Descriptive results
The results of descriptive analyses on personal and demographic, economic, biophysical, institutional and behavioral characteristics of the sampled farm households is given in Table 2. The results showed that 85% of the respondents are male household heads who possess a very low level of education. However, they have large family size (six, on average) and rich farming experience (23 years, on average). It is widely acknowledged that family size and composition affect the amount of labor available for farm, off-farm and household activities. It also determines the demand for food. Similarly, more experienced farmers are found to be able to identify soil erosion problems better than less experienced farmers (Shiferaw 2008).
Table 2
Independent variables and descriptive statistics
Variables | Mean | Standard deviation |
Sex (male) a | 85.00 | |
Education level (years) | 2.10 | 2.70 |
Farming experience (years) | 23.48 | 12.30 |
Family size (number) | 5.90 | 2.10 |
Economically active household members (number) | 2.37 | 0.10 |
Area of the plot (ha) | 0.44 | 0.17 |
Slope of the plot a | | |
Flat/gentle | 15.70 | |
Medium | 39.80 | |
Steep | 44.50 | |
Distance of the plot from dwelling a | | |
< 5 min | 14.10 | |
5–10 min | 39.50 | |
10–20 min | 31.00 | |
> 20 min | 15.40 | |
Security of tenure (yes) a | 90.80 | |
Livestock holding (TLU) a | | |
Less than 1 | 7.50 | |
1–3 | 43.30 | |
More than 3 | 49.20 | |
Off-farm activities a | | |
Never engaged | 65.80 | |
Petty trade | 29.20 | |
Wage labor | 5.00 | |
Extension contact a | | |
Once/month | 42.50 | |
Twice/month | 40.00 | |
> 2/month | 17.50 | |
a per cent (proportions)
Source
Own analysis from survey data, 2017.
Looking at the economic variables, the data shows that only 34.2% of the sample households are engaged in off-farm/non-farm activities. Off-farm/non-farm activities have served farmers in the study area as sources of additional income to purchase food crops mainly and other non-food commodities. Involvement in petty trading and wage labor accounted for 29.2 and 5.0% of off-farm employment opportunities, respectively. Majority of the respondents (about 93%) possess livestock (TLU). Number of economically active household members who live and work for the household also determines the labor available in the household which in turn may determine the type of SWC measures used by the farm households. Households with more labors may decide to use conservation measures which require more labor force but effective and efficient.
Concerning biophysical characteristics, it is undisputable that SWC measures may take some area that would have been used for cultivation (growing) of crops. Hence, it is assumed that farmers with larger farm plot area are more likely to use improved SWC measures to reduce soil erosion and conserve water in their farm plots than farmers with small farm plots (Semgalawe 1998). The survey result shows that the average size of farm plot for the sample households is 0.43 ha. This indicates that there is a serious shortage of farmland in the study area. Slope is one of the farm attributes that can aggravate land degradation in general and soil erosion in particular. Farmers who have farms in areas which are more prone to soil erosion are expected to experience more soil erosion and therefore recognize the impact of topsoil loss more easily than farmers with farms located on flat areas. In this study, 15.7%, 39.8%, and 44.5% plots were located on flat, medium and very steep slopes, respectively. It is expected, thus, that the steeper the slope of the farmland, the higher the probability of the farmers to adopt improved SWC technologies. Distance between farm plots and a homestead is important in which a considerable amount of time can be lost in walking long distances. In addition, it is easier for farmers to care their farm and to construct and maintain structural SWC practices and for manure application on the fields near their homesteads than fields that are far away. As it is indicated Table 2, about 15% of the farms are located more than 20 minutes away from homestead. During the FGDs, it was indicated that leaving crop residues on the cultivation field enhances soil fertility. However, when the land is located far away from homestead, other people may take the residues for home use (fuel energy), for animal feed, for fencing and even for sell. Thus, if the farm field is located near the farmhouse, it becomes easier to be managed and receives better attention.
The issue of tenure security is among the institutional variables considered in this study. Farmers in the study area have four major sources of land. These are 1) inheritance from family, 2) receiving from kebeles, 3) sharecropping, and 4) renting system. The survey result revealed that more than 90% of the respondents feel secure about their land holding. Further, it was found that 76% of the respondents believe that land belongs to the government; 89% expect to use the land throughout their lifetime; 94% think that they have the right to inherit the land to their children; and 93% believe that they can decide to invest on SWC. Land tenure has important implications for agricultural development in general and SWC in particular (Woldeamlak 2006). Land tenure arrangements in rural Ethiopia have undergone frequent changes since the 1974 revolution. The land reform proclamation, "Land-to-the tiller", which proclaimed that land cannot be sold or mortgaged is one in the Dergue regime. Then, in 1995 a new constitution has been enacted. In this proclamation farmers have been given the right to use their land indefinitely, but selling or mortgaging of land is still prohibited (Kebede 2006). It is generally concluded that a more secure tenure system provides the necessary incentives for farmers to decide on adoption of SWC measures on their farm plots (Tesfaye 2011).
The other institutional characteristics is contact with Development Agents (DAs). Having good relationship with DAs helps farmers to be aware of improved SWC practices in reducing hazard associated with soil erosion. The DAs can provide technical information and advice as well as training on improved SWC practices. In the survey, we found that about 43% of the farmers have interacted with DAs at least once a month.
Farmers’ perception on soil erosion
During the survey, farmers in the sample were asked to classify their farm plots, depending on the perception of degree of erosion problem (extent/severity), as low, medium and high. Thus, according to farmers’ perception, 12.5%, 41.7%, and 45.8% of the total farm plots were affected by low, medium and high erosion, respectively (Table 3). Soil erosion is a naturally occurring process on all land. The agents of soil erosion are water and wind, each contributing a significant amount of soil loss each year in the study area. The role of water in eroding the land is very high during rainy season. On the other hand, wind causes erosion during dry/windy season. Among the interviewed farmers, about 36% and 30% ranked cultivation of steep slopes and poor agricultural practices as the main causes of land degradation, respectively (Table 3).
Table 3
The distribution of farmer’s response by perception on extent and major causes of soil erosion
| Per cent |
Perception of soil erosion severity/extent | |
Low | 12.50 |
Medium | 41.70 |
High | 45.80 |
Major causes of soil erosion | |
Cultivation of steep slopes | 35.5 |
Poor agricultural practices | 29.5 |
Heavy rainfall | 20.5 |
Ceaseless cultivation | 14.5 |
Source: Own analysis from survey data, 2017. |
Farmers’ perception on structural SWC measures
The variables considered here are related to respondents' perception towards risks and comparative advantages of SWC technologies. These variables are important factors in influencing households' participation in improved/new SWC practices. The relative superiority of the technologies in terms of their advantages enable farmers to have favorable perception about the technologies, which in turn enhances decision in favor of adoption of the technologies. In order to get essential information and insight concerning farmers’ decision on adoption of improved SWC practices, looking at their perception on each practice to which they are employing is quite important. Hence, knowledge of farmers’ evaluative perception on technology attributes in the study area is an appropriate issue. In this study, a five-point Likert scale was used for this purpose and the result is depicted in Table 4.
Table 4
Distribution of farmers’ perception on structural SWC measures (5- point Likert-scale).
Perception Statement | 5-Point Likert Scale a | Chi-square |
SA | A | NO | D | SD |
1.Traditional structural SWC measures are more flexible | 87.5 | 11.7 | 0.8 | 0.0 | 0.0 | 6.89 |
2.Improved soil bund increases the quality of soil fertility | 30.0 | 40.9 | 5.0 | 22.5 | 1.6 | 72.05*** |
3.Improved stone bund needs more use of inputs | 39.1 | 54.2 | 1.7 | 5.0 | 0.0 | 16.91** |
4.Improved check dam requires frequent maintenance | 29.2 | 61.7 | 2.5 | 4.1 | 2.5 | 10.50 |
5.Improved soil bunds are possible to practice on small farm plots | 1.6 | 9.1 | 2.5 | 24.2 | 62.6 | 17.50 |
a SA = Strongly Agree; A = Agree; NO = No Opinion; D = Disagree; SD = Strongly Disagree |
***,**, significant at 1% and 5% probability level, respectively. |
Source
Own analysis from survey data, 2017.
As indicated in Table 4, almost all the respondents proclaimed that traditional structural SWC measures are more flexible. On the other hand, more than 70% of the farmers stated that improved soil bund increases soil fertility; more than 90% of the sampled households agreed that improved stone bunds need more inputs/materials; and, more than 90% of the respondents stated that improved check dams require frequent maintenance.
Factors affecting use of improved structural SWC measures
The results of Multinomial Logit (MNL) analysis conducted to assess factors affecting smallholder farmers’ adoption of improved structural SWC measures is given in Table 5. The dependent variable, adoption of improved SWC measures, has four categories: traditional or no adoption (base category), improved soil bund, improved stone bund, and improved check dam. The MNL model was run using 248 plots: traditional structures (113), improved soil bund (92), improved stone bund (25), and improved check dam (18). There are 12 explanatory variables that entered into the MNL model.
As can be seen in the lower part of Table 5, the MNL model is significant with a reasonable explanatory ability. Overall, the econometric analysis indicated that education, farming experience, number of economically active household members, extension contact, plot area, and plot distance from dwelling were found to affect farmer’s decision on the use of improved structural SWC measures significantly. However, these variables affect the use of one, two or all of the conservation structures at different sign, magnitude and significance level. In what follows, we discuss these significant predictors of farmers’ use of improved structural SWC measures in the study area.
Table 5
Multinomial Logit (MNL) model estimation results.
Variable | Improved Soil Bund | Improved Stone Bund | Improved Check Dam |
Coef. (Std. Err.) | Marginal effect | Coef. (Std. Err.) | Marginal effect | Coef. (Std. Err.) | Marginal effect |
Sex of household head | 0.45 (0.72) | 0.0221 | 0.68 (0.45) | 0.0221 | 0.05 (0.08) | 0.0036 |
Education level | 2.64 (0.67) | 0.0055 *** | 2.60 (0.58) | 0.003 *** | 2.47 (0.56) | 0.006 *** |
Farming experience | 0.29 (0.11) | 0.006 *** | 0.30 (0.15) | 0.001 ** | 0.24 (0.15) | 0.002 * |
Security of land tenure | 0.37 (0.88) | 0.008 | 0.83 (0.80) | 0.0162 | 0.10 (0.70) | 0.0024 |
Perception of soil erosion | 1.54 (1.26) | 0.003 | 0.86 (1.41) | 0.0129 | 1.68 (1.43) | 0.037 |
Extension contact | 1.93 (0.94) | 0.0052 ** | 1.22 (0.80) | 0.0194 | 0.01 (0.70) | 0.0002 |
Livestock holding | -0.37 (0.32) | -0.0007 | -0.31 (0.30) | -0.0049 | -0.27 (0.23) | -0.0062 |
Plot area | 1.40 (0.52) | 0.0028 *** | 1.22 (0.59) | 0.0184 ** | 1.92 (0.53) | 0.0426 *** |
Plot distance | -0.35 (0.37) | -0.0007 | -1.22 (0.59) | -0.001 ** | -0.73 (0.40) | -0.0165 * |
Slop of the plot | 2.11 (1.30) | 0.0043 | 1.43 (1.43) | 0.0215 | -4.44 (2.44) | -0.000 |
Off-farm activities | -3.67 (2.31) | 0.361 | -3.83 (2.40) | 0.070 | 0.24 (0.26) | 0.0055 |
Number of economically active household members | 0.62 (0.38) | 0.0013 * | 0.09 (0.33) | 0.0013 | 2.43 (1.49) | 0.054 |
Constant | -4.75 (3.73) | - | -6.85 (3.28) | - | -5.08 (3.00) | - |
Observations | 248 | 248 | 248 |
Log likelihood | 118.18 | 118.18 | 118.18 |
Chi squared | 134.97 *** | 134.97 *** | 134.97 *** |
Pseudo R2 | 0.36 | 0.36 | 0.36 |
*, **, *** significant at p < 0.1, p < 0.05, and p < 0.01 probability level, respectively. |
Dependent variable = existence of improved structural SWC structure on the farm plot
Source
Own analysis from survey data, 2017.
Educational level of household head
Education level is positively and highly significantly associated with the use of improved soil bund, stone bund and check dam. More precisely, a one-year increase in education will increase the probability of a household to use improved soil bund, stone bund and check dam by 0.55%, 0.3%, and 0.6%, respectively. This implies that household heads with relatively better formal education are more likely to use appropriate improved structural SWC practices and they are also able to anticipate the consequences of soil erosion than non-educated farmers. In addition, they have better understanding of their environment and risks associated with cultivation of marginal lands. Our result is in line with empirical evidence obtained from different parts of the country (e.g., Anley et al. 2007;Tizale 2007).
Farming experience
Farming experience is also positively and significantly related to the adoption of improved structural SWC measures in the study area. The result indicates that experienced farmers tend to use improved conservation strategies than non experienced farmers. In relation to this result, Shiferaw et al. (2008) asserted that experienced farmers are capable of detecting soil erosion problems more than non-experienced farmers. Similarly, Fekadu et al. (2013) pointed out that those farmers who have better farm experience have high chance of being participant. As observed from the result, a one-year increase in farming experience increases the probability of farmers’ adoption of improved soil bund, stone bund and check dam conservation by 0.6%, 0.1% and 0.2%, respectively.
Extension contact
Extension service on SWC practices was found to have a positive effect on adopting improved soil bund. However, it did not affect the adoption of improved stone bund or check dams. Farmers who receive extension message on SWC from development agents will be more encouraged to use improved SWC practice on their farm plots. Similarly, Yitayal et al. (2006) and Tizale (2007) reported that households with access to extension services and information have better understanding of land degradation problem and soil conservation practices and hence may perceive SWC practices to be profitable. As observed from the model result, as farmers get extension message/contents on SWC practices, the probability of using improved soil bund increases by 0.52%.
Plot area
The MNL model results indicate that plot area has a positive and significant effect on the likelihood of adopting all types of improved structural SWC structures. This is because farmers with larger farm plot are more likely to be able and willing to use improved SWC measures to reduce land degradation problems in plots located on sloppy areas. This result is in line with empirical studies that have shown a positive and significant effect of area of a plot on the decision to use conservation measures (for instance, Amsalu and De Graaff 2007; Kassa et al. 2013). Hence, plot size promotes conservation. The result shows that as plot area increases by a hectare, the probability of deciding to use improved soil bund, improved stone bund and improved check dam increases by 0.28%, 1.84% and 4.26%, respectively.
Distance of the plot from dwelling
Distance of the plot from the dwelling is related negatively with improved check dam and improved stone bund. The result from the model output indicates as the distance of the plot from dwelling of the household increases by one kilometer, the probability of using improved check dam and improved stone bund decreases by 1.65% and 1%, respectively. This result in line with the findings of Derajew et al. (2013).
Number of economically active household members
SWC activities demand labor which is a critical problem in a peak period of production and livestock rearing. In this study, number of economically active household members who actively participate in improved structural SWC relates positively and significantly with adoption of improved soil bund. The model result indicates that as the number of economically active household members increases by one person, the probability of using improved soil bund increases by 0.13%. We did not find any significant relationship between this variable and adoption of the other improved structural SWC measures. This result is in line with the findings of Tadesse and Belay (2004).