grassland in Flevoland and the feasibility of changing
fertilization regimes to BBF.
(Grasland Flevoland, z.d.).
Name: Erik Heijckers
Student number: 12428477
Supervisor: Dhr. dr. B. (Boris) Jansen, D. (Donya) Danesh Second examiner: Dhr. dr. J.R. (John) Parsons
Date: 30-05-2021, Heythuysen
University: University of Amsterdam, Future Planet Studies
Index
Tables ... 3
Figures ... 3
Abstract ... 4
1. Introduction ... 5
1.1 Background and problem formulation ... 5
1.1 Aims and stakeholders of the research ... 6
1.2 Research questions ... 6
2. Methods and Data ... 7
2.1 Fieldwork ... 7
2.2 Lab work ... 8
2.2.1 Auto analyzer ... 8
2.3 Analysis ... 8
3. Results ... 9
3.1 Results auto analyzer ... 9
3.1.1 Phosphorus ... 9
3.1.2 Nitrogen ... 10
3.2 Data on the farm ... 11
3.2.1 Amount of fertilizer ... 11 3.2.2 Cost of fertilization: ... 12 4. Discussion ... 13 4.1 Phosphorus ... 13 4.2 Nitrogen ... 13 4.3 PH values ... 14 4.4 Fertilizer ... 14
4.4.1 Change to sewage sludge ... 14
4.5 Further research ... 15
4.6 The validity of the research ... 15
5. Conclusion ... 16
Literature list ... 17
Acknowledgements ... 19
Appendix III: Data of Farms – interview ... 22
Appendix IV: additional calculation results... 23
Tables
Table 1: Table containing the results from the auto analyzer. These sample values have all been calculated to be mg/kg dry soil(ppm) and have the mean, min and max values added. The complete table of results per sample point are visible in Appendix I. These results were converted from µmol/l to mg/ kg dry soil ... 10Table 2: all fertilizer data combined. Showing the input on the fields, the output of nutrients due to mowing, the surplus that should be left in the field, the amount that is measured and then the proposed leaching. ... 11
Table 3: complete overview of all sample point data ... 20
Table 4: Table consisting of PH & EC content of the samples ... 21
Table 5: Data gathered on the farm by previous research. ... 22
Figures
Figure 1: Showing the fieldwork area, field A is researched in this paper. The map shows 2 windmill construction sites and 1 site where a windmill has been deconstructed. 20 Sample points are visualized in field A and the farm is situated between the two research fields. The highway A27 is visible on the left of field A in 2 gray lines. ... 7Figure 2: 1. Overview of the fieldwork area, gradual symbology is used to show the difference in values of PO4 across the field. 2. Overview of the fieldwork area, the same technique is used to show the difference in values of NO3 across the field. ... 9
Abstract
Both N and P utilization have exceeded their sustainable planetary boundaries, both are responsible for algae blooms and are harmful for the environment in a heightened quantity. However these nutrients are necessary to feed our global population and therefore different fertilizing regimes have to be implemented. The phosphorus supply is finite and it is necessary to find ways to make the phosphorus cycle circular. This research focusses on how Nitrogen and Phosphorus contents in agricultural grassland soils are spatially distributed, how that the Bio Based fertilizer “sewage sludge” could be implemented as fertilizer and how eager farmers would be to change to this fertilizer. It also aims to compare the amount of fertilizer used by a local farmer to the amount of nutrients that are available in the soil, investigating if less using fertilizer could be
possible. The research has been be conducted on 2 fields with different land uses (Grassland and sugar beets) around a farm in Almere. 40 samples have been analyzed using the auto analyzer, which will determine the concentrations of PO4, NH4+, NO3 & NO2. Spatial differentiations between the Nitrogen and Phosphorus have been observed and documented. Showing spatial differentiation and some extremer outliers. The values of Nitrogen and Phosphorus are calculated for the field and show a proposed leeching of nutrients. Which indicates an over-use of fertilizer. The use of Sewage sludge as replacement of artificial fertilizer could be effective if constraints such as; clean, cheap and easy to use sludge are being solved.
1. Introduction
1.1 Background and problem formulation
The world’s population has increased rapidly over the last 200 years, from 1 billion in 1800 to 7.7 billion in 2013 (Roser, Ritchie & Ortiz-Ospina, 2013). Over the last 50 years the world
population has in fact doubled, from about 3.5 to more than 7 billion. Subsequently, the world food demand also increased. Over the last 50 years it has approximately tripled (Bodirsky et al., 2015). This change is not only related to population growth but also to rising living standards, where individuals have a higher food demand as their wealth increases (Bordirsky et al., 2015).
To facilitate this rise in global food demand, the agricultural industry has grown and
increased its food output. In the past this growth was made possible by adding more nutrients to the soil using fertilizers such as green litter or animal manure (Smit et al., 2009). Until the 19th-century animal manure and human excreta collected in cities were also essential fertilizers. Especially in Flanders and Holland this practice was used and sometimes regulated by the government (Duncan Brown, 2003). Nowadays, the main ingredients for artificial fertilizers such as Nitrogen(N) can be artificially recovered from the atmosphere and Phosphorus(P), which is mined from large phosphate deposits (Rengel, 2020). These techniques allowed for further intensification and spread of
agriculture to nutrient poor environments(Smit et al., 2009).
Nitrogen, Phosphorus and Potassium (N, P, K) are the three main nutrients required for plant growth (White & Brown, 2010). They are key elements for the cells to grow, help transfer energy from sunlight to plants and stimulate root growth. This research will focus on N and P for the reasons stated below.
The world food production now relies heavily on the use of these mineral fertilizers. However, introducing these nutrients to local ecosystems by runoff or leeching is causing major environmental problems (Bennett, Carpenter & Caraco, 2001; Carpenter et al., 1998). Mining and transporting these fertilizers alters the global P & N cycles, causing them to accumulate in
agricultural soils(Galloway et al., 2008; Khasawneh, Sample & Kamprath, 1980). Increasing P levels in soils is subsequently increasing P values in aquatic bodies due to runoff and erosion, which leads to eutrophication in these bodies of water(Bennett, Carpenter & Caraco, 2001). Furthermore,
phosphorus deposits are finite, with 85% of the world reserves situated in 3 countries ”Morocco, China & the United States”(lou et al., 2018). When these deposits are exhausted is not clear, models range from a few decades to around the end of the 21st century (Déry & Anderson, 2007; Steen, 1998; Van Vuuren, Bouwman & Beusen, 2010). The common form of phosphorus in fertilizer is phosphate [PO4], which is thereforeused as agent to identify phosphorus in this paper.
Nitrogen, as stated before can be produced from the atmosphere everywhere since it is a very abundant resource (Galloway et al. 2003). Most fertilizers have Nitrogen in the form of ammonia [NH3], ammonium [NH4+] and nitrate [NO3-](Finch, Samuel & Lane, 2002). However when an excessive amount of this fertilizer is used, it will then also leach out of the soils to aquatic bodies. Causing acidification and eutrophication (Galloway et al., 2003). Furthermore, Nitrogen use is
criticized more and more due to its adverse effects to the environment(Townsend & Howarth, 2010). Both N and P utilisation have exceeded their sustainable planetary boundaries (Steffen et al., 2015). Therefore it is necessary to investigate possibilities to lower the use of fertilizer and to create circular nutrient streams, thus making sure that ecosystems are not overloaded with nutrients. Research suggests that Bio Based Fertilizers (BBF) together with better fertilization
policies/standards are the solution to these problems (Rengel, 2020). BBF together with trying to close the N and especially P cycles is critical for the future of food production (Galloway et al., 2008; Bennett, Carpenter & Caraco, 2001). There are many different BBF, however this paper will focus on the use of Sewage sludge as BBF (Chojnacka, Moustakas & Witek-Krowiak, 2020). Sewage sludge is seen as a high risk, high reward strategy. It is usually full with contaminants and therefore proper techniques will have to be implemented before it is used as fertilizer (Usman et al., 2012; Laturnus, von Arnold, & Grøn, 2007; Zuloaga et al., 2012) . However, it is available everywhere in the world.
Sewage sludge is very rich in Phosphorus an Nitrogen and as this is the end of the food cycle, the P and N cycles could be made circular if it is used as fertilizer (Usman et al., 2012). With sewage sludge as fertilizer, we would go back to the original cycles from the 19th century, where manure and human excreta where removed from the city and used as fertilizers.
1.1 Aims and stakeholders of the research
Agricultural grassland is the most common land use in the Netherlands (Land- en tuinbouw: ruimtelijke spreiding, grondgebruik en aantal bedrijven, 1980–2019 | Compendium voor de
Leefomgeving, 2020). This paper therefore aims to investigate how agricultural grassland soils interact with fertilizer, how these nutrients are spatially differentiated and how a BBF such as sewage sludge changes the N & P cycles. It aims to compare the amount of fertilizer used by a local farmer, with the amount of nutrients that are available in the soil. Therefore finding if the input of nutrients to the soil can be lowered via the use of sewage sludge and/or better fertilizing regimes, causing less leaching or erosion to water bodies. A cost analysis is performed to investigate if farmers would be interested in switching to BBF, especially sewage sludge.
The stakeholders involved in this research were the farming community, since they are always affected due to changes in fertilization methods. The government is involved regarding the use of the sewage sludge, since the use of sewage sludge as fertilizer in the Netherlands is
forbidden(Dirkzwager, van Engers& Van Den Berg, 1997). The government would need to ensure that laws are in place to facilitate the circularization of nutrient cycles and changing laws for fertilizing regimes. The local population is also indirectly affected by this research, as harmful algae blooms could be reduced due to better regulation and understanding in use of fertilizers.
1.2 Research questions
The main research question is: How are the Nitrogen and Phosphorus contents from agricultural grassland soils in Flevoland spatially distributed and how could fertilization change due to the use of sewage sludge as bio based fertilizer?
To answer this question, several sub-questions are formulated:
1. What are the Nitrogen and Phosphorus contents in the soil and how are they spatially distributed?
2. How efficient is the current fertilization regime?
3. How would the current fertilization regime differ from a fertilization regime by sewage sludge regarding nutrient availability and costs?
4. What is necessary for a change to BBF to be facilitated?
This paper is part of a larger research project regarding the use of BBF called LEX4BIO. Which is under supervision of Dhr. dr. B. (Boris) Jansen. Together with 4 other Bachelor Future Planet Studies students from the UvA, an overarching research goal is achieved. The other Bsc students are investigating the use of in-situ field tests for farmers and the potential for pollution by using sewage sludge as fertilizer by heavy metals. Therefor the pollution topic will not be discussed in this paper. One of the students is performing similar research to this paper, with the main difference being Land use.
2. Methods and Data
2.1 Fieldwork
The field work has been performed on and around a farm in Almere, Kluutweg 7. This location has been chosen to represent the whole of Flevoland, while it is representative for the region due to the same soil characteristics(NHI, 2008) Together with my supervisor Dhr. dr. B. (Boris) Jansen, Bram Buter(who has performed previous research at this site) and the 4 other bachelor FPS students that are performing the research for LEX4BIO. At the research site, 2 different land uses are being sampled. This paper focusses on agricultural grassland(Figure 1A), while the similar paper focusses on potatoes (Figure 1B).
To ensure a randomised pattern in sample locations, beforehand a map was created on ArcGIS with the location of the sample points. These points will be found in the field using the collector app. 20 samples have been extracted per land use since 20 samples per researched field is the minimum due to expected spatial differentiation of Nitrogen in the soil (Geisseler & Horwath, 2016), leading to a total of 40 samples that have been bagged separately. The soil samples have been taken from a depth of 30cm using an auger. These samples are around 200g each, to ensure that enough material is extracted for future lab work. Due to complications at the site, the
Figure 1: Showing the fieldwork area, field A is researched in this paper. The map shows 2 windmill construction sites and 1 site where a windmill has been deconstructed. 20 Sample points are visualized in field A and the farm is situated between the two research fields. The highway A27 is visible on the left of field A in 2 gray lines.
randomised sample points for field A could not be used and were chosen randomly on the spot. During the field work sample A9 had been lost and therefore no data is collected of this sample point.
2.2 Lab work
The samples extracted from the field are examined at the UvA, science park campus by myself and the 4 other Bachelor students from the LEX4BIO project. All the samples need to be prepared for the different testing machines. To calculate the different water content in the soil samples, each sample is weighed before and after it is put into an oven. Each sample point has been divided in two separate samples, since duplicate samples are necessary for the validity of the analysis resulting in a total 80 samples in total. A water extract is made from each soil sample with a 1:2.5 ratio(soil : water). The first batch was created with 20 mg soil and 50ml demineralized water, while the second batch was created with 30 mg soil and 75 ml demineralized water. The samples then proceed to the shaker for 2 hours. After shaking, the PH and EC values are measured using the measuring equipment(Consort C831) in the lab. The samples need to be centrifuged before filtering and go 20minutes at 2000 rpm. The filtration of the samples is executed using filter membranes of 0.45 µm, the centrifuged samples are carefully poured into the filtration machine to make sure only the liquid is poured in and the solid residue is discarded. The auto analyzer. Therefore each filtered sample will be divided into 3 extracts supplying each sampling machine with samples. This research will only make use of the 40 Auto analyzer samples from the land use “grassland”. To prepare the samples for the auto analyzer, a small tube is filled per sample with the filtered extract and put in a test-tube rack. Then it is passed to the lab that performs the auto analysis.
2.2.1 Auto analyzer
As mentioned before, the auto analyzer will be used to gather data for this research. The 40 grassland land use samples will be tested for concentrations of PO4 , NH4+, NO3 , NO2 & Total N. In the lab a Skalar San ++ autoanalyzer is used, which is based on the Continuous Flow Analysis. The water solution is sucked out of the sample and distributed through different channels in order to record all five variables from one sample. The data are gathered via colour coding with chemicals as the chemicals react to the amount of PO4 , NH4+, NO3 , NO2 & Total N that is in the water solution. These will then indicate the amount of Phosphorus and Nitrogen available in the soil.
2.3 Analysis
The farmer has been interviewed by an already ongoing research(unpublished) to find out what fertilizing regime has been used on the fields. How much does it cost to fertilize this pasture. What kind of fertilizer has been applied and in what quantity. This data has been communicated via email and is also used for this research and is visible in Appendix III (A. Tietema, personal
communication, 6 May, 2021).
The concentrations of PO4 , NH4+, NO3 , NO2 & Total N from the auto analyzer have been compared to the amount of fertilizer that has been used by the farmer. Thus establishing how effective the fertilization regime of the farmer has been and how much nutrients have eroded or leeched away. The spatial variation is compared qualitatively by comparing the results from the table and the figures with each other. Furthermore, the cost of fertilizer used by the farmer is compared to the possible cost if fertilizer would be replaced with sewage sludge as Bio Based fertilizer. The possible over-fertilization will be calculated and also included in the cost analysis to try and create a financial incentive for better fertilization management.
3. Results
3.1 Results auto analyzer
3.1.1 Phosphorus
The Phosphorus content of the research area is measured in PO4. The values across the field range between 1.67 mg/kg and 14.78 mg/kg with an average of 4.54 mg/kg (table 1). There are two outliers at the sample points A01 [14.78 mg/kg] and A12 [10.56 mg/kg] which are close to the farm and the windmill construction site respectively (Figure 2.1). Sample points A02 [7.11 mg/kg] & A17 [6.53 mg/kg] are also slightly higher than average.
Figure 2: 1. Overview of the fieldwork area, gradual symbology is used to show the difference in values of PO4 across the field. 2. Overview of the fieldwork area, the same technique is used to show the difference in values of NO3 across the field.
3.1.2 Nitrogen
The Nitrogen content is measured in NO3, NO2, NH4 and the total amount of N available in the soil. NO3 value ranges between 11.43 mg/kg and 76.34 mg/kg with an average of 18.90 mg/kg (table 1). There is one outlier at sample point A12 with a value of 76.34 mg/kg. The only sample point outside of this range is the previously mentioned outlier. The NO2 values range between 0.16 mg/kg and 0.43 mg/kg with a mean of 0.24 mg/kg (table 1). The NH4 values are between 3.94 mg/kg and 12.24 mg/kg with a mean of 9.01 mg/kg (table 1). The total N count in the soil ranges from 6.44 mg/kg to 22.86 mg/kg with a mean of 9.12 mg/kg. There is one outlier of 22.86 mg/kg at sample point A12.
Sample NO3[mg/kg dry soil] NO2[mg/kg dry soil] NH4[mg/kg dry soil]
Total N[mg/kg dry
soil] PO4[mg/kg dry soil]
A1 17.48 0.31 10.20 11.46 14.78 A2 20.98 0.23 10.42 8.89 7.11 A3 13.85 0.16 3.94 7.56 3.27 A4 16.22 0.21 10.28 7.13 4.99 A5 16.98 0.16 4.23 8.95 2.55 A6 19.42 0.25 12.24 8.85 2.11 A7 14.40 0.21 10.64 7.13 4.89 A8 11.43 0.21 10.23 6.44 4.70 A9 - - - - - A10 13.17 0.44 11.57 10.03 2.66 A11 14.77 0.21 10.35 7.56 2.05 A12 76.34 0.20 4.72 22.86 10.56 A13 19.35 0.30 10.47 8.41 1.67 A14 15.84 0.30 11.08 7.05 2.48 A15 11.43 0.16 4.47 7.65 2.71 A16 13.80 0.33 9.85 6.96 2.39 A17 13.17 0.17 4.12 7.11 6.53 A18 13.54 0.40 10.31 11.89 2.11 A19 16.78 0.23 10.25 8.74 4.39 A20 20.06 0.24 11.80 8.76 4.44 Mean 18.90 0.25 9.01 9.13 4.55 Min 11.43 0.16 3.94 6.44 1.67 Max 76.34 0.44 12.24 22.86 14.78
Table 1: Table containing the results from the auto analyzer. These sample values have all been calculated to be mg/kg dry soil(ppm) and have the mean, min and max values added. The complete table of results per sample point are visible in Appendix I. These results were converted from µmol/l to mg/ kg dry soil
3.2 Data on the farm
The total agricultural area of the farm is 42 ha, the surface area of the research area (Figure 1, field A) is 16 ha. The main fertilizer used on this field is slurry, produced by the 225 cows that live year round in the cowshed. The amount of slurry used is 75 m3/ha which accounts to 1200 m3 in total for this field. According to TEAGASC (2021), the nutrient content of slurry is 1 kg N/m3 & 0.6 kg P/m3. Which totals in 1200kg N & 720 kg P input on the field. Two different additional artificial fertilizer are used; Grasplus 14000 24% N & KAS 27% N. Both are used at 333 kg/ha which results in 5033 kg used per fertilizer on this field. The contents of these fertilizers combined are 1087.19 kg NH4 & 936.19 kg NO3 (Appendix IV).
3.2.1 Amount of fertilizer
The total contents of NO3, NO2, NH4 & PO4 within the research field are calculated with equation 1, the mean values (Table 1) and the bulk density of the soil of 1.54 g/cm3(Lamberink, 2013). This amounts to 87.32 kg of NO3, 1.16 kg of NO2, 41.62 kg of NH4, 42.18 Total N & 21.02 kg of PO4.
𝐾𝑔
ℎ𝑎= 𝑆𝑜𝑖𝑙 𝑠𝑎𝑚𝑝𝑙𝑒 ∗ 𝑏𝑢𝑙𝑘 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 ∗ 0.3𝑚 (𝑠𝑎𝑚𝑝𝑙𝑒 𝑑𝑒𝑝𝑡ℎ) ∗ 10000 𝑚
2 Eq. (1)
According to Beek (2007), the mowing of agricultural grassland takes up 206 kg/ha N & 20 kg/ha P every year in the Netherlands, this is a sample from the Vlietpolder in 2001. This totals in 3296 kg N output & 320 kg P output. Beek (2007) also states an atmospheric deposition of 31 kg/ha N per year resulting in 496 kg N.
P (kg y-1) N (kg y-1)
Input slurry 720 1200
Input artificial fertilizer NH4 = 1087 | NO3 = 936
Atmospheric deposition 496
Total Input 720 3719
Output by mowing 320 3296
Surplus 400 423
Amount currently in field PO4 = 21.02 NO3 = 87.32 | NH4 = 41.62 | Total N = 42.18
Proposed leaching 379.98 380.82(only used Total N)
Table 2: all fertilizer data combined. Showing the input on the fields, the output of nutrients due to mowing, the surplus that should be left in the field, the amount that is measured and then the proposed leaching.
The PH & EC values are visible in Appendix II. With the PH having an average value of 7.69, minimum of 7.55 and maximum of 7.81. The PH is similar across the sample points. The EC value has an average of 239, minimum of 212 and a maximum of 291. With A12 being the only somewhat higher outlier.
3.2.2 Cost of fertilization:
Total cost of fertilization at the moment according to previous research at this farm is €26 per 100 kilogram artificial fertilizer, which accounts to €2617 for the research field. The slurry used as fertilization is without cost since it is available on the farm (Appendix III).
4. Discussion
The results of this study have shown that the spatial distribution of Nitrogen and
Phosphorus(phosphate) is not uniform across the field, which was expected for N according to other research(Geisseler & Horwath, 2016). However the values do not differentiate from each other by a large margin, except for the outliers A01 & A12. Therefore it could be stated that the current fertilization regime distributes the minerals relatively effectively across the agricultural field, but improvements can be made to have more uniform values across the field. The extreme outliers in the data usually indicate incorrectly measured samples or some other contamination during data collection.
4.1 Phosphorus
In case of the PO4 however, the outliers could have an explanation. The PO4 values of A01 , A02 and A12 & A17 are high compared to the mean value. The higher values of A01 and A02 could be explained due to their close proximity to the farm, while the manure heaps (dry manure) of the farm were located close by these two points. Cow manure is very rich in phosphorus and could therefore explain a leeching of phosphorus to this part of the field (Kemppainen, 1889). To support this statement, during the sample collection on the farm a stream of brownish water was observed flowing from the manure heaps to the corner of the field(A01 and A02). This stream flowed as it had been raining at the farm that day. The high A12 value could be explained due to an excess of
fertilizer used at this particular spot, however this cannot be confirmed. There seem to be slightly higher values of PO4 between the A01 and A12 samples, at samples A17 and A04. This line with higher values could support the theory of an excess of fertilizer at A12. The fertilizing machine could have stopped at A12 and then driven back to the farm, causing a slight increase in PO4 in this line of sample locations. This could be possible due to the construction site around the windmill. Large mounds of sand were located just north of sample point A12. The average amount of PO4 [4.55] measured in the field is low compared to the optimal phosphorus amount of 30-50 mg/kg suggested by Beegle & Durst (2002). A possible explanation would be that there had been no recent
fertilization.
4.2 Nitrogen
The NO3 values in figure 2.2 also show an extreme outlier at sample point A12. The chosen depiction of Nitrogen is NO3 while this is one of the main components in the fertilizers used on the field. This is also the form of Nitrogen that will easily leech out of soils when present in high concentrations(Beek, 2007 ; Johnson et al., 2005). The outlier could be explained due it’s close proximity to the windmill construction site. While this sample point is located closest to the activities where large industrial equipment(bulldozer, cranes) and other vehicles are working. Studies show that the Nitrogen deposition from busy roads can be measured to around 100m from the source (Khalid et al., 2020 ; Middlecamp & Elliot, 2009). This outlier could therefore be explained due to the emissions of these vehicles and the resulting deposition of Nitrogen. However if the previously mentioned heightened PO4 value at this sample point is caused by an excess of fertilizer. The NO3 levels could also have been affected while these are also present in fertilizer.
The values of NO3 are surprisingly not heightened at the western side of the field even though there is a highway around 100m left of the field (Figure 1). It was expected that some deposition would be recorded (Khalid et al., 2020 ; Middlecamp & Elliot, 2009). The overall values of NO3, with a mean of 18.9 are average to relatively low. These values should range between 10-50 mg/kg for healthy plant development (Pattison, Moody & Bagshaw, 2010). All the values are therefore within the boundaries, except for sample point A12.
The NO2 values are very low, which can be explained by the fact that it quickly reacts to form other molecules such as NH4 and NO3 (Bleeker, 2018). These values however are not used while
some were below the limit of detection(LID) of the auto analyzer and therefore inaccurate. The NH4 values were unusable for the same reason.
4.3 PH values
The PH values of the samples are noted in Appendix II. They are fairly neutral and towards alkaline with a mean of 7.69. A more acid PH value is expected regarding the amount of fertilizer that is used. However the soil in Flevoland is reclaimed seabed and therefore very calcium rich. During data collection, broken sea shells could still be found in the soil. While the PH values are very similar, they are neglected for further research.
4.4 Fertilizer
Table 2 shows the differences in input of fertilizer and what is actually present in the soil. These are big differences. According to the data, the soil is being over fertilized and therefore is leeching nutrients. However, these data are not all recorded from the research site in Almere and they try to portray how that the soil characteristics could be. Therefore, this comparison is not a true representation of the reality.
The reduction of leeching could however be achieved through fertilizing more often, with fewer amount of fertilizer at a time. This could be more cost effective for the farmer, however it would require more time.
4.4.1 Change to sewage sludge
Communicating with the farmer, it became clear that if the farmer would completely switch to BBF it would have to be very convenient or cheaper than the current fertilization(personal communication, April 7, 2021). The Netherlands already uses a lot of BBF, due to the amount of livestock in the country. However to change from the additional artificial fertilizer to completely BBF, sewage sludge has to be used. As said before the Netherlands does not use sewage sludge for fertilizing at the moment due to regulations. However the best option would be to use pallets, converting sewage sludge to manageable pallets which improves distribution capacity and nutrient richness (Kominko, Gorazda, & Wzorek, 2017). This would also be beneficial for the farmers, while they could rely on the already familiar fertilization technique of artificial fertilizers.
Sewage sludge has depending on the origin, degree of dewatering and process of treatment very different nutrient contents. For example in the German Environment Agency (2018), 100 tonnes of wet sludge containing contains on average about 190kg of Nitrogen, of which 55kg are NH4, 195kg of phosphate (P2O5) and 30kg of potassium (K2O). While sewage sludge is always abundant, there is definitely a lot of nutrients to be gained.
The Netherlands is one of the few countries in Europe that has no agricultural use for sewage sludge(Rengel, 2020). Ireland and Portugal use as much as 90% of their sewage sludge for agricultural purposes. This proves it is possible to use the sewage sludge, however a lot of questions are being raised about the contamination of the soil due to heavy metals, pharmaceuticals etc. that have a bad effect on health. A lot of the sewage sludge used right now is not very expensive, and in some cases free for the farmers, while the municipality wants to get rid of the excess sewage. However due to increasing understanding of contaminants present in waste water and the effect they have, better treatment of sewage sludge is necessary before it is used as fertilizer(German Environment Agency, 2018 ; Rengel, 2020). These new and better techniques would increase the price of the existing sewage sludge.
4.5 Further research
Research into the water ways around the farm could be interesting for further research. If the contents of P & N are measured together with the input and output of these nutrients on the field. The precise amount of leeching can be calculated and reviewed for this specific soil.
Another interesting topic to investigate is if other techniques of fertilization change the spatial distribution of the N and P. With this research, it will be possible to point out the best fertilization methods.
4.6 The validity of the research
The validity of the research can be increased when using larger datasets, with more data, more precise results can be achieved. The spatial data from this research was interpreted
qualitatively due to time restraints and an error which occurred during data analysis. The exact location of the datapoints was lost, however the datapoints have been added again on the map at approximately the right location. The opinion of the local farmer about BBF has also been
documented in the research. To get an accurate representation however, multiple different farmers have to be questioned about the willingness and feasibility of sewage sludge as BBF.
The auto analyzer had broken down between the measurement of 2 batches of samples and had to be recalibrated. Due to different settings on the auto analyzer, the Limit of Detection was not the same for NH4 and NO2. Therefore only part of the NH4 and NO2 values were useful. This does however indicate that these values were extremely low already, just barely being above the LID. Nitrogen varies with soil water, so the Nitrogen values can differ after rain(Ockerman & Livingston, 1999). I tried to compensate this in the calculation of the data. By only calculating the dry weight of the soil and take out the water weight. However inaccuracies can always occur.
Sewage sludge has depending on the origin, degree of dewatering and process of treatment very different nutrient contents. For example in the German Environment Agency (2018), 100 tonnes of wet sludge containing contains on average about 190kg of Nitrogen, of which 55kg are NH4, 195kg of phosphate (P2O5) and 30kg of potassium (K2O).
5. Conclusion
The spatial distribution of N and P was as expected not uniform over the research field. However the values were all reasonably low and even below the optimal threshold for PO4. This is probably due to the fact of no recent fertilization. The Nitrogen values are within the expected margins, except for sample point A12. Which is probably due to the fact of the construction site. The difference in input and output does indicate that there is leeching of nutrients. The amount is difficult to calculate while values had to be gathered from other sources that this research field. The current fertilization regime is doing its job, no unexplainable differences in spatial distribution have been noticed. However, the use of artificial fertilizer could be reduced as the data now suggest over fertilization. If the Dutch government would allow for sewage sludge to be used, it could provide for the additional nutrients. The use of sewage sludge as fertilize in the Netherlands would only replace the artificial fertilizer, since animal manure still has to be used. The best option would be to convert sewage sludge to dry tablets. Therefore reducing the threshold for farmers to use this fertilizer. The cost of the sewage sludge would have to be lower than the cost of artificial fertilizer and contain safe amounts of nutrients.
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The data collected during the research has been stored in Github. This is open access and available with this link: https://github.com/erikheijckers/P-N-grassland-Almere.git
Acknowledgements
Special thanks to:Dhr. R.L. (Rutger) van Hall, who supported us in the lab in our experiments.
Dhr. B. (Bram) Buter, who joined us in the fieldwork and labwork. Thanks for his insight in the farm, while he already was performing ongoing research.
I was guided by mentor D. (Donya) Danesh. Special thanks to her for supporting me during the thesis.
Dhr. B. (Boris) Jansen. Who was my supervisor and provided us with this project and possibility to do research.
Appendices
Appendix I: Data for all sample points
Wet soil in sample [gram]
Water content [percent]
Water in soil sample
[gram] Dry soil in sample [gram]
Demiwater added to sample [ml] NO3 [µmol/l] NO2 [µmol/l] NH4 [µmol/l] Total N [µmol/l]
PO4 [µmol/l] DON [µmol/l] Water total (L) Zuurtegraad [pH] EC [µs] A01.1 30.19 21.35% 6.44505618 23.74 75 81.4 1.7 20 242 11 142 0.08144506 7.75 255 A01.2 30.34 21.35% 6.477078652 23.86 75 83.4 2.3 20 236 11.7 134 0.08147708 7.77 268 A02.1 30.86 23.24% 7.170788382 23.69 75 91.5 1.6 20 175 6.2 63 0.08217079 7.72 246 A02.2 30.34 23.24% 7.049958506 23.29 75 102 1.3 20 188 4.5 67 0.08204996 7.72 242 A03.1 20.02 23.29% 4.661696751 15.36 50 68.1 1 7.5 161 2.46 85 0.0546617 7.55 250 A03.2 20.12 23.29% 4.684981949 15.44 50 57.7 1 7.4 143 2.38 78 0.05468498 7.59 256 A04.1 30.3 22.34% 6.768041237 23.53 75 73.4 1.3 20 148 4.1 54 0.08176804 7.78 227 A04.2 30.8 22.34% 6.879725086 23.92 75 78.3 1.4 20 147 3.5 48 0.08187973 7.73 233 A05.1 20.03 22.40% 4.48672 15.54 50 78 1 8.5 190 1.56 103 0.05448672 7.6 246 A05.2 20.07 22.40% 4.49568 15.57 50 78.4 1 7.7 175 2.27 88 0.05449568 7.59 245 A06.1 30.43 32.18% 9.792352941 20.64 75 80 1.4 20 158 1.3 58 0.08479235 7.72 245 A06.2 30.5 32.18% 9.814878893 20.69 75 72.6 1.2 20 150 1.4 57 0.08481488 7.65 243 A07.1 30.21 23.93% 7.230222635 22.98 75 66.7 1.3 20 146 4.2 60 0.08223022 7.67 250 A07.2 30.37 23.93% 7.26851577 23.1 75 63.4 1.3 20 139 3 55 0.08226852 7.79 247 A08.1 30.18 21.39% 6.454682081 23.73 75 51.5 1.4 20 131 3.8 60 0.08145468 7.73 213 A08.2 30.21 21.39% 6.461098266 23.75 75 55.9 1.2 20 137 3.4 61 0.0814611 7.71 211 A09.1 - - - - -A09.2 - - - - -A10.1 30.36 29.03% 8.814193548 21.55 75 61.4 2.3 20 224 2.1 144 0.08381419 7.61 232 A10.2 30.55 29.03% 8.869354839 21.68 75 48.1 2.6 20 145 1.5 78 0.08386935 7.6 224 A11.1 30.23 22.09% 6.677309237 23.55 75 69.3 1.2 20 156 1.6 66 0.08167731 7.76 228 A11.2 30.14 22.09% 6.657429719 23.48 75 67.9 1.4 20 155 1.5 68 0.08165743 7.81 213 A12.1 20.21 25.32% 5.117553648 15.09 50 319 1 8.3 426 7.2 99 0.05511755 7.59 281 A12.2 20.32 25.32% 5.145407725 15.17 50 357 1.4 9.1 470 8.04 103 0.05514541 7.59 301 A13.1 30.11 22.83% 6.87359596 23.24 75 91.4 2.4 20 179 1.1 68 0.0818736 7.75 231 A13.2 30.31 22.83% 6.919252525 23.39 75 86.3 1.3 20 163 1.4 57 0.08191925 7.77 225 A14.1 30.17 26.00% 7.843055028 22.33 75 69.3 1.8 20 141 1.6 51 0.08284306 7.74 221 A14.2 30.07 26.00% 7.817058824 22.25 75 68.2 1.7 20 130 1.9 44 0.08281706 7.77 221 A15.1 20.58 21.98% 4.523456376 16.06 50 61.3 1 8.2 165 1.02 95 0.05452346 7.64 231 A15.2 20.18 21.98% 4.435536913 15.74 50 46.4 1 9.3 154 3.12 98 0.05443554 7.69 222 A16.1 30 18.93% 5.680147059 24.32 75 77.8 2.3 20 157 2 58 0.08068015 7.78 227 A16.2 30.3 18.93% 5.736948529 24.56 75 56.9 2 20 144 1.8 66 0.08073695 7.84 225 A17.1 20.03 25.09% 5.024590444 15.01 50 60.1 1 7.5 135 5.16 67 0.05502459 7.54 242 A17.2 20.65 25.09% 5.180119454 15.47 50 57.3 1 7.8 146 4.32 80 0.05518012 7.56 240 A18.1 30.45 22.00% 6.699 23.75 75 66.5 1.4 20 246 1.7 160 0.081699 7.63 252 A18.2 30.08 22.00% 6.6176 23.46 75 59.8 3.6 20 245 1.5 163 0.0816176 7.65 240 A19.1 30.13 21.31% 6.420105485 23.71 75 78.5 1.5 20 164 1.9 67 0.08142011 7.63 247 A19.2 30.04 21.31% 6.40092827 23.64 75 78.9 1.4 20 199 4.8 102 0.08140093 7.58 243 A20.1 29.95 29.50% 8.836020583 21.11 75 92.4 1.4 20 172 2.8 59 0.08383602 7.82 236 A20.2 30.2 29.50% 8.909777015 21.29 75 71.1 1.2 20 144 3.1 53 0.08390978 7.8 237 B01.1 30.44 28.25% 8.599918699 21.84 75 934 1.6 20 1057 4.5 105 0.08359992 7.54 280 B01.2 30.21 28.25% 8.534939024 21.68 75 889 1.4 20 982 5.6 74 0.08353494 7.49 324 B02.1 20.12 28.31% 5.695735294 14.42 50 461 1 8.4 542 6.19 72 0.05569574 7.59 280 B02.2 20.36 28.31% 5.763676471 14.6 50 421 10 9.1 524 3.75 84 0.05576368 7.57 290 B03.1 30.49 27.09% 8.258886827 22.23 75 1030 87.3 94 1284 2 72 0.08325889 7.54 344 B03.2 30.54 27.09% 8.272430427 22.27 75 832 1.2 20 926 4.6 76 0.08327243 7.59 315 B04.1 30.1 26.38% 7.93976378 22.16 75 172 3.4 20 240 2.5 46 0.08293976 7.4 302 B04.2 29.8 26.38% 7.860629921 21.94 75 297 1.3 20 371 2.5 55 0.08286063 7.66 307 B05.1 30.07 27.10% 8.148339161 21.92 75 1203 1.5 20 1251 3.5 28 0.08314834 7.48 347 B05.2 30.01 27.10% 8.13208042 21.88 75 1288 1.7 20 1364 5 56 0.08313208 7.46 345 B06.1 30.24 26.64% 8.057024221 22.18 75 481 1.8 20 530 5.7 28 0.08305702 7.43 272 B06.2 30.36 26.64% 8.08899654 22.27 75 510 1.4 20 566 0.7 37 0.083089 7.46 273 B07.1 30.19 27.98% 8.446011905 21.74 75 755 1.2 20 854 3.4 80 0.08344601 7.4 316 B07.2 30.11 27.98% 8.423630952 21.69 75 952 59 20 1074 3.5 44 0.08342363 7.38 344 B08.1 30.29 28.93% 8.763514563 21.53 75 527 1.8 20 592 15.4 45 0.08376351 7.41 311 B08.2 29.89 28.93% 8.647786408 21.24 75 524 1.5 20 591 8.2 48 0.08364779 7.45 306 B09.1 20.39 27.04% 5.513622449 14.88 50 915 4.2 8.2 1028 13.9 100 0.05551362 7.55 350 B09.2 20.25 27.04% 5.475765306 14.77 50 991 39.3 33.4 1205 7.59 141 0.05547577 7.53 346 B10.1 30.52 32.31% 9.861066236 20.66 75 530 6.6 20 601 1.8 46 0.08486107 7.52 336 B10.2 30.08 32.31% 9.718901454 20.36 75 295 1.6 20 359 0.9 43 0.0847189 7.58 319 B11.1 30.08 26.35% 7.927220217 22.15 75 675 1.8 20 755 4.9 61 0.08292722 7.88 342 B11.2 30.42 26.35% 8.016823105 22.4 75 993 1.6 20 1026 6 13 0.08301682 7.51 364 B12.1 29.8 25.15% 7.495334686 22.3 75 1277 71.3 47 1435 4.7 40 0.08249533 7.32 386 B12.2 29.88 25.15% 7.515456389 22.36 75 562 27.5 20 656 2.8 50 0.08251546 7.47 310 B13.1 20.05 29.29% 5.873598553 14.18 50 851 22 9.7 996 5 113 0.0558736 7.47 349 B13.2 20.08 29.29% 5.88238698 14.2 50 1183 48 35.1 1485 17.1 219 0.05588239 7.47 364 B14.1 30.15 29.58% 8.919375 21.23 75 756 2.4 20 904 14.9 128 0.08391938 7.58 319 B14.2 30 29.58% 8.875 21.13 75 827 1.9 20 1004 12.8 156 0.083875 7.52 329 B15.1 30.02 13.41% 4.025670498 25.99 75 468 1.7 20 544 3.6 54 0.07902567 7.55 280 B15.2 29.88 13.41% 4.006896552 25.87 75 469 1.2 20 535 3.7 46 0.0790069 7.69 272 B16.1 20.76 25.78% 5.352486188 15.41 50 274 1 7.6 359 7.08 77 0.05535249 7.48 319 B16.2 20.1 25.78% 5.182320442 14.92 50 340 1 10.5 485 7.06 134 0.05518232 7.53 316 B17.1 30.01 26.20% 7.86262 22.15 75 1368 1 20 1447 5.6 60 0.08286262 7.31 369 B17.2 30.05 26.20% 7.8731 22.18 75 1179 1.2 20 1256 6.3 57 0.0828731 7.36 344 B18.1 20.11 23.62% 4.749225092 15.36 50 539 196 13.3 855 5.46 106 0.05474923 7.54 306 B18.2 20.11 23.62% 4.749225092 15.36 50 520 85.5 7 649 4.97 36 0.05474923 7.58 285 B19.1 30.42 29.25% 8.897989031 21.52 75 702 1.2 20 801 8.3 80 0.08389799 7.61 293 B19.2 30 29.25% 8.775137112 21.22 75 711 1.2 20 813 14 83 0.08377514 7.68 291 B20.1 30.24 29.53% 8.928939641 21.31 75 764 5.1 20 885 1.5 96 0.08392894 7.41 348 B20.2 29.92 29.53% 8.834453507 21.09 75 1686 25.9 20 1759 2.4 29 0.08383445 7.33 441 BL 1 50 4.2 1 6.6 20 0.79 15 0.05 8.03 32
Appendix II: PH & EC Table
Sample PH EC (mS/cm) A1 7.76 261.5 A2 7.72 244 A3 7.57 253 A4 7.755 230 A5 7.595 245.5 A6 7.685 244 A7 7.73 248.5 A8 7.72 212 A9 - - A10 7.605 228 A11 7.785 220.5 A12 7.59 291 A13 7.76 228 A14 7.755 221 A15 7.665 226.5 A16 7.81 226 A17 7.55 241 A18 7.64 246 A19 7.605 245 A20 7.81 236.5 Mean 7.69 239.3684 Min 7.55 212 Max 8.81 291Appendix III: Data of Farms – interview
Farm Almere
Category Unit Extra info Total
Farm Barn size m^2 2459
Field size hectare 42
Manure/ha m^3 75 Population Race Holstein Frysian Total # cows 225 Production cows 130 Young cows 95
Flux # cows /year 25-30%
Input Food kg/year/total #cows 4047589 Water m^3/year 7500 Fertilizer ton/year Grasplus 14000 24% N 14 KAS 27% N 14
Output Cows output kg/year
&1000 kg 1
stier 32000
Milk production L/year/cow 10500
L/year/farm 1400000
Manure total m^3 6000
Manure own use (on
fields) 3150
OWN USE % 52,50%
Table 5: Data gathered on the farm by previous research.
Relevant interview questions:
Hoe veel mest wordt jaarlijks buiten de stal opgeslagen? 200 ton vaste mest
Hoe veel mest wordt er aan bouwland toegediend en hoe veel aan grasland? 75m3/ha drijfmest aan grasland, 100 ton vaste mest aan bouwland.
Wat is de verhouding tussen geïmporteerde, geëxporteerde, verwerkte vaste en dunne mest? 50% vaste mest wordt geëxporteerd, 50% wordt toegediend. 100% van de drijfmest wordt aan grasland toegediend. Geen import (excl. kunstmest).
Wat is de toedieningsmethode van de mest? Mbv. sleufkouter, sleepvoeten, zodenbemesting of bovengronds?
Appendix IV: additional calculation results
Data on the farmResearch area gathered from ArcGIS Pro: 160755 m2 -> 16 ha Total slurry driven on research field: 16 ha * 75 m3/ha = 1200 m3 Artificial fertilizers used:
Grasplus 14000 24% N: 8.1% NH4 ; 5.1% NO3 ; 10.8% NH2 14.000 kg/jaar
KAS 27% N: 13.5 % NO3 ; 13.5% NH4 14.000 kg/jaar
Input from Artificial fertilizer: 14.000 kg / 42 ha = 333.33 kg/ha Grasplus 14.000 24%N: 16 ha * 333.33 kg/ha = 5033.28 kg NH4: 5033.28 * 0.081 = 407.70 kg NO3: 5033.28 * 0.051 = 256.70 kg Kas 27%N: 16 ha * 333.33 kg/ha = 5033.28 kg NH4: 5033.28 * 0.135 = 679.49 kg NO3: 5033.28 * 0.135 = 679.49 kg
Bulk density: 1.54 g/cm3 = 1540 kg/m3 (Lamberink, 2013) Output grassland by mowing:
206 kg/ha N * 16ha = 3296 kg N 20 kg/ha P * 16 ha = 320 kg P Atmospheric deposition: 16 ha * 31 kg/ha = 496 kg
Total contents calculated with equation 1:
NO3: 0.0000189 * 1540 * 0.3 * 10000 = 87.318 kg NO2: 0.00000025 * 1540 * 0.3 * 10000 = 1.155 kg NH4: 0.00000901 * 1540 * 0.3 * 10000 = 41.6262 kg PO4: 0.00000455 * 1540 * 0.3 * 10000 = 21.021 kg Cost:
Artificial fertilizer = €26 per 100 kg.
Artificial fertilizer used: €0.26 * 5033.28 kg €0.26 * 5033.28 kg = €2617.30
