Veizaga 2009 Soil Water Aquacrop

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Katholieke Universiteit Leuven

Effect of Volcanic Rocks onSoil Water Holding Capacity

and on Crop WaterProductivity Modelling in

the Bolivian Altiplano

Promotors:Prof. D. RaesDr. S. Geerts

Master dissertation in partial fulfilmentof the requirements for the Degree of

Master of Water Resources Engineeringby: Alfredo Ronald Veizaga Medina

September 2009

i

Acknowledgements

First of all, to express my gratitude to my promoters, Professor Dirk Raes, who believed and

supported me at the beginning of this big challenge, as well as suggested important issues to

improve this research. And special gratitude to Dr. Sam Geerts, who was always beside to me,

thanks for the support and friendship which was very important to develop this investigation

and to complete the final document, even though he was sometimes very busy, he did not

hesitate in helping me “Muchas gracias Dr. Sam Geerts”.

Thanks to Flemish Interuniversity Council for University Development Cooperation (VLIR-

UOS) for giving me the opportunity and financial support to be here in Belgium as a student.

And to all of the staff, such as professors, assistants to Mrs. Greta Camps, Martine Gabriëls

and Hilde de Coninck from Inter University Programme in Water Resources Engineering

(IUPWARE), who organized the lectures and many activities to provide us a high education.

To Valentinus Tuts, Lore Fondu and Sabrina from the Laboratory of Soil and Water

Management at the Geo-Institute, K.U. Leuven (Department of Earth and Environmental

Sciences, who always were there to help me, during the laboratory work. To my classmates

and compatriots Paola Pacheco and Álvaro Gonzáles, who helped me to improve this

document in a given moment, as well as we shared many times while we were studying.

And to Institute de Researcher pour le débeloppement (IRD) and Juan Pablo Rodriguez for

soils data from the southern Bolivian Altiplano, to Karen Vancampenhout for helping me to

get some soil samples, and Armando Molina that sent me important articles related to my

topic, as well as to Dra. Carmen Del Castillo, Claudia Alcón, Cristal Taboada and Cleofe

Ruano thanks for being my friends.

Finally, thanks to my parents Alicia Medina and Hector Veizaga, and all my siblings, who

always encourage me to continue, despite of the problems in life.

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Table of contents

Acknowledgements ....................................................................................................................i

Table of contents.......................................................................................................................ii

List of symbols .........................................................................................................................iv

List of figures ...........................................................................................................................vi

List of tables ...........................................................................................................................viii

Summary ...................................................................................................................................x

Resumen ...................................................................................................................................xi

1 Introduction ......................................................................................................................1

1.1 Objectives .............................................................................................................................. 31.1.1 General Objective.............................................................................................................. 31.1.2 Specific Objectives............................................................................................................ 3

1.2 Outline of the thesis............................................................................................................... 3

2 Materials and methodology .............................................................................................4

2.1 Field work and laboratory...................................................................................................... 42.1.1 Study area .......................................................................................................................... 4

2.1.1.1 Climate...................................................................................................................... 52.1.1.2 Agriculture................................................................................................................ 6

2.1.2 Data collection................................................................................................................... 72.1.2.1 Soil samples for determining the soil physical characteristics ................................. 72.1.2.2 Soil samples for determining the gravel content (mass%)........................................ 7

2.1.2.2.1 Classifying the volcanic rocks.............................................................................. 82.1.3 Laboratory procedure for soil water retention curve (WRC) determination..................... 9

2.1.3.1 Volume, Bulk density and Porosity of volcanic rocks.............................................. 92.1.3.2 Experiments to determine water retention curve (WRC) between volcanic rockfragments (VRF) and soil types ............................................................................................... 102.1.3.3 Soil characteristics (Sibelite® M002)...................................................................... 122.1.3.4 Origin of soil and particle size................................................................................ 122.1.3.5 Soil water retention................................................................................................. 13

2.1.3.5.1 Water content ..................................................................................................... 132.1.3.5.2 Soil water retention curve................................................................................... 14

2.1.3.6 Procedure for pF 0 – 2 ............................................................................................ 162.1.3.7 Procedure for pF 2.3 to 2.8..................................................................................... 172.1.3.8 Procedure for pF 3.4 to 4.2..................................................................................... 172.1.3.9 Statistical analysis................................................................................................... 18

2.1.4 WRC separation of volcanic rock and WRC of soil from mixed samples ...................... 19

2.2 Aqua-Crop modelling .......................................................................................................... 202.2.1 Model Inputs.................................................................................................................... 20

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2.2.1.1 Climatic data........................................................................................................... 202.2.1.2 Crop data................................................................................................................. 202.2.1.3 Management data: the fertility effect on water productivity .................................. 212.2.1.4 Soil data.................................................................................................................. 22

2.2.2 Sensitivity analysis of the model to different soil physical properties ............................ 232.2.2.1.1 Simulation for local sandy soil (from southern Bolivian Altiplano).................. 232.2.2.1.2 Simulation for WRC of soil-VRF mixtures versus pure soil (Sibelite® M002) . 242.2.2.1.3 Simulations for WRC of sand-VRF mixtures and pure sand ............................. 242.2.2.1.4 Sensitivity analysis of the model to water content at field capacity andpermanent wilting for Sibelite®M002 soil .......................................................................... 242.2.2.1.5 Sensitivity analysis of the model to water content from laboratory data versusdata of pedotransfer functions.............................................................................................. 25

2.2.3 Water use efficiency (WUE) and statistical analysis....................................................... 25

3 Results and discussion....................................................................................................26

3.1 Field work and laboratory.................................................................................................... 263.1.1 Data collection................................................................................................................. 26

3.1.1.1 Soil samples for determining the soil physical characteristics ............................... 263.1.1.2 Soil samples for determining the gravel content..................................................... 26

3.1.2 Laboratory results for soil water retention curve (WRC) determination......................... 273.1.2.1 Volume, Bulk density and Porosity of volcanic rocks............................................ 273.1.2.2 WRC of different soil –VRF mixtures.................................................................... 28

3.1.2.2.1 Soil particle size of the fine earth (mixed samples)............................................ 283.1.2.2.2 Experiment 1, WRC for around 15 vol% of each class of VRF mixed with soil(Sibelite® M002)................................................................................................................. 293.1.2.2.3 Experiment 2, WRC for around 30 vol% of each class of VRF mixed with soil(Sibelite®M002) ................................................................................................................... 313.1.2.2.4 Experiment 3, WRC for around 15 vol% of each class of VRF mixed with siltyloam and sandy soils............................................................................................................ 36

3.1.3 WRC separation of volcanic rock and WRC of soil from mixed samples ...................... 45

3.2 AquaCrop modelling............................................................................................................ 473.2.1 Sensitivity analysis of the model to different soil physical properties ............................ 47

3.2.1.1.1 Simulation for local sandy soil (from southern Bolivian Altiplano).................. 473.2.1.1.2 Simulation for WRC of soil-VRF mixtures versus pure soil (Sibelite® M002) . 493.2.1.1.3 Simulations for WRC of sand-VRF mixtures and pure sand ............................. 523.2.1.1.4 Sensitivity analysis of the model to water content at field capacity andpermanent wilting for Sibelite®M002 soil .......................................................................... 553.2.1.1.5 Sensitivity analysis of the model to water content from laboratory data versusdata of pedotransfer functions.............................................................................................. 56

4 Conclusions and Recommendations .............................................................................59

4.1 Conclusions.......................................................................................................................... 59

4.2 Recommendations................................................................................................................ 61

5 References........................................................................................................................62

6 Annexes............................................................................................................................69

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List of symbols

Symbol Description UnitAMC Antecedent moisture classANOVA Analysis of varianceCICDA-AVSF Agronomes et vétérinaires sans frontièresB Above ground biomass production Mg ha-1

BET Brunauer, Emmett and Teller (method)ETa Actual evapotranspiration mmETo Reference evapotranspiration mmCEC Cation exchange capacity cmol+ kg-1

CN Curve number -CWP Crop water productivityE Soil evaporation mmEC Electrical conductivity mS/cmFAO Food and Agricultural Organization of the United NationsFC Field capacity vol%GIS Geographical information systemHIo Reference harvest index (AquaCrop model) -IBTEN Instituto Boliviano de Ciencia y Tecnología NuclearINRA Institute National de Recherches Agronomiques (France)IRD Institute de Researcher pour le débeloppement (France)IRD-CLIFA IRD for climate and functioning of agro-ecosystemsISWC Initial soil water content mmKSAT Saturated hydraulic conductivity mm day-1

MACA Ministerio de Agricultura de BoliviaMrf Mass of volcanic rock fragment kgMf Mass of particles <2.0 mm (fine earth) kgN Number of replicationn Soil porosity vol%nrf Porosity of volcanic rock fragments vol%OM Organic matter mass%Pexp Soil water depletion factor for canopy expansion -Psen Soil water depletion factor for canopy senescence -Psto Soil water depletion fraction for stomatal controlPWP Permanent wilting point vol%REW Readily available water mmRF Rainfed conditionsRO Surface runoffRm Gravimetric rock fragments content mass%Rv Volumetric rock fragment content vol%SAT Saturation vol%SRC-SILBECO Company that extracts and refines various types of minerals

SENAMHI Servicio Nacional de Meteorología e hidrología de BoliviaTAW Total available water (between FC and PWP) mm

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Symbol Description UnitTa Actual transpiration mmUSDA United States Department of AgricultureUSGS United States Geological SurveyVRF Volcanic rock fragmentVrf Volume of the volcanic rock fragments m3

Vt Total soil volume (fine earth and rock fragments) m3

WP * Normalized water productivity Mg ha-1

WP Crop water productivity kg m-3

WPb Biomass water productivity g m-2

WRC Water retention curveWUEETa Water use efficiency for actual evapotranspiration kg m-3

WUETa Water use efficiency for actual transpiration kg m-3

XRF X-ray Fluorescence (method)Y Total grain yield Mg ha-1

Ya Economically valuable yield Mg ha-1

Zn Minimum effective rooting depth mZx Maximum effective rooting depth mα Significant level of statistical test -θ Volumetric soil water content m3 m-3

θFC Volumetric water content at FC m3 m-3

θm Gravimetric soil water content m3 m-3

θPWP Volumetric water content at PWP m3 m-3

θSAT Volumetric water content at SAT m3 m-3

θrf Volumetric water content of rock fragment m3 m-3

θs Water content in soil without rock fragments m3 m-3

θt Volumetric water content of the soil-rock fragment mixture m3 m-3

ρb,f Bulk density of particles < 2.0 mm (fine earth) kg m-3

ρb,rf Bulk density of rock fragment kg m-3

ρb,t Bulk density of soil including fine earth and rock kg m-3

ρp Particle density of soil kg m-3

ρw Density of water kg m-3

ψg Gravitational potential m2s-1kgm-3

ψh Hydrostatic potential m2s-1kgm-3

ψm Soil matric potential m2s-1kgm-3

ψn Pneumatic potential m2s-1kgm-3

ψo Osmotic potential m2s-1kgm-3

ψt Total water potential m2s-1kgm-3

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List of figures

Figure 1.1. (a) Map of Bolivia showing the quinoa area productions in the southern BolivianAltiplano and (b) Geological map of the Bolivian Altiplano showing the volcanic areasformations (Source: adapted from Risacher and Ritz, 1991). ............................................2

Figure 2.1. Maps of South America and Bolivia showing the study area. .................................4Figure 2.2. Rainfall and temperatures from 10 years (Between 1975 and 1985) in Salinas de

Garci Mendoza nearby Irpani (SENHAMI). ......................................................................5Figure 2.3. Predominant vegetation around Irpani in the southern Bolivian Altiplano (Source:

J. P. Raffaillac, IRD). .........................................................................................................6Figure 2.4. Volcanic rock (Source: Gyllenhaal, 2001)...............................................................8Figure 2.5: Determination of volume with Archimedes´ principle (Source: Rubin J., 2008). .10Figure 2.6. Soil water holding capacity for different soil texture classes (Source:

http://www.nivaa.nl/explorer/pagina/pictures/pfcurvey.jpg). ..........................................15Figure 2.7. 08.01 Sandbox (Source: Aquagri, 2007)................................................................16Figure 2.8. Low pressure chamber (Source: Alfredo Veizaga). ...............................................17Figure 2.9. High pressure chamber and special rings (Source: Alfredo Veizaga). ..................18Figure 2.10. Above ground biomass production (B) in function of ∑Ta/ETo, showing the

biomass water productivity WPb for C3 group crops and WP for quinoa under low soilfertility (dotted line), (Source: Adapted from Raes et al., 2009a). ...................................22

Figure 3.1. Water retention curve: (■a) 17 vol% of VRF of class 1 with soil, (∆b) 14 vol% ofVRF of class 2 with soil, (×c) 14 vol% of VRF of class 3 with soil, (◊d) 17 vol% of VRF class 4 with soil, (□e ) pure soil (Sibelite® M002). Error bars are ± 1 standard deviation...........................................................................................................................................30

Figure 3.2. Water retention curve: (■a) 29 vol% of VRF of class 1 with soil , (∆b) 27 vol% of VRF of class 2 with soil, (×c) 30 vol% of VRF of class 3 with soil, (◊d) 25 vol% of VRF of class 4 with soil, and (□e) pure soil (Sibelite® M002). Error bars are ± 1 standarddeviation. ..........................................................................................................................33

Figure 3.3. Schematisation of macroporosity formation due to presence of VRF inside of thekopecky ring, for soil-VRF mixtures containing around 15 and 30 vol%, grey colourrepresents the fine earth and black colour represents the VRF. .......................................35

Figure 3.4. Water retention curve for soil-VRF mixtures composed by around 15 vol% (▲)and around 30 vol% of VRF (◊) versus pure soil (□), (soil: Sibelite® M002). .................35

Figure 3.5. Water retention curve: (■a) 16 vol% of VRF of class 1 with SL1, (∆b) 15 vol% of VRF of class 2 with SL1, (×c) 15 vol% of VRF of class 3 with SL1, (◊d) 16 vol% of VRF class 4 with SL1 and (□e) pure SL1. Error bars represent ± one standard deviation. ......38

Figure 3.6. Water retention curve: (■a) 16 vol% of VRF of class 1 with SL2, (∆b) 16 vol% of VRF of class 2 with SL2, (×c) 17 vol% of VRF of class 3 with SL2, (◊d) 16 vol% of VRF class 4 with SL2 and (□e) pure SL2. Error bars represent ± 1 standard deviation. ..........40

Figure 3.7. Ternary mixtures (clay, silt and sand), (a) 10 % of clay and (b) 15 % of clay, theblack areas are the macropores and the grey areas represent ternary mixtures (Source:Fiès and Bruand, 1998).....................................................................................................41

Figure 3.8. Water retention curve: (■a) 15 vol% of VRF of class 1 with S, (∆b) 15 vol% of VRF of class 2 with S, (×c) 15 vol% of VRF class 3 with S, (◊d) 16 vol% of VRF class 4 with S and (□e) pure S (Sand). Error bars represent ± 1 standard deviation. ...................43

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Figure 3.9. Water retention curve of volcanic rock fragments class 1(■), class 2 (∆), class 3 (x) and class 4 (♦) separated from total mixed samples for experiment 1. Error bars represent ± 1 standard deviation. ......................................................................................46

Figure 3.10. (a) Simulated grain yield versus actual evapotranspiration (ETa), simulated data(■) and trend curve (—); (b) Simulated grain yield versus actual transpiration (Ta),simulated data (♦) and trend line (—)...............................................................................48

Figure 3.11. Comparison of (a) simulated grain yield, (b) water use efficiency fortranspiration (WUETa) (■) and for evapotranspiration (WUEETa), (□) for pure soil versussoil-VRF mixtures. Error bars represent ± 1 standard deviation. ....................................51

Figure 3.12. Simulated grain yield in function of actual evapotranspiration (ETa) for: pure soil(X), soil-VRF mixture with 15 vol% (□), with 30 vol% of VRF, (■) and trend curve, that is a third order polynomial as an approximation for logistic function (—)......................52

Figure 3.13. Comparison of (a) simulated grain yield, (b) water use efficiency for actualtranspiration (WUETa) (■) and for actual evapotranspiration (WUEETa) (□), for pure sand versus sand-VRF mixtures (15 vol% of VRF). Error bars represent ± 1 standarddeviation. ..........................................................................................................................54

Figure 3.14. Sensitivity analysis, (a) effect of changing water content at field capacity on thesimulated grain yield and (b) on water use efficiency for actual evapotranspiration(WUEETa). Error bars represent ± 1 standard deviation. ...................................................55

Figure 3.15. Sensitivity analysis, (a) effect of changing in water content at permanent wiltingpoint on the simulated grain yield and (b) on the water use efficiency for actualevapotranspiration (WUEETa). Error bars represent ± 1 standard deviation.....................56

Figure A1. Beckman Coulter LS 13 320 laser diffraction particle size. ..................................70Figure A2. Differential volume (%) versus particle diameter (um) from Beckman Coulter LS

13 320 laser diffraction particle size, Sibelite® M002 (◊), SL1 (▲), SL2 (X) and Sand (□), soil used to carry out the WRC of pure soil and soil-VRF mixtures................................71

Figure A3. Cumulative volume (%) versus particle diameter (um) from Beckman Coulter LS13 320 laser diffraction particle size, Sibelite® M002 (◊), SL1 (▲), SL2 (X) and Sand (□), soils used to carry out the WRC of pure soil and soil-VRF mixtures. .............................71

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List of tables

Table 2.1. Main characteristics from scoria volcanic rock (Source: USGS, 2004 andEncyclopaedia Britannica, 2009)........................................................................................8

Table 2.2. Volcanic rocks classified according to size. ..............................................................9Table 2.3. Mixes to determine the WRC, experiment 1 and 2 are around 15 and 30 vol%

respectively of each class of VRF mixed with Sibelite® M002, and experiment 3 isaround 15 vol% of each class of VRF mixed with soil types, as well as the soil origin. .11

Table 2.4. Particle size, bulk density (ρb), particle density (ρp) and chemical characteristics ofSibelite® M002 (Source: SRC - SIBELCO, nd). ..............................................................12

Table 2.5. Matrical potential units (Source: Raes, 2004 and SensorsONE Ltd, 2009). ...........16Table 2.6. Most important crop inputs and program settings utilised for modeling quinoa. ...21Table 2.7. Dependable rainfall for average monthly data based on Rainbow software. ..........23Table 2.8. Soil physical characteristics from Irpani 2005-2006 (Source: Geerts et al., 2008b).

..........................................................................................................................................24Table 3.1. Soil texture at three depths in Irpani (mean ± 1 standard deviation).......................26Table 3.2. Textural class and gravel content in Irpani (mean ± 1 standard deviation).............27Table 3.3. Bulk density and porosity corresponding to each class of volcanic rock fragment

(mean ± 1 standard error, N=10). .....................................................................................28Table 3.4. Particle size analysis (texture) for the soil utilised in each experiment, N

(repetitions). Mean ± 1 standard deviation. ......................................................................28Table 3.5. Comparison of water content at saturation (θSAT), field capacity (θFC) and

permanent wilting point (θPWP) between different soil-VRF mixtures and pure soil(Sibelite® M002). The letters show significant statistical groups (α=0.05), m (mean) se(standard deviation) and N (repetitions). ..........................................................................31

Table 3.6. Comparison of volumetric water content at saturation (θSAT), field capacity (θFC)and permanent wilting point (θPWP) between different soil–VRF mixtures and pure soil(Sibelite® M002). The letters show significant statistical groups (α= 0.05), m (mean), se(standard deviation) and N (repetitions). ..........................................................................34

Table 3.7. Comparison of volumetric water content at saturation (θSAT), field capacity (θFC)and permanent wilting point (θPWP) between soil-VRF mixtures and pure soils. Differentletter means statistical differences (α=0.05), N (repetitions). (Mean ± 1 standarddeviation). .........................................................................................................................36

Table 3.8. Total bulk density (ρb,t), bulk density of VRF (ρb,rf) and bulk density of fine earth(ρb,f ) of pure SL1 and SL1- VRF mixtures. (Mean ± 1 standard deviation, N=4). ...........39

Table 3.9. Total bulk density (ρb,t), bulk density of VRF (ρb,rf ) and bulk density of fine earth(ρb,f ) of pure SL2 and SL2- VRF mixtures. (Mean ± 1 standard deviation, N=4). ...........41

Table 3.10. Total bulk density (ρb,t), bulk density of VRF (ρb,rf ) and bulk density of fine earth(ρb,f ) of pure sand (S) and S-VRF mixtures. (Mean ± 1 standard deviation, N=4)..........44

Table 3.11. Simulated grain yield, water use efficiency for actual transpiration (WUETa) andfor actual evapotranspiration (WUEETa), under rainfed conditions of southern BolivianAltiplano (mean ± 1 standard deviation). .........................................................................47

Table 3.12. Saturated hydraulic conductivity (KSAT) calculated based on soil texture (Sibelite®

M002) and volume of VRF (vol%) by means of pedotransfer functions, and soil physicalproperties from experiment 1 (E-1) and experiment 2 (E-2)............................................49

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Table 3.13. Simulated grain yield, water use efficiency for actual transpiration (WUETa) andactual evapotranspiration (WUEETa) for soil data (Sibelite® M002) from experiment 1 (E-1) and 2 (E-2), different letter indicates statistical differences (α= 0.05). (Mean ± 1standard deviation). ..........................................................................................................50

Table 3.14. Soil inputs used to carry out the simulation for quinoa production with andwithout VRF in the sandy soil (S). ...................................................................................53

Table 3.15. Comparison of simulated grain yield, water use efficiency for actual transpiration(WUETa) and for actual evapotranspiration (WUEETa) for sand-VRF mixtures versus puresandy soil (S), different letters mean statistical differences (α=0.05). (Mean ± 1 standarddeviation). .........................................................................................................................53

Table 3.16. Soil input model to asses the sensitivity analyses to water content at field capacity(θFC) and permanent wilting point (θPWP).* indicates the values changed in comparisonwith baseline. ....................................................................................................................55

Table 3.17. Soil physical characteristics from laboratory and pedotransfer functions for puresilty loam (SL1) and silty loam containing 15 vol% of volcanic rock fragments (VRF). 57

Table 3.18. Comparison of simulated grain yield, for soil inputs from pedotransfer functionsversus laboratory, (mean ± 1 standard deviation). Different letters means statisticaldifferences (α=0.05; N = 18). ...........................................................................................58

Table A1. Rainfall and temperatures from Salinas de Garci Mendoza nearby Irpani, between1975 and 1985 (Source: SENHAMI). ..............................................................................69

Table A2. Chemical soil properties of soils in Irpani...............................................................69Table A3.Total bulk density (ρb,t), bulk density of VRF (ρb,rf) and bulk density of fine earth

(ρb,f) of pure soil and soil - VRF mixture (Sibelite® M002). ............................................70

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Summary

The southern Bolivian Altiplano is one of the most arid places in this country, with many

climatic constrains, the average rainfall for instance is around 200 mm per year with a huge

inter annual variability. There is the main area of quinoa production worldwide, therefore is

important for the local economy. In the soil of quinoa production is possible to find volcanic

rock fragments (VRF), due to volcanic activity in the past. Because there is not enough

information about the influence of these volcanic rocks on the soil and quinoa yield, the

general objective is to determine the influence of VRF on soil water holding capacity and on

crop water productivity modelling.

The study area is located in Bolivia, specifically in Irpani at 19°37´ LS, 67°43´ LW and at

3700 m. a. s. l. Soil samples were collected in order to determine their texture, gravel content

and to categorize the volcanic rocks fragments, in four classes according their size, afterwards

in laboratory the water retention curve of samples composed by 15 and 30 vol% of VRF

mixed with different soil textures were determined. The crop water productivity model was

then used to test its sensitivity to volcanic rock fragments presence in the soil, with calibrated

crop parameters of quinoa, historical climatic data from the agro climatic GIS library of

Geerts et al. (2006), and under rainfed conditions as well as low fertility.

Depending on the bulk density and volcanic rock fragment content vol%, the simulated grain

yield did not change much, for silty loam soil containing 15 vol% of VRF under conditions of

Bolivian Altiplano, but as the content of volcanic rock fragment increased to around 30 vol%

the reduction of simulated grain yield became significant. This was because of VRF can hold

water at permanent wilting point, which reduces the total available water of soils. The VRF

contented in sandy soils of Irpani ranged from 12 to 30 mass% between 0 and 60 cm of depth.

It can be concluded based on the simulation for sandy soil-VRF mixtures that the grain yield

could be overestimated by around 6 %, if the effect of volcanic rocks on the water retention

curve is not taken into account in AquaCrop.

Keywords: Water holding capacity, volcanic rock fragments, water productivity model,

quinoa, soil-rock fragment mixtures, southern Bolivian Altiplano.

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Resumen

El Altiplano sur es uno de los lugares más áridos en Bolivia, con muchas limitaciones

climáticas, la precipitación media es alrededor de 200 mm por año con una gran variabilidad

interanual. Allí es la principal zona de producción de la quinua a nivel mundial, por lo tanto es

importante para la economía local. En suelos de producción de quinua es posible encontrar

rocas volcánicas (FRV), debido a actividad volcánica en el pasado. Debido a que no hay

suficiente información acerca de la influencia de estas rocas volcánicas sobre el suelo y el

rendimiento de quinoa, el objetivo general es determinar la influencia de FRV en la retención

de agua en el suelo y sobre modelización de la productividad de agua en cultivo.

El área de estudio es en Bolivia, específicamente en Irpani a 19 ° 37 ' LS, 67 ° 43' LO y 3700

m. s. n. m. Se recogieron muestras de suelo para determinar su textura, su contenido de grava

y clasificar los FRV, en cuatro clases según su tamaño, después en el laboratorio la curva de

retención de agua de las muestras compuestas por 15 y 30 vol% de FRV mezclado con

diferentes texturas del suelo se determinaron. Luego, el modelo AquaCrop se utilizó para

probar su sensibilidad a la presencia de rocas volcánica en el suelo, con parámetros calibrados

del cultivo de quinua, datos climáticos históricos de la biblioteca agroclimática-SIG de Geerts

et al. (2006), y bajo condiciones de secano y de baja fertilidad.

Dependiendo de la densidad y el contenido de FRV vol% el rendimiento no cambió mucho,

para suelos franco limosos con 15 vol% de FRV bajo condiciones de altiplano boliviano,

pero como el contenido del FRV incrementó a alrededor de 30 vol%, la reducción de

rendimiento simulado se convirtió en importante. Debido a que FRV pueden sostener agua a

punto de marchitamiento permanente, reduciendo el total agua disponible en el suelo. El

contenido de FRV en suelos arenosos de Irpani oscila de 12 a 30 peso % entre 0 y 60 cm de

profundidad. Concluyéndose, basado en la simulación de la suelo arenoso-FRV mezclados

que el rendimiento de grano pueden estar sobrestimado por alrededor del 6 %, si el efecto de

las piedras sobre la curva de retención de agua no se tiene en cuenta en AquaCrop.

Claves: Curva de retención de humedad, rocas volcánicas, modelización de productividad de

agua en cultivos, quinoa, suelo-roca volcánica mezclas, Altiplano sur de Bolivia.

INTRODUCTION

1

CHAPTER I

1 Introduction

The southern Bolivian Altiplano is one of the most arid places in Bolivia. It has special

characteristics such as drought, salinity, frost, hail as is mentioned by many researchers

(Jacobsen and Mujica, 1999; Garcia, 2003; Geerts et. al., 2008a). Despite of these difficult

conditions quinoa (Chenopodium quinoa Willd.) is being produced as the most important

crop. The southern Bolivian Altiplano is the principal area of quinoa production in the entire

world. Therefore it is important for the local economy. Most of local people have quinoa

production as main activity together with the livestock. The soils in this region are between

sandy to loamy sand and with important content of volcanic rocks.

The quinoa production area in the southern Bolivian Altiplano covers part of the Potosí and

Oruro departments , with a total area of approximately 37,200 km² (Figure 1.1.a) . In this area

volcanic rocks are distributed in the soils (du Bray et al., 1995), because this area has

important concentration of volcanoes, which were active in the past. According to Risacher

and Ritz (1991) during the Plio-Quaternary the western and the southern Altiplano were

indeed strongly affected by an intense volcanic activity. These volcanic rocks are locally

called as pomes (extrusive igneous rocks). They have a specific characteristic, namely its

porosity, which may influence the water holding capacity of soils in the southern Bolivian

Altiplano and in other places of the world. Nevertheless the amount of water that the volcanic

rocks can retain in their pores is often unknown. That is why this investigation intends to

determine the effect of volcanic rocks on the water holding capacity of soil mixed with stones.

In the first steep of the research, volcanic rocks were divided in four classes according to size;

they were mixed with different soil types in order to determine the water retention curve in

the laboratory. In a next step this study focuses on crop water productivity modelling.

Modelling is important, for many advantages such as to save time, money, to be able to

analyse the conjunctive effect of different crops responses to water stress, or to analyze future

scenarios based on validated knowledge. In this sense we also analyzed the under and

INTRODUCTION

2

overestimation of the water holding capacity, especially in soil in which the volcanic rock

content is important, and therefore its influence on water productivity modelling and possible

overestimation of quinoa yields by means of modelling. Figure 1.1.b presents the volcanic

formations in the study area which coincide with the quinoa area production.

(a) (b)

Figure 1.1. (a) Map of Bolivia showing the quinoa area productions in the southern

Bolivian Altiplano and (b) Geological map of the Bolivian Altiplano showing the

volcanic areas formations (Source: adapted from Risacher and Ritz, 1991).

In the first step of the research, volcanic rocks were divided into four classes according to

size. They were mixed with soil (Sibelite® M002) and different soil types, with different

textures in order to determine the water retention curve in the laboratory. In a next step this

study focuses on crop water productivity modelling. Modelling is important, for many

advantages such as to save time, money, to be able to analyse the conjunctive effect of

different crops responses to water stress, or to analyze future scenarios based on validated

knowledge. In this sense we also analyzed the under u over estimation of the water holding

capacity, especially in soil in which the volcanic rock content is important, and therefore its

La Paz

BoliviaDepartmetal LimitsReal Quinoa Area Production

N

EW

S

Santa Cruz

Pand o

Beni

Cochaba mba

Potosi Tarija

Oruro

Chuq uisasa

PERU

CHILE

ARGENTINA

Salt FlatsSandstones, Claystones, Mudstones, & ShaleSandstones & ShalesLakesVolcanic FormationsLimits

N

EW

S

Ti ticaca Lake

IRPANI

BOLIVIAN ALTIPLANO

INTRODUCTION

3

influence on water productivity modelling and possible misestimating of quinoa yields by

means of modelling.

1.1 Objectives

1.1.1 General Objective

To investigate the effect of volcanic rocks on the water holding capacity of sandy soils

in the Bolivian Altiplano and the consequences for crop water productivity (CWP)

modelling.

1.1.2 Specific Objectives

To determine the influence of volcanic rocks fragments (VRF) on the water retention

curve (WRC) of different soil types mixed with volcanic rocks fragments.

To model and compare the quinoa yield and water use efficiency for soil with and

without volcanic rocks fragments through a CWP modelling, with the model

AquaCrop.

1.2 Outline of the thesis

After introducing the thesis, Chapter II presents materials and methodology. This chapter

describes geographic, climatic and soil features of the study area, the material used at the

laboratory, and methodology of the software that was used.

Chapter III describes the results and discussion of the water retention curve from volcanic

rocks mixed with soil types, the determination of the underestimation u overestimation of the

water holding capacity of a soil with volcanic rocks, and its influence on water productivity

modelling. The estimation of quinoa yields from fields with soil mixed with volcanic rocks is

discussed. Based on the results from chapter III, conclusions and recommendations are

presented in the Chapter IV. At the end of this paper the references as well as annexes are

presented.

MATERIALS AND METHODOLOGY

4

CHAPTER II

2 Materials and methodology

2.1 Field work and laboratory

2.1.1 Study area

The study area is located in Bolivia (South America), specifically in the Irpani community

nearby Salinas de Garci Mendoza in the department of Oruro. Geographically it is located at

19°37´ latitude south and 67°43´ longitude west. The altitude is around of 3700 meter above

mean sea level. Figure 2.1 presents the map of South America and Bolivia showing the study

area.

Figure 2.1. Maps of South America and Bolivia showing the study area.

BolivianAltiplano


5

2.1.1.1 Climate

The southern Bolivian Altiplano and consequently the Irpani community have an semiarid to

arid climate with many climatic constrains such as drought, frost risk and hail especially in

the summer or austral period (François et al., 1999; Delaet, 2006; Geerts et al., 2006; Garcia

et al., 2007; Geerts et al., 2008b).

The historical behaviour of the precipitation and temperature of Salinas de Garci Mendoza (a

community near Irpani) is shown in Figure 2.2 and Table A1 in annexes.

-10

-5

0

5

10

15

20

25

J A S O N D J F M A M JMonth

Tem

per

atu

re°C

0

20

40

60

80

100

120

Pre

cip

itatio

n(m

m)

Pp T min T max T meam

Figure 2.2. Rainfall and temperatures from 10 years (Between 1975 and 1985) in Salinas

de Garci Mendoza nearby Irpani (SENHAMI1).

Salinas de Garci Mendoza has an average yearly precipitation of 332 mm. The rainiest month

is January with 112 mm as average. In terms of average temperature the maximum, minimum

and mean temperatures are -1.4 ºC, 7.8 °C and 16.7 ºC respectively (average between 1975

and 1985). On the other hand, Geerts et al., (2008b) indicate that the precipitation in the study

area is around 210 mm with a huge inter-annual variability. The same researcher mentioned a

total effective rainfall during 2005–2006 of 385 mm.

1 SENAMHI: “Servicio Nacional de Meteorología e Hidrología de Bolivia”.


6

2.1.1.2 Agriculture

In the study area, quinoa is the only crop that is able to assure a harvest and generate

economic ingresses. In this zone traditional varieties of greater grain size have been

developed known like quinoa Real (Bonifacio, 2001). Figure 2.3 presents the predominant

natural vegetation that constitutes of native pastures such as Festuca ortophilla, Stipa ichu

and tholas such as Baccharia mierophylla, Lepidohilium cuadrangulare (MACA, 1988).

Figure 2.3. Predominant vegetation around Irpani in the southern Bolivian Altiplano

(Source: J. P. Raffaillac, IRD).

In the southern Bolivian Altiplano quinoa is produced as monoculture, the fallow period are

between 1 and 3 years or more (Bottner et al., 2006; after Geerts, 2008). The fields are often

prepared from January to May, during the rainfall period through mechanized systems (Aroni,

2001; Risi, 2001). Quinoa is grown during rainy season which is from September to May

(Geerts, 2008). The sowing period starts from August when the temperature increases until

the middle of November (Aroni, 2001), to take advantage of the soil moisture due to snow in

winter (June to August).

Traditional sowing is done in manually dug sowing pits with interspacing of 1 m to 1.4 m, at

30 cm of depth or more according to soil moisture (Aroni, 2001; Risi, 2001; Debergh, 2007;

Geerts, 2008), nowadays mechanized sowing is being applied. To increase soil fertility, manure


7

of lama and sheep are sometimes applied before of sowing (CICDA-AVSF, 2004; after Debergh,

2007). The cultivated quinua varieties have great grain size with diameters more than 2 mm

(Bonifacio, 2001). Quinoa has a cycle up to 200 days in this area. According to Risi (2001)

the most important pests that affect the quinoa development are rodents and "q´hona q’hona"

(Eurysacca quinoae).

The harvesting is done manually and either the entire plants are extracted including the roots

(Risi, 2001) or they are cut just of the field surface, afterward they are staked in the field to

dry. Consequently the grain is separated by means of machinery or naturally, and then they

are brought to industrial plants to extract the saponins from the grains (Geerts, 2008).

2.1.2 Data collection

2.1.2.1 Soil samples for determining the soil physical characteristics

The soil samples were collected from a cropped field with quinoa by the project “Red

Quinoa” (IRD, 2009). The soil samples come from an experiment under randomized block

design, and the crop was sown in September of agricultural cycle 2004-2005. 12 soil samples

were collected at three depths 0-20 cm, 20-40 cm and 40- 60 cm, each soil sample weighing 1

kg. These samples were sent to be analysed at the INRA (“Institute National de Recherches

Agronomiques - Laboratoire d’Analyses de Sols d’Arras France”). At INRA physical analysis

of the soil was performed in order to determine the soil texture (mass%). The texture was

determined by sieving and the pipette method; procedure NF X 31-107 (Association

Française de Normalisation, 1994). Also chemical analysis was performed to determine the

organic mater (O.M.), pH, electrical conductivity (E.C.); cations exchange capacity (C.E.C),

Phosphorus (P2O5), Nitrogen (N), Magnesium (Mg), Potassium (K) and Sodium (Na).

2.1.2.2 Soil samples for determining the gravel content (mass%)

During the agricultural cycle of 2005-2006, 12 soil samples were collected from 0-20 cm, 20-

40 cm and 40-60 cm of depth by “Red Quinoa” project (IRD, 2009) in Irpani from fields

cropped with quinoa. These samples were sent to be analysed at IBTEN (“Instituto Boliviano

de Ciencia y Tecnología Nuclear”). The soil texture was determined by the method of the


8

Hydrometer of Bouyoucos (sedimentation), and the particles of sand, silt and clay were

calculated in mass percentage. The gravel content was determined by weighing the amount of

particles that did not cross the mesh of the sieve of 2 mm, that is to say, particles greater than

2 mm.

2.1.2.2.1 Classifying the volcanic rocks

According to Gardiner and Miller (2004) as well as USGS (2004), the extrusive or volcanic

igneous rocks (Figure 2.4) are produced when magma exits and cools outside of, or very near

the earth's surface.

Figure 2.4. Volcanic rock (Source: Gyllenhaal, 2001).

General characteristics such as chemical properties, texture and colour of volcanic rocks are

presented in the Table 2.1.

Table 2.1. Main characteristics from scoria volcanic rock (Source: USGS, 2004 and

Encyclopaedia Britannica, 2009).

Name of the rock: ScoriaBasic type: Volcanic rockGroup: IgneousStructure: It has many forms, manly prolonged and angularChemicalcomposition:

Composed of trioxide of silica and aluminium trioxide,among other components

Texture: Porous, spongy or frothy with many hollows andcavities, large and thick vesicles walls

Specific gravity(relative density): Greater than 1 g cm-3

Colour: Greyish, ash-gray, dark brown, black and red


9

During the agricultural season of 2004-2005, 10 soil samples were collected from a depth

between 0 and 30 cm, each soil sample had a weight of around 3 kilos and they were sieved in

order to separate the particles greater than 2.0 mm. Afterwards they were separated with an

automatic device in four classes according to size (Table 2.2). These four classes were chosen

because only these size sieves were available at the laboratory (IRD-CLIFA laboratory –

Viacha, Bolivia). The volcanic rocks from these samples were used for the determination of

the water retention curve of soil-rock mixtures.

Table 2.2. Volcanic rocks classified according to size.

Classes Diameter (mm)

Class 1 2.0-3.15Class 2 3.15-4.5Class 3 4.5-8.0Class 4 > 8.0

2.1.3 Laboratory procedure for soil water retention curve (WRC) determination

All laboratory measurements were performed at the Laboratory of Soil and Water

Management in the geo-institute, K.U. Leuven (Department of Earth and Environmental

Sciences).

2.1.3.1 Volume, Bulk density and Porosity of volcanic rocks

The volume of the rocks was calculated based on the Archimedes’ principle (Figure 2.5), for

each size class of volcanic rock fragment. Because of the porosity of the volcanic rocks they

were soaked for 72 hour prior to measure their volume. Archimedes’ principle states that the

volume of fluid displaced is equal to the volume of the portion of the object submerged

(Britannica Encyclopaedia, 2009). Tubes of 50 cc, 100 cc, and 200 cc of volume, scales of 0.1

and 0.001 of precision, and oven dryer were utilised.

Bulk densities of volcanic rocks were determined based on the following equation.


10

rf

rf

rfb V

M, [g cm-3] Eq. 2.1

Where the ρb,rf is the bulk density of volcanic rock fragments, Mrf is mass of volcanic rock

fragments dried at 105 °C during 24 hours and Vrf is the volume of the volcanic rocks.

Figure 2.5: Determination of volume with Archimedes´ principle (Source: Rubin J.,

2008).

The porosity (n) is the ratio of the volume of the pores to the bulk volume of the soil (Raes,

2004). It was calculated, based on equation 2.2 proposed by Carter and Ball (1990); after

Descheemaeker (2006).

rfp

rfbrfn

,

,1

[m3 m-3] Eq. 2.2

Where nrf [m3 m-3] is the rock fragment porosity and, ρp,rf [g cm-3] is the rock fragment particle

density. According to Raes (2004) for ρp,rf a value of 2.65 g cm-3 can be assumed.

2.1.3.2 Experiments to determine water retention curve (WRC) between volcanic

rock fragments (VRF) and soil types

Once, the volume of the volcanic rocks fragments (VRF) was determined, and taken into

account that the kopecky ring volume was 100 cm3; the amount of soil for every sample was


11

calculated by subtracting from 100 cc the volume of the rock. This volume of soil was

converted in mass units for a bulk density of 1.23 g cm-3. Afterwards the volcanic rocks and

soil were mixed to be filled inside of kopecky rings by hand, for continuing with the water

retention curve experiments. In other words we mixed the samples so that the bulk density of

the soil was at known value. This was mainly important for the soil (Sibelite® M002), because

it was reported that its water retention changes a lot with differences in settling (Bulk

density).

The procedure for WRC determination was performed for the following combinations

between volcanic rocks and soil types (Table 2.3). The numbers of blank samples were 5, 7,

and 4 respectively for experiment 1, 2 and 3, for soil type.

Table 2.3. Mixes to determine the WRC, experiment 1 and 2 are around 15 and 30 vol%

respectively of each class of VRF mixed with Sibelite® M002, and experiment 3 is around

15 vol% of each class of VRF mixed with soil types, as well as the soil origin.

Experiment Volcanic rock fragment(VRF) class

SamplesN

VRF (vol%)(m ± s e)*

Soiltype and origin

Class 1 10 17.0 ± 1.0

Class 2 10 14.0 ± 1.0

Class 3 10 14.0 ± 1.0

1

Class 4 7 17.0 ± 4.0

Class 1 5 31.0 ± 2.0

Class 2 5 30.0 ± 2.0

Class 3 5 29.0 ± 2.0

2

Class 4 5 25.0 ± 2.0

Silty loam soil(Sibelite® M002)

SIBELCO

Class 1 4 16.0 ± 0.4

Class 2 4 15.0 ± 0.2

Class 3 4 15.0 ± 0.2

Class 4 4 16.0 ± 1.0

Silty loam soilSL1

Leuven

Class 1 4 16.0 ± 0.2

Class 2 4 16.0 ± 0.1

Class 3 4 17.0 ± 0.2

Class 4 4 16.0 ± 1.0

Silty loam soilSL2

Bruges

Class 1 4 15.0 ± 0.4

Class 2 4 15.0 ± 0.4

Class 3 4 15.0 ± 1 .0

3

Class 4 4 16.0 ± 1 .0

Sandy soilS

Mol

* m= mean or average; se= standard deviation


12

2.1.3.3 Soil characteristics (Sibelite® M002)

The soil from SCR-SIBELCO that was first used in the study is called Sibelite® M002.

According to SCR-SIBELCO (nd) this material is high-purity silica produced from

cristobalite by iron-free grinding and accurate sieving by means of air-separators.

Granulometric data, physical characteristics as well as chemical analysis are presented in

Table 2. 4.

Table 2.4. Particle size, bulk density (ρb), particle density (ρp) and chemical

characteristics of Sibelite® M002 (Source: SRC - SIBELCO, nd).

Feature Value Unit MethodControl –sieve > 63 µmD10D50D90ρp

ρb

Specific surfacepHSiO2

Fe2O3

Al2O3

TiO2

K2OCaO

459702002.401.050.5999.50.030.200.030.050.01

%µmµmµmg cm-3

g cm-3

m2 g-1

-%%%%%%

Alpine®

Malvern MS 2000®

Malvern MS 2000®

Malvern MS 2000®

--BET®

-XRF®

XRF®

XRF®

XRF®

XRF®

XRF®

D50 is defined as the grain diameter at which 50 % of the particles are finerthan. Particle sizes D10 and D90 are associated with 10 % and 90 % finer than,XRF®: X-ray Fluorescence method; BET: Brunauer, Emmett and Teller method.

2.1.3.4 Origin of soil and particle size

The soils used for determining the WRC of mixtures (experiments 3) came from Mol which is

a municipality located in the province of Antwerp, from the region of Bruges in the province

of West Flanders and the fields located in Leuven in the province of Flemish Brabant in

Belgium. In order to determine the soil texture, samples were sent to be analysed at the

laboratory of Soil and Water Management at Geo-Institute, K.U. Leuven. The particle size

analysis was determined based on a Beckman Coulter LS 13 320 laser diffraction particle size


13

(Figure 1A of annexes), that posses a bench as well as universal liquid module, in order to

determine the size particles, they must be suspended in water at 20° C (Manek et al., 2005).

2.1.3.5 Soil water retention

“The total soil-water potential of the constituent water in soil at temperature T0 is the amount

of useful work per unit quantity of pure water that must be done by means of externally

applied forces to transfer reversibly and isothermally an infinitesimal amount of water from

the standard state to the soil liquid phase at the point under consideration.” (Bolt, 1976; after

Jury and Horton, 2004). The components of soil water potential are the following:

nhmogt Eq. 2.3

Where ψt [m2s-1kgm-3] is the total water potential; ψg [m2s-1kgm-3] is the gravitational

potential determined by the location of the point under consideration relative to the reference

level; ψo [m2s-1kgm-3] is the osmotic potential due to the presence of solutes in the soil

solution; ψm [m2s-1kgm-3] is the matric potential that is related with the forces by which the

water is held in the soil matrix; ψh [ m2s-1kgm-3] is the hydrostatic potential applied only

when the soil is saturated, and the pneumatic potential ψn [m2s-1kgm-3] results from external

gas pressure applied to the water (Young and Sisson, 2002; after Baetens, 2007).

2.1.3.5.1 Water content

To quantify the water content in soils, it can be done in two different units, as volumetric

water content θ and gravimetric water content θm (Jury and Horton, 2004). The next equations

were used to calculate θm and θ:

soildryofmasswaterofmass

m [kg kg-1] Eq. 2.4

mw

tb

, [m3 m-3] Eq. 2.5

Where ρb,t [kg m-3] is the total bulk density and ρw [kg m-3] is the water density. According to

Brakensiek (1986); after Baetens (2007), several equations can describe the relation between


14

the bulk densities of particles less than 2.0 mm and rock fragments particles greater than 2.0

mm.

rfbvfbvtb RR ,,, 1 [kg m-3] Eq. 2.6

m

vfbtb R

R

11

,, [kg m-3] Eq. 2.7

Where ρb,t [kg m-3] is the total soil bulk density, which includes particles < 2.0 mm and rock

fragments, ρb,f [kgm-3] is the particles < 2.0 mm bulk density (fine earth), Rm [kg kg-1] is the

rock fragments content in terms of mass, Rv [m3m-3] is the rock fragment in terms of volume

and ρb,rf [kgm-3] is the bulk density of rock fragments.

The bulk density of particles < 2.00 mm, the rock fragment content by mass or by volume can

be calculated based in the followings formulas:

rft

ffb VV

M

, [kg m-3] Eq. 2.8

Where Mf [kg] is the mass of particles <2.0 mm, Vt [m3] is the total soil volume including

rock fragments and Vrf [m3] is the volume of rock fragments.

t

rfv V

VR [m3 m-3] Eq. 2.9

t

rfm M

MR [kg kg-1] Eq. 2.10

Where Mrf [kg] is the mass of rock fragments, and Mt [kg] is the total mass soil, in which fine

earth plus rock fragment are included (Child and Flint, 1990; after Baetens, 2007).

2.1.3.5.2 Soil water retention curve

The soil WRC describes the relation between the water content and the matric potential (ψm),

this curve is important because it gives information on the water available for plants (Raes,

2004). The water content described by soil WRC ranges between water content at saturation

(θSAT) and the water content at air dryer. Due to laboratory limitations, we only measured

until permanent wilting point (θPWP).


15

The shape of soil WRC depends on the soil texture (Figure 2.6) and structure. For instance,

fine textured soil as clay strongly retain water and as a result have more water because of its

high specific surface (Raes, 2004). On the other hand, sandy soils have much largest pores

that drain at modest suctions (Jury and Horton, 2004), furthermore the specific surface is

smaller than the clay soils.

Figure 2.6. Soil water holding capacity for different soil texture classes (Source:

http://www.nivaa.nl/explorer/pagina/pictures/pfcurvey.jpg).

Two points are quite important in relation with the water holding capacity of the soil for

plants. These are: the field capacity (FC), which is the maximum water content that a soil will

hold after to be well drained for a few days. The water content at FC depends of the soil type,

but values between pF 2.0 and pF 2.5 are suggested as ψm at FC (Romano and Santini, 2002).

And, the permanent wilting point (PWP) that is defined as the soil water content at which the

leaves of sunflower plants wilt permanently, in general is assumed at pF 4.2 ( Raes, 2004;

Bohne, 2005; after Baetens, 2007).

According to Jury and Horton (2004) the relationship between matrical potential and water

content is not unique in a given soil because of the hysteresis; it means that ψm and θcan

follow a different wetting and drying curve. The water potential and ψm can be measured in

varied units, Table 2.5 shows the most common units.


16

Table 2.5. Matrical potential units (Source: Raes, 2004 and SensorsONE Ltd, 2009).

Volumetric water potential HeadEnergy per unit of volume Energy per unit of head

bar Kpa Kgf cm-2 cmH2O pF (Log cmH2O)-0.10 -10.0 0.10 102.00 2.0-0.33 -33.20 0.34 339.90 2.5

-15.00 -1549.40 15.30 15800.0 4.2

The conversion factor from energy per unit of volume to head is 1/( w g)

w=1000 kg m-3; g=9.81m s -2

2.1.3.6 Procedure for pF 0 – 2

In order to quantify the WRC for the points corresponding to pF 0 until 2, a device was used

that is called sand box (Figure 2.7).

Figure 2.7. 08.01 Sandbox (Source: Aquagri, 2007).

In the kopecky rings we fixed a piece of cloth at the bottom side of the sample with an elastic-

band, and were filled with the mixed samples of volcanic rocks fragments and soil, with

repetitions for every class (Table 2.3). Samples with pure soil were also added to determine

the WRC of the soil without rocks (blank samples). After filling of the kopecky rings, they

were saturated during 3 to 7 days according to the soil texture. The suction regulator was slid

down to 0 cm (pF 0) and it was left during a week. After a week the rings were carefully taken


17

out of the device and were weighed (balance with 0.01g of precision). The same procedure

was followed for the points of pF 0.5 (3.2 cm), 1.0 (10 cm), 1.5 (32 cm) and 1.8 (63 cm) until

reach pF 2.0 (100 cm).

2.1.3.7 Procedure for pF 2.3 to 2.8

For measuring the pF points from 2.3 till 2.8 a device called low pressure chamber (Figure

2.8) was utilised, together with ceramic plates of 1 bar. The ceramic plates were soaked for a

period of three days prior to use. Afterwards, soil samples that came from the previous point

(sand box) were placed on the ceramic plates with a layer of soil and were introduced inside

the pressure chamber, under a constant pressure of 0.2 bar during a week. After that time, the

samples were weighed. The same process was followed for the remaining point pF 2.8 (0.62

bar). Afterwards the samples were oven dried at 105 °C during 1 day in order to get the oven

dry weight of every sample.

Figure 2.8. Low pressure chamber (Source: Alfredo Veizaga).

2.1.3.8 Procedure for pF 3.4 to 4.2

In order to determine the WRC for the 3.4 and 4.2 pF points, the total samples were divided in

two parts for each point respectively. Afterwards, the samples were soaked in bottle during 3

to 7 days depending of soil texture, and the ceramic plates of 15 bar as well. Since the


18

samples were saturated, they were filled up in specially fabricated rings (PVC rings with

diameter of 7.1 cm and 2.6 cm of height). These special sample rings were created to limit the

sample height for the case of mixes (soil and volcanic rock fragment), and for pure soil

normal rings were utilised. Then the samples were placed on the ceramic plate, and they were

introduced to the pressure chamber at constant pressure during 7 days (Figure 2.9).

After 7 days in the pressure chamber at 2.5 bar. The samples were weighed and placed in the

oven dry at 105 °C during 1 day to determine the dry weight. The same procedure of

saturating, pressuring and drying was performed for determining the water content of pF 4.2.

For experiments 2 and 3 only the pF 4.2 were performed, due to the long stabilization time of

each pressure.

Figure 2.9. High pressure chamber and special rings (Source: Alfredo Veizaga).

2.1.3.9 Statistical analysis

In order to compare the water content at SAT, FC, and PWP among mixes of each volcanic

rock fragment class and pure soil for the experiments. ANOVA analysis and Duncan multiple

comparison tests were performed to assess differences among average values. The statistical


19

analyses were performed based on SAS 9.2 statistical package (SAS, 2007). And the

significance levels were α=0.05 percent.

2.1.4 WRC separation of volcanic rock and WRC of soil from mixed samples

In order to get a generic relation (pedo-transfer functions) for the determination of the WRC

of soil-rock fragment mixtures, with the data obtained in the experiment 1 (Table 2.3), we

tried to separate the water content at different suction heads for each class of volcanic rock

fragment from the mixes (soil plus volcanic rock fragments), given that the bulk density of

each volcanic rock fragment class and the pure soil were already known before mixing them.

We tried to do the separation based on the WRC for pure soil. We assumed that the soil part

of the mixes should have the same water content for each pF point as the pure soil (blank

samples) as the samples were prepared to have a constant ρb for the soil fraction. Then, the

total water content by volume (water content of the volcanic rocks and the soil) was

calculated for each pF point. From these values the water content of the pure soil part was

subtracted respecting the total volume of the volcanic rock fragment. In this way we tried to

obtain the WRC only for volcanic rocks fragments.


20

2.2 Aqua-Crop modelling

In order to develop the modelling part the new version of crop water productivity model

AquaCrop (version 3.0) was utilised (Steduto et al., 2009; Raes et al., 2009a; Raes et al.,

2009b). 18 years were simulated; first for local sandy soil without takes into account the

volcanic rock fragments, afterwards for the soil results obtained in the laboratory (Section

2.1.3) for pure soils as well as each class of soil-VRF mixtures. This is to check whether the

VRF have an important effect on final yield simulation, because the position of rock

fragments in the soil may have large influence on the internal drainage and water retention

(Pérez, 1998). Furthermore, rock fragments as mulch can reduce evaporation and runoff

(Kemper et al., 1994; after Cousin et al., 2003; Kosmas et al., 1998). On the other hand, if the

evaporation is high, the rock fragments instead of reducing the evaporation increase it,

because of their high calorific feature (Cousin et al., 2003), as explained before.

2.2.1 Model Inputs

2.2.1.1 Climatic data

The historical daily rainfall and monthly evapotranspiration was obtained from the

Agroclimatic GIS library of Geerts et al. (2006). 18 years historical daily rainfall of Rio

Mulatos, which is a located at 19°41’ LS, 66°46’ LW and 3815 m. a. s. l. (Geerts, 2008)

nearby Irpani, and the average monthly evapotranspiration from 25-30 years. Since the direct

effect of the temperature on the quinoa development was not analysed in detail, it was not

used in the model.

2.2.1.2 Crop data

The AquaCrop model was released with the quinoa crop parameters already calibrated based

on the calibration carried out by Geerts et al. (2009a). Further adjustments were carried out by

Geerts (unpublished data, 2009). From adjusted parameters, some calendar parameters for

southern Bolivian Altiplano were adjusted, because the crop cycle is around 200 or more

calendar days (Risi, 2001; Geerts, 2008). The most important crop inputs and program

settings are presented in Table 2.6.


21

Table 2.6. Most important crop inputs and program settings utilised for modeling

quinoa.

Crop input ValueUpper LowerPexp: Soil water depletion factor for canopy expansion (-)0.50 0.80

P sto: Soil water depletion fraction for stomatal control (-) 0.60P sen: Soil water depletion factor for canopy senescence(-) 0.98Sum (ETo) during stress period to be exceeded before senescence istriggered (-) 200

Soil fertility stress at calibration (%) 50Zn: Minimum effective rooting depth (m) 0.30Zx: Maximum effective rooting depth (m) 1.00Time from sowing to emergence (Calendar Days) 7.0Time from sowing to maximum rooting depth(Calendar Days) 83Time from sowing to start senescence(Calendar Days) 173Time from sowing to maturity (Calendar days) 200Time from sowing to flowering (Calendar days) 115WP*:Water productivity normalized for ETo and CO2 (g m-2) 10.4Water productivity normalized for ETo and CO2 during yieldformation (as % WP*) 90

HIo: Reference harvest index (%) 50

According to Geerts (2008); Geerts et al. (2008a), the model was calibrated for experimental

condition (with a good quinoa seed as well as pest management), that for farmers conditions

rarely happen. Given this condition, from the total yield (Y) 25 % should be subtracted, in

order to have the farmer yields (Geerts, pers. comm., 2009). The start growing cycle was set

for September 15th for each simulated year.

2.2.1.3 Management data: the fertility effect on water productivity

To run the simulation in management component of AquaCrop model, the file soil fertility

level was fixed to poor conditions, because the native soil fertility in the southern Bolivian

Altiplano is very low, according to Fleming and Galwey (1995); after Bosque et al. (2003)

and Geerts et al. (2008b). AquaCrop works with a normalized water productivity (WP*) in

order to calculate the above ground biomass (B). The WP* of a crop can be classified

according to its physiological photosynthesis pathway (Raes et al., 2009a). Quinoa is a C3

plant (Figure 2.10) as reported by many researchers (Jensen et al., 2000; Jacobsen et al., 2003;

Bois et al., 2006; Geerts, 2008).


22

Figure 2.10. Above ground biomass production (B) in function of ∑Ta/ETo, showing the

biomass water productivity WPb for C3 group crops and WP for quinoa under low soil

fertility (dotted line), (Source: Adapted from Raes et al., 2009a).

The above ground biomass or dry above ground biomass (B) of the crop is linear with

∑Ta/ETo, and the slope of this relation is the biomass water productivity (WPb). In the Figure

2.10 the dotted line shows that when the soil fertility is low this relation is linear until certain

point after that became no linear, because of the reduction of the soil nutrients, even if the

water content in the soil is high (Heng et al., 2007; Geerts, 2008) and according to Raes et al.

(2009a) the nutrient reservoir depletes while the crop develops, therefore in the model the

consequence of soil fertility on the adjustment of WP is not linear during the entire season.

All the simulations were done for rainfed (RF) conditions.

2.2.1.4 Soil data

The soil input that AquaCrop needs, are soil physical properties such as volumetric water

content at saturation (θSAT), volumetric water content at FC (θFC), volumetric water content at

PWP (θPWP) and saturated hydraulic conductivity (KSAT). These inputs can be obtained from

the AquaCrop data according to soil texture, or the users can create new files with their own

laboratory data (Raes et al., 2009a).

The initial water content is a very sensitive parameter (Geerts et al., 2009a), and it depends of

the soil water accumulation during the preceding fallow year for rainfed conditions (Raes et


23

al., 2009a; Geerts et al., 2009a). Frequency analysis was carried out for the yearly rainfall data

with the RAINBOW program (Raes et al., 2006) for yearly rainfall data (18 years), according

to procedure proposed by Raes (2004); Raes and Geerts (2008), Table 2.7 presents the

dependable rainfall found, to classify the follow years into 3 classes.

Table 2.7. Dependable rainfall for average monthly data based on Rainbow software.

*PE (%) Year Dependable rainfall (mm year -1)20 Wet 37850 Normal 23080 Dry 132

*PE: probability of exceedance

When the annual rainfall of previous year to be simulated felt above the normal rainfall 230

mm, the initial soil water moisture was set at 60 % of the totally available water (TAW), but if

it was less than 230 mm it was set at 50 % of the TAW.

2.2.2 Sensitivity analysis of the model to different soil physical properties

A sensitivity analysis of soil physical properties was done, to analyse how changes in these

inputs affects the simulated outputs. According to Beven (2001), as cited by Jacquin and

Shamseldin (2009), sensitivity analysis gives a measurement of the sensitivity of a quantity Y

under examination to small changes in the model parameters, with respect to some chosen

values.

2.2.2.1.1 Simulation for local sandy soil (from southern Bolivian Altiplano)

For these simulations, Table 2.8 presents the soil physical characteristics that were calculated

base on suction table and pressure plate methods for the case of WRC, and for KSAT it was

obtained in situ by means of the double ring infiltration apparatus (Geerts et al., 2008b). The

thickness of the soil was set at 1.2 m, the readily evaporable water (REW) as 7 mm and the

curve number (CN) 70, because KSAT is 720 greater than 250 mm day-1 for each simulated

case and the initial soil water content (ISWC) was set at 50 % and 60 % of total available

water (TAW), that are around to antecedent moisture class (AMC) II (Raes et al., 2009a).


24

Table 2.8. Soil physical characteristics from Irpani 2005-2006 (Source: Geerts et al.,

2008b).

Thickness (m) θSAT (vol%) θFC (vol%) θPWP (vol%) KSAT(mm day-1)

0.20 53.9 20.5 5.2 720.00.20 51.3 22.4 4.9 720.00.60 52.4 22.4 6.9 720.0

2.2.2.1.2 Simulation for WRC of soil-VRF mixtures versus pure soil (Sibelite® M002)

These simulations were performed with data (WRC) obtained from experiments 1 and 2

(Table 2.3). The KSAT for pure soil (Sibelite® M002) as well as for soil-VRF mixtures were

found through pedotransfer function (Saxton and Rawls, 2006), based on the soil texture

analysis and VRF content vol%. The thickness of the soil was set at 1 m of depth, the readily

evaporable water as 7 mm and the curve number 70.

2.2.2.1.3 Simulations for WRC of sand-VRF mixtures and pure sand

For these simulations, WRC for sand from experiment 3 (Table 2.3) was used. The initial soil

water content was set at FC or 100 % of TAW in order for the model simulates yields above

0.0 Mg ha-1 , given that the values of 50 % and 60 % of TAW were very low and the grain

yield was always 0.0 Mg ha-1. With the purpose of get KSAT for pure soil and soil-VRF

mixtures, pedotransfer function (Saxton and Rawls, 2006) were used, based on the soil texture

and the VRF content vol%. The thickness of the soil was set at 1 m, the readily evaporable

water as 7 mm and the curve number 70.

2.2.2.1.4 Sensitivity analysis of the model to water content at field capacity and permanent

wilting for Sibelite®M002 soil

The sensitivity analysis was carried out for soil (Sibelite® M002). It was done in order to asses

how sensitive is the model to maximum and minimum values of θFC as well as θPWP, these

values were found from experiment 1 and 2 considering mixed samples. And the average of

θSAT, θFC and θPWP were calculated considering only pure soil (to be considered as a baseline).

KSAT was obtained by means of pedotransfer functions (Saxton and Rawls, 2006) and taking


25

into consideration the soil texture. The thickness of the soil was set at 1 m, the readily

evaporable water as 7 mm and the curve number 70.

2.2.2.1.5 Sensitivity analysis of the model to water content from laboratory data versus

data of pedotransfer functions

In order to compare the laboratory data versus results obtained with pedotransfer functions,

SL1 (silty loam soil collected from Leuven) based on particle size distribution and a 15 vol%

gravel contend, the θSAT, θFC and θPWP was determined by means of pedotransfer function

(Saxton and Rawls, 2006). Afterwards, sensitivity analysis of AquaCrop was carried out for

using these inputs from pedotransfer functions. The simulated grain yield from data of

pedotransfer function was compared with the average of SL1-VRF mixtures composed by

each size class with 15 vol% of VRF from laboratory. The thickness of the soil was set at 1

m, REW as 7 mm and the CN 70.

2.2.3 Water use efficiency (WUE) and statistical analysis

From all different simulations the WUE was calculated. The WUE is also known as water

productivity (WP), WUE is defined as the ratio of the mass of economically valuable yield

(Ya) in [Mg ha-1] and the volume consumed by the crop (ETa) in [m3 ha-1] (Hatfield et al.,

2001; Geerts and Raes, 2009; Raes et al., 2009a)

a

aET ET

YWUE [kg m-3] Eq. 2.11

The WUE can be represent for the crop transpiration

a

aT T

YWUE [kg m-3] Eq. 2.12

Where: Ta [m3] is the actual crop transpiration.

In order to assess the effect of volcanic rock fragments on the crop yield, WUEET and WUET,

simulation for blank samples (pure soil) and soil-VRF mixtures were compared statistically

by means of pairwise T student test (Willems, 2007) in the SPSS statistical software.

RESULTS AND DISCUSSION

26

CHAPTER III

3 Results and discussion

3.1 Field work and laboratory

3.1.1 Data collection

3.1.1.1 Soil samples for determining the soil physical characteristics

According to the soil analysis done by INRA, the average soil texture until a depth of 60 cm

is loamy sand as shown in Table 3.1. These results coincide with the textural classes reported

by Geerts et al. (2008b) who reported a soil texture between loamy sand and sandy loam.

Table 3.1. Soil texture at three depths in Irpani (mean ± 1 standard deviation).

Depth Clay Fine silt Large silt Fine sand Large sand Textural

(cm) < 2 μm(%)

2-20 μm(%)

20-50 μm(%)

50-200 μm(%)

200-2000 μm(%) Class

0-20 4.1 ± 0.3 3.5 ± 0.2 4.5 ± 1.7 34.6 ± 8.1 53.4 ± 10.2 Sand20-40 5.3 ± 0.5 6.3 ± 0.5 6.6 ± 1.1 36.6 ± 3.0 45.2 ± 3.8 Loamy Sand40-60 7.3 ± 0.4 9.0 ± 1.6 10.3 ± 0.3 26.6 ± 3.6 46.8 ± 4.5 Sandy Loam

The chemical characteristics are presented in the Table A2 of the annexes, where pH is

slightly alkaline corresponding with Gardiner and Raymond (2004). With respect to natural

soil fertility, the soil has around 0.35 % organic matter, the phosphorus content (P2O5) 0.03 g

kg-1 and total N content 0.17 g kg-1 , according the soil analysis reported by Manu et al. (1991)

for semiarid conditions and sandy soil of Africa, these values are very low. Based on this

information, the conclusion is that the natural soil fertility is low. Previous investigation such

as, Fleming and Galwey (1995), cited by Bosque et al. (2003) and Geerts et al. (2008b) also

reported this result.

3.1.1.2 Soil samples for determining the gravel content

The soil physical analysis done by IBTEN is presented in Table 3.2, in which the gravel

content of the soil of Irpani varies from 13.2 to 29.1 mass% according to the depth. The

gravel content increases with the depth, given that between 40 cm and 60 cm the value is


27

around 30 %. On the other hand, the textural class of the soil in each soil layer is sandy loam,

which was also found in the analysis done by INRA (Table 3.1).

Table 3.2. Textural class and gravel content in Irpani (mean ± 1 standard deviation).

Depth Clay Silt Sand Gravel Textural(cm) (%) (%) (%) (%) Class

0-20 16.3 ± 1.2 10.0 ± 4.6 73.7 ± 4.9 13.2 ± 1.3 Sandy loam20-40 17.3 ± 1.5 9.0 ± 6.6 73.7 ± 7.4 20.6 ± 15.5 Sandy loam40-60 17.0 ± 2.0 3.3 ± 2.5 79.7 ± 4.5 29.1 ± 6.6 Sandy loam

The position of gravel (rock fragment) in the soil has large influence on the soil water content,

because it can affect the internal drainage, water retention and evaporation at the soil (Pérez,

1998). According to Kemper et al. (1994), after Cousin et al. (2003); Xiao-Yan (2002), rock

fragments applied as mulch on the soil reduced the water evaporation and runoff. On the other

hand, Cousin et al. (2003) state that if there is high evaporation, rock fragments instead of

reducing the evaporation increase it, because of their high calorific feature.

3.1.2 Laboratory results for soil water retention curve (WRC) determination

3.1.2.1 Volume, Bulk density and Porosity of volcanic rocks

Table 3.3 shows the results of bulk density and porosity corresponding to four size classes of

volcanic rock fragments. The bulk density of individual volcanic rock fragments ranges from

0.88 g cm-3 to 2.03 g cm-3 . Rust et al. (1999) found that the bulk density varied between 0.54

and 2.90 g cm-3 for many types of volcanic rocks. The same researcher reported for volcanic

rocks of Mauna Ulu (Hawaii), values from 1.27 to 1.49 g cm-3 that are close to the values

found.

The porosity values found in this study are low in comparison to the values reported by Rust

et al. (1999) with ranges between 0.50 and 0.59, but, these values were found with a more

precise method to measure the porosity (helium pycnometer).


28

Table 3.3. Bulk density and porosity corresponding to each class of volcanic rock

fragment (mean ± 1 standard error, N=10).

Classes Bulk density (g cm-3) Porosity (-)Class 1 (2.0 - 3.15 mm) 1.50 ± 0.10 0.44 ± 0.04

Class 2 (3.15- 4.5 mm) 1.76 ± 0.16 0.34 ± 0.06

Class 3 (4.5- 8.0 mm) 1.82 ± 0.10 0.31 ± 0.04

Class 4 (> 8.0 mm) 1.58 ± 0.35 0.40 ± 0.13

There is also a difference in bulk density among classes, where the smallest diameters of

volcanic rock (class 1) present less value of bulk density and high porosity. This difference

may be explained by the weathering stage according to Childs and Flint (1990) the smaller the

rock fragments, the more weathered. Furthermore, Descheemaeker (2006) states that smallest

rock fragments underwent intense weathering processes and as result increment of porosity.

But a thing that does not agree with this theory is the fact that the volcanic rock fragments

greater than 8 mm of diameter (class 4) have in average high porosity as the smaller volcanic

rock fragment group (class 1).

3.1.2.2 WRC of different soil –VRF mixtures

3.1.2.2.1 Soil particle size of the fine earth (mixed samples)

The particle size analysis carried out with the Beckman Coulter LS 13 320 laser diffraction

particle size analyzer is presented in Table 3.4. The soil collected from Mol has a textural

class of sand. While the soil collected from Leuven, Bruges and Sibelite® M002 of Sibelco

have as textural class which is silty loam, but with different percentages of sand, silt and clay.

Table 3.4. Particle size analysis (texture) for the soil utilised in each experiment, N

(repetitions). Mean ± 1 standard deviation.

Particle size(FAO)

Mol(N=2)

Leuven(N=2)

Bruges(N=2)

Sibelite® M002(N=1)

Sand (%) 99.5 ± 0.04 22.2 ± 0.52 25.1 ± 0.54 43.2Silt (%) 0.2 ± 0.03 69 ± 0.52 60.4 ± 1.30 51.4Clay (%) 0.3 ± 0.01 8.8 ± 0.02 14.5 ± 0.80 5.5

Textural class Sand Silty Loam Silty Loam Silty Loam


29

3.1.2.2.2 Experiment 1, WRC for around 15 vol% of each class of VRF mixed with soil

(Sibelite® M002)

The WRC for experiment 1 are presented in the Figure 3.1, in which, one can observe the

effect of volcanic rocks on the water holding capacity of the soil. Please note that the value of

pF 2.0 may be loss precise due to a failure of the sand box at this suction, the difference in θ

content between soil–VRF mixture and pure soil are greater, mainly at 0.0, 0.4, 1.0, 1.5, 1.8,

and 2.0 pF pressure suction points. These differences could be caused by the increment of

drainage, when VRF are present in the soil as is mentioned by van Wesemael et al. (1995)

who states that the reduction of θ could be explained by the increment in drainage and limited

retention capacity of moisture in stony soils because of macropores.(a) (b)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Wat er cont ent (m3m -3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Wat er content (m 3m -3)

pF

(c) (d)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water content (m 3m-3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water content (m 3m -3)

pF


30

(e)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water content (m 3m-3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50Wat er content (m 3 m-3)

pF

Figure 3.1. Water retention curve: (■a) 17 vol% of VRF of class 1 with soil, (∆b) 14

vol% of VRF of class 2 with soil, (×c) 14 vol% of VRF of class 3 with soil, (◊d) 17 vol%

of VRF class 4 with soil, (□e ) pure soil (Sibelite® M002). Error bars are ± 1 standard

deviation.

In order to compare the θSAT, θFC and θPWP among pure soil versus soil-VRF mixed samples of

each size class of VRF, ANOVA and Duncan multiple comparison test was carried out to

compare means, the results are shown in the Table 3.5.

According to the statistical test, there are differences in θSAT among pure soil and mixed

samples. Probably, these differences are because the VRF have less porosity (Table 3.3) than


31

the fine earth. The porosity value (0.54) of fine earth was calculated based on bulk density of

1.23 g cm-3 and assuming a particle density of 2.65 g cm-3 (Raes, 2004). There is no statistical

difference in θFC, this result agrees with the pedotransfer function developed by Saxton and

Rawls (2006), where the rock fragments do not appear to affect the θFC. On the other hand

there are differences in θPWP, in which for soil-VRF mixtures θPWP in general are slightly

greater than pure soil. Perhaps, this difference is because of the VRF effect (more very small

pores). In this respect, Duteau (1987), after Cuniglio et al. (2008) indicates that porous rock

fragments can attract water during drought periods, because of their micropores.

Table 3.5. Comparison of water content at saturation (θSAT), field capacity (θFC) and

permanent wilting point (θPWP) between different soil-VRF mixtures and pure soil

(Sibelite® M002). The letters show significant statistical groups (α=0.05), m (mean) se

(standard deviation) and N (repetitions).

θSAT (m3 m-3) θFC (m3 m-3) θPWP (m3 m-3)Mixtures and pure soilN m ± se N m ± se N m ± se

Pure soil 5 0.49 ± 0.01 a 5 0.31 ± 0.02 a 3 0.02± 0.00 b

soil + 17 vol% VRF class 4 7 0.46 ± 0.05 ba 7 0.33 ± 0.05 a 3 0.03± 0.01 ab

soil + 14 vol% VRF class 3 10 0.45 ± 0.01 b 8 0.32 ± 0.01 a 5 0.03± 0.00 asoil + 14 vol% VRF class 2 10 0.44 ± 0.01 b 6 0.32 ± 0.01 a 5 0.03± 0.00 ab

soil + 17 vol% VRF class 1 10 0.44 ± 0.01 b 7 0.32 ± 0.01 a 5 0.03± 0.00 a

And further, Brouwer and Anderson (2000) for the case of ironstones gravel found that θPWP

was still high in soil-rock fragment mixtures, due to high water content in the ironstone

gravel. Katsura et al. (2006) indicates that θ reduces more with increasing of pressure suction

head for pure soil than for rock.

3.1.2.2.3 Experiment 2, WRC for around 30 vol% of each class of VRF mixed with soil

(Sibelite®M002)

Figure 3.2 presents the WRC for experiment 2, where the soil-VRF mixtures of each class

show less θ between 0.0 and 2.8 pF pressure suction points in comparison with pure soil.


32

(a) (b)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water cont ent (m 3m-3 )

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water content (m 3m-3 )

pF

(c) (d)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water content (m 3m -3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Wat er content (m 3m-3 )

pF

(e)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water content (m3m-3)

pF


33

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Water con tent (m 3m -3)

pF

Figure 3.2. Water retention curve: (■a) 29 vol% of VRF of class 1 with soil , (∆b) 27

vol% of VRF of class 2 with soil, (×c) 30 vol% of VRF of class 3 with soil, (◊d) 25 vol%

of VRF of class 4 with soil, and (□e) pure soil (Sibelite® M002). Error bars are ± 1

standard deviation.

This difference could be explained, on the one hand by the fact that the VRF have less pores

than pure soil, as a consequence less water content, Katsura et al. (2006) found that the rock

(granite) had a smaller value (0.3 m3 m-3) of θSAT than the pure soil (0.5 m3 m-3), and the rock

fragment occupies the volume of soil, which is able to retain water (Baetens, 2007). On the

other hand, Fiès et al. (2002) and van Wesemael et al. (2003) reported that the water holding

capacity is affected by the texture and the increment of rock fragment content because of

increment of macropores.

Table 3.6 shows the result of Duncan multiple comparison on θSAT, θFC and θPWP, in which, it

can be seen that θSAT is greater for pure soil than the soil-VRF mixtures, as happened in the

experiment 1. θSAT of mixtures for experiment 2 as compared to experiment 1 shows lower

values, probably, this is because increment of the VRF as reported by Fiès et al. (2002) and

van Wesemael et al. (2003). The fact that the loss in water holding capacity is proportionally

higher for 30 vol% mixtures might be due to existence of macropores at the contact surface

between two volcanic rock fragments (drainage).


34

Table 3.6. Comparison of volumetric water content at saturation (θSAT), field capacity

(θFC) and permanent wilting point (θPWP) between different soil–VRF mixtures and pure

soil (Sibelite® M002). The letters show significant statistical groups (α= 0.05), m (mean),

se (standard deviation) and N (repetitions).

θSAT (m3 m-3) θFC (m3 m-3) θPWP (m3 m-3)Mixtures and pure soilN m ± se N m ± se N m ± se

Pure soil 7 0.50 ± 0.01 a 7 0.33 ± 0.02 a 3 0.03 ± 0.00 c

soil + 30 vol% VRF class 2 5 0.44 ± 0.05 b 5 0.31 ± 0.05 b 3 0.05 ± 0.00 asoil + 29 vol% VRF class 3 5 0.45 ± 0.01 b 5 0.31 ± 0.01 b 3 0.04 ± 0.00 b

soil + 31 vol% VRF class 1 5 0.43 ± 0.01 c 5 0.30 ± 0.01 b 3 0.05 ± 0.00 a

soil + 25 vol% VRF class 4 5 0.43 ± 0.01 c 5 0.31 ± 0.01 b 3 0.04 ± 0.00 b

According to the statistical analysis, θFC is slightly greater in pure soil as compared with all

classes of soil-VRF mixtures. When the mixed samples were composed by around 15 vol% of

VRF there were no statistical differences (Table 3.6). On this issue van Wesemael et al.

(1995); Petersen et al. (1968), cited by Baetens (2007) reported that at FC the fine earth water

content decreases with increment of rock fragment content. Recently Özhan et al. (2008)

reported a reduction of θFC because of the increment of volcanic rock from 25 to 50 mass% in

loamy soil mixtures.

In relation to θPWP among pure soil and soil-VRF mixtures present differences, in which the

soil containing VRF of each size class contain more water at PWP than pure soil. This result

was also found by Coile (1953); Brouwer and Anderson (2000) and Özhan et al. (2008) who

reported that soil-rock fragment mixtures can hold more water because of the water content in

the rocks. It is also observed that, while the VRF content increased from around 15 to 30

vol% the water content in the soil-VRF mixtures also increased. Özhan et al. (2008) found

that the θPWP increased as the rock fragment increased in soil-rock fragment mixtures, from 25

to 50 mass% of volcanic rock fragment (pumice), but only when the mixtures were for loamy

sand and sandy loam textural soil classes.

Summarising, soil–VRF mixtures in general have less θ at low suction, because of VRF

presence, which allows the formation of macrospores when 2 or more VRF are in contact

each other, this is schematized in Figure 3.3.


35

Figure 3.3. Schematisation of macroporosity formation due to presence of VRF inside of

the kopecky ring, for soil-VRF mixtures containing around 15 and 30 vol%, grey colour

represents the fine earth and black colour represents the VRF.

The macropores seem to increase when the VRF content increases from 15 to 30 vol%, it can

be seen when θ reduced for soil–VRF mixtures (30 vol% of VRF) at FC as shown in the

Figure 3.3 and Figure 3.4. In addition the roughness of VRF surface could have facilitated the

increment of macropores. The presence of macropores probably is directly related with

preferential flow, hence increment of drainage.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50Wat er content (m 3m-3)

pF

Figure 3.4. Water retention curve for soil-VRF mixtures composed by around 15 vol%

(▲) and around 30 vol% of VRF (◊) versus pure soil (□), (soil: Sibelite® M002).

The fact that at high suctions the soil-VRF mixtures have proportionally more water could be

explained by the greater content of micropores by the VRF in relation to the pure soil.

15% VRF 30% VRF

Macro-pores


36

3.1.2.2.4 Experiment 3, WRC for around 15 vol% of each class of VRF mixed with silty loam

and sandy soils

The result of the statistical analysis for experiment 3 is summarized in Table 3.7. The results

of the WRC are presented in the following order: first for SL1-VRF mixtures, in which SL1 is

silty loam soil texture composed of 22.2 % sand, 69.0 % silt and 8.8 % clay, secondly for

SL2-VRF mixtures in which SL2 is silty loam soil texture composed of 25.2 % sand 60.0 %

silt and 14.5 % clay, and afterwards for S-VRF mixtures in which S is a sandy soil.

Table 3.7. Comparison of volumetric water content at saturation (θSAT), field capacity

(θFC) and permanent wilting point (θPWP) between soil-VRF mixtures and pure soils.

Different letter means statistical differences (α=0.05), N (repetitions). (Mean ± 1

standard deviation).

Soil textural classes andsoil-VRF mixtures

VRF(vol%) N θSAT

(m³ m-³)N θFC

(m³ m-³)N θPWP

(m³ m-³)Pure SL1 0 4 0.56 ± 0.01 a 4 0.31 ± 0.00 a 4 0.11 ± 0.01 aSL1-VRF mixture (class 1) 16 4 0.50 ± 0.01 b 4 0.29 ± 0.01 b 3 0.12 ± 0.01 aSL1-VRF mixture (class 2) 15 4 0.50 ± 0.01 b 4 0.29 ± 0.01 b 3 0.12 ± 0.01 aSL1-VRF mixture (class 3) 15 4 0.50 ± 0.01 b 4 0.29 ± 0.00 b 3 0.11 ± 0.01 aSL1-VRF mixture (class 4) 16 4 0.49 ± 0.01 b 4 0.29 ± 0.01 b 3 0.12 ± 0.00 aPure SL2 0 4 0.48 ± 0.01 a 4 0.44 ± 0.01 a 5 0.16 ± 0.00 bSL2- VRF mixture (class 1) 16 4 0.43 ± 0.01 b 4 0.40 ± 0.01 b 3 0.17 ± 0.00 bSL2-VRF mixture (class 2) 16 4 0.43 ± 0.01 b 4 0.40 ± 0.01 b 3 0.16 ± 0.00 bSL2-VRF mixture (class 3) 17 4 0.43 ± 0.01 b 4 0.40 ± 0.01 b 3 0.17 ± 0.00 bSL2-VRF mixture (class 4) 16 4 0.44 ± 0.02 b 4 0.41 ± 0.02 b 3 0.19 ± 0.03 a

Pure S 0 4 0.38 ± 0.00 a 4 0.07 ± 0.00 b 5 0.006 ± 0.00 cS-VRF mixture (class 1) 15 4 0.37 ± 0.00 a b 4 0.09 ± 0.01 a b 3 0.029 ± 0.00 bS-VRF mixture (class 2) 15 4 0.36 ± 0.00 b 4 0.10 ± 0.01 a 3 0.029 ± 0.00 bS-VRF mixture (class 3) 15 4 0.36 ± 0.02 b 4 0.09 ± 0.02 a b 3 0.030 ± 0.00 bS-VRF mixture (class 4) 16 4 0.38 ± 0.02 a b 4 0.10 ± 0.03 a 3 0.041 ± 0.00 a

SL1: silty loam with 22.2 % sand, 69.0 % silt and 8.8 % clay, SL2: silty loam with 25.2 % sand, 60 % silt and14.5 % clay and S: sandy soil with 99.5 % sand, 0.2 % silt and 0.3 % clay.

SL1- VRF mixtures

Figure 3.5 shows that the θSAT and θFC of pure SL1 are greater as compared to SL1-VRF

mixtures for each size class of VRF. This result is also corroborated by the statistical analysis


37

(Table 3.7). On the other hand the values of θSAT, θFC and θPWP found in this experiment for

pure SL1 are in the range of values reported by Raes (2004).

a) b)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50 0.60Wat er content (m³m-³)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50 0.60Wat er content (m³m-³)

pF

c) d)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50 0.60Wat er content (m³m-³)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.2 0 0.30 0.40 0.50 0.60Wat er content (m³m-³)

pF

e)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50 0.60Wat er content (m³m-³)

pF


38

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40 0.50 0.60Wat er content (m³m-³)

pF

Figure 3.5. Water retention curve: (■a) 16 vol% of VRF of class 1 with SL1, (∆b) 15

vol% of VRF of class 2 with SL1, (×c) 15 vol% of VRF of class 3 with SL1, (◊d) 16 vol%

of VRF class 4 with SL1 and (□e) pure SL1. Error bars represent ± one standard

deviation.

For the case of θPWP, no significant differences were found between pure SL1 and SL1-VRF

mixtures. This result agrees with the pedotransfer function proposed by Saxton and Rawls

(2006), in which the VRF did not affect the θPWP. This is probably because the amount of

micropores is almost the same in both, given that the SL1 soil is composed by 69.0 % silt and

8.8 % clay. The micropores volume is related with the pore volume of the clay matrix in the

soil (Boivin et al., 2004). The content of silt, but mainly clay can hold more water due to its

high specific surface (Jury and Horton, 2004). In addition the clay–silt phase also forms

micropores as described by Fiès and Bruand (1998) and Boivin et al. (2004). And for the case

of VRF was also found that they have high content of micropores in experiments 1 and 2.

There are no statistical differences of θ among SL1-VRF mixtures; this means that the size of

VRF chosen to develop this experiment has the same effect on θSAT, θFC as well as θPWP

(Table 3.7). Probably, this is because the size of VRF inside of the mixture slightly affected

the bulk density of fine earth, as shown in Table 3.8, and consequently it slightly changed the


39

pore space. The values of ρb,rf, ρb,f and ρb,t given in Table 3.8, 3.9 and 3.10 were calculated

based on the Equations 2.1, 2.7 and 2.8 respectively (Section 2.3.3.1 and 2.3.3.2).

Table 3.8. Total bulk density (ρb,t), bulk density of VRF (ρb,rf) and bulk density of fine

earth (ρb,f ) of pure SL1 and SL1- VRF mixtures. (Mean ± 1 standard deviation, N=4).

Pure soil and mixturesVRF

(vol%)ρb,t

(g cm-³)ρb,f

(g cm-³)ρb,rf

(g cm-³)Pure SL1 0 1.24 ± 0.01 - -SL1- VRF mixture (class 1) 16 1.36 ± 0.01 1.29 ± 0.02 1.62 ± 0.00

SL1 -VRF mixture (class 2) 15 1.36 ± 0.02 1.28 ± 0.02 1.80 ± 0.00

SL1- VRF mixture (class 3) 15 1.34 ± 0.01 1.27 ± 0.01 1.81 ± 0.00SL1 -VRF mixture (class 4) 16 1.32 ± 0.01 1.26 ± 0.02 1.64 ± 0.05

SL2- VRF mixturesa) b)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.10 0.20 0.30 0.40 0.50Wat er content (m 3m-3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.10 0.20 0.30 0.40 0.50Wat er content (m3 m-3)

pF

c) d)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.10 0.20 0.30 0.40 0.50Wat er content (m 3m-3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.10 0.20 0.30 0.40 0.50Wat er content (m 3m-3)

pF


40

e)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.10 0.20 0.30 0.40 0.50Wat er content (m 3m -3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50Water content (m 3m-3)

pF

Figure 3.6. Water retention curve: (■a) 16 vol% of VRF of class 1 with SL2, (∆b) 16

vol% of VRF of class 2 with SL2, (×c) 17 vol% of VRF of class 3 with SL2, (◊d) 16 vol%

of VRF class 4 with SL2 and (□e) pure SL2. Error bars represent ± 1 standard deviation.

The Figure 3.6 shows that the SL2–RVF of each VRF class always has less water content than

pure SL2. This occurs from SAT until suction pF 2.8, which is also corroborated by the

statistical test (Table 3.7). If one compares these curve with those found for SL1-VRF

mixtures and pure SL1, the differences between them are less significant at suction pF points

of 2.0 and 2.8. Perhaps it occurred because the SL2-VRF mixtures had to be prepared with

moistened soil (pure SL2 with a high content of clay), using moistened soil appears to change


41

the ρb,f due to compaction of fine earth (Table 3.9). Indeed the ρb,f inside of mixtures changed

considerably for each SL2- VRF mixture, probably an effect of this is an increment of

macropores when two or more VRF were in contact each other, and these became bigger

while clay was undergone shrinkage as shown in Figure 3.7 (Fièst et al., 2002; Boivin et al.,

2004).

Table 3.9. Total bulk density (ρb,t), bulk density of VRF (ρb,rf ) and bulk density of fine

earth (ρb,f ) of pure SL2 and SL2- VRF mixtures. (Mean ± 1 standard deviation, N=4).

Pure soil and mixturesVRF

(vol%)ρb,t

(g cm-³)ρb,f

(g cm-³)ρb,rf

(g cm-³)Pure SL2 0 1.42 ± 0.02 - -SL2- VRF mixture (class 1) 16 1.62 ± 0.04 1.60 ± 0.04 1.74 ± 0.06

SL2 -VRF mixture (class 2) 16 1.55 ± 0.02 1.49 ± 0.03 1.83 ± 0.06

SL2- VRF mixture (class 3) 17 1.55 ± 0.02 1.51 ± 0.03 1.77 ± 0.09SL2 -VRF mixture (class 4) 16 1.46 ± 0.04 1.45 ± 0.03 1.51 ± 0.28

It was also observed that either for pure SL2 and SL2-VRF mixtures θFC remained high as in

clay soil. The possible reason is that SL2 were compacted, it can indeed be seen in the Table

3.9, in which the ρb,f reached high values. In addition the organic matter of these samples

appeared to be important, according to Saxton and Rawls (2006) organic matter generally

increases the water holding capacity of soil. The size of VRF did not affect the water holding

capacity of the SL2- VRF mixtures at SAT or in FC, but it did at PWP, in which the SL2- VRF

mixture composed by class 4 presented the highest values, probably it may be an effect of

high micropores content in this size class of stone.

Figure 3.7. Ternary mixtures (clay, silt and sand), (a) 10 % of clay and (b) 15 % of clay,

the black areas are the macropores and the grey areas represent ternary mixtures

(Source: Fiès and Bruand, 1998).


42

As happened during experiments 1 and 2 (Figure 3.3), the SL-VRF mixtures lost more water

at low suction, in comparison with pure SL soil, apparently because the increment of drainage

and preferential flow trough of those macropores. The formation of macropores in the SL-

VRF mixtures as shown in Figure 3.7, in part seems to be helped by the shrinkage of clay,

since clay is a shrinkage agent (Boivin et al., 2004). This could have occurred especially for

the case of SL2-VRF mixtures, since it contained 15 % of clay. In this respect Fiès et al.

(2002) stated that the macropores formation is due to filling or shrinkage, but shrinkage only

occurs when the clay content in the soil-rock fragment mixture is greater than 30 %. To study

this, he used as coarse material glass an inert material and without any porosity.

S-VRF mixtures

a) b)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40Wat er content (m³m-³)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40Wat er content (m³m-³)

pF

c) d)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40Water cont ent (m³m-³)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40Water content (m³m-³)

pF


43

e)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30 0.40Wat er content (m³m-³)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40Wat er content (m 3m -3)

pF

Figure 3.8. Water retention curve: (■a) 15 vol% of VRF of class 1 with S, (∆b) 15 vol%

of VRF of class 2 with S, (×c) 15 vol% of VRF class 3 with S, (◊d) 16 vol% of VRF class

4 with S and (□e) pure S (Sand). Error bars represent ± 1 standard deviation.

The WRC for S-VRF mixtures of each size class and pure S are given in Figure 3.8. The θSAT,

θFC and θPWP values for pure S reported in Table 3.7 are comparable to the values reported by

Ceballos et al. (2002); Saxton and Rawls (2006) and Heng et al. (2009). The θFC was very

low, possibly due the fact that this soil is composed by 99.5 % of sand.

According to the Table 3.7 and Figure 3.8, there are statistical differences of the θ between

pure S and S-VRF mixtures. θSAT changes only slightly between pure S and mixtures, but θFC


44

and θPWP of S-VRF mixtures were greater than pure S soil. As mentioned before the probable

reason is because the VRF have high mesopores and micropores content as compared with

sand, specially the VRF of class 4. This result was also reported by Özhan et al. (2008) for the

case of loamy sand soil mixed with 25 mass% of volcanic rock (pumice), in which the

mixture got 30 % more water content as compared with pure loamy sand at FC and PWP.

In contrast with the WRC of SL-VRF mixtures, the size of VRF affected the water holding

capacity of S-VRF mixtures, as shown in the statistical analysis (Table 3.7), in which the S-

VRF mixtures formed by VRF of class 1 and 4 have greater θSAT as compared with mixtures

formed by VRF of class 2 and 3. Possibly, the VRF of class 1 and 4 have more macropores

(Table 3.3). With regards to the θFC the difference between them is lower, probably it can be

explained by the fact that ρb,t did not changed a lot among them (Table 3.10).

With respect to θPWP the difference among them are small, but the S-VRF mixtures composed

by VRF of class 4 shows the greatest values, in this respect Coile (1953) found that the size of

stone affected its θPWP, in which soil igneous rock of 2-5 mm and 13-19 mm of size got 2.7

and 3.8 mass% of water respectively. The fact that VRF of class 4 contains more water is

possibly because they have more micropores than the other classes, given that θPWP was

reported as an indirect indicator of textural microporosity in soils (Armas-Espinel et al.,

2003), whereas the θSAT and θFC as indicators of macroporosity as well as mesoporosity

respectively (Luxmoore, 1981, after Armas-Espinel et al., 2003).

Table 3.10. Total bulk density (ρb,t), bulk density of VRF (ρb,rf ) and bulk density of fine

earth (ρb,f ) of pure sand (S) and S-VRF mixtures. (Mean ± 1 standard deviation, N=4).

Pure soil and mixtures VRF(vol%)

ρb,t

(g cm-³)ρb,f

(g cm-³)ρb,rf

(g cm-³)Pure S 0 1.67 ± 0.01 - -S-VRF mixture (class 1) 15 1.67 ± 0.01 1.68 ± 0.01 1.61 ± 0.00

S-VRF mixture (class 2) 15 1.69 ± 0.02 1.67 ± 0.03 1.80 ± 0.00

S-VRF mixture (class 3) 15 1.71 ± 0.02 1.69 ± 0.02 1.83 ± 0.00S-VRF mixture (class 4) 16 1.67 ± 0.05 1.67 ± 0.02 1.69 ± 0.29

From Table 3.10, it is also observed that rocks did not produce strong compaction on fine

earth in the mixed samples, or in some cases the bulk density of fine earth remained the same


45

as pure sand, in contrast to SL-VRF mixture, in which the local compaction provoked by VRF

(rocks) was more significant (Table 3.8 and Table 3.9).

3.1.3 WRC separation of volcanic rock and WRC of soil from mixed samples

The Figure 3.9 shows the WRC for volcanic rock fragments of class 1 up to class 4 (Table

2.2). It is observed that from saturation pF 0 until pF 2 it is possible to separate the WRC of

volcanic rock fragments from the total mixed samples, taking into account the bulk density of

VRF and its volume and the bulk density of the soil. But the water content between pF 2.3

and pF 3.4 show a strange behaviour, instead of reducing it increases. Possibly, the

assumption that the fine earth of the mixture and the VRF form two separate structures was

wrong for 2.3, 2.8 and 3.4 pressure suction pF points. Instead, a new composite structure

should be taken into account (Figure 3.9). And for the water content at pF 4.2 shows

reasonable values for all the VRF size classes.

From the Figure 3.9, it seems to be impossible to separate the water content of the volcanic

rock and soil from the total mixed samples especially at 2.3, 2.8 and 3.4 pF pressure suction

points by this method. Brouwer and Anderson, (2000) almost followed a similar

methodology, but they utilised a diatomaceous earth of great uniformity and known WRC.

According to Raes (pers. comm., 2009) the soil-VRF mixtures appears to form a new very

specific soil structure and therefore it is impossible to separate them. Furthermore, Brakensiek

and Rawls (1994), as cited in Miller and White (1998) states that the rock fragments change

the structure and the pore space amount in the soil. It was observed that the VRF inside of the

mixture changed the bulk density of the fine earth and therefore the pore space as presented in

Table 3A of annexes. Zimmerman and Bodvarsson (1995), for soil-rock fragment mixtures

proposed next equation for obtaining the volumetric water content:

rfvsvt RR )1( Eq.4.1

Where θt [mm-3] is the total water content of the soil-rock fragment mixture, Rv [m3m-3] the

volumetric rock fragment content, θs [m3 m-3] and θrf [m3 m-3] are the volumetric water


46

content in the soil without rock fragments and the water content of rock fragment,

repetitively. Based on equation 4.1 the θrf can be arranged as the next equation:

vvtrf RR /)1( Eq.4.2

We tried to calculate θrf for VRF based on the equation 4.2. And the problem was again with

2.3, 2.8 and 3.4 pF points pressure suctions, the WRC was as shown in the Figure 3.9.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.05 0.10 0.15 0.20 0.25Water content (m 3m-3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.05 0.10 0.15 0.20 0.25Water content (m3m -3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.10 0.20 0.30Water con tent (m 3m-3)

pF

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.00 0.05 0.10 0.15 0.20 0.25Wat er content (m 3m -3)

pF

Figure 3.9. Water retention curve of volcanic rock fragments class 1(■), class 2 (∆), class

3 (x) and class 4 (♦) separated from total mixed samples for experiment 1. Error bars

represent ± 1 standard deviation.


47

At field capacity all the macro-pores are emptied by the gravitational forces, remaining with

water in the mesopores and micropores, which are held by the capillary and adsorption forces

to the soil matrix (Raes, 2004). Zimmerman and Bodvarsson (1995) states that the equation

4.1 should be applied only if soil rock fragment are in capillary equilibrium with each other,

when that is not so, the case of dual porosity should be considered. Ma and Shao (2008) states

that stony soils, in fact, have 2 porous media one is the fine earth (continuous pore system)

and the rock fragment (discontinuous pore system) when the rock is enwrapped by fine earth,

the rock could hold water if it is not impermeable, but its hydraulic conductivity may be lower

than fine earth and act as sink or source of water in the soil-rock fragment mixture.

3.2 AquaCrop modelling

3.2.1 Sensitivity analysis of the model to different soil physical properties

3.2.1.1.1 Simulation for local sandy soil (from southern Bolivian Altiplano)

The grain yield simulated by the AquaCrop model, WUETa as well as WUEETa calculated are

presented in Table 3.11, in which the average grain yield 0.95 Mg ha-1 is quite realistic, given

that in the Bolivian Altiplano with low rainfall, marginal lands and without fertilization, the

average yield does not exceed 1 Mg ha-1 (Mujica et al., 2001).

Table 3.11. Simulated grain yield, water use efficiency for actual transpiration (WUETa)

and for actual evapotranspiration (WUEETa), under rainfed conditions of southern

Bolivian Altiplano (mean ± 1 standard deviation).

Years Grain yield(Mg ha-1)

WUETa(kg m-3)

WUEETa(kg m -3)

18 0.95 ± 0.81 0.53 ± 0.17 0.29 ± 0.13

The WUEETa ranges from 0.11 kg m-3 to 0.52 kg m-3 as shown in the Figure 3.10a, the lowest

values were found during the dry years, whereas the greatest values were found during wet

years. Geerts et al. (2008b) also reported a value of 0.50 kg m-3 for rainfed condition at the

same study area, during a wet year (385 mm) and with capillary rise (CR) contribution of 45


48

mm. The maximum value was 0.52 kg m -3 for 364 mm of rainfall, while it was only 0.45 kg

m-3 for 660 mm of rainfall. This corroborates the previously mentioned that the nutrient

reservoir depletes while the crop develops, and therefore the relation between yield and Ta

become non-linear, despite of high rainfall (Heng et al., 2007; Geerts, 2008; Raes et al.,

2009a).

From the Figure 3.10a it can also be seen that the WUEETa for the quinoa crop has a logistic

curve as reported by Geerts (2008) and Geerts and Raes (2009). While the WUETa seems to

have a linear behaviour (Figure 3.10b), WUETa is also called transpiration efficiency as can be

found in Zhang et al. (1998). WUETa ranges from 0.34 kg m-3 to 0.78 kg m-3 according to

seasonal rainfall during the crop development.

It was also observed that during wet years, for instance when the rainfall was 563 mm and

411 mm the WUETa were 0.65 kg m-3 and 0.78 kg m-3 respectively, which means more WUETa

with less rainfall. In this respect Zhang et al. (1998), found that rainfed plus irrigated wheat

had lower WUETa in comparison with rainfed wheat, concluding that the excess of water

decreased WUETa because of higher transpiration caused by greater leaf area and higher

stomatal conductance.

Y = -4E-08x3 + 5E-05x2 - 0.008x + 0.64R2 = 0.94

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 200 400 600ETa(mm)

Gra

iny

ield

(Mgh

a-1)

y = 0.007x - 0.22R2 = 0.97

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 100 200 300 400Ta(mm)

Gra

inyi

eld

(Mgh

a-1)

Figure 3.10. (a) Simulated grain yield versus actual evapotranspiration (ETa),

simulated data (■) and trend curve (—); (b) Simulated grain yield versus actual

transpiration (Ta), simulated data (♦) and trend line (—).

(a) (b)


49

3.2.1.1.2 Simulation for WRC of soil-VRF mixtures versus pure soil (Sibelite® M002)

Table 3.12 show that the KSAT value calculated by means of pedotransfer functions for the

pure soil (Sibelite® M002) was 670 mm day-1, that is almost similar to the value found by

(Tuts, unpublished data, 2009) 640 mm day-1, that was determined by means of laboratory

permeameter.

The KSAT for soil-VRF mixtures decreases with increment of rock content %vol (Rv) in

agricultural soil because decreases the amount of soil matrix in which water could be stored or

conducted (Miller and White, 1998; Saxton and Rawls, 2006; Verbist et al., 2009). On the

other hand Ma and Shao (2008) state that rock fragment content in the soil is an important

factor to obstruct infiltration because of reducing the cross-sectional area for water flow.

Table 3.12 also presents the water content at SAT, FC and PWP for experiments 1 and 2 used

to carry out the simulations.

Table 3.12. Saturated hydraulic conductivity (KSAT) calculated based on soil texture

(Sibelite® M002) and volume of VRF (vol%) by means of pedotransfer functions, and

soil physical properties from experiment 1 (E-1) and experiment 2 (E-2).

The result of simulations and statistical analysis for grain yield, WUETa and WUEETa with soil

data obtained from experiment 1 as well as experiment 2 are presented in Table 3.13.

According to the statistical analysis (pairwise comparison), there are no significant

Soil inputs VRF(vol%)

KSAT(mm day- 1)

θSAT(vol%)

θFC(vol%)

θPWP(vol%)

Pure soil 0 670 49.0 31.0 2.0Soil- VRF mixture (class 1) 17 530 44.0 32.0 3.0Soil -VRF mixture (class 2) 14 530 44.0 32.0 3.0

Soil- VRF mixture (class 3) 14 530 45.0 32.0 3.0E-1

Soil -VRF mixture (class 4) 17 530 46.0 33.0 3.0Pure soil 0 670 51.0 33.0 3.0

Soil- VRF mixture (class 1) 31 420 43.0 30.0 5.0Soil -VRF mixture (class 2) 30 420 44.0 32.0 5.0Soil- VRF mixture (class 3) 29 420 45.0 31.0 4.0

E-2

Soil -VRF mixture (class 4) 25 420 43.0 31.0 4.0


50

differences of grain yield for pure soil and soil-VRF mixtures composed by around 15 vol%

of each size class of VRF, but there are significant differences for WUETa and WUEETa. For

the soil inputs from experiment 2, there are significant differences of grain yield, WUETa and

WUEETa for pure soil as compared with the soil-VRF mixtures (Table 3.13).

Table 3.13. Simulated grain yield, water use efficiency for actual transpiration (WUETa)

and actual evapotranspiration (WUEETa) for soil data (Sibelite® M002) from experiment

1 (E-1) and 2 (E-2), different letter indicates statistical differences (α= 0.05). (Mean ± 1

standard deviation).

Soil inputsVRF

(vol%) YearsGrain yield(Mg ha -1)

WUETa

(kg m- 3)WUEETa

(kg m-3)Pure soil 0 18 1.38 ± 0.95 a 0.52 ± 0.17 a 0.361 ± 0.13 b

Soil-VRF mixture (class 1) 17 18 1.33 ± 0.88 a 0.52 ± 0.17 a 0.359 ± 0.13 cSoil-VRF mixture (class 2) 14 18 1.33 ± 0.88 a 0.52 ± 0.17 a 0.359 ± 0.13 dSoil-VRF mixture (class 3) 14 18 1.33 ± 0.88 a 0.52 ± 0.17 a 0.359 ± 0.13 e

E-1

Soil-VRF mixture (class 4) 17 18 1.37 ± 0.88 a 0.51 ± 0.17 b 0.364 ± 0.13 aPure soil 0 18 1.39 ± 0.87 d 0.52 ± 0.18 a 0.37 ± 0.12 a

Soil-VRF mixture (class 1) 31 18 1.22 ± 0.89 b 0.51 ± 0.18 a 0.34 ± 0.13 cSoil-VRF mixture (class 2) 30 18 1.30 ± 0.86 e 0.51 ± 0.18 a 0.35 ± 0.13 bSoil-VRF mixture (class 3) 29 18 1.29 ± 0.90 a 0.51 ± 0.18 b 0.35 ± 0.13 b

E-2

Soil-VRF mixture (class 4) 25 18 1.28 ± 0.88 c 0.52 ± 0.18 c 0.35 ± 0.13 b

From the grain yield, WUETa and WUEETa simulated based on the data for each soil-VRF

mixtures for experiment 1 and 2; the average was calculated in order to compare it with the

values found for pure soil. The results are presented in the Figure 3.11. Where, there are not

great differences in WUETa or in WUEETa between pure soil and soil-VRF mixtures, even

when the VRF increases from 15 to 30 vol%.

There is an inverse relation between the VRF content and simulated grain yield, for the type

of rock fragment found in Bolivia. In other words the increment of VRF in the soil reduces

the simulated grain yield, given that the soil contain around 15 vol% of VRF reduces in 3.2 %

the simulated grain yield in comparison to pure soil. When the soil has around 30 vol% of

VRF the simulated grain yield reduces 8.3 % in comparison with pure soil (Figure 3.11). Not

taking into account the changes in WRC the simulation leads to an overestimation of quinoa

yields.


51

The grain yield reduction of soil-VRF mixtures in comparison with pure soil perhaps could be

explained by the fact that VRF affect the water balance. Mainly TAW, that was greater for

pure soil than soil-VRF mixtures (Table 3.12). More run off (RO) was generated by soil-VRF

mixture especially when the VRF increased from around 15 to 30 vol%, hence less infiltrated

water in the soil profile and root zone were produced. The increment of deep percolation

(drainage out of the soil profile) could occur in wet years, because θSAT was lower than for

pure soil.

0.5

0.8

1.1

1.4

1.7

2.0

2.3

Pure soil Soil-VRFmixt ure(15 vol %)

Soil-VRFmixt ure(30 vol %)

Gra

inY

ield

(Mg

ha-1

)

0.3

0.4

0.5

0.6

Pure soil Soil-VRFmixture(15 vol %)

Soil-VRFmixt ure(30 vol %)

WU

E(k

gm

-3)

Figure 3.11. Comparison of (a) simulated grain yield, (b) water use efficiency for

transpiration (WUETa) (■) and for evapotranspiration (WUEETa), (□) for pure soil

versus soil-VRF mixtures. Error bars represent ± 1 standard deviation.

It was also observed that the simulated soil evaporation (E) increases slightly when the VRF

increases. In literature it was found that the rock fragment can reduce (Cousin, 2003) or

increase (Pérez, 2000) the soil evaporation, or it can have an ambivalent effect on the

evaporation rates, reducing it in wet conditions and increasing it in dry conditions (van

Wesemael et al., 1995). In addition they could have either positive as negative effect on the

plant development, for instance Kosmas et al. (1998) states that E is hampered and retarded

under rock fragments during the day, so that plants could adsorb it. But Adams (1967), cited

by Poesen and Lavee (1994) found that rock fragments applied as mulch for sorghum

produced a high temperatures close to the plant that could provoke problems in certain

physiological functions.

(a) (b)


52

The E was found to be slightly greater in soil containing VRF, as was mentioned before the

VRF could reduce or increase the E according to the climatic conditions along to the crop

cycle. AquaCrop posses inside its management field file the mulch application in order to

reduce the E, but for the case of VRF this seems not applicable because the mulch will

permanently reduce the E, which is not coinciding with the literature as mentioned above.

R2 = 0.94

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 100 200 300 400 500 600 700ET a(mm)

Gra

inyi

eld

(Mg

ha-

1 )

Figure 3.12. Simulated grain yield in function of actual evapotranspiration (ETa) for:

pure soil (X), soil-VRF mixture with 15 vol% (□), with 30 vol% of VRF, (■) and trend

curve, that is a third order polynomial as an approximation for logistic function (—).

The WUEETa function for pure soil, soil-VRF mixture with around 15 vol% and 30 vol% of

VRF is presented in Figure 3.12, in which the WUEETa function for quinoa has a logistic curve

as indicated by Geerts et al (2009b).

3.2.1.1.3 Simulations for WRC of sand-VRF mixtures and pure sand

Table 3.14 presents the soil inputs that were used in the simulation for sandy soil with and

without VRF. The KSAT for pure sand was calculated by means of pedotransfer function of

Saxton and Rawls (2006), this value almost agrees with the value of 6420 mm day-1 reported

by Ceballos et al. (2006) for sandy soils.


53

Table 3.14. Soil inputs used to carry out the simulation for quinoa production with and

without VRF in the sandy soil (S).

(1)TAW: ( FC - PWP )* 1000*Z, in which Z=1m.

The result of the simulations as well as the statistical analysis for grain yield, WUETa and

WUEETa are given in Table 3.15. The simulated grain yield ranges between 0.71 Mg ha-1 and

0.84 Mg ha-1, this result agrees with the normal grain yield for rainfed condition of Bolivian

Altiplano that was reported to be less than 1.0 Mg ha -1 (Risi, 2001).

Table 3.15. Comparison of simulated grain yield, water use efficiency for actual

transpiration (WUETa) and for actual evapotranspiration (WUEETa) for sand-VRF

mixtures versus pure sandy soil (S), different letters mean statistical differences

(α=0.05). (Mean ± 1 standard deviation).

Soil inputs VRF(vol%)

Years Grain yield(Mg ha -1)

WUETa

(kg m-3)WUEETa

(kg m- 3)Pure S 0 18 0.81± 0.90 b 0.37± 0.24 a 0.23± 0.18 a

S-VRF mixture (class 1) 15 18 0.76± 0.88 c 0.36± 0.25 b 0.21± 0.18 bS-VRF mixture (class 2) 15 18 0.84± 0.92 a 0.38± 0.25 a 0.23± 0.18 aS-VRF mixture (class 3) 15 18 0.74± 0.87 d 0.35± 0.24 c 0.21± 0.18 cS-VRF mixture (class 4) 16 18 0.71± 0.87 e 0.34± 0.24 d 0.20± 0.18 d

According to pairwise test comparison, the simulated grain yields for each sand-VRF

mixtures are different to the ones found for pure sand. With respect to WUEETa and WUETa for

pure sand as compared with S-VRF mixtures, there are differences with S-VRF mixtures

composed by VRF of class 1, 3 and 4. The WUEETa found are lower than the value reported by

Geerts et al. (2008b), which was 0.32 kg m-3 under rainfed conditions of Bolivian Altiplano

(107 mm) and for sandy soils. Most probably, this is because the laboratory soil was even

more sandy, having a lower TAW. The differences of yield among pure sand and sand-VRF

Soil inputsVRF

(vol%)KSAT

(mm day-1)θSAT

(vol%)θFC

(vol%)θPWP

(vol%)TAW (1)

(mm)Pure S 0 5979 38.0 7.0 0.06 64

S-VRF mixture (class 1) 15 4704 37.0 9.0 0.29 61S-VRF mixture (class 2) 15 4704 36.0 10.0 0.29 71S-VRF mixture (class 3) 15 4704 36.0 9.0 0.30 60S-VRF mixture (class 4) 16 4704 38.0 10.0 0.41 59


54

mixtures could be explained by the differences of TAW as shown in Table 3.14, in which

sandy soil containing 15 vol% of VRF generally presents lower TAW, because the VRF hold

more water at PWP, water that could be used by the plants, despite of having more water at

FC. It was also observed as an average of 18 years that the deep percolation and E were more

in sand-VRF mixtures than pure sand, but pure sand generated more RO.

In order to quantify the reduction of simulated grain yield, WUEETa and WUETa for sandy soil

containing 15 vol% of VRF in relation with pure S, the average from the all VRF classes was

calculated (18 years), the result is given in Figure 3.13, in which the reduction are 5.4 %, 7.0

% and 4.5 % for simulated grain yield, WUEETa and WUETa respectively, taken as baseline

the results of pure sand.

0.0

0.2

0.4

0.6

0.8

1.0

Sand Sand -VRF(15 vol %)

Gra

inyi

eld

(Mg

ha-1

)

0.0

0.1

0.2

0.3

0.4

0.5

Sand Sand -VRF(15 vol %)

WU

E(K

gm

-³)

Figure 3.13. Comparison of (a) simulated grain yield, (b) water use efficiency for actual

transpiration (WUETa) (■) and for actual evapotranspiration (WUEETa) (□), for pure

sand versus sand-VRF mixtures (15 vol% of VRF). Error bars represent ± 1 standard

deviation.

These results could be extrapolated to sandy soil from Bolivian Altiplano, based on the Table

3.2 the gravel content (VRF) was around 17 mass% between 0 and 40 cm of depth. However,

it was more abundant with 30 mass% at deeper layer. But, it still can be extrapolated, because

the root density is inversely related, which means that the root density is greater nearby to

surface. As a conclusion when the VRF is not considered, the model could overestimate the

grain yield by around 6 percent.

(a) (b)


55

3.2.1.1.4 Sensitivity analysis of the model to water content at field capacity and permanent

wilting for Sibelite®M002 soil

The soil inputs used to carry out this sensitive analysis are shown in the Table 3.16.

Table 3.16. Soil input model to asses the sensitivity analyses to water content at field

capacity (θFC) and permanent wilting point (θPWP).* indicates the values changed in

comparison with baseline.

Soil input θSAT(vol%)

θFC(vol%)

θPWP(vol%)

KSAT

(mm day-1)Maximum FC 50.0 37.0* 3.0 670Maximum PWP 50.0 33.0 5.0* 670Base-line 50.0 33.0 3.0 670Minimum FC 50.0 28.0* 3.0 670Minimum PWP 50.0 33.0 2.0* 670

Figure 3.14 presents the sensitivity analysis of the model to changes in θFC, according to the

soil inputs presented in Table 3.16. The change of θFC from 33.0 vol% to 37.0 vol% caused an

increment of 9 % for the simulated grain yield and 6 % for WUEETa. While the change from

33.0 vol% to 28.0 vol% produced a reduction of 8 % and 5 % for the simulated grain yield

and WUEETa respectively.

0.0

0.5

1.0

1.5

2.0

2.5

Max FC Baseline Min FC

Gra

inyi

eld

(Mg

ha-1

)

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Max FC Baseline Min FC

WU

EET

a(K

gha

-1)

Figure 3.14. Sensitivity analysis, (a) effect of changing water content at field capacity on

the simulated grain yield and (b) on water use efficiency for actual evapotranspiration

(WUEETa). Error bars represent ± 1 standard deviation.

(a) (b)


56

Figure 3.15 shows the effect of changing water content at PWP on the grain yield and

WUEETa, it can be observed that the grain yield and WUEETa presented a reduction of 4 % and

3 % respectively, when the water content at PWP was changed from 3.0 vol% to 5.0 vol%.

On the other hand, when changes from 3.0 vol% to 2.0 vol% were done, both the grain yield

and WUEETa increased to 2 %.

0.20

0.50

0.80

1.10

1.40

1.70

2.00

2.30

Max PWP Baseline Min PWP

Gra

inyi

eld

(Mg

ha-1

)

0.20

0.24

0.28

0.32

0.36

0.40

0.44

0.48

Max PWP Baseline Min PWP

WU

EET

a(Kg

ha-1

)

Figure 3.15. Sensitivity analysis, (a) effect of changing in water content at permanent

wilting point on the simulated grain yield and (b) on the water use efficiency for actual

evapotranspiration (WUEETa). Error bars represent ± 1 standard deviation.

The water content at FC and PWP are very sensitive parameters, which agrees with the results

found by Geerts (2008); Geerts et al. (2009b) who stated that the water content at FC and

PWP are among most sensitive parameters of AquaCrop model. The values of θFC (28.0

vol%) and θPWP (5.0 vol%) correspond to soil-VRF mixtures composed by around 30 vol% of

VRF. According to the sensitivity analysis the simulation based on these values presented a

reduction on the grain yield and WUEETa in comparison with pure soil. Therefore when the

VRF content in the soil is around 30 vol%, it should be considered if not the grain yield could

be overestimated in approximately 10 %, if the soil is silty loam.

3.2.1.1.5 Sensitivity analysis of the model to water content from laboratory data versus

data of pedotransfer functions

According to Table 3.17, the values of θSAT, θFC, θPWP that were derived by means of

pedotransfer function (Saxton and Rawls, 2006) do not change with the increment of VRF

(a) (b)


57

content to 15 vol%. This is because they consider gravel (VRF) as inert material, which does

not hold water by matric potential (Saxton and Rawls, 2006). The pedotransfer functions only

change the saturated hydraulic conductivity in response to VRF.

The laboratory data shows that θSAT and θFC of SL1-VRF mixtures are lower in comparison

with pure SL1.This is most probably because of the formation of macropores when VRF are in

contact each other, as reported by Fiès et al. (2002), on the other hand the total bulk density

ρb,t (fine earth plus VRF) was found to increase while the VRF increases. With respect to the

θPWP is slightly greater as compared with pure SL1, probably because the VRF have high

micropores content (Armas-Espinel et al., 2003), hence it can hold more water at PWP (Coile,

1953; Brouwer and Anderson, 2000; Özhan 2008).

The soil inputs used to carry out the simulation for silt loam with 15 vol% of VRF content are

presented in Table 3.17, the KSAT was assumed to be 780 mm day-1 for the laboratory inputs.

Table 3.17. Soil physical characteristics from laboratory and pedotransfer functions for

pure silty loam (SL1) and silty loam containing 15 vol% of volcanic rock fragments

(VRF).

Table 3.18 presents the simulated grain yield, in which there are statistical differences

according to the pairwise t test comparison. The average values of simulate grain yield for

data from pedotransfer functions are greater than the values from laboratory. This is mainly

because the differences in TAW, in which the pedotransfer function, predict more TAW than

laboratory data. Then, there can be variation of the harvest index (HI) produced by water

stress during dry years (Steduto et al., 2009; Geerts et al., 2009b).

Pedotransfer function Laboratory dataPhysicalproperties VRF=0 vol% VRF=15 vol% VRF=0 vol% VRF=15 vol%

θSAT (vol%) 52.4 52.4 55.8 49.6θFC (vol%) 29.4 29.4 31.0 29.2θPWP (vol%) 8.3 8.3 11.1 11.6ρb,t (g cm-3) 1.26 1.26 1.24 1.34KSAT (mm day-1) 984 780


58

Table 3.18. Comparison of simulated grain yield, for soil inputs from pedotransfer

functions versus laboratory, (mean ± 1 standard deviation). Different letters means

statistical differences (α=0.05; N=18).

Soil data VRF(vol%)

Average grain yield(Mg ha-1) Difference- (%)

Pedotransfer 15 1.11± 0.87 a

Laboratory 15 1.00± 0.80 b10.5

When the soil inputs are derived from pedotransfer functions, the simulated grain yield could

be overestimated by 10.5 %, taking as a base the inputs of laboratory.

CONCLUSIONS AND RECOMMENDATIONS

59

Chapter IV

4 Conclusions and Recommendations

Based on the objectives and results obtained in the chapter developed before, the conclusions

and recommendations are the followings:

4.1 Conclusions

The gravel content or volcanic rock fragment of the soil of Irpani ranged from 13.2 % to 29.1

mass% according to the depth, in which deeper soil layers have more gravel content. The

textural class was sandy loam with very low natural soil fertility.

The bulk density of volcanic rock fragment ranged from 0.88 g cm-3 to 2.03 g cm-3 and the

porosity from 0.31 to 0.44. Volcanic rock fragments of size class 1 (2-3.15 mm) and size class

4 (>8mm) presented the highest values.

Sibelite® M002 soil–volcanic rock fragment mixtures in general had less water at low suction,

because of the presence of the volcanic rocks, which allowed the formation of macrospores

when 2 or more volcanic rocks were in contact each other, that increased drainage. Probably

these macropores increased proportionally with volcanic rocks from 15 to 30 % vol. In

contrast at high suctions the soil-volcanic rock mixtures had proportionally more water, this

may be attributed to a greater content of micropores by the rocks in relation to the pure soil.

For silty loam soils the content of volcanic rock fragments (VRF) by 15 vol% affected the

water retention curve at low suction. For this soil the volumetric water content at saturation

and field capacity was affected by the formation of macropores, provoking lower water

content in silty loam-VRF mixtures as compared with pure silty loam soil.

For sandy soils the water retention curve was affected at field capacity and permanent wilting

point, in which sandy soil containing 15 vol% volcanic rock fragments held more water, due

to high content of mesopores and micropores by the rocks from southern Bolivian Altiplano.


60

For silty loam soils, the content of volcanic rock fragments can strongly increase the bulk

density of the mixed samples. And even locally increase the bulk density of the fine earth

close to the rock fragment due to local compaction. In contrast, for sandy soil the content of

rock fragments could only slightly change the bulk density of the mixture.

The size of volcanic rock fragment chosen to carry out the experiments did not affect the

water retention curve of mixed samples (silt loamy-VRF mixtures) at saturation or at field

capacity, but it did slightly at permanent wilting point. In general way, the size of volcanic rock

fragment chosen in these experiments did not strongly affect the water retention curve, but the

amount of volcanic rock fragment did.

For sand, the size of VRF affected the water holding capacity of sand-VRF mixtures

particularly at permanent wilting point, in which the VRF of class 4 (>8 mm) showed to hold

more water due to greater amount of micropores.

The crop water productivity model AquaCrop was then used to test its sensitivity to volcanic

rock fragment presence in the soil input file. The grain yield simulated through the AquaCrop

model (0.95 Mg ha-1) was quite realistic for the Bolivian Altiplano condition (low rainfall,

marginal lands with very low soil fertility), since the average yield does not exceed 1 Mg ha-1.

Depending on the bulk density and volcanic rock fragment content vol% the soil water

balance and grain yield did not change much, for silty loam soil (Sibelite® M002) containing

around 15 vol% of volcanic rock fragment and under conditions of Bolivian Altiplano. But as

the content of volcanic rock fragment increased to around 30 vol%, the reduction of simulated

grain yield became significant (8.3 %). Therefore when the volcanic rock fragment content in

the soil is around 30 vol% overestimation on the simulated grain yield could occur.

The evaporation was found to be slightly greater in soil containing volcanic rock fragment

that could reduce or increase according to the climate conditions along to the crop cycle and

depending of the author that is cited. Therefore, to include a generic effect of stones on the

evaporation reduction in AquaCrop modelling is not possible.


61

The sensitivity analysis of AquaCrop to changes of volumetric water content at field capacity

and permanent wilting point on simulated grain yield, for silty loam soil (Sibelite® M002)

showed that maximum error in modelling by not taking into account the volcanic rock

fragment ranged from 9% to –3% on the yield estimation.

In the simulations carried out using sand-VRF mixtures. The effect of sandy soil containing

15 vol% of VRF on the simulated grain yield was a reduction by 5.4 % as compared with pure

sand. This was because mixtures had lower total available water, because VRF held more

water at permanent wilting point, that could had been used by the plants, despite of having

more water at field capacity.

The sandy soils from southern Bolivian Altiplano have as gravel content (VRF) around 17

mass% between 0 and 40 cm of depth, and 30 mass% between 40 and 60 cm. It can be

concluded based on the simulation for sandy soil-VRF mixtures that the grain yield could be

overestimated by around 6 %, if the stones on the water retention curve are not taken into

account in AquaCrop.

4.2 Recommendations

In pedotransfer functions, the bulk density of the rock fragments should be included into the

relation to change water content at saturation, field capacity and permanent wilting point more

correctly.

The volcanic rock fragment and the total bulk density (fine earth and rock fragments) appear

to affect the water content in soil-volcanic rock fragments mixtures. Therefore, multiple

regressions should be carried out, in order to investigate which of the soil physical

characteristics affect most highly the water retention curve in soil that contains porous rocks.

And in order to derive specific pedotransfer functions.

REFERENCES

62

5 References

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Bonifacio, A., 2001. Aspectos agrícolas y de mejoramiento de la quinua en Bolivia in quinua.In: Izquierdo, J., Mujica, A., Jacobsen, S.-E., Marathee, J. P., Morón, C. (Eds.). Memoriasde reunión técnica y taller de formulación de proyecto regional sobre producción ynutrición humana en base a cultivos andinos. Cultivos andinos. CD versión 1.0. FAO,Santiago, Chile.

Bosque, H., Lemeur, R., Van Damme, P., Jacobsen, S.-E., 2003. Ecophysiological analysis ofdrought and salinity of quinoa (Chenopodium quinoa Willd.). Food Reviews International19, 111-119.

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Ceballos A., Martínez-Fernández, J., Santos, F., Alonso, P., 2002. Soil-water behaviour ofsandy soils under semi-arid conditions in the Duero Basin (Spain). J. Arid Environ.51, 501-519.

Childs, S., Flint, A. L., 1990. Physical properties of forest soils containing rock fragments. In:Gessel, S.P., Weetman, G.F., Powers, R.F. (Eds.), Sustained productivity of forest soils.University of British Columbia, Vancouver.

Coile, T. S., 1953. Moisture content of small stone in soil. Soil Sci. 75, 203-208.

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ANNEXES

69

6 Annexes

Table A1. Rainfall and temperatures from Salinas de Garci Mendoza nearby Irpani,

between 1975 and 1985 (Source: SENHAMI).

Temp. Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Annual

Tmin -7.2 -6.0 -3.3 -1.5 0.7 2.8 3.8 3.1 2.9 -0.1 -5.7 -6.3 -1,7***

Tmax 13.4 16.1 15.7 19.5 15.3 19.7 20.7 20.3 16.1 15.9 14.8 13.1 7,8***

Tmean 3.6 5.4 6.6 9.3 8.0 11.0 12.1 11.5 9.3 8.0 4.9 3.8 16,7***

pp 0.0 3.4 6.8 2.9 5.0 33.8 111.9 99.6 61.7 4.2 1.8 0.9 331,99*

* Sum and *** Average

Table A2. Chemical soil properties of soils in Irpani.

Unit 0-20cm

20-40cm

40-60cm

Averagecm

C/N - 11.90 11.50 11.83 11.74OM g kg-1 3.60 3.18 3.66 3.48pH - 7.47 7.45 7.70 7.54

N total g kg-1 0.16 0.17 0.18 0.17CaCO3 total g kg-1 < 1 < 1 < 1 < 1P2O5 - Olsen g kg-1 0.03 0.03 0.03 0.03CEC -Metson cmol+kg-1 3.21 3.77 4.69 3.89

MgO g kg-1 0.10 0.12 0.16 0.13Mg g kg-1 0.06 0.07 0.10 0.08K2O g kg-1 0.22 0.20 0.25 0.22K g kg-1 0.18 0.17 0.21 0.19

Na2O g kg-1 0.04 0.04 0.03 0.04Na g kg-1 0.03 0.03 0.02 0.03

N of NO3 mg kg-1 5.42 3.60 2.32 3.78N of NH4 mg kg-1 0.57 0.63 1.23 0.81

EC mS cm-1 0.04 0.03 0.03 0.03CEC: Cation Exchange Capacity; OM: Organic Matter.EC: Electrical Conductivity (Salinity).

ANNEXES

70

Table A3.Total bulk density (ρb,t), bulk density of VRF (ρb,rf) and bulk density of fine earth

(ρb,f) of pure soil and soil - VRF mixture (Sibelite® M002).

Pure soil and mixtures VRF(vol%)

N ρb,t

(g cm-³)ρb,f

(g cm-³)ρb,rf

(g cm-³)Pure soil 0 5 1.23 ± 0.02 - -

Soil -VRF mixture (class 1) 17 10 1.36 ± 0.02 1.33 ± 0.03 1.50 ± 0.10



E1

Soil -VRF mixture (class 4) 17 7 1.30 ± 0.06 1.30 ± 0.04 1.54± 0.39

Pure soil 0 7 1.23 ± 0.00 - -




E2


E1: Experiment 1; E2: Experiment 2

Figure A1. Beckman Coulter LS 13 320 laser diffraction particle size.

ANNEXES

71

0

1

2

3

4

5

6

7

8

9

10

0.01 0.1 1 10 100 1000 10000Part icle diameter (um)

Dif

fere

ntia

lvol

ume

(%)

Figure A2. Differential volume (%) versus particle diameter (um) from Beckman

Coulter LS 13 320 laser diffraction particle size, Sibelite® M002 (◊), SL1 (▲), SL2 (X)

and Sand (□), soil used to carry out the WRC of pure soil and soil-VRF mixtures.

0

10

20

30

40

50

60

70

80

90

100

0.0 1 0.1 1 10 100 1000 10000Part icle diameter (um)

Cu

mm

ula

tive

volu

me

(%)

Figure A3. Cumulative volume (%) versus particle diameter (um) from Beckman

Coulter LS 13 320 laser diffraction particle size, Sibelite® M002 (◊), SL1 (▲), SL2 (X)

and Sand (□), soils used to carry out the WRC of pure soil and soil-VRF mixtures.

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Veizaga 2009 Soil Water Aquacrop

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