Precision Agric (2012) 13:713–730 DOI 10.1007/s11119-012-9273-6 REVIEW PAPER
Factors influencing the adoption of precision agricultural technologies: a review for policy implications Yeong Sheng Tey • Mark Brindal
Published online: 20 July 2012 Ó Springer Science+Business Media, LLC 2012
Abstract Increasing pressure for food security and sustainability as well as a need to halt environmental degradation has focused attention on increasing the efficient use of farm resources. One answer to aspects of that problem is the use of precision agricultural technologies (PATs). To facilitate their adoption, initiatives have been fostered in developed countries since the 1980s. Despite a low rate of adoption elsewhere, similar efforts in recent years have been initiated in developing countries. Given this, understanding those underlying factors that influence the adoption of PATs is vital. It is timely to review these factors and to draw policy implications from that review for future actions. This review, based on studies investigating the limited adoption of PATs in ‘experienced’ countries, extrapolates their findings to explain why farmers have or have not adopted PATs. At the same time, this review summarizes the key insights for more effectively targeting ‘new’ followers: e.g. it provides some answers to the question of who is more likely to adopt PATs. Additionally, the review points to the limitations of current research in the area and suggests a robust economic model or multidisciplinary approach be adopted for future investigation. Keywords
Precision agricultural technologies Adoption Factors Policy
Introduction Traditional/conventional agricultural practices have rarely achieved optimal efficiency, either in terms of maximal yield or minimal cost of production. Under such systems, inputs (e.g. fertilizer and pesticides) are applied, in order to prevent nutritional deficiency or losses in stand or yield, at a uniform rate over an entire farm (Khanna et al. 1999). Such Y. S. Tey M. Brindal School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia Y. S. Tey (&) Institute of Agricultural and Food Policy Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia e-mail:
[email protected]
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decisions are neither informationally driven nor based on a prescribed need, but typically made to avoid risk. More specifically, they overlook field variability. Over-application of fertilizers, as an example, results in input losses through leaching and runoff. These practices generate adverse effects on resource quality (e.g. soil and water). There are, in turn, consequential impacts for plants, ecosystems, the economy, and population. Resource misallocation, therefore, has serious implications for sustainability vis-a`-vis food security. Such realizations have focused attention on increasing the efficient use of farm resources. One answer to specific aspects of this problem is the adoption of precision agriculture, a concept introduced about two decades ago. Precision agriculture is a production system that involves crop management according to field variability and site-specific conditions (Seelan et al. 2003). Precision agricultural technologies (PATs) are those technologies which, either used singly or in combination, as the means to realize precision agriculture. Using PATs, data are collected to assist farmers in making guided sub-field decisions, including applications of fertilizers and pesticides, distribution densities for seeds, irrigation application rates, and tillage regimes (Daberkow and McBride 1998). The ultimate objective is the management of crop and soil variability in a manner which increases profitability and reduces environmental destruction (Fountas et al. 2005). Decisions which are better than those that would be made with traditional/conventional agricultural practices have the potential to boost the efficient use of resources, reduce input costs, and minimize environmental degradation. At the same time they would improve yield and crop quality. Currently the available commercial technologies include Global Positioning Systems, Geographic Information Systems, yield monitors, near, far, and proximal remote sensors, and variable-rate applicators (Robertson et al. 2012). For either the farmer or an appointed third party to implement and entrench these technologies on individual farms, voluntary adoption of PATs is required to capitalize the aforementioned benefits (Kutter et al. 2011). Since the 1980’s, to facilitate such adoption, public and private initiatives have been fostered within the agricultural industry in developed countries. In more recent years, similar efforts have been initiated in developing countries, including Brazil (Silva et al. 2011), China (Maohua 2001), India (Mondal et al. 2011), and Uruguay (Alvarez and Nuthall 2006). Such initiatives have largely targeted specific crops which yield higher economic returns (e.g. cotton, corn, sugar cane, wheat, and rice) (Khanna et al. 1999). Such diffusion has led many other countries to invest in PATs notwithstanding their limited adoption in ‘experienced’ countries (Mondal and Tewari 2007; Swinton and Lowenberg-DeBoer 2001; Hudson and Hite 2003; Kitchen 2008). Given the above-mentioned backdrop, understanding those underlying factors that influence the adoption of these technologies is vital. It is timely to review these factors and to draw policy implications for future actions. Our review is based on a number of studies that have been devoted to investigating the limited adoption of PATs in ‘experienced’ countries. The findings can be extrapolated to explain why any particular farmer has or has not adopted PATs. At the same time, this review summarizes the key insights for more effectively determining and providing direction for ‘new’ followers: e.g. it provides some answers to the question of who is more likely to adopt PAT’s.
The benefits and costs of using PATs A primary goal of farmers using PATs is to increase profitability. Conceptually, this is possible through more cost-effect use of farm inputs (chemicals, fuel, labor and
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machinery), yield gain and selective harvesting (for quality products) (Chen et al. 2009). PATs can help a farmer to achieve these goals through an understanding of the variability of soil properties, crop requirements and other factors in yield variability: thereby facilitating more informed farming prescription and decision making (Maohua 2001). If a farmer has accurate information on nutrients needed on each grid element of land, the precise application of fertilizer could reduce input costs. Such a concept of profitability is, of course, based on the assumption that the net savings made from any precision application (e.g. in fertilizer costs) more than offsets the costs of either additional labor, the purchase of more specialized application equipment, or the sacrifice of amenity. Indeed one of the inherent complexities of PATs is that, unless highly specialized equipment which automatically adjusts application rates is employed, farm management decisions will logically involve some simplification of application through the grouping of parts of the property with similar characteristics. If a property shows 10 areas each needing differing application rates, lacking sophisticated application equipment, the farmer might group areas with similar needs, settling on three or four application rates rather than 10. While this approach must achieve sub-optimal input, efficiency is nevertheless higher than the ‘‘one size fits all’’ regime. Considering equipment, labor costs and amenity values, such a regime might represent the most efficient solution at that time for that farm. Consequently, results in respect to the profitability of PATs, have been mixed. For the purposes of discussion here, we consider wheat production in various countries. Heisel et al. (1996) and Timmermann et al. (2002) have, fundamentally, demonstrated that information-armed application of inputs (e.g. herbicide) would result in saving significant input costs. Cost saving, nevertheless, does not immediately lead to profitability. For example, Carr et al. (1991) and Biermachera et al. (2009) have shown an insignificant difference in the return of fertilizer application via PATs and traditional methods. Lowenberg-DeBoer and Aghib (1999)and Swinton and Lowenberg-DeBoer (1998) have in fact suggested that the application of soil sampling tests for soil fertility would not lead to profitability. Furthermore, these mixed results on the profitability of PATs can be attributed to technical and investment factors. The former, in particular, poses challenges in making full use of PATs. Besides requiring sophisticated knowledge in respect to mechanical operation for data collection, PATs also involve a high-level of complex data management, interpretation, and decision making in respect to agronomic solutions (Robertson et al. 2012). While these skills differ among individual farmers, the investment factor (e.g. the initial cost to acquire these technologies) varies across time. With the increasing availability of PATs, investment costs in these technologies have decreased over time (Jochinke et al. 2007). However, prices have remained relatively high. Incentives or subsidies have not generally been provided to enhance the affordability of PATs. Average low, medium, and high investment costs of up to US$17,300, US$45,000 and US$75,000, respectively, are reported (Robertson et al. 2007). Therefore, the uncertainty of profitability is compounded by feasibility considerations and high investment costs. PATs also offer opportunity to increase crop quality and yield. A classic example of these benefits can be found in viticulture. Grape quality, which is reflected in wine as its final product, basically varies across vineyards. Similarly, grape yield is not consistent from vineyard to vineyard. To an extreme extent, even uniformly managed fields in any given vineyard produce disparities regarding yield and quality (Bramley and Hamilton 2007). This is because yield and quality are influenced by spatial variability in the physical properties of soil and in soil fertility (Tardaguila et al. 2011). PATs, therefore, at least
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theoretically allow variable-rate application of the needed amounts of foliar fertilizers to increase the yield in fields with low production and to improve the quality specifications in some parcels within a vineyard (Arno´ et al. 2012). Meantime, many studies suggest that PATs have the potential to reduce agriculturecaused environmental impacts (e.g. Fuglie and Bosch 1995; Khanna and Zilberman 1997; Oriade et al. 1996; Schnitkey and Hopkins 1997; Hudson and Hite 2003). Improved the matching of farm inputs with crop need avoids excess application (Reichardt and Ju¨rgens 2009). Applying only the nitrogen needed for crops to reach their maximum potential yield, as an example, could reduce nitrate contamination in groundwater and the pollution of downstream water sources (Biermachera et al. 2009). This is particularly important since agricultural non-point source pollution is a major consideration in the contamination of many of the world’s waterways. Therefore, while boosting economic efficiency in onfarm activities, PATs offer environmental protection. A number of studies have demonstrated the economic and ecological superiorities of PATs over their counterparts (Silva et al. 2007; Sylvester-Bradley et al. 1999; Takacs-Gyorgy 2008). Most PATs are created for information enriching purposes. As such, they are generally seen unfavorably compared to those technologies (e.g. genetically modified crop variety and bio-enhanced fertilizer) which generate direct beneficial implications (Robertson et al. 2012). Unlike many other PATs, guidance technologies (e.g. light bars and auto steering systems) of global positioning system (GPS) do offer direct benefits to their users. The main function of GPS is to locate positions of interest accurately. It gives the current and past location to the implements, providing a means to till, apply farm inputs (e.g. pesticides, fertilizers, and lime), and harvest on the ‘right’ parcels, especially when the farmland is large. Controlled traffic, hence, helps to avoid skips and overlaps in farm work. Consequently, the PAT helps to reduce extended working time and fatigue. Without GPS guidance, as an example, a machinery track that used to harvest sugarcane is more likely to drive over the assigned row. In destroying the produce, the loss is not only to a decline in current yield but also in future yield due to soil compaction (Palaniswami et al. 2011). Based on the representative issues above, we perceive the costs and benefits of using PATs to be complex. Given this complexity, a review of a number of individual analyzes that have investigated the factors influencing farmers to either adopt or not adopt PATs is meaningful since it should facilitate a better understanding of policy options which encourage PAT adoption.
Methodology Agricultural practices are adaptive, risk-averse actions carried out by individual farmers. Such actions result in multifaceted behavior albeit arising from perceived benefits. This has given rise to an extensive adoption literature, studying a range of agricultural practices, such as fertilizers, pesticides, conservation practices, sustainable practices, climate change adaptive measures, agro-forestry innovations, and other technologies. Some of these findings have been reviewed, e.g. Pattanayak et al. (2003) and Mercer (2004) on agroforestry innovations; Fleming and Vanclay (2010) on climate change adoptive measures, and Knowler and Bradshaw (2007) on conservation practices. However, most review studies have not given rise to an easy method for carrying out a review. Therefore, the task is challenging, especially in respect to presenting and discussing the findings in a well thought-out format. Among others, the method used by Knowler and Bradshaw (2007) is considered a valuable adjunct to our interest. They have
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demonstrated a simple and structured way to conduct such a review. Though one might see such a method as a meta-analysis, it is practically distinct without involving relatively complex statistical procedures. The outputs, nevertheless, do not diverge and do meet the objective of conducting a review. The simplicity of Knowler and Bradshaw’s (2007) method is described by five core components, which have captured the major details of past studies. These core components are author(s), country, specific adoption subject(s), analytical method and model significance. In addition, we should pay attention to two of its surrounding components: sample size and number of variables. This need arises because individual analytical methods have different requirements in achieving statistical significance (with respect to sample size while factoring in differing numbers of variables). While the method above provides a review backdrop, special focus should be placed on summarizing the findings of past studies. To be more precise, information on the insignificant and significant variables tested must be captured collectively. Note should be taken that the latter can appear as a positive or negative sign. Regardless of their sign, they are the central interest of this paper and will be used for our discussion. Up to this point, it is clear that our review involves two tasks: a review of the ‘‘backdrop’’ and a review of ‘‘significant factors’’. As mentioned earlier, carrying out such tasks is not easy. Though Knowler and Bradshaw (2007) have lent credence to our proposed methods, they have not equipped us with their working procedures. Hence, the intent of this section is to present ‘‘add-on’’ information on these procedures in the following subsections. Data This review summarizes what has been done and found by past studies. It requires a comprehensive tool to search and identify a pool of relevant studies. As ScopusÒ was comprehensive in journal coverage as well as readily and conveniently available, it was used for this purpose. Taking the subject of this review as the guide, two keyword sequences were used: (1) adoption/use/application and (2) precision agriculture/precision agricultural technologies/Global Positioning Systems (GPS)/yield monitoring systems/ remote sensing systems/soil sampling regimens/variable-rate applicators. The search yielded result indicating more than 50 published papers. Though the object was matched, the aims of the majority of these papers were not in our interest. We, however, did not discard them but saved them for careful reading. This was because much of the information they contained proved useful as an input for our writing. Only 15 papers were identified to be closely related to the subject of this review. These peer-reviewed studies can be categorized into two groups: ex-ante group and ex-post group. The ex-ante group, which described the predictive nature of an investigation, was formed by five studies: Rezaei-Moghaddam and Salehi (2010), Marra et al. (2010), Adrian et al. (2005), Hudson and Hite (2003) and Hite et al. (2002). The ex-post group, which described an ‘‘after the event’’ nature of an inquiry, was made up by 10 studies: Robertson et al. (2012), Walton et al. (2008), Larson et al. (2008), Isgin et al. (2008), Roberts et al. (2004), Daberkow and McBride (2003), Fernandez-Cornejo et al. (2002), Roberts et al. (2002), Khanna (2001) and Daberkow and McBride (1998). In retrospect, the objective was to review factors which explain why farmers have or have not adopted PATs. In other words, we were interested in the occurrence (adoption/ non-adoption) that has taken place. Hence, the five ex-ante studies, which used
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willingness-to-pay, intention to use and attitudes to predict the future adoption of PATs, were excluded. Consequently, we limited our review to the 10 ex-post studies. Some of these studies looked at the adoption of system-based precision agriculture as a whole while others considered the use of one or a few PAT(s). As a collective set, these selected studies provided 25 analyzes for our review and synthesis. Review procedures Our first task was to review the background to the 10 selected studies. While following the earlier discussion, with the intention of ensuring compatibility, the initial task was implemented by formatting them on the basis of the seven identified core components: (1) author(s), (2) country, (3) PATs, (4) analytical methods, (5) sample size, (6) number of variables and (7) model significance. That was done by recording these details using the program Microsoft WordÒ. While most information was straight forward in the text, analytical methods, sample size, number of variables, and model significance were not. These happened because different samples and/or set of variables were used to test on one or more of the PATs; in some cases, these details were simply not included in the text. To handle this, we referred to the tables of analytical outputs, which presented these details along their empirical findings. Further analysis was carried out to meet the objective of this paper: to review the factors influencing the adoption of PATs. By focusing on their tables of analytical outputs, we were provided with the summarized findings. Special attention was paid to statistical significance of the tested variables. Their denotations (e.g. *** denotes statistical significance of 5 %) gave a clear-cut identification of insignificant, positive significant and negative significant factors. These findings were keyed into individual spreadsheets, each spreadsheet representing a single study, in the program of Microsoft ExcelÒ. Knowing that tested variables may vary across these studies, we did not have a pre-determined set of variables in each spreadsheet. We rather captured them progressively (e.g. the first spreadsheet recorded 10 variables studied in the ith paper; the 10 variables were saved for the second spreadsheet and new variables investigated in the jth paper were added to and placed at the bottom of the list; and the procedures went on from one study to another). Collectively, the format enabled a final spreadsheet to perform the eventual inventory of the list of significant factors that influenced the adoption of PATs. When performing such a review, one should be mindful of possible setbacks, either caused by data entry error or oversight. To avoid this pitfall, each member of our study team carried out the task by following the specified working procedures that are described above. Individual outputs were compared but no difference was spotted. Thus, no rectification was required. This result could be credited to our structured review procedures.
A review of explanatory factors in the adoption of PATs Details of reviewed studies and analyzes Farmers are the end-users of PATs. To adopt PATs, farmers must engage in behavioral change. Table 1 provides details of the 10 reviewed studies, which yielded 25 analyzes. They explain the adoptive decisions of farmers with respect to PATs. Their details are
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Walton et al. (2008)
Larson et al. (2008)
Isgin et al. (2008)
Roberts et al. (2004)
Daberkow and McBride (2003)
Fernandez-Cornejo et al. (2002)
Roberts et al. (2002)
Khanna (2001)
2
3
4
5
6
7
8
9
USA
USA
USA
USA
USA
USA
USA
USA
USA
Australia
Country
NA means not available, Sig. denotes the estimated model is significant
Daberkow and McBride (1998)
Robertson et al. (2012)
1
10
Author(s)
No.
Probit
Variable-rate inputs technology
Tobit
Precision agriculture
Logit
Logit
Precision agriculture
Logit
Variable-rate inputs technology
Variable-rate inputs technology
Logit Logit
Grid soil sampling
Soil sampling
Logit Logit
Yield monitor/Out GPS
Logit
Yield monitor/GPS
Precision agriculture
Logit
Variable-rate lime technology Precision agriculture
Probit Probit
Variable-rate fertilizer technology
Probit
Logit
Precision agriculture Soil sampling
Logit
Precision agriculture Probit
Logit
Precision agriculture
Precision agriculture
Logit
Precision agriculture
Logit
Probit
Remote sensing
Soil sampling
Logit Probit
Variable-rate & yield mapping Soil sampling
Logit Logit
Yield mapping
Analytical method
Variable fertilizer rates
Precision agricultural technologies
Table 1 Details of 25 adoption analyzes drawn from 10 reviewed study
950
405
650
NA
NA
NA
NA
NA
4 040
8 429
1 131
1 131
1 131
1 131
1 131
491
491
491
491
1 215
827
827
1 170
1 170
1 170
Sample size
11
11
10
10
10
10
10
10
7
11
10
10
10
10
10
10
9
9
10
11
13
13
7
7
8
No. of variables
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Sig.
Model significance
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arranged in order of author(s), country, PATs, analytical method, sample size, number of variables and the significance of the model to our structured review. Because the use of PATs is more prevalent in developed countries, studies analyzing the adoptive decision making process has, to date, only been undertaken in these countries. Such studies have been conducted predominantly in the United States of America and, to a lesser extent, in Australia. They have investigated adoptive decision making processes within five PATs: (1) GPS, (2) yield monitoring systems, (3) remote sensing systems, (4) soil sampling regimens and (5) variable-rate applicators. In addition, some studies have examined precision agriculture as a whole. It is interesting to note that the most widely adopted GPS guidance technologies (e.g. light bars, auto steer system) have not been studied as much as their prevalence would indicate. This is perhaps because their popularity has not created a ‘problem’ which invites investigation. Assuming that, for the farmer, profitability is the main objective in using PATs, analysis of the selected studies has been built, using an economic approach, on the theory of utilitymaximization. This theory assumes that farmers make rational choices to maximize their profit within their resource capabilities (Edwards-Jones 2006). Using such an economic approach, farmer decisions can be conceptualized in two ways. This, in turn, determines the selection of analytical method. Firstly, farmers make decision to adopt or not to adopt PAT(s). When facing with such binary choice, a Logit or Probit analytical method is employed. Both are discrete choice models in which the dependent variable is dichotomous and truncated on both ends at 0 (non-adoption) and 1 (adoption). They generate similar results when tested on a large sample; their results differ when used on a small sample. Though there is no statistical theory for preferring one of them over the other, estimation of the Probit model is relatively more complicated due to the fulfillment of the assumption of normal distribution (Hill et al. 2008). This perhaps explains the popularity of the Logit model over the Probit model in our reviewed studies. Secondly, farmers have to decide on the adoption and the frequency or intensity of using PAT(s). To analyze such decisions statistically, a Tobit model is suitable. It is a choice behavior model that was proposed by Tobin (1958) to describe the relationship between a limited dependent variable and a set of independent variables. The Tobit model is preferred over binary Logit and Probit models when the decisions on the adoption and the intensity of use have to be done simultaneously (Feder and Umali 1993). Having said that, its ultimate application depends on whether such concepts arise. Sample sizes are critical in determining the performance of analytical methods. A minimum of 10 observations per estimated parameter is required for the Logit model (Hair 2010). Sample sizes of 60 are the minimum demanded for the Probit model (Chen et al. 2005) and 500 are suggested for Tobit (Nelson 1981). In most1 of the selected analyzes, these requirements have been fulfilled. Consequently, all of these reviewed models are significant. Their findings provide an empirical basis for our review. In addition, we should mention that sample sizes in our review are quite large in comparison to those reviewed by Knowler and Bradshaw (2007). Because our reviewed studies fulfill the minimum requirement of sample sizes, they do not provide an example of how a model could perform poorly due to a gap in meeting a desired sample size. For demonstrating this important point, a number studies that were reviewed by Knowler and Bradshaw (2007) can be borrowed for the purpose: with a small sample size of less than 100, not more than 20 % of the variants are explained in individual analyzes that were conducted by Shortle 1
Details on sample size have not been provided by Roberts et al. (2002) for their applications of the Logit method.
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and Miranowski (1986), Marra and Ssali (1990), and Carlson et al. (1994). This observation coincides with Hair’s (2010) caution that the complexity of analytical methods for small samples may result in little statistical power for the test to identify significant results. Significant factors influencing the adoption of PATs Derived from the 25 analyzes in the 10 selected studies, a total of 34 significant factors have been found to explain the adoptive decision making of PATs. These significant factors are presented in Table 2. They can be grouped within seven categories: (1) socioeconomic factors, (2) agro-ecological factors, (3) institutional factors, (4) informational factors, (5) farmer perception, (6) behavioral factors and (7) technological factors. Interpretation of these individual factors should be based on the assumption that other things remain constant. Socio-economic factors Socio-economic factors refer to the personal background of the farm’s main decision maker. Because information-intensive technologies require a high level of human capital, the farmer’s capacities and abilities clearly influence his/her adoptive decision to use PATs on their farm (Daberkow and McBride 1998). Socio-economic factors found significant to our review are operator age, years of formal education and years of farming experience.
Table 2 Significant factors influencing the adoption of PATs Categories Socio-economic factors
Variables Operator age
Formal education
Years of farming experience Agro-ecological factors
Land tenure
Part-owner farmers
Farm specialization
Full-owner farmers
Farm size
Farm income/profitability
Farm sales
Soil quality
Variable fertilizer rates
Percentage of main crop in total farmland
Livestock sales
Percentage of farmland as county land area
Debt-to-asset ratio
Percentage of cropped land to total farmland
Production value
Percentage of farmland as large farms
Owned land minus rented land
Off-farm employment
Yield Institutional factors Informational factors
Distance from a fertilizer dealer
Use of forward contract
Region
Development pressure
Use consultant
Perceived usefulness of extension services in implementing precision farming practices
Farmer perception
Perceived profitability of using precision agriculture
Behavioral factors
Willingness to adopt variable-rate technology
Technological factors
Yield mapping
Farm has irrigation facility
Use of computer
Generated own map-based input prescription
Sources Various studies indicated in Table 1
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Data on operator age can be recorded as a continuous variable or recoded as a dummy variable (1 = operator age is over ith year-old; 0 = operator age is below jth year-old). Age has been shown to be a significant explanatory factor, which has a negative relationship with the adoption of high-technological practices, such as computers (Batte et al. 1990). This has been considered a consequence of older farmers having shorter planning horizons, diminished incentives to change and less exposure to PATs (Roberts et al. 2004). Younger farmers, in contrast, have a longer career horizon and are more technologicallyorientated (Larson et al. 2008). They may be more motivated to try PATs than their older counterparts. Thus far, this hypothesis has only been advanced by Roberts et al. (2004) in their analyzes of variable-rate applicators. Other studies have either found age is a positive determinant (e.g. Isgin et al. 2008; Daberkow and McBride 1998) or an insignificant factor (e.g. Daberkow and McBride 2003; Robertson et al. 2012). In view of this mixed picture, the function of operator age in the adoptive decision does not lend itself to an easy explanation. The farm decision makers or the household head’s formal education attainment can be measured in the number of years of formal education or ordinal education levels. Since the implementation of PATs require substantial technological and informationally driven analytical skills and knowledge-based interpretation, more highly educated farmers are most likely to meet the human capital requirements (Larson et al. 2008). Therefore, formal education attainment is hypothetically expected to be positively related to the adoption of PATs. Indeed, such results have been found in a number of adoption studies on various PATs (e.g. Walton et al. 2008; Larson et al. 2008). Farming experience is used to quantify how long farmers have been involved in agricultural production activities. This continuous variable has an ambiguous impact on the adoption of PATs. Greater experience can lead to better knowledge of spatial variability in the field (Khanna 2001) and to operational efficiency to the extent that farmers learn by doing (Adhikari et al. 2009). More experienced farmers may feel less need for the supplementary information provided by PATs and, hence, eschew their adoption (Isgin et al. 2008). On the other hand, uncertainty regarding farm investment reduces with learning and experience (Feder 1982). Confidence is, hence, boosted in PATs. This induces more riskaverse farmers to adopt PATs as long as they are profitable (Daberkow and McBride 2003). Empirically, however, this factor has been largely insignificant. A single exception – positive impact has been found in the adoption of variable-rate applicator by Khanna (2001). Agro-ecological factors Agro-ecological factors are sometimes known as ‘‘farm biophysical factors’’. As the nomenclature implies, this factor embodies both on-farm natural endowments and operational factors to explain the adoption of PATs. Among natural endowments, soil quality has been found to be the single significant factor. Commonly found significant on-farm operational factors include land tenure, farm size and financial status. Judging soil quality is not easy. In literature, this factor is represented by a ratio of an average yield per acre or maximum yield per acre. It is sometimes labeled as a continuous variable of average yield or yield difference between the most and the least productive farmland. A ‘blanket’ rate of fertilizer application in a parcel, as an example, that results in below average yield means that the soil of the poorer quality is less responsive. When noting that a relatively more productive parcel is offset by unproductive ones, the knowledge of spatial variability is more likely to induce the adoption of PATs. This has
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been supported by statistically significant findings in a number of our reviewed studies (e.g. Isgin et al. 2008; Khanna 2001). ‘‘Land tenure’’ is a descriptor differentiating self-owned farm land from property which is rented from a third party. This factor can be captured through (1) a dummy variable (1 = owned land; 0 = rented land) or (2) two dummy variables: part-owned land (1 = yes; 0 = no) and fully owned land (1 = yes; 0 = no) or (3) a continuous variable of percentage of owned land as a ratio of total farmland. A farmer is more likely to manage self-owned land in a more favorable manner than rented land (Roberts et al. 2004). With such ownership, he is more likely to enjoy the benefits accruing to his farm management and, thus, increase the incentive for the adoption of PATs. Though this factor has been insignificant in some cases, its impact on adoption has been generally consistent across a range of studies (e.g. Roberts et al. 2002; Isgin et al. 2008). ‘‘Farm size’’ refers to the total land available to a farmer for agricultural production. Traditionally, this factor is measured by total acreage of farmland. Our review has revealed that this continuous variable can also be represented by expressing (1) the ratio main crop acreage relative to the total cropped area, (2) the percentage of farmland in crop or (3) the proportion of farmland which is larger than a defined acreage. This factor is a proxy for economies of scale, an important consideration in any attempt to acquire high level technologies. As investment costs, informational costs and uncertainty increase the critical size of farms likely to adopt PAT’s increases also (Feder et al. 1985). This is the result of larger farms having a greater capacity to absorb costs and risks while, at the same time, allowing those factors to be spread over a greater productive base. Therefore, PATs are more likely to be adopted on large farms. In a significant number of cases, the adopters of PATs have collectively been found to come from large farms (e.g. Walton et al. 2008; Robertson et al. 2012). Financial status is a conceptual factor used to express the continuous variables of farm sales, livestock sales, production value, farm income/profitability, and debt-to-asset ratio. Each of these variables can be used as a proxy for farm capital. Investments in innovations like PATs require high entry costs and carry greater risk of PATs than investments in mature technologies (Diederen et al. 2003). In addition, it is always difficult to raise external capital for high risk investment. A farmer who has greater capital, therefore, has greater financial capacity to adopt PATs. If the results turn out to be unfavorable, losses are potentially affordable. However, this factor has proved insignificant in most cases (e.g. Larson et al. 2008; Roberts et al. 2002) or has had mixed results (e.g. Daberkow and McBride 1998; Isgin et al. 2008). Institutional factors Institutional factors are indicators that enable or disable a farmer’s inclination towards behavioral change. Significant factors identified, to date, include ‘‘farm region’’ and ‘‘development pressure’’. ‘‘Farm region’’ describes the general location of farms. An alternative reference to this factor is ‘‘farm location’’ if the study is based on a more specific perspective. Due to the difficulty in capturing data on natural resources (e.g. soil fertility, climate, and rainfall), either farm region or location is used as a stand-in for the integrative representation of these other factors. It is captured as a dummy variable (1 = a farm is situated in a location; 0 = otherwise). Heterogeneity of natural resources influences performance and subsequent adoptive decision making (D’Emden et al. 2006). Farms which are located in resource-rich areas, hence, are more likely to adopt PATs. However, its significance as a factor
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impacting on adoption has been mixed. The explanation has been offered that farms are unlikely to be managed in the same way even when they are under similar production conditions (Schmitzberger et al. 2005). ‘‘Development pressure’’ is a factor which considers whether a farmer is faced with pressing urban growth surrounding his farm. This factor can be recorded as a dummy variable (1 = yes; 0 = no). ‘‘Yes’’ is indicative that a farmer has observed considerable development in the neighborhood of his farm. Moreover, as arable land is increasingly given up for development, the pressure for change to more productive agricultural practices increases. Under such circumstance, farmers who are under such pressure are hypothesized to adopt PATs. This factor has been found particularly significant by Isgin et al. (2008). Informational factors Information is the key to the diffusion of innovations (Rogers 2003). Given the difficulty of quantifying information, it can be characterized by (1) the accessibility to the information from a certain source (1 = yes; 0 = no), (2) expressed as the perceived usefulness of the information that is obtained from a certain source (1 = yes; 0 = no) or (3) the frequency of receiving the information within a time period. Information on agricultural practices is typically sourced from extension services or consultants. However, such public services are intended for mass consumption, limiting the official’s focus and availability to assist a specific farm. Additionally, PATs are technically complex. Hiring a third party’s services (such as cropping consultant) to set-up and use PATs is more relevant. Therefore, adopters of PATs are more likely to be those who have hired consultants. Indeed, this has been evidenced in the research of Robertson et al. (2012) and Larson et al. (2008). The latter, nevertheless, has also shown that adoption is more likely if the information on PATs provided by extension services is seen as useful. Farmer perception Farmer perception refers to their personal subjective evaluation of innovation attributes. Among the perceived attributes suggested by Rogers (2003), perceived relative advantage is used to assess how well an innovation is thought to offer increased benefits in excess of those technologies that one intends to replace. Among other relative advantages, profitability is a major concern when considering any capital-intensive agricultural technology, including PATs. Realistically and perceptually, rational farmers do not want to make losses on their investment. Hence, the probability of adopting PATs is expected to be higher if PATs are seen to result in profit (1 = yes; 0 = no). This hypothesis has been sustained through the findings in Walton et al. (2008). Behavioral factors ‘‘Behavioral factors’’ are used to depict the psychology of a farmer. These factors play a particularly important role in decision making when an innovation does not offer direct benefits (Lynne et al. 1988). PATs, as noted earlier, offer a combination of economic and environmental benefits. In view of this, ‘‘intention’’ has been posited as an antecedent to the adoptive decision making process, particularly in leading to environmental-related behaviors (Lamba et al. 2009; Calkins and Thant 2011). Empirically, intention is represented either by an ordinal variable of likelihood or a dummy variable of willingness to
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adopt PATs. This variable is conceptualized to capture the motivational factors that influence a behavior. However, it somewhat depends on non-motivational factors (e.g. time and capital) to decide how hard an individual is willing to try or how much effort he is planning to exert, in order to perform the behavior (Ajzen 1991). Collectively, expression of higher likelihood or willingness to adopt PATs indicates that he has actual control over the behavior and is therefore more likely to realize it. As such, adoptive decisions emerge from intentionality. This factor is found to have a positive impact on the adoption of PATs, especially when the cost of acquiring them is being subsidized (Khanna 2001). Technological factors Technological factors embody a number of indicators in the use of technologies, including irrigation facilities, PAT’s, and computers. The latter, in particular, as part of farm management, implies that the farmer has some knowledge of technological operation regardless whether the computer is used for record keeping or other purposes. A major component in the operation of PATs is to examine potential problems by analyzing the recorded information in a computing device (Larson et al. 2008). As such, computer technology is the integral part of precision agriculture (Roberts et al. 2004). Therefore, computer use has commonly been found as a predictor for the adoption of PATs (e.g. Isgin et al. 2008; Daberkow and McBride 1998, 2003).
Policy implications From the review of significant factors influencing the adoption of PATs in the earlier section, the adoption of PATs is a result of multi-dimensional considerations. Extrapolated from the discussion, the adoption of PATs is positively associated with (1) socio-economic factors (farmers who are older and have higher education level), (2) agro-ecological factors (farmers whose farm has better soil quality, is self-owned, and is large), (3) institutional factors (farmers who face greater pressure for sustainability), (4) informational factors (farmers who have hired consultants and agreed on the usefulness of extension services), (5) farmer perception (farmers who perceived that PATs would bring profitability), and (6) technological factors (farmers who have used computers). These findings provide empirically based implications for countries that have ventured into or that are about to get involved in the promotion of PATs. For effective diffusion of PATs, their promotional and educational initiatives should be well targeted toward one or more selected segments. While our review has provided related cues for potential segments, agricultural officials and private companies should also evaluate different market segments. Borrowing from a marketing perspective, Kotler (2003) suggests that such an evaluation can be done by looking at two key factors: the segment’s overall attractiveness and the objectives. Does a potential segment possess general attractiveness, such as size, growth, and economies of scale? Does investing in the segment meet the objectives for a given productive resource? Any attempt to answer these two questions can be partly assisted by an Agricultural Census dataset, which is commonly available in individual countries. A part of the dataset provides details on (1) socio-economic factors (gender, age, household size, ethnic, marital status, educational attainment, employment status of farm holder), (2) agro-ecological factors (size, land tenure, location, capital, sales/profit and main crop of farm) and (3) technological factors (use of irrigation and technology on-farm). These variables are
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relatively ‘fixed’ over time. Thus, the dataset renders a valuable basis for making the decision on which segments to target on the basis that they present the greatest opportunity of success. On the other hand, information on (1) institutional factors, (2) informational factors and (3) farmer perceptions have other important functions. As these factors are modifiable, intervention is able to add more weight to the likelihood of PATs adoption. Firstly, public education on resource conservation will lead to consequential awareness. Increasing awareness will increase pressure on a farmer to use PATs in order to conform to the public demand for sustainability. However, this social pressure will not succeed if farmers are not exposed to the stimulant. Therefore, promotion of the issue should be targeted at mass societal groups. Secondly, carefully tailored information on PATs should be made available to farmers through effective channels at affordable prices (or even complimentarily). While extension officers are not hired to focus specifically on the promotion of PATs, they should be equipped with the necessary knowledge. If a third party’s service is preferred over extension services, local government should instigate public–private partnerships or appoint third parties at the cost of a packaged remuneration as a step-up extension agency. Lastly, farm business is profit-orientated. Adoption is unlikely to happen if PATs are not perceived to be profitable or offer amenities and services valued by producers. Lehman et al. (1993) point out that a farmer still might adopt a new agricultural practice even though it might not result in a direct profit. This is made possible through a number of financial initiatives, such as capital subsidies for the set-up and maintenance of PATs, tax reduction for adopters of PATs, cuts in interest rates, and complimentary PATs technical assistance to save costs and boost yields. One or more of these could indirectly reshape farmer perceived profitability and improve actual farm profitability.
Conclusions PATs have been promoted to enhance the efficiency of on-farm resource use under the intertwined banners of enhancing sustainability and food security. Many countries have considered investing in these technologies. While PATs are more commercially available in developed countries, adoption has grown at a modest rate. This phenomenon has attracted a number of domestic studies to identify the factors underlying adoption in varied forms. Capitalizing on these disparate research efforts, the intent of this paper has been to review their findings, searching for those commonalities which might, partially at least, explain the farm-level adoption of PATs. In doing so it has been our aim to offer policy cues for both ‘experienced’ countries and ‘new’ entrants. To meet the objective of this paper, a total of 25 separate analyzes of the factors influencing PATs adoption have been drawn from 10 selected studies. Based in developed countries, the dependent variable of these analyzes has been the answer to the question whether a farmer has or has not adopted or how frequently (or intensely) a farmer has used a single PAT or an aggregate of PATs. Employing the economic approach, a Logit or Probit analytical methodology has been used to analyze the binary adoption choice while a Tobit analytical method has been utilized to assess the intensity of use. Each of these analyzes has been significant in their model fit and, thus, renders an empirical basis for our review. Findings from the 25 reviewed analyzes have indicated that as many as 34 significant factors might help explain the adoption of PATs. These factors have been grouped
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according to (1) socio-economic factors, (2) agro-ecological factors, (3) institutional factors, (4) information factors, (5) farmer perception, (6) behavioral factors and (7) technological factors. Some of these relatively ‘‘fixed’’ factors are useful for market segmentation and targeting purposes. Other ‘‘modifiable’’ factors can be reshaped through interventions. It is also important to add some caveats to this review. While the review is useful in clarifying those factors positively associated with the adoption of PATs, low adoption rates in ‘‘developed’’ countries suggest that many farmers, possessing all of these factors, have never the less chosen not to adopt. Therefore, while the factors should be utilized in the manner suggested, they cannot be regarded either as precise or comprehensive. Research should therefore continue to discover those trigger factors which, at present, elude us. More research effort should be devoted to understanding the adoption of PATs in ‘experienced’ countries and the search for ‘‘trigger’’ factors; ex-ante studies should seek to provide preliminary insights on the interest or intention of ‘‘new’’ entrants to adopt PATs. As noted earlier, the benefits and costs of using PATs are complex. Decision making in respect to the studies selected for this review, however, has been entirely based on an economic approach. Current models in the approach are not sufficient to represent the totality of those considerations which lead to the adoption of PATs. Past studies have largely ignored the informational, behavioral, and social aspects of decision making. They have also overlooked the policy context within which the agricultural enterprise operates. Understanding these diverse considerations is essential since it might directly impinge on the primary motivation for the adoption of PATs. Acknowledgments We thank two anonymous reviewers and Jim Schepers (Co-Editor) for their constructive comments concerning ways to improve the quality of an earlier version of this paper.
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