The Nexus between livelihood diversification and farmland management strategies in rural Ethiopia

The purpose of this study was to determine whether or not livelihood diversification strategies are significant predictors of rural households’ farmland management practices. To this end, the two-limit Tobit econometric model was employed to analyse the nexus between livelihood diversification and farmland management. The study incorporated 151 farm households in Gozamin District, East Gojjam, in Ethiopia. A survey questionnaire was used to gather data. Both descriptive and inferential statistics were used as methods of data analysis. The result indicated that livelihood diversification, measured by the inverse Herfindahl–Hirschman diversity index, has a positive and significant effect on sustainable land management activities. Besides, farm households who participated in agricultural extension packages and those engaged in farm cooperatives and in non-farm wage employment activities joined significantly more in sustainable rural land management practices. The integrated rural livelihood and sustainable land management strategy that can help jointly maximise the farm household livelihood and the land management practices is required. Rural livelihood development policies need to promote and adapt rural institutions like farm cooperatives and comprehensive agricultural extension services. *Corrsopnding author: Geremew Worku Kassie, Department of Economics, Business & Economics College, Debre Markos University, Debre Markos, Ethiopia E-mails: geremew_worku@dmu.edu.et, geremewworku@yahoo.com Reviewing editor: Goodness Aye, University of Agriculture Makurdi Benue State, Nigeria Additional information is available at the end of the article


PUBLIC INTEREST STATEMENT
Ethiopia strives to ensure sustainable agricultural development as more than 78% of its population depends on agriculture. To this end, each household has to be engaged in good farmland management practice that enhances agricultural productivity. This is because sustainable agricultural development requires environmentally friendly land management practices. Furthermore, households in rural districts of Ethiopia use different livelihood diversification strategies primarily to ensure their livelihood security. This scenario, in one way or the other, may have its own impact on their farmland management practice in particular and on the environment in general. Therefore, this study will be of public interest since it attempts to determine whether or not livelihood diversification strategies are significant predictors of rural households' farmland management practice. The study showed that farm household livelihood diversification activities have positive effect on farmland management practices. Besides, the two-limit Tobit econometric model developed in this study may be applicable in other developing nations with similar characteristics as north-east central Ethiopia.

Introduction
Livelihood diversification is defined by several scholars in different ways. It is the scope and combination of activities and choices (Liu & Liu, 2016); a means of gaining a living (Loison & Loison, 2016); comprises the capabilities, assets and activities required for a way of living (Farrington, Carney, Ashley, & Turton, 1999;Scoones, 1998). It is also defined as the course by which households establish progressively diverse livelihood portfolios (Niehof, 2004); adequate stocks and flows of cash to meet basic needs (Hilson, 2016), and it is a form of self-insurance (Barrett, Reardon, & Webb, 2001). Livelihood diversification is a major means by which many individuals reduce risk. It is widely understood as a form of self-practice in which people exchange some foregone expected earnings for reduced income variability achieved by selecting a portfolio of assets and activities (Abdulai & CroleRees, 2001;Adesina, Mbila, Nkamleu, & Endamana, 2000;Barrett, Reardon, et al., 2001). Farm household diversification refers to income strategies of rural individuals in which they increase their number of activities, regardless of the sector or location (Brandth & Haugen, 2011;Loison & Loison, 2016;Martin & Lorenzen, 2016).
In situations of high-risk agricultural sector and poverty incidences, poorer farm households with constraints of critical assets will be forced to engage in alternative incomes by participating in lowyield and sometimes risky non-farm activities (Barrett, Reardon, et al., 2001;Makita, 2016;Martin & Lorenzen, 2016;Loison & Loison, 2016). Farm households in sub-Saharan Africa strongly rely on farming and exploitation of natural resources. Specifically, in Ethiopia, the complex inter-linkages of poverty, population growth and environmental degradation cause decline in farm plot sizes, lead to landlessness and expansion of farming to marginal lands (Belay & Bewket, 2015;Bezabih, Gebreegziabher, Gebremedhin, & Köhlin, 2010;Pender & Gebremedhin, 2008). Therefore, rural households in sub-Saharan African countries usually need to cope with poverty and income variability (Abdulai & CroleRees, 2001). As a result, diversification is usually connected with livelihood survival and distress under abating conditions, as well as with livelihood security under improving economic conditions (Niehof, 2004). Different scholars mentioned several types of livelihood diversification activities. There are four distinct rural livelihood strategies, namely: on-farm agricultural production, unskilled on-farm or offfarm wage employment and non-farm earnings from trades, commerce and skilled employment and the fourth mixed strategy combines all the three strategies (Gebru & Beyene, 2012;Hilson, 2016;Sherren, Loik, & Debner, 2016). The components of rural livelihood diversification are also classified by sector as farm or non-farm, by function as wage employment or self-employment or by location as on-farm or off-farm (Bowen & De Master, 2011;Loison & Loison, 2016). It is also argued that rural people establish their livelihoods via three main strategies: agricultural intensification; livelihood diversification; and migration (Barrett, Bezuneh, & Aboud, 2001). The types of livelihood diversification activities by the farm households may have either positive or negative effects to the rural land management system. In addition, the rural land management practice is also determined by the socio-economic characteristics of farm households. Therefore, it is pertinent to examine the effect of households' livelihood diversification strategies on farmland management practice in Gozamin Rural District in East Gojjam, Ethiopia. household income was found to be generated from non-farm livelihood diversification activities (Gebru & Beyene, 2012). Non-farm activities have the potential to help households reduce poverty by granting them with a form of insurance against the threats of farming and minimising reliance on natural resources (Davis, 2003;Deininger & Jin, 2006;Hilson, 2016;Lanjouw & Lanjouw, 2001;Martin & Lorenzen, 2016;Neupane & Thapa, 2001;Reardon, 1997).
There are theories that explained why farm households diversify, instead of specialising their livelihood activities. The major justifications for diversification as summarised by (Sherren et al., 2016) are attribute to (i) self-insurance against risk, (ii) an ex-post coping strategy, (iii) inability to specialise due to incomplete factor markets and (iv) consumption diversification where there are incomplete product markets. In addition, there are incentives for allocating labour to the non-farm activities including: better comparative returns, inadequate farm output, a need for non-farm income sources to purchase for farm inputs and risky returns to farming (Barrett, Reardon, et al., 2001;Gebru & Beyene, 2012;Marenya & Barrett, 2007;Martin & Lorenzen, 2016;Reardon, 1997). The extent of risktaking or aversion behaviour of the farm household also positively determines the necessity for diversification as a form of insurance, and conversely may also determine negatively the degree of access to other ways of settling and coping with shocks and risks (Ellis, 2000). Generally, these drivers have been divided along a spectrum of "necessity versus choice", on the one extreme, "push factors", and on the other extreme, "pull factors" (Hilson, 2016;Makita, 2016;Orenstein & Hamburg, 2009;Sherren et al., 2016;Van Hulst, Helena, & Posthumus, 2016). The dwarf productivity growth of agriculture, the declining soil mineral content of the farmland and the ever-continuing population growth push farm households in the north-east central Ethiopia of Gozamin District to diversify their livelihood strategy, so as to minimise risk, maximise their personal income and to guarantee and smooth consumption expenditure.
For instance, a poor farm household's labour input allocation may be transited from natural resource-dependent livelihood activities to other environmentally non-harmful off-farm and non-farm income generating activities, such as trade, and rural small-scale manufacturing practices. Subsequently, the adverse impact on natural resources caused by firewood and charcoal production is expected to decline (Davis, 2003;Farrington et al., 1999). In addition, smallholder farmers use a variety of activities to adapt to climate variability and change. These practices include diversification activities like planting trees that could minimise the pressures of natural capital and diversification of livelihood strategies and land use management (Delden et al., 2015;Ito et al., 2016;Reardon, 1997;Tai-yang et al., 2011). The growth of non-farm income sources if accessible in remote rural areas might reduce the need for landless dwellers to carry out extractive practices in local environments for their livelihood. This has been called the "substitution of employment for the environment" (Delden et al., 2015). In addition, livelihood diversification is an effective way of solving the problem caused by poverty and environmental degradation (Delden et al., 2015;Ito et al., 2016). For instance, in China, livelihood diversity helps minimise households' dependence on environmental resources, thereby helping environment rehabilitation (Zhang et al., 2011). Off-farm employment sometimes demands the outflow of the rural population and reduces regional population and environmental stresses, which are beneficial for keeping sustainable achievements (Ito et al., 2016;Taiyang et al., 2011;Zhang et al., 2011). As an external "pull" factor, the rapid development of China's overall economy effectively promotes the non-agricultural transfer of the rural labour force and improvement of household livelihoods, so as to indirectly promote the restoration of the ecological environment (Wang, Gao, Wang, & Li, 2016).
The effect of livelihood diversification on environment is one of the integral issues in any agrarian countries. Eradication of extreme poverty and hunger will be successfully attained if the country could promote sustainable livelihood and agriculture (World Bank Group, 2012). In countries such as Ethiopia, diversification in agriculture sector is highly related to using and exploiting natural resources; conversely, it may help rehabilitate farmland from degradation as far as it is undertaken in an environmentally sustainable manner. For instance, in north-east central Ethiopia, small-scale farmers continue to diversify their income bases to non-farm and off-farm activities to maximise their income and livelihood sources. However, previous empirical studies did not try to examine the effect of farm household livelihood diversification activities on sustainable land management practices particularly in the context of developing countries. This study attempted to examine the nexus between livelihood diversification strategies and farmland management practices at farm household level by employing data compiled from the survey of 151 farm households in Gozamin District of north-east central Ethiopia.

The model
This study developed the econometric model to estimate the effects of livelihood diversification strategies of farm households on sustainable land management practices. The extent of household's livelihood diversification is measured by the inverse Herfindahl-Hirschman Diversity (IHHD) index using the formula: where y j represents the proportional contribution of each livelihood activity j to household i's overall income. Farm livelihood activity consists of three broad categories of farm household income sources, namely: on-farm, off-farm and non-farm practices (Ellis, 2000;Ellis & Freeman, 2004), that indicate the maximum value of the IHHD index is limited to be three. In this model, all sample farm households are assumed to be engaged in at least one farm activity, on-farm activities, which mainly may include the production of crops and livestock. Thus, the IHHD lies between one and three. Similarly, the sustainable land management (SLM) index, the dependent variable, is constructed from eight different sustainable land management indicators and practices, which are performed in the study area. These indicators include tree plantation, terracing, fallowing, manure and compost, soil-bund, gully check, shelterbelt and strip cropping by the farm household's own farmlands. Based on the institutional and socio-economic characteristics of the individual farm households, some of them adapt these sustainable land management practices in various extents while others do not. All scores are added and divided by eight to find a Sustainable Land Management (SLM) index for individual farm household (i) as illustrated below: (1) i where s n represents eight different sustainable land management practices notably conducted in the study area. The SLM index lies between zero and one, inclusive.
The farm household may allocate the food crop land for more environmentally sustainable livelihood activities like tree planting and perennial cropping purposes. On the other hand, depending on the household characteristics and institutional set-up, some livelihood strategies may have adverse effects on the farmland management systems. As a rational economic agent, the farm household is expected to adapt land management techniques for sustainable agricultural practices if the benefit gained from adopting them is higher than otherwise.
If the dependent variable is censored, i.e. having a lower limit and/or upper limit, then the least squares estimators of the regression parameters are biased and inconsistent. This study employs a censored regression model (Greene, 2012;Maddala, 1992), which is a generalisation of the standard Tobit model. It is censored since some proportion of farmers from the sample did not participate in sustainable land management practices, and a dependent variable will take a limit value for some proportion of the observations, which is zero from below. As the dependent variable, sustainable land management index has a lower limit of zero and an upper limit of one; the two-limit Tobit model is found to be a more reliable measure.
where γ is the lower limit, that is zero, and is the upper limit, which is one in the dependent variable of this model. The study estimated this model using the Maximum Likelihood (ML) procedure. The log-likelihood function can be presented as follows, assuming the disturbance term, , follows a normal distribution with mean 0 and constant variance 2 , where Φ(⋅) and (⋅) represent the cumulative distribution function and the probability density function of the standard normal distribution, respectively, and I i and I i are indicator functions with: For a standard Tobit model, for each observation (i), where i, 1, … , n, the dependent variable is defined by: The log-likelihood function of the censored regression model can be maximised with respect to the parameter vector ( � , ) � using standard non-linear optimisation algorithms. (3)

Data and variables
The data for this study were collected from a survey of 151 farm households in Gozamin District located in north-east central Ethiopia. First, the study area was selected purposefully so as to find the representative farm households of rural Ethiopia. Second, a survey questionnaire was used by employing simple random sampling technique to select sampled farm households ( Figure 1). Table 1 below, farm households do not use sustainable land management practices with equal extent and efforts. Some adopt all sustainable land management practices, others partly, while others do not adopt any of the practices.

As indicated in
As seen in Table 2, out of the total 151 farm households, 71.52% of them are engaged in off-farm and non-farm livelihood activities. Similarly, 75.4% of them are found to actively participate in sustainable land management activities.
During the survey season, the farm households' average income was 23371 ETB and from on-farm income, off-farm income and non-farm income diversification activities, the farm households earned on average 20177 ETB, 1235 ETB and 1971 ETB, respectively.   Table 3 reveals coefficient and marginal estimates of the two-limit Tobit model. Since the Tobit model is non-linear, the estimated coefficients cannot result in a correct measure of the effect of the explanatory variables on the dependent variable. Therefore, the most fitting method is to use marginal effects rather than their coefficients. For comparison, the multivariate probit result has already been computed and seen in Appendices 1 and 2.

Empirical results
On the margin, farmers who become members of the cooperatives are more likely to participate in sustainable land management practices by more than 15.6% units than the non-member counterparts. As farmers organise themselves through cooperatives, it is more likely that their knowledge about farmland protection could be enhanced. Furthermore, the government development agencies in Ethiopia use cooperatives as a channel to give technology and environmental protection awareness for farm households. This result is in line with (Bravo-Monroy, Potts, & Tzanopoulos, 2016) who found that being a member of community organisations stimulated adoption of organic coffee management practices. Similarly, Wang et al. (2016) found out that being a member of regional environmental cooperatives and having membership in farmers' organisations led to the improvement in farm households' perceptions towards agricultural environmental schemes and to adopt

Table 3. Two-limit Tobit estimation of sustainable farmland management index
Notes: Standard errors are in parentheses.
Agricultural extension service users are more likely to participate in sustainable land management practices; keeping other factors constant, farm households who have access to agricultural extension services are found to be 35.2% units more likely to participate in sustainable land management practices. More importantly, extension services are supposed to enhance the knowledge of farm households in better farming that includes the adoption of environmentally sustainable farming techniques. As farm households get access to agricultural extension services, they can protect their land and will engage themselves in land management practices to increase their land productivity. Similarly, previous studies revealed that contact with extension workers and getting technical assistance helped farm households improve their land management and soil conservation practices (Delden et al., 2015;Fraser, Dougill, Mabee, Reed, & McAlpine, 2006;Grace et al., 2016;Kondylis, Mueller, Sheriff, Zhu, & Bank, 2016;Kotzé, Sandhage-Hofmann, Meinel, du Preez, & Amelung, 2013;Mirzabaev et al., 2015;Nyanga, Kessler, & Tenge, 2016;Onduru, de Jager, Gachini, & Diop, 2016;Reed, 2008;Reed et al., 2009;Sherren et al., 2016;Tittonell, 2014;Zhang et al., 2015).
Wage collected from non-farm income sources has also a positive effect on sustainable land management practices. On the margin, as a farm household head gets employed in non-farm activities, the land management practices increased by 22.7% units. The justification on this econometric result is that the intensity of on-farm farming may be declining, while sustainable land management practices such as fallowing and tree plantations, which are less labour-intensive, would increase. Various international studies also supported this result (Bhandari & Grant, 2007;Gautam, Webb, Shivakoti, & Zoebisch, 2003;Holden, Shiferaw, & Pender, 2004;Paudel & Gopal, 2004;Robalino, 2007;Scherr, 2000;Templeton & Scherr, 1999;Thapa, 1996).
Farm households who have primary education show less engagement in sustainable land management activities by 10.5% units than those who have no formal education. Perhaps this is because instead of being engaged in sustainable land management practices, farm households with better education may be engaged more in specialisation and higher productive activities. Education could also take away labour time from the land management practices. Some previous studies also found out similar findings (Kilgore & Snyder, 2016;McLaren, Parkhill, Corner, Vaughan, & Pidgeon, 2016;Pender & Gebremedhin, 2008;Schulze et al., 2015;Yesilonis, Szlavecz, Pouyat, Whigham, & Xia, 2016). However, some other previous studies (Ersado, Amacher, & Alwang, 2004;Pender & Kerr, 1998;Pender, Nkonya, Jagger, Sserunkuuma, & Ssali, 2004) found out that education has affected positively the land management conservation practices.
Finally, livelihood diversification that is measured by Inverse Herfindahl-Hirschman Diversity index reveals statistically significant and positive effects on the adoption decision of sustainable land management practices. Keeping other factors constant, as farm households' measure of livelihood diversification increased by one unit, the marginal effect of sustainable land management index was found to increase by 56.9% units. This result may be justified; first, some livelihood diversification activities in the study area such as vegetable and tree plantations on farm plots can be reinforced with enhancing the sustainable land management practices. Second, as farm households diverge their livelihood strategies, their personal income is likely to be maximised, and that will motivate farmers to invest in better farmland management practices like protecting their land from erosion and any other potential land degradation effects. Finally, farm households allocate their labour to livelihood diversification activities that will help smooth their annual consumption expenditure through non-farm and off-farm income sources that may help reduce the intensity of on-farm agricultural practices. Using Ordinary Least Square and quantile regression techniques, Mishra and Moss (2013) brought consistent results.
In conclusion, livelihood diversification, as measured by Inverse Herfindahl-Hirschman Diversity index, reveals an upbeat and significant effect on sustainable land management index of a farm household. Besides, participating in agricultural extension packages and being members of farm cooperatives have significant effects on households' sustainable land management practices. On the other hand, farm households who have completed a primary education participate less in sustainable land management practices compared to those without formal education.

Implications
Comprehensive rural development policy needs to be designed and implemented in order to empower farm households. Off-farm and non-farm rural livelihood diversification strategies need to be incorporated in the rural development policies. Most importantly, the off-farm and non-farm rural development strategies will help farm households to employ their labour hour efficiently throughout the year; consequently, there will not be wasted idle labour hours. Besides, in Ethiopia, the integrated rural livelihood-sustainable land management strategy that can help maximise both the rural livelihood and the land management practices of farm households needs to be formulated. Finally, rural development institutions like cooperatives and agricultural extension packages have to be incorporated in rural development strategies so as to improve the capacity of farm households to participate in livelihood diversification and sustainable land management practices. The rural development policy towards livelihood diversification needs to incorporate farm households in institutionalising cooperatives like tree plantation, and other environmental cooperatives that can enhance farmland management practices shall be mainstreamed. Note: Standard errors are in parentheses. *Statistical level of significance at 10%. **Statistical level of significance at 5%. ***Statistical level of significance at 1%.