J. Agr. Sci. Tech. (2021) Vol. 23(3): 487-498

Application of Weather Forecasts in Farm Management Decisions: The Case of

L. Parsi1 and H. Maleksaeidi1*

ABSTRACT

Weather forecasts have potential for improving adaptation and resilience of agricultural systems to climate changes; however, there is still uncertainty on the factors affecting the use of this information in farm management decisions. This survey study was conducted on the application of weather information by 213 farmers selected through a stratified random sampling technique in 21 rural areas of , in . The results indicated that perception of the reliability of weather information providers, availability of weather forecast information, self-efficacy, and subjective norm were the key drivers for using weather forecast information in the farm management decisions. Based on the results, confidence to the information providers was low among farmers. In addition, social norm about using weather information in practice was not strong in the study area. The results of the study highlighted the need for improving beliefs and values of farmers and their communities about the importance of using weather forecast information for adaptation to climate change.

Keywords: Adaptation to climate change, Agricultural decision making, Weather threats.

INTRODUCTION live in the arid and semi-arid regions of developing countries and are vulnerable to Agricultural production is significantly climate change due to lack of facilities affected by extreme temperature events (Biglari et al., 2019; Erena et al., 2019; (Jamshidi et al., 2018; Bijani et al., 2017; Trinh et al., 2018; Kumar et al., 2016; Hatfield and Prueger, 2015; Zhang et al., Maleksaeidi and Karami, 2013; Altieri and 2017; Chen et al., 2016), the variation in the Nicholls, 2017). In such a situation, it is rainfall patterns (Khatri-Chhetri et al., 2017; important to empower farmers in order to Crane et al., 2011), wind speed trends make the right management decisions on the (Sharratt et al., 2015), and climate disasters farm for withstanding climate changes, Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 such as floods and droughts (Yu et al., 2014; adapting to these changes, and improving Maleksaeidi et al., 2016). Based on the their resilience capacity to mitigate the statistical estimates, the loss of yield due to impacts of such changes (FAO, 2014). climate change could be over 35% for rice, Many authors including Roudier et al. 20% for wheat, 60% for maize and 13% for (2014), Kolawole et al. (2014), Takle et al. barely, depending on the region and the (2014), Morton et al. (2017), and Nidumolu severity of changes (Khatri-Chhetri et al., et al. (2018) have emphasize that the 2017). This drop in agricultural productivity capability to forecast climate changes on a has led to increased poverty and food seasonal or shorter time scale, adoption of insecurity in the world, particularly among information disseminated from these marginalized and subsistence farmers who forecasts and regulating agricultural

1 Department of Agricultural Economic and Extension, College of Agriculture, University of Kurdistan, Sanandaj, Islamic Republic of Iran * Corresponding author; e-mail: [email protected]

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practices according to this information are the second-largest economic sector in Iran the key points for building resilience and that provides employment opportunities for adaptation to climate change threats. For 23% of the country's population and supplies example, in 1992, when the Brazilian 80% of the country's food (Mohammadian government warned farmers about the Mosammam et al., 2016), various studies occurrence of the El Niño and provided have indicated significant impacts of climate them free drought tolerant seeds, the country change on agricultural productivity in Iran. witnessed an increase in the agricultural For instance, Nassiri et al. (2006) estimated productivity as a result of farmers' that by 2025, with an increase of 2.7°C in adaptation to this phenomenon (Patt et al., the average temperature, the average yield of 2005). Generally, a precise prediction of rainfed wheat - as one of the most important climate change gives farmers an opportunity cereals in Iran (Mohammadian Mosammam to approach weather threats, allows them et al., 2016)- would be reduced by 18%. In enough time to prepare for changes, make addition, based on a simulation, Moradi et informed decisions and tailor their al. (2014) estimated that if no adaptation management activities to these changes strategy were applied, the corn yield would (Petersen and Fraser, 2001; Losee and drop between 1 and 42% by 2100 due to Joslyn, 2018). climate change. Overall, without paying Over the past two decades, advances in attention to the adaptation strategies, climate technology have made it possible to improve change would limit agricultural production the quality and lead-time of weather in different areas of Iran (Karimi et al., forecasts and their interpretations (Kusunose 2018). In such circumstances, providing and Mahmood, 2016; Kenkel and Norris, weather forecasts and using them in practice 1995). Theoretically, with increasing is crucial for farmers to prepare for climate uncertainty in weather and climate patterns, changes and adaptation to these changes it is expected that the value of weather (Losee and Joslyn, 2018). information for farmers will increase and, Farmer's decision to rely on weather under an ideal management scenario, we see forecast information is likely a function of more application of such information in many factors (Kusunose and Mahmood, decision making (Kusunose and Mahmood, 2016). These include availability of weather 2016). However, lack of sufficient studies forecasts (Changnon, 2004; Frisvold and on the adoption of weather information in Murugesan, 2013; Artikov et al.. 2006), practice by users, particularly by poor confidence in accuracy of these forecasts resource and vulnerable farmers who live in (Kusunose and Mahmood, 2016; Kox et al., Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 developing countries such as Iran, has led to 2015; Hu et al. (2006); Changnon (2004) a lack of scientific evidence for this issue and Artikov et al. (2006)), trust in the (Haigh et al., 2018). reliability of the sources making the Iran is one of the developing countries forecasts (Kox et al., 2015; Artikov et al., where agricultural production faces great 2006; Hu et al., 2006), perception of the deal of uncertainties in climate. The main forecasts’ usability (Changnon, 2004; characteristic of arid or semi-arid climate of Sharifzadeh et al., 2010), subjective norms Iran is low rainfall and high evaporation (Artikov et al., 2006; Sharifzadeh et al., (Amiri and Eslamian, 2010). The average 2010), and self-efficacy such as ability for annual rainfall of 224 to 275 mm has made interpreting and understanding weather Iran one of the driest countries in the world forecasts and having adequate motivation for (Keshavarz et al., 2013). In addition, the using theses information (Articov et al., average temperature in this country is 2006; Hu et al., 2006). In this regard, access predicted to increase from 1.5 to 4°C, if the to scientific evidence about using weather CO2 concentration doubles by 2100 (Amiri information in practice by farmers is and Eslamian, 2010). While agriculture is necessary to clear the value of climate

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information for farmers as the main on-farm wheat yields between 2006-2010 as a result decision makers (Roudier et al., 2014). In of drought. Based on this, in 2010, Jihad-e- addition, an understanding of the factors Agriculture Organization of Khuzestan affecting the application of weather Province converted 130 hectares of land information by farmers in agricultural from the city of Veys to a training site and decision making may create a base to organized training courses in various fields, formulize policy commendations that can including the use of weather information for increase the use of this information among farmers. However, investigations have not farmers, thereby not only reducing their been conducted on farmers in the area about vulnerability to climate change but also the the use of meteorological information in costs involved in producing this information agricultural decisions, as well as their level due to its use in practice. of trust in and access to this information. Therefore, the aim of this study was to Accordingly, in this study, Veys city was understand Iranian farmers' use of weather selected as the study area. Figure 1 shows information, as well as factors affecting the the geographic location of the study area. use of this information in farm management decision by the studied farmers. Survey of Farmers

MATERIALS AND METHODS A survey was conducted on farmers in the rural areas of Veys. The total number of Study Area farmers in the study area was 480. The sample size was determined as 213, based Veys is the capital of Veys District in Bavi on Cochran (1963) formula. This number of County, Khuzestan Province, southwest of farmers was randomly selected based on Iran. This area is a part of River stratified random sampling from 21 villages. Basin (Molaali and Babaee, 2017). Veys has Each village was considered as a stratum fertile fields, large gardens, and vast modern and then farmers were randomly selected in farms. Based on the country divisions in proportion to the farmers’ population in each 2016, Veys consists of 30 villages, 9 of village. Farmers were interviewed which have been vacant for various reasons, individually through a structured mainly climate disasters including drought questionnaire. Face validity of the (Statistical Center of Iran, 2016). While questionnaire was confirmed by a panel of experts of climatology and agricultural Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 Khuzestan is one of the main agricultural pillars in Iran, temperature in different parts extension and education. Before survey was of this province, such as Veys, sometimes done, the questionnaire was pre-tested with reach over 50°C. However, the predictions 30 farmers in the rural areas of to indicate an increase in the average correct the questions and examine their temperature of this area in the coming years reliability by calculating Cronbach's Alpha. (Masoudi and Elhaeesahar, 2016). Also, The questionnaire was divided into three though the average annual rainfall in the sections. The first section included socio- region is 226 mm, it is predicted that economic characteristics of the farmers. In reducing rainfall in the future years will turn the second section, farmers were asked to different parts of this region into a drier area state if they used weather forecast (Masoudi and Elhaeesahar, 2016). Now also, information in farm management decisions. climate change, particularly drought, has Farmers were asked to specify their response caused serious challenges for farmers in the on a double spectrum (YES= 1 or NO= 0). area. For example, Molaali and Babaeei The third section was designed to assess (2017) found that Veys and two other cities availability of weather forecasts, farmers’ near it experienced a sharp fall in irrigated perception about the accuracy of weather

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Figure 1. Geographic location of the study area. forecasts, farmers’ perception about the al., 2006), α is a constant (the value of the reliability of the weather information criterion when the predictor is equal to providers, farmers’ perception about zero), X1, X2, …, Xn represent the predictors forecasts' usability, subjective norm and (independent variables) and B1, B2, …, Bn, self-efficacy to interpret and apply these are the partial regression coefficients that forecasts in the farm management decisions. indicate the change in probability of Each of these variables was assessed using membership for any 1-unit change in the several closed-ended questions on a 5-point independent variable. B weights are Likert scale (Completely disagree= 1; calculated through maximum likelihood Disagree= 2; No idea= 3; Agree= 4; estimation after transforming the dependent Completely agree=5). Table 1 shows variable into a logit variable (Meyers et al., research variables, items used to measure 2006). them and the calculated Cronbach's Alpha Also, coefficients for variables measured with (2) Likert spectrum. Where, signifies the total distinction value that indicates the quantitative Analysis characteristics of i th farmer, Equation (1) can be converted to Equation (3). Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 Binary logistic regression analysis was used to explore factors influencing use of weather forecasts information by the studied (3) farmers. Forward stepwise (conditional) If 0.50, then the i th farmer is method was used to eliminate variables. The classified as a user of weather forecast information, and if 0.50, then the i th farmer is classified as a non-user of weather forecast information. Indices that are applied for evaluation of logistic model here is as follows: logistic regression model here are Cox and (1) Snell and the Nagelkerke tests, Wald test and the significance level of Chi square (χ2). Where, represents probability of putting i th farmer in a group that uses weather SPSS version 23 was used for doing forecast information in farm management analyses. decisions, e is exponential function and has a value of approximately 2.718 (Meyers et

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Table 1. Variables, definitions, measuring items and Cronbach's Alpha coefficients. Cronbach's alpha Variables Definition Statements Source coefficients Dependent variable Using weather forecast information is a binary Using weather forecast information in farm variable. It was given a value of 1, if a farmer management decisions (Y) used weather information in farm management decisions, and 0 if not. Independent variables Socio-economic variables: Age (Year) (X1) Farm experience (Year) (X2) Education (Years of schooling) (X3) Farm size (ha) (X4) Access to weather forecasts is possible for me Changnon via: (2004); Availability The ability to access 1) Radio Frisvold and of weather weather information via 2) National TV Murugesan forecasts media and other tools 3) Web sites and web sources (2013); 0.77 (X5) 4) Newspaper Artikov et al. 5) Satellite TV (2006) 6) Contacting the 1559 weather phone answering service I confidence in the accuracy of forecast Kusunose and regarding: Mahmood Perceived 1) Temperature (2016); accuracy of Individual's confidence in 2) Chance of precipitation Kox et al. 0.74 weather the accuracy of weather 3) Amount of precipitation (2015); Artikov forecasts forecasts. 4) Chance of thunderstorm et al. (2006), Hu (X6) 5) Chance of storm et al. (2006) 6) Chance of dust I trust to the reliability of weather information Perceived providers including: reliability of Individual's trust to the 1) Radio Kox et al. the weather reliability of the weather 2) National TV (2015); Artikov information information providers and 3) Web sites and web sources et al. (2006), Hu providers the informants. 4) Newspaper et al. (2006) 0.72 (X7) 5) Satellite TV 6) Contacting the 1559 weather phone answering service 1) Weather forecasts are simple to interpret

Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 Individual's perception of and use in practice. Perceived the applicability of 2) Weather forecasts are produced according Changnon forecasts’ weather forecasts to local conditions. (2004); usability information in practice. 3) Weather forecasts are provided on time for Sharifzadeh et 0.76 (X8) use. al. (2010) 4) Predictions for crop-specific critical periods are available. Individual's perception of 1) My family believe that weather forecasts Articov et al. the community should influence my crop-related decisions. (2006); Subjective expectations about using 2) My friends and neighbors believe that I Sharifzadeh et norm or not using weather should use weather forecasts in farm al. (2010), Hu et (X9) forecasts in farm management decisions. al. (2006) management decision. 3) Most people who are valuable to me believe that I should use weather forecasts in farm 0.79 management decisions. Individual capabilities to 1) I have enough literacy to understand and Articov et al. Self-efficacy interpret, understand and use of weather forecasts. (2006); Hu et (X10) use of weather forecasts 2) I have enough motivation to use weather al. (2006) 0.71 in farm management forecasts. decision.

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RESULTS Predictive Model of Factors Affecting Use of Weather Information Overview of Profiles of Farmers As described in the methodology section, The mean age of farmers was 35.04 years logistic regression analysis using forward with a Standard Deviation (SD) of 10.56. They conditional method was applied for had 1-40 years of farming experience (SD= developing a predictive model of factors 8.58) (Table 2). Majority of farmers were male determining use of weather forecasts (95.8%) and married (93.4%). Agriculture was information by the surveyed farmers. Data the main occupation of about 80% of the analysis revealed that only 37.09% of studied farmers. Based on Table 2, the average farmers in the study area used weather education level of farmers was 8.12 years with forecast information in the farm the minimum of 0 and maximum of 16 (SD= management decisions, while the majority of 2.94). Also, the mean farm size of farmers was them (62.91%) did not use weather 1.9 ha, with the minimum of 0.6 and information in practice. Table 3 indicates the maximum of 6 ha (SD= 0.87). goodness of fit indices for the logistic Based on Table 2, the mean value of model. Based on this table, the logistic regression went up to four steps. As shown availability of weather forecasts among 2 farmers was moderate (16.77 out of 30). in this table, the Negelkerke R revealed that Also, from farmers' point of view, the 41% of the variation in the use of weather accuracy of weather forecasts information forecast information in the farm was moderate (15.05 out of 30). However, management decisions is explained by the farmers had relatively little confidence in model. This percentage is appropriate, as the reliability of the weather information values of logistic regression scales are most providers such as radio and national TV of the time much lower than the corresponding ones for a linear model (11.53 out of 30). From the perspective of 2 the farmers, the usability of weather forecast (Norusis, 2005). Chi-square (χ ) information was moderate (10.30 out of 20). improvements presented in Table 3 evaluate While farmers' perceptions of their ability to the contribution of each predictive variable to the model. Since, the value of Chi-square understand and apply weather forecast 2 information (self-efficacy) was relatively (χ ) has improved in each step and the high (6.71 out of 10), based on the findings significance level of chi-squares in Table 2, the mean value of subjective improvements are smaller than 0.05, it can Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 norm among farmers was moderate (7.20 out be concluded that independent variables can of 15). increase the predictive power of the model Table 2. Summary statistics of the research variables used in the binary logistic model. Variablesa Min Max Mean ±Std deviation Age (years) (X1) 22 75 35.04 10.56 Farm experience (Year) (X2) 1 40 15.33 8.58 Education (Years of schooling) (X3) 0 16 8.12 2.94 Farm size (ha) (X4) 0.6 6 1.9 0.87 Availability of weather forecasts (X5) 7 22 16.77 2.49 Perceived accuracy of weather forecasts (X6) 9 20 15.05 4.70 Perceived reliability of the weather information providers (X7) 6 27 11.53 3.60 Perceived forecasts’ usability (X8) 4 20 10.30 3.54 Subjective norm (X9) 3 15 7.20 2.83 Self-efficacy (X10) 2 10 6.71 2.18 a Range of variables: (X5)= 6-30; (X6)= 6-30; (X7) =6-30; (X8) =4-20; (X9)= 3-15; (X10)=2-10.

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Table 3. Fit indices for the logistic regression model. Cox and Snell Chi-square (χ2) Steps Nagelkerke R2 -2 log likelihood df Sig R2 improvement 1 0.14 0.19 249.38 31.53 1 0.000 2 0.22 0.30 228.69 20.68 1 0.000 3 0.26 0.36 215.73 12.96 1 0.000 4 0.30 0.41 204.22 11.50 1 0.001

compared to the situation where they are not variable entered into the regression model used. In addition, reducing the value of -2 was perceived reliability of the weather log likelihood in each step represents an information providers. This variable shows a increase in the model power by adding each positive coefficient for the logistic model new variable in each step. (B= 0.29). By increasing one unit in the Table 4 indicates the variables entered into value of this variable, the probability of the regression model and their coefficients membership of a farmer in the group that of influence. As shown in this table, of the uses weather information in practice will ten independent variables examined, increase 1.47 times. The significance level including socio-economic variables and of the Wald statistic (0.000) shows this other independent measures (see Table 1), parameter is different from zero. The second four variables including perceived reliability variable that entered into the logistic model of the weather information providers, was availability of weather forecasts (B= availability of weather forecasts, self- 0.26). According to the regression results in efficacy, and subjective norm entered into Table 4, by increasing one unit in the value the logistic model. In this table, the of this variable, the probability of positivity of B coefficients indicates that by membership of a farmer in the group that increasing the value of independent uses weather information in practice will variables, the probability of using weather increase 1.30 times. Self-efficacy was the forecast information in the farm third variable that entered the regression management decisions improves. Also, the model (B= 0.66). In fact, by increasing one significance of Wald statistics indicates the unit in the value of this variable, the significance of coefficients of the variables probability of membership of a farmer in the entered into the regression model. In group that uses weather information in farm- addition, the "odds ratio" is an estimate of level decisions will increase 1.93 times. The increasing the percentage of membership in last variable that entered into the equation Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 the target group (a group that uses weather was subjective norm (B= 0.25). Based on information in farm-level management Table 4, by increasing one unit in the value decisions) by increasing one unit in the of subjective norm, the probability of predictive variable. membership of a farmer in the group that Based on the results in Table 4, the first uses weather information in practice will Table 4. Variables entered in the logistic regression (forward conditional) model at the end of step 4. Odd Variables B SE Wald df Sig ratio (Exp(B)) Perceived reliability of the weather information providers 0.29 0.06 24.41 1 0.000 1.47 (X7) Availability of weather forecasts (X5) 0.26 0.08 10.73 1 0.001 1.30 Self-efficacy (X10) 0.66 0.12 32.11 1 0.000 1.93 Subjective norm (X9) 0.25 0.07 11.57 1 0.001 1.28 Constant 6.26 1.46 18.35 1 0.000 5.14

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increase 1.28 times. Finally, the regression The results of analysis showed that the equation is constructed with these four majority of farmers do not use weather variables. information in their farm management Based on Equation (2), the following decisions. This finding is consistent with the formula is obtained: results of study by Sharifzadeh et al. (2010), Yi = 6.26 + 0.29X7 + 0.26X5 + 0.66X10 + which showed that wheat growers in Fars 0.25X9 (4) Province of Iran rarely use weather Then, by merging Equation (4) into information in agronomic decision making. Equation (3), the following equation is The results of logistic regression analysis obtained: indicated that using weather information by farmers was influenced by their perception of the reliability of the weather information providers, availability of weather forecast (5) information, their self-efficacy, and subjective norm. Accordingly, it is expected DISCUSSION that by increasing farmers' trust in the reliability of the weather information providers and the informants, the extent of Production in the agricultural sector faces using this information in farm management many uncertainties, most of which are due to decisions will be increased. While this day-to-day weather variability and annual finding is in line with the results of the study climate fluctuations (Kusunose and by Artikov et al. (2006) and Hu et al. Mahmood, 2016). Under such situations, it (2006), another part of our study showed is necessary to look for options that will that farmers in the study area had little trust improve the adaptation and resilience of in the reliability of the resources they used farmers, particularly vulnerable smallholder to receive weather information. This issue farmers in developing countries, against could be one of the reasons for not applying these threats (Keshavarz et al., 2017). From weather information by the majority of the the viewpoint of many researchers (e.g. studied farmers. The availability of weather Morton et al., (2017) and Nidumolu et al. information is the second factor that is (2018)), regulating agricultural activities expected by increasing it, using information based on weather forecasts information is a in farm management decisions will be main option for building resilience and increased by farmers. This finding is in adaptation to climate change. In theory, it agreement with the results of studies by Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 seems that with the advancement of weather Changnon (2004) and Artikov et al. (2006). forecast technologies and the dissemination On the other hand, the results of study of the information, farmers quickly get this showed farmers access to weather information and use it in their management information in the study area is at medium decisions on the farm. However, in practice, level. Perhaps, as Hosseini et al. (2009) farmers' decision for adaptation to weather point out, one of the reasons for this is the forecast information is influenced by a lack of farmers' access to internet websites variety of factors (Kusunose and Mahmood, due to the shortage of proper 2016) and their recognition is essential for telecommunication infrastructure in rural understanding the value of this information areas of Iran. Based on the results of logistic to farmers and improving its use in farm regression analysis, the third factor affecting management decisions. Therefore, the aim the use of weather information by farmers is of this study was to understand the use of their level of self-efficacy regarding their weather information in practice by farmers ability to understand and use this and factors affecting it. information. This finding also confirms the results of studies by Articov et al. (2006),

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Hu et al. (2006) and Sharifzadeh et al. forecasts in agriculture, involving farmers, (2010). Based on the results, social norm particularly the local leaders who are often was the fourth and last effective factor in trusted by farmers, in the dissemination of prediction of the application of weather climate information can play an important information by farmers. This finding role in using this information in the farm indicates that more pressure from the management decisions. Considering the community for using weather information importance of meteorological information leads to more use of this information in availability in the application of this practice. This is in congruent with the results information in practice, in order to increase of studies by Articov et al. (2006), Hu et al. the access of farmers groups to such (2006), and Sharifzadeh et al. (2010). information, it is recommended that Despite this fact, contrary to the findings of different, multiple, and varied channels be the study by Articov et al. (2006), farmers in used for transmitting this information to the present study did not feel considerable different groups of farmers. Finally, pointing pressure from the community for using out the tips for future research will be weather forecast information in the farm worthwhile. Firstly, due to the limitations of management decisions. This is another quantitative research methods such as their reason for not using weather information by reliance on self-reported measures, it is the majority of farmers in the study area. suggested that qualitative research also be used to obtain more accurate and deeper information on the use of climate CONCLUSIONS information in farm management decisions. Secondly, other important variables can This research adds new knowledge to the certainly be useful in explaining and existing scientific literature about factors forecasting the use of climate information in driving application of weather forecasts in farm management decisions by farmers. farm management decisions. The study Hence, it is necessary to consider such indicated that perception of the reliability of variables in the future studies. the weather information providers, availability of weather forecast information, self-efficacy, and subjective norm are the REFERENCES key determinants of using weather forecast information in farm management decisions 1. Amiri, M. J. and Eslamian, S. S. 2010. by farmers. Based on the results, the amount Investigation of Climate Change in Iran. J. Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 of confidence in the information providers Agr. Sci. Tech., 3(4): 208-216. was low among farmers, while availability 2. Altieri, M. A and Nicholls, C. I. 2017. The of weather information by farmers and Adaptation and Mitigation Potential of Traditional Agriculture in a Changing social norm about using weather forecast in Climate. Clim. Change., 140(1): 33-45. practice was moderate in the study area. 3. Artikov, I., Hoffman, S. J., Lynne, G. D., The lack of social norms regarding the use Pytlik Zillg, L. M., Hu, Q., Tomkins, A. J., of weather forecasts in agriculture Hubbard, K. G., Hayes, M. J. and Waltman, emphasizes the need for improving beliefs W. J. 2006. Understanding the Influence of and values of farmers and their communities Climate Forecasts on Farmer Decisions as about the importance of climate information Planned Behavior. J. Appl. Meteorol. Clim., in coping with climate change as an 45: 1202-1214. important step in the application of this 4. Biglari, T., Maleksaeidi, H., Eskandari, F. information in practice. Also, given the and Jalali, M. 2019. Livestock Insurance as a Mechanism for Household Resilience of importance of confidence in the information Livestock Herders to Climate Change: providers as one of the most important Evidence from Iran. Land Use Policy, 87: determinants of application of weather 104043.

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5. Bijani, M., Ghazani, E., Valizadeh, N. and 16. Hosseini, S. J. F., Niknami, M. and Chizari, Haghighi, N. F. 2017. Pro-Environmental M. 2009. To Determine the Challenges in Analysis of Farmers' Concerns and the Application of ICTs by the Agricultural Behaviors towards Soil Conservation in Extension Service in Iran. J. Agric. Ext. Central District of Sari County, Iran. Inte. Rural Dev., 1(1): 27-30. Soil. Water. Conserv. Res., 5(1): 43-49. 17. Hu, Q., Pytlik Zillg, L. M., Lynne, G. D., 6. Changnon, S.A. 2004. Changing Uses of Tomkins, A.J., Waltman, W. J., Hayes, M. Climate Predictions in Agriculture: J., Hubbard, K. G., Artikov, I., Hoffman, S. Implications for Prediction Research, J. and Wilhite, D. A. 2006. Understanding Providers, and Users. Weather Forecast., Farmers’ Forecast Use from Their Beliefs, 19: 606-613. Values, Social Norms, and Perceived 7. Chen, Ch., Chen, X. and Xu, J. 2016. Obstacles. J. Appl. Meteorol. Clim., 45: Impacts of Climate Change on Agriculture: 1190-1201. Evidence from China. J. Environ. Econ. 18. Karimi, V., Karami, E. and Keshavarz, M. Manag., 76: 105-124. 2018. Climate Change and Agriculture: 8. Cochran, W. G. 1963. Sampling Techniques. Impacts and Adaptive Responses in Iran. J. 2nd Edition, John Wiley and Sons, Inc., Integr. Agric., 17(1): 1–15. New York. 19. Kenkel, P. L. and Norris, P. E. 1995. 9. Crane, T. A., Roncoli, C. and Hoogenboom, Agricultural Producers' Willingness to Pay G. 2011. Adaptation to Climate Change and for Real-Time Mesoscale Weather Climate Variability: The Importance of Information. J. Agr. Resour. Econ., 20(2): Understanding Agriculture as Performance. 356-372. NJAS – Wagen. J. Life Sc., 57: 3-4. 20. Keshavarz, M., Maleksaeidi, H. and Karami, 10. Erena, E., Brotons, J. M., Conesa, A., E. 2017. Livelihood Vulnerability to Manera, F. J., Castaner, R. and Porras, I. Drought: A Case of Rural Iran. Int. J. 2019. Influence of Climate Change on Disast. Risk. Re., 21: 223–230. Natural Degreening of Lemons (Citrus 21. Keshavarz, M., Karami, E. and Vanclay, F. limon L. Burm. f). J. Agr. Sci. Tech., 21: 2013. The Social Experience of Drought in 169-179. Rural Iran. Land Use Policy, 30: 120– 129. 11. FAO. 2014. Enabling Farmers to Face 22. Khatri-Chhetri, A., Aggarwal, P. K., Joshi, Climate Change. Available on: P. K. and Vyas, S. 2017. Farmers' www.fao.org/3/a-i4020e.pdf (Accessed 22 Prioritization of Climate-Smart Agriculture Jun 2018) (CSA) Technologies. Agric. Syst., 151: 184– 12. Frisvold, G.B. and Murugesan, A. 2013. Use 191. of Weather Information for Agricultural 23. Kolawole, O. D., Wolski, P., Ngwenya, B. Decision Making. Weather Clim. Soc., 5: and Mmopelwa, G. 2014. Ethno- 55-69. Meteorology and Scientific Weather Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 13. Jamshidi, O., Asadi, A., Kalantari, Kh. and Forecasting: Small Farmers and Scientists’ Azadi, H. 2018. Perception, Knowledge, and Perspectives on Climate Variability in the Behavior towards Climate Change: A Okavango Delta, Botswana. Clim. Risk Survey among Agricultural Professionals in Manag., 4–5: 43–58. Hamadan Province, Iran. J. Agr. Sci. Tech., 24. Kox, T., Gerhold, L. and Ulbrich, U. 2015. 20: 1369-1382. Perception and Use of Uncertainty in Severe 14. Haigh, T., Koundinya, V., Hart, Ch., Klink, Weather Warnings by Emergency Services J., Lemos, M., Mase, A. S., Prokopy, L., in Germany. Atmos. Res., 158-159: 292-301. Singh, A., Todey, D., Widhalm, M. 2018. 25. Kumar, A., Sharma, P. and Joshi, S. 2016. Provision of Climate Services for Assessing the Impacts of Climate Change on Agriculture: Public and Private Pathways to Land Productivity in Indian Crop Farm Decision-Making. B. Am. Meteorol. Agriculture: An Evidence from Panel Data Soc., doi:10.1175/BAMS-D-17-0253.1, in Analysis. J. Agr. Sci. Tech., 18: 1-13. press. 26. Kusunose, Y. and Mahmood, R. 2016. 15. Hatfield, J. L. and Prueger, J. H. 2015. Imperfect Forecasts and Decision Making in Temperature Extremes: Effect on Plant Agriculture. Agric. Syst., 46: 103–110. Growth and Development. Weather. Clim. 27. Losee, J. E. and Joslyn, S. 2018. The Need Extremes., 10(Part A): 4-10. to Trust: How Features of the Forecasted

496 Weather Forecasts in Farm Management Decisions ______

Weather Influence Forecast Trust. Int. J. 37. Nidumolu, U., Lim-Camacho, L., Gaillard, Disast. Risk. Re., doi: E., Hayman, P. and Howden, M. 2018. 10.1016/j.ijdrr.2018.02.032, in press. Linking Climate Forecasts to Rural 28. Maleksaeidi, H. and Karami, E. 2013. Livelihoods: Mapping Decisions, Social-Ecological Resilience and Information Networks and Value chains. Sustainable Agriculture under Water Weather. Clim. Extremes., in press. Scarcity. Agroecol. Sust. Food., 37: 262– 38. Norusis, M. 2005. SPSS 14.0 Statistical 290. Procedures Companion. Prentice Hall, New 29. Maleksaeidi, H., Keshavarz, M., Karami, E. Jersey, USA. and Eslamian, S. 2016. Climate Change and 39. Patt, A., Suarez, P. and Gwata, Ch. 2005. Drought: Building Resilience for an Effects of Seasonal Climate Forecasts and Unpredictable Future. In: “Handbook of Participatory Workshops among Subsistence Drought and Water Scarcity: Environmental Farmers in Zimbabwe. PNAS, 102(35): Impacts and Analysis of Drought and Water 12623-12628. Scarcity”, (Eds.): Eslamian, S. and 40. Petersen, E. and Fraser, R. 2001. An Eslamian, F. CRC Press, Taylor and Francis Assessment of the Value of Seasonal Group, New York. Forecasting Technology for Western 30. Masoudi, M. and Elhaeesahar, M. 2016. Australian Farmers. Contributed Paper Trend Assessment of Climate Changes in Presented at the 45th Annual Conference of Khuzestan Province, Iran. Natural Environ. the Australian Agricultural and Resource Change., 2(2): 143- 152. Economics Society, Adelaide, 22 – 25 31. Meyers, L. S., Gamst. G. and Guarino, A. J. January 2001. 2006. Applied Multivariate Research: 41. Roudier, P., Muller, B., d’Aquino, P., Design and Interpretation. SAGE Roncoli, C., Soumaré, M. A., Batté g, L. and Publication Inc., London. Sultan, B. 2014. The Role of Climate 32. Mohammadian Mosammam, H., Forecasts in Smallholder Agriculture: Mosammam, A. M., Sarrafi, M., Tavakoli Lessons from Participatory Research in Two Nia, J. and Esmaeilzadeh, H. 2016. Communities in Senegal. Clim. Risk Analyzing the Potential Impacts of Climate Manag., 2: 42–55. Change on Rainfed Wheat Production in 42. Sharifzadeh, M., Zamani, Gh. H. and Hamedan Province, Iran, via Generalized Karami, E. 2010. Factors Affecting the Additive Models. J. Water and Clim. Application of Meteorological Information Change., 7(1): 212-223. in Farmers' Decision Making. Iranian J. 33. Molaali, P. and Babaee, O. 2017. A Review Agri. Econom. Devel. Res., 41-42(4): 541- of the Effects of Drought on the Grain Yield 555. (In Persian). in the Vays, , and Salamat Regions 43. Sharratt, B. S., Tatarko, J., Abatzoglou, J. of the Khuzestan Province. In: “Global T., Fox, F. A. and Huggins, D. 2015. Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021 Changes and Natural Disaster Implications of Climate Change on Wind Management: Geo-Information Erosion of Agricultural Lands in the Technologies”, (Eds.): Pirasteh, S. and Li, J. Columbia Plateau. Weather. Clim. Springer International Publishing, Extremes., 10(A): 20-31. Switzerland. 44. Statistical Center of Iran. 2016. Statistical 34. Moradi, R., Koocheki, A. and Nassiri, M. Yearbook of Khuzestan Province. Available 2014. Adaptation of Maize to Climate on: Change Impacts in Iran. Mitig. Adapt. https://nnt.sci.org.ir/sites/Apps/yearbook/Lis Strateg. Glob. Chang., 19: 1223-1238. ts/year_book_req/Item/newifs.aspx. 35. Morton, L. W., McGuire, J. M. and Cast, A. 45. Takle, E. S., Anderson, Ch. J., Andresen, J., D. 2017. A Good Farmer Pays Attention to Angel, J. and Elmore, R. W. 2014. Climate the Weather. Clim. Risk Manag., 15: 18-31. Forecasts for Corn Producer Decision 36. Nassiri, M., Koocheki, A., Kamali, G. A. Making. Earth Interact., 18(5): 1-8. and Shahandeh, H. 2006. Potential Impact of 46. Trinh, T. Q., RañolaJr, R. F., Camacho, L. Climate Change on Rainfed Wheat D. and Simelton, E. 2018. Determinants of Production in Iran. Arch. Agron. Soil Sci., Farmers’ Adaptation to Climate Change in 52: 113-124. Agricultural Production in the Central

497 ______Parsi and Maleksaeidi

Region of Vietnam. Land Use Policy, 70: Regions. ‏Environ. Model. Softw., 62: 454- 224–231. 464. 47. Yu, Ch., Li, Ch., Xin, Q., Chen, H., Zhang, 48. Zhang, P., Zhang, J. and Chen, M. 2017. J., Zhang, F., Li, X., Clinton, N., Huang, X., Economic Impacts of Climate Change on Yue, Y. and Gong, P. 2014. Dynamic Agriculture: The Importance of Additional Assessment of the Impact of Drought on Climatic Variables Other than Temperature Agricultural Yield and Scale-Dependent and Precipitation. J. Environ. Econ. Manag., Return Periods over Large Geographic 83: 8-31.

کاربرد پیش بینیهای هواشناسی در تصمیمات مدیریت مزرعه: مورد ایران

ل. پارسی، و ح. ملک سعیدی

چکیده

پیص بیىیَای ًَاضىاسی دارای ظرفیتی برای بُبًد سازگاری ي تابآيری سیستمَای کطايرزی با تغییراتاقلیمیَستىذ،َرچىذَمچىاندرمًردعًاملمؤثردراستفادٌازایه پیص بیىیَادرتصمیمات مربًطبٍمذیریتمسرعٍاطمیىانيجًدوذارد. یکمطالعٍپیمایطیدرمًردکاربرداطالعاتًَاضىاسی تًسط 312 کطايرز از 31 مىطقٍ ريستایی يیس در استان خًزستان کٍ از طریق ريش ومًوٍگیری تصادفی طبقٍایاوتخابضذوذ، وطاندادکٍاطمیىانبٍ ارائٍدَىذگاناطالعاتًَاضىاسی،دردسترس بًدن پیص بیىیَایًَاضىاسی،خًدکارآمذیيَىجاررَىی محرکَایاصلیاستفادٌاز ایهپیص بیىیَا درتصمیماتمربًطبٍمذیریتمسرعٍَستىذ. براساسیافتٍَا،اطمیىانبٍ ارائٍدَىذگاناطالعاتدربیه کطايرزانکمبًد.عاليٌبرایه،َىجاراجتماعیدرمًرداستفادٌازاطالعاتًَاضىاسیدرعملدر مىطقٍمًردمطالعٍقًیوبًد.وتایجتحقیقحاکیازویازبٍارتقاءاعتقاداتي ارزشَایکطايرزاني

جًامعآوُادرمًرداَمیتاستفادٌاز پیص بیىیَایًَاضىاسیبرایسازگاریباتغییراتاقلیمیاست. Downloaded from jast.modares.ac.ir at 11:05 IRST on Tuesday September 28th 2021

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