Agricultural Risk Management and Production Efficiency among Peasant Farmers in Taraba State,North Eastern Nigeria

Abstract

This study was on agricultural risk management and production efficiency among peasant farmers in Wukari Local Government of Taraba State with specific objectives to; determine the socio-economic characteristics of the peasant farmers in the area; identify the risk faced by farmers in the area; analyse the risk management strategies and determine their management efficiency in combating the production risk. The Data for the study were collected using structured questionnaire. Descriptive and inferential statistics were used to analyse the data collected. The study showed that 88.8% faced risk from time to time while 5.6% of the respondents often face risk and another 5.6% never experienced risk at all. Diversification (56%) has been the major approach of risk management by respondents in the study area while integration (32%) and forward contracting (11.2%) methods were less employed. The coefficients of age (0.1044) was significant 1%, sex (0.7008) was significant at 5%, Educational level (0.3478) was significant at 1%, access to extension (2.6627) was significant at 1% and agribusiness risk (3.215) was significant at 1%. They were all positive indicating that they have positive relationship with the productivity of farmers. The allocative cost efficiency of risk management among the farmers stands at maximum: 20.57, minimum: 16.28 and mean: 18.55 indicating that the efficient level of production is very low thus demanding adequate management to increase production efficiency. It was recommended that the Government and stakeholders should help the farmers in time of risk to boost their morale for adequate production.

Keywords

Agricultural risks production efficiency peasant farmers management uncertainty

Introduction

Risks and uncertainties are major issues confronting agricultural production which is biological and seasonal in nature hence, no one can tell accurately the nature of agricultural decision and its possible outcomes. It is thus difficult to take the right decisions when the production environment is risky and uncertain. Farmers are left with critical decision on crops to be planted, seed rates, fertilizer application and application of other essential inputs. These decisions are not fixed depending on the nature of weather risks and other associated risks. A livestock farmer has to take a number of decisions to expand his dairy cattle herd and he has to wait for several years to get back the investment and also income from the investment. Changes in weather, prices and other socio-economic factors occur between time periods in which investment decisions are made and the final outcome. Due to this the farmers have considered various management strategies relevant to the risk and uncertainty conditions. If everything in farming and livestock rearing goes with certainty then every farmer becomes a better manager, but this is not the case with real farming situation. Only a few farmers can become efficient particularly those who could understand risk situations in farming follow the relevant risk management strategies. The world as a whole has been making progress towards improving food security and nutrition. This is obvious from the substantial increase in per capital output, food supplies achieved globally and for a large proportion of the population of the developing world (Alexandratos, 1995).

Risk in agribusiness investment has become very high in recent years due to climate change, terrorism and communal clashes. Agribusiness sector in Nigeria is full of risks and uncertainties stemming from various factors. This is obvious considering the fact that agribusiness investment depends on quirks of the environment and nature. Agricultural activities are exposed to greater risk. In fact, agricultural activities are more susceptible to the physical and natural uncertainties than other enterprises. Agricultural activities involve extensive, direct and continuous contact with the forces of nature and in this part of the world where scientific methods are less developed; predicting nature can be highly inaccurate, this makes the primary role of agriculture as the major supplier of food and raw materials to the agro-industrial processing and manufacturing industry ineffective. The effectiveness of this primary role of agriculture with less risk can aid the traditional role of agriculture as a great employer of labour and income generation and a contributor to foreign exchange earnings through exports. In Nigeria the rate of achievement of the linkage between agricultural and industrial sector has remained belated as a result of risks. This is why Thun and Hoenig (2009) and Juha and Pentti (2008) maintained that there is a need for research work to fortify the concept of risk management.

The Nigerian agribusiness environment is full of risks and uncertainties arising from several factors and these risks affect the efficient conversion of input to output (Bauer & Bushe, 2003). The arrays of hazards in Nigerian agriculture result in low productivity and output instability. Human and economic factors added to the situation making agriculture a high-risk venture. Taraba State which is high in food production in Nigeria is in the centre stage of the devastating effects of risk factors associated with agricultural production. For example, most crops grown in water prone areas were completely wiped out in the recent flood disaster in Nigeria in 2012 and the recent crisis between indigenes of the communities led to burning of farms, store/ware houses for harvested produce in 2018 and 2019. This has necessitated the need to examine the issue of risk management in agricultural production as it concerns the peasant farmers, as this has implication for food security in Nigeria. Hence, it is important to give optimum attention to the peasant farmers, especially in the area of risk management of their farms and store/ware houses as this has implication for food security in Nigeria. Attention to peasant farmers is equally important because they make up a large portion of the population and as their farms represent an important part of the nation’s assets. In this regard, the research intends to find solutions to some questions: what are the socio-economic characteristics of the peasant farmers in the study area? what agricultural risks do these peasant farmers face? what risk management strategies are being adopted to mitigate agricultural risks faced? how are these risk management strategies related to efficient conversion of inputs into output?

The objectives of the study were to determine the socio-economic characteristics of the peasant farmers in the study area, identify the agricultural risks faced by peasant farmers in Wukari local government area, analyse risk management strategies that are adopted by the peasant farmers to mitigate agricultural risks being faced, determine how these risk management strategies relates to efficient conversion of inputs to outputs.

Methodology

The Study Area

This study was carried out in Wukari local government area of Taraba State, Nigeria. Wukari local government is one of the local government areas of Taraba state. It is the has fifteen districts namely; Wukari, Avyi, Matar-fada, Gidan–idi,Tsokundi, Nwokyo, Rafin-kada, Choku, Kente, Chinka, jibu, Assa, Bantaje, Arufu and Akana.

Wukari local government headquarters has the Donga River flowing through and the Benue River forms a boundary with Nassarawa state to the northwest. It has an area of 4,308km and a population of 241,546 at the 2006 census. The town is the base of the Wukari Federation, a traditional state. Wukari is an agrarian community; producing fish from the Donga and Benue Rivers and also crops of various types; Maize, Millet, Guinea corn, Yam, Rice and beans are utilizing the available land therein.

The soil types of the study area is loamy and fertile for crop production. The climate of Wukari is marked by dry and wet season. The dry season starts in November and ends in March while the rainy season starts in April and till October.

Sampling Procedure

Multiple random sampling techniques of the respondents was adopted. In the first stage fives (5) wards (Akwana, Jibu, Hospital, Kente and Puje) were randomly selected from ten (10) wards in the local government area and twenty-five (25) respondents were randomly selected in each ward to make a total of 125 as shown by Table 1.

Table 1.

Classification of Study Area.

No	Wards	No of Respondents
1	Akwana	25
2	Jibu	25
3	Hospital	25
4	Kente	25
5	Puje	25

Source: The authors.

Data Collection: The primary source of data was generated using structured questionnaire which was administered in the sampled area.

Data Analysis Technique

Descriptive and inferential statistics were used to analyze the data collected. Specifically, descriptive statistics like frequency and simple percentage were used to analyze objective (a) and (b).

Analytical Technique

Stochastic Frontier Cost Function

The stochastic frontier function is specified as:

Y_{i} = f (X_{i j}; β) + V_{i} - U_{i} (i = 1, 2, n)

(1)

where

$Y_{i}$ is Output of the ith firm;

$X_{i j}$ is Vector of actual jth inputs used by the ith firm;

ß is Vector of production coefficients to be estimated;

$V_{i}$ is Random variability in the production that cannot be influenced by the firm

and $U_{i}$ is Deviation from maximum potential output attributable to technical inefficiency of ith farmer.

The above specifications have been expressed in terms of a production function, with the U_i interpreted as technical inefficiency effects, which cause the firm to operate below the stochastic production frontier. To specify a stochastic frontier cost function, the error term specification is simply altered from ( $V_{i} - U_{i}$ ) to ( $V_{i}$ + $U_{i}$ ). This substitution would transform the production function defined by Equation (1) into the cost function: The stochastic frontier cost function is specified as:

Ln C_{a} = f (P_{a}, Y_{a} : β) + (V_{i} + U_{i})

(2)

where:

$C_{a}$ is total cost of production of the ith firm,

$P_{a}$ is input prices,

$Y_{a}$ is Output of the ith firm,

β is Parameters to be estimated,

$V_{i}$ is Systematic component which represents random disturbance cost due to factors outside the scope of the firm, $U_{i}$ is one sided disturbance term used to represent cost inefficiency and is independent of $V_{i}$

The cost efficiency (CE) of an individual firm is defined in terms of the ratio of observed cost ( $C_{b}$ ) to the corresponding minimum cost ( $C_{min}$ ) under a given technology:

CE = {exp}^{U}

(3)

where:

CE = Cost efficiency,

$C_{b}$ = the observed cost and represents the actual total production cost; $C_{min}$ = the minimum cost and represents the frontier total production cost. In this study, the empirical model of the stochastic frontier cost function is specified as:

\log C_{1} = β 0 + β_{1} \log P_{1} + β_{2} \log P_{2} + β_{3} \log P_{3} + β_{4} \log P_{4} + β_{5} \log P_{5} + β_{6} \log Y_{1} + V_{1} + U_{1}

(4)

where:

$C_{1}$ is Total production cost. (Naira);

$P_{1}$ is Cost of pesticide (Naira),

$P_{2}$ is Cost of clearing (Naira),

$P_{3}$ is Cost of planting (Naira)

$P_{4}$ is Cost of labour (Naira),

$P_{5}$ is Cost of harvesting (Naira),

$P_{6}$ is Cost of seedling (Naira),

$Y_{1}$ is output of crop (Naira).

The V_i are random variables which are assumed to be normally distributed N (0, σ $V_{2}$ ), and independent of the $U_{i}$ which are non-negative random variables, assumed to be half normally distributed |N (0, $\sum_{u}$ 2) | and account for the cost inefficiency in production.

Inefficiency Model

The cost inefficiency model is specified as follows:

{CE}_{1} = δ_{0} + δ_{1} Z_{1} + δ_{2} Z_{2} + δ_{3} Z_{3} + δ_{4} Z_{4} + δ_{5} Z_{5}

(5)

where:

CE = Cost inefficiency of i th farmer

Z_{1} = Age of farmers (years)

Z_{2} = Primary occupation

Z_{3} = Farming experiences (years)

Z_{4} = Contact with extension agents

(1 contacted, 0 otherwise)

Z_{5} = Farm size

Z_{6} = Household sizes

Z_{7} = Marital status

The stochastic frontier production and the inefficiency model will be estimated for the cocoa farmers to determine their efficiency. $U_{i}$ provides information on level of the allocative efficiency of the ith farm. The allocative efficiency of individual farmers is defined in terms of the ratio of the predicted minimum cost (C_i*) to observed cost (C_i).

That is $AE = \frac{C_{i *}}{C_{i}} = \exp$

Thus, allocative efficiency is an inverse function of cost efficiency and so, ranges between zero and one.

Result and Discussion

The socio-economic characteristics of age, gender, marital status, household size, educational status, farming experience, primary occupation, farm practice and crop grown are presented in Table 2 below.

Table 2.

Socio-economic Characteristics of the Respondents.

Items	Respondents	Frequency	%
Gender	Male Female	95 30	76 24
Age	<30 31–40 41–50 51–60	22 36 61 07	17.6 28.0 48.8 5.6
House hold sizes	1–5 6–10 11–15	56 62 07	44.8 49.6 5.6
Primary occupation	Civil servant Artisan Farming	54 64 07	43.2 51.2 5.6
Years of farming experience	6–10 11–15 16–20 21–23	30 14 48 33	24 11.2 38.4 26.4
Crop grown	Cereals Tuber Legume	41 77 07	32.8 61.6 5.6
Farming practice	Mixed Mono cropping	103 22	82.4 17.6
Marital status	Married Single	104 21	83.20 16.80
Educational status	Secondary Tertiary No education	34 84 07	27.20 67.2 5.6

Source: The author.

Age and Gender of Respondents

Table 2 shows the age and gender of the respondents. The results show that majority (76.8%) of the respondents were within the ages of 31–50 years, which indicates that most of the respondents were youths, some energetic and could afford to engage in agricultural production and other non-farm economic activities.

The result on gender shows that majority (76%) of the respondents were male, while female constitute only 24% in the study area. The possible reason for this could be attributed to labour- intensive nature of farming, which the male can withstand more than the female respondents.

Marital Status of Respondents

Marital status refers to the categories of the respondents involved in the study as to whether they are married or otherwise. Table 2 revealed that 83.20% of the respondents were married, while 16.8% were single. This may be due to the fact that married respondents are more engaged in the farming activities because they have to farm to feed their family.

For household size, the result showed 94.4% of between 1–10 persons, while 5.6% respondents has about 11–15 persons in their household. 43.2% of the respondents are civil servants, 51.2% are artisans while 5.6% are predominantly farmer as their primary occupation. 64.8% of respondents have between 16–23 years of farming experience. 61.6% of respondents grow tuber, 32.8% grow cereals and 5.6% grow legume. 82.4% of respondents practice mixed farming while 17.6% practice mono cropping

Sources of Agribusiness Risk

As presented in Table 3, the risk sources identified to be affecting peasant farmers in the study area were strategic risk, environmental risk, operational risk, business risk, financial risk, market risk and institutional risk. Though, nearly all the sampled farmers identified the seven risk sources but their degree of influence were found not to be the same on the productivity or profitability of their farming business. The result is in line with the findings of Alimi and Ayanwale (2005) as well as Mikhayloa (2005) who reported that the identified risks were the major sources of risk in agribusiness operations.

Table 3.

Distribution of Respondents by Various Source of Agribusiness Risk.

Risk source and Components	Ranks
Risk source and Components	0	1	2	3	4	Total
Market, sovereign and credit risk
Price commodities effects on business	07	24	62	32	–	125
Change in demand effects on business	14	35	46	30	–	125
Supply of goods effects on business	07	42	22	44	10	125
Interest on loan effects on business	14	27	21	24	39	125
Access to loan from financial  institutions	07	21	–	34	63	125
Loan collected effects on business	21	14	07	27	56	125
Foreign exchange effects on business	–	–	–	49	76	125
Settlement, liquidity and operational risk
Business partners roles in Business	14	22	27	34	28	125
Partners do their obligations as expected	22	27	28	27	21	125
How do get funds for your business?	–	21	36	47	21	125
How do take note of your assets?	14	41	14	41	15	125
Rate of fraudulence in your business	21	07	21	07	69	125
Theft activity in your business	27	29	29	14	26	125
Breach or termination of contract	20	21	15	19	50	125
Business policy in daily transactions	–	08	28	34	55	125
Strategic, compliance, reputational and environmental risk
Rate of encountering natural disasters	27	55	–	43	–	125
Reputation maintenances	19	13	08	42	43	125
Government rules and regulation adherence	47	35	14	15	14	125
Health and workers safety cares	35	21	15	34	20	125
Firm reputation/quantity produce	21	14	27	42	21	125
Enforcement of business/investment rules	35	21	15	34	20	125

Source: The author, Field Survey, 2018.

Note: 0 = Daily, 1 = Monthly, 2 = Twice a year, 3 = Yearly, 4 = others specify.

Allocative Cost Efficiency of Risk Management among Peasant Farmers in Wukari Local Government Area

The estimates of the parameters of Stochastic Frontier Cost function for peasant farmers is contained in Table 4. The variables used in the regression analysis have direct relationship with total yield produced. The cost of production increases by the value of each coefficient as the quantity of each variable is increased by 1%. The coefficients for cost of seedlings, cost of fertilizer, cost of clearing, cost of planting, cost of harvesting and cost of labour were all positives, with the estimates of cost of clearing significant at 1%, while cost of (labour, harvesting, planting) were significant at 10%. This indicated that more costs are incurred on labour, harvesting and planting among the farmers in the study area. The estimates of fertilizer and seedlings was not significance, indicating poor usage of fertilizers among the farmers in the study area. The coefficient of cost of pesticides was negative and statistically significant at 1%, this showed that majority of the respondents in the study area incurred more cost on acquiring pesticides among the farmers. The coefficient of fertilizer used was positive but not significant at any level, indicating that fertilizer usage among the farmers is not popular, but if intensified, the usage might lead to high productivity. Thus, increasing the use of fertilizer will add to the total profit by minimizing its cost in an efficient manner. This is in conformation with Tijjani and Bakari (2014) in their analysis of determinants of allocative efficiency of rainfed rice production in Taraba State, Nigeria. Where they found that addition of fertilizer to rice production increases rice productivity.

Table 4.

Maximum Likelihood Estimates of the Stochastic Frontier Production Function.

Variables	Parameter	Coefficients	Standard Errors	P > /z/
Cost of pesticides	$β_{1}$	−1.0459	0.2450	0.000***
Cost of seedlings	$β_{2}$	0.3238	0.2560	0.206
Cost of fertilizer	$β_{3}$	0.1856	0.2826	0.511
Cost of clearing	$β_{4}$	4.1973	0.3413	0.000***
Cost of planting	$β_{5}$	0.7088	0.3287	0.031*
Cost of harvesting	$β_{6}$	1.6014	0.7938	0.044*
Cost of labour	$β_{7}$	1.2087	0.4800	0.012*
Constant	$β_{o}$	−15.5994	3.6125	0.0000***
Variance parameters
Sigma squared	σ2	0.6042	0.0438*	0.5242
Gamma	Γ	0.0188	0.5784	0.6064
Loglikelihood	LLF	−252.96908

Source: The author.

Note: ***, **, * Signify 1%, 5% and 10% respectively.

The result of the diagnostic statistics shows that the variance parameters for the frontier cost function are statistically significant at 10% level. The estimate of the sigma squared (0.0438) * indicates that the distributional forms of error terms are well specified. The gamma estimate (0.6064) shows the 60.64% of the variation among the respondents is due to differences in allocative efficiency. Thus, the results of the diagnostic statistics confirmed the relevance of stochastic frontier cost function, using the maximum likelihood estimates.

Table 5 showed the table of efficiency scores. The maximum efficiency score for the farmers in the study area is 20.57, while the minimum efficiency score was 16.28. The mean efficiency score for peasant farmers in the study area was 18.55. This showed that the efficiency level in their production is very low, adequate management and practices will increase their production efficiency.

Table 5.

Table of Efficiency Scores.

Efficiency Scores	Frequency	%	Cum %
15–16	12	12.5	12.5
17–18	15	15.63	28.13
19–20	62	64.58	92.70
> 20	07	7.30	100
Max	20.57	Mean	18.55
Min.	16.28

Source: The author, Field Survey 2018.

Table 6 showed the cost inefficiency model of the Stochastic Frontier analysis. The cost inefficiency model showed that the coefficients of age, sex, marital status, education level, access to extension and Agric-business risk were all positives. This shows that these variables have some positive relationship with the productivity of farmers in the study area. The estimates of age, education level, primary occupation, access to extension, membership of cooperative association, Agric-business risk, and credit sources were statistically significant at 1%, and they influence or affect the productivity of the farmers in the study area. The positive coefficient and statistical significance of the estimates of age, indicated that the farmers in the study area are very efficient in their production and are still in productive age. It is generally noted in agricultural production that as one getting older, the productivity reduces.

Table 6.

Inefficiency Model of the Stochastic Frontier Analysis.

Variables	Parameters	Coefficients	Standard Errors	P > /z/
Age	$Y_{1}$	0.1044	0.0237	0.000***
Sex	$Y_{2}$	0.7008	0.2383	0.004**
Marital status	$Y_{3}$	0.1932	0.4399	0.662
Educ level	$Y_{4}$	0.3478	0.0816	0.000***
Occupation	$Y_{5}$	−0.8757	0.2075	0.000***
Farming exp	$Y_{6}$	−0.0040	0.0181	0.826
Access to extension	$Y_{7}$	2.6627	0.5061	0.000***
Membership of association	$Y_{8}$	−1.2778	0.1413	0.000***
Household sizes	$Y_{9}$	−0.6363	0.1940	0.002**
Agric-business risk	$Y_{10}$	3.2015	0.4888	0.000***
Credit source	$Y_{11}$	−0.8471	0.1460	0.000***
Constant		10.4743	0.6581	0.000***
R-squared (0 0.8431)Adj R-squared (0.8226)	No. of Obs.125	Prob > F0.0000	Root MSE\|0.46756

Source: The author, Field Survey 2018.

Note: ***, **, and * Signify 1%, 5% and 10% respectively

Access to credit was statistically significant and have a negative coefficient, this showed that

Farmers in the study area have poor access to credit, and this affect their productivity.

The result further revealed that the coefficient of extension contact was also positive. This showed that if farmers have unrestricted access to extension agents, their technical efficiency will increase. The coefficient of household size, primary occupation and farming experience were negative, this may reduce the technical efficiency of the farmers, due to the important of those variables in efficiency measurement.

Where;

$Z_{1}$ = Technical efficiency

$X_{1}$ = Total yield

$X_{2}$ = Market risk, sovereign risk and credit risk

$X_{3}$ = Settlement risk, liquidity and operational

$X_{4}$ = Strategic risk, compliance reputational and environmental risk

The correlation in Table 7 above showed the relationship between the technical efficiency, total yield of the farmers, total outcome of market risk, sovereign risk and credit risk, total outcome of settlement risk, liquidity and operational and total outcome of strategic risk, compliance reputational and environmental risk.

Table 7.

Correlation Analysis of Efficiency and Agricultural Risk Management Strategies Adopted by Peasant Farmers in the Study Area.

	$Z_{1}$	$X_{1}$	$X_{2}$	$X_{3}$	$X_{4}$
$Z_{1}$	1.0000
$X_{1}$	0.6963	1.0000
$X_{2}$	0.0077**	0.0245*	1.0000
$X_{3}$	0.1933	0.1959	0.0480*	1.0000
$X_{4}$	−0.4122	−0.5758	0.2065	0.0660*	1.0000

Source: The author, Field Survey, 2018.

Note: Sig *10%, **5% and *1%.

Conclusion

Agricultural production is faced with several risks which limit farmers output, however, many farmers lack adequate skills and knowledge in coping with the risk, it is thus good that help may be provided to the farmers in form of loans/credit, extension services to help better production. These will go a long way in increasing food availability and farmers’ income as they will produce enough and in turn increase the country’s food security.

Recommendation

In order to mitigate the effects of risk and to enhance production efficiency of the farmers in the study area, the following recommendations were made.

The government and donor Agencies should encourage farmers with funds in time of disaster such as Crisis, drought, flood, fire disaster, insect/pest attack and so on.

The peasant farmers should organize themselves into Associations and pull their resources together to give loan to members that are affected by risks and unexpected disaster

The rural Extension workers/services should be encouraged to visit farmers and teach them risk and uncertain management strategies to make them prepare for the future.

Footnotes

ORCID iD

Maryanne Odufa Egbeadumah

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