ADOPTION RATE AND USE INTENSITY OF BUNDLED CLIMATE INFORMATION SERVICES AND CLIMATE- SMART AGRICULTURAL TECHNOLOGIES IN ETHIOPIA Technical Report Abonesh Tesfaye, Gebermedihin Ambaw, Maren Radeny and Dawit Solomon November 2025 To cite this report Tesfaye A., Ambaw G., Radeny M., Solomon D. 2025. 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. 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 enumerators. A word of thanks also goes to district level officials who facilitated data collection and the enumerators who collected the household data. 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 Cover photo: AICCRA/ Gebermedihin Ambaw Disclaimer This technical report has not been peer-reviewed.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. © 2025 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, and Digeluna Tijo districts in Ethiopia. A simple random sampling technique was employed to select 482 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 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 84%, 79.8%, and 39% for site-specific fertilizer recommendation, Climate-smart integrated small ruminant innovations (SmaRT Pack), and user-centric bundled climate information and digital agricultural 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, 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 research 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) Maren Radeny is a Science 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 Food insecurity status of respondents ............................................ 18 Adoption rate and use intensity of SmaRT Pack innovation by district . 21 Adoption rate and use intensity of site-specific fertilizer recommendation by district ................................................................................. 23 Adoption rate and use intensity of climate information services and agro-advisories by district ........................................................... 24 Spillover from Adopter Farmers .................................................... 27 Factors affecting adoption of CIS and CSA technologies and determinants of intensity of adoption ............................................ 27 Factors affecting the adoption of SmaRT Pack innovations and determinants of use intensity ....................................................... 27 Factors affecting the adoption of site-specific fertilizer recommendation and determinants of use intensity ................................................. 29 Factors affecting the adoption of CIS and agro-advisories and determinants of use intensity ....................................................... 30 Conclusions ..................................................................................... 33 References ...................................................................................... 35 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 CIAT 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) 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 climate information and digital agricultural 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, along with seasonal weather forecast across different sites in three regions of Ethiopia, namely Oromia, Amhara, and central Ethiopia regions. In 2025, these efforts evolved into the Harmonized Digital Fertilizer and Agronomic Solutions (HaFAS) platform, led by MoA and EIAR with CGIAR partners including ICRISAT and CIAT. Institutionalized into the national system, HaFAS is now implemented at scale by universities, NGOs (e.g., Self-Help Africa), research institutes (EIAR/RARIs), and international partners (GIZ, PxD, Digital Green, ICRISAT), reaching 44,197 smallholder farmers in 2025, increasing the number of beneficiaries to 116,997 since 2022. The delivery of user-centric bundled climate information and digital ag-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, 1 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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), recorded voice (voice blast), development agents (DAs), and a call center (7860). Delivery of these climate agro-advisories is facilitated through a partnership 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. To date, through the Ethiopian Digital Agro-Climate Advisory Platform, AICCRA and CIMMYT have generated climate forecasts and translated them into actionable agro-advisories, which are delivered bi-monthly to more than 373,606 beneficiaries (34.5% women), including 54,506 reached in 2025. These advisories are disseminated across 169 districts 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). Since 2021, 95,750 smallholder farmers, including 6,150 in 2025, have accessed SmaRT Pack innovations through CBBPs. 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 2 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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. METHODOLOGY Sample size and study locations The study was conducted in three regions in Ethiopia: Central Ethiopia, Amhara, and Oromia. Doyogena, Menz, and Digeluna Tijo were the three 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 482 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%) 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. 3 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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). 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 4 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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. 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 = Y * i > 0 if Yi* > 0 (3) Yi* = yi β +ui and Di*= xi΄α + ɛi , Yi = 0 otherwise (4) Y * i = 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). 𝑛 𝐹𝐴𝑗 AIj =∑ ( )/𝑁𝑃 (6) 𝐹𝑅𝑗 𝑗=1 5 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Where AIj = is adoption index of the jth farmer; j = 1, 2, 3…, n., n is the total number of farmers, 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 climate information and digital agricultural 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. 6 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Table 1. Definition of variables and expected signs for the double hurdle model Description Measurement Expected Supportive literature sign Dependent variables Adoption decision 1= farmer adopts the SSFR, CIS and/or CSA technologies, 0= otherwise Intensity of adoption -Proportion of sheep - Number of improved sheep breeds produced breeds to total sheep using any of the combinations of the SmaRT produced Pack components to total number of sheep the household owned. -Adoption index - Adoption index calculated based on the NPS and Urea fertilizer applied in relation to the -Proportion of land to recommended level. total cropland - Proportion of hectares of land allocated for CIS and agro-advisory technology to total cropland. Independent variable Age of the household Number of years +/- Beshir (2014); Milkias & Abdulahi (2018); head Endiris et al. (2021) Gender of household Male = 1; 0 = otherwise +/- Launio et al. (2018); Aliyi et al. (2021) head Education level of the No education = 0 + Challa & Tilahun (2014); Habtamu and household head Basic education =1 Krishna (2021) Elementary education =2 Secondary education = 3 College education = 4 7 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Table 1. Contd. Description Measurement Expected Supportive literature sign 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 + Ponguane and Mucavele (2018); Atinafu et Ethiopian Birr 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 1= Yes, 0= No - Hagos, F. and Holden, S.T. (2017) fertilizer 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) 8 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) RESULTS AND DISCUSSION Descriptive statistics results of the farm household characteristics in the three districts Most respondents from both groups in all three 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. Comparing the 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 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 three districts, adopters and non-adopters of Digeluna Tijo 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. 9 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Table 2. Farm household characteristics (Continuous variables) District of the study Age Family TLU Landholding Farming Farm Non-farm (years) size (No.) (ha) experience Income Income (ETB) (No.) (years) (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 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) Gender Education level Availability of District of the study 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 Adopters 86 14 8 3 54 35 - 52 Tijo Non-adopters 88 12 12 6 59 24 - 61 10 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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 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 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 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. 11 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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 Yield in kg/0.25ha and barley in ha NPS Urea Wheat Barley Wheat Barley Doyogena Wheat Barley Wheat Barley Adopters 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 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 120 100 80 60 40 20 0 Adopters Non adopters Adopters Non adopters Adopters Non adopters Doyogena Menze Digeluna Tijo manure compost herbicides and pesticides Figure 2. Application of agricultural inputs 12 Respondents (%) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Table 5. Access to extension services, credit facility, training, and distance to marketplaces Do you have Do you have Do you get Are you member Distance to District of the study access to credit access to training of cooperatives marketplace (km) facility extension services 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 Adopters 60 40 95 5 98 2 100 - 9 Non-adopters 51 49 86 14 - 100 96 4 7 13 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) When respondents were asked if they got any support from the government in the last 12 months, in Doyogena more than half of the respondents and in Digaluna Tijo more than 70% of respondents confirmed that they got support particularly on agronomic advice and training on the application of the right amount of agricultural input. When respondents were asked if they are aware of any by-laws, policies and initiatives the government implements in relation to climate change, almost all adopters and non-adopters in Digaluna Tijo stated that they are aware of them and involved in the implementation. Similarly, in Doyogena 84% non-adopters and 65% adopters indicated that they are aware of those initiatives and programs and majority of them reported that they are involved in the initiatives and programs. 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. 120 100 80 60 40 20 0 Adopters Non adopters Adopters Non adopters Adopters nonadopters Doyogena Menze Digeluna Tijo Figure 3. The proportion of respondents who think the climate has changed. 14 Respondents (%) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 120 100 80 60 40 20 0 Adopters Nonadopters Adopters Nonadopters Adopters NonAdopters Doyogena Menze Digeluna Tijo high temprature drought heavy and irregular rain Figure 4. The proportion of respondents who recognized the impact of climate change through extreme events. 100 80 60 40 20 0 Adopters Nonadopters Adopters Nonadopters Adopters Nonadopters Doyogena Menze Digeluna Tijo crop failure livestock reduction soil fertlity reduction pest and disease Figure 5. The proportion of respondents who perceived the livelihood impact of climate change. 15 Respondents (%) Respondents (%) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 100 80 60 40 20 0 Adopter Nonadopter Adopter Nonadopter Adopter Nonadopter Doyogena Menze Digeluna Tijo sales of assets relaying on saving engaging in income generating activity Figure 6. The proportion of respondents who used coping mechanisms To understand soil erosion rate and fertility status of the area, respondents were asked how they perceived soil erosion rate on their plots of land, in Doyogena more than 60% from both groups reported that they don’t observe soil erosion on their land. However, in Digeluna Tijo majority of the respondents from both groups stated that there is no erosion on their land. Unlike the three places, there is moderate erosion in Menz district as reported by majority of respondents. Similarly, soil fertility status is on decline in Menz district while it is improving in Doyogena (Table 6). 16 Respondents (%) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Table 6. Soil erosion rate and fertility status Soil erosion (%) Soil fertility status (%) District of the study No erosion Moderate Gully erosion On decline Improving Unchanged Doyogena Adopters 65 29 6 22 49 29 Non-adopters 68 28 4 6 56 38 Menze Adopters 6 94 - 73 17 10 Non-adopters 8.3 83.3 8.3 73 6 21 Digeluna Tijo Adopters 83 17 - 8 83 9 Non-adopters 78 22 - - 90 10 17 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Food insecurity status of respondents Food insecurity status of respondents was assessed by using UNFAO based household food security questionnaire which includes eight indicators of food insecurity. Looking at farmers’ responses, most of the households in Digeluna Tijo districts reported experiencing several signs of food insecurity over the past 12 months, including not having enough food to eat, inability to consume healthy and nutritious food, eating limited types of food, reducing meal sizes and running out of food. However, most respondents did not report the most severe indicators, such as going hungry or not eating for an entire day. In contrast, households in the other districts, both from the beneficiary and control groups, reported experiencing these indicators to a lesser extent, suggesting relatively better food security conditions compared to Digeluna Tijo (Figures 7 & 8). 18 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) A B 80 80 60 60 40 40 20 20 0 0 Non Adopters Non Adopters Non Adopters Non Adopters Non Adopters Non Adopters adopters Aadopters adopters adopters Aadopters adopters Doyogena Menze Digaluna Tijo Doyogena Menze Digaluna Tijo No Yes No Yes C D 80 80 60 60 40 40 20 20 0 0 Non Adopters Non Adopters Non Adopters Non Adopters Non Adopters Non Adopters adopters Aadopters adopters adopters Aadopters adopters Doyogena Menze Digaluna Tijo Doyogena Menze Digaluna Tijo No Yes No Yes Figure 7. Proportion of respondents who experienced food insecurity in the past 12 months due to insufficient money or resources: (A) did not have enough food; (B) were unable to eat healthy and nutritious food; (C) ate only a few kinds of food; (D) skipped meals. 19 Respondents (%) Respondents (%) Respondents (%) Respondents (%) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) A B 80 100 80 60 60 40 40 20 20 0 0 Non adopters Adopters Non Adopters Non adopters Adopters Non Adopters Non Adopters Non Adopters Aadopters adopters Aadopters adopters Doyogena Menze Digaluna Tijo Doyogena Menze Digaluna Tijo No Yes No Yes C D 100 100 80 80 60 60 40 40 20 20 0 0 Non Adopters Non Adopters Non Adopters Non Adopters Non Adopters Non Adopters adopters Aadopters adopters adopters Aadopters adopters Doyogena Menze Digaluna Tijo Doyogena Menze Digaluna Tijo No Yes No Yes Figure 8. Proportion of respondents who experienced food insecurity in the past 12 months due to insufficient money or resources: (A) ate less than they felt they should; (B) ran out of food; (C) felt hungry; (D) did not eat for an entire day. 20 Respondents (%) Respondents (%) Respondents (%) Respondents (%) Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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 7). In Doyogena, more than 90% recipients adopted the improved ram technology, while in Menz all recipients adopted the technology. Improved feed and forage technology was received and adopted by 98% of farmers in Menz but only a third of them adopted it in Doyogena. For improved health services, almost all recipients in Menz adopted and used the technology, compared to 88% in Doyogena. Regarding market linkage services, majority of farmers in Menze adopted and used the services when compared to the rate in Doyogena. The overall adoption rate of the bundled SmaRT Pack technologies stands at 68%, reflecting significant uptake among farmers across the districts. Table 8 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 7. The adoption rate of SmaRT Pack innovation Rate of farmers adopted and used SmaRT Pack innovation (%) District Improved ram Improved feed Improved Improved market and forage health service linkage Doyogena 92 29 88 41 Menz 100 98 100 89 Average 96 64 94 65 The overall adoption rate of innovation = 79.8% With an adoption rate of 79.8%, 4,908 of the 6,150 farmers reached in 2025 are now using the SmaRT Pack innovation, bringing the total number of users to 65,836 since 2021. 21 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Table 8. Use intensity of adoption of SmaRT Pack innovation Use intensity Doyogena Menz Total Frequency Percent Frequency Percent Frequency Percent Low adopters 2 4 6 12 8 8 Medium 11 22 - - 11 11 adopters High adopters 38 74 45 88 83 81 Total 51 100 51 100 102 100 The primary sources of information for adopters on the adoption and use of the SmaRT Pack innovations in the Doyogena district were the Areka and Worabe Research Centers, and ICARDA, while for the Menz district, it was the Debre Berhan Research Center, ICARDA and Ministry of Agriculture (Figure 9). Breeding cooperatives and development agents were the main information delivery channels (Figure 10). In Doyogena, the most preferred channel of information was breeding cooperatives, while in Menz district, both breeding cooperatives and development agents were the most preferred channels. Almost all adopters in both districts reported receiving the information on time. Areka and Areka and ICARDA ICARDA Debreberhan Debreberha and ICARDA n and ICARDA MoA MoA Figure 9. Source of information in Doyogena and Menz districts (left to right) Breeding cooperative Breeding s cooperatives Developmen Developmen t agents t agents Neigbours Neigbours Figure 10. Channel of information in Doyogena and Menz districts (left to right) 22 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Adoption rate and use intensity of site-specific fertilizer recommendation by district The adoption rates of site-specific fertilizer recommendations (NPS and Urea) and seasonal weather forecasts in Doyogena were 86%, 85% and 79% respectively. The overall adoption rate of innovation was 84% (Table 9). Table 9. Adoption rate of site-specific fertilizer recommendation Rate of farmers adopted and used site-specific fertilizer District recommendation (%) NPS Urea Seasonal weather forecast Doyogena 86 85 79 The overall adoption rate of innovation = 84% With an adoption rate of 84%, 37,125 of the 44,197 farmers reached in 2025 are using the site-specific fertilizer recommendations, bringing the total number of users to 90,200 since 2022. Table 10 presents the use intensity of site-specific fertilizer recommendations in Doyogena district. About 35% of the farmers were categorized under the high adopter group while 37% were under the low adopter group. More than a quarter were grouped under medium adopters’ category. Table 10. Use intensity of adoption of site-specific fertilizer recommendation Use intensity Doyogena 23 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Frequency Percent Low adopters 35 37 Medium adopters 26 28 High adopters 33 35 Total 94 100 The primary sources of information on site-specific fertilizer recommendations were the Ministry of Agriculture and CIAT. Development agents served as the main and preferred channel for delivering this information (Figures 11 and 12). Almost all farmers reported receiving recommendations and advisory services on time. When asked about the affordability of the recommended location-specific fertilizer rates, 72% confirmed that they could afford the suggested application rates. MoA and CIAT MoA and CIAT LERSHA and LERSHA and CIAT CIAT Figure 11. Source of information in Doyogena and Digeluna Tijo districts (left to right) Development Development agents agents Recorded Recorded voice blast voice blast Call center Call center Mobile app Mobile app SMS text SMS text Figure 12. Channel of information in Doyogena and Digeluna Tijo districts (left to right) Adoption rate and use intensity of climate information services and agro-advisories by district Table 11 presents the adoption rates of CIS (Climate Information Services) technologies and agro advisories across various agricultural activities in Digeluna Tijo. 24 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Adoption rates varied significantly by activity, where planting date, harvesting time and input supply were adopted by more than half of the respondents. On the other hand, weeding time, application of agro-chemicals and fertilizer was adopted by more than 40% of respondents. Advisory on land preparation and mechanization was adopted by more than a third of the respondents. Sub-seasonal forecasting was the least adopted at the rate of 4.5%. The overall adoption rate of the CIS technologies in Digeluna Tijo stands at 39%. The intensity of adoption was categorized into three groups, with more farmers found under the high adopters’ group (Table 12). The main sources of information about CIS and agro-advisories were LERSHA, MoA, EIAR, and development agents, while the main channels to deliver the information included development agents, call centers, SMS text, mobile app and radio (Figure 13). All respondents reported that they received the information on time. 25 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Table 11. Adoption rate of CIS technologies Rate of adoption of CIS and agro-advisories (%) District Sub- Land Planting Weeding Fertilizer Agro- Harvesting Input Mechanization seasonal preparation date time application chemical time supply forecast application Digeluna Tijo 4.5 35 59 41 47 44 59 56 36 The overall adoption rate of innovation = 39% With an adoption rate of 39%, 21,257 of the 54,506 farmers reached in 2025 are using user-centric bundled climate information and digital ag-advisories, bringing the total number of users to 78,695 since 2021. Table 12. Use intensity of adoption of CIS technologies and agro-advisories Use intensity Frequency Percent Low adopters 13 20 Medium adopters 7 10 High adopters 46 70 Total 66 100 26 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Main source of information Channel of the information Recorded voice blast LERSHA Call center MoA Mobile app ATA SMS text Development agents Radio Figure 13. Source and channel of information in Digeluna Tijo districts (left to right) Spillover from Adopter Farmers Adopter farmers were asked how many people outside their household, including friends, neighbours, fellow farmers, and relatives, they had informed about three innovation packages: Bundled Small Ruminant Innovations, Site-Specific Fertilizer Recommendations (SSFR), and user-centric bundled climate information and digital agricultural advisories. On average, each adopter reported passing information on to approximately 2.5 individuals. This average reflects direct interpersonal spillovers within local social networks and suggests that adopters act as modest but meaningful conduits for disseminating agricultural knowledge. 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 27 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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 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 of the household head significantly and 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. 28 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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). 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 of 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 29 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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 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 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 30 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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 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 13. Factors affecting the probability of adoption and use intensity of SmaRT Pack technology Variables Probit model Truncated model Coefficient Standard Coefficient Standard Marginal error error 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 -0.037 0.026 0.001 0.005 -0.002 experience 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 -0.045* 0.079 0.003 0.016 -0.008 market 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 14. Factors affecting the probability of adoption and use intensity of site- specific fertilizer recommendation Variables Probit model Truncated model Coefficient Standard Coefficient Standard Marginal error error 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 0.006 0.024 -0.019*** 0.006 -0.008 market Membership to 1.172*** 0.023 0.172** 0.085 0.291 farmers group 31 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) Affordability of - - -0.111*** 0.039 -0.054 price of fertilizer Constant 0.876* 0.494 0.961*** 0.142 Log likelihood -116.29 LR X2 (7) 78.94 Prob > X2 0.000 Pseudo R2 0.25 No of 209 observation Note: ***, **, * refer to the levels of significance at 1%, 5%, and 10%, respectively. Table 15. Factors affecting probability of adoption and use intensity of CIS and agro- advisory Variables Probit model Truncated model Coefficient Standard Coefficient Standard Marginal error error 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 0.011 0.021 0.008 experience 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 114 observation Note: ***, ** refers to the level of significance at 1% and 5%, respectively. 32 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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, 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 482 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 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 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 intensity of use 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 intensity of use 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 33 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) 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. 34 Adoption Rate and Use Intensity of Bundled CIS and CSA Technologies in Ethiopia Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) REFERENCES Abdulla, A.M. (2015). 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