Under the Cover: The spatial effect of Shade
Trees on Topsoil Nutrient Concentrations
in Huíla, Colombia
Daniel Derksen
10784373
University of Amsterdam
Primary supervisor:
Dhr. Dr. B. Jansen
Secondary supervisor:
Dhr. Dr. K. F. Rijsdijk
13-7-2018
Abstract
Coffee has become an integrated part of our day to day life. As a consequence, coffee is also the major source of income for its 25 million producers. The production puts a lot of pressure on various natural systems. Causing water pollution, deforestation, soil erosion and biodiversity losses. Large intercropped trees, or shade trees, help decrease these effects. This research looks at the effect of shade trees on soil nutrient concentration over distance in the topsoil of a coffee plantation in Huila, Colombia. Moreover, a social study is included to find a possible knowledge gap between farmers and the scientific community. Effects on the soil should be a direct result of organic material deposited by the shade trees. Through field sampling in 3 radiuses around multiple shade trees, data was gathered. The samples were analyzed for exchangeable elements (Ca2+, Mg2+, Na+, K+,) and organic carbon, using ICP-analysis and Walkley-Black respectively. With statistical analysis, significance was tested between nutrient concentrations and distance away from shade trees. No significance was found over distance in the topsoil. A comparison between topsoil /and subsoil showed a significant difference between the two layers. Large amounts of mulch, originating from multiple sources, explains both results. The share of mulch from shade trees was insignificant compared to the mulch from the weeds. However, the total mulch did cause higher nutrient concentrations in the topsoil. Future research requires a comparison between fields with higher shade tree presence. The social survey exposed the presence of an economic gap rather than a knowledge gap. Low coffee prices cause farmers to knowingly choose yield over sustainability.Content
ABSTRACT 2CONTENT 3
INTRODUCTION 5
Global and Local Importance of (Sustainable) Coffee 5
Sustainability 5
Shade trees 5
Theoretical framework 6
Shade trees 7
Mulch to topsoil 7
Topsoil fertility and distance 7
METHODS 9
Sampling 9
Field sampling 9
Plot selection and Parameters 10
Tree selection 11
Soil sample analysis 11
Statistical analysis 12
Survey 12
RESULTS 13
Lab results per element 13
Statistical analysis per element 14
Comparison between topsoil and subsoil 15
Comparison between plot 1 and 2 16
Fraction analysis 18
Survey Results 19
Question 1
Presence of shade trees 20
Question 2
Advantages 20
Question 3
Disadvantages 20
Question 4
Shade tree species 20
Question 5
Influences agricultural practices 20
DISCUSSION 21
Results 21
Methods 21
Parameter influences 22
Future research 22
Survey 22
Recommendations future students 23
Preparation 23
Flexibility 23
Dependence 23
CONCLUSION 24
ACKNOWLEDGEMENTS 25
REFERENCES 26
APPENDICES 29
Zipfile 29
Conversion to mmol/g 29
Mind the gap - Thoughts on the coffee chain 29
Introduction
Global and Local Importance of (Sustainable) Coffee
Coffee has become a globalized commodity and a major foreign exchange earner in many developing countries. The global coffee chain has changed dramatically as a result of deregulation, new consumption patterns, and evolving corporate strategies (Ponte, 2002). Back in the late 70s and early 80s, civil society movements began to challenge the image of coffee as an exotic commodity. This trend has continued and is reflected in the publics’ perception of coffee as product. Since the turn of the century, attention shifted further towards the origin of coffee. Arabica beans from single origin microlots, with unique and complex taste profiles, are sold at higher prices. The information about the exact origin and the producing farmer is much sought after (Coffeetalk, 2016). The sale of special, organic and fair-trade coffees is now ubiquitous. In order to allow access to mainstream markets and maintain differentiation, environmental and social ideals were translated into certifications (Raynolds, 2000). At the forefront of special and certificate coffee, is Colombia. The coffee giant produces an average of 11.5 million 60kg bags of coffee per year (ICO, 2014), more than 10% of the global production. In Colombia, coffee emerged as dominant export product since the mid-19th century. Since 1950 the national federation of coffee growers (FNC) has pushed Colombia to the international stage with its high-quality beans. The nations’ dependence on coffee has decreased since the 1980’s (Equal Exchange, 2005). Directed towards sustainable producing, coinciding with the global trend in awareness mentioned earlier, the importance grew for the social role of coffee. Socially responsible trading was shown trough public development locally. More opportunities and schooling was given to the more than 500 thousand coffee families (Andrade, 2013; FNC, 2017).Sustainability
Despite, the reliance on coffee and the opportunities and life it offers to people, it has a downside for nature. As coffee grows only within the tropics, growing coffee substitutes mostly natural forests. Combined with irresponsible farming practices this can, among other things, lead to a loss in biodiversity, water pollution, land degradation and increased vulnerability to pests and diseases (Bote, 2011; Ibanez & Blackman, 2016).Environmental concern (ministerio de Agricultura, 2012) increased the amount of certificates present in the farming regions. The three most important certificates are UTZ, fair trade and rainforest alliance. The certificates are incentives as they grant farmers a higher sales price for their coffees. Although the certificates emphasize different aspects of the coffee production, they all include multiple rules to encourage sustainability. One criteria, included in all three, is the presence of shade trees. Albeit in a percentage of canopy cover, multistrata vegetation presence or the requirement of shade tree planting (van der Vossen, 2005; IFOAM, 2010; Voedingscentrum, 2018).
Shade trees
Studies on shade trees have shown that they can have multiple, beneficiary effects. Firstly, the shade grown coffee provide multiple ecological services. Planting shade trees is a form of carbon sequestration (Steffan-Dewenter, 2007; Youkhana, 2009). A study by Dulorme (2003) showed a 1.9 mg C per ha per year increase of carbon through organic material deposits, only a slight difference from natural vegetation. Secondly, shade-grown coffee incites higher biodiversity, in part by retaining some of the natural biodiversity. Studies on biodiversity have shown shaded agroforestry systems to support an average of 60-70% of primary forest species. In contrast to 20% in unshaded systems (Steffan-Dewenter, 2007). Secondly, lower erosion rates are found in shade-grown coffee (Ibanez & Blackman, 2016) and soil properties are maintained through organic material and the trees’ rooting system. Thirdly, due to the slower growth of shade-grown coffee, as a consequence of receiving less sunlight, more sugars develop inside the berry (Bote, 2011). This results in a better taste and quality. Apart from the natural advantages, shade trees can provide a farmer with extra resources and income. For instance, as firewood or as agricultural product, like fruits or leguminous crops. Multiple sources of income will make a farmer less vulnerable to the coffee harvest and fluctuating coffee prices (Tscharntke, 2011). J. Beer (1987) assessed the advantages, disadvantages and desirable characteristics of shade trees for coffee, cacao and tea.Positive effects on soil fertility and sustainability are described in his research and are further discussed in the theoretical framework.
Despite these benefits, in coffee regions around the world, the amount of shade trees is in decline (Tscharntke, 2011). As climates are changing, areas with the right conditions for coffee growth are shifting and diminishing. This is especially true for the more vulnerable Arabica coffee, which only grows at high altitudes (Bunn, 2015). Without a clear understanding of the services provided by the shade trees, farmers instead aim to increase yield by replacing shade trees with coffee plants. With fluctuating prices, farmers try to secure their income by moving towards a more monocultural system (Bote, 2011; Bunn, 2015). In Colombia, this trend would be a shift back to old, unsustainable, way of coffee growing (Willey, 1975; Beer, 1987; Bote, 2011; Tscharntke, 2011; Bunn et al., 2015). This trend is difficult to understand given the recent researches published on the importance of shade trees. This could possibly reflect a gap between knowledge and practice (Perfecto, 2005). In this research, the benefit of organic material deposits by shade trees on the topsoil properties will be investigated. Through soil sample analysis the different concentrations of elements in the topsoil will be determined as a function of distance from the shade trees. These elements are: Total organic carbon (representing soil organic matter), magnesium, calcium, sodium, and potassium. The aim for this research is to answer the question: To what extent does shade trees’ organic material deposits influence topsoil nutrient concentration over distance on a coffee plantation in Huila, Colombia? Sub-questions will be: 1. To what extent are the topsoil organic carbon concentrations over distance affected by shade trees’ mulch? 2. To what extent are the topsoil exchangeable elements concentrations over distance affected by shade trees’ mulch? 3. To what extent are the carbon concentrations and the concentrations of the exchangeable elements related? 4. Do the years of production have an effect on topsoil nutrient concentrations? This research is done in collaboration with Math van Soest, another immensely capable earth science student. The required fieldwork will be done together. However, in this paper the focus is on topsoil differentiation, whereas Math van Soest will look at subsoil data and effects.
As shown by Perfecto (2005), feedback between research knowledge and practice can be lacking, suggesting a gap in knowledge or absent knowledge transfers. To include this alleged gap, a small survey is added to this research in an attempt to help understand the what and how of farmers’ choices concerning shade trees. The aim of this survey is to answer the question: To what extent are the coffee farmers aware of the effects of shade trees?
Theoretical framework
In this paragraph, a background is given on the importance of measuring nutrients from the topsoil over distance. First, the effects of shade trees for the soil are listed using an article by Beer from 1987. Next, the role of mulch is clarified with key articles by Palm (1990) and Robinson (1965). Lastly, the nutrient concentrations are linked to soil fertility.Shade trees
In 1987 J. Beer published an article/report to offer assistance in finding the best shade tree species per site. Beer describes four categories of benefits with one called improvement of soil fertility and/or soil protection. The article lists seven factors in this category: 1. The growth of the shade tree root system can improve soil drainage and aeration. 2. The provision of mulch and organic material helps retain moisture and increase the organic matter content. 3. Erosion reduction on slopes. 4. Decomposition rate of soil organic material decreases through lower soil temperatures. 5. Nutrient recycling of nutrients that are outside of the crops’ root system. 6. Nitrogen fixation by shade tree root nodules. 7. Shaded crop plantations require less chemicals, including herbicides, which could otherwise have a negative effect on the activity and abundancy of soil organisms. Some of the effects of these soil organism are the fixation of nitrogen and the decomposition of organic material. For this research number 2 is the main focus point. The mulch from the shade trees’ residual material is an important source of organic material and nutrients in the topsoil. Number 5, nutrient recycling, is closely related as the recycling by the shade trees’ roots end up being the nutrients found in the mulch. However, since this part of the process takes place in the subsoil, this paper will primarily look at the mulch and topsoil part of the nutrient recycling.Mulch to topsoil
In 1965 a paper by J. Robinson was published which showed an overall chemical difference with the appliance of mulch within the first 10 centimeters, or topsoil. The research contained an analysis on pH, K+, Ca2+, Mg2+, P, Mn and organic carbon of a latosolic coffee soil in Kenya. The research was conducted to check previous hypothesizes and researches on the benefits of mulch. They found that the regular application of mulch lowered the soils’ acidity, increased organic carbon and, among other things, results indicated that mulch increased mobilization and leaching (Robinson, 1965). Within the topsoil total pore space was significantly increased, as an effect of which, rainfall acceptance improved. Youkhana (2009) likewise showed a significant increase of soil carbon and nitrogen on sites that had mulch. As mentioned before, a way to add mulch is by planting shade trees in between the crops. But, not all mulch is the same. One of the most common shade trees used in Colombia, is the native Inga Edulis or Guama (Palm, 1990). The Inga is a tropical leguminous tree and its mulch, consisting of leaves and large bean shaped fruits, is high in readily available nitrogen and the tree itself is also a nitrogen fixator. As a natural product, mulch is not a homogeneous substance. Palm (1990) proved that the effect of lignin in the Inga trees’ leaves slows down the nitrogen decomposition. The study showed that different mulch from the same tree can have contrasting effects to the soil, not to mention different mulch from different trees. To sum up, although mulch has been proven to have positive effects on the soil, more research is necessary to optimize mulch with crops’ demand.Topsoil fertility and distance
From mulch, organic carbon and nitrogen concentrations are mentioned as the main soil fertility properties that are influences strongly by shade trees (Robinson, 1965; Palm, 1990).
Nitrogen (N) is an essential nutritional element, that is frequently deficient in agricultural soils. Nitrogen enters the soil mostly through the decomposition of soil organic matter (SOM) by micro-organisms (Li, 2009). On coffee farms nitrogen fixation can be improved with shade trees, with the Guama as example.
Carbon, another essential nutrient, is a measurable component of SOM. In turn, SOM plays a critical role in the chemical, physical and biological function of agricultural soils. SOM affects (mostly positively) the fertility of soil through nutrient turnover, the cation exchange capacity, and degrading pollutants (Griffin, 2013). It also improves the sustainability of a soil through increased soil buffering
capacity and soil structure. As the SOM is difficult to measure directly, soil organic carbon (SOC) is measured as a good indicator of the SOM. According to Griffin (2013), a reasonable estimate of SOM from SOC for many soils assuming an average organic matter content of 58% organic carbon is therefore: Organic matter (%) = total organic carbon (%) x 1.72 (eq. 1) For this research, this conversion is not applied and the results will only mention the total organic carbon. It is however, important to remember that the organic carbon concentrations found in this paper are a good indicator of the total organic matter present in the topsoil. The total organic matter content is a direct result of deposits on the surface, which increases topsoil fertility. Griffin mentions that changes in soil organic carbon are most likely to be seen in the first 10 cm of a soil, or topsoil, as it contains the highest concentration of organic matter. In his case, in western Australia, 60% of all the organic matter in the top 30 cm is found in the first 10 cm. Gindaba (2005) showed an increase in carbon and exchangeable elements concentrations in the soil surface under the canopy of shade trees. In this research, it is thus hypothesized that the organic carbon and CEC elements concentrations in the topsoil will decline with increasing distance away from the shade trees.
Methods
Sampling
Field sampling
This research investigates the effect of single shade trees on the topsoil nutrient concentration around it. The methods used to find and assess the correct data are based on the methods used by Olusegun Ekanade (1987) and Robinson (1965). Ekanade searched for the effect of the Kola shade tree on the soil properties of cocoa plantations in Nigeria as a function of relative distance to the cocoa trees. He randomly selected the Kola trees and took soil samples at three distances between the selected tree and the nearest cocoa trees (figure 1). The sampling methods practiced in this research were similar to the extent that the shade trees were selected randomly and samples were taken at three distances. These distances are 1, 2 and 3 meters (A, B and C). Robinson (1965) conducted a similar research but used a different sampling method. The distances from which samples were taken were relative to the canopy cover of the selected shade trees. The first sample from underneath the canopy cover, the second sample underneath the boundary and the third sample outside of the canopy cover. However, since the plots under investigation in this research are less structured compared to the plots in the Ekanade paper and because the shade trees vary in size, the distances are not measured relative towards the nearest trees or relative to the canopy cover (plot descriptions in next paragraph). Instead, a radial system was used, taking samples at three set distances (1, 2, and 3 meters) and from four directions. These directions are relative to the slope of the hill (figure 2). To collect the samples, a shovel, measuring tape, markers and plastic bags were used. Samples were taken from the topsoil at approximately 10-15 cm deep, depending on the soil classification. For each shade tree, four samples are taken per distances 1, 2 and 3. This adds up to a total of 12 topsoil samples per shade tree, 36 samples per plot and a total of 72 topsoil samples used in this research. Samples were taken from two plots. Before the sampling, the soil on the plot was classified. This was done as part of the metadata as well as to determine the depth of topsoil and subsoil. Figure 2. Diagrammatic illustration of sampling pattern of shade trees on coffee plantation. Dots represent sampling points and orientation are relative to the slope. Figure 1. Diagrammatic illustration of planting pattern of cocoa and kola and the sample points. From Ekanade (1987).
Plot selection and Parameters
From the plots available for the sampling, two plots with the most in common were selected. Three criteria were followed: 1. Similar orientation 2. Similar shade cover 3. Soil classification The parameters of the selected plots are given in table 1. Among these parameters there were still some notable differences. The total shade cover was higher on plot 1. The coffee plants on plot 1 were younger. The shade tree species on plot 1 varied whereas plot 2 only contained Plantain.
Parameters Plot 1 Plot 2
Slope, in degrees -24O -20O Orientation, in degrees relative to the North 15 O 7O Coffee tree density, trees per m2 0.4 0.6 Coffee tree height, in meters 0.5-0.7 1.0-1.1
Shade tree species Arayan, Roblé Plantain
Total years of coffee growth 10 30
Mulching Yes yes
Soil classification Calcic Gleysol Calcic Gleysol or Clayic Regosol
Altitude 1750-1800 1700-1720
Table 1. Plot parameters at the Finca 1810, in Pitalito, Colombia.
Tree selection
After plot selecting, the tree selection followed. Three trees were randomly selected per plot, following two criteria: 1. No interference from other shade trees. 2. Situated on a continuous slope. Since the samples were taken in a radial system around a single shade tree, it was important that there were no other shade trees in close proximity. Trees in close proximity could have an extra effect on the data, making it difficult to evaluate the effect of a shade tree around which the samples were taken. As mentioned before, most Arabica is grown at high elevation in hill covered regions. To take the slope of the plots into account, the selected trees and their 3 meter radiuses were situated on a continues slope with a slope angle which was in line with the plots average slope angle.
The research was carried out on a farm located near to the town of Pitalito in the region of Huila in Colombia. The farm is placed on the north side of a hill between 1650 and 1810 meters above sea level. Plot 1 had different types of shade trees whereas plot 2 only had plantain. There are certain variables that were not included in the table. For instance, when growing coffee, a farmer usually cuts his/her coffee trees after 7-12 years, when they grow back he or she cuts them again after the same period. When this cycle is repeated 3 times the trees are usually replaced with new coffee trees. This is done to improve the quality of the berry. It also explains the tree height compared to the years coffee has been grown on a certain plot. The coffee trees of plot 1 for instance, had been cut 3 years earlier.
Soil sample analysis
The intent for this research was to analysis the 72 (or 144, including subsoil samples) in Amsterdam in the lab facilities of the UvA. Using an ICP analyzer for the exchangeable elements and using a CNS analyzer to measure carbon and nitrogen concentrations. Unfortunately, due to unforeseen problems with importing the samples, they were not allowed into the country.The lab analysis was therefore done in cooperation with Ambilab, a private soil analysis lab situated in Pitalito, Colombia. Before the analysis at Ambilab, the samples per tree and per distance were combined to form one sample. This lowered the cost of the analysis but as an effect excluded the variable for direction and diminishing the sample size from 72 to 18. Subsequently, the samples were analyzed for total organic carbon and the exchangeable elements. This was done using the Walkley-Black method and ICP with the ammonium acetate method respectively. Walkley-Black method The Walkley-Black method is based on the oxidation reaction of organic matter through the addition of potassium dichromate (Cr2O72-), followed by back titration of ferrous sulphate to extract excessive dichromate. The amount of carbon in the sample is related to the amount the dichromate that is used in the solution. After subtracting the amount of excessive dichromate from the total amount added at the start, an estimate can be made for the amount of carbon that was in the sample (Gelman, 2011).
ICP, ammonium acetate method
The measurement of the exchangeable elements (Mg2+, Ca2+, Na+, K+) was done using the ammonium acetate method. First, a solution is made from the soil sample by adding a 1 M NH4OAc solution. To extract all the elements, the solution is shaken for 2 hours after which the solution is separated from the solids by centrifuging. After adding ammonium, the alkali and alkaline cations (including the exchangeable elements) are displaced and can subsequently by analysed using ICP-OES (Brix, 2008). ICP-OES is an atomic emission spectroscopy based analysis method. The solution is heated to a plasma and hit with high intensity light (Kim et al. 2009). After which the different elements emit specific wavelengths.
Statistical analysis
The statistical analysis is equivalent to the one used by Ekanade (1987). Using Matlab, a one-way analysis of variance (ANOVA) was conducted for all five soil property variables (the 4 exchangeable elements and organic carbon). The ANOVA tests for significant differences between the means of the concentrations at three distances. A p-value of <0.05 would indicate a decline or increase over distance. All five elements are also transformed to fractions, making it possible to do an ANOVA on the five elements combined. In case of a significant result, the ANOVA is followed by fisher’s least significant difference (LSD) test. The LSD test is used to see which of the three-distance means caused the significance.Because of the differences between plot 1 and 2, a t-test is performed to see whether these parameter differences have an effect on the topsoil nutrient concentrations. Absolute values are compared for the exchangeable elements and organic carbon. Another t-test is done to compare the topsoil carbon data with the subsoil carbon data from Math van Soest. This is done to confirm the hypothesis that organic carbon from mulch is largely found in the topsoil.
Survey
A hypothesis was stated earlier on a knowledge gap between farmers and scientific research. Therefore, besides a soil analysis on the effect of shade trees for the soil properties, this research will also incorporate a social study. A qualitative survey attempts to find out what farmers know about the effects of shade trees and find the motivations to their agricultural practices. The survey aids in making later recommendations to be more applicable to the people it concerns. The survey consists of the following five questions: 1. Do you have shade trees on your coffee plantation? 2. To your knowledge, what are the advantages of the presence of shade trees? 3. What are the disadvantages of the shade trees? 4. What kind of tree species do you use as shade trees? Why that species? 5. Who or what influences you in your decisions concerning your agricultural practices? The results from the survey will be written down in a table with key answers highlighted. The answers given by the farmers will be written down including the names of the farmers, location and the number of hectares they cultivate. Since the survey is not an official poll, a more qualitative approach is more fitting. Some concluding remarks will be made on the basis of the stories that were gathered, additional details that were not included in the table will be mentioned. The results and the concluding remarks should, by all means, not represent the position of the average coffee farmer in the region. It could however serve as an attention point for future research into the agricultural system. Bringing to consideration that not every farmer is the same and that there are possibly different external variables at play that need to be incorporated in future studies.
Results
Lab results per element
The results received from the laboratory analysis are given in table 2. The results for the exchangeable elements were given by Ambilab in meq/100grams sample, for organic carbon the results were given in percentages. The results were converted to SI-units, mmol/g, for concentrations. The calculation for the conversions are given in the appendix. The topsoil data was used for further research.ID Plot Tree Distance Soil Calcium [mmol/g] Magnesium [mmol/g] Sodium [mmol/g] Potassium [mmol/g] Organic Carbon [mmol/g]
1 1 1 1 Top 0,022 0,003 0,009 0,005 5,650 2 1 1 1 Sub 0,067 0,083 0,010 0,186 1,454 3 1 1 2 Top 0,113 0,031 0,007 0,005 5,827 4 1 1 2 Sub 0,079 0,010 0,008 0,168 1,081 5 1 1 3 Top 0,104 0,012 0,007 0,176 3,422 6 1 1 3 Sub 0,075 0,005 0,007 0,117 0,812 7 1 2 1 Top 0,126 0,027 0,008 0,004 5,533 8 1 2 1 Sub 0,086 0,009 0,008 0,162 0,808 9 1 2 2 Top 0,102 0,033 0,007 0,012 4,903 10 1 2 2 Sub 0,084 0,010 0,009 0,168 1,087 11 1 2 3 Top 0,139 0,049 0,007 0,007 5,504 12 1 2 3 Sub 0,086 0,006 0,008 0,133 0,847 13 1 3 1 Top 0,103 0,014 0,006 0,181 5,452 14 1 3 1 Sub 0,100 0,010 0,009 0,099 0,352 15 1 3 2 Top 0,097 0,013 0,007 0,012 0,055 16 1 3 2 Sub 0,099 0,006 0,009 0,007 1,494 17 1 3 3 Top 0,131 0,023 0,009 0,004 5,770 18 1 3 3 Sub 0,117 0,007 0,010 0,154 0,973 19 2 4 1 Top 0,142 0,022 0,009 0,013 5,553 20 2 4 1 Sub 0,115 0,007 0,011 0,141 1,224 21 2 4 2 Top 0,185 0,027 0,008 0,171 4,499 22 2 4 2 Sub 0,136 0,008 0,010 0,164 1,162 23 2 4 3 Top 0,143 0,010 0,009 0,004 2,799 24 2 4 3 Sub 0,133 0,003 0,009 0,161 0,881 25 2 5 1 Top 0,183 0,013 0,008 0,184 3,942 26 2 5 1 Sub 0,230 0,006 0,014 0,196 1,999 27 2 5 2 Top 0,217 0,037 0,009 0,004 4,500 28 2 5 2 Sub 0,140 0,008 0,009 0,161 1,216 29 2 5 3 Top 0,199 0,031 0,009 0,005 3,607 30 2 5 3 Sub 0,135 0,007 0,010 0,011 1,080 31 2 6 1 Top 0,311 0,057 0,009 0,004 4,543 32 2 6 1 Sub 0,186 0,021 0,010 0,156 1,303 33 2 6 2 Top 0,188 0,019 0,008 0,186 3,635 34 2 6 2 Sub 0,141 0,004 0,010 0,118 0,775 35 2 6 3 Top 0,205 0,037 0,009 0,004 3,565 36 2 6 3 Sub 0,150 0,006 0,010 0,133 0,636 Table 2. Presence of soil nutrients (Ca2+, Mg2+, Na+, K+, C) in mmol/g. First column is the sample ID number. 2nd to 5th column are the main variables; sampled plot, tree number, meter distance and soil layer (top or sub) respectively. Column 6 to 10 are
Figure 3 shows the scatterplots of the nutrient concentrations per element over distance in the topsoil. Visualizing the distribution of the nutrients. Figure 3. Scatterplots of nutrient concentrations in mmol/gram and µmol/gram for sodium over distance in meters in topsoil. In order for the hypothesis to be true, nutrient concentration need to decrease away from the tree. This means that it is expected that concentrations in these figures would go from top left to bottom right, in either a linear or exponential relation. However, this cannot be concluded from these figures.
Statistical analysis per element
To test for any significance, ANOVA was conducted on all elements using Matlab 2017a. The P-value results of each element are presented in table 3. Significance level alpha was set at 5%.These results indicate no significant change in concentration over distance. Element P-Value Calcium 0.9883 Magnesium 0.8611 Sodium 0.5555 Potassium 0.7530 Carbon 0.3161 0 0.5 1 1.5 2 2.5 3 3.5 4 distance [m] 0 0.2 0.4 concn [mmol/g]
Calcium concentration over distance
0 0.5 1 1.5 2 2.5 3 3.5 4 distance [m] 0 0.05 0.1 concn [mmol/g]
Magnesium concentration over distance
0 0.5 1 1.5 2 2.5 3 3.5 4 distance [m] 6 8 10 concn [µ mol/g]
Sodium concentration over distance
0 0.5 1 1.5 2 2.5 3 3.5 4 distance [m] 0 0.1 0.2 concn [mmol/g]
Potassium concentration over distance
0 0.5 1 1.5 2 2.5 3 3.5 4 distance [m] 0 5 10 concn [mmol/g]
Comparison between topsoil and subsoil
Additional two sample t-tests were done to see whether there were significant differences between topsoil and subsoil concentrations. The null hypothesis (no difference) is rejected if there is a significant difference with alpha set at 5%. The results of the t-tests help in order to find the effect of mulch in general, as organic material is largely found in the topsoil. The means for all elements between topsoil and subsoil were compared.
Element Mean Topsoil Mean Subsoil P-value
Calcium 0.1506 0.1199 0.0950 Magnesium 0.0255 0.0120 0.0168 Sodium 0.0080 0.0094 0.0016 Potassium 0.0545 0.1354 0.0010 Carbon 4.3754 1.0657 4.8689e-11 Table 4. Topsoil and subsoil comparison using two-sample t-tests, significance level set at 5%. Means of the concentrations are given in mmol/gram.
The results, given in table 4, show a significant difference in all elements except calcium. Higher concentrations of sodium and potassium are found in the topsoil. The mean concentration of carbon is more than four times higher in the topsoil, indicating a clear effect of surface mulch. With these results, the next step was to test whether the exchangeable elements are related to the carbon concentrations, as was found by the aforementioned Griffin (2013). To do this, the concentrations of the exchangeable elements were divided by the carbon concentrations of the same sample. This provided the concentrations normalized for carbon. The same t-tests were done on these normalized concentrations. The elements are related to carbon if their significance from before disappeared after normalization. Significance for magnesium and calcium disappeared. Element Mean Topsoil normalized for Carbon Mean Subsoil normalized for Carbon P-value Calcium 0.1325 0.1247 0.9364 Magnesium 0.0192 0.0113 0.5684 Sodium 0.0085 0.0099 0.8396 Potassium 0.0257 0.1414 1.2597e-6 Table 5. Topsoil and subsoil comparison normalized for organic carbon, significance level set at 5%. Means of the elements in mmol/gramC.
Comparison between plot 1 and 2
This comparison was done to answer the fourth sub question of this research, whether the years of production have an influence on the nutrient concentrations in the topsoil. Five t-tests were again conducted in which the means for every element in the topsoil were compared between plot 1 and 2. The results are presented in table 6.
Element Mean Topsoil plot 1 Mean Topsoil plot 2 P-value
Calcium 0.1041 0.1971 2.8984e-4 Magnesium 0.0228 0.0282 0.4317 Sodium 0.0074 0.0086 0.0015 Potassium 0.0451 0.0638 0.6348 Carbon 4.6794 4.0714 0.3863 Table 6. Plot comparison using two-sample t-tests, significance level set at 5%. Means of the concentrations are given in mmol/gram. A significant difference was found between plot 1 and 2 in the concentrations of calcium and sodium. These results do not give a clear effect on the influence of production years as will be discussed later.
Table 8. Results in fractions for all measured nutrients.
Table 7. Data for all measured nutrients concentrations in mmol/gram.
Fraction analysis
As mentioned in the methods, an ANOVA test was also done on the fractions of the nutrients. A conversion to fractions enables the analysis for concentration over distance between all five elements, hereby increasing the robustness of the dataset. The data was transformed to fractions by dividing each individual amount by the sum of the values found at the three distances per tree. Table 7 presents the concentrations data. Table 8 presents the fractions after transforming the concentrations.
The ANOVA results for the fractions do not show a significance for the exchangeable elements or for all elements combined. The results are presented in table 9. From the boxplots presented in figure 4 and 5 we can also conclude that there does not appear to be a significant difference between the three distances after comparing the elements in fractions.
With these results, no significant decreases in nutrient concentrations are found over the three meters away from the shade trees. The main hypothesis of this research is therefore not confirmed. Group of elements P-Value Exchangeable Elements 0.6692 All elements 0.8089
Table 9. P-values retreived frrom the fraction analysis for exchangeable elements (Ca2+, Mg2+, Na+, K+) and exchangeable elements plus total carbon.
Figure 5. Boxplots of all elements in fractions at three distances.
Figure 4. Boxplots of exchangeable fractions at three distances.
Survey Results
The survey results are qualitative and should not be used to represent any other farmers or in effect, a larger group of farmers. The answers are presented in table 10. In this paragraph, the answers are discussed and interpreted to answer the research question concerning the farmers knowledge about shade trees.
Rodrigo Papamilla
Gomez Heli Gonzalez Sutuclara Archimiro Oyos Jalimendez Javier Olmedo Regel Peregrino Gomez Galindez Oscar Hoyos
Muralla Ana Lucia Urbano Peña
Name of the farm and location
El silencio, San
Augustin El Paraiso, San Augistin Trebl, San Augustin El Paraiso, San Augustin El Rodeo, San Augustin Finca Filadelphia, San Augustin Finca 1810, Ptalito Hectares 6 5 2.5 5 2 1 5.5 Do you have shade trees on your plantation?
Yes Yes Yes Yes Yes Yes Yes
To your knowledge, what are the advantages of (shade) trees? Protection for small coffee plants. Climate control when warm. Income through timber and personal uses. Soil quality increases, more Organic material. Income through timber and personal uses. Fruits/berries are bigger and taste better. Climate control during cold weather. Organic matter production. Health of plants, greener leaves. Climate control when plants were smaller. Shade is good because it makes the ground more fertile. Personal uses. Quality of the coffee is better, because the berries grow slower and climate control/ protection. Soil, organic matter that falls. Have you ever experienced disadvantages of shade trees (e.g. lower yield)? Too much shade will lead to less production. Competition with coffee over nutrients, causing lower yield Less
production Less production Less production Less production Less production
What kind of trees do you have? Why that species? Guama, Cerinde, oranges, mango, plantain. Cachimbo, Guama, Cedro, Avocado, plantain. Guama, Carbonero, Cedrillo, Guasimo, fruits Guama, Avocado, Fruits, Cachimbo. Guama,
Plantain. PlantainCachimbo. , Caucho, Plantain.
Concerning your agricultural practices. Who or what influences you in your decisions? Extra space and started planting trees for income and use. Through experience, he found out some other benefits. External advice by Nestlé. His daughter studied agronomy and that is why he started planting shade trees but also why he cut down some of the trees. Experience and external advice through meetings with the FNC. Experience and external advice through meetings with the FNC. Experience, in other place they used Guama to make the soil more fertile. Believe concerning organic farming. External information from buyer and company. External from FNC, experience. Table 10. Farmer interviews data and answers to survey. Key words are in bolt. Tree species are given a colour when use is specified (besides shade). Blue for timber, green for soil fertility, orange for own use, black if not specified.
Question 1 Presence of shade trees
Yes. All farmers had shade trees on their plantations.Question 2 Advantages
Protection and climate control, soil fertility, mulch provision, better quality coffee. All of these answers agree with the advantages of shade trees mentioned by J. Beer (1987). As became clear, most of these answers came from experience, as with the mentioned disadvantages. However, it is interesting to note that the effect on, the invisible, nutrient concentrations was known.Question 3 Disadvantages
Less production, nutrient competition. Lower production was mentioned by all. Although all farmers mentioned the shade as constricting factor, Helí Gonzalez-Sutuclara added that shade trees get into a completion over nutrients with coffee plants. This is a sign of thorough knowledge about soil ecology.
Question 4 Shade tree species
Gauma, Plantain, Cachimbo. These are the most common trees that were mentioned. They represent roughly three groups of secondary use, besides shade. Guama or Inga, as mentioned before, was present because of its positive effect on soil fertility. Although this was not specified by all farmers. Plantain was present on all farms, it is used in most meals, and for some it provided an extra source of income. Cachimbo is a native tree species and has a large canopy cover with a relatively thin base, meaning that it provides a lot of shade for its nutrient usage. Its secondary use, if specified, is as a source of timber.Question 5 Influences agricultural practices
Experience, external advice through a cooperative, the FNC, university and/or buyers.
Of course, experience is the most prominent form of knowledge acquirement, fastest feedback from nature to the farmer. Nonetheless, within this group of farmers it was not the only form. Knowledge and equipment is shared in groups of farmers, in so called cooperatives. The national federation of coffee farmers (FNC) hosts lectures to teach farmers and the farming community. Farmer Helí Gonzalez got information through his daughter who studied agro economics at the university. Some of the interviewed farmers attained information from their buyers, who gave them tips to help increase their yields.
Discussion
The main goal of this research was to test if nutrient concentrations in the topsoil of a coffee farm are influenced by shade trees. Previous research was by Ekanade (1987) and Robinson (1965) showed the effect of shade trees over distance. In this paper, no significant effect was found. This chapter starts with a further interpretation of the results. Secondly, the errors in the methods will be discussed, including the parameter influences. Thirdly, tips for further research will be given. Fourthly, the survey will be discussed. Lastly, recommendations are provided for bachelor students who wish to set up their own research.Results
The ANOVA tests on the concentrations of the exchangeable elements and organic carbon did not provide a significant difference over distance away from the tree. To answer the first two sub questions, to no extent can an effect be found of shade trees’ mulch on the exchangeable elements concentrations and organic carbon concentrations over distance. This is in contrast with the results found by Ekanade (1987) and Robinson (1965). The low robustness of the dataset used in this research could be of influence. To combat this, all the elements were converted to fractions. The elements could hereby be grouped into one, increasing the robustness and enabling an analysis between them. The ANOVA from this group did also not a return a significant difference between the distance. Besides robustness there were other variables and parameters that could explain the results found in this research, as will be discussed later. However, mulch was clearly present in the fieldwork area. To see whether the mulch had an effect on the soil, a comparison was done between topsoil and subsoil nutrient concentrations. With significance levels set at 5%, all elements except calcium (p-value of 0.0950) were significantly higher or lower. Hence, it is concluded that mulch has an effect on the topsoil nutrient concentrations. As stated by Griffin (2013) exchangeable elements and organic carbon are both released by mulch. By doing t-tests on the concentration normalized on organic carbon, significances with magnesium and sodium disappeared and the p-value for calcium increased from 0.0950 to 0.9364. A correlation between organic carbon and the exchangeable elements suggests that they come from the same origin: mulch. Therefore, these results confirm the influence of mulch (organic matter) on the topsoil fertility.The cultivation age differed between the two plots, plot 1 was 10 years old and plot 2 was 30 years under cultivation. T-tests were done between the data of plot 1 and 2 to find differences as a consequence of longer cultivation. A significant difference was found for calcium and sodium (2.8984e-4 and 0.0015 respectively). Ekanade (1987) did a similar comparison and found significance in all elements. The results in this paper could have been influenced by other plot specific parameters, especially with the low robustness of the dataset in this research.
Survey
The survey results showed that the farmers who were interviewed had knowledge about technical concepts concerning ecological and soil chemistry related processes. Within the interviewed group of farmers, a clear knowledge gap, as depicted by perfecto (2005) seemed to be absent. However, an economic gap did become apparent. Because of low coffee prices, most shade tree benefits are overruled by a necessity for a higher yield, sometimes incited by the buying companies.
Methods
It proved to be difficult to send 144 soil samples to the Netherlands for analysis. Dutch customs did not allow the samples into the country and they were sent back to Colombia. Consequentially, this research needed to be completed with the 36 samples at Ambilab. Since only 18 of these samples were taken from the topsoil, the robustness decreased drastically. As these samples were comprised of the four samples per distance for four directions (up, right, down, left) relative to the hill, the variable for slope was now lost. This also meant that any effect within a radius would go unnoticed, for instance the influence of a coffee plant on a sample taken closely to it. Nitrogen was missing in the analysis. As mentioned in the theoretical framework, nitrogen is a good indicator for soil fertility as it is frequently depleted in agricultural soils. Nitrogen analysis throughthe use of CNS analysis was planned to be conducted in Amsterdam on the 144 samples. Including nitrogen in the dataset would have increased robustness and could possibly indicate a decline in concentration away from the shade trees.
Parameter influences
There were also parameter influences that should be taken into account when looking at the results of this paper. Due to the fact that this research was bound to one coffee plantation, the main characteristics of this plantation need to be included. The total shade cover, mulch presence, and shade tree species, were the main characteristics for the plots in this research. Since the entire plantation was located on the north side of a hill, the plantation got less sunlight. The farmer therefore chose to have only a few shade trees on the plantation. The effect of mulch from shade trees is thus smaller than on a farm with shade trees intercropped more frequently. The effect of mulch, as seen in the significant difference between topsoil and subsoil, can be explained by the mulch that originated from the weeding of plants that were growing in between the coffee plants. This weeding was done when this research was being conducted and it proved to be a very effective way to hold moisture and to redistribute organic matter. However, a disadvantage of only using the weeds for mulch is that deeper laying nutrients will be lost instead of getting brought back into the system by deeper laying roots from shade trees. The two plots that were selected for the sampling had a lot in common but they differed in age (time as agricultural field), and species of shade tree. In this research, no differentiation has been made between tree species, but plot 2 had only plantain as shade tree and plot 1 had a variety of native tree species. Because of these inconsistencies, the production age difference of 10 and 30 years becomes hard to pinpoint. The significant difference between plot 1 and 2 found in only calcium and sodium could have originated from other effects, as for instance tree species. All of these parametrical influences could be overcome with robustness. More data points would lower the impact of outliers and would make it easier to find more significant effects.Future research
As for this research, the absence of the effect of mulch by shade trees could be explained by the little share of mulch on the plots that originated from the trees. Further study is needed between plots with a lot of shade trees and plots similar to the ones in this research. This would make the effect of mulch from shade trees more apparent. Since a suggested research would look at field differences, a different sampling method should be applied. Samples could be taken randomly on a field, or in a raster/matrix. To lower the effect of other parameters, field differences need to be considered. This research has been done in cooperation with a research on subsoil differentiations. Future research could extent on the vertical effects of shade trees, possibly by taking samples at various depths between 0-50 cm.Survey
The survey included in this research is not based on social academic rules or standards. It has been added to this earth scientific research because the larger context in which this research has been performed requires a holistic view. The farmers’ livelihood and choices are vital to any other aspect of his or her farm. It would therefore be of great benefit to include a social side to any research that is conducted in an agricultural context. The survey presented here is short, but aided in giving a perspective into the coffee system. The survey was done with the help of Vanessa Mendez who at the time worked for Nestlé and brought us to the farmers whom she had to interview for her job at Nestlé. Since these farmers produce for Nestlé, question 5 is automatically biased. All of the interviewed farmers, to a degree, got information from Nestlé. This emphasizes the qualitative aspect of this survey.Recommendations future students
In this paragraph recommendations are provided for future students who are setting up their own research. These recommendations are based on the experiences gained from conducting this research. They will be divided in 3 categories: preparation, flexibility, and dependence. Preparation It goes without saying that preparation is an important part of most undertakings. Nevertheless, for the fieldwork described in this paper the priorities for preparation were different.
Starting your research, you will first need a subject and a basic story. In the case of this investigation, the subject area was going to be coffee and soil. Reading into some previous research and experiments the subject got more specified to shade trees and soil nutrient concentrations. In combination with a broader system story about a decline in shade trees and sustainability risks, it became clear we had to go to a coffee farm. Without knowing the exact details of the research, what methods were going to be used, it was time for the next step in the preparation. With an understandable story in our back pocket we could now start arranging getting there and getting access to a farm. Through meetings with people at the Coffeecompany, we got in contact with a coffee buyer for Latin and South-America, Mild Coffee Company. They then helped to get in contact with people in Colombia who were able to get us in the right place and give us information about facilities we needed. At this point we had not yet talked to any earth scientist, that is why going into too much details concerning the practicalities of the research could have been confusing. A simple story and simple request helped us get results. In this case, we needed access to a farm and access to a lab. Details on the facilities were only required once access was granted. Advancing step by step, using the right story per step. Additionally, meeting in person proved much more productive in respect to communicating via emai. Flexibility With Colombia on the other side of the Atlantic, we did not have time to see the fieldwork area before doing the fieldwork. This means that from the start we had to deal with uncertainty. Having an entire research planned out does not help. Instead, prepare to be flexible and creative. Make sure you have some way to gather results, albeit rudimentary. This could be by doing an analysis at a lab near the research area or bringing test kits yourself. International shipment is always a risk. The sampling methods used in this research were also adapted to the circumstances of the fieldwork area, shade trees were planted closely and randomly between the coffee trees. Methods used by Ekanade (1987) could therefore not be implemented. Always have a plan B and also discuss this with your supervisor. Dependence Until you have your results you have to live with uncertainty and dependence. This is also the part that makes doing your own research so rewarding. You are the one that has to do it, otherwise nothing will happen. This does mean that you will be dependent on other people to do things for you and help you get to where you want to go. Arranging and managing this will require a lot of work and will be probably be the most time-consuming aspect of your research. To combat the eerie feeling of not being in control, waiting for responses, be assertive and patient at the same time. Prepare your options and extra plans. If someone does not respond or something will not go through, prepare for another way to get there. If someone does respond or something does go through, immediately take the next step, this will get you momentum. This research was done in Colombia where Math and I experienced some cultural differences we had to work around during our investigation. These cultural differences are probably not constricted to Colombia alone. When asked if something is possible, especially via text messages, Colombians will probably say yes. It could very easily turn out not to be possible at all. We experienced this when we asked about doing our own laboratory analysis at SENA, an educational institute for coffee research. We got a yes which turned into a no when we got there. That is why it is advised to always have a plan B, even if something is given as a certainty.
Conclusion
This research has been about understanding and combining two phenomena in coffee production and in the context of shade trees. On the one hand, the many benefits found with the implementation of shade trees on a coffee farm in previous research. On the other hand, the seemingly contrasting trend of a decreasing presence of these shade trees on coffee plantations. This thesis studied thre effect of mulch from shade trees on the fertility of the topsoil. Through soil sample analysis of 18 soil samples at 1, 2, and 3 meters away from 6 shade trees no significant effect was found for the elements of Ca2+, Mg2+, Na+, K+ and total organic carbon. The five elements appeared to be correlated and thus originated from the same mulch. A significant difference was found between topsoil and subsoil, which could be explained by the abundant presence of mulch from different sources, mostly weeds. Although no fertility difference was found in the topsoil there is still reason to believe the positive effects found in other research. Fellow student and fieldwork partner, Math van Soest, studied the effect of shade trees on soil fertility in the subsoil of the coffee plantation in Huíla, Colombia. In his research, a significant decline was found in subsoil fertility away from the shade trees as a possible effect of the rooting systems. Due to an unfortunate combination of events the robustness of this research is missing. Future research is therefore required to find or exclude the effect of mulch by shade trees. The social study included in this paper helped understand the decline in shade tree presence. Farmers who were interviewed knew about the benefits of shade trees, they nevertheless argued that because of low coffee prices they have to increase their yield to make a living. A knowledge gap became an economic gap. As long as good quality and sustainable coffee is not promoted enough the decline in shade grown coffee will continue.
Acknowledgements
First off, I would like to express my gratitude for my research partner and friend since high school, Math van Soest. Without you this might whole thing would just have been a great idea. Instead, we really did go to Colombia and we will talk about this adventure for years to come, at least for dos años. Great appreciation goes out to Boris Jansen, who is listed in the role of supervisor, but more often than not, was our mentor and supporter. Thank you for offering us the freedom and the guidance to do our own research. Special thanks go out to all the people who invested their time to help us make this research a reality. Both through small steps and giant leaps. Jasper Uhlenbusch, Jantien Rutte, Hector Posada, Gloria Marcela, Walter Ariza, Ana Lucia, Vanessa Mendez, Oscar Hoyos and Oscar Valbuena, you have all been a blessing. In particular, Ana Lucia, for your incredible hospitality, finca 1810 is a paradise. Vanessa Mendez, you should have been mentioned on every page because we cannot thank you enough for all your effort. Translating, hosting, taking us with you on your trips to the coffee farms, letting us see the reality of the coffee industry but also the real and amazing Colombia. You are an angel.References
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Appendices
Zipfile
The zip. file in which this paper is sent contains the following appendices: - Dataset received from Ambilab, exchangeable element concentrations for 36 samples; topsoil and subsoil. Data data is given in meq/100g. - Dataset received from Ambilab, total organic carbon concentrations for 36 samples; topsoil and subsoil. Data is given in percentages found per sample. - All data converted to mmol/gram. Calculation given below. - Matlab script for statistical analysis, data visualization and data conversions, for topsoil and subsoil.Conversion to mmol/g
To converted the exchangeable element concentration data from meq/100g to mmol/g, the following calculation was applied: Meq/100g divided by 100 = meq/g Meq/g is equivalent to mmol/g for sodium and potassium. Since calcium and magnesium have an oxidation state of 2+, their values were multiplied by 2 to convert meq/g to mmol/g.Mind the gap - Thoughts on the coffee chain
As mentioned before, I am a coffee enthusiast and it drove me to set up this research. It was tough to see that the stories I believed in, about fair trade, direct trade and the smiling coffee farmers in commercials did, in large parts, not come from reality. That is why I wanted to add a short paragraph on the coffee chain and include advice for other coffee enjoyers and coffee businesses. The ever-increasing demand in coffee has made coffee producing countries eager to keep a large market share. For Colombia, this is especially true. Overproduction has started to become apparent in many coffee-producing regions, including Huíla. Certificates are only part of the solution, they grant only a slight benefit. Coffee produced under the rainforest alliance is an improvement. However, it is not an improvement to actual rainforest. Fairtrade tries to guarantee a price that makes it possible for a farmer to live by, but they do not differentiate between good or bad coffee, or good or bad agricultural practices. Certificates are only as good as their lowest bars. Corruption and insufficient oversight are inherently part of these certificates. Farmers interviewed in this research had a good idea about what was good for their land, but making a living had and will always have priority. Oscar Hoyos, the farmer who produces for the Coffeecompany is a perfect example, he is one of the few organic producing farmers but he cannot make a living. The demand for organic coffee is not high enough so he has to sell most of his coffee to the commodity market for a very low price. Coffeecompany buys 15% of his coffee for a higher price with premiums for quality and taste characteristics. Even for a farmer who has a rich European buyer it is apparently hard to come by.I you enjoy a nice cup of coffee or need it as fuel, please consider drinking organic and fairtrade, to start with. The next step is to start drink specialty coffee from direct trade and microlots. We do not need more coffee, we just need better coffee. If we can get the market to really reward the farmers who produce sustainable coffee, more will follow. For businesses in the coffee sector I just hope you invest in knowing how your product is produced and where it comes from. Investing in the chain and aiming for transparency might not seem cost effective but your consumers will love it.
