Final Report

Improving Fertilizers Management through the understanding of organic matter quality
David Arguello Jacome
Registration Number: 840601018140
MSc. Internship report (SOQ-70424)
Department of Soil Quality, Wageningen University, the Netherlands.
Supervisors: Gerard Ros PhD. and Karst Brolsma PhD.
Examiner: Prof. Rob Comans PhD.
August 2015

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Table of Contents
Abstract..........................................................................................................................................................i
1. Introduction ..........................................................................................................................................1
2. General Objective: ................................................................................................................................3
2.1. Specific objectives:........................................................................................................................3
3. Research Questions ..............................................................................................................................3
4. Hypotheses ...........................................................................................................................................3
5. Literature Review..................................................................................................................................3
6. Materials and Methods.........................................................................................................................5
6.1. Pot experiment .............................................................................................................................5
7. Results...................................................................................................................................................8
7.1. Pot Experiment .............................................................................................................................8
7.1.1. Grass Samples Results...........................................................................................................9
7.1.2. Soil Sample Results .............................................................................................................12
8. Discussions..........................................................................................................................................17
9. Conclusions .........................................................................................................................................19
References ..................................................................................................................................................19
Annexes.......................................................................................................................................................24

i
Abstract
Soil fertility has been defined as the capacity of a soil to provide the best growth conditions for a crop. These
growth conditions are related to nutrient availability, organic matter quality, water holding capacity, among
other. It is known that soil organic matter plays an important role in soils because it affects the soil chemically,
physically and biologically, but changes of total carbon in soil are slow. Many indicators have been developed
to estimate and characterize the quality of soil organic matter and organic residues. However, most of these
indicators are time consuming, expensive to measure and they cannot predict the effect on soil total carbon.
Conversely, there are indicators that are easy to measure like the hot water carbon. This indicator has the
capacity to estimate the effect of management practices on the soil total carbon. But what do we do after
these indicators have been measured? How farmers should address the fertilization of soils with different levels
of fertility (as organic matter content)? To answer this questions a pot experiment with two soils with different
organic matter content, three types of fertilizer (inorganic, slurry and digestate) and three dose of application
(50%, 100% and 150%) was carried out. The results showed that application of organic fertilizers to soils with
high organic matter levels lead to a higher production of biomass (fresh and dry). Additionally, HWC was
affected by the type of fertilizer and dose of application, this corroborates the capacity of HWC to predict
changes in soil organic matter. We conclude that a differentiated application of organic residues in soil with
different organic matter contents would be the best option in practice. Soils with low OM content would need
a higher application of organic residues to increase the amount of SOM in the long term.
Keywords: Organic matter quality, Organic fertilizers, Hot water carbon, Potentially Mineralizable Nitrogen.

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1. Introduction
Soil organic matter is the fraction of the soil that consists of plant or animal tissue in various stages of
decomposition (Murphy, 2014). Soil organic matter is one of the most important components of soil because
it affects the physics, chemistry and biology of soils (Stevenson, 1982). The content of organic matter in the
top 15 cm varies between 1-5% in most agricultural soils (Schnitzer, 1991). Even when present at low
quantities, organic matter plays an important role. Several researches have shown that OM enhance aggregate
stability, improves water holding capacity, increase the CEC of soils and other soil properties which are related
to soil fertility (Murphy, 2014).
A definition of soil fertility can be the soil’s ability to offer the required conditions for optimal plant growth.
(Stockdale, et al., 2002). This ability is the result of physical, chemical and biological processes acting together
to provide nutrients, water, aeration and stability to the plant and at the same time avoid contaminants that
may inhibit plant growth (Stockdale, et al., 2002). In this research, we will focus on the definition of soil
fertility as the ability of soils to provide nutrients for plant growth.
It is important to highlight that the quality of organic matter affects how it behaves in the soil. It has been
suggested that organic matter could be classified into three categories: Active organic matter (plant residues
and living microbial biomass), slow organic matter (somewhere in between active and stable organic matter
that consists primarily of detritus, partially broken down cells and tissues that are only gradually decomposing)
and stable organic (Fenton, et al., 2008). The first two categories contribute to soil fertility because during its
decomposition several nutrients are released (Mohammadi, et al., 2011). The stable organic matter, also
known as humus, influences in a minimum degree the soil fertility. However, it is important for soil fertility
management because it determines soil structure and cation exchange capacity (Murphy, 2014).
The importance of measuring soil organic matter lies in the role that OM plays on soil fertility. However,
changes in the total content of soil organic matter could be very slow (Ghani, et al., 2002). In order to estimate
the quality of soil organic matter, several techniques and indicators have been developed which focus on
different fractions of organic matter like the soil microbial carbon. Many of these techniques are expensive
and time consuming, therefore they have not been adopted as a standard procedure to assess soil fertility.
Nevertheless, there are some techniques that are less demanding of time and economic resources. Among
these techniques are the hot water carbon extraction, bacterial fungal ratio and potentially mineralizable
nitrogen.
Hot water carbon extraction (HWCE) is an easy method to estimate the readily mineralizable fraction of total
carbon (Ct) in arable soils (Chodak, et al., 2003, Ghani, et al., 2003). The hot water extractable carbon has
been considered as a heterogeneous pool (Hamkalo and Bedernichek, 2014), because it contains elements
from vegetative cells of microorganism which are destroyed by the high temperature (≥80O
C) and also non
microbial-organic substances. The HWCE gives a comparable estimate of microbial activity as the fumigation-
extraction techniques. In an experiment performed by Sparling, et al. (1998) they found that the HWCE
method extracted 43% of the microbial carbon which is similar to the 40-45% obtained with the fumigation
extraction technique.
Potentially Mineralizable Nitrogen (PMN) is an indicator that measures the amount of nitrogen that mineralizes
under constant and optimum conditions. It can be used to estimate the amount of N that should be added to
fulfil the crop requirements (Cabrera and Kissel, 1988). It is often measured to judge the capacity of soil
organic matter to supply inorganic N, in particular nitrate (Gregorich, et al., 1994). Additionally, the correct
application of N avoids losses of fertilizers and reduce production costs.
Besides, the “green revolution” in the 1940’s changes the way our food was produced. Many inorganic
fertilizers and pesticides were introduced in agriculture. Farmers were able to produce up to three or four
times more food. However, the ecosystem and society suffered an invisible damage (Sala and Bocchi, 2014).
The balance in the ecosystem was disturbed and our food production system became dependent of chemical
inputs. Nowadays, the use of organic fertilizers (manures, slurries, digestate, compost, etc.) are the new

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trend. Many studies have shown that organic fertilizers have the capacity to supply many of the nutrients that
are needed for crop production (Abubaker, et al., 2012, Cavanagh, et al., 2011, Fouda, et al., 2013).
Among the different option of organic fertilizers, there are few that have been accepted for most farmers
around the world. Slurry of many types (cow, pig, etc.) has been used as a source of nutrients for plant
production. However, non-regulated applications have been related to an increase in the amount of nitrogen
and phosphorus leached to ground waters (Cavanagh, et al., 2011, Triolo, et al., 2013). Digestates are another
option of organic fertilizer. The production of biogas is based on anaerobic digestion of organic residues. The
by-products of this process are called digestates. (Al Seadi and Lukehurst, 2012, Holm-Nielsen, et al., 2009,
Lukehurst, et al., 2010, Schievano, et al., 2009). These digestates have different properties than the common
organic fertilizers. The content of nitrates tend to be higher and the amount of labile C is lower because it is
consumed during the digestion to produce methane.
Grasse are able to uptake the main forms of nitrogen: ammonia and nitrate (Bowman and Paul, 1988). The
sources for this element could be inorganic or organic fertilizers. The main differences between them
availability of the nutrient for the plant. In the case of inorganic fertilizers, the N is available almost
immediately. On the other hand, organic fertilizers have a slower release of N because they depend of the
mineralization process to convert organic compounds into mineral form of N (Murphy, 2014). The range of N
that grass can uptake is really high, it has been reported that grass can take up to 500 kg N/ha (Aavola, et
al., 2005). It has been proved that Lolium perenne presented a nearly linear response in dry matter production
up to N levels of 500 kg ha-1
(Aavola, et al., 2005). However, higher doses of N should be avoided due to the
excess of this element is susceptible to increase losses to the environment. Aavola and Kärner (2008)
concluded that application rates over the 100 kg/ha should be avoided because approximately 1/3 of the
nutrient will not contribute to the formation of the harvestable part of the biomass.
Additionally, several factors can affect the nitrogen uptake of grass: an increase in the frequency of defoliation
could increase the accumulation of N in Lolium species(Aavola and Kärner, 2008); soil type could affect the
nitrogen uptake when N applied to soils is leached out of the system due to the high mobility of the nutrient
(nitrates) or the low water holding capacity of soils (e.g. sandy); weather could also affect the N uptake due
to the increase of water in the soil could lead to losses by leaching; application timing can also affect the N
uptake of grasses. Cookson (2000) found that on average denitrification and volatilization of N were significant
lower during winter application than autumn or spring application because of conditions which encouraged
denitrification during autumn and volatilization during spring.
Nonetheless, when measurements of organic matter quality has been done there is a general doubt among
farmers: what should we do with these information? All around the world, there are companies specialized in
soil management and they give advice to the farmers but one of the biggest problems in agriculture is the
inherent variability of soils. Soils can differ in textures and fertility (nutrient and organic matter content) within
a relative small area. The question that arises is what should farmers do when within the boundaries of a farm
there are big differences in soil organic matter? Is the most profitable option to fertilize all the area with the
same amount of fertilizers or should they fertilize (distribution of organic manure) only the soils with low levels
of fertility in terms of organic matter? These questions need to be answer in order to increase the effectiveness
of the current nutrient management strategies. This research will focus on understand how farmers should
address the fertilization of soils with different levels of fertility (as organic matter content).
The current investigation is focused on carbon and nitrogen as the main variables to be evaluated. The main
reason to focus on C and N is that these are the most important elements that help to characterize organic
matter quality. Nitrogen is important because it is an essential element for crops and soil microorganisms,
and it has to be provided by the farmers either from inorganic or organic sources. Carbon is taken into account
because it has a narrow relationship with N. Several studies have highlighted the influence of C:N ratios on
the balance between N mineralization and immobilization (Hodge, et al., 2000, Janssen, 1996).

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2. General Objective:
To investigate the effect of organic matter content and quality on the bioavailability of nitrogen in order to
guide farmers in optimizing the use of organic manures and consequently improve the management of
fertilization programs.
2.1. Specific objectives:
 To define the most relevant indicators (biological, physical and chemical) of organic matter quality
through research of scientific literature.
 To denote the principal methods to measure and characterize nitrogen from soil and organic fertilizers
through research of scientific literature.
 To study the effect of organic matter content and quality in the production of biomass of grass (Lolium
perenne L).
 To determine the amount of nitrogen provided by organic matter to the different pools in soil (total,
bioavailable, potentially mineralizable).
 To test the addition of different organic fertilizers and its effects on soil microbial biomass.
 To give a general recommendation about fertilizer strategies (dose, form, timing) when soils differ in
quality.
3. Research Questions
 What are relevant indicators or indexes available reflecting the organic matter quality in soils?
 How variable are these indicators over time and how are they influenced by processes in
rhizosphere?
 What are key variables affecting their size and turnover rates (if applicable)?
 If soils differ in quality/ fertility (soil organic matter in particular) how should farmers account for
this variation?
4. Hypotheses
The application of fertilizers (chemical fertilizers and/or organic manure) in soils with low organic matter
content/quality, where nutrient availability is the main bottleneck for dry matter production, will be more
profitable than the application of the same amount of fertilizers in soils with high organic matter
content/quality because it will be an extra contribution of nutrient for crops and the effect on microbial
community in high/low fertile soils (in terms of organic matter).
5. Literature Review
A literature review was performed in order to answer some of the research question related to indicators of
soil organic matter quality. The criteria used to assess whether the indicator were relevant of not was based
on the replicability, accuracy, cost-efficiency, acceptability in the scientific community and time consuming of
the methodologies. The results of this literature review is a summary of the most relevant indicators reported
by scientific literature. The search criteria was focused on the following key words: SOM quality, SOM
indicators, methodologies for SOM measurements, organic matter fractionation, HWC, PMN, BF ratio and
indicators and methodologies of nitrogen characterization. The papers revised were published between 1980
and 2015.
Below it is presented the results of the literature review. The first part is related to the most relevant indicators
of organic matter quality. These indicators have been classify into three categories: physical, biological and
chemical. This classification is based on the type of reactants used during the extraction or the soil’s portion
that has been measured. The second part of this section is about the methods to measure and characterize
nitrogen in soils and organic amendments.
Indicators of Organic Matter Quality
a) Physical

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Particulate Organic Matter (POM): It has been described as a transitory pool between fresh residues
and stable organic matter. It is important because its short turnover makes it an important source of
C and nutrients through the mineralization process(Haynes, 2005). It is the precursor of other forms
of organic matter (microbial biomass, soluble, non humic, humic). It can be obtained through two
different methods (based on several extractant with different densities) resulting in two different
terms: light fraction organic matter (LF) and sand sized fraction organic matter (SSF). In agricultural
soils, LF typically contains 2-18% of total organic C and 1-16% of total N (Gregorich and Janzen,
1996). On the other hand, SSF in general makes up 20-45% of total organic C and 13-40% of total
N (Bowman, et al., 1999). Two main factors affects the amount of LF and SSF: amount of residue
input (fertilizer input, burning of residues, harvesting and disposal of residues, etc.) and rate of
residue decomposition (irrigation, seasonality).
Dissolved Organic Matter (DOM): it is the organic material that is dissolved in the soil solution. It
behaves as a reactive component of soils solution and can form soluble complexes with multivalent
cations (e.g. heavy metals), thus influencing their bioavailability or movement through the soil profile
(e.g. leaching). It can be subdivided in dissolved organic carbon (DOC) and dissolved organic nitrogen
(DON). DOC normally accounts for 0.05 to 0.40% of the SOC in agricultural soils (Campbell, et al.,
1999). DON accounts for 0.15-0.19% of total N (Haynes, 2000). The effects of management on DOM
has not been well documented (Haynes, 2005). However, several studies have related its variability
to several agricultural practices like addition of crop residues (Graham, et al., 2002), conversion for
conventional to organic management (Lundquist, et al., 1999), reduce of tillage (Haney, et al., 1999).
Hot Water Extractable Carbon (HWEC): It has been suggested that HWC is mainly formed by organic
substances from microbial origin, especially carbohydrates (Redl, et al., 1990). Thus, it is believed
that is involved in aggregates stability. However, it may also originate from root exudates, organic
matter weakly absorbed to soil particles (Haynes, 2005). This fraction can be obtained following two
methods. The first one is boiling the soil sample in distilled water for 60 min (Körschens, et al., 1990).
The second method is soil sample in distilled water for 16 hours at 80OC sequentially after extraction
of CWEC (Ghani, et al., 2003). This indicator is easy to performance and has well reproducible results.
The amount found in agricultural soils is variable and depends on the amount of organic C.
b) Biological
Microbial Biomass: It is the organic material associated with cells of soil living microorganism. Among
the several functions of this indicator are the agent of transformation and cycling of organic materials
and nutrients, formation and decay of humic material, dynamic source and sink of plant nutrients (St
Luce, et al., 2014), etc. This indicator can be subdivided in microbial biomass Carbon (MBC) and
microbial biomass Nitrogen (MBN). They can be determined by two methods 1. Fumigation extraction
or 2. Substrate induce respiration (Gregorich, et al., 1994, Marinari, et al., 2006). Although it is a
smart way to determine the micro-organism amount, it doesn’t determine the enzyme activity.
Average variation of this indicators in agricultural soils are 1-5% of organic C and 1-6% of total N
(Haynes, 2005).
Poly Lipids Fatty Acids (PLFA): This method is used as an alternative to measure microbial biomass
(Frostegård and Bååth, 1996). The principle behind this method is the extraction of lipids from the
soil and later the fatty acids methyl esters are separated and identify using gas chromatography/
mass spectrometry. Several PLFA with a chain length less than 20 C atoms are considered to be
predominantly bacterial origin (Harwood, 2012). On the other hand, Federle (1986) stated that the
PLFA 18:2w6 constituted 43% of the total PLFA of some fungi species. Therefore, the 18:2w6 PLFA
can be used as indicator of the fungal biomass.
Potentially Mineralizable C: It is determined by measuring the CO2 produced during an incubation
time. This CO2 indicates the total metabolic activity of the microorganism that consume C during the

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decomposition of organic matter. PMC can be affected by many other factors like temperature,
moisture content, aeration, sample pre-treatment (Keeney, 1982).
Potentially Mineralizable N: it measures the net flux of N that is released during the process of
mineralization (Haynes, 2005, Ros, et al., 2011). However, mineralization and immobilization process
occurs at the same time. Thus, a portion of the N released during mineralization will be captured by
the microflora. This means that C:N ratio of the available substrate will play a significant role in the
amount of mineralizable N. In the same way as PMC, PMN can be affected by many factors (Bremner
and Mulvaney, 1982).
A broad generalization is that PMC and PMN account for between 1.5-5.0% of the total organic C and
N (Gregorich, et al., 1994, Haynes, 1999, Whalen, et al., 2000).
c) Chemical
Total organic C: This indicator is the sum of organic material (both living and death) present in the
soil excluding living plant material. This indicator can be obtained through wet or dry oxidation
procedures. In the most commonly used wet oxidation method, organic C is oxidized by potassium
dichromate in the presence of sulphuric acid with external heating (Yeomans and Bremner, 1988). In
dry oxidation (combustion) methods, organic C is converted to CO2 by burning the organic matter in
air or O2 in a furnace (Soon and Abboud, 1991). The produced CO2 can be measured by different
methods. Among these methods are (1) Titrating the CO2 adsorbed in NaOH with acid, (2) thermal
conductivity, (3) infrared adsorption measurement techniques. A average value of organic carbon in
soils is 7-60 g C/kg (Haynes, 2005).
Soil Total N: This indicator can also be obtained through wet or dry oxidation procedures. In the last
150 years, two methods have dominated the laboratories: (1) the Kjehldahl method which is a wet-
oxidation procedure and the Dumas method which is a dry combustion method (Bremner and
Mulvaney, 1982, McGill and Figueiredo, 1993) .
C/N ratio: This ratio also provide information of the soil’s capacity to store and recycle energy and
nutrients. Agricultural practices such as cultivation, fertilization and residue management influence
the soil C:N ratio. (Gregorich, et al., 1994). It can be also calculated for organic residues. In this case
the C/N ratio represents the stability of the residue in the soil. High C/N ratio means that it takes
longer time to decompose these sort of residues (e.g. wood, lignin, hemicellulose, etc.).
For this research two main indicators have been selected: Hot water carbon (HWC) and potentially
mineralizable nitrogen (PMN). The reason to select this indicators are the following;
 HWC is one of the simplest methods to measure changes in the labile fraction of soil organic
matter. It has been found a strong linear relationship between HWC and microbial biomass
(Ghani, et al., 2003). Therefore if HWC is mainly form by microbial C, this indicator will tell
us how big the microbial community in the soil and how active it is.
 PMN as explained before will tell us how much N will be mineralize in a period of time.
Therefore this indicator will tell us how much N will be provided by the organic material added
to the soil.
 Both indicators (HWC and PMN) are less expensive and time consuming than other indicators.
6. Materials and Methods
6.1. Pot experiment
Plants

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Plants of Rye Grass (Lolium perenne L.) were grown directly in the pots. A germination layer of quartz sand
was used to avoid any soil effect on the germination of grass. One gram of pure seeds per pot was sown and
then covered with a plastic sheet until germination occurred. After germination, the pots were placed in a
room with semi controlled environmental conditions.
Soils
Two sandy soils with high and low fertility level (expressed in organic matter content) were used. Pots of 1.5
kg were filled up with each soil. Additionally, the pots were weighted with the purpose of determine the amount
of water that will be needed to maintain 60% of the field water holding capacity. The pots were watered every
day with demineralized water.
Treatments
A multifactorial experiment was performed with 3 factors. The factors were: Soil Organic Matter Quantity (high
or low), fertilizer type (Control, Chemical Fertilizer, Slurry and Digestate) and dose of fertilization (High or
150%, Medium or 100% and Low or 50%). A total of 8 treatments was used in this experiment (Table 1). For
the chemical fertilizer treatment three commercial fertilizers were used: KAS (27% N), TSP (45% P2O5) and
Kali60 (60% K2O). Cow manure as digestate and slurry will be used for the other treatments. The nitrogen
availability for slurry was defined as 43% of ammonia and 6% of organic nitrogen. The amount of fertilizers
to be applied (Table 2) was calculated in base to the amount of nutrient available originally in each soil and
the amount of nutrients provided by the organic manure (Table 3). The fertilizers were added to the pots
during the preparation of the soil in order to have a homogeneous distribution of them and to avoid any
damage to roots due to salt concentration.
A mistake was made during the calculation of the amount stock solution to be applied per pot. The amount of
P and K was significantly lower that the amount provided by the organic fertilizers. Five times less P and three
times less K was added (Table1).
In order to obtain statistical relevance, the experiment was done in triplicate. Therefore this experiment had
a total of 48 objects (pots).
Table 1. Amount of fertilizers added per treatment.
Fertilizer Dose
Amount
Applied
[g/pot]
Amount
Applied
[gKAS/pot]
Amount
Applied
[gTSP/pot]
Amount
Applied [g
Kali60/pot]
N applied
[mg/pot]
P
applied
[mg/pot]
K
applied
[mg/pot]
Slurry
50 17.7 n/a n/a n/a 92.07 13.78 107.62
100 35.3 n/a n/a n/a 184.14 27.57 215.24
150 53.0 n/a n/a n/a 276.20 41.35 322.86
Digestate 100 35.3 n/a n/a n/a 168.94 22.27 185.20
Chemical
50 n/a 0.08 0.03 0.18 21.6 5.89 89.66
100 n/a 0.16 0.03 0.18 43.2 5.89 89.66
150 n/a 0.24 0.03 0.18 64.8 5.89 89.66
Measurements
Organic Fertilizer Characteristics
The chemical composition of the organic fertilizers was perform using the Near Infrared Reflectance
Spectroscopy (NIRS). According to several studies this technology is faster than the analytical
methods and its accuracy is quite good (Fan, et al., 2013, Moron and Cozzolino, 2004, St Luce, et al.,
2014).
The main chemical characteristics determined with the NIRS on the digestate and slurry were: Dry
matter, crude protein content, organic matter content, Total Nitrogen, Mineral N, Organic N,
Phosphorus, Potassium, Magnesium and Sodium. Table 2 shows the most important results of this
analysis.

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Table 2. Chemical composition of the organic residues used in the experiment.
Description
Digestate
[g/kg]
Slurry
[g/kg]
Dry matter 63 90
Crude Ash 22 23
Organic Matter 41 67
N-Tot. 4,78 5,21
N-NH3 2,23 2,41
N-org 2,55 2,8
P 0,63 0,78
K 5,24 6,09
Mg 0,6 0,7
Na 0,6 0,6
C/N ratio 4 6
C/N-org ratio 8.03 11.96
Original Soil Properties
The original soils were analysed to determine its original chemical, biological and physical properties. The
variables determined were: soil texture, pH, CEC, organic matter and clay content and macronutrient content
(N-P-K). The amount of bioavailable nutrients was also determined with a 0.01M CaCl2 extraction (N-P-K).
For organic matter determination, the samples were ashed for two hours at 500O
C and the percent of weight
loss was calculated. The percent loss on ignition (LOI) was converted to % organic matter using the following
equation:
% OM = (% LOI* 0.7) – 0.23; (Iqbal, 2014)
Organic Matter Quality
Soil samples were taken after harvesting. Three main indicators of soil quality were measured: Hot water
carbon extraction, fungal bacterial ratio and potentially mineralizable nitrogen.
Hot Water Carbon Extraction (HWCE): Samples were analysed using the method described by Ghani, et al.
(2003). Fresh soil samples (approximately 4 grams) were weight and put in 50 ml test tubes. 30 ml of distilled
water were added to the sediments in the same tubes. Later, the samples were shaken for 30 minutes.
Afterwards, the tubes were centrifuged during 20 minutes at 3600 RPM. After the centrifuge, the supernatant
was discarded (Cold water extract). Another 30 ml of distilled water was added to the soil pellets. Tubes were
left for 16 hours in the oven (80O
C). After that time, tubes were centrifuged for 20 minutes at 3600 RPM.
Finally, the supernatants were filtered through 45 µm filters. This C fraction has been described as a good
indicator for assessment of land management effects and availability of carbon to microbial population (Fan,
et al., 2013).
Potentially Mineralizable Nitrogen (PMN): Samples were analysed using the methodology proposed by Waring
and Bremner (1964). PMN was determined by measuring the amount of ammonium produced under
waterlogging conditions. 5 grams of air-dried soil were put in a test tube. 12.5 ml of water were added and
the solution was placed in a constant temperature cabinet (40O
C) for 7 days. After this period, the solution
was analysed with colorimetric analysis method. This indicator helped us to estimate the release of N for plant
uptake (de Vries, et al., 2013).
Nutrient bioavailability
Soil samples were taken after harvesting. To determine the effect of organic matter on the bioavailability of
nitrogen and its different forms (nitrate or ammonia), a 0.01 M CaCl2 extraction was performed (Houba, et al.,
2000). The samples were dried during 24 hours at 40O
C. After drying, samples were sieved up to 2mm. Then
three grams of sample were put into 50 ml test tubs. 30 ml of CaCl2 were added and the suspensions were

8
shaken overnight. Afterwards, samples were centrifuged at 3000 rpm during 10 minutes. 10 ml of the
supernatant were pipetted into test tubs and the measurements were done using a segmented flow analyser
(SFA).
Biomass and Element Concentration Analysis
After 5 weeks of growing, the grass was cut above the soil surface and fresh weight of shoots and roots was
measured. After washing with demi water, the plants were dried at 75O
C, and the dry weight of shoots and
roots was measured. After grinding, the material was delivered to the laboratory for the microwave digestion
and analysis of the macronutrient N.
Statistical Analysis
All the statistical analysis were done using the software Genstat version 17.0. Due to the experimental setup,
the data was unbalance for a factorial analysis, therefore an unbalance Analysis of Variance was performed.
In the case of the grass samples, the analysed variables were: fresh and dry weight (g/pot), nitrogen uptake
(mg N/pot) and crude protein content (mg CP/kg). In order to fulfil the requirements of normality and
homogeneity for the ANOVA analysis, mathematical transformations were used to adjust the different variables
for the proposed analysis. Fresh and Dry weight were transformed using the square root. Nitrogen uptake and
crude protein content were not transformed
The Shapiro-Wilk’s test (Shapiro and Wilk, 1965) and visual inspection of their histograms, normal Q-Q plots
and box plots showed that the residues from the square root of fresh weight (p=0.113), square root of dry
weight (p=0.227), N uptake (p=0.236) and crude protein content (p=0.591) were approximately normally
distributed. In the same way, a Bartlett’s test showed that variances from the residues of the square root of
fresh weight (p=0.870), square root of dry weight (p=0.170), nitrogen uptake (p=0.154) and crude protein
content (p=0.160) were homogeneous.
In the case of soils samples, the main variables analysed were: Hot Water Carbon (HWC), Potentially
Mineralizable Nitrogen (PMN), N-NO3 in the soil solution, P-available and K-available. In the same way, the
data was transformed to fulfil the normality and homogeneity requirements of the ANOVA. PMN and HWC were
transformed using logarithm (base 10). K available was analysed using the inverse (1/K-available). NO3 and
P-available were transformed using the inverse of the square root [1/sqrt(x)].
The Shapiro-Wilk’s test (Shapiro and Wilk, 1965) and visual inspection of their histograms, normal Q-Q plots
and box plots showed that the residues from the logarithm of PMN (p=0.72), logarithm of HWC (p=0.851),
the inverse of K-available (p=0.076), the inverse of the square root of N-NO3 (p=0.65) and the inverse of the
square root of P-available (p=0.357) were approximately normally distributed. In the same way, a Bartlett’s
test showed that variances from the residues of PMN (p=0.057), HWC (p=0.428), K-available (p=0.990), N-
NO3 (p=0.092) and P-available (p=0.950) were homogeneous.
7. Results
7.1. Pot Experiment
During the harvesting of the pot experiment, fresh and dry weight of shoots were measured. These plants
were 5 weeks (31 days) old starting from germination (Figure 1.). Additionally, soil samples were taken and
sent to the laboratory for further analysis.

9
Figure 1. Setup of the experiment.
Table 3. Summary of results from the Analysis of Variance Unbalanced for the grass samples
Factors
Fresh weight
[g/pot]
Dry matter
[g/pot] N uptake [g/pot]
Crude protein
content (g/kg DM)
F p F p F p F p
OM Level 24.49* <0.001 12* 0.002 27.79 * <0.001 20.93 * <0.001
Fertilizer Type 66.76* <0.001
67.71
* <0.001 109.03 * <0.001 19.34 * <0.001
Dose Level 1.36 0.272 1.06 0.359 12.67 * <0.001 8.62 * 0.001
OM Level x Fertilizer Type 1.23 0.315 1.12 0.354 1.11 0.361 3.63 * 0.023
OM Level x Dose Level 0.09 0.916 0.11 0.898 0.14 0.869 1.94 0.161
Fertilizer Type x Dose Level 2.07 0.143 1.49 0.241 16.06 * <0.001 5.61 0.008
OM Level x Fertilizer Type x
Dose Level
2.06 0.144 2.99 0.065 3.12 0.058 0.57 0.572
(*) Values are statistically significant.
7.1.1. Grass Samples Results
OM-level had an effect on the fresh weight of grass. An average increase of 27.48% on fresh weight was
present when grasses were grown on the soil with high organic matter level. Additionally, fertilizer type also
had an effect on the fresh weight. Fresh weight showed an average increase of 123.19% and 72.9% when
slurry and digestate were the source of nutrient respectively, in comparison to the control treatment (Figure
2). No significant interactions between the SOM level, fertilizer type and dose level were found (Table 3).

10
Figure 2. Effects of organic matter level and fertilizer type on fresh weight production.
Likewise, OM-level had an effect on the dry matter content. An average increase of 21.4% on dry matter was
present when grasses were grown on the soil with high organic matter level. Additionally, fertilizer type also
had an effect on the dry matter content. An average increase of 156% and 78.9% on dry matter was found
when slurry and digestate were added respectively, in comparison to the control treatment (Figure 3). No
significant interactions between organic matter level, fertilizer type and dose level were found (Table 1).
Figure 3. Effects of organic matter level and fertilizer type on dry matter production.
Fertilizer type had an effect on the nitrogen uptake. Approximately 220% and 160% more N was taken up by
grass plants when slurry and digestate were the sources of nutrients, respectively. Additionally, an interaction
with the dose level factor was observed on the slurry treatment. An increase of 50% to the normal dose of
slurry produced an increase of 21.9% in the N uptake. In the same way, a decrease of 50% to the normal
dose of slurry caused a decrease of 28.1% in the N uptake. No changes on N uptake were found when different
doses of inorganic fertilizer were applied.
c cd
b
a
d cd
b
ab
0
5
10
15
20
25
30
Inorganic Control Digestate Slurry
FreshWeight[g/pot]
High OM Low OM
c
cd
b
a
cd d
b
a
0
1
2
3
4
5
6
7
8
9
DryMatter[g/pot]
High OM
Low OM

11
Figure 4. Effect of the interaction between fertilizer type and dose level on N-uptake [g N/pot].
Fertilizer type also had an effect on the crude protein content. Similarly to the N uptake results, an increase
of 44.4% and 23.7% was observed when digestate and slurry where the source of nutrients respectively, in
comparison with the control treatment (Figure 5). Additionally, an interaction with the dose level factor was
observed on the slurry treatment. An increase of 50% to the normal dose of slurry produced an increase of
14.63% in the N uptake. In the same way, a decrease of 50% to the normal dose of slurry caused a decrease
of 17.96% in the N uptake (Figure 5). No changes on N uptake were found when different doses of inorganic
fertilizer were applied.
Figure 5. Effect of the interaction between fertilizer type and dose level on crude protein content [g CP/kg
DM]
A second significant interaction effect was found between soil organic matter level and fertilizer type on the
crude protein content. The inorganic fertilizer treatment showed an increase of 37.7% in crude protein content
when grasses were grown in soils with high organic matter level (Figure 6). All the treatments showed the
same tendency but the differences were not significant (Table 3).
d
c
d
b
d
c
d
a
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
0,45
Inorganic Slurry Control Digestate
Nuptake[gN/pot]
LOW NORMAL HIGH
c cc
b
c
a
c
a
0
50
100
150
200
250
300
350
CrudeProteinContent[g/kgDM]
LOW NORMAL HIGH

12
Figure 6. Effect of the interaction between fertilizer type and soil organic matter level on crude protein
content [g CP/kg DM]
7.1.2. Soil Sample Results
Table 4. Summary of results from the Analysis of Variance Unbalanced for the soil samples
Factors PMN [mg N/kg] HWC [mg/kg] N-NO3 [mg/kg]
P-available
[mg/kg] K-available [mg/kg]
F p F p F p F p F p
OM Level 337.47 * <0.001 2846.91 * <0.001 65.14 * <0.001 665.98 * <0.001 144.27 * <0.001
Fertilizer Type 0.30 0.823 5.04 * 0.006 16.08 * <0.001 1.66 0.194 54.53 * <0.001
Dose Level 0.88 0.424 0.75 0.480 6.96 * 0.003 2.18 0.130 5.49 * 0.009
OM x Fertilizer 1.81 0.166 8.23* <0.001 1.14 0.346 2.47 0.080 12.07 * <0.001
OM x Dose 0.24 0.789 0.28 0.754 1.24 0.303 2.36 0.110 7.18 * 0.003
Fertilizer x Dose 1.90 0.166 7.56 * 0.002 8.63 * 0.001 10.47 * <0.001 5.53 * 0.009
OM x Fertilizer x
Dose 0.11 0.894 1.97 0.155 5.09 0.012 3.33 * 0.048 2.39 0.108
Note: (*) Values are statistically significant
OM-level had an effect on the Potentially Mineralizable Nitrogen. Approximately 72.5% more PMN was present
in the soils with high OM-level (Figure 7). No significant interaction between soil organic matter level, fertilizer
type and dose level were found (Table 4).
c
cd
a
bc
e de
ab
bc
0
50
100
150
200
250
300
350
400
CrudeProteinContent[g/kgDM]
High OM Low OM

13
Figure 7. Effect of the organic matter level on potentially mineralizable nitrogen [mg N/kg]
SOM level had an effect on the amount of HWC extracted (Table 4). On average, the high OM level soil had
153.25% more HWC than the soil with low OM level. Additionally, in the soils with low OM level, more HWC
was extracted when slurry and digestate were the source of nutrients (Figure 8).
Likewise, a highly significant two-way interaction effect between fertilizer type and dose level on the hot water
carbon was found (Table 4). Slurry with the highest dose showed an increase of 16.78% on the HWC extracted
in comparison with the control treatment (Figure 9). The normal dose of the inorganic treatment showed also
an increase of 11.81% on the HWC extracted in comparison with the control treatment. The other treatments
did not show any significant differences (Figure 9).
Figure 8. Interaction Effect between organic matter level (High and Low) and type of fertilizer on Hot Water
Carbon [mg/kg].
0
20
40
60
80
High OM Low OM
PMN[mgN/kg]
a
b b
ab
d d
c c
0
200
400
600
800
1000
1200
1400
1600
HWC[mg/kg]
High OM Low OM

14
Figure 9. Interaction effect between type of fertilizer and dose level on hot water carbon [mg/kg].
There is a highly significant three-way interaction effect between organic matter level, fertilizer type and dose
level on the mineral nitrogen (N-NO3) (Table 4). The digestate treatment showed the highest content of
mineral N (Figure 10). The digestate treatment in the soil with high organic matter content showed 200%
more mineral nitrogen than the slurry with high dose in the same type of soil. On the other hand, the slurry
with high dose on the soil with low organic matter level showed 50% more N in comparison with the digestate
treatment.
Figure 10. Three-way interaction between soil organic matter level, fertilizer type and dose level on mineral
nitrogen content [kg/ha].
There is a significant three way interaction between soil organic matter level, fertilizer type and dose level on
the P-available content (Table 4). More P was available in the soil with high organic matter level (figure 11)
No significant differences in P-available were found between different fertilizers or doses on the soil with high
organic matter level (Figure 11). On the other hand, a reduction of 34.2% was found when 50% less slurry
was added in comparison to the normal dose.
bcd bc
ab
bcd
d
bcd
cd
a
800
850
900
950
1000
1050
1100
1150
HWC[mg/kg]
LOW NORMAL HIGH
bcd bcd
bcd
fg efg g
bcd
bc b
g def
ab
cde g
a
bcd
0
5
10
15
20
25
30
LOW NORMAL HIGH LOW NORMAL HIGH
High OM Low OM
N-NO3[kg/ha]

15
Figure 11. Three-way interaction between soil organic matter level, fertilizer type and dose level on P-
available [mg P/kg].
There were three different two-way interaction that affected the K-available. The first interaction was between
organic matter level and fertilizer type (Table 4). A higher K-available was present in soils with low organic
matter (Figure 12a). On the low OM soil, slurry and digestate showed an increase of 129.13% and 127.88%
respectively on the amount of K-available in comparison to the control treatment. On the soil with high OM
level, slurry and digestate showed an increase of 179.95% and 257.07% respectively on the amount of K
available.
The second significant two-way interaction was between organic matter level and dose level (Table 4). Higher
K-available was present in the soil with low organic matter content. An increase of 121.67% on the amount
of K-available was found in the treatments with the normal dose of fertilizer applied on the soil with low OM
level. A high standard error is observed in the treatments with high dose of fertilizers, this is an indicative that
the amount of fertilizers added varied greatly (Figure 12b).
The third significant two-way interaction was between fertilizer type and dose level (Table 4). The highest K-
available was present when slurry with high dose was added as fertilizer. No differences were found between
slurry high dose, slurry normal dose and digestate in the amount of K-available (Figure 12c). The inorganic
fertilizers had the lowest K-available together with the control treatments. This last result is another suggestion
that there was a mistake in the application of the inorganic fertilizers (P and K) during the experiment.
a
ab
ab
de ef fg
b ab
ab
g
cd c
ab
def
a
cde
0
0,5
1
1,5
2
2,5
LOW NORMAL HIGH LOW NORMAL HIGH
High OM Low OM
P-available[mgP/kg] Inorganic Slurry Control Digestate

16
Figure 12. Two-way interactions between soil organic matter level and fertilizer type (a); Soil organic matter
and dose level (b); fertilizer type and dose level (c) on K-available [mg K/kg].
In addition to the analysis of variances, scatter plots were performed in order to identify any significant
relationships among the tested variables. However, due to the nature of the experiment the data was not
useful to elaborate regressions. Figure 13 shows the existent relationship between hot water carbon and
potentially mineralizable nitrogen. The treatments with high organic matter level showed the highest PMN and
HWC.
Figure 13. Plot of Hot water carbon against potentially mineralizable N.
Figure 14. Shows that the change in HWC (HWC –HWCcontrol), had no significant relation with the nitrogen
uptake [mg N/pot]. We can see that N uptake varied from 11 to 349 mg N/pot. On the other hand, HWC
changed over time from -135.7 to 215.3 mg/kg. The negative numbers in the HWC mean that more carbon
was extracted in comparison with the control treatment. Additionally, we can see that the fertilizer type had
an effect on the amount of N uptake. Inorganic treatment had the lowest N uptake. This could be related to
d d
ab
cc
bc
a
a
0
10
20
30
40
50
60
70
80
Kavailable[mgK/kg]
High OM Low OM
e
cd
e
d
bc
b
b
a
CONTROL HIGH LOW NORMAL
High OM Low OM
c
b
c
a
c
a
c
a
LOW NORMAL HIGH
0
400
800
1200
1600
0 20 40 60 80 100
HWC[mg/kg]
PMN [mg N/kg]
High OM + Chemical
High OM+Control
High OM+Digestate
High OM+ Slurry
Low OM+ Chemical
Low OM+Control
Low OM+ Digestate
Low OM+ Slurry
(a) (b) (c)

17
the mistake made during the calculation of the amount of stock solution to be added. Perhaps P or K were
limiting factors.
Figure 14. Plot of Hot water carbon change due to fertilization against N uptake [mg N/pot].
8. Discussions
Our results showed that biomass (fresh and dry) was affected by SOM level. These results are in accordance
with the results of Bauer and Black (1994). They quantified the contribution of a unit increment of soil organic
matter content to soil productivity by evaluating the production of spring wheat. They found that dry matter
of the aboveground biomass was higher on the two soil with highest organic matter content (5711 and 5737
kg DM/ha) in comparison with the two soils with low organic matter content (4769 and 5804 kg DM/ha). These
results could be explain with the fact that organic matter is in close relation with the amount of microorganism
present in the soils. A high SOM content will mean a higher amount of microorganism. These microorganism
participate actively in the decomposition of organic residues like slurry or digestate. During the decomposition
of organic residues the microbes could absorb the mineral N in order to obtain energy. If the amount of N is
higher than the requirements of microbes, they will release this N (inorganic) to the soil solution where plants
can take up.
Additionally, biomass (fresh and dry) was affected by the addition of slurry. The results are in contradiction
with the results found by Orrico, et al. (2012). They found no significant differences between the used of
different bio fertilizers type (manures and digestate). They concluded that fertilizer type had no effect on the
production of Brachiaria brizantha cv. Piata, when applied in the proper manner (by correcting the level of
nitrogen according to its level content in each fertilizer). In our experiment, the nutritional characteristics of
the organic fertilizers used are variable but their variation are not so big, therefore the doses of N, P and K
are quite comparable. The response of biomass to the slurry application could be related to its slower
mineralization rate. The higher content of organic matter in the slurry (Table 2) suggest that microbes will
need more N to decompose this substrate. This will lead to a higher immobilization during the first stages of
decomposition. Later, with the gradually decay of the microbial biomass, N will be released and this will lead
to a more constant provision of nitrogen for the plants.
Besides, nitrogen uptake was also influenced by slurry application at different doses. This result is in
accordance with the results found by Gomez-Garrido, et al. (2014). They found that high doses of pig slurry
increased the N-uptake in barley. A higher dose of slurry caused a higher provision of nitrogen. If more
nitrogen is present in the soil solution, the plant will take up more. On the other hand, in our experiment the
0
50
100
150
200
250
300
350
400
-200 -100 0 100 200 300
N-uptake[mgN/pot]
Change in HWC [mg/kg]
High OM+ Inorganic
High OM+Digestate
High OM+Slurry
Low OM+Inorganic
Low OM+ Digestate
Low OM+ Slurry

18
inorganic treatment did not show any differences between the different doses of application. This result is in
contradiction with the results of many studies (Estavillo, et al., 1996, Griffin, et al., 2002, Gunnarsson, et al.,
2010, Smith and Hadley, 1989) which have shown that inorganic fertilizers have higher nitrogen uptake
efficiency than organic fertilizers because the immediate availability of N. After checking the calculations made
to determine the amount of inorganic fertilizer to be applied, it was found that approximately five times less
phosphorus and three times less potassium was added to these treatments. A possible P limitation could be
the reason why there were no differences between the inorganic treatments in our experiment. We were not
able to confirm this speculation because the P-content in the plant samples were not measured. Further
analysis to the plant samples will be needed to corroborate this explanation.
However, digestate treatment showed the same crude protein content than the slurry treatment with the
higher dose of application (Figure 5). Perhaps the mineralization rate of digestate was higher in comparison
to the slurry fertilizer. This suggestion could be corroborated with the finding of Peters and Jensen (2011).
They found a strong negative correlation between net N mineralization and C:N-org ratio of solid fraction from
animal slurry in an incubation experiment. The lower C:N-org ratio of the digestate (Table 2) used in our
experiment may have promoted rapid N mineralization and increased plant available N in soil.
Nevertheless, another explanation to the previous result could be the chemical characteristics of the digestate.
It is known that anaerobic digestion enhances the content of available mineral N (Odlare, et al., 2008).
Likewise, the content of mineral nitrogen in the soil solution was higher when digestate was applied (Figure
10). So, this means that more nitrogen is available for crop uptake. Similar results have been reported by
Johansen, et al. (2013). They found that digestate had considerably more NH4+
and NO3−
available than in
the control treatments. Additionally, Johansen also found the labile C of other treatments influenced the
mineralization-immobilization turnover of N possibly by net immobilization in the biomass of a proliferating
microbial community. The digestate had the lowest labile C content because during the digestion C from the
substrate is use to produce methane (Schievano, et al., 2009).
Furthermore, the crude protein content was also affected by the interaction between organic matter levels
and fertilizer type. Even though only the chemical treatment showed a significant difference between high or
low soil organic matter content, there is a tendency on all fertilizers type to perform better in the high organic
matter soil (Figure 6). An explanation for this could be that organic matter is closely related to microbial
activity. A higher content of microbes means that any organic residue (compost, slurry, digestate, etc.) will
be decompose faster. During decomposition many nutrients are released and become available for plants, this
process is known as mineralization (Murphy, 2014). This was corroborated by the results from the potentially
mineralizable nitrogen (PMN). More PMN was present in the soil with high organic matter content (Figure 7).
This suggest that SOM affects the amount of N that could be available during a growing season.
The easily extractable carbon (Hot water carbon) was affected by the interaction between soil organic matter
level and fertilizer type. A possible cause of the increase of HWC on soils with high OM is close the relationship
between organic matter content and microbial biomass. As explained by (Ghani, et al., 2003), HWC is mainly
formed by microbial C, therefore higher microbial biomass will be traduce into a higher HWC.
Additionally, the interaction between fertilizer type and dose level also influenced HWC (Figure 9). This result
could be due to the differences in fertilizers composition. As explained by Ghani, et al. (2002), (2003), HWC
not only extracts microbial C but root exudates, soluble carbohydrates and amino acids. Slurry is loaded with
a huge amount of microbes (fungi and bacteria), therefore more HWC would be extracted from the soils were
this fertilizer was added. On the other hand, digestate has less labile carbon due to during the digestion
process, labile C is consumed to produce methane. Therefore, less HWC would be extracted when digestate is
the applied fertilizer. Unexpectedly, the highest HWC was present when inorganic fertilizer was added followed
by the slurry treatment. Root exudates could also influence the HWC increasing its value, but we could prove
this hypothesis because no measurements were performed to determine the amount of C provided by roots.
We were not able to replicate the correlation between HWC and PMN found by Ghani, et al. (2003). The main
reason for this was the use of two soils with markedly differences in the organic matter level. However, we

19
found the same tendency and more HWC was related with more PMN. The explanation of this relationship
between HWC and PMN might be related to the fact that HWC is mainly formed by microbial C. This microbial
carbon gives us an idea of the amount of microbial biomass present in the soil. If we have more microbes in
the soil, decomposition will be faster and therefore more nitrogen will be mineralized and will be available for
the crops.
Our results suggest that a differentiated application of organic residues in function of the soil organic matter
content would have an effect on the biomass production of grass. However, we have to take into account the
possible scenarios that a farmer might have. The first scenario is where a farmer apply all the organic residue
(slurry, manure, compost or digestate) in the soil with low OM content and all the inorganic fertilizers in the
soil with high OM content. In this scenario the short term effect would be an increase in the production of the
less fertile soil. However, in the long term the soil with high OM content will suffer a reduction in its fertility
due to the depletion of OM.
The second scenario is completely opposite to the first scenario. In this case, the farmer apply all the organic
matter in the soil with high OM content and all the inorganic fertilizer in the soil with low OM content. In the
short term, a higher production of grass will be found in the soil with high OM content. In the soil with low OM
content, the productivity will remain the same but not for long. In the long term, the soil with low OM content
will deteriorate substantially. All the characteristics that are affected by OM content will be deteriorated like
decrease of water holding capacity, decrease in the cation exchange capacity, decrease in the microbial
community, etc. Finally, the soil with low OM content will be no longer fertile.
The third and last scenario would be when a farmer apply the organic residue in an optimal way. This means
that organic residues will be used in both fields (low OM and High OM) but the field with low organic matter
content will received a small percentage (an option could be 10%) more than the high OM field. In this
scenario, the short term results are a similar production in both fields. But the bigger impact would be in the
long term. The soil with low OM content will increase the amount of OM over time. Additionally, the soil with
high OM content will maintain its fertility.
9. Conclusions
There are many indicators that could help us to understand and estimate the organic matter quality in soils.
However, many of these indicators are difficult to measure due to high cost or time consumption. Additionally,
the turnover rate depends on environmental factors that sometimes cannot be controlled (e.g. temperature,
moisture, pH, etc.). Moreover, the extraction procedure used for estimating this indicators also affect the size
of them. For instance, if the temperature or time used during the HWC extraction is increased, the result will
differ greatly. However, more research and modelling is needed to determine if the HWC indicator is accurate
enough to predict changes in SOM.
In conclusion, if a farmer has a field were soils differs in SOM quality, one option could be to increase the
amount of organic fertilizers (slurry, compost, digestate) to the plot with the low organic matter content. This
differential application will increase the production of the soil with low organic matter level without
compromising the fertility of the soil with high OM level. Additionally, soils with low organic matter levels need
the constant application of organic residues because this is the only way to increase total organic matter
content in the long term.
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24
Annexes
Annex A. Average and Standard Error of Fresh and Dry Weight, N Uptake and Crude Protein Content
OM
Level
Fertilizer
Type
Dose Level
Fresh weight
[g/pot]
Dry matter
[g/pot]
N uptake
[g/pot]
Crude protein
(g/kg DM)
Mean
Std.
Dev. Mean
Std.
Dev. Mean
Std.
Dev. Mean Std. Dev.
High
OM
Inorganic
HIGH 10,67 2,87 3,03 0,92 0,12 0,02 268,65 37,30
LOW 13,00 1,63 3,81 0,44 0,17 0,02 276,19 12,86
NORMAL 13,33 1,25 3,86 0,45 0,15 0,02 247,95 15,38
Control CONTROL 11,67 1,70 3,31 0,25 0,13 0,00 239,86 20,11
Digestate NORMAL 17,67 2,05 4,97 0,72 0,26 0,03 333,50 6,26
Slurry
HIGH 27,33 5,44 9,22 1,80 0,45 0,04 308,80 32,37
LOW 19,67 0,47 6,33 0,33 0,24 0,03 239,59 21,13
NORMAL 24,33 4,71 8,06 1,93 0,34 0,03 277,33 49,59
Low
OM
Inorganic
HIGH 8,33 0,94 2,66 0,31 0,08 0,02 197,96 17,11
LOW 7,33 0,94 2,26 0,36 0,06 0,01 171,11 16,10
NORMAL 7,67 0,94 2,45 0,27 0,08 0,01 206,67 9,97
Control CONTROL 8,00 0,00 2,38 0,06 0,08 0,01 202,91 11,08
Digestate NORMAL 16,33 1,25 5,22 0,44 0,26 0,03 305,86 28,92
Slurry
HIGH 20,67 3,77 6,61 1,37 0,34 0,07 318,98 7,65
LOW 18,67 0,94 6,50 0,52 0,22 0,02 209,75 3,70
NORMAL 21,00 3,56 7,07 1,44 0,30 0,04 270,35 34,76

25
Annex B. Photos from the Pot Experiment
Photos 1 and 2: Filling of pots (left) and addition of the slurry treatment (right)
Photos 3 and 4: Sowing of seed (left) and plastic cover added during germination stage.

26
Photos 5 and 6: Germination of grass plants (left) and Pots during the experiment (right)
Annex C. Chemical analysis of the soil with High Organic Matter level
Note: Analysis performed by BLGG.

27
Annex D. Chemical analysis of the soil with Low Organic Matter level
Note: Analysis performed by BLGG.

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