Profit efficiency among maize farmers and implications for poverty alleviation and food security in Ghana

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Profit Efficiency Among Maize Farmers and Implications for Poverty Alleviation and Food Security in Ghana Camillus Abawiera Wongnaa , Dadson Awunyo-Vitor , Amos Mensah , Faizal Adams PII: DOI: Reference:

S2468-2276(19)30767-7 https://doi.org/10.1016/j.sciaf.2019.e00206 SCIAF 206

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Scientific African

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22 January 2019 7 September 2019 10 October 2019

Please cite this article as: Camillus Abawiera Wongnaa , Dadson Awunyo-Vitor , Amos Mensah , Faizal Adams , Profit Efficiency Among Maize Farmers and Implications for Poverty Alleviation and Food Security in Ghana, Scientific African (2019), doi: https://doi.org/10.1016/j.sciaf.2019.e00206

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Profit Efficiency Among Maize Farmers and Implications for Poverty Alleviation and Food Security in Ghana Camillus Abawiera Wongnaa*, Dadson Awunyo-Vitor, Amos Mensah and Faizal Adams Department of Agricultural Economics, Agribusiness and Extension, Kwame Nkrumah University of Science and Technology, Private Mail Bag, University Post Office, Kumasi, Ghana Corresponding Author Camillus Abawiera Wongnaa

Department of Agricultural Economics, Agribusiness and Extension, Kwame Nkrumah University of Science and Technology, Private Mail Bag, University Post Office, Kumasi, Ghana-West Africa Email: [email protected], Phone: +233243389509 / +233206752159

Abstract In this study, we analysed the profit efficiency of Ghanaian maize farmers using data collected from 576 farmers in four agro ecological zones of Ghana. The stochastic frontier translog profit function was the main method of analysis. We find that maize production in Ghana is profitable but profitability will be adversely affected if prices of relevant inputs (pesticides, fertilizer, herbicides, labour and seeds) and farm size increased. Generally, the mean profit efficiency for all farmers was estimated to be 48.4% and maize farmers in the transitional zone were most efficient in their profit levels. Finally, profit efficiency was found to be influenced by educational level, gender, access to agricultural extension officers, good roads and credit. Improvement in the quality of road infrastructure in Ghana’s maize producing areas could help lower the prices of production inputs. Stakeholders (Government and Non-Governmental Organizations) in the maize industry are encouraged to help put subsidies on prices of production inputs to make them accessible and cost effective in employing them. They are also advised to help provide extension agents with appropriate incentives that will make them committed to their duties. Implementation of the above recommendations among others will improve profit efficiency of Ghanaian maize farmers and make the maize industry an avenue for job creation and a recipe for helping Ghana to meet the sustainable development goals on no poverty and zero hunger. Key Words: efficiency; maize; stochastic frontier; translog profit function; single step

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Introduction Maize (Zea mays L.) ranks first as Ghana’s most important cereal produced and consumed (Morris et al, 1999; FAO, 2008; MiDA, 2010; MOFA, 2012). Occupying an area of about one million hectares and constituting about 50-60% of Ghana’s cereal production, it also ranks first based on area cultivated and follows cocoa as the second most important crop (MiDA, 2010; MOFA, 2012). Maize grows well in almost every part of Ghana. It grows in the northern savannah, transitional, forest and coastal savannah zones of the country (Angelucci, 2012). The main maize producing areas in Ghana are Eastern, Ashanti and Brong Ahafo regions that account for over 80% of total maize production in Ghana. The three northern regions (Northern, Upper East and Upper West Regions) supply the rest (Angelucci, 2012). Economically, almost every part of the crop is significant in Ghana. That is, a large variety of both food and non-food products can be produced from the leaves, grain, tassel, cob and stalk of the maize plant. Maize production is a major source of food for most Ghanaians and therefore very important for ensuring Ghana’s household food security. Domestic unfulfilled demand for maize for consumption in Ghana was projected to reach 267,000 metric tonnes by 2015 if urgent measures were not taken to bridge the gap (MiDA, 2010). Maize is an integral part of poultry and livestock feeds and a substitute for the brewing industry (MiDA, 2010; Angelucci, 2012). In fact, estimated demand for maize for poultry feed was projected to grow from 73,000 metric tonnes in 2010 to 118,100 metric tonnes by 2015 (MiDA, 2010). Despite the above economic benefits of maize production, yield in Ghana’s maize farms is one of the lowest worldwide. According to reports from IFPRI and MOFA, current yields in Ghana stand at 1.73 metric tonnes/ha and 1.92 metric tonnes/ha respectively (Ragasa et al, 2014; MOFA, 2015; Andam et al, 2017). Also, unfortunately, over 70% of Ghana's maize production is produced by smallholders who do not have access to required resources needed for increasing productivity, making them prone to recording low yields (SARI, 1996). Low yield in Ghana’s maize production results in low output which fails to respond to demand in the maize industry. This, under normal circumstance, is expected to increase maize prices for farmers. However, the reality in Ghana is that maize prices are still low for resource poor farmers who are unable to store their produce and wait for better future prices. Farmers are therefore unable to get maximum returns from resources committed to maize production. With low returns, maize production becomes very unattractive to farmers causing them to only cope to survive which reduces supply, thereby exacerbating the maize demand gap indicated earlier, especially for domestic consumption and the poultry industry. With maize being a food security crop in Ghana, if the supply shortage persists, it will lead to food insecurity in Ghana and make the country failing to meet the Sustainable Development Goals (SDGs) on no poverty and zero hunger. The question then is, apart from price increases, what other ways are available for resource poor farmers to increase their profits and efficiencies so as to make the maize industry an avenue for job creation in Ghana and hence the decision to analyse profits and profit efficiency of Ghanaian maize farmers in the current study? Smallholders, who contribute the majority of agricultural production in Ghana, must be helped to produce beyond subsistence level to higher profitability levels by employing their production resources efficiently. Estimation of profit efficiency provides policy makers with more information than those obtained from other efficiency measures. A profit efficiency function is able to capture specializations at the firm level which allows higher revenues reserved 2

by firms that produce differentiated products to compensate for higher costs (Sadiq and Singh, 2015). In fact, profit efficiency of farms has significant implications for development strategies employed in Ghana which is dominated by the primary sector. Therefore, an understanding of profit efficiency levels and its relationship with farmer and farm characteristics can help farmers realize their potential and take critical steps needed to improve their profit efficiencies. This study reveals such steps and will help develop Ghana’s maize subsector and consequently create jobs to provide incomes to the unemployed. This will help reduce poverty, hunger will be eradicated, and the first two sustainable development goals will be achieved. The current study focused on the potential of improved profit efficiencies of Ghana’s maize farmers in helping Ghana to meet the first two sustainable development goals by employing the stochastic frontier profit function. The rest of the article is organized as follows. The next section presents the methodology employed in the study. This is followed by the results and discussion. Finally, we present the conclusion and recommendation. Materials and Methods Study area The study took place in the Guinea savannah, Transitional, Forest and Coastal Savannah zones of Ghana. Even though maize production is at its peak in the transitional belt of the country, it does well in almost all agro ecological zones of Ghana. Because of this, even though the major producing zones could have also been the focused of this study, a nationwide approach presents more valid and reliable findings that is more representative of the nation, the reason this study used data from the major agro ecological zones of the country. With a land area of about 125,430km2, the Guinea Savannah zone lies in the North eastern corridor of Ghana’s Northern Region. The climate here is the tropical continental type and the vegetation type is that of the Guinea savannah. In the centre of the Brong-Ahafo Region and the North of Ashanti Region is found the Transitional zone. This zone covers an area of about 2300 km2. The wet semiequatorial climate as well as the Savannah woodland and forest vegetation characterize the zone. The forest zone, which comprises the rain and semi-deciduous forests, lies in the Western, Eastern, Ashanti and parts of Brong-Ahafo Regions. The zone covers a land area of about 134,354 km2. It has the semi equatorial type of climate and the vegetation is semi-deciduous forest. The Coastal Savannah zone, with a land area of about 20,000 km2, is located in the Accra Plains, Ho-Keta Plains and parts of Winneba and Cape Coast. Rainfall is the main climatic factor and normally comes in two peaks with the main season falling between March and July and the minor one occurring between September and October. Analytical framework The study employed descriptive statistics in analysing socioeconomic characteristics of the respondent maize farmers. Also, the stochastic frontier translog profit function approach was employed to analyse the profit efficiency of maize farmers in Ghana. This methodology remains the approach employed in estimating profit efficiency of agricultural production as it allows a joint estimation of the parameters in the profit function as well as the inefficiency model. The approach has been employed in studying the profit efficiency of crops such as maize, rice, cowpea, cocoyam, cassava, coffee, Irish potato as well as catfish and milk producers (Kolawole, 3

2006; Hyuha et al, 2007; Ogunniyi, 2008; Ng’ang’a, 2010; Ogunniyi, 2011; Assa et al, 2012; Tsue et al, 2012; Oladeebo & Oluwaranti, 2012; Bidzakin et al, 2014; Ansah et al, 2014; Mulie, 2014; Sadiq and Singh, 2015; Rahman et al, 2016). Application of a profit function in this study is an expansion of the production decisions made by a maize farmer. In accordance with the theory of production, a maize farmer is assumed to choose variable inputs and output bundles that maximize his/her profit subject to a technology constraint (Sadoulet and De Janvry, 1995). The underlying production function for maize can be generalized as ℎ (𝑞, 𝑥, 𝑧) = 0 where 𝑞 is a vector of maize output, 𝑥 is a vector of variable inputs employed in maize production, 𝑧 is a vector of fixed inputs employed in maize production and ℎ is a technology that enhances the productivity and profitability of maize production. Assuming the technology to be homogeneous across farms, restricted profit1 (gross margin)2 function is specified as follows: Maximize 𝑝. 𝑞 − 𝑤𝑥, Subject to ℎ(𝑞, 𝑥, 𝑧) = 0 (1) Where 𝑝 is a vector of prices of outputs and 𝑤 is a vector of prices of variable inputs. Considering a set of inputs and outputs, the profit maximizing input demand and output supply functions are generally respectively expressed as: 𝑋 = 𝑥 (𝑝, 𝑤, 𝑧) (2) 𝑄 = 𝑞 (𝑝, 𝑤, 𝑧) (3) Substituting equations (2) and (3) into (1) gives a profit function which is the maximum profit that the maize farmer can obtain given prices of 𝑝 and 𝑤, availability of fixed factors 𝑧 and production technology ℎ. The profit function can therefore be written as 𝜋 = 𝑝′𝑞( 𝑝, 𝑤, 𝑧) − 𝑤′𝑥(𝑝, 𝑤, 𝑧) (4) The profit estimated in equation (4) is then employed in the Ali and Flinn (1989) normalized profit function which is used in this study and outlined in equation (5) given the fact that the study is dealing with a single output, that is, maize (Sadoulet and De Janvry, 1995). Hence for maize, we have: 𝜋𝑗 = 𝑓(𝑝𝑖𝑗 , 𝑍𝑘𝑗 , 𝐷𝑖𝑗 ). exp(𝑒𝑗 ) (5) 𝑒𝑗 = 𝑣𝑗 − 𝑢𝑗 (6) Where 𝜋𝑗 = normalized profit of 𝑗𝑡ℎ maize farm defined as gross revenue less variable cost, divided by commodity prices from farm 𝑗, 𝑝𝑖𝑗 = prices of the variable inputs on 𝑗𝑡ℎ maize farm, 𝑍𝑘𝑗 = 𝑘𝑡ℎ fixed factors on 𝑗𝑡ℎ maize farm, 𝐷𝑖𝑗 = exogenous variables on 𝑗𝑡ℎ maize farm, 𝑒𝑗 = an error term comprising 𝑣𝑗 (random error term) and 𝑢𝑗 (inefficiency effects) of the maize farm 𝑗 and 𝑗 = 1, … … . 𝑛, is the number of farms in the sample. When 𝑢𝑗 = 0, the firm lies on the profit frontier but if 𝑢𝑗 > 0, the maize farm is profit inefficient. The inefficiency effects (𝑢𝑗 ) in equation (6) which are non-negative random variables are assumed to be identically and independently distributed such that 𝑢𝑗 is defined by the truncation (at zero) of the normal distribution with a mean of 𝑛

𝑢𝑗 = 𝛿0 + ∑ 𝛿𝑑 𝑤𝑑 + 𝜔 𝑑=1 1

Analysis of variance (ANOVA) was used to investigate whether there were differences in profits between the alternative farm size groups (Okurut and Thuto, 2009). 2 The use of restricted profit (gross margin) as an indicator of profit in profit efficiency studies was employed by Kolawole (2006), Ogunniyi, (2008), Ng’ang’a (2010), Ogunniyi (2011), Tsue et al (2012), Assa et al (2012) and Bidzakin et al (2014)

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(7) and variance where 𝑤𝑑 are the variables representing socio-economic characteristics of maize farm 𝑗 to explain inefficiency and 𝛿0 and 𝛿𝑑 are the unknown parameters to be estimated. The profit efficiency of the maize farm in the context of stochastic frontier is given by: 𝜎𝑢2

𝑖

𝜃𝑗 = 𝐸[(𝑒𝑥𝑝(−𝑢𝑗 )|𝑒𝑗 )] = 𝐸 [𝑒𝑥𝑝 (−𝛿0 − ∑ 𝛿𝑑 𝑤𝑑𝑗 ) |𝑒𝑗 ] 𝛿=1

(8) Where 𝜃𝑗 is profit efficiency of maize farmer 𝑗 and lies between 0 and 1 and is inversely related to the level of profit inefficiency. 𝐸 is the probability or expectation operator. This is achieved by obtaining the expressions for the conditional expectation 𝑢𝑗 upon observed value of 𝜃𝑗 . To know whether a Cobb-Douglas or translog functional form is the best for our data, we performed a likelihood ratio test to test the null hypothesis that the Cobb-Douglass functional form adequately fits the data. The generalized likelihood ratio test statistic is given as: 𝜆 = 2(ln𝐿𝐶𝑜𝑏𝑏−𝐷𝑜𝑢𝑔𝑙𝑎𝑠𝑠 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑓𝑜𝑟𝑚 − ln𝐿𝑡𝑟𝑎𝑛𝑠𝑙𝑜𝑔 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑓𝑜𝑟𝑚 ) (9) where 𝜆 is distributed as chi-square with 𝑅 degrees of freedom (𝑅 is the number of independent variables including a constant). The Cobb-Douglass functional form will be rejected in favour of translog functional form if 𝜆 exceeds the appropriate chi-square critical value. The best functional form according to our test results was that of the translog and therefore the translog functional form was employed. The empirical models that were estimated are represented by equations (10) and (11) as follow3s: 6

6

6

6

2

2

1 𝑙𝑛𝜋 ′ = 𝛼0 + ∑ 𝛼𝑖 𝑙𝑛𝑝𝑖 + ∑ ∑ 𝑟𝑖𝑘 𝑙𝑛𝑝𝑖 𝑙𝑛𝑝𝑘 + ∑ ∑ ∅𝑖𝑙 𝑙𝑛𝑝𝑖 𝑙𝑛𝑧𝑙 + ∑ 𝛽𝑙 𝑙𝑛𝑧𝑙 2 𝑖=1 2

2

𝑖=1 𝑘=1

𝑖=1 𝑙=1

𝑙=1

+ ∑ ∑ 𝜑𝑙𝑞 𝑙𝑛𝑧𝑙 𝑙𝑛𝑍𝑞 + 𝑣𝑗 − 𝑢𝑗 𝑙=1 𝑞=1

(10) 16

𝑢𝑗 = 𝛿0 + ∑ 𝛿𝑑 𝑤𝑑 + 𝜔 𝑑=1

(11) Where 𝑟𝑖𝑘 = 𝑟𝑘𝑖 for all 𝑘, 𝑖, 𝑢𝑗 is the inefficiency effect variable, 𝜔 is a truncated random variable, 𝛿0 is a constant term, and 𝛼0 , 𝛼𝑖 , 𝑟𝑖𝑘 , ∅𝑖𝑙 , 𝛽𝑙 , 𝜑𝑙𝑞 , 𝛿0 and 𝛿𝑑 (𝑑 = 1, … . ,16) are parameters to be estimated. It is important to note that the above model for the inefficiency effects (equation 11) can only be estimated if the inefficiency effects are stochastic and have a particular distributional specification. Hence there is interest to test the null hypotheses that the inefficiency effects are not present, 𝐻0 : 𝛾 = 𝛿0 = ⋯ = 𝛿16 = 0; the inefficiency effects are not stochastic, 𝐻0 : 𝛾 = 0; 3

The Cobb-Douglass functional form can be obtained from the authors

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and the coefficients of the variables in the model for the inefficiency effects are zero, 𝐻0 : 𝛿1 =

⋯ = 𝛿16 = 0. These null hypotheses are also tested using the generalized likelihood-ratio statistic, 𝜆, defined in equation (9) (Coelli & Battese, 1996). The description of the rest of the variables are Table 1. Variables employed in the study and their a priori expectations Variable Description and Measurement A priori Source Expectation Normalized restricted profit (gross margin4) 𝜋′ Normalized price5 of variable input 𝑝𝑖 (𝑓𝑜𝑟 𝑖 = 1, … . . , 6) Ansah et al (2014), 𝑝1 (𝐹𝐸𝑇𝑃 ) Normalized price of fertilizer Sadiq and Singh (2015) Normalized price of agrochemical +/Rahman (2003), (𝐴𝐺𝑅 ) 𝑝2 𝑃 Oladeebo & Oluwaranti (2012), Ansah et al, 2014 Ogunniyi (2011), Ansah 𝑝4 (𝑆𝐸𝐷𝑃 ) Normalized price of seed et al (2014), Sadiq and Singh (2015) Ogunniyi (2011), Ansah 𝑝5 (𝐿𝐴𝐵𝑃 ) Normalized price of labour et al (2014), Mulie (2014), Sadiq and Singh (2015) Ogunniyi (2008) 𝑝6 ( 𝑀𝐴𝑃 ) Normalized price of manure + Quantity of fixed input (𝑙 = 1, 2) 𝑍𝑙 Farm size, measured in hectares +/Bidzakin et al (2014), 𝑍1 Ansah et al (2014), Mulie (2014) Sadiq and Singh (2015) 6 Capital employed, measured as depreciation Sadiq and Singh (2015) 𝑍2 on farm tools and implements variables explaining inefficiency effects +/𝑤𝑑 (𝑑 = 1, … . ,18) Have good roads (1 = Yes, 0 = otherwise), + Wongnaa & Awunyo𝑤1 Vitor. (2017) Farm located in northern savannah (1 = + Addai (2011), Wongnaa 𝑤2 northern savannah, 0 = others), & Awunyo-Vitor (2018) Farm located in transitional (1 = transitional, + Addai (2011), Wongnaa 𝑤3 0 = others), & Awunyo-Vitor (2018) Farm located in forest (1 = forest, 0 = others) + Addai (2011), Wongnaa 𝑤4 4

Gross margin is computed as total revenue less total variable costs in Gh¢. Price of fertilizer is the average market price in Gh¢/kg of fertilizer. Price of Agrochemical is the average market price in Gh¢/litre of agrochemicals. Price of seed is the average market price in Gh¢/kg of seed. Price of labour is the average market price in Gh¢/man-day of labour. This includes paid labour, as well as paid equivalence of family and help group labour. Price of manure is the average market price in Gh¢/kg of manure. 6 The straight line depreciation method is employed. 5

6

& Awunyo-Vitor (2018) Farm located in coastal savannah (1 = + Addai (2011), Wongnaa 𝑤5 coastal savannah, 0 = others & Awunyo-Vitor (2018) Note: All normalized variables are normalized by dividing by the price of maize, 𝑝𝑦 7 Table 1 (continued) Variable Description and Measurement A priori Source Expectation Gender (1 = male, 0 = female), + Wongnaa & Awunyo𝑤6 Vitor. (2017) AGE = Age, measured in years +/Assa et al (2012), Sadiq 𝑤7 and Singh (2015) Education (number of years of schooling) + Hyuha et al (2007), 𝑤8 Ogunniyi (2011), Assa et al (2012), Mulie (2014), Sadiq and Singh (2015) Size of household (number of people under +/Oladeebo & Oluwaranti 𝑤9 farmer’s care) (2012), Sadiq and Singh (2015) Experience in producing maize (years of + Rahman, S. (2003), 𝑤10 producing maize) Hyuha et al (2007), Ogunniyi (2011), Assa et al (2012), Sadiq and Singh (2015) Farm size, measured in hectares +/Ogunniyi (2008), Ansah 𝑤11 et al (2014) Land fragmentation (1 = owns at least two +/Wongnaa & Awunyo𝑤12 plots, 0 = otherwise) Vitor. (2017), Wongnaa & Awunyo-Vitor (2018). Contact with extension officers (number of + Hyuha et al (2007), 𝑤13 visits received) Ogunniyi (2011), Assa et al (2012), Mulie (2014), Sadiq and Singh (2015) Belonging to a maize farmer group (1 = + Wongnaa & Awunyo𝑤14 membership of a group, 0 = otherwise) Vitor. (2017) Access to credit (1 = Yes, 0 = otherwise) + Oladeebo & Oluwaranti 𝑤15 (2012), Hyuha et al (2007), Assa et al (2012), Mulie (2014) Access to ready maize market (1 = available + Wongnaa & Awunyo𝑤16 maize market, 0 = otherwise) Vitor. (2017) Seed variety used (1 = improved, 0 = + Wongnaa & Awunyo𝑤17 7

Price of maize is the average prevailing market price in Gh¢/kg of maize.

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traditional)

Vitor. (2017), Wongnaa & Awunyo-Vitor (2018). Note: All normalized variables are normalized by dividing by the price of maize, 𝑝𝑦 presented in Table 1. Equations (10) and (11) representing the profit function and the inefficiency model of the stochastic frontier profit function are estimated simultaneously in a one-step estimation procedure using the method of maximum likelihood (Kumbhakar et al., 1991; Battese and Coelli, 1995). This is because the two-step procedure has long been recognized as biased as the model estimated in the first step is not well specified (Battese and Coelli,1995; Wang and Schmidt, 2002). Data Collection The study used data collected for a PhD dissertation on Economic Efficiency and Productivity of Maize Farmers in Ghana in the Department of Agricultural Economics, Agribusiness and Extension of Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. The data was primary data and was collected through a cross-sectional survey of 576 maize farmers for the 2014 rainy season in the major agro ecological zones. A multi-stage sampling procedure was employed for the data collection. With reference to the level of maize production in each district, two districts/municipalities were purposively sampled from each agro ecological zone. This was the first stage and resulted in the selection of East Gonja and West Mamprusi districts out of 54 districts from the Northern Savannah zone, Nkoranza and Ejura Sekyedumase districts out of 28 districts from the Transitional zone, Fanteakwa and Sekyere South districts out of 75 districts from the Forest zone as well as Gomoa and Ketu districts out of 59 districts from the Coastal Savannah zone. The second stage was the random selection of nine (9) communities from each sampled district/municipality. This was out of 34 communities in the East Gonja district, 23 communities in the West Mamprusi district, 21 communities in the Nkoranza district, 16 communities in the Ejura Sekyedumase district, 33 communities in the Fanteakwa district, 21 communities in the Sekyere South district, 22 communities in the Gomoa district and 20 communities in the Ketu district (GSS, 2014). Finally, a random sampling of eight (8) maize farmers from a list of maize farmers obtained from agricultural extension officers operating in each community constituted the third stage of the data collection exercise. Table 2 presents the population of maize farmers in each community sampled in the various districts for the study. The disadvantage with the current design is that each farmer in the population does not have an equal probability of inclusion as it was not a self-weighting one. As a result, the unweighted sample mean is a biased and inconsistent estimator of the population mean. When selection probabilities differ across farmers, each farmer in the survey stands represents a different number of farmers in the population. Consequently, to control for selection bias, before the sample data is used to calculate estimates for the population, it is weighted to ensure that each group of farmers is properly represented, making the sample data representative of the population. Sample means will therefore be unbiased estimates of the population means (Deaton, 1997). To obtain the sampling weights, the probability that each farmer was selected is first computed. This probability is given by the product of the probability that the 8

district/municipality of a farmer in his/her agro ecological zone was selected, the probability that the community of the farmer in his district/municipality was selected and the probability that the farmer was selected in his/her community. Weight is the opposite of probability and therefore is given by the reciprocals of sampling probabilities. That is, the rule here is to weight according to the reciprocals of sampling probabilities because households with low (high) probabilities of selection stand proxy for large(small) numbers of households in the population. After computing the sampling probabilities, the probability that a farmer was selected depends on his/her community of residence is tested. The weight of each farmer is not taken into account if the aforementioned test is rejected. In this study, two variables, viz. description of the community in which farmer lives-rural or urban, as well as number of maize farmers living in a farmer’s community of residence (proxies for characteristics of community in which farmer lives), were used as explanatory or independent Table 2. Villages or communities selected in the various districts selected for the study East Gonja Makango (145)

West Mamprusi Walewale (44)

Nkoranza

Ejura Sekyedumase Sekyeredumase (1120)

Fanteakwa

Salaga (159)

Zoorini (39)

Donkro Nkwanta (1511) Banofour (729)

Ejura (1968)

Masaka (161)

Gbani (148)

Bonsu (115)

Ajamasu (540)

Akyem Hemang (120) Juaso (158)

Yankanjia (54) Kalande (74) Kpembe (152) Kayitypee village (41) Akyenteteyi (189) Yayayili (63)

Porigu (154) Samani (55) Publini (135) Naasoro (67)

Abountem (817) Babiani (194) Dotobiri (881) Seseman (876)

Juaho (1214) Tarkoso (657) Durobo (587) Asuogya (814)

Begoro (456) Abompe (89) Ayeikrom (321) Dwenase (412)

Agona (412) Bipoah (120) Afamaso (308) Hiamakyene (421)

Gomoa Amanfi (402) Bewadze (871) Simbrofo (645) Apam (145) Takyiman (521)

Tinkaya (711) Yakurani (64)

Koforidua (172) Nkoranza Zongo (469)

Mbanaa (1229) Bisu (1551)

Saamang (214) Nsutam (302)

Morso (225) Wiamoase (651)

Dago (108) Oguan (219)

Osino (412)

Sekyere South Akrofonso (614)

Gomoa

Ketu

Ankamu (115)

Akame (125)

Jamasi (891)

Nkran (421)

Hatsukope (226)

Abrakaso (546)

Agavedzi (89)

Torkor (231) Viepe (435)

Denu (206) Blekusu (314) Agbozume (218) Klikor (814)

Note: Population of maize farmers in each community are shown in parenthesis Source: Survey, 2015; GSS, 2014 variables whereas the probability that a farmer was selected was the dependent variable in a linear regression. The hypothesis of dependence of selection of farmer on community of residence was rejected as the estimated coefficients were not significant. As a result, the weight of each farmer was not taken into account. The test results are presented in Table 6. A structured questionnaire was the main data collection instrument employed in this research. Its design consisted of both close ended and open ended questions. The questions included questions on socioeconomic characteristics of Ghana’s maize farmers, farm factors 9

likely to influence production efficiency, inputs employed in maize production and their quantities and prices, total output in maize farms, etc. Results and discussion Descriptive analysis The distributions of characteristics as well as prices of variable inputs and output of the respondents are presented in Tables 3 and 4. The findings reveal that 77.3% of respondents were males. The implication is that males dominate in Ghana’s maize production. This is consistent with Ansah et al (2014). The average age of respondents was 45 years (Table 4). This depicts an aging maize farmer population for Ghana and this could have a negative effect on the profit

10

Table 3. Distribution of Socio economic characteristics of respondent maize farmers Variable

Frequency

%

71 505 576

12.3 87.7 100

445 131 576

77.3 22.7 100

326 181 69

56.6 31.4 12.0

Total Farmers’ level of Education Illiterate

576

100

203

35.2

Primary Junior High School

85 201

14.8 34.8

Senior High School

70

12.2

Tertiary (University, Polytechnic, Training College)

17

3.00

Total Access to extension No Yes Total Access to good roads Bad(Rough/Marshy/Tarred with potholes) Good(Rough and smooth /Tarred but not asphalt/Asphalt) Total Type of maize variety Traditional Improved Total Land fragmentation Owns at least two plots Owns one plot Total Membership of a group No Yes Total Access to credit No Yes Total Cropping system Monocroping Mixed Cropping

576

100

330 246 576

57.3 42.7 100

291 285 576

50.5 49.5 100

345 231 576

59.9 40.1 100

222 354 576

38.5 61.5 100

435 141 576

75.5 24.5 100

474 102 576

82.3 17.7 100

86 490

14.9 85.1

Ready market No Yes Total Sex of maize farmer Male Female Total Farmers’ age group (Years) 18-45 46-60 Greater than 60

Source: Survey, 2015

11

Table 4. Descriptive statistics of characteristics of respondents Variable Min Max M Age (Years) 18 77.5 44.83 Educational level (Years of schooling) 0 17 6.13 Maize farming experience (Years) 1 49.5 13.89 Land area cultivated (ha) 0.22 69.7 3.04 Farm plots owned by farmer 1 5 1.34 Number of extension visits received 0 25 3.04 Amount of credit received (Gh¢/ha) 0 4500 162 Output of maize (Kg) 10 40000 2753 Gross margin 50 5000 510 2013 price of maize (Gh¢/Kg) 0.4 1.7 0.97 Capital 5.6 42.33 12.92 Size of household 2 34 7.611 Price of manure (Gh¢/Kg) 0 1 0.012 Price of fertilizer (Gh¢/Kg) 0.4 3.1 1.069 Price of agrochemicals (Gh¢/Litre) 4.75 31.5 9.78 Price of maize seed (Gh¢/Kg) 1 15 2.823 Price of labour (Gh¢/Man-day) 4.5 20 11.97 Source: Survey, 2015

SD 12.45 5.457 11.21 10.24 2.141 5.125 466 3435 241 0.24 5.724 4.719 0.066 0.862 36.45 1.496 8.175

efficiency of the farmers. This agrees with Oladeebo & Oluwaranti (2012) as well as Ansah et al (2014). Over 60% of the farmers received formal education and generally, they had six years of schooling (Tables 3 and 4), indicating that most of them ended in the primary school. This corroborates similar results reported by Hyuha et al (2007), Oladeebo & Oluwaranti (2012) and Ansah et al (2014). On average, a farmer had about seven (7) people in his/her household (Table 4). This is quite high and is good for the farmers since it will make available labour for carrying out husbandry activities on time (Oladeebo & Oluwaranti, 2012). With a mean of 14 years of maize farming (Table 4), there is no doubt that the farmers are experienced in maize production, especially with similar results being reported by Oladeebo & Oluwaranti (2012). It can also be found in Table 3 that 57.3% of the respondents had no access to extension service. For those that had access to extension service (Table 3), generally, extension officers visited them only thrice, implying poor provision of extension service to them (Table 4). This confirms the findings of Hyuha et al (2007). Most farmers (82.3%) never received credit for maize production (Table 3). For the few of them (17.7%) that received credit, the average amount received was just Gh¢162.008, given the relatively high capital requirement for maize production (Table 4). The little or no credit received by the farmers could halt or delay application of required production inputs to crops. This is because farmers will have little or no money to purchase the inputs, thereby making them profit inefficient (Hyuha et al, 2007). The findings are also that, generally, Ghana’s maize farming activities are on a small scale which ranges from 0.22ha to 69.7ha with an average of 3.04ha (Table 4). This is contrary to Bidzakin et al (2014) which reported a mean farm size of 8.8ha for maize farmers in northern Ghana. The difference could be 8

1US$ = Gh¢5.56

12

due to the existence of the Savannah Accelerated Development Authority (SADA) which

13

supported farmers in northern Ghana with production inputs for which reason they could expand their farms. Almost 50% of the farmers interviewed (49.5%) remarked their roads were good as against 50.5% of them that gave a bad impression of their roads (Table 3). The use of traditional maize seeds was common among the respondent farmers (59.9% of respondents) and this could pose a negative effect on their profit efficiencies (Table 3). Profitability analysis of maize production Table 5 presents the yield, costs, gross margin and returns on investment per hectare of a maize farm in Ghana. Generally, gross margin (restricted profit) was used as an indicator of profit. The average yield of maize grain produced was 1800kg/ha. In general, an average total revenue of Gh¢1746.00/ha of maize was calculated for the farmers using an average output price of Gh¢0.97/kg of maize grain. The average total variable cost incurred was Table 5. Costs and Returns of maize production in Ghana Quantity of Unit price of input/output input/output (Gh¢) Gh¢) Total Revenue Maize yield (kg/ha) 1800 0.97 Variable Cost Fertilizer (kg/ha) 140 1.1 Herbicide (Litres/ha) 4.9 9.5 Pesticicide (Litres/ha) 0.2 10.1 Seed (kg/ha) 18 2.8 Labour (man-days/ha) 64 12 manure (kg/ha) 24 0.01 Transportation cost Processing cost Total Variable cost Gross margin Return on investment(%) Source: Survey, 2015

Total cost/Revenue Gh¢)

1746 154 46.6 2.02 50.4 768 0.24 145 70 1236 510 41.3

Gh¢1236.00/ha. The results showed that over 60% of the total variable costs consisted of labour costs. This is a clear indication of the importance of labour input in maize production. This corroborates Rahman et al (2016) in which labour cost was found to have accounted for 40.3% of total variable cost in Bangladeshi maize production. The mean gross margin calculated was Gh¢510.00/ha. This is in line with Bidzakin et al (2014) that also reported a gross margin of Gh¢948.00/ha for maize farmers in northern Ghana. The gross margin result is supported by the results of the return on investment. On average, the return on investment was 41.3%. This means that for every one Ghana Cedi (Gh¢1.00) invested in maize production in Ghana, Gh¢0.413 was gained. The results show that generally, maize production is profitable in Ghana. Similar profitable levels were reported in similar recent studies (Ogunniyi, 2011; Sadiq and Singh, 2015; Rahman et al, 2016). Maize production can therefore provide income to maize farmers on a 14

sustainable basis if they are to remain in it. This can help reduce poverty and consequently hunger among farming communities in Ghana. The maize industry therefore has great potentials to help Ghana achieve the sustainable development goals on no poverty and zero hunger. Determinants of profit in Ghana’s maize farms Table 6 presents the results of the likelihood ratio tests of relevant hypotheses that were tested in the study. The results rejected the Cobb-Douglas functional form in favour of the more flexible translog functional form. The estimates of the translog functional form were therefore used to explain the determinants of profit as well as its efficiency. The hypotheses of whether or not the inefficiency effects are not present, whether or not the inefficiency effects are not stochastic as well as whether or not all the coefficients of the variables in the model for the inefficiency effects are zero are strongly rejected by the data employed in this study, making the traditional average response function not an adequate representation for the agricultural production in the study area in favour of the stochastic frontier and inefficiency models defined by equations (10) and (11). Table 7 also presents the variance parameters as well as tests of multicollinearity and heteroscedasticity for the estimated profit function. The gamma (𝛾) value (0.76) is high and implies that there are inefficiencies in the profits obtained by maize farmers and therefore ordinary least squares should be rejected in favour of the stochastic frontier function (Piesse and Thirtle, 2000). Table 6. Hypothesis tests results for choice of cobb-douglas versus translog functional forms and coefficients of the explanatory variables for the technical inefficiency effects Null Hypothesis L(H0) Decision 𝜆 𝜒2 𝐻0 : 𝛽𝑖𝑗 = 0 -120.11 34.32 10.21 Rejected -75.23 18.42 8.54 Rejected 𝛿𝑚 = 0 -154.14 71.54 11.52 Rejected 𝐻0 : 𝛾 = 𝛿0 = ⋯ = 𝛿16 = 0 -109.81 64.31 7.41 Rejected 𝐻0 : 𝛾 = 0 -138.42 54.23 9.58 Rejected 𝐻0 : 𝛿1 = ⋯ = 𝛿16 = 0 Chi-square critical values (𝜒2) are significant at 5%, L(H0) = Log likelihood function, 𝜆 = Test statistic, 𝛽𝑖𝑗 = Parameters in the square and cross terms in the translog functional form and 𝛿𝑚 = Inefficiency model parameters. Source: Survey, 2015 That is, about 76% of total variance of composed error of the profit function are explained by the variance of the variables explaining profit inefficiency, as such variables can be controlled by the farmers. That is, only 23% of profit variation results from random shocks (measurement errors, adverse weather conditions, disease infection, pest infestation, etc). This therefore represents the importance of incorporating profit inefficiency in the profit function. Lambda ( 𝜆), which is statistically significant at 5% is the ratio of the U and V error terms and is far greater than one (1) for the profit function, affirming the presence of great profit inefficiencies among respondent maize farmers. Lambda ( 𝜆) and sigma squared (𝜎 2 ) are significant at 5%, implying a good fit and correctness of the specified distributional assumption. The aforementioned results reveal the existence of inefficiencies among maize farmers in Ghana and hence the appropriateness of the application of the stochastic frontier profit function in modeling profit efficiencies of the farmers. 15

The Wald chi-square statistic (241.21) presented in Table 7 is significant at 1%, indicating joint Table 7. Variance parameters and other relevant diagnostic tests of model fit Variable Coefficient Standard Errors Sigma squared 13.45** 0.024 Gamma 0.76*** 0.019 Lambda 121.3** 0.061 Log likelihood -320.8 Likelihood ratio stat 4.78*** Number of farmers 557 Wald 241.21*** Mean VIF (multicollinearity) 1.345 Breusch Pagan (heteroscedasticity) 0.7416 Selection of farmer depends on community description (rural or urban) 0.0113 0.013 Selection of farmer depends on community farmer population 0.00042 0.0005 Note: The asterisks indicate levels of significance. *** is significant at 1%, ** is significant at 5%

Source: Survey, 2015 significance of the model. The mean Variance Inflation Factor (VIF) (1.345) is small, revealing that the model has no problem with multicollinearity (Edriss, 2003). Finally, the Breusch Pagan (BP) statistic (0.7416) is not significant, which reveals the absence of heteroscedasticity. The maximum likelihood estimates of the parameters of the stochastic frontier translog profit model are presented in Table 8. The dependent variable was gross margin (restricted profit) normalized by the price per kilogramme of maize output and the main independent variables included prices of all the variable inputs and the quantities of the fixed inputs (each normalized by the price per kilogramme of maize output) employed in maize production, notably land and capital. The signs of most of the variables in the model are theoretically expected. Table 9 also presents the profit elasticities of the normalized prices of variable inputs and quantities of the fixed inputs used in the production of maize in the study area. The coefficient of the variable representing price of fertilizer is negatively related to the profitability of maize and significant at 1%. The results in Table 9 show that a rise in the price of fertilizer by 1% will decrease the profitability of maize production by 0.245%. This finding corroborates the findings of Ansah et al (2014) and Sadiq and Singh (2015). It however disagrees with Assa et al (2012). The influence of price of agrochemicals on the profitability of maize production is also negative and significant at 5%. It could be inferred from Table 9 that a 1%increase in the price of agrochemicals will cause a decline in the profit from maize farming by 0.345%. Similarly, price of seed, with negative coefficient, is significant at 5%. A 1% rise in the price of seed will decrease profits by 0.045%. This is in line with Sadiq and Singh (2015) even though it is inconsistent with Assa et al (2012). Price of labour also had the expected negative sign and was significant at 1%. The magnitude of the elasticity shows that an increase in wage rate by 1% will lead to a decline in the profitability of maize farming by 6.281%. The results further showed that wage rate was the most important variable determining profit in maize farming in the Ghana. This is not surprising, given the labour intensive nature of maize production. This agrees with Ansah et al (2014) and Sadiq and Singh (2015). The coefficient of farm size is negative and significant at 5%. The results in Table 9 further show that a unit percent rise in farm size by maize farmers in Ghana will decrease maize 16

farm profits by 2.452%. This is due to underutilization of farm capacity. That is, small scale farmers

17

Table 8. Maximum likelihood estimates of stochastic frontier profit function Variable Coefficient Standard Error 4.456 0.218 Constant lnFETp -0.145*** 0.287 lnAGR -0.214** 0.344 lnSEDp -0.345** 0.245 lnLABp -0.513*** 0.011 lnMAp -0.015 0.034 lnLAD -0.546** 0.213 lnCAP 0.345*** 0.151 1 0.158 0.042 lnFETpxlnFETp 2 1 lnAGRpxlnAGRp 2 1 lnSEDpxlnSEDp 2 1 lnLABpxlnLABp 2 1 lnMApxlnMAp 2 1 lnLADxlnLAD 2 1 lnCAPxlnCAP 2

-0.348***

0.013

-0.582*

0.022

0.055***

0.048

0.789**

0.081

0.245***

0.078

0.002

0.041

lnFETpxlnAGRp 0.254** 0.033 lnFETpxlnLABp 0.054 0.121 lnFETpxlnSEDp -0.614** 0.154 lnFETpxlnMAp -0.224 0.041 lnFETpxlnLAD -0.233*** 0.187 lnFETpxlnCAP -0.081 0.028 lnAGRpxlnLABp -0.345** 0.045 lnAGRpxlnSEDp 0.014* 0.058 lnAGRpxlnMAp -0.475 0.061 lnAGRpxlnLAD 0.002** 0.027 lnAGRpxlnCAP 0.054 0.058 lnLABpxlnSEDp 0.041 0.124 lnLABpxlnMAp -0.034 0.069 lnLABpxlnLAD 0.078** 0.024 lnLABpxlnCAP 1.342 0.098 lnSEDpxlnMAp -0.456* 0.042 lnSEDpxlnLAD -0.159 0.035 LnSEDpxlnCAP -0.714* 0.045 lnMApxlnLADq 0.038 0.031 lnLADxlnCAP 0.248*** 0.189 Note: The asterisks indicate levels of significance. *** is significant at 1%, ** is significant at 5% and * is significant at 10%. Source: Survey, 2015

18

Table 9. Profit Elasticities Variable Elasticity Fertilizer price -0.245 Agrochemicals -0.345 Seed price -0.045 Wage -6.281 Manure price -1.456 Farm size -2.452 Capital 0.131 Source: Survey, 2015 Table 10. Actual and Expected profits of farmers cultivating different farm sizes Farm size Group (ha) Description Actual Profit (Gh¢) Expected Profit (Gh¢) 0.22 – 1.00 Small 482 1145 1.01 – 2.00 Medium 511 1684 >2.00 Large 619 2124 F-Statistic 1.458 2.489 Prob (F-Statistic) 0.001 0.000 Source: Survey, 2015 do not have the ability to meet the demands of owning large maize farms. The resources available to them can only cater for small farms so if they mistakenly start large farms, such farms will be poorly managed resulting in profit losses. This is supported by the results in Table 10 which reveals a significant difference between the profits received by farmers cultivating different farm sizes (small, medium and large) and that profit generated by cultivating small sizes would be high enough if there were to be fully efficiency compared to large farm size farmers. This finding agrees with Ansah et al (2014) and disagrees with Sadiq and Singh (2015). Finally, the coefficient of capital employed in the study area had the expected positive sign. The variable is significant at 1% and the profit elasticity in Table 9 shows that an increase in capital by 1% will increase the profitability of Ghana’s maize production by 0.131%. The results corroborate those of Kolawole (2006), Assa et al (2012) and Sadiq and Singh (2015), even though it was inconsistent with Bidzakin et al (2014). Profit efficiency of maize farmers in Ghana Table 11 presents the descriptive statistics of profit efficiency scores for the farmers. The average profit efficiency for the pooled sample was 45.9%, with a standard deviation of 25.2% and 0.024% and 81.3% as the minimum and maximum respectively, implying that the farmers produce far below the profit frontier with as high as 51.6% of potential maximum profit lost to inefficiency. The results imply that farmers would be able to increase profits from their maize farms by about 54.1% by using their disposable resources more efficiently (at the present state of technology). Also, the results revealed that farmers in the transitional zone are more profit efficient in their use of their production resources than those in other agro ecological zones. This is because, the deep, friable soils and the relatively dispersed tree cover in the transitional zone allows for more continuous cultivation and greater use of mechanized equipment. In fact, 19

considering a trend that has been observed all over West Africa, the transitional zone has become progressively more important for maize production (Smith et al, 1994). This can be the result of a combination of factors, including the presence of favourable agro-ecological conditions, availability of improved productivity enhancing technologies, a relative abundance of underutilized land, and a well-developed road transport system (Morris et al, 1999). This is followed by maize farmers in the coastal savannah zone, forest zone and the northern savannah zone respectively. The observed profit efficiency scores across the various agro ecological zones indicate that maize farmers in Ghana still produce below optimal profit levels and therefore can improve their profits with efficient use of resources and technologies available to them. This finding is consistent with earlier studies in Ghana and other developing African countries (Ogunniyi, 2011; Ansah et al, 2014; Bidzakin et al, 2014; Sadiq and Singh, 2015). In fact, the average profit efficiency for the nation is quite low and presents threats to Ghana succeeding in meeting the sustainable development goals on no poverty and zero hunger. Table 11. Profit Efficiency Scores of Maize Farmers in Ghana Pooled/Zone Minimum Maximum Mean (%) (%) (%) Pooled 0.024 81.3 45.9 Northern Savannah 2.48 89.4 34.3 Transitional 0.028 86.7 78.9 Forest 0.074 87.4 41.3 Coastal Savannah 0.037 85.2 53.8 Source: Survey, 2015

Standard Deviation (%) 25.2 17.8 22.4 28.4 27.1

Figure 1: Distribution of predicted profit efficiencies in agro ecological zones

Percentage of maize farmers

70 60 50 Pooled sample 40

Northern savannah

30

Transitional

20

Forest Coastal savannah

10 0 0-20

21-40

41-60

61-80

81-100

Profit efficiency

Source: Survey, 2015

20

The distribution of profit efficiencies among the maize farmers is presented in Figure 1. The figure shows that 65% of maize farmers in the pooled sample have profit efficiencies in the range of 41% to 60%. The average profit efficiency of maize farmers in this sample lies in this range. The figure also shows that whereas the average profit efficiency of farmers in the northern savannah zone fell within the range of 21% to 40% that of farmers in the transitional zone lied in the range of 61% to 80%. Finally, the mean profit efficiencies of maize farmers in the forest and coastal savannah zones were in the ranges of 41% to 60% and 41% to 60% respectively. The results depict a wide range of profit efficiencies among maize farmers in Ghana. Factors influencing profit efficiency of maize farmers in Ghana Many farmer and farm factors or characteristics were hypothesized to influence profit efficiency of maize producers in Ghana. With the inefficiency variable being the dependent variable, the coefficients are interpreted as the effect of each variable on profit inefficiency even though it is also possible to interpret the coefficients directly as influencing profit efficiency by considering their reverse signs (Assa et al, 2012). Table 12 presents the coefficients of the variables that were hypothesized to affect farm-specific profit efficiencies of maize producers in Ghana. The results of northern savannah and forest zones dummies were positive and significant at 1% and 5% respectively. This implies that maize producers in northern savannah and forest zones are less profit efficient compared to those in the coastal savannah zone. The dummy for living in the transitional zone was however negative and statistically significant at 1%, indicating relatively higher profit efficiency levels for owning maize farms in the transitional vis-à-vis the coastal Table 12. Sources of profit efficiency of maize farmers Variable Parameter Constant Have good roads 𝑤1 Farm located in northern savannah 𝑤2 zone Farm located in transitional zone 𝑤3 Farm located in forest zone 𝑤4 Gender 𝑤6 Age of farmer 𝑤7 Years of schooling 𝑤8 Size of household 𝑤9 Experience in producing maize 𝑤10 Farm size, measured in hectares 𝑤11 Land fragmentation 𝑤12 Contact with extension officers 𝑤13 Belonging to a maize farmer group 𝑤14 Access to credit 𝑤15 Access to ready maize market 𝑤16 Seed variety used 𝑤17 21

Coefficient 1.258 -0.345*** 2.345***

Standard Error 0.245 0.048 0.548

-0.358*** 2.581** -0.258* 0.014 -0.045*** -0.058 -0.089 0.021 -0.331 -0.122*** -0.397 -0.312*** -0.451 -0.4.18***

0.041 0.891 0.211 0.289 0.514 0.032 0.074 0.093 0.044 0.057 0.415 0.841 0.025 0.348

Note: The asterisks indicate levels of significance. *** is significant at 1%, ** is significant at 5% and * is significant at 10%. Source: Survey, 2015 savannah zone. Male-gender is negatively related to profit inefficiency and is significant at 10%. This means that male maize farmers are more efficient in their profit levels than female maize farmers. The dominance of males in maize farming as shown in Table 3 explains the experience males have in growing maize which enhances their profit levels. Also, men normally participate in training programmes organized by agricultural extension officers (Betty, 2005 in Sienso et al, 2013). Added to this is the fact that women do not normally own farm lands, and even if they do, such lands are normally small and located in unfertile areas of their communities that are unproductive (Sienso et al, 2013). Rural women are normally always unable to purchase required maize production inputs (fertilizers, seeds, herbicides, etc) because men have access to credit than them. This makes adoption of new farming methods by women farmers difficult. Notwithstanding the effort of women in food production in Africa, especially handling close to 80% of the work, they receive only about 5% of the resources that are normally given to farmers through extension officers (FAO, 2002 in Sienso et al, 2013). In addition, with the crucial roles played by women at home (cooking, cleaning, child care, etc.), it is not surprising that they will not be as profit efficient as their men counterparts. Moreover, sometimes women are unable to access agricultural information and technologies because of restrictions placed on them by culture (social norms, religion, traditions, etc.) (Shamsudeen et al, 2013). The influence of road infrastructure on profit efficiency is positive and significant at 1%. This means that maize farmers with farms close to good roads have higher profit efficiencies visà-vis those whose farms are located in areas with dilapidated roads. The reason is, good roads in producing areas enhance the inflows and outflows of production inputs and outputs respectively which increases maize production and prevents postharvest losses. Reduction of postharvest losses adds to farmers’ marketable produce and revenues which eventually increases their profits and hence their profit efficiencies. This result is in line with that of Rahman (2003) that identified poor rural infrastructure as one of the main obstacles to the development of agriculture in Bangladesh. According to the study, access to especially input markets enhances timely availability and application of production inputs at competitive prices, and as a result, increases farm profits. Formal education, measured in years of schooling, had a positive influence on profit efficiency and is significant at 1%. This means that educated farmers have higher profit efficiencies than illiterate ones. That is, giving farmers education will help reduce their profit inefficiencies. This is because, education gives them knowledge of the kind of productivity enhancing inputs and technologies to apply in their farms as well as the required quantities to be applied of each. The result is in line with previous similar studies in Ghana and other countries (Ogunniyi, 2011; Assa et al, 2012; Bidzakin et al, 2014; Sadiq and Singh, 2015) even though it disagrees with the findings of Ansah et al (2014) in Ghana. The coefficient of extension is negative and significant at 1% in the model. The implication is that producers with access to regular extension service are more profit efficient than those with few contacts with agricultural extension agents. This is because improved inputs developed by agricultural scientists get to farmers via agricultural extension agents. That is, extension helps farmers to become aware of proven modern production technologies that can help improve yield and consequently profit efficiency. This result corroborates the findings of 22

previous similar studies conducted by Rahman (2003), Kolawole (2006), Ogunniyi (2011), Assa et al (2012), Mulie (2014) as well as Sadiq and Singh (2015). Farm credit is also positively related to profit efficiency and significant at 1%. This indicates the occurrence of higher profit efficiencies for recipients of credit vis-à-vis nonrecipients. That is, with access to credit, maize farmers can expand and improve their farms (Tijani et al, 2006). Credit also allows farmers to easily adopt innovations or technologies that have proven to be beneficial to their farms. This finding also agrees with Hyuha et al (2007) and Assa et al (2012) that also reported positive effects of credit on profit efficiency. Finally, the variable representing use of improved seed was found to be significant and positively related to profit efficiency of maize farmers. The results in Table 12 show that improved seed use was significant at 1%. The positive effect of improved seed use could be the high yielding and early maturing nature of most recently developed seeds in the country’s research institutes as well as similar ones imported into the country. This agrees with the findings of Sserunkuuma et al (2001) that one of the main reasons for the low productivity of maize is the extensive use of unimproved maize seeds as it is common to see most farmers using seeds from their previous production seasons. Conclusions and Recommendations The study analysed the profit efficiency of Ghanaian maize farmers and made recommendation for its improvement as well as its implication for poverty alleviation and food security in Ghana. Maize production was found to be profitable and therefore could be a source of job creation in Ghana. With income from being employed in the maize industry, maize production will be helping to reduce poverty and hunger in Ghana. Generally, maize farm profit increased with increased prices of farm inputs and this could be due to the poor state of the roads leading to the producing centres, making it difficult for production inputs to be transported to where they are really needed. This is because, over 50% of the respondents stated the roads in their producing areas were in a poor state. Also, an increase in farm size by Ghanaian maize farmers decreased their profit levels. This is because small scale farmers do not have the ability to meet the demands of owning large maize farms. Generally, maize farmers in Ghana are seriously inefficient in their profit levels and the lost in profit is due to inefficiencies in their production activities. Profits can therefore be increased by employing their resources more efficiently. The results further show that maize farmers in the transitional zone of Ghana are more efficient in their profit levels than those in other agro ecological zones, suggesting the transitional zone as promising for commercial maize production. Male maize farmers were found to be more profit efficient than female ones. Profit efficiency was also increased by access to good road infrastructure. Formal education played a key role in improving the profit efficiency of maize farmers in the study area. Access to extension service by the respondent farmers also led to an increase in their profit efficiencies. Furthermore, farmers with access to farm credit performed better with regards to their profit efficiencies than those with no access. Finally, uses of fertilizer, pesticide and improved seed helped improve their profit efficiencies. Improvement in the quality of road infrastructure in Ghana’s maize producing areas could help lower the prices of production inputs so that their costs will not contribute so much to reducing farm profits and efficiency. Stakeholders (Government and Non-Governmental Organizations) in the maize industry are encouraged to help in putting subsidies on the prices of 23

production inputs to make them accessible and cost effective in employing them. They are also advised to help provide extension agents with appropriate incentives that will make them committed to their duties. Maize farmers are also urged to choose their farm sizes based on the production resources available to them. Seminars and workshops aimed at disseminating most productive cultural practices as well as productivity enhancing inputs and technologies could also be organized by agricultural extension officers for maize farmers and this will improve the knowledge of farmers in current trends in maize production. Moreover, government could liaise with financial institutions to come out with efficient measures that will make loan acquisition by maize farmers very easy. Implementation of the above recommendations will improve profit efficiency of Ghanaian maize farmers and make the maize industry an avenue for job creation and a recipe for helping Ghana to meet the sustainable development goals on no poverty and zero hunger. Acknowledgements We are especially indebted to the staff of the Ministry of Food and Agriculture in the West Mamprusi, East Gonja, Nkoranza, Ejura Sekyedumase, Fanteakwa, Sekyere South, Gomoa and Ketu districts/municipalities of Ghana for the information they provided about the maize crop and also assisting in the data collection. We are also grateful to the respondent maize farmers in the aforementioned districts/municipalities without whose co-operation the study could not have taken place. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of Interest The authors declare that they have no conflict of interest

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