African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6531

Full Length Research Paper

Production of indigenous poultry among smallholder farmers in Tigania West Meru County, Kenya

Evans Mwiti Mathiu
  • Evans Mwiti Mathiu
  • Department of Agricultural Economics and Extension, School of Agriculture, University of Embu. P. O. Box 6-60100, Embu, Kenya.
  • Google Scholar
Samuel Njiri Ndirangu
  • Samuel Njiri Ndirangu
  • Department of Agricultural Economics and Extension, School of Agriculture, University of Embu. P. O. Box 6-60100, Embu, Kenya.
  • Google Scholar
Samuel Chege Mwangi
  • Samuel Chege Mwangi
  • Department of Agricultural Economics and Extension, School of Agriculture, University of Embu. P. O. Box 6-60100, Embu, Kenya.
  • Google Scholar

  •  Received: 22 January 2021
  •  Accepted: 13 April 2021
  •  Published: 31 May 2021


The study was conducted to evaluate factors that significantly influence indigenous poultry production among smallholder farmers in Tigania west Mer County using the production function approach. The study relied on primary data collected by administering structured questionnaires to 359 respondents randomly selected from 5 wards using the multi stage stratified random sampling and probability proportionate to size techniques. A cross sectional survey design and primary data on poultry yields, inputs and farmer demographics were collected. A one step Cobb Douglas production function in logarithmic form was used to estimate the frontier production. From the analyses, results showed that over 50% of the deviations in poultry output resulted from production constraints. Further, off farm income, technology adoption, flock size, extension access and gender of respondents positively influenced indigenous poultry production, while land under cultivation had a negative influence. Results revealed a possibility to increase indigenous poultry production using improved feeds and vaccinations since they were the most limiting resources. In addition, policy interventions aimed at enhancing technology adoption and improving provision of extension services would serve as a motivation to persuade farmers increase stock and apportion more credits to poultry production. This would increase output leading to economies of scale thus increased returns among smallholder farmers.

Key words: Indigenous chicken, production, smallholder farmer.


The evolution of poultry production has signified a growing importance among small and medium-scale farmers residing in the rural areas (Milkias et al., 2019). Chickens are the most popular poultry worldwide (Hirwa et al., 2019). The birds are among common livestock species reared in rural areas (Goraga et al., 2018). Milkias et al. (2019) notes that the global poultry population is approximately 16.2 billion, of which 71.6% is found in developing countries. In Africa, village poultry contributes over 70% of poultry products and 20% of animal protein intake (Kejela et al., 2019). In Sub Saharan Africa (SSA), rural chicken production accounts for about 60% of poultry with indigenous chickens constituting 70% of the total chicken population (Kejela et al., 2019). Hirwa et al. (2019) clarifies that in East Africa, over 80% of human population lives in rural areas and over 75% of these households keep indigenous chickens. The population of poultry in Kenya is estimated to be about 29 million (Padhi, 2016), of which 75% are indigenous chickens (Delabouglise et al., 2020). Kamau et al. (2018) observed that 80% of the poultry production originates from smallholders with 7.1 and 9.4% of meat and egg products originating from indigenous chickens, respectively.

Poultry production plays a significant economic role and is practiced by about 80% of the rural populations (Moussa et al., 2019). In addition, poultry products are important sources of cheap and quality protein thus reducing malnutrition among rural populations (Njuguna et al., 2017). The poultry enterprise has potential to promote economic growth in developing countries through employment, provision of income and sustenance of rural populations (Mwobobia et al., 2016). In Kenya, poultry production remains a key enterprise among smallholder farmers (Ayieko et al., 2015), with about 70% of the rural inhabitants deriving their livelihood from poultry production (Kamau et al., 2018). Further, the poultry enterprise has been identified as an exit strategy in addressing food scarcity among rural households (Milkias et al., 2019).

In the Kenyan economy, poultry production breeds are well adapted to varied environmental conditions and remains essential among different customs since it promotes asset accumulation (Mbuza et al., 2017).  Kamau et al. (2018) illustrated that indigenous chickens are disease resistant, have ability to withstand feed fluctuation and are hardy. Additionally, the poultry enterprise is one of the cluster areas for value chain development in Kenya (Ayieko et al., 2015). Besides, rural poultry enterprises encounter several challenges in production especially among smallholder farmers. These challenges include inadequate institutional support and inefficient management (Kirui, 2014). Further, trade barriers resulting from lack of streamlined market structures hamper rural poultry production (Magothe et al., 2012). As a result, there exists an unmet demand of poultry products since production is below potential levels (Padhi, 2016). This leads to low yields and incomes thus a need to develop policies that support smallholder poultry farmers in rural areas (Kingori et al., 2010).

Globally, the production of indigenous poultry among smallholder farmers encounters several challenges. In Kenya, this includes the rising costs of farm inputs resulting from competition of key raw materials (Abadi et al., 2018). In addition, poultry farmers are unable to control diseases related to poultry as a result of the increasing costs of vaccines and management fees (Milkias et al., 2019). Further, Abadi et al. (2018) indicates that there is limited information regarding both input and output markets which leads to stiff competition within the industry. Besides, increase in prices of poultry products has reduced consumption among low-income rural families to about 7% (Goraga et al., 2018). This is so despite poultry being a key contributor to economic development and vital  towards achieving the socio-economic pillars of Vision 2030 (Delabouglise et al., 2020).

Owing to these challenges, there have been Government and Non-Governmental programs that provide support for the poultry industry (Baliyan and Masuku, 2017). These programs intend to increase production and bridge the gap between the increasing demand and low supply of poultry products (Atieno, 2016). In addition, various stakeholders have initiated programs aimed at supporting indigenous poultry farming as an investment to alleviate poverty especially among the young populations in rural areas (Bukunmi and Yusuf, 2015). Despite these concerted efforts, indigenous poultry production in rural areas continues to generate unrewarding returns. Kejela et al. (2019), ascribed this to weaknesses in the management and use of traditional production methods. This presents poultry production as a risky enterprise among smallholder farmers in rural areas (Kirui, 2014).

The low productivity shows the inability of farmers to fully utilize available technologies hence leading to low yields. Ambetsa et al. (2020) argued that though improved technology is one of the strategies for increasing agricultural productivity, optimal productivity can only be obtained when the technology is efficiently used. Besides, the high poverty levels entangled with the limitation of factors of production have made it difficult for farmers to increase production through use of more resources (Tiruneh and Geta, 2016). This constraint implies a need to examine agricultural production particularly among smallholder farmers (Ayinde et al., 2015). Rasha et al. (2018) explains that an analysis of production is vital in selecting cost effective input combinations and determine gains resulting from improving existing technologies. Increased productivity enables farmers increase returns without additional inputs and technologies thus better yields (Kirui, 2014). Due to inadequate knowledge regarding utilization of farm resources, high risk of uncertainties often characterizes the entire process of production among smallholder rural farmers (Ambetsa et al., 2020).

Despite the importance of poultry in rural areas, there is limited information focusing on indigenous chicken farming among smallholders. Further, very few studies have described this enterprise in Meru County.

This is so despite indigenous poultry farming being a major contributor to livelihoods among smallholder farmers in rural areas. Therefore, the objective of this study was to evaluate indigenous poultry production and identify characteristics that influence productivity among smallholder indigenous chicken farmers in Tigania west, Meru County.


The  theoretical  framework of this study is grounded on the CD production function. Ambetsa et al. (2020) explains that production functions express the connection between inputs and outputs. The production function indicates, in either mathematical or graphical form, the quantity of output that can be obtained from a combination of inputs (Kamiyama et al., 2016). In particular, it demonstrates the maximum attainable output per unit of input with the current level of technology (Tiruneh et al., 2017). Besides, Wollie et al. (2018) explain that production functions specify the minimum quantity of input per unit of output given available technology. The CD production function is widely used in economics, to represent the input-output relationship. Kamiyama et al. (2016) notes that the relationship is non-monetary, that is, it only relates physical inputs to physical outputs such that prices and costs are not considered.

The production function is one of the key concepts of main stream neoclassical theories, used to define marginal product and distinguish the defining emphasis of economics (Ntakyo et al., 2019). The primary purpose of the production function is to address the utilization of factor inputs and their corresponding distribution of proceeds in production. This is achievable by hypothesizing the technological estimates of achieving optimal yields (Nwigwe et al., 2016). In agricultural production, efficient allocation of farm resources enables farmers to attain farm objectives. It avails farmers the opportunity of improving their productivity and income. Tiruneh et al. (2017) expounds that at the micro economic level efficient allocation of farm resources such as farm land, credit facilities, fertilizer, labour, among others contributes to food production, employment creation, industrial raw materials and export products for foreign exchange earnings (Kelemu and Negatu, 2016). Though production functions can be expressed in various functional forms, the CD form is commonly used for its simplicity and flexibility coupled with its ability to provide parameters that are easy to estimate and interpret (Wollie et al., 2018).

Empirical model

To study the effect of socioeconomic and institutional factors on indigenous poultry production among smallholder farmers, the CD production function was used. The model was analyzed in a single-step rather than the two step procedures because it generates estimates which are not biased compared to the ones generated by the two-step method as explained by Kamiyama et al. (2016). Further, the CD production function was applied as a parametric stochastic frontier production approach as explained by Mwangi et al. (2020). The approach is the frequently used and entails the description of the technology which may be restrictive in most cases (Ajibefun,  2008).  Further,  the stochastic frontier model attributes deviations from the frontier function into inefficiencies and random errors thus accurate and less sensitive to measurement errors in data (Ndirangu et al., 2018). In addition, it allows testing of hypothesis regarding the goodness of fit for the model. This model estimates the production function by fitting observed data and minimizing measures of their distance from the expected frontier (Abdul and Isgin, 2016).


Description of study area

The study was conducted in Tigania West Sub-County (Figure 1) which is the largest producer of indigenous poultry in Meru County. The County is located along the extensive Mt. Kenya and has altitudes ranging from 3000 to 5,199 m above sea level (GoK, 2018). GoK (2018) elaborates that along the equator, the county lies about 0° 6’ North and 0° 1’ South and between latitudes 370 West and 380 East. This predisposes the County to various microclimates and influences its natural settings. Agro-ecological zones (AEZs) vary from upper highlands to lower midlands with a bimodal rainfall pattern (Okoth, 2019), with quantities varying from 300 to 2500 mm annually (Labeyrie et al., 2016). Temperatures range from 8 to 32°C during cold and hot seasons, respectively. Okoth (2019) notes that land is agricultural with livestock rearing being a common enterprise. Poultry production is concentrated among farmers in Tigania West majority being indigenous chicken. Figure 1 shows an illustration of the study area.

Research design and sampling technique

Cross-sectional survey research design was applied in this study since it has a high magnitude of precision and accuracy (Ambetsa et al., 2020). In addition, the design allows researchers to collect data at a single point thus saving on resources and time (Wambua et al., 2019). Ambetsa et al. (2020) further explains that cross sectional survey helps researchers describe populations within the study area in regard to the findings and define the extent to which the results relate to the sampled population. Further, multi stage stratified random sampling technique was used for the study. This approach is reasonable and pledges a perfect depiction of the target population as explained by Wambua et al. (2019). First, purposive sampling was applied to select Meru County and Tigania West sub-county chosen as the study area given the concentration of indigenous poultry farming. Further, Mbeu, Nkomo, Kianjai, Akithi and Athwana wards were used as stratums and probability proportionate to size procedure applied to get the number of farmers from each ward. Systematic random sampling technique was used to select producers using the Watson (2001) formula and applied by Watson and Meester (2015). Additionally, Watson and Meester (2015) clarified that the formula is most preferred for studies that consider populations that are above 10,000.

Data analysis

The study used both qualitative and quantitative data. The Statistical Package for Social Sciences (SPSS) version 13.0 was used for descriptive analysis. The log linearized form of the Cobb Douglas (CD) production function was used in determining factors  that    affect   indigenous   poultry   production.   The   CD production function was analyzed in single step regression to ensure that the estimated parameters were not biased (Ogolla, 2016). To achieve this, production inputs were transformed into log form and the selected factors were regressed against the poultry output. Data were collected in one season covering the year 2020 among smallholder farmers who own landholdings less than 2 ha.


Socioeconomic characteristics of respondents

Table 1  illustrate    descriptive   analysis   of    socio-economics characteristics of smallholder poultry farmers. From the results (Table 1), the average age of household heads was 46.55 years ranging from 21 to 82 years. The wide variation in age indicates the predominance of old farmers noting that indigenous chicken farming in the study area was perceived as a reserve for the aging population. The average years of indigenous poultry production were 20.02 ranging from 1 to 50 years. This elucidates that majority of the smallholders were highly experienced thus informed on matters regarding indigenous poultry farming. Further, the results imply that the farmers had adequate skills in poultry management, a key element in achieving a profitable enterprise.

Education has been regarded as acritical element among rural populations in developing countries since it equips people with skills and knowledge necessary in poverty eradication. Results in Table 1 show that respondents had an average of 9.98 years of schooling. A possible explanation is that access and enrolment to formal education in the study area was low with majority of the farmers having spent 8 years while schooling which is equivalent to primary education. Further, results show that some respondents had no form of formal education while the most educated smallholder farmer spent 18 years in formal education. This implies that a sizeable proportion of smallholders were unable to understand basic information regarding markets and contemporary innovations. Concerning the size of household, sampled farmers had an average of 5.62 members with the smallest household having 1 member and 12 members for the largest. Most of the households had 5 members signifying that respondents had sufficiently large households to offer family labor thus reduced production costs. On average, respondents had 1.75 acres of land ranging from 0.01 to 20 acres with majority measuring 1 acre denoting that respondents were smallholder farmers Besides, land fragmentation in the study area was common and poultry production encountered competition from other enterprises such as crop production.

Farmers earned an average income of KES 25,300 per year from indigenous chicken with the highest returns  being   two   million    shillings    while   majority managed to earn KES 20,000. Compared with off farm activities, farmers earned an average of KES 8,900 annually with the highest off farm employment generating KES 80,000. Majority of the respondents who engaged in off farm employment earned utmost KES 5,000. This implies that indigenous poultry production was more lucrative than off farm employment while the latter enabled farmers generate extra income for use in procurement of critical farm inputs and technologies. Moreover, respondents engaged in non-farm activities concentrated less in farm operations with limited resources and time allocated for poultry production consequently reducing their working capital. From the results, proximity to poultry markets averaged 5 KM with farmers spread approximately 4.7 KM from the mean market distance. This indicates that respondents received firsthand market information and were able to derive market benefits such as subsidized inputs and extension services. In addition, respondents had a better access to information regarding input and output markets thus empowered to identify markets that offer auspicious output prices consequently generating adequate returns.

Institutional characteristics among respondents

Table 2 describes institutional characteristics among respondents. The results reveal that majority (62.7%) of the households were male headed while 37.3% of the households were female headed. This indicates that smallholder  poultry  production  in  Tigania   West  was dominated by males owing to its lucrative nature to generate extra income for households.

Majority (52.1%) of the respondents had limited access to training and extension regarding poultry production. This implies that most of the farmers were not adequately informed on current and emerging issues in poultry production. In regard to group membership, 93.9% of the respondents were engaged in at least one organized group. This implies that farmers had enriched bargaining powers in both input and output markets. In addition, farmer groups can be used as channels for provision of information regarding modern innovations, appropriate production and marketing.

From the results in Table 2, 79.4% of the respondents had access to financial credits while 20.6% had no access to financial aid. This implies that most of the smallholder farmers were financially empowered to timely procure inputs and adopt technologies that boost production. This was achievable owing to the fact that 62.7% of the farmers had guaranteed tenure for their farms thus can use the title deeds as collateral to acquire loans. Contract markets for poultry products were available but only 1.4% of the respondents engaged in this avenue. This was possibly due to unfair contractual obligations and unrewarding product prices offered by participants in this market.

Analyses of poultry production among smallholder farmers

The microeconomic theory assumes that producers engage in production to maximize profits and minimize costs (Mwangi et al., 2020). The conventional econometric approaches tend to build on this standard by estimating production, cost and profit function parameters (Atieno, 2016). In this study, the Cobb Douglas production function as used by Najjuma et al. (2016) and Ambetsa et al. (2020) was estimated and applied  to   determine   factors   that  affect  production among smallholder indigenous poultry farmers. The gamma parameter (γ) shows that 50.548% of the deviations in poultry production resulted from production constraints. Besides, sigma squared (σ2) had a value of 0.638 that was significant at 1% level denoting a perfect goodness of fit.

The coefficient of multiple determination (Table 3) was 0.913 denoting that 91.3% of the variation in poultry production was explained by the explanatory variables included in the model. In addition, the remaining 8.7% was accounted for by the error term and variables not considered in the model. The results revealed an F-value of 95.97 which was highly significant at 1% level signifying that all variables in the model were paramount in explaining the variation of indigenous poultry production. The study revealed a return to scale of 1.019, this shows an estimated constant return to scale implying that a proportionate increase in input use, would increase output in indigenous chicken production in the study area by the same proportion.

The input-output analyses (Table 3) revealed that amount of credit borrowed, quantity of feeds and frequency of vaccination were significant determining factors of indigenous poultry production in the study area. From the results, a 1% increase in amount of credit would increase indigenous chicken production by 0.29%. Further, 1% increase in quantity of feeds would increase production by 0.37% while a percentage increase vaccination dosage would increase production among respondents by 0.35%. These results concurred with Bukunmi and Yusuf (2015). Besides, results revealed that output was highly responsive to feeds, frequency of vaccination and amount of credit.

Effect of selected factors on indigenous chicken production

The assessment of variations in production is important since it helps in making recommendations on policy review since the degree of efficiency alone is inadequate. To test whether socio-economic and institutional factors had any significant contribution in explaining farm production among smallholder indigenous poultry farmers, one step Cobb Douglas production function was used and results illustrated in Table 4.

Gender of the household head had a positive and significant coefficient related to indigenous poultry production at 5% level of significance. Results shows that when the gender of household head was male, the marginal effect of increasing production among respondents increased by 19.69%. This result coincided with the findings by Honfoga et al. (2017) who found out that if the gender of head of the household was a male, agricultural productivity would easily increase compared to households headed by female. This is attributed to ease of resource access by the males as compared to females as described by Ndirangu et al. (2018).

The coefficient off farm income was positive and significant at 5% level. This implied that off farm income was directly related to poultry production thus a unit increase in off farm income would increase poultry output by 36.94%. This infers that farmers who earned more income from non-farm activities were more productive in indigenous poultry than those who had no income from non-farm activities. This result upheld those of Wabomba (2015), Shettima et al. (2015) and Abate et al. (2019). The explication of this impression is that off farm employment empowered poultry farmers to timey procure inputs by ensuring a consistent flow of income and eliminating financial constraints (Aheisibwe et al., 2018). Conversely, Ndirangu et al. (2018) explained  that  off  farm  employment  had  a  negative influence on agricultural production. The researchers claimed that the involvement of farmers in off farm occupations reduced the time and resources devoted to the farm operations hence low productivity.

Adoption of modern technologies had a positive and significant coefficient. This coincided with prior anticipations and inferred that by smallholder farmers embracing modern innovations, indigenous poultry production increased by 29.19% compared to use of traditional production techniques. The likely explanation is that since technologies improves the quality of factors of production, it is possible for smallholder farmers to expand the production frontier through utilization of same quantity of output. In addition, technologies enable farmers attain a given output level with fewer resource as discussed by Ambetsa et al (2020) and Mwangi et al. (2020). Besides, the findings coincided with Mukhtar et al. (2018) who observed that producers who embraced modern innovations in Kano state of Nigeria had higher levels of farm production.

Concerning contact with extension agents, the coefficient was directly related to poultry output and significant at 10% level. This implies that an increase in extension contacts would increase poultry production by 9.41%. This is attributed to that extension serves as a source of information thus educating farmers on emerging production technologies that promote production. In addition, farmers with more access and increased contacts with extension experts were well enlightened with trending issues in both input and output poultry markets. This result coincided with the findings of Ntabakirabose (2017) who found that training in agriculture had a positive and significant impact on technical knowledge which equips farmers with technical skills and practical knowledge in adoption of  improved  technologies  hence  increasing  levels of production. In regard to the flock of indigenous chicken, the coefficient was positive and statistically different at 1% level of probability. This implies a unit increase in the flock size would increase indigenous chicken production 1.43%. A possible explanation is that a large flock enabled farmers derive the benefits of economies of scale thus reduced costs and increased returns. This enables farmers produce optimally due to reduced production constraints and enhanced empowerment as expounded by Mwangi et al. (2020).

In regard to size of land under poultry production (land size), the parameter was negative and significant at 1% level. This explains that high levels of poultry output were recorded among farmers who had allocated small portions of their farms for indigenous chicken production compared to their counterparts with large land sizes. This inferred that poultry yield decreased with increase in land under indigenous chicken production in Tigania West Sub-county. These results substantiate that farmers with small land sizes were more productive contrary to their associates with large plots of land. The reasonable justification is that, farmers with small land sizes could be part of the farmers who depend on their farms for occupation. This therefore inspires them to give farming greater attention for higher yields hence more resourceful despite the size. This result was in agreement with the findings of Chepng’etich et al. (2015) and Mwangi et al. (2020). Folorunso and Adenuga (2013) and Dessale (2019) argued that the inverse relationship between land size and production in agriculture was attributable to the decrease in management aptitudes among smallholders which reduces the efficacy of production.


The results of the current study reveal that improved feeds and vaccination were the most limiting resources in indigenous poultry production in the study area. This implies that a possibility existed to increase production through efficient utilization of these inputs. The study further showed that in indigenous poultry farming, production was influenced by size of the flock, adoption of technologies and engagement in nonfarm employments. These enabled farmers acquire essential inputs leading to improved output hence economies of scale and better returns. Besides, results revealed that access to extension services increased level of production while output was high among respondents who allocated small portions of their farms for indigenous chicken farming. From the findings, this study finds that enhanced accessibility of extension services would educate smallholder farmers on emerging innovations and technologies.

In addition, the study noted a need to develop policy interventions geared towards  subsidizing  the  costs  of modern production innovations in poultry farming while promoting efficient distribution and availability of poultry vaccines since this input was found to be an important component towards increased poultry production. Farmers need to apportion more of acquired credits for indigenous chicken production since this will enable them acquire improved feeds and enlarge their stock thus benefiting from the economies of scale. Besides, this will reduce costs per unit of production leading to increased returns thus improving the farmers bargaining power in both input and output markets.


The authors have not declared any conflict of interests.


Abadi T, Gezahegn M, Teklehaimanot A (2018). Assessment of factors affecting adoption of exotic chicken breed production in North Western Zone of Tigray, Ethiopia. International Journal of Livestock Production 9(11):293-299.


Abate TM, Dessie AB, Mekie TM (2019). Technical efficiency of smallholder farmers in red pepper production in North Gondar zone Amhara regional state, Ethiopia. Journal of Economic Structures 8(1):18.


Abdul MM, Isgin T (2016). Technical Efficiency of Cassava Production in the Savannah Zone of Northern Ghana: Stochastic Frontier Analysis. Journal of Biology, Agriculture and Healthcare 6(20):62-72.


Aheisibwe AR, Lokina RB, Hepelwa AS (2018). Technical Efficiency in Seed Potato Production Systems in Uganda. Journal of Economics and Behavioral Studies 10(3):122-140.


Ajibefun IA (2008). An evaluation of parametric and non-parametric methods of technical efficiency measurement: application to small scale food crop production in Nigeria. Journal of Agriculture and Social Sciences 4(3):95-100.


Ambetsa FL, Mwangi SC, Ndirangu SN (2020). Technical efficiency and its determinants in sugarcane production among smallholder sugarcane farmers in Malava sub-county, Kenya. African Journal of Agricultural Research 15(3):351-360.


Atieno OM (2016). Factors influencing poultry production among poultry farmers in Eldoret Town, Uasin Gishu Country, Kenya. A Research Project Report.


Ayieko MOD, Bett EK, Kabuage LW (2015). Analysis of indigenous chicken marketing participation decisions: The case of producers from Makueni County, Kenya. East African Agricultural and Forestry Journal 81(1):12-17.


Ayinde IA, Aminu RO, Ibrahim SB (2015). Technical efficiency of maize production in Ogun State, Nigeria. Journal of Development and Agricultural Economics 7(2):55-60.


Baliyan SP, Masuku MB (2017). Socio-Economic Factors as Determinants of Farm Management Skills among Broiler Poultry Producers in Botswana. International Journal of Agricultural Economics 2(2):27.


Bukunmi FR, Yusuf HA (2015). Analysis of socio-economic factors influencing poultry egg production among poultry farmers in Ondo State, Nigeria. Current Journal of Applied Science and Technology pp. 1-7.


Chepng'etich E, Nyamwaro SO, Bett EK, Kizito K (2015). Factors that influence technical efficiency of sorghum production: A case of small holder sorghum producers in Lower Eastern Kenya. Advances in Agriculture 2015:1-11.


Delabouglise A, Le Thanh NT, Xuyen HTA, Nguyen VYB, Tuyet PN, Lam HM, Boni MF (2020). Poultry farmer response to disease outbreaks in smallholder farming systems in southern Vietnam. Elife 9:59212.


Dessale M (2019). Analysis of technical efficiency of small holder wheat-growing farmers of Jamma district, Ethiopia. Agriculture and Food Security 8(1):1.


Folorunso ST, Adenuga KM (2013). An analysis of technical efficiency of ginger crop production in Jaba Local Government Area, Kaduna State, Nigeria. Advances in Applied Science Research 4(5):85-90.


Goraga Z, Wilbert C, Caron L (2018). Ethiopian native chicken productivity, aims of production and breeding practices across agro-climatic zones. International Journal of Livestock Production 9(8):198-205.


Government of Kenya (GoK) (2018). Meru County Government. County Integrated Development Plan 2018-2022.


Hirwa CDA, Kugonza DR, Kayitesi A, Murekezi T, Semahoro F, Uwimana G, Habimana R (2019). Phenotypes, production systems and reproductive performance of indigenous chickens in contemporary Rwanda. International Journal of Livestock Production 10(10):213-231.


Honfoga BG, Tognon IA, Chikou A (2017). Profitability and sustainability of modern fish farming in Benin: An on-farm experimental appraisal of two production systems of Clarias gariepinus. Journal of Development and Agricultural Economics 9(9):243-249.


Kamau CN, Kabuage LW, Bett EK (2018). Impact of improved indigenous chicken breeds on productivity. The case of smallholder farmers in Makueni and Kakamega counties, Kenya. Cogent Food and Agriculture 4(1):1477231.


Kamiyama H, Kashiwagi K, Kefi M (2016). Technical efficiency among irrigated and non-irrigated olive orchards in Tunisia. African Journal of Agricultural Research 11(45):4627-4638.


Kejela Y, Banerjee S, Taye M (2019). Some internal and external egg quality characteristics of local and exotic chickens reared in Yirgalem and Hawassa towns, Ethiopia. International Journal of Livestock Production 10(5):135-142.


Kelemu K, Negatu W (2016). Analysis of levels and determinants of technical efficiency of wheat producing farmers in Ethiopia. African Journal of Agricultural Research 11(36):3391-3403.


Kingori AM, Wachira AM, Tuitoek JK (2010). Indigenous chicken production in Kenya: A review. International Journal of Poultry Science 9(4):309-316.


Kirui K (2014). Factor influencing performance of poultry farming projects in Bureti sub county, Kericho, Kenya (Doctoral dissertation, University of Nairobi).


Labeyrie V, Deu M, Dussert Y, Rono B, Lamy F, Marangu C, Leclerc C (2016). Past and present dynamics of sorghum and pearl millet diversity in Mount Kenya region. Evolutionary Applications 9(10):1241-1257.


Magothe TM, Okeno TO, Muhuyi WB, Kahi AK (2012). Indigenous chicken production in Kenya: I. Current status. World's Poultry Science Journal 68(1):119-132.


Mbuza F, Manishimwe R, Mahoro J, Simbankabo T, Nishimwe K (2017). Characterization of broiler poultry production system in Rwanda. Tropical Animal Health and Production 49(1):71-77.


Milkias M, Molla M, Tilahun S (2019). Productive and reproductive performance of indigenous chickens in Gena Bossa District of Dawro Zone, Ethiopia. International Journal of Livestock Production 10(1):24-32.


Moussa H.O, Keambou TC, Hima K, Issa S, Motsa'a SJ, Bakasso Y (2019). Indigenous Chicken production in Niger. Veterinary and Animal Science 7:100040.


Mukhtar U, Mohamed Z, Shamsuddin MN, Sharifuddin J, Iliyasu A (2018). Application of data envelopment analysis for technical efficiency of smallholder pearl millet farmers in Kano state, Nigeria. Bulgarian Journal of Agricultural Science 24(2):213-222


Mwangi TM, Ndirangu SN, Isaboke HN (2020). Technical efficiency in tomato production among smallholder farmers in Kirinyaga County, Kenya. African Journal of Agricultural Research 16(5):667-677.


Mwobobia RM, Amwata DA, Kanui TI, Nguku SA (2016). Comparing production characteristics of poultry farmers in Katulani District, Kitui County, Kenya. Livestock Research for Rural Development 28(5):20-36.


Najjuma E, Mbeche R, Kavoi MM (2016). Assessment of technical efficiency of open field tomato production in Kiambu County, Kenya (stochastic frontier approach). Journal of Agriculture, Science and Technology 17(2):21-39.


Ndirangu SN, Mbogoh SG, Mbatia OLE (2018). Evaluation of the Elasticity of Farm Output among Smallholder Farmers in Selected Agro-Ecological Zones of Embu County, Kenya. Asian Journal of Agricultural Extension, Economics and Sociology, pp 1-10.


Njuguna C, Kabuage LW, Bett EK (2017). Economic analysis of indigenous chicken production: The case of smallholder farmers in Makueni and Kakamega Counties, Kenya.


Ntabakirabose G (2017). An economic analysis of the factors influencing maize productivity and efficiency in Rwanda: a case study of Gatsibo District. Doctoral dissertation -JKUAT.


Ntakyo PR, Berg VDM, Mugisha J (2019). Market production and productivity: The effects of cash cropping on technical efficiency in staple crop production. African Journal of Agricultural Research, 14(19):828-842.


Nwigwe C, Okoruwa V, Adenegan K, Olajide A (2016). Technical efficiency of beef cattle production technologies in Nigeria: A stochastic frontier analysis. African Journal of Agricultural Research 11(51):5152-5161.


Ogolla M (2016). Factors Influencing Poultry Production Among Poultry Farmers in Eldoret Town, Uasin Gishu County, Kenya. Doctoral dissertation, University of Nairobi.


Okoth NW (2019). Factors Influencing Adoption of Climate-smart Agriculture as a Climate Change Adaptation in Tigania West-Meru County Kenya. Doctoral dissertation, University of Nairobi.


Padhi MK (2016). Importance of indigenous breeds of chicken for rural economy and their improvements for higher production performance. Scientifica (2016).


Rasha RK, Liza HA, Manjira S, Kazal MMH, Rayhan SJ (2018). Financial profitability and resource use efficiency of boro rice production in some selected areas of Mymensingh in Bangladesh. Research in Agriculture Livestock and Fisheries 5(3):293-300.


Shettima BG, Amaza PS, Iheanacho AC (2015). Analysis of Technical Efficiency of Irrigated Vegetable Production in Borno State, Nigeria. Journal of Agricultural Economics, Environment and Social Sciences 1(1):88-97.


Tiruneh WG, Chindi A, Woldegiorgis G (2017). Technical efficiency determinants of potato production: A study of rain-fed and irrigated smallholder farmers in Welmera district, Oromia, Ethiopia. Journal of Development and Agricultural Economics 9(8):217-223.


Tiruneh WG, Geta E (2016). Technical efficiency of smallholder wheat farmers: The case of Welmera district, Central Oromia, Ethiopia. Journal of Development and Agricultural Economics 8(2):39-51.


Wabomba OW (2015). Determinants of technical efficiency of soybean production among farmers in Bungoma County, Kenya (Doctoral dissertation, Moi University).


Wambua DM, Ndirangu SN, Njeru LK, Gichimu BM (2019). Effects of recommended improved crop technologies and socio-economic factors on coffee profitability among smallholder farmers in Embu County, Kenya. African Journal of Agricultural Research 14(34):1957-1966.


Watson DM (2001). Pedagogy before technology: Re-thinking the relationship between ICT and teaching. Education and Information technologies 6(4):251-266.


Watson RR, Meester DF (Eds.) (2015). Handbook of eggs in human function (Vol. 9). Wageningen Academic Publishers.


Wollie G, Zemedu L, Tegegn B (2018). Economic efficiency of smallholder farmers in barley production in Meket district, Ethiopia. Journal of Development and Agricultural Economics 10(10):328-338.