Factors influencing hybrid maize farmers’ risk attitudes and their perceptions in Punjab Province, Pakistan

Shoaib Akhtar, Ll Gu-cheng, Raza Ullah, Adnan Nazir, Muhammad Amjed lqbal, Muhammad Haseeb Raza, Nadeem lqbal, Muhammad Faisal

1 College of Economics & Management, Huazhong Agricultural University, Wuhan 430070, P.R.China

2 Institute of Agricultural and Resource Economics, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan

3 The Management School, Lancaster University, LA1 4YX, United Kingdom

1. lntroduction

Farmers have to work in an environment intricate by different kinds of vulnerabilities and uncertainties that are always encouraged by natural environment, market faults and social uncertainties (Ellis 2000; Akcaoz and Ozkan 2005).To evade many types of risks, growers should invest in term of time and money to develop some approaches and to take different adaptive measures. These investments have more expectation of return, but also at the same time they have more failure of risk (Alderman 2008). Farming risks arise mainly due to the variability of climate, the density of biological diseases, production seasonality, the different geographical production area and consumer of agricultural production (McNeilet al. 2015; Ullahet al. 2015), regular natural catastrophes (World Bank 2011), the production and prices unpredictability of agriculture products, imperfect input/output markets (Musser and Patrick 2002) and the absence of financial facilities along with partial extent and design of risk management strategies such as credit and insurance (Musser and Patrick 2002; Jain and Parshad 2006). Some of these categories may overlap each other.

Since farming is a key source of revenue for farmers,therefore it is imperative for agricultural households to recognize and overcome risks (Drollette 2009). The concern about risk in agriculture should be left not only to the agricultural household but also to the whole society, as the risk averse nature of farmers may result in misallocation of resources that lessen overall welfare. Even if the farmer is risk neutral, the presence of risk could have an impact on production decisions due to its impact on expected marginal productivity when randomness occurs inside the production or cost functions (Just and Pope 1979). Understanding of the risk sources can help farmers in taking wise decisions related to crop management and adaptive measures. To analyze farmers’ decision in risky and uncertain conditions,it is necessary to observe how they perceive risk and react in risky situations (Lucas and Pabuayon 2011).

Maize being the highest yielding cereal crop in the world is of significant importance for countries like Pakistan, where rapidly increasing population, food and fodder demand have already out stripped the available food, feed and fodder supplies. Out of total maize production, about 60% is used in poultry feeds, 25% in industries and remaining is used as food for human and animals. Maize accounts for 0.5%in gross domestic product (GDP) and 2.7% in agriculture value addition. In 2016–2017, maize was cultivated on 1 334 thousand hectares, and the production of maize was 6.130 million tonnes, showing an increase of 16.3% from the previous year of 5.271 million tonnes (GOP 2016a). Maize enjoys an important position in the existing cropping systems of Pakistan. It ranks the third after wheat and rice and is grown in almost all the provinces of the country, but Punjab and Khyber Pakhtunkhwa are the main areas of production.In Punjab Province, both hybrid and non-hybrid maize varieties are being grown. The introduction of hybrid maize varieties is mainly attributed to the efforts of private sector.Hybrid maize varieties are very popular among the farming communities mainly due to their higher yield potential which generates higher returns to the growers. Like other crops,hybrid maize crop also has to face environment (excessive rainfall, hail storm, flood, lodging of crop, drought), biological(insect/pest diseases related to maize crop), institutional(government support price policy negligible, lack of credit facility, lack of insurance companies, lack of government research institute in producing hybrid seed) and economic issues (higher input and lower output prices, lack of market facilities) ( Abidet al. 2015; Gorstet al2015; Abidet al. 2016;Iqbalet al. 2016). All these problems lead growers in a very uncertain situation which could result in dissatisfaction and disenchantment among growers.

Due to financial constraints and limitation of resources,capability of Pakistan to adapt to exposed risks at national as well as at farm level is very limited (Abidet al.2015).Moreover, the existence of public institutions at local level are unable to provide support to farmers because of their limited resources. The crop loan insurance scheme (CLIS)was launched in Pakistan during 2008, however, the scheme is still at an immature stage (Kassamet al. 2014; Iqbalet al.2016) and farmers mostly rely on traditional methods to manage farm level risks.

Assessing farmers’ perceptions and attitudes towards risk are crucial factors shaping farmers’ decision when faced with an uncertain situation (Akcaoz and Ozkan 2005). Decisions made by farmers can be analyzed in risky and uncertain situations by considering their risk perceptions and attitudes towards risk (Lucas and Pabuayon 2011). Previous studies on the impacts of social, economic and demographic factors on farmers’ perceptions of risk and risk attitudes showed mixed results. Characteristics of farms and farm household impact risk perceptions and risk attitudes of farmers. Literacy and agricultural experience lead farmers to understand risk sources; their incidence and severity, and consequently effect their perceptions and enhance their capabilities to manage farm risk more efficiently. Earlier literature has found that risk preferences diverge (Flatenet al. 2005) momentously based on age (Kammar and Bhagat 2009; Kisaka-Lwayo and Obi 2012; Ashraf and Routray 2013; Iqbalet al. 2016),education (Khanet al2010; Dadzie and Acquah 2012),income (Einavet al2010), agricultural experience (Lucas and Pabuayon 2011), off-farm income (Ullahet al. 2015), contract farming (Luet al2017) and farm size (Lucas and Pabuayon 2011; Iqbalet al. 2016). Climate information is of significant importance in managing production risk in agriculture arising from climate variability (Chaudhary and Aryal 2009). Farmers access to extension workers enables understanding and management of agricultural risks through the adoption of effective risk management strategies (Arce 2010).

The inadequate information on farmers’ risk attitudes and risk perceptions poses a big challenge for researchers and policy makers to develop a comprehensive risk management system at the farm level (Ellis 2000; Ayinde 2008; Lucas and Pabuayon 2011). Hence, to develop an effective policy to help farmers with risk management at the farm level, risk information at the farm level needs to be considered locally. Hybrid maize is becoming more and more important food stuff in developing countries, and it is a critical issue to analyze farmers’ risk perceptions,policy preferences, and behaviors (Leiserowitz 2005; Denget al. 2017). Therefore, this research aims to explore risk perceptions and risk attitudes of hybrid maize growers and analyze the potential impacts of various factors on their risk perceptions and attitudes towards risk. The findings may provide better understanding of the farmers’ perceptions and attitudes towards risk that ultimately shape their decisions under risky situations. Knowing the influence of various farm and farm household characteristics on farmers’risk perceptions and attitudes may help policy makers to chart out sound policies for the adaptation of farmers to constantly changing bio-physical environment in which they operate.

2. Data and methods

2.1. Study area

The rural areas of Punjab Province were selected for this research. It is the second largest and most populous province of the country, located in the semi-arid lowland zone (Abidet al.2015). Punjab has 20.63-million-hectare geographical area, of which 59% is cultivated. The main criteria for selecting Punjab Province as the study area include that: i) Punjab shares 53% of the overall agriculture GDP and 74% for the entire cereal production of the country(Badaret al. 2007; Abidet al. 2015); ii) 81.3% of the total hybrid maize, and all of spring hybrid maize is produced in Punjab province only (GOP 2016b); iii) like other crops the hybrid maize crop in Punjab is highly exposed to various kinds of risk, i.e., climate risk, biological risk, price risk and financial risk (World Bank 2011).

The study was conducted in four maize producing districts of Punjab based on their shares to total maize production in the province following the statistics from these four districts show some variation in climate, socioeconomic structure and exposure to various kinds of risks (BOS 2016). Location of the selected districts is given in Fig. 1. The annual average minimum and maximum temperature in Punjab ranges from 16.3 to 18.2°C and 29.3 to 31.9°C, respectively, between the period 1970–2001. Punjab receives 50 to 75% rainfall during the monsoon (Abidet al. 2015). The pattern of rainfall is different in different agro-ecological zones of Punjab, rain-fed zones receive the highest quantity of rainfall followed by irrigated zones receiving a lower quantity of rainfall (Mohammad 2005). There are two main seasons in Punjab, Province, i.e., Rabi (November–April) and Kharif(May–October). Major crops cultivated in these districts are sugarcane, maize, wheat, potato and cotton (Naqvi and Ashfaq 2014).

Fig. 1 Map of study districts in Punjab Province, Pakistan.

2.2. Sampling

To full fill the study objective, a multi-stage random sampling technique was used to select the study area and farm household. In the first step, Punjab Province was selected as the main study area based on its higher contribution towards total agricultural GDP. In the second step, four hybrid maize growing districts were selected at random. In the third step from each selected districts, one village was selected using random sampling techniques. In the final stage, farm households were selected from each village from the list of farmers provided by the revenue department.Specifically, 100 hybrid maize growers were selected(interviewed) from each village. Yamane’s formula (Yamane 1967) was used for farm household sample selection in the study area, which is given as below:

Where,n, sample size;N, total number of farmers in the study area;e, margin of error, used as ±15% (0.15).

The interview schedule included all the relevant information regarding socioeconomic characteristics of the farm and farm household, income sources, perception of the farmers about different risk sources for the hybrid maize crop, and indicators to assess farmers’ risk attitudes and risk perceptions. Prior to the start of the survey, a pretesting was done to avoid missing any essential information.

2.3. Risk perception

Farmers were asked to score the severity and incidence of risk source (climate, market, biological and financial risks)on a Likert scale from 1 (very low) to 5 (very high) based on their understanding of each risk source. Following Cooper(2005), the given scores were then pooled in a risk matrix and were classified as low if the score is from 2 to 5 and higher if its range from 6 to 10. Fig. 2 shows risk matrix.

2.4. Risk attitude

Fig. 2 Risk matrix.

An equally likely certainty equivalent (ELCE) model was used to figure out the attitudes of farmers toward risks.Certainty equivalents (CEs) were derived for a sequence of risky outcomes and matched them with utility values(Biniciet al. 2003). For example, farmers were asked to identify the monetary value of a certain outcome that made them indifferent in a choice amid two risky outcomes of total annual household income (PKR 80 000) and PKR 0 each with same probabilities (in this example the utility related with PKR 80 000 is 1 and with PKR 0 is 0).Suppose that the reply is PKR 41 000; this is the certainty equivalent (CE) of the agriculturalists for the income level of PKR 80 000 and PKR 0 with same probabilities. The farmer was once more enquired to state the monetary value of a sure outcome that make him indifferent between the two risky outcomes of PKR 41 000 and PKR 0 with equal probability. This process continued till appropriate data points were found. The similar method is followed for the other half of the income distribution to get the CE points and match them with utility values. The farmer response of PKR 41 000 is the CE for uncertain payouts of PKR 80 000 and PKR 0 with equal probabilities (0.5 each) and utility values for this CE are calculated as:

u(41 000)=0.5u(0)+0.5u(80 000)=0.5(0)+0.5(1)=0.5 (2)

uis utility, in our case, is a function of wealth, but we use it as a function of income (Olarindeet al. 2007). After finding few certainty equivalent points and matching them with utility values, a cubic utility function was applied for assessment of the utility of each individual respondent. The equation of cubic utility function is:

Where,αare the parameters andwrepresents the wealth of the farmers and their attitudes toward risk, which is dependent on several factors. This cubic utility function is associated with risk aversion, risk preferring and risk indifferent behavior (Biniciet al. 2003). As utility is frequently estimated on an ordinary scale, the shape of utility function on an ordinary scale can be transmuted into a quantitative measure of risk aversion called absolute risk aversion (Arrow 1964; Pratt 1964; Raskin and Cochran 1986). The absolute risk aversion is arithmetically written as:

ra(w) is a parameter of absolute risk aversion,′ andare the first and second order derivatives of wealth (w),respectively. Following Olarindeet al. (2007), income is substituted for wealth. If individual is risk averse, then coefficient of absolute risk aversion is positive, negative if individual prefers risk and zero if individual is indifferent to risk. The risk attitudes of farmers are included in the study as 1, if individual reflect risk averse nature and 0,otherwise.

2.5. Dependent and independent variables

Based on the contemporary review of relevant literature farm household characteristics like age, education, maize farming experience, off-farm income, contract farming, family size,contact to extension agents and farm characteristics like distance from main market, maize farming area are used as independent variables as these factors can influence farmers’ risk attitudes and risk perceptions (Ullahet al.2015; Iqbalet al. 2016). The dependent variables used in the study are risk attitudes and farmers’ perceptions of four types of risks, i.e., climate risk (high rain fall, flood, storm,lodging), biological risk (insect/pest diseases and other hybrid maize crop related diseases), price risk (high input prices, low output prices and market related issues) and financial risk (non-availability of credit, high interest rate and finance related issues).

2.6. Probit regression

By following Ullahet al. (2015) and Iqbalet al. (2016) and keeping in view the dichotomous nature of the dependent variables, a probit model is used in the present study which is given as:

Where,Yiis the dichotomous dependent variable, in our studyYishows the high-risk perceptions and risk averse behavior.xiis a vector of independent variables used in the analysis (such as socio-economic characteristics of the farm and farming households),βiis the vector of unknown parameter (to be estimated) andεis the error term.

3. Results and discussion

3.1. Descriptive statistics

The descriptive statistics of the variables used in the analysis are presented in Table 1. In the analysis, two types of variables were used, i.e., continuous and discrete choice dummy variables. Results showed that mean age of the farmers was 45 years with 7 years of average educational background. On average, farmers had 12 years of hybrid maize farming experience. The average distance that farm from the main city market was 16 km. The average hybrid maize farm size in the selected area was 33 acres.An average six members were included in the family size.Analysis also indicated that 78% of the farmers show risk averse attitude as they were not ready to take any type of opportunity that involves any type of risk. Ellis (2000)documented growers’ decision related to farm productionby using income method and defined risk attitude as “a person is described as risk averse if he chooses a situation in which a given income is certain to a situation yielding the same expected value for income but involves uncertainty”.Analysis also divulged that risk of high input prices was the most perceived risk by more than three fourths of the hybrid maize growers while financial risk was the least perceived by hybrid maize growers. The climate risks (high rainfall,flood, lodging, hail storm, temperature and drought) and biological risks (insect/pest diseases, other hybrid maize crop related diseases) were perceived by less than three fourths of the growers as shown in Table 1.

Table 1 Depiction of variables used in the model1)

3.2. Factors affecting risk attitude

Probit regression was used in the present study to explore the factors affecting farmers’ risk attitudes and risk perceptions. The findings of the probit approach presented in Table 2 indicate that distance from the main market, offfarm income, and location dummies for Sahiwal District were the imperative and significant factors determining the risk attitudes of the sampled growers. The negative coefficient of age presented that older growers are more likely to take risks as compared to younger growers. The findings were related with those by Dadzie and Acquah (2012), Ullahet al.(2015) and Iqbalet al. (2016). Our findings recommend that with an increase in the year of schooling of the farmer,the risk aversion attitude also increases. Education of the farmers (decision maker) expands his/her information on several sources of risk, its effects at farm level and possible strategies which can be used to protect their earnings from various source of risk. The results were in line with the studies of Lucas and Pabuayon (2011) and Iqbalet al.(2016). Furthermore, the farmers situated far away from the main market are less risk averse in nature as compared to the farmers near to the main market. The probable reason may be the difference of information level as farmers in distant areas have lesser opportunities to meet input/output dealers and progressive growers and are mostly unaware of the emerging risks. Growers with low off-farm incomes are found to be more risk averse in nature compared to growers with higher off-farm incomes. Higher off-farm incomes may indicate a greater risk bearing capacity and represents a form of diversification that would have an influence on farmers’ risk attitudes. The finding is in line with results of Lamb (2003) and Iqbalet al. (2016) who also documentedthat growers with lower incomes are more risk averse and avoid uncertain situations.

Table 2 Parameters estimates of probit model

Moreover, the results indicate that farmers’ access to extension workers has a negative relationship with their risk attitudes. Access to extension workers enhance farmers’understanding of the risks from various sources and enable them to better manage farm level risks. However, this relationship is statistically insignificant at 5% probability level. It is very important for the growers to have more and more access to market related information, crop management information during disease and contact with input and output dealers and agricultural extension workers during farming. Having access to agricultural information can enhance the farm productivity and at the same time transform the risk attitudes of farmers (Ayinde 2008). Other variables including hybrid maize farming experience, family size, and location dummies (Faisalabad, Okara, Sahiwal)have a positive and insignificant impact, but farming area has an insignificant and negative impact on farmers’ attitudes towards risk. Growers with more farming experience are more risk averse in nature compared to farmers with less farming experience. Our findings are in contrast to the results of Ayinde (2008) who stated that farming experience and risk averse attitude have negative relationship. Our findings also point to the importance of farm size in relation to farmers’ risk attitudes. The results suggest that larger farmers tend to take more risks compared to smallholders.Large farm size are associated with greater wealth and greater capacity to absorb risks arising from various sources.The findings are in contrast to the results of Lucas and Pabuayon (2011) and Ullahet al. (2015) who documented a positive effect of farming area on risk averse attitude of farmers. The findings also revealed that farmers with larger family size tend to be more risk averse in nature. Dadzie and Acquah (2012) and Ullahet al. (2015) also found similar findings for the effect of family size on farmers’ risk averse attitude and argued that with higher family size the consumption needs of the household raises which translates into risk attitudes of the farmers.

3.3. Factors affecting risk perceptions

Table 2 represents the determinants affecting hybrid maize growers’ perceptions of various kinds of risks. The impact of farm and farm household characteristics on farmers’ perception of risk are mix and mostly insignificant.Previous studies documented a mixed effect of farm and farm household characteristics on farmers’ risk perception(Lucas and Pabuayon 2011; Ullahet al.2015). Our findings suggest that age of the growers has a negative impact on farmers’ perception of biological risk (insect/pest diseases,other hybrid maize crop related disease) but positive affect on farmers’ perception of price risk, climate risk and financial risk. Aged farmers’ consider price risk, climate risk, and financial risk to be the potential threats to their farm enterprise while younger farmers perceived biological risks are the main source of risk that can change their farm income. Similarly, farmers with more schooling years perceived risk of climate and finance to be the main risk sources that can undermine their incomes from farm sector while farmers with lower educational attainments identified price and biological risks as main risks source. Farmers with more farming experience, consider biological risks as the main threats as compared to farmers with lower farm experience. Ullahet al. (2015) and Udmaleet al. (2014)also indicated that farmers with more farming experience have higher risk perception of climate risks. Farmers with larger farm size, consider risks of climate and finance to be major risk sources while small holders identified biological and price risks as major sources of risks at their farm.Farmers with larger family size perceived price and financial risks to be major threats while farmers with smaller family size considered biological and climate risks to be the main sources of risk at their farm. Distance from main markets has a positive impact on farmers’ perception of all risk sources.Higher off-farm income reduces farmers’ concerns of financial risks, however, higher off-farm income is associated with higher perceptions of biological, price and climate risks. Our results also indicate that contract farming has a positive impact on farmers’ perception of all risk sources.One possible explanation for this may the fact that the higher risk perception induce farmers to adopt contract farming to overcome negative shocks resulting from various risks and uncertainties. Similarly, farmers with more contacts with agricultural extension worker perceived price and biological risks to be higher threats to their farm earnings. More contacts with extension workers have a negative effect on farmers’ perceptions of climatic and financial risk sources.The coefficients of location dummies indicate that farmers in Faisalabad District perceived financial risk as the major threat while farmers from Okara District perceived climatic and financial risks to be the main sources of risks to their farm incomes. Similarly, farmers from Sahiwal District perceived price, climate and financial risks to be the main sources of risks.

4. Conclusion

The present study was conducted in Punjab Province of Pakistan using cross-sectional data of 400 hybrid maize growers with the main objective of assessing farmers risk attitudes and risk perceptions. Analysis also explores the effect of various socio-economic and institutional factors on farmers’ perceptions and attitudes. Majority of the farmers were aware of different sources of risk to which hybrid maize crop was exposed and they also ranked those risks according to their observations and knowledge. The findings suggest that most of the farmers bear risk averse attitude and the risk averse attitude may have implications in farmers’farm and risk management decisions. Growers ranked price, biological, climate and financial risks as major risks to their hybrid maize crop. In addition, analysis also revealed that distance from farm to main market, off-farm income and age, maize farming experience, access to extension agent, and location are the determinants significantly (either negatively or positively) influencing farmers’ risk attitudes and risk perceptions. Although the findings of this research are specific to the selected hybrid maize growing districts of Punjab Province in Pakistan, they may have wider intimations particularly for developing countries whose economies are mainly dependent on agricultural sector.It is important to consider these factors during developing and executing risk management strategies at farm level.More investment on farmers’ education and better access to institutional services such as farm advisory services could help farmers better anticipate and manage the risks at farm level. The results may also be valuable for policy makers and other researchers to comprehend how farm and farm household characteristics play their roles in shaping farmers’attitudes towards risk which may, in turn, influence their risk management decisions at farm level.

Acknowledgements

This research work was financially supported by the National Natural Science Foundation of China (NSFC, 71473100;NSFC-CGIAR, 71461010701).

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