinnati.
The name and contents of two posters are as below respectively:
The effects of organic matter application and intensive tillage and traffic on some soil structural properties
L. Abdollahi, L.J. Munkholm and P. Schjønning
Aarhus University, Department of Agroecology, Denmark
Introduction
Modern mechanized agriculture is characterized by large inputs of mechanical energy to soil from traffic and tillage operations. When applied as kinetic energy by power-harrows, mechanical energy fractures the soil structural elements and in addition disperses soil colloids (e.g. Watts et al., 1996). Dispersed clay may cement on soil surfaces (on the topsoil or on inner surfaces of aggregates) and hence affect soil friability (e.g. Schjønning et al., 2012). Traffic by heavy machinery will induce vertical as well as shear strain. The reaction of soil to compaction is known to be influenced by organic carbon (OC). Soane et al. (1981) reviewed results indicating that OC decreased the maximum impact of compaction at the most sensitive water content. More recently, Schjønning et al. (2007) showed that management-derived increases in OC boosted the resilience of soil to compaction. The purpose of this study is to evaluate the effects of mechanical inputs to soil with different levels of OC.
Materials and Methods
The field experiment was carried out in Denmark at Research Centre Foulum. The soil is a coarse sandy loam (Typic Hapludult) with 82 g clay (<2 µm), 116 g silt (2-20 µm) and 776 g sand (20 µm - 2 mm) â„ kg soil. The basic field experiment, initiated 13 years prior to sampling, includes four adjoining fields in a cash-crop rotation including winter wheat (Triticum aestivum L.). Different organic matter management strategies were applied: fertilization with slurried manure, mineral fertilizer and straw incorporation (treatment ORG), or fertilization with only mineral fertilizers and with all crop residues removed (treatment MIN). The main plots with MIN and ORG treatments were replicated three times in a randomized block design. The soil was rotovated (treatment ROT), compacted (treatment PAC) or left undisturbed (treatment REF) as split-plot treatments in the main plots with organic matter management. The mechanical treatments took place immediately after each mouldboard ploughing operation in a two-year period prior to sampling. Treatment PAC included wheel-by-wheel traffic by a small tractor with narrow tires, while treatment ROT involved a Howard RotoLabour tine cultivator that operated to a depth of ca. 0.1 m in the soil and with a high ratio between rotovation speed and tractor driving speed. The six combinations of treatments are labeled MIN-REF, MIN-ROT, MIN-PAC, ORG-REF, ORG-ROT and ORG-PAC.
In the spring of two consecutive years 13-14 years after start of the experiment, we sampled cubes of soil from the 6-13 cm layer in the field grown with winter wheat. The cubes were taken to the laboratory and stored at 2oC until analyses could take place. In the field, a drop-shatter test was performed as described by Schjønning et al. (2002), and soil fragmentation quantified as the mean weight diameter (MWD) of the aggregate size distribution. In the laboratory, subsamples from the soil cubes were taken to a Yoder-type measurement of wet aggregate stability and a measurement of clay dispersibility as described by Schjønning et al. (2002)
Results and discussion
The organic treatment with no mechanical energy input (ORG-REF) gave rise to the highest friability (least MWD; Figure 1). Soil compaction (PAC) reduced soil friability in the MIN as well as in the ORG treatments, but most pronounced for the MIN soil. Rotovation (ROT) increased the MWD and hence decreased soil friability for the MIN as well as the ORG soil but significantly only for the latter.
Thirteen years of amendment with animal slurry and plant residues (ORG) increased aggregate stability (trend with P~0.12) and decreased clay dispersion compared to non-organic soil (MIN)(Table 1). Both mechanical treatments (PAC and ROT) increased significantly the clay dispersion as compared to the REF treatment, meaning that clay dispersibility is highly sensitive to mechanical energy input, which is in accordance with Watts et al. (1996). Also the stability of macroaggregates to mechanical breakdown was reduced by the mechanical treatments, but only significant for the ROT treatment. We interpret this as a puddling effect of the kinetic energy applied in the rotovation process; appa-rently this kind of energy is more injurious for soil aggregate stability.
Figure 1. Effects of different management systems on the Mean Weight Diameter (MWD) determined from the size distribution of aggregates following a drop shatter test. Bars labeled by identical letters are not significantly different (P=0.05).
Table 1. Treatment effects on water stable aggregates and clay dispersion.
Numbers followed by identical letters are not significantly different (P=0.05).
Organic
Treatments
Mechanical
Treatments
MIN
ORG
REF
ROT
PAC
Water stable aggregates
mg aggr. g-1 soil
538a
593a
589a
541b
566ab
Clay Dispersion
mg clay g-1 soil
5.27a
4.40b
4.55b
4.90a
5.06a
Conclusions
The friability of the organic soil was less affected by soil compaction than the soil dressed only with mineral fertilizers
Thirteen-fourteen years of amendment with organic manure and incorporation of straw increased macro-aggregate stability and decreased clay dispersion
Soil compaction and rotovation decreased macro-aggregate stability and increased clay dispersion
Our results indicate that soil organic matter may help soils cope with the detrimental effects of traffic and tillage
