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Science Nature 2(1), pp.071-085 (2019) e-ISSN: 2654-6264 DOI: https:// doi.org/ 10 .305
98/SNVol2Iss1pp071-085year2019 DEVELOPMENT OF A LAND DEGRADATION
ASSESSMENT MODEL BASED ON FIELD INDICATORS ASSESSMENT AND PREDICTION
METHODS IN WAI SARI, SUB-WATERSHED KAIRATU DISTRICT, WESTERN SERAM
REGENCY, MALUKU PROVINCE, INDONESIA Silwanus M. Talakua1,*, Rafael M.
Osok1 1Department of Soil Science, Faculty of Agriculture, Pattimura University Jl. Ir.
Martinus Putuhena, Kampus Poka, Ambon, Indonesia 97233 Received : November 30,
2018 Revised : March 10, 2019 Published : March 11, 2019 Copyright @ All rights are
reserved by S.M. Talakua and R.M. Osok Corresponding author: *Email:
[email protected] 0 7 1 Development of Land Degradation Assessment
Model Based on Field Indicators Assessment and Prediction Methods In Wai Sari, Sub
Watershed Kairatu District, Western Seram Regency, Maluku Province, Indonesia
Abstract The study was conducted in Wai Sari sub-watershed, Western Seram Regency
Maluku to develop an accurate land degradation assessment model for tropical small
islands.
The Stocking field land degradation measurement and RUSLE methods were applied to
estimate soil loss by erosion and the results of both methods were statistically tested in
order to obtain a correction factor. Field indicators and prediction data were measured
on 95 slope units derived from the topographic map. The rates of soil loss were
calculated according to both methods, and the results were used to classify the degree
of land degradation.
The results show that the degree of land degradation based on the field assessment
ranges from none-slight (4.04 - 17.565 t/ha/yr) to very high (235.44 - 404.00 t/ha/yr),
while the RUSLE method ranges from none-slight (0.04-4.59 t/ha/yr) to very high 203.90
- 518.13 t/ha/yr. However, the RUSLE method shows much higher in average soil loss
(133.4 t/ha/yr) than the field assessment (33.9 t/ha/yr).
The best regression equation of logD/RP = - 0.594 + 1.0 logK + 1.0 logLS + 1.0 logC or
D = 0.2547xRxKxLSx CxP was found to be a more suitable land degradation assessment
model for a small-scale catchment area in the tropical small islands. Keywords: Wai Sari
sub watershed, land degradation, field assessment model, RUSLE ARTICLES I.
Introduction Land degradation in developing countries [1-50] has became a major
concern since it has been related to environmental problems [6], food security and the
productivity of agricultural land [23], and poverty [4]. According to [47], land
degradation encompasses not only soil degradation, but also vegetation degradation,
forest, cropland, water resources, and biodiversity degradation of a nation.
Therefore [21] stressed that the impact of land degradation is a drain on economic
growth in rural areas and has an affect on national economic growth pattern. Land
degradation is a very complex phenomena as it involves a set of bio-physical and
socio-economic processes and some of them occur at different spatial, 0 7 2
Development of Land Degradation Assessment Model Based on Field Indicators
Assessment and Prediction Methods In Wai Sari, Sub Watershed Kairatu District, Westen
Seram Regency, Maluku Province, Indonesia temporal, economic and cultural scales [23,
46].
Land degradation reduces the capacity of land to provide ecosystem goods and services
over a period of time [25], [47], and the potential of land to support agricultural systems
and their value as economic resources [37]. According to [48], the impacts of land
degradation in lowering the productivity of agricultural land can be temporary or
permanent.
The complexity of land degradation means its definition differs from area to area,
depending on the subject to be emphasized [6]. In Indonesia, soil erosion has been
considered as one of the major and most widespread forms of land degradation, as a
result of land use changes and human activities [36, 47]. Recent report of Ref. [47] stated
that degraded land in Indonesia is 24.3
million ha in 2013, and it is caused mainly by erosion due to inappropriate land uses,
and no soil and water conservation practices are applied in such areas. The extend of
land degradation in Maluku is related to the deforestation activities in the past, land
conversion from natural forest to agricultural and plantation areas, and the rapid
expansion of agriculture and settlement area in the hilly areas in particularly in Ambon
island. Study in Ref.
[22] showed that soil erosion is the major factor in determining the capability of land to
support agriculture development in Wai Batu Merah watershed of Ambon Island, and
the environmental quality of this watershed has declined as indicated by erosion,
flooding and sedimentation during the rainy season, and water deficiency during the dry
season.
Many different methodologies have been used to study soil erosion and land
degradation such as field measurements, mathematical models, remote sensing,
environmental indicators, including the use of simple models based on indicators that
synthesize complex processes. The emperical soil erosion models, such as Universal Soil
Loss Equation (ULSE) [50] and the Revised-USLE (RUSLE) [28] have been worldwide
applied to assess soil loss by water [24, 26, 30]. In Indonesia, both models have been
used to predict soil loss from medium to lage watersheds [20, 27, 29, 31-32, 39].
A number of studies have been conducted in Ambon and Seram islands using USLE
model to estimate erosion rates from small watersheds [14, 17, 42-44], and it is very
likely that the predicted soil loss from the study areas tend to be much higher than
other studies in Indonesia and other regions. The study found that the high rate of soil
loss is largely due to high rainfall erosivity index (R-value in USLE) or indicates the
impact of high rainfall in the study areas. This happen, because the climate condition in
the study areas is considerably different to the place where the model was originally
developed.
The assessment of land degradation based on the measurement of the actual indicators
in the field both qualitatively and quantitatively was discussed by Stocking [37]. Stocking
introduced field indicators as pedestals, rills, gully, plant/tree root exposure, armaour
layers, exposure of below ground portions of fence posts and other structures, the rock
exposure, tree mound, and sediment in tunnel or drains.
Tstcos he ckingla degradation field indicators assessment and RUSLE method to develop
a more accurate model based on the extent and amount of land degradation field
indicators in a small watershed of Maluku Province. The local land conditions which are
considered as the cause of land degradation including climate, soil, topography,
vegetation and land use and humans influence are studied in detail. So, the results of
this study will support efforts in protecting land degradation of the watershed,
especially by local governments.
Wai Sari Sub watershed is part of Wai Riuapa river basin located in Kairatu Sub District,
Western Seram Regency, Maluku Province in Indonesia. Wai Sari sub watershed is a
source of water supply to local communities as well as irrigation water to paddy fields.
Wai Sari also provides natural resource in particularly agriculture, plantations and
forestry products to support 0 7 3 Development of Land Degradation Assessment Model
Based on Field Indicators Assessment and Prediction Methods In Wai Sari, Sub
Watershed Kairatu District, Westen Seram Regency, Maluku Province, Indonesia daily
needs of local communities.
However, some of human activities especially in the exploitation of forest resources have
became the causal agents of land degradation in this watershed, as indicated by soil
erosion and sedimentation during the rainy season. II. EXPERIMENTAL METHOD 2.1.
Place And Time of Places Research The research was conducted in Wai Sari Sub
watershed, Wai Riuapa river basin in Kairatu District of West Seram Regency, Maluku
Province, Indonesia. 2.2.
Materials and Tools Materials and tools used in this study included a slope unit map (as
the field map at scale of 1: 10,000), land degradation indicators sheet, compass,
altimeter, clinoneter, global position system (GPS), auger, munsell soil colour chart, roller
meter, measuring stick, hoes, shovels, field knives, aquades, H2O2, HCl, sample rings,
plastic soil samples, calculators, and digital camera. Microsoft Office 2007, Minitab16,
SPSS17, and ArcGIS10.1
were used to data processing and report writing. 2.3. Research Methods The method
used in this study was survey, and the field observation was conducted on 95 slope units
showing slope steepness and flow directions derived from a topographic map.
The measurement of land degradation indicators in the field was carried out on all slope
units according to the field measurement method [37]. The indicators were pedestals,
rills, gully, plant/tree root exposure, armour layers, exposure of below ground portions
of fence posts and other structures, the rock exposure, tree mound, build up against
barriers, and sediment in drains. Each indicator was identified and measured.
At the same time, the soil loss prediction factors of the RUSLE [28] included rainfall
erosivity (R), soil erodibility (K), topography (slope length and steepness) (LS), actual
vegetation (C) and soil conservation practices (P) were also measured on each slope
unit. Data processing consisted of three stages. Firstly, the calculation of soil loss
(t/ha/yr) using both methods, Scking’f ield measurement [37], and RUSLE methods
which is A = R x K x LS x C x P [28].
The level of land degradation was determined using the criteria of FAO in Ref. [19] ie
none-slight = 0-20 t/ha/yr; moderate = 20-50 t/ha/yr; high = 50-200 t/ha/yr; very high
= >200 t/ha/yr. Secondly, the results of both methods were statistically tested by using
the Pre-Analysis Test included Linearity Test and Independence Test.
The results was used to indicate the nonlinearity and independence between land
degradation data of the field measurement and prediction. The results of the linearity
model test were indicated by the value of "Deviation from Linearity = 05 , which means
that the nonlinear is significant at the 5%. The results of the independence model test
were indicated by the Durbin-Watson statistical value in the model (range 1.5 – 2.5),
which means that the residual is uncorrelated or the assumption of independence is
satisfied at the 5% significance level.
Thirdly, to apply T-different test (Paired T-test) [12] on the results of land degradation
field measurements and prediction as is indicated by the P-value = 0.05. At the
significance level of 5%, if the data is completely different and can be continued with the
development of a more accurate model. The test of the developed-model of land
degradation was carried out by linear and non-linear multiple regression-correlation
analysis [5, 19], with tic mol is Y + ß1X1 ß2X2ß3X3+ ß4X4i + e we Yhe rate of soil loss
according to the field assessment method [37]; ßointcept effie; i -X5i= land degradation
factors of RUSLE prediction method [28] namely rainfall erosivity, soil erodibility,
topography (slope length and sts)etiond covatn measurs; ß - ß5 = regression coefficient
for X1-X5 factors ; i eror All data were analyzed using the MS Exel 2007, SPSS17 and
Minitab16 programs.
0 74 Development of Land Degradation Assessment Model Based on Field Indicators
Assessment and Prediction Methods In Wai Sari, Sub Watershed Kairatu District, Westen
Seram Regency, Maluku Province, Indonesia III. RESULTS AND DISCUSSION 3.1. Land
Degradation by Field Assessment Method [37] The result of the study as presented in
Fig. 1 and Fig.
2 shows that the actual field phenomenas or land degradation field indicators in the Wai
Sari Sub- watershed are pedestals, plant/tree root exposure to identify sheet erosion, rill
indicator as rill erosion, gully indicator as gully erosion. According to the calculation of
soil loss rates, the level of land degradation can be classified as follows: none to slight
degradation level (soil loss 4.04 - 17.56 t/ha/yr) covers the area of 220.75 ha or 66.24%,
with field indicators are pedestal (average height = 2.00 cm); trees/plant root exposure
(1.91cm); rills (average depth 2.70cm), and soil bulk density (average 1.12 g/cm3). The
occurence of pedestal, roots exposed and rill indicators is predicted longer than 24.7
years with the average soil loss is 0.76 mm/yr (lower than other levels of land
degradation) The moderate degradation level (20.06 - 43.76 t/ha/yr) covers the area of
57.57 ha or 17.29%, with field indicators are pedestal (average height = 2.04 cm),
trees/plant root exposure (2.62 cm), rill (average depth = 9.05 cm), and soil bulk density
(average 1.08 g/cm3).
The formation of pedestal, plant roots exposure and rill indicatorss is predicted about
16.9 years with the average of soil loss of 2.60 mm/yr (higher than the slight
degradation level, but lower than high and very high land degradation levels). The high
degradation level (51.91- 194.21 t/ha/yr) covers the area of 50.14 ha or 15.04%, with
field indicators are pedestal (average height=1.92cm), trees/plant root exposure (2.16
cm), rills (average depth=18.14cm), gully (average depth=62.09cm), and soil bulk density
(average 1.10 g/cm3). The pedestal, plant roots exposure, rills and gully have been
formed within 9.1
years, resulting in an average degradation due to soil loss of 9.01 mm/yr (higher than
slight and moderate degradation levels). The level of very high degradation (235.44-
404.00 t/ha/yr) covers the area of 4.76 ha or 1.43% of the total study area. The field
indicators are pedestal (average height 1.95cm), trees/plantexposed root (1.05 cm), and
soil bulk density (average 1.10 g/cm3).
The occurence of pedestal and trees/plant root exposure is predicted less than 0.75 yr
with the average of soil loss is 29.20 mm/yr (higher than other land degradation levels in
the study area). The study found that at the slightly degradation level, vegetation
conditions are generally still in good Figure 1.
The level of land degradation based on Field Indicators Assessment [37] in Wai Sari Sub
Watershed. Figure 2. Map of land degradation level based on Field Indicators
Assessment [37] in Wai Sari Sub Watershed. 0 7 5 Development of Land Degradation
Assessment Model Based on Field Indicators Assessment and Prediction Methods In Wai
Sari, Sub Watershed Kairatu District, Westen Seram Regency, Maluku Province,
Indonesia condition, especially the density of higher and lower vegetation, and the
vegetation stratification is relatively well-formed.
In this condition, vegetation canopy protects soil surface from the direct impact of
rainfall, and reduces the destructive energy of droplets on the ground surface by
intercepting the falling rainfall. Vegetation also minimizes the rate and volume of
surface flow by holding some of the water for its own use, creating surface roughness
and increasing infiltration, and prevents the formation of crust on the surface [8].
The effect of vegetation canopy and ground covers in reducing soil erosion varies with
upper cover (aerial cover) and ground cover (contact cover) [9, 43], and structure of
vegetation stratification [3]. However, in many areas, the type of land uses presents the
effects of plants, vegetation and soil cover on soil loss within a catchment [45].
In addition, the existence of biomass of living plants and litter plays a role in reducing
erosion by capturing the energy of raindrops, ground cover can also reduce the rate of
water, and the volume of surface runoff and soil loss, while plant roots will physically
stabilize the soil and increase resistance to erosion. In contrast, at the high to very high
degradation levels, the condition of vegetation shows loss of native vegetation, and
change in both the density (the upper and lower vegetation) and stratification of
vegetation due to various human activities such as deforestation, land-use conversion
from forest to agriculture, plantations and settlement.
Under a such condition, the high erosivity energy of rainfall cannot be properly
intercepted by vegetation canopy, so the soil surface is openned to direct impact of
raindrops. The field indicators found at this degradation level are pedestal, plant/root
exposure, rill and gully with higher depth and their formation are relatively recent
compared to other levels of degradation.
Although degradation processes may occur without human interference [37], however,
according to [18] the land degradation is commonly accelerated by human intervention
in the environment. Similarly, as reported by [16] that soil erosion is a function of land
use, and loss of vegetation cover will increase erosion from none to moderate erosion.
According to [45] the convertion of forest into agricultural land will increase erosion by
changing in vegetation cover and land processing, while on the agricultural lands, the
increasing of soil erosion is due to the repeated of soil treatment and removal of
vegetation cover on soil surface. Therefore, change in land use from natural forest to
perennial crops/plantations, annual crops and bare soil will increase land degradation
from slight to high levels [37]. Moreover, according to [41] deforestation is the cause for
the increase in surface flow and erosion rates in a watershed.
According to Hopley (1999) quoted by Ref. [13] that loss of vegetation due to
deforestation, agricultural practices, land preparation for settlements, burning of forests
and grasslands is considered to be a causal agent of erosion in Balikpapan East
Kalimantan.
Loss of forest trees and the increasing of shrubs and imperata cylindrica areas will
increase the rates of surface flow and soil erosion. According to Ref. [11] loss of
vegetation and humus on the plantation areas has created serious erosion problems.
The results of study in Lampung Province in Ref. [35] concluded that changes in land use
from forests to annual crops led to increased erosion.
Intensive infrastructure development such as settlements is considered to be one of the
human activities that cause land degradation [34]. Loss of ground cover will also
accelerate the formation of sheet erosion, rill and gully erosion (Field, 1997) quoted by
Ref. [34]. 3.2. Results of Prediction of Land Degradation by the RUSLE Method [28] The
levels and amount of land degradation based on the RUSLE soil loss predicted method
[28] as shown in Fig. 3 are none to slight degradation, soil loss is 0.04-4.59 t/ha/yr
covering the area of 143.5 ha or 43.05%.
This degradation level is generally found on slopes with low LS-factor values (flat to
almost flat slope steepness, 0-6%) and on land uses with high C-factor values such as
cultivated field, mixed garden, settlements and imperata 0 7 6 Development of Land
Degradation Assessment Model Based on Field Indicators Assessment and Prediction
Methods In Wai Sari, Sub Watershed Kairatu District, Westen Seram Regency, Maluku
Province, Indonesia cylindrica areas.
This degradation level also occurs on slopes with high LS-factor values (slope steepness
20-70%) and on secondary forest with very low C-factor values. At the moderate
degradation level, soil loss is 41.60-48.33 t/ha/yr covering the area of 9.33 ha or 2.99%.
This degradation level occurs in soils with slightly high K-factor values (Dystrudepts) and
slopes with medium LS-factor values (gently to moderately slope steepness, 8-16%) and
shrub and mixed garden land uses. At the high degradation level, soil loss is
60.80-192.80 t/ha/yr covering the area of 0.04 ha or 0.003%, which generally occurs on
slopes with high LS-factor values (moderately to very steep slope steepness, 15-85%)
and low K-factor values.
The land uses are shrub, cultivated field, mixed garden, clove and coconut gardens. At
the very high degradation level, soil loss is 203.90-518.13 t/ha/yr covering the area of
93.67 ha or 28.12% of the total study area. This degradation level, occurs in soil with
slightly high K-factor values (Dystrudepts) and high LS-factor values (moderately steep -
very steep, 30-85%).
The land uses are shrub, cultivated field, mixed garden, clove and coconut gardens, and
imperata cylindrica areas. The study shows that at the slight degradation level, although
the slope condition is steep to very steep, the vegetation cover of secondary forest is
sufficient enough to reduce the impacts of slope steepness by protecting soil surface
from raindrops and runoff.
According to [28], soil loss per unit area generally increases along with the increasing of
slope length and steepness. In the other words, soil loss can be decreased by reducing
slope length and steepness. Soils with flat or almost flat slopes are usually stable and
have very low soil loss.
However, the rate of soil loss will be rapidly increasing once the slope steepness
increases to 2% or 5%, as stated by [1] that on a slope of 10%, erosion will increase to
eight times higher, and soil erosion will continue to increase on steeeper slopes. The
study in Lampung by [35] indicated that land cover especially with dense canopy such as
natural forest has lower soil loss compared to the plantation.
Similar study in Thailand, as reported by [49] that land degradation due to erosion on
forest is lower than field cultivation. The high to very high degradation levels occured in
the study area show that slope, soil properties, land uses are considered to be the major
factors influencing soil erosion at this levels.
The condition of slope and soil erodibility factors in the study area indicate high
susceptibility of soil to erosion, transportability of sediment and the amount and rate of
flow on the surface as slope steepness is dominated by steep to very steep slopes, and
soil is dominated by medium to slightly high erodibility factors. According to [28], soil
loss per unit area generally increases with the increasing of slope length and steepness,
and the high value of soil erodibility factor increase the erosion rate of the study area.
The impacts of land uses on erosion is also significant at this levels due to low
vegetative covers (both vegetation canopy and ground covers), so the vegetative covers
are not sufficient enought to protect soil surface from detachment and sediment
transport by surface flow. Hopley in 1999 quoted by Ref. [13] stated that loss of
vegetation caused by deforestation, agricultural practices, land preparation for
settlements, burning of forests and grasslands is considered as a major factor
determining the erosion problems in Balikpapan East Kalimantan. Figure 3.
Level and extent of land degradation based on Predicted Method (RUSLE) [28] in Wai
Sari Sub Watershed. 0 7 7 Development of Land Degradation Assessment Model Based
on Field Indicators Assessment and Prediction Methods In Wai Sari, Sub Watershed
Kairatu District, Westen Seram Regency, Maluku Province, Indonesia Brandt in 1988
cited by Ref.
[11] stated that the ability of ground cover to protect soil surface from raindrops to
erode is greater than higher trees, because the raindrops are collected before dripping
from the leaves and hit the ground surface with greater power. 3.3. Development of
Land Degradation Assessment Model The results of Linearity Pre-analysis Test show no
linear relationship between the level of land degradation assessment based on field
indicators and RUSLE prediction method at the 95% confidence level as indicated by Sig.
of Deviation from Linearity = 0.000 * (<0.05).
However, they are independent to each other at a 95% confidence level according to the
Independence Pre-analysis Test and is indicated by the Durbin-Watson statistical value
of 1,718 (range 1.5 - 2, 5). While the results of Independent Samples T-test analysis show
that there is no different in land degradation levels resulting from the field indicators
assessment and the RUSLE methods at the 95% confidence level, as indicated by a
Sig.2-tailed value of 0.000 * (<0.05).
This study also shows that the average annual soil loss predicted by RUSLE method is
higher (133.4 t/ha/yr) than field indicators measurement (33.9 t/ha/yr) as depicted in Fig.
4 to Fig. 11. This implies that the rate of predicted soil loss tends to be over estimated
for Wai Sari sub watershed and in consequently it provides a higher land degradation
levels compared with the actual field measurement in the study area.
Therefore, the field indicators measurement is considered to be a more acceptable
method in assessing land degradation levels based on soil loss in a small watershed in
the tropic, as they represent the actual erosion phenomena in the field. Figure 4. Root
exposure in cultivated field. Land degradation by field indicators assessment = 23.60
t/ha/yr. Land degradation based on predicted soil loss (RUSLE) = 344.43 t/ha/yr (over
estimated) Figure 5.
Pedestal in cultivated field. Land degradation by field indicators assessment = 33.67
t/ha/yr. Land degradation based on the predicted soil loss (RUSLE) = 248.53 t/ha/yr
(over estimated) Figure 6. Pedestal in coconut garden. Land degradation by field
indicators assessment = 7.87 t/ha/yr. Land degradation based on the predicted soil loss
(RUSLE) = 190.80 t/ha/yr (over estimated) Figure 7. Root exposure in coconut garden.
Land degradation by field assessment = 7.21 t/ha/yr. Land degradation based on the
predicted soil loss (RUSLE) = 190.80 t/ha/yr (over estimated) Figure 8. Root exposure in
mixed garden. Land degradation by field indicators assessment = 9.25 ton/ha/thn. Land
degradation based on the predicted soil loss (RUSLE) = 85.59 t/ha/yr (over estimated)
Figure 9. Rill in mixed garden. Land degradation by field indicators assessment = 47.20
t/ha/yr.
Land degradation based on the predicted soil loss (RUSLE) = 85.59 t/ha/yr (over
estimated) 0 7 8 Development of Land Degradation Assessment Model Based on Field
Indicators Assessment and Prediction Methods In Wai Sari, Sub Watershed Kairatu
District, Westen Seram Regency, Maluku Province, Indonesia Figure 10. Pedestal in clove
garden. Land degradation by field indicators assessment = 6.05 t/ha/yr.
Land degradation based on the predicted soil loss ( RUSLE) = 8.77 t/ha/yr ( over
estimated) Figure 11. Rill in shrubs area. Land degradation by field indicators assessment
= 106.90 t/ha/yr. Land degradation based on the predicted soil loss ( RUSLE) = 190.65
t/ha/yr (over estimated) The development of land degradation assessment model based
on the results of field measurements shows the best regression equation as follow: logD
/ RP = -0.594 + 1.0 logK + 1.0 logLS + 1.0 logC, or D = 0.2547xRxKxLSxCxP, with a P
value of 0.000 * (<0.05) at 95% confidence level, and D is the amount of land
degradation in a slope unit (t/ha/yr), R is the rainfall erosivity (ton.m/ha/cm-rain), 0,2547
is the correction factor to RUSLE model, K is the soil erodibility value of a slope unit, LS
is the index of topographic factors (slope lenght and steeepness values), C is the value
of plant or vegetation index factors, P is the value of soil conservation index factors.
By integrating the 0,2547-correcting factor into the RUSLE, the predicted soil loss in the
study area can be reduced to almost 26% compared to the original model (RUSLE).
According to Ref. [37], the application of a model from one place to very different
conditions of soil, climate, slope and land use may lead to greater uncertainties
associated with estimates of average annual soil loss.
Therefore, it is important to obtain suitable local data that can suit for the study area
particularly climate, soil type, topography, and land uses, and output of the model
should be validated using measured field data. Further, Ref. [37] stated that erosion
factors of the original empirical equation are rated on a numeric scale and the combined
effects of multiple factors of rainfall, slope, soil type, land uses and vegetation cover
determines the average of annual soil loss.
So, the equation does not express an actual measurement of the land degradation in
the field, but provides the potential land degradation based on the predicted annual soil
loss in a study area. The study in Ref. [10, 15, 40] showed that the assessment of land
degradation due to erosion can be measured by using field indicators proposed by Ref.
[37]. While according to Ref.
[7], the accuracy of actual land degradation indicators at the farmer's land level is
required in development and improvement methods for soil and water conservation
planning at the catchment scale in the highlands of East Africa. In addition, the field
assessment techniques have considerably advantages compared to standard
experimental approaches in measuring soil loss and soil quality changes, because, their
results express the actual processes and evidences that occur in the field, but can not be
created in the laboratory.
The field assessment techniques also allow the involvement of farmers and local
communities, so they can improve their perspective and accept the technology [38]. This
study has developed a land degradation assessment model based on the actual field
data measurement, and the model is suitable for small watersheds in small islands of
Maluku Province. 4.1. Conclusions 1. The level of land degradation based on field
assessment methods [37] are none-slight degradation (4.04 - 17.565 t/ha/yr) covering
220.75 ha or 66.24%, moderate degradation (20.06 - 43.76 t/ha/yr) covering 57.57 ha or
17.29%, high degradation (51.91 - 194.21 t/ha/yr) of 50.14 ha or 15.04 % and
degradation is very high 235.44 - 404.00 t/ha/yr covering an area of 4.76 ha or 1.43% of
the total study area.
The field indicators of land degradation found in the Wae Sari Sub-watershed are
pedestal and trees/plant roots exposure to identify sheet erosion, and rill (indicators of
rill erosion) and gully (indicator of gully erosion). 0 7 9 Development of Land
Degradation Assessment Model Based on Field Indicators Assessment and Prediction
Methods In Wai Sari, Sub Watershed Kairatu District, Westen Seram Regency, Maluku
Province, Indonesia 2.
The results of land degradation level based on the RUSLE soil loss predicted method
[20] are none-slight of land degradation of 0.04-4.59 t/ha/yr with an area of 143.5 ha or
43.05%, moderate level 41.60-48.33 t/ha/yr, with an area of 9.33 ha or 2.99%, high level
of 60.80-192.80 t/ha/yr covering an area of 0.04 ha or 0.003%, and very high level
between 203.90-518.13 t/ha/yr covering an area of 93.67 ha or 28.12% of the total
study. 3.
The development of land degradation assessment model based on the field assessment
provides the best regression equation as: Log D / RP = -0.594 + 1.0 log K + 1.0 logLS +
1.0 log C or D = 0.2547xRx KxLSx CxP. This model can reduce the rate of soil loss by
erosion till almost 26% compared to the original model (RUSLE), and therefore, it has
been considered as the suitable model for assessing land degradation of small
catchment areas in small islands of Maluku. 4.2. Suggestion The present model,
D=0.2547xRxKxLSxCxP is still needed to be tested at other small watersheds in Maluku,
so the accuracy of the model can be improved and it will be more adaptable to Maluku
condition.
Acknowledgement 1. We are grateful to thank the Ministry of Research, Technology and
Higher Education of Republic of Indonesia in providing the research fund. 2. We would
like to thank Prof. Michael A. Stocking, MPhil., PhD at the University of East Anglia,
Norwich, UK who provided the form of land degradation methods based on field
indicators assessment. 3.
We also thank Luohui Liang, Managing Coordinator for People, Land Management and
Environmental Change (UNU/PLEC) in Tokyo-Japan, who sent the book of Land
degradation – guidelines for field assessment. Conflict of Interest The authors declare
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Dep. Agric., Agric. Handb. No. 537. *Corresponding Author Brief CV Dr. Ir. Silwanus
Matheus Talakua, MP was born in Ambon, Maluku Province on January 3rd, 1965. He
got his Bachelor of Agriculture (S1) from Pattimura University in 1991 with specialist in
Soil Sciences/Soil and Water Conservation.
In 1999 he completed his Master Degree Program (S2) at Padjadjaran University in
Bandung with the main spesification in Soil science, Land Reclamation and
Rehabilitation. In 2009, he finished his Doctor (S3) degree at Padjadjaran University with
spesification in Soil Science, Land Degradation and Rehabilitation. Since 1993, he is
working at the Soil Science and Agroecotechnology Study Program, Faculty of
Agriculture, Pattimura University teaching Soil Physics, Basic Soil Science, Hydrology, Soil
and Water Conservation, Geodesy and Cartography, Degradation and Land
Rehabilitation.
He is also teaching Soil Physics, Land Resource Conservation, Land Rehabilitation, Land
Use Planning, and Statistical Analysis at Land Management Study Program,
Postgraduate Program, Pattimura University. The researchs that he had done were the
Study of Soil Degradation Through Estimation of Potential Erosion by the USLE method
in the Wai Ruhu Watershed at Sirimau Sub District Ambon (1991), Determination of
Erosion Hazard Levels in the Wai Lela Watershed at Ambon Bay Baguala Sub District
Ambon City (1999 ), Analysis of Some Physical Properties of Soil, Land Use and
Properties of Soil Profiles on Infiltration Processes in Wae Tonahitu Watershed at
Ambon Bay Baguala Sub District, Ambon City (2002), Evaluation of Soil Degradation and
Control Effort in the Wai Riuapa Watershed at Kairatu Sub District of Maluku Province (
2006), Inventory of Sago Potential and Sago Mapping in Bula Sub District East Seram
District (2009), Sago Development Survey in South Buru District (2009), Effects of Land
Use on Soil Degradation Due to Erosion in Kairatu Sub District at West Seram District of
Maluku Province (2009), Identification of watershed characteristics in Wai Batu Merah
Watershed at Ambon City of Maluku Province (2012).
The Effect of Land Use Extent and Vegetation Density on Soil Degradation in Mixed
Plantation and Shifting Cultivation in Kairatu District, West Seram Regency, which was
published in the Agrinimal Journal. Faculty of Agriculture Pattimura University, Vol. 3,
No. 1 (2013). ISSN: 2088-3609. Evaluation of Land Capability and Landuse Planning in
the Wai Tina Watershed, South Buru Regency, Maluku Province, which was published in
the Agrologia Journal of the Agriculture Faculty Pattimura University Vol. 3, No. 1 (2014).
ISSN: 2301-7287.
Water Efficiency on Irrigation System in Way Bini of Waeapo Sub District, Buru District of
Maluku Province, which was published in the Agrologia Journal of Agriculture Faculty
Pattimura University Vol. 5, No. 2 (2016). The Effects of Land Use Factors on Soil
Degradation at Mixed Plantation in the Sub District of Kairatu, West Seram District,
Maluku Province, published in the Agrologia Journal of the Agriculture Faculty Pattimura
University Vol. 7, No. 1 (2018).
Determination of Land Capability Class and Land Rehabilitation Planning at Wai Batu
Merah Watershed in Ambon City, Maluku Province, which was published in the
Agrologia Journal Vol. 7, No. 1 (2018). Dr. Talakua was also a speaker at several scientific
meetings such as Annual Scientific Meeting (PIT) XXVIII Indonesian Hydraulic
Engineering Expert Association 0 8 4 (HATHI) (2011).
"The Role of Integrated Watershed Management in Regional Development" at the
Meeting for the Watershed Management of Integrated Wae Apu Watershed in Buru
Regency (2011). Soil from the Side of Empowerment, Conservation. General Meeting of
GPM Male Pastors in Maluku Province (2011). The Importance of Integration in
Watershed Management at the Coordination Meeting on the Planning of the Integrated
Management of the Wae Manumbai Watershed in Aru Islands Regency (2012).
"Maluku in the Aspect of Disaster Presenters Papers on Activities for Dissemination of
Disaster Risk Reduction in Ambon City for Elementary, Junior High School, Senior High
School Teachers in Ambon City in collaboration with Ambon City Government cq
Ambon City Regional Disaster Management Agency with Hope World Wide Indonesia
(2013). As Oral Presenter in National Joint Conference Unpatti-Unpad. Sustainable
Development For Archipelago Region in Pattimura University (2017).
Determination of Innovative Patterns of Land Conservation at Wai Batu Merah, Wai
Tomu, Wai Batu Gajah, and Wai Batu Gantung to Support Flood and Sediment Barriers
at the National Seminar for the 51st Anniversary of the Faculty of Agriculture, Mataram
University in Lombok at Nusa Tenggara Barat Province (2018). He attended the
International Seminar on Sago and Spices For Food Security.
Sail Banda (Small Island for Our Future) (Ambon, 2010), Disaster Risk Reduction Training
Program at Yogyakarta (2014), Training Data Analysis Experimental Design 2 Factors
With Minitab 17 Program (2016), Basic training on ArcGIS by the Center For The
Development Of Spatial Data (PPIDS) of Pattimura University (Ambon, 2018),
International Seminar Sago feed The World (Ambon, 2018). He has published two books:
1."Sago, Hope and Challenges" First Edition on November 2010.
Publisher: PT Bumi Aksara Jl. Sawo Raya No.18. Jakarta-13220 ISBN 978-979-010-518-8.,
and 2. Land Degradation, Method of Analysis and Its Application on Land Use on 2016,
Publisher : Plantaxia Publisher Yogyakarta. ISSN: 978-602-6912-13-8. Dr. Talakua was a
Secretary of the soil physics and mechanics devision of The Indonesian Soil Science
Association (HITI) branch Maluku and Irian Jaya (1993-1996), and a member of the
Indonesian Soil Science Association (HITI) (Deputy Chairman of the HITI KOMDA
Maluku), the secretary of the Watershed Forum of Maluku Province, a member of the
Water Resources Council of Maluku Province, a member of Regional Spatial Planning
Coordination Team (BKPRD) of Ambon City, and a member of the Resources
Management Coordination Team of Ambon-Seram River Region of Maluku Province.
0 8 5
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