RJOAS January 2025
by Wardani Delvira Citra, Irianto Heru, Rahayu Wiwit (Faculty of Agriculture, University of Sebelas Maret, Indonesia)
The 2023 Census of Agriculture (ST2023) recorded a decrease in Agricultural Households (RTUP) in the Sragen Regency, subsequently impacting the overall production of the agricultural sector. One of the substantial factors contributing to this decline is the limited involvement of the younger generation. This tendency is particularly evident in Sragen Regency, which has fewer millennial farmers than other rice production centers across Central Java Province. In light of this challenge, this research aims to define the factors influencing the regeneration of Gen Z rice farmers in Sragen Regency. The study, with its practical implications, involved selecting two hundred respondents using simple random sampling techniques in six sub-districts, with proportional distribution utilizing proportional stratified random sampling. The gathered data is analyzed using the PLS-SEM method with SmartPLS 3.0 software, and the results show how personal factors, agricultural resources, socio-economics, and external support positively influence farmer regeneration.
The agricultural sector is vital to Indonesia's development. Exponential population growth and increased consumption impact the growing food demands. Its success relies heavily on the availability and quality of the human resource (Sergeevna et al., 2021). Skilled labor is crucial for sustaining development programs and creating economic opportunities, which allow better access to capital, markets, and technology (Supatminingsih, 2022). However, the aging farmer population challenges its growth because of the reluctance to adopt modern technologies, resulting in lower productivity and efficiency. This demographic shift may lead to a shortage of farmers in the following years, ultimately contributing to labor scarcity (Irianto et al., 2019). The 2023 Census of Agricultural (ST2023) showed how Central Java Province experienced a decline in the number of Agricultural Households (RTUP) by 1.68% in the last 10 years (BPS-Statistics, 2023). This decline also affected rice production, which in 2022 was recorded at 9.3 thousand tons, decreasing to 9.08 thousand tons in 2023. This phenomenon also happened in Sragen Regency, which is known as the center of rice production in Central Java. In 2013, the number of RTUPs in Sragen Regency was recorded at 142,187 households, which will decrease to 138,967 in 2023. This decrease happens because aged farmers were not replaced in 10 years, reflected in the low number of millennial farmers in the Sragen Regency. Commonly, the younger generation who enter the agricultural sector undergo a process of regenerating farmers in the family, where farming management is passed on from parents to their children (Anwarudin et al., 2020). In the context of this regeneration, Gen Z has the potential to realize farmer regeneration as they are at the stage of character development, where the attitudes and values held are forming to adopt certain ideologies (Susilowati, 2016). The process of their success is influenced by various factors from the agricultural sector and their characteristics (Fischer & Burton, 2014). Besides, external support, such as government, family, and community assistance, is important in encouraging farmer regeneration (Taopik et al., 2018). Another study by Bavorová et al. (2024)shows that socio-economic factors influence regeneration success, including income, social status, and access to resources. Therefore, integrating those mentioned factors may provide an adequate comprehension of the factors that influence the regeneration of Gen Z rice farmers in Sragen Regency.
This research tests a structural model involving individual factors, agricultural resources, socio-economics, and external support influencing farmer regeneration.
Personality factors are inherent in a person's career choice (Widiawati, 2024). Fischer & Burton (2014) found that individual characteristics influenced farmer regeneration. A person's self-identity, emotional attachment, beliefs, and career path can encourage entering or leaving the agricultural sector (Bertolozzi-Caredio et al., 2020; Coopmans et al., 2021). Emotional attachment to agriculture drives potential successors to look for new ways to expand the business by combining new knowledge and perspectives (Plana-Farran et al., 2023). May et al. (2019) found that beliefs about the agricultural sector influenced agricultural succession. The belief that he can succeed encourages individuals to overcome obstacles and seek information to succeed in this sector (Handayani et al., 2022).
The agricultural resources influence farmer regeneration, including the amount and availability of farmland and production factors (Coopmans et al., 2021). Land access and limited land influence regeneration (Saleh et al., 2021). Large-scale farming attracts the youth in agricultural succession compared to small-scale farming (Żmija et al., 2020). The availability of infrastructure, such as roads and irrigation, functions as a means of increasing and distributing agricultural production (Subroto & Sapha, 2016). Adequate water supply plays a role in ensuring agricultural productivity and will encourage family farming succession (Rayasawath, 2018).
Children's perceptions of their parents' socioeconomic conditions influence their skills (Mahbub, 2016). The younger generation's perception of agricultural work is a form of positive (good) or negative (bad) assessment (Widiyanti et al., 2018). Higher farming income influences their willingness to take over and be successors (Abdala et al., 2021). According to Shahzad et al. (2021), agricultural land area is also an essential factor in farming handover since it reflects the economic advantages of the land. Individual perceptions regarding occupation's social prestige impact a person's career choice (Susanto et al., 2021). Farming is unclean and tiring work that provides little prestige (Arvianti et al., 2020). Nevertheless, Sudrajat et al. (2020) found that community perceptions of socio-cultural value may encourage farmer regeneration.
Family life and agricultural activities have a reciprocal relationship. Significant moments in the family life cycle usually happen with the change from one conceptual phase to another in the regeneration process (Coopmans et al., 2021). When prospective successors are prepared to manage the family farm at a young age, they will try to continue it (Pessotto et al., 2019). The community environment, such as people's appreciation of agriculture, impacts farmer job satisfaction (Coopmans et al., 2021). Undoubtedly, government support, such as the assistance of entrepreneurship, counseling, capital, and equipment, will influence the acceleration of farmer regeneration (Anwarudin et al., 2020).
This research uses five variables: individual, agricultural, environmental, socio-economic, and external support factors, as well as farmer regeneration. These variables are used to test hypotheses with the following conceptual framework:
Based on the conceptual framework of this study shown in Figure 1, the research hypothesis is formulated as follows: H1: Personal factors have a positive effect on farmer regeneration, H2: Agricultural environment factors have a positive effect on farmer regeneration, H3: Socio-economic factors have a positive effect on farmer regeneration, H4: External support factors have a positive effect on farmer regeneration.
This research was conducted in six sub-districts in Sragen Regency, namely Gemolong, Masaran, Mondokan, Plupuh, Sidoharjo, and Tanon, which were selected purposively considering that these areas had the highest number of food crop households and rice production (BPS-Statistics, 2023). A quantitative approach is used with a survey method, where the questionnaire is the main instrument and aims to determine the influencing factors of Gen Z rice farmers' regeneration in Sragen Regency.
The population in this study consisted of children of rice farmers aged 12–27 years, with a total of 1,496 individuals. Simple random sampling is used in this study with the sample size in SEM analysis. Hair et al., (2018) recommend a minimum sample size of 100 if the model consists of ≤5 latent constructs, with each construct having more than three items and high item communality (>0, 6). However, larger samples are generally recommended because they provide more stable solutions and reliable analysis results. Therefore, this research uses 200 samples (Purwanto & Sudargini, 2021). The number of samples in each sub-district was distributed using proportional stratified random sampling to ensure proportional representation from each sub-district. Based on this technique, Gemolong District had 35 respondents, Masaran District had 35 respondents, Sidoharjo District had 35 respondents, Plupuh District had 32 respondents, Tanon District had 36 respondents, and Mondokan District had 27 respondents.
This study uses three main approaches for data collection: interviews, observation, and documentation. Interview is a method that involves asking respondents verbal questions based on a prepared questionnaire. Meanwhile, observation requires researchers to visit research sites to engage with respondents directly. Documentation augments the data by compiling relevant research materials.
Partial Least Squares-Structural Equation Modelling (PLS-SEM) is utilized to analyze the influence of individual factors, agricultural resources, socio-economics, and external support on farmer regeneration. SEM–PLS tests include three main stages: (1) Testing the measurement model (outer model) including factor loading analysis, Cronbach's alpha, composite reliability, AVE, and HTMT; (2) Structural model test (inner model) includes R², Q², PLSpredict, and f²; (3) Hypothesis testing to determine the relationship between variables.
Two hundred respondents in this study showed various demographic characteristics. Most were men of 111 people, or around 56%, and 44% were women, averaging 20 years old. Respondents in the early adulthood category were the largest group, with a percentage of 56.5%. In comparison, the lowest number was in the adult category, at 15%. The education level of most is high school, and only 3% have a bachelor's degree in agriculture. Generation Z is less interested in choosing agricultural institutes as an education option (Isyanto et al., 2023).
Next, the PLS-SEM results were evaluated in two stages: measurement and structural model assessments (Purwanto & Sudargini, 2021). This assessment is done before hypothesis testing (Styaningrum et al., 2024). The measurement model assessment carried out several analyses to confirm the validity and reliability of the measurement model of (Hair et al., 2018). In this research, each item has a loading factor above 0.70 and AVE > 0.50 per the criteria (Hair et al., 2019). The discriminant validity test showed that the heterotrait – monotrait ratio (HTMT) value was <0.90. As Franke & Sarstedt (2019), the HTMT test has a threshold value of <0.90.
Based on Table 1, it can be seen that reliability is met when the value Cronbach's alpha and value composite reliability respectively >0.70 Ghozali & Latan (2015) and 0.60 (Ahmad et al., 2016). This condition means that the measurement model has achieved validity and reliability. A structural model was tested to represent the relationship between variables in the research construct afterward (Hair et al., 2018). R value2 is in the moderate category, namely 0.695, while Q2 has a value of 0.487. Hair et al. (2019) stated that the value of Q2 <0.50 has moderate predictive ability.
Based on Table 2, the values of Q²_predict and The RMSE on PLS are more significant than those of Q²_predict and RMSE on LM. Four MAE values in PLS are lower than MAE values in LM. So, this model has moderate predictive power (Shmueli et al., 2019). The f² test on personal factors is 0.119, agricultural resources factors 0.067, socio-economic factors 0.106, and external support factors 0.091. Testing this research hypothesis uses the bootstrapping method in SmartPLS 3.0 to evaluate each factor's significant influence on rice farmers' regeneration at a significance level of 5%. The results are shown in Table 3.
Based on Table 3, it can be seen that personal, agricultural resources, socioeconomic, and external support factors influence farmer regeneration. Personal factors include self-identity, emotional attachment, beliefs in farming, and participation in training. Agricultural resource factors include land area, access, input availability, and infrastructure. Socioeconomic factors involve Gen Z's perception of income, land area, social prestige, and socio-cultural values. External support factors include socialization, land inheritance, community appreciation, group support, and government policies.
Personality factors weigh 0.265 with a P value of 0.001, meaning that individual factors have a positive and significant effect on the regeneration of rice farmers. This study's results align with research by Plana-Farran et al. (2023)that individual factors positively influence the desire of prospective farmer successors to continue farming in the future. Bertolozzi-Caredio et al. (2020) stated that the individual dimension is central to farmer regeneration, especially in influencing the younger generation's potential and willingness to be involved in family farming. The people of Sragen Regency not only consider farming as a profession but also as a tradition that must be preserved. This phenomenon was also expressed by Francis et al. (2017), who stated that farmers view farming as a way of life rather than just a job, which includes cultural and emotional values, family traditions, norms, and ideology. This societal ideology explains why individuals are the main factor in the farmer regeneration process.
The weight of agricultural resource factors is 0.213 with a P value of 0.000, meaning that agricultural resource factors have a positive and significant effect on the regeneration of rice farmers. Agricultural environmental indicators in this research include land area, land access, input availability, and infrastructure. The results of this research align with research on horticultural farming in Italy by Cavicchioli et al. (2018), which found that agricultural resource factors influence farm succession. This finding also aligns with Rayasawath (2018), who states that the availability of labor and agricultural resources influences the succession of family farming. The agricultural resources in Sragen Regency are supportive, with large amounts of privately owned agricultural land and easy access to land to increase the scale of farming. Ease of access to agricultural land in an area facilitates the regeneration of farmers. This convenience provides opportunities for the younger generation to expand the scale of their farming business in the future, whereas, in rural communities, ownership of more extensive land provides prestige (Kovách et al., 2022).
Socioeconomic factors weigh 0.279 with a P value of 0.000, meaning that an increase in socioeconomic factors will significantly increase the regeneration of rice farmers. In line with research by Cavicchioli et al. (2018), socioeconomic conditions positively affect family farming succession in the younger generation, where farming with higher income will increase the possibility of potential successors continuing it. Also, in line with research conducted by Bavorová et al. (2024), socioeconomic factors have a positive and significant effect on farm succession. Research by Shahzad et al. (2021) found that differences in family socioeconomic conditions influence the desire of prospective farming successors to continue farming. Gen Z in Sragen Regency views farming as generating a high income even though it is unstable, so a side job is needed. As Shahzad et al. (2021)found, most potential successor candidates need additional sources of income, either through diversification of farming businesses or activities outside the agricultural sector, to overcome financial vulnerability due to economic challenges and climate change.
The external support factor weighs 0.233 with a P value of 0.000, meaning that external support has a positive and significant effect on the regeneration of rice farmers in Sragen Regency. Research by Rodriguez-Lizano et al. (2020) aligns with this research, where the findings reveal that socio-economic factors and external support, such as family support, influence the success of family farming succession. Social support from family, friends, or the community forms a positive mindset. It increases self-confidence in managing farming, encouraging the younger generation to enter the agricultural sector (Boye et al., 2024).
Research findings show that personality factors, agricultural resources factors, socio-economic factors, and external support factors positively affect farmer regeneration. Socio-economic variables have the most dominant influence on farmer regeneration, which shows that the regeneration of rice farmers depends on Gen Z's perception of income, land area, social prestige, and socio-cultural values. Therefore, improving the socio-economic welfare of farmers through programs to increase income land access will increase prestige and attract the interest of the younger generation in the regeneration of the agricultural sector. It is recommended that further research expand the research by adding other variables that influence the regeneration of rice farmers, considering the critical role of the younger generation in introducing innovation and diversity in agricultural practices for the sustainability of the agricultural sector in the future.
Original paper, i.e. Figures, Tables, References, and Authors' Contacts available at http://rjoas.com/issue-2025-01/article_01.pdf