Socioeconomic factors and farmer’s perception of soil erosion and conservation were examined with special reference to Wadi Kufranja catchment, northern Jordan. Field data were collected through a household field survey, and soil erosion loss was calculated and mapped using the Revised Universal Soil Loss Equation (RUSLE), within a GIS/RS environment. In-situ field measurements of soil erosion were also conducted to assess splash, sheet and runoff soil erosion. The estimated potential average annual soil loss is 10 ton·ha -1 year for the watershed. 42.1% (5317. 23 ha) of the watershed area was estimated to have moderate soil loss (5 - 25 ton·ha -1·years -1). Soil erosion risk is severe to extreme over 31.2% (3940.56 ha) of the catchment, whereas the calculated soil loss is 25 - 50 and >50 ton·ha -1·year -1. The measured sheet and splash soil erosion in W. Kufranja was 10 ton·ha -1·year -1 from tillage land, and 3 ton·ha-1·year-1 from the fallow land, with an average ranges from 8 to 10 ton·ha -1·year -1. Similarly, the maximum measured soil erosion on the eastern margin of W. Kufranja was 12.7 ton·ha -1·year -1, while the minimum soil erosion was 2.9 ton·ha -1·year -1. The collected household socioeconomic/conservation data have been subjected to multivariate statistical analysis. Through factor analysis, the twenty one variables were reduced into four significant factors which account for 69.7% of the variation in the original variable. Stepwise multiple regression analysis revealed that the total variance explained by three independent variables was 0.585 (R = 0.765, R2 = 0.585). Out of the total variance, forest clearance explained 34.7%, fallow land 7.7%, and land use/land cover 16.1% respectively. The F-value for forest clearance, fallow land, and land use/land cover are significant at 0.1% level. Most of the farmers aware that poor land management, deforestation, overgrazing, traditional cultivation (cultivation up-and-down the slope, and mono-cropping), and population pressure, are the major direct and indirect causes of soil erosion. By contrast, vegetative measures (i.e., afforestation and tree planting), adoption of structural soil and water conservation measures (terraced farming, check dams and gully control), and crop system management were recommended to control soil erosion.
Soil degradation by accelerated erosion is a serious environmental problem in the highlands region of Jordan. Erosion of topsoil leads to declining soil quality and productivity, thus restricting the area of potential future agriculture. Rapid population growth, the associated population movements into rural areas, and land use practices and land cover changes since the 1950’s, have led to soil degradation. Eroded soil materials from the denudational slopes are deposited over wadi floors, agricultural lands, irrigation canals, even on roads, and more seriously in reservoirs constructed across the wadis draining to the rift. Several studies were carried out to estimate soil erosion in Jordan on local, regional, and national scales [
Recently, the revised Universal Soil Loss Equation (RUSLE) model has been employed in conjunction with RS and GIS technology to estimate the annual soil loss in different areas in central and northern Jordan [
Wadi Kufranja catchment constitutes the present study area. It is located in the northern highlands of Jordan, and lies between 32˚14' to 32˚22' north latitudes and 35˚21' to 35˚47' east longitudes (
topography indicate past landside events, probably of Pliocene and Quaternary age (<5 Ma) [
Land-cover types vary from natural vegetation (forests) mixed with crop-land. Four scattered associations of forests are distinguished throughout the watershed [
Data were obtained from a variety of sources including remote sensing, DEM, and GIS for estimating soil erosion loss; field measurements of soil erosion, and from a household questionnaire survey. The basic methods/ tools employed to assess soil erosion are illustrated (
Soil erosion in northern Jordan was conducted over the past two decades using different soil erosion estimation
techniques such as: in-situ field measurements, and soil erosion estimation using geospatial technology (remote sensing and GIS).
Several different empirical and physical process based models have been developed since 1930s to predict soil loss [
where,
A: the computed soil loss per unit area [ton∙ha−1∙year−1], R: runoff erosivity factor (rainfall and snowmelt) in [MJ mm ha−1∙hr −1∙year−1], K: soil erodibility factor (soil loss per erosion index unit for a specified soil measured on standard plot, 22.1m long, with uniform 9% (5.16˚) slope, in continuous tilled fallow) [ton∙ha∙hr∙ha−1 MJ−1∙mm−1], L: slope length factor (ratio of soil loss from the field slope length to soil loss from standard 22.1m slope under identical conditions) (dimensionless), S: slope steepness factor (ratio of soil loss from the field slope to that from the standard slope under identical conditions) (dimensionless), C: cover-management factor (ratio of soil loss from a specified area with specified cover and management to that from the same area in tilled continuous fallow) (dimensionless), and P: the support practice factor (ratio of soil loss with a support practice-con- tour tillage, strip-cropping, terracing-to soil loss with row tillage parallel to the slope (dimensionless).
The RUSLE model was applied with Arc GIS 10.1 and ERDAS Imagine 8.5, along with LANDSAT ETM+ 2010 to estimate soil erosion loss [
Two plots for field measurements of sheet, runoff (soil traps) and splash soil erosion [
The terrain mapping unit (TMU) (or homogenous domain) approach was elaborated by Meijerink [
A TMU represents a natural division (or) portion of land surface containing a set of ground conditions which differ from the adjoining units, and it’s boundaries can be easily demarcated [
The study watershed consists of 2295 farms distributed over 18 villages and towns [
where:
n is sample size, N is total no. of households and e is the significance level (in our case the significance level is 7%). The resultant sample size was 187 farms which represent the whole watershed. A structured questionnaire was designed and distributed to collect information on socioeconomic conditions of farmers, soil erosion status, soil and water conservation, and the perception of soil erosion extent, causes and impacts, and the awareness of soil conservation measures and practices. The information also covers the household characteristics, land ownership, income and expenditure, and the awareness of governmental soil erosion projects including cost. The collected information is considered an essential factor when making decisions on soil and water conservation projects and practices [
Factor analysis was employed to condense the original variation, measured in terms of a large number of variables (21 variables), into variation in terms of a few factors, each of which contributes a known amount to the total variation It is performed on the standardized variables using correlation matrix to dismiss the effect of different measurement units on the determination of factor loadings. Factor loadings represent a simple correlation between properties of each factor. Eigenvalues illustrate the amount of variance explained by each factor. Factors with eigenvalues > 1 explained more the total variation in the data than farmers’ socioeconomic characteristics and perception of soil erosion, while factors with eigenvalues <1 explained less the total variation than the individual farmer. Thus, only factors with eigenvalues >1 were considered for interpretation of results [
Some important socioeconomic variables were eliminated in the present analysis. All households in W. Kufranja for example, are owned only by males. Therefore, the gender variable does not contribute to variation in perception. All farmers also inherited their farms through successive generations, thus, household size is generally small (1.5 - 2.5 ha only) due to continuous fragmentation of the agricultural lands. This process is expected
Socioeconomic/soil conservation variables | Measurement level | Value |
---|---|---|
Dependent variable/Soil erosion rate | ton∙ha−1∙year−1 | Continuous |
Covariates: | ||
X1. Age (years) | Number | Discrete |
X2. Educational level | ≥Grade 6 | Discrete |
X3. Distance between house and the farm | Km | Continuous |
X4. Size of agricultural holding | Dunum | Discrete |
X5. Total of farm capital | Assets/JD | Continuous |
X6. Farm annual economic/revenue | Total household income/JD | Continuous |
X7. Farm annual expenditure | Farm expenditure/JD | Continuous |
X8. Farm labor | Number | Discrete |
X9. Farm accessibility | Easy/difficult | Dummy |
X10. Dependency ratio | Number | Discrete |
X11. Adopted method(s) of soil conservation | 0 (or) 1 | Dummy |
X12. Indigenous knowledge/practical experience in soil conservation | 0 (no), 0.3 (low), 0.6 (moderate), 1 (high) | Continuous |
X13. Proper training in soil conservation | Frequency/year (or)years | Discrete |
X14. Awareness of soil conservation cost | 1 (yes), 0 (no) | Dummy |
X15. Beneficial from governmental conservation projects | 1 (yes), 0 (no) | Dummy |
X16. Does government help farmers in their conservation efforts | 1 (yes), 0 (no) | Dummy |
X17. Are you willing to participate in conservation measures cost | 1 (yes), 0 (no) | Dummy |
X18. Does forest clearance accelerate soil erosion | 1 (yes), 0 (no) | Dummy |
X19. Does land cover and land use changes accelerate soil erosion | 1 (yes), 0 (no) | Dummy |
X20. Does fallow land accelerate soil erosion | 1 (yes), 0 (no) | Dummy |
X21. Does climate change accelerate soil erosion | 1 (yes), 0 (no) | Dummy |
to affect long-term investment in soil conservation activity, and encourage soil erosion. Hence, the law of agricultural land parceling and the resultant lands fragmentation has been excluded from the analysis, since most households are homogeneous in terms of size, and cultivation practices.
The data layers (maps) extracted for K, LS, R, C, and P factors of the RUSLE model were integrated within the raster calculator option of the Arc GIS spatial analyst in order to quantify, evaluate, and generate the soil erosion loss map for Wadi Kufranja [
The results of the present investigation in the Wadi Kufranja watershed are comparable with similar studies carried out in north Jordan [
Erosion loss class | Numeric range (ton∙ha−1∙year −1) | Percentage (%) | Area (ha) | |
---|---|---|---|---|
Minimal | 0 - 5 | 26.7 | 3372.21 | |
Low | 5 - 15 | 36.5 | 4609.95 | |
Moderate | 15 - 25 | 5.6 | 707.28 | |
Severe | 25 - 50 | 7.9 | 997.77 | |
Extreme | >50 | 23.3 | 2942.79 | |
Source: Farhan et al., (2013).
12.7 ton∙ha−1∙year−1, while the minimum soil erosion measured was 2.9 ton∙ha−1∙year−1. It is concluded that vegetation and land use, rainfall intensity during the storm event (mm/hr−1), the amount of rainfall for the storm event (mm), conservation measures, and slope form, are the most variables influencing soil erosion [
The measured and calculated soil erosion per year from the different plots in Jerash area are comparable to those estimated by adopting the RUSLE model [
Soil Loss Categories Tones/ha/year−1 (Area %) | Terrain units | ||||
---|---|---|---|---|---|
Extreme | Severe | Moderate | Low | Minimal | |
>50 | 25 - 50 | 15 - 25 | 5 - 15 | 0 - 5 | |
0.09 | 1.04 | 0.73 | 1 | 11.16 | Moderately Sloping Wadi-Side Slopes |
0.07 | 1.24 | 0.88 | 0.58 | 9,6 | Steep Wadi-Side Slopes |
0 | 0.07 | 0.21 | 0.26 | 5.16 | Slightly Sloping Wadi-Side Slopes |
0 | 0.23 | 0.23 | 0.17 | 5.07 | Remnants of Erosion Surfaces: Steeply Sloping |
0.07 | 0.2 | 0.27 | 0.17 | 9.04 | Remnants of Erosion Surfaces: Moderately Sloping |
0 | 0 | 0.03 | 0 | 6.46 | Alluvial Fan |
0 | 0.04 | 0.07 | 0.03 | 0.34 | Isolated Erosion Hills |
0 | 0.03 | 0.02 | 0 | 0.83 | Structural Terraces Slight Sloping |
0 | 0 | 0.21 | 0.03 | 0.75 | Structural Terraces-Moderates Sloping |
0 | 0 | 0.22 | 0.29 | 1.74 | Structural Terraces Steeply Sloping |
0 | 0.09 | 0.23 | 0 | 0.41 | Old Complex Landslides |
0 | 0.03 | 0.16 | 0 | 0.4 | Irregular Slopes |
0 | 0 | 0.35 | 0.41 | 6.47 | Slightly Sloping Straights Slopes |
1.13 | 2.26 | 1 | 0.77 | 9.41 | Slopes Dissected by Gullies |
0 | 0 | 0.03 | 0.04 | 0,58 | Flat Summits |
0 | 0.06 | 0.07 | 0 | 0.64 | Infilled Wadis |
0 | 0.02 | 0.03 | 0 | 0.84 | Recent Landslides |
0 | 0.09 | 0.58 | 0.57 | 9.9 | Remnants of Erosion Surfaces: Slightly Sloping |
0 | 0.14 | 0 | 0 | 0.13 | Dissected Fault Scrap |
Source: Farhan et al. [
The old landslide terrain unit is considered as degraded terrain, where it shows a low rate of soil loss generally, although relatively steep (10˚ - 15˚, 15˚ - 20˚, and 20˚ - 30˚ slope categories) and a high amount of rainfall are dominant. The soil erosion rate decreases here because the change in land use/cover from bare soil/rangeland to forest and shrubs stabilized the landslide areas. The flat summit terrain shows the lowest rate of soil erosion. Also, soil erosion is high on the remnants of erosion surfaces although flat to slightly sloping terrain are common (
With reference to the perceptions of farm households participating in the present survey, it was possible to identify five major direct causes of soil erosion in W. Kufranja: poor land management (42.3% of the respondents), deforestation and grazing, land use abuse and traditional cropping system, urbanization and natural causes (
Indirect causes of soil erosion are comparably quite crucial since, these severely affect soil erosion through direct causes. Population pressure, land tenure and continuous fragmentation of agricultural land, poverty, traditional farming, and education were perceived as indirect causes of soil erosion in the area under consideration (
Recent studies indicates that through applying soil conservation measures in central and northern Jordan, the annual erosion rate has decreased from 78 ton∙ha−1∙year−1, before installation of soil conservation structures, to 33 ton∙ha−1∙year−1, 20 years after installation of conservation structures [
The relation between socioeconomic/soil conservation variables was examined using factor analysis and multiple stepwise regression. Factor analysis was employed to reduce the large number of variables, and to condense the original variation measured in terms of 21 variables, into variation in terms of a few factors, each of which contributes to a known amount to the total variation. Through factor analysis, the 21 variables were reduced into four significant factors which account for 69.7% of the variation in the original variables.
The loadings of the rotated factors are shown in
Factor Number | Eigenvalue | Percentage of variance | Cumulative percentage of total variance |
---|---|---|---|
I II III IV | 6.752 4.683 2.351 1.761 | 30.6 20.2 11.4 7.5 | 30.6 50.8 62.2 69.7 |
Factor no. | Factor lable | Factor loading |
---|---|---|
Factor I: Age Distance Farm capital Farm revenue Farm expenditure Farm labor Factor II: Forest clearance Dependency ratio Education Conservation methods Factor III: Government help Conservation cost Proper training Factor IV: Fallow land Land use, land cover | Farm economic characteristics (1) (3) (5) (6) (7) (8) Education and conservation knowledge (18) (10) (2) (11) Government policy in soil conservation (16) (14) (13) Land use/land cover changes (20) (19) | 0.461 0.646 0.610 0.776 0.883 −0.617 0.562 0.551 0.502 0.488 0.636 −0.466 −0.454 0.571 0.439 |
struct the conservation measures such as stone bunds, the most common structural measure in our region historically. By contrast, young farmers have abandoned this technique beginning in the 1970s. Factor I reflects the economic characteristics of the households. Factor II shows positive loading on forest clearance, dependency ratio, education, and adopted method(s) of conservation methods. Young and educated household owners are clearly aware of soil erosion hazards due to forest clearance and overgrazing. They are also familiar with different conservation methods installed in that area by the German Agricultural Technical Cooperation Team and the Jordanian Ministry of Agriculture Jordan four decades ago. Such conservation measures at that period of time were provided almost cost-free for each household. This may explain the farmers’ unwillingness to invest in conservation measures, or to participate in paying for soil conservation costs. Factor II is considered an indicator of education and conservation knowledge, since the aforementioned four variables are correlated positively with factor II. The third factor accommodates positive loading on governmental help, conservation cost, and proper training in soil conservation. Therefore, it is considered as the government policy in soil conservation. Factor IV loaded positively on fallow land, and land use/land cover, hence, it is an indicator of land use/land cover changes. Factor IV is labeled as the land use/land cover factor. With the rotated factor solution illustrated above, the role of socioeconomic variables in household spatial variability was clearly explored, although, most farmers and farms are homogeneous in terms of several socioeconomic variables such as: farms ownership, gender, household size, land tenure, land parceling and the resultant land fragmentation.
Stepwise multiple regression analysis was employed to identify the predictor variables of soil erosion. (the dependent variable) among the socioeconomic/soil conservation variables (the independent variables). The model used is in the form given below:
where:
Yi = soil erosion loss (ton∙ha−1∙year−1) estimated for each household parcel,
a = is a constant (the point where the line crosses the Y axis when X = 0,
b = regression coefficient,
The results of stepwise regression are illustrated in
It is clear that anthropogenic factors such as: long and continuous human interference with land resources, deforestation and overgrazing in the past and present, land use changes and farming practices, poor conservation measures, and fragmentation of holdings are major causes of soil erosion [
Variables | R | R2 | Increase in R2 % | F value for variable | Sig. % |
---|---|---|---|---|---|
Forest clearance Fallow land Land use/land cover | 0.589 0.651 0.765 | 0.347 0.424 0.585 | 34.7 7.7 16.1 | 13.875 3.928 11.489 | 0.1 0.1 0.1 |
where most of the younger generation also choose either higher education/professions, or join the army rather than practice farming. Moreover, farmers who cultivate land owned by others may be less likely to invest in soil conservation [
The present results of the RUSLE model reveal the severity of soil erosion in Wadi Kufranja watershed. The mean soil loss estimated for the catchment was 10 ton∙ha−1∙year−1, with the five erosion risk classes, ranging from 0.0 to 1865 ton∙ha−1∙year−1. Areas of 53.1723 km2 (5317.23 hectares) and 39.4056 km2 (3940.56 hectares) were classed as suffering moderate or very severe soil erosion. Similarly, the measured sheet and splash soil erosion in W. Kufranja was 10 ton∙ha−1∙year−1 from tillage cultivated lands, and 3 ton∙ha−1∙year−1 from the fallow land, with an average ranging from 8 to 10 ton∙ha−1∙year−1. The measured maximum soil erosion on the eastern margin of W. Kufranja was 12.7 ton∙ha−1∙year−1, while the minimum soil erosion measured was 2.9 ton∙ ha−1∙year−1. Vegetation and land use abuse, traditional cultivation, lack of conservation measures, and rainfall intensity for storm events (mm/hr−1), are considered the most significant factors influencing soil erosion. Subsequently, several terrain units in the middle and lower reaches of the catchment must be prioritized for conservation, where high soil erosion rates predominate. Here, the combined effect of K, LS, and C factors, also accounts for high soil erosion loss across the study area. The household survey recognized poor land management, deforestation and overgrazing, land use abuse and traditional cropping system, urbanization and natural causes, as the major direct causes of soil erosion. Whereas, population pressure, land tenure and continuous fragmentation of agricultural land, poverty, traditional farming, and education were perceived as the major indirect causes of soil erosion.
Four significant factors, accounting for 69.7% of the variation in the original variables were revealed through factor analysis. Factor I reflects the economic characteristics of the households, while factor II is considered an indicator of education and conservation knowledge. Factor III represents the government policy in soil conservation, while factor IV is described as the land use/land cover factor. Stepwise regression shows that forest clearance, fallow land, and land use/land cover are contributing for 16.1% of the variation in the original variables. Stepwise regression indicates that 58 percent of variation in the dependent variables is explained by forest clearance, fallow land, and land use/land cover. The F-values for these predictor variables are significant at 0.1%. The R-squared value (0.585) denotes that about 58 percent variation in the dependent variable (soil erosion rate) is explained by the predictors (forest clearance, fallow land, and land use/land cover) for W. Kufranja watershed.
According to farmers’ perceptions, structural measures which have been practiced for a long time are considered the most efficient soil conservation measures adopted to reduce soil erosion. However, to be effective, structural measures by themselves must be integrated with agronomic measures. Contour stone terraces (or stone bunds) are used both on hillslopes, grazing and barren lands for soil and water conservation, and for afforestation purposes in both high and low rainfall conditions. The terraces break the slope and reduce the velocity of surface water. Similarly, check dams are normally constructed on gullies and small ravines formed by erosive activity of water (
In view of such attitudes, the authors suggest that government may offer the farmers soft loans encouraging them to restore and renew the old damaged conservation structures, and to install new structures. Vegetative measures are also recommended by respondents to prevent further forest degradation. In this context, forest plantation could be expanded in the watershed between 100 to 400 m a.s.l (
The farmers’ perceptions and socioeconomic determinants of soil erosion in the present case study are based on geospatial techniques (GIS and RS), field measurements of soil erosion, and a household survey. Few significant variables were employed. Thus, it is possible to extend the adopted techniques using large parameters to generate more precise information which can help in formulating more efficient soil conservation plans. A long- term monitoring program for soil erosion estimation, and evaluation of the impact of conservation structures in reducing soil loss and improving farms productivity, are essential in order to achieve sustainable agriculture.
YahyaFarhan,DalalZregat,AliAnbar, (2015) Assessing Farmers’ Perception of Soil Erosion Risk in Northern Jordan. Journal of Environmental Protection,06,867-884. doi: 10.4236/jep.2015.68079