Agroforestry Practices for Climate Change Adaptation and its Contribution to Farmers’ Income

Agroforestry practices offer a unique opportunity to address climate change impacts while securing the livelihoods of the rural communities. This study was carried out in Tillotama municipality of Rupandehi district, Nepal. Agroforestry system practices at the study site were identified through reconnaissance survey and discussions with ward officials. With 10% sampling intensity, purposive sampling was adopted for the study using the structured questionnaire, key informant interview, and field observation. For mean comparison, one-way ANOVA and Least Significant Difference (LSD) as post-hoc tests were carried out. Local communities were adopting eight different types of agroforestry practices under four agroforestry systems, namely agri-silvicultural, silvo-pastoral, agro-silvopastoral and silvi-fishery. The agroforestry system shared up to 50.54% of total households’ income, in which income from agriculture was the highest. Agroforestry income was


Introduction
Agroforestry is a climate-smart production system and is considered more resilient than mono-cropping (Charles et al., 2014;Haile et al., 2019).It is one of the most experimented land-use systems across landscapes and agro-ecological zones in Nepal (Nair, 2007;McCord et al., 2015).With food shortages and increased threats of climate change, interest in agroforestry is gathering for its potential to address various on-farm adaptation needs, and fulfill many roles in AFOLU (agriculture, forestry, and other land-use) related mitigation pathways (Mbow et al., 2014).It can play a crucial role in improving resilience to uncertain climates through micro-climate buffering and regulation of water flow (Stigter, 2015).When it provides assets and income from carbon, wood energy, improved soil fertility, and enhancement of local climate conditions, it provides ecosystem services and reduces human impacts on natural forests (Moreno et al., 2018).Most of these are direct benefits for local adaptation while contributing to global efforts to control atmospheric greenhouse gas concentrations (Rosenzweig and Tubiello, 2007).Furthermore, agroforestry provides a particular example of a set of innovative practices that are designed to enhance productivity in a way that often contributes to climate change mitigation through enhanced carbon sequestration, and that can also strengthen the system's ability to adapt to adverse impacts of changing climatic conditions (Verchot et al., 2007;Mbow et al., 2014).
Climate change is projected to affect agricultural and natural ecosystems around the world, and there is no reason to expect that agroforestry systems will be spared (Luedeling et al., 2014).As the impacts of climate change have become apparent around the world, adaptation has attracted increasing attention (Mimura et al., 2015).With the world's population increase, the need for more productive and sustainable use of the land becomes more urgent.To meet the demand for food by 2050, world food production will have to increase by over 60% (Mckenzie and Williams, 2015).But the shortfall in domestic cereals production in the developing world was expected to widen from around 100 million tons in 1997 to around 190 million tons in the year 2020 (Rosegrant et al., 2001;Verchot et al., 2007).In many regions of the world, there will be limited ability for new varieties and increased fertilizer use to further increase the yields (Huang, Pray and Rozelle, 2002;Balemi and Negisho, 2012).
Agroforestry systems include both traditional and modern land-use system dynamics, and ecologically based natural resource management systems that diversify and sustain production in order to increase social, economic, and environmental benefits for land users at all scales (Pandey, 2007).Agroforestry as a treebased system combines trees and/or shrubs, animals, and agronomic crops.It provides a particular example of a set of innovations designed to enhance REDD+ through carbon substitution, carbon conservation, and carbon sequestration in the agricultural landscape (Charles, Nzunda and Munishi, 2014).The rapid increase in Earth's surface temperature and changing precipitation pattern has resulted in direct implications to multiple sectors and livelihood of communities (Rao and Leal Filho, 2015).The poorest and vulnerable people are being affected the most (Mustafa, 2011).The data trend from 1975 to 2005 shows that the mean annual temperature has increased by 0.06°C, while the mean rainfall has decreased by 3.7 mm (-3.2%) per month per decade (MoE, 2012).Similarly, mean annual temperature is predicted to be increased between 1.3°C to 3.8°C by the 2060s and 1.8°C to 5.8°C by the 2090s while annual precipitation could reduce by the range of 10 to 20 percent across the country Nepal (Joshi and Singh, 2020;MoE, 2010).Studies also indicate that the observed warming trend is not uniform across the country.Agroforestry land-use management is necessary for increasing soil carbon stocks and socio-economic development of farmers; and the research on the carbon sequestration rate of agroforestry is necessary for making future policies and strategies on the issue of climate change.However, there were limited research (Neupane and Thapa, 2001;Regmi, 2003) carried out in the field of agroforestry, mostly focusing on soil fertility and local livelihood.

Methodology Study Area
The study was carried out in Gangolia village of Tilottama municipality in Rupandehi district, which lies in the Southern part of Lumbini Province of Nepal with the coordinates of 27°37′48″ N latitude and 83°27′36″ E longitude.The district Rupandehi lies in the southern and western parts of Nepal.On the East, it shares a border with Nawalparasi district, on the West with Kapilvastu district, on the North with Palpa district, and on South with India.The elevation of the district lies between 100 m to 1229 m from sea level.The total area of the district is 1,360 km 2 with 16.1% in Churia Range and the rest in the Terai1 region.Recently, the Government of Nepal is planning to extend the agroforestry system in the Rupandehi district by considering an agroforestry pocket area.Only a few farmers have been practicing different types of agroforestry systems for few decades, although such type of study is lacking in this area.

Data Collection
The primary data were collected from the study site by employing a combination of social survey methods involving participatory techniques such as on-site field observation, household survey questionnaire, and key informant interview.The sampling used for this study was purposive sampling with a sampling intensity of 10% (Kombo and Tromp, 2006).Out of the total 304 households in Gangolia village of Rupandehi district, 33 households (10% sampling intensity) were sampled.
The various relevant and related secondary data were derived from published research papers, articles, newspapers, brochures, leaflets, annual reports, progress report and other publications of various related authorities.Secondary databases of precipitation and temperature were collected from the Department of Hydrology and Meteorology.

Data Processing and Analysis
Quantitative data were analyzed using descriptive and inferential statistics such as percentage, mean, frequency distribution, and use of graphics and parametric test i.e., F-test (ANOVA).F-test was used to compare the income of farmers from the agroforestry system with its determining factors like caste, wellbeing ranking, education level, and family size.Similarly, rainfall and temperature data of 30 years  were analyzed using the Least square curve fitting technique i.e., Y=a+bt where, y=temperature or rainfall, t=time (year), a and b are constant estimated.

Socio-Demographic Status of Respondents
The age group of the respondents mostly lies between 35 to 60 years.The major castes/ethnic groups in the study area were Brahmin/Chhetri, Janjati, Dalit, and others represented by 30%, 50%, 10%, and 10%, respectively, among the sampled households.Literacy level among the sample respondents was primary level (33.3%), secondary level (30%), and higher secondary and above (36.7%).Most of the households (HHs) had 4 to 12 members.Among the sampled households, about 3.3% HHs were having less than 6 family members, 40% having 6 to 7, 40% sampled households 7 to 9 family members and 16.6% HHs more than 10 family members.

Annual Income of Farmer
The majority of the household income was from agroforestry (50.19%) followed by remittances/pensions (21.01%), services, business, and wages to be 15.79%, 9.27%, and 3.73%, respectively.An increase in size of these parameters brought about an increase in the household's annual income and, thus, contributing to poverty alleviation.Contribution of agroforestry components on total farm income of the farmers showed that mean annual income from agriculture was found to be 42%, followed by livestock, fisheries, poultry, tree and fuelwood with 27%, 16%, 11%, 3%, and 1%, respectively (Figure 2).

Mean Test of Agroforestry Income of the Farmer with respect to different Socio-economic Variables
Distribution of the socio-economic factors influencing agroforestry income showed overall significance to only wellbeing status (rich/medium/poor) of the household.In regard to caste, Brahmin/Chhetri with Janjati was significant; but Brahmin/Chhetri with Dalit and Other are insignificant at a 5% level of significance.Similarly, education level and family size are also insignificant to the total annual income of agroforestry income.So, we can conclude that level of education and family size did not affect the income from agroforestry.

Climate Change Adaptation Strategies through Agroforestry System Practices
Almost 30 years' climatic data from 1989 to 2018 of Bhairahawa meteorological station showed that the average annual rainfall was in increasing trend with 0.034 cm per year which is shown in figure 3. The trend of the maximum temperature was also in increasing order of 0.026°C per year as shown in figure 4.  The strategies adopted by the farmers against climate change were found mainly in the form of using chemical fertilizers and pesticides (25%), diversification of income-generating activities (27%), agroforestry (18%), and changing the crop calendar (30%).Similar findings like crop-livestock diversification and multiple cropping strategies were reported by several scholars (Assoumana et al., 2016;Gebreeyesus, 2017;Mekuria and Mekonnen, 2018).The respondents who practiced agroforestry experienced various benefits such as improved soil fertility rate (32%), improved micro-climate (24%), increased catchment for pump set and boring (29%), and increased wood products (15%), although these methods are not sufficient to control climate change effects.
Figure 5: Specific adaptation strategies adopted at household level

Conclusion and Recommendation
Agroforestry has a significant impact on the livelihood of people engaged in agriculture primarily and on those who have low adaptive capacity.In the study area, trees on farmland were found as part of traditional practices.Agroforestry shares about 50% of total HH's income, in which the income from agriculture was highest.Income from the agroforestry system was found highly dependent on the socio-economic status of the households.The temperature has been increased by 0.026°C per year and rainfall by 0.04 cm per year.Change in the cropping calendar was found as a major climate change adaptation strategy by the farmers.
Agroforestry is one of the best options to make the community more resilient from adverse impacts of climate change through increased income and environmental services.Thus, the promotion of agroforestry practices in private land should be emphasized by the government.The practice of agroforestry should be done on a large scale to mitigate the adverse effects of climate change.Commercialization of agroforestry products should be done to enhance the farmer's income.To encourage the farmers to practice agroforestry practices, they should be provided with capacity building, training, and information to make them aware of the benefits of agroforestry.

Acknowledgement
We are grateful to Tribhuvan University's Institute of Forestry, Office of Dean, Pokhara/ NORHED-SUNREM project for assisting under Faculty Strategic Research Grant scheme.The authors would like to acknowledge Ms. Sushma Bhattarai, Ms. Neeru Thapa, Mr. Prabin Pandit, Mr. Ajay Bhandari, and Mr. Prasiddha Khadka for assisting in providing valuable sources of information for this study.The authors are thankful to the local people of Gangolia village, Rupandehi district for their generous support during the fieldwork.

Figure 1 :
Figure 1: Map of the study area

Figure 2 :
Figure 2: Gross Annual income of Farmer with Yearly Earnings from AF System

Figure
Figure 3: Average annual precipitation

Table 1 :
Mean Test of Agroforestry Income of the Farmer with respect to different Socio-economic Variables