was then determined with a Euro Elemental Analyzer coupled with a Finigan Delta
Infrared Mass Spectrometer (IRMS). The results, building on a pairwise comparison
of d
13
C values for maize leaves in the same alley, consistently detected significantly
higher d
13
C values (p < 0.03) in the rows close to the hedgerows, as compared to
those of the maize plants located in the central row of the alley. The results also
showed a relationship between available NO 3
À
–N in the soil and the sampled maize
leaves after 120 days of planting, with d
13
C signatures becoming depleted with
increasingly available NO 3
À –N (R
2
¼ 0.50–0.92). These findings led to the development of a newly developed framework for showing available NO 3
À – N and
13
C
isotopic discrimination in maize plants (Fig. 7.8). The approach highlighted the
impact of fertilizer application on crop water uptake and the dynamics between
crop response drivers, such as water and N availability. The proposed
13
C discrimination framework, in combination with data on N availability and N uptake into
shoots, showed that N deficiency was a major cause of maize yield decline when
using the hedges.
7.5.1.3 Soil Erosion Modeling
Soil erosion models aim to meet the practical needs of soil conservation and also
advance the level of scientific understanding regarding soil erosion processes
(Morgan 2005). Efforts to develop soil erosion assessment tools have resulted in
a number of empirical and process based models being developed, e.g., the
Universal Soil Loss Equation (USLE) (Wischmeier and Smith 1978), the European
Soil Erosion Model (EUROSEM) (Botterweg et al. 1998) and the Griffith University Erosion System Template (GUEST) (Misra and Rose 1996). Because of the
close relationship between crop production and erosion processes, integrated crop,
soil and tree models that represent both processes and allow a spatial representation
of biophysical impacts are desirable. The WaNuLCAS model was developed to
represent tree–soil–crop interactions in a wide range of agroforestry systems in
which trees and crops overlap in space and/or time (van Noordwijk and Lusiana
1999). The model is able to predict event-based water induced erosion and can thus
be used to explore the positive and negative effects of various combinations of trees
and crops and their management, as well as the soil and weather conditions, on runoff and soil loss. Pansak et al. (2010) successfully applied WaNuLCAS to evaluate
the ability of the model to predict water induced soil erosion, in order to gain a
better understanding of the effectiveness of various SCT regimes at controlling
erosion. WaNuLCAS also allows one to assess the magnitude and dynamics of
those key processes influencing the efficiency of SCT in the mountainous
environments of north-east Thailand. A dataset, encompassing a 3 year period,
originating from field experiments in Loei province, was employed in this case,
with results indicating that the model was able to effectively predict soil loss and
run-off levels (R
2
¼ 0.80 and 0.82 respectively) at the tested sites. Model
simulations demonstrated that the key parameters for effective soil erosion control
were an adequate representation of soil cover development, and that the sustenance
7 Soil Conservation on Sloping Land: Technical Options and Adoption Constraints
255
Infrared Mass Spectrometer (IRMS). The results, building on a pairwise comparison
of d
13
C values for maize leaves in the same alley, consistently detected significantly
higher d
13
C values (p < 0.03) in the rows close to the hedgerows, as compared to
those of the maize plants located in the central row of the alley. The results also
showed a relationship between available NO 3
À
–N in the soil and the sampled maize
leaves after 120 days of planting, with d
13
C signatures becoming depleted with
increasingly available NO 3
À –N (R
2
¼ 0.50–0.92). These findings led to the development of a newly developed framework for showing available NO 3
À – N and
13
C
isotopic discrimination in maize plants (Fig. 7.8). The approach highlighted the
impact of fertilizer application on crop water uptake and the dynamics between
crop response drivers, such as water and N availability. The proposed
13
C discrimination framework, in combination with data on N availability and N uptake into
shoots, showed that N deficiency was a major cause of maize yield decline when
using the hedges.
7.5.1.3 Soil Erosion Modeling
Soil erosion models aim to meet the practical needs of soil conservation and also
advance the level of scientific understanding regarding soil erosion processes
(Morgan 2005). Efforts to develop soil erosion assessment tools have resulted in
a number of empirical and process based models being developed, e.g., the
Universal Soil Loss Equation (USLE) (Wischmeier and Smith 1978), the European
Soil Erosion Model (EUROSEM) (Botterweg et al. 1998) and the Griffith University Erosion System Template (GUEST) (Misra and Rose 1996). Because of the
close relationship between crop production and erosion processes, integrated crop,
soil and tree models that represent both processes and allow a spatial representation
of biophysical impacts are desirable. The WaNuLCAS model was developed to
represent tree–soil–crop interactions in a wide range of agroforestry systems in
which trees and crops overlap in space and/or time (van Noordwijk and Lusiana
1999). The model is able to predict event-based water induced erosion and can thus
be used to explore the positive and negative effects of various combinations of trees
and crops and their management, as well as the soil and weather conditions, on runoff and soil loss. Pansak et al. (2010) successfully applied WaNuLCAS to evaluate
the ability of the model to predict water induced soil erosion, in order to gain a
better understanding of the effectiveness of various SCT regimes at controlling
erosion. WaNuLCAS also allows one to assess the magnitude and dynamics of
those key processes influencing the efficiency of SCT in the mountainous
environments of north-east Thailand. A dataset, encompassing a 3 year period,
originating from field experiments in Loei province, was employed in this case,
with results indicating that the model was able to effectively predict soil loss and
run-off levels (R
2
¼ 0.80 and 0.82 respectively) at the tested sites. Model
simulations demonstrated that the key parameters for effective soil erosion control
were an adequate representation of soil cover development, and that the sustenance
7 Soil Conservation on Sloping Land: Technical Options and Adoption Constraints
255