Climate-smart agricultural practices: a case of dairy cooperative farmers in Agula and Maychew, Northern Ethiopia

Abstract This study examined the climate-smart agriculture (CSA) practices of dairy farmers in Agula and Maychew, Northern Ethiopia. Data was collected through focus group discussions (FGDs) and questionnaires. The study explored farmers’, implementation of three CSA practices – improved fodder, manure management, and replacement of unproductive cows. The determinants of CSA adoption were analyzed using a binary logistics model at significance levels of 1%, 5%, and 10%. Results showed that improved fodder was adopted by 60.1% in Agula and 18.2% in Maychew. The adoption of manure management (p = .229) and replacement of unproductive cows (p = .935) did not vary significantly. The replacement of unproductive cows had the highest adoption rate (45.9%). Improved fodder adoption was significantly higher among male-headed farms (p = .017). Manure management adoption was positively associated with gender (p = .034), number of cows (p = .081), and access to climate information (p = .063). Replacement of unproductive cows was associated with number of cows (p < .001), farm income (p = .049), and access to extension services (p = .006). FGDs revealed that farmers were able to perceive climate change and its effects on their dairy business. This study underscores the need for CSA practices to adapt to climate change impact on animals and mitigate greenhouse gas emissions from dairy farms.


Introduction
Long-term changes in rainfall intensity and variability [1], along with increased frequency and severity of floods, droughts, and extreme temperatures, pose significant challenges to livestock productivity [2,3].Climate change affects livestock through its impact on the availability and quality of animal feeds, water resources, growth and milk production, disease prevalence, and reproduction [4].These effects have implications for global food and nutritional security, with varying impacts based on breed, production system, and geographic region [5].Sub-Saharan African countries are particularly vulnerable to these impacts due to their heavy reliance on agriculture [6].
Ethiopia's economy is heavily influenced by the livestock sector, which plays a crucial role in national economic development [7] and contributes to improving the nutritional status of the population [8].Dairy animals constitute 31.5% of the total livestock population [9].Despite this potential, the productive and reproductive performances of dairy cattle in the country remain low, largely due to factors such as scarce and lowquality feed, poor genetics, and weak healthcare management [10].Projections for milk production in Ethiopia indicate a deficit of approximately 3.185 million L by 2028 due to increasing demand and rapid population growth [11].
Maximizing the contribution of the dairy sector to meet the demand and supply requirements of dairy products in Ethiopia would require a substantial amount of land, which puts negative pressure on this scarce resource.Consequently, policymakers and experts have suggested climate-smart intensification of milk production as a means to address this challenge [12].Enhancing production can be achieved through improved veterinary services, farm management practices, and access to quality fodder.Therefore, dairy farmers need to implement climate-smart agriculture (CSA) practices to enhance on-farm productivity, improve resilience to climate change, and reduce greenhouse gas (GHG) emissions [6, [12][13][14][15].
CSA, as a strategy in the livestock sector, aims to promote sustainable livestock productivity while reducing environmental impacts and addressing climate change adaptation and mitigation efforts [15].At the farm level, dairy farmers employ a variety of adaptation and mitigation techniques, including increased concentrate feeding, forage production, composting, biogas production, replacement of unproductive animals with productive ones, zero-grazing, and water harvesting [12,14,16].These practices help reduce GHG emissions through improved digestion, reproductive performance, and reduced use of synthetic fertilizers and fossil fuels [6].
To promote the adoption of CSA practices, it is necessary to understand the current status and factors influencing their adoption.This information would facilitate informed policymaking and targeted investments to enhance adoption.However, there are limited studies on the implementation of CSA practices in Ethiopian dairy systems.Therefore, this study was conducted in Tigray, one of the regional states most susceptible to climate change, with the objectives of assessing dairy farmers' perception of climate change impacts and examining the current status of and influencing factors of the implementation of CSA practices.

Study area
This study was conducted in two areas in the Tigray regional state, namely Agula (13 � 41 0 30 00 N, 39 � 35 0 30 00 E) and Maychew (12 � 46 0 47.17"N, 39 � 32 0 18.10"E).These locations were purposively selected due to the presence of active dairy cooperatives, as indicated by information obtained from the local Bureau of Agriculture and Rural Development office (Figure 1).
Agula includes three active dairy cooperatives: Abay, Daero, and Selam.Daero dairy cooperative was established in 2005 and has 15 active members.Selam cooperative was established in 2007 and has 30 active members, while Abay cooperative was established in 2014 and has 24 active members.These cooperatives collect milk from member farmers at a price of 22 Birr/L ($0.75/L) and sell the milk to local restaurants and cafeterias.None of the cooperatives possess a milk processing plant.Farmers in Agula receive professional and extension services from Technical and Vocational Education Training institutions, universities, and non-governmental organizations.
In Maychew, the study focused on Tegulu dairy cooperative, which was established in 2019 and had 45 active members.The cooperative operates two milk collection centers and a milk cooling facility.The milk processing plant has a capacity of 600 L per day.During the study period, dairy farmers in Maychew sold their milk to the cooperative at a price of 25 Birr/L ($0.85/L).The cooperative also provides farm inputs, such as animal feed, to its members.Both governmental and non-governmental organizations offer professional and extension services to the dairy farms in Maychew.

Research design and strategy
The aim of this study was to assess the extent of implementation of CSA practices among dairy farmers.The research design involved a preliminary site visit to familiarize the researchers with the study locations and the dairy farmers who were members of the dairy cooperatives.Data collection tools, including questionnaires and checklists, were utilized.To ensure the validity of the study, key informant interviews with experts were conducted to validate the questionnaires and plan field visits.The questionnaires were pre-tested and revised for clarity.Focus group discussions (FGDs) were also conducted as part of the data collection process.

Target population and sample size
The target population for this study comprised dairy farmers who were members of the dairy cooperatives.A list of member dairy farmers (114) was obtained from the respective cooperatives.Sampling was based on farmers' consent, and 29 farmers who did not provide consent were excluded.Consequently, a total of 85 voluntary dairy cooperative farmers (41 from Agula and 44 from Maychew) participated in the study.For the FGDs, 16 dairy cooperative farmers (eight participants from each study site) were selected by extension officers, taking into consideration factors such as gender, farming experience, ability for selfexpression, and consent to participate.

Data sources and methods of data collection
A mixed data collection approach was employed, utilizing both a questionnaire survey and FGDs.
The survey involved the use of a structured questionnaire written in English, which was translated into the local language, Tigrigna.Data were collected from farm owners or family members who had knowledge of farm management practices.The collected information encompassed socio-economic characteristics, land holdings, farmers' perceptions of climate change, its effects on dairy farming, and the implementation of CSA practices.Data enumerators, fluent in Tigrigna, were trained prior to the survey and supervised during data collection.
Additionally, quantitative data were triangulated with information obtained from the FGDs conducted at each study site.Each FGD comprised eight farmers, including both male and female participants who had also taken part in the questionnaire survey.The FGDs explored climate events commonly experienced in the study locations and their potential impacts on dairy farming.They also assessed the factors influencing the acceptance of CSA practices among dairy farmers.

Data management and analysis
Data obtained from the questionnaire survey were summarized and analyzed using Microsoft Excel version 2013 and SATA version 15.Descriptive statistics were employed to analyze socio-economic and farm characteristics.The adoption of three selected CSA practices was compared between Agula and Maychew using a chi-square test.Qualitative data collected through FGDs were transcribed and categorized.The determinants of CSA practice adoption were analyzed independently for the three practices: improved fodder, manure management, and replacement of unproductive cows [12,14,16].A binary logistic regression model was utilized for the analysis.The results are summarized and presented in appropriate tables.

Empirical model
Dairy farmers were categorized as adopters or non-adopters of a specific CSA practice.The study employed a standard logistic regression (logit model) to examine binary choices, specifically the adoption versus non-adoption of a CSA practice.This model provides empirical estimates of how changes in socio-economic and exogenous variables influence the probability of adopting a particular practice [17].The adoption level was represented as a dichotomous variable (adopter ¼ 1, non-adopter ¼ 0).Three different models were used to assess the impact of independent variables on the three CSA practices.
The binary logistic model used in the study is specified as follows, based on Quddus [41]: where: � Yi (the dependent variable) is the level of adoption (1 ¼ adopters, 0 ¼ non adopters).� P refers to the probability of the variable Y being equal to 1 given the input variable X. � X denotes the input variables or features that are used to predict the outcome Y. � bo represents the intercept or bias term, while bi (where i ranges from 1 to 10) represents the coefficients associated with the input variables X. � e represents Euler's number, a mathematical constant approximately equal to 2.71828.It is used as the base of the natural logarithm.
The logit transformation of P is defined as: The study considered 10 explanatory variables to assess their combined effects on the adoption of CSA practices, encompassing demographic, socio-economic, and institutional characteristics.Based on previous research reports, the independent variables described below were taken into account.

Gender
Gender was used as a dummy variable with a value of "1" for male farmers and "0" for female farmers.Female farmers are generally less likely to implement CSA practices due to the perception that it requires more work.Also, male farmers may have greater access to information on climate change and, consequently, be better equipped to implement adaptation and mitigation practices [18].

Education level
Education level was used as a categorical variable with three possible values: "0" for farmers with no formal education, "1" for those with primary education, and "2" for those with secondary or higher education.Farmers with higher education levels are expected to have a better understanding of the situation and be more capable of implementing CSA practices [19].

Farm experience
Farm experience was employed as a continuous variable representing the number of years of farming experience.Farmers with longer experience are likely to have been exposed to both past and present climatic conditions, making them more inclined to implement CSA practices [20].

Farm income
Farm income was adopted as a continuous variable expressed in Ethiopian birr, indicating the monthly earnings associated with dairy farming.Farmers with higher incomes tend to take more risks and have better access to information, potentially leading to a higher adoption of CSA practices [6].

Access to credit
Access to credit was used as a dummy variable with a value of "1" if the farmer has access to credit services and "0" otherwise.Credit can alleviate cash flow issues, enabling farmers to purchase necessary inputs and implement CSA practices [21].Therefore, this study hypothesized a positive correlation between access to credit and the adoption of CSA practices.

Access to extension services
Access to extension services was used as a dummy variable with a value of "1" if the farmer has access to extension services and "0" otherwise.Farmers with access to extension services are generally better informed, which can positively influence the adoption of CSA practices [22].

Access to climate information
Access to climate information was adopted as a dummy variable with a value of "1" if the farmer has access to information about climate change and "0" otherwise.Information on climate change through various media can create awareness and favorable conditions for the adoption of CSA practices [23].

Number of cows
The number of cows was used as a continuous variable indicating the number of cows owned by the farmer.Farmers with larger herds are more likely to adopt CSA practices [21].

Land size
Land size was used as a continuous variable expressed in hectares, representing the size of land allocated to dairy farming.Farmers with larger land holdings can diversify their income sources and reduce their vulnerability to climate change risks, potentially increasing the adoption of CSA practices [19].

Household size
Household size was used as a continuous variable indicating the number of family members in the household.A larger household size provides a sufficient labor force, which can enhance the likelihood of implementing CSA practices [23].
Prior to data analysis, the study checked for multicollinearity using the variance inflation factor (VIF) for both categorical and continuous independent variables.The VIF values were found to be less than 10, indicating the absence of multicollinearity [24].Consequently, all the hypothesized independent variables were included in the model.

Socio-demographic characteristics
Table 1 presents the socio-demographic characteristics of the respondents.It reveals that the dairy farming business is male-dominated in both study sites.The respondents had an average age of approximately 47.2 (± 1.02) years.About 17.1% of respondents in Agula and 18.2% in Maychew had no formal education.The primary level of education was the highest attained by the majority of respondents in Agula.However, in Maychew, 61.4% of respondents had achieved a secondary or higher education level.On average, dairy farmers in Agula owned approximately 0.58 ha of land for their dairy business, while farmers in Maychew owned only 0.15 ha.The farmers in Agula had been engaged in the dairy business for a longer period, with an average of 9.8 years, compared to 7.6 years for farmers in Maychew.The average household size in Agula was 6.6, while in Maychew

Implementation of CSA practices
Table 2 presents the adoption of CSA practices among dairy farmers.The practices included improved fodder development, manure management, and replacement or culling of unproductive dairy cows.In Agula, the majority of farmers (60.9%) practiced improved fodder development, while only 18.2% of farmers in Maychew adopted this practice.However, there was no significant variation in the adoption of manure management (p ¼ .229)or replacement of unproductive cows (p ¼ .935) between the two sites.
Table 3 displays the frequency of farms that adopted the various CSA strategies.It can be observed that approximately one third of the farms (29%) did not adopt any of the CSA practices.The results further revealed that 24.7% of farmers adopted the replacement of unproductive cows only, 10.6% adopted improved fodder development only, 7% adopted both manure management and improved fodder, and 11% adopted all three CSA practices.

Factors affecting the implementation of CSA practices
The results of the binary logistic regression analysis for the three CSA practices are presented in Table 4.The findings revealed a mixed relationship between gender and the implementation of CSA practices.Specifically, there was a significant relationship (p ¼ .017) between gender and the use of improved fodder development.However, no significant relationship was found between gender and the adoption of manure management (p ¼ .298)or the replacement of unproductive cows (p ¼ .261).
Land size was found to have a significant positive association with the adoption of improved fodder development (p ¼ .022)and manure management (p ¼ .034).However, it was found to have a non-significant role (p ¼ .526) in the adoption of the replacement of unproductive cows.The number of cows on the farm also showed a significant association with the adoption of manure management (p ¼ 0.081) and the replacement of unproductive cows (p < .001).
Access to climate information was found to have a significant positive association (p ¼ .0063)with the adoption of manure management.Furthermore, farm income (p ¼ .049)and access to extension services (p ¼ .006)were found to play a significant role in the adoption of the replacement of unproductive cows.

Focus group discussions
An FGD was conducted with dairy farmers in each study area to gather their perspectives on climate change, observed changes, and the impact on dairy farming in their locality.The outcomes of the discussions are summarized in Table 5.
Farmers reported noticeable local climate change and variability, which manifested in  changes in temperature, seasonality and amount of rainfall, and intensity of winds.They were able to connect climate change indicators with their impact on dairy farming.Farmers highlighted that the availability of animal feeds has decreased due to the recent decline and unpredictability of rainfall.Consequently, the cost of livestock feeds has risen, making the sector less attractive to new entrants.Additionally, farmers expressed concerns about the increasing temperature, which puts stress on dairy cows.This has led to a higher prevalence of diseases such as mastitis, pneumonia, and foot rot.Furthermore, there has been a rise in reproductive problems like premature birth, congenital issues, and abortion.Farmers also noted that changes in temperature and rainfall patterns have resulted in lower milk output and decreased milk quality.The FGDs revealed that the majority of farmers were unaware of specific CSA practices.However, many farmers were implementing adaptive strategies such as manure management, improved fodder production, and replacing unproductive cows based on their instincts, without fully understanding the benefits of these practices as part of CSA.
Farmers cited several constraints that hindered the adoption of CSA practices, including lack of knowledge, financial limitations, insufficient land size, land ownership status, high input costs, and inadequate promotion of CSA practices.

Discussion
The perception of climate change and its impact on dairy farming plays a crucial role in the adoption of CSA practices.The findings from the FGDs align with the existing literature and highlight the farmers' ability to identify the various manifestations of climate change.This is in line with the Intergovernmental Panel on Climate Change [25] guideline, which recognizes climate change and variability as global and local phenomena that manifest in diverse weather occurrences.The farmers participating in the FGDs also demonstrated an understanding of the potential impacts of climate change on dairy cattle and animal feed availability.Their observations are consistent with reports by Polley et al. [26] and Henry et al. [27], which indicate that climate change and variability have  implications for forage production and quality, milk production, animal diseases, and reproduction.The farmers' recognition of the impact of climate change and variability on the prevalence of animal diseases and the availability of animal feed is noteworthy.They understood that rising temperatures can indirectly affect dairy cattle by subjecting them to stress and increasing their susceptibility to diseases.This understanding aligns with the scientific understanding that higher temperatures can lead to an increase in microbial activity, a rise in disease incidences, and a decline in host resistance [28].While the farmers expressed uncertainty regarding the relationship between climate change and reproductive issues in dairy cows, they correctly identified heat stress as a significant factor in reproductive problems [29].
The effects of climate change on dairy cattle are primarily attributed to changes in precipitation, humidity, and the frequency of floods and droughts [4].Dairy cattle, in turn, contribute significantly to GHG emissions [30].The introduction of high-producing breeds and the intensification of dairy farming can help reduce GHG emissions [11].Therefore, it is proposed that dairy farms should adopt CSA practices such as manure management, increasing feed digestibility through fodder development, and replacing unproductive cows [12].
Increasing cow productivity is essential for reducing GHG emissions.Moreover, maintaining a larger number of young animals as replacement stock can lower production costs for the same amount of output [31].Increasing the lifetime productivity of individual animals and herds can lead to a 15-30% reduction in methane (CH 4 ) emissions, as demonstrated by Knapp et al. [32].It is recommended to cull unproductive cows or replace them with heifers to reduce the emission intensity associated with such cows [12].However, this study revealed that less than half of the farmers (approximately 45%) adopted the practice of replacing unproductive cows, with no significant difference observed between Agula and Maychew.During the FGDs, farmers mentioned that the rising cost of cows hindered them from adopting this practice.Consequently, they keep the cows until their milk production significantly decreases, and then sell them for meat purposes.
Mitigating CH 4 emissions from manure can be achieved through the implementation of effective manure management techniques [33].Approaches such as anaerobic digestion for biogas and fertilizer production, composting, and covering manure heaps in pits or under shade can help reduce emissions [34].However, only a few farmers in both Agula and Maychew adopted manure collection, application practices, and composting, and none of the farms implemented biogas production.During the FGDs, farmers expressed that the land holding status, which is based on leasing, and the small land size posed challenges in adopting these resource-intensive practices.It was observed that manure collection and application practices were slightly higher in Agula, where some farmers engage in crop production alongside dairy farming and use the manure as fertilizer.However, the practice of open-field disposal of manure should be discouraged, as it leads to GHG emissions, nutrient leaching, organic matter loss, and undesirable odors that impact the environment negatively [35].
Most dairy farmers in Agula and Maychew primarily rely on crop residue and green forage as feed for their dairy cows.However, they have observed that changes in rainfall patterns have affected the availability of these feeds.It is important to note that these feeds have high fiber content and low digestibility, leading to significant enteric CH 4 production.Relying solely on such feeds is not conducive to promoting CSA practices.Therefore, it is crucial to focus on increasing the digestibility of feeds to improve rumination time and subsequently reduce CH 4 emissions [14].
One effective strategy for mitigating the impact of feed on enteric CH 4 production and enhancing the resilience of animal feed and livestock production is the use of improved fodder.Feeding improved fodder has been shown to decrease enteric CH 4 production, making it a climate-smart choice [36].In Agula, the majority of farmers have adopted this practice, while in Maychew, a few farmers have embraced it.During the FGDs, farmers explained that the high cost of fodder seeds, such as alfalfa and elephant grass, posed a barrier to adopting this CSA practice.However, farmers in Agula mentioned that they received assistance through community outreach programs conducted by nearby higher education institutions, such as Mekelle University, which provided them with free seeds.Additionally, farmers in Agula have relatively larger land areas available for growing fodder compared to farmers in Maychew.Among the fodders, alfalfa was the most common in Agula, while green maize was more prevalent in Maychew.
The levels of adoption of CSA practices among farmers varied, as indicated by the results.Some farmers implemented only one or a combination of two practices, while a few farmers adopted all three CSA practices.This finding aligns with Maindi et al.
[6], who reported that the frequency of CSA practice adoption varies among farmers for various reasons.The implementation of different CSA practices was found to be influenced by social factors (such as gender and education), economic factors (including farm income, number of cows, and land size), and institutional factors (such as access to credit, extension services, and climate information) [37].
The results showed that the adoption of CSA practices was significantly influenced by gender, farm revenue, education, extension services, number of cows, and land size.Gender had a significant and positive influence on the adoption of CSA practices, which is consistent with the findings of Mulwa et al. [38].This could be attributed to the labor requirement for fodder development.Maindi et al. [6] stated that women often have limited control over financial services, labor, and resources like land.However, gender did not have an impact on the adoption of manure management and replacement of unproductive cows, contrary to the findings of Deressa et al. [18] and Maindi et al. [6], who reported gender differences in the adoption of certain CSA practices.
The education status of farmers did not significantly contribute to the adoption of improved fodder and the replacement of unproductive cows.This finding contradicts the results of Ajayi et al. [39], who demonstrated that the education level of producers plays a positive role in the adoption of improved practices in dairy farming.However, education did play a significant role in the adoption of manure management, supporting the findings of Ajayi et al. [39], Jones et al. [40], Quddus [41] and Farid et al. [19], which highlighted the influence of farmers' education levels on the adoption of improved farming practices.
Farm income had a positive and significant effect on the adoption of the replacement of unproductive cows.This can be attributed to the economic implications of purchasing replacement cows instead of keeping unproductive ones.Farmers with higher income are more likely to be able to afford the cost of culling unproductive cows and acquiring new productive ones.This finding is consistent with the studies conducted by Gbetibouo et al. [42], Jones et al. [40], and Maindi et al. [6], which demonstrated the positive impact of income on the adoption of CSA practices.These studies suggest that higher-income farmers are more willing to take risks and have better access to information compared to lower-income farmers.However, the lack of association between income and the adoption of improved fodder and manure management could be due to the farmers' limited willingness to invest money in adopting such practices.
Access to extension services was expected to have a positive relationship with the adoption of CSA practices.However, the findings revealed that access to extension services only influenced the adoption of the replacement of unproductive cows.This could be attributed to the focus of the extension services provided.According to the farmers' feedback during the FGDs, the extension services they received did not specifically cover CSA technologies and practices.Instead, the services primarily focused on animal health and production.Since the productivity of cows can decline due to diseases and related factors, the extension experts are more likely to advise farmers to cull less productive cows.Although the impact of access to extension services was not significant for the other two practices, this finding supports the results of Below et al. [22] and Gbetibouo et al. [42], indicating that access to extension services that address CSA practices increases the likelihood of adoption.
The ownership of a higher number of dairy cows was found to have a significant positive relationship with the adoption of CSA practices, specifically the adoption of manure management and the replacement of unproductive cows.During the FGDs, farmers explained that as the number of cows increases, there is a greater need to collect and store manure.Dairy farmers have an incentive to manage the manure in order to increase the income from its sale to crop farmers or to apply it as fertilizer for fodder.The number of cows could also be associated with the adoption of the replacement of unproductive cows because farmers are more willing to take the risk of reducing the herd size if they have enough other cows as a backup.This finding is supported by Arakelyan [33], who found that increasing the number of cows increases the likelihood of adopting climatesmart practices, indicating that farmers have greater resources to invest.
In the present study, the landholding size in the study area was relatively small.However, it was found to have a significant positive impact on the adoption of improved fodder and manure management.These findings are consistent with the research conducted by Arakelyan [33] and Parwada et al. [43], which indicated that larger land size is more likely to influence the adoption of climatesmart practices.This result is further supported by Maindi et al. [6], who highlighted that manure management and improved fodder are complementary practices that are both affected by land size.The farmers' observations during the FGDs also aligned with these findings, as they mentioned that composting or directly applying manure to fodder improved productivity due to its positive effects on soil fertilization and water retention capacity [44].
Adopting CSA practices is a challenging endeavor in general.It often demands a significant investment that exceeds the capacity of dairy farmers in developing countries [13].Promoting the adoption of CSA practices among these farmers necessitates support in terms of knowledge, skills, and technology provisions.This can be accomplished by implementing comprehensive policy initiatives that involve various stakeholders.

Conclusion
Based on the findings of this study, it is evident that dairy farmers in the study area possess awareness regarding climate change and its impact on their farming operations.Consequently, there is a growing necessity to adapt to the effects of climate change and mitigate GHG emissions from dairy farms through the adoption of CSA practices.However, the level of adoption among dairy farmers was found to be limited, highlighting the need to address this deficiency appropriately.The adoption of CSA practices was influenced by factors such as land size, access to extension services and climate information, and income level.These factors should be taken into consideration when formulating strategies and policies for climate-smart dairy development.Moreover, special attention should be given to raising farmers' awareness about the specific benefits associated with each CSA practice.

Figure 1 .
Figure 1.Map of the study sites (prepared by the authors using ArcGIS 6.1).

Table 1 . Socio-demographic and farm characteristics.
Note: Numbers in parentheses are the standard error (SE) for the proportions.

Table 4 . Binary logistic regression model results with marginal effects.
Reference is no formal education.��� : Significant at the 1% level; �� : Significant at the 5% level; � : Significant at the 10% level. a