**4. Discussion**

In this paper, we explored the geographic contexts where irrigation adoption studies were conducted and the set of causal factors that were reportedly associated with irrigation adoption decisions. Based on the results of the systematic review, our first hypothesis held true. That is, the geographic contexts in which irrigation adoption studies were often conducted were biased. Geographic regions with less than 1% area equipped for irrigation, very low (less than 0.2%) and high (above 0.8%) percent of cropland, low market accessibility index (less than 0.1), and average annual precipitation with less than 463 mm/year and greater than 1219 mm/year, were highly underrepresented in this collection of case studies. In other words, these case studies were significantly biased toward areas where at least some amount of irrigation was already being practiced. An explanation for this bias towards irrigated areas could be that the research was motivated by the need to identify challenges

and/or opportunities associated with further expansion. Additionally, low cropland areas were also understudied, because research might have been focused more on areas having a moderate or higher amount of cropland cover to encourage further agricultural growth and development. Usually, farmers in areas with a high percentage of cropland cover, because of the limited scope for further (land) expansion, are more likely be using intensive agricultural practices (like irrigation) to increase their crop productivity, hence the focus was towards areas with moderate amount of cropland. Further, highly accessible regions were over-represented in this collection, because research is often conducted in locations (and with communities) that are easily accessible (or reachable) as compared to remote or hard to reach locations [105]. There is also evidence that farmers with greater market access had stronger incentives to adopt irrigation for market production [106]. Hence, regions with low market accessibility were understudied and accordingly underrepresented. Similarly, regions with low and high average annual rainfall were also underrepresented and this might be due to the overall 'unsuitability' of this technology within these regions. For instance, if a region receives abundant rainfall, farmers might have a natural inclination to rely on rainfall for agricultural activities rather than investing in new technology, as irrigation is generally a substitute for rainwater [107]. For regions with low average annual rainfall, although irrigation technology can be very useful nevertheless, reliable access to water might hinder its widespread diffusion and subsequent adoption [108].

The second hypothesis that we tested in this paper held partially true as only the Demographic category of factors was observed as the most common among all the case study regions. This indicated that demographic factors such as a farmer's age, gender, household assets, income diversification options, and perceptions toward climate change (see Figure 3 for a complete list), significantly affected farmers' decisions to adopt (or not) irrigation irrespective of the geographic context. However, some distinct regional variations were also seen. For instance, studies from North America explained irrigation adoption behavior of farmers using a combination of only demographic, biophysical, social capital, farm-enterprise, and institutional factors. Factors related to place or technology did not feature in the case studies from this region. Similarly, for cases from Near East, only categories of factors such as demographic, farm enterprise, biophysical and social capital were observed. Both institutional and technology related factors were least observed among all these case studies. Further, the highest frequency was of the cluster with Biophysical, Demographic, Farm Enterprise, and Social Capital factors (B, D, F, S), followed by the cluster with Biophysical, Demographic, Farm Enterprise, Institutional, and Social Capital factors (B, D, F, I, S), suggesting that irrigation adoption decisions around the world are best explained by the combination of multiple and coupled factors instead of a single variable.

Moreover, majority of the case studies in this collection were from geographic regions of Asia and Africa and were clustered with a greater (and often similar) number of factors as compared to the rest. This suggests that some common challenges might possibly exist with regard to irrigation technology diffusion and adoption within these regions, even though the study sites within these regions (See Appendix A for more information on study locations) were different from each other in many other aspects beyond just percentage of irrigation or average annual precipitation (national wealth, population densities, etc.). A recent study on understanding sustainability challenges in three different rural landscapes, namely, Australia, central Romania, and southwestern Ethiopia, found similarities among these three different social-ecological systems, even though the systems examined appear to be very different on the surface [109], thus, highlighting the need for a comprehensive analysis to identify and better comprehend such common challenges.

Although a nearly similar set of factors were observed from case studies of Asia and Africa, many of the study sites from Africa with little to no irrigation (less than 1%) were understudied, while all those from Asia were over-studied and hence over-represented in this collection (Figure 12). One explanation for this research bias could be that the farmers in the study sites within Africa might still be in their early adoption phase. Given the low percentage of irrigated areas, one can argue that in these sites only a few individuals

are taking the risk of investing in this technology. Moreover, this technology might not have been completely diffused within these sub-regions of Africa (east, west, and south), and as a result, this topic might be highly understudied within these sites because there is first a need to properly introduce this technology to the people, make them aware of its use and benefits, and only then can the adoption process be studied. Furthermore, based on the results of the frequency analysis, institutional and social capital related factors were most commonly observed in cases from this study region compared to others. These categories include factors like access to informational services, credit facilities, extension services, skill development programs, supporting policies, incentives, and subsidies. A study by Wozniak (1987) [110] highlighted the important role played by education and information on the new technology, particularly for early adoption. Another study by Diederen et al. (2003) [111] presented empirical evidence for explaining the differences in adoption behavior of innovators, early adopters, and laggards. Their findings suggested that innovators (~first or early users of technology) made more use of external sources of information. In a more recent study on the adoption of improved seed varieties by farmers in Ethiopia, the findings suggested that farmers' awareness about the available seed varieties is an important factor for the actual adoption to take place [112]. Teha & Jianjun (2021) [113] in their study on the adoption of small-scale irrigation found that 'government promotion' in the form of incentives and training positively affected a farmer's irrigation adoption decision. Thus, some kind of external support like extension and credit services are vital for farmers for enhancing the diffusion and adaptation of successful technologies and practices [114,115]. With limited information and support, a farmer's decision-making is primarily based on intuition and can be less efficient [116].

However, the results of this meta-study are limited in scope, since only peer-reviewed research articles that were available in the English language, in the two selected databases, and published on and after the year 2000 were considered for this analysis. Such a restriction on the publication date was imposed because the global irrigation dataset used in this analysis is based on the nationally reported statistics from around the year 2000. Further only articles that investigated the factors associated with irrigation adoption were selected for this analysis irrespective of the theoretical frameworks applied to examine a farmer's adoption behavior. Due to this, certain factors might be emphasized more than others. For instance, a social network analysis approach was used to assess the barriers to climate change adaptations in Spain [117]. Because of the specific framework used in this study, the barriers identified were mostly categorized within social capital and institutional categories (see Appendix A for study details). Similarly, another case study from Nepal, used risk perception and motivation theory to understand farmers preparedness to cope with the impacts of climate-change hazards [118], and as a result, only the factors characterized as demographic were identified from this case study. Moreover, conference proceedings and grey literature were also excluded from the dataset due to inconsistent methodology and results reporting. Such sources may have contained useful and unique insights, but issues of comparability with information gathered from peer-reviewed would have unduly complicated the analysis.

Despite the limitations mentioned above, the global representativeness analysis highlights the multiple (geographic) biases that exist with respect to studying farmers' irrigation adoption decision-making. More research on this topic is being conducted in regions that have little to high percentage of irrigation (>1%), are readily accessible, receive moderate amounts of average annual rainfall, and have moderate amounts of cropland cover. These results suggest the need to expand research efforts, particularly in areas with low irrigation and cropland cover to identify constraints to and help accelerate economic growth, poverty reduction, and food and livelihood security for rural communities in these regions.
