The impact of tillage practices on the
aggregate stability of a clay soil
Susanne de Bruin May 2021, AmsterdamSupervisors: Elly Morriën, Erik Cammeraat, Anne Uilhoorn, Rutger van Hall Future Planet Studies, Amsterdam
Abstract
This study is focused on the concept of nature inclusive agriculture, which is part of the vision of the Dutch Ministry of Agriculture, Nature and Food Quality's (ministerie LNV) about circular agriculture. The research aim of this paper is to describe the impact of tillage practices on the aggregate stability of the soil. Tillage practices can cause problems in agriculture as they have significantly harmful effects on the soil. A few examples are: soil compaction, soil degradation, a decrease in soil productivity and a decline in soil organic matter, which can all contribute to a decline in aggregate stability
.
This study will also take four different types of organic matter (POXC, HWC, total organic matter and total carbon content) into account and their relation to aggregate stability. This research is based on the theory that tillage leads to lower aggregate stability, and to loss of organic matter from the top soils, which can eventually lead to soil degradation. Samples were collected from cauliflower fields in Hem, the Netherlands. The samples were examined in a laboratory, by the use of wet sieving methods, to calculate the aggregate stability. These results are compared with earlier collected data and discussed within the selected framework of sustainable agricultural practices. Results showed that tillage practices did not have a significant effect on aggregate stability within the indicated 5-month time period (P > 0.05). However, the different fields had a significantly different aggregate stability (P < 0.0001). All four types of organic matter had a significantly positive moderate correlation with the aggregate stability in the soil. We conclude that aggregate stability does not react to soil management effects strategies on the short term (< year).Keywords: Sustainable agriculture – tillage practices – aggregate stability – microbial activity – soil organic matter – carbon – POXC – HWC – soil fertility – crop production
For the general public
De invloed van microben en organisch materiaal op de aggregaat stabiliteit in de bodem. Door de toenemende groei van de bevolking zal ook de vraag naar voedsel toenemen. Hierdoor zullen conventionele landbouwmethodes, zoals intensief ploegen, de overhand krijgen, ook in Nederland. Dit onderzoek heeft het doel om de invloed van intensief ploegen uit te leggen, door de link tussen microben en organisch materiaal en aggregaat stabiliteit in de bodem te onderzoeken. Het onderzoek is verricht in bloemkoolvelden in Hem in
Nederland. De monsters die uit het veld zijn genomen, worden in het laboratorium onderzocht op aggregaat stabiliteit, microbiële activiteit, organisch materiaal en twee vormen van organische koolstof. Door middel van deze resultaten kan er met een nieuwe blik naar duurzame landbouw worden gekeken.
Kernwoorden: Duurzame landbouw – ploegen – aggregaat stabiliteit – microben – organisch materiaal – organische koolstof – voedselproductie – vruchtbare bodem
Content list
1. Introduction ... 4
1.1 Increasing SOM in cauliflower fields ... 4
1.2 Tillage ... 4
1.3 Aggregate stability ... 5
1.4 Soil organic matter ... 6
2. Methods and Data ... 7
2.1 Study Area ... 7 2.2 Data Collection ... 8 2.2.1 Fieldwork ... 8 2.2.2 Lab analysis ... 8 2.3 Statistical Analysis ... 11 3. Results ... 12
3.1 Effects of Tillage treatment on aggregate stability ... 12
3.2 Aggregate stability and organic matter ... 13
4. Discussion ... 14
4.1 Aggregate stability ... 14
4.2 Soil organic matter and aggregate stability ... 15
5. Conclusion ... 16
6. Literature list ... 18
7. Acknowledgements ... 22
8. Appendices ... 23
8.1 Extensions of the methodologies ... 23
8.2 Extension of the results ... 29
1. Introduction
Due to a rise in the global population, food demand has been increasing. Consequently, the need for agricultural crops on global level is also increasing, and is expected to intensify in the coming
decades (Tilman et al., 2011). This could, among others, lead to a more intensive use of croplands to meet the rising demands. However, this form of agricultural expansion can have significant
consequences regarding the impacts on soil degradation. For example, intensification of agriculture can have harmful effects on the stability of soil aggregates (Sithole et al., 2019). A decrease in aggregate stability and other important soil qualities can cause a decrease in crop production (Adeyemo, 2010), therefore causing a reinforcing feedback loop. This is considered an urging
problem, especially in the Netherlands, as food production in the Netherlands covers 54% of its total surface (CBS, 2018). With more than half of the land use in the Netherlands attributed to agricultural purposes, intensifying the agricultural methods will have major impacts on the Dutch, and even global, environment.
In this paper, the impact of intensive agricultural practices, namely tillage practices, on the aggregate stability of the soil will be researched. Moreover, this research aims to include different types of organic matter content and their relation to aggregate stability. Although numerous scientific assumptions have been made about soil organic matter, soil aggregates, and microbial activity, there is a significant knowledge gap when it comes to research about the relations between the stability of soil aggregates and soil organic matter loss from the soil (Abiven et al., 2009).
1.1 Increasing SOM in cauliflower fields
This research will be part of an overarching project called “Duurzame Bloemkool” (Sustainable Cauliflower). This project was commissioned by the province Noord Holland and carried out in collaboration with Amsterdam Green Campus, the University of Amsterdam and two entrepreneurs, Wim Reus and Pé Slagter. This project studies the biodiversity, water quality, crop quality, yield and soil quality of multiple agricultural fields, cultivated by cauliflower. For four consecutive years the cauliflower fields are treated with different field management treatments in order to study the beneficiary effects of no-till practices and manure on the previously stated qualities. The idea behind this project springs from the circular agricultural vision from the Ministry of Agriculture, Nature and Food quality and fixates on a nature inclusive management in regional agriculture. If feasible, this project could lead to a more sustainable and nature inclusive approach of agricultural land management in a broader region, or globally.
1.2 Tillage
As stated before, harvesting and farming methods can have a harmful effect on the environment. One of those methods, to be discussed in this paper, is tillage. Tillage is a form of agricultural land preparation used to increase yield and as a short-term cost-effective farm practice. This agricultural practice is used to kill weeds, prepare a seedbed, manage crop waste and absorb nutrients in the
and where an aggressive, intensive, form of tillage is used to plough the fields (Sithole et al., 2019). This industrial form of agriculture became extra popular due to its higher crop yields and favorable labor efficiency (Reganold et al., 1987). Consequently, this form of intensive agriculture is favored by most Dutch farmers as well (Bos et al., 2014). However, the effects of intensive tillage practices can have significantly harmful effects on the soil. A few examples are: soil compaction, soil degradation, a decrease in soil productivity and a decline in soil organic matter, which can all contribute to a decline in aggregate stability (Adeyemo, 2010). When zooming in on the effects of tillage on the specific soil qualities, it clearly shows that aggregate stability is impacted by intensive tillage
practices in two ways. Aggregate stability is indirectly effected because soil erosion, due to intensive tillage practices, can lead to slaking, disruption or breakdown of the aggregates (Lal, 2003).
Moreover, research shows that tillage methods intensify the processes of disaggregation, due to the disruption and separation of macroaggregates. The disaggregation of the soil can also enhance the effects of the previously mentioned soil erosion (Spiegel et al., 2003). The next paragraph will explain the significance of aggregate stability and the importance of the link between soil organic matter and aggregate stability will be laid out.
1.3 Aggregate stability
Aggregates are structures in the soil, constructed out of primary soil particles. Clay, sand and silt are tangled around soil organic matter and microorganisms, in groups of aggregates by the use of SOM. Aggregate groups are distinguished as the groups of particles that cohere more strongly to each other than to surrounding particles (Amézketa, 1999; Nimmo, 2013). Furthermore, aggregate stability, density, size, composition, and impact on the transport of solutes, colloids, fluids, and heat are all important physical aggregate characteristics (Nimmo, 2013; Scow, 1997).
Research has proven that multiple factors contribute to the stability of aggregates (Abiven et al., 2009; Amézketa, 1999; Bissonnais, 1996). The major soil properties contributing to the stability of the aggregates are; aluminum and iron oxides, cation content, clay mineralogy, texture, microbial activity and soil organic matter. Since agricultural practices can influence the abundance and characteristics of organic matter (OM), this can be assumed to be one of the most important factors when researching aggregate stability (Abiven et al., 2009).
On the other hand, aggregates have a beneficial role in OM stabilization in the soil. Aggregates are useful in regulating the availability of water, oxygen and organic matter in the soil to preserve the soil from decomposing (Yang et al., 2020). Moreover, aggregates provide a habitat for microbial activity. This is beneficial for agriculture as microbes decompose organic matter, recycle and
regulate carbon, nitrogen and phosphorous, help control diseases and improve overall soil structure (Helgason et al., 2010; Pineda et al., 2010; Vega, 2007). Consequently, the turnover and formation of aggregates in the soil are crucial for storage of soil organic carbon (SOC), as they also provide
physical protection against too much decomposition by soil microbes for the SOC (de Nijs &
Cammeraat, 2020; X. Wang et al., 2014). SOM is decomposed by microorganisms because it is a food resource for fungi and bacteria. Whereas these microbes are in turn food resources for
microarthropods and protists (Kooijman et al., 2020). Altogether, an increase in aggregate stability will have beneficiary effects on the soil. Consequently, the effects of soil management and land use
1.4 Soil organic matter
As aggregates are relying on soil organic matter for consistency, an increase in SOM would be assumed to also have beneficiary effects on the soil. SOM has a reinforcing relationship with the stability of the aggregates. An increase in SOM content is assumed to cause an increase in aggregate stability, whereas an increase in aggregate stability is assumed to lead to an increase in SOM. This section further explains the different types of soil organic matter examined in this paper.
The organic matter in the soil can be divided into multiple subgroups. Mostly, SOM is divided into total C, total N, and total S. this proposed paper focusses merely on the organic carbon content in the soil. The carbon content can be divided into two groups, hot-water extractable carbon (HWC) and permanganate-oxidizable carbon (POXC). HWC is assumed to be easier extractable than POXC (Ghani et al., 2003). Whereas the POXC is actively involved in nutrient cycling, can be used as an indicator for a healthy soil and has demonstrated to be comparatively sensitive to land management changes and variation in environment (Culman et al., 2012; Jenny et al., 2019; Weil et al., 2003b). In this paper, four types of organic matter will be measured, the total organic matter, the organic carbon content, the HWC and the POXC.
To meet the rising food demand with higher yields, but lower harmful consequences, impacts of harmful agricultural practices must be studied. This research focusses on the impact of tillage practices on the aggregate stability. It aims to describe the relations between different fractions of soil organic matter and the aggregate stability of the soil. The expected outcome of this research is that less intensive tillage practices will have a beneficial influence on the aggregate stability of the soil. This means that it is expected that fields with no-till practices will have a higher aggregate stability and this difference is expected to increase over time. Moreover, it is expected that the measured organic matter fractions correlate strongly to the stability of the aggregates in the soil.
2. Methods and Data
To measure the impact of tillage on aggregate stability and the relation between the SOM and the aggregate stability, samples have been collected in a fieldwork study. The first fieldwork study was done in March 2020 and the second fieldwork study was done in Augustus 2020, after the first crop season and tillage application. Next, those samples have been analyzed in a laboratory and put in to data tables. This section discusses the methods that have been utilized in order to produce the desired results.
2.1 Study Area
The fieldwork study has been performed on a marine clay soil in two agricultural fields in Hem, the Netherlands (52°9 - 52°39 N and 5°0 - 5°12 E) between March 2020 and March 2021 (see Figure 1). The area has an elevation of around -1 mASL and a slope around 0.25° (Pons & Oosten, 1974). Hem is characterized by a temperate, humid climate, with an average annual rainfall of 788 mm (67mm in January and 78mm in August) and an average temperature of 11.2°C, annually (4.4°C in January and 18.6°C in August). The exact classification of the soil, according to the Dutch National Key Registry of the Subsurface, is pMn85A, with young marine clay as parent material and a calcium-rich leek-/woudeerdgrond. The WRB would classify this as a Calcaric Regosol. Moreover, geomorphological features that are found in this area are tidal plains and tidal creek ridges (Pons & Oosten, 1974). This fieldwork was part of the first and second findings of the evaluation results on the physical qualities of the soil. Field A and B have previously been leveled and smoothed for farming production and were fertilized with different straw rich manure and were tilled as described in Table 1 and the legend in Figure 1.
Veld
Farm A B
Pé No-Till (PA) Tilled (PB) Wim Tilled (WA) No-Till (WB)
Table 1: The assigned fields for Tilled and No-till for farmer Wim and Pé used in this research.
2.2 Data Collection
The data collection proposed by this paper can be divided into two parts. The first part contains the sample collection during the fieldwork. The second part of the data collection has been done in a laboratory.
2.2.1 Fieldwork
In Hem, the Netherlands, two experimental sites were created (Figure 1), where 46 sampling spots are evenly distributed over all the plots. Because a significantly bigger difference between sample spots can be found in the top horizon, the samples were derived from 0-10 cm soil depth (Spiegel et al., 2003). Prior to the experiment, chemical, physical and biological measurements were performed in March 2020 to create baseline measurements for the values. For these samples, there was no difference in tillage practices as the project started after the baseline samples were taken. Figure 1 shows that one No-Till plot and one Tilled plot were created in field B both fertilized with goat manure. Moreover, field C shows 5 plots: three no-Till and two Tilled plots. The plots in field C were all fertilized with a different manure: goat, horse or pig. The different types of fertilizer used are named but have no further use in this proposed thesis. Field B (from farmer Pé) and field C (from farmer Wim) each belong to a different farm, where cauliflower is produced. The second
measurements were performed in August 2020, after the tillage application and after the cropping season, in order to measure the effects of a difference in tillage practices.
2.2.2 Lab analysis
Aggregate stability
The measurement of the aggregate stability was done by the wet sieving method, according to Bissonnais (1996). This wet sieving method was carried out using a Wet Sieving Apparatus. The stability of aggregates can be measured by putting the soil samples into the sieves from the machine. First of all, the samples were made by dividing the field sample from each point into four smaller samples. This meant there were four samples for each point in the field, so 80 samples for Pé and 40 samples for Wim, for each time period. This repetition reduces the probability of accepting the wrong hypothesis. Next, the sieves were soaked into low electrolyte water in the Wet Sieving Apparatus for three minutes exactly, to separate the unstable aggregates from the rest of the sample. After, the stable aggregates are separated from the sand and organic fraction by the use of an ultrasonic probe. This leaves stable and unstable aggregates dissolved in water to be dried in an
depth review on the protocol of the wet sieving apparatus used in the laboratory can be found in Appendix 8.1
%𝑊𝐴𝑆 = 𝑊1
𝑊1 + 𝑊2 𝑥 100%
Equation 1: where %WAS = percentage wet aggregate stability, w1 = weight of stable soil aggregates, w2 =weight of unstable soil aggregates. Based on (W. D. Kemper & Rosenau, 1986).
POXC
To measure the POXC values of the soil samples, the tests were carried out according to Weil et al., 2003. First of all, the air-dried soil samples were weighed in tubes. Next, the samples had to be mixed with deionized water and KMnO4 solution in an oscillating shaker. After a few minutes of settling, the samples were mixed with deionized water in a centrifuge tube. Lastly, the soil samples were tested on their absorption of four standard concentrations of KMnO4 in the
spectrophotometer. Permanganate Oxidizable C could then be determined with
Equation 2: where a is the intercept and b is the slope of the standard curve, Abs is the absorbance of the unknown soil sample, and Wt is the mass of soil (kg) used in the reaction. Based on (Weil et al., 2003a).
𝑃𝑂𝑋𝐶 (𝑚𝑔 𝑘𝑔45 𝑠𝑜𝑖𝑙 ) = [0.02 𝑚𝑜𝑙 𝐿45
= −(𝑎 + 𝑏 × 𝐴𝑏𝑠)] × (9000 𝑚𝑔 𝐶 𝑚𝑜𝑙45) × (0.02 𝐿 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 𝑊𝑡45) Equation 2: where a is the intercept and b is the slope of the standard curve, Abs is the absorbance of the unknown soil sample, and Wt is the mass of soil (kg) used in the reaction. Based on (Weil et al., 2003a).
HWC
The measurement of the HWC values was performed as described in Figure 2 based on (Ghani et al., 2003). The first step was to extract readily water-soluble carbon from the soil samples, which might come from animal excreta, recent liming, or soluble plant residues. The second stage involved extracting labile soil carbon components at 80 8C for 16 hours.
Figure 2: Schematic description of procedure for extracting water-soluble (WSC) and hot-water extractable C (HWC) (Reprinted from: Ghani et al., 2003).
LOI
Next, the loss-of-ignition (LOI) values can be effective indicators for the amount total organic matter in the soil. LOI values are measured by combusting soil samples and calculating the weight loss after the ignition of the samples (Salehi et al., 2011). This type of measurement has proved to be an effective method to estimate soil organic matter (Cambardella et al., 2001; Konen et al., 2002). The LOI measurements and analysis were done based on (Salehi et al., 2011). An estimation of the percentage of SOM in the samples was calculated with Equation 3: Where the Soil Organic Matter percentage can be calculated from the sample weights. Based on (Schulte & Hopkins, 2015)
𝑆𝑂𝑀HIJ =
soil weight after combustion − oven dry soil weight
oven dry soil weight × 100
Equation 3: Where the Soil Organic Matter percentage can be calculated from the sample weights. Based on (Schulte & Hopkins, 2015)
CNS
Lastly, the total carbon content was measured by the CNS method, which can achieve elemental recoveries of up to 98 percent (D. Wang & Anderson, 1998). When this data is required to determine short to medium-term changes in soil organic C storage, this kind of accuracy is important. These particular changes in carbon storage are an example of results of changes in the environment or the agricultural practices (Wright & Bailey, 2001). The CNS methodology can be used to measure the total amounts of nitrogen, carbon and sulfur. However, this proposed research uses the measured carbon. This proposed paper used an elemental combustion analyzer for solid materials: rock powder or soil. It is based on quick and absolute combustion of the soil sample, which lead to a transformation of the solid samples into the gas phase. Next, the concentrations of previously mentioned elements were then measured by gas chromatography in the combustion analyzer (Costech, 2015).
2.3 Statistical Analysis
The programming tool Rstudio was used to statistically evaluate data obtained from as many samples as possible during the thesis timeframe (Horton & Kleinman, 2015).
Independent variable(s)
1. Tillage practices (T or NT) 2. Different fields (PA, PB, WA, WB)
Dependent variable(s) • POXC values • HWC values • LOI values • CNS values • Aggregate stability
The independent variables count as a categorical variable and are also the control variables. The dependent variables all count as continuous variables. Next, three hypotheses were constructed to be tested in a programming tool. The first and most important hypothesis to be tested was whether tillage practices are of influence on the aggregate stability. Here, the null hypothesis is: tillage practice has no significant influence on the dependent variables. The second hypothesis was whether there was a difference in aggregate stability over the different fields. Here, the null hypothesis concludes: the different fields have no significant difference in aggregate stability. Both hypotheses could be tested with the same statistical test, a mixed ANOVA test. This test examines both variables at once, which reduces the possibility of a type 1 error. After this mixed ANOVA test, multiple posthoc tests (like Bonferonni or Tukey) were done to show the differences between the variables.
The third hypothesis to be tested was how the first four dependent variables (POXC, HWC, LOI and CNS) affect variable six, the aggregate stability. Here, the null hypothesis is: the POXC values, HWC values, LOI values, and CNS values do not correlate with the aggregate stability. This hypothesis was tested with a multiple regression test and a partial correlation test.
Before testing, the assumptions associated with the test: linearity, normality, homogeneity, were tested first. The scripts of the statistical tests can be found in Appendix 8.3.
3. Results
3.1 Effects of Tillage treatment on aggregate stability
Figure 3 shows the measurements of the aggregate stability, before and after the tillage application, per field, for the four chosen fields. All measured fields had a measured aggregate stability above 80%, and the mean aggregate stability for all fields was around 90%. The measured aggregate stability was highest for the fields owned by farmer Pé. Figure 3 shows that for the Tilled field from Pé (PB) and the No-Till field from Wim (WA), there has not been a significant change in aggregate stability over time. The percentage of stable aggregates in field PB and WA stayed considerably high, compared to the other fields. On the other hand, the aggregate stability in the No-Till field from Pé (PA), decreased by 4.6% after the crop season of the cauliflowers. Moreover, in the Tilled field from Wim (WB), the aggregate stability increased by 1.09% after the crop season. However, the changes in aggregate stability over time were too small to be significant (mixed ANOVA test; F= 0.71; df=108; P = 0.4
).
On the other hand, the statistical test did show a significant difference between the four different fields (mixed ANOVA test; F= 19.18; df=108; P < 0.0001). A bar graph on the fields before and after the tillage application can be found in Appendix 8.2.Figure 3: Median aggregate stability of a marine clay soil in Hem, the Netherlands (2020). Where PA and WB were used for tillage and PB and WA were used for No-Till practices. For fields P (farmer Pé) n = 10 points, and for fields W (farmer Wim) n = 5. Mixed ANOVA test, df = 108, p < 0.0001 for the fields and p > 0.05 for the different time periods. Box = 25th and 75th percentiles; points are outliers. Made to test extreme outliers; no extreme outliers. The aggregate stability was measured before and after the harvesting of cauliflowers (2020).
3.2 Aggregate stability and organic matter
Table 2 shows the aggregate stability and the different fractions of soil organic matter per chosen field from the two farmers. Table 2 shows a correlation between the five variables, as the changes per field seem coherent. For all five variables, the Tilled field from Wim has the lowest values, and the No-Till field from Wim has the second lowest values. The Tilled field from Pé has the highest values for all five variables, and the No-Till field from Pé has the second highest values. Figure 4, Figure 5, Figure 6 and Figure 7 show the regression lines of the four variables with the aggregate stability for each point. All four organic matter fractions show a significant correlation with the aggregate stability (Multiple Regression Analysis, F = 6.4, p = 0.001). Figure 7 shows that
permanganate-oxidizable carbon has the lowest correlation coefficient; around 0.55, and thus the lowest correlation with aggregate stability. Carbon percentage, hot water extractable carbon and organic matter by LOI have a very similar correlation coefficient around 0.65. Figure 4 shows that carbon percentage has the strongest correlation with aggregate stability. A bar graph on all five variables for each of the four fields can be found in Appendix 8.2
Table 2: The measured aggregate stability and different fractions of soil organic matter, including CNS, HWC, LOI and POXC (mean ± sd). Values are different scales. Measured on four different fields in a marine clay soil in Hem, the Netherlands.
Field Aggregate Stability (%) CNS (%C) HWC (mg/l) LOI (g) POXC (mg)
Pe T 96.78 (+/- 2.13) 4.42 (+/- 0.45) 141.80 (+/- 20.16) 0.071 (+/- 0.009) 1071.34 (+/- 287.76)
Pe NT 94.96 (+/- 1.94) 3.42 (+/- 0.51) 98.17 (+/- 17.89) 0.055 (+/- 0.011) 808.37 (+/- 190.38)
Wim T 86.14 (+/- 3.33) 2.42 (+/- 0.09) 60.99 (+/- 15.58) 0.038 (+/- 0.004) 546.57 (+/- 54.52)
Wim NT 92.44 (+/- 3.46) 2.54 (+/- 0.11) 70.60 (+/- 4.92) 0.047 (+/- 0.003) 586.70 (+/- 26.03)
Figure 4: Correlation between Aggregate Stability (AS) and % Carbon in soil. r = 0.6519632; p < 0.0001; formula:
AS = 0.79200 + 0.04187*%C
Figure 5: Correlation between aggregate stability and Hot Water extractable Carbon. r = 0.6383726; p < 0.0001; formula:
Figure 6: Correlation between aggregate stability and Organic Matter (OM) extracted by Loss of Ignition. r = 0.6388664;
p < 0.001; formula: AS = 0.8167 + 2.0093*OM
Figure 7: Correlation between aggregate stability and Permanganate-Oxidizable Carbon. r = 0.5686734; p < 0.01;
formula: AS = 8.554e-01 + 9.172e-05*POXC
4. Discussion
In this study, field samples were used to investigate the effects of tillage practices on aggregate stability and the link between aggregate stability and different types of organic matter. The results of the laboratory research showed that tillage practices had a weak impact on aggregate stability for the first harvest period. The expected outcome was that the fields with no-till practices would have a higher aggregate stability. However, this was only the case for the fields of farmer Wim and not for the fields of farmer Pé. Moreover, the expected outcome was that, over time, the difference in aggregate stability between the Tilled and No-Till fields had increased. However, this was not the case for the fields from farmer Wim. For the fields of farmer Pé the difference in aggregate stability did increase. Nonetheless, this means that the field with No-Till practices had a lower aggregate stability than the year before, which was not expected as an outcome. However, the ANOVA test showed that the changes in aggregate stability over time were not significant (P = 0.4). P < 0.5 is false for this test, leading to the statement that aggregate stability was not influenced by tillage practices over a 5-month time period. Furthermore, a significant correlation between aggregate stability and the four types of organic matter was found.
4.1 Aggregate stability
The mean aggregate stability measured in this paper lays between 85 and 95%, which seems remarkably high. Caravaca et al. (2004) studied the aggregate stability for semiarid Mediterranean soil and found a mean aggregate stability around 40% for cultivated soils, and a mean aggregate stability around 83% for forested soils. Many of the researched soils were similar to this paper: a Calcaric regosol, with a clay loam soil. However, the aggregate stability of the semiarid soils is significantly lower compared to this paper, especially for the cultivated soils. Again, this can be a
measured aggregate stability percentages similar to this research. In their research done in
Mediterranean Calcaric regosols in southern France they studied multiple vineyards with a silty clay soil. Here, one of the fields had similar results compared to this paper, with aggregate stability ranging from 75 to 90%. This field had a significantly higher aggregate stability compared to the other vineyards. Again, this can be explained by the climate, as the field with the highest aggregate stability is located in the most humid area. Lastly, a study done in Norway, by Kværnø & Øygarden (2006), comparing multiple soils, showed similar results in aggregate stability percentages compared to this paper. This study indicated that a silty clay loam soil and a clay loam soil have a significantly higher aggregate stability compared to a silt soil and clayey soils had a higher aggregate stability overall. Therefore, the texture could have contributed to the high aggregate stability as well, as the study site, used for this paper, consisted of a marine clay soil.
A decrease in aggregate stability after the harvest can be related to multiple conditions. According to Beare et al. (1994), high temperatures and low precipitation are unfavorable soil aggregating
conditions that can influence the outcome of research similar to this research. The data for the second measurement was collected after an exceptionally hot and dry summer, around 2.5 degrees Celsius above average and around 20 mm precipitation below average (KNMI, 2020). This might have impacted the results of the research. Multiple studies have indicated that a hot, arid climate does not have a beneficial effect on the aggregate stability. First of all, a study done in Bolivia with climatological gradients proved that, for all lands used, climate impacted the aggregate stability of the soils. With higher mean annual rainfall, there was an increase in soil aggregate stability (Cerdà, 2000). Moreover, in other comparable climatological gradient studies in Israel the same observation was made (Lavee et al., 1991, 1996). Lastly, a study done in the Baetic Mountain System in Spain found a significant decrease in aggregate stability when moving from the wettest areas to the driest (Cerda et al., 1996). On the other hand, other studies implicated different results. Research done in Spain and Crete showed that human interference as cultivation, grazing and forest could cause lower aggregate stability at the wet areas (Boix et al., 1995; Cerdà, 1998). The previously mentioned observations also lead to the conclusion that an expected climate change induced reduction of annual rainfall will have significantly negative consequences for worldwide agriculture.
Many studies proved that less severe tillage practices would lead to a higher aggregate stability (Bottinelli et al., 2017; Paul et al., 2013). However, those studies were performed over a significantly larger time period compared to this research. Moreover, the fields researched within these studies have been exposed to the different tillage treatments for a significantly larger time compared to this research. Consequently, it is possible that, for this research, the effects of the different tillage treatments could not have been measured yet. This can also be found in other studies, where the research took longer than a few years to find significant results of the effects of tillage (Hajabbasi & Hemmat, 2000), or where the research showed no results of the effects of tillage within a short time period (Liang et al., 2011). This leads to the recommendation to repeat this particular research to acquire a more conclusive overview of the effects of tillage on aggregate stability.
4.2 Soil organic matter and aggregate stability
0.0001). This section discusses the change in aggregate stability over the different fields, and the correlation between the change in aggregate stability and the different types of measured organic matter. The results showed that the aggregate stability in the soil had a significant correlation with the other four variables, POXC, HWC, LOI and CNS (Figure 4 - Figure 7). The correlation coefficients measured in this paper are significantly positive, but not very strong. Other researchers showed a similar link between the previously mentioned variables.
The relation between aggregate stability and organic matter has been discussed for decades. In their research Kemper & Koch (1966) found a strong relation between aggregate stability and soil organic matter in the form of a logarithmic curve regression. Meaning that changes in organic matter below 1 percent will have the most effect on the aggregate stability of the soil (Figure 8).
Figure 8: relation of aggregate stability to organic matter in wester soils (reprinted from: Kemper & Koch, 1966). The positive correlation between aggregate stability and HWC, POXC, carbon content and organic matter measured by LOI has been proven before by many studies (Almajmaie et al., 2017; Angers et al., 1999; Bouajila, 2008; Duchicela et al., 2013; Pikul et al., 2007; Saha et al., 2011) The results of this paper showed a lower correlation between aggregate stability and POXC than for the other organic matter types. However, Bongiorno et al. (2019) studied the physical soil properties in ten European long-term field experiments and found POXC the most positively correlated to water stable
aggregates from the labile carbon fractions. Moreover, they found that POXC was the most sensitive to tillage practices. This would be an interesting statement to investigate when repeating this research in the future. A soil health study done by Fine et al. (2017) showed that POXC was the most accurate single indicator of overall soil quality. Their research included a large number of samples (n = 930) from various locations around the United States, representing various pedo-climatic
conditions. Nonetheless, the quantitative relationships between existing indicators and soil functions are mostly unexplored.
time span and the change in climate conditions. This leads to the conclusion that aggregate stability will suffer significantly if average annual rainfall decreases as a result of climate change. Another conclusion was drawn that the effects of climate is larger than the effect of tillage in the first year of this research. Furthermore, it would be advised to repeat this research with a bigger time span. Nonetheless, the different fields had a significantly different aggregate stability. This was further elaborated upon by looking at the correlations between aggregate stability and the four different types of organic matter (HWC, POXC, organic matter content and organic carbon content). All four types of organic matter had a significantly positive moderate correlation with the aggregate stability in the soil. POXC had the lowest correlation coefficient, although other studies suggested POXC had the highest correlation with aggregate stability.
6. Literature list
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7. Acknowledgements
The research for this paper was carried out as part of the overarching project called “Duurzame Bloemkool” (Sustainable Cauliflower). This project was commissioned by the province Noord Holland and carried out in collaboration with Amsterdam Green Campus, the University of Amsterdam and two entrepreneurs, Wim Reus and Pé Slagter. Elly Morriën and Erik Cammeraat are thanked for supervising this research. Anne Uilhoorn is thanked for supervising the statistical analysis of this research. Rutger van Hall is thanked for supervising the laboratory analysis of this research
8. Appendices
8.1 Extensions of the methodologies
2
Contents
On these operating instructions ... 3
1. Introduction ... 3
2. Applications ... 3
3. Operating principles ... 4
4. Procedure ... 4
Eijkelkamp Soil & Water is not responsible for (personal damage due to (improper) use of the product. Eijkelkamp Soil & Water is interested in your reactions and remarks about its products and operating instructions.
3
On these operating instructions
When the symbol shown on the left is placed before a piece of text, this means that an important instruction follows.
When the symbol shown on the left is placed before a piece of text, this means that an important warning follows pointing out a risk of injury to the user or damage to the device.
The user is always responsible for it own personal protection.
Text in italics means that the actual text is shown on the display screen or instrument.
1. Introduction
The wet sieving apparatus is used to determine the aggregate stability of a soil, which is the resistance of soil structure against mechanical or physico-chemical destructive forces.
temperature and gas diffusion, water transport and seedling emergence and therefore it is an important soil characteristic for farmers.
which are separated from adjoining structural elements by surfaces of weakness.
Soil texture, soil structure, and the type of clay mineral, organic matter content and type, cementing agents impact of heavy machinery, treading by animals and raindrop splash. Physico-chemical forces are e.g. slaking,
of clay minerals, dissolving of cementing agents, air explosion or reduction in pore water suction. Slaking downward transportation with surface runoff water.
2. Applications
Due to the impact of aggregate stability on plant growth and soil loss, applications of the wet sieving will be especially useful for researchers and scientists on soil erosion, land degradation and conservation, agriculture, sustainable agriculture.
Scientists on salinization problems may have advantage determining wet aggregate stability using wet sieving, to control deterioration of soil structure or to determine possible impacts of amelioration practices on aggregate stability.
Determining aggregate stability will give information on the sensitivity of soils to water and wind erosion, which might be prevented e.g. by mulching the soil surface. Information on soil aggregate stability will improve
4
3. Operating principles
The wet aggregate stability is determined on the principle that unstable aggregates will break down more easily than stable aggregates when immersed into water.
are destroyed. Sand grains and plant roots will remain on the sieve and only aggregates are considered. After drying the cans with the aggregates, the weight of both stable and unstable aggregates can be determined. Dividing the weight of stable aggregates over total aggregate weight gives an index for the aggregate stability.
4. Procedure
Determining aggregate stability using the wet sieving apparatus:
1. Weigh 4.0 grams of 1- to 2- mm air-dried aggregates into the sieves.
2. Pre-moistened the aggregates. Depending on the sample leave the sample 5-10 minutes before submerging them.
3. Place the sieves in the sieve holder. 4. Place the weighed (numbered) cans. 5. Place the sieve holder on de build-in stop.
cover the soil. The water can be put into the cans
are moved under water (so add enough water to the boxes). Build-in stop
5
7. Place the sieve holder in the working position by putting the sieve holder in the second hole on the shaft
8. Check if the mains switch is in the “Off” position 9. Put the Adapter into a wall plug
10. Start the motor by putting the mains switch into “3 min” position and allow it to raise and lower the holder for 3 min. ± 5 s. (stroke = 1.3 cm, at about 34 times/min) At the end of this time the motor will stop automatically.
11. Raise the sieve holder out of the water and place it in the leak out position, by putting the sieve holder in
When there is no water leaking out of the sieves anymore, than take the (numbered) cans (containing the particles and aggregate fragments that have broken loose from the aggregates and come through the sieves) on a tray.
12. Replace these cans with another set of weighed (numbered) cans
13. Fill the cans with a dispersing solution (containing 2 g sodium hexametaphosphate/L) for soils with pH > 14. Place the sieve holder in the working position
15. Start the motor by putting the mains switch into “Continue” position and continue sieving until only sand particles ) are left on the sieve. If some aggregates remain stable after 5 to 8 min of sieving in the dispersing solution, stop the sieve by putting the mains switch in the “Off” position, and rub them across the screen with a rubber tipped rod until they are disintegrated.
16. Continue sieving until materials smaller than the screen openings have gone through.
Second hole on the shaft
First hole on the shaft
6
To disperge the aggregates can take some time (the more clay the longer it takes); there is no standard reacting time. Use of the reagent in combination with washing is the best method to be sure that all the aggregates are disperged.
17. Raise the sieve holder and place it in the leak out position.
When there is no dispersion solution leaking out of the sieves anymore, than take the (numbered) cans and place them on a separate tray. These cans contain the materials from the aggregates that were stable, except for sand particles too large to get through the screen.
18. Both sets of cans are placed in a convection oven at 110 °C until the water has evaporated. 19. The weight of the materials in each can is then determined by weighing the can, plus contents, and of the dispersing solute along with the soil. Consequently, 0.2 g should be subtracted from the weight of the contents to obtain the soil weight.
20. The fraction stable is equal to the weight of soil obtained in the dispersing solution cans divided by the sum of the weights obtained in the dispersing solution cans and distilled water cans.
Dispersion of the aggregates may also be achieved using an ultrasonic probe, in which case, the dispersing solution can be distilled water rather than the sodium salt solution, which eliminates the need for the 0,2 g correction indicated in step 19.
The test procedure is now: Step 1 to 10 see above
12. Than held the ultra sonic probe into the water for 30 s at a medium frequency 13. Place the sieve holder in the working position
14. Start the motor by putting the mains switch into “Continue” position and continue sieving until only sand particles (and root fragments) are left on the sieve. If some aggregates remain stable after 5 to 8 minutes of sieving in the dispersing solution, stop the sieve by putting the mains switch in the “Off” position, and rub them across the screen with a rubber tipped rod until they are disintegrated.
15. Continue sieving until materials smaller than the screen openings have gone through. 16. Raise the sieve holder and place it in the leak out position.
When there is water leaking out of the sieves anymore, than take the (numbered) cans and place them on a separate tray. These cans contain the materials from the aggregates that were stable, except for sand particles too large to get through the screen.
17. Both sets of cans are placed in a convection oven at 110 °C until the water has evaporated. 18. The weight of the materials in each can is then determined by weighing the can, plus contents, and subtracting the weight of the can. The fraction stable is equal to the weight of soil obtained in the “ultra sonic probe” cans divided by the
8.2 Extension of the results
Figure 9: the aggregate stability (mean ± SEM) of a marine clay soil in Hem, the Netherlands. Where PA and WB were used for tillage and PB and WA were used for No-Tillage practices. For fields P (farmer Pé) n = 10 points, and for fields W (farmer Wim) n = 5. Mixed ANOVA test, df = 108, p < 0.0001 for the fields and p > 0.05 for the different time periods. The aggregate stability was measured before and after the harvesting of cauliflowers (2020).
Figure 11: The aggregate stability and different fractions of organic matter, including CNS, HWC, LOI and POXC, in a marine clay soil in Hem, the Netherlands (mean ± SEM) (2020), for all four fields. Scales have been adapted to fit the graph.
8.3 Scripts of the statistical analysis
Script of the first two hypotheses (ANOVA)
# installing packages library(tidyverse) library(ggpubr) library(rstatix) library(outliers) library(readxl) # loading data Meting_aggregate_stability_1_ <- read_excel("Downloads/Dataset_anova_susanne.xlsx",) View(Meting_aggregate_stability_1_) Meting_aggregate_stability <- Meting_aggregate_stability_1_
Meting_aggregate_stability <- Meting_aggregate_stability %>% convert_as_factor(boerderij, veld, punt)
Meting_aggregate_stability <- na.omit(Meting_aggregate_stability)
# Converting to long format library(reshape2)
data.long <- melt(Meting_aggregate_stability, id = c("punt", "boerderij","veld"), # keep these columns the same
measure = c("Before","After"), # Put these two columns into a new column variable.name="moment") # Name of the new column
summary(data.long)
# visualization bxp <- ggboxplot(
data.long, x = "moment", y = "value", color = "veld", palette = "jco"
)
bxp <-bxp + labs(x = "moment", y = "Aggregate Stability (%)") bxp
# assumptions # no outliers data.long %>%
group_by(moment, veld) %>%
identify_outliers(value) #no extreme outliers
# normality data.long %>%
group_by(moment, veld) %>% shapiro_test(value)
ggqqplot(data.long, "value", ggtheme = theme_bw()) + facet_grid(moment ~ veld)
# Homogneity of variance data.long %>%
levene_test(value ~ veld)
# Homogeneity of covariances
box_m(data.long[, "value", drop = FALSE], data.long$veld)
# Two-way mixed ANOVA test # anova for field and time
aov.res1 <- aov(value ~ veld*moment + Error(punt/moment), data=data.long) summary(aov.res1)
get_anova_table(aov.res1)
# anova for farm and tim
aov.res2 <- aov(value ~ boerderij*moment + Error(punt/moment), data=data.long) summary(aov.res2)
# anova for farm, field and time
aov.res3 <- aov(value ~ boerderij*veld*moment + Error(punt/moment), data=data.long) summary(aov.res3)
# Post-hoc tests
# Effect of field at each moment one.way <- data.long %>% group_by(moment) %>%
adjust_pvalue(method = "bonferroni") one.way
# Pairwise comparisons between fields pwc <- data.long %>%
group_by(moment) %>%
pairwise_t_test(value ~ veld, p.adjust.method = "bonferroni") pwc
# Effect of time at each level of field one.way2 <- data.long %>%
group_by(veld) %>%
anova_test(dv = value, wid = punt, within = moment) %>% get_anova_table() %>%
adjust_pvalue(method = "bonferroni") one.way2
# Pairwise comparisons between time points at each field
# Paired t-test is used because we have repeated measures by time pwc2 <- data.long %>%
group_by(veld) %>% pairwise_t_test(
value ~ moment, paired = TRUE, p.adjust.method = "bonferroni" ) %>%
select(-df, -statistic, -p) # Remove details pwc2
#visualization ?sd
agg <- aggregate(value ~ veld + moment, data.long, mean) tmp <- aggregate(value ~ veld + moment, data.long, sd) names(tmp)[3] <- "StdErr" agg <- merge(agg, tmp) rm(tmp) p <- ggplot(agg, aes(x = veld, y = value, fill = moment)) + geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = value - StdErr, ymax = value + StdErr), position = position_dodge(0.9),
width = 0.25)
p <- p + labs(x = "field", y = "Aggregate Stability (%)") p <- p+scale_fill_brewer(palette="jco")
p
Scripts of the third hypothesis (correlation) HWC
library(ggpubr) theme_set(theme_pubr()) library(readxl) # load data Data_agg_HWC <- read_excel("Desktop/Thesis/HWC_aggr.xlsx") #visualisation
ggplot(Data_agg_HWC, aes(x = AggStability, y = HWC)) + geom_point() + stat_smooth() # correlation coefficient cor(Data_agg_HWC$AggStability, Data_agg_HWC$HWC) # 0.6383726 # computation
model1 <- lm(AggStability ~ HWC, data = Data_agg_HWC) model1
# formula: AggStab = 0.8402486 + 0.0009073*HWC
model2 <- lm(HWC ~ AggStability, data = Data_agg_HWC) model2
#formula: HWC = -317.3 + 449.1*aggstab
#regression line
geom_point() +
stat_smooth(method = lm)
r <- r + labs(x = "HWC (mg/L)", y = "Aggregate Stability") r #model summary summary(model1) summary(Data_var) POXC # installing packages library(tidyverse) library(ggpubr) theme_set(theme_pubr()) library(readxl) # load data Data_agg_POXC <- read_excel("Desktop/Thesis/agg_POXC.xlsx") #visualisation
ggplot(Data_agg_POXC, aes(x = aggstab, y = POXC)) + geom_point() +
stat_smooth()
# correlation coefficient
# computation
model1 <- lm(aggstab ~ POXC, data = Data_agg_POXC) model1
#formula: aggstab = 8.554e-01 + 9.172e-05*POXC
model2 <- lm(POXC ~ aggstab, data = Data_agg_POXC) model2
#formula: POXC = -2464 + 3526*aggstab
#regression line
r <- ggplot(Data_agg_POXC, aes(POXC, aggstab)) + geom_point() +
stat_smooth(method = lm)
r <- r + labs(x = "POXC (mg)", y = "Aggregate Stability") r LOI # installing packages library(tidyverse) library(ggpubr) theme_set(theme_pubr()) library(readxl) # load data Data_agg_LOI <- read_excel("Desktop/Thesis/LOI_agg.xlsx")
#visualisation
ggplot(Data_agg_LOI, aes(x = aggstab, y = LOI)) + geom_point() + stat_smooth() #correlation coefficient cor(Data_agg_LOI$aggstab, Data_agg_LOI$LOI) #0.6388664 # computation
model1 <- lm(aggstab ~ LOI, data = Data_agg_LOI) model1
#formula: aggstab = 0.8167 + 2.0093*LOI
model2 <- lm(LOI ~ aggstab, data = Data_agg_LOI) model2
#formula: LOI = -0.1325 + 0.2031*aggstab
#regression line
r <- ggplot(Data_agg_LOI, aes(LOI, aggstab)) + geom_point() +
stat_smooth(method = lm)
r <- r + labs(x = "Organic Matter by LOI (g)", y = "Aggregate Stability") r
library(tidyverse) library(ggpubr) theme_set(theme_pubr()) library(readxl) # load data Data_agg_CNS <- read_excel("Desktop/Thesis/Agg_CNS.xlsx") #visualisation
ggplot(Data_agg_CNS, aes(x = aggstab, y = CNS)) + geom_point() + stat_smooth() # correlation coefficient cor(Data_agg_CNS$aggstab, Data_agg_CNS$CNS) #0.6519632 # computation
model1 <- lm(aggstab ~ CNS, data = Data_agg_CNS) model1
#formula: aggstab = 0.79200 + 0.04187*CNS
model2 <- lm(CNS ~ aggstab, data = Data_agg_CNS) model2
#formula: CNS = -6.142 + 10.151*aggstab
r <- ggplot(Data_agg_CNS, aes(CNS, aggstab)) + geom_point() +
stat_smooth(method = lm)
r <- r + labs(x = "C (%)", y = "Aggregate Stability") r Multiple Regression # installing packages library(tidyverse) library(ggpubr) theme_set(theme_pubr()) library(readxl) library(plotrix) # load data Data_var <- read_excel("Desktop/Thesis/Variables.xlsx") summary(Data_var) #building model
model <- lm(aggstab ~ POXC + CNS + LOI + HWC, data = Data_var) summary(model)
#interpretation
summary(model)$coefficient
8.035e-confint(model)
#Residual standard error
sigma(model)/mean(Data_var$aggstab)
#making data for barplot so it fits
meanAgg <- mean(Data_var$aggstab)*100 meanPOXC <- mean(Data_var$POXC)/10 meanCNS <- mean(Data_var$CNS)*10 meanLOI <- mean(Data_var$LOI)*1000 meanhwc <- mean(Data_var$HWC) seAgg <- std.error(Data_var$aggstab)*100 sePOXC <- std.error(Data_var$POXC)/10 seCNS <- std.error(Data_var$CNS)*10 seLOI <- std.error(Data_var$LOI)*1000 seHWC <- std.error(Data_var$HWC) data <- data.frame(
variable= c('AggStab', 'POXC', 'CNS', 'LOI', 'HWC'),
value= c(meanAgg, meanPOXC, meanCNS, meanLOI, meanhwc), se=c(seAgg, sePOXC, seCNS, seLOI, seHWC)
)
# Most basic error bar ggplot(data) +
geom_errorbar( aes(x=variable, ymin=value-se, ymax=value+se), width=0.4, alpha=0.9, size=1.3)
#bring variables together
var_vis <- read_excel("Desktop/Thesis/Variablen_vis.xlsx")
data.vis <- melt(var_vis, id = c("punt", "field"), # keep these columns the same
measure = c("aggstab","POXC", "CNS", "LOI", "HWC"), # Put these two columns into a new column
variable.name="variable") # Name of the new column
#visualization
MakeMean <- aggregate(value ~ variable + field, data.vis, mean) Makesd <- aggregate(value ~ variable + field, data.vis, sd) names(Makesd)[3] <- "StdErr"
MakeMean <- merge(MakeMean, Makesd) rm(Makesd) s <- ggplot(MakeMean, aes(x = field, y = value, fill = variable)) + geom_bar(stat = "identity", position = "dodge") +
