ADOPTION RATE AND USE INTENSITY OF BUNDLED CLIMATE INFORMATION SERVICES AND CLIMATE- SMART AGRICULTURAL TECHNOLOGIES IN ETHIOPIA Technical Report Abonesh Tesfaye, Gebermedihin Ambaw, Dawit Solomon December 2024 To cite this report Tesfaye A., Ambaw G., Solomon D., 2024. AICCRA Technical Report: Adoption Rate and Use Intensity of Bundled Climate Information Services and Climate- Smart Agricultural Technologies in Ethiopia. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Acknowledgments Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) is a project that helps deliver a climate-smart African future driven by science and innovation in agriculture. It is led by the Alliance of Bioversity International and CIAT and supported by a grant from the International Development Association (IDA) of the World Bank. About AICCRA Reports Titles in this series aim to disseminate interim research on scaling climate services and climate-smart agriculture in Africa and stimulate feedback from the scientific community. Cover photo: ILRI Disclaimer This working paper has not been peer-reviewed. Any opinions stated herein are those of the author(s) and do not necessarily reflect the policies or opinions of AICCRA, donors, or partners. Licensed under a Creative Commons Attribution – Non-commercial 4.0 International License. © 2024 Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Partners Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) ABSTRACT This study aims to understand the adoption rate and use intensity of enhanced climate information services and climate-smart agriculture technologies in the Doyogena, Menz, Bekoji, and Digeluna Tijo districts in Ethiopia. A simple random sampling technique was employed to select 523 respondents from the four districts. Both descriptive and econometric methods were used to analyze the data. Descriptive statistics such as mean, frequency, and percentages were used to assess the rate and intensity of adoption of the improved technologies while the double-hurdle econometric model was employed to estimate determinants of the likelihood of adoption decision and use intensity of these technologies. Our findings revealed an adoption rate of 73%, 68%, and 18% for site-specific fertilizer recommendation, Climate-smart integrated small ruminant innovations (Smart Pack), and user-centric bundled digital climate agro advisories, respectively. The results further indicated that the education level of the respondents, farm household assets formation such as ownership of farmland and tropical livestock unit, the access road to marketplaces, and farmers group membership were the main determinants of the probability of adoption and use intensity of these technologies. This study highlights the importance of prioritizing and promoting asset building and accumulation among farm households, investing in developing farmers' training centers and infrastructure such as access roads, and encouraging and facilitating smallholder farmers to join farmers' groups and cooperatives. These may empower farm households to efficiently utilize improved agricultural technologies and advance the uptake and sustainability of these technologies, contributing to the enhanced welfare of smallholder farmers. Keywords Climate-smart agriculture technologies; climate information services; adoption rate; adoption intensity; double hurdle model; smallholder farmers; Ethiopia. ABOUT THE AUTHORS Abonesh Tesfaye is a senior consultant at the International Livestock research Institute, Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Gebermedihin Ambaw is Research Officer at the International Livestock research Institute, Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Dawit Solomon is Eastern and Southern Africa Program Leader at the International Livestock research Institute, Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) CONTENTS Introduction ...................................................................................... 1 Methodology ..................................................................................... 3 Sample size and study locations ..................................................... 3 Method of data analysis ................................................................ 4 Results and discussion ...................................................................... 9 Descriptive statistics results of the farm household characteristics in the three districts .............................................................................. 9 Adoption rate and use intensity of smart pack innovation by district ... 15 Adoption rate and use intensity of site-specific fertilizer recommendation by district ................................................................................. 17 Adoption rate and use intensity of climate information services and agro-advisories by district ........................................................... 19 Factors affecting adoption of CIS and CSA technologies and determinants of intensity of adoption ............................................ 21 Factors affecting the adoption of smart pack innovation and determinants of use intensity ............................................................................. 21 Factors affecting the adoption of site-specific fertilizer recommendation and determinants of use intensity ................................................... 23 Factors affecting the adoption of CIS and agro-advisories and determinants of use intensity.......................................................... 24 Conclusions ..................................................................................... 26 References ...................................................................................... 29 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) ACRONYMS AICCRA Accelerating Impacts of CGIAR Climate Research for Africa ABC Alliance of Bioversity and International Center for Tropical Agriculture CIMMYT International Maize and Wheat Improvement Center CIS Climate-Smart Villages CSA Climate-smart agriculture EDACaP Ethiopian Digital Agro-climate Advisory Platform GAS Green Agro-Solutions ICARDA International Center for Agricultural Research in Dry Areas SSFR Site-Specific Fertilizer Recommendation Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 1 INTRODUCTION Ethiopia is susceptible to the impacts of climate change and the irregularity of climate variability due to its reliance on rain-fed agriculture and natural resources as drivers of economic growth (Bouteska et al., 2024; Dendir and Simane, 2019). The country has frequently experienced extreme events, like droughts and floods, in addition to rainfall variability and increasing temperatures, which contributes to limited agricultural production and exacerbates food insecurity among poor and vulnerable households (FDRE, 2023). Given the nation’s vulnerability to climate change impacts, there is a pressing need for broader investment in the adoption of innovative agricultural technologies and practices to improve the resilience of smallholder farmers to drought and enhance their welfare (Zegeye et al., 2020; Ahmed et al., 2024). Cognizant of this challenge, the Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) project in Ethiopia has developed various enhanced climate information services (CIS) and climate-smart agriculture (CSA) practices and innovations tailored to address the specific needs of the project area, to improve the resilience of smallholder farmers in the face of a changing climate. These technologies are site-specific fertilizer recommendations, user-centric bundled digital climate agro advisories, and Climate-smart integrated small ruminant innovations (Smart Pack). Site-specific fertilizer recommendations (SSFR) are formulated using a tested, data-driven machine-learning method. Evidence shows that the lack of a context- specific and evidence-based advisory system that enables the targeted application of the right amount and type of input at the right place and at the right time plays a pivotal role in undermining the productivity of major crops in Ethiopia. The SSFR ensures that the correct type and amount of fertilizer are applied in the most suitable locations and, at optimal times, tailored to the specific crop, climate, and soil conditions. The Alliance of Bioversity and International Center for Tropical Agriculture (CIAT), in collaboration with Green Agro Solution PLC (LERSHA) and Digital Green under the AICCRA project, have been engaged to develop the SSFR for wheat, maize, barley, and teff. The recommendations provided are NPS and urea application rate, compost and vermicompost application rate, and yield prediction across different sites in three regions of Ethiopia, namely Oromia, Amhara, and central Ethiopia regions. The delivery of user-centric bundled digital climate agro advisories was the other innovative technology delivered to farmers to provide timely and tailored information about weather patterns, rainfall projections, and climate-related risks, enabling them to make informed decisions about their farming practices. Delivery of climate advisory services requires a series of steps, including generating, translating, communicating, and gathering feedback. The Ethiopian Digital Agro-climate Advisory Platform (EDACaP), which has been upgraded lately, generates climate forecasts and translates them into agro-advisories. The platform provides weather forecasts, climate prediction, and agro-climate advisory at seasonal and sub-seasonal time scales. Decision-relevant agro- climate advisories are communicated to various users through the LERSHA platform via different communication channels such as short messages (SMS), recoded voice (voice blast), development agents (DAs), and a call center (7860). Delivery of these climate agro-advisories is facilitated through a partnership Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 2 among LERSHA, International Maize and Wheat Improvement Center (CIMMYT), Ministry of Agriculture (MoA), and AICCRA to communicate seasonal (3-4 months lead time) and sub-seasonal (10-15 days lead time) weather forecasts, agro- advisories on land preparations, crop choice, planting and weeding time and application of fertilizer and agro-chemicals to smallholder farmers through various communication channels in five regional states (Amhara, Oromia, central Ethiopia, Sidama and Somali). Climate-smart integrated small ruminant innovations (Smart Pack), led by the International Center for Agricultural Research in Dry Areas (ICARDA), is the third agricultural innovation introduced to small ruminant keepers. Under the Smart Pack Ethiopia program, ICARDA and partners developed several innovations, including the dissemination of improved rams, feed and forage development, animal health interventions, and innovative market outlets with capacity building and innovative credit accessibility through cooperative organization. AICCRA, ICARDA, and Ethiopian universities are collaborating to introduce the Smart Pack to benefit small ruminant keepers. AICCRA is engaged in facilitating the scaling of these innovations through various mechanisms. One such approach is to demonstrate the framework on the ground for scaling these climate-smart small ruminant innovation packages involving research and extension systems. Three sheep value chain sites were identified through a consultative process to demonstrate the framework in four regions in Ethiopia. These are the Southwest Ethiopia Peoples’ region (Bonga district), Central Ethiopia region (Doyogena district), Amhara region (Menz district), and one goat site in South Ethiopia regional state (Konso district). Enhancing the welfare of smallholder farmers through the introduction of improved agricultural technologies and innovations has recently gained increased attention. The focus now goes beyond introducing these agricultural technologies to improve agricultural production alone; it rather promotes the use of improved technologies and innovations to advance the welfare of smallholder farmers. In this regard, the effectiveness and uptake of CIS and CSA interventions among smallholder farmers in the project areas remain understudied. There are gaps in understanding the extent to which farmers adopt these technologies and the factors influencing their adoption. This highlights the need for a comprehensive adoption study to assess the current situation, identify barriers to adoption, and develop strategies to enhance the uptake and sustainability of these innovations. Therefore, this adoption study was initiated with the following specific objectives: (i) assess the rate of adoption of these enhanced climate information services and climate-smart agriculture technologies, (ii) examine the intensity of adoption of these enhanced climate information services and climate-smart agriculture technologies and (iii) identify the key factors influencing the likelihood of adoption and use intensity of these agricultural technologies and innovations among smallholder farmers. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 3 METHODOLOGY Sample size and study locations The study was conducted in three regions in Ethiopia: Central Ethiopia, Amhara, and Oromia. Doyogena, Menz, Bekoji, and Digeluna Tijo were the four districts from the three regions considered for the study. A multi-stage sampling approach involving purposive and random sampling procedures was employed to draw sample respondents. The first stage involved the purposive selection of the four districts where the CIS and CSA innovations were introduced. In the second stage, intervention villages/kebeles were purposely selected from each district. In the third stage, stratified random sampling1 was used to classify farmers into adopters and non-adopters of CIS and CSA technologies in each kebele. At the final stage, a simple random sampling method was used to select 523 sample respondents for the three agricultural technologies under study. Doyogena district is located in the central Ethiopia region. The altitude ranges from 2420 to 2740 meters above sea level (m.a.s.l.). The mean annual rainfall ranges from 1,000 to 1,400 mm, while the temperature ranges from 12.6°C to 20°C. The farming system is characterized by an Enset (Ensete ventricosum) – cereal–livestock production system. The main crops grown include wheat, barley, legumes, and vegetables, such as potatoes. Livestock production includes cattle, sheep, and poultry production. The top layer of the soil in the areas is black clay loam, rich in organic matter, while the sub-soil covers a depth of one meter, which is red clay loam. Most households practice subsistence farming with an average land area of less than 0.5 hectares. The Menz district is one of the five districts in the Northern Shoa zone of the Amhara Regional State. The district is located between 39°- 44°E longitude and 10 ° - 24°N latitude with an altitude ranging between 1680 to 4000 meters above sea level (Ayele & Husain, 2015). The average temperature of the district varies between 5 to 18°C, while the annual rainfall ranges from 700mm to 1200mm. The district has a total population of 20,469, of which 14,568 (71.2%) were male and 5,901 (28.8%) were female. The central highland consists of black-clay and reddish-brown heavy loam soil with 53% black, 30% brown, and the remaining 17% sandy soil. Mixed crop-livestock production systems mainly characterize agriculture, and farmers are limited to barley, wheat, and sheep production (BoA, 2019). Bekoji district is located in Southeastern Ethiopia, in the Oromia region. It is 227 km from Addis Ababa. The study area was located at latitude 070 32’ 37’’ N and 1 Stratified random sampling is a type of probability sampling method that involves dividing a population into subgroups or strata based on certain characteristics and then selecting a random sample from each stratum. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 4 longitude 390 15’ 21’’ E, at an altitude of 2780 meters above sea level. The maximum and minimum temperatures were 3.8 and 20.4 °C, respectively, with an annual rainfall of 939 mm. The soil type of the area was Clay soil (Nitosols) with a pH of 5.0. Digeluna Tijo is a district in part of the Arsi zone in the Oromia region, Ethiopia. The administrative center of this district is Sagure town. The district is geographically located at 07°45’ N latitude and 39°09’ E longitude. The altitude of this district ranges from 2500 to 3560 meters above sea level. The mean annual rainfall of the district ranges from about 1000 mms to 1500 mms, while the annual mean temperature ranges between 15° C and 22° C (OESPO, 2003). The land use in the district shows that 41,630 hectares are currently cultivated, about 8,650.5 hectares is grazing land, 23,158.96 hectares is covered by forest, and 19,260.5 hectares is uncultivated (DTDARDO, 2015). The district has a high potential for cereal crops, pulses, and livestock. The major grains grown in the area are wheat, barley, maize, beans, peas, and linseed. Figure 1. Study locations Method of data analysis The data was analyzed using both descriptive and econometric methods. Descriptive statistics were used to analyze the rate and intensity of adoption of CIS and CSA technologies while the double-hurdle econometric model was used to analyze determinants of adoption decision and use intensity of these technologies. The double hurdle model assumes that farm households face two hurdles in any agricultural decision-making process, such as the participation decision (the decision to adopt) followed by intensity (the decision on the extent of adoption). Hence, the double hurdle model allows for the simultaneous consideration of the determinants of the decision to adopt agricultural technology in the household and the determinants of use intensity through two separate stages. The model uses both probit and truncated regression at different stages. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 5 The first stage involves running a probit regression to determine factors affecting farm households’ decision to participate in technology adoption. While in the second stage, truncated regression is used to analyze the intensity of adoption on the individuals who passed the first hurdle. The probability of adoption can be specified as follows: Di = 1 if Di* > 0 and 0 if Di* ≤ 0 (1) Di* = xi΄α + ɛi (Adoption /first hurdle) (2) Where, Di* is a latent adoption variable that takes the value of 1 if a household adopts CIS and CSA technologies and 0 otherwise, x is a vector of household characteristics, and α is a vector of parameters. Not all adopters are at the same level of adoption. The intensity of adoption (Y) decision has an equation given as in a truncated regression function: Yi = Yi * > 0 if Yi* > 0 (3) Yi* = yi β +ui and Di*= xi΄α + ɛi , Yi = 0 otherwise (4) Yi * = yi β + ui (intensity /second hurdle) (5) Where Yi* is the observed intensity measured using the proportion of the area of land allocated for the improved technologies and the proportion of improved sheep breeds produced using smart pack innovation, yi is a vector of household characteristics, and β is a vector of parameters while ɛi and ui are the error terms assumed to be independent and normally distributed, where, ɛi ~ N (0, 1) and ui ~ N (0, σ2). The double hurdle log-likelihood is the sum of the truncated regression and the probit models. In adoption analysis, the adoption rate can be represented by the number of members of a society who start using a new technology or innovation during a specific period. While the intensity of adoption measures the land area under a given technology. In our study, the adoption rate is measured by calculating the share of households using CIS and CSA technologies. In contrast, the intensity of adoption is measured by calculating the adoption index for each of the improved CIS and CSA technologies. The first technology considered is climate-smart integrated small ruminant innovations (Smart Pack). The intensity of adoption was measured based on the number of improved sheep breeds produced using any combination of the Smart Pack components to the total number of sheep the household owned. The second technology is site-specific NPS and urea fertilizer recommendation. To measure the intensity of adoption of site-specific fertilizer recommendations, the adoption index of households was calculated based on equation (6) following Maddalla (1997). AIj =∑ ( 𝐹𝐴𝑗 𝐹𝑅𝑗 ) /𝑁𝑃 𝑛 𝑗=1 (6) Where AIj = is adoption index of the jth farmer; j = 1, 2, 3…, n., n is the total number of farmers. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 6 FA is the level or quantity of fertilizer the farmer applied. FR is the recommended level or quantity of fertilizer the farmer is supposed to apply, and NP is the number of practices. The third technology under study is user-centric bundled digital climate agro advisories. Here the intensity of adoption was measured based on the proportion of land on which CIS and agro-advisories services are implemented to total cropland. Since wheat is the major crop in the area, the proportion of land under wheat was considered for the analysis. When the adoption index is above 50%, the household is categorized as a high adopter; at 50%, the household is categorized as a medium adopter, and for an adoption index of less than 50%, the household is grouped under a low adopter. In our estimation of factors affecting the likelihood of adoption of improved CIS and CSA technologies, the dependent variable of the probit model takes a dichotomous value depending on the farmers' decision either to adopt or not to adopt the improved agricultural technologies while the truncated regression model would have a continuous value which is the intensity of adoption of the technologies. Since we have three improved agricultural technologies under consideration, we would have three dependent variables: (i) the number of improved sheep breeds produced using any of the combinations of the smart pack components to a total number of sheep the household owned, (ii) adoption index calculated based on the NPS and urea fertilizer applied in relation to the recommended level and (iii) proportion of hectare of land allocated for CIS and agro-advisories to total cropland. The definition of model variables and their expected signs are given in Table 1. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 7 Table 1. Definition of variables and expected signs for the double hurdle model Description Measurement Expected sign Supportive literature Dependent variables Adoption decision 1= farmer adopts the CIS and/or CSA technologies, 0= otherwise Intensity of adoption -Proportion of sheep breeds to total sheep produced -Adoption index -Proportion of land to total cropland - Number of improved sheep breeds produced using any of the combination of the Smart Pack components to total number of sheep the household owned. - Adoption index calculated based on the NPS and Urea fertilizer applied in relation to the recommended level. - Proportion of hectare of land allocated for CIS and agro-advisory technology to total cropland. Independent variable Age of the household head Number of years +/- Beshir (2014); Milkias & Abdulahi (2018); Endiris et al. (2021) Gender of household head Male = 1; 0 = otherwise +/- Launio et al. (2018); Aliyi et al. (2021) Education level of the household head No education = 0 Basic education =1 Elementary education =2 Secondary education = 3 College education = 4 + Challa & Tilahun (2014); Habtamu and Krishna (2021) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 8 Table 1. Contd. Description Measurement Expected sign Supportive literature Family size Number of household members +/- Challa and Tilahun (2014); Djibo and Maman, (2019) Land holding Land size in hectare + (Abay et al., 2016); Milkias and Abdulahi (2018); Yigezu et al. (2018); Farming experience Number of years +/- Beshir et al. (2020) Farm and off farm income Income from on farm and off farm activity in Ethiopian Birr + Ponguane and Mucavele (2018); Atinafu et al. (2022) TLU Tropical livestock unit + Ngeno (2018) Availability of labor 1 = Yes; 0= No + Makokha et al. (2001); Million and Belay (2004) Affordability of price of fertilizer 1= Yes, 0= No - Hagos, F. and Holden, S.T. (2017) Access to credit 1 = Yes; 0 = No + Simtowe et al. (2016); Feyisa (2020) Access to extension service 1 = Yes; 0 = No + Asfaw et al. (2012) Group membership 1 = Yes; 0 = No + Atinafu et al. (2022) Distance to market Distance to the nearest marketplace in km - Amare and Simane (2017) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 9 RESULTS AND DISCUSSION Descriptive statistics results of the farm household characteristics in the three districts Most respondents from both groups in all four districts were male-headed households. The mean age of respondents ranged between 41 and 47 years, while the average family size varied between 5 to 8. From the result, less than a tenth of adopters in the four districts were illiterate. Compared to the other three districts, a fifth of adopters in Doyogena received basic education, followed by 17% of adopters in Menz. A higher proportion of non-adopters in Digeluna Tijo and Bekoji reported joining elementary school, while a third of adopters from the same district enrolled in secondary school. Comparing total Livestock unit (TLU) size among adopters and non-adopters of the three districts, on average, those adopters in Menz owned 10 TLU, followed by those adopters in Digeluna Tijo with 7.4 TLU. Among the four districts, adopters and non-adopters of Digeluna Tijo and Bekoji have the largest average land holding size of 1.6 ha and 1.1 ha, respectively. Similarly, both adopters and non-adopters from the same district earned the highest farm income (ETB2 100,873 and 79,745, respectively) compared to the other two districts, while non-adopters in Doyogena earned the highest off-farm income (ETB 38,700). On average, those adopters in the Menz district reported the highest farming experience (26 years). The labor demand of the farm household was met by involving family and hired labor. Tables 2 and 3 present the detailed farm household characteristics. 2 ETB refers to Ethiopian Birr. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 10 Table 2. Farm household characteristics (Continuous variables) District of the study Age (years) Family size (No.) TLU (No.) Landholding (ha) Farming experience (years) Farm Income (ETB) Non-farm Income (ETB) Doyogena Adopters 47 8 4 0.6 22 15,500 28185 Non-adopters 47 7 3 0.5 20 8900 38700 Menz Adopters 43 5 10 0.9 26 30200 14400 Non-adopters 41 5 5 0.8 24 24150 13516 Digeluna Tijo and Bekoji Adopters 44 7 7.4 1.6 22 100,873 4680 Non-adopters 43 6 5.2 1.1 21 79,745 11294 Table 3. Farm household characteristics (Categorical variables) District of the study Gender Education level Availability of Labor (%) Male (%) Female (%) No Edu. (%) Basic Edu. (%) Elementary (%) Secondary (%) College (%) Doyogena Adopters 73 27 6 22 35 31 6 90 Non-adopters 82 18 14 14 51 20 2 89 Menz Adopters 79 21 8 17 47 28 - 83 Non-adopters 74 26 13 11 51 26 - 94 Digeluna Tijo and Bekoji Adopters 86 14 8 3 54 35 - 52 Non-adopters 88 12 12 6 59 24 - 61 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 11 Major crops grown in the study areas were wheat, barley, beans, potato, and Enset (Ensete ventricosum). Regarding farm input use, a combination of NPS and urea were the main types of fertilizer applied. Looking at the average application rate, adopters and non-adopters in Digeluna Tijo and Bekoji applied the largest amount of NPS and urea per 0.25ha of their barley farmland. In addition to inorganic fertilizer, most respondents in all the districts applied manure to their cropland. Regarding the application of compost, only those respondents who live in Menz and Digeluna Tijo reported that they have applied compost to their cropland (Figure 2). The application of herbicides and pesticides was a common practice among all respondents. The area under Barely Farm was the biggest among adopters and non-adopters of Digeluna Tijo and Bekoji compared to the other districts. Similarly, adopters and non-adopters from the same districts produced the highest average yield compared to the other two districts. In the two districts, adopters and non-adopters reported higher barley production. Crop production in all the districts is under rainfed agriculture, and according to most of the respondents, men and women in the household participated in decision-making about planning seasons and managing crop production. Over 90% of adopters in each district have reported access to extension services. Whereas more adopters and non-adopters in Digeluna Tijo and Bekoji districts have access to credit services. Furthermore, nearly all adopters and non-adopters from the same district have joined the farmers’ cooperative, and nearly all adopters of Doyogena and Menz are members of the farmers’ cooperative. Regarding training, the majority of adopters both in Doyogena and Digeluna Tijo and more than 60% of adopters in Menz have reported that they have received training from farmers' cooperatives. Digeluna Tijo district was the farthest when comparing the average distance of marketplaces from respondents’ villages. Tables 4-5 illustrate the detailed descriptive analysis results. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 12 Table 4. Agricultural input use, area under wheat and barley, and yield in kg District of the study Kg of fertilizer/0.25ha The area under wheat and barley in ha Yield in kg/0.25ha NPS Urea Wheat Barley Wheat Barley Doyogena Adopters Wheat Barley Wheat Barley 24 9 29 10 0.24 0.10 350 150 Non-adopters 21 14 27 10 0.20 0.10 330 110 Menz Adopters 12 - 78 12 0.52 0.52 610 770 Non-adopters - - 56 10 0.30 0.50 420 810 Digeluna Tijo and Bekoji Adopters 7.6 193 5 107 0.1 1.1 290 4000 Non-adopters 6.9 119 8 66 0.1 0.8 270 3350 Figure 2. Application of agricultural inputs 0 20 40 60 80 100 120 Adopters Non adopters Adopters Non adopters Adopters Non adopters Doyogena Menze Digeluna Tijo R e s p o n d e n ts ( % ) manure compost herbicides and pesticides Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 13 Table 5. Access to extension services, credit facility, training, and distance to marketplaces District of the study Do you have access to credit facility Do you have access to extension services Do you get training Are you member of cooperatives Distance to marketplace (km) Yes (%) No (%) Yes (%) No (%) Yes (%) No (%) Yes (%) No (%) Doyogena Adopters 57 43 92 8 92 8 100 - 4 Non-adopters 41 59 92 8 - 100 - 100 5 Menz Adopters 47 53 93 7 62 38 93 7 4 Non-adopters 45 55 68 32 - 100 - 100 4 Digeluna Tijo and Bekoji Adopters 60 40 95 5 98 2 100 - 9 Non-adopters 51 49 86 14 - 100 96 4 7 Respondents were also asked if they think the climate has changed over the past 20 years and if they have recognized any climate change impacts on their livelihood, and the coping mechanisms they used. Almost all adopters and most non-adopters from the three districts think that the climate has changed over time, and these changes were manifested through extreme events such as heavy and irregular rain, high temperatures, and drought. As a result, most respondents reported that their livelihood was affected in various ways, such as crop failure, livestock reduction, pests and diseases, and soil fertility reduction. Major coping mechanisms included sales of assets, relying on saving and engaging in additional income-generating activities. Figures 3 - 6 show respondents’ opinions about climate change, its impacts, and their coping mechanisms. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 14 Figure 3. The proportion of respondents who think the climate has changed Figure 4. The proportion of respondents who recognized the impact of climate change through extreme events 0 20 40 60 80 100 120 Adopters Non adopters Adopters Non adopters Adopters nonadopters Doyogena Menze Digeluna Tijo R e s p o n d e n ts ( % ) 0 20 40 60 80 100 120 Adopters Nonadopters Adopters Nonadopters Adopters NonAdopters Doyogena Menze Digeluna Tijo R e s p o n d e n ts ( % ) high temprature drought heavy and irregular rain Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 15 Figure 5. The proportion of respondents who perceived the livelihood impact of climate change Figure 6. The proportion of respondents who used coping mechanisms Adoption rate and use intensity of smart pack innovation by district The adoption rates of improved technologies, including ram, feed and forage, health services, and market linkage, showed differences between Doyogena and Menz (Table 6). In Doyogena, nearly all recipients adopted and used the improved ram technology, while a majority in Menz also adopted it. Improved feed and forage technology was adopted by over 60% of farmers in Menz but only by a few in Doyogena. The Chi-square test confirmed a significant difference between the two districts in this regard. For improved health services, almost all 0 20 40 60 80 100 Adopters Nonadopters Adopters Nonadopters Adopters Nonadopters Doyogena Menze Digeluna Tijo R e s p o n d e n ts ( % ) crop failure livestock reduction soil fertlity reduction pest and disease 0 20 40 60 80 100 Adopter Nonadopter Adopter Nonadopter Adopter Nonadopter Doyogena Menze Digeluna Tijo R e s p o n d e n ts ( % ) sales of assets relaying on saving engaging in income generating activity Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 16 recipients in Menz adopted and used the technology, compared to 41% in Doyogena. Regarding market linkage services, adoption was higher in Doyogena than in Menz, with the Chi-square test indicating a significant relationship between location and adoption of these services. The overall adoption rate of the bundled smart pack technologies stands at 68%, reflecting significant uptake among farmers across the districts. Table 7 highlights the use intensity of smart pack innovations, showing that most farmers fell into the high adopters’ group, while only a few were in the low adopters’ group Table 6. The adoption rate of Smart Pack innovation District Rate of farmers adopted and used smart pack innovation (%) Improved ram Improved feed and forage Improved health service Improved market linkage Doyogena 98 6 41 74 Menz 93 66 96 68 Average 96 36 69 71 The overall adoption rate of the innovation = 68% Table 7. Use intensity of adoption of Smart Pack innovation Use intensity Doyogena Menz Total Frequency Percent Frequency Percent Frequency Percent Low adopters 3 6 1 2 4 4 Medium adopters 6 12 7 13 13 13 High adopters 40 81 45 85 85 83 Total 49 100 53 100 102 100 The primary sources of information for adopters on the adoption and use of the smart pack innovations in the Doyogena district were the Areka Research Center and ICARDA, while for the Menz district, it was the Debre Berhan Research Center and ICARDA and Ministry of Agriculture (Figure 7). Breeding cooperatives and development agents were the main information delivery channels (Figure 8). In Doyogena, the most preferred channel of information was breeding cooperatives, while in Menz district, the most preferred channel was development agents. Almost all adopters in both districts reported receiving the information on time. Figure 7. Source of information in Doyogena and Menz districts (left to right) Areka and ICARDA Debreberhan and ICARDA MoA Areka and ICARDA Debreberhan and ICARDA MoA Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 17 Figure 8. Channel of information in Doyogena and Menz districts (left to right) Adoption rate and use intensity of site- specific fertilizer recommendation by district The adoption rates of site-specific fertilizer recommendations and seasonal weather forecasts varied across districts. In Doyogena, 69% and 71% of farmers adopted and used NPS and Urea recommendations, respectively, while 67% utilized seasonal weather forecasts. In Digeluna Tijo and Bekoji, adoption rates were higher, with 74% and 78% adopting NPS and Urea, respectively, and 80% using seasonal weather forecasts. The overall adoption rate of the innovation stands at 73% (Table 8). Table 8. Adoption rate of site-specific fertilizer recommendation District Rate of farmers adopted and used site-specific fertilizer recommendation (%) NPS Urea Seasonal weather forecast Doyogena 69 71 67 Digeluna Tijo and Bekoji 74 78 80 Average 72 75 74 The overall adoption rate of the innovation = 73% Table 9 presents the use intensity of site-specific fertilizer recommendations. More farmers are in the high adopters’ group in Digeluna Tijo and Bekoji than in Doyogena. The frequency of low adopters is nearly similar in both districts. Table 9. Use intensity of adoption of site-specific fertilizer recommendation Use intensity Doyogena Digeluna Tijo and Bekoji Total Frequency Percent Frequency Percent Frequency Percent Low adopters 25 48 27 42 52 45 Medium adopters 10 19 6 10 16 13 High adopters 17 33 31 48 48 42 Breeding cooperatives Development agents Neigbours Breeding cooperatives Development agents Neigbours Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 18 Total 51 100 64 100 115 100 The primary sources of information on site-specific fertilizer recommendations were the Ministry of Agriculture and CIAT in Doyogena and LERSHA and CIAT in Digeluna Tijo. In both districts, development agents served as the main channels for delivering this information. Additionally, in Digeluna Tijo, channels such as recorded voice blasts, SMS, call centers, and radio were used to reach farmers (Figures 9 and 10). All farmers in Digeluna Tijo and the majority in Doyogena reported receiving recommendations and advisory services on time. When asked about the affordability of the recommended location-specific fertilizer rates, most farmers in Doyogena and all farmers in Digeluna Tijo confirmed that they could afford the suggested application rates. Figure 9. Source of information in Doyogena and Digeluna Tijo districts (left to right) Figure 10. Channel of information in Doyogena and Digeluna Tijo districts (left to right) MoA and CIAT LERSHA and CIAT MoA and CIAT LERSHA and CIAT Development agents Recorded voice blast Call center Mobile app SMS text Radio Development agents Recorded voice blast Call center Mobile app SMS text Radio Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 19 Adoption rate and use intensity of climate information services and agro-advisories by district Table 10 presents the adoption rates of CIS (Climate Information Services) technologies and agro advisories across various agricultural activities in Digeluna Tijo. Adoption rates varied significantly by activity, with sub-seasonal forecasting being the most adopted at 38%, followed by fertilizer application (32%), and harvesting time (27%). Other activities, including crop choice (13%), land preparation (14%), planting date (14%), weeding time (19%), agro-chemical application (16%), input supply (7%), and mechanization (4%), had comparatively lower adoption rates. The overall adoption rate of the CIS technologies in Digeluna Tijo stands at 18%. The intensity of adoption was categorized into three groups, with more farmers found under the high adopters group (Table 11). The main sources of information about CIS and agro-advisories were LERSHA, MoA, ATA, and development agents, while the main channels to deliver the information included recorded voice blasts, call centers, SMS text, and radio (Figure 11). All respondents reported that they received the information on time. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 20 Table 10. Adoption rate of CIS technologies District Rate of adoption of CIS and agro-advisories (%) Sub- seasonal forecast Crop choice Land preparation Planting date Weeding time Fertilizer application Agro- chemical application Harvesting time Input supply Mechanization Digeluna Tijo 38 13 14 14 19 32 16 27 7 4 The overall adoption rate of the innovation = 18% Table 11. Use intensity of adoption of CIS technologies and agro-advisories Use intensity Frequency Percent Low adopters 3 5 Medium adopters 19 30 High adopters 41 65 Total 63 100 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 21 Figure 11. Source and channel of information in Digeluna Tijo districts (left to right) Factors affecting adoption of CIS and CSA technologies and determinants of intensity of adoption The parameter estimates of the probit and truncated regression models estimated using STATA version 17 are reported in Tables 12-14. The values of the Pseudo R2, the log-likelihood, and the LR-Chi2 indicated that the fitted regression models are statistically significant. According to the model results, different factors explain the adoption decision and the intensity of using the CSA and CIS technologies. Factors affecting the adoption of smart pack innovations and determinants of use intensity The decision to adopt smart pack innovations was influenced by factors such as gender, family size, farmland, farm income, and distance to the marketplace. Accordingly, the gender of the household head positively and significantly determined the probability of adoption of smart pack innovation, indicating that male-headed households were more likely to adopt the smart pack innovation than their counterparts. This may be because male-headed households are more likely to get information about new technologies and have more access to resources and membership in cooperatives than female-headed households. These findings are consistent with studies such as Gebre et al. (2019), Gebremariam and Wünscher (2016), and Huyer (2016). Household family size was another significant variable in determining a household’s adoption decision of smart pack innovation, indicating that families with larger households are less likely to adopt smart pack innovation. A probable reason could be that households with larger family members may secure their household needs through off-farm activities instead of investing in adopting smart pack innovation. This finding is in Main source of information LERSHA MoA ATA Development agents Channel of the information Recorded voice blast Call center Mobile app SMS text Radio Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 22 line with the findings of Kamau et al. (2024), Tran et al. (2019), Uhunamure et al. (2019). Our result further indicated that there was a significant and positive relationship between farm size and the adoption of smart pack innovation, i.e., the probability of adoption increases with a bigger farm size. This may imply that households with bigger farm sizes would be willing to allocate part of their land to try new technologies such as smart pack innovation technologies. Quddus (2022) and Kamau et al. (2024) also found a positive relationship between farm size and adoption of livestock technologies. Distance to the marketplace had the expected negative and significant effect on the probability of adoption of smart pack innovation, indicating that the likelihood of adoption decreases with marketplaces that are far away from farmers' villages. This may be because a relatively closer distance of farmers’ homes to the marketplaces enables the marketing of inputs and outputs by facilitating timely input delivery and output disposal, resulting in lower transport costs. Beshir (2014) also found similar results showing an inverse relationship between market access and the probability of adopting improved agricultural technologies. Our result also revealed a negative association between farm income and the probability of adopting smart pack innovation. The negative association may imply that the likelihood of adoption decreases with more farm income since farmers with sufficient farm income may consider themselves well- off and may not consider investing in adopting new technologies. Turning to the intensity of smart pack innovation, factors such as the age of the household head, family size, TLU, and land were determinates of use intensity of adoption. Age significantly positively influenced the intensity of the adoption of smart pack innovation. According to the marginal effect result, an increase in the age of the household head by one year would increase the intensity of the use of smart pack innovations by 0.1%. This could be because older farmers might have experience and accumulated human capital over the years. This finding is in line with Beshir et al. (2012) and Beshir et al. (2022). Although family size negatively affected the probability of adoption decision in our probit regression result, in this study, it positively influenced the use intensity of smart pack technologies. The marginal effect result indicated that a unit increase in family size increases the use intensity of smart pack innovation by 0.5%. A previous study by Susie (2017) also reported similar results where a larger family size was associated with a better intensity of adoption of agricultural technologies. Similarly, TLU has a significant positive effect on the intensity of adoption, indicating that households with more TLU intend to increase the use of smart pack innovation. The marginal effect revealed that an increase in one unit of tropical livestock unit would increase the intensity of use of smart pack innovation by 0.1%. On the other hand, land owned by the household negatively influenced the use intensity of smart pack innovation, though there was positive effect of land in the adoption decision in our probit model result. The marginal effect result revealed that an increase in one hectare of land may decrease the use intensity of the innovation by 4%. This may be due to the less land-intensive nature of keeping small ruminants considering the number of TLU per household in the study area. In addition, farm size plays a critical role in the adoption of new technologies in Ethiopia, given the small size of land holding in the country. Farmers with small farms are hesitant to apply agricultural innovations, since they are afraid of the uncertainty of obtaining the claimed benefit (Harvey et al., 2014; Josephson et al., 2014). Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 23 Factors affecting the adoption of site-specific fertilizer recommendation and determinants of use intensity Among the explanatory variables hypothesized to influence the adoption decision of site-specific fertilizer recommendation, factors such as age, gender, farm income, and group membership were found to have significant effects. Age had a negative and significant influence on the probability of adoption of site-specific fertilizer recommendations. This implies that older household heads are more resistant to adopting site-specific fertilizer recommendations than young household heads. This may be because young farmers are more flexible and likely to take risks than their older counterparts. This finding is in accordance with the findings of Tufa and Tefera (2016) and Ayele et al. (2022), who showed that the probability of adoption of improved technology decreases as the age increases. The probability of adoption of site-specific fertilizer recommendations was also negatively related to the gender of the household head, indicating that female household heads were more likely to adopt site-specific fertilizer recommendations compared to male-headed households. This unexpected result may be explained by the fact that female-headed households in the study area might have gained access to resources and information on improved agricultural technologies, hence the increased probability of adoption by female-headed households. This finding is contrary to studies such as Hailu et al. (2014) and Tesfaye et al. (2014), who confirmed that as compared to male-headed households, female-headed households were less likely to adopt technologies due to their lower labor endowment, lower farmland holding and livestock ownership, and less access to information on improved agricultural technologies. Our findings also showed a negative and significant impact of farm income on the adoption decision of the site-specific recommendation, which was unexpected compared to similar studies (e.g., Mthethwa et al., 2022). Membership of farmers' cooperatives was found to have a positive relationship with the adoption of fertilizer recommendations. According to Tufa and Tefera (2016), membership in farmers' organizations helps farmers access affordable agricultural inputs and encourages farmers to adopt improved technologies. Abebaw and Haile (2013), Aweke (2013), and Ketema et al. (2016) also reported that cooperative membership has a significant positive impact on adoption of agricultural technology, including the adoption of fertilizer. The intensity of the use of site-specific fertilizer recommendations was determined by age, distance to market, membership in the farmer's group, and affordability of fertilizer price. According to our findings, age had a negative and significant influence on the use intensity of site-specific fertilizer recommendations. As the respondent's age increases by a year, the intensity of the adoption of fertilizer recommendations decreases by 0.7 %. Our result is contrary to the findings of Berlie and Tegegne (2024), who found that age positively influences the use of the recommended rates of fertilizer. The other significant determinant of fertilizer recommendation was distance to marketplace. It has the expected negative sign indicating the importance of proximity to a marketplace in facilitating marketing of inputs and outputs. This result is consistent with Beshir et al. (2012). Intensity of use of site-specific fertilizer recommendation was positively and significantly influenced by membership of farmers cooperatives. Compared to respondents who were not members, farmers cooperatives membership would increase the intensity of use of site-specific Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 24 fertilizer recommendation by 29%. This may be because organizations such as farmers cooperatives facilitate access to information and input, that could be an incentive for farmers to adopt the technology. This result is in line with the findings of previous studies, such as Tefera et al. (2020). The use intensity of fertilizer recommendation was negatively affected by the affordability of inorganic fertilizer according to our analysis. The perception of high price of fertilizer (less affordability of fertilizer) by respondents decreases the use intensity by 5%. Previous studies such as Hagos and Holden, (2017) indicated that the main limitation Ethiopian farmers face in using the recommended amount of fertilizer is high price. Factors affecting the adoption of CIS and agro-advisories and determinants of use intensity The probit model result showed that family size and tropical livestock unit were the two independent variables found to influence respondents’ adoption decisions of CIS and agro-advisories. Similar to the finding in section 3.4.1., family size had a significant negative relationship with adopting CIS and agro-advisories. Although these findings are in line with Kamau et al. (2024), Tran et al. (2019), Uhunamure et al. (2019), they are contrary to the findings of a literature review conducted by Yirga and Alemu (2016), where they assessed the factors that determined adoption decision of agricultural technologies, and family size was identified as one of the factors that positively influenced adoption decision of agricultural technologies as large family size is associated with a higher labor endowment to accomplish agricultural tasks on time. The other significant variable that positively determined the adoption decision was TLU. The likelihood of adopting CIS and agro-advisories increases for respondents with more TLU since Ethiopia's livestock is the major income source for most rural people (Abdulla, 2015). The findings of some studies (e.g., Hasen, 2015; Ketema et al., 2016) also confirmed that livestock holding has a significant positive effect on farmer’s decision to adopt agricultural technology. According to our findings, factors that determine the use intensity of CIS and agro-advisories were the education level of the household head, TLU, and availability of labor. Education positively and significantly influenced the intensity of CIS and agro-advisories. The marginal effect result implied that as the education level of respondents increases, the use intensity increases by 7% because a better education level might favor respondents in accessing and timely utilization of advisories disseminated. Soumya et al. (2021) also reported similar findings where education level was positively associated with the intensity of adoption of agro advisories. Our finding was consistent with the findings in section 3.4.1 above regarding the influence of TLU on use intensity. TLU has a significant positive effect on the use intensity of CIS and agro-advisories. The marginal effect result showed that an increase in one unit of TLU would increase the intensity of use of CIS and agro-advisories by 3.8%, indicating that households with larger TLU have greater likelihood of adoption and use intensity of new agricultural technologies such as CIS and agro advisories because livestock plays a significant role as income sources in rural Ethiopia (Asresie and Zemedu, 2015) that would encourage farmers to invest in the adoption and intensification of these technologies. The availability of labor positively influenced the intensity of the use of CIS and agro-advisories. This hints that households with sufficient labor force use CIS and agro-advisories properly as the availability Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 25 of labor increases the use intensity by 3%. A possible reason could be that improved practices are labor intensive and households with relatively more labor use the technologies more efficiently (e.g., Beshir, 2014; Hailu, 2008). Table 12. Factors affecting the probability of adoption and use intensity of smart pack technology Variables Probit model Truncated model Coefficient Standard error Coefficient Standard error Marginal effect (dy/dx) Age - 0.007 0.025 0.011*** 0.004 0.001 Gender 0.704** 0.371 -0.114 0.074 0.033 Family size -0.132** 0.069 0.020*** 0.011 0.005 Education -0.009 0.163 -0.021 0.033 -0.002 Farming experience -0.037 0.026 0.001 0.005 -0.002 TLU -0.004 0.041 0.016** 0.007 0.001 Land 1.025** 0.476 -0.256* 0.141 0.039 Labor 0.259 0.225 -0.086 0.048 0.003 Distance to market -0.045* 0.079 0.003 0.016 -0.008 Farm income -0.000*** 0.00 -0.013 0.012 -0.008 Constant 2.939*** 0.873 -0.015 0.149 Log-likelihood 28.34 LR X2 (10) 36.41 Prob > X2 0.000 Pseudo R2 0.39 No of observation 200 Note: ***, **, and * refer to the significance levels at 1%, 5%, and 10%, respectively. Table 13. Factors affecting the probability of adoption and use intensity of site- specific fertilizer recommendation Variables Probit model Truncated model Coefficient Standard error Coefficient Standard error Marginal effect (dy/dx) Age -0.019* 0.011 -0.007** 0.003 -0.007 Gender -0.835*** 0.265 -0.077 0.059 -0.185 Family size 0.035 0.063 0.014 0.018 0.013 Farm income -0.031*** 0.013 0.046 0.041 0.042 Distance to market 0.006 0.024 -0.019*** 0.006 -0.008 Membership to farmers group 1.172*** 0.023 0.172** 0.085 0.291 Affordability of price of fertilizer - - -0.111*** 0.039 -0.054 Constant 0.876* 0.494 0.961*** 0.142 Log likelihood -116.29 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 26 LR X2 (7) 78.94 Prob > X2 0.000 Pseudo R2 0.25 No of observation 209 Note: ***, **, * refer to the levels of significance at 1%, 5%, and 10%, respectively. Table 14. Factors affecting probability of adoption and use intensity of CIS and agro-advisory Variables Probit model Truncated model Coefficient Standard error Coefficient Standard error Marginal effect (dy/dx) Age 0.001 0.017 -0.001 0.004 -0.000 Gender -0.288 0.040 -0.041 0.102 -0.087 Family size -0.184** 0.079 0.033 0.019 -0.024 Farming experience 0.011 0.021 0.008 Education -0.091 0.189 0.116*** 0.048 0.067 TLU 0.109** 0.047 0.023*** 0.010 0.038 Labor -0.111 0.147 0.068** 0.033 0.028 Off farm income -0.058 0.072 0.0103 0.090 -0.019 Constant 1.305 0.958 -0.348 0.226 Log likelihood -65.08 LR X2 (8) 33.60 Prob > X2 0.000 Pseudo R2 0.21 No of observation 114 Note: ***, ** refers to the level of significance at 1% and 5%, respectively. CONCLUSIONS The study aimed to assess the adoption rate and use intensity of three agricultural technology packages among smallholder farmers living in the Doyogena, Menz, Bekoji, and Digeluna Tijo districts in Ethiopia. The technologies introduced were smart pack innovation, site-specific fertilizer recommendations, climate information services, and agro-advisories. A simple random sampling technique was employed to select 523 respondents from the three districts. Both descriptive and econometric methods were used to analyze the data. Descriptive statistics such as mean, frequency, and percentages were used to assess the rate and intensity of technology adoption, while the double-hurdle econometric model was used to estimate the determinants of the likelihood of adoption decision and use intensity of these technologies. Our findings indicated that in both districts almost all respondents reported that they have adopted improved ram technology. In the Menz district, all respondents adopted improved health services, while most of the respondents in Doyogena Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 27 adopted improved market linkage. Regarding use intensity of smart pack innovation, more respondents are under the high adopter’s category in both districts. Our result further indicated that nearly three-fourths of respondents from both districts have adopted the recommended fertilizer application rate and its packages. Comparing the use intensity between the districts, more farmers in Digeluna Tijo were in the high adopter category than those in Doyogena. Unlike the other two technologies, there was a relatively low adoption rate and use intensity of CIS and agro-advisories. Looking at the determinants of the likelihood of adopting these technologies, our result provided empirical evidence of the positive impacts of farmland, TLU, and membership into cooperatives on respondents’ adoption decisions. Proximity to the marketplace also significantly contributed to the adoption decision of respondents. The use intensity of these technologies was explained by factors such as education level, TLU, labor, and membership in cooperatives. The closeness of marketplaces also played a key role in promoting the use intensity of these agricultural technologies. Although most respondents reported that they had received extension services, this factor had no significant impact on explaining both the adoption decision and the intensity of use of these agricultural technologies. To promote the adoption and use intensity of these agricultural technologies, our study suggests improving the education level of farmers, enhancing farm household asset formation, encouraging and facilitating cooperative membership and investing in infrastructure such as access road. The impacts of access to extension services on the adoption decision and use intensity have to be given due consideration since these services are instrumental in bridging the gap between agricultural research and practical farming and serving as a catalyst for the dissemination of knowledge, technology, and best practices among farmers (Abhijeet et al., 2023). Therefore, a policy that advocates advancing the uptake and sustainability of these innovations may need to prioritize and promote asset building and accumulation among the farm households, development of educational and farmers training centers and infrastructure such as access roads, encourage and facilitate smallholder farmers to join farmers groups and cooperatives to enable the farm household to utilize these agricultural technologies and share knowledge amongst themselves efficiently. The successful implementation and utilization of climate information services and climate-smart agriculture technologies can enhance smallholder farmers' welfare. ACKNOWLEDGMENTS This study was funded by the Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) project supported by a grant from the International Development Association (IDA) of the World Bank, which helps deliver a climate-smart African future driven by science and innovation in agriculture for Africa. The authors are grateful to the Alliance of Bioversity and CIAT, The International Maize and Wheat Improvement Center (CIMMYT), the International Center for Agricultural Research in Dry Areas (ICARDA) and Green Agro Solution PLC. (LERSHA) for their valuable input in the designing of the survey tool, and their assistance in the selection of Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 28 enumerators. A word of thanks also goes to district level officials who facilitated data collection and the enumerators who collected the household data. Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 29 REFERENCES Abdulla, A.M. (2015). Adoption of small ruminants fattening package in agro- pastoralist areas Dugda Dawa district, southern Oromia, Ethiopia. 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