Determinants of multiple maize technology packages adoption in Ethiopia: Evidence from Sidama region

Determinants of multiple maize technology packages adoption in Ethiopia: Evidence from Sidama region | 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 Determinants of multiple maize technology packages adoption in Ethiopia: Evidence from Sidama region Ashenafi Guye, Tewodros Tefera, Million Sileshi, Abdi-Khalil Edriss This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4428885/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Adoption of improved agricultural technology packages is vital in Ethiopia, as the expansion of cultivable land appears nearly exhausted and population size has skyrocketed. However, the country has been shown low adoption rate. Thus, this study aimed to explore the factors that hinder or facilitate the adoption of multiple maize technology packages in the northern Sidama zone of Ethiopia. A multistage sampling procedure was applied to gather cross-sectional data from 424 farm households owning 545 maize plots. A multivariate probit model was applied to address the study objectives. Of total plots, improved maize seed, fertilizer, and row planting were adopted on about 54, 45, and 44 percent, respectively. The conditional probability results have also confirmed that maize technology packages have complementarity (positive relationship). This infers that agriculture-focused policies that influence the adoption of a single component of technology packages can have a reinforcing advantage over the adoption of other technologies. Furthermore, the results from model showed that farmers with higher family size, plot size, age, tropical livestock unit, ox (en), off-farm income, access to credit and extension services, membership in institutions, and the number of plots are more likely to adopt at least one of the improved maize technology packages. However, distance to maize plots affected adoption negatively. Therefore, it is crucial to reinforce and deliver quality extension services, provide credit access, motivate youth to be involved in farming activities, inspire membership and ease the system to access inputs and technologies for broader adoption of technology packages. Multiple maize technologies Adoption Multivariate probit Sidama Ethiopia Figures Figure 1 1. Introduction Ethiopia ranked second in Africa with over 123 million people, following Nigeria, and showed the fastest-growing economy in the region [ 1 ]. However, it also remains one of the most poverty-stricken developing countries, with $ 1,020 per capita gross national income, though it aims to achieve lower-middle-income status by 2025 [ 1 ]. The economic growth of the country is highly dependent on the performance of agriculture, which is basically the milestone of the country’s economy [ 2 , 3 ]. Currently, this sector contributes about 32.4% of GDP, 75% of export earnings, 73% of job opportunities, and 70% of raw materials for domestic industries [ 4 , 5 ]. Even though Ethiopia has a rich agricultural history, it has been facing many challenges in the sector. Agricultural productivity is low due to a lack of private investment, fragmented markets, farmers' limited access to resources, low subsidies, soil degradation, low application of modern agricultural inputs, inefficiencies, subsistence farming, poor infrastructure, vulnerability to environmental and climate-related shocks, reliance on outdated farming methods, and rain-fed agriculture leading to fluctuations in yields [ 6 – 11 ]. Non-stop internal conflict and the worst drought in recorded history are a factor in the worsening of these challenges. On the other hand, as the best prospect sector for growth in Ethiopia, the government developed and ended two consecutive growth and transformation plans (GTP I and II) and has embarked on a ten-year economic development plans (2021–2030) where agriculture is at the top of priority sectors [ 12 ]. Over the next ten years, the agriculture industry is predicted to grow by 6.2% annually. In a country where land is scarce, the adoption of agricultural technologies plays a crucial role in enhancing agricultural productivity and reducing food insecurity [ 13 – 15 ]. Thus, to hit the target, the use of improved agricultural technology packages is a critical approach [ 5 ]. However, the adoption of agricultural technologies at the national level is still significantly lower as compared to many developing countries [ 17 – 21 ]. This indicates a need for additional evidence-based initiatives and strategies to promote the adoption of agricultural technologies in a wider scope. Numerous studies have been conducted in Ethiopia on the factors that affect the adoption of agricultural technologies [ 22 – 28 ] and on maize technology adoption specifically [ 29 – 34 ]. However, these studies analyzed the determinants of the adoption of agricultural technologies, focusing on the adoption of a single component of the technology package, paying little attention to correlations among the technologies, and the possible simultaneity of the multiple adoption decisions. Even though the majority of the farmers have been adopting a combination of technologies, none of them showed farmers’ multiple adoption decisions towards maize technology packages. Moreover, in terms of methodology, most of the previous studies conducted adopted univariate or binary choice models to investigate determinants of agricultural as well as maize technology adoption, assuming adoption of those technologies is mutually exclusive with little or no interdependency among them. Yet, smallholders adopt several complementary or substitution technologies at a time, and using binary choice models can be biased and inefficient. Hence, adoption decisions may be correlated and best characterized by a multivariate probit model. Maize ( Zea mays L.) is a vital food crop and is broadly grown by smallholder farmers in several regions of Ethiopia, though its national average yield (39 quintal/ha) is by far below the world’s average (56.6 quintal/ha) [ 35 ]. This demonstrates that Ethiopia is producing below the actual attainable yield. The strategy to reduce the yield gap is to increase agricultural productivity through the adoption of improved maize technology packages. Sidama is one of the maize-producing regions in Ethiopia [ 36 ]. Despite its maize production potential, no comparable studies were conducted in this area. The findings of this study will provide evidence-based empirical information for government and development partners for relevant interventions. In addition, the findings of this study can support policymakers in developing appropriate policies to improve the production and productivity of maize crop by encouraging wider adoption. As a result, the livelihood of farm households will be improved through increased income and food security. Therefore, this study aimed to assess the factors that drive or hamper the adoption of multiple maize technology packages in selected districts of the Sidama region of Ethiopia. 2. Study methodology 2.1. Study area description This study was undertaken in three randomly selected districts (Woredas) namely, Hawassa Zuria, Boricha, and Shebadino in Sidama Region, Ethiopia (Fig. 1 ). Sidama is one of the 13 regions in Ethiopia and located in the Southeastern part of Ethiopia, 275 km south of Addis Ababa. It is surrounded by the Oromia Region in the North, North-East and South-East, the Gedeo Zone in the South and the Wolaita Zone in the West. The capital of the region is located in Hawassa. Sidama is the most densely populated region in Ethiopia. In 2020 by considering the land mass of the region, the crude population density of the region is 674 persons per km 2 . The livelihood of the people in the region highly depends on rain-fed agriculture and mixed farming systems. The region is known for its maize production potential in the country [ 36 ]. Therefore, improving maize productivity through modern maize technology adoption has a magnificent impact on the livelihood of land scarcely populated communities of the region. (Fig. 1 ) displays the study area location map. 2.2. Data sources, types, and collection method In this study, both primary and secondary data were collected to address the research objectives. The primary data set consists of farm family characteristics, socio-economic, institutional, infrastructural, plot and different elements in the maize production packages that were collected. This data set was gathered through semi-structured questionnaires. Secondary data was collected from government and non-governmental offices, the Central Statistics Authority (CSA), the Sidama Regional Bureau of Agriculture, and the Plan and Development Commission Bureau. The collection of relevant secondary information was often based on both published and unpublished documents. 2.3. Sampling procedures The study was mainly based on cross-sectional data obtained from a farm household selected through a multi-stage sampling technique. In the first stage, three Woredas mentioned above were randomly selected from 10 potential maize-producing Woredas of the Sidama Regional State. Eleven maize producing Kebeles were selected in the second stage, by using a simple random sampling method (Table 1 ). In the third stage, 424 farm households were randomly selected and interviewed based on probability proportional to the sizes of each Kebele and Woreda (Table 1 ). Among these households, there were 102 non-adopters and 322 adopters. Additional data were gathered at the plot level, resulting in 545 observations on the number of plots cultivated with maize during the 2022 production period. The schedule was first pre-tested on 12 randomly selected maize farming households, and some modifications were made to the questionnaire before the execution of the formal survey. Enumerators who are familiar with the study area, who can understand the local language, and who have prior experience in data collection were trained, recruited, and supervised by the researcher. Table 1 Distribution of sample maize farming households across Woreda and Kebele Woreda (district) Kebele (village) Maize farming households Sample size Hawassa Zuria Jaraqarara 953 63 Jaradamuwa 337 22 Labukoromo 890 59 Doyootilicho 842 56 Samaejersa 523 35 Boricha Qonsorechafa 554 37 Qonsoregoge 510 34 Shebadino Sadeqa 608 40 Alawoano 378 25 Qonsoreano 454 30 Morochoshondolo 348 23 Total 6,398 424 Source: (WoARD, 2022). 2.4. Data analysis method Descriptive and inferential statistics were used to analyze the demographic and socio-economic characteristics of farmers. A multivariate probit model (MVP) was used to identify factors influencing the adoption decisions of multiple maize technology packages in the study area. 2.5. Determinants of improved seed, chemical fertilizer, and row planting adoption Following the seminal work of Cappellari and Jenkins [ 37 ] and others [ 29 , 38 – 41 ] multivariate probit model (MVP) was adopted to explore the factors that determine the adoption of multiple maize technology packages in the study area. In binary choice models, the information about a farmer’s adoption of a single technology package does not alter the likelihood of the decision to adopt another technology. Thus, probit and logit models were widely used to model discrete decisions. In addition, a bivariate model has a limited applicability to estimate interdependent decisions. Subsequently, Dorfman [ 42 ] adopted multinomial logit to model multiple adoption decisions. However, still multinomial logit method has its own drawbacks, even if it estimates the influence of the explanatory variables on dependent variables that includes multiple choices with unordered response categories [ 43 ]. Multinomial logit classifies the household into different categories based on the technology packages adopted. As a result, it cannot take into consideration the factors that cause households to choose a particular set of the technology packages at the same time. It assumes that the household that assigned to a particular adoption category will not participate in another adoption category. Thus, multinomial logit is only appropriate when the adoption categories are independent and mutually exclusive. On the contrary, MVP is appropriate when different technology packages are interdependent, and it fills the gap in the aforementioned methods. The MVP is a method of interrelated binary response regression model that estimates the effect of a set of dependent variables on each of the different maize technology packages adoption at the same time. It allows free correlation of noise terms and correlations between adoptions of different maize technology packages [ 44 ]. This study assumed that the selected maize technology packages are correlated and not mutually exclusive, and therefore the use of MVP is the best fit. Furthermore, the random utility assumption is a basis for modeling the observed outcome of the multiple maize technology packages adoption. Theoretically, smallholder producers in developing countries generally, and in Ethiopia in particular, produce crops in uncertain and risky production schemes. Hence, producers would invest in a given technology if the expected utility from the adoption \({U}_{m}\) is higher than the expected utility \({U}_{0}\) without adoption [ 45 ]. Consider the \({j}^{th}\) farm household \((j=1,\dots ., N)\) confronting a decision to adopt or not to adopt the available yield-improving maize technology packages on the plot z ( \(z=1,\dots .., Z\) ) over a given time boundary. Let \({U}_{0}\) denotes the gains to the maize producers from the old technology adoption, and \({U}_{m}\) denote the gains from improved technology packages adoption, where \(m\) represents the choice of improved maize seed (IS), chemical fertilizer (F), and row planting (R). As aforementioned, the smallholder producers decide to adopt the \({m}^{th}\) maize technology packages on a plot \(z\) of if \({Y}_{jzm}^{*}={U}_{jzm}^{*}-{U}_{0}>0\) . This net gain ( \({Y}_{jzm}^{*}\) ) that the smallholders derive from the adoption of \({m}^{th}\) improved maize technology packages on a pilot \(z\) is a latent variable and influenced by observable factors ( \({X}_{jz}\) ) as well as the noise term ( \({\epsilon }_{jz}\) ). \({Y}_{jzm}^{*}={X}_{jz}^{{\prime }}{\beta }_{m}+{\epsilon }_{jz}\) (m = IS, F, R) (1) Where: \({X}_{jz}^{{\prime }}\) represents observed household, socioeconomic, institutional, and plot characteristics; \({\epsilon }_{jz}\) represents unobserved characteristics; m denotes the type of maize technology available; and \({\beta }_{k}\) denotes the vector of parameters to be estimated. The unobserved choice in Eq. (1) can be translated into the observed binary outcome equation for each technology choice as follows: $${Y}_{k}=\left\{\begin{array}{c}1 if {Y}_{jzm }^{*}>0\\ 0 otherwise \end{array}\right.(m=IS, F, R)$$ 2 In the MVP model, where the adoption of multiple maize technology packages is possible, the error terms jointly follow a multivariate normal distribution (MVN) with a zero conditional mean and variance normalized to unity. $$\varOmega =\left[\begin{array}{c} \begin{array}{ccc}{\rho }_{IS}& {\rho }_{CF}& {\rho }_{RP}\end{array}\\ \begin{array}{cccc}{\rho }_{SI}& 1& {\rho }_{IF}& {\rho }_{IP}\\ {\rho }_{FC}& {\rho }_{FS}& 1& {\rho }_{CP}\\ {\rho }_{PR}& {\rho }_{PI}& {\rho }_{CP}& 1\end{array}\end{array}\right]$$ The correlation between the stochastic components of the different types of maize technology packages that is unobserved is denoted by the off-diagonal elements in the covariance matrix. [ 45 – 47 ]. Table 2 demonstrates the variables expected to influence the adoption of multiple maize technology packages and the anticipated hypotheses. Table 2 Definition, measurement, and expected influence of variables used in the analysis Variables Description and unit Expected signs Dependent Variable: Adoption Adoption in this study refers to a farm household that adopted the maize technology packages (improved maize seed, chemical fertilizer (Urea and NPS) and row planting) on maize farming plots. These variables are represented as dummy variables, with a value of 1 indicating adoption and 0 indicating non-adoption for each technology. Explanatory variables AgeHH Age of the head of household (years) ± EduHH The education level of the head of household (years of formal education/grade) + SexHH Sex of head of the household (1 = male) ± Plot size Plot size allotted to maize production (in hectares) + Number of plots Number of maize plots owned by farm households (number) ± Household size Number of family members living under one roof (in adult equivalent) ± Livestock ownership(TLU) Livestock holding size (in TLU) ± Oxen owned Number of oxen owned by farm households (number) + Off-farm income The household income earned from off-farm employment (in ETB) ± Access to credit Access of household to credit (1 = yes) + Extension contact Agricultural extension advice delivered (number of times/frequency) + Membership to institutions Membership of household to various farmers-based institutions(1 = member) + Distance to a market center The average distance of a household to reach the major market center(in minutes) Distance to the main road The average distance of a household to reach the all-weather road (in minutes) Distance to farmers’ training center The average distance of a household to reach the farmers’ training centers (in minutes) Distance to maize plots The distance of household to reach the maize plots (in minutes) Source: Literature review 3. Results and discussion 3.1. Adoption status of maize technology packages Table 3 presents the status of the adoption of maize technology packages in the study area at the plot level. Maize technology packages considered in this study were improved maize seed, chemical fertilizer, and row planting. Of 545 maize plots of sampled farm households in the Sidama region, improved maize seed, chemical fertilizer, and row planting were adopted on 54, 45, and 44% of the maize plots, respectively. The row planting adoption rate is relatively lowest as compared to other maize technology packages. However, the percentage indicates the farming household that applied row spacing according to research recommendations [ 36 ]. Table 3 Distribution of maize technology package adoption at plot level (%) Maize technology package Frequency Percent Improved seed(IS) 294 53.94 Chemical fertilizer(F) 245 44.95 Row planting(R) 236 43.30 Source: Own survey data 3.2. Descriptive statistics of explanatory variables used in the analysis The summary statistics of variables that were supposed to influence the adoption of improved seed, chemical fertilizer and row planting are included in the MVP model and provided in Table 4 . The selection of these variables was based on the relevant literature review [ 27 , 33 , 38 , 39 , 48 ]. These variables include a range of household characteristics, biophysical, socio-economic, institutional, infrastructural, and plot-level characteristics. About 95% of sample households were male-headed in the study area, which is comparable to findings reported by Zenga [ 49 ] in Ethiopia. Concurrent to this report, 95, 94, and 96% of adopters of improved maize seed, chemical fertilizer, and row planting respectively were male-headed whereas 94% of non-adopters for each improved maize seed and chemical fertilizer and 93% of row planting were also male-headed. The average years of attending formal education of sampled households was 5.1 on average, though about 25 percent of the respondents were illiterate. Adopters of improved maize seed, chemical fertilizer, and row planting attained 5.4, 5.5, and 5.3 years of formal schooling respectively. Whereas non-adopters of improved maize seed, chemical fertilizer, and row planting attained 4.72, 4.67, and 4.89 years of schooling respectively. The age of the household head ranges from 24 to 80 with an average age 45 years which is comparable to the findings reported by Zegeye[ 50 ] The mean age of the adopters of improved maize seed, chemical fertilizer, and row planting were roughly 45 years individually and non-adopters of the same variables were about 46 years. The average family sizes of farming households range roughly from 2 to 10 with a 4.7 average value of an adult equivalent. However, the family size of adopters of improved seed, chemical fertilizer, and row planting were 4.90, 4.93, and 4.80 respectively and non-adopters of the same variables were 4.51, 4.54, and 4.65 respectively. Moreover, the unit of tropical livestock owned by the sampled households was averaged at 2.85 TLUs varying from 0 to 15.75 TLUs. However, adopters of improved seed, chemical fertilizer, and row planting owned 3.7, 3.76, and 3.6 TLUs respectively and non-adopters of the same variables owned 1.9, 2.1, and 2.2 TLUs, respectively. The average maize plot size owned by sampled households was 0.55 ha, whereas adopters of improved maize seed, chemical fertilizer, and row planting owned 0.55, 0.71, and 0.76 ha, respectively and non-adopters owned 0.55, 0.42, and 0.40 ha, respectively. Besides, adopters of improved maize seed, chemical fertilizer, and row planting earned off-farm income of ETB 12107.80, ETB14281.60, and ETB9993.60 and non-adopters of the same variables earned off-farm income averaging at ETB7747.50, ETB6684.30, and ETB10180.70 respectively. However, the sampled household earned ETB 10099.68 on average annually. Among institutional variables, about 61% of improved seed as well as chemical fertilizer adopters and 59% of row planting adopters were able to access credit and 43, 47, and 48% of non-adopters of the same variables were also able to access credit. On average, farmers contacted extension agents 2.12 times per production season. About 87, 86, and 77% of adopters of improved seed, chemical fertilizer, and row planting were members of one or multiple social institutions, respectively and 49, 53, and 64% non-adopters of the same variables were also members. These results imply that the farm households who used improved maize technology packages were comparably male, educated, and have bigger family sizes, owned greater plot sizes and tropical livestock units as well as got greater access to credit, extension, and membership to institutions. These results are comparable to the findings reported by [ 26 , 50 – 52 ]. Table 4 shows descriptive statistics of explanatory variables used in the analysis. Table 4 Descriptive statistics of sampled farming household characteristics Variables Improved maize seed Fertilizer Row planting Total sample Adopters Non-adopters Adopters Non-adopters Adopters Non-adopters Mean (SE) Mean(SE) Mean (SE) Mean(SE) Mean(SE) Mean(SE) Mean (SE) Sex of household head 0.95(0.012) 0.94(0.014) 0.94(0.014) 0.94(0.012) 0.96**(0.012) 0.93(0.014) 0.948(0.009) Education of household 5.4**(0.24) 4.72(0.25) 5.55**(0.27) 4.67(0.23) 5.30(0.270) 4.89(0.232) 5.1(0.176) Age of household head 45.2(0.628) 46.4(0.76) 45.15(0.721) 46.27(0.66) 45.77(0.755) 45.76(0.64) 45.76(0.487) Family size 4.90***(0.09) 4.51(0.09) 4.93**(0.102) 4.54(0.082) 4.809(0.098) 4.65(0.086) 4.72(0.064) Pilot size 0.550(0.026) 0.556(0.19) 0.709**(0.20) 0.426(0.02) 0.759**(0.21) 0.39(0.02) 0.553(0.092) Livestock size owned 3.70***(0.17) 1.87(0.108) 3.76***(0.18) 2.11(0.118) 3.63***(0.20) 2.26(0.111) 2.85(0.111) Oxen size owned 1.12***(0.04) 0.57(0.045) 1.13***(0.05) 0.65(0.044) 1.03***(0.05) 0.737(0.042) 0.867(0.033) Off-farm income 12107***(1327) 7747(1033) 14281***(1618) 6684(798) 9993.6(1176) 10180.7(1233) 10099.68(864.2) Access to credit 0.61***(0.03) 0.438(0.03) 0.61***(0.03) 0.47(0.028) 0.59**(0.031) 0.478(0.028) 0.5302(0.02) Access to extension visit 2.55***(0.13) 1.55(0.135) 2.39**(0.143) 1.85(0.127) 2.79***(0.15) 1.559(0.116) 2.093(0.095) Membership to institution 0.87***(0.02) 0.529(0.031) 0.86***(0.02) 0.59(.028) 0.805***(0.02) 0.644(0.027) 0.6990(0.02) Distance to main road 19.05(1.387) 20.1(0.47) 19.05(1.38) 20.95(1.47) 19.85(1.57) 19.98(1.31) 19.92(1.01) Distance to main market 49.28**(1.20) 52.3(1.25) 50.66(1.326) 50.68(1.15) 51.47(1.31) 50.07(1.16) 50.68(0.870) Distance to FTC 14.26 (0.641) 15.5(0.75) 14.28(0.697) 15.30(0.68) 15.13(0.832) 14.62(0.586) 14.84(0.490) Distance to maize plots 2.81***(0.24) 8.176(1.07) 3.15***(0.55) 7.02(0.825) 2.98(0.281) 7.039(0.885) 5.28(0.523) Number of maize plots 1.67***(0.04) 1.37(0.038) 1.66***(0.05) 1.43(0.038) 1.66***(0.05) 1.440(0.034) 1.53(0.030) **and ** * are significant at the 5 and 1 percent probability levels, respectively. Source: Own survey data 3.3. Econometric estimations 3.3.1. Correlation among improved seed, chemical fertilizer, and row planting The conditional probabilities of adopting improved seed, fertilizer, and row planting are given in Table 5 . The conditional probability of adopting improved seed, fertilizer and row planting, however, highlighted the existence of the relationship. For instance, the probability of adopting improved seed increases from 54–72%, 70%, and 83% conditional on the adoption of fertilizer, row planting, and both fertilizer and row planting, respectively. The probability of adopting fertilizer also increases to 60%, 52%, and 61% conditional on adopting improved seed, row planting, and both improved seed and row planting respectively. Furthermore, the conditional probability of adopting row planting rises marginally from 43–56%, 50%, and 58% conditional on adopting improved seed, fertilizer, and both improved maize seed and fertilizer. This implies the existence of a positive correlation (complementarities) among the maize technology packages in this study. Table 5 Unconditional and conditional probabilities of adoption Maize technology packages Condition Improved seed(IS) Fertilizer(F) Row planting(R) P(Yk = 1) 0.54 0.45 0.43 P(Yk = 1|IS = 1) 1.000 0.60*** 0.56*** P(Yk = 1|F = 1) 0.72*** 1.000 0.50*** P(Yk = 1|R = 1) 0.70*** 0.52*** 1.000 P(Yk = 1|IS = 1, F = 1) 1.000 1.000 0.58*** P(Yk = 1|IS = 1, R = 1) 1.000 0.61*** 1.000 P(Yk = 1|F = 1, R = 1) 0.83*** 1.000 1.000 Note: YK is a binary variable representing the adoption status concerning choice k (k = IS, F and R). Table 6 presents the estimates of pairwise correlation coefficients of the error terms in the three equations. From the MVP model estimations, the results revealed that the correlation coefficients of the error terms are statistically significant for all pairs of equations, and in this case, two of the three cases are statistically different from zero (Table 6 ), approving the appropriateness of the MVP specification. The finding of the correlation coefficients of the error term shows that there is a positive relationship (complementarity) among the maize technology packages considered in this study. The simulated maximum likelihood estimation results also revealed that there were positive and significant relationships between household decisions to adopt improved seed and fertilizer (ρ21) and improved seed and row planting (ρ31). In addition, there were positive but insignificant relationships between the adoption of fertilizer and row planting (ρ32). The complementarity of these technologies is expected, especially in relatively bigger and commercialized farms, and these findings are in line with research recommendations. Table 6 Correlation coefficient estimation for the error terms from the three adoption equations of improved seed, fertilizer and row planting Parameter Coefficient Standard error P-value 95% of Confidence interval ρ21 0.2599843 0.0734561 0.000 0.1112214 0.3973512 ρ31 0.2970848 0.0720593 0.000 0.1502671 0.4310818 ρ32 0.0042945 0.0745312 0.954 -0.140844 0.1492523 Joint probability (success) 0.1866(0.180) Joint probability (failure) 0.2447(0.219) Predicted probability IS = 0.54(0.25) F = 0.45(0.23) R = 0.43(0.20) Likelihood ratio test of rho21 = rho31 = rho32 = 0:chi2 (3) = 26.5233 Prob > chi2 = 0.0000 Note: The indexes refer to the equations: 1 = improved seed, 2 = fertilizer, 3 = row planting 3.3.2. Determinants of improved seed, fertilizer, and row planting adoption Table 7 presents the coefficients of the MVP adoption model. The MVP model fits the data reasonably well. The Wald test that all regression coefficients are jointly equal to zero is rejected [Wald chi2 (48) = 341.61, \({\chi }^{2}\) =0.0000]. Furthermore, it suggests that coefficients are jointly significant and that the explanatory power of the variables included in the model was satisfactory. The likelihood ratio test of the null hypothesis of independence between maize technology sets is significant (chi2 (3) = 26.5233, p-value= 0.0000<0.01) implying that the multiple uses of technology is not mutually exclusive but interdependent. Thus, the null hypothesis ( \(Ho\) ) that all the correlation coefficient \(\rho \left(rho\right)\) values are jointly equal to zero is rejected, denoting the best fit of the model and consequently supporting the application of MVP modeling. Despite the motivation of farmers to adopt a combination of maize technology packages, there are many factors that influence their decision to choose a particular technology packages. From the sixteen explanatory variables included in the analysis, age of household head, family size, plot size, livestock and oxen ownership, off-farm income, access to credit and extension services, membership in institutions, number of maize plots owned, and plot distance were significantly influenced at least one of the maize technology packages adoption in the study area. Among household characteristics, the age of the household was found to be positively and significantly related to maize row planting adoption at a 5% significance level. The marginal effect of 0.004 for age suggests that an increase in the age of farmers by one year would increase the probability of adopting row planting by 0.4%. Age is a good proxy for farming experience in agricultural activities. Therefore, as farmers get older, they gain expertise related to improved technology adoption and application. This infers that the greater the experience of farmers, the more likely they are to adopt improved technology packages. This result is in line with the study by [ 54 , 55 ]. The household size of farm households has a positive and significant effect on the adoption decision for chemical fertilizer packages. The marginal effect for household size is 0.026 suggesting an increase in family size in AE would increase the probability of adopting chemical fertilizer by 2.6%. Household size is a proxy for labor availability, and larger households can ease labor constraints during peak production season and help to effectively practice technology packages adoption compared to their counterparts. This demonstrates that larger household size, the more the adoption will be, because the adoption of multiple maize technology packages requires more labor. This result is consistent with the findings of [ 33 , 48 , 56 , 57 ]. Livestock ownership was found to have a positive relationship with improved seed, fertilizer, and row planting at a 1% significance level. The marginal effect of livestock ownership is 0.045, 0.038 and 0.031 for improved seed, chemical fertilizer, and row planting, respectively. This deduces that as the ownership of livestock increases by one TLU the probability of adopting improved seed, chemical fertilizer, and row planting would increase by 4.5, 3.8, and 3.1%, respectively. Livestock ownership is an indicator of being wealthy in a rural part of Ethiopia. Farmers in the study area raise the issue of financial constraints to purchase market inputs, as well as hiring labor and renting oxen during the production season. Thus, farmers owning more livestock units solved the financial shortage partly through income from livestock sales. This finding is consistent with the study by [ 9 , 19 , 27 , 48 ]. In addition, apart from livestock ownership, oxen ownership was also found to be positively related to improved seed and fertilizer adoption at a 1% significance level. The marginal effect of 0.054 and 0.078 for improved seed and chemical fertilizer adoption, respectively, implies that an increase in ox (en) ownership would increase the probability of adopting improved seed and fertilizer by 5.4 and 7.8%, respectively. Ownership solves the issues of both labor and financial shortages. This is because farm households that have ox (en) can plow relatively more farmland, prepare their land well, and sow on time, which would assist them to get a better yield and improve their food security and income. As a result, they have a greater probability of adopting improved technology packages than their counterparts. This result is consistent with the findings of [ 52 , 59 , 60 ]. Furthermore, off-farm income has a positive and significant relationship with fertilizer application at a 1% significance level. The marginal effect of 0.0001 for off-farm income suggests that as the off-farm income increases by one Ethiopian birr, the probability of adopting chemical fertilizer would increase by 0.01%. Purchasing chemical fertilizer on time has been the bottleneck for Ethiopian smallholder farmers due to late supply and skyrocketing prices. Thus, income from off-farm employment solves the financial liquidity problem during peak production season. This finding is in line with the findings of [ 26 , 41 , 61 ]. Among institutional variables, access to credit was found to have a positive and significant relationship with improved seed and row planting adoption at a 5% significance level. The marginal effect of access to credit is 0.064 and 0.070 for adopting improved seed and row planting, respectively. This denotes that those farm households that have access to credit are more likely to adopt improved seed and row planting by 6.4 and 7%, respectively, than those who have no access to it. Access to credit solves cash constraints that households could face at the time when they want to purchase agricultural inputs and hire labor (rent oxen). Hence, access to credit paves the way for the timely application of modern farm inputs. Thus, farmers who have access to credit have a higher possibility of adopting the technology packages than their counterparts. Thus, access to credit increases the likelihood of adopting maize technology packages. The current findings harmonize with past findings [ 48 , 61 – 63 ]. Extension contact was found to have a positive and significant effect on row planting adoption at a 1% significance level. The marginal effect of 0.038 for frequency of extension contact implies that as the frequency of extension contact increases, the probability of adopting row planting would increase by 3.8%. The plausible reason for this is that extension contact serves as the center of information sources as well as technical know-how for the improved agricultural technology packages. This is evident as the household head's extension contact raises awareness, provides timely access to and uses of information, and fills skill gaps to adopt improved technologies. Extension contact is a potential force that speeds up the effective adoption of improved agricultural technologies by farmers. Therefore, the more access to technical knowledge and information farmers have, the more likely they are to adopt technology packages. This result is in line with the findings [ 33 , 41 , 48 , 58 ]. Membership in any of the formal or informal farmer-based institutions has positive and significant influences on the adoption of improved maize seed at a 5% significance level. The marginal effect of membership in farmer-based institutions is 0.128, inferring that as membership in institutions increases, the probability of improved maize seed adoption would increase by 12.8%. Membership in any form of institution is a proxy for information and input access. Membership in social institutions helps the farmers get labor during peak production season. Farmers require more labor support from relatives and social institutions for labor sharing during peak production times for sowing, planting, weeding, harvesting and threshing. The social networks that they have in farmer-based institutions help farmers access information about market prices, improve production packages, and share their experiences. Thus, being a member of farmer-based institutions increases the likelihood of a farmer adopting the maize technology packages. This finding is consistent with [ 66 – 68 ]. Among plot-level characteristics, maize plot size was found to have a positive relationship with the adoption of row planting. The marginal effect for plot size is 0.13, which infers that an increase in plot size by one hectare would increase the probability of adopting row planting by 13%. The size of maize plots owned is an indicator of wealth in rural parts of Ethiopia; thus, households with more land can afford the use of commercialized inputs such as improved seed and fertilizer as compared to their counterparts, because row planting goes along with improved maize varieties and chemical fertilizer adoption. Farmers are unwilling to adopt row planting with local varieties in the study area. This finding is consistent with the study [ 10 , 39 ]. However, this result contradicts the finding [ 69 ], which found that farmers with small and marginal plots are more likely to adopt than farmers with large plots. A number of plots owned were also found to have a positive and significant effect on maize row planting adoption at a 5% significance level. The marginal effect of 0.057 on number of plots owned deduces that as the number of plots owned increases by one plot, the probability of adopting row planting would increase by 5.7%. This means that farm households with several numbers of maize plots would adopt maize technology packages more than their counterparts. This is because producing on several plots spreads the risk of crop loss compared to a lesser number of plots. In addition, crop failure may not concurrently occur on all the plots owned by the farm households. This in turn encourages farmers to adopt improved maize technology packages and make necessary investment on the plots. This finding is also in harmony with the findings by [ 54 , 70 ]. In line with the prior expectation, the maize plots distance from farmers’ residences affected the adoption of improved seed, chemical fertilizer, and row planting negatively and significantly at a 1% significance level. The marginal effect of -0.009,-0.005, and − 0.007 for plot distance implies that as the distance of the maize plot increases by one minute, the probability of adopting improved maize seed, chemical fertilizer, and row planting would decrease by 0.9, 0.5, and 0.7%, respectively. This is plausible because, as the residence of the farmers is far away from the plots, they give less attention and are less likely would be preparation of land, sow, input use, weed, and harvest. The fact behind this is that the distance to plots is associated with extra transportation costs, energy, and time. This result is line with the findings [ 13 , 39 , 56 ]. Table 7 MVP simulation results for household maize technology packages adoption Improved maize seed Chemical fertilizer Row planting Variables Coefficient Std. Err. Marginal effect Coefficient Std. Err. Marginal effect Coefficient Std. Err Marginal effect Sex of the household 0.037 0.272 0.0113 -0.178 0.272 -0.058 0.352 0.239 0.118 Education of the household head 0.007 0.016 0.0021 0.017 0.016 0.005 0 .007 0.016 0.002 Age of the household 0.001 0.006 0.0004 0.001 0.006 0.0003 0.011** 0.006 0.004 Family size -0.024 0.017 0.0152 0.081** 0.041 0.026 -0.042 0.041 -0.014 Plotsize -0.025 0.052 -0.0075 0.079 0.127 0.026 0.392** 0.159 0.131 Livestock size(TLUs) 0.148*** 0.029 0.045 0.117*** 0.030 0.038 0.092*** 0.025 0.031 Oxen ownership 0.175** 0.092 0.054 0.238** 0.093 0.078 0.084 0.094 0.028 Off-farm income 4e-06 3e-06 1e-06 0.0001*** 3e-06 3.4e-06 -2.4e-06 3e-06 -8e-07 Access to credit 0.208** 0.122 0.064 0.189 0.120 0.062 0.209** 0.119 0.070 Extension contact 0.039 0.030 0.0123 -0.021 0.030 -0.007 0.113*** 0.030 0.038 Membership to institutions 0.415** 0.167 0.128 0.249 0.170 0.082 -0.102 0.168 -0.034 Distance to the main road 0.002 0.003 0.0005 0.002 0.003 0.0005 0.002 0.003 0.0007 Distance to a main market center -0.004 0.003 -0.0014 0.002 0.003 0.0008 0.0008 0.003 0.0003 Distance farmers’ training center -0.006 0.005 -0.0019 -0.005 0.005 -0.002 0.002 0.005 0.0006 Distance to maize plots -0.029*** 0.006 -0.0090 -0.016** 0.008 -0.005 -0.023*** 0.005 -0.007 Number of plots 0.136 0.093 0.0421 0.069 0.089 0.023 0.171** 0.084 0.057 Constant -1.132** 0.481 -1.526*** 0.492 -1.905*** 0.446 Joint probability (success) = 0.186(0.20) joint probability (failure) = 0.244 (0.22) MVP(SML, number of random draws = 100) Log-likelihood= -920.84532 Number of obs = 545 Wald chi2(48) = 341.61, \({\chi }^{2}\) =0.0000 *** and ** are significant at 1% and 5% probability levels, respectively. Source: Model output 4. Conclusion and recommendations Adoption of improved agricultural practices in Ethiopian agriculture is out of the question, since expanding the production area (land) seems nearly impossible. Thus, increasing agricultural productivity through the use of improved agricultural technology packages is an important solution to improve the supply side, alleviate poverty, and address food insecurity. Hence, the country has been implementing agricultural development initiatives starting since decades ago. Despite the efforts to promote the adoption of agricultural technologies by Ethiopian farmers in most rural areas, adoption rates in the country have been very low, and this is also the case in the study area. Therefore, understanding the factors that encourage or impede the adoption of maize technology packages is important for planning and implementing different strategies that improve yield and productivity. Therefore, the objective of this study was to identify the factors that influence the adoption of multiple maize technology packages in the northern Sidama zone of the Sidama national regional state of Ethiopia. The MVP result revealed that education level, age, family size, tropical livestock unit and oxen ownership, off-farm income, access to credit and extension contact, membership in institutions and several plot ownership were significantly and positively related to the adoption of at least one maize technology packages, whereas the distance to maize plots significantly and negatively affected the adoption of maize improved seed, chemical fertilizer, and row planting in the study area. Based on the research findings, the following policy recommendations are made: the concerned bodies engaged in maize technology packages promotion need to address important variables identified in this study. The government should strengthen and deliver quality extension services, encourage membership in farmers-based social institutions to underpin farmer-to-farmer knowledge and input access, avail credit access, encourage off-farm employment before peak production season, deliver production inputs on time for affordable prices, and technologies that save labor to promote row planting and to achieve broader adoption of the technology packages. This study was limited to using cross-sectional datasets. Hence, it might not appropriately capture farmers’ re-adjustment decisions of resource allocations in response to their adoption of improved seed, fertilizer and row planting based on changes in perception, weather as well as market prices. Therefore, future research should focus on adoption dynamics using more representative panel data. Declarations Acknowledgements We greatly acknowledge the Ethiopian Ministry of education for funding this study. Further, we acknowledge the respondents for cooperating us for relevant data collection. Data collectors and agricultural experts from regional office to development agents are highly appreciated for their support during data collection. Author contributions We declare that we are the authors of this research article. During data collection and analysis, all ethical and technical academic standards were followed. Every source of information used in this article has been duly acknowledged. AG developed the proposal, conceived and proposed the methodology, collected the field data, analyzed and interpreted the data and wrote the paper. TT, MS, and AE contributed to the methodology, supervised the entire research work, commented on the draft manuscript and approved the final manuscript to submission. Funding statement This study was funded by the Ethiopian Ministry of Education (Hawassa University). Data availability The corresponding author confirms that the data used in this study will be available up on request. The data is not openly presented to keep the privacy of the responding individuals. Ethics approval and consent to participate Ethical approval was obtained from the Haramaya University post-graduate research office committee to conduct this study. Thus, this study was carried out in accordance with the University research ethics standards and guidelines. Informed consent Each respondent’s participation in this study was based on informed consent. The informed consent form was read out to the respondents in local language before the beginning of data collection. Respondents were well-informed about the purpose of the study, confidentiality of the information, voluntary based participation, and their right to withdraw from the interview at any point in time while interviewing. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviews received at journal 07 Jul, 2024 Reviews received at journal 03 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers invited by journal 14 Jun, 2024 Editor assigned by journal 29 May, 2024 Submission checks completed at journal 29 May, 2024 First submitted to journal 16 May, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4428885","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311451894,"identity":"17bbde8a-cf63-419d-b814-9d5849dcbac3","order_by":0,"name":"Ashenafi Guye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDCCA1CSX/4wiCkhQ7wWyRlsCSAtPMRrMbjBYwBiEdbCdyM78cOPP3cSG273fH51o8aCh4H98NEN+LRI3sjdLNnb9iyxcc7ZbdY5x4AO40lLu4FPi8GN3A0SvA2HE5sZcrcZ57ABtUjwmBHSsvnnnz+HE9sYcp4Z5/wjTss2aR62w4k9EjnMj3PbiNAieebtNmvZtsPGM3iOmTHn9knwsBHyC9/x3M033/w5LLv/ePPjzznf6uT42Q8fw6sFGbBJgElilYMA8wdSVI+CUTAKRsHIAQCsWVLr6FPMJgAAAABJRU5ErkJggg==","orcid":"","institution":"Haramaya University","correspondingAuthor":true,"prefix":"","firstName":"Ashenafi","middleName":"","lastName":"Guye","suffix":""},{"id":311451895,"identity":"e90e0d69-6f73-4012-8557-884c99c818c7","order_by":1,"name":"Tewodros Tefera","email":"","orcid":"","institution":"REFOOTURE Ethiopia Project Manager and Co-strategic lead","correspondingAuthor":false,"prefix":"","firstName":"Tewodros","middleName":"","lastName":"Tefera","suffix":""},{"id":311451896,"identity":"5b9a1c35-bafe-494a-adf7-927f81d3ba98","order_by":2,"name":"Million Sileshi","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Million","middleName":"","lastName":"Sileshi","suffix":""},{"id":311451897,"identity":"eabea3c1-cac9-4a74-9f7c-d8a35b9adf6b","order_by":3,"name":"Abdi-Khalil Edriss","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Abdi-Khalil","middleName":"","lastName":"Edriss","suffix":""}],"badges":[],"createdAt":"2024-05-16 06:34:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4428885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4428885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58112451,"identity":"736be801-40b3-4179-8605-7bf76151defd","added_by":"auto","created_at":"2024-06-11 09:52:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137567,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area location map\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4428885/v1/9c88bbb6fdff7ee576180958.png"},{"id":58113273,"identity":"dcc4ea22-6b96-4bd9-a83c-1c071283615f","added_by":"auto","created_at":"2024-06-11 10:00:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1167999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4428885/v1/7ab389df-c5e6-4736-a439-a2871cc463ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of multiple maize technology packages adoption in Ethiopia: Evidence from Sidama region","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEthiopia ranked second in Africa with over 123\u0026nbsp;million people, following Nigeria, and showed the fastest-growing economy in the region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, it also remains one of the most poverty-stricken developing countries, with \u003cspan\u003e$\u003c/span\u003e1,020 per capita gross national income, though it aims to achieve lower-middle-income status by 2025 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The economic growth of the country is highly dependent on the performance of agriculture, which is basically the milestone of the country\u0026rsquo;s economy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, this sector contributes about 32.4% of GDP, 75% of export earnings, 73% of job opportunities, and 70% of raw materials for domestic industries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEven though Ethiopia has a rich agricultural history, it has been facing many challenges in the sector. Agricultural productivity is low due to a lack of private investment, fragmented markets, farmers' limited access to resources, low subsidies, soil degradation, low application of modern agricultural inputs, inefficiencies, subsistence farming, poor infrastructure, vulnerability to environmental and climate-related shocks, reliance on outdated farming methods, and rain-fed agriculture leading to fluctuations in yields [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Non-stop internal conflict and the worst drought in recorded history are a factor in the worsening of these challenges.\u003c/p\u003e \u003cp\u003eOn the other hand, as the best prospect sector for growth in Ethiopia, the government developed and ended two consecutive growth and transformation plans (GTP I and II) and has embarked on a ten-year economic development plans (2021\u0026ndash;2030) where agriculture is at the top of priority sectors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Over the next ten years, the agriculture industry is predicted to grow by 6.2% annually. In a country where land is scarce, the adoption of agricultural technologies plays a crucial role in enhancing agricultural productivity and reducing food insecurity [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Thus, to hit the target, the use of improved agricultural technology packages is a critical approach [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the adoption of agricultural technologies at the national level is still significantly lower as compared to many developing countries [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This indicates a need for additional evidence-based initiatives and strategies to promote the adoption of agricultural technologies in a wider scope.\u003c/p\u003e \u003cp\u003eNumerous studies have been conducted in Ethiopia on the factors that affect the adoption of agricultural technologies [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and on maize technology adoption specifically [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, these studies analyzed the determinants of the adoption of agricultural technologies, focusing on the adoption of a single component of the technology package, paying little attention to correlations among the technologies, and the possible simultaneity of the multiple adoption decisions. Even though the majority of the farmers have been adopting a combination of technologies, none of them showed farmers\u0026rsquo; multiple adoption decisions towards maize technology packages.\u003c/p\u003e \u003cp\u003eMoreover, in terms of methodology, most of the previous studies conducted adopted univariate or binary choice models to investigate determinants of agricultural as well as maize technology adoption, assuming adoption of those technologies is mutually exclusive with little or no interdependency among them. Yet, smallholders adopt several complementary or substitution technologies at a time, and using binary choice models can be biased and inefficient. Hence, adoption decisions may be correlated and best characterized by a multivariate probit model. Maize (\u003cem\u003eZea mays L.)\u003c/em\u003e is a vital food crop and is broadly grown by smallholder farmers in several regions of Ethiopia, though its national average yield (39 quintal/ha) is by far below the world\u0026rsquo;s average (56.6 quintal/ha) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This demonstrates that Ethiopia is producing below the actual attainable yield. The strategy to reduce the yield gap is to increase agricultural productivity through the adoption of improved maize technology packages. Sidama is one of the maize-producing regions in Ethiopia [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Despite its maize production potential, no comparable studies were conducted in this area. The findings of this study will provide evidence-based empirical information for government and development partners for relevant interventions. In addition, the findings of this study can support policymakers in developing appropriate policies to improve the production and productivity of maize crop by encouraging wider adoption. As a result, the livelihood of farm households will be improved through increased income and food security. Therefore, this study aimed to assess the factors that drive or hamper the adoption of multiple maize technology packages in selected districts of the Sidama region of Ethiopia.\u003c/p\u003e"},{"header":"2. Study methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study area description\u003c/h2\u003e\n \u003cp\u003eThis study was undertaken in three randomly selected districts (Woredas)\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e namely, Hawassa Zuria, Boricha, and Shebadino in Sidama Region, Ethiopia (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Sidama is one of the 13 regions in Ethiopia and located in the Southeastern part of Ethiopia, 275 km south of Addis Ababa. It is surrounded by the Oromia Region in the North, North-East and South-East, the Gedeo Zone in the South and the Wolaita Zone in the West. The capital of the region is located in Hawassa. Sidama is the most densely populated region in Ethiopia. In 2020 by considering the land mass of the region, the crude population density of the region is 674 persons per km\u003csup\u003e2\u003c/sup\u003e. The livelihood of the people in the region highly depends on rain-fed agriculture and mixed farming systems. The region is known for its maize production potential in the country [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, improving maize productivity through modern maize technology adoption has a magnificent impact on the livelihood of land scarcely populated communities of the region. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) displays the study area location map.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Data sources, types, and collection method\u003c/h2\u003e\n \u003cp\u003eIn this study, both primary and secondary data were collected to address the research objectives. The primary data set consists of farm family characteristics, socio-economic, institutional, infrastructural, plot and different elements in the maize production packages that were collected. This data set was gathered through semi-structured questionnaires. Secondary data was collected from government and non-governmental offices, the Central Statistics Authority (CSA), the Sidama Regional Bureau of Agriculture, and the Plan and Development Commission Bureau. The collection of relevant secondary information was often based on both published and unpublished documents.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Sampling procedures\u003c/h2\u003e\n \u003cp\u003eThe study was mainly based on cross-sectional data obtained from a farm household selected through a multi-stage sampling technique. In the first stage, three Woredas mentioned above were randomly selected from 10 potential maize-producing Woredas of the Sidama Regional State. Eleven maize producing Kebeles\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e were selected in the second stage, by using a simple random sampling method (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In the third stage, 424 farm households were randomly selected and interviewed based on probability proportional to the sizes of each Kebele and Woreda (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Among these households, there were 102 non-adopters and 322 adopters. Additional data were gathered at the plot level, resulting in 545 observations on the number of plots cultivated with maize during the 2022 production period. The schedule was first pre-tested on 12 randomly selected maize farming households, and some modifications were made to the questionnaire before the execution of the formal survey. Enumerators who are familiar with the study area, who can understand the local language, and who have prior experience in data collection were trained, recruited, and supervised by the researcher.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of sample maize farming households across Woreda and Kebele\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWoreda (district)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKebele (village)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaize farming households\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eHawassa Zuria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJaraqarara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJaradamuwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLabukoromo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDoyootilicho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSamaejersa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoricha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQonsorechafa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQonsoregoge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eShebadino\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSadeqa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlawoano\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQonsoreano\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMorochoshondolo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSource: (WoARD, 2022).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data analysis method\u003c/h2\u003e\n \u003cp\u003eDescriptive and inferential statistics were used to analyze the demographic and socio-economic characteristics of farmers. A multivariate probit model (MVP) was used to identify factors influencing the adoption decisions of multiple maize technology packages in the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Determinants of improved seed, chemical fertilizer, and row planting adoption\u003c/h2\u003e\n \u003cp\u003eFollowing the seminal work of Cappellari and Jenkins [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] and others [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] multivariate probit model (MVP) was adopted to explore the factors that determine the adoption of multiple maize technology packages in the study area. In binary choice models, the information about a farmer\u0026rsquo;s adoption of a single technology package does not alter the likelihood of the decision to adopt another technology. Thus, probit and logit models were widely used to model discrete decisions.\u003c/p\u003e\n \u003cp\u003eIn addition, a bivariate model has a limited applicability to estimate interdependent decisions. Subsequently, Dorfman [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] adopted multinomial logit to model multiple adoption decisions. However, still multinomial logit method has its own drawbacks, even if it estimates the influence of the explanatory variables on dependent variables that includes multiple choices with unordered response categories [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Multinomial logit classifies the household into different categories based on the technology packages adopted. As a result, it cannot take into consideration the factors that cause households to choose a particular set of the technology packages at the same time. It assumes that the household that assigned to a particular adoption category will not participate in another adoption category. Thus, multinomial logit is only appropriate when the adoption categories are independent and mutually exclusive.\u003c/p\u003e\n \u003cp\u003eOn the contrary, MVP is appropriate when different technology packages are interdependent, and it fills the gap in the aforementioned methods. The MVP is a method of interrelated binary response regression model that estimates the effect of a set of dependent variables on each of the different maize technology packages adoption at the same time. It allows free correlation of noise terms and correlations between adoptions of different maize technology packages [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. This study assumed that the selected maize technology packages are correlated and not mutually exclusive, and therefore the use of MVP is the best fit.\u003c/p\u003e\n \u003cp\u003eFurthermore, the random utility assumption is a basis for modeling the observed outcome of the multiple maize technology packages adoption. Theoretically, smallholder producers in developing countries generally, and in Ethiopia in particular, produce crops in uncertain and risky production schemes. Hence, producers would invest in a given technology if the expected utility from the adoption \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{m}\\)\u003c/span\u003e\u003c/span\u003e is higher than the expected utility \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{0}\\)\u003c/span\u003e\u003c/span\u003e without adoption [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. Consider the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({j}^{th}\\)\u003c/span\u003e\u003c/span\u003e farm household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((j=1,\\dots ., N)\\)\u003c/span\u003e\u003c/span\u003e confronting a decision to adopt or not to adopt the available yield-improving maize technology packages on the plot z (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(z=1,\\dots .., Z\\)\u003c/span\u003e\u003c/span\u003e) over a given time boundary. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{0}\\)\u003c/span\u003e\u003c/span\u003e denotes the gains to the maize producers from the old technology adoption, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{m}\\)\u003c/span\u003e\u003c/span\u003e denote the gains from improved technology packages adoption, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\)\u003c/span\u003e\u003c/span\u003e represents the choice of improved maize seed (IS), chemical fertilizer (F), and row planting (R). As aforementioned, the smallholder producers decide to adopt the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}^{th}\\)\u003c/span\u003e\u003c/span\u003e maize technology packages on a plot \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(z\\)\u003c/span\u003e\u003c/span\u003e of if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{jzm}^{*}={U}_{jzm}^{*}-{U}_{0}\u0026gt;0\\)\u003c/span\u003e\u003c/span\u003e. This net gain (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{jzm}^{*}\\)\u003c/span\u003e\u003c/span\u003e) that the smallholders derive from the adoption of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}^{th}\\)\u003c/span\u003e\u003c/span\u003e improved maize technology packages on a pilot \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(z\\)\u003c/span\u003e\u003c/span\u003e is a latent variable and influenced by observable factors (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{jz}\\)\u003c/span\u003e\u003c/span\u003e) as well as the noise term (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{jz}\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({Y}_{jzm}^{*}={X}_{jz}^{{\\prime }}{\\beta }_{m}+{\\epsilon }_{jz}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003e(m\u0026thinsp;=\u0026thinsp;IS, F, R)\u003c/em\u003e (1)\u003c/p\u003e\n \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{jz}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e represents observed household, socioeconomic, institutional, and plot characteristics; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{jz}\\)\u003c/span\u003e\u003c/span\u003e represents unobserved characteristics; \u003cem\u003em\u003c/em\u003e denotes the type of maize technology available; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{k}\\)\u003c/span\u003e\u003c/span\u003e denotes the vector of parameters to be estimated. The unobserved choice in Eq. (1) can be translated into the observed binary outcome equation for each technology choice as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${Y}_{k}=\\left\\{\\begin{array}{c}1 if {Y}_{jzm }^{*}\u0026gt;0\\\\ 0 otherwise \\end{array}\\right.(m=IS, F, R)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the MVP model, where the adoption of multiple maize technology packages is possible, the error terms jointly follow a multivariate normal distribution (MVN) with a zero conditional mean and variance normalized to unity.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\varOmega =\\left[\\begin{array}{c} \\begin{array}{ccc}{\\rho }_{IS}\u0026amp; {\\rho }_{CF}\u0026amp; {\\rho }_{RP}\\end{array}\\\\ \\begin{array}{cccc}{\\rho }_{SI}\u0026amp; 1\u0026amp; {\\rho }_{IF}\u0026amp; {\\rho }_{IP}\\\\ {\\rho }_{FC}\u0026amp; {\\rho }_{FS}\u0026amp; 1\u0026amp; {\\rho }_{CP}\\\\ {\\rho }_{PR}\u0026amp; {\\rho }_{PI}\u0026amp; {\\rho }_{CP}\u0026amp; 1\\end{array}\\end{array}\\right]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe correlation between the stochastic components of the different types of maize technology packages that is unobserved is denoted by the off-diagonal elements in the covariance matrix.\u003c/p\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. Table 2 demonstrates the variables expected to influence the adoption of multiple maize technology packages and the anticipated hypotheses.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTable\u0026nbsp;2 Definition, measurement, and expected influence of variables used in the analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription and unit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpected signs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variable: Adoption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption in this study refers to a farm household that adopted the maize technology packages (improved maize seed, chemical fertilizer (Urea and NPS) and row planting) on maize farming plots. These variables are represented as dummy variables, with a value of 1 indicating adoption and 0 indicating non-adoption for each technology.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExplanatory variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgeHH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of the head of household (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEduHH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe education level of the head of household (years of formal education/grade)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSexHH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex of head of the household (1\u0026thinsp;=\u0026thinsp;male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlot size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlot size allotted to maize production (in hectares)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of maize plots owned by farm households (number)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of family members living under one roof (in adult equivalent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLivestock ownership(TLU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLivestock holding size (in TLU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOxen owned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of oxen owned by farm households (number)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOff-farm income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe household income earned from off-farm employment (in ETB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess of household to credit (1\u0026thinsp;=\u0026thinsp;yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtension contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural extension advice delivered (number of times/frequency)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMembership to institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMembership of household to various farmers-based institutions(1\u0026thinsp;=\u0026thinsp;member)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to a market center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe average distance of a household to reach the major market center(in minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to the main road\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe average distance of a household to reach the all-weather road (in minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to farmers\u0026rsquo; training center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe average distance of a household to reach the farmers\u0026rsquo; training centers (in minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to maize plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe distance of household to reach the maize plots (in minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eSource: Literature review\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Adoption status of maize technology packages\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the status of the adoption of maize technology packages in the study area at the plot level. Maize technology packages considered in this study were improved maize seed, chemical fertilizer, and row planting. Of 545 maize plots of sampled farm households in the Sidama region, improved maize seed, chemical fertilizer, and row planting were adopted on 54, 45, and 44% of the maize plots, respectively. The row planting adoption rate is relatively lowest as compared to other maize technology packages. However, the percentage indicates the farming household that applied row spacing according to research recommendations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of maize technology package adoption at plot level (%)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize technology package\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved seed(IS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical fertilizer(F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRow planting(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Own survey data\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Descriptive statistics of explanatory variables used in the analysis\u003c/h2\u003e \u003cp\u003eThe summary statistics of variables that were supposed to influence the adoption of improved seed, chemical fertilizer and row planting are included in the MVP model and provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The selection of these variables was based on the relevant literature review [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These variables include a range of household characteristics, biophysical, socio-economic, institutional, infrastructural, and plot-level characteristics. About 95% of sample households were male-headed in the study area, which is comparable to findings reported by Zenga [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] in Ethiopia. Concurrent to this report, 95, 94, and 96% of adopters of improved maize seed, chemical fertilizer, and row planting respectively were male-headed whereas 94% of non-adopters for each improved maize seed and chemical fertilizer and 93% of row planting were also male-headed. The average years of attending formal education of sampled households was 5.1 on average, though about 25 percent of the respondents were illiterate. Adopters of improved maize seed, chemical fertilizer, and row planting attained 5.4, 5.5, and 5.3 years of formal schooling respectively. Whereas non-adopters of improved maize seed, chemical fertilizer, and row planting attained 4.72, 4.67, and 4.89 years of schooling respectively. The age of the household head ranges from 24 to 80 with an average age 45 years which is comparable to the findings reported by Zegeye[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] The mean age of the adopters of improved maize seed, chemical fertilizer, and row planting were roughly 45 years individually and non-adopters of the same variables were about 46 years. The average family sizes of farming households range roughly from 2 to 10 with a 4.7 average value of an adult equivalent. However, the family size of adopters of improved seed, chemical fertilizer, and row planting were 4.90, 4.93, and 4.80 respectively and non-adopters of the same variables were 4.51, 4.54, and 4.65 respectively.\u003c/p\u003e \u003cp\u003eMoreover, the unit of tropical livestock owned by the sampled households was averaged at 2.85 TLUs varying from 0 to 15.75 TLUs. However, adopters of improved seed, chemical fertilizer, and row planting owned 3.7, 3.76, and 3.6 TLUs respectively and non-adopters of the same variables owned 1.9, 2.1, and 2.2 TLUs, respectively. The average maize plot size owned by sampled households was 0.55 ha, whereas adopters of improved maize seed, chemical fertilizer, and row planting owned 0.55, 0.71, and 0.76 ha, respectively and non-adopters owned 0.55, 0.42, and 0.40 ha, respectively. Besides, adopters of improved maize seed, chemical fertilizer, and row planting earned off-farm income of ETB\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e12107.80, ETB14281.60, and ETB9993.60 and non-adopters of the same variables earned off-farm income averaging at ETB7747.50, ETB6684.30, and ETB10180.70 respectively. However, the sampled household earned ETB 10099.68 on average annually.\u003c/p\u003e \u003cp\u003eAmong institutional variables, about 61% of improved seed as well as chemical fertilizer adopters and 59% of row planting adopters were able to access credit and 43, 47, and 48% of non-adopters of the same variables were also able to access credit. On average, farmers contacted extension agents 2.12 times per production season. About 87, 86, and 77% of adopters of improved seed, chemical fertilizer, and row planting were members of one or multiple social institutions, respectively and 49, 53, and 64% non-adopters of the same variables were also members. These results imply that the farm households who used improved maize technology packages were comparably male, educated, and have bigger family sizes, owned greater plot sizes and tropical livestock units as well as got greater access to credit, extension, and membership to institutions. These results are comparable to the findings reported by [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows descriptive statistics of explanatory variables used in the analysis.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of sampled farming household characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eImproved maize seed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFertilizer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRow planting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal sample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdopters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-adopters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdopters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-adopters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdopters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-adopters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean(SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean (SE)\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 household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96**(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.948(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation of household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4**(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.72(0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.55**(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.67(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.30(0.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.89(0.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.1(0.176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.2(0.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.4(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.15(0.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.27(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.77(0.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.76(0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.76(0.487)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.90***(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.93**(0.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.54(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.809(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.65(0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.72(0.064)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePilot size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.550(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.556(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.709**(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.426(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.759**(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.553(0.092)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock size owned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70***(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.76***(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11(0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.63***(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.26(0.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.85(0.111)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxen size owned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12***(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13***(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65(0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03***(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.737(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.867(0.033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOff-farm income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12107***(1327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7747(1033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14281***(1618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6684(798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9993.6(1176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10180.7(1233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10099.68(864.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to credit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61***(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.438(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61***(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59**(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.478(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5302(0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to extension visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.55***(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55(0.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39**(0.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85(0.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.79***(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.559(0.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.093(0.095)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMembership to institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87***(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.529(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86***(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59(.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.805***(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.644(0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6990(0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to main road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.05(1.387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.05(1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.95(1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.85(1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.98(1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.92(1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to main market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.28**(1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.3(1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.66(1.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.68(1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.47(1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.07(1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.68(0.870)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to FTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.26 (0.641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.28(0.697)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.30(0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.13(0.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.62(0.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.84(0.490)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to maize plots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.81***(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.176(1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15***(0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.02(0.825)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.98(0.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.039(0.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.28(0.523)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of maize plots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67***(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66***(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.66***(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.440(0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.53(0.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e**and ** * are significant at the 5 and 1 percent probability levels, respectively. Source: Own survey data\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Econometric estimations\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Correlation among improved seed, chemical fertilizer, and row planting\u003c/h2\u003e \u003cp\u003eThe conditional probabilities of adopting improved seed, fertilizer, and row planting are given in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The conditional probability of adopting improved seed, fertilizer and row planting, however, highlighted the existence of the relationship. For instance, the probability of adopting improved seed increases from 54\u0026ndash;72%, 70%, and 83% conditional on the adoption of fertilizer, row planting, and both fertilizer and row planting, respectively. The probability of adopting fertilizer also increases to 60%, 52%, and 61% conditional on adopting improved seed, row planting, and both improved seed and row planting respectively. Furthermore, the conditional probability of adopting row planting rises marginally from 43\u0026ndash;56%, 50%, and 58% conditional on adopting improved seed, fertilizer, and both improved maize seed and fertilizer. This implies the existence of a positive correlation (complementarities) among the maize technology packages in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnconditional and conditional probabilities of adoption\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMaize technology packages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved seed(IS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertilizer(F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRow planting(R)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(Yk\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(Yk\u0026thinsp;=\u0026thinsp;1|IS\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(Yk\u0026thinsp;=\u0026thinsp;1|F\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(Yk\u0026thinsp;=\u0026thinsp;1|R\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(Yk\u0026thinsp;=\u0026thinsp;1|IS\u0026thinsp;=\u0026thinsp;1, F\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(Yk\u0026thinsp;=\u0026thinsp;1|IS\u0026thinsp;=\u0026thinsp;1, R\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(Yk\u0026thinsp;=\u0026thinsp;1|F\u0026thinsp;=\u0026thinsp;1, R\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: YK is a binary variable representing the adoption status concerning choice k (k\u0026thinsp;=\u0026thinsp;IS, F and R).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the estimates of pairwise correlation coefficients of the error terms in the three equations. From the MVP model estimations, the results revealed that the correlation coefficients of the error terms are statistically significant for all pairs of equations, and in this case, two of the three cases are statistically different from zero (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e), approving the appropriateness of the MVP specification. The finding of the correlation coefficients of the error term shows that there is a positive relationship (complementarity) among the maize technology packages considered in this study.\u003c/p\u003e \u003cp\u003eThe simulated maximum likelihood estimation results also revealed that there were positive and significant relationships between household decisions to adopt improved seed and fertilizer (ρ21) and improved seed and row planting (ρ31). In addition, there were positive but insignificant relationships between the adoption of fertilizer and row planting (ρ32). The complementarity of these technologies is expected, especially in relatively bigger and commercialized farms, and these findings are in line with research recommendations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficient estimation for the error terms from the three adoption equations of improved seed, fertilizer and row planting\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% of Confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eρ21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2599843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0734561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1112214 0.3973512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eρ31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2970848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0720593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1502671 0.4310818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eρ32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0042945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0745312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.140844 0.1492523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJoint probability (success)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1866(0.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJoint probability (failure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2447(0.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePredicted probability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIS\u0026thinsp;=\u0026thinsp;0.54(0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.45(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u0026thinsp;=\u0026thinsp;0.43(0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eLikelihood ratio test of rho21\u0026thinsp;=\u0026thinsp;rho31\u0026thinsp;=\u0026thinsp;rho32\u0026thinsp;=\u0026thinsp;0:chi2 (3)\u0026thinsp;=\u0026thinsp;26.5233 Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.0000\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The indexes refer to the equations: 1\u0026thinsp;=\u0026thinsp;improved seed, 2\u0026thinsp;=\u0026thinsp;fertilizer, 3\u0026thinsp;=\u0026thinsp;row planting\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Determinants of improved seed, fertilizer, and row planting adoption\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the coefficients of the MVP adoption model. The MVP model fits the data reasonably well. The Wald test that all regression coefficients are jointly equal to zero is rejected [Wald chi2 (48)\u0026thinsp;=\u0026thinsp;341.61, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\chi }^{2}\\)\u003c/span\u003e\u003c/span\u003e=0.0000]. Furthermore, it suggests that coefficients are jointly significant and that the explanatory power of the variables included in the model was satisfactory. The likelihood ratio test of the null hypothesis of independence between maize technology sets is significant (chi2 (3) = 26.5233, p-value= 0.0000\u0026lt;0.01) implying that the multiple uses of technology is not mutually exclusive but interdependent. Thus, the null hypothesis (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Ho\\)\u003c/span\u003e\u003c/span\u003e) that all the correlation coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \\left(rho\\right)\\)\u003c/span\u003e\u003c/span\u003e values are jointly equal to zero is rejected, denoting the best fit of the model and consequently supporting the application of MVP modeling. Despite the motivation of farmers to adopt a combination of maize technology packages, there are many factors that influence their decision to choose a particular technology packages. From the sixteen explanatory variables included in the analysis, age of household head, family size, plot size, livestock and oxen ownership, off-farm income, access to credit and extension services, membership in institutions, number of maize plots owned, and plot distance were significantly influenced at least one of the maize technology packages adoption in the study area. Among household characteristics, the age of the household was found to be positively and significantly related to maize row planting adoption at a 5% significance level. The marginal effect of 0.004 for age suggests that an increase in the age of farmers by one year would increase the probability of adopting row planting by 0.4%. Age is a good proxy for farming experience in agricultural activities. Therefore, as farmers get older, they gain expertise related to improved technology adoption and application. This infers that the greater the experience of farmers, the more likely they are to adopt improved technology packages. This result is in line with the study by [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe household size of farm households has a positive and significant effect on the adoption decision for chemical fertilizer packages. The marginal effect for household size is 0.026 suggesting an increase in family size in AE would increase the probability of adopting chemical fertilizer by 2.6%. Household size is a proxy for labor availability, and larger households can ease labor constraints during peak production season and help to effectively practice technology packages adoption compared to their counterparts. This demonstrates that larger household size, the more the adoption will be, because the adoption of multiple maize technology packages requires more labor. This result is consistent with the findings of [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLivestock ownership was found to have a positive relationship with improved seed, fertilizer, and row planting at a 1% significance level. The marginal effect of livestock ownership is 0.045, 0.038 and 0.031 for improved seed, chemical fertilizer, and row planting, respectively. This deduces that as the ownership of livestock increases by one TLU the probability of adopting improved seed, chemical fertilizer, and row planting would increase by 4.5, 3.8, and 3.1%, respectively. Livestock ownership is an indicator of being wealthy in a rural part of Ethiopia. Farmers in the study area raise the issue of financial constraints to purchase market inputs, as well as hiring labor and renting oxen during the production season. Thus, farmers owning more livestock units solved the financial shortage partly through income from livestock sales. This finding is consistent with the study by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, apart from livestock ownership, oxen ownership was also found to be positively related to improved seed and fertilizer adoption at a 1% significance level. The marginal effect of 0.054 and 0.078 for improved seed and chemical fertilizer adoption, respectively, implies that an increase in ox (en) ownership would increase the probability of adopting improved seed and fertilizer by 5.4 and 7.8%, respectively. Ownership solves the issues of both labor and financial shortages. This is because farm households that have ox (en) can plow relatively more farmland, prepare their land well, and sow on time, which would assist them to get a better yield and improve their food security and income. As a result, they have a greater probability of adopting improved technology packages than their counterparts. This result is consistent with the findings of [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, off-farm income has a positive and significant relationship with fertilizer application at a 1% significance level. The marginal effect of 0.0001 for off-farm income suggests that as the off-farm income increases by one Ethiopian birr, the probability of adopting chemical fertilizer would increase by 0.01%. Purchasing chemical fertilizer on time has been the bottleneck for Ethiopian smallholder farmers due to late supply and skyrocketing prices. Thus, income from off-farm employment solves the financial liquidity problem during peak production season. This finding is in line with the findings of [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong institutional variables, access to credit was found to have a positive and significant relationship with improved seed and row planting adoption at a 5% significance level. The marginal effect of access to credit is 0.064 and 0.070 for adopting improved seed and row planting, respectively. This denotes that those farm households that have access to credit are more likely to adopt improved seed and row planting by 6.4 and 7%, respectively, than those who have no access to it. Access to credit solves cash constraints that households could face at the time when they want to purchase agricultural inputs and hire labor (rent oxen). Hence, access to credit paves the way for the timely application of modern farm inputs. Thus, farmers who have access to credit have a higher possibility of adopting the technology packages than their counterparts. Thus, access to credit increases the likelihood of adopting maize technology packages. The current findings harmonize with past findings [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtension contact was found to have a positive and significant effect on row planting adoption at a 1% significance level. The marginal effect of 0.038 for frequency of extension contact implies that as the frequency of extension contact increases, the probability of adopting row planting would increase by 3.8%. The plausible reason for this is that extension contact serves as the center of information sources as well as technical know-how for the improved agricultural technology packages. This is evident as the household head's extension contact raises awareness, provides timely access to and uses of information, and fills skill gaps to adopt improved technologies. Extension contact is a potential force that speeds up the effective adoption of improved agricultural technologies by farmers. Therefore, the more access to technical knowledge and information farmers have, the more likely they are to adopt technology packages. This result is in line with the findings [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMembership in any of the formal or informal farmer-based institutions has positive and significant influences on the adoption of improved maize seed at a 5% significance level. The marginal effect of membership in farmer-based institutions is 0.128, inferring that as membership in institutions increases, the probability of improved maize seed adoption would increase by 12.8%. Membership in any form of institution is a proxy for information and input access. Membership in social institutions helps the farmers get labor during peak production season. Farmers require more labor support from relatives and social institutions for labor sharing during peak production times for sowing, planting, weeding, harvesting and threshing. The social networks that they have in farmer-based institutions help farmers access information about market prices, improve production packages, and share their experiences. Thus, being a member of farmer-based institutions increases the likelihood of a farmer adopting the maize technology packages. This finding is consistent with [\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong plot-level characteristics, maize plot size was found to have a positive relationship with the adoption of row planting. The marginal effect for plot size is 0.13, which infers that an increase in plot size by one hectare would increase the probability of adopting row planting by 13%. The size of maize plots owned is an indicator of wealth in rural parts of Ethiopia; thus, households with more land can afford the use of commercialized inputs such as improved seed and fertilizer as compared to their counterparts, because row planting goes along with improved maize varieties and chemical fertilizer adoption. Farmers are unwilling to adopt row planting with local varieties in the study area. This finding is consistent with the study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, this result contradicts the finding [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], which found that farmers with small and marginal plots are more likely to adopt than farmers with large plots.\u003c/p\u003e \u003cp\u003eA number of plots owned were also found to have a positive and significant effect on maize row planting adoption at a 5% significance level. The marginal effect of 0.057 on number of plots owned deduces that as the number of plots owned increases by one plot, the probability of adopting row planting would increase by 5.7%. This means that farm households with several numbers of maize plots would adopt maize technology packages more than their counterparts. This is because producing on several plots spreads the risk of crop loss compared to a lesser number of plots. In addition, crop failure may not concurrently occur on all the plots owned by the farm households. This in turn encourages farmers to adopt improved maize technology packages and make necessary investment on the plots. This finding is also in harmony with the findings by [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn line with the prior expectation, the maize plots distance from farmers\u0026rsquo; residences affected the adoption of improved seed, chemical fertilizer, and row planting negatively and significantly at a 1% significance level. The marginal effect of -0.009,-0.005, and \u0026minus;\u0026thinsp;0.007 for plot distance implies that as the distance of the maize plot increases by one minute, the probability of adopting improved maize seed, chemical fertilizer, and row planting would decrease by 0.9, 0.5, and 0.7%, respectively. This is plausible because, as the residence of the farmers is far away from the plots, they give less attention and are less likely would be preparation of land, sow, input use, weed, and harvest. The fact behind this is that the distance to plots is associated with extra transportation costs, energy, and time. This result is line with the findings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMVP simulation results for household maize technology packages adoption\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eImproved maize seed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eChemical fertilizer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eRow planting\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarginal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMarginal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStd.\u003c/p\u003e \u003cp\u003eErr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMarginal effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation of the household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 .007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlotsize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.392**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock size(TLUs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.148***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.117***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.092***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxen ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.175**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.238**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOff-farm income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.4e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-8e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to credit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.208**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.209**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.113***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMembership to institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.415**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the main road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to a main market center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance farmers\u0026rsquo; training center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to maize plots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.016**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.023***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of plots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.171**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.057\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 \u003cp\u003e-1.132**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.526***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.905***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eJoint probability (success)\u0026thinsp;=\u0026thinsp;0.186(0.20)\u003c/p\u003e \u003cp\u003ejoint probability (failure)\u0026thinsp;=\u0026thinsp;0.244 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eMVP(SML, number of random draws\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003cp\u003eLog-likelihood= -920.84532\u003c/p\u003e \u003cp\u003eNumber of obs = 545\u003c/p\u003e \u003cp\u003eWald chi2(48) = 341.61, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\chi }^{2}\\)\u003c/span\u003e\u003c/span\u003e=0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*** and ** are significant at 1% and 5% probability levels, respectively. Source: Model output\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":"4. Conclusion and recommendations","content":"\u003cp\u003eAdoption of improved agricultural practices in Ethiopian agriculture is out of the question, since expanding the production area (land) seems nearly impossible. Thus, increasing agricultural productivity through the use of improved agricultural technology packages is an important solution to improve the supply side, alleviate poverty, and address food insecurity. Hence, the country has been implementing agricultural development initiatives starting since decades ago. Despite the efforts to promote the adoption of agricultural technologies by Ethiopian farmers in most rural areas, adoption rates in the country have been very low, and this is also the case in the study area. Therefore, understanding the factors that encourage or impede the adoption of maize technology packages is important for planning and implementing different strategies that improve yield and productivity.\u003c/p\u003e \u003cp\u003eTherefore, the objective of this study was to identify the factors that influence the adoption of multiple maize technology packages in the northern Sidama zone of the Sidama national regional state of Ethiopia. The MVP result revealed that education level, age, family size, tropical livestock unit and oxen ownership, off-farm income, access to credit and extension contact, membership in institutions and several plot ownership were significantly and positively related to the adoption of at least one maize technology packages, whereas the distance to maize plots significantly and negatively affected the adoption of maize improved seed, chemical fertilizer, and row planting in the study area.\u003c/p\u003e \u003cp\u003eBased on the research findings, the following policy recommendations are made: the concerned bodies engaged in maize technology packages promotion need to address important variables identified in this study. The government should strengthen and deliver quality extension services, encourage membership in farmers-based social institutions to underpin farmer-to-farmer knowledge and input access, avail credit access, encourage off-farm employment before peak production season, deliver production inputs on time for affordable prices, and technologies that save labor to promote row planting and to achieve broader adoption of the technology packages. This study was limited to using cross-sectional datasets. Hence, it might not appropriately capture farmers\u0026rsquo; re-adjustment decisions of resource allocations in response to their adoption of improved seed, fertilizer and row planting based on changes in perception, weather as well as market prices. Therefore, future research should focus on adoption dynamics using more representative panel data.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe greatly acknowledge the Ethiopian Ministry of education for funding this study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFurther,\u0026nbsp;we acknowledge the respondents for cooperating us for relevant data collection. Data collectors and agricultural experts from regional office to development agents are highly appreciated for their support during data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eWe declare that we are the authors of this research article. During data collection and analysis,\u0026nbsp;all ethical and technical academic standards were followed. Every source of information used in this article has been duly acknowledged. AG developed the proposal, conceived and proposed the methodology, collected the field data, analyzed and interpreted the data and wrote the paper. TT, MS, and AE\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003econtributed to the methodology, supervised the entire research work, commented on the draft manuscript and approved the final manuscript to submission. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e This study was funded by the Ethiopian Ministry of Education (Hawassa University).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe corresponding author confirms that\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe data used in this study will be available up on request. The data is not openly presented to keep the privacy of the responding individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eEthical approval was obtained from the Haramaya University post-graduate research office committee to conduct this study. Thus, this study was carried out in accordance with the University research ethics standards and guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003eEach respondent\u0026rsquo;s participation in this study was based on informed consent. The informed consent form was read out to the respondents in local language before the beginning of data collection. Respondents were well-informed\u0026nbsp;about the purpose of the study, confidentiality of the information, voluntary based participation, and their right to withdraw from the interview at any point in time while interviewing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors of this research article declare that there is no financial or non-financial conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Bank (WB). Ethiopia Overview: Development news, research, data| World Bank, Worldbank.org. https://www.worldbank.org/en/country/ethiopia/overview. 2022.\u003c/li\u003e\n \u003cli\u003eEthiopian Economic outlook (EEO). The Story behind the Numbers. Ethiopia Economic Review. 2016.\u003c/li\u003e\n \u003cli\u003eWB, World Bank. 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Application of multivariate probit on determinants of sesame farmers market outlet choices in Gimbi District, Ethiopia. \u003cem\u003eAfr. J. Agric. Res.\u003c/em\u003e 2017; 12(38):2830-2835. Doi: 10.5897/AJAR2017.12605.\u003c/li\u003e\n \u003cli\u003eJerop R, Dannenberg P, Owuor G, Mshenga P, Kimurto P, Willkomm M, Hartmann GG. Factors affecting the adoption of agricultural innovations on underutilized cereals: The case of finger millet among smallholder farmers in Kenya. \u003cem\u003eAfr. J. Agric. Res.\u003c/em\u003e 2018; Vol. 13(36), pp. 1888-1900. Doi: 10.5897/AJAR2018.13357.\u003c/li\u003e\n \u003cli\u003eDorfman JH. Modeling multiple adoption decisions in a joint framework. \u003cem\u003eAJAE\u003c/em\u003e. 1996; 78, 547-557. Doi: 10.2307/1243273.\u003c/li\u003e\n \u003cli\u003eGreene WH. Econometric Analysis. 4th Edition, Prentice Hall, Englewood Cliffs. 2000.\u003c/li\u003e\n \u003cli\u003eBelderbos R, Carree M, Diederen B, Lokshin B, Veugelers R. Heterogeneity in R and D cooperation strategies. \u003cem\u003eInt. J. Ind. Organ.\u003c/em\u003e 2004; 22, 1237-1263. Doi: org/10.1016/j.ijindorg.2004.08.001.\u003c/li\u003e\n \u003cli\u003eBorges J, Foletto L, Xavier VT. An interdisciplinary framework to study farmers decisions on adoption of innovation. Insights from Expected Utility Theory and Theory of Planned Behavior. \u003cem\u003eAfr. J. Agric. Res.\u003c/em\u003e 2015; 10(29):2814-2825.DOI: 10.5897/AJAR2015.9650.\u003c/li\u003e\n \u003cli\u003eTeklewold H, Kassie M, Shiferaw, B. Adoption of Multiple Sustainable Agricultural Practices in Rural Ethiopia. \u003cem\u003eJ. Agric. Econ.\u003c/em\u003e 2013; 64(3): 597\u0026ndash;623. https://doi.org/10.1111/1477-9552.12011.\u003c/li\u003e\n \u003cli\u003eWainaina P, Tongruksawattana S, Qaim, M. Tradeoffs and complementarities in the adoption of improved seeds, fertilizer, and natural resource management technologies in Kenya. \u003cem\u003eJ. Agric. Econ.\u0026nbsp;\u003c/em\u003e2016; 47:351-362.https://hdl.handle.net/10419/104815.\u003c/li\u003e\n \u003cli\u003eZegeye MB, Fikire AH, Bekele G. Determinants of multiple agricultural technology adoption: evidence from rural Amhara region, Ethiopia. Cogent Econ Financ. 2022; 10:1, 2058189. https://doi.org/10.1080/23322039.2022.2058189.\u003c/li\u003e\n \u003cli\u003eZenga D. Land ownership and technology adoption revisited: Improved maize varieties in Ethiopia. \u003cem\u003eLand Use Policy\u003c/em\u003e. 2018; Volume 72, pp. 270-279. https://DOI.org/10.1016/j.landusepol.2017.12.047.\u003c/li\u003e\n \u003cli\u003eZegeye F. Production efficiency, commercialization of cereal crops and multidimensional poverty among farm households in major \u003cem\u003e\u0026lsquo;Teff\u0026rsquo;\u0026nbsp;\u003c/em\u003egrowing areas of Ethiopia. A Dissertation submitted to center for rural development studies presented in partial fulfillment of the requirements for the degree of Philosophy in Development Studies (Rural Development). Addis Ababa University, Ethiopa. 2021.\u003c/li\u003e\n \u003cli\u003eMwungu CM, Shikuku KM, Kinyua I, Mwongera C. Impact of adopting prioritized climate-smart agricultural technologies on farm income and labor use in rural Tanzania. Invited paper presented at the 6th ACAE, September 23\u0026ndash;26. Abuja, Nigeria. 2019.\u003c/li\u003e\n \u003cli\u003eKassa B. Factors affecting agricultural production in Tigray Region, Northern Ethiopia. Dissertation of PhD, University of South Africa. 2015.\u003c/li\u003e\n \u003cli\u003eAddis Y, Sani S. Impact of Adoption of Improved Agricultural Production Technologies on Cereal Crops Productivity and Farmers\u0026rsquo; Welfare in Central Ethiopia. \u003cem\u003eInd. J. Sci. Technol.\u003c/em\u003e 2021; 14(44): 3280-3287. https://doi.org/ 10.17485/IJST/v14i44.1306.\u003c/li\u003e\n \u003cli\u003eKetema M, Kibret K, Hundessa F, Bezu, T. Adoption of Improved Maize Varieties as a Sustainable Agricultural Intensification in Eastern Ethiopia. Implications for Food and Nutrition Security.\u003cem\u003eTURJAF.\u003c/em\u003e2021; 9(6):998-1007. https://doi.org/10.24925/turjaf.v9i6.998-1007.3937.\u003c/li\u003e\n \u003cli\u003eUllah A, Shah A, Bavorova A, Prasad Kandel M G. Adoption of hand tractor technology in terrace farming: Evidence from the Hindu Kush Himalayan (HKH), Pakistan. \u003cem\u003eHeliyon 9 (2023) e14150.\u0026nbsp;\u003c/em\u003ehttps://doi.org/10.1016/j.heliyon.2023.e14150.\u003c/li\u003e\n \u003cli\u003eZegeye MB. Adoption and Ex-post Impact of Agricultural Technologies on Rural Poverty: Evidence from Amhara Region, Ethiopia. Cogent Econ Financ. 2021: 9:1, 1969759. https://doi.org/10.1080/23322039.2021.1969759.\u003c/li\u003e\n \u003cli\u003eShita A, Kumar N, Singh S. \u0026lsquo;The impact of agricultural technology adoption on income inequality: a propensity score matching analysis for rural Ethiopia. \u003cem\u003eIJIDS\u003c/em\u003e. 2020; Vol. 12, No. 1, pp.102\u0026ndash;114. https://doi.org/10.1504/IJIDS.2020.105013.\u003c/li\u003e\n \u003cli\u003eAhmed H, Anang BT. Impact of Improved Variety Adoption on Farm Income in Tolon District of Ghana. \u003cem\u003eAGRISE\u003c/em\u003e. 2019; 19(2), 105-115. http://dx.doi.org/10.21776/ub.agrise.2019.019.2.5.\u003c/li\u003e\n \u003cli\u003eHawas LD, Degaga DT. Factors affecting improved agricultural technologies adoption logistic model in study areas in east Shewa zone, Ethiopia. Pennsylvania western university, clarion, Pennsylvania.\u003cem\u003e\u0026nbsp;JSDA.\u003c/em\u003e 2023\u003cem\u003e;\u0026nbsp;\u003c/em\u003e25(1).\u003c/li\u003e\n \u003cli\u003eHamza D. Barley technologies adoption and its contribution to farm households\u0026rsquo; income and food availability in semen Shewa zone, Amhara region, central Ethiopia. PhD Dissertation, Addis Ababa University, Addis Ababa. 2018.\u003c/li\u003e\n \u003cli\u003eMuriithi BW, Affognon HD, Diiro GM, Kingori SW, Tanga CM, Nderitu PW, Ekesi S. Impact assessment of Integrated Pest Management (IPM) strategy for suppression of mango-infesting fruit flies in Kenya. \u003cem\u003eJ. Crop. Prot.\u003c/em\u003e 2016; 81:20-29. http://dx.doi.org/10.1016/j.cropro.2015.11.014.\u003c/li\u003e\n \u003cli\u003eWossen T, Abdoulaye T, Alene A, Haile M, Feleke S, Olanrewaju A, Manyong V. Impacts of extension access and cooperative membership on technology adoption and household welfare. J. Rural Stud. 2017; 54:223-233. http://dx.doi.org/10.1016/j.jrurstud.2017.06.022.\u003c/li\u003e\n \u003cli\u003eTefera T, Tesfaye G, Elias E, Diro M, Koomen I. Drivers for adoption of agricultural technologies and practices in Ethiopia. A study report from 30 woredas in four regions. Capacity Building for Scaling Up of Evidence-based Best Practices in Agricultural Production in Ethiopia Project Report No. NS_DfA_2016_1CASCAPE. 2016.\u003c/li\u003e\n \u003cli\u003eOuma JO, De Groote H. Determinants of improved maize seed and fertilizer adoption in Kenya. \u003cem\u003eAfr. J. Agric. Mark.\u003c/em\u003e 2017;\u003cem\u003e\u0026nbsp;\u003c/em\u003evol.5 (6), pp 001-008.\u003c/li\u003e\n \u003cli\u003eElsheikh SE, Hashim AA, Faki HH, Elamin EM. Factors Affecting Adoption of Improved Varieties of Sorghum, Millet, Groundnut and Sesame in North Kordofan State. 2018. https://doi.org/10.19080/ARTOAJ.2018.13.555889.\u003c/li\u003e\n \u003cli\u003eGideon DA, Joshua AB, Dennis SE, Franklin, N. M. Adoption of improved maize variety among farm households in the northern region of Ghana. Cogent Econ Financ. 2017; 5:1, 1416896; doi: 10.1080/23322039.2017.1416896.\u003c/li\u003e\n \u003cli\u003eKwarteng AT, Aidoo R, Sarfo-Mensah P. Determinants of the extent of adoption of maize production technologies in Northern Ghana. \u003cem\u003eAfr. J. of Agri. Res.\u003c/em\u003e 14(19): 819-827. Doi: 10.5897/AJAR2019.13912.\u003c/li\u003e\n \u003cli\u003eMmbando FE, Baiyegunhi LJ. Socio-economic and institutional factors influencing adoption of improved maize varieties in Hai District, Tanzania. \u003cem\u003eJ. Hum. Ecol.\u0026nbsp;\u003c/em\u003e2016;\u003cem\u003e\u0026nbsp;53\u003c/em\u003e(1), 49\u0026ndash;56. https://doi.org/10.1080/09709274.2 016.11906955.\u003c/li\u003e\n \u003cli\u003eVarma P. Adoption and the impact of system of rice intensification on rice yields and household income: an analysis for India. \u003cem\u003eAppl Econ.\u0026nbsp;\u003c/em\u003e2019; 51(45):4956\u0026ndash;72. http://dx.doi.org/10.1080/00036846.2019.1606408.\u003c/li\u003e\n \u003cli\u003eDjibo O, Maman NM. Determinants of agricultural technology adoption: Farm household\u0026rsquo;s evidence from Niger. J. of Dev. and Agri. Econ. 2018; 11(1). https://doi.org/10.5897/JDAE2018.0998.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Woreda (plural woredas) is an administrative division of Ethiopia, managed by a local government and equivalent to district.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The smallest administrative unit of Ethiopia, contained within a woreda\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e ETB\u0026thinsp;=\u0026thinsp;0.018 US dollar or 1US DOLLAR\u0026thinsp;=\u0026thinsp;56.704ETB (Source: National Bank of Ethiopia (NBE), April 12, 2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHttps://Nbe.Gov.ET/Exchange/Banks-Exchange-Rates/\u003c/span\u003e\u003cspan address=\"http://Https://Nbe.Gov.ET/Exchange/Banks-Exchange-Rates/\" targettype=\"URL\" 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-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discoverfood","sideBox":"Learn more about [Discover Food](https://www.springer.com/44187)","snPcode":"","submissionUrl":"","title":"Discover Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple maize technologies, Adoption, Multivariate probit, Sidama, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-4428885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4428885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdoption of improved agricultural technology packages is vital in Ethiopia, as the expansion of cultivable land appears nearly exhausted and population size has skyrocketed. However, the country has been shown low adoption rate. Thus, this study aimed to explore the factors that hinder or facilitate the adoption of multiple maize technology packages in the northern Sidama zone of Ethiopia. A multistage sampling procedure was applied to gather cross-sectional data from 424 farm households owning 545 maize plots. A multivariate probit model was applied to address the study objectives. Of total plots, improved maize seed, fertilizer, and row planting were adopted on about 54, 45, and 44 percent, respectively. The conditional probability results have also confirmed that maize technology packages have complementarity (positive relationship). This infers that agriculture-focused policies that influence the adoption of a single component of technology packages can have a reinforcing advantage over the adoption of other technologies. Furthermore, the results from model showed that farmers with higher family size, plot size, age, tropical livestock unit, ox (en), off-farm income, access to credit and extension services, membership in institutions, and the number of plots are more likely to adopt at least one of the improved maize technology packages. However, distance to maize plots affected adoption negatively. Therefore, it is crucial to reinforce and deliver quality extension services, provide credit access, motivate youth to be involved in farming activities, inspire membership and ease the system to access inputs and technologies for broader adoption of technology packages.\u003c/p\u003e","manuscriptTitle":"Determinants of multiple maize technology packages adoption in Ethiopia: Evidence from Sidama region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 09:52:24","doi":"10.21203/rs.3.rs-4428885/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-22T04:40:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-12T10:55:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-08T00:13:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-03T23:12:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77566083852177833592122648673082570250","date":"2024-07-01T05:01:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258975913351898567329257439297178108666","date":"2024-06-28T10:30:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205176794175303187465610512686699048438","date":"2024-06-27T20:33:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271089968530327814307456123042219469373","date":"2024-06-17T08:41:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-14T15:55:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-29T16:18:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-29T16:17:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Food","date":"2024-05-16T06:32:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discoverfood","sideBox":"Learn more about [Discover Food](https://www.springer.com/44187)","snPcode":"","submissionUrl":"","title":"Discover Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a136f609-d992-4954-adcb-79b07b36dbde","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-16T09:36:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 09:52:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4428885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4428885","identity":"rs-4428885","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}