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Soil erosion modeling using LISEM in

agricultural fields at the foot of the

Sierras Chicas, Córdoba, Argentina

The effect of soil bunds on erosion

MSc thesis by Siebrand van der Hoeven

March 2016

Soil Physics and Land Management Group

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Soil erosion modeling using LISEM in agricultural fields at the

foot of the Sierras Chicas, Córdoba, Argentina – The effect of soil bunds on erosion

Master thesis Soil Physics and Land Management Group submitted in partial fulfilment of the degree of Master of Science in International Land and Water Management at

Wageningen University, the Netherlands

Study program: MSc International Land and Water Management

Student registration number: 900814-347-030

SLM 80324

Supervisors: WU Supervisor: Dr. ir. Michel Riksen; Dr. ir. Violette Geissen; Célia P.

Martins Bento Host supervisor: Dr. ing. Santiago Reyna

Examiner:

Prof. dr. Coen Ritsema

Date:

04/03/2016

Soil Physics and Land Management Group, Wageningen University

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Abstract The Limburg Soil Erosion Model (LISEM) was used to simulate the runoff and soil losses in a maize

field and a soybean field at the foot of the Sierras Chicas in Córdoba, Argentina. Four years ago, soil

bunds were constructed to reduce soil erosion and guide excess water to a channel. This modelling

exercise was performed in order to evaluate the effectiveness of soil bunds in terms of soil and water

loss reduction. The second objective was to evaluate the capability of LISEM to adequately model the

effect of the bunds by adjusting the digital elevation model. The digital elevation model was adjusted

to incorporate the higher elevation at the location of the soil bunds. During the fieldwork, soil,

vegetation and catchment parameters were gathered as input. Weirs were installed at key points to

calibrate and validate discharge and sediment losses. Small erosion plots were installed to verify the

small scale erosion rates. The amount of data for calibration was limited, mostly due to the low

number of rainfall events. The modelled soil losses were 15% lower in the situation with bunds,

excluding the channel. However, in the model, the bunds forced the accumulated runoff along the

bund to flow into the channel. This accumulation of runoff leads to high erosion rates in the channel.

One effect of modelling soil bunds, by adjusting the digital elevation model, was successfully imitated.

Namely, the model was capable of simulating the longer flow pathway, which increased infiltration

and is one effect of soil bunds. However, in LISEM the local depressions, which make the soil bunds

retain runoff and sediments, are removed. The second problem is that, in some locations, the model

forcibly breaches the bunds, which leads to a concentration of water flow. Both faults meant that the

modelled values of water and soil losses were higher than the measured values.

Key words: Limburg Soil Erosion Model; Soil bunds; Physically modeling soil conservation; Argentina

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Table of contents Abstract ................................................................................................................................................... 3

Table of contents ..................................................................................................................................... 4

1 Introduction ..................................................................................................................................... 5

1.1 Research questions.................................................................................................................. 6

2 History of soil erosion models ......................................................................................................... 7

3 Model selection ............................................................................................................................... 8

4 Conceptual framework of LISEM ..................................................................................................... 9

5 Materials and methods ................................................................................................................. 11

5.1 Study area .............................................................................................................................. 11

5.2 Fieldwork ............................................................................................................................... 17

5.2.1 Plot and weir installation ............................................................................................... 18

5.2.2 Water, sediment and organic matter losses quantification .......................................... 20

5.3 LISEM input data ................................................................................................................... 21

5.3.1 Input parameters ........................................................................................................... 21

5.3.2 Rainfall ........................................................................................................................... 26

5.4 LISEM map creation ............................................................................................................... 29

5.5 Calibration and validation ..................................................................................................... 31

6 Results ........................................................................................................................................... 33

6.1 Input parameters ................................................................................................................... 33

6.2 Initial run ............................................................................................................................... 35

6.3 Calibration ............................................................................................................................. 36

6.4 Validation .............................................................................................................................. 41

6.5 Research questions................................................................................................................ 43

7 Discussion ...................................................................................................................................... 49

7.1 Fieldwork performance ......................................................................................................... 49

7.2 Model performance .............................................................................................................. 51

8 Conclusions .................................................................................................................................... 52

9 Recommendations......................................................................................................................... 53

10 Bibliography ............................................................................................................................... 54

11 Appendices ................................................................................................................................ 58

11.1 Appendix A: Interview on the topic of the farm land management ..................................... 58

11.2 Appendix B: Simulated rainfall intensity data ....................................................................... 62

11.3 Appendix C: Results of input parameter measurements ...................................................... 63

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1 Introduction Erosion in agricultural fields is one of the primary problems for farmers since the start of agriculture.

Erosion through water, in particular in areas with intense rainfall events, causes major problems and

endangers future food production (Pimentel et al., 1987, Poesen et al., 1996). Since the green

revolution, occurring between the 1940s and 1960s, modern technologies have allowed for immense

increases in agricultural production worldwide (Godfray et al., 2010). These technologies include the

use of machines, agrochemicals and large scale mono cultures, which in turn affects the rate at which

water erosion occurs (Lal, 1998, Pimentel and Pimentel, 1990), with high potential costs for society

(Pimentel et al., 1995).

It has become possible to model the physical processes of soil erosion using the growing computing

power of the modern era. Nowadays, soil erosion models are an important tool in estimating and

predicting erosion behaviour (Merritt et al., 2003). As an extension, the impact of soil conservation

measures, which are vital in minimizing the impact of soil erosion (Morgan, 2009), can also be

modelled. However, nature is complex, while the use of models is limited by the possibility to acquire

the necessary input parameters. Vegetative soil conservation measures can be relatively simple to

simulate as they affect existing input parameters (Nearing et al., 2005). On the contrary, many

conservation measures also influence input parameters indirectly, which can be solved by applying a

corrective factor to simulate the impact of these measures (Hessel and Tenge, 2008). An aspect of

structural soil conservation measures that is generally corrected by a factor is artificial elevation,

such as terraces, ditches and bunds. These measures can be incorporated in the digital elevation

model (DEM) if the spatial scale is appropriately detailed; however, this is practically never the case

(Hessel and Tenge, 2008). The adjustment of the DEM to incorporate structural measures in erosion

models can have useful advantages. Corrective, empirically based, factors require testing for each

unique measure and environment, while physical models can be used more flexibly and are able to

represent spatial variation.

The first objective in this research was to perform a modelling exercise to identify the advantages

and disadvantages of adjusting the DEM in order to model the impact of bunds, a structural

conservation measure, on soil erosion. The model used is the physically based Limburg Soil Erosion

Model (LISEM), which simulates erosion during single rainfall events over a spatially distributed grid.

The LISEM is a well-documented model and the user is able to select the grid and catchment size (De

Roo et al., 1996a, De Roo et al., 1996b).

The study area was located 25km north of Córdoba city, Argentina, where soil bunds have recently

been constructed in order to reduce water erosion. Argentina is one of the countries that is leading

current agricultural expansion. Between 1961 and 2013, the area cultivated increased from 47.5

million hectares to 110.3 million hectares (FAOSTAT, 2015). As a result, deforestation also increased

(Grau et al., 2005). In the Chaco of Central Argentina, around 80% of land formerly covered by forests

is now cultivated (Zak et al., 2008). However, looking solely to erosion in agricultural fields, the

currently used conservation tillage practices have decreased erosion in many parts compared to the

conventional tillage when the agricultural expansion started (Viglizzo et al., 2011).

In Córdoba province, located in the centre of Argentina, the agricultural area has doubled in size, as

in 2008 agricultural land covered 14.4 million hectares (INDEC, 2009), 87% of the province, compared

to 7.2 million hectares in 1988 (INDEC, 1992). In the northern part of Córdoba, forest area decreased

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from 44% to 8% over thirty years, while cultivated land expanded to 746,000 hectares by 1999 from

an earlier 83,600 hectares (Zak et al., 2004). Most of this land has come under modern production,

which includes mechanization, use of agrochemicals, such as fertilizers and pesticides, and mono

cultures, in particular soybean. The trend of agricultural expansion was located in particular in the

fertile Pampa plains (Manuel-Navarrete et al., 2009). Research on soil quality and water erosion is

sparse and has only been done in other parts of Córdoba Province or the Pampa area (Irurtie and

Mon, 1996; Cantu et al., 2007; Campitelli et al., 2010), not in the vicinity of the study site.

The study area is part of a farm that constructed the soil bunds and channels in several fields to

reduce soil erosion and increase water infiltration. The farm manager has observed a decrease in soil

erosion after the construction, but has not quantified the actual reduction. Therefore, the second

objective is to evaluate the effectiveness of these bunds and channels by modelling the soil and

water losses in two fields with and without soil bunds and then comparing the results.

The two objectives are approached by applying LISEM to two catchments; a maize field of 103ha, and

a soybean field of 37ha. The fields have a maize-soybean crop rotation, and are located just north of

Córdoba city, at the foot of the Sierras Chicas. The soil is the fertile, albeit highly erodible, Pampa soil.

At the same time, the piedmont location means that slopes in the study area are up to 6%. Combined

with the highly intense rainfall events that occur at times, this creates a situation with a high

potential for water erosion. LISEM is used to model rainfall events and provides both spatial and

temporal data of water and soil losses. Calibration of the model is using data from small local weirs,

while local soil losses from small erosion plots are used to validate the map of soil detachment.

1.1 Research questions The first research question is:

What is the reduction of soil and water losses as a result of constructing soil bunds and

channels in maize and soybean fields in Córdoba, Argentina, according to fine spatial modeling?

The sub-questions are:

a) What are the water losses with and without soil bunds?

b) What are the soil losses with and without soil bunds?

The second research question is:

What are the advantages and disadvantages of adjusting the DEM to model the effect of

bunds on soil erosion?

The sub-questions, where the first sub-question is a continuation of the first main research question,

are:

a) Do the results at the catchment outlet reflect the impact of the soil bunds on soil and water

erosion?

b) What is the spatial distribution of the soil erosion rates?

c) Does the spatial distribution of the soil erosion rates reflect the impact of the soil bunds?

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2 Erosion model background In this chapter, first, the state of erosion modelling is described, which shows that the possibility

already exists to model elevation changes spatially. Then, the selection of the erosion model is

supported. Lastly, the conceptual framework of the selected model is briefly explained.

2.1 History of soil erosion models The importance of studying water erosion, especially in agriculture, has resulted in a wide array of

erosion models. This is logical as erosion increasingly takes place in agricultural fields, since

agricultural management generally leads to higher soil erodibility and erosion can lower crop

production (Pimentel et al., 1987). In addition, agricultural fields often have a homogeneity within

fields which reduces the necessary complexity (Renard et al., 1997). Based on empirical knowledge,

laboratory experiments and physical understandings, researchers have developed models to simulate

many erosion processes (Bull and Kirkby, 1997, Prosser and Rustomji, 2000, Merritt et al., 2003).

These advances have several advantages such as the better understanding of erosion processes and

their use as tools for decision and policy makers (Renschler and Harbor, 2002, Jakeman and Letcher,

2003, Gobin et al., 2004). One of the first and most notable models is the Universal Soil Loss Equation

(USLE) based on soil erosion data over decades and empirical equations that were developed in the

1940s and 50s (Wischmeier and Smith, 1978). As computer technology and computation became

more advanced, so did erosion models. This allowed for the development of models based on

physical equations, with higher spatial and temporal resolution (Merritt et al., 2003).

Models can be divided between empirical, conceptual and physically-based models (Wheater et al.,

1993). Lal (1994) gives a concise explanation of the advantages and disadvantages of different model

types. Empirical models are mainly based on observation and rely heavily on statistical analysis of

large datasets. They apply well to tested conditions, but require the setup of an entirely new

database for a previously untested situation. In contrast, physically-based models aim to represent

the underlying spatial and temporal processes of soil erosion using a set of key factors. For this

reason, physically-based models can be used by acquiring values for these key factors.

On the other hand, Merritt et al. (2003) discuss 17 models and find empirical models still have much

merit, stating that these models require relative simple inputs at the cost of representing catchment

heterogeneity. However, acquiring an accurate input dataset proves difficult because of this

heterogeneity. Merritt et al. (2003) are critical of physically-based models, stating that the physical

equations applied are often scaled up or down without justification and therefore lacking validity.

The model should always be calibrated and validated to assess the accuracy of its results. Thus, the

physically-based model is a closer representation of the reality. However, this comes at the cost of

higher requirements in terms of input parameters to reflect spatial heterogeneity, as well as a higher

sensitivity to input errors, which can accumulate quickly.

In conclusion, the state of erosion models has advance to a point at which it is possible to include

spatially detailed elevation differences, required to physically model soil bunds. In the next sub-

chapter a model was selected that incorporates the qualities necessary for this research.

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2.2 Model selection The model that was selected is the Limburg Soil Erosion Model (LISEM). LISEM is a distributed,

physically-based, model that simulates soil losses and runoff during single rainfall events. The model

was developed in 1996 for erosion modeling in Limburg, the south-eastern province of the

Netherlands (De Roo et al., 1996b, De Roo et al., 1996a). LISEM was selected because 1) the model is

well-documented, 2) uses a spatially detailed grid that creates a flow path based on the DEM, and 3)

uses single rainfall events, where the runoff can be viewed at selected time frames.

Documentation is important to understand the mechanics behind the model and to allow for easy

use. In addition, the model has been successfully validated in many environments. The spatial detail

is important to replicate the increased elevation at the location of the soil bunds. Lastly, the runoff

and erosion intensities can be viewed at any point during the modelled rainfall event. This means

that the spatial erosion patterns can be reviewed and compared to the expected patterns.

It is important to realise that grid size affects the accuracy of the results; a larger grid size ignores

some of the local heterogeneity, while a smaller grid size exacerbates existing errors. The same

applies to the size of time steps. Therefore, a quick review was done on the capabilities of LISEM to

handle varying grid and time step sizes. A study by Hessel (2002a) compared the scaling of grid size

and time step length in detail using LISEM. Some conclusions were that larger cell size means a lower

discharge at the outlet, due to lower average slopes and increasing dispersion of water in numerical

solution of the kinematic wave, and increased soil losses, which is paired with increased deposition,

leading to an overall lower sediment yield at the outlet. Increasing the time step length, with a grid

size of 10m, shows little influence on discharge up to 30 seconds, after which it starts to decrease

notably, and bigger steps have a large influence on soil loss as the erosion decreases greatly. The

later study by Hessel and Tenge (2008) ,that modelled the effect of soil and water conservation

measures, found that a catchment is too large to model with spatial resolution that is high enough to

model such fine features. Instead, they used a P-factor, an erosion-reducing factor used in, for

example, the USLE (Renard et al., 1997). In the case of this thesis, the area modelled is smaller, and

the bunds and ditches are broad.

It should be noted that, according to Jetten et al. (2003), the prediction of spatial distribution of

erosion has a low accuracy, which lowers the higher the spatial resolution becomes. The reason for

this is mostly due to large heterogeneities in the field, which is not completely captured by the

spatial data. Therefore, its usefulness is restricted by the availability of spatially and temporally

variable measurements of input data.

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2.3 Conceptual framework of LISEM LISEM has been well documented and, as a result, a clear manual for the use of the LISEM can be

found here: http://www.itc.nl/lisem/download/lisemmanualv2x.pdf.

LISEM is an event-based model, meaning that modeling is done per rainfall event. The model

incorporates rainfall data with data on soils, vegetation, land management and topography. In LISEM,

the user is responsible for measuring and putting in the right variables (Jetten, 2002). As a result, the

vegetation variables are not derived from assigning a crop to an area, but from providing the

vegetation variables individually. For example, in the model, the leaf area index and the plant height

can be given as two completely unrelated values, while in reality they are very much related.

The water flow in the model is based on the kinematic wave method, which determines the path of

the water. For each grid cell per time step, the water and sediment balance is calculated in eight

directions using the kinematic wave. The kinematic wave method is a variation on the Saint-Venant

equations of continuity and momentum. In the kinematic wave a steady, uniform flow is assumed in

which friction and gravity forces balance each other (Chow et al., 1988). In the LISEM, the kinematic

wave is combined with the Manning’s equation to solve the momentum equation (Jetten, 2002). The

kinematic wave method redistributes water to flow towards the outlet, the lowest point. This is

calculated using finite differences, which leads to numerical errors. The explicit method, which

calculates the next state in a cell based on the previous state, requires the time step length to be less

than or equal to the cell size divided by the kinematic wave celerity (Chow et al., 1988). If the time

steps are too large, instability, occurs which means the errors amplify. However, LISEM uses an

implicit method, which calculates the next state based on both the current and the later state. This

means that grid size and time step length do not cause instability, but mostly influence the accuracy

of the modeling results (Hessel, 2005).

A flow diagram from the LISEM manual schematically shows the parameters and processes that make

up the water and sediment balance (figure 1). Rainfall (P) falls in a grid cell on the vegetation and

partly is intercepted and partly falls through (Th), based on the cover and leaf-area index (LAI). The

water balance in the cell is then calculated based on throughfall, runoff from upslope and water

stored in local depressions. Local depression capacity (Ss) is based on the random roughness and

slope. The water can infiltrate (I) based on the surface type, which has its appropriate hydraulic

conductivity, soil depth and moisture content. Infiltrated water disappears in the subsoil. The

infiltration method that is used is the Green & Ampt model (Green and Ampt, 1911). The method is

based on the Darcy equation (Jetten, 2002) and describes the infiltration over time in the vertical

direction. The Green & Ampt model can contain two soil layers, however this is not necessary in this

study as the soil is uniform.

As the water from rainfall and upslope inflow fills up the local depressions, runoff starts to occur. The

amount of sediments suspended (e) is the detachment by splash (Ds), based on aggregate stability

and rainfall intensity, and the detachment by runoff (Df) minus the deposition of sediments (Dp).

Detachment or deposition is based on the cohesion of the soil, the median grain size, slope and

manning’s n, which affect the overland flow (Qi) and transport capacity. The kinematic wave routes

the water and sediment flow to downslope cells until they end up in a channel. In the channel, the

channel parameters affect the flow, detachment and deposition routing runoff and sediments

towards the catchment outlet.

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The outputs are a hydrograph and sediment graph for each outlet, and total erosion and

sedimentation maps at desired intervals.

Figure 1 Flow diagram of the LISEM model showing how parameters and processes interact (Jetten, 2002)

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3 Materials and methods In this chapter the study area is described, as well as the fieldwork done and the processing of the

data in order to run the LISEM. Finally the method of calibration and validation is explained.

3.1 Study area The study area is located in a large farm, located just north of Córdoba city, near the town of Rio

Ceballos. The farm, Santo Domingo, is 5500ha large, of which 3200ha are cultivated. The area is on

the border of the Pampas, the great fertile lowlands in the eastern part of Argentina, which stretches

to the southeast of Córdoba city. To the west of the study area the Sierras Chicas rise up, a mountain

range that stretches from north to south. The farm is located at the foot of this mountain range, in

the transitional Piedmont belt that lies between the mountains and the plain (figure 2).

Figure 2 The study area, located in the Córdoba province, just north of Córdoba city. On the left the location of the province in the northern half of Argentina, and on the right the study area (red indicating the mountains, yellow the piedmont and green the plains)

The mountains are reverse fault-bounded blocks with an origin in the Precambrian and Palaeozoic.

The blocks are metamorphosed Pampean sediments (Baldo et al., 1996, Rabassa et al., 1996). The

plains are a result of fluvial and aeolian processes, largely influenced by the rivers coming down from

the mountains. The Piedmont area is located around 500 m- a.s.l. and has gently rolling hills that

form the landscape. The soil is composed of fluvial sediments, which contain loess, silts and sands

(Carignano, 1999).

The climate has changed considerably over the past 200,000 years, alternating between arid and

temperate or subtropical (Iriondo and Kröhling, 1995, Carignano, 1999). Currently, the climate of

Córdoba ranges from temperate to sub humid. Most notable is that rainfall is concentrated in the

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warm summer months, December to March, while in the cold winter months, May to September,

almost no precipitation occurs (figure 3). Temperatures can drop to below zero during winter nights

and become as high as 38 degrees Celsius on summer days (SMN, 2015). The average annual rainfall

over the past 20 years is 846mm, ranging between 574 and 1327mm (table 1). In the past 10 years,

there were 21 rainfall events of more than 70mm in a day, 7 events of more than 100mm in a day,

with the biggest events being 149mm in March 2010 and 138mm, preceded by 52mm the previous

day, in October 1999 (SMN, 2015). Directly after the study period, on 15 February 2015, a rainfall

event of 180mm occurred (SMN, 2015).

Table 1 The average, minimum and maximum monthly rainfall (1995-2014) at Córdoba airport, Pajas Blancas, 20 km south of the study area (source: SMN (2015))

Average rainfall Minimum rainfall Maximum rainfall

January 118 47 200 February 129 19 270 March 135 31 293 April 64 3 169 May 18 0 51 June 5 0 18 July 7 0 31 August 5 0 31 September 34 1 107 October 82 10 256 November 113 0 281 December 135 0 356

Figure 3 Monthly average rainfall, and average minimum and maximum temperatures between 1994-2014 (source: SMN (2015))

The information about land and crop management was largely derived from the interview with the

farm manager (see Appendix A), and from observation in the fields and on the farm.

According to the farm manager, erosion has been a pressing problem for many years. During large

rainfall events, water has come down the Sierras Chicas and caused severe erosion in the agricultural

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fields. Water erosion was reduced by a switch to minimum tillage in the early 1990s, and since

1991/2 direct sowing and no tillage are used. This reduced both the use of machines and the soil

erosion in the field, while increasing soil moisture and organic matter content. Erosion was further

reduced by the introduction of soil conservation structures; dikes, grassed waterways and bunds. In

the fields without conservation signs of erosion can be seen (figure 4a), and in the case that bunds

cannot drain the water, bunds can be breached leading to gullies as well (figure 4b).

Figure 4 (a) A gully incised circa 30cm in a field without bunds and (b) signs of gully formation in a field with bunds

There are two fields that were studied, located closely to each other. One was cultivated with Zea

mays (maize) and the other with Glycine max (soybean). Each year, maize and soybean are rotated.

Thus the fields have two year maize-soybean crop rotation scheme, and residues from the previous

year are still present when the current crop is sown. In the soybean field there were still maize stubs

standing and maize litter covered around 45-60% of the soil. In the maize field 30-40% of the soil was

covered with some soybean residues. Since the soil bunds were constructed, sowing is done in the

direction of the slope, perpendicular to the soil bunds. Soybean was spaced with 20cm and rows

were 50cm apart. Maize was spaced 25cm by 50cm.

In both fields some forest areas are left untouched. Soybean was sown on 11 and 12 October and

would be harvested in March. Maize was sown between 28 November and 1 December, and grows

until harvest in July. On 2 February soybean had an average height of 102cm and maize of 226cm.

Both crops were sown using direct sowing, and at that same time nitrogen fertilizer was applied in

the maize field.

Besides the sowing machine (Agrometal IOM & Agrometal TX Mega), the only machines used are a

mosquito tractor (Metalfor Múltiple 2800), spraying pesticides, and a harvester (unkown). The

mosquito tractor enters the field around four times a year depending on the presence of weeds and

pests. There is a variety of weeds, such as Sorghum halepense, Conyza bonariensis, Chloris spp,

Senecio argentinus, and various grasses. Herbicides are sprayed in most cases, while fungicides and

insecticides are used to a lesser extent. In soybean, the insecticides are used more regularly than in

maize.

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The soil has an A horizon in the top 10 to 30cm, after which it gradually transits from an AC horizon

into the parent material, a C horizon (figure 5).

Figure 5 Soil profiles of the soil in the, wet, maize (left) and, dry, soybean (right) fields.

The topsoil has a texture that is evenly distributed between clay, silt and sand (table 2). The organic

matter in the topsoil indicates healthy microbiological activity. Based on a pF curve, the pore space

can hold 20mm/10cm of water available for plant intake.

Table 2 Texture and organic matter in the soil

Texture Clay (%) Silt (%) Sand (%) Organic matter (%)

Maize Topsoil (0-10cm) Clay Loam 27.4 40.0 32.6 4.6

Subsoil (40-50cm) Silt Loam 26.5 71.4 2 2.1

Soybean Topsoil (0-10cm) Loam 26.3 44.2 29.5 5.1

Subsoil (40-50cm) Silty Clay Loam 34.7 52.0 13.3 1.6

As mentioned before, nitrogen is applied when maize is sown, varying between 80 and 130kg/ha,

depending on the local fertility. Fertility is mapped based on yields per location in the previous year

using software in the harvesting machine that is linked to a Global Positioning System (GPS). This

information is used in the next year to locally apply the optimal nitrogen concentration. In general,

the soil contains abundant nutrients, with a good level of nitrogen but relatively low amount of

phosphorus (table 3).

Table 3 Overview of nitrogen (N), phosphorus (P) and potassium (K) available in the soil

Annual N production (kg/ha)

Available P (mg/kg)

Available K (mg/kg)

Maize Topsoil (0-10cm) 44 1.2 348

Subsoil (40-50cm) 10 1.9 193

Soybean Topsoil (0-10cm) 42 1.1 398

Subsoil (40-50cm) 13 < 0.2 99

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The forest areas have not changed and their cover has neither increased nor decreased, although the

plant composition in the periphery may have changed due to weeds invading the forest. Both fields

have soil bunds that end at a channel that drains excess runoff water. In addition, the maize field has

two dike structures perpendicular to the channel, one in the middle of the field and one at the end

(figure 6). A small retention basin is located in front of the second dike. On the north side there is a

road which acts as a barrier. The northeast boundary of the catchment is delimited by a bund that

runs from the road to the dike.

The maize field (92.6ha) is shaped in a U form, with the channel in the middle of the field. Before the

current bunds were made in 2010/11, older bunds, built in 1993, existed. The current bunds have a

height of 20 to 25cm, although along the bunds this height can vary, and the width is 3 to 5m. The

earlier bunds were wider in spacing and designed based on a different contour-measuring method.

The slope from east to west is on average 1.7%, and can increase to 6% on the southern slopes. The

soil under the maize is mainly bare, with minimum soybean litter from the previous year and, in

some places, weeds.

Figure 6 The maize field, slopes down towards the east with a dike on the east and west sides and a channel in the middle that connects the dikes.

The soybean field is larger, but divided by two channels. These channels divide the field into smaller

catchments and only one of these catchments (34.7ha) is modelled (figure 7). The soil bunds in this

field are more pronounced than in the maize field. The bunds are in average higher, approximately

30cm, and have the same width of 3 metres everywhere. The bunds in this catchment all drain into a

channel south of it. The average slope is 1.8% and is 4.2% at the steepest point, sloping down

halfway the field in the southeast direction. The soil is covered by a dense litter layer, a result from

the residues of the maize cultivated in the previous year. In this field, weeds seem to be more

persistent as well.

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Figure 7 The soybean field, that slopes down towards the southeast-east, with a channel draining the entire southern side. The rainfall gauge in the northwest is the same gauge as the one in the southeast of the maize field map.

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3.2 Fieldwork Fieldwork included installing erosion plots and local weirs, weekly monitoring of the vegetation and

soil, and monitoring the plots and weirs after each rainfall event. Fieldwork was performed between

9 December 2014 and 13 February 2015. Installation of the plots and weirs was finished for soybean

on 28 November and for maize on 9 December. Three plots and three weirs were installed in each

field, meaning six plots and six weirs in total (figure 8 & 9). The weirs were installed to determine the

outflow after rainfall events from sub-catchments, delineated by bunds, in the field. Runoff plots

were built to verify the spatial erosion maps created by LISEM with local values in the field.

Figure 8 Location of the weirs and plots in the maize field, with the map of figure 4 for reference

Figure 9 Location of the weirs and plots in the soybean field, with the map of figure 5 for reference

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3.2.1 Plot and weir installation

The plots were installed between bunds, not too close to the bund upslope or downslope, according

to the slope direction. Plots were 1 meter wide by 3 meter long (figure 10). The long side was parallel

to the sloping direction, and an outlet was located at the lowest side. The outlet allowed water and

sediment to discharge through a PVC hose into a 200L barrel. The first barrel was connected by

another PVC hose to a second 200L barrel in case the first would overflow during an extreme event.

The plot was enclosed by metal sheets of 30cm height, which were hammered 10cm deep into the

soil. The PVC hose had a diameter of 51mm and was attached to the outlet using a hose clamp. The

plot outlet was shaped trapezoidal in order to concentrate the runoff and sediment flow into the

hose.

Figure 10 Setup of small erosion plots, on the left a plot in the soybean field, right-top a schematic representation of a plot, and in the right-bottom two pictures of the outlet from inside and outside the plot

Weirs were installed at the location where the sub-catchment, enclosed by the soil bunds, discharges

into the grass channel. The discharge is mainly guided through the ditch that was formed upslope of

each bund. The field is also separated from the channel by a high bund, with an opening where the

ditch and the channel meet. At this meeting point the weirs were installed.

The weir consisted of three metal sheets; one was 140cm long and 10cm high, and two others 30cm

high and 40cm long (figure 11). The long one was first hammered in the ground perpendicular to the

ditch, until it was less than a centimetre above the ground and at horizontal level. During a rainfall

event of sufficient intensity, water and sediments run off and discharged at the weir. Water would

accumulate before overflowing. At two points of the weir, the overflow was captured and led to a

respective barrel. To calculate the total water and sediment losses, the ratio between the width of

the overflow catchers and the total weir width was used.

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Figure 11 Setup of the local weir, on the top-left the schematized position of the weir in the field, on the top-right a scheme of the overflow catchers, and the bottom images are of the field situation after installation of a weir.

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3.2.2 Water, sediment and organic matter losses quantification

After each rainfall event the water and sediment quantities in the barrels of the plots and weirs were

measured. To calculate the volume of water in the barrel, the relation between water level and

water volume was calibrated before installation. To do this, the height of the water level in the barrel

was measured per increase of water volume. Thus, it was possible to convert a measured water level

to water volume. After each rainfall event of more than 10mm the barrels were checked for runoff

and sediments. If these were present, then the losses of sediment, water and organic matter were

measured.

The following procedure was followed to quantify the runoff water and sediments in the barrels. First,

the water level in the barrel was measured using a measuring tape and converted to volume of water.

This volume was multiplied by the ratio between the width of the runoff catchers and the weir width.

The maximum capacity that could be held by the barrels was approximately 220 liters. Thus a runoff

quantity of 660 liters was the maximum that could be measured.

To quantify the sediments, the runoff water was stirred very well inside the barrel. Using a measuring

cup, exactly 1L of a representative mix was put in a bottle. The sediments in the bottle were left to

settle as much as possible. Then the contents of the bottle were split into two parts. First, the upper

part, which was mostly water and perhaps some fine sediment, was discharged in a different storage

bottle using a measuring cup and the volume was noted. The water in the storage bottle was stirred

very well and an aliquot was put in a tube. The water in the tube was dried at 1050C for 24 hours and

the sediments left were weighed. This weight was multiplied by the storage bottle volume and

divided by the tube volume to get the sediment weight in the first part of the 1L bottle.

The second part of water and mostly sediments was stirred very well and then filtered using an 8μm

pore size filter. The sediments were weighed, heated at 1050C for 24 hours, and weighed again for

the sediment weight of the second part.

The sediments in the 1L bottle were then quantified by adding the weight of the first and second part

to arrive at the total fine sediment weight in 1L of barrel water. This quantity was multiplied by the

total volume of water in the barrel to estimate the total sediment present in a barrel. The samples

were stored in a freezer (-70C) while they were not used in an experiment.

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3.3 LISEM input data

3.3.1 Input parameters

The input parameters of the soil were separated for the soybean and maize fields. The parameters of

vegetation are different for soybean, maize and forest. Some parameters, such as random roughness

and saturated soil moisture level, were considered constant during the study period. Others – leaf

area index, plant cover, plant height, crust fraction, and initial soil moisture content – were different

for each rainfall event. The forest area did not have any notable changes. The soil underneath the

forest was assumed to have the same properties as the rest of the field. The sampling procedures of

the input parameters are described in this chapter. An overview is given in table 4. The input

parameters are generally measured using standard methods (Hessel, 2002b). For some parameters,

the methods were different from the methods used to program and calibrate LISEM, in which case an

appropriate conversion was considered.

Table 4 Overview of the LISEM input parameters, the measuring frequency, number of samples per field per sampling, and the type of method used for sampling

Input parameter Frequency Number of samples

Methods

Leaf area index - - Literature Fraction soil cover Every two weeks 3 Visually estimated Plant height Every two weeks 60 Measured using tape Manning’s n Once - Literature Random roughness Once 9 Measured; chain method Fraction stones Once - Visually estimated Fraction crusts Every two weeks 3 Visually estimated Saturated hydraulic conductivity Once 3 Laboratory experiment Saturated volumetric soil moisture content

Once 3 Weighing the moisture loss by oven-drying

Initial volumetric soil moisture content

Weekly 9 Weighing the moisture loss by oven-drying

Suction at wetting front Once - Literature Soil depth - - Estimated Saturated volumetric soil moisture content of crusts

Once - Based on the saturated volumetric soil moisture and literature

Aggregate stability Once 6 Wet sieving Cohesion Once 10 Torvane device Root cohesion Once - Estimated Median grain size Once Composite Optical laser diffraction

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3.3.1.1 Leaf area index

The leaf area index was taken from literature on soybean (Weber et al., 1966, Blad and Baker, 1972,

Taylor, 1980), maize (Maddonni and Otegui, 1996, Steduto and Hsiao, 1998) and semi-deciduous

forest (Grubb, 1977, Granier et al., 2000). Appropriate values were taken based on the spacing of the

crops and the growing stage.

3.3.1.2 Vegetation cover

Soil cover by vegetation was estimated in the field by observation, and top view photographs were

made for reference.

3.3.1.3 Plant height

Plant height was measured every two weeks by taking the height of 20 plants at three locations in

each field using measuring tape. An average value of all 60 plant heights per field was used as input

parameter.

3.3.1.4 Manning’s n

The factor Manning’s n was introduced in the Manning’s equation, where it represents an empirical

factor of the surface’ resistance to flow (Manning et al., 1890). It is used in LISEM as a factor that

affects the overland flow velocity. Manning’s n was estimated from the tabulated values for

floodplains presented by Te Chow (1959).

3.3.1.5 Random roughness

The pin roughness meter is used normally to acquire the random roughness, the standard deviation

of the micro relief, for LISEM. However, this is a labour intensive method, while the simpler

alternative, the chain method, is practically correlated (Saleh, 1993), thus the chain method was used.

The random roughness using the chain method was calculated as follows:

𝐶𝑟 = (1 − 𝐿2 𝐿1⁄ ) ∙ 100

Where Cr is the random roughness according to the chain method, L1 is the length of the chain and

L2 is the horizontal length of a surface. In this case, L1 is 107.2 centimetres. The random roughness

as used in LISEM is acquired through a conversion:

𝑅𝑅 = 1.1986 ∙ 𝐶𝑟 + 0.0255 ∙ 𝐶𝑟2

Where RR is the random roughness in centimetres.

3.3.1.6 Crust fraction

Soil was considered crusted if the top soil surface layer was harder than the regular soil below. The

crust fraction was estimated by observation every two weeks for each plot. The soil in the plot was

then compared to the surrounding soil to ensure the crust fraction in the plot was representative for

the field.

3.3.1.7 Stone fraction & road width

Both stone fraction and road width affect the rain drop impact on the soil and infiltration. However,

in the maize and soybean fields neither was present.

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3.3.1.8 Saturated hydraulic conductivity

To measure the saturated hydraulic conductivity, a sample of the top 10cm was taken near each plot.

Samples were stored in a cooling cell (5 0C) until the experiment was performed in Wageningen.

Before the experiment the samples were completely saturated. Each sample was then put on a filter

and the conductivity was measured by letting water pass vertically through the sample, as described

by Stolte (1997). A water layer on top of the sample was kept constant and the measured results

were used as input to determine the conductivity. Conductivity was calculated using the formula:

𝐾𝑠𝑎𝑡 =𝑉

𝑡 ∙ 𝐴∙

𝑙

𝑙 + 𝑑

Where Ksat is the saturated conductivity (cm/day), V is volume of water (ml), t is time needed to

collect V (days), A is the surface area of the sample (cm2), l is the height of the sample (cm) and d is

the thickness of the water layer on top of the sample (cm). The unit used in the model was millimetre

per hour, thus the results should be divided by 2.4. There is currently a more accurate method in

which water cannot freely fall from the bottom of the sample, but here the old method was used

since the model parameter is based on this method.

The conductivity through the crust was derived from literature. Hoogmoed and Stroosnijder (1984)

found an infiltration rate of 10mm/hr, a tenth of the infiltration rate in their crust-free soil. Morin

and Van Winkel (1996) found that the infiltration rate of clay and sand soil reduced to values of 1 to 5

mm/hr. Thus, the choice was made to select a saturated hydraulic conductivity of crusts of a fifth of

the normal hydraulic conductivity, and to use this parameter as a calibration variable.

3.3.1.9 Volumetric moisture content

The saturated volumetric moisture content (m3/m3) is the volume of water per total sample volume

in a saturated sample. This was done by taking a known sample volume and completing saturating

said sample. The sample was weighed, dried at 1050C for 24 hours, and weighed again. The

difference in weight is the mass of water evaporated. This mass was converted to volume, and then

used to calculate the saturated volumetric moisture content.

The initial volumetric moisture content (-) is the moisture content in the soil at the start of a rainfall

event. Soil samples were collected every week at three depths (0-5cm, 15-20cm and 30-35cm)

nearby each plot. From these samples, a known weight was dried at 1050C for 24 hours and weighed

again. The difference in weight is the moisture content, which divided by the total sample mass is the

gravimetric dry-weight based moisture content. The gravimetric soil moisture content was then

converted to the volumetric soil moisture content, using the formula:

𝜃 = 𝑤 ∙ 𝜌𝑏 𝜌𝑤⁄

Where 𝜃 is the volumetric soil moisture content (-), w is the gravimetric soil moisture content (-), ρb

is the soil bulk density (g/cm3) and ρw is the water density (g/cm3). The soil bulk density varied per

field and per depth (table 5).

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Table 5 Bulk density (average ± standard deviation) in the soybean and maize field at three different depths.

Depth (cm) Soybean field (g/cm3) Maize field (g/cm3)

0-5 0.97 ± 0.05 1.14 ± 0.1 15-20 1.34 ± 0.06 1.38 ± 0.03 30-35 1.23 ± 0.1 1.34 ± 0.07

3.3.1.10 Soil water tension at the wetting front

The tension at the wetting front or wetting front capillary pressure head (cm) is an important Green-

Ampt infiltration parameter, which can be derived based on the texture of the soil (Rawls et al.,

1983).

3.3.1.11 Soil depth

The soil is uniform and deep. Soil depth (mm) was estimated to be 2 meters based on the holes made

when installing the plots.

3.3.1.12 Aggregate stability

Aggregate stability (-) was measured using the wet sieving method (Kemper and Rosenau, 1986).

Composite soil samples were gathered from the top-soil (0-10cm) near each plot, totalling 6 samples.

The samples were stored in a cooling cell until aggregate stability was measured. First, 24h before

measuring, the samples were removed from the storage space and left to air dry. All samples were

measured in duplicates. The wet sieving apparatus consists of metal cups, small plastic cups with a

sieve in the bottom and the apparatus itself which stirs the content of the plastic cups through the

liquid in the metal cups.

First the metal cups were dried and weighed. Soil was sieved at 2mm, after which 4g were put in one

of the plastic sieving cups. The metal cups were filled with distilled water to a level at which the soil

in the plastic cups was completely submerged. The water puts pressure on the aggregates, breaking

down the weaker aggregates (Kemper and Rosenau, 1986). At the same time, the machine stirred the

soils softly through the water for three minutes, so that the broken down aggregates sank through

the bottom of the plastic sieving cup into the metal cup. The metal cup with water was replaced with

a metal cup with dispersing solution. The soil samples had a pH below 7, meaning the dispersing

solution was 2g NaOH/L. The still stable aggregates were submerged and the apparatus worked for 5

to 10 minutes, at least until all the soil was dispersed. The leftover material, such as sand grains or

roots, was thrown away. All the metal cups were placed in the oven for 24h at 1050C, after which

they were weighed again. The difference in weight was the respective weight of aggregates

dispersed in distilled water (Wdw) and in dispersing solution (Wds). Aggregate stability was then

calculated:

𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑖𝑛𝑑𝑒𝑥 =𝑊𝑑𝑠

𝑊𝑑𝑠 + 𝑊𝑑𝑤

However, LISEM requires an input of the median number of drops needed to half the size of

aggregates on a sieve. This number is on average a factor 90 higher in comparison to the wet sieving

method (Ouessar et al., 1993), thus values of the aggregate stability index were multiplied by 90. The

test was done twice for each sample, thus 12 in total.

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3.3.1.13 Cohesion

Cohesion (kPa) was measured using a Torvane device, which gives an indication of the soil shear

strength (Zimbone et al., 1996). Cohesion was measured ten times near each plot in soils with

current moisture content and in soils that were saturated. The latter was done by wetting the topsoil

to approach saturation point. The measurements in saturated soils were done to verify how much

influence saturation has on the soil cohesion.

Besides the soil cohesion, the soil is also reinforced by plant roots which creates a soil-root matrix

that strengthens the soil more than the two separately (Gyssels et al., 2005). Cohesion due to roots

(kPa) depends on the tensile strength of roots and root-area ratio (Wu et al., 1979). However, this

relation does not adequately reflect the complexity of the soil-root matrix and it was revised by Baets

et al. (2008). They explain how measuring the cohesion of root-permeated soils can either measure

the resistance of the soil or of the roots, whichever is weaker.

In the maize field the roots were absent at the surface, except for exactly next to the plant, and the

presence of litter was minimum. Therefore, the cohesion measured in the maize field represents

solely the resistance of the soil. In the soybean field, however, the top soil was permeated with roots

and litter from the previous year. For that reason, the cohesion measured with the torvane in the

soybean field was considered to reflect root cohesion more than soil cohesion.

3.3.1.14 D50 value of the soil

The median particle diameter (D50) of the soil (μm) was determined by the size of soil particles.

Grain size was measured using optical laser diffraction, both with and without dispersion of the soil.

Dispersion is used to break down aggregates in the soil, a main source of variation in size distribution

measurements (Chappell, 1998). The value of median particle diameter was taken from the grain size

distribution of dispersed soil.

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3.3.2 Rainfall

The precipitation data was acquired from on-farm pluviometers that were checked by farm

employees each day at approximately 7:00 AM. Over the period of January 2011 to February 2015,

these data were acquired daily and communicated to the farm office and written down. The location

of the three pluviometers of interest to this study area is shown in the two maps found in the study

area description (figure 6 & 7).

The meteorological station at the airport Pajas Blancas, Córdoba, 20km south of the study area,

provided 6 hour data over the period 1995 to 31 March 2015 (SMN, 2015). Detailed rainfall intensity

data, on the temporal scale of minutes, was not available. In this study a desired time step of 5

minutes was used to represent changes in intensity, a key factor in modelling the water erosion.

Rainfall does not fall with a constant intensity over a period, but varies over time. Rainfall has a

number of properties that characterize the event. Depth (mm) is the precipitation fallen. Duration

(hours) is the time span of a single event. Intensity (mm/hour) is the depth over a selected time

period and the peak intensity is the maximum rainfall on a fine temporal scale in a single rainfall

event, although one event can have several peaks of different intensities. These properties are

represented in a hyetograph, which is a graphical representation of rainfall depth (mm) over time

(hours).

The movement of a storm can be described by the Poisson process (Onof et al., 2000). The Poisson

process stochastically models the rainfall that falls in a certain spatial location based on the speed of

the storm and the rainfall stored at that point of storm, with the highest intensity produced in the

centre of the storm (Onof et al., 2000). This rainfall process is described by a triangular hyetograph of

the rainfall intensity (mm/hour) i against time (hours) t (Ellouze et al., 2009).

Figure 12 Triangular representation of a hyetograph

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The geometry of a simplified triangular hyetograph (see figure 12) is described by the following

equations:

𝐷 =1

2∙ 𝑑 ∙ ℎ

𝑛 =𝑑

∆𝑡

𝑖𝑗 =2 ∙ ℎ

𝑛∙ 𝑗; 0 ≤ 𝑗 ≤

1

2𝑑

𝑖𝑗 = −2 ∙ ℎ

𝑛∙ 𝑗;

1

2𝑑 ≤ 𝑗 ≤ 𝑑

Where D is the total event depth (mm), d is the event duration (hours), h is the peak intensity

(mm/hour), n is the number of time intervals (-), ∆t is the time interval (hours), and i the intensity

(mm/hour) at time j (hours). As D is known from daily data for each rainfall event, either d or h has to

be found to solve the previous equations.

Vidal and Cousillas (1982) created an intensity-duration-frequency (IDF) curve for Balcarce, Buenos

Aires Province, that has a similar climate, with an average yearly rainfall of 800mm concentrated in

the summer months. Based on the results of Vidal and Cousillas (1982), the relation between the

peak intensity and total event depth is described by (see also figure 13):

ℎ = 12.626 ∙ 𝐷0.5343

When the total depth is known and the peak intensity is calculated, then the duration of rainfall

event can be described by:

𝑑 =2 ∙ 𝐷

ℎ

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Figure 13 Relation between the peak intensity of an event (mm/hour) and the total depth of a rainfall event (mm) based on Vidal and Cousillas (1982)

In a different study in the Córdoba province by García et al. (2001), the relation between maximum

rainfall at different temporal scales was calculated and, on average, 0.153 of the daily rainfall falls in

5 minutes, while an hour accounts for 0.543 parts of the daily precipitation. Based on this study, the

average peak intensity of a storm is 1.84 times the total depth. This coincides with a rainfall event of

one hour modelled using the intensity-depth relation based on Vidal and Cousillas (1982).

Using these formulas the total rainfall depth that is available was converted to intensities at intervals

of 5 minutes. The intensity data were adjusted for the total quantities of each local pluviometer. The

rainfall input file is a table with rows being time and columns representing each rain gauge. Rainfall

was assumed to start at the same time for each pluviometer. The events that were monitored on 6

January, 12 January and 9 February 2015 were used. In addition, several events were modelled for

other periods, when vegetation cover is different.

The locations of the three pluviometers that are located close to the fields were assigned a single

raster cell in ArcGIS. The location of the rain gauges was used to divide the field in terms of their

corresponding gauge by means of interpolation, using the inverse distance weighting (IDW)

interpolation method in ArcGIS. This map was converted to an ID map which assigns a rainfall gauge

to each cell and which corresponds to a rainfall file that contains rainfall data for each respective

gauge. The intensity data of the rainfall events were converted to separate .TBL files as input for

LISEM.

00

50

100

150

200

250

300

0 50 100 150 200 250

Pe

ak in

ten

sity

(m

m/h

ou

r)

Rainfall event depth (mm)

Intensity(mm/hour)

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3.4 LISEM map creation The maps used as input in LISEM were derived from three base maps, which represent the elevation,

land use and soil type (Jetten, 2002). Impermeable areas, normally represented by a separate base

map (Jetten, 2002), were not present in either of the two study fields. The base maps were created in

a geographic information system program, ArcGIS in this case. The base maps that were created in

ArcGIS (version 10.3) are raster files. These were then converted to American Standard Code for

Information Interchange (ASCII) files. The ASCII files were converted to the LISEM input maps in a

separate program called Nutshell. The base maps were then combined with their respective

parameter values to create the input maps (table 6). The rainfall was distributed according to the

location of the rain gauges.

Table 6 Input parameters and their respective base map

Input parameter Unit Base map

Rainfall mm Table Catchment boundaries DEM Sine of gradient in flow direction - DEM Local drainage direction network DEM Outlet location Manually selected Leaf area index m2/m2 Land use Fraction soil cover - Land use Plant height m Land use Manning’s n - Land use Random roughness cm Soil Fraction stones - Soil Fraction crusts - Soil Saturated hydraulic conductivity mm/h Soil Saturated volumetric soil moisture m3/m3 Soil Initial volumetric soil moisture m3/m3 Soil Suction at wetting front cm Soil Soil depth mm Soil Ksat of crusts mm/h Soil Aggregate stability # Soil Cohesion kPa Soil Root cohesion kPa Land use Median particle diameter μm Soil

A soil type map was created for the soybean and maize fields separately. Forest areas in each field

had respective values of their vegetation parameters. In the soybean field there are two types of land

use: soybean and forest. Similarly, in the maize field there are maize and forest. In ArcGIS, the forest

and crop areas were delineated by editing a polygon feature, which was then converted to a raster.

The land use map was used to create the input maps of the leaf area index (LAI), plant cover, plant

height and Manning’s n. The grid size was 2 by 2 metres, which is detailed enough to model the form

of the broad soil bunds. To create the Digital Elevation Model, a topographic map, provided by the

farm Santo Domingo, was used. This map contained contour intervals of 1m altitude and had an

unknown reference system. The map was georeferenced in ArcGIS to ensure that the distances in

this reference system were converted to the actual scale before the map was put in LISEM. In ArcGIS,

the contour lines were converted to a triangulated irregular network (TIN) and then to a raster.

30 | P a g e

Both fields have a channel leading from west to east. The LISEM has an option to model channels,

however, these channels are supposed to be less than 20% the width of the cell size. In this study,

such channels would not have the correct purpose. Instead, channels were simulated by making a

path of 3 cells wide, which is 5 centimetres lower than field level. The channel has a barrier of 30

centimetres high on each side, which is similar to the bunds in the field. This ensures that water

entering the channel does not leave until it reaches the outlet. Runoff inside the channel can still

erode the soil, and likewise sediment can deposit. The barriers on the sides of the channel have an

opening where a bund meets the channel. The location of the main and any sub outlets were

assigned manually. LISEM then creates a hyetograph for both the main and sub outlets. The location

of the sub outlets coincides with the location of the weirs in the field. The temporal resolution used

was 30 seconds.

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3.5 Calibration and validation The initial run, based on the measured and estimated input parameters, generally contains errors.

These errors can stem from inaccurate parameters and the model itself. For this reason, calibration

was done by adjusting selected parameters in order to match the outcome of the model to the

measured runoff and sediment losses in the field. LISEM creates a hydrograph and sediment graph

for the outlet of the catchment. Additional points, for which these graphs are produced as well, were

manually selected.

In both the scenario with and without bunds, the main outlet is located at the lowest point of the

field in the channel. The water and sediment at this point was not measured. In the scenario with

bunds, the main outlet is at the same location, but three additional outlet points were selected at the

location of the local weirs. Each field contained three weirs at the point where a soil bund discharges

into a channel. The calibration was done by matching the hydrographs and sedigraphs to the results

of the runoff and sediments that pass the three weirs installed in each field, as described in chapter

5.1. There were three weirs used to minimize the impact of errors and outliers.

Overflow from each weir was captured by two plastic containers that led runoff and sediments to

respective barrels with an approximate capacity of 200 litres. This method provides information on

total runoff and sediment lost, but beyond this it does not supply information over time to calibrate

hydrographs and sediment graphs. The calibration of runoff and sediment data was done by eye to

match the runoff and sediment data.

Calibration was limited by the number of rainfall events that actually led to runoff and the fact that

only total runoff and sediments were measured. Therefore, two input parameters, saturated

conductivity and Manning’s n, were adjusted several times to observe their impact on the results

(table 7). In addition, the duration-intensity relation of the rainfall events was also adjusted for the

same reason. The most influential of these three parameters was finally used to calibrate the model.

Table 7 Input parameters of the LISEM model used for calibration

Parameter Explanation

Saturated conductivity The hydraulic conductivity measured is often too high and is therefore commonly the first parameter to be adjusted (De Roo et al., 1996a, De Roo and Jetten, 1999). Lower calibrated values can be a result of disturbance of the sample or that a field will not be completely saturated (Hessel et al., 2003). A higher conductivity means a higher rate of infiltration

Manning’s n Manning’s n influences the velocity of the water, which in turn affects timing of the runoff and sediments at the outlet. Manning’s n also influences the time available for water to infiltrate.

Rainfall event duration & peak intensity

The rainfall duration and the intensity determine how quickly water starts flowing and in which quantity runoff flows, as well as the impact of splash detachment.

32 | P a g e

Calibration was done using a single rainfall event, but using all the runoff and sediment data of both

fields. Validation was performed using the data from the last rainfall event. Furthermore, LISEM

created a map with the sediment eroded and a map with sediment deposited with the same spatial

resolution as the input maps. The results of these maps were verified by comparing those values with

the runoff and sediments measured in the barrels of each plot.

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4 Results In the results, first, the rainfall events are presented, including measured input parameters for each

event. Then, the initial run was performed for each rainfall event using these parameters. Then, the

calibration parameters were adjusted in order to match the outcomes of the initial run to the

measured values at the weirs. The results of each event were run again using the calibrated

parameters and then validated. Lastly, the results related to each sub-question are presented. The

adjusted parameters were applied to all events once more.

4.1 Input parameters There were three rainfall events on 06-01-15, 12-01-15 and 09-02-15 that had sufficient quantity and

intensity to cause runoff into the barrels. The rainfall depth (mm), duration (hrs) and peak intensity

(mm/hr) per event are presented in table 8 and the rainfall intensity dataset in Appendix B:

Simulated rainfall intensity data.

Table 8 Rainfall data of the three major rainfall events occurring during the monitoring period

6 January 2015 12 January 2015 9 February 2015

Gauge 1 2 3 1 2 3 1 2 3 Depth (mm) 64 70 72 56 60 48 68 62 56 Peak intensity (mm/hour) 116 122 124 108 112 100 120 114 108 Duration (minutes) 66 69 70 62 64 58 68 65 62

The measured values of each model input parameter can be found in Appendix C: Results of input

parameter measurements. In table 9 an overview is presented of the input data that were gathered.

The soil parameters are very similar in both fields, except for the hydraulic conductivity, which was

four times higher in the soybean field. This could be attributed to the denser root system of the

soybean plants, at the time the sample was taken, which affected this soil property. Crusts appeared

more commonly in the maize field. Vegetation parameters are initially higher in the soybean field,

but eventually the LAI and plant height of maize become greater in a later stage. The forest area has

separate vegetation parameters (table 10), as they were considered constant during the monitoring

period.

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Table 9 Parameters input dataset for LISEM for the events of 6 January, 12 January and 12 February 2015

Input parameter Unit 6 January 12 January 9 February Maize Soybean Maize Soybean Maize Soybean

Average rainfall mm Leaf area index m2/m2 2.7 3.2 3.1 3.5 5.3 4.0 Fraction soil cover - 0.18 0.65 0.29 0.67 0.58 0.75 Plant height m 0.73 0.75 1.28 0.83 2.26 1.02 Manning’s n - 0.030 0.040 0.035 0.040 0.035 0.040 Random roughness cm 11.83 10.59 11.83 10.59 11.83 10.59 Fraction stones - 0 0 0 0 0 0 Fraction crusts - 0.4 0.05 0.38 0.05 0.22 0.05 Sat. hydraulic conductivity mm/h 6.98 28.27 6.98 28.27 6.98 28.27 Sat. volumetric soil moisture - 0.25 0.25 0.25 0.25 0.25 0.25 Initial volumetric soil moisture - 0.10 0.11 0.13 0.13 0.11 0.13 Suction at wetting front cm 27.0 10.0 27.0 10.0 27.0 10.0 Soil depth mm 2000 2000 2000 2000 2000 2000 Sat. conductivity of crusts mm/h 1.40 5.65 1.40 5.65 1.40 5.65 Aggregate stability - 27.0 27.1 27.0 27.1 27.0 27.1 Cohesion kPa 27.43 41.38 27.43 41.38 27.43 41.38 Root cohesion kPa 0 0 0 0 0 0 Median grain size μm 13.43 12.62 13.43 12.62 13.43 12.62

Table 10 Land use parameters of the forest area

Input parameter Unit Forest

Leaf area index m2/m2 8 Fraction soil cover - 0.95 Plant height m 4 Manning’s n - 0.1

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4.2 Initial run The model was run using the rainfall data presented in table 8 and the input parameters from tables

9 and 10. This was done for all three events in both fields, both with and without bunds. The results

(table 11) show the total amount of soil detached by splash or flow erosion, the deposition and the

total amount of soil that has left the main outlet. The soil loss is higher in the maize field, also in

terms of tons per hectare; an area of 92.6 hectares in the maize field compared to 34.7 hectares in

the soybean field. This is likely a result of the difference in hydraulic conductivity between the two

fields.

Splash detachment is similar in both scenarios, which can be expected as vegetation and soil cover

are the same. However, flow detachment in the fields with bunds is lower. At the same time

sediment deposition is much lower in the scenario with bunds, which leads to a higher simulated

total soil loss. However, total soil loss was expected to be lower in the scenario with bunds, and this

discrepancy will be discussed later using the results after calibration.

Table 11 Overview of the splash and flow detachment, deposition and soil loss, in tons, as produced by LISEM using the base input parameters

Splash detachment Flow detachment Deposition Total soil loss

Tons Tons Tons Tons Tons/Ha

6 January

Maize - no bunds 290.5 607.4 343.2 553.4 6.0

Maize - bunds 303.4 497.9 193.6 604.9 6.5

Soybean - no bunds 88.6 121.0 124.1 83.5 2.4

Soybean - bunds 90.6 100.7 57.1 131.2 3.8

12 January

Maize - no bunds 235.2 471.7 307.1 399.0 4.3

Maize - bunds 244.7 431.8 185.0 489.3 5.3

Soybean - no bunds 69.2 77.0 95.9 48.1 1.4

Soybean - bunds 70.2 73.1 47.3 92.8 2.7

9 February

Maize - no bunds 237.7 504.0 313.1 427.6 4.6

Maize - bunds 246.4 448.6 183.3 509.7 5.5

Soybean - no bunds 74.3 80.8 98.5 54.5 1.6

Soybean - bunds 75.5 75.8 48.3 99.6 2.9

The model also simulated runoff and sediment at the sub-outlets, which correspond to each weir.

These results are presented in tables 12 and 13, in which the sediment output at the main outlet

corresponds to the total soil loss in table 11. The amount of runoff and sediments at the weirs that

was simulated is very high in comparison to the measurement capacity of the weirs, which was

approximately 1300 litres. This was important for the calibration process. The values at the main

outlet are highest, as expected, but in the sub-catchments there doesn’t seem to be a correlation

between runoff and catchment areas. The runoff simulated at weir 3 of the soybean field was the

highest of all sub-catchments, which cannot be explained by catchment area or variation in the

rainfall distribution. The large differences of simulated runoff and sediment between the 6 weirs

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were caused by differences in slope, especially the slope parallel to the bunds, which influence the

rate at which runoff accumulates.

Table 12 LISEM non-calibrated results in the maize field with bunds for the events of 6 and 12 January, and 9 February

6 January 2015 12 January 2015 9 February 2015

Area Runoff Sediment Runoff Sediment Runoff Sediment

Outlet Hectares Liters Kilogram Liters Kilogram Liters Kilogram

Main 92.6 38,481,808 604,888 32,415,412 489,278 33,965,016 509,705

Weir 1 1.0 418,929 4,807 348,242 3,728 321,567 3,414

Weir 2 1.4 110,224 991 91,605 703 84,864 588

Weir 3 3.2 37,772 333 32,587 247 38,002 283

Table 13 LISEM non-calibrated results in the soybean field with bunds for the events of 6 and 12 January, and 9 February

6 January 2015 12 January 2015 9 February 2015

Area Runoff Sediment Runoff Sediment Runoff Sediment

Outlet Hectares Liters Kilogram Liters Kilogram Liters Kilogram

Main 34.7 8,382,488 131,228 5,266,968 92,769 5,919,953 99,610

Weir 1 1.3 278,871 3,674 212,814 2,867 217,115 2,923

Weir 2 3.4 33,419 363 27,190 294 28,402 318

Weir 3 2.5 1,221,435 15,686 939,782 12,309 964,503 12,598

4.3 Calibration The measured data from the plots and weirs were converted to volume of runoff, and weight of

sediment and organic matter (table 14 & 15). Not all barrels contained runoff and sediments, while

other barrels were completely full. The runoff volumes from the plots did not particularly differ

between the soybean and maize fields. The runoff volumes measured at the maize field weirs were

much higher than the volumes in the soybean field. The model results from the base run (table 12

and 13) were compared to the values from the barrels from their respective weirs. The values

simulated are much higher than the measured values.

This discrepancy was mainly caused by three problems that occurred and that had a big impact on

the results and the way these can be interpreted. First, the first event was not monitored separately

due to miscommunication between the researcher and the farm. Thus, the barrels that were

monitored on the 14th of January contained runoff and sediments of both the event of 6 January and

12 January. The events of 6 and 12 January are combined, however, it should be noted that the

barrels from the weirs in the maize field were already full during the field visit on 12 January before

the rainfall event of that day. Second, the barrels from the weir in the maize field were completely

full after each event, thus providing only a minimum quantity of runoff and sediments. Third, some

barrels were not secured firmly enough in the soil, and had drifted out of their hole during a rainfall

event, preventing water and sediments from entering. In a few cases this happened, but there was

some runoff and sediments in the barrel.

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Table 14 Runoff and sediment quantities from the plots and weirs from the first two events, 6 and 12 January 2015. The asterisk (*) indicates that barrels were completely full (1300 liters or more).

Plot Runoff volume (L) Sediment weight (g)

Maize

1 no runoff

2 50 68.2

3 15 20.8

Soybean

1 85 97.8

2 25 24.2

3 27 34.6

Weir Runoff volume (L) Sediment weight (g)

Maize

1 891 1363.8

2* 1359 1751.2

3* 1309 1696.6

Soybean

1 no runoff

2 no runoff

3 no runoff

Table 15 Runoff and sediment quantities from the plots and weirs from the event of 9 February 2015. The asterisk (*) indicates that barrels were completely full (1300 liters or more).

Plot Runoff volume (L) Sediment weight (g)

Maize

1 230 376.4

2 unknown

3 unknown

Soybean

1 30 31.0

2 no runoff

3 230 343.9

Weir Runoff volume (L) Sediment weight (g)

Maize

1* 1298 2006.8

2 no runoff

3* 1378 1614.2

Soybean

1 no runoff

2 no runoff

3 624 719.5

Very large ratios can be observed between the values that were measured in the field and the results

produced by LISEM, by comparing tables 12 and 13 to tables 14 and 15. This gap is difficult to close

by calibration without changing the input parameters to such an extent that the parameters will be

physically impossible. Nevertheless, a simple sensitivity analysis of the calibration parameters was

performed to understand the impact of these parameters on the result.

The effect of adjusting the hydraulic conductivity and Manning’s n on the model outcomes are shown

in tables 16 and 17. Increasing the hydraulic conductivity has a much larger impact in the soybean

field than in the maize field, as the base value of the hydraulic conductivity is larger in the soybean

field. Manning’s n has an effect in particular on the sediments, as a result of retarding the flow. In

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addition, the effect of extending the duration of each rainfall event is simulated, and this is combined

with doubling the conductivity once more to see the combined effect (tables 18 & 19). Doubling the

rainfall event duration, means that the intensity is halved. Multiplying the hydraulic conductivity by

three, which means a conductivity of 84.8mm/hr, in the soybean field leads to virtually all the rain to

infiltrate quickly enough to prevent the initiation of runoff (table 17). Likewise, multiplying the

hydraulic conductivity by two, while the intensity is halved, has the same effect (table 19). The effect

of extending the rainfall event duration reduces runoff in the entire field. This also reduces the

sediment passing all weirs, yet the sediment yield at the main outlet is practically unchanged.

Table 16 Calibration of the rainfall events in the maize field with bunds by multiplying the hydraulic conductivity and Manning's n parameters.

Hydraulic conductivity x2 Hydraulic conductivity x3 Manning’s n x2

Runoff (l) Sediment (kg) Runoff (l) Sediment (kg) Runoff (l) Sediment (kg)

6 January

Weir 1 330,290 4,058 263,747 3,474 413,381 3,473

Weir 2 87,200 798 69,782 661 96,058 483

Weir 3 29,211 277 22,734 228 33,114 124

12 January

Weir 1 270,098 3,074 210,572 2,556 341,852 2,548

Weir 2 71,414 527 55,875 417 77,553 397

Weir 3 25,134 203 19,327 164 27,563 98

9 February

Weir 1 231,498 2,636 165,912 2,027 317,059 2,340

Weir 2 61,375 426 44,236 304 74,759 369

Weir 3 28,688 227 21,520 177 32,994 114

Table 17 Calibration of the rainfall events in the soybean field with bunds by multiplying the hydraulic conductivity and Manning's n parameters; the hyphen (-) indicates that no runoff occurred

Hydraulic conductivity x2 Hydraulic conductivity x3 Manning’s n x2

Runoff (l) Sediment (kg) Runoff (l) Sediment (kg) Runoff (l) Sediment (kg)

6 January

Weir 1 81,349 1,476 20 1.50 254,605 2,624

Weir 2 11,999 187 7 0.99 25,273 154

Weir 3 347,658 5,951 15 1.17 988,082 8,241

12 January

Weir 1 46,919 882 - - 196,643 2,050

Weir 2 7,276 115 - - 17,969 113

Weir 3 195,579 3,449 - - 742,356 6,630

9 February

Weir 1 45,557 908 - - 204,192 2,136

Weir 2 6,992 117 - - 19,229 119

Weir 3 191,370 3,493 - - 784,754 6,874

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Table 18 Calibration of the rainfall events in the maize field with bunds by extending the duration of the rainfall event and its combination with multiplying the hydraulic conductivity parameter

Rain event duration x2 Rain event duration x2, Hydraulic conductivity x2

Runoff (l) Sediment (kg) Runoff (l) Sediment (kg)

6 January

Weir 1 332,129 4,259 209,958 2,898

Weir 2 87,712 574 55,706 347

Weir 3 29,471 197 17,761 112

12 January

Weir 1 271,687 3,176 164,583 2,063

Weir 2 71,947 379 43,868 230

Weir 3 25,382 134 14,943 67

9 February

Weir 1 233,311 2,733 114,713 1,488

Weir 2 61,985 305 30,903 149

Weir 3 28,909 155 16,026 66

Table 19 Calibration of the rainfall events in the soybean field with bunds extending the duration of the rainfall event and its combination with multiplying the hydraulic conductivity parameter; the hyphen (-) indicates that no runoff occurred

Rain event duration x2 Rain event duration x2, Hydraulic conductivity x2

Runoff (l) Sediment (kg) Runoff (l) Sediment (kg)

6 January

Weir 1 85,674 1,520 - -

Weir 2 12,410 146 - -

Weir 3 380,870 6,358 - -

12 January

Weir 1 50,955 896 - -

Weir 2 7,679 84 - -

Weir 3 223,247 3,724 - -

9 February

Weir 1 48,994 880 - -

Weir 2 7,401 75 - -

Weir 3 216,232 3,632 - -

The measured values from the event of 9 February give the most information. The measured runoff

values from this event were used to calibrate the model. The only calibration parameter that was

used is the hydraulic conductivity, which is the most powerful parameter in terms of influencing the

runoff and sediment results. The reason is that adjusting other parameters might be more

appropriate, but there is no measured reference to know whether the adjustment is correct. Thus it

is futile to devise values for additional calibration parameters. In this calibration process the

hydraulic conductivity is increased until it reaches the point that the modelled runoff at one weir

matches the measured values.

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This results in a factor of 8.00 for the maize field, which means a hydraulic conductivity of 56.3

mm/hr. This factor means a calibrated runoff value of 1,262 litres, compared to the measured value

of 1,298 litres at weir 1 (table 20). In addition, the runoff at weir 2 is considerably lower, which

coincides with an absence of runoff and sediment at that weir on 9 February.

For the soybean field, the calibration results in a factor of 2.66, meaning a hydraulic conductivity of

74.9 mm/hr. This is based on a calibrated runoff of 687 litres, compared to the measured runoff of

624 litres in weir 3 on 9 February (table 21).

Table 20 The calibrated model runoff and sediment results compared to the measured results from the weirs in the maize field, 9 February

Calibrated model results Measured values

Runoff Sediment Runoff Sediment

Outlet Liters Kilogram Liters Kilogram

Main 496,148 25,766

Weir 1 1,262 39 1298 2.0

Weir 2 219 8 no runoff

Weir 3 1,729 10 1378 1.6

Table 21 The calibrated model runoff and sediment results compared to the measured results from the weirs in the soybean field, 9 February

Calibrated model results Measured values

Runoff Sediment Runoff Sediment

Outlet Liters Kilogram Liters Kilogram

Main 45 14

Weir 1 477 12 no runoff

Weir 2 39 2 no runoff

Weir 3 687 28 624 0.7

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4.4 Validation Although, after calibration, the results of the event of 9 February were in line with the measured

results, this is not the case for the results of the two events in January (tables 22 & 23). This was not

caused by errors in measured discharge at the weirs, as the event of 12 January contained less

rainfall, but simulated higher runoff. Likely, the parameters that vary over time are the main reason

why the modelled discharge in the January events is still much higher than the measured results. In

the case of the maize field, the maize was still growing rapidly between January and February, which

can explain the relatively higher runoff and sediment values. The results from the event of January 12

in the soybean field was within the range of expectations, but the event of January 6 did produce

very high runoff results.

Table 22 LISEM calibrated results in the maize field with bunds for the events of 6 and 12 January

6 January 12 January

Runoff Sediment Runoff Sediment

Outlet Liters Kilogram Liters Kilogram

Main 4,341,018 121,869 2,640,978 74,807

Weir 1 58,122 1,040 34,242 501

Weir 2 16,010 136 9,783 63

Weir 3 3,951 42 2,846 20

Table 23 LISEM calibrated results in the soybean field with bunds for the events of 6 and 12 January

6 January 12 January

Runoff Sediment Runoff Sediment

Outlet Liters Kilogram Liters Kilogram

Main 357,727 12,809 6,413 459

Weir 1 12,777 278 3,172 55

Weir 2 2,505 47 692 13

Weir 3 51,842 1,129 6,202 118

Additional information is provided by the plots. The soil losses in the plots were compared to the

modelled values of the average soil loss in the entire field (table 24). The values do not show

particular correlation, but are in the same order of magnitude, with the exception of the results from

the maize field on 9 February. This confirms that the calibration was appropriate in terms of the

effect on the modelled results.

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Table 24 A comparison of the average soil losses as measured in the plots versus the average in the entire field as modelled using LISEM. Plots that had no runoff and sediments were ignored if the barrels had floated upwards, preventing runoff from entering.

Average soil losses (g/m2)

Plots Field (modelled)

Maize

Average of 6 & 12 January 14.8 13.4

9 February 125.5 0.02

Soybean

Average of 6 & 12 January 17.4 89.8

9 February 62.5 17.5

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4.5 Research questions The sub-questions for the first research question are:

c) What are the water losses with and without soil bunds?

d) What are the soil losses with and without soil bunds?

The results of the water losses are presented in table 25. In the two events of January, water losses

in the situation without bunds are approximately three quarters in comparison to the losses in the

situation with bunds.

The results of the soil losses are presented in table 26. The soil losses in the situation with soil bunds,

including the channels, were still higher than in the situation without bunds. The differences

between both situations in terms of deposition and splash detachment are very small. In all cases

flow detachment is higher in the scenario with bunds.

Table 25 Overview of infiltration and total water losses, in milimetres and cubic meters, as produced by LISEM using the calibrated hydraulic conductivity parameter

Infiltration Total water loss

mm mm Cubic meters

6 January

Maize - no bunds 63.4 3.7 3411

Maize - bunds 62.4 4.7 4344

Soybean - no bunds 69.4 0.8 273

Soybean - bunds 69.1 1.0 358

12 January

Maize - no bunds 55.8 2.1 1962

Maize - bunds 55.1 2.9 2643

Soybean - no bunds 53.9 0.014 5

Soybean - bunds 53.9 0.018 6

9 February

Maize - no bunds 63.3 0.22 203

Maize - bunds 63.0 0.54 496

Soybean - no bunds 57.3 0 0

Soybean - bunds 57.3 1.3∙10-4 0.05

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Table 26 Overview of the splash and flow detachment, deposition and soil loss, in tons, as produced by LISEM using the calibrated hydraulic conductivity parameter

Splash detachment Flow detachment Deposition Total soil loss

Tons Tons Tons Tons Tons/Ha

6 January

Maize - no bunds 309.4 64.6 246.3 90.4 0.98

Maize - bunds 310.5 101.0 243.1 121.9 1.32

Soybean - no bunds 92.2 5.2 71.9 5.0 0.14

Soybean - bunds 92.2 13.3 70.2 12.8 0.37

12 January

Maize - no bunds 249.9 39.3 210.3 45.5 0.49

Maize - bunds 250.5 76.4 211.3 74.8 0.81

Soybean - no bunds 71.2 0.5 28.5 0.4 0.010

Soybean - bunds 71.2 1.3 27.7 0.5 0.013

9 February

Maize - no bunds 252.8 9.3 200.9 6.6 0.072

Maize - bunds 252.8 36.4 202.6 25.8 0.28

Soybean - no bunds 76.6 0.002 35.3 7.0∙10-5 2.1∙10-6

Soybean - bunds 76.6 0.054 35.1 0.014 4.0∙10-4

The soil losses were higher in the situation with bunds. However, the soil bunds are soil and water

conservation structures and were expected to lower the runoff and erosion. The farm management

had also observed a reduction in erosion. This discrepancy between modelled and observed erosion

rates can be explained by several factors. The first possibility is that the model was not capable of

properly simulating the effect of the bunds. This factor is discussed as part of the second research

question. A second possibility was that, due to the construction of soil bunds, erosion took a

different form. This means that instead of gully formation which obstructs farming practices, the

erosion consisted primarily of sheet erosion.

Another possibility is that the channel structure concentrated the flow and was thus more prone to

erosion. The channels cover 0.71 ha in the maize field and 0.66 ha in the soybean field. To verify this

possibility, the soil losses in the fields with bunds, in kg of soil, were further divided by soil loss in the

channel and losses in the rest of the field (table 27). Soil loss in the channel forms a high portion of

the total losses. Considering this, the erosion in the rest of the field is lower than in the case without

bunds. This means that the bunds reduce the overall erosion in the crop area. At the same time, flow

and sediment concentrates in the channels which can potentially become highly eroded over time.

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Table 27 Soil losses in the field with bunds and without bunds. The soil losses in the field with bunds are further divided between soil loss in the channel and in the rest of the field

Soil losses with bunds Soil losses without bunds

Soil loss reduction

Event Total (kg) In the channel (kg) In the field (kg) Total soilloss (kg) In the field (%)

Maize

6 January 121,869 45,485 76,384 90,421 16%

12 January 74,807 36,557 38,250 45,490 16%

9 February 25,766 22,145 3,621 6,625 45%

Soybean

6 January 12,809 8,540 4,269 5,009 15%

12 January 459 158 301 354 15%

9 February 14 14 0.03 0.07 57%

46 | P a g e

The additional sub-questions for the second research question are:

a) Do the results at the catchment outlet reflect the impact of the soil bunds on soil and water

erosion?

b) What is the spatial distribution of the soil erosion rates?

c) Does the spatial distribution of the soil erosion rates reflect the impact of the soil bunds?

The answer to the first sub-question is a logical outcome of the answer to the first research question.

The total erosion in terms of water and soil losses were simulated to be higher in the situation with

bunds. This can possibly be explained by a failing modelling capacity of the LISEM, a change in flow

patterns and a concentration of flow into the channels. These three possibilities were investigated in

order to answer the second and third sub-questions.

The spatial distribution of soil erosion was captured by maps of the runoff, soil losses and infiltration,

created by LISEM. In figures 14 to 17 runoff, in litres per seconds, is presented at a time interval of 40

minutes in the maize field and 37 minutes in the soybean field. For the case of the soybean field, the

rainfall intensity on the eastern side was too low to generate runoff at the captured point in time. In

the situation without bunds, the flow is not interrupted by bunds and flows more homogenously

than in the situation with bunds. However, the runoff concentrates itself in natural flow paths, which

may lead to gully formation. For both fields, it can be stated that in the situation with bunds, the

water reaches the bunds and starts flowing towards the channel. The concentration of flow leads to

higher erosion rates, which is reflected in the higher flow detachment, and reduces the time for this

water to infiltrate.

In figure 15, the simulated runoff occurs in a large area of the maize field, but the average runoff rate

is lower compared to the situation with bunds, see figure 14. Runoff and erosion are higher in the

case with bunds, which is a result of the concentration of flow along the bunds, and is also due to the

drainage direction network created by LISEM, which prevents the creation of local elevation

depressions. These local depressions occur both in reality and in the model upslope of each bund.

The bunds have a big impact on both the result of the model and on the actual erosion patterns. In

the field situation, the bunds do not only slow down water and sediment flow, but also form small

depressions. These depressions are formed because there is some elevation variation of the bund

along the contour line, which means that the bund varies very slightly in altitude. These depressions

are actually units of water retention, where water and sediments accumulate and infiltrate. The

accumulation continues until the storage capacity is reached, at which point water and sediment will

continue flowing. However, the drainage direction network is always continuous and it ignores local

depression.

In some cases, the model guides the runoff pathway through the bunds, practically breaching the

bund. This is not according to the reality, and would only occur if a bund is severely eroded. This

should be taken into consideration when determining the reliability of the models results. In the field

without bunds a clear trend can be seen in both the maize and soybean field; runoff concentrates

roughly at the location of the channels.

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Figure 14 Runoff in the maize field with bunds 40 minutes following the start of the rainfall event on 12 January. The x and y axes represent distance in meters.

Figure 15 Runoff in the maize field without bunds 40 minutes following the start of the rainfall event on 12 January. The x and y axes represent distance in meters.

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Figure 16 Runoff in the soybean field with bunds 37 minutes following the start of the rainfall event on 12 January. The x and y axes represent distance in meters.

Figure 17 Runoff in the soybean field without bunds 37 minutes following the start of the rainfall event on 12 January. The x and y axes represent distance in meters.

49 | P a g e

5 Discussion The bunds were expected to significantly reduce water erosion. In case studies, Hengsdijk et al.

(2005) found a reduction in runoff of approximately 50%, while a different study by Herweg and Ludi

(1999) found an average reduction in annual runoff of 14% and in soil loss of 48%. In comparison, the

reduction in soil loss in this study, discounting the channel, was 16% and 15% in the maize and

soybean field, respectively, for the events of 6 and 12 January. The reduction was 45% and 57%,

respectively, for the event of 9 February.

While the simulated results did not conform to the expectations, more understanding was gained

about the challenges in this type of DEM-based erosion modelling. The maps gave insight in the

spatial distribution, which show a reduction of erosion in the field due to the bunds, but an increase

of flow erosion along the bunds and in the channel.

After reviewing the literature it seems that no studies, using LISEM or other models, on modelling

water conservation, retention or harvesting measures on field or catchment scale have been

published. A possible explanation is given in the study by Hessel and Tenge (2008), in which the

authors reasoned that the cell size is too large, while spatial data is insufficiently detailed.

Furthermore, we saw that in the model the local elevation depressions, spanning several cells, were

ignored to create a continuous drainage network. To solve this, a three-dimensional model is

required, which includes the horizontal surface water retention, a well-studied subject (Schwilch et

al., 2012, Reij et al., 2013). Similarly, a DEM based erosion model, that includes terraces, should

include subsurface erosion, which is driven by vertical water movement. However, this type of

erosion, called piping, still requires research in order to incorporate it into field and catchment scale

erosion models (Wilson et al., 2013).

The methodology and results included uncertainties and were influenced by a number of mistakes.

These are discussed in the following sub-chapter about the fieldwork performance. The limitations

on the capability of the model to simulate the effect of the bunds is further discussed in the model

performance sub-chapter.

5.1 Fieldwork performance The fieldwork could be performed according to the methodology that was devised. The only

exception was the steering of the sediments inside the barrel to take a sample for sediment

quantification. Mixing the water and sediments using this method was difficult, as the barrels were

buried in the ground. The highest concentration was likely still at the bottom of the barrel, especially

in the cases where the barrels were completely full. A higher sediment concentration at the bottom

of the barrel will have affected the measured sediment results, which can explain why the measured

and modelled sediments lack correlation.

The weirs functioned properly, as runoff overflowing the weirs had entered the barrels easily. The

actual performance during a rainfall event was not verified. The setup, with the runoff and sediment

catcher, can have influenced the uniform overflow, which influences the ratio between measured

values and actual overflow values. Overflow is not likely to be entirely uniform to start with, in

particular at low discharge, even if the weir is perfectly levelled. Furthermore, a small depression was

observed in front of the weirs, where water would first accumulate before flowing over the weir. This

50 | P a g e

means that runoff from small events was possibly not registered. It also can influence the measured

ratio between runoff and sediments.

The field was considered to be covered homogenously with its crop. However, it should be noted

that close to the forest areas, roads, depressions immediately before and after the bunds, and

channels the crop growth was less. Along the border of the forest there were often patches of a few

meters wide that were bare. Also, the sowing machines sometimes got disconnected from the soil

just before or after a bund. This has resulted in some bare patches in the field. These bare areas are

random, but always close to a bund. LISEM has the option to model wheel tracks of agricultural

machinery, but this option was not used. Each time machines enter the field, they take slightly

different routes through the field. The greatest effect was not noticed in the compaction of the soil,

but the damage to crops.

The form of the rainfall events simulated here, using the triangular hyetograph, are hypothetical,

and may not be the correct representation of the actual rainfall intensity during an event. For

example, in a study 25km south of Córdoba City rainfall events of 1.5 to 2.5 hours were evaluated

(Esmoriz et al., 2008). In their study, the rainfall duration appeared to be independent from total

rainfall depth. In addition, the daily rainfall may have been distributed among two or more smaller

events during that day. There are studies that support the simplification of rainfall events to a certain

level. Chargui et al. (2013) stated that most rainfall events that they observed in central Tunisia had

shapes similar to the modelled event, in which most events also only had one peak. Woolhiser and

Osborn (1985) also noted that, at the end of a rainfall event, there commonly is a period of low

intensity.

The measured input parameters were within the range that is usually found for these soil types; clay

loam and silt loam in the maize field and loam and silty clay loam in the soybean field. Hydraulic

conductivity was the parameter with the greatest difference between the two fields. This was likely

caused by an error, caused by macro-pores that opened up in the soil samples during transportation.

In the calibration process, this difference was decreased to 56.3mm/hr and 74.9mm/hr in the maize

and soybean fields, respectively. This is about three to five times as much as normally measured

(Clapp and Hornberger, 1978, Rawls et al., 1982). The value of the soil cohesion was higher than the

one used for this soil type in the European Soil Erosion Model (EUROSEM), a very similar model to

LISEM (Morgan et al., 1998).

In the process of calibration, it was not possible to verify whether the forms of hydrograph and

sediment graph are correct as only total runoff and sediment losses were measured. Therefore, it is

difficult to say which parameters have correctly influenced the results. It is possible that the adjusted

saturated hydraulic conductivity has overcorrected in favour of changing other parameters. Whereas

in a measured hydrograph or sediment graph the effect would be visible, in this case it is not.

Calibration is also restricted by the limited amount of reliable runoff and sediment data from the

weirs. This is due to some barrels floating out of their holes, preventing runoff from entering, due to

some barrels becoming completely filled, providing only a minimum runoff value, and due to a

limited amount of rainfall events that generated runoff. In the local hydrological calendar, rainfall

events would normally already start in December (SMN, 2015). In the monitoring year (2014),

December was completely dry, while a large event occurred on the 3 and 4 October 2014, before the

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monitoring period started. Another large event took place on 14 and 15 February 2015, just after the

monitoring period ended.

There are some aspects that support the reliability of the models results, despite the large measure

of uncertainty in the calibration process. First, the simulated data of 9 February at the weirs in the

maize field are similar to the data measured, with full barrels for weirs 1 and 3, but no runoff at weir

2. Second, the average soil loss across the field in the model is in the same range of the sediment

losses measured in the plots.

5.2 Model performance The effect of the bunds is also reflected on the models results, but with different outcomes. Similar

to reality, there are natural depressions formed between the bund and the natural slope. Artificial

depressions are also formed by the simplification of the elevation data, which is based on contour

lines on intervals of 1 meter elevation. Another source of artificial depressions is the grid size, which

is too big to reflect the smooth surface of reality. That is to say, the pathway that runoff would take

in reality, is blocked by a cell, because the cell is formed as a square. This cell is, on average, part of a

bund, but the other part contained the pathway that water in reality would take. A simplified

illustration is presented in figure 18, in which an artificial depression is formed in cell C2.

A B C D E

1 0

2 2 2 1

3 3 2 2 2 1

4 3 3 3 3 2

5 4 4 4 3 3 Figure 18 A simplified scheme of a sloping field in which an artificial depression (cell C2) is formed. The grey area indicates the bund, while numbers 0-4 indicates the elevation

LISEM requires a continuous drainage network, and will forcibly ignore depressions to ensure such.

By ignoring the depressions, the effect of bunds on water retention and soil conservation is

diminished. As a result, the runoff that is concentrated just upslope of a bund can continue to flow

and erode, leading to higher runoff and soil loss outcomes at the main outlets. Furthermore, by

looking at the runoff maps, it appears that some bunds have been breached. Water flows along a

bund, but at some points the bunds are ignored. This means a high concentration of water and

erosion, which simulates the potential development of gullies. However, in reality the bunds are not

breached, at least not in the rainfall events that were modelled. This form of breaching is unintended

and likely a side effect of the method that LISEM uses to fill the depressions. As a result, some bunds

that receive runoff from bunds further upslope can have a high amount of runoff, while other bunds

have part of their runoff drained by a breach.

Lastly, the model does not have an input parameter directly taking into account the litter cover in the

field. The low runoff values in the soybean field could partially be explained due to a thick layer of

litter, that was absent in the maize field. Litter has multiple effects, such as reducing splash

detachment, slowing down runoff and stabilizing the soil. These factors are not taken directly into

account and can have an important influence on water and sediment yields and may explain some of

the high model results, especially during the initial run.

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6 Conclusions The first research question that was investigated in this report is:

What is the reduction of soil and water losses as a result of constructing soil bunds and

channels in maize and soybean fields in Córdoba, Argentina, according to fine spatial modeling?

The bunds work properly by reducing the water erosion in the field. Based on modelled results, it

was not possible to conclude whether the overall amount of water in the field was increased by the

introduction of the bunds. The modelled results show that, outside the channel, soil losses

diminished by 15% to 57% in both fields due to the bunds. In return, runoff and sediment

detachment by flow is concentrated along the bunds and in the channel.

The second research question is:

What are the advantages and disadvantages of adjusting the DEM to model the effect of

bunds on soil erosion?

The model was not capable of fully representing the effects that bunds have on water and sediment

transport. The model does properly simulate the effect of bunds by creating a longer drainage

pathway, which decreases the overall slope and increases the time available for water to infiltrate

into the soil. However, another aspect of the soil bund is that they force a new pathway along a

contour line, just upslope of the soil bunds. This pathway accumulates the water flowing downslope.

The accumulation of water increase the runoff velocity and volume, increasing its transport capacity.

The effect of depressions, which is a water harvesting factor, is not modelled, making the model

inadequate for simulating soil conservation measures, like bunds, that have a water harvesting

aspect. This is due to elevation depressions being removed from the elevation model. The effect of

disrupting and redirecting the flow was in general successful, although the drainage direction

algorithm forced a breach in some of the bunds.

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7 Recommendations These are recommendations for the farm management. It was observed in the field that often the

pathway upslope of the soil bunds was cut off from the channel. The channels have a barrier of soil

on each side. This barrier is higher than the field and prevents water flowing along the bund from

entering the channel. In case the runoff quantities are low, the water will be retained and can

infiltrate. However, in case of great rainfall events, the runoff can start flowing over the lowest point

of a soil bunds and thus create a breach. This leads to high erosion rates and likely gully forming

downslope of the breach. It is recommended to create a passage from the field to the channel, at the

point where both meet. This passage may be higher than the field to promote water retention, but

should be lower than the lowest point of the soil bund. This will cause excess water to leave the field

and prevents the erosion of the soil bunds and subsequent gully formation.

The soil bunds interrupt runoff and sediment flow, thus lowering the erosion rates and gully

formation due to the concentration of flow. Regardless, during large rainfall events the area between

bunds will generate some runoff and sediment flow. A portion will settle upslope of the bunds,

mostly due to the depressions mentioned and due to a decrease in drainage slope. An accumulation

of sediment will increase the elevation of the area upslope of the bunds. Over time, several years,

this elevation can reach the level of the bunds, negating their effect. Therefore, it is important to

maintain the area upslope of bunds by deepening this area regularly.

It is furthermore recommended to use this soil, extracted by deepening the area upslope of the bund,

to restore the soil bunds. Water erosion and the wheels of agricultural machinery can lower the soil

bunds and this may eventually lead to a breach of the bund. Not only the bunds should be

maintained regularly, but also the channel should be monitored. Erosion in the channel will not

influence crop yield, but over a longer time period gullies may form in the sides of the channel and

create incision into the surrounding field. Hence, it is important to maintain plant cover in the

channels and to monitor erosion in the channel after several large storms.

To guarantee the positive effect of the bunds, regular maintenance is important. The state of the

bunds should be monitored yearly to prevent bunds from breaching during storm events. Further

attention should be paid to the connection between the bunds and the channel. Water should be

able to discharge into the channel before it reaches the height of the bunds, in order to prevent

breaching.

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9 Appendices

9.1 Appendix A: Interview on the topic of the farm land management Respondent: Marcelo Budovski (farm manager)

Interviewer: Siebrand van der Hoeven

Date: 09-02-2015

The interview was done in Spanish and translated in English (cursive).

1a. ¿En esos campos habían usado una sistema de maíz-soja-maíz-soja en los últimos 5 o 10 años? Was a maize-soybean rotation used in these fields for the past 5 to 10 years?

Exactamente, tenemos una sistema de rotación entre soja un año y maíz el otro año. Indeed, we have a system of rotation between soybean one year and maize in the next.

1b. ¿Cambiaron entre maíz y soja cada año o también sembraron otros tipos de cultivos? Did you switch between maize and soybean every year or did you also plant other crop types?

También cada dos años después de cultivar soja en el verano, sembramos garbanzo en 10% de los campos durante el invierno. Esto no ha pasado todavía en los campos de maíz y soja. Every two years during the winter we also sow chickpea in 10% of the fields. This is done in fields that were cultivated that year with soybean during the summer. This [ed. cultivating chickpea] has not happened in the maize and soybean fields [ed. which were used for this study].

2. ¿Cuándo introdujeron siembra directa en esos campos? When was direct sowing introduced in these fields?

Introducimos siembra directa en los años 1991/2. We introduced direct sowing in the year 1991/2.

3. ¿Qué tipo de sistema de siembra se utilizaba antes de la siembra directa? Which sowing system was used before the introduction of direct sowing?

Antes usábamos una sistema de labranza mínima por unos treinta años. Before [ed. 1991/2] we used a minimum tillage system for about thirty years.

4. ¿Hubo una transición entre la labranza convencional y siembra directa, utilizando la labranza reducida/mínima en el medio? Si es así, ¿cuánto tiempo ha sido aplicado antes de la siembra directa? Por cuántos años ha sido la siembra convencional utilizado? Was there a transition between conventional tillage and direct sowing, using minimum tillage in between? If yes, then for how much time was was mininum tillage applied before the direct sowing system? For how many years was conventional sowing and tillage used?

En los 80’s cambiamos de labranza convencional a la labranza mínima. In the eighties we changed from conventional tillage to minimum tillage.

5. ¿Cómo considera usted la severidad de la erosión hídrica antes y después la introducción de labranza mínima, en una escala de 1 (muy poca la erosión del agua) a 10 (muy fuerte la erosión hídrica)? How do you consider the severity of water erosion before and after the introduction of direct sowing on a scale of 1 to 10 (1 means little erosion and 10 means severe water erosion).

La erosión hídrica era un 6 o 5 durante la época en cual usamos labranza mínima. Después con la siembra directa era un 3. During the time we used miminum tillage the water erosion was a 5 or a 6. Afterwards, when we introduced direct sowing, water erosion was a 3.

6. ¿Cree usted que las medidas ya aplicadas (diques, terrazas, canales) está siendo efectivo? En una escala del 1 al 10, ¿cómo clasificar la reducción de la erosión del agua después de la aplicación de estas medidas de mitigación? Do you think that the applied measures, such as dikes, soil bunds and channels, have been effective?

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On a scale of 1 to 10 [ed. same scale as in question 5], how do you classify the reduction of water erosion since the conservation measures were applied?

Ahora con las medidas aplicadas la erosión hídrica sería un 1 o 2. Hace 3/4 años empezamos con construir las medidas. Now, with the measures applied, the water erosion is reduced to a 1 or 2. Since 3 to 4 years have we started construction these measures.

7. ¿Si las terrazas se erosionan, hay una forma en cual les restauran? ¿Aquello ya tenían que hacer una vez y en cual campo(s) fue? In case the soil bunds erode, do you have a way of repairing them? Has this happened already and which fields was this the case?

Cada invierno vamos a controlar las terrazas. Si una terraza es demasiada baja en una parte la reparamos con pala y rastra. Eso hacemos nosotros mismos. Every winter we check the soil bunds. If there is a part that is too low, then we repair it with shovels and machines. We do this ourselves

8. También se puede ver en algunos puntos que las terrazas no tienen una salida al canal. Además en el canal del campo de soja falta pastura en muchas partes. ¿Porque es así? It is possible to see that in some cases the bunds do not discharge in the channel. In addition in the channel of the soybean field much of the grass is gone. Why is that?

Si, esa parte es una mala construcción. La terraza debería tener una salida al canal, si no el agua sube y pasa sobre la terraza causando erosión en la parta abajo. Desde ahora vamos a hacer esa parte nosotros mismos. Primero construimos el canal y ponemos la pastura, y solo en el año siguiente empezamos construir las terrazas. La falta de pastura en ese canal es por la lluvia y la escorrentía cual daño el canal y las plantas. Yes, that is a construction mistake. The bunds should discharge into the channel, if not the water rises and passes over the bunds, which causes erosion in the part below the bund. From now on we ourselves will create the exit from the bunds to the channel. We will first construct the channel and put grass in the channel, and only after a year we will construct the bunds. The lack of grass in that channel is caused by rainfall and runoff which damaged the channel and the plants.

9. A partir de su experiencia y observaciones en el campo, ¿tienes alguna idea sobre cómo reducir con mayor eficacia los problemas de erosión del agua que aún existen? Using your experience and observations in the field; do you have more ideas to more efficiently reduce any still existing water erosion problems?

No, ahora estamos en un buen camino; hay poca erosión con las medidas que tenemos. Además no existen muchas otras cosas que podemos hacer. Si no, sería imposible hacer la agricultura. No, at this point we are on the right track; with the current measures there is little erosion. In addition there is little else that we can do or it would be impossible to cultivate.

10. ¿Cuáles fueron los principales cambios que se producen en la gestión del campo en que se aplica la siembra directa, es decir, en términos de fertilizantes y pesticidas a usar? ¿Se disminuyen o aumentan, y por qué? ¿Tuvo que cambiar el tipo de fertilizante o pesticidas que se aplican? ¿Por qué? After the introduction of direct sowing, what were the greatest changes in the land management, such as use of fertilizers and pesticides? Did you use more or less, and why? Was it also necessary to change the type used, and why?

Los cambios principales fueron que ahora usamos menos maquinas en el campo. Cuando usamos labranza, primero tenía que pasar una máquina para soltar el suelo, después una para romper los terrones de suelo, y al final una para difundir y aplanar el suelo. Ya no usamos este sistema, cual tenía la gracia que también mato a todas las hierbas malas en el campo. Por eso usamos más herbicidas ahora y también más fertilizantes. El uso de insecticidas no ha cambiado, tampoco el uso de fungicidas. The greatest changes were that we now use less machines in the field. When we used plowing, first a machine had to loosen the soil, after which another machine had to break the clumps of soil, and

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lastly one to smoothen the soil. We do not use this system, which also destroyed the weeds, anymore. That is why we use more herbicides now and also more fertilizers. The use of insecticides and fungicides has not changed.

11. ¿Ha observado un aumento o una disminución (en el tiempo) sobre plagas después de la introducción de la siembra directa? ¿Ha observado un aumento o disminución en la resistencia a las malas hierbas herbicidas aplicados? ¿Si se observó un aumento, la resistencia era a la que el herbicida (s)? Over time have you observed an increase or a decrease of weeds and pests after the introduction of direct sowing? Similarly has the resistance of weeds increased or decreased, and if so; to which herbicide?

Si, vemos más malezas ahora, especialmente las hierbas malas. Usamos mucho Roundup que tiene glifosato. Este herbicida mata a todas las plantas salvo los cultivos. Durante los años las hierbas han desarrollado una tolerancia contra la herbicida. Eso significa que el uso de glifosato es menos efectivo ahora. Yes, we have more pests now, especially weeds. We use Roundup, which has glyphosate. This herbicide kills the weeds but not the crops. During the years the weeds have developed a tolerance against the herbicide. This means the use of glyphosate is less effective today.

12. ¿Ha cambiado la cobertura superficial de los montes (área forestal) durante los últimos 5 años? ¿En case que sí, cuanto es la diferencia? Has the area covered by forest changed in the last five years? If so, what is the difference?

No, los montes no han cambiado nada. Sí es posible que en los bordes de los montes había un cambio en vegetación, que ahora hay más hierbas malas. Adentro los montes todavía sigue siendo la vegetación original. No, the forest area has not changed. It is possible that at the edges of the forest there has been a change in vegetation, now that there are more weeds. Within the forest the original vegetation still persists.

13. En el campo de maíz se puede ver en Google Earth que había otras terrazas antes las terrazas de hoy. ¿Cuándo cambiaron de esas terrazas a las terrazas nuevas y cuál fue la razón? On Google Earth it is possible to see that the field maize had other soil bunds before the current ones. When did you change to the new soil bunds and what was the reason?

Marcelo: Las primeras terrazas fueren instaladas hace muchos años, creo en 1993. No sé exactamente porque cambiamos de terrazas. Las terrazas nuevas fueron instaladas hace 4 años y al mismo tiempo construyeron los dos diques y el canal en ese campo. Otra razón es que fue difícil para las maquinas pasar a esas terrazas. The first bunds were constructed a long time ago, I think in 1993. I do not know exactly why we change the soil bunds. The new bunds were constructed four years ago and at the same time the dikes and channel were constructed in that field. Another reason was the machines had difficulty passing over those bunds.

14. En el campo de soja se puede ver muchos más rastrojo y residuos de plantas que en el campo de maíz. ¿Existe una razón especial o se puede ver al revés en el año siguiente? In the soybean field one can see much more stubbles and crop residues than in the maize field. Is there a special reason for this or will it be the other way around in the next year?

Sí, la mayor cantidad del rastrojo solo es por el cultivo anterior. Yes, the majority of the stubbles is a result of the previous round of cultivation.

15. En el campo de soja también se puede ver que en unas partes no sembraron por unos metros. Además falta muchas veces las plantas cultivos justo antes y después una terraza [ed. siembran perpendicular a las terrazas]. ¿Porque no llegaron las semillas allá al suelo? In the soybean field one can see that some parts no sowing was done for a couple of meters. Also often no crops are seen just before and after a soil bunds [ed. as sowing is done perpendicular to the bunds]. Why did the seeds not enter the soil in those places?

Las máquinas de siembra tienen que pasar sobre las terrazas y allá se puede perder unas semillas, sí.

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Si la máquina no mantiene contacto con el suelo para. El conductor debería volver unos metros y empezar de nuevo, pero no siempre lo hace. Por eso existen eses lugares sin plantas, que tienen alrededor el tamaño de una máquina [ed. alrededor 15m]. The sowing machines have to pass over the terraces and there they can lose some seeds, yes. If the machine does not maintain contact with the soil, it stops. The driver has to return a few meters and start again, but this does not always happen. That is why some fallow spaces exist, which are around the size of a machine [ed. about 15m].

16. ¿Cuáles son las fechas en cual sembraron y cuando intentan hacer el cosecho? What are the sowing dates of the two fields and when do you intent to harvest?

La fecha de siembra fue el 11 y 12 de octubre para soja y el 28 de noviembre hasta el primero de diciembre para maíz. Está planificado el cosecho de soja en ese campo para marzo [alrededor el 15] y el cosecho de maíz para julio [alrededor el 15]. The sowing date was 11 and 12 October for soybean and 28 November until 1 December for Maize. We plan to harvest soybean in March and maize in July.

17. ¿Si usan fertilizantes, donde en el campo y que es la cantidad? If you use fertilizers, where in the field do you use it and in what quantity?

En soja no usamos fertilizantes, sí usamos inoculantes de raíces y curasemillas. En el campo de maíz usamos Nitrógeno y lo aplicamos al mismo tiempo que hacemos la siembra. La cantidad es variable según los ambientes dentro de un mismo lote. Pero en promedio 110 kg de urea por hectárea. Con un rango de 80 a 130 kg, según zona. In soybean we do not use fertilizers, we do use root inoculants and seed treatment. In the maize field we apply nitrogen at the same time of sowing. The quantity applied varies according to the environment within a field. On average 110kg of urea is applied per hectare, ranging between 80 to 130kg/ha.

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9.2 Appendix B: Simulated rainfall intensity data Rain gauges location: 1. northwest of the maize field, 2. in between the maize and soybean field, and

3. east of the soybean field.

Note: the value at minute 5 is the average intensity over the time period of 0 to 5 minutes.

Table 28 Modelled rainfall intensity data over 5 minute time steps during three rainfall events in January and February 2015

6 January 2015 12 January 2015 9 February 2015

Time Rain gauges Rain gauges Rain gauges

(minutes) 1 2 3 1 2 3 1 2 3

0 0 0 0 0 0 0 0 0 0

5 8.8 8.9 8.9 8.7 8.8 8.6 8.9 8.8 8.7

10 26.5 26.6 26.7 26.2 26.4 25.9 26.6 26.4 26.2

15 44.1 44.4 44.5 43.7 43.9 43.2 44.3 44.0 43.7

20 61.8 62.1 62.3 61.2 61.5 60.5 62.0 61.6 61.2

25 79.4 79.9 80.1 78.7 79.1 77.8 79.7 79.2 78.7

30 97.1 97.7 97.9 96.2 96.6 103.4 97.5 96.8 96.2

35 116.1 118.3 115.6 111.5 112.8 88.2 114.8 105.6 102.8

40 100.6 111.9 115.6 85.7 93.1 70.9 108.1 96.8 85.7

45 82.9 94.1 97.9 68.2 75.5 53.6 90.4 79.2 68.2

50 65.3 76.4 80.1 50.7 58.0 36.3 72.7 61.6 50.7

55 47.6 58.6 62.3 33.2 40.4 19.0 54.9 44.0 33.2

60 30.0 40.8 44.5 15.7 22.8 5.2 37.2 26.4 15.7

65 12.4 23.1 26.7 3.5 7.0 0 19.5 8.8 3.5

70 1.8 7.1 8.9 0 0 0 5.3 0 0

75 0 0 0 0 0 0 0 0 0

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9.3 Appendix C: Results of input parameter measurements

Table 29 Leaf area index (LAI) of soybean, maize and forest at maturity and the number of days necessary for plants to grow to maturity

Time to max LAI (d) max LAI (m2/ m2)

Soybean 60 4

Maize 70 5.4

Forest Constant 8

Table 30 Fraction of the soil covered by vegetation

6-1-2015 19-1-2015 2-2-2015

Soybean plot 1 0.65 0.65 0.70

Soybean plot 2 0.70 0.70 0.80

Soybean plot 3 0.70 0.70 0.75

Maize plot 1 0.15 0.35 0.55

Maize plot 2 0.20 0.40 0.60

Maize plot 3 0.20 0.45 0.60

Table 31 Plant height of soybean (cm)

28-11-2014 15-12-2014 6-1-2015 19-1-2015 2-2-2015

SP1 SP2 SP3 SP1 SP2 SP3 SP1 SP2 SP3 SP1 SP2 SP3 SP1 SP2 SP3

22 22 21 38 43 46 65 70 86 84 95 82 98 107 106

26 25 16 39 37 43 70 78 75 80 90 95 89 91 94

14 16 17 44 43 45 67 73 76 88 97 93 103 107 93

16 14 15 41 50 41 80 76 87 89 92 93 116 96 114

17 24 24 37 27 39 85 84 73 88 98 93 93 118 98

24 26 23 48 34 41 75 78 88 91 92 93 91 93 115

21 12 22 42 43 31 76 76 73 84 82 80 92 97 112

16 23 29 47 46 40 72 83 66 90 98 93 88 116 98

22 17 23 44 44 38 69 67 87 96 98 96 102 106 117

19 23 24 38 40 39 78 68 83 86 90 81 103 95 107

23 18 20 56 45 42 76 85 81 100 81 94 87 92 88

28 15 25 38 41 34 77 67 72 97 85 101 112 107 100

20 13 11 37 45 41 72 82 72 92 91 94 90 111 105

18 18 17 42 50 36 65 76 72 83 84 88 110 99 111

25 26 17 45 35 35 82 71 68 92 99 83 111 110 92

10 18 26 50 43 42 87 67 75 98 98 86 94 102 103

23 20 24 42 41 40 73 72 68 82 94 98 97 110 112

22 27 16 48 46 36 74 82 77 81 100 82 111 98 97

21 26 2 45 32 31 76 87 72 84 95 90 93 110 106

27 15 27 40 50 39 77 68 72 79 87 93 97 91 117

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Table 32 Plant height of maize (cm)

9-12-2014 15-12-2014 6-1-2015 19-1-2015 2-2-2015

MP1 MP2 MP3 MP1 MP2 MP3 MP1 MP2 MP3 MP1 MP2 MP3 MP1 MP2 MP3

9 10 5 14 13 16 67 74 70 156 181 185 224 225 228

7 7 6 15 15 16 65 70 68 192 199 185 229 247 238

9 9 11 18 13 13 72 75 76 184 186 203 222 244 232

6 10 7 13 15 13 80 50 63 178 174 189 227 213 229

7 7 7 19 16 13 67 67 78 162 169 174 242 207 216

9 8 10 16 15 15 78 77 76 183 174 174 228 229 207

8 6 8 18 15 16 89 72 65 186 173 204 224 223 239

9 8 12 13 18 16 82 75 79 174 200 198 204 223 246

10 6 5 18 16 19 72 65 72 184 196 183 223 240 243

9 9 9 13 16 15 73 82 75 199 182 191 228 240 219

8 6 9 19 11 13 80 70 60 187 180 176 241 219 237

7 9 10 22 15 15 81 71 75 204 193 197 208 212 230

10 6 10 14 16 12 75 70 67 176 164 200 216 241 231

10 8 8 19 13 11 72 73 78 197 179 186 228 226 218

9 7 9 13 8 12 68 70 70 192 158 183 218 228 208

10 7 9 18 12 12 78 75 74 177 194 174 201 220 219

7 9 8 16 13 14 82 74 75 180 165 194 222 240 226

6 10 14 16 14 12 70 75 71 183 167 173 233 205 214

6 8 9 16 9 19 80 77 74 194 164 199 235 222 222

7 8 7 17 18 13 73 66 78 201 180 181 232 245 226

Table 33 Random roughness (cm) measured in soybean and maize field using the chain method

Length 1 Length 2 Length 3 Average length Random roughness

Soybean plot 1 96.5 103 98.5 Soybean plot 2 103 96.4 97 Soybean plot 3 99.8 94 103.2 99.04 10.59

Maize plot 1 96.8 92.5 103.8 Maize plot 2 100 96.7 99.9 Maize plot 3 99.1 95.5 99.7 98.22 11.83

Table 34 Fraction of crusts (-) estimated in January and February 2015

6-1-2015 19-1-2015 2-2-2015

Soybean plot 1 0.05 0.05 0.05

Soybean plot 1 0.05 0.05 0.05

Soybean plot 1 0.05 0.05 0.05

Maize plot 1 0.40 0.35 0.25

Maize plot 1 0.40 0.30 0.20

Maize plot 1 0.40 0.40 0.20

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Table 35 Saturated hydraulic conductivity (cm/day) measured in the top soil (0-10cm)

Saturated hydraulic conductivity

Soybean plot 1 19.39

Soybean plot 1 87.51

Soybean plot 1 96.64

Maize plot 1 16.10

Maize plot 1 296.24

Maize plot 1 17.40

Table 36 Saturated volumetric soil moisture (-) in the top soil (0-10cm)

Saturated volumetric soil moisture

Soybean plot 1 0.214

Soybean plot 1 0.245

Soybean plot 1 0.286

Maize plot 1 0.257

Maize plot 1 0.247

Maize plot 1 0.254

Table 37 Gravimetric (GSM), volumetric (VSM) and adjusted volumetric soil moisture (VSM_adj) (-) in the soybean field before each rainfall event

06-01-2015 12-01-2015 09-02-2015

Depth GSM VSM VSM_adj GSM VSM VSM_adj GSM VSM VSM_adj

0-5cm 0.16 0.15 0.08 0.26 0.25 0.12 0.24 0.23 0.11

15-20cm 0.16 0.21 0.10 0.20 0.27 0.13 0.18 0.25 0.12

30-35cm 0.16 0.22 0.11 0.19 0.26 0.13 0.18 0.23 0.12

0-5cm 0.14 0.14 0.07 0.22 0.22 0.11 0.23 0.22 0.11

15-20cm 0.16 0.21 0.10 0.20 0.27 0.13 0.17 0.23 0.11

30-35cm 0.16 0.21 0.10 0.21 0.28 0.14 0.18 0.24 0.12

0-5cm 0.19 0.18 0.09 0.22 0.22 0.11 0.22 0.21 0.10

15-20cm 0.18 0.24 0.12 0.19 0.25 0.13 0.15 0.20 0.10

30-35cm 0.20 0.26 0.13 0.19 0.26 0.13 0.16 0.22 0.11

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Table 38 Gravimetric(GSM), volumetric (VSM) and adjusted volumetric soil moisture (VSM_adj) (-) in the maize field before each rainfall event

06-01-2015 12-01-2015 09-02-2015

Depth GSM VSM VSM_adj GSM VSM VSM_adj GSM VSM VSM_adj

0-5cm 0.14 0.16 0.08 0.23 0.26 0.13 0.24 0.27 0.13

15-20cm 0.18 0.25 0.13 0.18 0.25 0.13 0.18 0.25 0.12

30-35cm 0.18 0.25 0.12 0.20 0.26 0.13 0.17 0.23 0.12

0-5cm 0.16 0.18 0.09 0.23 0.26 0.13 0.22 0.25 0.12

15-20cm 0.20 0.27 0.14 0.19 0.26 0.13 0.16 0.22 0.11

30-35cm 0.19 0.25 0.13 0.19 0.26 0.13 0.18 0.23 0.12

0-5cm 0.14 0.16 0.08 0.27 0.30 0.15 0.23 0.26 0.13

15-20cm 0.18 0.25 0.13 0.19 0.26 0.13 0.21 0.29 0.15

30-35cm 0.18 0.24 0.12 0.19 0.25 0.13 0.20 0.27 0.13

Suction at wetting depth

Maize: clay loam: 27.00cm

Soybean: loam: 10.01cm

Table 39 Aggregate stability () as the median number of drops needed to half the size of aggregates on a sieve

Aggregate stability index Median number of drops

Test 1 Test 2 Average

Soybean plot 1 0.32 0.28 0.30 26.72

Soybean plot 1 0.33 0.30 0.31 28.21

Soybean plot 1 0.31 0.27 0.29 26.30

Maize plot 1 0.30 0.26 0.28 25.39

Maize plot 1 0.30 0.29 0.29 26.42

Maize plot 1 0.34 0.31 0.32 29.04

Table 40 Cohesion (kPa) measured using a torvane in soybean and maize field

Soybean (Large vane:0.02) Maize (Normal vane: 0.1)

Plot 1 Plot 2 Plot 3 Plot 1 Plot 2 Plot 3

15.50 11.18 13.73 26.48 25.50 35.31

14.32 14.32 16.08 20.59 33.34 30.40

14.51 13.93 15.69 29.42 24.52 27.46

16.08 15.89 6.67 27.46 22.56 25.50

15.10 14.71 15.89 28.44 28.44 23.54

9.22 14.51 14.71 23.54 26.48 32.36

14.32 13.93 15.30 31.38 22.56 23.54

15.89 14.71 15.69 31.38 23.54 33.34

13.53 13.93 13.53 27.46 31.38 25.50

7.45 13.53 14.71 24.52 22.56 34.32

Average 13.59 14.06 14.20 27.07 26.09 29.13

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Table 41 Distribution of the median grain size, as well as d10 and d90, before and after dispersing soil aggregates

D10 (µm) D50 (µm) D90 (µm)

Soybean soil 3.43 34.17 111.66

Dispersed soybean soil 3.62 36.96 117.7

Maize soil 1.6 13.43 53.14

Dispersed maize soil 1.42 12.62 53.32