Degree and Drivers of Crop-Livestock Integration Based on Micro-Level Study Evidence from Afar National Regional State

Degree and Drivers of Crop-Livestock Integration Based on Micro-Level Study Evidence from Afar National Regional State | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Degree and Drivers of Crop-Livestock Integration Based on Micro-Level Study Evidence from Afar National Regional State Mohammed Adem, Prof. Lilly Grace Eunice This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7438761/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2025 Read the published version in Discover Sustainability → Version 1 posted 13 You are reading this latest preprint version Abstract This research examines the extent of crop-livestock integration and the factors affecting it in Asayita district. The study employed multistage sampling, incorporating stratified and simple random selection methods, to choose kebeles and 360 families. We employed the crop-livestock integration index and the fractional response model to analyse the data. The average degree of the crop-livestock integration index was 0.25 among rural households in the study area. The fractional response model indicates that the gender of the household head, years of schooling, availability of adult labour, farm size, livestock unit, remittance income, and credit utilisation all exert a statistically significant positive influence on the crop-livestock integration index, while distance from canals and market issues negatively impact it. The study's findings suggest measures that household heads, regional and district administrations, as well as national and international organisations, may employ to enhance their crop and livestock integration initiatives. crop-livestock integration fractional response regression Degree crop-livestock integration index Figures Figure 1 Figure 2 1. Introduction Integrated farming systems encompass the amalgamation of several elements, including crops, livestock, poultry, aquaculture, agroforestry, horticulture, and apiculture, into a cohesive agricultural framework. The origins of this concept can be linked to previous agricultural practices where farmers historically integrated both crops and livestock on their farms. Nonetheless, the modern iteration of integrated farming systems emerged in the mid-20th century as a reaction to the challenges encountered by small-scale farmers in developing nations. The aforementioned practice is gaining global popularity, particularly among small-scale farmers seeking sustainable methods to enhance production and profitability[ 1 ].Crop-livestock integration denotes the planned amalgamation of crop and livestock production to attain reciprocal advantages and enhance agricultural productivity. When one entity's output is the other's input, and vice versa, the integration is more thorough. Mixed farming denotes the integration of crop cultivation and livestock husbandry overseen by a singular farm manager. Moreover, integration may involve exchange or trade between specialised crop growers and livestock owners [ 2 ]. Integrated crop-livestock systems are a sustainable way to boost farming productivity by combining plants and animals, which helps improve important processes in farming. Moreover, crop-livestock combination can significantly improve resilience to climatic anomalies [ 3 ]. According to [ 4 ] integrated farming system defines as a collection of production systems that exhibit interdependence, interconnectivity, and often, interlocking interactions among a small number of crops, animals, and associated subsidiary industries. The goal is to maximize the efficiency of nutrient use within each system while reducing negative environmental effects. Integrated Farming System is characterized by its interconnected and interdependent nature, where the primary and secondary outputs of one system are utilized as essential inputs for another, resulting in a cohesive and integrated entity. The primary objective of integrated farming is to foster symbiotic relationships among its various components, hence minimizing reliance on external inputs. This technique is based on the concept of minimizing waste by utilizing byproducts as useful resources for other products within the system. The incorporation of crops and cattle into farming systems makes a substantial contribution to economic prosperity by diversifying income, optimizing resource utilization, mitigating risks, and enhancing value [ 5 ]. Mixed crop-livestock systems account for the majority of domestic grain and livestock production for families in developing nations [ 6 ]. In developing countries, mixed systems account for 65% of beef production, 75% of milk production, 55% of lamb production, and almost 50% of global cereal production [ 7 ]. African smallholder farmers were especially vulnerable to supply-side challenges such as climate change extremism and the potential for fragmentation. Integration is a strategic approach to managing risk that allows for the coordination of different activities on a home farm, such as crops and animals, in order to optimize the use of resources and improve the livelihoods of the farm household. The escalating population demand on land frequently leads to the amalgamation and intensification of crop-livestock systems [ 8 ] . In the agricultural landscape of the Afar National Regional State, there is a shift from the traditional practice of small-holder farmers specializing in either crop or livestock production to a more integrated approach. This means that households are now engaging in a variety of agricultural activities to generate income and ensure that they have enough food for their households. Nevertheless, within this change, some crucial matters remain unanswered. Although smallholder farmers are increasingly adopting crop-livestock integration, there is limited understanding of the diversity in the level of integration among rural farm households in the research area. The lack of knowledge in this area prevents the implementation of specific interventions and the development of policies that aim to promote sustainable farming practices and enhance rural livelihoods. Certain rural households employ an integrated strategy, distributing their labor between crop and animal production to secure stable income and consumption for their families. In contrast, others remain solely focused on either crop or livestock farming. Yet, the factors influencing this divergence in farming strategies remain ambiguous, impeding efforts to design custom-made interventions to promote agricultural integration and resilience. Despite the recognized importance of crop-livestock integration for employment generation, economic resilience, and overall household well-being, empirical evidence linking integration levels to these broader development objectives is lacking. Consequently, policymakers and stakeholders are unable to connect the full potential of integrated farming systems to enhance food security, economic prosperity, and rural livelihoods in the region. In light of these challenges, there is an urgent need for comprehensive research to address the following objectives estimating the level of crop-livestock integration among smallholder farmers in the Asayita district and analyzing the determinants of the level of crop-livestock integration, considering factors such as household demographics, farm characteristics, access to resources, and institutional support. This study seeks to deliver empirical insights into crop-livestock integration dynamics, guide evidence-based policy creation, and enhance the sustainable advancement of agricultural systems in the designated area. This research endeavour seeks to explore two primary enquiries within the study area: What is the degree of integration between crop and livestock activities among the local farming community? What factors contribute to the observed level of crop-livestock integration? 2. Methodology 2.1. Description of the study area The study was carried out in Ethiopia's Afar region in Asayita, district. It is included in the administrative boundaries of the Awisi zone. Topographically, it is obviously plain. There are ten kebles and one town administration in this district. Its borders are as follows: Dubti to the west, Afambo to the south, the Awash River to the north, which divides it from Elidar, and Djibouti to the east to [ 9 ]. The village is located at an elevation of 300 meters (980 feet) and has latitude and longitude of 11°34′N 41°26′E. The predominant method of raising cattle in the district is the pastoral and agro-pastoral system. There are 71,383 cattle, 16,943 sheep, 23,086 goats, 3,277 camels, and 482 donkeys in the district [ 10 ] . 2.2. Sample size and sampling technique This research adopted a multi-stage sampling technique. The initial stage utilized purposive sampling to choose the Asayita district based on its agricultural output potential (both crops and livestock). In the second stage, four of the ten kebeles in the district were selected after stratifying them into two categories: near and far, based on their distance from the main canal to their kebele. Two kebeles from each stratum were chosen using simple random sampling techniques. The study considered how far the kebeles were from the main canal as an important factor because it greatly affects how rural families combine their crop and animal farming. Ultimately, we employed a basic random sampling method to identify households. The total number of households in the selected kebeles was 3,594. The sample size for this investigation was calculated using the formula established by [ 11 ]. n= \(\:\:\:\:\:\frac{N}{1+N\left({e}^{2}\right)}\) --------------------------------------- (1) Where: n = sample size (360), N = household size (3,594) and e = level of precision (0.05) 2.3. Sources and methods of data collection The district and regional agricultural offices, along with smallholder farmers, supplied primary and secondary data for this study. To gather primary and secondary data, sample surveys, both formal and informal, would be employed. In order to comprehend the general context and degree of crop-livestock integration participation, secondary data will be gathered through document inspection. Key informant interviews (KII), focus groups (FGD), semi-structured and structured questions, and observation would be used to gather primary data from small-holder farmers. Additionally, semi-structured questions were used to perform home surveys. 2.4. Techniques for data analysis The data were examined utilizing both descriptive and inferential statistical methods. Alongside descriptive and inferential statistics, a fractional response probit regression model and a crop-livestock index were employed. 2.4.1. Estimating Crop-Livestock Integration Index Crop-livestock integration refers to the practice of combining crop production and livestock rearing in a mutually beneficial manner [ 12 ]. To measure the level of crop-livestock integration, researchers have developed the crop-livestock integration index (CLII), which provides a quantitative assessment of the degree of integration on a farm or regional scale [ 13 ]. The crop-livestock integration index is used to quantitatively assess the level of integration between crops and livestock in a farming system and provide insights for enhancing the effectiveness and sustainability of agricultural practices. The Crop-Livestock Integration Index (CLII) is a tool used to measure and assess the level of integration between crop and livestock production systems. The index provides a numerical score that represents the degree of integration, with higher scores indicating higher levels of integration. The agricultural industry extensively uses it to gauge the level of crop-livestock integration. This study Favors CLII over other methods for assessing the level of crop-livestock integration among farm households in the Asayita district. because it captures the various dimensions of CLI, including physical interactions, nutrient cycling, economic benefits, and other indicators of agricultural production activities. This crop-livestock integration index takes into consideration land use percentage, nutrient cycling, income diversification, labor integration, and efficient utilization of resources. This paper articulates the CLII model as follows: CLII = \(\:\sum\:_{i=1}^{n}(\frac{Ni\left(w\right)}{{T}_{w}}\) ) -------------------- (2) Where, ‘CIII’= Crop-Livestock Integration Index, ‘i’= 𝑖 th households, ‘n’ = the number of integration indicators, ‘N i ’ = normalized value which includes (land use, nutrient cycling, income diversification, labor integration, and efficiency of resource use), ‘𝑤’ is weights of each indicator and ‘T w ’ is the total weight of crop-livestock integration index. The value of CLII fluctuates between zero (0) and one (1). Consequently, 0 signifies the absence of integration (exclusively crop or livestock) and one represents the pinnacle of integration [ 14 ]. 2.4.2. Determinants of Crop-Livestock Integration Level This situation necessitates the employment of a fractional response probit regression model since some respondents' values of the dependent variable (CLII) are zeros or one, indicating neither extreme nor no integration. We have a vector of independent variables (x) and a continuous dependent variable (y) in [0, 1] under the general formulation for the fractional response regression model. Since y is in [0, 1], fit a regression to find the mean of y conditional on x: E(y/x). Restrict that E(y/x) is also in [0, 1]. [ 15 ], Consider the following model for the conditional expectation of the fractional response variable. E (y i /x i ) = G (x i β) + ε i , i = 1,2............N----------------------------(3) Where 0 ≤ y i ≤ 1 denotes the dependent variable CLII and (the k ×1 vector) i x refers to the explanatory variables of observation i typically, G (.) is a distribution function. Table 1 Definition and units of measurement for explanatory variables used in the fractional response model. Variables Description Measurement Expected sign Gender of Households Male/female 1 if male,0 otherwise + Age of Respondents Years + Year of schooling Years + Adult labor Number + Distance from cannel Hours - Tropical Livestock Unit TLU + Non-farm job Yes/no 1 if yes, 0 otherwise - Remittance income Yes/no 1 if yes, 0 otherwise + Credit Utilization Yes/no 1 if yes, 0 otherwise + Market problems Yes/no 1 if yes, 0 otherwise - Occurrence of shocks Yes/no 1 if yes, 0 otherwise - Farm Size Hectare + Access to training Yes/no 1 if yes, 0 otherwise + Source: Different Literature 3. Results and Discussion 3.1. Descriptive statistics of categorical and continuous variables Table 2 Descriptive statistics of categorical variables with crop-Livestock integration index Variables Variable descriptions Crop-Livestock integration index (CLII) Freq. % mean St. Dev. T- value Sex of Households Male 252 70 0.29 0.22 0.000*** Female 108 30 0.17 0.22 Non-farm job Yes 158 44 0.25 0.22 0.805 No 202 56 0.26 0.23 Remittance income Yes 134 37 0.28 0.23 0.048** No 226 63 0.24 0.22 Credit Utilization Yes 113 31 0.26 0.21 0.421 No 247 69 0.24 0.23 Market problems Yes 339 94 0.24 0.22 0.000*** No 21 6 0.48 0.17 Occurrence of shocks Yes 306 85 0.26 0.23 0.382 No 54 15 0.23 0.24 Access to training Yes 168 47 0.27 0.23 0.2965 No 192 53 0.24 0.23 Note: *** and * Significant at P < 0.01 and p < 0.1 respectively Source: Survey result, 2024 Table two shows how different socioeconomic factors of households affect the crop-livestock integration index. 70% of the homes in the sample were headed by men, and the remaining 29% by women. The mean values for men and women were 0.29 and 0.17, respectively, with a standard deviation of 0.22. The T-test results revealed a statistically significant relationship between household sex and crop-livestock integration index at the one percent probability level. In terms of remittance income, the T-test results demonstrated a statistically significant association between household remittance income and the crop-livestock integration index at the 5% probability level. Furthermore, when it comes to market problems, 94% of respondents reported having an issue with agricultural output. At the 1% level of likelihood, there is a statistically significant relationship between market problems and the crop-livestock integration index. Table 3 Descriptive statistics of continuous variables with crop-Livestock integration index Variables Crop-Livestock Integration Index (CLII) Mean St. Dev. Min. Max. Correlation Coefficient Age of Respondents 46.4 12.03 22 80 0.10 Year of schooling 5.49 5.00 0 15 0.16*** Adult labour 3.29 1.70 2 12 0.34*** Distance from cannel 28.53 15.75 0 90 -0.19*** Tropical Livestock Unit 17.25 18.53 0 67 0.17 * Farm Size 3.46 1.64 0.5 6 0.22*** Note: ***, ** and * Significant at P < 0.01, p < 0.05 and p < 0.1 respectively Source: Survey result, 2024 According to descriptive statistics of continuous variables with crop-livestock integration index, the connection between household year of schooling, adult labour, and farm size is positive at the 1% probability level. However, the results of this study for distance from the cannel revealed that the minimum and maximum hours from the cannel were 0 and 90 minutes, with mean and standard deviation of 28.53 and 15.75, respectively, and a negative correlation 3.2. Degree of crop-Livestock integration index This study employed the crop-livestock integration index to assess the extent of integration among farm households in the Asayita district. The findings illustrated in Fig. 2 depict the level of crop-livestock integration among the farmers in the study area. Respondents exhibiting the highest integration possessed the largest index, while those with minimal integration had the smallest index. Among the total sample households 39% recorded an integration index value of 0. This implies that those farmers did not integrated crop and livestock or participating in to one agricultural activity. About 10% had diversity index between 0.01 up to 0.3, 46% had between 0.31up to 0.6 and about 5% had diversity index between 0.61 and 1. The study found that rural residents in the area have a low level of crop-livestock integration, as indicated by a mean index of 0.25. This value agrees with the finding of Adem and Tesafa (2020), on level of income diversification index by using Simpson diversification index with value of 0.24. This is primarily due to their reliance on a single agricultural activity (crop or livestock production) for their livelihood, making them vulnerable to risks and uncertainty. Factors contributing to low integration include limited irrigation, insufficient knowledge about integration techniques, inadequate government support, weak extension services, and limited access to resources etc. 3.3. Determinants of crop-Livestock integration index Table 4 FRM results on level of Crop-livestock Integration Predictor variables dy/dx Coefficient Robust std. Err Z -value p>|z| Sex of Households 0.105 0.365 *** 0.089 4.10 0.000 Age of Respondents -0.001 -0.002 0.003 -0.66 0.511 Year of schooling 0.007 0.023*** 0.007 3.08 0.002 Adult labor 0.040 0.132*** 0.021 6.44 0.000 Distance from cannel -0.003 -0.012 *** 0.003 -3.89 0.000 Tropical Livestock Unit 0.001 0.005 ** 0.002 2.52 0.012 Non-farm job -0.022 -0.072 0.073 -0.99 0.323 Remittance income 0.040 0.131* 0.072 1.81 0.071 Credit Utilization 0 .033 0 .109 0.075 1.45 0.147 Market problems -0.210 -0.596 *** 0.097 -6.12 0.000 Occurrence of shocks 0.019 0.064 0.109 0.59 0.557 Farm Size 0.031 0.103*** 0.028 3.69 0.000 Access to training 0.008 0.0028 0.070 0.40 0.693 Constant -1.092 0.268 -4.06 0.000 Number of obs = 360, Wald chi2(13) = 177.54, Prob > chi 2 = 0.000, Log pseudolikelihood = -186.47, Pseudo R 2 = 0.086. ***, ** and * indicates statically significant at 1, 5 and 10% respectively The gender of the household head influences crop-livestock integration activities due to culturally determined roles, constraints on social mobility, and disparities in asset ownership and access. Male headship significantly and positively influences the crop-livestock integration index at a 1% probability level. Consequently, with all other factors held equal, the crop-livestock integration index increases by 10.5% when the household head is male (male-headed households). Conversely, the female equivalents are less inclined to engage in crop-livestock integration activities. The likely explanation is that female heads have greater obligations in home administration (non-farm activities), while their male counterparts are more inclined to participate in various farming tasks that enhance their income. This outcome aligns with prior research on gender roles in agricultural practices [ 17 ]. The year of schooling is statistically significant at the 1% probability level and exhibits a positive correlation with the crop-livestock integration index. This indicates that for each additional year of education attained by the household, the integration of crops and livestock increases by 0.7%, provided all other factors remain unchanged. This outcome aligns with prior research indicating that educated individuals integrate both crops and livestock into their livelihood strategies. Adult labour is statistically significant at the 1% level and corresponds positively with the crop-livestock integration index. This implies that if there is one more adult labour in the family, the crop-livestock integration index grows by 4%, provided everything else remains constant. This result is consistent with previous research of [ 18 ] and[ 19 ], which argue that adult labour is crucial for tasks such as feeding livestock, managing crop rotations, and implementing sustainable practices, which can lead to improved yields and farm resilience. Distance from the cannel is statistically significant at the 1% level and adversely correlates with the crop-livestock integration index. This means that if the irrigation cannel runs for an additional hour, the crop-livestock integration index will fall by 0.3%, while everything else remains constant. The distance from irrigation canals has a substantial impact on crop-livestock integration because it affects water accessibility, raises operational costs, limits crop diversification, and degrades soil quality. These issues can impede farmers' capacity to successfully manage crops and livestock, resulting in lower yield and economic viability. This conclusion is consistent with[ 20 ]. Tropical livestock units (TLUs) play an important role in increasing crop-livestock integration. TLUs help to make farming systems more resilient and productive by promoting nutrient recycling, diversifying revenue sources, maximizing resource usage, and boosting food security. Crop-livestock synergy not only increases total agricultural productivity, but it also promotes sustainable practices, which benefit farmers and communities. Promoting the integration of TLUs into farming systems has the potential to significantly improve both economic viability and household nutritional outcomes. In this study, TLUs were statistically significant at the 5% level and correlated positively with the crop-livestock integration index. This means that if there is one more TLU, the crop-livestock integration index will rise by 0.1%, while everything else remains unchanged. This finding is connected to the results of[ 21 ],remittance revenue is a valuable resource that promotes crop-livestock integration. Remittances help farmers build more resilient and productive agricultural systems by increasing investment capacity, diversifying revenue sources, facilitating access to technologies, and boosting livelihood strategies. The findings of this study show that remittance income was statistically significant at the 10% level and positively connected with the crop-livestock integration index. This suggests that if there is one more remittance, the crop-livestock integration index will increase by 0.4%. This result agrees with the finding of[ 22 ] and.[ 20 ]. Market problems pose significant challenges to crop-livestock integration, affecting farmers' decision-making, productivity, and sustainability. As hypothesized, market problems were significantly and negatively related to level of crop-livestock integration at 1% probability level. This implies that market problems lead lower the degree of crop- livestock integration. If the other factors remain constant, the marginal effect of farm household ‘level of crop-livestock integration index decreases by 21%. This conclusion is consistent with [ 23 ]. Farm sizes can considerably improve crop-livestock integration by capitalizing on economies of scale, diversifying productivity, and maximizing resource use. Larger farms have more access to financial and physical resources, allowing them to invest in technology and infrastructure that support integrated operations. At the 1% probability level, farm size was found to be strongly and positively associated to crop-livestock integration, as expected. If all other variables remain constant, the marginal effect of farm size on household crop-livestock integration index increases by 3.1%. This conclusion is congruent with [ 24 ]. 4. Conclusion and Recommendation Crop and livestock production provide rural households with a variety of income-generating opportunities. This study uses survey data from 360 randomly selected households from four kebeles in Asayita district , Afar region Ethiopia, to measure the level of crop-livestock integration and examine it determinants. The study used distance from the main cannel as a stratification variable since it is an important feature for rural households when integrating crop and livestock production activities. To answer the stated key study questions, results from descriptive analysis, indexing, and econometric estimate were applied. Based on the crop-livestock integration index, the independent T-test results show that sex of household head, remittance income, credit utilization, and market problem all had a statistically significant effect on the level of crop-livestock integration. Descriptive statistics of continuous variables with crop-livestock integration index show that year of schooling, adult labour, tropical livestock unit, and farm size are all significantly correlated. This study employed the crop-livestock integration index to measure the level of crop-livestock integration among farm households. The study discovered that rural populations in the area have a low level of crop-livestock integration, as evidenced by a mean index of 0.25. Under no, low, medium and high level of crop-livestock integration index 38.6%, 10.6%, 46.1% and 4.7% of households categorized respectively. This is primarily because they rely on a single agricultural activity (crop or livestock production) for a living, putting them vulnerable to risks and uncertainties. The fractional response regression probit model was used to address the question "what are the factors that determine the level of crop-livestock integration index among farm households." The model's results showed that male-headed households, years of schooling, credit use, adult labor, tropical livestock unit, remittance revenue, and farm size all had a statistically significant beneficial effect on households' crop-livestock integration index. In contrast to negatively associated variables, the above variables boost respondents' crop-livestock integration index. However, distance to the irrigation cannel and market issues had a negative and considerable effect on the degree of crop-livestock integration index. To significantly raise the level of crop-livestock integration in Asayita district, the following measures and actions should be adopted by household heads, regional and district administrations, national and international organizations. The concerned body must strive harder to expand access to education in the study region in order to explore the present opportunity of crop-livestock integration to improve their livelihood strategies. They can establish an enabling environment for farmers to engage in agricultural activities by implementing supportive legislation, providing training and resources, encouraging research, and promoting partnerships. All responsible body improve infrastructure such as roads, storage facilities, and marketplaces, as well as water management systems to meet crop irrigation and animal needs. Finally, use a collaborative approach to increase integration, agricultural productivity, sustainability, and livelihoods, thereby contributing to larger global goals such as the Sustainable Development Goals. Declarations Funding The authors have not received funding for this review. Disclosure statement The authors have disclosed that they do not have any potential conflicts of interest to report. Ethics Approval/Declarations/ This study was conducted in compliance with ethical standards and followed all applicable guidelines for research involving human participants. Before the commencement of data collection, verbal informed consent was obtained from all participants, ensuring they were fully informed about the study's purpose, procedures, and their rights, including the right to withdraw at any time without consequences. All efforts were made to ensure the privacy and confidentiality of the participants, and data were anonymized to prevent the identification of individuals. If you have specific ethics guidelines or further requirements for this journal, please let us know so that we can address them accordingly. Consent to Participate This research did not involve special human participants, animals, or any subjects requiring formal consent to participate. Therefore, this section is not applicable. Consent for Publication This study does not contain any special and unethical data from individual persons. Therefore, this section is not applicable. Availability of Data and Material The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to the sensitive nature of the research and the privacy of participants, the data cannot be made publicly available. For further information or access to the data, please contact Mohammed Adem at [email protected] Code Availability This study did not involve the use of any not known software application or custom code. We only use STATA and SPSS software. Therefore, this section is not applicable. Authors' Contributions Mohammed Adem Ali: The corresponding author prepared the questionnaire, collected the data, developed the methodology, analysed and interpret the data. Prof. B. Lilly Grace Eunice: The second author reviewed the appropriateness of the methodologies, performed grammar corrections, contributed to the design development, and provided general comments and feedback. Both authors have read and approved the final manuscript. 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Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2025 Read the published version in Discover Sustainability → Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 07 Sep, 2025 Editor assigned by journal 07 Sep, 2025 Editor invited by journal 29 Aug, 2025 Submission checks completed at journal 29 Aug, 2025 First submitted to journal 29 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7438761","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512708370,"identity":"cd9693b4-3932-4a69-97b8-3150497a9698","order_by":0,"name":"Mohammed Adem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBADxgYG5gNAWkKGFC1sCSAtPKRo4TEAMQhr0W0/nfiYp8ZOtl/szOdXN2oseBjYDx/dgE+L2ZnczcY8x5KNZ87O3WadcwzoMJ60tBt4tRzI3SbNw8acuOF27jbjHDagFgkeM/xazr/d/pvnX33i/ts5z4xz/hGj5UbuNmbetsOJG6RzmB/nthGl5e1mybl9x41n3E4zY87tk+BhI+iX87kbP7z5Vi3bPzv58eecb3Vy/OyHj+HVAgJM0LhgkwCThJSDAOMPCM38gRjVo2AUjIJRMPIAADzpS9QcIVO6AAAAAElFTkSuQmCC","orcid":"","institution":"Andhra University","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Adem","suffix":""},{"id":512708371,"identity":"8a5274b7-5359-4452-a775-5002ac15b9ad","order_by":1,"name":"Prof. Lilly Grace Eunice","email":"","orcid":"","institution":"Andhra University","correspondingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Lilly Grace","lastName":"Eunice","suffix":""}],"badges":[],"createdAt":"2025-08-23 05:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7438761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7438761/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-025-02294-3","type":"published","date":"2025-11-26T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91202815,"identity":"c4809bdb-e0e9-440c-87d0-41e723b1b938","added_by":"auto","created_at":"2025-09-12 15:59:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical representation of the research area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7438761/v1/637e6b4cb7bc3e3a49604b00.png"},{"id":91202814,"identity":"4b4ed259-983c-49ec-9354-9c6ca4d5dd16","added_by":"auto","created_at":"2025-09-12 15:59:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDegree of Crop-Livestock Integration\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7438761/v1/f51d1376a9644b477f984a97.png"},{"id":97178271,"identity":"2ce4608e-edb0-4fae-80ba-39152ffe5071","added_by":"auto","created_at":"2025-12-01 16:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1156591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7438761/v1/eb177956-dd7c-4bd7-a46c-252f7d2dbeaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Degree and Drivers of Crop-Livestock Integration Based on Micro-Level Study Evidence from Afar National Regional State","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIntegrated farming systems encompass the amalgamation of several elements, including crops, livestock, poultry, aquaculture, agroforestry, horticulture, and apiculture, into a cohesive agricultural framework. The origins of this concept can be linked to previous agricultural practices where farmers historically integrated both crops and livestock on their farms. Nonetheless, the modern iteration of integrated farming systems emerged in the mid-20th century as a reaction to the challenges encountered by small-scale farmers in developing nations. The aforementioned practice is gaining global popularity, particularly among small-scale farmers seeking sustainable methods to enhance production and profitability[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Crop-livestock integration denotes the planned amalgamation of crop and livestock production to attain reciprocal advantages and enhance agricultural productivity. When one entity's output is the other's input, and vice versa, the integration is more thorough. Mixed farming denotes the integration of crop cultivation and livestock husbandry overseen by a singular farm manager. Moreover, integration may involve exchange or trade between specialised crop growers and livestock owners [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Integrated crop-livestock systems are a sustainable way to boost farming productivity by combining plants and animals, which helps improve important processes in farming. Moreover, crop-livestock combination can significantly improve resilience to climatic anomalies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] integrated farming system defines as a collection of production systems that exhibit interdependence, interconnectivity, and often, interlocking interactions among a small number of crops, animals, and associated subsidiary industries. The goal is to maximize the efficiency of nutrient use within each system while reducing negative environmental effects. Integrated Farming System is characterized by its interconnected and interdependent nature, where the primary and secondary outputs of one system are utilized as essential inputs for another, resulting in a cohesive and integrated entity. The primary objective of integrated farming is to foster symbiotic relationships among its various components, hence minimizing reliance on external inputs. This technique is based on the concept of minimizing waste by utilizing byproducts as useful resources for other products within the system.\u003c/p\u003e\u003cp\u003eThe incorporation of crops and cattle into farming systems makes a substantial contribution to economic prosperity by diversifying income, optimizing resource utilization, mitigating risks, and enhancing value [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Mixed crop-livestock systems account for the majority of domestic grain and livestock production for families in developing nations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In developing countries, mixed systems account for 65% of beef production, 75% of milk production, 55% of lamb production, and almost 50% of global cereal production [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. African smallholder farmers were especially vulnerable to supply-side challenges such as climate change extremism and the potential for fragmentation. Integration is a strategic approach to managing risk that allows for the coordination of different activities on a home farm, such as crops and animals, in order to optimize the use of resources and improve the livelihoods of the farm household. The escalating population demand on land frequently leads to the amalgamation and intensification of crop-livestock systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eIn the agricultural landscape of the Afar National Regional State, there is a shift from the traditional practice of small-holder farmers specializing in either crop or livestock production to a more integrated approach. This means that households are now engaging in a variety of agricultural activities to generate income and ensure that they have enough food for their households. Nevertheless, within this change, some crucial matters remain unanswered. Although smallholder farmers are increasingly adopting crop-livestock integration, there is limited understanding of the diversity in the level of integration among rural farm households in the research area. The lack of knowledge in this area prevents the implementation of specific interventions and the development of policies that aim to promote sustainable farming practices and enhance rural livelihoods.\u003c/p\u003e\u003cp\u003eCertain rural households employ an integrated strategy, distributing their labor between crop and animal production to secure stable income and consumption for their families. In contrast, others remain solely focused on either crop or livestock farming. Yet, the factors influencing this divergence in farming strategies remain ambiguous, impeding efforts to design custom-made interventions to promote agricultural integration and resilience. Despite the recognized importance of crop-livestock integration for employment generation, economic resilience, and overall household well-being, empirical evidence linking integration levels to these broader development objectives is lacking. Consequently, policymakers and stakeholders are unable to connect the full potential of integrated farming systems to enhance food security, economic prosperity, and rural livelihoods in the region. In light of these challenges, there is an urgent need for comprehensive research to address the following objectives estimating the level of crop-livestock integration among smallholder farmers in the Asayita district and analyzing the determinants of the level of crop-livestock integration, considering factors such as household demographics, farm characteristics, access to resources, and institutional support. This study seeks to deliver empirical insights into crop-livestock integration dynamics, guide evidence-based policy creation, and enhance the sustainable advancement of agricultural systems in the designated area. This research endeavour seeks to explore two primary enquiries within the study area: What is the degree of integration between crop and livestock activities among the local farming community? What factors contribute to the observed level of crop-livestock integration?\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Description of the study area\u003c/h2\u003e\u003cp\u003eThe study was carried out in Ethiopia's Afar region in Asayita, district. It is included in the administrative boundaries of the Awisi zone. Topographically, it is obviously plain. There are ten kebles and one town administration in this district. Its borders are as follows: Dubti to the west, Afambo to the south, the Awash River to the north, which divides it from Elidar, and Djibouti to the east to [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The village is located at an elevation of 300 meters (980 feet) and has latitude and longitude of 11\u0026deg;34\u0026prime;N 41\u0026deg;26\u0026prime;E. The predominant method of raising cattle in the district is the pastoral and agro-pastoral system. There are 71,383 cattle, 16,943 sheep, 23,086 goats, 3,277 camels, and 482 donkeys in the district [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Sample size and sampling technique\u003c/h2\u003e\u003cp\u003eThis research adopted a multi-stage sampling technique. The initial stage utilized purposive sampling to choose the Asayita district based on its agricultural output potential (both crops and livestock). In the second stage, four of the ten kebeles in the district were selected after stratifying them into two categories: near and far, based on their distance from the main canal to their kebele. Two kebeles from each stratum were chosen using simple random sampling techniques. The study considered how far the kebeles were from the main canal as an important factor because it greatly affects how rural families combine their crop and animal farming. Ultimately, we employed a basic random sampling method to identify households. The total number of households in the selected kebeles was 3,594. The sample size for this investigation was calculated using the formula established by [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003en=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\frac{N}{1+N\\left({e}^{2}\\right)}\\)\u003c/span\u003e\u003c/span\u003e --------------------------------------- (1)\u003c/p\u003e\u003cp\u003eWhere: n\u0026thinsp;=\u0026thinsp;sample size (360), N\u0026thinsp;=\u0026thinsp;household size (3,594) and e\u0026thinsp;=\u0026thinsp;level of precision (0.05)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Sources and methods of data collection\u003c/h2\u003e\u003cp\u003eThe district and regional agricultural offices, along with smallholder farmers, supplied primary and secondary data for this study. To gather primary and secondary data, sample surveys, both formal and informal, would be employed. In order to comprehend the general context and degree of crop-livestock integration participation, secondary data will be gathered through document inspection. Key informant interviews (KII), focus groups (FGD), semi-structured and structured questions, and observation would be used to gather primary data from small-holder farmers. Additionally, semi-structured questions were used to perform home surveys.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Techniques for data analysis\u003c/h2\u003e\u003cp\u003eThe data were examined utilizing both descriptive and inferential statistical methods. Alongside descriptive and inferential statistics, a fractional response probit regression model and a crop-livestock index were employed.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Estimating Crop-Livestock Integration Index\u003c/h2\u003e\u003cp\u003eCrop-livestock integration refers to the practice of combining crop production and livestock rearing in a mutually beneficial manner [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To measure the level of crop-livestock integration, researchers have developed the crop-livestock integration index (CLII), which provides a quantitative assessment of the degree of integration on a farm or regional scale [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The crop-livestock integration index is used to quantitatively assess the level of integration between crops and livestock in a farming system and provide insights for enhancing the effectiveness and sustainability of agricultural practices. The Crop-Livestock Integration Index (CLII) is a tool used to measure and assess the level of integration between crop and livestock production systems. The index provides a numerical score that represents the degree of integration, with higher scores indicating higher levels of integration. The agricultural industry extensively uses it to gauge the level of crop-livestock integration. This study Favors CLII over other methods for assessing the level of crop-livestock integration among farm households in the Asayita district. because it captures the various dimensions of CLI, including physical interactions, nutrient cycling, economic benefits, and other indicators of agricultural production activities. This crop-livestock integration index takes into consideration land use percentage, nutrient cycling, income diversification, labor integration, and efficient utilization of resources. This paper articulates the CLII model as follows:\u003c/p\u003e\u003cp\u003eCLII =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}(\\frac{Ni\\left(w\\right)}{{T}_{w}}\\)\u003c/span\u003e\u003c/span\u003e) -------------------- (2)\u003c/p\u003e\u003cp\u003eWhere, \u0026lsquo;CIII\u0026rsquo;= Crop-Livestock Integration Index, \u0026lsquo;i\u0026rsquo;= \u0026#119894;\u003csup\u003eth\u003c/sup\u003e households, \u0026lsquo;n\u0026rsquo; = the number of integration indicators, \u0026lsquo;N\u003csub\u003ei\u003c/sub\u003e\u0026rsquo; = normalized value which includes (land use, nutrient cycling, income diversification, labor integration, and efficiency of resource use), \u0026lsquo;\u0026#119908;\u0026rsquo; is weights of each indicator and \u0026lsquo;T\u003csub\u003ew\u003c/sub\u003e\u0026rsquo; is the total weight of crop-livestock integration index. The value of CLII fluctuates between zero (0) and one (1). Consequently, 0 signifies the absence of integration (exclusively crop or livestock) and one represents the pinnacle of integration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Determinants of Crop-Livestock Integration Level\u003c/h2\u003e\u003cp\u003eThis situation necessitates the employment of a fractional response probit regression model since some respondents' values of the dependent variable (CLII) are zeros or one, indicating neither extreme nor no integration. We have a vector of independent variables (x) and a continuous dependent variable (y) in [0, 1] under the general formulation for the fractional response regression model. Since y is in [0, 1], fit a regression to find the mean of y conditional on x: E(y/x). Restrict that E(y/x) is also in [0, 1]. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Consider the following model for the conditional expectation of the fractional response variable.\u003c/p\u003e\u003cp\u003eE (y\u003csub\u003ei\u003c/sub\u003e/x\u003csub\u003ei\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;G (x\u003csub\u003ei\u003c/sub\u003e β) +\u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, i\u0026thinsp;=\u0026thinsp;1,2............N----------------------------(3)\u003c/p\u003e\u003cp\u003eWhere 0\u0026thinsp;\u0026le;\u0026thinsp;y\u003csub\u003ei\u003c/sub\u003e \u0026le;\u003cem\u003e1\u003c/em\u003e denotes the dependent variable CLII and (the \u003cem\u003ek\u003c/em\u003e\u0026times;1 vector) \u003cem\u003ei\u003c/em\u003e\u003cb\u003ex\u003c/b\u003e refers to the explanatory variables of observation \u003cem\u003ei\u003c/em\u003e typically, \u003cem\u003eG (.)\u003c/em\u003e is a distribution function.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDefinition and units of measurement for explanatory variables used in the fractional response model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables Description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpected sign\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender of Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale/female 1 if male,0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of Respondents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from cannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical Livestock Unit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTLU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-farm job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes/no 1 if yes, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemittance income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes/no 1 if yes, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCredit Utilization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes/no 1 if yes, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes/no 1 if yes, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccurrence of shocks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes/no 1 if yes, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHectare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes/no 1 if yes, 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Different Literature\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Descriptive statistics of categorical and continuous variables\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of categorical variables with crop-Livestock integration index\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable descriptions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eCrop-Livestock integration index (CLII)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFreq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSt. Dev.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT- value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex of Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNon-farm job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRemittance income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.048**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCredit Utilization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMarket problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOccurrence of shocks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAccess to training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.2965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote: *** and * Significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 respectively\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource: Survey result, 2024\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable two shows how different socioeconomic factors of households affect the crop-livestock integration index. 70% of the homes in the sample were headed by men, and the remaining 29% by women. The mean values for men and women were 0.29 and 0.17, respectively, with a standard deviation of 0.22. The T-test results revealed a statistically significant relationship between household sex and crop-livestock integration index at the one percent probability level. In terms of remittance income, the T-test results demonstrated a statistically significant association between household remittance income and the crop-livestock integration index at the 5% probability level. Furthermore, when it comes to market problems, 94% of respondents reported having an issue with agricultural output. At the 1% level of likelihood, there is a statistically significant relationship between market problems and the crop-livestock integration index.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of continuous variables with crop-Livestock integration index\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eCrop-Livestock Integration Index (CLII)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSt. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCorrelation Coefficient\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of Respondents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult labour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.34***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from cannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.19***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical Livestock Unit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.22***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cb\u003e***, ** and * Significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 respectively\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource: Survey result, 2024\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to descriptive statistics of continuous variables with crop-livestock integration index, the connection between household year of schooling, adult labour, and farm size is positive at the 1% probability level. However, the results of this study for distance from the cannel revealed that the minimum and maximum hours from the cannel were 0 and 90 minutes, with mean and standard deviation of 28.53 and 15.75, respectively, and a negative correlation\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Degree of crop-Livestock integration index\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study employed the crop-livestock integration index to assess the extent of integration among farm households in the Asayita district. The findings illustrated in Fig.\u0026nbsp;2 depict the level of crop-livestock integration among the farmers in the study area. Respondents exhibiting the highest integration possessed the largest index, while those with minimal integration had the smallest index. Among the total sample households 39% recorded an integration index value of 0. This implies that those farmers did not integrated crop and livestock or participating in to one agricultural activity. About 10% had diversity index between 0.01 up to 0.3, 46% had between 0.31up to 0.6 and about 5% had diversity index between 0.61 and 1. The study found that rural residents in the area have a low level of crop-livestock integration, as indicated by a mean index of 0.25. This value agrees with the finding of Adem and Tesafa (2020), on level of income diversification index by using Simpson diversification index with value of 0.24. This is primarily due to their reliance on a single agricultural activity (crop or livestock production) for their livelihood, making them vulnerable to risks and uncertainty. Factors contributing to low integration include limited irrigation, insufficient knowledge about integration techniques, inadequate government support, weak extension services, and limited access to resources etc.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Determinants of crop-Livestock integration index\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFRM results on level of Crop-livestock Integration\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edy/dx\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRobust std. Err\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ -value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u0026gt;|z|\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex of Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.365 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of Respondents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.023***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.132***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from cannel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.012 ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTropical Livestock Unit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-farm job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemittance income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.131*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCredit Utilization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 .033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 .109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.210\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.596 ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccurrence of shocks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.103***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNumber of obs\u0026thinsp;=\u0026thinsp;360, Wald chi2(13)\u0026thinsp;=\u0026thinsp;177.54, Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000, Log pseudolikelihood = -186.47, Pseudo R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.086. ***, ** and * indicates statically significant at 1, 5 and 10% respectively\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe gender of the household head influences crop-livestock integration activities due to culturally determined roles, constraints on social mobility, and disparities in asset ownership and access. Male headship significantly and positively influences the crop-livestock integration index at a 1% probability level. Consequently, with all other factors held equal, the crop-livestock integration index increases by 10.5% when the household head is male (male-headed households). Conversely, the female equivalents are less inclined to engage in crop-livestock integration activities. The likely explanation is that female heads have greater obligations in home administration (non-farm activities), while their male counterparts are more inclined to participate in various farming tasks that enhance their income. This outcome aligns with prior research on gender roles in agricultural practices [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The year of schooling is statistically significant at the 1% probability level and exhibits a positive correlation with the crop-livestock integration index. This indicates that for each additional year of education attained by the household, the integration of crops and livestock increases by 0.7%, provided all other factors remain unchanged. This outcome aligns with prior research indicating that educated individuals integrate both crops and livestock into their livelihood strategies.\u003c/p\u003e\u003cp\u003eAdult labour is statistically significant at the 1% level and corresponds positively with the crop-livestock integration index. This implies that if there is one more adult labour in the family, the crop-livestock integration index grows by 4%, provided everything else remains constant. This result is consistent with previous research of [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which argue that adult labour is crucial for tasks such as feeding livestock, managing crop rotations, and implementing sustainable practices, which can lead to improved yields and farm resilience. Distance from the cannel is statistically significant at the 1% level and adversely correlates with the crop-livestock integration index. This means that if the irrigation cannel runs for an additional hour, the crop-livestock integration index will fall by 0.3%, while everything else remains constant. The distance from irrigation canals has a substantial impact on crop-livestock integration because it affects water accessibility, raises operational costs, limits crop diversification, and degrades soil quality. These issues can impede farmers' capacity to successfully manage crops and livestock, resulting in lower yield and economic viability. This conclusion is consistent with[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTropical livestock units (TLUs) play an important role in increasing crop-livestock integration. TLUs help to make farming systems more resilient and productive by promoting nutrient recycling, diversifying revenue sources, maximizing resource usage, and boosting food security. Crop-livestock synergy not only increases total agricultural productivity, but it also promotes sustainable practices, which benefit farmers and communities. Promoting the integration of TLUs into farming systems has the potential to significantly improve both economic viability and household nutritional outcomes. In this study, TLUs were statistically significant at the 5% level and correlated positively with the crop-livestock integration index. This means that if there is one more TLU, the crop-livestock integration index will rise by 0.1%, while everything else remains unchanged. This finding is connected to the results of[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e],remittance revenue is a valuable resource that promotes crop-livestock integration. Remittances help farmers build more resilient and productive agricultural systems by increasing investment capacity, diversifying revenue sources, facilitating access to technologies, and boosting livelihood strategies. The findings of this study show that remittance income was statistically significant at the 10% level and positively connected with the crop-livestock integration index. This suggests that if there is one more remittance, the crop-livestock integration index will increase by 0.4%. This result agrees with the finding of[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMarket problems pose significant challenges to crop-livestock integration, affecting farmers' decision-making, productivity, and sustainability. As hypothesized, market problems were significantly and negatively related to level of crop-livestock integration at 1% probability level. This implies that market problems lead lower the degree of crop- livestock integration. If the other factors remain constant, the marginal effect of farm household \u0026lsquo;level of crop-livestock integration index decreases by 21%. This conclusion is consistent with [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Farm sizes can considerably improve crop-livestock integration by capitalizing on economies of scale, diversifying productivity, and maximizing resource use. Larger farms have more access to financial and physical resources, allowing them to invest in technology and infrastructure that support integrated operations. At the 1% probability level, farm size was found to be strongly and positively associated to crop-livestock integration, as expected. If all other variables remain constant, the marginal effect of farm size on household crop-livestock integration index increases by 3.1%. This conclusion is congruent with [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion and Recommendation","content":"\u003cp\u003eCrop and livestock production provide rural households with a variety of income-generating opportunities. This study uses survey data from 360 randomly selected households from four kebeles in Asayita \u003cem\u003edistrict\u003c/em\u003e, Afar region Ethiopia, to measure the level of crop-livestock integration and examine it determinants. The study used distance from the main cannel as a stratification variable since it is an important feature for rural households when integrating crop and livestock production activities. To answer the stated key study questions, results from descriptive analysis, indexing, and econometric estimate were applied. Based on the crop-livestock integration index, the independent T-test results show that sex of household head, remittance income, credit utilization, and market problem all had a statistically significant effect on the level of crop-livestock integration. Descriptive statistics of continuous variables with crop-livestock integration index show that year of schooling, adult labour, tropical livestock unit, and farm size are all significantly correlated.\u003c/p\u003e\u003cp\u003eThis study employed the crop-livestock integration index to measure the level of crop-livestock integration among farm households. The study discovered that rural populations in the area have a low level of crop-livestock integration, as evidenced by a mean index of 0.25. Under no, low, medium and high level of crop-livestock integration index 38.6%, 10.6%, 46.1% and 4.7% of households categorized respectively. This is primarily because they rely on a single agricultural activity (crop or livestock production) for a living, putting them vulnerable to risks and uncertainties. The fractional response regression probit model was used to address the question \"what are the factors that determine the level of crop-livestock integration index among farm households.\" The model's results showed that male-headed households, years of schooling, credit use, adult labor, tropical livestock unit, remittance revenue, and farm size all had a statistically significant beneficial effect on households' crop-livestock integration index. In contrast to negatively associated variables, the above variables boost respondents' crop-livestock integration index. However, distance to the irrigation cannel and market issues had a negative and considerable effect on the degree of crop-livestock integration index.\u003c/p\u003e\u003cp\u003eTo significantly raise the level of crop-livestock integration in Asayita district, the following measures and actions should be adopted by household heads, regional and district administrations, national and international organizations.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe concerned body must strive harder to expand access to education in the study region in order to explore the present opportunity of crop-livestock integration to improve their livelihood strategies.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThey can establish an enabling environment for farmers to engage in agricultural activities by implementing supportive legislation, providing training and resources, encouraging research, and promoting partnerships.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAll responsible body improve infrastructure such as roads, storage facilities, and marketplaces, as well as water management systems to meet crop irrigation and animal needs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFinally, use a collaborative approach to increase integration, agricultural productivity, sustainability, and livelihoods, thereby contributing to larger global goals such as the Sustainable Development Goals.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have not received funding for this review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have disclosed that they do not have any potential conflicts of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval/Declarations/\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in compliance with ethical standards and followed all applicable guidelines for research involving human participants. Before the commencement of data collection, verbal informed consent was obtained from all participants, ensuring they were fully informed about the study\u0026apos;s purpose, procedures, and their rights, including the right to withdraw at any time without consequences. All efforts were made to ensure the privacy and confidentiality of the participants, and data were anonymized to prevent the identification of individuals.\u003c/p\u003e\n\u003cp\u003eIf you have specific ethics guidelines or further requirements for this journal, please let us know so that we can address them accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not involve special human participants, animals, or any subjects requiring formal consent to participate. Therefore, this section is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not contain any special and unethical data from individual persons. Therefore, this section is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to the sensitive nature of the research and the privacy of participants, the data cannot be made publicly available.\u003c/p\u003e\n\u003cp\u003eFor further information or access to the data, please contact Mohammed Adem at [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve the use of any not known software application or custom code. We only use STATA and SPSS software. Therefore, this section is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMohammed Adem Ali: The corresponding author prepared the questionnaire, collected the data, developed the methodology, analysed and interpret the data.\u003c/p\u003e\n\u003cp\u003eProf. B. Lilly Grace Eunice: The second author reviewed the appropriateness of the methodologies, performed grammar corrections, contributed to the design development, and provided general comments and feedback.\u003c/p\u003e\n\u003cp\u003eBoth authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eN.A.DAR et al., INTEGRATED FARMING SYSTEMS FOR SUSTAINABLE AGRICULTURE, 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSlingerland M. \u003cem\u003eMixed farming: scope and constraints in West African Savanna\u003c/em\u003e. 2000. [Online]. 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Animal. 2013;7(4):673\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S1751731112002091\u003c/span\u003e\u003cspan address=\"10.1017/S1751731112002091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"crop-livestock integration, fractional response regression, Degree, crop-livestock integration index","lastPublishedDoi":"10.21203/rs.3.rs-7438761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7438761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research examines the extent of crop-livestock integration and the factors affecting it in Asayita district. The study employed multistage sampling, incorporating stratified and simple random selection methods, to choose kebeles and 360 families. We employed the crop-livestock integration index and the fractional response model to analyse the data. The average degree of the crop-livestock integration index was 0.25 among rural households in the study area. The fractional response model indicates that the gender of the household head, years of schooling, availability of adult labour, farm size, livestock unit, remittance income, and credit utilisation all exert a statistically significant positive influence on the crop-livestock integration index, while distance from canals and market issues negatively impact it. The study's findings suggest measures that household heads, regional and district administrations, as well as national and international organisations, may employ to enhance their crop and livestock integration initiatives.\u003c/p\u003e","manuscriptTitle":"Degree and Drivers of Crop-Livestock Integration Based on Micro-Level Study Evidence from Afar National Regional State","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 15:59:16","doi":"10.21203/rs.3.rs-7438761/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-08T12:20:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T08:55:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T08:26:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260739504272799118138518202682028264915","date":"2025-09-23T03:56:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T05:37:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254261401666357276324789885099881702472","date":"2025-09-12T16:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218791623524032189771339006631439294752","date":"2025-09-12T15:54:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136077200798129822737111644000599877468","date":"2025-09-08T06:02:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-07T15:49:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-07T15:44:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T15:10:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-29T13:43:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-08-29T13:39:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25a6b6e4-7973-404f-b259-68529c8d99d1","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:00:01+00:00","versionOfRecord":{"articleIdentity":"rs-7438761","link":"https://doi.org/10.1007/s43621-025-02294-3","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-11-26 15:57:09","publishedOnDateReadable":"November 26th, 2025"},"versionCreatedAt":"2025-09-12 15:59:16","video":"","vorDoi":"10.1007/s43621-025-02294-3","vorDoiUrl":"https://doi.org/10.1007/s43621-025-02294-3","workflowStages":[]},"version":"v1","identity":"rs-7438761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7438761","identity":"rs-7438761","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}