New insights on efficiency and productivity analysis: Evidence from vegetable-poultry integration in rural Tanzania

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New insights on efficiency and productivity analysis: Evidence from vegetable-poultry integration in rural Tanzania Naphtal Habiyaremye , Martin Paul Tabe-Ojong Jr. , Justus Ochieng , Takemore Chagomoka PII: DOI: Reference:

S2468-2276(19)30751-3 https://doi.org/10.1016/j.sciaf.2019.e00190 SCIAF 190

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Scientific African

Received date: Revised date: Accepted date:

16 January 2019 23 May 2019 4 October 2019

Please cite this article as: Naphtal Habiyaremye , Martin Paul Tabe-Ojong Jr. , Justus Ochieng , Takemore Chagomoka , New insights on efficiency and productivity analysis: Evidence from vegetable-poultry integration in rural Tanzania, Scientific African (2019), doi: https://doi.org/10.1016/j.sciaf.2019.e00190

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New insights on efficiency and productivity analysis: Evidence from vegetable-poultry integration in rural Tanzania Naphtal Habiyaremyea, Martin Paul Jr.Tabe-Ojongb, Justus Ochiengc, Takemore Chagomokad

a

International Livestock Research Institute (ILRI), P.O. Box 30709 - 00100, Nairobi, Kenya

([email protected]) b

Institute for Food and Resource Economics, University of Bonn Nussallee 21, 53115 Bonn,

Germany ([email protected]) c

World Vegetable Center (WorldVeg) Eastern and Southern Africa, P.O. Box 10, Arusha,

Tanzania ( [email protected]) d

Seed Co -TheAfrican Seed Company, Accra, Ghana

([email protected])

Abstract Despite its dependence on agriculture, food production does not seem to match the population increase in Tanzania. Exacerbating the situation, poverty in the light of per capita income keeps increasing making it one of the poorest economies in the world. Recent policy debates have been geared at probing into the situation and seeking ways of reversing this troublesome and disturbing trend. Increasing productivity and improving production efficiency are steps in the right direction of augmenting food production. We collected data from 250 vegetable farmers in the Babati district of Tanzania and assess the efficiency and productivity level of the integration of poultry systems into their vegetable activity. Specifying the Cobb-Douglas functional form in the stochastic production frontier, we find evidence of the direct impact of farm size, fertilizer quantity, labour cost and seed cost on productivity. While younger farmers are observed with the greatest level of efficiency, male farmers are more technically efficient than their female counterparts. Though surprising (because of the historical attribution of women to vegetables), this is somewhat expected as males are better connected in society and have higher access to improved and novel farming technologies and techniques which are relevant in increasing efficiency. The mean technical efficiency of this production system is 0.44 which is sharply below most studies on vegetables. This re-affirms and re-echoes to policy the need for effective policy developments that underscore the use of improved farming technologies.

Key words: Vegetable, poultry, Cobb-Douglas production function, stochastic frontier analysis JEL codes: D24, Q12, Q18, C21

1. Introduction Agriculture continues to be the cornerstone for most developing nations especially in subSaharan Africa (SSA). This is because of its employment capacity, provision of food, and its contribution to the foreign reserves through exports. Although transiting to a market economy, the economy of Tanzania is still reliant on agriculture for its 85% share of exports and quarter contribution to the Gross Domestic Product (GDP) (CIA, 2018). Like

in other agrarian

economies, 65% of the labor force is also employed in this vibrant primary sector (CIA, 2018). Despite its dependence on agriculture, food production does not seem to match the country’s population increase. Exacerbating the situation, poverty in the light of per capita income keeps increasing making it one of the poorest economies in the world.

Recent policy debates have been geared at probing into the situation and seeking ways of reversing this troublesome and disturbing trend. Increasing productivity and improving production efficiency are steps in the right direction of augmenting food production (GHI, 2017) and attaining agricultural growth and economic development (Poudel et al., 2015). Productivity growth can be obtained from the efficient use and allocation of farm resources as well as from the use of improved techniques in farming. These techniques could be the use of improved seeds, fertilisers, improved farming systems, and better land and other environmental friendly technologies. However as opined by Poudel et al. (2015), agricultural growth and development is far more dependent on the efficiency in utilizing a particular system or technology than on the system or technology itself.

The Green revolution which recorded numerous successes in terms of reducing poverty and increasing productivity through the use of improved technologies in Asia has gradually been initiated in developing nations (though recently changing) to include the integration of novel farming systems like the integrated vegetable cum poultry system and the integrated vegetable cum piggery system. The government of Tanzania has partnered with both national and

international developmental organizations for the provision of these improved farming technologies to farmers. This is the case with the Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) project which aims to offer pathways to end hunger and reduce poverty among smallholder farmers in Tanzania. Vegetable cum poultry farming is one of the sustainably intensified farming system initiated and supported by the Africa RISING project in different districts of Tanzania. Vegetables have received considerable attention and are generally produced by smallholder farmers who own about two hectares of land (Marble & Fritschel., 2014). For instance, out of 8.8 million hectares of used land in Tanzania, around 115,000 ha are allocated to vegetable production and a total of 635,000 tonnes of vegetables were produced in the year 2007/2008 (NSCA, 2008). Vegetable farming is described as a valued economic activity that provides income to farmers and offers employment opportunity mostly to women and young people in poor rural areas (Everaarts al., 2015). Vegetable production provides an opportunity to enhance productivity and increase smallholders’ income (Ali, 2000). Weinberger and Lumpkin (2007) argued that vegetables are more profitable than cereals crops which had earlier been contended by Joshi et al. (2006) in India where the net profit of radish and eggplant ranged between Rs 5591/ha and Rs 12094/ha respectively while that of maize and paddy ranged from Rs 2519/ha to Rs 10384/ha. Furthermore, the set of vegetables in Bangladesh, Cambodia, Laos, Niger, North Vietnam, and South Vietnam gave a net return on area and labor inputs that is higher than that of rice (Weinberger & Lumpkin, 2007). Poultry production, on the other hand is regarded as an investment opportunity that offers quick and high profits to rural poor farmers (Epprechtet al.,, 2007; Sonaiya, 2007). Based on the non-separable farm household model which exists in most developing economies as a result of thin and missing markets, the consumption and production decisions of most households are not separable. This implies households can approach product markets and generate more revenue from the sales of poultry products that are in excess of their consumption. For instance, Oladeebo and Ambe-Lamidi (2007) estimated the profitability of poultry farming among youth farmers in Nigeria using net income, gross margin, and gross return per naira invested. Their findings revealed that, based on all the three indicators, poultry production is profitable and a worthy enterprise to venture in.

The poultry sector contributes about 3 % of the GDP derived from agriculture in Tanzania, equivalent to 1% of the total national GDP (TALIRI, 2015). However, despite the central role that poultry plays, its potential is not fully explored (Goromela et al.,2007; Marwa et al., 2016; Mutayoba et al., 2012). It is even argued that if this sector is managed effectively and efficiently, its contribution to the national economy could be higher (Marwa et al., 2016). Hence, improving the productivity of poultry is vital for eliminating poverty, improving food security and enhancing the wellbeing of rural communities and in the process helping the country to meet its Sustainable Development Goals (SDGs) obligation. Vegetable and poultry farming systems have the potential of generating more revenue, however, they are facing a number of challenges such as low yields and high cost of productions. If these two farming systems are to be produced in an efficient way, their productivity would be more than the present levels. From the standpoint of the cost of production, labor and fertilizer costs are the main costs incurred during vegetable production while feeds alone occupy over 60% of total variable costs in the process of poultry farming (Tijjani et al., 2012). Vegetables provides vitamins to poultry that increases their productivity and poultry manure is an organic fertilizer to vegetables that increases vegetable yield (Adekiya & Agbede, 2016). Therefore, the integration of vegetable cum poultry production systems results in the reduction of poultry feed cost and reduces the costs of fertilizers. Despite the above mentioned importance of vegetables and poultry in the agricultural sector in Tanzania, many households are unable to produce at the frontier level to satisfy household food demand. Therefore, for a country like Tanzania with reducing agricultural land, increase in food production can only arise from agricultural intensification through the diffusion and adoption of pro-poor and environmental friendly technologies in production. The agricultural system in Tanzania is heterogeneous with the predominance of many farmers operating on small farms. Land in Tanzania is owned by the government with the possibility of a leasing contract for up to 99 years (CIA, 2018). Because of its relevance in agricultural production, land has been the topic for numerous international and national policy debates. However, in Tanzania, in the past decades, there has been relatively few policy reforms with regards to land ownership especially foreign land ownership. Land is arguably a crucial constraint in the attainment of increased food production. Moreso, economic theory purports that limited land and land ownership has the

possibility of restricting a household’s access to credit which it can use for improving productivity and efficiency. The literature on productivity and efficiency in sub-Saharan Africa has been on the increase since policy has always sought ways of improving productivity and efficiency in production. Most of these studies have focused on technical efficiency1 and utilized both the parametric and non-parametric forms in estimating technical efficiency. However, most of the literature is only centered on cereals like maize, beans and rice (Boubacar et al., 2016; Mango et al., 2015; N'cho et al., 2017; Ng’ombe, 2017; Ogada et al., 2014); and more recently root and tuber crops (Asante et al., 2014; Binam et al., 2005; Okoye et al., 2016). Very few studies have focused on African leafy vegetables despite their increasing role in food security and income generation (Akamin et al., 2017; Haji and Andersson, 2006; Srinivasulu et al., 2015; Tabe-Ojong and Molua, 2017). Even at this, the studies on vegetable farms only targeted specific vegetables cultivated by households which are usually not representative of most farm households, hence limiting policy development. Farm households usually engage in the cultivation of numerous vegetables for purposes of food and other livelihood options. Nowadays, vegetable farmers are increasingly integrating poultry and other livestock in their production systems. These novel farming systems can be described as pro-poor and environmental friendly since the output of one production process is used as the input of the next process. Despite this important feedback mechanism, very little is known if its ability to increase productivity and translate into the better living conditions of the farm households. It is thus vital to understand how efficient integrated poultry -vegetable farming systems is. This is the gap this study tries to address especially drawing special attention on the role of farm size and the gender construct. Analyzing farm efficiency continues to be relevant for policy since it gives clear insights into farm competitiveness and their ability to increase productivity and the efficient use of farm resources. In this paper, we use data from vegetable farms in the Babati district of Tanzania to test possible effects of farm sizes and the gendered nature of vegetable production on the inefficiency level of farmers. To do this, we estimate a stochastic production frontier model of the factors affecting 1

Focus is on technical efficiency since it entails the optimal allocation of scarce resources purchased within a defined budget

vegetable production in the area and subsequently affixing socio-economic determinants to the generated efficiency scores in the first stage. This study thus provides an understanding of the resource use efficiency in vegetable farms in Tanzania, amidst building the framework and complementing the literature in the light of efficiency and productivity analysis in Africa. 2. Analytical framework Estimating productivity and efficiency is one of the core areas of research in production economics. The microeconomic theory of production provides the theoretical framework for most empirical research on efficiency (Tabe-Ojong and Molua, 2017) and simply defines production as the transformation of inputs to outputs. Productivity on the other hand is just an index of the value of output to that of inputs employed in agricultural production. We motivate our analysis by specifying the production function of a farmer ( of inefficiency in production, the

to be (

. In the absence

farmer would produce

(

(1)

However, in the real world, farmers are usually faced with a series of shocks which reduces their production efficiency. The stochastic production frontier analysis assumes that this efficiency reduces the potential output of the firm. Specifically, (

(2)

is the efficiency level of the

farmer and ranges between 0 and 1. A value of 1 implies that

the farmer is producing an optimal amount of output given his present technology embodiment while

is indicative of the inefficient use of inputs given the farmer’s current production

technology. Because of the non-negativity characteristic of output, the level of technical efficiency is assumed to be positive (

).

2.1 Stochastic frontier model The seminal paper of Farrell (1957) led to the development of numerous approaches in analyzing productivity and efficiency (Abdul-Salam and Phimister, 2017). The two common methods used in literature are the Stochastic Frontier Analysis (SFA) and the Data Envelope Analysis (DEA). SFA came as a result of the groundbreaking works of Aigner et al. (1977) and Meeusen and Van

den Broeck (1977) in estimating a Stochastic frontier production function. The authors estimated two error terms: a stochastic error and a one sided error signifying technical inefficiency. DEA on the other hand was independently brought forth by Charnes et al. (1978). DEA is nonparametric approach which employs mathematical programming and assumes that the deviation from the production frontier is due to technical inefficiency only. The strength of DEA stems from its ability to accommodate numerous inputs and outputs and its non-specification of a functional form. However, its deterministic nature coupled with its implicit assumption of no stochastic error are major drawbacks to its use. Agricultural production (typically rain-fed) is usually pruned to shocks like weather and climate risks, the incidence of pests and diseases, and other downside risk measures. Ignoring this and attributing all to inefficiency is a strong assumption with a possibility of biasing our analysis. We thus adopt the SFA in estimating the technical efficiency of vegetable farmers in Babati since it differentiates deviation from the frontier into the two components of inefficiency and the idiosyncratic error. SFA is a parametric approach that relies on an econometric specification. Being an econometric approach, it makes possible for hypothesis testing and the setting of a confidence interval (Wadud and White, 2000). It is represented as: (

(3)

(

(4)

is the output of the parameter estimates. individual terms

farm,

is the vector of the inputs used by the farmer and

is the

, as mentioned previously is the error term which is made up of two .

conventionally refers to specification or measurement errors that

are outside the influence of farmers. It is assumed to follow a normal distribution with mean zero and variance

.

on the other hand is a one-sided stochastic error term which reflects

inefficiency. This is of utmost importance because it specifies the deviation to achieving the frontier optimal production by a farmer.

and

follow the assumption of independence and

are identically distributed across representative observations (Belotti et al., 2012). The inefficiency component as exhibited in literature follows four different distributions: normalgamma, half-normal distribution (Aigner et al., 1977), exponential distribution (Meeusen and van den Broeck, 1977), and the truncated normal. Based on previous studies and for comparison purposes, we make use of the half-normal and exponential frameworks to ascertain the model fit

(

of the data. In the half-normal, variance 3.

while

in the exponential model has a

. Estimation approach

Two estimation approaches are conventionally used to estimate the inefficiency effects of farmers: the two stage estimation procedure and the one stage estimation technique. In the two stage technique, the production function and the efficiency scores are predicted in the first stage while the factors driving efficiency are estimated in the second stage. However as concluded by Wang and Schmidt (2002), this method leads to bias and inconsistent estimates as it poses serious issues with regards to the assumptions on the random variable

. This is probably

because of the potential correlation between the covariates in the first and second stages leading to the violation of the independently, identically and normally distributed (i.i.d.) assumption in the frontier function. Moreover, there is the potential of omitted variable bias since the efficiency determining covariates are not considered in the first stage (Koirala et al., 2016). Therefore for this study, our estimation takes the form of single stage estimation wherein the production function is simultaneously estimated with the inefficiency effects. The technical efficiency score is estimated by: = ( Where

(

(

(

=

is the actual output of the farm while

(

(5)

is the maximum attainable output (frontier

output). The above equation signifies that TE takes values ranging from zero to one with production bounded by a deterministic quantity, (

.

3.1 Empirical model There exists a long standing debate on the ideal functional form to be used in explaining the production frontier (Michler and Shively, 2015), with many studies using the Cobb-Douglas specification. Based on data and the context of the study, studies (Abdul-Salam and Phimister, 2017) are using the more flexible translog specification. However, it is not trivial estimating the coefficients of the translog model as a result of input collinearity. We therefore employ the Cobb-Douglas production function for the analysis of the technical efficiency of vegetable farmers in the Babati district of Tanzania because of its appropriateness in representing a

particular production technology. More so, its efficiency in modelling multiple inputs makes it the best for this analysis. Perhaps, its interpretation is relatively easy as they just signify elasticities. It is specified as: ∑

(6) ∑

Where

(7)

signifies the farm and inputs respectively. The dependent variable,

vegetable production which is log-transformed.

is value of

represents the productive input of the farm

(farm size, labor cost, fertilizer cost, pesticide cost, income).

represents the vector

efficiency drivers like age, gender and education level of the household head, extension access and the integration of poultry into vegetable farming. All the above covariates have been found by previous studies (Baráth et al., 2018; Binam et al., 2005; Boubacar et al., 2016; Chirwa, 2007; Latruffe et al., 2016; Martinez Cillero et al., 2017; Solís et al., 2009; Villano et al., 2015) to be significant determinants of productivity and technical efficiency. Finally parameter estimates for the frontier function while

and

and

are the

are the parameter estimates for the

determinants of technical efficiency. Also included in the analysis are regional dummies for the various villages. 4. Data and descriptive statistics This paper uses a cross-sectional data from Babati District, one of the major vegetable growing districts in Tanzania. The survey is conducted in five villages of Babati district (Bermi, Galapo, Matufa, Seloto, and Shaurimoyo). Structured questionnaires are used to collect information from 250 vegetable farming households. Information on farm inputs, production system as well as output with socio-economic and institutional characteristics are collected. In addition, farmers’ demographic information is also documented. The information gathered facilitated in establishing the determinants of technical efficiency of vegetable farmers in this district. The demographic factors such as gender, age, and education level of the head of the household are expected to positively influence efficiency. The more educated farmers are more knowledgeable and open to novel farm technologies and avenues with potentials to increase their output. They also tend to apply different production techniques which increase their total output and making them more technically efficient than less educated farmers (Asadullah & Rahman,

2009; Fadzim et al., 2017). Male headed households are more likely to be technically efficient than female headed households despite their historic attribution to vegetable cultivation. This is probably because of the energy consuming nature of this integrated system and males’ access to production resources (Asante et al., 2014). Moreso, male headed households have high access to information and are well connected in the society improving their access to novel farm technologies and techniques. Similarly younger farmers are expected to exhibit lower efficiency levels than their older counterparts. Age has been increasingly used as a proxy for experience which is expected to reduce inefficiency levels. Older farmers are highly networked in their communities and have built up their skill and know-how level as a result of continuous farming. Indeed, this built skills work to their advantage in reducing technical inefficiency and increasing productivity. On the other hand, younger farmers can still be observed with higher technical efficiency based on their youthful attributes. Their energetic nature and participation in many programs meant to improve their livelihoods is a plus for them. As a matter of fact, young farmers are less risk averse than their older counterparts making them engage into ventures which can improve their yields (Masunda & Chiweshe, 2015; Mugera & Featherstone, 2008). In addition, household size is expected to influence the production efficiency in a positive way since many household members can bring different ideas that can increase the productivity at the same time providing the labor demand for agricultural production. Households with access to extension services are expected to be more efficient as they get more information on improved technologies, improved vegetable seed as well as other important technical advice that increase productivity. These informal means of getting information are very common in most farming systems and have been proven to impact efficiency if they are efficiently and effectively done. Likewise, farm size, quantity of fertilizer and quantity of poultry manure applied are expected to positively affect technical efficiency since they increase the total quantity of vegetable produced. Some farmers in Babati cultivate on marginal lands making them dependent on soil supplements like fertilizers and organic manure. Descriptive statistics of the variables used in the model are presented in the Table 1 below. On average, smallholder farmers own about 9 chickens and allocate 0.09 hectares of land to vegetables with the application of 11.5 Kgs of fertilizers, 77.5 Kgs of poultry manure and 1.7

liters of pesticides. Furthermore, the seed cost is on average Tsh. 6,722 while the labor cost is Tsh. 91,182. In turn, they produced on average, around 863.5Kgs of vegetables per household and could get an average income of Tsh. 96,519 from vegetable and poultry farming. Vegetable farmers in the study area have a fair access to extension services as 67.6% of household’s access to educational and extension services. Male headed households are predominant in the sample (84%) while on average the household size is 5 people per household; indicating a serious concern given the average land owned of 1.2 hectares. On average, the age of the head of the household is 47 years while the education level is 7 years of formal schooling, implying that at least, the majority of farmers have primary education. Table 1. Descriptive statistics of variables used in the model Variable

Mean

Std. Dev.

Min

Max

Flock size (number)

9.46

15.576

0

200

Extension (=1 if access)

0.676

0.469

0

1

Age of the household head (number)

47.388

12.937

19

98

Gender of household head (=1 if male)

0.840

0.367

0

1

Education level of household head

7.032

2.660

0

16

Household size (number)

5.208

2.101

1

12

Land owned (in hectares)

1.198

1.176

0

6.171

Vegetable farm size (in hectares)

0.090

0.083

0.0008

0.607

Quantity produced (in Kgs)

863.476

1300.681

0

6750

Quantity fertilizer (in Kgs)

11.548

14.151

0

60

Poultry manure quantity (in Kgs)

77.579

142.816

2

600

Quantity pesticides (in Liters)

1.697

3.835

0

32

Total labor cost (in Tsh)

91181.600

102692.400

250

725000

Seed cost (in Tsh)

6721.992

9083.476

0

60000

V- P income (in Tsh)

96519.060

129728.200

0

1370000

(number)

5. Results and discussion As explained above, we use the one stage approach in estimating the technical efficiency of farmers in the Babati district of Tanzania wherein we employ the Cobb-Douglas production function estimating both the half normal and the exponential specifications. Table 2 below presents the full results of both the input output relationship and the inefficiency model. Despite presenting both results of the half normal and exponential methods, we base our interpretation on the half normal as the likelihood ratio test conducted proves that it is of better fit than the exponential model. The gamma value (γ=0.544) signifies the presence of technical efficiency and the appropriateness of the frontier model in capturing this inefficiency. Specifically, it tells us that half of the variation in output is as a result of the inefficiency of the farmers. The signs of the parameters in the input output relationship all depict signs in accordance with a priori expectations and are consistent with economic theory. The most important input determinants of vegetable farmers in the study area are farm size, amount of fertilizer used, the cost of labor, and the cost of seeds. They all have a significant and positive effect on the quantity of vegetable produced. Interpreting the coefficients is straightforward and represents elasticities since the variables are all transformed into logs. Farm size is highly significant (1% level) indicating that a 1% increase in the size of vegetable farm leads to a corresponding 0.55% increase in the quantity of vegetable produced. This signifies the importance of land as the primary input of any agricultural production process. This finding is similar to previous studies on productivity and efficiency (Latruffe et al., 2016; Mango et al., 2015; N'cho et al., 2017; Ng’ombe, 2017). Recent evidence with the use of the crop cuts measure of yields and use of remote sensing methods like the Geographical Positioning System (GPS) to measure farm sizes have debunked this oft-touted relationship. To the authors, measurement errors in self reporting yields and farmland cultivated have marked these results. Due to financial and material constraints, we couldn’t do the crop cut gold standard measure of yields or use GPS for measuring plot sizes. We used self-reported data by the households. Since this is recalled data, we are not sure of the reliability and validity of the responses of the farmers, making our results on this liable to some systematic errors. To reduce this nuance, we cautioned farmers on the effective reporting of the yields and farm sizes.

The amount of fertilizer used is also significant at the 1% level of probability indicating the importance of fertilizers. From the findings, a 1% increase in the use of fertilizers increases vegetable output by 0.16%, a much lower effect than farm size. Farmers make use of both organic and synthetic fertilizers in a bid to improve the quality of the soil and subsequently their output. Labor which has increasingly been proven to be a significant and crucial determinant of agricultural production also showed a positive and significant effect on the quantity of vegetable produced. Labor captured as the total cost of labor in man-days depicted a higher coefficient than fertilizer quantity. Economically, a 1% increase in the labor cost increases the quantity of vegetable produced by 0.34%. Farm families which invest more in into labor are certain of higher yields, possibly because of more labor hours invested into vegetable cultivation. Similarly, the cost of vegetable seeds also depicted a similar relationship though with a lower coefficient. Farmers utilize both local indigenous seeds and improved seeds. The local seeds which are usually low yielding cost little or nothing since they are retained from previous production, or collected from friends. The improved seeds which are high yielding are usually not easy to obtain since they entail a purchasing cost. The total elasticity of production which refers to the returns to scale is derived by adding up all the input elasticities. Summing up all the input elasticities, we obtain 1.246. This implies that the production function is exhibiting increasing returns to scale and doubling input use will increase production by approximately 125%. Thus, the elasticity of output is an increasing function of farm size, fertilizer quantity, labor cost and seed cost. Table 2: Estimation of vegetable production and technical efficiency in Tanzania Variable

Half normal

Exponential

Farm size

0.558***

0.555***

(0.080)

(0.760)

0.163***

0.175***

(0.063)

(0.062)

0.018

0.020

(0.046)

(0.044)

Fertilizer quantity

Pesticide quantity

Labour cost

Capital

Seed cost

Manure quantity

Constant

0.347***

0.359***

(0.075)

(0.073)

0.012

0.022

(0.032)

(0.031)

0.146***

0.148***

(0.026)

(0.026)

0.002

0.001

(0.051)

(0.049)

3.132***

2.535***

(1.042)

(0.981)

Variance parameters Lambda(λ)

2.069

Gamma(γ)

0.544

sigma u

1.491

Sigma v

0.720

Inefficiency model Flock size

Extension

Age

Educational level

Farm size

Gender

Household size

Capital

-0.104

-0.124

(0.010)

(0.014)

-0.198

-0.376

(0.253)

(0.380)

0.018*

0.023

(0.011)

(0.016)

0.037

0.067

(0.045)

(0.069)

1.159

1.888

(1.610)

(2.367)

-0.590*

-0.729

(0.316)

(0.466)

-0.055

-0.054

(0.054)

(0.082)

-3.47e-06*

-4.65e-06

Village dummies

Constant

Log pseudolikelihood Wald test

(2.01e-06)

(3.34e-06)

0.127

0.198

(0.086)

(0.131)

0.305

-1.307

(0.805)

(1.239)

-375.929

-3.75.800

328.37

362.58

(0.0000)

(0.0000)

Notes: *, *** signify the level of significance at 10 and 1 percent respectively. Parenthesis are the standard errors

We now move to the second part of the table which is the core of the paper. In this model, it is important to indicate that the negative coefficients represent positive drivers of efficiency while the positive coefficients signify factors which rather decrease the efficiency level. The lambda value (λ=2.069) implying that inefficiency accounts for most of the frontier deviation than statistical noise. Thus vegetable farmers operate below the frontier level as a result of technical efficiency. Here, we are particularly interested in the role of gender in technical efficiency. Our gender variable is significant at the 10% level of significance and positively related to technical efficiency. This is indicative of the fact that men are more technically efficient than women. This finding though partially in line with a priori expectations deviates from previous studies (Akamin et al., 2017) who found female farmers to be more technically efficient than male farmers. Like in most parts of rural Africa, vegetable cultivation has been left to rural women. However, because of its increased importance and energy demands, many more male farmers are becoming interested in vegetable farming. With their increased social networks and connections, they quickly access important agronomic information as well as improved methods of production which are relevant in increasing technical efficiency. Another significant source of technical efficiency is the age of the farmers which according to previous studies is an important proxy for representing farmer’s experience. The coefficient on age is positive and statistically significant at the 10% level of significance implying that younger farmers are more technically efficient than older farmers. Age increases experience up to a certain limit and then start decreasing as a result of the conservative nature of older farmers. Thus, age and technical efficiency follow an inverted u–shape relationship. A plausible

explanation is that younger vegetable farmers are more novel technology and risk loving. This trait of theirs makes them invest into novel technologies and adopt novel farm techniques which are very relevant for technical efficiency. Perhaps older farmers are less informed since they believe in their own knowledge base and abilities. Moreover, they are less energetic to carry on with the energy demanding farming activities. This findings are consistent with earlier works by (Asante et al., 2014; Asefa, 2011; Chirwa, 2007; Okoye et al., 2016). Lastly, household income represented as capital shows the expected positive impact on technical efficiency. This is a very important determinant of technical efficiency especially in low-income countries where farm and household capital as well as farm resources are limited. To increase farm and household incomes, most farm families are beginning to diversify and commercialize their farming. Farm size and educational level depicted the expected negative effect on technical efficiency, though insignificant. Most educated households will rarely participate in vegetable farming and even if they do, it will more or less be a part time of extra activity. The number of chicken owned exhibited a positive though insignificant relationship with TE. Most households who owned chickens use their manure in vegetable farms and they used some of the vegetables as feed to the poultry. This indicates a high level of resource use efficiency and sustainability in production.

5.1 Distribution of technical efficiency The mean technical efficiency level ranges from 0.0029 to 0.95 with a mean efficiency score of 0.44. This range is quite high and points to the existence of ample opportunities to increase the production of vegetables by 56% using the current state of technology .This efficiency score is relatively low as compared to previous studies (Akamin et al., 2017; Haji and Andersson, 2006) on vegetable farms which reported scores ranging from 66 to 67% respectively. Table 3, below reports the key summary statistics of the technical efficiency level of vegetable farmers. Table 3: Descriptive summary of technical efficiency

Statistics

Technical efficiency

Mean

0.440

Standard deviation

0.210

Minimum

0.003

Maximum

0.950

Note: Technical efficiency is conventionally expressed as percentages

Table 4 presents the frequency distribution of the technical efficiency scores. As observed, more than 50% of the farmers depicted efficiency scores lower than 50%. This indicates substantial inefficiency level amongst vegetable farmers with high skewness between 0.30 and 0.70. Just a single farmer operated close to the frontier with an efficiency level of 0.95. This is indicative of the fact that most farmers are not efficiently using their resources. Figure 1, is a kernel density histogram estimate for the density of technical efficiency of vegetable cum poultry farmers in the Babati district of Tanzania. From the figure, majority of farmers fall in the 0.40 to 0.70 efficiency range. This implies that, a large range of farmers are technically inefficient. Disaggregating efficiency levels by villages (figure 2), we find different efficiency distributions. Galapo is observed with the highest number of efficient farmers. This is most probably due to the agro-ecological differences as well as access to farm inputs.

Table 4: Frequency Distribution of Technical efficiency Efficiency level

Frequency

Percentage

<0.10

14

5.600

0.10
30

12

0.20
24

9.600

0.30
37

14.800

0.40
36

14.400

0.50
36

14.400

0.60
53

21.200

0.70
17

6.800

0.80
2

0.800

0.90
1

0.400

250

100

Total

1.5 0

.5

1

Density

2

2.5

Kernel density estimate

0

.2

.4

.6

Technical efficiency

Fig 1. Distribution of technical efficiency

.8

1

Galapo

Matufa

0

0

1

Shaurimoyo

1

2

3

4

Seloto

.5

0

Density

1

2

3

4

Bermi

0

.5

1

0

.5

1

te Density kdensity te Graphs by Villages

Fig 2. Distribution of technical efficiency by villages 6. Conclusion Agriculture continues to be the main engine driving the development of rural economies with increasing productivity and efficiency in production having the potential to alleviate poverty and increase food and nutrition security. This has made productivity to be a central issue in most policy debates in most developing countries. With the advent of improved technologies and novel techniques of farming, most governments are increasingly partnering with international non-governmental organizations (NGOs) for the delivery of these improved techniques to farmers. This is the case with the integrated vegetable cum poultry farming recently introduced in the Babati district of Tanzania. Despite its introduction, most farmers are still food insecure and highly impoverished. It is against this background that we gave a first attempt in understanding the productivity and efficiency level of farmers employing the Cobb-Douglas production function in the stochastic frontier production analysis. Employing a cross sectional survey of both integrators and non-integrators and applying the single stage stochastic frontier analysis, we found farm inputs like farm size and fertilizer input to have a significant bearing in

increasing the productivity of farm households. Costs measures like labor cost and seed cost are also found to positively determine the output level of the farm. Despite in contrast to previous studies, we argue that high cost is an indication of good quality seeds. Farmers who invest in quality seeds and who employ a well-paid (skilled) labor force are certain of higher returns in terms of output. For the efficiency model, older farmers were found to exhibit higher efficiency levels than their younger counterparts, probably as a result of the experience effect. Female farmers were also observed to have greater efficiency scores probably because of the historic attribution to vegetable farming. This may have certainly built some sense of self-efficacy in them which leads to reduced efficiency. Finally, household income showed a negative effect on technical efficiency. Households with a huge income will rather diversify or engage in other nonfarm activities. This will of course reduce their efficiency level since they allocate less time to their farming activity. The findings are suggestive of the important role of farm inputs in driving agricultural production. Farm inputs are the key resources underlying the production of most farm output in agrarian economies. Government should intervene in the provision of such farm inputs to farmers. Most farmers are operating below the frontier level with a mean efficiency score of 0.44, which is low and indicative of the presence of avenues to turn tables around and increase their efficiency. In this regard, attention should be specifically taken to women since they are observed with high efficiency scores. Acknowledgements The first author is grateful to GIZ/BEAF for funding his stay in Arusha, Tanzania. The research was supported by World Vegetable Center (WorldVeg) through Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) grant number AID-BFS-G-11-00002, with funding by the U.S. Agency for International Development (USAID). The opinions expressed in this paper are entirely those of the author(s) and do not necessarily reflect the views of the USAID. The authors also appreciate long term strategic donors to the World Vegetable Center: Republic of China (Taiwan), UK aid from the UK government, Australian Centre for International Agricultural Research (ACIAR), Germany, Thailand, Philippines, Korea, and Japan.

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