They can check soil erosion to some extent, increase soil fertility, reduce salinity; alkalinity,acidity, and desertification, ultimately improve soil health which keeps the land suitable for the sustainable production of diversified products . According to Sharma et al. , agroforestry-related practices, such as, the use of multi-purpose tree species, relay-cropping, terracing and contour cultivation, soil and stone bunds, strip as well as alley cropping are appropriate to fulfil the needs of low-resource farmers by restoring and increasing land productivity. In fact, the mainstream literature on mountainous agroforestry farming systems tends to support the assertion that agroforestry could ameliorate the living conditions of the local population and protect the natural reserves from human disturbance .
Elsewhere in Tanzania, hydroponic grow system in the East Usambara Mountains, the study by Reyes et al. for example, indicated that the households who practiced improved agroforestry systems obtained twice as much annual gross income compared with their counterpart farmers who used traditional practices. They also found that about 40% of farmers who practiced improved agroforestry were securing enough food throughout the year, compared with only 18% for farmers who used traditional practices.However, empirical evidence which shows the effect of agroforestry farming systems on poverty and income inequality is lacking, at least in the context of mountain areas. The evidence would help policy-makers and other stakeholders to formulate suitable policies, plans and strategies to achieve sustainable development in these areas.
It should be noted here that, the levels of income-inequality in mountain areas may vary significantly between farming households,farmland locations and farmers’ economic characteristics. To the best of our understanding, these characteristics have not yet received adequate attention among scholars as most studies attempted to evaluate income inequality in mountain areas did not consider these. In addition, studies which disaggregate inequality based on differences in household and farm characteristics such as, farmland location, gender, and access to extension, as well as membership to community-based financial institutions, like the Savings and Credit Co-operative Society and Village Community Bank , are lacking. Equally important, much of the previous research on inequality uses time series or panel data focusing on broader scales, such as national, regional and multinational levels.
Studies which use cross sectional data while focusing on small scale sites and locations such as, hamlets,indoor garden villages and wards of mountain areas are lacking. Where the cross-sectional data is applied, most researchers have ignored the effects of variation in household personal characteristics, farming characteristics,economic characteristics as well as the existing transforming structures and processes. In the Karakoram valleys of Pakistan, for example, Ullah et al. investigated the nexus between financial inclusion and improvement in the living standards of mountain people, and reduction in economic poverty ,multi-dimensional poverty and income inequality using the Quasi Experimental Designs, Foster, Greer and Thorbecke poverty measures, Alkire et al. methodology,Gini Index and Quintile technique. In addition, they used a Logistic Regression technique to identify the major drivers of economic poverty, multidimensional poverty and income inequality using non-disaggregated cross sectional data. They found a positive synergy among inclusive finance and living standards and a negative connection between financial inclusion and economic poverty, multidimensional poverty as well as income inequality.
Their results of logistic regression showed that financial inclusion was a potential determinant of economic poverty reduction though it was found to be an insignificant tool for eradicating multidimensional poverty.In Poland, Kata and Wosiek used time series data covering a period of2004-2018 to investigate the redistributive effects of agricultural policy and the importance of income inequalities among agricultural holdings for sustainable agricultural development. They used the process of concentration of production factors in this sector, as well as, the level of budget support using pooled data.Specifically, they applied the Gini coefficient, concentration index, and multi-variate regression analysis to establish a relationship between the processes of income polarization in agriculture and the process of concentration of production factor as well as the level of budget support.