Identification and prioritization of agricultural risks and their management strategies adopted by cotton growers in the Punjab, Pakistan

45 Afzal and Luqman Int. J. Biosci. 2020 RESEARCH PAPER OPEN ACCESS Identification and prioritization of agricultural risks and their management strategies adopted by cotton growers in the Punjab, Pakistan Muhammad Kashif Afzal1, Muhammad Luqman2* 1PhD Scholar, Department of Agricultural Extension, College of Agriculture, University of Sargodha, Pakistan 2Assistant Professor, Department of Agricultural Extension, College of Agriculture, University of Sargodha, Pakistan Key words: Cotton, Agricultural risks, Risk management strategies. http://dx.doi.org/10.12692/ijb/16.6.45-59 Article published on June 16, 2020 Abstract Cotton is the major cash crop of Pakistan. Pakistan is also included in the list of top ten cotton producing countries of the world. Inspite of its significance in the national economy and in overall agricultural production, the average cotton production in Pakistan is declining since last five years. A number of factors are responsible for this decline of cotton production including the perception that overall agriculture has become a risky business. With this rational the present research was formulated for the identification and prioritization of agricultural risks and their management strategies adopted by cotton growers in the Punjab, Pakistan. Data were collected from 400 cotton growers of three selected districts of the Punjab. Face-to-face interviews were conducted for data collection. The collected data were analyzed using SPSS. The results shows that majority of the respondents had age upto 35 years. Majority of the respondents were literate having educational level up to ten years of schooling. Family landholding of 40.0% of respondents was 6-10 Acres. Overall rating of risks that were being faced by cotton growers in the research area shows that “Human Risks” is on the top with highest mean value (4.26/5.00). Respondents practiced a wide variety of risk management strategies. Out of these adoption of multiple income sources was on the top with highest mean (3.58/5.00). * Corresponding Author: Muhammad Luqman  muhammad[email protected] International Journal of Biosciences | IJB | ISSN: 2220-6655 (Print), 2222-5234 (Online) http://www.innspub.net Vol. 16, No. 6, p. 45-59, 2020 46 Afzal and Luqman Int. J. Biosci. 2020 Introduction Agriculture is one of the oldest professions of human civilization (Hanif et al., 2010). The history of human civilization reveals that agriculture sector is the major provider of food and fiber around the world and still possesses the same significant role in insuring food security (FAO, 2011). The livelihoods of majority of the people in the whole world are linked with this sector directly or indirectly (Berchoux et al., 2019). Role of agriculture in the development of any country is very much significant especially in case of developing countries where majority of the poor and food insecure people are residing (Tadesse et al., 2016).Agricultural production is directly linked with various factors that may be climatic, non-climatic and socio-economic (Fahad et al., 2018). All of these factors are beyond the control of farmers due to which agriculture is regarded as a risky business around the globe (Hardaker et al., 2015). Due to the occurrence of different risks, entire agricultural production is quite unpredictable that results into fluctuation of prices of food commodities (Sookhtanlo and Sarani, 2011). The typology and intensity of risks associated with agricultural production is varying from region to region. High level of risks in agriculture is very much common in developing countries (Pervez et al., 2016).However, in most of the cases four major types of risks are being identified as market risks, production risks, financial risks, institutional risks and human risks (Arbuckle et al., 2015; Jianjun et al., 2015; Wang et al., 2017; Duong et al., 2019 and many others).All of these risks are very much sensitive and expressively linked with each other (Ullah and Shivakoti 2014). The linkage and correlation between different types of risks were also reported by Tangermann (2011).With special reference to Pakistan Iqbal et al., (2016) reported three major types of agricultural risks as production, marketing and financial. Pakistan is developing as well as possesses agricultural based national economy. Agriculture is the single largest sector of economy that provides 38.5% employment to the total country’s national labour force. The percentage share of agriculture sector in comparison to other sectors of economy i.e. industry and services during the last five years is evident clear from the Fig. 1 given below. Cotton is the major source of fiber in the whole world and is being cultivated in more than 100 countries including Pakistan (Cianchetta and Davis, 2015). Cotton is well known cash crop of Pakistan and contributes significantly in overall national economy. This is considered as the white gold for Pakistan’s economy. Pakistan is the 4th largest producer of cotton and 3rd largest exporter of raw cotton. Last year cotton production is declined by 17.5% (Government of Pakistan, 2019).Multiple factors are responsible for this decline including climatic and non-climatic attributes (Iqbal et al., 2018). Occurrence of diverse nature of risks is one of the main features of cotton production in Pakistan like other regions of world with wide range of frequency and magnitude (Kouser and Qaim, 2014).The prevalence of risks in cotton crop was also reported by Zulfiqar et al., (2016). These risks instigate from multiple sources like climate variability, state national policies, international market policies, access to crop insurance/microcredit services, provision of extension or rural advisory services, geographical location etc. The mitigation of these risks is largely depends upon the perceptions of farmers about its intensity and frequency (Meraner and Finger, 2017). Risk management is a complex process and comprises of different steps. First step is the identification and nature of risk and 2nd step is to evaluate its consequences at farm and societal level. Conclusively, risk assessment is the major element of any risk management strategy. Adoption of risk management strategies at farm level is very much essential to involve generations in farming (Hardaker et al., 2004). Different risk management strategies are available for farmers to adopt (Ullah et al., 2015). Three main types of risk management strategies were identified by Okunmadewa (2003) as presentation, 47 Afzal and Luqman Int. J. Biosci. 2020 mitigation and copping strategies. The responsibility of selection of any of these management strategies lies on the shoulder of farm manager. In developing countries a number of research studies were conducted to probe out typology of agricultural risks, risk sources and management strategies adopted by farmers. However, limited studies are available in case of developing countries like Pakistan. Adoption of risk management is also very important to mitigate the risks faced by cotton growers in the country. Multiple factors manipulate the adoptability of different risk management strategies by cotton growers in Pakistan (Zulfiqar et al., 2016). Limited literature is available regarding identification of factors involved in adoption of risk management strategies by cotton growers in Pakistan. The basic objective of the current study was to identify and prioritize the risks associated with cotton production along with identification and prioritization of risk management strategies adopted by cotton growers to minimize the mentioned risks. Methodology Description of research area The study was conducted in the Punjab province of Pakistan. On the basis of population, Punjab is the largest province of Pakistan. The province is very much famous for its maximum share in total agricultural production of the country (Government of the Punjab, 2018). The share of the Punjab province in total production of cotton crop is 64.0%. Cotton is widely grown in southern region of the Punjab. Research design Cross-sectional Survey Research Design was used. This design allows collection of data from different groups of respondents at one point in time. Mix method approach was adopted keeping in mind the complexities of present research study. Personal faceto-face interviews were conducted for the collection of Quantitative data. For supporting qualitative data, focus group meetings and key informant interviews were conducted to collect qualitative data. Sampling procedure Both probability (Simple Random Sampling) and non-probability (Purposive Sampling) sampling procedures were used in the current research study. List of top ten (10) cotton growing districts of the Punjab was prepared. From that list three (03) districts were selected through Simple Random Sampling technique. List of farmers using differed ICT tools in the selected districts was obtained from Directorate of Agricultural Information, Lahore. The validity of the lists were first check from NADRA and then from Agriculture Extension Department of the respective District. Cotton growers using ICTs was selected through Purposive Sampling procedure. From the list of each district respondents were selected through Simple Random Sampling technique using web link random.org. The targeted study districts are hereby highlighted on the map of the Punjab as shown in Fig. 2. Sample size The population from which the sample was selected as the units of analysis covered all cotton growers within randomly selected districts. In order to ensure generalization of the present research, a sample size of four hundred (400) cotton growers were selected. The said sample size was calculated by using the formula framed by Fisher (Fisher et al., 1998) as given below. & The Fisher formula comprises of two parts; the first part of the formula was used for computing sample size for an infinite population. The result of that first part of formula was then used into the 2nd part of formula for computing sample size of the known/finite population. Where: n = sample size for infinite population Z = 1.96 (at 95% Confidence level) p = estimated proportion of cotton growers (0.1) 48 Afzal and Luqman Int. J. Biosci. 2020 q = 1-p d = precision of the estimate at 5% (0.05) The sample size will be; n = n = sample size for infinite population n =138 The adjusted sample size for the finite population of cotton growers in the selected districts is: = = adjusted sample size n = estimated sample size for infinite population N = Finite population size = ≈ 132 household respondents will be selected from District Muzaffargarh = ≈ 134 household respondents will be selected from District Bahawalpur = ≈ 132 household respondents will be selected from District Khanewal. Total sample size obtained was 398. However, the sample size was increased to four hundred (400) for easy data collection and analysis. Research instruments Structured interview schedule was used for quantitative data collection. For obtaining qualitative data interview guide was used. Reliability of interview schedule was measured through SPSS. The Value of Cronbach's Alpha of Items on Likert Scale was 0.773. According to Hair et al. 1998, the value of Cronbach's Alpha of Items on Likert Scale should be 0.7 of higher. It mean than internal consistency of the research instrument used in the present research was good and acceptable and the statements of all the variables on likert scale are found to be reliable. Content Validity of both the research instruments was checked through panel of experts and then by pre-testing (by conducting interviews from 50 Cotton growers). Data analysis The collected data were analyzed using SPSS. Descriptive statistics were used for the interpretation of data. Results and discussion Section-I: Socio-economic characteristics of respondents Age Data regarding present age (at the time of data collection during the year 2018) is tabulated in Table 1. Data presented in Table 1 shows that, nearly half (49.3%) of respondents were with age upto 35 years. This indicates that majority of the population in rural areas of the study districts were fall in category of young. Young people tend to adopt latest technologies at higher rate as compared to old age group. Table 1. Frequency and percentage of respondents according to their age. Age Frequency Percentage Upto 35 Years 197 49.3 36 Years to 45 Years 85 21.3 46 Years to 55 Years 101 25.3 56 Years and Above 17 4.3 Total 400 100.0 The results of present study are in line with the findings of Iqbal et al. (2018) who concluded that average age of respondents in cotton growing districts (Khanewal, Vehari, Bahawalpur, Bahawalnagar, Muzaffar Garh and RajanPur) was 46 years. The data also indicates that only 4.3% of respondents were fall in category of 56 years or above. This shows that proportion of old age group in the targeted study districts was very low. In contrast with the findings of the present study, Naveed and Anwar (2015) reported 49 Afzal and Luqman Int. J. Biosci. 2020 that majority (39.2%) of respondents are in the age group of 36-45 years. They studied the information needs of cotton growers in district Bahawalpur (one of the leading cotton producing districts of the Punjab and also one of the targeted districts of present research). Educational level Education is one the most important and significant socio-economic factors that play key role in the adoption of improved agricultural technologies by the farmers. The data concerning educational level of respondents is presented in Table 2. Table 2. Frequency and Percentage of respondents according to their educational level. Educational level Frequency Percentage Illiterate 109 27.2 Upto Primary 108 27.0 Matriculation 100 25.0 Intermediate 49 12.3 Graduation or Above 34 8.5 Total 400 100.0 Data tabulated in Table 2 presents the educational status possessed by the respondents at the time of data collection. The data indicate that 27.2% of respondents were illiterate. This indicates that high level of illiteracy is still prevalent among rural households in the targeted research area. On the other hand 72.8% of respondents were found literate. Among literate respondents majority (27.0%) of the respondents possessed educational level upto primary (5 years of schooling) and only 8.5% had educational level graduation or above. This shows that in rural areas of the targeted study districts in particular and generally in all the districts of the Punjab, higher level education (University level) is not so common. Table 3. Frequency and Percentage of respondents according to their family land holding. Size of family land holding Frequency Percentage Upto 5 Acres 142 35.5 6 to 10 Acres 160 40.0 11 Acres or Above 98 24.5 Total 400 100.0 This may be due to the non-availability of higher educational institutions in rural areas. This has been noticed that all the public and private sector universities and higher degree awarding institutions are located in urban localities. In connection with findings of the present study Naveed and Anwar (2015) reported that in district Bahawalpur (one of the leading cotton producing districts of the Punjab province), among literate cotton growers, majority (31.6%) had education upto eight years (middle) of schooling. Educational status of farmers plays key role in adoption of improved agricultural technologies as reported by Bakhsh et al. (2005) while identifying factors effecting yield of cotton in district Sargodha (Punjab province). Size of family land holding Land holding serves as one of the prime physical assets for farmers in the whole world but particularly in developing and low income countries like Pakistan. The data regarding size of family land holding of respondents in the targeted research areas was collected and presented in Table 3. 50 Afzal and Luqman Int. J. Biosci. 2020 Table 4. Mean and SD of production risks. Production Risks Mean SD High/Low rainfall 4.14 0.508 Flood 4.13 0.589 High/Low Temperature 4.02 0.557 Drought 3.77 0.609 Hail storm 3.69 0.772 Wind Storm 3.69 0.729 Overall Mean 3.90/5.00 0.627 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. The data tabulated in Table 3 shows that majority of the respondents (40.0%) possessed agricultural land ranging between 6-10 acres in the targeted population. Only 24.5% of respondents had agricultural land upto 11 acres. This indicates that small landers are in majority in the targeted districts of the present study like other parts of the country. Section II: Agricultural risks and management strategies Typology of risks being faced by Cotton growers Different types of risks are being faced by cotton growers in the research area. These risks are divided into eight different categories as explained by different researchers researched on agricultural risks and management strategies adopted by farmers in different regions of the globe. These risks are illustrated in the Fig. 3 given below.All of the above mentioned risks are hereby explained on the basis of self-perception of respondents in the research area one by one in the proceeding sections. Production risks Production risks are also referred to as weather related risks. Mean and SD of different production risks is presented in Table 4. Table 5. Mean and SD of biological risks. Biological Risks Mean SD Insect attack 4.14 0.594 Disease attack 4.00 0.619 Rodents 3.63 0.556 Overall Mean 3.93/5.00 0.590 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. Data presented in Table 4 shows that among different types of production related risks being faced by cotton growers in the research area “high/low intensity of rainfall” is on the top with highest mean value (x = 4.14/5.00 and SD 0.508). The production risk which is ranked at the last was “wind and storm” with lowest mean ((x = 3.69/5.00). Overall mean value of all the production risks were 3.90/5.00, shows that majority of the respondents “agreed” regarding prevalence of production risks in the targeted research area. In connection with these findings, Qasim and Ahmad (2016) reported that weather related risk sources like inadequate rainfall (x = 4.91/5.00), severe weather conditions (x = 4.39/5.00) and natural disasters (x = 4.09/5.00) are the leading risk sources for farmers of Pothwar (rain-fed) region in the Punjab province of Pakistan. They concluded that weather is the major source of production risks for Pakistani farmers. All the production risks are mainly related to weather, which is beyond human control as climatic events are natural. 51 Afzal and Luqman Int. J. Biosci. 2020 Table 6. Mean and SD of input risks. Input risk Mean SD Shortage of Fuel (Diesel) & Electricity 4.32 0.735 Adulteration in pesticides 4.24 0.760 Non-availability of fertilizers at the time of peak season 4.19 0.769 Shortage of certified seed 4.17 0.777 Irrigation water shortage 4.05 0.769 Adulteration in fertilizers 4.03 0.847 Overall Mean 4.17/5.00 0.776 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. Adverse climatic conditions and production risks lead to low crop production. Several research studies shows that variation in temperature and humidity are the leading sources of production related risks faced by farmers in different regions of the world (Van Asseldonk and Lansink, 2003; Richards et al., 2004; Musshoffet al., 2006 and Cafiero et al., 2007). Table 7. Mean and SD of market risks. Market risk Mean SD Middleman monopoly 4.30 0.729 Fluctuations in the prices of inputs 4.26 0.743 Fluctuations in market rates of cotton 4.20 0.757 Buyers monopoly 4.00 0.653 International gambling in cotton market 3.97 0.833 Lack of main cotton market in area 3.94 0.585 Money inflation 3.90 0.573 Overall Mean 4.08/5.00 0.696 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. Biological risks Biological risks are the major sources that significantly associated with production of field crops. These risks are closely linked with production risks. In this regard Miller et al., (2004) reported that raise the intensity of production risks. Biological risks prevail at each and every step of production technology of each crop (Ashraf et al., 2013). The data regarding biological risks was collected and their mean and SD is tabulated in Table 5 given below. Table 8. Mean and SD of harvesting and transportation risks. Harvesting and transportation risk Mean SD Less availability of labour (other than picking) 3.41 1.034 Non-or less availability of skilled labour (other than picking) 3.41 0.985 Non-availability of mechanical pickers 3.41 1.084 Contamination in cotton during picking 3.37 1.110 High costs of picking 3.34 1.098 Non-availability of skilled pickers 3.32 1.098 Contamination during transportation 3.31 1.098 Shortage of skilled spraying labour 3.29 1.132 Inappropriate handling and storage of cotton 3.19 1.204 Overall Mean 3.34/5.00 1.094 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. 52 Afzal and Luqman Int. J. Biosci. 2020 The data regarding biological risks as presented in Table 5 shows that “insect attack” is on the top with highest mean ((x = 4.14/5.00 and SD 0.594). Overall mean value of biological risks was 3.93/5.00 with SD 0.590. This show that high majority of the respondents were agreed regarding occurrence of biological risks in the research area. Table 9. Mean and SD of human risks. Human risks Mean SD Illiteracy 4.48 0.671 Limited access to Extension & Advisory Services 4.39 0.764 Lack of interest in farming 4.37 0.787 Small landholdings 4.31 0.787 Poverty & Food Insecurity 4.31 0.848 Poor Health Conditions/Illness 4.27 0.801 Lack of technical knowledge 4.22 0.850 Lack of skills 4.20 0.830 Produce got theft 3.81 0.518 Overall mean 4.26/5.00 0.762 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. Input risks In agricultural production inputs play an eminent role. Timely application of inputs to the respective crops enhances its production. Farmers mostly face input related risks due to limited of non-availability of inputs like seed, fertilizers, irrigation water and pesticides (Khan et al., 2013). Different types of input related risks were identified in the present research and presented in table 6 with mean values and SD. Table 10. Mean and SD of financial risks. Financial risks Mean SD Poor net return from crop 3.62 1.095 High prices of inputs 3.51 1.004 High interest rates gained by local agri. Dealers/investors 3.44 1.070 Black marketing of inputs 3.41 1.075 No other income source except farming 3.14 1.245 High interest rate upon loan 3.12 1.165 Sluggish cooperative societies system 2.84 1.178 Overall Mean 3.30/5.00 1.119 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. The data tabulated in Table 6 regarding input related risks being faced by cotton growers in the research area shows that “shortage of fuel & electricity” is on the top with highest mean (x=4.32/5.00) and 0.735 SD. On the other hand “adulteration in fertilizers” is placed at the end on the basis of lowest mean value (x = 4.03/5.00) among all the input related risks. The overall mean value of all the input related risks was found to be 4.17/5.00 (SD 0.766) shows that large majority of the respondents were “agreed” regarding occurrence of input related risks in the research area. In connection with present findings, Rahman et al., (2019) concluded that irrigation water availability and timely application of balanced fertilizers are the main contributors towards better crop production in Pakistan. 53 Afzal and Luqman Int. J. Biosci. 2020 Table 11. Mean and SD of Legal/Institutional Risks. Legal/institutional risks Mean SD Uncertain financial policies (credit, saving and insurance) 3.19 1.146 Uncertain land policies and tenure system 3.18 1.109 Uncertain trade and market policies 3.16 1.204 Uncertain monetary and tax policies 3.11 1.141 Political instability 3.07 1.100 Lack of policies for land reforms 2.96 1.242 Irrelevant agricultural policies 2.88 1.218 Corrupt patwar system 2.78 1.213 Non-existence of effective farmers union 2.74 1.210 Overall Mean 3.01/5.00 1.176 Scale: 1 = S. Disagree, 2 = Disagree, 3 = Undecided, 4 = Agree, 5 = S. Agree. Table 12. Mean and SD of Risk Management Strategies being adopted by respondents. Risk Management Strategies Mean SD Adoption of multiple income sources 3.58 1.163 Obtained loan from Banks 3.34 1.158 Sale of physical/financial assets 3.27 1.204 Sharing of information within farming community 3.26 1.185 Adopt conservation agricultural technologies 3.22 1.214 Ensure timely supply of inputs 3.20 1.166 Use of ICTs for updated market information 3.18 1.221 Crop insurance policies 3.10 1.120 Personal insurance policies 3.09 1.169 Adopt suitable prevention measures against insect/pest attack 3.08 1.154 Contract farming 3.06 1.152 Growing crops other than cotton having high economic return 3.05 1.168 Pest control using biological methods 3.05 1.186 Maintaining feed/inputs reserves 3.05 1.127 Use of ICTs for weather forecast 3.04 1.169 Cooperation of farmers 3.04 1.228 Growing multiple crop varieties 3.03 1.154 Crop diversification 3.02 1.163 Establish strong linkages with Extension 2.95 1.159 Small dams/turbine scheme 2.80 1.245 Scale: 1 = V. Low, 2 = Low, 3 = Neutral, 4 = High, 5 = V. High. Market risks Different market oriented risks are being faced by Pakistani farmers like other farm producers of majority of the low income countries especially with agrarian nature of economy. According to the report of SDPI (2018), small land holders in Pakistan face multidimensional types of risks like climate related, market oriented and institutional ones. Different types of market oriented risks prevail in the research area with their respective mean values are presented in Table 7 given below. Data presented in Table 8 shows that among market related risks being faced by cotton growers of the