Scholarly article on topic 'Impacts of the precision agricultural technologies in Iran: An analysis experts' perception & their determinants'

Impacts of the precision agricultural technologies in Iran: An analysis experts' perception & their determinants Academic research paper on "Agriculture, forestry, and fisheries"

CC BY-NC-ND
0
0
Share paper
Keywords
{"Sustainable agriculture" / "Precision agriculture" / "Impact assessment" / Boushehr / Iran}

Abstract of research paper on Agriculture, forestry, and fisheries, author of scientific article — Somayeh Tohidyan Far, Kurosh Rezaei-Moghaddam

Abstract Nowadays agricultural methods developments that are productively, economically, environmentally and socially sustainable are required immediately. The concept of precision agriculture is becoming an attractive idea for managing natural resources and realizing modern sustainable agricultural development. The purpose of this study was to investigate factors influencing impacts of precision agriculture from the viewpoints of Boushehr Province experts. The research method was a cross sectional survey and multi-stage random sampling was used to collect data from 115 experts in Boushehr province. According to the results, experts found underground and surface waters conservation, rural areas development, increase of productivity and increasing income as the most important impacts of precision agricultural technologies. Experts’ attitudes indicate their positive view toward these kinds of impacts. Also behavioral attitude has the most effect on impacts.

Academic research paper on topic "Impacts of the precision agricultural technologies in Iran: An analysis experts' perception & their determinants"

Impacts of the Precision Agricultural Technologies in Iran: An analysis Experts' perception & their determinants

PII: DOI:

Reference:

Accepted Manuscript

To appear in:

Somayeh Tohidyan Far, Kurosh Rezaei-Moghaddam

Information Processing in Agriculture

S2214-3173(16)30132-9 https://doi.Org/10.1016/j.inpa.2017.09.001 INPA 100

Received Date: Revised Date: Accepted Date:

8 December 2016

18 September 2017

19 September 2017

Please cite this article as: S. Tohidyan Far, K. Rezaei-Moghaddam, Impacts of the Precision Agricultural Technologies in Iran: An analysis Experts' perception & their determinants, Information Processing in Agriculture (2017), doi: https://doi.org/10.1016/j.inpa.2017.09.001

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Impacts of the Precision Agricultural Technologies in Iran: An analysis Experts'

perception & their determinants

Somayeh Tohidyan Far and Kurosh Rezaei-Moghaddam1 Ph.D student and Associate Professor, Dept. of Agricultural Extension and Education, College of

Agriculture, Shiraz University, Iran.

Abstract

Nowadays agricultural methods developments that are productively, economically, environmentally and socially sustainable are required immediately.The concept of precision agriculture is becoming an attractive idea for managing natural resources and realizing modern sustainable agricultural development. The purpose of this study was to investigate factors influencing impacts of precision agriculture from the viewpoints of Boushehr Province experts. The research method was a cross sectional survey and multi-stage random sampling was used to collect data from 115 experts in Boushehr province. According to the results, experts found underground and surface waters conservation, rural areas development, increase of productivity and increasing income as the most important impacts of precision agricultural technologies. Experts' attitudes indicate their positive view toward these kinds of impacts. Also behavioral attitude has the most effect on impacts.

Keywords: Sustainable agriculture, Precision agriculture, Impact assessment, Boushehr, Iran.

1. Introt

In rec

)duction

recent years, agriculture has turned into an industry in response to food provision and food security and human relation with environment has been changed due to achievement in different technologies [16]. In this respect, agricultural systems emphasize on utilizing inputs that are produced by fossil fuels such as chemical fertilizers, pesticides, herbicides, and agricultural machinery with high fuel consumption. Although applying such technologies has increased yield and

1 Corresponding author. Kurosh Rezaei-Moghaddam. Tel: +98 711-2277703. Fax: +98 711-2286072

E-mail: rezaei@shirazu.ac.ir. postal address: Dept. of Agricultural Extension and Education, College of Agriculture,

Shiraz University, Iran. Postal code: 71441-65186

workforce efficiency, it has destroyed many natural resources on which agricultural systems continuance relies on. Thus, this destruction will first affect farmers and then society [4]. Agricultural products produced through modern agriculture based on green revolution methods bring about many problems for human health and destroy natural resources due to applying improper production patterns, unsustainability of production systems, loss of basic resources, and consequently threaten production facilities, hence this issue makes the production process impossible. Therefore, agricultural methods developments that are productively, economically, and socially sustainable are required immediately [19].

With the introduction of agricultural informatization, the traditional agriculture has been reformed by advanced ICTs, eventually contributing to the significant improvements in agricultural productivity and sustainability [28]. The concept of precision agriculture, based on information technology, is becoming an attractive idea for managing natural resources and realizing modern sustainable agricultural development [13].

Precision agriculture

i crop

Precision agriculture is an integrated crop management system that combines information technologies with rational agricultural industries and attempts to provide amounts and type of inputs based on actual needs of cultivation in small farms that are located inside a large farm [11]. Also, precision agriculture is considered a farm management system on the basis of information technology to determine, analyze and manage changes inside a farm for profitability, sustainability and optimum conservation of farms [7]. This system focuses on site-specific management of production. Precision agriculture presents a new concept in sustainable use of the agricultural resources and is defined as a management concept that combines communications and information technologies for managing temporal and spatial changes in the farm [6]. The basic goal of PA is to optimize yield with minimum input and reduced environmental pollution [14].

Precision agriculture with the purpose of inputs management will provide distinguished production methods for agricultural producers and like any other technology it may enable farmers to collect data with the purpose of identifying effective variables on farm's potential yield. Moreover, farmers can make decisions regarding inputs and use them in variables rates [17].

Impacts of precision agriculture

Various researches have been reported that assess the impacts of precision agriculture technologies. This approach can not only decrease costs, but can also increase yields. Furthermore, accurately applying chemicals and fertilizers only where needed

reduces the potential for ground and surface water pollution [10]. Precision agriculture will not only help cost saving but also has considerable environmental benefits [7]. Increased efficiency through accurate machinery guidance systems alone can deliver quantifiable returns to farmers. Accurate auto-steer systems could save farmers 5-15% on input costs (fuel, pesticides and fertiliser) by reducing over- or under-lapping and by increasing the timeliness of operations, such as facilitating the spraying of pesticides at night. Boosting yield, improving economic production, and compensating costs are taken into account as the advantages of applying precision agriculture technologies [2]. Dobermann et al [5] believed that along with economic benefits, environmental benefits such as decrease of greenhouse gases emission and pollution caused by fertilizers and pesticides have to be considered [8]. By reducing over-application and under-application of inputs such as nutrients and pesticides, this strategy has the potential to improve profitability for the producer and also to reduce the threat of ground or surface water contamination from agricultural chemicals [22]. According to Zhang et al. [27] people expected impacts of using precision agriculture in profitability for producers and ecological and environmental profits. Adopting precision agriculture will affect job opportunities (providing consulting services, supporting services, specialized tools, etc.) and agricultural structures, especially farms sizes distribution in rural areas and using chemical fertilizers, pesticides and other agricultural inputs efficiently will decrease environmental problems [21]. According to Swinton and Lowenberg-DeBoer [23] precision agriculture has brought about 57% profitability. Another study demonstrated that precision agriculture technologies resulted in farms profitability due to increase of yield and inputs costs reduction. Meanwhile, financial administration improvement causes risk administration improvement and farms management abilities improvement [26]. Boosting productivity, profitability and sustainability, improving product quality, efficient product management, preserving soil, water and energy resources, conserving underground and surface waters, optimizing production efficiency, minimizing environmental impacts and risks which is done with the purpose of environmental and economic sustainability are other stipulated impacts of these technologies [18].

Different scientists have presented various models to examine attitudes and behaviors. Theories that used in this article discussed in following.

Theory of reasoned action

This theory is based on psychology and defines the relationship between attitudes and behavior. Based on this theory adoption of innovation would be affected by individual and social factors. Individual factor is defined as a positive or negative belief toward forming behavior or it is considered as the same attitude toward

forming behavior, and social factor is subjective norms or the impact of social pressure on the person whether it results in forming the behavior or not [9].

Theory of planned behavior

In psychology, theory of planned behavior is introduced as a linkage between attitude and behavior. This theory is presented based on the theory of reasoned According to TPB theory individuals' behaviors will be determined by their intentions affected by attitude, subjective norm, and perceived behavioral control [18].

Technology acceptance model

The technology acceptance model (TAM) was proposed by Davis as an instrument to predict the likelihood of a new technology being adopted within a group or an organisation [17]. Based on the theory of reasoned action, the TAM is founded upon the hypothesis that technology acceptance and use can be explained in terms of a user's internal beliefs, attitudes and intentions. As a result it should be possible to predict future technology use by applying the TAM at the time that a technology is introduced. The original TAM gauged the impact of four internal variables upon the actual usage of the technology. The internal variables in the original TAM were: perceived ease of use (PEU), perceived usefulness (PU), attitude toward use (A) and behavioural intention to use (BI) [25].

Shyu and Huang [20] conducted a study based on technology acceptance model and its results revealed that perceived usefulness and perceived enjoyment variables affected attitude of use. Moreover, attitude of use and perceived usefulness influenced use intention. Nan et al. [15] examined developed information technology acceptance model based on TAM model. Results showed that short-term behavioral intention variable was affected by direct impacts of compatibility, attitude of use, and perceived usefulness. The causal relationship between short-term behavioral intention, compatibility, attitude of use and long-term behavioral intention was positive. In addition, perceived ease of use and perceived usefulness had positive effect on attitude of use. Chen et al. [3] presented a model for investigating intention toward modern technologies by combining TAM and TPB models. According to the results innovation traits such as perceived usefulness and perceived ease of use brought about developing a positive attitude toward using modern technologies and attitude, subjective norms and perceived behavioral control affected intention towards use.

Many studies carried out on environmental behaviors have demonstrated that knowledge is an important behavior predictor, since this variable can influence the whole process of decision-making in a way that a person may make a wrong decision

through wrong information and knowledge [12]. According to Tress [24] there was a relationship between perceived transitional difficulty toward sustainable activities and attitude toward these activities. The way in which people find transition toward sustainable activities difficult will show a more negative attitude. According to literature Iran should follow precision agriculture technologies more seriously through relying on its potential capacities [18]. Consequently, this end cannot be achieved without cooperation of those who are in charge of agriculture. Because of the key role of agricultural experts in affecting adoption of innovation by farmers, the purpose of this study was to investigate factors influencing impacts of precision agriculture from the viewpoints of Boushehr Province experts. According to literature reviews the following framework is presented to investigate factors affecting impacts

(Fig. 1).

Fig. 1

e facto

2. Research method

A cross-sectional survey was used to collect data using questionnaire. The list of indices to measure the dependent variable was provided through three steps. In the first step, information related to the impacts of precision agriculture was gathered based on documents and resources in other countries. In the second step, a pre-pilot study was done on pioneer farmers domiciled at Marvdasht regions for confirming the impacts. In the last step some experts of Jihad-e-Keshavarzi Organization in Fars Province were interviewed.

Data to test the model was gathered among agricultural experts in - southern province- in Iran in 2016. .This province has a proper capability for expanding agricultural activities qualitatively and quantitatively and is one of the pioneers in introduction and diffusion of new technology. Boushehr includes 9 towns and has a hot climate, and its average annual precipitation is between 200 to 250 millimeters. In 2007, its total under cultivation fields was about 236053 hectares. 5142 hectares of these fields are allocated to water farming and the remaining 184811 hectares are allocated to dry farming. The majority of under cultivation fields is dedicated to wheat in 167351 hectares and tomato in 14519 hectares. Farming in gardens of Bousher province is done in 40661 hectares, of which, dates are the main part with 37265/2 hectares. Statistical population in this research includes all Jihad-e-Keshavarzi experts working in Bousher province. A multi-stage random sampling was used to gather data. The number of samples was estimated based on studied population and Cochran's formula and 115 experts of Boushehr province were interviewed and the required data was gathered through questionnaires. The validity of questionnaire was tested by experts of Agricultural Extension and Education

Department, Shiraz University. The questionnaire was pilot-tested with 30 randomly selected agricultural experts out of sample. Based on the feedback from the pilot test, the questionnaire was refined and a revised final questionnaire was developed. The Cronbach alpha coefficients for the variables have been presented in Table 1. Table 2 shows the definition of variables.

Table 1 Table 2

LISREL

Data from the questionnaires were encoded and analyzed using LISREL software. Structural equation modeling (SEM), a multivariate analysis, is an appropriate method for analyzing latent variables, such as constructs developed from survey items. SEM is similar to multiple regression but is used to analyze and calculate variance explained in endogenous and exogenous latent variables. The relationship between constructs (or latent variables) is represented by the paths coefficients [1]. Apart from descriptive statistics and inferential techniques, frequencies, percentage, mean score, standard deviation, coefficients of correlation and structural equation modeling were used to analyze the data.

3. Results and Discussioi 3-1- Impacts of the precision agricultural technologies Environmental Impacts

In table 3 frequency and mean for each precision agriculture technologies impacts are illustrated. Due to mean of environmental impacts of precision agriculture technologies, experts have found underground and surface waters conservation with the mean 4.02 as the most important environmental impact of this plan. In this way 69.6% of experts have assessed the high impact of using precision agriculture technologies on underground and surface waters conservation and only 0.9% of them believe the impact is very low. The results of Sudduth et al. [22] study is in accord with this finding. Table 3 shows that weeds management and energy sources conservation with mean 3.93 are placed in the second rank after underground and surface waters conservation. Thus, 72.2% and 60% of experts defined a high impact of precision agriculture technologies on weeds management and energy sources conservation and 1.7% and 2.6% of the sample defined a low impact. None of the experts considered that precision agriculture technologies had no impact on weeds management and energy sources conservation. Also, 73.9% of experts reported using

precision agriculture technologies resulted in pest management and just 2.6% of sample believe that using precision agriculture technologies had very low impact on pests management. It must be mentioned that pests management has mean equal to 3.87. According to the results, plant disease management and producing healthy products have, respectively, means of 3.86 and 3.82. The results of Jochinke et al. [8] confirm this finding. Based on table 3, 54.8% of experts assessed an average impact of using precision agriculture technology on green gases emissions. This environmental impact has the minimum value with mean 3.06 and experts have taken it into account less than other environmental impacts as an impact of precision agriculture technologies.

Table 3

<& Social Impacts

According to table 4 experts have socially introduced rural areas development with mean 3.93 as the most important impact of using precision agriculture technologies. The results of this factor revealed that 70.4% of experts assessed a high impact of precision agriculture technology on rural areas development and only 1.7% of the sample reported that impact was very low. According to the above-mentioned table, decrease of social class gap with mean 2.41 was the lowest value as the impact of using precision agriculture technologies. Meanwhile, on average experts assessed immigration as a social impact of precision agriculture technologies. This index has a mean equal to 2.47.

Table 4

Technical Impacts

According to table 5 experts found increase of productivity with mean 3.94 as the most important technical impact of precision agriculture technologies. 71.3% of experts assessed a high impact of precision agriculture technologies on increase of productivity and 14.8% of them assessed very high impact. None of the experts considered that precision agriculture technologies had no impact on productivity. This result is compatible with Jochinke et al. 's [8] study. Quality of products and farm condition improvement with mean 3.90 are placed in the second rank after increase of productivity. Thus, 67% and 74.8% of experts explicated the impact of using precision agriculture technologies on quality of products and farm condition improvement are high. Based on table 5 agricultural lands expansion and agricultural operations time with mean 3.74 and 3.76 were placed in the lowest rank. But it should be noted that means of both factors are more than 3 and the majority of sample have assessed average and high impact of precision agriculture technologies on agricultural lands expansion and agricultural operations time.

Table 5

Economic Impacts

Table 6 demonstrates experts economically consider increase of income with mean 3.99 as the most significant impact so that 63.5% of sample considered a high impact of precision agriculture technologies on increase of income and 20% of them have assessed a very high impact. After income, improvement and prosperity of agricultural status with mean 3.98 is placed in the second rank. The results of improvement and prosperity of agricultural status showed that 61.7% of experts assessed a high impact of precision agriculture technologies on improvement and prosperity of agricultural status and 21.7% of them assessed a very high impact. According to this table, decrease of risk with rank 3.53 is placed in the lowest rank. But it should be mentioned that mean of this factor is over 3 and the majority of sample have assessed average and high impact of precision agriculture technologies on decrease of risk.

of preci 6 *

Mean of experts' attitude toward precision agriculture impacts indicates that experts look at precision agriculture technologies positively. According to table 7 mean of experts' attitude in all fields is more than 3.

Table 7

is is moi

3-2- Correlation among variables

Table 8 provides correlation coefficients between variables. The correlation coefficients between behavioral attitude with perceived usefulness, individual innovativeness and impacts of the precision agricultural technologies were computed 0.52, 0.42 and 0.56 respectively. The coefficients were significant at the level of significance 0.01.

Coefficients revealed that there was a significant and positive relationship between perceived ease of use and attitude of confidence (0.56, P=0.01), individual innovativeness (0.36, P=0.01) and knowledge of precision agriculture (0.22, P=0.05). Correlation analysis of perceived usefulness with other variables indicated that it has a positive relationship with attitude of confidence (r=0.64, P=0.01), individual innovativeness (r=0.42, P=0.01) and impacts of the precision agricultural technologies (r=0.48, P=0.01). The results of Pearson correlation analysis showed a positive relationship between attitude of confidence and individual innovativeness (r=0.54, 0.01), knowledge of precision agriculture (r=0.31, P=0.01), and impacts of the precision agricultural technologies (r=0.36, P=0.01). Correlation coefficients

revealed a positive relationship between individual innovativeness and knowledge of precision agriculture (r=0.27, P=0.01), and impacts of the precision agricultural technologies (r=0.38, P=0.01). Meanwhile, there was a positive relationship between knowledge of precision agriculture and impacts of the precision agricultural technologies (r=0.30, P=0.01).

Table 8

Measurement model evaluation

One of the criteria for measurement model evaluation is Chi-Square/Degree of Freedom which should be less than three. This value is 1.15. The next to evaluate the model is p-value which should be more than 0.05, in table 9 it is seen equal to 0.43. Computing Goodness-of-Fit, Adjust Goodness-of-Fit, Normed Fit Index, Non-normed Fit Index, and Comparative Fit Index are required for model fit in such a way that their values should be higher than 0.90. Moreover, Root Mean Square Residual and Root Mean Square Error of Approximation should be less than 0.05 and 0.10. According to the results the indices were higher than 0.9. Root Mean Square Error of Approximation for measurement model and Root Mean Square Residual were computed 0.06 and 0.02 respectively.In fact, these variables present a proper model for defining attitude toward the impacts of the precision agricultural technologies.

impacts

Table 9

The results showed that (Fig. 2) there is a positive and meaningful causal effect between perceived usefulness and behavioral attitude (X=0.40, p<%1). The results of Shyu and Huang [20], Nan et al. [15], Chen et al. [3] confirm this finding. Individual innovativeness is an external variable that has direct effect on behavioral attitude (X=0.22, p<%1). Analyzing results indicated a positive effect of perceived ease of use on behavioral attitude (X=0.12, p<%5). This finding is consistent with the results of Nan et al. [15] and Chen et al. [3]. These variables accounted for 24% of behavioral attitude changes (SMC=0.24). The findings demonstrate that behavioral attitude has the most direct effect on impacts of the precision agricultural technologies and the causal relationship of this variable was 0.64 (P=0.64, p<%1). After behavioral attitude, perceived usefulness has the most significant effect on impacts of the precision agricultural technologies (X=0.33, p<%1). Based on the results perceived transitional difficulty has direct negative effect on impacts of precision agricultural technologies (X=0.17, p<%5). From the results perceived ease of use and individual innovativeness variables had indirect effects on impacts of precision agricultural technologies. These variables anticipate 39% of changes of impacts of precision agricultural technologies (SMC=0.39).

Fig. 2

4. Conclusion and recommendations

Today, useful technologies along with strategy of environment conservation as well as viewpoints change from remedial strategies to preventive strategies in using such technologies are at the center of attention. Efforts are made to emphasize applying modern sciences in agriculture that concern production and productivity boost and environmental conservation. Information technology in agriculture called precision agricultural technologies is considered among modern technologies. This kind of agricultural system is farming precision management based on inputs data and knowledge and regards using inputs in terms of farms needs and site-specific management. Precision agriculture is referred to a systematic approach for remaking the whole system of agriculture for developing sustainable, low input and high-yield agriculture. Precision agriculture methods are able to enhance economic and environmental sustainability of production. According to the results of this paper, applying precision agriculture systems is regarded as a means of achieving sustainable agriculture, a move towards which is inevitable for all countries, especially developed ones, as a result of environmental problems and food security provision for growing population. Because other agricultural systems such as traditional and organic may not provide growing population food security. Agricultural Jihad-e-Keshavarzi experts are one of the factors that introduce these kinds of technologies to farmers and experts' positive attitude toward the impacts of precision agricultural technologies plays an important role in accelerating the diffusion of them. Boushehr Province experts found underground and surface waters conservation, weeds management, energy resources conservation and pests management as the most important environmental impacts of precision agricultural technologies. The most significant social impact of this agricultural system refers to rural areas development. The most important technical impacts of precision agricultural technologies is increase of productivity, increasing products quality, and improving farm condition. Experts defined the most significant economic impacts of these technologies as increasing income, improving and prospering agricultural status. Experts' attitudes indicate their positive view toward these kinds of impacts. Hence, governmental planners in agriculture must take into account precision agriculture implementation in agricultural development plans. In addition, they must try to consider supportive services to have access to precision agricultural technologies, extension and training activating, and material and spiritual incentive in their activities and in particular they have to emphasize on providing training infrastructures.

Behavioral attitude has the most effect on attitude towards impacts. While knowledge and awareness are considered as an introduction to create an attitude, the results of

this study showed that knowledge of precision agriculture had no effect on behavioral attitude. One of its reasons may be resulted from lack of knowledge regarding this technology so that experts' knowledge and information in this field must be increased. Therefore, they will be able to apply it in farms after gaining required information and encourage farmers to adopt such technologies through providing necessary conditions and facilities. Creating learning groups and providing condition for group discussion are suggested for facilitating precision agriculture learning. Lack of researches in this field in Iran is another reason of experts' lack of knowledge. Researches must apply theoretical knowledge of precision agriculture in the country of Iran. Approving necessary credits for research and encouraging researchers to plan and apply the relevant plans in precision agriculture are essential too. Perceived usefulness is considered as the most significant variable affecting behavioral attitude and the second factor influencing attitude of precision agriculture technologies. Therefore, education should focus on justifying perceived usefulness of those technologies to experts so that teaching precision agriculture at universities should be regarded more. Planning in-service training courses for experts, forming a network of experts, teachers, and technicians, developing and performing internship programs for experts are suggested. Finally, officials and relevant policy-makers can establish strategic planning based on the results of this study to diffuse these kinds of technologies.

ed on th

References

[1] Adrian A.M, Norwood S.H, Mask P.l. Producers perception and attitudes toward precision agriculture technologies. Comp Ele Agric. 2005; 48(3) : 256-271.

[2] Breazeale D. A. precision agriculture fertilization program for Alfalfa hay production: will it pay for itself? University of Nevada, cooperative extension. Fact sheet. 2006. Link: https://www.unce.unr.edu/publications/files/ag/2007/fs0723.pdf

[3] Chen Ch, Fan Y, Farn Ch. Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior. Trans Res Part C. 2007; 15: 300-311.

[4] Clark S.R, Hirsch B.A. Roots change. Funders Agricultural Working Group. 2001.

[5] Doberman A, Blackmore S, Cook S.E, Adamchuk V.I. Precision farming: Challenges and future directions. Proceeding of the 4th International Crop Science Congress, 26 Sep -1Oct 2004. Brisbane, Australia; 2004.

[6] Fountas S, Pedersen S, Blackmore S. ICT in precision agriculture: Diffusion of technology. 2005. Link: http: // departments. Agri. Huji. ac.ir.

[7] Hosseini M, Chizari M, Bordbar M. Evaluation of the possibility of precision agriculture from the view point of Agricultural experts in Fars Province. Iranian Agric Exten Edu. 2010; 6(2): 35-47. (In Persian)

[8] Jochinke D.C, Noonon B.J, Wachsmann N.G, Norton R.M. The adoption of precision agriculture in an Australian broad acre cropping system—Challenges and opportunities. Field Crops Res. 2007; 104:68-76.

[9] King R.C, Gribbins M.L. Internet technology adoption as an organizational event: An exploratory study across industries. Proceedings of the 35th Hawaii International conference on System Sciences. Hawaii, Spanish; 2002. P. 139-145.

[10] Krishnan M, Foster Ch.A, Strosser R.P, Glancey J.L, Sun J. Adaptive modelling and control of a manure spreader for precision agriculture. Comp El Agri. 2006; 52:110.

[11] Malek-Saeidi H, Rezaei-Moghaddam K. Application of ecological knowledge system towards precision agriculture. Human and Environment. 2008. p: 76-91. (In Persian)

[12] Malek-Saeidi H. Factors affecting attitude and knowledge of agricultural specialists of Jihad-e-Keshavarzi Organization of Fars and Khuzestan Provinces toward organic farming. Master thesis. Ahvaz University, Iran. 2007. (In Persian)

[13] Maohua W. Possible adoption of precision agriculture for developing countries at the threshold of the new millennium. Com Ele in Agric. 2001; 30:45-50.

[14] Mondal P, Basu M. Adoption of PA Technologies in India and Some Developing Countries: Scope, Present Status and Strategies. Pro Natu Sci. 2009; 19: 659-666.

[15] Nan Zh, Xun-hua G, Guo-qing C. Extended Information Technology Initial Acceptance Model and Its Empirical Test. Sys Engin - Theory and Practice. 2007; 27(9): 123-130.

[16] Rokeneddin Eftekhari A, Heidari Sareban V. The role of organic farming in food security. Jihad. 2006; 271: 120-137. (In Persian)

[17] Salehi S, Rezaei-Moghaddam K, Ajili A. Application of yield monitoring technologies: A model for sustainable agriculture. Iranian Agric Exten Edu. 2008; 4(1):15-32. (In Persian)

[18] Salehi S. Factors affecting attitude and intention to use of agricultural specialists of Jihad-e-Keshavarzi Organization of Fars and Khuzestan Provinces toward precision agriculture technologies. Master thesis. Ahvaz University, Iran. 2007. (In Persian)

[19] Sharma, A.K. A handbook of organic farming. Agrobios, India; 2005. p. 20-30.

[20] Shyu S.H, Huang J. Elucidating usage of e-government learning: A perspective of the extended technology acceptance model. Gove Inf Quar. 2011; 28: 491-502.

[21] Srinivasan A. Hand Book of precision agriculture: principles and applications. New York: Food Products Press; 2006.

[22] Sudduth K.A, Drummond S.T, Kitchen N.R. Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Computers and Electronics in Agriculture. 2001; 31:239-264.

[23] Swinton S.M, Lowenberg-DeBoer J. Evaluating the profitability of site-specific farming. J Pro Agric. 1998; 11:439-446.

[24] Tress B. Converting to organic agriculture-Danish farmers views and motivation. Danish J Geo. 2005; 101: 131-144.

[25] Turner M, Kitchenham B, Brereton P, Charters S, Budgen, D. Does the technology acceptance model predict actual use? A systematic literature review. Inf Software Tech. 2010; 52: 463-479.

[26] Van Zyl S.F. The impact of precision farming on the profitability of selected maize irrigation farms in the Northern Cape Province. Master thesis. Department of Agricultural Economics, Extension and Rural Development Faculty of Natural and Agricultural Sciences. University of Pretoria. 2010.

[27] Zhang N, Wang M, Wang N. Precision agriculture- a worldwide overview. Comp Ele Agric. 2002; 36: 113-132.

[28] Zhang Y, Wang L, Duan Y. Agricultural information dissemination using ICTs: a review and analysis of information dissemination models in China. Inf Pro Agric. 2016;

3(1):17-29.

Knowledge of precision agriculture

Perceived transitional difficulty

Individual innovativeness

Perceived usefulness

Attitude of confidence

Perceived ease of use

Behavioral attitude

Fig 1: The

Impacts of the precision agricultural technologies

eoretical framework

Knowledge of precision 0.11

agriculture

Perceived transitional difficulty

Table 1. Cronbach's alpha coefficients for research variables 1

Variables Cronbach's alpha coefficient

Behavioral attitude 0.75

Perceived ease of use 0.74

Perceived usefulness 0.77

Attitude of confidence 0.80

Individual innovativeness 0.77

Knowledge of precision agriculture 0.97

Perceived transitional difficulty 0.85

Impacts of the precision agricultural technologies 0.94

Table 2. Definition of variables

variables definition

Perceived usefulness Perceived usefulness is defined as the extent to which a person believes that using the system

will enhance his or her job performance (Venkatesh and Davis, 2000). The variable was measured using items related to increased productivity, reduction of production costs, better control on farm activities and etc. The questions were in the form of five-point scales labeled from strongly agree to strongly disagree.

Perceived ease of use According to Davis (1989) perceived ease of use is defined as the extent to which a person believes that using the system will be free of physical and mental effort (Lu et al., 2005). This variable was estimated using items related to learning easiness, mental effort required to use these technologies, effectiveness of using experts' opinions in application of these technologies and etc. The questions were in the form of five-point scales labeled from strongly agree to strongly disagree.

Behavioral attitude Taylor and Todd (1995) defined attitude scale which measured whether individuals like or dislike using the technology and how they felt using the technology. We operationally defined attitude to use as the prospective expert's positive or negative feeling about adopting precision agricultural technologies. This variable was estimated with desirable or un- desirable use of these technologies, reasonable or unreasonable use of the technologies and positive and negative attitude toward the precision agricultural technologies. The questions were in the form of five-point scales labeled from strongly agree to strongly disagree

This variable measures the confidence of a producer to learn and use precision agricultural technologies. Through Loyd and Gressard (1984) scale a number of questions were posed in relation to having certainty to learn precision agricultural technologies in educational classes and workshops and also in having self-confidence in using these technologies and etc. The questions were in the form of five-point scales labeled from strongly agree to strongly disagree.. Individual innovativeness is defined as "the willingness of an individual to try out any new technology". It was estimated through items proposed by Agarwal and Prasad (1998). The questions were in the form of five-point scales labeled from strongly agree to strongly disagree. The individual familiarity with the features and technologies of precision agriculture reveals individual knowledge in relation to this agricultural system. In order to measure this variable a number of questions were raised in the field of the precision agricultural technologies and the questions were in the form of five-point scales labeled from none to very high.

Attitude of confidence

Individual innovativeness

Knowledge of precision agriculture

Perceived transitional difficulty

Impacts of precision agriculture

Perceived transitional difficulty toward the precision agriculture is considered as an index for measuring individual perception and idea regarding transitional difficulty from conventional agriculture toward adoption and application of the precision agriculture. It was measured by questions about possibility of precision agriculture implementation with attention to farm conditions and economical and educational situations in Iran. The questions were in the form of five-point scales labeled from strongly agree to strongly disagree.

Impacts of precision agriculture includes individual attitudes and beliefs about the likely impacts of these kinds of technologies. Concerning the impacts of precision agriculture technologies on technical, social, economic and environmental fields, thirty seven questions were asked. Six- point scales ranging from none to very high were used to evaluate the impacts.

Table 3. Environmental impacts of precision agriculture

Variable Positive impact No impact Standard deviation Rank mean Priority

Very high high average low Very low

Frequency Percent Frequency Percent Frequency Percent Frequency Percent Frequency Percent Frequency Percent

Pest management 12 10.4 85 73.9 13 11.3 2 1.7 3 2.6 0.71 3.87 3

Plant disease management 11 9.6 85 73.9 13 11.3 4 3.5 2 1.7 0.69 3.86 4

Weeds management 16 13.9 83 72.2 10 8.7 4 3.5 2 1.7 0.72 3.93 2

Energy sources conservation 25 21.7 69 60.0 12 10.4 6 5.2 3 2.6 0.87 3.93 2

Underground and surface waters conservation 22 19.1 80 69.6 8 7.0 4 3.5 1 0.9 0.69 4.02 1

Underground and surface waters pollution 15 13.0 69 60.0 17 14.8 7 6.1 4 3.5 3 2.6 1.06 3.65 9

Soil pollution 17 14.8 27 23.5 54 47.0 10 8.7 4 3.5 3 2.6 1.10 3.29 10

Soil erosion 18 15.7 67 58.3 11 9.6 14 12.2 3 2.6 2 1.7 1.07 3.66 8

Soil compaction 12 10.4 77 67.0 11 9.6 9 7.8 3 2.6 3 2.6 1.03 3.66 8

Pesticides consumption 17 14.8 69 60.0 17 14.8 7 6.1 4 3.5 1 0.9 0.96 3.73 6

Fertilizer consumption 18 15.7 68 59.1 15 13.0 9 7.8 3 2.6 2 1.7 1.02 3.72 7

Greenhouse gas emissions 9 7.8 23 20.0 63 54.8 11 9.6 5 4.3 4 3.5 1.04 3.06 12

Biodiversity 8 7.0 39 33.9 50 43.5 11 9.6 4 3.5 3 2.6 1.02 3.23 11

Producing healthy products 20 17.4 73 63.5 12 10.4 4 3.5 4 3.5 2 1.7 0.99 3.82 5

Table 4. Social impacts of precision agriculture

Variable Positive impact No impact Standard deviation Rank mean Priority

Very high high average low Very low

Frequency Percent Frequency Percent Frequency Percent Frequency Percent Frequency Percent Frequency Percent

Job opportunities 13 11.3 73 63.5 20 17.4 3 2.6 3 2.6 2 1.7 1.06 3.68 4

1" 0.9"

Work force 10 8.7 33 28.7 56 48.7 5 4.3 3 2.6 1.81 2.95 6

1" 0.9" 1" 0.9" 2" 1.7" 4" 3.5"

Welfare 16 13.9 71 61.7 17 14.8 7 6.1 3 2.6 1 0.9 0.92 3.75 3

Immigration Note: * show 8 7.0 25 21.7 55 47.8 9 7.8 5 4.3 1 lologie 0.9 s on tl 2.29 lese inde 2.47 xes ar 7

> expe: Is who belie ve that the in îpacts of prec ision 1 armin 1 techr

Snegative.ap 7 6.1 25 21.7 57 49.6 8 7.0 4 3.5 2 1.7 2.38 2.41 8

2" 1.7" 8" 7.0" 2" 1.7"

Life style 13 11.3 30 26.1 53 46.1 9 7.8 5 4.3 4 3.5 1.35 3.14 5

1" 0.9"

Life quality satisfaction 17 14.8 73 63.5 14 12.2 7 6.1 2 1.7 2 1.7 0.95 3.78 2

Rural areas development 17 14.8 81 70.4 11 9.6 4 3.5 2 1.7 0.73 3.93 1

Table 5. Technical impacts of precision agriculture

Variable Positive impact No impact

Very high high average low Very low e

> e ■a 'C

> o > u > u > u > u > o ■a r oi ri

cu aj aj aj aj aj a ■a a oí P

cr aj u_ (D CL cu aj U- (D CL cu aj U- (D CL cr aj u_ cD CL cu aj u_ cD CL cu aj u_ cD CL J tS

Productivity 17 14.8 82 71.3 11 9.6 3 2.6 2 1.7 0.71 3.94 1

Quality of products 18 15.7 77 67.0 14 12.2 4 3.5 1 0.9 1 0.9 0.79 3.90 2

Agricultural lands 12 10.4 79 68.7 13 11.3 6 5.2 4 3.5 1 0.9 0.90 3.74 8

expansion

Sustainability of 11 9.6 83 72.2 13 11.3 4 3.5 4 3.5 0.79 3.80 5

products

Management of 15 13.0 77 67.0 14 12.2 6 5.2 3 2.6 0.81 3.82 3

inputs consumption

Agricultural 11 9.6 81 70.4 12 10.4 8 7.0 2 1.7 1 0.9 0.85 3.76 7

operations time

Farm condition 12 10.4 86 74.8 12 10.4 4 3.5 1 0.9 0.64 3.90 2

improvement

Depreciation of 17 14.8 71 61.7 17 14.8 7 6.1 2 1.7 1 0.9 0.89 3.79 6

machinery

Create a database with information about the status of the land 14 12.2 79 67.7 15 13.0 1 0.9 6 5.2 0.85 3.81 4

Table 6. Economic impacts of precision agriculture

Variable Positive impact No impact Standard deviation Rank mean Priority

Very high high average low Very low

Frequency Percent Frequency Percent Frequency Percent Frequency Percent Frequency Percent Frequency Percent

Profitability 25 21/7 67 58.3 18 15.7 2 1.7 2 1.7 1 0.9 0.86 3.93 4

Risk 13 11.3 67 58.3 17 14.8 9 7.8 5 4.3 4 3.5 1.14 3.53 6

Reduce the cost of inputs 14 12.2 73 63.5 19 16.5 5 4.3 4 3.5 0.85 3.76 5

Yield 20 17.4 75 65.2 15 13.0 4 3.5 1 0.9 0.72 3.94 3

Income 23 20.0 73 63.5 15 13.0 3 2.6 1 0.9 0.71 3.99 1

Improvement and prosperity of agricultural status 25 21.7 71 61.7 12 10.4 6 5.2 1 0.9 0.78 3.98 2

Table 7. The mean of the impacts of the precision agriculture technologies from the view point of experts

Variable Standard deviation Mean

The impact of precision agriculture 0.56 3.66

Environmental impacts 0.67 3.97

Social impacts 0.81 3.26

Technical impacts 0.61 3.83

Economic impacts 0.67 3.86

Table 8. The correlation coefficients matrix between variables

Variables

3 itta

oi via

if n o c f o e

vi ita

v o n n

c er a f o e tg T3

¿3 13

oi iti

§ 3 d iff

e fi ie

er la ei

e ult ol

ht ul ol

f ci n

of ir h

cts ga cet

Behavioral attitude 1

Perceived ease of use 0.06 1

Perceived usefulness 0.52** 0.30** 1

Attitude of confidence o.35** 0.56** 0.64** 1

Individual innovativeness 0.42** 0.36** 0.42** 0.54** 1

Knowledge of precision agriculture 0.16 0.22* 0.10 0.31** 0.27** 1

Perceived transitional difficulty -0.17 -0.02 -o.10 -0.06 -0.08 -0.14 1

Impacts of the precision agricultural 0.56** 0.19* 0.48** 0.36** 0.38** 0.30** -0.10 1

technologies

Table 9: Model evaluation overall fit measurements.

Goodness of fit measure Recommended obtained results

criterion in this research

Chi-square/degree of freedom (X2/df) <3 1.15

p-value >0.05 0.43

Normed Fit Index (NFI) >0.90 0.91

Non-Normed Fit Index (NNFI) >0.90 0.99

Comparative Fit Index (CFI) >0.90 1.00

Goodness-of-Fit Index (GFI) >0.90 0.99

Adjust Goodness-of-Fit Index (AGFI) >0.90 0.97

Root Mean Square Residual (RMSR) <0.05 0.02

Root Mean Square Error of Approximation (RMSEA) <0.1 0.06

Highlights

We proposed a model to investigate factors influencing impacts of precision agriculture.

We estimated impacts of precision agriculture from the viewpoints of experts. Experts' attitudes indicate their positive view toward these kinds of impact s. Behavioral attitude has the most effect on impacts.