Skip to main content

Adoption analysis of agricultural technologies in the semiarid northern Ethiopia: a panel data analysis

Abstract

Agricultural technology change is required in developing countries to increase the robustness to climate-related variability, feed a growing population, and create opportunities for market-oriented production. This study investigates technological change in the form of adoption of improved wheat, drought-tolerant teff, and cash crops in the semiarid Tigray region in northern Ethiopia. We analyze three rounds of panel data collected from smallholder farms in 2005/2006, 2009/2010, and 2014/2015 with a total sample of 1269 households. Double-hurdle models are used to assess how the likelihood (first hurdle) and intensity of technology adoption (second hurdle) are affected by demographic, weather, and market factors. The results indicate that few smallholders have adopted the new crops; those that have adopted the crops only plant small shares of their land with the new crops, and that there has been only a small increase in adoption over the 10-year period. Furthermore, we found that high population density is positively associated with the adoption of improved wheat, and previous period’s rainfall is positively associated with the adoption of drought-tolerant teff. The adoption of cash crops is positively associated with landholding size and access to irrigation. The policy implications of these results are that the government should increase the improved wheat diffusion efforts in less dense population areas, make sure that drought-tolerant teff seed is available and affordable after droughts, and promote irrigation infrastructure for production of cash crops.

Background

Adoption of improved agricultural technologies is an important means of adapting to climate change, improving agricultural productivity, and facilitate the transition from subsistence agriculture to market-oriented agriculture (Bezu et al. 2014; De Janvry and Sadoulet 2002; Mendola 2007; Minten and Barrett 2005; Yu et al. 2011; Zilberman et al. 2012). Among the technologies adopted by farmers in the Ethiopian highlands are improved wheat, drought-tolerant teff, and cash crops (Belay et al. 2006; Shiferaw et al. 2014; Wale and Chianu 2015). In this paper, we investigate to what extent farmers in the semiarid Tigray region of Ethiopia have adopted improved wheat, drought-tolerant teff, and cash crops and which factors explains the adoption and intensity of adoption.

Technology diffusion often takes years and can best be captured using panel data. However, most studies on the adoption of improved wheat in semiarid agriculture in Ethiopia use cross-sectional data (Kelemu 2017; Kotu et al. 2000; Lobell et al. 2005; Matuschke et al. 2007; Shiferaw et al. 2014; Tesfaye et al. 2016). One of the few studies including a time dimension is Abera’s (2008), which used cross-section household data from 2001 with recall data back to 1997 and estimated factors affecting adoption of improved wheat in northern and west Shewa zones of Ethiopia. He analyzed how farmer and farm characteristics are correlated with adoption and intensity of adoption, but does not cover important supply-side constraints that need attention.

Studies of drought-tolerant teff in Ethiopia include Wale and Chianu (2015) and Belay et al. (2006). Wale and Chianu (2015) examined farmers’ demand for drought-tolerant teff using cross-sectional data. The study of Belay et al. (2006) used data from an experiment on village demonstration plots, including 41 farmers in 2002 and 2003, and found that farmers adopt drought-tolerant teff varieties when there is limited rainfall. To the best of our knowledge, empirical studies of the adoption of drought-tolerant teff using rich panel data from semiarid agriculture are missing.

Adoption of cash crops is mainly associated with access to irrigation and has a dual advantage. First, irrigation and adoption of cash crops typically allow the smallholders to harvest more than one time per year, which lead to improved land productivity. Second, the adoption of cash crops leads to improved output market integration and increased income. Ethiopia has adopted smallholders’ commercialization as part of its economic transformation strategy (Gebremedhin et al. 2009). The development of irrigation reduces the production risk in semiarid areas and expansion of public investments in infrastructures improve market access. This has improved agricultural productivity and enhanced market participation by Ethiopian smallholders (Gebregziabher et al. 2009; Hailua et al. 2015).

The main contribution of this study is threefold: first, we provide new insight into the development in the adoption of the three improved agricultural technologies improved wheat, drought-tolerant teff, and cash crops in Tigray, Ethiopia. Second, we provide new insight into factors affecting the likelihood of adoption and intensity of adoption for these improved agricultural technologies. Third, we discuss policy implications for how to best integrate and reap the benefits from the promotion of improved wheat, drought-tolerant teff and cash crops, given their importance for food productivity, food security, and market integration.

Theoretical framework

Household’s adoption decision of new technology is usually modeled as a choice between traditional and new technology. A farm household adopts the new agricultural technology when the expected benefit from adoption is higher than without adoption (Amare et al. 2012; Bezu et al. 2014; Ma and Shi 2015). More recently, the literature has started to investigate constraints that could cause only partial adoption across and within farms.

The theoretical framework of this study builds on the state-contingent partial adoption framework for new technologies in a risk-exposed economy, as in Holden and Quiggin (2017). Partial and state-contingent adoption reflects that household choices may be affected by factors such as stochastic weather events, market imperfections in input and output markets, limited knowledge about the performance of new technologies under different states of nature, limited availability and high cost of technologies, and heterogeneity in farm and household characteristics.

Climate change and climate risk may affect technology adoption as illustrated by the state-contingent production approach (Holden and Quiggin 2017). This approach states that farmers’ adoption decision depends on their perception of risk associated with the choice of the new technology relative to alternative technologies and the states of nature that may be realized after adoption decisions are made. Limited knowledge of the performance of new technologies under alternative states of nature may be one constraint. Partial adoption and exposure to different states of nature can over time help farmers build realistic and more accurate expectations about alternative technologies and thereby influence the adoption and adaptation process. Hence, households exposed to earlier weather shocks and who are risk-averse are more likely to choose a less risky technology such as drought-tolerant crop varieties when they have developed their knowledge about these (Amare et al. 2012; Antle 1987; Holden and Quiggin 2017).

Another research string important for our study is the literature on technology diffusion. Pan et al. (2018) investigated how technology diffusion processes affect farmers’ adoption decisions. They found that factors making it easy to learn about the benefits of new technologies have a positive impact on adoption rates. Examples of such factors are extension services, field demonstrations, market integration, and viewing and learning from other farmers. Other studies also point to learning externalities, social learning diffusion, communication patterns, and following successful neighbors’ practices as drivers of technology diffusion (Conley and Udry 2010; Genius et al. 2014). In total, these studies point in the direction of a gradual increase in adoption of improved agricultural technologies over time, if they are available and affordable.

Based on the theoretical framework, we propose the following hypotheses for testing:

  • H1: There is a gradual increase in the adoption and intensity of adoption of the three improved agricultural technologies over the 10-year time period.

  • H2: Improved wheat is more likely to be adopted in areas with high population pressure and by more land-constrained households (high farm-level population pressure).

  • H3: Drought-tolerant teff is more likely to be adopted in areas with more rainfall variability and in areas exposed to recent rainfall shocks (droughts).

  • H4: Cash crops are more likely to be adopted in areas with good market access (short distance to markets).

Method

Survey design and data

The data are collected in Tigray in northern Ethiopia. The region is semiarid and exhibits high population pressure (Appendix Table 5), seasonal and erratic rainfall, relatively low agricultural potential, and limited access to sizeable markets. The data used in this study come from three rounds of farm household surveys conducted in 2005/2006, 2009/2010, and 2014/2015 production seasons (Table 1).

Table 1 Summary of statistics of variables used in the analysis by survey year (mean values)

The panel sample is based on a survey conducted in 1998/1999 using a two-stage sampling technique and described in Hagos and Holden (2003). In the first stage, communities were selected from the rural districts of the region to reflect differences in agricultural potential, population density, agroecology, market access, and access to irrigation. In the second stage, 25 households were randomly sampled from a list of farm families in the selected communities for detailed interviews. Most of the technologies of interest for this study were introduced in the study region after year 2000, and we use data from the three survey rounds in 2006, 2010, and 2015, each covering the previous year’s cropping seasons. Over time, some households dropped out of the sample, and new were added, resulting in an unbalanced household panel.

To examine farmers’ technology adoption decisions, we use information on household and farm characteristics including land and non-land endowments, farm-level population pressure, indicators of access to infrastructure (marketplace and road), and rainfall at community level. We construct long-term average annual rainfall, variation (standard deviation) in average annual rainfall, and 1- and 2-year lagged annual rainfall at the community level from the monthly satellite record of the African Rainfall Climatology Version 2 (ARC2) for the years 2003–2014Footnote 1.

Presuming that access to technology differs according to the features of agroecology and accessibility of public services, we divide the households into three access-to-agricultural-technologies groups. The first access group is households residing in the mid and highland agroecology with access to improved wheat (Group 1). In Ethiopia, wheat is a mid and highland crop (Doss et al. 2003; Kotu et al. 2000) and is distributed to households in this agroecology. The second access group is households who live in drought-affected agroecologies with access to drought-tolerant teff (Group 2). Promotion of the adoption of drought-tolerant teff is an important strategy for adapting to the changing climate in these areas. The third access group is households who live in communities with access to irrigation and, thereby, are able to grow cash crops (Group 3). Access to irrigation such as a dam or groundwater that can be used to grow crops facilitate the adoption of cash crops. We will later refer to these three regionally determined access groups as the households with access to improved wheat, access to drought-tolerant teff, and access to cash crops, respectively.

Estimation method: double-hurdle model

The technology adoption literature proposes various econometric methods that can be used in modeling the behavior of households’ demand for new agricultural technology and identify the factors that can explain adoption decisions (Heckman 1979; Maddala and Nelson 1975; Wooldridge 2010). We present results based on Cragg’s double-hurdle models that allow variables to have different effects on adoption and intensity of adoption. In the first hurdle, we estimate a probit model to determine the probability that the households adopt the new agricultural technologies. In the second hurdle, we use a truncated regression model to determine the intensity of the adoption. We estimate the double-hurdle models for the adoption of the three technologies separately using the subsample that has access to the respective technologies.

We first run parsimonious models with the key explanatory variables of interest: household level and average community-level population pressure (family size/farm size), average community level rainfall and rainfall variability over the last 12 years, 1 and 2 years of lagged deviations from average rainfall, distance to market, and farm-level access to irrigation in the case of cash crops. We then assess the robustness of these results by including additional household control variables with and without a correlated random effects (CRE) approach (see elaboration below). The control variables include household head characteristics (gender, age, age squared, and literacy status), family labor (number of adult males and females), household resource endowments (number of oxen, mobile phone ownership (dummy), and size of owned land). Two-year dummies are also included to capture change over time (2010 and 2015). We will refer to these control variables by the vector X.

We specify the following Craggit double-hurdle model:

Hurdle 1: Probability of adoption, binary probit

$$ P\left({w}_{ij t}=1\right)={\alpha}_p{P}_{ij t}+{\alpha}_r{R}_{ct}+{\alpha}_d{D}_{ct}+\left({\alpha}_n{X}_{ij t}+{\gamma}_n{\overline{X}}_{ij}\right)+{u}_i+{e}_{ij t}\kern0.5em $$
(1)

Hurdle 2: Intensity of adoption, truncated regression model

$$ {Y}_{ij t}={\beta}_p{P}_{ij t}+{\beta}_r{R}_{ct}+{\beta}_d{D}_{ct}+\left({\beta}_n{X}_{ij t}+{\delta}_n{\overline{X}}_{ij}\right)+{\mu}_i+{\varepsilon}_{ij t}\ \mathrm{if}\kern0.5em w=1,\kern0.5em 0\ \mathrm{otherwise}, $$
(2)

where wijt is a variable indicating whether or not the household adopt the new technology, taking the value of 1 if the household adopts the technology and 0 otherwise; Yijt is the observed intensity of adoption measured as the log of area planted with the technology for the households that have adopted the technology; Pijt represents household and community population pressure; Rct is a vector representing the rainfall variables; Dct is the distance to market; and Xijt is a vector of the control variables as explained above. To control for unobserved heterogeneity, the means of the time-varying X variables, \( {\overline{X}}_{ij}, \)are included, which is the Mundlak (1978) and Chamberlain (1982), approach, also known as the correlated random effects (CRE) approach (Wooldridge 2010). This approach controls for other time-constant unobservable variables in a similar way as household fixed effects do in a linear panel data model. i, j, and t are individual household, technology type, and time identifiers, respectively; α and β are the parameters to be estimated for the n X-variables, and uiand μiare normally distributed random effects, constant for each household over time; eijt and εijtare error terms assumed to be independent and normally distributed, eijt~N(0, 1) and εijt~N(0, σ2).

A limitation of the CRE approach is that it takes many degrees of freedom and that may affect significance levels in small samples such as in the second stage of our double-hurdle models. We, therefore, run models without and with this specification as a robustness check. We have also tested for attrition bias, but found no significant effect on our results, and hence report the results without attrition controls.

Results

Descriptive analysis

Table 1 presents the mean values of technology adoption rates and intensity of adoption by technology and year in our panel, as well as the key variables of interest for our study. The adoption rates measure the share of households using each crop in the region they are available, while the adoption intensity measures the area the adopters planted with each crop. The areas are measured in tsimdi; one tsimdi is approximately 0.25 ha. Average farm size in tsimdi is also included in the table, for comparison with areas planted with the new crops of interest to our study.

We observe that the adoption rate for the improved wheat increased from 12.9% in 2006 to 18.4% in 2010 and decreased to 13.8% in 2015, indicating an initial increase and then stagnation and decline in adoption. The pattern for adoption intensity shows a similar trend over time. On average across years, adopters of improved wheat had planted about 5% of their farm area with improved wheat.

Drought-tolerant teff had adoption rates of 6, 3.9, and 16%, respectively over the 3 years, indicating a stagnant low rate first but then a substantial increase in the adoption rate. The adoption intensity was stagnant and small from 2006 to 2010 but then more than doubled from 2010 to 2015. On average across years, adopters of drought-tolerant teff had planted about 3% of their farm area with drought-tolerant teff.

For cash crops, we see an initial increase in adoption rate from 11.5 to 18.4%, and then a weak decline to 16%. On average across years, adopters of cash crops had planted about 3% of their farm area with cash crops.

Overall, we see low adoption rates and only small shares of the farms of adopters covered by the new crops. Only for drought-tolerant teff do we see a clear trend towards increasing adoption. For the two other technologies we see a stagnation or decline in the adoption rates over time. Hence, we do not find support for our Hypothesis H1 stating, “There is a gradual increase in the adoption and intensity of adoption of the three improved agricultural technologies over the 10-year time period.”

Estimation results

The results of the double-hurdle model for adoption and intensity of adoption are presented in Table 2 for improved wheat, Table 3 for drought-tolerant teff, and Table 4 for cash crops. We discuss one technology at a time in the following three sections. The three technologies are largely adopted in different areas and do, to a very small extent, compete for the same land. We can, therefore, consider their adoption as independent processes. The adoption for each technology is estimated for the areas that have access to these technologies and where these technologies are suitable.

Table 2 Double-hurdle estimation factors affecting adoption of improved wheat (Craggit model)
Table 3 Double-hurdle estimation factors affecting adoption of drought-tolerant teff (Craggit model)
Table 4 Double-hurdle models for adoption of Cash crops (Craggit models)

To verify whether the results are robust, we present the results from three different double-hurdle models for each technology. The first is a parsimonious version that includes only the key variables of interest, the second includes additional controls, and the third includes the means of the RHS variables including additional controls (CRE approach). In our interpretation, we give most weight to the results that are significant across all three model versions. We focus primarily of the assessment of our four hypotheses in the interpretation of the results.

Improved wheat adoption

The results for the improved wheat models are presented in Table 2. Our Hypothesis H2 stated, “Improved wheat is more likely to be adopted in areas with high population pressure and by more land-constrained households (high farm-level population pressure)”. Table 2 shows that farm-level population pressure is strongly and robustly positively correlated with adoption of improved wheat. This result is significant at 1% level in two of three model variants, and significant at 5% level in the third. The intensity of adoption was negatively correlated with community-level population pressure and significant at 1 and 5% levels in two of three models. This means we have support only for the second part of the hypothesis, that more land-constrained households are more likely to adopt improved wheat.

The results further show that improved wheat adoption was more likely in areas with lower average rainfall, higher rainfall variability, and two years after a negative rainfall shock. This indicates that improved wheat adoption can be a response to droughts in areas with lower than average and more variable rainfall. These results were also robust to the alternative model specifications. Finally, improved wheat adoption was not significantly affected by distance to markets.

Drought-tolerant teff adoption

We formulated the following hypothesis H3 that “Drought-tolerant teff is more likely to be adopted in areas with more rainfall variability and in areas exposed to recent rainfall shocks (droughts)”. We see from Table 3 that the standard deviation for rainfall is insignificant in all models. Furthermore, the lagged negative rainfall shock variables were also insignificant, while the one-year lagged positive rainfall shock variable was highly significant and positive in all three versions of the adoption (first hurdle) models. We, therefore, have to reject our hypothesis H3.

Intensity of adoption of drought-tolerant teff was found to be higher in areas with larger distance to markets. This result was highly significant (1% level) in all three models. The year dummy for 2015 was significant and negative in all three models. This should point in direction of dis-adoption of drought-tolerant teff from 2006 to 2015, but Table 1 indicates that adoption has increased over time. This difference could be due to the unbalanced sample or changes in drivers over time.

Cash crop production

We have assessed factors associated with cash crop production in areas with access to irrigation in Table 4. We hypothesized (H4) that cash crops are more likely to be adopted in areas with good market access. The distance-to-market variable is, however, insignificant in all models and we, therefore, must reject hypothesis H4. On the other hand, we see that farm-level population pressure is highly significant and positive in the first hurdle, indicating that cash crops are more likely to be grown on farms with high family size/farm size ratio. This may be because such households have more labor to grow labor-intensive crops. Furthermore, cash crops are more likely to be grown in areas with higher rainfall variability and after a year with good rainfall. This may indicate that food crops are given priority after years with lower rainfall. The negative signs for the year dummy variables are not consistent with the probabilities of growing cash crops across years in Table 1. This difference could be due to the unbalanced sample or changes in drivers over time.

Discussion

We will here discuss strength and limitations of our study and assess the adoption rates we find in comparison with other studies in Ethiopia, to assess the external validity of our findings.

Our study provides new evidence based on household panel data over a 10-year period for crop varieties and crops that are relevant for adaptation to climate change by smallholder farm households in a semiarid environment. The strengths of our study include the consistency of data collection methods over time, use of good data on rainfall and rainfall variability over time and space and having data from areas with substantial variation in population pressure, market access, and access to irrigation. A limitation of our study is that we do not have detailed data on access to extension services that may have affected the technology diffusion processes. Another limitation is that we have not assessed how these technologies are combined with other yield-enhancing technologies such as fertilizer. We are aware that fertilizer use intensity has increased substantially in our study areas during the same period. We leave these issues for other studies. We know that extension programs to stimulate the adoption of agricultural technologies have been part of the Ethiopian government’s policies since the mid-1990s (Wubeneh and Sanders 2006).

Large farm household surveys in Ethiopia seem to indicate that use of improved seeds of wheat and teff is modest not only in our study areas but in the whole country. Bachewe et al. (2014), based on the Feed the Future survey of 7000 households in 251 kebeles in 84 woredas in 2013, found that only 18% of all households used improved seeds in the main growing season. Those who adopted improved seeds used on average 14 kg/ha of seeds. This implies an average rate of 2 kg/ha for the total sample. This is data for all crops and adoption rates are lower for each crop but this baseline report does not present disaggregated data on improved seed adoption rates by crop and variety type.

We may wonder why we see so limited adoption of improved varieties in Ethiopia compared with some other African countries such as Kenya, Zambia, and Zimbabwe (Ethiopian Agricultural Transformation Agency 2017). There has been a large increase in the number of new varieties released in Ethiopia in the period 2000–2011 compared with earlier periods according to National Crop Variety Register (Firew et al. 2016). Cereal varieties also dominate with about 200 new varieties released in the period 2000–2011. Of these, 50 varieties are new wheat varieties, and 20 are new teff varieties. Very few of these varieties are commercialized and adopted by farmers however. Seed production is dominated by a few old varieties (Ethiopian Agricultural Transformation Agency 2017). One of the reasons for limited adoption in semiarid areas like Tigray may be that only 11% of the cereal varieties released are adopted to low rainfall areas (ibid.). The large agroecological heterogeneity, including large local variation in soils, elevation, and rainfall makes it very challenging to test and identify the best-suited varieties in each location. Taste preferences may also matter, and local varieties may be well adapted to local conditions. Furthermore, most farmers are used to recycle their own seeds. Spielman et al. (2012) found that only about 28% of the wheat and teff producers purchased new seeds of these crops every year.

In contrast to this, we see large increases in fertilizer use also in the semiarid areas in Tigray over the last couple of decades. This may indicate that traditional varieties are responsive to fertilizer. There exists limited knowledge of how the new varieties would perform compared with the local varieties under varying local conditions although they may have performed well under research station conditions.

Most varieties are developed and distributed by the Ethiopian Government, but the private sector is growing in importance. The Agricultural Transformation process may lead to better availability and promotion of improved crop varieties.

Of the various crops for which improved seed was multiplied and distributed by the seed multiplier agency of Ethiopia, wheat remains the first crop in the last three decades (Dixon et al. 2006). Another benefit of growing improved wheat in the highland of Ethiopia is its rust resistance. About 68% of Ethiopia, particularly the study region, is a semiarid highland and local wheat is affected by “leaf rust” (Puccinia striiformis) and “stem rust” (P. graminis) during maturity period (Kotu et al. 2000). This reduces not only productivity but also the quality of the crop. We do not know whether the farmers in our survey are aware of these advantages of improved wheat.

Teff is a typical crop of Ethiopia but it cannot grow anywhere else, and we observe few works similar to our study. According to the study of Belay et al. (2006), it demonstrated that farmers select the improved teff variety that exhibited early maturity in Gojam, Ethiopia. A similar study conducted in the semiarid northern Ethiopia shows that farmers prefer the drought-tolerant teff variety not only from its early maturity and drought tolerance ability but also it generates a meaningful yield and by-product difference compared with the local teff (Wale and Chianu 2015).

Shiferaw et al. (2014), using the International Maize and Wheat Improvement Center (CIMMYT) and Ethiopian Institute of Agricultural Research (EIAR) data collected in 2011, found that that wheat is the most important cereal in the most populated regions of the country (Tigray, Amhara, Oromia and SNNP) in terms of area share, total production, home consumption, and market integration. About 70% of households grew improved wheat varieties, and the average area planted with wheat per household for those growing wheat was 2.6 tsimidi.

Wale and Chianu (2015) assessed adoption of drought-tolerant teff using a sample of 395 households from South Gondar and North Wollo (Amhara region) in 2006/2007. They found that 64% sampled housheolds accessed drought-tolerant teff and 35% had adopted the technology. A similar study in the Amhara region that examined adoption of new teff varieties using a sample of 115 farm households in 2014/2015 found that 13% had adopted such varieties, and the average area planted with improved teff by the adopters was about 1.2 tsimidi (Cafer and Rikoon 2018).

Conclusion

We use household panel data for the period 2006–2015 from the semiarid Tigray region in northern Ethiopia to assess the adoption of improved wheat, drought-tolerant teff, and cash crops among smallholder farmers. In particular, we have assessed the effects of rainfall and rainfall variability, farm- and community-level population pressure, and market access on the likelihood and intensity of adoption of these technologies. Overall, we find low adoption rates and small areas planted with these crop varieties and crops even among the adopters of the technologies. The adoption of improved wheat and cash crops had stagnated and even declined in the study period while adoption of drought-tolerant teff was on the increase.

Lower rainfall and higher rainfall variabilities and recent negative rainfall shocks were associated with higher adoption rates for improved wheat and so was higher farm level population pressure. Surprisingly, drought-tolerant teff showed higher adoption rates after positive rainfall shocks and intensity of adoption was higher in areas more distant from markets. Higher rainfall variability and recent positive rainfall shocks were associated with higher adoption rates for cash crop and so was farm-level population pressure. These findings illustrate that interactions between climate variables such as rainfall and rainfall variability may interact with population pressure and affect technology adoption in unpredictable and sometimes surprising ways.

Several policy implications can be drawn from our results. First, there is a need for increase diffusion efforts for improved wheat in less-dense population areas suitable for wheat production. Farmers in these areas are significantly less likely to adopt improved wheat, and we find it likely that improved diffusion efforts could increase the adoption in these areas. Second, there is a need for making drought-tolerant teff seeds available and affordable after droughts. We find the counterintuitive results that farmers are less likely to adopt drought-tolerant teff in the years after a negative rainfall shock. We find it likely that this partly can be attributed to lack of available and affordable of seeds in the years after droughts. Third, to increase the production of cash crops, one should promote irrigation efforts. We find that production of cash crops is significantly related to the access to irrigation.

Given that climate change is likely to affect future weather conditions, our study contributes to the limited literature on climate change adaptation in semiarid areas in Africa. The complexity and seriousness of the issues imply that much more research is needed within this area.

Availability of data and materials

The authors declare that the data and file supporting the findings of this study are available within the article.

Notes

  1. 1.

    These are available online IRI/LDEO Climate Data Library:

    http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.ARC2/..

Abbreviations

CRE:

Correlated random effect

HH:

Households

RHS:

Right-hand sides

St. Dev.:

Standard deviation

References

  1. Abera HB (2008) Adoption of improved tef and wheat production technologies in crop-livestock mixed systems in northern and western Shewa zones of Ethiopia. University of Pretoria. Ph.D. dissertation http://hdl.handle.net/2263/25364

  2. Amare M, Asfaw S, Shiferaw B (2012) Welfare impacts of maize–pigeonpea intensification in Tanzania. Agric Econ 43(1):27–43 https://doi.org/10.1111/j.1574-0862.2011.00563.x

    Article  Google Scholar 

  3. Antle JM (1987) Econometric estimation of producers’ risk attitudes. Am J Agric Econ 69(3):509–522 https://doi.org/10.2307/1241687

    Article  Google Scholar 

  4. Bachewe F, Berhane G, Hirvonen K, Hoddinott J, Hoel J, Tadesse F, Yohannes Y (2014) Feed the Future (FtF) of Ethiopia–baseline report 2013. Ethiopia Strategy Support Program and International Food Pol Res Inse, Addis Ababa http://essp.ifpri.info/files/2014/12/FTF-baseline-report-FINAL-for-website.pdf

    Google Scholar 

  5. Belay G, Tefera H, Tadesse B, Metaferia G, Jarra D, Tadesse T (2006) Participatory variety selection in the Ethiopian cereal tef (Eragrostis tef). Exp Agric 42(1) https://doi.org/10.1017/S0014479705003108

  6. Bezu S, Kassie GT, Shiferaw B, Ricker-Gilbert J (2014) Impact of improved maize adoption on welfare of farm households in Malawi: a panel data analysis. World Dev 59:120–131 https://doi.org/10.1016/j.worlddev.2014.01.023

    Article  Google Scholar 

  7. Cafer AM, Rikoon JS (2018) Adoption of new technologies by smallholder farmers: the contributions of extension, research institutes, cooperatives, and access to cash for improving tef production in Ethiopia. Agric Hum Values 35(3):685–699

    Article  Google Scholar 

  8. Chamberlain G (1982) Multivariate regression models for panel data. J Econ 18(1):5–46 https://doi.org/10.3386/t0008

    Article  Google Scholar 

  9. Conley TG, Udry CR (2010) Learning about a new technology: pineapple in Ghana. Am Econ Rev 100(1):35–69 https://doi.org/10.1257/aer.100.1.35

    Article  Google Scholar 

  10. De Janvry A, Sadoulet E (2002) World poverty and the role of agricultural technology: direct and indirect effects. J Dev Stud 38(4):1–26 https://doi.org/10.1080/00220380412331322401

    Article  Google Scholar 

  11. Dixon J, Nalley L, Kosina P, La Rovere R, Hellin J, Aquino P (2006) Adoption and economic impact of improved wheat varieties in the developing world. J Agric Sci 144(6):489–502 https://doi.org/10.1017/S0021859606006459

    Article  Google Scholar 

  12. Doss CR, Mwangi W, Verkuijl H, De Groote H (2003) Adoption of maize and wheat technologies in Eastern Africa: a synthesis of the findings of 22 case studies: CIMMYT

    Google Scholar 

  13. Ethiopian Agricultural Transformation Agency (2017). A roadmap towards the transformation of the Ethiopia seed. Seed system development strategy. Vision, systemic challenges, and prioritized interventions. Working strategy document. Available at http://extwprlegs1.fao.org/docs/pdf/eth172079.pdf. Accessed 24 Jan 2020.

    Google Scholar 

  14. Firew M, Mizan A, Agidew B, Tafa J, Yeshitila M, Getachew T, Million E, Getachew A, Abdulesemed A, Daneil M (2016) Crop variety registry. Issu NO 19/2016. Minstry of AGrilcture and Natural Resource, Addis Abab Available at http://publication.eiar.gov.et:8080/xmlui/bitstream/handle/123456789/2404/CROP%20V. Accessed 2 Feb 2020

    Google Scholar 

  15. Gebregziabher G, Namara RE, Holden S (2009) Poverty reduction with irrigation investment: An empirical case study from Tigray, Ethiopia. Agric Wat Mang 96(12):1837–1843 https://doi.org/10.1016/j.agwat.2009.08.004

    Article  Google Scholar 

  16. Gebremedhin B, Jaleta M, Hoekstra D (2009) Smallholders, institutional services, and commercial transformation in Ethiopia. Agric Econ 40(s1):773–787 https://doi.org/10.1111/j.1574-0862.2009.00414.x

    Article  Google Scholar 

  17. Genius M, Koundouri P, Nauges C, Tzouvelekas V (2014) Information transmission in irrigation technology adoption and diffusion: Social learning, extension services, and spatial effects. Am J Agric Econ 96(1):328–344 https://doi.org/10.1093/ajae/aat054

    Article  Google Scholar 

  18. Hagos, F., & Holden, S. (2003). Rural household poverty dynamics in northern Ethiopia 1997-2000: analysis of determinants of poverty. Paper presented at the Chronic Poverty Research Centre International Conference, Manchester.

    Google Scholar 

  19. Hailua G, Manjureb K, Aymutc K-M (2015) Crop commercialization and smallholder farmers livelihood in Tigray region, Ethiopia. J Dev Agric Econ 7(9):314–322 https://doi.org/10.5897/JDAE2019.1134

    Google Scholar 

  20. Heckman JJ (1979) Sample selection bias as a specification error. Econometrica J Econom Soc:153–161

  21. Holden ST, Quiggin J (2017) Climate risk and state-contingent technology adoption: shocks, drought tolerance, and preferences. Eur Rev Agric Econ 44(2):285–308 https://doi.org/10.1093/erae/jbw016

    Google Scholar 

  22. Kelemu K (2017) Determinants of farmers access to information about improved wheat varieties: case of farmers in major wheat growing regions of Ethiopia. Int J Res Agric Sci 4(1)

  23. Kotu BH, Verkuijl H, Mwangi W, Tanner D (2000) Adoption of improved wheat technologies in Adaba and Dodola Woredas of the Bale Highlands, Ethiopia: CIMMYT

    Google Scholar 

  24. Lobell DB, Ortiz-Monasterio JI, Asner GP, Matson PA, Naylor RL, Falcon WP (2005) Analysis of wheat yield and climatic trends in Mexico. Field Crop Res 94(2-3):250–256 https://doi.org/10.1016/j.fcr.2005.01.007

    Article  Google Scholar 

  25. Ma X, Shi G (2015) A dynamic adoption model with Bayesian learning: an application to US soybean farmers. Agric Econ 46(1):25–38 https://doi.org/10.1111/agec.12124

    Article  Google Scholar 

  26. Maddala, G. S., & Nelson, F. (1975). Switching regression models with exogenous and endogenous switching. Paper presented at the Proceedings of the American Statistical Association.

    Google Scholar 

  27. Matuschke I, Mishra RR, Qaim M (2007) Adoption and impact of hybrid wheat in India. World Dev 35(8):1422–1435 https://doi.org/10.1016/j.worlddev.2007.04.005

    Article  Google Scholar 

  28. Mendola M (2007) Agricultural technology adoption and poverty reduction: a propensity-score matching analysis for rural Bangladesh. Food Policy 32(3):372–393 https://doi.org/10.1016/j.foodpol.2006.07.003

    Article  Google Scholar 

  29. Minten, B., & Barrett, C. B. (2005) Agricultural technology, productivity, poverty and food security in Madagascar. Productivity, Poverty and Food Security in Madagascar (January 2005). https://ssrn.com/abstract=716142 or https://doi.org/10.2139/ssrn.716142.

  30. Mundlak Y (1978) On the pooling of time series and cross section data. Econometrica J Econom Soc:69–85

  31. Pan Y, Smith SC, Sulaiman M (2018) Agricultural extension and technology adoption for food security: evidence from Uganda. Am J Agric Econ 100(4):1012–1031 https://doi.org/10.1093/ajae/aay012

    Article  Google Scholar 

  32. Shiferaw B, Kassie M, Jaleta M, Yirga C (2014) Adoption of improved wheat varieties and impacts on household food security in Ethiopia. Food Policy 44:272–284 https://doi.org/10.1016/j.foodpol.2013.09.012

    Article  Google Scholar 

  33. Spielman DJ, Kelemwork D, Alemu D (2012) Seed, fertilizer, and agricultural extension in Ethiopia, Food and agriculture in Ethiopia: Progress and policy challenges, pp 84–122 Available at http://extwprlegs1.fao.org/docs/pdf/eth172079.pdf. Accessed 20 Jan 2020

    Google Scholar 

  34. Tesfaye S, Bedada B, Mesay Y (2016) Impact of improved wheat technology adoption on productivity and income in Ethiopia. Afr Crop Sci J 24(s1):127–135 Available at http://www.bioline.org.br/abstract?cs16022. Accessed 20 Jan 2019

    Article  Google Scholar 

  35. Wale E, Chianu J (2015) Farmers’ demand for extra yield from improved tef [(Eragrostis tef (Zucc.) Trotter)] varieties in Ethiopia: implications for crop improvement and agricultural extension. J Agric Sci Technol 17(6) http://journals.modares.ac.ir/article-23-5111-en.html

  36. Wooldridge, J. M. (2010) Econometric analysis of cross section and panel data: MIT press. Available at https://pdfs.semanticscholar.org/ac93/a61c65fe3af707f19b7446f48756e4c7bd60.pdf. Accseed 12 June 2018.

    Google Scholar 

  37. Wubeneh NG, Sanders J (2006) Farm-level adoption of sorghum technologies in Tigray, Ethiopia. Agric Syst 91(1):122–134 https://doi.org/10.1016/j.agsy.2006.02.002

    Article  Google Scholar 

  38. Yu, B., Nin-Pratt, A., Funes, J., & Gemessa, S. A. (2011) Cereal production and technology adoption in Ethiopia. Ethiopia Strategy Support Program II (ESSP II), ESSP II Working Paper, 31.

    Google Scholar 

  39. Zilberman D, Zhao J, Heiman A (2012) Adoption versus adaptation, with emphasis on climate change. Ann Rev Resour Econ 4(1):27–53 1941-1340/12/1010-0027$20.00

    Article  Google Scholar 

Download references

Acknowledgements

We thank the conference participants at 30th International Agricultural Economics (ICAE) in Vancouver, Canada from July 28 to August 2, 2018, for their critical comments on the earlier version of the manuscript.

Funding

Data collection has been funded by NORAD through the NOMA and NORHED programs, especially the “Climate-Smart Natural Resource Management and Policy” (CLISNARP) collaborative research and capacity-building program between the School of Economics and Business at Norwegian University of Life Sciences, Mekelle University, Ethiopia, and LUANAR in Malawi.

Author information

Affiliations

Authors

Contributions

MG designed the study and carried out the data collection process and quantitative analysis. He wrote the introduction, theoretical framework, methodological section as well as results and discussions with SH. FA conceived the study and did the refining and sequence alignment, critical proof reading, and editing of the paper. He participated in the rewriting of the methodological section as well as the results and discussions. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Menasbo Gebru.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 5 Population density (persons/km2) by tabia and survey period

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gebru, M., Holden, S.T. & Alfnes, F. Adoption analysis of agricultural technologies in the semiarid northern Ethiopia: a panel data analysis. Agric Econ 9, 12 (2021). https://doi.org/10.1186/s40100-021-00184-6

Download citation

Keywords

  • Semiarid areas
  • Climate risk
  • New crop varieties
  • Double-hurdle
  • Northern Ethiopia

JEL Classification

  • O33
  • Q12
  • Q16
  • R34
\