“…e study was conducted in Manyatta and Runyenjes subcounties in Embu County, Kenya, where most of the marketed coffee is produced by smallholder farmers. e area is located in Upper Midland (UM) zone, 2-3 agroecological zones, within an altitude range from 1600 to 1800 m above sea level [15]. e rainfall pattern in the study area is bimodal but the annual quantity ranges between 1120 and 1495 mm.…”
Section: Description Of the Study Sitementioning
confidence: 99%
“…e number of farmers from each cooperative society was determined using the following formula applied by [15]:…”
Section: Sampling Procedures and Sample Sizementioning
Despite the increase in area under coffee in Kenya in the last decade, productivity has been on the decline. Numerous production technologies have been developed through on-station research but there has been limited on-farm research to assess the impact of these technologies at the farm level. On the other hand, smallholder farmers are endowed differently and this would positively or negatively affect the adoption of recommended technologies and hence coffee productivity. This study was carried out to evaluate the effects of socioeconomic factors and technology adoption on smallholder coffee productivity at the farm level. The study employed stratified random sampling where 376 farmers were randomly sampled from six cooperative societies which had been preselected using probability proportional to the size sampling technique. The effects of socioeconomic factors and technology adoption on coffee productivity were analyzed using the stochastic Cobb-Douglas production function. The study revealed that off-farm income, access to credit, type of land tenure, and land size had significant positive effects on coffee productivity. Therefore, coffee farmers should be encouraged to diversify their income sources and to embrace credit financing, as the government reviews land use policies to avail adequate agricultural land. The study further revealed that the adoption of recommended application rates of manure, fungicides, and pesticides had significant positive effects on coffee productivity. The adoption of these technologies should therefore be enhanced among small-scale farmers to improve coffee productivity at the farm level.
“…e study was conducted in Manyatta and Runyenjes subcounties in Embu County, Kenya, where most of the marketed coffee is produced by smallholder farmers. e area is located in Upper Midland (UM) zone, 2-3 agroecological zones, within an altitude range from 1600 to 1800 m above sea level [15]. e rainfall pattern in the study area is bimodal but the annual quantity ranges between 1120 and 1495 mm.…”
Section: Description Of the Study Sitementioning
confidence: 99%
“…e number of farmers from each cooperative society was determined using the following formula applied by [15]:…”
Section: Sampling Procedures and Sample Sizementioning
Despite the increase in area under coffee in Kenya in the last decade, productivity has been on the decline. Numerous production technologies have been developed through on-station research but there has been limited on-farm research to assess the impact of these technologies at the farm level. On the other hand, smallholder farmers are endowed differently and this would positively or negatively affect the adoption of recommended technologies and hence coffee productivity. This study was carried out to evaluate the effects of socioeconomic factors and technology adoption on smallholder coffee productivity at the farm level. The study employed stratified random sampling where 376 farmers were randomly sampled from six cooperative societies which had been preselected using probability proportional to the size sampling technique. The effects of socioeconomic factors and technology adoption on coffee productivity were analyzed using the stochastic Cobb-Douglas production function. The study revealed that off-farm income, access to credit, type of land tenure, and land size had significant positive effects on coffee productivity. Therefore, coffee farmers should be encouraged to diversify their income sources and to embrace credit financing, as the government reviews land use policies to avail adequate agricultural land. The study further revealed that the adoption of recommended application rates of manure, fungicides, and pesticides had significant positive effects on coffee productivity. The adoption of these technologies should therefore be enhanced among small-scale farmers to improve coffee productivity at the farm level.
“…Furthermore, the global financial and economic crisis have made the situation worse through the disruption of agricultural supply chains and market, weakening the ability of the agricultural sector to address food security. Despite intervention in agriculture, crop, and livestock production system are still sensitive to drought and other extreme weather events especially in arid and semi-arid areas as indicated by Ndirangu et al (2017). This confirms the existence of fragile local mechanism for coping and poor resilience to cushion against future climate change shocks.…”
mentioning
confidence: 69%
“…Kenya has experienced extreme rainfall events in every 3 years cycle on average based on the 1989-2011 analysis (Ndirangu et al 2017). In the same period, severe droughts and changes in rainfall variability have become inevitable.…”
Food production in Kenya and Africa in recent past has experienced vagaries of weather fluctuations which ultimately have affected crop yield. Farming in Kenya is localized in specific Agro-ecological zones, hence understanding crop growth responses in particular regions is crucial in planning and management for purposes of accelerating adoption. A number of strategies for adoption and adaptation to changing weather patterns have been deployed yet only limited challenges have been partially addressed or managed. This chapter examines previous methods used in classifying agro-ecological zones and further provides additional insightful parameters that can be adopted to enable farmers understand and adapt better to the current variable and unpredictable cropping seasons. The chapter scrutinizes past and current documented information on agro-ecological zonal valuations coupled with the use of earth observation components such as air temperature at surface, land surface temperature, evapotranspiration, soil, temperature, and soil and moisture content in order to better understand and effectively respond to new phenomena occurring as a result of climate change in the marginal agricultural areas. Significant variations in precipitation, ambient temperature, soil moisture content, and soil temperature become evident when earth observation data are used in evaluation of agro-ecological lower midland zones IV and V. The said variations cut across areas within the agro-ecological zones that have been allocated similar characteristics when assigning cropping seasons. The chapter summarizes the outcomes of various streams of contributions that have reported significant shifts or changes in rainfall and temperature patterns across Kenya and wider Eastern Africa region. The chapter highlights the need for re-evaluation of the agro-ecological zones based on the recent earth observation datasets in their diversity. The research emphasizes the use of multiple climate and soil-related parameters in understanding climate change in the other marginal areas of Kenya.
“…Furthermore, in this case agricultural sector workers who are classified as family workers do not have their own income so they have to depend on other parties or family members who have income to meet their needs. An increase in family members who have no income if it is not accompanied by an increase in food production will reduce the availability of food for everyone in the household so that it will have an impact on low food security (Ndirangu, Mbogoh and Mbatia, 2017). In addition to the decline in food production, the population's food consumption tends to increase.…”
This study aims to determine the effect of workers in agricultural sector, income, access to clean water, and regional spending on food security in East Nusa Tenggara Province. This study uses panel data which is a combination of cross-section data from 22 districts/cities in East Nusa Tenggara Province and time series data from 2018-2021 which is then analyzed using multiple regression. The results show that the best estimation model is the Fixed Effect Model with the Generalized Least Squares (GLS) method. Based on the estimation result, it shows that the adjusted R2 is 0.91461. Assuming ceteris paribus, partially income and access to clean water has a significant positive effect on food security. Meanwhile, workers the agricultural sector has a significant negative effect on food security and local government expenditure has a non-significant positive effect on food security.
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