
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12825-3
10.1016/j.heliyon.2024.e36794
e36794
Research Article
“A quantitative analysis on the adoption of soil, water, and forest conservation technologies in the upper Gelana watershed, Northeast Ethiopian highlands”
Tesfahun Tsedey tset4884@gmail.com
ab⁎
Abegaz Assefa assefa.abegaz@gmail.com
a
Abate Esubalew esubalewabate@gmail.com
c
a Department of Geography & Environmental Studies, Addis Ababa University, P.O.Box 1176, Addis Ababa, Ethiopia
b Department of Urban Environmental Studies, Kotebe University of Education, P.O.Box 31248, Addis Ababa, Ethiopia
c Center for Rural Development, Addis Ababa University, P.O.Box 1176, Addis Ababa, Ethiopia
⁎ Corresponding author. Department of Geography & Environmental Studies, Addis Ababa University, P.O.Box 1176, Addis Ababa, Ethiopia. tset4884@gmail.com
23 8 2024
15 9 2024
23 8 2024
10 17 e367949 10 2023
21 8 2024
22 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Globally, the degradation of soil, water, and forests has had a significant impact on both livelihoods and the environment. This issue is particularly severe in developing countries, including Ethiopia. Despite extensive efforts to implement conservation measures for soil, water, and forests in the highlands of Ethiopia, there has been a lack of thorough evaluation and documentation regarding the adoption of these practices by rural households. It is crucial to have scientific and up-to-date information at various spatial scales in order to effectively monitor existing practices, scale up successful initiatives, and promote sustainable regional development. Therefore, this paper focuses on analyzing the adoption of soil, water, and forest conservation activities by households in the upper Gelana watershed, South Wollo zone, Amhara Regional State of Ethiopia. The field data collection for this study took place from January to March 2022, from 150 rural household heads. Data analysis was carried out using SPSS software version 23. Descriptive statistics, Pearson bivariate correlation, and multinomial logistic regression were used. The survey findings revealed that 69 % of the respondents had implemented various soil, water, and forest conservation measures at different stages. The Pearson correlation results indicated a positive relationship between the adoption of soil, water and forest conservation practices. The multinomial logistic regression analysis has revealed that age, gender, access to credit, and access to extension services, significantly influenced the households’ decision behaviour to adopt soil conservation practices. Age, access to extension service, and access to water resource were significant predictors of adoption of water conservation practices; whereas age, educational status, and access to extension service were significant predictors of adoption of forest conservation practices. This study underscores the significance of institutional factors in driving the adoption of technology in the research area. It further recommends policies that prioritize the dissemination of information on effective strategies, improvement of access to extension services, water resources, and credit facilities to promote sustainable watershed management. This study is exceptional in its innovative approach, which explores the convergence of these vital conservation domains within the distinct setting of the upper Gelana watershed. Studying the adoption of these technologies is crucial for informing policy-making and designing effective interventions that promote sustainable watershed practices. In this case, the Ministry of Agriculture, and development agents should scale up the adoption of these practices and take remedial actions for those not yet adopted.

Keywords

Agricultural resource degradation
Conservation
Pearson correlation
Multinomial logistic regression
Rural households' adoption behaviour
==== Body
pmc1 Introduction

The need for proper management of soil, water and forest resources is indicated as an important strategy in the four goals (Goals 2, 6, 12, and 15) of the 17 Sustainable Development Goals of the United Nations [1]. However, according to the Food and Agriculture Organization (FAO) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), approximately 33 % of the Earth's soils are currently degraded [2]. Furthermore, projections suggest that by 2050, this figure could rise to over 90 %, indicating a concerning trend towards widespread soil damage [2]. Specific to Ethiopia, it is estimated that 1.9 billion tons of soil be wiped out every year, while 410 million tons of silt are produced [3]. Therefore, it is essential to implement effective soil conservation strategies to address soil loss and degradation and mitigate the long-term effects on agricultural output [4,5]. This will help to achieve SDG-2.4, which aims at ensuring sustainable food production systems and implementing resilient agricultural practices, and that progressively improving land and soil quality [1].

Anticipated impacts of climate change, including changes in temperature, rainfall patterns, and distribution, are likely to affect Ethiopia and other regions of Africa. These changes are expected to have significant effects on the national economy, rural livelihoods, and rain-fed agricultural production [6,7]. To promote climate-resilient agriculture across diverse regions and localities, countries must both adapt to and mitigate the effects of climate change -aligned with SDG-6.5 that highlighted the need for implementation of integrated water resources management at all levels [1]. Various agricultural water resource management practices, such as water harvesting from rooftops, river diversion, and pond development, have been recommended and implemented to achieve this objective [[8], [9], [10], [11], [12]]. Similarly, forest degradation poses a significant threat to agriculture, especially in drought-prone regions of Ethiopia [13,14]. Forest degradation has various impacts, including accelerated soil erosion, depletion of surface and groundwater sources, and the loss of ecosystem services [[15], [16], [17], [18]]. Empirical evidences are available that demonstrates the positive outcomes of implementing forest conservation measures, such as tree planting, reforestation and agroforestry in reversing the trends of agricultural land degradation [[19], [20], [21], [22]]. Such practices are in line with SDGs-12.2 and 15.2, which stated the need for implementation of sustainable management of natural resources and all types of forests [1]. These measures have been shown to enhance ecosystem services and improve rural livelihoods [[19], [20], [21], [22]]. The practice of maintaining or enhancing the productive capacity of land, including soil, water, and vegetation, at the local level is commonly known as soil and water conservation [23,24]. Sustainable watershed management involves the sustainable management of a watershed system through the use of appropriate technological solutions. This approach aims to ensure the long-term sustainability of land, agriculture, and forestry, or a combination thereof, to conserve natural resources. It also emphasizes the importance of implementing adequate institutional and economic measures to support these conservation efforts [25]. These initiatives are designed to maintain or improve soil fertility while simultaneously preventing or minimizing soil erosion, compaction, and salinity. The suggestions provided align with the principles outlined in the 2030 Agenda for Sustainable Development, particularly within Sustainable Development Goal 12: Responsible Consumption and Production [26]. Furthermore, these initiatives also aim to effectively conserve or manage water resources. Sustainable watershed management, as a comprehensive approach, involves engaging multiple stakeholders and generating evidence through pilot activities to inform local and regional policies and strategies, which is aligned with SDG-1. b [1]. This integrated approach has been crucial in achieving positive outcomes [27,28]. The concept of interconnectedness and the methodological approach of adopting a holistic perspective to development are essential in achieving the UN Sustainable Development Goals. This is because these goals encompass both synergies and trade-offs, highlighting the intricate relationships between different aspects of sustainable development [[29], [30], [31]]. This aligns with the goals of enhancing food security and nutrition (SDG 2), mitigating greenhouse gas emissions (SDG 13), alleviating strain on water resources (SDG 14) and land resources (SDG 15), and fostering productivity and economic growth (SDG 8). However, the choice and implementation of these initiatives should be tailored to the unique local conditions.

Since the 1970s, Ethiopia has integrated soil and water conservation techniques into its watershed management programs [32,33]. Initially spearheaded by the government and bolstered by food-for-work initiatives until the late 1990s, the implementation primarily focused on engineering solutions aimed at reducing soil erosion. Assistance was provided by governmental and non-governmental organizations, along with the World Food Program (WFP). Despite the success of certain management practices, widespread adoption of these measures was not uniformly achieved [34,35].

Agriculture continues to be the primary economic sector in Ethiopia, especially in the South Wollo zone, despite its declining contribution to national revenue [6,34]. Factors such as reliance on rain-fed agricultural systems, traditional farming methods, limited adoption of agricultural technologies, and the impacts of climate change collectively contribute to the low production levels within the agricultural sector [[35], [36], [37]]. The Tehuledere area, located in Ethiopia's highlands, is facing significant challenges due to soil degradation, depletion of agricultural water resources, and forest degradation. These issues have resulted in a decline in crop production and livestock productivity in the region [38]. Given that subsistence farmers make up the majority of the population in the study area, it is essential to enhance and modernize the subsistence agricultural system. This upgrade is crucial for reducing poverty levels and improving the overall quality of life for the local population [38,39]. The limited adoption of soil, water, and forest conservation technologies could potentially contribute to the persistent low agricultural output levels [40,41]. Therefore, in this study, our null hypothesis is defined as follows: "Households' adoption practices of soil, water, and forest conservation technologies are not associated with households' demographic characteristics (age, gender, and educational status), institutional factors (access to extension services, and access to credit services), and access to water resources."

Several recent studies have investigated the factors that influence the adoption of soil, water, and forest conservation measures in Ethiopia [5,[41], [42], [43]]. The existing studies have primarily focused on a limited range of conservation measures and have not thoroughly examined the factors influencing the adoption of soil, water, and forest conservation strategies. Additionally, despite facing significant challenges such as prolonged drought and poverty, the study area lacks comprehensive research on the adoption of soil, water, and forest conservation practices. Therefore, one of the novelties of this study is that, it can solve the scarcity of empirical literature on the adoption of these practices. This study takes an innovative approach by exploring the convergence of these conservation domains in this specific setting. By focusing on the concurrent adoption of various conservation technologies, this study aims to provide a comprehensive understanding of the relationships and synergies that can arise among soil, water, and forest conservation practices. This perspective fills a research gap and offers valuable insights into the integrated stewardship of natural resources in this region. Furthermore, it can raise awareness among local land managers and development agents, as well as policy makers on the extent of adoption of diverse conservation practices. The result could also serve as a feedback for the long-term conservation works implemented in the watershed [44].

2 Research methods

2.1 Description of the study area

The Upper Gelana watershed is located in the Tehuledere district of Ethiopia's South Wollo zone, within the Amhara regional state. Geographically, it extends between latitudes 11°34′44″ and 11°45′4″ N and longitudes 39°34′11″ and 39°45′2″ E (Fig. 1). This region is situated approximately 491 km north of Addis Ababa and ranges in elevation from 1365 to 3328 m above mean sea level.Fig. 1 The study area: map of Ethiopia (A), Amhara (B), and the study area (upper Gelana - watershed) (C).

Fig. 1

The present-day topography has been shaped by tectonic and volcanic processes, characterized by Cenozoic volcanic rock formations and further molded by fluvial erosion. The area is characterized by rugged terrain and relatively low-lying plains, with slopes varying from gentle to very steep inclines. The drainage system of the Upper Gelana watershed flows into the Awash River. The study area is divided into three agro-climatic zones: hot (Kola), temperate (Woina-Dega), and cool (Dega) climatic zones [45]. With an average annual rainfall of 516.6 mm, the watershed exhibits a mono-modal distribution of precipitation [46]. Between June and September, rainfall accounts for approximately 59 % of the total. The research area has four main types of soil: Leptosols, Cambisols, Vertisols, and Regosols [44,45]. According to a report by the Amhara Design and Supervision Works Enterprise in 2011, as part of the East Amhara Development Corridor Integrated Land Use Planning Project, the Gelana watershed is one of the most densely populated areas in the district. In 2007, it had an estimated population of 64,965 residents, resulting in an impressive average population density of 260 people per square kilometre. In a specific sub-area known as Keble 08, the population stands at 4,152 individuals, consisting of 2,087 females and 2,065 males. Agriculture has traditionally been the primary economic driver and source of livelihood for the community [47]. In the region, subsistence-level mixed crop and cattle farming is practiced, with farmers cultivating a variety of crops to meet their daily needs. The main cereal crops grown in the area include sorghum, maize, barley, and wheat, which serve as reliable sources of sustenance. Additionally, farmers also cultivate fruits and vegetables, with popular crops such as oranges, bananas, papayas, mangoes, lemons, and avocados being regularly grown. However, in recent years, the landscape has undergone changes with the emergence of chat (Catha edulis) as a major cash crop and primary source of income in the study area. Chat is a significant export item in Ethiopia, generating substantial income and foreign currency for the country. Furthermore, its stimulating effects on health and psychology are increasingly recognized nationwide. Despite its importance, there is a lack of clear legal framework and policy governing the production, transportation, and consumption of chat in Ethiopia. This regulatory gap has presented challenges for farmers, traders, and consumers alike, as they operate in a legal grey area [48,49].

2.2 Data requirements, sources, and methods of collection

The data collection for this research was conducted in 2022 over a span of three months, from January to March. A thorough household-level survey was carried out using a well-designed questionnaire that collected various types of data, including demographic, socioeconomic, institutional, and biophysical information about households. The survey questionnaire was designed to gather both open-ended and closed-ended responses, allowing for a nuanced understanding of the characteristics and experiences of the households. Prior to the survey, the researcher established connections with Kebele administrators in the study area and scheduled meetings with sample respondents. During the pilot test, the researcher explained the objective of the study and obtained consent from the participants. The feedback received from administrators during this phase helped refine the household survey questionnaire, ensuring its effectiveness in gathering accurate and relevant data. On the survey day, enumerators received a concise orientation on eliciting accurate responses from household heads. They then administered the questionnaires, ensuring that each respondent fully understood the purpose and objectives of the study.

2.3 Survey population and sampling technique

The Upper Gelana watershed was selected as the focus of this study due to its critical importance as a region prone to drought and famine in north-eastern Ethiopia, as reported by the Agriculture and Rural Development Office of Tehuledere district in the South Wollo Zone, Amhara Regional State. This region is particularly noteworthy for its high proportion of individuals experiencing chronic food insecurity, making it an ideal location for investigating the factors contributing to this issue [50]. The Upper Gelana watershed was chosen as the study site due to its accessibility and its history of collaboration with government and non-governmental organizations in managing soil, water, and forest resources. This provided a comprehensive understanding of the ecological and social dynamics of the region. From this watershed, eight villages were selected as the focus of this study. The Upper Gelana watershed covers approximately 1,538 ha in total. The survey population consisted of households from these eight villages, with the heads of households chosen as the unit of analysis, and data collected from each household. After identifying the sample villages within the watershed, a sample size of 150 households was calculated to ensure a representative representation sample of the population. This sample size allowed for a detailed examination of the relationships between household characteristics and environmental practices [51]. Subsequently, established guidelines for determining an adequate sample size for multinomial logistic regression modelling were employed, ensuring that our study had sufficient statistical power to accurately estimate the relationships between household characteristics and environmental practices.n=N1+N(e2)

n=41521+4152(0.08×0.08)

the notation “n" represents the sample size, while “N" represents the population size, and “e" signifies the desired level of precision, which was set at 8 %. A random sampling approach was used to select a total of 150 household heads for a household survey.

2.4 Methods of data analysis

The collected data were analysed using the Statistical Package for the Social Sciences (SPSS) version 23. Descriptive and inferential statistical methods were employed, including descriptive statistics to examine the percentage distribution and central tendencies (means) of households' demographic and socioeconomic backgrounds. Inferential statistics, specifically bivariate correlation and a one-sample t-test were used to assess the relationships between the postulated explanatory variables influencing households' adoption of soil, water, and forest conservation practices. The bivariate correlation coefficient (r) was calculated to measure the statistical association between the variables, and the one-sample t-test was used to determine whether the correlation was statistically significant at p-values of 0.1, 0.05, and 0.01.

Multinomial logistic regression analysis was conducted to predict the adoption status of different soil, water, and forest conservation techniques while controlling for household characteristics as independent/explanatory variables. The ultimate goal of the study was to elucidate how these factors are interrelated with one another, including determining whether farmers who adopted a particular technique were also more likely to adopt other sustainable farming practices.

2.5 Description, measurement, and hypothesis of variables

2.5.1 Description of variables

This study examined a range of biophysical soil conservation techniques, including composting, green manure, soil bund, stone bund, and strip cropping, alongside traditional soil conservation methods. The study also considered technologies for water conservation, such as river diversion, roof rain harvesting, and pond development. Furthermore, the investigation encompassed techniques for forest conservation, including tree planting, agroforestry, and commercial forestry.

2.5.2 Soil conservation and management tools

Soil conservation is a holistic approach that acknowledges the soil as a dynamic ecosystem, where microorganisms play a crucial role in maintaining its fertility by decomposing organic matter, releasing essential nutrients, and facilitating air and water circulation. For this investigation, we examined five distinct soil conservation techniques (Table 1).Table 1 Soil conservation: description of used technologies/practices and unit of measurement.

Table 1Variables	Description and unit of measurement	
COMPOST	Adoption of compost 1 if adopted, otherwise 0	
GREENMAN	Adoption of green manure 1 if adopted, otherwise 0	
SOILB	Adoption of soil bund 1 if adopted, otherwise 0	
STONEB	Adoption of stone bund 1 if adopted, otherwise 0	
STRPCR	Adoption of strip cropping 1 if adopted, otherwise 0	

Composting is a sustainable practice that recycles organic waste and replenishes the soil with vital nutrients. By incorporating compost into the soil, the amount of organic matter increases, and essential nutrients such as nitrogen are supplemented. This natural process can significantly reduce the need for synthetic fertilizers [[52], [53], [54]]. Green manuring is a sustainable agricultural technique that involves planting specific crop species, such as clovers and field beans, to enhance the soil's quality and fertility [55]. This eco-friendly approach enables farmers to enhance their crops without depending on synthetic chemicals or fertilizers, making it a compelling alternative to conventional methods [56]. Soil bunds are an effective technique for mitigating surface runoff, decreasing sediment concentrations in runoff water, and preventing soil erosion while simultaneously improving soil moisture content [57]. Notably, soil bunds are the most widely utilized structural measures for soil and water conservation (SWC) across cultivated lands with high rainfall, as they can enhance both groundwater recharge and soil water retention simultaneously. Stone bunds, which are constructed by placing rocks in a line along the contour of the land, are a type of structural measure. Their widespread adoption in agriculture is attributed to their capacity to retain moisture within the soil, enabling crops to flourish even during periods of drought [58]. Strip cropping, a farming technique that involves cultivating crops in alternating strips across the land, creates a buffer zone that prevents soil erosion and sedimentation during heavy rainfall or strong winds. By planting different crop types in adjacent strips, farmers can retain soil moisture, reduce runoff, and promote soil health [59].

2.5.3 Agricultural water conservation

Agricultural water conservation involves a variety of approaches, including water harvesting, which is crucial for collecting and managing surface runoff to provide a reliable water source for agriculture and other purposes. In Ethiopia, the government has initiated a soil and water conservation program to promote rainwater harvesting systems as an alternative solution to address water scarcity. This initiative was prompted by severe droughts in the Tigray, Wollo, and Hararge regions from 1971 to 1974, which led to the establishment of the Food-for-Work (FFW) program, providing employment opportunities for individuals affected by drought. Water harvesting is an essential method for improving crop yields in areas with insufficient rainfall for agriculture. Water harvesting farm ponds play a critical role in increasing crop productivity and farm income in rain-fed regions, particularly in the face of climate change. In addition, roof rainwater harvesting is a simple yet effective technique for collecting and storing rainwater that falls on rooftops, utilizing the roof as a catchment area. The collected water is then transferred to storage tanks or reservoirs for future use. Another technique, floodwater diversion, is used to redirect the flow of rivers or streams toward nearby fields for irrigation. This technique involves constructing structures such as dams or canals to divert water from its natural course and channel it to crops that require watering. By doing so, farmers can supplement their crops with additional water, reducing their reliance solely on rainwater [[8], [9], [10],60,61] (Table 2).Table 2 Agricultural water conservation: description of used technologies/practices and unit measurement.

Table 2Variables	Description and unit of measurements	
River diversion	Adoption of river diversion 1 if adopted, otherwise 0	
Roof rain	Adoption of roof rain harvesting 1 if adopted, otherwise 0	
Pond development	Adoption of pond development 1 if adopted, otherwise 0	

2.5.4 Forest conservation and management tools

Forest conservation and management strategies are crucial for the sustainability of the environment and economy, as forests are one of the most important natural resources on the planet, providing numerous societal benefits. In Ethiopia, forests are essential for meeting the social, economic, cultural, and spiritual aspirations of both current and future generations. To assess the state of forests in the research area, three conservation strategies were examined: agroforestry, tree planting, and commercial tree planting (Table 3). Agroforestry is a sustainable approach that serves both protective and productive forest functions, valued by societies [62]. It offers numerous benefits, including improved food security, income generation, climate change mitigation and adaptation, and biodiversity conservation [63]. Tree planting is an effective strategy for repairing degraded areas, enhancing carbon sequestration, producing timber and non-timber forest products, and establishing natural corridors. Additionally, the planting of trees can improve soil fertility, water quality, and microclimates. Farmers who engage in commercial tree planting can receive financial incentives to invest in tree development and care, diversifying their agricultural revenue streams and creating job opportunities [64]. Commercial tree planting also helps alleviate pressure on natural forests by providing an alternative source of timber and non-timber products. This approach can open up new revenue streams and job opportunities, making it an appealing option for farmers [65].Table 3 Forest conservation: description of used technologies/practices and unit of measurement.

Table 3Variables	Description and unit of measurement	
TREE	Adoption of tree planting 1 if adopted, otherwise 0	
COMMTREE	Adoption of commercial tree planting 1 if adopted, otherwise 0	
AGROFORESTRY	Adoption of agroforestry 1 if adopted, otherwise 0	

2.5.5 Independent variables

To estimate the model parameters, a set of explanatory variables was selected based on previous empirical studies. These variables were chosen because they are expected to influence farm household decisions regarding soil, water, and forest management techniques (Table 4). These factors can have a significant or insignificant positive or negative impact on the adoption of soil, water, and forest conservation practices by farmers.Table 4 Explanatory variables and their unit measurements.

Table 4Factors	Description and unit of measurements	
AGE	Age of the household head Continuous variable measured in years	
SEX	Gender of the household head Dummy variable: 0 - female, 1 - male	
EDUC	Educational level of the household head Interval variable: 0 - illiterate, 1 - able to read and write, 2 - primary (grade 1–8), 3 - secondary (grade 9–12), 4 - diploma/degree	
EXTEN	Access to extension services Dummy variable: 0 - no, 1 - yes	
ACCWATER	Access to water is a Dummy variable: 0-no, 1- Yes	
CREDIT	Access to credit is Dummy variable. 0- no, 1- Yes	
Note: EDUC = educational level; EXTEN = extension; ACCWATER=Access to water.

3 Results

3.1 Household characteristics

The survey results indicated that, out of the 150 sampled household heads, 66 % were male and 34 % were female. It is worth noting that female-headed farmers’ encountered challenges related to family labour, as they were responsible for both farming and household duties. In rural Ethiopia, men generally have better access to resources, information, and socioeconomic opportunities, and are expected to have fewer household responsibilities compared to women [34,57,66]. The survey results also showed that 41.3 % of household heads had no formal education, indicating a significant literacy gap within the sample population. Furthermore, 40.7 % of households were able to read and write, while 12.7 % had completed primary education, 4.7 % had attended high school, and only 0.7 % held a BA/BSC degree (Table 5). Notably, more than half of the sampled respondents were lacking formal education, which may have limited their access to information on newly introduced soil, water, and forest management techniques. In terms of demographics, the average age of the sampled household heads was 51.9 years, ranging from 18 to 82 years. The household size varied significantly, with a minimum of 1 and a maximum of 9 family members, resulting in an average household size of 4.7 members. In relation to resource access, 71.3 % of the sampled households had access to agricultural credit, but none had access to credit for soil, water, or forest conservation practices over the past five to ten years.Table 5 Descriptive data sampled households.

Table 5Variable name	Description	N	Marginal percentage	
Gender	Female	51	34.0 %	
Male	99	66.0 %	
Educational status	Illiterate	62	41.3 %	
Read and write only	61	40.7 %	
Elementary (1–8 grade)	19	12.7 %	
High school (9–12) grade	7	4.7 %	
BA/BSc.	1	0.7 %	
Access to extension service	No	22	14.7 %	
Yes	128	85.3 %	
Access to credit	No	43	28.7 %	
Yes	107	71.3 %	
Total	150		

The study findings indicated an average livestock holding of 3.1 animals per household (Table 6). The range of livestock sizes varied considerably, ranging from a minimum of 1 to a maximum of 6 animals per household. Land availability posed a challenge in the study area due to population growth, resulting in varying land sizes per household ranging from 0.1 ha to 0.5 ha, with an average land size of 0.3 ha. Access to basic necessities was also limited in the study area. While 90.7 % of the sampled participants had access to water, the remaining 6.7 % did not have sufficient water for their daily activities. In terms of economic indicators, the average annual income of the sampled households ranged from 3000 to 50,000 Ethiopian Birr (ETB), with an average income of 10,672.58 ETB. The role of strong grassroots institutions is crucial for successful watershed management [67]. In the context of watershed management, two types of institutions need to regularly connect and collaborate: one involving internal stakeholders and the other involving external stakeholders [67]. The first type exists at the community level in the form of self-help groups, user groups, or the watershed community. These groups need to be empowered and united at the watershed level to establish a platform for collective action. The second set of institutions includes external agencies such as the government, non-governmental organizations, local administration, and researchers. These institutions need to collaborate to achieve synergy and prioritize the capacity building and financial sustainability of grassroots-level institutions. According to this study, almost 85.3 % of households accessed the extension service for guidance on soil, water, and forest conservation practices.Table 6 Descriptive result for continuous variables.

Table 6Household characteristics	Min	Max	Mean	
Age of the household-head (yrs.)	18	82	51.29	
Land size (ha.)	0.1 Ha.	0.5 Ha.	0.3 Ha.	
Annual income (ETB)	3000	50000	10678.52	

3.2 The association between soil, water, and forest conservation

Table 7 reveals a positive correlation between the adoption of soil, water, and forest conservation practices. Farmers who employed composting were more likely to adopt other innovative agricultural techniques, such as green manure, strip cropping, and agroforestry. Moreover, there was a favourable association between soil conservation methods like soil bunds, stone bunds, strip farming, ponds, and commercial tree planting (Table 7). Notably, households that practiced tree planting were most likely to adopt agroforestry and commercial tree planting. The analysis also shows that households that utilized one method of soil conservation were often found to be using another. This pattern is particularly pronounced in households that had already adopted or embraced one method of soil conservation and were more likely to apply another.Table 7 Correlation matrices of dependent variables for Upper Gelana watershed.

Table 7VARIABLES	COMP	GREEENM	SOILB	STONEB	STRCRP	POND	ROOFRAIN	RIVERDIR	TRP	AGROF	COMMT	
COMP	1											
GREENM.	0.31**	1										
SOILB.	−0.167*	−0.14	1									
STONEB.	−0.01	−0.09	0.35**	1								
STRCRP.	0.211*	−0.067	0.23*	0.27**	1							
POND.	−0.132	0.165	0.24**	0.104	−0.008	1						
ROOFRA.	−0.029	−0.022	0.48**	0.20*	0.183*	0.444**	1					
RIVERDI	0.153	0.071	−0.011	0.057	0.404**	0.104	0.231**	1				
TRP	0.035	0.075	−0.06	0.098	0.399**	0.02	0.14	0.299**	1			
AGROFO	0.201*	0.360**	−0.17*	0.001	−0.026	−0.268**	−0.131	0.081	0.18*	1		
COMMT	0.069	−0.14	0.40**	0.176*	0.264**	0.033	0.483**	0.226**	0.20*	−0.075	1	
Note; COMP = compost, GREENMAN = green manure, SOILB = soil bund, STONEB = stone bund, STRCRP = strip cropping, POND= Pond, ROOFRAI= Roof rain harvesting, RIVERDI= River diversion, TRP = Tree planting, AGROF= Agroforestry, COMMT = commercial tree planting.

N = 150; and ***, **, and * statistically significant at 1 %, 5 %, and 10 % probability levels, respectively.

3.3 Factors affecting households’ adoption behaviour of soil conservation techniques

The study employed multinomial logistic regression to predict the adoption status of various soil conservation techniques, including compost, green manure, soil bunds, stone bunds, and strip cropping. The analysis controlled for five household characteristics: age, gender, educational status, access to extension services, and access to credit. As previously stated in the introduction, our null hypothesis posited that these household characteristics have no association with the adoption of these soil conservation technologies. Our analysis was based on a dataset collected from 150 household respondents, which was entered into SPSS software for analysis. We conducted tests to evaluate the model's fitting and goodness of fit (Table 8). The model fitting test revealed that the model significantly improved upon the null model (χ2 = 82.780, df = 20, P < 0.001), indicating a strong fit.Table 8 The “Model fitting” and “goodness of fit” tests.

Table 8Model	Model Fitting Criteria	Likelihood Ratio Tests	
−2 Log Likelihood	Chi-Square	df	Sig.	
Intercept Only	409.983				
Final	327.203	82.780	20	0.000	
Goodness-of-Fit	
	Chi-Square	df	Sig.		
Pearson	422.469	440	0.718		
Deviance	302.825	440	1.000		

Moreover, the Deviance and Pearson chi-square tests confirmed the goodness of fit of the model for the data analysis (χ2 (422.5), df = 440; p = 0.718) and (χ2 (302), df = 440, p = 1.00), respectively (Table 8). Non-significant test results suggest that the model accurately fits the data [68]. These findings gave us confidence in using the model for our analysis.

Table 9, presents the summary model results of the multinomial logistic regression. The results show the likelihood ratio tests of the overall contribution of each independent variable (household's age, gender, educational status, access to extension services, and access to credit) to the model. Except for educational status, the other four variables were found to be significant predictors of the households' behaviour in adopting soil conservation practices (p < 0.05) in the study area. The intercept is also significant at p < 0.001 level.Table 9 Summary model results of factors affecting rural households' adoption behavior of soil conservation techniques.

Table 9Effect	Model Fitting Criteria	Likelihood Ratio Tests	
−2 Log Likelihood of Reduced Model	Chi-Square	df	Sig.	
Intercept	348.003	20.800	4	0.000	
Age	341.015	13.812	4	0.008	
Gender	336.761	9.558	4	0.049	
Educational status	332.220	5.017	4	0.286	
Access to extension service	341.767	14.564	4	0.006	
Access to credit	349.833	22.630	4	0.000	
Note: The chi-square statistic is the difference in −2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

In multinomial logistic regression, households' adoption of a given soil conservation technology is considered as the reference (or baseline category) against which all other adopted technologies being compared. Accordingly, in Table 10, the data sets indicate that the reference/baseline technology is compost. The ‘B' columns present regression coefficients expressed in the metric of log-odds for each predictor/independent variable, and the Exp(B)- columns present odds ratios, i.e., the exponential regression coefficient of the predictor. Age (B = 0.060, and Exp(B) = 1.062, p < 0.01), access to extension service (B = 1.589, and Exp(B) = 4.900, p < 0.1), and access to credit (B = 0.998, and Exp(B) = 2.714, p < 0.05) were positively significant predictors in discriminating households' adoption between compost and green manure. These results imply that as the household heads are getting older in age, and as they get access to extension, and credit services, they are more likely to adopt green manure rather than compost; because ‘B' coefficients are positive and the odds ratios Exp (Bs) are greater than 1. The odds ratios of 1.062, 4.90, and 2.714, respectively, for age, access to extension, and credit services indicate that for every unit increase in age, access to extension, and credit services, the odds of a household's adoption of green manure changes positively by factors of 1.062, 4.90, and 2.714, respectively. Age (B = 0.073, and Exp(B) = 1.076, p < 0.05); and access to credit (B = 3.186, Exp(B) = 24.197, p < 0.01) are positive significant predictors in discriminating households' adoption between compost and soil bund. As the household heads are getting older in age, and as they get access to credit services, they are more likely to adopt soil bund rather than compost; because ‘B' coefficients are positive and the odds ratios Exp (Bs) are greater than 1. Coefficients of gender (B = 1.472, and Exp(B) = 4.359) indicates male headed households are more likely to adopt soil bund than compost compared to female headed households. Age (B = 0.064, and Exp(B) = 1.066, p < 0.05); and access to credit (B = 2.337, and Exp(B) = 10.35, p < 0.05) are positive significant predictors in discriminating households' adoption between compost and stone bund.Table 10 Multinomial logistics regression predictors of rural households’ soil conservation adoption behaviour in the upper Gelana watershed, with adoption reference practice of compost.

Table 10Soil conservation practice	Green manure	Soil bund	Stone bund	Strip cropping	
B Exp(B)	B Exp(B)	B Exp(B)	B Exp(B)	
Intercept	−3.035**		−5.998***		−3.292**		1.071		
Age	0.060***	1.062	0.073***	1.076	0.064**	1.066	−0.035	0.965	
Gender	−0.050	0.951	1.472**	4.359	−0.319	0.727	−0.606	0.545	
Educational status	−0.306	0.736	−0.452	0.636	−0.798*	0.450	0.238	1.269	
Access to extension service	1.589*	4.900	0.589	1.803	−0.031	0.970	−1.889**	0.151	
Access to credit	0.998**	2.714	3.186***	24.197	2.337**	10.350	−0.777	0.460	
Reference practice	Compost	

On the other hand, educational status (B = −0.798, and Exp(B) = 0.450, p < 0.1); and access to extension services (B = −1.889, and Exp(B) = 0.151, p < 0.05) are negative significant predictors in discriminating households' adoption between compost and stone bund; and between compost and strip cropping, respectively (Table 10). The odds ratios of 0.45 and 0.151 for stone bund and strip cropping, respectively, imply that with literate educational status, and access to extension services, the probabilities of a household head's adoption of stone bund and strip cropping change by a factor of 0.45 and 0.151, respectively, because the odds of adoption probability are decreasing. Educational status (B = 0.798, and Exp(B) = 2.220, P < 0.010) was a significant predictor in discriminating households' adoption between compost and stone bund. This implies that educated households are more likely to adopt compost than stone bund. Age (B = −0.064, and Exp (B) = 0.938, P < 0.5) and access to credit (B = −2.337 and Exp(B) = 0.097, p < 0.5) are also negative significant predictors in discriminating households' adoption between compost and stone bund. The odds ratios of 0.938 and 0.097 imply that the adoption probability is decreasing when their age is increasing and when they have access to credit.

Table 11 presents the multinomial regression coefficients for the adoption of green manure as the reference/baseline technology compared to other technologies. The results show that age (B = −0.060, Exp(B) = 0.942, p < 0.05), access to extension services (B = −1.589, Exp(B) = 0.204, p < 0.1), and access to credit (B = −0.998, Exp(B) = 0.368, p < 0.05) are negative significant predictors of households' adoption of green manure compared to compost and strip cropping. These variables indicate that as age increases, access to extension services decreases, and access to credit decreases, the probability of adopting green manure decreases compared to compost and strip cropping Moreover, the odds ratios suggest that the probability of adopting green manure is decreasing compared to compost (odds ratio: 0.942) and strip cropping (odds ratio: 0.909). On the other hand, access to credit (B = 2.188, Exp(B) = 8.916, p < 0.05) is a positive significant predictor of households' adoption of soil bund compared to green manure. The odds ratio of 8.916 indicates that as households gain access to credit services, they are more likely to adopt soil bund rather than green manure. Furthermore, the coefficient for gender (B = 1.522, Exp(B) = 4.581) suggests that male-headed households are more likely to adopt soil bund than green manure compared to female-headed households.Table 11 Multinomial logistics regression predictors of rural households’ soil conservation adoption behaviour in the upper Gelana watershed, with adoption reference practice of green manure.

Table 11Soil conservation practice	Compost	Soil bund	Stone bund	Strip cropping	
B	EXP(B)	B	EXP(B)	B	EXP(B)	B	EXP(B)	
Intercept	3.035**		−2.963*		−0.257		4.106**		
Age	−0.060***	0.942	0.013	1.013	0.004	1.004	−0.095*	0.909	
Gender	0.050	1.051	1.522**	4.581	−0.269	0.764	−0.557	0.573	
Educational status	0.306	1.358	−0.146	0.864	−0.492	0.612	0.544	1.723	
Access to extension service	−1.589*	0.204	−1.000	0.368	−1.620	0.198	−3.478***	0.031	
Access to credit	−0.998**	0.368	2.188**	8.916	1.339	3.814	−1.776**	0.169	
Reference practice	Green manure	

The results of the multinomial logistic regression analysis indicate that age (B = 0.073, Exp(B) = 1.076, P < 0.01), gender (B = 1.472, Exp(B) = 4.359, P < 0.05), and access to credit (B = 3.186, Exp(B) = 24.197, P < 0.01) (Table 12) are significant predictors in distinguishing households' adoption of soil bund from compost. These findings suggest that as household heads age, male-headed households with access to credit services are more likely to adopt soil bund than compost. Furthermore, gender (B = 1.522, Exp(B) = 4.581, P < 0.05) and access to credit (B = 2.188, Exp(B) = 8.916, P < 0.05) were significant predictors in distinguishing households' adoption of soil bund from green manure. These results imply that male-headed households with access to credit services are more likely to adopt soil bund than green manure. In contrast, gender (B = −1.522, Exp(B) = 0.218, P < 0.1) and access to credit (B = −2.188, Exp(B) = 0.112, P < 0.5) were significant predictors in distinguishing households' adoption of green manure from soil bund, suggesting that female-headed households are more likely to adopt green manure than soil bund. Additionally, gender (B = 1.791, Exp(B) = 1.413, P < 0.05) and access to extension service (B = 1.791, Exp(B) = 1.413, P < 0.05) were significant predictors in distinguishing households' adoption of soil bund from stone bund, indicating that male-headed households with access to extension services are more likely to adopt soil bund than stone bund in the study area. The results also show that age (B = −0.095, Exp(B) = 0.90, P < 0.1), access to extension (B = −3.478, Exp(B) = 0.031, P < 0.01), and access to credit (B = −1.776, Exp(B) = 0.019, p < 0.01) were significant predictors in distinguishing households' adoption of strip cropping from green manure, implying that as household heads age and have access to credit services, they are more likely to adopt strip cropping than green manure. Finally, age (B = −0.100, Exp(B) = 0.905, P < 0.1), educational status (B = −1.858, Exp(B) = 2.817, P < 0.1), access to extension (B = −1.858, Exp(B) = 0.156, P < 0.1), and access to credit (B = −3.114, Exp(B) = 0.044, p < 0.05) were significant predictors in distinguishing households' adoption of strip cropping from stone bund, suggesting that with increasing household heads’ age and educational status, and have access to credit services and extension services, they are more likely to adopt strip cropping than stone bund.Table 12 Multinomial logistics regression predictors of rural households’ soil conservation adoption behaviour in the upper Gelana watershed: with adoption reference practice of soil bund.

Table 12Soil conservation practice	Compost	Green manure	Stone bund	Strip cropping	
B	EXP(B)	B	EXP(B)	B	EXP(B)	B	EXP(B)	
Intercept	5.998***		2.963*		2.706		7.070***		
Age	−0.073***	0.929	−0.013	0.987	−0.009	0.991	−0.109**	0.897	
Gender	−1.472**	0.229	−1.522*	0.218	−1.791**	0.167	−2.079**	0.125	
Educational status	0.452	1.571	0.146	1.157	−0.346	0.708	0.690	1.993	
Access to extension service	−0.589	0.555	1.000	2.718	−0.620	0.538	−2.478**	0.084	
Access to credit	−3.186***	0.041	−2.188**	0.112	−0.849	0.428	−3.964***	0.019	
Reference practice	Soil bund	

Age (B = −0.064, Exp(B) = 0.93, P < 0.05), educational status (B = 0.798, Exp(B) = 2.220, P < 0.01), access to credit (B = −2.337, Exp(B) = 0.097, P < 0.05) (Table 13) were significant predictors in discriminating households' adoption between compost and stone bund. On the other hand, gender (B = 1.791, Exp(B) = 1.413, P < 0.05) is significant predictors in discriminating households' adoption between soil bund and stone bund. Age (B = −0.100, Exp(B) = 0.905, P < 0.01), educational status (B = 1.036, Exp(B) = 2.917, P < 0.01), access to extension (B = −1.858, Exp(B) = 0.156, P < 0.01), access to credit (B = −3.114), Exp(B) = 0.044, P < 0.05) were significant predictors in discriminating households' adoption between strip cropping and stone bund.Table 13 Multinomial logistics regression predictors of rural households’ soil conservation adoption behaviour in the upper Gelana watershed, with adoption reference practice of stone bund.

Table 13Soil conservation practice	Compost	Green manure	Soil bund	Strip cropping	
B	EXP(B)	B	EXP(B)	B	EXP(B)	B	EXP(B)	
Intercept	3.292*		0.257		−2.706	1.009	4.363*		
Age	−0.064**	0.938	−0.004	0.996	0.009	5.998	−0.100*	0.905	
Gender	0.319	1.376	0.269	1.309	1.791**	1.413	−0.287	0.750	
Educational status	0.798*	2.220	0.492	1.635	0.346	1.859	1.036*	2.817	
Access to extension service	0.031	1.031	1.620	5.053	0.620	2.338	−1.858*	0.156	
Access to credit	−2.337**	0.097	−1.339	0.262	0.849	2.337	−3.114**	0.044	
Reference practice	Stone bund	

Access to extension (B = 1.889, Exp(B) = 6.611, P < 0.05) is significant predictors in discriminating households' adoption between compost and strip cropping. Age (B = 0.095, Exp(B) = 1.115, P < 0.05), access to extension (B = 3.478, Exp(B) = 32.389, P < 0.001) and access to credit (B = 1.776, Exp(B) = 5.905, P < 0.05) (Table 14) were significant predictors in discriminating households' adoption between green manure and strip cropping. Age (B = 0.109, Exp(B) = 1.115, P < 0.05), gender (B = 2.079, Exp(B) = 7.993, P < 0.05), access to extension (B = 2.478, Exp(B) = 11.916, P < 0.05), access to credit (B = 3.964, Exp(B) = 52.644, p < 0.001) were significant predictors in discriminating households' adoption between soil bund and strip cropping. Age (B = 0.100, Exp(B) = 1.105, P < 0.01), educational status (B = −1.036, Exp(B) = 0.355, p < 0.01), access to extension (B = 1.858, Exp(B) = 6.410, P < 0.01), access to credit (B = 3.114, Exp(B) = 22.518, P < 0.05) were significant predictors in discriminating households' adoption between stone bund and strip cropping. The odds ratios of 1.105, 0.355, 6.410 and 22.518, respectively, for age, educational status, access to extension, and credit services indicate that for every unit increase in age, educational status, access to extension, and credit services, the odds of a household's adoption of green manure changes positively by factors of 1.105, 0.355, 6.410 and 22.518, respectively. The result indicates that elder households and lower educated but having high access to extension service and credit have better probability of adoption of stone bund than strip cropping.Table 14 Multinomial logistics regression predictors of rural households’ soil conservation adoption behaviour in the upper Gelana watershed, with adoption reference practice of strip cropping.

Table 14Soil conservation practice	Compost	Green manure	Soil bund	Stone bund	
B	EXP(B)	B	EXP(B)	B	EXP(B)	B	EXP(B)	
Intercept	−1.071		−4.106**		−7.070**		−4.363*		
Age	0.035	1.036	0.095*	1.100	0.109**	1.115	0.100*	1.105	
Gender	0.606	1.834	0.557	1.745	2.079**	7.993	0.287	1.333	
Educational status	−0.238	0.788	−0.544	0.580	−0.690	0.502	−1.036*	0.355	
Access to extension service	1.889**	6.611	3.478***	32.389	2.478**	11.916	1.858*	6.410	
Access to credit	0.777	2.176	1.776**	5.905	3.964***	52.644	3.114**	22.518	
Reference practice	Strip cropping	

3.4 Factors affecting households’ adoption behaviour of agricultural water resource conservation techniques

The “model fitting” and “goodness of fit” were tested and presented in Table 15. The model fitting test showed that the model significantly fits to analyse the data over the null model (χ2 = 46.731, df = 12, P < 0.001). Moreover, the Deviance and Pearson chi-square tests showed the “Goodness of Fit” of the model for the data analysis (χ2 (208.6), df = 214; p = 0.591) and (χ2 (200), df = 214, p = 0.739), respectively (Table 15). Non-significant test results are indicators that the model fits the data well. These results gave us the confidence to use the model for our data analysis.Table 15 Model fitting information and goodness-of-fit test.

Table 15Model Fitting Information	
Model	Model Fitting Criteria	Likelihood Ratio Tests	
−2 Log Likelihood	Chi-Square	df	Sig.	
Intercept Only	284.754				
Final	238.023	46.731	12	0.000	
Goodness-of-Fit	
	Chi-Square	df	Sig.	
Pearson	200.365	214	0.739	
Deviance	208.616	214	0.591	

Table 16 presents the multinomial logistic regression summary model results. The results showed the likelihood ratio tests of the overall contribution of each independent variable (household's age, gender, educational status, access to extension services, access to credit, and access to water) to the model. Age, access to extension, and access to water were found to be significant predictors of the households' behaviour to adopt water conservation practices (p < 0.05) in the study area.Table 16 Summary model results of factors affecting rural households' adoption behaviour of agricultural water resource conservation techniques.

Table 16Effect	Model Fitting Criteria	Likelihood Ratio Tests	
−2 Log Likelihood of Reduced Model	Chi-Square	df	Sig.	
Intercept	239.414	1.391	2	0.499	
Age	245.954	7.931	2	0.019	
Gender	241.204	3.181	2	0.204	
Educational status	238.123	0.101	2	0.951	
Access to extension service	258.016	19.993	2	0.000	
Access to water	245.376	7.353	2	0.025	
Access to credit	241.140	3.117	2	0.210	
Note: The chi-square statistic is the difference in −2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

The results of Table 17 indicate that age (B = 0.055, Exp(B) = 1.057, p < 0.05), access to extension service (B = 3.660, Exp(B) = 38.867, p < 0.05), and access to water (B = −2.552, Exp(B) = 0.078, p < 0.05) are significant predictors in distinguishing households' adoption between roof rain harvesting and river diversion. These findings suggest that as household heads increase in age, gain access to extension services and water, they are more likely to adopt roof rain harvesting over river diversion. The odds ratios of 1.057, 38.867, and 0.078 imply that a one-unit increase in age, access to extension services, and water is associated with a corresponding increase in the probability of adopting roof rain harvesting by 1.057, 38.867, and 0.078 times, respectively. Similarly, age (B = 0.059, Exp(B) = 1.061, p < 0.05), access to extension (B = 1.786, Exp(B) = 5.98, p < 0.05), and access to water (B = −2.331, Exp(B) = 0.781, p < 0.05) were significant predictors in distinguishing households' adoption between pond development and river diversion. These results indicate that as household heads increase in age, are educated, and have access to extension services and water, they are more likely to adopt pond development over river diversion. Furthermore, gender (B = 0.745, Exp(B) = 2.106, p < 0.1) and access to extension (B = −1.874, Exp(B) = 0.154, p < 0.1) were significant predictors in distinguishing households' adoption between pond development and roof rain harvesting. In addition, age (B = −0.059, Exp(B) = 0.943, p < 0.05), access to extension (B = −1.786, Exp(B) = 0.168, p < 0.05), and access to water (B = 2.33, Exp(B) = 10.283, p < 0.5) were significant predictors in distinguishing households' adoption between pond development and river diversion to pond. Finally, gender (B = 0.745, Exp(B) = 6.512, p < 0.1) and access to extension (B = −1.874, Exp(B) = 6.512, p < 0.1) were significant predictors in distinguishing households' adoption between roof rain harvesting and pond development.Table 17 Multinomial logistic regression predictors of rural households’ agricultural water resource conservation adoption behaviour in the upper Gelana watershed.

Table 17Water conservation practices	Roof rain harvesting	River diversion	River diversion	
B	EXP(B)	B	EXP(B)	B	EXP(B)	
Intercept	−1.639		1.639		1.362		
Age	0.055**	1.057	−0.055**	0.946	−0.059**	0.943	
Gender	−0.495	0.610	0.495	1.640	−0.250	0.779	
Educational status	−0.046	0.955	0.046	1.047	−0.034	0.967	
Access to extension service	3.660***	38.67	−3.660***	0.026	−1.786**	0.168	
Access to water	0.552**	0.078	2.552**	12.834	2.331**	10.289	
Access to credit	−0.016	0.984	0.016	1.016	−0.854	0.426	
Water conservation practices	Pond development	Pond development	Roof rain harvesting	
Intercept	−1.362		0.277		−0.277		
Age	0.059**	1.061	0.004	1.004	−0.004	0.996	
Gender	0.250	1.284	0.745*	2.106	−0.745*	0.475	
Educational status	0.034	1.034	0.080	1.083	−0.080	0.923	
Access to extension service	1.786**	5.968	−1.874*	0.154	1.874*	6.512	
Access to water	−2.331**	0.097	0.221	1.247	−0.221	0.802	
Access to credit	0.854	2.348	0.870	2.387	−0.870	0.419	
Reference practice	River diversion	Roof rain harvesting	Pond development	

3.5 Factors affecting rural households' adoption behaviour of forest conservation techniques

The “model fitting” and “goodness of fit” were tested and presented in Table 18. The model fitting test showed that the model significantly fits to analyse the data over the null model (χ2 = 127.945, df = 92, P < 0.5). Moreover, the Deviance and Pearson chi-square tests showed the “Goodness of Fit” of the model for the data analysis (χ2 (129.97), df = 160; p = 0.961), and (χ2 (159), df = 160, p = 0.503), respectively (Table 19). Non-significant test results are indicators that the model fits the data well [69]. These results gave us confidence to use the model for our data analysis.Table 18 The “Model fitting” and “goodness of fit” tests.

Table 18Model Fitting Information	
Model	Model Fitting Criteria	Likelihood Ratio Tests	
−2 Log Likelihood	Chi-Square	df	Sig.	
Intercept Only	271.341				
Final	143.395	127.945	92	0.008	
Goodness-of-Fit	
	Chi-Square	df	Sig.	
Pearson	159.182	160	0.503	
Deviance	129.977	160	0.961	

Table 19 Summary model results of factors affecting rural households' adoption behaviour of forest conservation techniques.

Table 19Likelihood Ratio Tests	
Effect	Model Fitting Criteria	Likelihood Ratio Tests	
−2 Log Likelihood of Reduced Model	Chi-Square	df	Sig.	
Intercept	135.517a	1.391	2	0.491	
Age	238.719	103.201	2	0.021	
Gender	136.131b	0.613	2	0.736	
Education	155.540b	20.023	8	0.010	
Access Extension	146.319b	10.802	2	0.005	
Access Credit	136.746b	1.229	2	0.541	
The chi-square statistic is the difference in −2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

Table 19 shows the results of likelihood ratio tests of the overall contribution of each independent variable (household's age, gender, educational status, access to extension services, access to credit, and access to water) to the model. Access to extension was found to be a significant predictor of the households' behaviour to adopt forest conservation practices (p < 0.005) in the study area.

Table 20 presents the summary model results of the multinomial logistic regression analysis. The likelihood ratio tests evaluated the overall contribution of each independent variable (household age, gender, and educational status, access to extension services, access to credit, and access to water) to the model. The results showed that access to extension services was a significant predictor of households' behaviour in adopting forest conservation practices in the study area (p < 0.05). Furthermore, extension services were found to be significant predictors in discriminating households' adoption between Agroforestry and commercial planting (B = −2.91, Exp(B) = 0.54, p < 0.01). Additionally, extension services were significant predictors in discriminating households' adoption between agroforestry and tree planting (B = 1.514, Exp(B) = 4.545, p < 0.1). The results also showed that extension services were significant predictors in discriminating households' adoption between commercial forestry and tree planting (B = 2.913, Exp(B) = 18.408, p < 0.001). Finally, extension services are significant predictors in discriminating households' adoption between tree planting and agroforestry (B = −1.514, Exp(B) = 0.220, P < 0.1).Table 20 Multi-nominal logistic regression predictors of rural households’ agricultural forest resource conservation adoption behaviour.

Table 20Forest Resource Conservation	Agroforestry	Tree planting	Agroforestry	Commercial forestry	Tree Planting	Commercial forestry		
B	Exp(B)	B	Exp(B)	B	Exp(B)	B	Exp(B)	B	Exp(B)	B	Exp(B)	
Intercept	6.740		4.734		−2.006		−6.740		2.006		−4.73			
Age	26.044	210.6	0.298	1.347	−25.74	6.581	−26.044	4.0–12	27.746	1121	−0.298	0.742		
Gender	0.316	1.371	−0.142	0.867					0.458	1.581	0.142	1.153		
Education	−18.603	8.335	−16.43	7.27	2.167	8.731	18.603	7780	−15.06	2.882	5.578	264.4		
Access to Extension	−2.913***	0.054	−1.399	0.247	1.514*	4.545	2.913***	18.40	−1.514*	0.220	1.399	4.050		
Access Credit	−1.106	0.331	−0.699	0.497	0.408	1.503	1.106	3.024	−0.408	0.665	0.699	2.011		
Access water	−1.319	0.267	−0.985	0.374	0.334	1.397	1.319	3.739	−0.334	0.716	0.985	2.677		
The reference category: Commercial forestry.	Tree planting	Agroforestry		

4 Discussion

4.1 Adoption of soil conservation

The null hypothesis of this study posited that age, gender, educational status, access to extension services, and access to credit do not have an association with households' adoption practices of soil conservation technologies (compost, green manure, soil bund, stone bund, and strip cropping). The results indicate that age has a significant positive impact on the adoption of soil conservation practices (p < 0.01; Table 9). Specifically, the study revealed that as the age of the household head increases, adoption of consolidated/integrated soil conservation practices increases, however with varied adoption odds among the five practices (Table 10, Table 11, Table 12, Table 13, Table 14). As the household head gets older, he/she is more likely to adopt green manure (odds ratio = 1.06), soil bund (odds ratio = 1.08), and stone bund (odds ratio = 1.07) than adopting compost (Table 10). Contrary to our result [66], have reported that as the age of a household increases adoption of soil conservation practices decreases. Conversely, the study revealed a decreasing and significant odds association between increasing household heads’ age and the adoption of compost and strip cropping, in reference to the adoption of green manure (Table 11), soil bund (Table 12), and stone bund (Table 13). This implies that older households are less likely to adopt the former practices compared to the latter ones. The results of this study are consistent with previous studies [4,17,70].

Male-headed farmers have been found to have better access to information and resources, including land, labor, and inputs, compared to female-headed farmers [57]. The study's findings indicate that the gender of households has a significant impact on the adoption of conservation practices. Specifically, the result revealed that female headed households are more likely to adopt green manure and compost, whereas, male headed households to adopt soil bund. The suggests that female-headed households may have better practices in compost and green manure than their male counterparts, while male-headed households are more likely to adopt soil bund. This result is consistent with previous studies [5,41], which reported that male headed households are more likely to be engaged in implementation and maintenance of soil bund than female headed households; may be due to the fact that soil bund requires significant physical effort or extra time, because female headed households are more likely to engage in housekeeping businesses [66].

On the other hand, the multinomial logistic regression revealed that educational status of household head has no significant association with the adoption of integrated soil conservation practices (Table 9). Contrary to our finding, different studies have reported positive and significance association between increased household's educational status and adoption of soil conservation practices [66,71].

The availability of credit services can play a crucial role in promoting the adoption of conservation measures by farmers [11]. In this study, access to credit was found to have a positive and significant association with the adoption of integrated soil conservation practices (p < 0.001; Table 9). This indicates that the household head's access to credit and adoption of consolidated/integrated soil conservation practices increases, however with varied adoption odds among the five practices (Table 10, Table 11, Table 12, Table 13, Table 14). This implies that as the household head gets access to credit, he/she is more likely to adopt green manure (odds ratio = 3.19), soil bund (odds ratio = 2.34), and stone bund (odds ratio = 10.35) than adopting compost (Table 10). Contrary to our result [66], have reported that as the age of a household increases adoption of soil conservation practices decreases. This indicates that farmers were motivated to invest in various physical soil conservation strategies due to the use and availability of financing. Conversely, the study revealed a decreasing and significant odds association between household head's access to credit and adoption of compost, green manure, and strip cropping in reference to the adoption of green manure (Table 11), soil bund (Table 12), and stone bund (Table 13).

Access to extension services refers to the contact of development agents with farmers to deliver extension services. The extension service plays a great role in raising awareness about soil, water, and forest conservation practices and the possibility for a farmer to decide to practice soil, water, and forest conservation activities [5]. The results of this study indicate a positive and significant relationship between access to extension services and the adoption of soil conservation practices (Table 9), however with varied likelihood adoption odds among the five practices (Table 10, Table 11, Table 12, Table 13, Table 14). This implies that as households get access to extension services, they are more aware about the outcomes of adopting different soil conservation practices. The results of this study are consistent with previous studies.

4.2 Adoption of water conservation

Water is a crucial resource for both commercial and subsistence agricultural production, making water resource security a fundamental requirement for agricultural productivity. The null hypothesis posited that age, gender, educational status, and access to extension services do not have an association with households' adoption practices of agricultural water resource conservation practices (roof rain harvesting, river diversion, and pond development). The study revealed that age, access to extension services, and access to agricultural water resources are significant predictors of households' behaviour in adopting water conservation practices (Table 16, p < 0.05). Specifically, the study revealed that as the age of the household head increases, adoption of consolidated/integrated water conservation practices increases, however with varied adoption odds among the three practices (Table 17). As the household head gets older, he/she is more likely to adopt roof rain harvesting (odds ratio = 1.06), and pond development (odds ratio = 1.068), in reference to adoption of river diversion (Table 17).

Like adoption of water conservation, the extension service plays a great role in raising awareness about water conservation practices and the possibility for a farmer to decide to adopt it. The results of this study indicate a positive and significant relationship between access to extension services and the adoption of water conservation practices (Table 17), however with varied likelihood adoption odds among the three practices. This implies that as households get access to extension services, they are more aware about the outcomes of adopting different agricultural water resource conservation practices. Additionally, the study established a positive and significant relationship between the adoption of water conservation practices and access to water.

4.3 Adoption of forest conservation

The study also examined the adoption of forest conservation practices, such as tree planting, agroforestry, and commercial tree planting, in relation to six independent variables: age, gender, educational status, and access to extension services, access to water, and access to credit. Extension services play a pivotal role in disseminating information about soil, water, and forest conservation practices to farmers at the local level [[71], [72], [73], [74], [75], [76]]. When farmers have access to development agents who provide them with information and advice on innovative conservation measures and technologies, it encourages them to invest in their land. The results indicated that access to extension services was a significant predictor of households' behaviour in adopting forest conservation practices especially between access to extension services and tree planting and commercial forestry.

5 Conclusion, theoretical and practical significance of the research

5.1 Conclusions

Assessment of adoption of conservation practices of agricultural resources is ever more vital with the growing food demand of the increasing population. In this line, this study examines the adoption of soil, water, and forest conservation methods by smallholder farmers in the upper Gelana watershed in Ethiopia's north-eastern highlands, using household survey data. The findings of the descriptive analysis indicate that 69 % of households have implemented at least one conservation technique, while 31 % have either not adopted or discontinued these practices. The Pearson correlation analysis reveals a significant association between soil, water, and forest conservation technologies. The multinomial regression model highlights factors such as age, gender, access to farm credit, and extension services as key predictors of adoption, with education having no significant impact. Improved access to credit, water, and extension services is found to increase the likelihood of adopting watershed management practices. The study emphasizes the role of institutional factors as crucial determinants influencing technology adoption in the region. Recommendations include policy initiatives to disseminate information on effective strategies, improve access to extension services, water, and credit, and promote collaboration among local stakeholders, decision-makers, administrators, and technical experts, drawing inspiration from successful models in countries like China. The study also suggests the integration of natural resource technologies and institutions for sustainable watershed management practices.

5.2 Theoretical and practical significance of the research

Studying the adoption of soil, water, and forest conservation technologies at watershed level has theoretical significance as it deepens understanding of the factors driving individuals and households to engage in sustainable practices. The study approaches used in this study can be adopted by other researches elsewhere in the highlands of Ethiopia and sub-Saharan Africa region. The results of the study can also serve as a baseline empirical evidence for future studies. Analysing adoption patterns helps researchers identify barriers and facilitators of conservation technology adoption, thereby informing the development of improved policies and interventions. Furthermore, exploring adoption contributes to theoretical frameworks in environmental conservation and sustainable development, offering valuable insights for decision-making and fostering long-term environmental stewardship.

On a practical level, studying the adoption of these technologies is crucial for informing Ministry of Agriculture, development agents, and policy-makers in order to support and scale-up adopted practices, and designing effective interventions to promote sustainable land use practices; leading to improved resource management and environmental sustainability.

5.3 Future research directions

Future research should consider employing action research designs that include direct observation of individuals' engagement in technology adoption and feedback mechanisms. Additionally, longitudinal studies maybe important that track developments/dynamics of households’ adoption behaviours over time, which could help identify specific exposures and relationships between interventions and outcomes.

6 Limitations of the study

Although the sample population was selected using an appropriate sampling framework, unavoidable errors, such as, ‘sampling error’ (because the study didn't cover the whole population), and administrative errors, maybe reported in the results of this study. The study is ‘a snap-shop’, therefore, it doesn't show dynamics of households' adoption behaviour. Since this study is a local study at a watershed level, the results may not reflect the households' adoption behaviour or factors affecting adoptions of soil, water and forest conservation practices of the entire highland region of Ethiopia. Regardless of these limitations, we believe that this study presents dependable watershed level results with in its scope.

Data availability

Data will be made available on request.

CRediT authorship contribution statement

Tsedey Tesfahun: Writing – review & editing, Writing – original draft, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Assefa Abegaz: Visualization, Supervision, Methodology, Formal analysis, Conceptualization. Esubalew Abate: Writing – review & editing, Supervision, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

The authors would like to thank 10.13039/501100007941 Addis Ababa University for providing financial support from its “Thematic Research Fund” for data collection. We would like to thank the sampled household farmers for their time in line with providing their demographic and socioeconomic data, and adopted practices during the survey questionnaires completion. Last but not least, we are grateful to the district and village agricultural development agents and administrators for their assistance during the fieldwork.
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