Soil organic carbon, nitrogen and soil respiration during the rainy season in the silvopastoral system of Rivas, Nicaragua

Texto completo

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‘Soaked Silvopastoral Soils’

Soil organic carbon, nitrogen and soil respiration

during the rainy season in the silvopastoral system

of Rivas, Nicaragua

--MSc Thesis--

Roy Remme

Registration number: 851228-686100 MSc Climate Studies, Wageningen UR Supervisor: Dr. Marcel Hoosbeek Chair group: Earth System Science Course code: ESS-80439

March 31, 2011

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Preface

Before you lies the last piece of work for my MSc programme Climate Studies at Wageningen UR and the final product of my study for the FUNCiTREE project. This thesis concludes about nine months of research, which I can look back on with joy. I am grateful to have been a part of the FUNCiTREE project in Nicaragua, all in all it has been a very educational and interesting experience. Of course, this product would not have been here without a number of helping hands.

First of all, I want to thank my supervisor, Marcel Hoosbeek, for getting me involved in the project and for his advice throughout the whole study. Many thanks go to the people from CATIE, especially Fabrice DeClerck for setting up my trip to Rivas and getting me started up and Andre Nieuwenhyse for his supervision and advice in the field. His critical questions were vital for the improvement of my research.

A very big thank you goes to all my colleagues and friends at the FUNCiTREE office in Belén, Rivas. First and foremost, Dalia Sánchez for her dedication to the project and my wellbeing in Belén and all the little problems she helped me solve. I want to thank Guillermo Ponce for his advice and help in the laboratory of Escuela Internacional de Agricultura y Ganadería (EIAG), Rivas. Amalia, many thanks for the excellent food, good company and assistance. My time in Belén would not have been half as fun without my companions and fellow students. Ryan, thanks for all the talks, laughs, beers and trips around beautiful Nicaragua. Sofia, thanks for your latin warmth and the surfing trips to playa Maderas in Lola. Diana, gracias por las clases de español. He avanzado mucho gracias a ti. Will y Flor, muchas gracias por tu amistad, ayuda y los bailes. Israel, muchas gracias por todo tu apoyo en campo y tus bromas. Gracias a ti estoy un pocito Nica. A todos mis amigos, fue un honor poder trabajar y vivir con ustedes, nunca lo olvidaré. Many thanks also goes to the farmers that allowed me to dig soil pits and do measurements in their fields. Also, thanks to the people at the EIAG for letting me use the laboratory and at INETER for putting their meteorological data at my disposal.

I want to thank a number of people from the Earth System Science (ESS) group at Wageningen UR for their help. It was a pleasure to work with Eef Velthorst in the ESS laboratory. Your help with all the analyses was greatly appreciated and I enjoyed companionship during the coffee breaks. I want to thank Bart Kruijt and Jan Elbers for their quick digital responses to my distress calls every time the research equipment broke down.

Finally, I want to thank Jacomien for the distraction she offered in between the hard work, both in Nicaragua and during writing. My parents deserve recognition for supplying me with fresh research equipment and sharing some great times with me all around Nicaragua. Thank you, Liset and Paul for proof-reading and correcting parts of my work.

Roy Remme

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Summary

Soil organic carbon (SOC) pools and the flows in and out of this pool are key components in the global carbon cycle. The tropics play a particularly important role in the terrestrial carbon budget. The purpose of this study is to contribute to the understanding of SOC and nitrogen pools and the emission of CO2 to

the atmosphere through soil respiration in silvopastoral systems (SPS) in Rivas, Nicaragua, focussing on the rainy season which lasts from May to late October. Soil respiration and its controlling factors, including soil temperature, air temperature, precipitation and substrate supply through the decomposition of soil organic matter (SOM) have been studied.

For this research silvopastoral fields with two local tree species, namely Guazuma ulmifolia and

Crescentia alata and two soil types, (Vertic) Haplustolls and Haplusterts, have been used. Three areas within the silvopastoral field have been studied: the area under the tree canopies, the leaf litter cone directly to the southwest of the tree and open pasture. In the field soil respiration measurements have been done, soil samples and meteorological data have been collected. Soil samples have been analyzed in the laboratory for a number of soil characteristics, including soil texture and SOC and nitrogen content of three SOM fractions: the light fraction (LF), physically occluded SOM (iPOM) and mineral associated SOM (maOM). Statistical analysis has been done to study the relations between soil respiration and temperature, precipitation, SOC and nitrogen.

Haplusterts have a higher clay content and also more total SOC and nitrogen in the top 50 cm of soil than (Vertic) Haplustolls. For both soil types the maOM fraction contained much larger SOC and nitrogen pools than the LF and iPOM fractions. SOC and nitrogen pools LF are the smallest. SOC and nitrogen pools under the tree canopy and in the leaf litter cone are generally larger than in open pasture. A positive correlation was found between soil clay content and SOC and nitrogen in the maOM fraction, also resulting in a positive correlation between clay content and total SOC and nitrogen. August and September were the wettest and coolest months measured during the study, while October was substantially drier. Soil temperature was approximately 2 °C lower under the tree canopy compared to the open field. Average daytime soil respiration was lowest under the tree canopy, and significantly higher in the leaf litter cone and the open pasture. Average soil respiration was highest in the leaf litter cone. Soil respiration was higher in October than in August and September. No clear correlation was found between soil respiration and SOC and nitrogen. Clay content, temperature and soil temperature correlated positively with soil respiration. Precipitation quantities correlated negatively with soil respiration, especially under very wet conditions. Interactions between controlling variables could only partially explain variations in soil respiration. The most important controlling variables were soil temperature, air temperature and precipitation. SOC and nitrogen in the different SOM fractions were better predictors of soil respiration than total SOC and nitrogen.

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Resumen

Las reservas de carbono orgánico del suelo (SOC) y los flujos de entrada y salida de este grupo son componentes importantes en el ciclo global del carbono. Los trópicos son especialmente importantes en el balance de carbono terrestre. El propósito de este estudio es contribuir a la comprensión de las reservas de SOC y nitrógeno, y las emisiones de CO2 a la atmósfera a través de la respiración del suelo en

los sistemas silvopastoriles (SSP) de Rivas, Nicaragua, centrándose en la estación lluviosa que dura de mayo hasta el fin de octubre. La respiración del suelo y sus factores de control fueron estudiados, incluyendo la temperatura del suelo, temperatura del aire, precipitación y el suministro de sustrato a través de la descomposición de la materia orgánica del suelo (SOM). Para esta investigación se han utilizado utilizaron los campos silvopastoriles con dos especies de árboles locales, a saber, Guazuma ulmifolia y Crescentia alata, y dos tipos de suelo, (Vertic) Haplustolls y Haplusterts. Tres áreas dentro del campo silvopastoril se estudiaron: el área bajo la copa de los árboles, el cono de la hojarasca directamente al suroeste del árbol y los pastizales abiertos. Se realizaron las mediciones de respiración del suelo, recogieron las muestras de suelo y los datos meteorológicos en el campo. Las muestras de suelo se analizaron en el laboratorio para una serie de características del suelo, incluyendo la textura del suelo y el contenido de SOC y nitrógeno de tres fracciones de SOM: la fracción ligero (LF), físicamente ocluido SOM (iPOM) y minerales asociados SOM (maOM). Se hicieron el análisis estadístico para estudiar las relaciones entre la respiración del suelo y la temperatura, precipitación, SOC y nitrógeno.

Haplusterts tienen un mayor contenido de arcilla y más SOC y nitrógeno total en los primeros 50 cm del suelo que (Vertic) Haplustolls. Para los dos tipos de suelo, la fracción maOM tiene reservas de SOC y nitrógeno mucho más grande que las fracciones de LF y iPOM. Las reservas de SOC y nitrógeno son las más pequeñas en LF. Las reservas de SOC y nitrógeno bajo la copa de los árboles y en el cono de la hojarasca son generalmente más grandes que en los pastizales abiertos. Se observó una correlación positiva entre el contenido de arcilla del suelo y el SOC y nitrógeno en la fracción maOM, lo que también resulta en una correlación positiva entre el contenido de arcilla y el SOC y nitrógeno total. Agosto y septiembre fueron los meses más húmedos y frescos, mientras que octubre fue considerablemente más seco. La temperatura del suelo fue de aproximadamente 2 °C menos bajo la copa del árbol en comparación con los pastizales abiertos. La respiración promedio del suelo durante el día fue el más bajo debajo de la copa del árbol y significativamente mayor en el cono de la hojarasca y el pasto abierto. La respiración del suelo fue mayor en el cono de la hojarasca. En octubre la respiración del suelo fue mayor que en agosto y septiembre. No hay una correlación clara entre la respiración del suelo y el SOC y nitrógeno. El contenido de arcilla, la temperatura y la temperatura del suelo se correlacionó positivamente con la respiración del suelo. Precipitación se correlacionó negativamente con la respiración del suelo, especialmente en condiciones muy húmedas. Las interacciones entre las variables de control sólo pudrieran explicar las variaciones en la respiración del suelo parcialmente. Las variables de control más importantes son la temperatura del suelo, temperatura del aire y la precipitación. El SOC y nitrógeno en las distintas fracciones de la MOS fueron mejores predictores de la respiración del suelo que el total del SOC y nitrógeno.

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Contents

1. Introduction ... 1

2. Methodology ... 5

2.1 Field set-up ... 5

2.1.1 Geology and soil types ... 5

2.1.2 Tree species selection ... 5

2.1.3 Site selection ... 6

2.1.4 Research areas in the silvopastoral field ... 9

2.2 Soil respiration ... 10

2.3 Soil samples ... 11

2.4 Laboratory analysis ... 12

2.4.1 Bulk density ... 12

2.4.2 Soil texture ... 12

2.4.3 Total carbon and nitrogen ... 12

2.4.4 Physical fractionation ... 13

2.5 Data overview and statistical analysis ... 15

2.5.1 Obtained data ... 15

2.5.2 Methods of data analysis ... 16

3. Results ... 17

3.1 Soil Texture ... 17

3.2 Carbon and nitrogen supply ... 18

3.2.1 Measured SOC and nitrogen pools ... 18

3.2.2 Carbon pools in the top 50 cm of soil ... 20

3.2.3 Nitrogen pools in the top 50 cm of soil ... 21

3.2.4 Carbon and nitrogen relations ... 22

3.2.5 Relation between carbon and nitrogen and soil texture ... 22

3.3 Temperature and precipitation... 25

3.3.1 Air temperature and precipitation ... 25

3.3.2 Soil temperature ... 27

3.3.3 Soil temperature over the course of a day ... 27

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3.4.1 Spatial variability and species variation ... 28

3.4.2 Long-term Temporal Variation ... 29

3.4.3 Short-term Temporal Variation ... 29

3.5 Effects of carbon and nitrogen pools on soil respiration ... 31

3.5.1 Soil respiration and total soil organic carbon and nitrogen... 31

3.5.2 Soil respiration and soil fractions ... 31

3.6 Effect of soil texture on soil respiration ... 32

3.7 Temperature ... 32

3.7.1 Air temperature ... 32

3.7.2 Soil Temperature ... 33

3.8 Precipitation ... 35

3.8.1 Precipitation quantities ... 36

3.8.2 Single rain-event ... 36

3.9 Effects of cow dung and urine on soil respiration ... 37

3.10 Interactions ... 37

3.10.1 Silvopastoral system ... 37

3.10.2 Tree species and soil type ... 38

4. Discussion ... 41

4.1 Soil respiration in the Rivas SPS ... 41

4.2 Soil texture, SOC and nitrogen and soil respiration ... 42

4.3 Temperature and soil respiration ... 43

4.4 Precipitation and soil respiration ... 43

4.5 Interactions of factors controlling soil respiration ... 44

4.6 Uncertainties ... 45

4.7 Future research ... 47

5. Conclusion ... 49

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1.

Introduction

The gravity of future climate change has widely been accepted by both the scientific community and policymakers and mitigation measures are being discussed globally. An important part of this discussion focuses on terrestrial carbon sequestration. For effective strategies an accurate understanding of the global carbon cycle and its components is essential. Key components in the global carbon cycle are the soil organic carbon (SOC) pools and the flows in and out of this pool (Batjes 1996; Wang et al. 2006; Lal 2008) The tropics play a particularly important role in the terrestrial carbon budget because they contain over 40% of the world’s soil carbon (Raich et al. 2006). The purpose of this study is to contribute to the understanding of these SOC pools and the emission of CO2 to the atmosphere through soil respiration in silvopastoral systems (SPS) in Rivas,

Nicaragua, focussing on the rainy season.

The soil carbon pool is one of the five global carbon pools (Lal 2008). In the surface meter alone the world’s soils contain approximately 1550 Pg SOC (Eswaran et al. 1993; Lal 2008). Globally the yearly input from organic litter and the loss through respiration are both estimated to be approximately 60-100 Pg C y-1, balancing the cycle (Raich and Potter 1995; Rustad et al. 2000; Lal 2008). Soil respiration is the second largest carbon flux in terrestrial ecosystems (Wang et al. 2006). Changes in the SOC pools through land-use changes have contributed 35% of the cumulative gain in atmospheric CO2

since approximately 1750 (Lal 2008). A small change in the global soil respiration flux could potentially rival annual CO2 emissions from the burning of fossil fuels (Raich and Schlesinger 1992).

Global warming could have a positive feedback effect on CO2 emissions from soil carbon pools,

increasing soil respiration, which in turn will further exacerbate warming. The development of a better understanding of controls on soil respiration and its components is critical, especially the decomposition of soil organic matter (SOM) (Rustad et al. 2000).

SOM is available in soils in different fractions, affecting the accessibility and stability of SOC and nitrogen. The long term storage of carbon is determined by SOM stabilization mechanisms in the soil (Six et al. 2002; von Lützow et al. 2007). Six et al. (2002) defined three mechanisms which are of importance for the stabilization of SOM: chemical stabilization, physical protection and biochemical stabilization. Based on these stabilization mechanisms different SOM pools have been defined, that increasingly stabilize SOC and nitrogen. The unprotected SOM pool consists of partially decomposed plant residues that are not closely associated with soil minerals and also known as the light fraction (LF). SOM that is physically protected within soil microaggregates, or occluded particulate organic matter (iPOM) is already more stabile, as it is protected from microbes and enzymes. Mineral associated organic matter (maOM), is considered as the most stabile fraction, being chemically stabilized (Six et al. 2002; von Lützow et al. 2007).

In the global carbon cycle soil respiration is one of the major pathways, with emission estimates ranging from 68 to 100 Pg C yr-1 (Rustad et al. 2000), coupling below- and aboveground metabolism in terrestrial ecosystems (Giardiana and Ryan 2000). Soil respiration refers to a series of metabolic processes that break down organic molecules in the soil to liberate carbon dioxide (CO2), with three

principle components: respiration from SOM, root respiration and surface litter respiration (Raich and Schlesinger 1992; Rustad et al. 2000; Luo and Zhou 2006). In other words, soil respiration is the production of CO2 by plant parts and organisms in soil, while they gain energy from catabolising

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2 and abiotic factors (Bahn et al. 2010). These factors include soil temperature, soil moisture, ecosystem productivity, carbon substrate and nitrogen availability, plant root densities and activities, soil organism population levels, soil physical and chemical properties and vegetation types (Raich and Tufekcioglu 2000; Rustad et al. 2000; Lal 2005; Luo and Zhou 2006).

The most important controlling factors are temperature, soil moisture and substrate supply (Raich and Tufekcioglu 2000; Lee et al. 2004; Luo and Zhou 2006; Bahn et al. 2010). Temperature is probably the most important controlling factor of soil respiration because all aspects of soil respiration are affected by temperature (Raich and Schlesinger 1992; Lee et al. 2004; Luo and Zhou 2006; Bahn et al. 2010). In ecosystems without extended periods of drought, soil temperature usually suffices to explain most of the seasonal variation of soil respiration (Bahn et al. 2010). The sensitivity of soil respiration to temperature change is smaller in tropical regions (Raich and Schlesinger 1992).

Precipitation and consequential changes in soil moisture are important factors that control soil respiration, but the relationship between soil respiration and soil water content is very complex and not well understood (Raich and Schlesinger 1992; Luo and Zhou, 2006). Soil moisture diffuses substrates and O2, thus indirectly affecting soil respiration. Soil respiration is generally around its

maximum rate in intermediate soil moisture levels and decreases again with high moisture content (Luo and Zhou 2006). In most cases precipitation has a positive relation with soil moisture, except for in extreme conditions, for example when precipitation rate is higher than the infiltration rate into the soil, causing surface runoff (Seneviratne et al. 2010). Precipitation has direct effects on soil respiration. Short wetting can cause an instantaneous excitation of microbes, but also a delayed pulse. Longer periods of rainfall can promote the growth of microbial biomass, increasing basal respiration. Heavy rainfall temporarily increases soil respiration in temperate climates, releasing large quantities of carbon to the atmosphere in a short period of time (Lee et al. 2004).

Substrate supply is an important factor which controls soil respiration, because this provides the carbon-based organic compounds and nitrogen necessary for the process (Luo and Zhou 2006). Aboveground photosynthetic activity, carbon and nitrogen substrates in SOM, above- and belowground litter production and the microbial community strongly affect soil respiration through substrate supply (Luo and Zhou 2006; Wang et al. 2006). SOC concentration, especially from recently produced labile SOC in litter significantly influences soil respiration (Wang et al. 2006). Nitrogen affects root respiration, litter decomposition and primary plant productivity, thus controlling soil respiration (Luo and Zhou 2006).

Soil respiration is regulated by multiple interacting controlling factors, which tend to be difficult to separate. Soil respiration is usually most responsive to the limiting factor (Luo and Zhou 2006). Interactions between moisture and temperature account for most of the variability of soil respiration, especially when neither of these factors are at their extremes (Lee et al. 2004; Luo and Zhou 2006). Ecosystem productivity and substrate supply also interact with temperature and moisture (Luo and Zhou 2006; Bahn et al. 2010). This study focuses on the most important controlling factors, their interactions and their effects on soil respiration.

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3 the majority has focused on the northern hemisphere and large natural systems in the tropics. Raich and Schlesinger (1992) commented that few measurements of annual soil respiration had been made in the tropics at the time of writing. There are examples of tropical studies on SOC and soil respiration (e.g. Raich et al. 2006; Falloon et al. 2007). Still, the dataset is highly restricted and heavily underrepresents many biomes (Bahn et al. 2010). Relatively little is known about soil carbon and soil respiration in parts of the neo-tropics, including parts of Central America. More research has been done in some tropical regions than in others. There is less information available for seasonal regions than for the humid tropics (Arroyo-Mora et al. 2005). There are still uncertainties about the magnitude of SOC pools in tropical regions (Canadell et al. 2007). There is very little known about both SOC pools and soil respiration in Nicaragua. Searches through scientific literature databases provided no results for either subject.

This study will focus on silvopastoral systems (SPS) in the Rivas department in southwest Nicaragua. SPS is an agroforestry system which combines fodder plants such as leguminous herbs and grasses with trees and shrubs (Haile et al. 2010; Pagiola et al. 2007). Silvopasture provides a number of advantages compared to ordinary pasture systems. Besides providing pasture for livestock other on-site benefits include additional production from tree components such as fuel wood, fodder and timber; the maintenance and improvement of pasture productivity; and diversification of production (Pagiola et al. 2005). Other benefits include the increase of biodiversity, the increase of water infiltration and decrease in surface runoff and soil erosion, increased soil fertility through the continuous supply of organic matter, and the diversification of farm income sources (Pagiola et al. 2007; Reis et al. 2010). In areas where large parts of land have been modified due to cattle-ranching and agricultural practices, SPS can be seen as important tools to achieve livestock production and conservation goals (Pezo and Ibrahim 1998; Schroth et al. 2004). For this study SPS fields were used that were used for cattle grazing. Compared to treeless agricultural systems, SPS have the potential to enhance carbon sequestration, because trees can be a significant sink of atmospheric carbon and function as a long term storage (Haile et al. 2010). SPS practices have been found to fix significant amounts of carbon in soils (Pagiola et al. 2007). In silvopastoral systems above- and belowground productivity and the quality and quantity of litter inputs differ from grassland systems (Haile et al. 2010).

To date SPS studies have focused on carbon sequestration and soil organic carbon pools at SPS level. For soil respiration in general Bahn et al. (2010) claim that little is known about spatial variation, although it is evident that even over short distances this can differ substantially. Little is known about spatial variations in SOC pools and soil respiration within silvopastoral fields, for example differences between the areas that receive different amounts and types of litter. For SPS three different areas can be distinguished: the open field which receives only grass litter, the area under the tree canopy that receives mainly tree litter and, areas around the tree that receive litter from both trees and grass. In this thesis spatial differences will be studied, based on these differences in litter input.

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4 precipitation is 1400 mm (INETER 2000). The land has been widely modified over the past centuries and the present-day landscape of Rivas is dominated by pastures (56.7%). Other important land-uses are crop cultivation, secondary forest fragments and riparian forests (Sánchez et al. 2004). With respect to economic activities the department of Rivas is dedicated mainly to agriculture, ranching, and artisanal fishing. Annual crops and extensive cattle ranching are the dominant production types (Gómez et al. 2004). The municipalities of Belén and Rivas have been classified as municipalities with high poverty and medium to low poverty respectively (INIFOM 2007). Various SPS practices can be found in the Rivas area, but this study focuses on isolated trees in pastures. These may have been planted here after the establishment of pastures, they could be products of the process of natural regeneration or they may have been retained by producers when the land was cleared (Pezo and Ibrahim 1998; Harvey and Haber 1999).

Figure 1.1 Map of Nicaragua (left) and the department of Rivas (right). On the Nicaragua map the department of Rivas has been marked in red. Source Rivas map: Sánchez et al. 2010.

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2.

Methodology

In this chapter the methods that have been used throughout this study will be explained. A number of steps had to be taken to select research plots, do field measurements, obtain samples, analyze soil samples in the laboratory and analyze the obtained data. First of all, the methodology used during the fieldwork, from July to November 2010, will be explained. Specific tree species and adequate field sites needed to be selected (section 2.1). Over the course of the fieldwork, soil respiration measurements were taken (section 2.2) and soil samples were collected (section 2.3). A number of analyses were carried out in laboratories in Rivas, Nicaragua and Wageningen, the Netherlands (section 2.4). Finally, the obtained dataset was statistically analyzed (section 2.5).

2.1 Field set-up

In this section the geological history, soil types and the methodology used to select tree species and research plots will be explained. The field set-up used for the measurements and sampling will be elaborated.

2.1.1 Geology and soil types

The soils in the Rivas area largely belong to the Rivas complex and were formed on marine parent material of young Tertiary age. The parent materials consist of clays and sands of varying thickness. The Rivas complex has been lightly folded and eroded, resulting in a landscape with rolling to steep hills. Ephemeral rivers (gullies) are deeply incised and bordered by fluvial deposits that range from clay higher up in the deposit to gravel at the bottom. Alluvial fans with slopes of less than 10% have been formed as a result of erosion in the hills. These areas are largely heavy-textured and range from clay loam on the fringes of valleys to heavy clay in the central areas. The clays are largely montmorillonitic, causing the formation of Vertisols which swell upon wetting and form deep cracks upon drying. These soils become very hard when dry. In some areas the soils may have horizons with lime concretions (<1 cm diameter) between 50 and 100 cm depth, because the parent material has some calcium carbonate. Steeper slopes have highly variable soils and on slopes where soil movement is evident they range between 0 and 50 cm in depth. Loamy to clayey soils are found on more gradual, less steep slopes, frequently with a well-developed dark topsoil. The mineralogy of these soils is the same as in the alluvial fans, but the Vertisol character is less expressed, with lower clay contents (Buurman and Hoosbeek 2009).

This study focuses on the flat parts of the landscape to eliminate the effects of slope on soils. Two soil types were identified in the flatter parts of the Rivas area: (Vertic) Haplustolls and Haplusterts.

The Haplusterts are Vertisols that are found mainly in the central (flat) parts of alluvial fans in the Rivas area. (Vertic) Haplustolls are Mollisols that have been described to be found on sloping parts of the alluvial fans of the Rivas area (Buurman and Hoosbeek 2009). Both soil types have been found in the research area, table 2.1 and table 2.2 give an overview of soil types per individual plot. The area in which (Vertic) Haplustolls were found can be described as an alluvial fan, but was not sloping. All research plots were found in nearly completely flat areas.

2.1.2 Tree species selection

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6 alata, commonly known as Jícaro(figure 2.1).The tree species are of a similar size and have a fairly similar crown architecture. A database with individuals of both species was available, with information on location, size, the land owner among others.

Figure 2.1 Selected tree species, G. ulmifolia (left) and C. alata (right).

Guazuma ulmifolia

G. ulmifolia is one of the five dominant tree species in the Rivas SPS. This species is also one of the most used species as a source of forage and food in the dry season (Sánchez et al. 2010). It is a non leguminous species (Flores et al. 1998), that grows to a maximum height of about 8 meters in the open field. It loses its leaves during the end of the dry season, in February and March. Flowers and fruits are produced in the largely leafless period in the dry season, between January and April (Francis 1991; Cordero and Boshier 2003). Crowns of trees growing in open areas tend to be spreading and very limby (Francis 1991). The tree has a wide variety of uses, producing timber for carpentry, coal, fodder and feed for livestock (Francis 1991; Cordero and Boshier 2003).

Crescentia alata

C. alata is a species that is tolerant to very wet and very dry conditions. It produces flowers and large gourd-like seeds along its stem. The species does not lose its leaves throughout most of the dry season and is therefore only marginally deciduous. Only near the end of the dry season the leaves are dropped, but new leaves are produced shortly thereafter, before the start of the rainy season (Rockwood 1974; Cordero and Boshier 2003). Flowers and fruits are produced year round (Cordero and Boshier 2003). C. alata is also a non leguminous species (Kassa et al. 1997). A primary use of C. alata in SPS is shade for livestock. The ripe fruits are eaten by livestock and can be used as feed supplements. The empty gourds can be used for handicrafts (Cordero and Boshier 2003).

2.1.3 Site selection

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7 surrounding trees which could affect soil characteristics. There are no other trees within at least 20 meters distance of any of the selected trees. All trees needed to be of a relatively similar age, size and shape to decrease differences impact on soils as much as possible. All trees used in this study are at least 15 years of age, according to the farm owners. All C. alata trees were located on soils that have been classified as Haplusterts, while most of the G. ulmifolia were found on soils classified as (Vertic) Haplustolls. The two G. ulmifolia individuals located on Haplusterts, were in the same pasture.

Table 2.1 Information on the location and size of G. ulmifolia individuals.

Tree code

X-coordinate

Y-coordinate

Comunidad Tree height (m)

Canopy diameter (m)

Stem

diameter (cm)

Soil type

G3 612054 1285283 Cantimplora 10.60 15.13 65.00 Haplustoll

G8 611759 1285778 Cantimplora 10.50 16.78 112.80 Haplustert

G17 611692 1285816 Cantimplora 10.80 15.09 90.00 Haplustoll

G18 612089 1284980 Cantimplora 8.50 13.03 43.00 Haplustoll

G19 611977 1285204 Cantimplora 7.00 10.75 56.20 Haplustoll

G20 612218 1285306 Cantimplora 9.00 12.55 71.00 Haplustert

All selected individuals of G. ulmifolia are located in the comunidad Cantimplora, in the northern part of the research area. This is a relatively flat part of the research area, with a relatively high number of isolated G. ulmifolia trees. All selected trees are relatively close together, located in three adjacent pastures, two trees per pasture. Two of the pastures were frequently used for grazing by cattle and contain trees of similar age and size. The third pasture was rarely used and towards the end of October it was in a quite poor condition. The vegetation was not edible for cattle or horses and it stood approximately one meter high. The two trees, the largest and oldest trees selected, started losing their leaves in October. Especially individual G8 was in poor condition towards the end of the rainy season. It had lost the majority of its leaves and a large portion of its branches had died. Field conditions and the soil type could have contributed to this. The soil was nearly continuously saturated between August and October, with frequent ponding. G. ulmifolia are usually not found on Haplusterts, which could also contribute to its poor state.

Table 2.2 Information on the location and size of C. alata individuals.

Tree code

X-coordinate

Y-coordinate Comunidad

Tree height (m)

Canopy diameter (m)

Stem

diameter (cm)

Soil type

J1 615078 1278333 Las Mesas 7.20 9.25 37.50 Haplustert

J7 612005 1283979 Cantimplora 8.80 16.38 - Haplustert

J10 612010 1283344 Cantimplora 7.40 12.50 53.00 Haplustert

J14 615143 1278319 Las Mesas 7.60 8.95 46.80 Haplustert

J32 624981 1250877 San Antonio 8.00 7.45 33.80 Haplustert

J33 625268 1251028 San Antonio 8.20 13.05 74.00 Haplustert

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9 The selected C. alata trees are far more spread out than the selected G. ulmifolia trees due to the lack of isolated individuals. C. alata is a very common species in Rivas but very few met the prerequisites for selection. Two individuals were found in the comunidad Cantimplora, relatively close to the individuals of G. ulmifolia. Two individuals were selected in a pasture in the comunidad Las Mesas and two individuals were selected in the southern part of the research area, in the comunidad San Antonio de la Chocolata.

2.1.4 Research areas in the silvopastoral field

In this study three areas under and around a tree were compared for soil characteristics and soil respiration (figure 2.2). These three areas have been chosen because of differences in litter input and environmental conditions. In each area soil respiration experiments have been done and soil samples have been taken. The first area is under the canopy of the tree. The second area is in the leaf litter cone, three meters to the southeast of the tree. The leaves of the tree fall mainly towards the southeast because the prevailing wind comes from the northwest. This is especially the case in the dry season between November and April. In the rainy season the wind direction changes more and there is also less litter fall. The final area is in open pasture, 10 meters away from the tree. This area was preferably chosen in the northwest to keep possible interaction of the soil with tree litter to a minimum. If there was another tree towards the northwest another direction has been chosen. Cattle graze in all areas, affecting the grasses and herbs. Due to sufficient grass growth in the rainy season grazing by cattle does not affect the available tree litter.

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10

2.2 Soil respiration

The soil respiration measurements form one of the main pillars of this study. Field measurements were done from August 3 – September 6 and October 20 – November 2, 2010 with portable soil respiration equipment. A PP Systems Soil Respiration System was used, consisting of the SRC-1 Soil Respiration Chamber and the EGM-4 Environmental Gas Monitor and a soil temperature probe. Stored data was downloaded to the computer after each day of measuring. With this equipment a soil respiration measurement takes no more than two minutes, meaning it was possible to do sufficient repetitions per research plot. For each measurement both soil respiration (in µmol m-2 s-1) and soil temperature (°C) were recorded.

Figure 2.3 Schematic overview of the experimental design, with soil respiration measurements and soil sampling being done under the tree canopy, in open pasture northeast of the tree and in the leaf litter cone southwest of the tree. Rain gauges were placed in the open pasture and cow dung was randomly sampled.

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11 Besides regular measurements, also a number of respiration measurements were done on cow dung and in a cow urine patch. Cow dung was selected randomly, both under the tree canopy and in the open field. At least one measurement was done each day per piece of dung and repeated over a time period of days to five weeks, to measure the decrease of respiration over time. One cow urine patch was measured for the effects of urine on soil respiration. Measurements were done in the urine patch and control measurements were done in the directly adjacent pasture.

2.3 Soil samples

Soil samples were taken for all three research areas of six trees of each species. To obtain the samples soil pits were dug of at least 50 cm deep. The soil pits were large enough to sit in, in order to observe the face and make soil descriptions. While digging, one face of the soil pit was not disturbed, which was used to describe the soil pit, take soil samples and the bulk density samples. Soil samples were taken at 5-15 cm depth and at 30-40 cm depth. Bulk density samples were taken at 7.5-12.5 cm depth and 32.5-37.5 cm depth. At each depth three samples were taken, these will be referred to as sub-samples. Figure 2.4 depicts the sampling scheme used in the soil pits. Three bulk density rings of 100 cm3 each were hammered into the undisturbed pit face at the two depths. After hammering in the rings, the soil surrounding them was bagged for laboratory analysis. Approximately 800 grams of wet soil was bagged per sub-sample. The bulk density rings were removed from the soil and the contents of the rings was bagged and stowed in a cooler to prevent evaporation. In total 216 bulk density samples and 216 bagged samples were taken. The bulk density samples were analyzed locally (see paragraph 2.4.1).

Figure 2.4 Sampling scheme of the soil pit face. Sampling depths are 5-15 cm and 30-40 cm. The circles indicate the bulk density rings. The grey area around these rings indicates the area where the bagged soil samples were taken from.

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12 remaining sample was discarded. All samples were shipped to Wageningen UR for laboratory analysis.

2.4 Laboratory analysis

The soil samples which were taken were analyzed for a number of characteristics, in laboratories both in Rivas, Nicaragua and in Wageningen, the Netherlands. Soil characteristics which have been analyzed are bulk density, soil texture, total soil organic carbon and total nitrogen content and carbon and nitrogen pools of the different SOM fractions.

2.4.1 Bulk density

The bulk density samples were weighed at the project office in Belén, Nicaragua for their wet weight. Afterwards they were brought to the soil laboratory of the Escuela Internacional de Agricultura y Ganadería (EIAG) in Rivas, where they were dried in an oven at 105 °C for 24 hours. After drying the samples were weighed again for their dry weight. While weighing the dry weight of the samples the oven was left on at 60 °C to prevent the attraction of moisture. With the dry weight of the sample (gr) and the known volume of the bulk density rings (cm3) the bulk density in grams per cm3 of soil for each sample could be determined, using the following equation:

Bulk density = Weight dry sample / Volume bulk density ring

2.4.2 Soil texture

Soil texture analysis was done in the Earth System Science laboratory in Wageningen. This analysis was done only for soil samples from under the tree canopy, at two depths. Composite samples were used, composing of the three sub-samples per depth of each soil pit. So, per plot one composite sample was analyzed for the soil layer at 5-15 cm depth and one composite sample was analyzed for the soil layer at 30-40 cm depth. A total of 24 samples were analyzed. For the analysis a Coulter-Beckman Grain-sizer, type LS230 was used. The Fraunhofer.rfd methodology was used.

In order to relate soil texture to soil respiration it was necessary to estimate the soil texture of the top 50 cm of soil. This was done using the results from the two measured soil layers. To estimate the average soil texture of the two soil types for the top 50 cm of soil, two assumptions were made. First, it was assumed that the soil texture at 5-15 cm depth was representative for the soil layer at 0-20 cm depth. Second, it was assumed that the soil texture of the soil layer at 30-40 cm depth was representative for the soil layer at 20-50 cm depth. For each sampled soil the following equation was used to calculate individual soil texture fractions:

S0-50 = (2 * S5-15 + 3 * S30-40) / 5

In which S0-50 is the percentage of a soil texture fraction (clay, silt or sand) in the soil layer at 0-50 cm depth, S5-15 is the measured percentage of a soil texture fraction (clay, silt or sand) in the soil layer at 5-15 cm depth and S30-40 is the measured percentage of a soil texture fraction (clay, silt or sand) in the soil layer at 30-40 cm depth.

2.4.3 Total carbon and nitrogen

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13 possible and homogenized. Between 10 to 15 mg of each composite sample was weighed in and analysed for total SOC and total nitrogen in an Interscience element analyser, type EA 1108, using standards composed of Acetanilide. This analysis gave the weight fraction of carbon and nitrogen compared to the total weight of the analyzed sample. The respective units are gr C gr-1 sample and gr N gr-1 sample. These weight fractions were used to calculate total SOC and total nitrogen in kg m-2 in the two sampled soil layers. The weight fractions were multiplied by the bulk densities of each sample to calculate the total grams SOC and N per cm3. To convert this to kg m-2 for a soil layer with a thickness of 10 cm, the grams SOC and N were divided by 1000 and then multiplied by 100000. This can be expressed in the following equation:

Ptotal = (g * BD)/1000 * 100000

In which Ptotal is the total pool of SOC or nitrogen respectively in kg m-2 for a soil layer with a thickness of 10 cm, g is the weight fraction of SOC or nitrogen in gr gr-1 sample and BD is the bulk density of the sample in gr cm-3.

In order to relate total SOC and nitrogen to soil respiration it was necessary to estimate the soil texture of the top 50 cm of soil. The total SOC and nitrogen in the two sampled soil layers were used to estimate carbon pools in the top 50 cm. The layer at 5-15 cm depth was assumed to be representative for the top 20 cm of the soil. So soil SOC and nitrogen pools of the soil layer of 0-20 cm depth were estimated by multiplying the SOC and nitrogen pools of the 5-15 cm layer by a factor two. The layer at 30-40 cm depth was assumed to be representative for the soil layer from 20-50 cm depth. This means that the SOC and nitrogen content of the soil layer at 20-50 cm depth was calculated by multiplying the SOC and nitrogen content of the 30-40 cm layer by a factor three. So for estimating the carbon content of the top 50 cm of soil the following equation was used:

P0-50 = 2 * P5-15 + 3 * P30-40

In which P0-50 is the total SOC or nitrogen pool of the top 50 cm of soil, P5-15 is the total SOC or nitrogen pool of the soil layer at 5-15 cm depth and P30-40 is the total SOC and nitrogen pool of the soil layer at 30-40 cm depth.

2.4.4 Physical fractionation

A physical fractionation method was used to separate three different SOM fractions: the light fraction (LF), the physically occluded fraction (iPOM) and the mineral associated fraction (maOM). The fractionation method applied was based on density fractionation. This fractionation method isolates the LF of SOM that is weakly associated with soil minerals from heavier fractions (organo-mineral complexes) (von Lützow et al. 2007), in other words, the iPOM and maOM fractions. For this study the density solution used to separate the SOM fractions was sodium polytungstate (3Na2WO4.9WO3.xH2O) (SPT). The density of 1.6 g cm

-3

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14 ADD GRAPHIC

The same 72 ground composite samples were used which were also used for analysing total SOC and total nitrogen. Approximately 10 grams of each sample was weighed and put in centrifuge tubes. 50 ml of the density solution SPT was added, the tubes were shaken by hand 5 times and then left to stand for one hour. After, the tubes were put in an MSE centrifuge, type Mistral 6000 for 23 minutes at 4500 rpm, which is similar to 5900 g in this centrifuge. The centrifuge tubes were emptied over a Büchner funnel with a 0.7 µm glass filter. The filter was rinsed with distilled water and the residue, the light fraction (LF), was rinsed off the filter and into a glass beaker and oven dried at 40 ºC.

To the precipitate that was left behind in the centrifuge tube (the heavy fractions), 50 ml SPT was added again and it was hand shaken to bring the precipitate into solution. Then a dispersion method was applied. An ultrasonic probe was hung into the solution for 5 minutes to destroy soil aggregates and liberate the light material of the iPOM fraction. After the ultrasonic treatment the solution was left to stand for half an hour. The solution was again centrifuged for 23 minutes at 4500 rpm. The centrifuge tubes were emptied over a Büchner funnel with a 0.7 µm glass filter. The filter was thoroughly rinsed with distilled water and the residue, the iPOM fraction was rinsed off the filter, into a glass beaker and oven dried at 40 ºC. Because of the high clay content in the final fraction (maOM) it was not possible to rinse this fraction over a glass filter. It was also not possible to centrifuge this fraction in large 1000 ml centrifuge tubes, dissolved in water. The clay particles would not entirely sink, so the water could not be removed through suctioning. So, this fraction was rinsed into a beaker glass using distilled water and also set in the oven to dry, still containing a large quantity of SPT.

When dry, all the fractions were weighed. It was assumed that all SPT was washed off the LF and iPOM fractions. For the maOM fraction it was known that a large quantity of SPT was mixed in with this fraction and that there was therefore a large error while weighing. To calculate the actual

Soil Sample

Light fraction (LF)

Heavy fraction (iPOM and maOM)

Occluded fraction (iPOM) Mineral associated fraction (maOM) Density fractionation

Dispersion and 2nd density fractionation

SOM fractions

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15 weight of the maOM fraction (WmaOM) the weight of the LF and iPOM fractions (WLF and WiPOM) were subtracted from the initial weight of the sample (Wtotal), so:

WmaOM = Wtotal - WLF - WiPOM

Samples of SOM fraction sample were analysed for SOC and nitrogen contents in the Interscience analyser, using the same methodology as for total SOC and N (paragraph 2.4.3). The same calculations were used to convert the weight fraction of SOC and nitrogen found in the samples to kg m-2. Because of the high quantities of SPT residue in the maOM fraction a correction needed to be made in the calculations for this fraction. A correction factor (KmaOM) was calculated by dividing the weight of the maOM fraction with SPT (WmaOM+SPT) by the actual, calculated weight (WmaOM):

KmaOM = WmaOM+SPT / WmaOM

The weight fractions of SOC and nitrogen in the maOM fraction were multiplied by KmaOM to correct for the portion of SPT in the analysed sample.

In order to relate SOC and nitrogen in the different SOM fractions to soil respiration it was necessary to estimate the soil texture of the top 50 cm of soil. The SOC and nitrogen in the two sampled soil layers were used to estimate carbon pools in the top 50 cm. The method used for each fraction is the same as described in paragraph 2.4.3, using the following equation:

P0-50 = 2 * P5-15 + 3 * P30-40

In which P0-50 is the SOC or nitrogen pool of the top 50 cm of soil, P5-15 is the SOC or nitrogen pool of the soil layer at 5-15 cm depth and P30-40 is the SOC and nitrogen pool of the soil layer at 30-40 cm depth.

2.5 Data overview and statistical analysis

This section will give a brief overview of the data which has been collected and analyzed and which methods have been used during this analysis.

2.5.1 Obtained data

In the field soil respiration and soil temperature data were collected directly, and 24 hour precipitation data from rain gauges was collected when possible. In the laboratory data was collected for bulk density, soil texture, total SOC and nitrogen and SOC and nitrogen pools of three SOM fractions.

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16

Table 2.3 Variables used in this study, including units and the source of the data.

Variable Unit Source

Soil respiration µmol CO2 m -2

s-1 Field measurements

Soil temperature °C Field measurements

Precipitation (rain gauges) mm Field measurements

Bulk density g cm-3 Laboratory EIAG Rivas

Soil clay content % Laboratory ESS-CC Wageningen

Soil silt content % Laboratory ESS-CC Wageningen

Soil sand content % Laboratory ESS-CC Wageningen

Total SOC (0-50 cm) gC m-2 Laboratory ESS-CC Wageningen

Total nitrogen (0-50 cm) gN m-2 Laboratory ESS-CC Wageningen

Carbon content LF gC m-2 Laboratory ESS-CC Wageningen

Carbon content iPOM gC m-2 Laboratory ESS-CC Wageningen

Carbon content maOM gC m-2 Laboratory ESS-CC Wageningen

Nitrogen content LF gN m-2 Laboratory ESS-CC Wageningen

Nitrogen content iPOM gN m-2 Laboratory ESS-CC Wageningen

Nitrogen content maOM gN m-2 Laboratory ESS-CC Wageningen

Average daily temperature °C INETER Rivas

Precipitation (24 hours) mm INETER Rivas

Precipitation (48 hours) mm INETER Rivas

2.5.2 Methods of data analysis

The data has been visualized and analyzed using Microsoft Excel and SPSS Statistics 17.0. A number of statistical methods have been used to analyze significant differences and relationships in the collected data. The statistical methods that have been used in this study are independent sample t-tests, univariate and multivariate ANOVAs and linear regression models.

For spatial and temporal comparisons of soil respiration, soil characteristics and SOC and nitrogen pools univariate and multivariate ANOVAs and independent sample t-tests have been applied. Where possible, ANOVAs have been applied. Only in situations where there were only two data sets, such as with tree species and soil type, independent t-tests have been used. Univariate and multivariate ANOVAs have been used because the key interest was to study the spatial differences in soil respiration.

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17

3.

Results

In this chapter the results of this study will be displayed. First the controlling factors of soil respiration will be analyzed. Soil texture of different research fields and soil types will be analyzed (section 3.1). The SOC and nitrogen pools will be analyzed in section 3.2, looking at total carbon and nitrogen pools, different SOM fractions and relations between SOC and nitrogen. Air temperature, precipitation and soil temperature will be analyzed in section 3.3. Soil respiration will be analyzed on temporal and spatial variation and differences between tree species (section 3.4). Relations between soil respiration and SOC and nitrogen pools (section 3.5), soil texture (section 3.6), temperature (section 3.7) and precipitation (section 3.8) will be analyzed. The effects of cow dung and urine will be analyzed in section 3.9. Finally, in section 3.10 soil respiration will be analyzed based on interactions between individual controlling factors.

3.1 Soil Texture

The results for the soil texture analysis of the soil samples below the tree canopies can be seen in figure 3.1. The figure shows that although there are differences between individuals, all samples contain considerable clay fractions, ranging between 22.2% and 39.4%. All samples contain quite similar fractions of silt, ranging from 40.8% to 51.5%. Sand fractions range from 14.9% to 36.5%. Differences were larger based on soil types than on tree species for all fractions.

Figure 3.1 Soil texture at 5-15 cm depth (left) and at 30-40 cm depth (right) for all individual trees. G# indicate G. ulmifolia trees, J# indicate C. alata trees.

The results from the deeper soil layer (30-40 cm depth) show large differences between the two soil types. Trees G3, G18, G19 and G20 are all located on Haplustolls while G8, G17 and all C. alata trees are located on Haplusterts. Sand fractions are significantly (P=0.005) higher in Haplustolls than in the Haplusterts, and silt and clay fractions significantly smaller (P=0.012 and P=0.000 respectively). Figure 3.2 shows the average soil fractions per soil type. Haplustolls change significantly with depth, with a large increase in the sand fraction (P=0.005) and decreases in silt and clay fractions (P=0.005 and P=0.004 respectively). No significant differences were found in soil texture between 5-15 cm depth and 30-40 cm depth for the Haplusterts.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% G 3 G 8 G 1 7 G 1 8 G 1 9 G 2

0 J1 J7

J1 0 J1 4 J3 2 J3 3

30-40 cm Clay Silt Sand

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% G 3 G 8 G 1 7 G 1 8 G 1 9 G 2

0 J1 J7

J1 0 J1 4 J3 2 J3 3

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18

Figure 3.2 Average soil texture for the two soil types at 5-15 cm depth and 30-40 cm depth.

Average soil texture was calculated for the top 50 cm of soil according to the method explained in paragraph 2.4.2. Haplusterts have considerably larger clay and silt fractions than Haplustolls, in which the sand fraction is the largest fraction (table 3.1).

Table 3.1 Average soil texture ± S.E. of the top 50 cm of soil per soil type.

Soil type Clay (%) Silt (%) Sand (%) Haplustolls 20.4 ± 1.4 34.9 ± 3.1 44.7 ± 4.5

Haplusterts 30.6 ± 1.6 45.3 ± 0.7 24.1 ± 1.8

3.2 Carbon and nitrogen supply

SOC and nitrogen pools of the soil have been studied at various levels. The results will be presented in this paragraph.

3.2.1 Measured SOC and nitrogen pools

The sizes of all measured SOC pools decrease with soil depth (table 3.2). The total SOC pool is larger in C. alata fields for all locations in the field at both soil depths. In G. ulmifolia fields SOC pools were largest in the leaf litter cone at both depths for both total SOC and all measured SOM fractions. In C. alata fields the total SOC pool and the SOC pool in the maOM fraction under the tree canopy and in the leaf litter pit are substantially larger than in open pasture at both depths. This trend cannot be seen for the carbon in the LF and the iPOM fraction. For G. ulmifolia fields the total SOC pool is at least twice as small at 30-40 cm depth, compared to the layer at 5-15 cm depth. By far the largest portions of the SOC in the LF and the iPOM fraction are found in the upper layer of the soil for the fields of this tree species. In C. alata fields most SOC of these two fractions is also found in the upper soil layer, but the decrease is not as strong.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Haplustolls 5-15 Haplustolls 30-40 Haplusterts 5-15 Haplusterts 30-40

Sand (%)

Silt (%)

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19

Table 3.2 Soil carbon pools (gC m-2) ± S.E. at 5-15 cm depth and 30-40 cm depth per tree species and location in the field.

Tree species Location Total SOC

5-15cm

Total SOC 30-40 cm

C(lf) 5-15 cm

C(lf) 30-40 cm

C(iPOM) 5-15 cm

C(iPOM) 30-40 cm

C(maOM) 5-15 cm

C(maOM) 30-40 cm

G. ulmifolia Canopy 2308.9 ± 188.0 920.4 ± 167.6 64.8 ± 11.8 7.1 ± 1.8 356.3 ± 141.8 23.5 ± 10.4 1887.8 ± 225.8 889.7 ± 166.1

Leaf litter cone* 2807.4 ± 507.5 1385.9 ± 370.5 93.0 ± 20.0 12.0 ± 3.8 468.9 ± 142.7 79.8 ± 37.5 2245.5 ± 518.4 1294.1 ± 380.0

Pasture* 2078.9 ± 231.7 997.8 ± 231.1 61.7 ± 23.1 4.7 ± 1.2 240.9 ± 108.0 9.9 ± 3.9 1776.2 ± 183.6 983.2 ± 232.7

C. alata Canopy 3466.5 ± 335.8 1987.7 ± 50.8 115.0 ± 23.8 56.4 ± 35.0 176.3 ± 55.4 132.7 ± 62.8 3175.2 ± 382.4 1798.6 ± 77.4

Leaf litter cone 3209.6 ± 355.0 1894.7* ± 88.8 80.6 ± 20.0 21.5* ± 10.6 262.3 ± 87.0 81.5* ± 35.2 2866.7 ± 370.2 1791.7* ± 118.8

Pasture 2231.8 ± 192.8 1329.3 ± 77.0 115.2 ± 40.1 12.0 ± 4.5 254.4 ± 124.2 34.9 ± 16.3 1862.1 ± 298.5 1282.4 ± 62.2 * Due to missing values only 5 of the 6 plots were used to calculate means.

Table 3.3 Soil nitrogen pools (gN m-2) ± S.E. at 5-15 cm depth and 30-40 cm depth per tree species and location in the field.

Tree species Location Total N

5-15cm

Total N 30-40 cm

N(lf) 5-15 cm

N(lf) 30-40 cm

N(iPOM) 5-15 cm

N(iPOM) 30-40 cm

N(maOM) 5-15 cm

N(maOM) 30-40 cm

G. ulmifolia Canopy 188.4 ± 9.7 70.1 ± 5.1 4.2 ± 1.5 0.6 ± 0.4 22.3 ± 13.1 1.4 ± 0.9 161.9 ± 20.9 68.1 ± 5.7

Leaf litter cone* 222.0 ± 28.2 99.1 ± 14.7 7.4 ± 3.2 0.6 ± 0.2 28.9 ± 16.0 4.1 ± 2.2 185.7 ± 34.7 94.5 ± 15.5

Pasture* 166.4 ± 17.5 72.1 ± 8.1 12.5 ± 6.8 0.4 ± 0.2 15.6 ± 6.6 0.8 ± 0.7 138.3 ± 20.4 70.9 ± 8.4

C. alata Canopy 265.2 ± 28.2 143.7 ± 10.7 8.6 ± 2.1 5.6 ± 4.5 16.2 ± 7.1 8.0 ± 5.0 240.4 ± 33.2 130.1 ± 12.4

Leaf litter cone 226.4 ± 26.4 123.2* ± 15.3 2.1 ± 0.5 0.6* ± 0.2 4.5 ± 0.8 1.8* ± 0.5 219.7 ± 26.7 121.0* ± 15.6

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20 Table 3.3 shows the sizes of the nitrogen pools in the soil layers at 5-15 cm and 30-40 cm depth. The sizes of all measured nitrogen pools decrease with soil depth. Differences in total nitrogen pools in the 5-15 cm soil layer in the leaf litter cone and open pasture are negligible between the fields of the two tree species. The nitrogen pool under C. alata trees is however substantially larger than under G. ulmifolia. In the deeper soil layer the total nitrogen pool is larger in the fields with C. alata. For the fields of this tree species the nitrogen pools are largest under the tree canopy, while the nitrogen pools generally seem to be largest in the leaf litter cone of G. ulmifolia fields. This is not true for the nitrogen content at 5-15 cm depth, where the nitrogen content in the LF is highest in open pasture.

3.2.2 Carbon pools in the top 50 cm of soil

The different carbon pools in the top 50 cm of soil were calculated according to the method explained in paragraph 2.4.3 and paragraph 2.4.4. There are clear differences in total SOC between the fields of the different tree species (table 3.4). Fields with C. alata have significantly higher total SOC pools (P=0.001). The total SOC in the fields of this tree species are highest under the tree canopy, slightly lower in the leaf litter cone and significantly lower in open pasture (P=0.002 and P=0.015 compared to the canopy and the leaf litter cone respectively). The same trend can be seen for the maOM fraction. The LF of the soil contains the least SOC. The iPOM fraction contains about twice as much, but by far the most SOC is stored in the maOM fraction. The SOC pool in the LF under the tree canopy is larger than that in open pasture and in the leaf litter cone this is the smallest. The SOC pool of the iPOM fraction is largest in the leaf litter cone, but differences with the other two areas are small.

Table 3.4 Estimated average soil carbon pools (gC m-2) ± S.E. of the top 50 cm of soil of the different tree species plots and locations within these plots. Average total SOC and carbon in different SOM fractions are displayed. Whether differences are significant between locations is indicated in superscript. Sets used to indicate significance are: A; a; #; and π for G. ulmifolia and X, Y; x; α; and +, - for C. alata.

Tree species Location Total SOC CLF CiPOM CmaOM G. ulmifolia Canopy 7379A ± 842 151a ± 24 783# ± 298 6444π ± 890

Leaf litter cone* 9772A ± 2125 221a ± 42 1177# ± 360 8373π ± 2164

Pasture* 7151A ± 1047 137a ± 48 511# ± 222 6501π ± 1025

C. alata Canopy 12896X ± 750 399x ± 143 750α ± 287 11746+ ± 972

Leaf litter cone* 12545X ± 918 240x ± 52 856α ± 260 11449+ ± 1126

Pasture 8451Y ± 444 266x ± 89 613α ± 285 7571- ± 584

* Due to missing values only 5 of the 6 plots were used to calculate means.

In the G. ulmifolia fields the leaf litter cone contains the most SOC. The open pasture contains the least SOC, although the difference with SOC under the tree canopy is small. The same can be noted for the maOM fraction and the LF. The SOC pool in the LF of the soil is considerably smaller than SOC pool in the iPOM fraction and also smaller compared to the fields with C. alata. The differences between areas of the field are considerable when looking at the iPOM fraction, although they are not significant. Under the tree canopy the SOC pool is considerably larger than in open pasture, but considerably smaller than in the leaf litter cone.

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21 soil types. Total SOC pools are much higher in the Haplusterts than in Haplustolls in all areas of the field. For both soil types total SOC was highest in the leaf litter cone. For Haplustolls plots no significant differences were found between the three areas in the field for total SOC or the SOC in the different SOM fractions. In Haplusterts plots the total SOC (P>0.01) and carbon in the maOM fraction (P>0.03) were significantly higher than in the open pasture. The carbon pool in LF is larger in Haplusterts plots. The variation in the iPOM carbon pool is much larger between the three areas for Haplustolls than for Haplusterts, with the leaf litter cone pool being the largest. The carbon in the maOM fraction is substantially larger in Haplusterts than in Haplustolls for all areas.

Table 3.5 Estimated average soil carbon pools (gC m-2) ± S.E. of the top 50 cm of soil per soil type and location within the field. Average total SOC and carbon in different SOM fractions are displayed. Significant differences between locations are indicated in superscript. Sets used to indicate significance are: A; a; α; and π for G. ulmifolia and X, Y; x; γ; and +, - for C. alata.

Soil type Location Total SOC CLF CiPOM CmaOM

Haplustolls Canopy 6143A ± 435 142a ± 37 703α ± 358 5297π ± 615

Leaf litter cone* 6576A ± 657 187a ± 65 1276α ± 633 5114π ± 225

Pasture* 5499A ± 487 157a ± 83 415α ± 236 4928π ± 508

Haplusterts Canopy 12135X ± 750 342x ± 112 799γ ± 253 10995+ ± 900

Leaf litter cone# 13123X ± 897 250x ± 36 906γ ± 190 11967+ ± 1020

Pasture 8746Y ± 378 227x ± 70 624γ ± 233 7894- ± 493

*Due to missing values only 3 of the 4 plots were used to calculate means.

#

Due to missing values only 7 of the 8 plots were used to calculate means.

3.2.3 Nitrogen pools in the top 50 cm of soil

Using the measured nitrogen pools from the two soil layers the nitrogen pools in the top 50 cm of soil were calculated (paragraph 2.4.3 and 2.4.4). Nitrogen is divided over the three different soil fractions, with by far the largest nitrogen pool in the maOM fraction (table 3.6). C. alata fields contain more total nitrogen on average than G. ulmifolia fields. In the latter fields the largest nitrogen pools are found in the leaf litter cone. Differences between total nitrogen pools under the tree canopy and in open pasture are small. The amount of nitrogen in the LF is largest in open pasture, while the iPOM nitrogen pool is largest in the leaf litter cone. Also the maOM nitrogen pool is largest in the leaf litter cone.

Table 3.6 Estimated average nitrogen pools (gN m-2) ± S.E. of the top 50 cm of soil of the different tree species plots and locations within these plots. Average total nitrogen and nitrogen in different SOM fractions are displayed. Whether differences are significant between locations is indicated in superscript. Sets used to indicate significance are: A; a; #; and π for G. ulmifolia and X, Y; x; α; and + for C. alata.

Tree species Location Total N NLF NiPOM NmaOM G. ulmifolia Canopy 587.2 A ± 30.3 10.2a ± 3.2 48.9# ± 29.0 528.1π ± 55.8

Leaf litter cone* 741.4 A ± 97.5 16.5a ± 6.5 70.0# ± 34.7 654.8π ± 110.2

Pasture* 549.0 A ± 47.1 26.1a ±13.4 33.6# ± 15.0 489.3π ± 55.9

C. alata Canopy 961.5 X ± 85.1 33.8x ±17.1 56.3α ± 28.9 871.3+ ± 101.2

Leaf litter cone* 874.9 XY ± 95.0 5.1x ± 1.0 15.2α ± 3.4 854.6+ ± 97.1

Pasture 604.1 Y ± 59.8 7.7x ± 3.6 21.4α ± 6.9 574.9+ ± 57.8

Figure

Figure 1.1 Map of Nicaragua (left) and the department of Rivas (right). On the Nicaragua map the department of Rivas has

Figure 1.1

Map of Nicaragua (left) and the department of Rivas (right). On the Nicaragua map the department of Rivas has p.14
Table 2.1 Information on the location and size of G. ulmifolia individuals.

Table 2.1

Information on the location and size of G. ulmifolia individuals. p.17
Table 2.2 Information on the location and size of C. alata individuals.

Table 2.2

Information on the location and size of C. alata individuals. p.17
Figure 2.2 Research sites in Rivas: Cantimplora (top left and right), Las Mesas (bottom left) and San Antonio (bottom right)

Figure 2.2

Research sites in Rivas: Cantimplora (top left and right), Las Mesas (bottom left) and San Antonio (bottom right) p.18
Figure  2.2  Schematic  overview  of  an  isolated  tree  in  the  Rivas  SPS.  The  prevailing  wind  direction  and  the  three  research

Figure 2.2

Schematic overview of an isolated tree in the Rivas SPS. The prevailing wind direction and the three research p.19
Figure  2.3  Schematic  overview  of  the  experimental  design,  with  soil  respiration  measurements  and  soil  sampling  being

Figure 2.3

Schematic overview of the experimental design, with soil respiration measurements and soil sampling being p.20
Figure 2.4 Sampling scheme of the soil pit face. Sampling depths are 5-15 cm and 30-40 cm

Figure 2.4

Sampling scheme of the soil pit face. Sampling depths are 5-15 cm and 30-40 cm p.21
Figure 2.5 Schematic overview of fractionation method. Three  SOM fractions (LF, iPOM and maOM) were obtained from

Figure 2.5

Schematic overview of fractionation method. Three SOM fractions (LF, iPOM and maOM) were obtained from p.24
Figure 3.1 Soil texture at 5-15 cm depth (left) and at 30-40 cm depth (right) for all individual trees

Figure 3.1

Soil texture at 5-15 cm depth (left) and at 30-40 cm depth (right) for all individual trees p.27
Table 3.1 Average soil texture ± S.E. of the top 50 cm of soil per soil type.

Table 3.1

Average soil texture ± S.E. of the top 50 cm of soil per soil type. p.28
Table 3.2 Soil carbon pools (gC m -2 ) ± S.E. at 5-15 cm depth and 30-40 cm depth per tree species and location in the field

Table 3.2

Soil carbon pools (gC m -2 ) ± S.E. at 5-15 cm depth and 30-40 cm depth per tree species and location in the field p.29
Table 3.3 Soil nitrogen pools (gN m -2 ) ± S.E. at 5-15 cm depth and 30-40 cm depth per tree species and location in the field

Table 3.3

Soil nitrogen pools (gN m -2 ) ± S.E. at 5-15 cm depth and 30-40 cm depth per tree species and location in the field p.29
Table 3.4 Estimated average soil carbon pools (gC m -2 ) ± S.E. of the top 50 cm of soil of the different tree species plots and  locations within these plots

Table 3.4

Estimated average soil carbon pools (gC m -2 ) ± S.E. of the top 50 cm of soil of the different tree species plots and locations within these plots p.30
Figure 3.3 Relations between carbon and nitrogen for the total SOC and nitrogen pools (top left), the maOM fraction (top right), the LF (bottom left) and the iPOM fraction (bottom right)

Figure 3.3

Relations between carbon and nitrogen for the total SOC and nitrogen pools (top left), the maOM fraction (top right), the LF (bottom left) and the iPOM fraction (bottom right) p.33
Figure 3.5 The amount of SOC (black diamonds, left axis) and nitrogen (grey squares, right axis) in the top 50 cm of soil in

Figure 3.5

The amount of SOC (black diamonds, left axis) and nitrogen (grey squares, right axis) in the top 50 cm of soil in p.34
Figure  3.8  Daily  temperature  (grey  squares)  and  precipitation  (black  circles)  for  the  two  periods  in  which  field

Figure 3.8

Daily temperature (grey squares) and precipitation (black circles) for the two periods in which field p.36
Figure  3.9  Mean  soil  temperature  over  the  course  of  the  day  in  the  tree  research  locations:  under  the  tree  canopy

Figure 3.9

Mean soil temperature over the course of the day in the tree research locations: under the tree canopy p.38
Figure 3.10 Average soil respiration over the course of the day for both tree species: above is G

Figure 3.10

Average soil respiration over the course of the day for both tree species: above is G p.40
Figure 3.11 The relation between soil  respiration (µmol CO 2 m -2   s -1 ) and total SOC (left) and total nitrogen (right)

Figure 3.11

The relation between soil respiration (µmol CO 2 m -2 s -1 ) and total SOC (left) and total nitrogen (right) p.41
Figure 3.13 Relationship between the average clay content of the top 50 cm of the soil and mean soil respiration under the

Figure 3.13

Relationship between the average clay content of the top 50 cm of the soil and mean soil respiration under the p.42
Figure 3.14 Mean  daily soil respiration in relation to daily mean temperature for the three research locations: under the

Figure 3.14

Mean daily soil respiration in relation to daily mean temperature for the three research locations: under the p.43
Figure  3.16  The  relation  between  average  daily  soil  respiration  and  average  daily  soil  temperature  for  August  17  to

Figure 3.16

The relation between average daily soil respiration and average daily soil temperature for August 17 to p.44
Figure 3.17 The relation between average daily soil respiration under the tree canopy and average daily soil temperature

Figure 3.17

The relation between average daily soil respiration under the tree canopy and average daily soil temperature p.44
Figure  3.19  Mean  daily  soil  respiration  in  relation  to  the  amount  of  precipitation  over  48  hours  for  the  three  research

Figure 3.19

Mean daily soil respiration in relation to the amount of precipitation over 48 hours for the three research p.45
Figure  3.18    Mean  daily  soil  respiration  in  relation  to  the  amount  of  precipitation  over  24  hours  for  the  three  research

Figure 3.18

Mean daily soil respiration in relation to the amount of precipitation over 24 hours for the three research p.45
Figure 3.20 Soil respiration over time in the leaf litter cone and open pasture on August 27 in G

Figure 3.20

Soil respiration over time in the leaf litter cone and open pasture on August 27 in G p.46
Table  3.16  Model  type  2  best  model  fits  and  predictors  for  linear  regression  models  of  soil  respiration  based  on  all

Table 3.16

Model type 2 best model fits and predictors for linear regression models of soil respiration based on all p.48
Table  3.15  Model  type  1  best  model  fits  and  predictors  for  linear  regression  models  of  soil  respiration  based  on  all

Table 3.15

Model type 1 best model fits and predictors for linear regression models of soil respiration based on all p.48
Table 3.17 Model type 1 best model fits and predictors for linear regression models of soil respiration based on collected

Table 3.17

Model type 1 best model fits and predictors for linear regression models of soil respiration based on collected p.49
Table 3.18 Model type 2 best model fits and predictors for linear regression models of soil respiration based on collected

Table 3.18

Model type 2 best model fits and predictors for linear regression models of soil respiration based on collected p.49

Referencias

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