© 2015 Asian Economic and Social Society. All rights reserved ISSN(P): 2304-1455/ ISSN(E): 2224-4433 Volume 5(7), 158-166 158 FARMERS’ ATTITUDES TOWARD RISKS IN PADDY PRODUCTION OF NORTH- WEST SELANGOR INTEGRATED AGRICULTURAL DEVELOPMENT AREA (IADA), MALAYSIA Amin Mahir Abdullah and Abdullahi Auwal Gindi Faculty of Agriculture, Universiti Putra Malaysia, Malaysia Suryani Darham Institute of Agricultural and Food Policy Studies, Universiti Putra Malaysia, Malaysia Alias Radam Faculty of Economics and Management, Universiti Putra Malaysia, Malaysia Abstract1 The main objective of this research is to determine the reliability of the new developed instruments of farmers’ attitudes toward types of risks in paddy production. The different income level groups farmers’ attitude were compared. The data were collected from 286 paddy farmers at Selangor Integrated Agricultural Development Area (IADA), Malaysia. The Cronbach’s alpha coefficient of 0.894 was obtained, which is above the minimum acceptable value of 0.70. One way Analysis of Variance (ANOVA) test was conducted to determine whether significant difference on the attitude toward risk exist among different income level groups farmers. The ANOVA result shows significant differences in their attitudes toward risks with respect to three categories of risks: Production risk F (2, 282) = 21.477, P = 0.001, Financial risk F (2, 283) =21. 506, P= 0.001, and Environmental risk F (2, 283) = 27.245, P = 0.001. Keywords: Risk, attitude, income & reliability 1. INTRODUCTION 1.1. Background of the Study Agricultural sector, as worldwide phenomena, is exposed to variety of risks which include climate variability, weather related hazards of cyclone and flood, pest and diseases, commodity price fluctuation, change in consumer demand among others (Okezie & Amin, 2012).These results in variability in production and yield and unpredictability of the output. As in the rest of the world, climate change affects Malaysian agricultural sector in terms of production, this will also have socio-economic impact on the people in the sector and nation as a whole (Najim et al., 2007). The decision in environment for production activities are characterized by uncertainty or the absence of perfect or complete information or knowledge. Farmers usually make decisions now, which will affect their outputs or yield later. Farmers are not sure of the changes of some of the factors such as changes in weather, government policies, as well as the changes of new technologies which will be difficult for them to predict future with certainty. Corresponding author’s Name: Abdullahi Auwal Gindi Email address: gindiauwal@yahoo.com Asian Journal of Agriculture and Rural Development journal homepage: http://aessweb.com/journal-detail.php?id=5005 mailto:gindiauwal@yahoo.com Asian Journal of Agriculture and Rural Development, 5(7)2015: 158-166 159 Climate change will affect production as well as socio economic condition of farmers and economic of the Nation at large. Changing in climatic factors, results in a year to year variability of crop production, physical damage, loss of harvest, and drop in productivity. The report of the damages of the 2006 to 2007 floods in Johor displaced 110,000 people, damaging an estimated RM0.35 billion worth of infrastructure and causing RM2.4 billion in economic losses and an estimated RM84 million worth of agricultural produce was damage or lost affecting about 7,000 farmers (Okezie & Amin, 2012). Despite continuous increase of government subsidy for paddy farmers, the paddy planting area is decreasing as the farmers often experience adverse impacts of climate variation (Alam et al., 2010). Paddy like any other crops faces with similar uncertainties like adverse weather conditions such as drought and floods as well as pest and disease infestations. To the best of our knowledge, documented attitude of Malaysian paddy farmers toward different categories of risks associated with the paddy production is limited or unavailable, based on this assertion, the objective of this study is to identify and characterize different types of risks associated with paddy production and to evaluate the attitude of Malaysian paddy farmers toward different categories of risks. This study was carried out in the North West Selangor Integrated Agriculture Development Area (IADA) where paddy is the main crop of cultivation. The granary area was selected for it is one of the important paddy production areas. The IADA in North West Selangor consist of eight areas- Sawah Sempadan, Sungai Burung, Sekinchan, Sungai Leman, Pasir Panjang, Sungai Nipah, Panchang Bedena and BaganTerap. The total paddy planting area is 18,980 ha. There are total of 10,300 paddy farmers (producers) in the area. In 2011 the average yield was 5.5 tons/ ha. In attempt to maximize rice production in Malaysia, and also to minimize or mitigate risk factors associated with production, different management strategies were adopted by both farmers and government, ranging from informal practices such as avoidance of highly risky crops and crops and incomes diversifications to formal measures such as insurance (Dadzie & Acquah, 2012). They pointed out that households whose consumption levels are close to subsistence devote a large share of land to safer, traditional varieties of rice and other cereals than riskier, high-yielding varieties. Another research conducted by Jain & Parshad (2006) point out that subsistence household spatially diversify their plots reduce the impact of weather shocks that vary by location. 2. LITERATURE REVIEW Farmers’ attitudes to risks are the major determinants of the rate of diffusion of new technologies among them and of the outcome of rural development programs (Dadzie & Acquah, 2012). To make any technology or program successful or efficient, there is need to understand the farmers’ decision process, to have knowledge and understanding of risk and uncertainty associated with their decision making as well as the attitude of the various farmers’ groups toward risk. The degree to which farmers are willing to accept risk in production is related to their level of production technology as well as their demographic compositions of their households and hence the attitude of farmers toward risk was used as a measure of their investment behavior. Several approaches have been used by different researchers in measuring attitude toward risk as cited by Dadzie and Acquah (2012) whose ranges from direct and indirect approaches, economic anthropology, farmer risk programming, expected utility and safety- first theory approach, an interview method eliciting certainty equivalents and experimental gambling approach among others. Dadzie and Acquah (2012) used Equally Likely Certainty Equivalent with Pure Hypothetical Risky Prospective (ELCEPH) model in measuring attitude of "Agona Duakwa" farmers towards risk in crop production. Ellis (2000) used income variance approach in measuring attitude of farmers toward risk in production. He characterizes the farmers based on their degree of taking decision involving risk as either risk- preferring/loving/taking, risk neutral and risk averse. Others researchers such as Mauro et al. (2009) used eleven independent variables in their research on farmers knowledge and priori risk analysis across Canadian farmers in explaining their attitudes Asian Journal of Agriculture and Rural Development, 5(7)2015: 158-166 160 regarding benefits and risks of Roundup Ready Wheat (RRW). The variables include, among others, age of respondents, trust in corporations, formal education of the respondents, financial wellbeing and trust of government policies. Another study Hindi & Mahmoud (2011) measures attitudes toward risks of Jordan Valley Vegetable farmers. He used utility function model in measuring their attitudes toward risks and also examine the relationship between their demographic characteristics and their attitudes toward risks. This study and development of the likert scale was based on Bard & Berry (2000), and also Carlsson et al. (2005) was referred, this differentiate our work from many researchers conducted in this area. 3. METHODOLOGY The aim of this research study is to determine the reliability of the instruments intended to measure attitudes of farmers toward various risk categories and compare the attitude of difference farmers’ income level groups toward the various categories of risk associated with paddy production in the area. An instrument consisting of 47 statements or items referring to four different categories of risks designed for the study are as follows: Production Risk (PR) with 15 items, Financial Risk (FR) with 16 items, Social Risk (SR) with 10 items and Environmental Risk (ENR) with 6 items. Data were collected through an in depth survey of farmers in the area of Integrated Agricultural Development Area (IADA), North-West Selangor, Malaysia. The target group of the survey was the paddy farmers. The survey sampling technique was based on proportionate/simple random sampling procedure, with each farmer having the same opportunity (probability) of being selected. Respondents were then presented with these 47 statements which sought to measure their attitudes toward risks in rice production. A five point Likert scale was used where the respondents were requested to indicate the extent of agreement or disagreement with the statements, where 1 was "strongly disagree" to 5 "strongly agree". 3.1. Evaluation of reliability Reliability is the extent of how reliable is the said measurement model in measuring the intended constructs. Is the items measured what it supposed to be measured? Internal consistency is possible by Cronbach’s alpha, which is the most important reliability index being used in measuring reliability of the questionnaire where the values greater than 0.7 was considered acceptable (Field, 2009). The computation of alpha (α) is based on the reliability of a test relative to other test, and it estimates the proportion of variance that is systematic or consistent with a set of test scores. The value of alpha ranges from 0 (if no variance is consistent) to 1 (if all variance is consistent) and its formula is given as α = 𝑛 𝑛−1 [1 − 𝑛𝛴Vi 𝑉𝑡𝑒𝑠𝑡 ] where: ‘n’ is the number of questions, ‘Vi’ is the Variance of scores on each question and ‘Vtest’ is the total variance of overall scores on the entire test. One way Analysis of Variance (ANOVA) technique was used to test the claims that there is no difference among farmers’ income level groups in their attitudes toward risks in paddy production. Based on the data obtained from respondents’ net income (yield value), ranges from low of RM 1106 to high RM of 69631, farmers were categorized into three categories; low income, middle income and higher income level farmers (Hindi & Mahmoud, 2011). The assumptions of the use of ANOVA for the analysis was checked and fulfilled which include the dependent variable to be normally distributed and measured at least at the interval/ratio scale (Field, 2009) homogeneity/equality of variance (Howell, 2007) and the cases represent random samples from the population and scores on the test variables are independent of each other. 4. RESULT AND DISCUSSION Table 1 shows the median age of survey respondents is about 48 years (58%), more than ninety percent (90%) of the respondents are male while only less than 5% are female. About eighty seven percent (87%) of the respondents are married with the mean number of dependents of five (5) children and maximum number of eleven (11) children per respondent. In terms of different ethnic Asian Journal of Agriculture and Rural Development, 5(7)2015: 158-166 161 groups that are involved in paddy production from Table 1, it shows that about ninety three percent (93%) of the respondents belong to a Malay ethnic group while the remaining 5.6% and 0.7% of the two ethnic groups are Chinese and India respectively. Educational level of the respondents also is portrayed in Table1. From the Table, only 3.1% of the respondents reported having never been to school. The majority of respondents (57%) attended secondary school level of education while 35.5% and 5.9% attended primary and tertiary levels of education respectively. From Table 1, on average, each respondent had 6.22 hectares of the total farmland area. The median years of experience by the respondents in the paddy production was about 21 years, and maximum of 63 years of experience. The net income of the respondents from paddy produce was also reported in Table 1. The mean net income (yield value) was RM 17, 2726.8 per season; with range of yield values from RM 1106.65 to RM 69,631.00 per season. Table 1: Descriptive statistics of paddy farmers (N = 286) Variable Mean Percent (%) St. Deviation Minimum Maximum Age 48.34 58.40 11.74 20 75 Gender Male 95.5 Female 4.5 Marital status Single 10.1 Married 86.7 Widow/Widower 3.2 Number of dependents 5.00 1 11 Years of Experience 21.79 13.58 0 63 Ethnic group1 Malay 93.7 Chinese 5.6 India 0.7 Educational level2 No school 3.1 Primary School 35.5 Secondary School 57.5 Tertiary Schools 5.9 Farm size 6.22 4.5 0.5 24 Total Yield value 17276.80 13217.67 1106.65 69631 1 Ethnic group: 1 (Malay) 2 (Chinese) 3 (India) 2Education categories: 1 (None), 2 (Primary school), 3 (secondary School) 4 (collage/ University) 4.1. Reliability The following Tables of reliability and statistics (Table 2) give us the value of the coefficient of Cronbach’s alpha value (0.894) which is above the minimum acceptable value of 0.70. The values indicate that about 90% variance is consistent or in other words, only 10% variances are not consistent or reliable in measuring the intended constructs. Also in Table 2, score statistics give the score that is related to the scale entity that is the summary statistics for the 47 items. The summated scale ranges from a low of 47 to a high of 235 with the mean of 173.28 and standard deviation of 20.94. Table 3 is the Item Total Statistics, comprises with ‘Scale means if item deleted’ which present mean of the summated remaining items by excluding individual item from the list i.e. if PR1 is deleted, the mean of the sum of the remaining items will be 169.5455 units. ‘Scale variance if item deleted’ is the third column of the Table 3; it gives the variance for the remaining summated items by excluding a particular individual item from the list. ‘Correlated total item correlation’ is the Asian Journal of Agriculture and Rural Development, 5(7)2015: 158-166 162 correlation of the item in questioned with the summated score for all other items. The rule of thumbs is that these values should be at least 0.40 (Anastasiadou, 2011). ‘Alpha if item deleted’ this is probably the most important column in the Table. This represents the scale’s Cronbach’s alpha reliability coefficient for internal consistency if the individual item is removed from the scale. In Table 3, the scale’s Cronbach’s alpha would be 0.895 if item PR1 were removed from the scale. This value is then compared to the Alpha coefficient value of Table 2 (0.894) to see if one wants to delete the item in order to increase alpha value to 0.895. Gliem & Gliem (2003) provide the following rule of thumb: Alpha value > 0.9 is excellent, > 0.8 is good, > 0.6 is questionable, > 0.5 is poor and < 0.5 is unacceptable. Table 2: Reliability and scale statistics Cronbach’s alpha Mean Variance STD No. of Item 0.894 173.28 438.83 20.74 47 Table 3: Item-total statistics Item Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item- Total Correlation Cronbach's Alpha if Item Deleted PR1 169.545 431.821 .148 .895 PR2 169.758 426.394 .224 .894 PR3 170.010 416.053 .440 .891 PR4 169.709 415.245 .474 .890 PR5 169.209 427.114 .355 .892 PR6 169.786 419.270 .378 .892 PR7 169.804 414.762 .461 .890 PR8 169.174 422.699 .476 .891 PR9 169.055 423.821 .528 .891 PR10 169.213 420.947 .447 .891 PR11 169.377 426.495 .289 .893 PR12 169.318 422.716 .428 .891 PR13 169.153 430.362 .251 .893 PR14 169.440 419.412 .447 .891 PR15 170.244 413.568 .338 .893 FR1 169.727 423.869 .310 .893 FR2 170.132 421.989 .278 .893 FR3 169.909 424.651 .253 .894 FR4 169.874 424.103 .265 .893 FR5 170.178 412.793 .424 .891 FR6 169.912 419.561 .321 .893 FR7 170.286 413.096 .427 .891 FR8 169.954 419.356 .344 .892 FR9 169.933 416.118 .415 .891 FR10 170.080 421.071 .305 .893 FR11 169.318 424.211 .369 .892 FR12 169.307 425.028 .353 .892 FR13 170.087 417.343 .381 .892 FR14 169.559 418.184 .443 .891 FR15 170.136 414.034 .402 .891 FR16 169.835 425.134 .256 .893 SR1 169.538 420.902 .413 .891 SR2 169.580 415.957 .518 .890 SR3 169.230 420.262 .518 .890 Asian Journal of Agriculture and Rural Development, 5(7)2015: 158-166 163 SR4 169.395 422.921 .391 .892 SR5 169.451 417.694 .482 .890 SR6 169.639 418.526 .375 .892 SR7 169.139 423.840 .452 .891 SR8 169.055 422.544 .471 .891 SR9 168.965 426.062 .406 .892 SR10 169.461 417.927 .478 .890 ENR1 169.115 422.320 .526 .891 ENR2 169.087 422.768 .477 .891 ENR3 169.538 424.137 .331 .892 ENR4 169.195 426.411 .364 .892 ENR5 169.458 425.898 .315 .892 ENR6 169.975 423.042 .254 .894 One way Analysis of Variance (ANOVA) test was conducted to determine the significance difference between farmers’ income level groups in their attitudes toward risks categories associated with paddy production. The ANOVA result in Table (4) shows that there were significant difference between farmers’ income level groups in their attitudes toward risks with respect to three categories of risks: Production risk F (2, 282) = 21.477, P = 0.001, Financial risk F (2, 283) = 21.506, P= 0.001, and Environmental risk F (2, 283) = 27.245, P = 0.001. Only Social risk category shows statistically no difference between farmers’ income level groups. From the ANOVA result (Table 4), the attitude of farmers’ level income groups toward production, financial and environmental risks differs between low income level, middle income and high income level farmers, but there is no difference in their attitude toward social risk. Table 4: One way ANOVA results of categories of risk associated with paddy production ANOVA Sum of Squares df Mean Square F Sig.(p) Production Risk Between Groups 18.842 2 9.421 21.477 .000 Within Groups 123.706 282 0.439 Total 142.549 284 Financial Risk Between Groups 15.561 2 7.781 21.506 .000 Within Groups 102.385 283 0.362 Total 117.947 285 Social Risk Between Groups .362 2 0.181 0.554 0.575 Within Groups 92.419 283 0.327 Total 92.781 285 Environmental Risk Between Groups 19.124 2 9.562 27.245 .000 Within Groups 99.326 283 0.351 Total 118.450 285 Having confirmed from the ANOVA test, that there is a significant difference among farmers’ income level in their attitudes toward risks associated with paddy production except one category, yet ANOVA test only provides us with the information that difference exists between the groups, but there is no information about which among the groups differ from the others? To solve this problem, Fisher’s LSD (Least Significant Different) Post hoc pair wise multiple comparisons were used to determine which mean differs from another. Asian Journal of Agriculture and Rural Development, 5(7)2015: 158-166 164 From multiple comparison Table (5) of the three groups of farmers’ income level, it shows that there is difference between all the three income level groups in their attitudes toward production risk with Low income level (M = 3.34, SD = 0.736), Middle income level (M = 3.692, SD = 0.613) and High income level farmers (M = 2.34, SD = 0.236). In respect of their attitudes toward financial risk, there is a significant difference between Low income level (M = 3.647, SD = 0.615), Middle income level (M = 3.20, SD = 0.52) and High income level farmers (M = 2.95, SD = 0.44), but statistically there is no difference in the attitude of farmers toward financial risk between middle income level farmers and high income level farmers. Likewise the comparisons of the attitude of farmers’ income level groups toward environmental risk shows statistical significant difference between Low income (M = 3.34, SD = 0.528), Middle (M = 3.84, SD = 0.624) and High (M = 4.08, SD = 0.84), but no significant difference between middle income and high income level farmers in their attitude toward environmental risk. Table 5: LSD Post hoc pairwise multiple comparisons of farmers’ level of income groups Multiple Comparisons (I) Income category (J) Income category Mean Difference (I-J) Std. Error Sig.(p) 95% Confidence Interval Lower Bound Upper Bound Production Risk Low Income Middle Income -.23968* .080 .003 -.3976 -.081 Higher Income 1.11299* .217 .000 .6845 1.541 Middle Income Higher Income 1.35267* .216 .000 .9269 1.778 Financial Risk Low Income Middle Income .44490* .072 .000 .3018 .588 Higher Income .69782* .197 .000 .3088 1.086 Environmental Risk Middle Income -.49915* .071 .000 -.6401 -.358 Low Income Higher Income -.73360* .194 .000 -1.1167 -.350 *. The mean difference is significant at the 0.05 level The effect size was calculated from the ANOVA result, in order to identify the strength or magnitude of the existing differences among the farmers’ income level groups in their attitudes toward different types of risks associated with paddy production. The eta square (ƞ2) formula was used in estimating the effect size. The formula is ƞ2 = dfbetF/dfbetF + dfwt or ƞ2 = SSbet/SStot where: dfbet is degree of freedom between the group, dfwt is degree of freedom within the group, F is F value, SSbet is sum of square between the group and SStot is sum of square total (Cohen et al., 2003). The eta square (ƞ2) values of production, financial and environmental risk are 0.129, 0.13 and 0.161 respectively. As it was indicated from the calculated eta square (ƞ2) values, based on Cohen (1988) criteria (<= 0.01 to 0.05 is small effect size, 0.06 to 0.13 is medium or moderate effect size, and >= 0.14 is large effect size), it shows that the strength or magnitude of the differences in their attitude toward production and financial risks are moderate (0.13) while on the environmental risk the difference is large (0.16). 5. SUMMARY AND CONCLUSION The main objective of this research is to determine the reliability of the new developed instruments intended to measure attitudes of farmers toward various categories or types of risks associated with paddy production. Also the study aims to compare the attitudes of different farmers’ income level groups toward the various types of risks. The data used for the analysis were collected through an in depth survey from 286 farmers in the area of Integrated Agricultural Development Area (IADA), North-West Selangor, Malaysia. Asian Journal of Agriculture and Rural Development, 5(7)2015: 158-166 165 Reliability test was conducted on the 47 items of the questionnaire to measure internal consistency and reliability of the measurement model in measuring the intended constructs. The value of the coefficient of Cronbach’s alpha of 0.894 was obtained which is greater than the minimum acceptable value of 0.70. One way analysis of variance (ANOVA) test was used to determine whether a significant difference exists between farmers’ income level groups in their attitudes toward different types of risks associated with paddy production. Out of four types, only one (social risk) shows no significant difference between farmers’ income level groups on their attitudes toward risks. Fisher’s LSD Post hoc pairwise comparison was also conducted to determine which mean group among three groups of farmers differ from another. From the result of the analysis, it shows that there is difference between all the income level groups in their attitudes toward production and financial risks. The magnitude or effect size was computed using eta square (ƞ2) and the difference in their attitude toward production and financial risks are moderate while the difference in their attitude toward environmental risk is large. 5.1. Implication Research on attitude toward risk is very important in any decision making process. Attitude is considered the major constraint on adoption rate of any new technology or innovations by farmers, and consequently, paddy production is affected by this attitude. Therefore, there is a need for policy makers to consider and investigate the attitude of farmers (as a priority) towards any new technology or innovations in farming practices in order to bridge the gap of rejection and hence improve acceptance rate. Acknowledgement We would like to express our appreciation and gratitude to Universiti Putra Malaysia for providing the research grant under Research Universiti Grant Vote no: 91662 to support this study. Views and opinions expressed in this study are the views and opinions of the authors, Asian Journal of Agriculture and Rural Development shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. Reference Alam, M., Siwar, C., Murad, W., Molla, R. I., & Ekhwan, M. (2010). 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