What do farmers believe to be the most effective method to improving their resilience? And finally, how do agroforestry practices help farmers adapt to exposure to climate-related hazards? To address these questions, we undertook a field study of a farmer-managed agroforestry development project in the Nyando District in western Kenya. We compare farmers who have been involved in World Agroforestry Centre ICRAF agroforestry development projects for 2 to 4 years to neighboring farmers with no agroforestry training.
We used household surveys, in-depth interviews and focus group discussions to provide both a qualitative and a quantitative dimension to our analysis. We used a mixed methods approach that combined household surveys, in-depth interviews, focus group discussions and field observations to investigate our research questions. These discussions generated a list of key indicators that farmers feel are most important to the well-being of their households.
This type of mixed method approach has been strongly encouraged in the literature to better capture the reality on the ground [ 16 ]. The Lower Nyando sublocation is characterized by low productivity, erratic rainfall and severe soil erosion. Elevation is 1, m with an average annual rainfall of 1, mm [ 17 ].
This area is predominantly of the Luo tribe. The Middle Nyando sublocation has higher productivity, cooler temperatures and more equitably distributed rainfall. Average elevation is 1, meters with average rainfall of 1, mm per year [ 17 ]. Middle Nyando has a mix of Luo and Kalinjin tribes. Maize is the staple crop in both sublocations, with sugar cane and coffee also grown as cash crops in Middle Nyando. All members of these groups were included in the treatment group. ICRAF provided a mix of tree species to farmers, including: Acacia mellifera and Acacia polyacantha, Albizia coriaria, Calliandra calothyrsus, Casuarina equisetifolia, Cordia abyssinica, Faidherbia albida, Gliricidia sepium, Grevillea robusta, Markhamia lutea, Senna siamea, and Warburgia ugandensis.
Two additional community groups were selected as the control group based on their proximity to the treatment groups. None of the farmers in the control group had participated in agroforestry training in the past. Due to the distinct climatic differences between Lower and Middle Nyando, it is difficult to compare groups across sublocations. In addition, farmers in the two sublocations differ in ethnicity, market access, land size and other key characteristics.
Treatment and control households within sublocations are fairly similar, as can been seen across the basic household variables presented in Table 1. A t-test for differences of means was carried out to assess differences between all treatment and control groups and unless noted in the table, no significant differences were observed.
Despite similarities in basic household characteristics, it is important to note that the control and treated households were located 1 to 2 km apart.
We surveyed households in June and July to capture basic household characteristics, agroforestry practices used and biophysical farm observations. Three households were removed from the dataset due to their extreme differences across key household parameters listed in Table 1. We conducted 20 in-depth interviews with 13 farmers 6 women , 4 village elders 1 woman and 3 community leaders all men. In addition, we held seven interactive focus group discussions with agriculture-oriented community groups.
Questions focused on observed changes in climate, farming practices productivity constraints, agroforestry practices, future goals and how households had coped with the most recent floods and droughts.
Men and women were split into subgroups for a part of each focus group discussion. Detailed methodology is presented in Thorlakson [ 18 ]. Key variables used for statistical analysis were measured in a way consistent with the literature in the field.
We used an estimate of total livestock value as a proxy for household wealth, as livestock is the most frequently cited indicator of farmer wealth among the Luo and Kalinjin tribes of western Kenya [ 19 ]. We collected other indicators of wealth housing material, type of roof, and so on but found little variation among these indicators across households in our sample.
Livestock holdings were converted into an economic value using current local market prices. We measured farm productivity by converting current seasonal crop production to economic units using average crop prices in the region. Soil erosion intensity was measured on a nine-point scale using two on-farm observations, type of erosion present and intensity of observed erosion [ 20 ]. We estimated total above ground tree biomass using Kuyah et al. To assess the impacts of the agroforestry development project on farmer well-being we used household wealth and farm productivity as dependent variables.
We used matching techniques to increase the similarities between the treatment and control groups [ 22 , 23 ]. Matching gives additional weight to households across treatment groups that are most similar on selected parameters.
Parameters used for matching included: household size, land tenure, household head educational level, soil type and gender of household head, as these measures were all noted in the literature to affect subsistence farmer well-being [ 24 ].
This analysis method was validated after incorporating the basic household parameters into the regression as well, achieving similar results.
We transcribed all field observations, interview notes and focus group discussion notes, and tagged major topics and keywords. Common themes that emerged included agroforestry use, climate change, drought, farm constraints, farming techniques, floods, labor, erosion, rainfall change and well being. In , the Nyando District provided a unique opportunity to study the impact of climate-related hazards on farmers, as both a drought and a flood had recently affected the area.
Lower and Middle Nyando experienced drought-like conditions in September and October when the short-rains season failed.
Specific data on the intensity of the drought in this region is sparse, but food shortage was widespread in the region due to water shortages [ 27 ]. In addition, the Lower Nyando region was also hit with a significant flood in March and April of that displaced people and destroyed 7 homes across the Nyando District [ 28 ].
Farmers interviewed grouped climate-related hazards into three major topics: increased variability of the timing of rains, droughts, and floods. We focus on these three major types of hazards, though we understand that other changes are also expected to occur due to climate change. Farmers attribute this decrease to a combination of the drought, flood and rain variability experienced in the previous 12 months.
Maize, the staple crop in the region, followed similar production trends Table 2. Due to the climate-related hazards experienced in the to season, households reported experiencing intense periods of hunger. Average duration of hunger periods for households were 4. A hunger period, as defined by farmers, is a time when the household had severe difficulties obtaining enough food to feed all household members.
During periods of hunger, the most common coping strategy reported was to reduce food consumption through restricting the size, diversity and number of meals taken each day. Households that were involved in off-farm activities intensified their work in these areas and others engaged in casual farm labor.
See Table 3 for a list of common coping strategies. Farmers were also forced to use more detrimental coping strategies to cope with the reduced productivity in to From discussions with farmers, we defined detrimental coping strategies as those that have harmful long-term impacts on household productivity.
Farmers reported selling oxen reserved for plowing during periods of drought, leading to lower farm productivity the following season as people then had to plow by hand. This consumption had negative repercussions, as many farmers were forced to plant fewer seeds the following season due to the depletion of their personal seed stores and constraints in capital. Some Middle Nyando farmers reported being forced to lease part of their farms for 2 years to wealthy farmers in the area in order to feed their families.
This coping mechanism is especially detrimental as it prevents farmers from accessing their main source of livelihood, their land, for 2 years. According to some farmers, engaging in casual labor during periods of hunger also represents a detrimental coping strategy as it delays the planting in their own farms. Farmers involved in an agroforestry development project typically used fewer detrimental coping strategies during hunger periods.
Farmers with mature trees on their land were able to sell seedlings, timber and firewood and consume fruit from their trees during periods of hunger. Farmers reported that this diversification of coping strategies allowed them to rely less on other traditional coping strategies. See Table 3 for a comparison across groups. The most effective way farmers found to reduce their vulnerability to these climate-related hazards was to diversify income to include off-farm activities.
Farmers who engaged in off-farm activities, such as wage-earning jobs or owning small shops, reported being better able to cope with climate-related hazards than their farming neighbors.
Farmers with higher average farm productivity also reported fairing better during rainfall variations as they had more stores to draw on when current production was low. We discussed with farmers how they believed they could improve their overall standard of living when exposed to the hazards described above. Food security, as defined by farmers, is the ability to obtain an adequate diet for all household members throughout the year, without being forced to use long-term savings to purchase food.
To achieve food security, farmers reported being interested in opportunities to start small business ventures or obtaining credit to purchase farm implements to improve their farm productivity. Farmers also expressed interest in opportunities to improve their agricultural knowledge and to learn about alternative income opportunities as other indirect pathways to improve food security. It is only after a household reaches relatively food security that they begin investing in long-term processes for improving other components of their well-being.
Farmers also reported that they have begun to put more emphasis on the environmental conservation of their land. Both treatment and control focus groups concluded that their well-being had significantly declined due to soil erosion on their farms. A number of community groups had recently been formed to focus on environmental issues and soil conservation practices in the area, suggesting that environmental conservation is perceived as a key way through which communities believe they can improve their well-being.
Constraints to achieving well-being are summarized in Table 4. Most farmers agreed that unpredictable weather and lack of access to capital are the two largest constraints to improving their lives, but environmental degradation was also cited as a major concern. In addition to food security, most farmers also cited an ability to cope with shocks and stresses as a key characteristic of a successful household.
During times of stress, successful households are food secure for 2 to 3 months longer, often giving support to their neighbors and family. Successful households are not forced to sell livestock or belongings, take their children out of school, or significantly reduce meal portions during exposure to outside shocks.
Currently, households feel unable to deal with the unexpected problems that arise from extreme weather events, sicknesses, job loss, low cash crop market prices, and so on. Farmers continuously reiterated their need to find better ways in which to deal with exposure to outside shocks, particularly rainfall variability and drought, which frequently disrupt their lives. Farmers were most interested in improving their off-farm incomes, diversifying income sources and improving general farm productivity to reduce their sensitivity to climate-related hazards.
During interviews, farmers also emphasized their desire to remain autonomous in deciding what type of specific adaptation measures they choose to employ. Many farmers complained that some specific climate-change adaptation measures suggested by agricultural extension workers or non-profit organizations, such as planting drought-resistant maize, were actually detrimental to their farm yields during normal or heavy rains.
With the uncertainties farmers face in weather patterns from year to year, they were unwilling to invest in strategies that were less productive under certain weather conditions. Farmers reported being interested in receiving information and advice on potential adaptation strategies, as long as outside constituents did not decide what activities would take place in their communities without their consent.
When interpreting these results, it is important to note that the agroforestry project assessed has only been in operation for 2 to 4 years and thus the long-term effects of agroforestry involvement are not captured in our analysis.
Our results suggest that agroforestry improves farm productivity and household wealth. Farmers found trees improved their farm productivity by decreasing soil erosion and increasing soil fertility. Farmers who reported no change in farm productivity explained either they had not planted nitrogen-fixing trees in their fields or the trees were not yet mature enough to assess the effects.
Many farmers expressed concern that planting trees in their fields would reduce productivity of their crops and were unwilling to take such a risk. All farmers who have begun intercropping trees reported significant improvements to their productivity after incorporating nitrogen rich leaves into their soil. For farms using agroforestry techniques, our quantitative data suggests a slight improvement in farm yields in both Lower and Middle Nyando when compared with the control group.
The high uncertainty in the quantitative results of the study was likely in part due to the small sample size, short duration of agroforestry participation and the non-randomized selection of households. During focus group discussions, farmers ranked the potential income benefits from trees as the most helpful aspect of the trees on their land.
The excitement farmers expressed in the income benefits from tree products stemmed in part from the limited opportunities for income generation in the area. Among the farmers in Lower Nyando who have had trees for 4 years, Benefits were reported from the sale of fuel wood, timber, fruit and seedlings and through savings in food purchases due to an increase in farm productivity. Farmers who had not seen improvement in their income after planting trees explained that their trees were still too young to provide any benefits.
In our quantitative analysis we used livestock holding as a surrogate for household income as it is difficult to measure wealth in real terms among small-scale farmers. Household wealth, as measured by current livestock holdings, improved for Lower Nyando participants involved in an agroforestry project.
It is not surprising that Middle Nyando farmers have not improved their wealth through agroforestry involvement, as these farmers planted their trees only 2 years ago and do not yet have mature trees that can provide timber, fruit or fuel wood for sale. Due to their remote location, Middle Nyando farmers have also had less success selling tree seedlings to neighboring communities so have been unable to receive substantial income benefits from this source.
Lower Nyando farmers, however, reported selling fruit, timber, fuel wood and seedlings on a regular basis to local markets. From discussions with farmers in the area, it appears that the inconclusive results on wealth in Middle Nyando are in part due to a lack of infrastructure in the area, that is currently acting as a barrier to access markets for their tree crops. Involvement in agroforestry practices also provides a number of other general improvements that helped farmers increase the environmental sustainability of their farms.
Soil erosion was particularly detrimental to people affected by the floods in Lower Nyando, with many farmers complaining of decreased soil fertility due to the intense soil erosion during the heavy rains. Sources: Borgen Project Photo:. Tag Archive for: Subsistence Farming. As the places where people farm grow drier, famine and drought become more of a risk. Sub-Saharan Africa contains 19 of the 25 poorest countries in the world.
This includes the Central African Republic, which is nearly self-sufficient in crops but ranks as the poorest country in Africa GDP due to poor livestock quality. Subsistence farming , or smallholder agriculture, is when one family grows only enough to feed themselves. Without much left for trade, the surplus is usually stored to last the family until the following harvest. Unfortunately, there are higher trade taxes placed on the continent compared to other regions. This is due to roads that lead toward ports rather than other countries, as well as rigorous tariffs and inspection laws between borders.
Working to boost intra-African trade , regional economic communities RECs face immense challenges and policymakers are focusing on RECs in order to increase regional integration. Initially, cocoa was as a smallholder crop but has grown in popularity due to global demand. Robusta is a typical coffee bean grown in Africa, commonly used for instant coffee. It faces competition with the higher quality Arabica beans exported from Asia and South America.
Overall, the exposure of cash crops to the world market has expanded growth in Africa but also slowly eroded farmer incomes. Cash crop farmers receive very small proportions of the final traded price.
Women make up the largest share of the agricultural labor force in Africa. In sub-Saharan Africa, child labor increased over the to period, in contrast to continued progress in the rest of the world.
Most child labor is unpaid, going on in family farms and not between employment with a third-party. Families that do subsistence farming anecdotally report high career aspirations for their children. The high child labor rates are not necessarily an alternative to school, but an act performed for the necessary family income that leads to subsistence and high attendance rates.
In a sense, child work often contributes to improving the family farm that they may eventually inherit. Focus on agribusiness can help improve the lives of farmers. Agribusiness also involves technology, such as mobile apps used as a means to reach farmers and track data on land conditions.
By increasing local production of chemical fertilizers, the lives of African farmers could improve. Globally, Africa consumes only one percent of fertilizer and produces even less. The results agree with literature that many farmers practice farming to derive livelihood as these results has indicated.
Additionally, so, these farmers do not have any form of employment as farming is their solely occupation for living. Lastly, they do employ laborers during harvesting period for assistance in harvesting with 8. The results display that subsistence farming had a significant role in rural areas of Nyandeni Local Municipality. The literature review revealed that subsistence farming in Nyandeni Local Municipality is decline in with several factors.
Table 4 below is displaying challenges which are fundamental to the reduction of subsistence farming in the study area. Farmers were choosing more than one challenge they were facing in farming.
This is crucial challenge because farmers in the study area had no source of funding which would have assisted them in purchasing modern farming inputs instead of using traditional and obsolete farming technique. The lack of knowledge is contributing to decline of farming as these farmers had only primary education which is just basic education which do not contribute much in farming and which was the reason of using indigenous knowledge for farming.
This had contributed to decline in subsistence farming due to changing climate which led to prolong dry spells as result of drought, making it very hard for farmers to have water available for farming. This is the main cause because households in the study area they just farm using indigenous knowledge not being assisted. They lack pesticides and herbicides for diseases, lack improved seeds which withstand climate variability and knowledge in terms of farming, such factors contributed vastly to decline in subsistence farming.
Lastly, lack of farming equipment is one the challenges that subsistence farmers agreed that it contributed to decline in farming. The multiple regression results for factors affecting subsistence farming are presented in Table 5.
The dependent variable in the multiple regression was subsistence farming. The explanatory variables were quantified as those related to socioeconomic factors of rural household practicing subsistence farming. For all the variables with a positive coefficient, it implies that as any of them increases, so does Food Production.
Table 3 summarizes the empirical results of multiple regression. The direction of influence of the variable is shown by the signs of the coefficients. A positive sign of the coefficient implies that the particular variable has no influence on the household production and a negative value on the coefficient shows that the particular variable has influence on the household production.
Table 5 shows the estimated coefficient, standard error and significance value of the variables in the model. The activities in households are controlled by the household head. Age usually influence one's health condition; thus it can be stated that old people are more likely to have health problems. When the household head stops participating in agricultural activities, it is likely that everyone in the household neglect agricultural activities.
The results reveal a negative relationship between age and subsistence farming, hence the negative coefficient. From the results, age has a negative coefficient, meaning that age has negative influence on subsistence farming status.
That clearly demonstrate that, the more people get older, their participation in agricultural practices decreases. This simply means that old people participate less in agricultural activities. Mashamaite stated that age influences productivity in agriculture. Rural households are mostly headed by males 60 per cent.
The results reveal a negative relationship that exists between sex and subsistence farming in rural areas. Sex has a negative coefficient and is significant at 1 per cent significance level. This relationship means that male-headed households are more likely not to produce their own food through subsistence farming. Mathebula et al. There is a positive relationship that exists between family size and subsistence farming production.
Family size is significant at 1 per cent significance level. The results, therefore, mean that the larger the household size, the more the household is likely to produce its food through subsistence farming.
This may be because there is enough farm labour through family labour. However, subsistence production becomes necessary because it increases food availability in the household. Education plays a crucial role in agriculture, as it determines the literacy rate. It makes it easy for an individual to acquire information from various sources.
Education widens one's chances of being formally employed. When one is formally employed, they are unlikely to participate in agricultural activities. However, with that being said, education has a positive effect on subsistence farming in rural areas. The results reveal that years spent in school is significant at 5 per cent significance level. The results estimate that the more the years a farmer spend in school increases, there greater is their involvement in subsistence farming in rural households.
Extension service tends to be inefficient in rural areas. Rural households lack information and innovations in agriculture. Provision of proper extension services to rural people would result in better and improved agriculture in rural areas. The results indicate that extension service has a positive influence in subsistence farming in rural areas. Extension service has a positive coefficient and is significant at 5 per cent significance level. This simply means that a unit increase in extension service results in an increase in subsistence farming in rural areas.
Experience in farming encourages people to participate in agricultural activities. The more people participate in farming the more they see its benefits than purchasing everything from the markets. The results suggest that there was a positive relationship that exists between subsistence farming and farming experience. It is significant at 5 per cent significant level and has a positive coefficient. Mathebula stated that farming experience extends a zeal of farming in rural households and it assists in innovating new skills in the field.
Household income is directly proportional to own-food production. That means income and subsistence farming were positively related. The results show a positive coefficient of income and it was significant at 1 per cent significance level. People with high income were likely to be involved in agricultural activities since they can afford agricultural inputs.
The results indicate that a unit increase in household income level results in an increase subsistence farming. Households with little or no reliable income are hesitant to invest the money they have in agriculture; they rather buy something to eat at the current moment. Mashamaite suggested that rural households must invest more in agriculture because income may not be enough to meet household expenditure. Employment status negatively influences subsistence farming. The results indicate a negative coefficient and employment was significant at 5 per cent significance level.
Households with employed heads were likely not to produce their food, they purchase from the market. Employed people have no time to participate in agricultural activities, they invest their time in their jobs. Unemployed individuals tend to take part in agricultural production because they believe it is cheaper than to buy from the markets. Mashamaite stated that most people in rural areas are unemployed and are dependent on social grants to have food on the table. The study was investigating factors affecting subsistence farming in Nyandeni Local Municipality.
The study results revealed that subsistence farming in the study area was practiced by male with an average age of 60 years and family size of 6 people per household. Subsistence farmers had a farm size of 2 Ha which they were using to practice crop and vegetable farming with a farming experience of 10 years. Households were strictly practicing subsistence farming to provide food for home consumption and only sell surplus for income generation as to supplement their social grant securities.
Subsistence farming in the study area was declining because majority of the farmers lack funding and knowledge, lack of water availability, lack of extension services and lack of farming equipment. The study concludes that subsistence farming is influenced by socio-economic factors such as age, sex, and family size, access to extension services, farming experience, employment, household income and education status. Therefore, the study recommends that policymakers and government must embark in education trainings which are aimed in increasing importance of farming in rural areas.
The study further suggests that government and NGO extension personnel must be made available in rural areas so that they can disseminate farming information. The government must promote sustainable food production by ensuring collaboration of all stakeholders in government, private sector and NGOs or CBOs. Should subsistence agriculture be supported as a strategy to address rural food insecurity?
Agr, 48 4 : The direct and indirect economic contribution of small-scale black agriculture in South Africa. Agrekon, 2 ,
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