Does family life cycle influence farm households' adoption decisions concerning sustainable agricultural technology?

Li, Fenni; Zhang, Junbiao; Ma, Chunbo Article Does family life cycle influence farm households' adoption decisions concerning sustainable agricultural technology? Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Li, Fenni; Zhang, Junbiao; Ma, Chunbo (2022) : Does family life cycle influence farm households' adoption decisions concerning sustainable agricultural technology?, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 25, Iss. 1, pp. 121-144, https://doi.org/10.1080/15140326.2021.2012408 This Version is available at: https://hdl.handle.net/10419/314155 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Does family life cycle influence farm households’ adoption decisions concerning sustainable agricultural technology? Fenni Li, Junbiao Zhang & Chunbo Ma To cite this article: Fenni Li, Junbiao Zhang & Chunbo Ma (2022) Does family life cycle influence farm households’ adoption decisions concerning sustainable agricultural technology?, Journal of Applied Economics, 25:1, 121-144, DOI: 10.1080/15140326.2021.2012408 To link to this article: https://doi.org/10.1080/15140326.2021.2012408 © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 03 Mar 2022. Submit your article to this journal Article views: 2146 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 Does family life cycle influence farm households’ adoption decisions concerning sustainable agricultural technology? Fenni Li a,b , Junbiao Zhang a,b and Chunbo Ma c a College of Economics & Management, Huazhong Agricultural University, Wuhan, China; b Hubei Rural Development Research Center, Huazhong Agricultural University, Wuhan, Hubei, China; c School of Agriculture and Environment, University of Western Australia, Perth, Australia ABSTRACT The literature examining the role played by family life cycle in farm households’ decisions to adopt sustainable agricultural technology (SAT) remains scant. To bridge this gap, we evaluate the impact of family life cycle on farm households’ adoption of SATs by using multivariate probit regression analyses of survey data from 902 farm households in Hubei Province, China. The results show that most farm households belong to the middle stages of the family life cycle. Straw returning is the most frequently adopted SAT among farm households. Farm households’ SAT adoption decisions are closely related to family life cycle. SAT adoption by farm households differs at each family life cycle stage. Based on this finding, when stimulating SAT adoption, the government should consider the different capital endowments and needs of farm households at different family life cycle stages as well as conduct differential and targeted technology promotion measures. ARTICLE HISTORY Received 14 January 2021 Accepted 25 November 2021 KEYWORDS Family life cycle; sustainable agricultural technologies; adoption decision; multivariate probit model 1. Introduction Sustainable agricultural technology (SAT), a production technology that improves agricultural productivity and rural livelihood and, more critically, conserves agricultural resources and maintains harmony between nature and the economy (Khonje, Julius, Petros, Hirpa, & Alene, 2018; Zeng, Zhang, He, & Chen, 2019), has been promoted by the efforts of numerous national and international organizations. However, several studies have found that the adoption rate of SATs is relatively low in China, Ethiopia, Tanzania, and other developing countries or regions (Kassie, Jaleta, Shiferaw, Mmbando, & Mekuria, 2013; Teklewold, Kassie, & Shiferaw, 2013; Zeng, Zhang, He, & Chen, 2019). Bourdieu (1986) stated that only with a minimum level of capital endowment can individuals make behavioural decisions. Therefore, to determine the factors influencing SAT adoption and to stimulate farm households’ adoption of SATs, scholars have mainly focused on the personal characteristics of the household head or the characteristics of the farm household (Li, Chen, Tang, & Feng, 2020), such as educational attainment (Khonje, Julius, Petros, Hirpa, & Alene, 2018), risk preference (Andres, Pennings, & Dianne, CONTACT Junbiao Zhang [email protected] College of Economics and Management, Huazhong Agricultural University, Room 301, Crop Sciences Building, 1st Shizishan Street, Wuhan, Hubei, China JOURNAL OF APPLIED ECONOMICS 2022, VOL. 25, NO. 1, 121–144 https://doi.org/10.1080/15140326.2021.2012408 © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. AGRICULTURAL ECONOMICS 2016), household size (Teklewold, Kassie, & Shiferaw, 2013), farm size and number of livestock owned (Kassie, Teklewold, Jaleta, Paswel, & Olaf, 2015), number of labourers (Zeng, Zhang, He, & Chen, 2019), and household income (Kassie, Jaleta, Shiferaw, Mmbando, & Mekuria, 2013), without taking into account the dynamic perspective of the family life cycle. In fact, considering the family life cycle when observing SAT adoption is of great significance for understanding Chinese farm households’ behavioural logic (Chang, Li, Xie, & Zhao, 2020; Li, Chen, Tang, & Feng, 2020). First, China has a strong sense of “family orientation” (Li, Chen, Tang, & Feng, 2020). The production and resource allocation activities of Chinese farm households are carried out in a way that is centered on the family (Liang, Lin, & Zhang, 2015). Furthermore, based on the analytical framework of “capital endowments → production technology behaviour choice”, farm households’ technology adoption decisions are deeply affected by their capital endowments (Li, Chen, Tang, & Feng, 2020; Xu et al., 2020). There is a close association between the capital endowments and the family life cycle of farm households (Wang & Wu, 2017). Capital endowments accumulate as the family life cycle develops (Xu et al., 2020). Hence, analyzing farm households’ SAT adoption in the context of family life cycle may be beneficial to the development of the analytical framework from a pattern of “capital endowments → production technology behaviour choice” to one of “family life cycle → capital endowments → production technology behaviour choice” and to the enrichment of the literature on farm households’ technology adoption behaviour. Second, family life cycle is a compound concept that includes several household characteristics, such as labour resources and capital accumulation (Chao & Wan, 2016; Su, Feng, & Zhu, 2020), and it can more comprehensively reflect capital endowments at different stages (Su, Feng, & Zhu, 2020). In other words, farm households at different family life cycle stages have different levels of capital endowment and different constraints in terms of household burden, ability to resist risk, capital accumulation, production needs, etc. (Leinbach & Smith, 1994; Li, Chen, Tang, & Feng, 2020; Neulinger & Rado, 2018; Perz & Walker, 2002; Xu et al., 2020), which results in differentiated agricultural production and management decisions by farm households related to their current family life cycle characteristics (Chang, Li, Xie, & Zhao, 2020; He, Zhang, & Tian, 2013; Perz & Walker, 2002; Sherbinin, Vanwey, Mcsweeney, Aggarwal, & Walker, 2008). Thus, an exploration from the family life cycle perspective can better explain differences in farm households’ adoption decisions concerning technology (Su, Feng, & Zhu, 2020). In fact, many researchers have found that family life cycle is significantly correlated with personal or household behaviour. For example, Zinda and Zhang (2017) argued that the livelihood strategy formulation of farm households is largely affected by their family life cycle. A study by Amirtha and Sivakumar (2018) concerning India found that family life cycle influences women to engage in e-shopping. Liang, Lin, and Zhang (2015) pointed out that family life cycle produces a marked effect on farm scale choice in southern China. Leinbach and Smith (1994) stated that the off-farm employment participation of farm households is closely related to their family life cycle. However, to the best of our knowledge, no previous studies have attached importance to the role played by family life cycle in farm households’ SAT adoption in China. Similarly, family life cycle has been applied less frequently in the field of farm households’ technology adoption behaviour. Therefore, we include the consideration of family life cycle in the analysis of SAT adoption, attempting to fill the gap in the literature concerning the impact of family 122 F. LI ET AL. life cycle on farm households’ agricultural production behaviour. Furthermore, the literature on SAT adoption has concentrated mainly on the adoption of a particular sustainable agricultural technology by farm households. In fact, SAT is a technology package consisting of various subtechnologies. It is possible for farm households to adopt a mix of SATs for agricultural production processes (Kassie, Jaleta, Shiferaw, Mmbando, & Mekuria, 2013; Li, Zhang, & He, 2019). Therefore, it is necessary to investigate multiple SATs and construct an accurate model to explore farm households’ adoption decisions. Additionally, prior studies on SAT adoption have mostly explained the reasons why farm households adopt or do not adopt SATs by way of one attribute of SATs, ignoring the fact that SATs have multiple attributes pertaining to capital, labour, and production risk. For example, Sall, Norman, and Featherstone (2000) merely pointed out that improved seed technology is capital intensive. Luo, Qin, Wang, and Wang (2016) simply identified soil testing and fertilizer recommendations as a labour-intensive technology. Zeng, Zhang, He, and Chen (2019) merely recognized that no-/mini-tillage technology would increase output risk. Thus, these conclusions are not only likely to fail to illustrate the reasons why farm households differ in their adoption of and need for SATs but are also insufficient as guides to farm households’ sustainable agricultural production practices. Based on this foundation, using the multivariate probit model and survey data from 902 farm households in Hubei Province, China, and taking four SATs as examples, we attempt to accomplish the following goals: (1) to reveal the importance of family life cycle on farm households’ SAT adoption decisions in order to expand the scope of application of the concept of family life cycle and to enrich the literature concerning farm households’ technology adoption behaviour, and (2) to shed light on the heterogeneity of SAT adoption by farm households at different family life cycle stages in order to provide a basis for accurately understanding the main extension agents of SATs with different attributes, thus improving the pertinence of SAT extension measures and realizing the optimization of agricultural technology popularization strategies. The structure is as follows: Section 2 presents family life cycle divisions in rural China and research hypothesis. Sections 3 introduces the data, variable and method. Section 4 reveals the estimation results. Section 5 reports the conclusion and relevant policy implication. 2. Concept definition and research hypothesis 2.1. Family life cycle divisions in rural China Family life cycle describes the process of marriage, childbirth, children leaving home, and death within a household (Xu et al., 2020). Sorokin, Zimmermann, and Galpin (1931) first proposed this concept and divided family life cycle into four stages. Many scholars have amended and perfected family life cycle theory in accordance with the reality of the situation within nations and classified family life cycle into seven (Lansing & Kish, 1957), eight (Duvall, 1971), nine (Wells & Gubar, 1966), and 13 stages (Murphy & Staples, 1979). Glick (1947) introduced a division of family life cycle into six stages: formation, expansion, stabilization, contraction, empty nest, and disintegration, a model which is recognized as the classical family life cycle theory and is commonly used internationally (Xu et al., 2020). JOURNAL OF APPLIED ECONOMICS 123 However, there are differences in family culture and lifestyle between rural China and other countries (He, Zhang, & Tian, 2013; Xu et al., 2020). Specifically, due to a lack of independent income, newlyweds in rural China usually live together with their parents for a period of time rather than living alone (Li, 2005; Xu et al., 2020). Thus, the establishment of a new household in rural China begins with financial separation rather than marriage (Li, Chen, Tang, & Feng, 2020; Liang, Lin, & Zhang, 2015). Second, considering birth control policy in China, most farm households have only one child in the household rather than the large number of children typical in Western households (Ye, Cai, Chen, & Xia, 2019). Third, considering the deep influence of Confucian culture in contrast to individualistic family relationships in Western societies, Chinese farm households support their elderly parents and live with them, forming multigenerational households (He, Zhang, & Tian, 2013; Li, Chen, Tang, & Feng, 2020). This structure indicates that households containing elderly parents as the main figures gradually vanish and are integrated into a family life cycle stage dominated by their children (Liang, Lin, & Zhang, 2015; Xu et al., 2020). In general, the concept of family life cycle in rural China is so complicated (Liang, Lin, & Zhang, 2015) that classical family life cycle theory is insufficient to explain the issue in China. Under these circumstances, Chinese scholars have adjusted their classification of family life cycle to suit the reality of the situation within rural China. For instance, based on the number and ages of children, Li (2005) divided the family life cycle into four stages, including beginning families, maturing families, matured families, and aging families. Based on the ages of children and the elderly, some scholars have classified the family life cycle into five stages (Liang, Lin, & Zhang, 2015; Peng & Wu, 2017), such as a classification that employs the stages of “young couple family, growing nuclear family, mature nuclear family, extended family, and empty nest family” (Liang, Lin, & Zhang, 2015) or a classification using stages of “start-up, growth, maturity, expansion, and decline” (Peng & Wu, 2017). Others have extended this classification and divided the family life cycle into six stages based on household size and demographic structure (Chao & Wan, 2016; He, Zhang, & Tian, 2013; Li, Chen, Tang, & Feng, 2020; Xu et al., 2020), such as a classification that employs the stages of “couple nuclear family, growing nuclear family, mature nuclear family, extended family I, extended family II, and shrinking family” (Li, Chen, Tang, & Feng, 2020), a classification using the stages of “starting period, rearing period, burden period, stable period, maintenance period, and empty nest period” (Xu et al., 2020), a classification that employs the stages of “couple nuclear family, standard nuclear family, extended nuclear family, lineal family, extended lineal family, and shrinking family” (He, Zhang, & Tian, 2013), or a classification using the stages of “bachelorhood, newlywed, full nest 1, full nest 2, full nest 3, and empty nest” (Chao & Wan, 2016). Furthermore, Zhu, Yang, and Rao (2017) constructed a model of family life cycle employing seven stages: single family, newlywed family, growing nuclear family, mature nuclear family, extended family, empty nest family, and family living alone. As mentioned above, different studies employ divergent approaches to the classification of family life cycle. Derrick and Lehfeld (1980) stated that the division of family life cycle should be determined by research purpose and data. Thus, referring to the literature above (Chang, Li, Xie, & Zhao, 2020; Xu et al., 2020), we divide the family life cycle in rural China into six stages. Specifically, if farm households are married but have no children, they are considered to belong to the starting period. If farm households have 124 F. LI ET AL. children or grandchildren, the youngest children or grandchildren are under 16 years old, and other members of the family are between the ages of 17 and 64, then they are considered to belong to the rearing period. If farm households have children or grandchildren, the youngest children or grandchildren are under 16 years old, and the household contain members over the age of 65, then they are considered to belong to the burden period. If all household members are between the ages of 17 and 64, then the farm households are considered to belong to the stable period. If the household contain members over the age of 65 and all children or grandchildren are over the age of 16, then the farm households are considered to belong to the maintenance period. If all household members in the household are over the age of 65, then the farm households are considered to belong to the empty nest period. The details are shown in Table 1. 2.2. Research hypothesis Many scholars have found that the labour supply, income level, and risk aversion level of farm households all change with the continuous evolution of the household’s family life cycle (Liang, Lin, & Zhang, 2015; Xu et al., 2020). For example, Lansing and Kish (1957) argued that there is an inverted U-shaped correlation between the family life cycle of farm households and their income level. A study by Su, Feng, and Zhu (2020) showed that farm households’ risk aversion level is closely related to their family life cycle. Zhu, Yang, and Rao (2017) and Xu et al. (2020) noted that the family life cycle of farm households affects their labour supply. The above factors have an important influence on SAT adoption by farm households. Based on this fact, the hypothesis of this study is as follows. First, there is a close relationship between family life cycle and farm households’ income level (Brown, Venkatesh, & Viswanath, 2005; Zhu, Yang, & Rao, 2017). Household income level differs over different stages of the family life cycle (Li, 2005; Zhu, Yang, & Rao, 2017). Additionally, scholars have reached consensus that household income level exerts a positive impact on farm households’ SAT adoption. That is, the higher the income level is, the fewer farm households are limited by financial conditions in the adoption of SATs (Christine & David, 1982; Kassie, Jaleta, Shiferaw, Mmbando, & Mekuria, 2013; Kassie, Teklewold, Jaleta, Paswel, & Olaf, 2015; Teklewold, Kassie, & Shiferaw, 2013). Therefore, family life cycle may influence the SAT adoption decisions of farm households by affecting household income level. Specifically, for farm households in the early family life cycle stage, capital accumulation is limited due to recent separation from the original households and the Table 1. Family life cycle divisions in rural China. Family life cycle Division Starting period Young and childless couples Rearing period Children or grandchildren born, the youngest children or grandchildren are under the age of 16, and there are household members between the ages of 17 and 64 Burden period Children or grandchildren born, the youngest children or grandchildren are under the age of 16, and there are household members over the age of 65 Stable period All household members are between the ages of 17 and 64 Maintenance period All children or grandchildren are over the age of 16 and there are household members over the age of 65 Empty nest period All household members are over the age of 65 JOURNAL OF APPLIED ECONOMICS 125 formation of new households (Zhu, Yang, & Rao, 2017), and thus, such households are constrained in terms of capital and less motivated to adopt SATs. When the family life cycle of farm households develops to the stage that is characterized by childbearing and paternal aging, the presence of elderly individuals and minors in the household increases support and maintenance burden and reduces the financial stability and income level of farm households (Amirtha & Sivakumar, 2018; Amirtha, Sivakumar, & Hwang, 2020; Li, 2005; Li, Chen, Tang, & Feng, 2020; Su, Feng, & Zhu, 2020; Walker, Perz, Caldas, & Silva, 2002; Zhu, Yang, & Rao, 2017); as a result, farm households are likely to face financial constraints in SAT adoption. As farm households’ family life cycle reaches the stage where all household members are adults, off-farm work by adult children not only contributes to a high household income level (Amirtha & Sivakumar, 2018; Li, 2005) but also provides a good financial base, thereby granting farm households abundant funds for SAT adoption. When farm households reach a later family life cycle stage, although the physical condition and work capacity of their members deteriorate, long-term capital accumulation allows such households to maintain financial stability; as a result, these farm households are less limited by financial conditions in SAT adoption. Second, family life cycle may lead to changes in the number of labourers available for farm production in the household (Su, Feng, & Zhu, 2020; Xu et al., 2020; Zhu, Yang, & Rao, 2017). Farm households have different numbers of labourers at different family life cycle stages (Li, 2005; Zhu, Yang, & Rao, 2017). Additionally, positive impacts by the number of labourers on farm households’ SAT adoption have been found by most researchers (Feder, Just, & Zilberman, 1985; Supaporn, Kobayashi, & Supawadee, 2013; Thangata & Alavalapati, 2003). That is, the greater the number of labourers among farm households, the greater the availability of labour resources to assist in technology application (Ofuoku, Egho, & Enujeke, 2008) and the higher the likelihood of farm households adopting SATs. Thus, family life cycle may influence farm households’ decision to adopt SATs by affecting the supply of labourers who engage in sustainable agricultural production (Su, Feng, & Zhu, 2020). Specifically, for farm households in the early family life cycle stage, the labour force at this stage is adequate, young, and able-bodied (Li, 2005; Peng & Wu, 2017), and therefore, such households have abundant labour resources to assist in SAT adoption. When the family life cycle of farm households develops into the middle stage, household merging or expansion of household size allows for greater allocation of labour resources to farm production (Amirtha & Sivakumar, 2018; Li, 2005; Li, Chen, Tang, & Feng, 2020). Additionally, growing children could add to the household labour pool (Barbieri, Bilsborrow, & Pan, 2005; Leinbach & Smith, 1994; Li, 2005; Perz, Walker, & Caldas, 2006; Sherbinin, Vanwey, Mcsweeney, Aggarwal, & Walker, 2008; Su, Feng, & Zhu, 2020; Zinda & Zhang, 2017), thus enabling farm households to have adequate labour resources (Walker, Perz, Caldas, & Silva, 2002) and meet the labour demand for SAT adoption. As the family life cycle of farm households evolves to the stage where all household members are adults, off-farm work by members may affect the number of labourers allocated to agricultural production (Perz, Walker, & Caldas, 2006; Su, Feng, & Zhu, 2020; Zinda & Zhang, 2017), thus influencing the labour resources available for technology adoption and the likelihood of farm households adopting SATs (Kassie, Teklewold, Jaleta, Paswel, & Olaf, 2015). When the family life cycle of farm households enters the later stage during which all household members are elderly individuals, reduction in household size and the gradual 126 F. LI ET AL. decline of members’ work capacity render the labour resources allocated to farm production insufficient (Chang, Li, Xie, & Zhao, 2020; Peng & Wu, 2017; Su, Feng, & Zhu, 2020), which affects the SAT adoption by these households. Finally, farm households’ risk aversion level is closely related to their family life cycle (Su, Feng, & Zhu, 2020). Farm households have different risk aversion levels at different family life cycle stages (García & Gruat, 2003; Su, Feng, & Zhu, 2020), and farm households’ technology adoption decisions are strongly influenced by risk aversion level (Andres, Pennings, & Dianne, 2016; Kassie, Teklewold, Jaleta, Paswel, & Olaf, 2015). According to the stochastic production function proposed by Just and Pope (1978), farm households with high risk aversion levels are more cautious (Rogers, 1983) and conservative in making adoption decisions concerning new agricultural technologies (Luo, Lin, & Qiu, 2021), preferring risk-mitigating production elements and technologies (Hou, Qiu, Bai, & Xu, 2014; Su, Feng, & Zhu, 2020). Therefore, family life cycle may influence the SAT adoption decisions of farm households by affecting risk aversion level. Specifically, in the early family life cycle stage, the inexperience of farm households dispose them toward high risk aversion (Zhang, He, & Yang, 2020), leading to a cautious adoption of SATs and other new technologies. When the family life cycle of farm households enters the stage in which there are minors in the household, the presence of those minors would increase the risk aversion level (Amirtha, Sivakumar, & Hwang, 2020; Yi & Zhu, 2017) and result in a conservative adoption decision concerning SATs and other new technologies by farm households. The cause of this risk aversion is that minor is an unproductive, socially dependent population (Leinbach & Smith, 1994) that does not contribute to the labour supply and provides negative savings. When the family life cycle of farm households enters the middle stage, the acquisition of experience makes them less risk averse (Walker, Perz, Caldas, & Silva, 2002; Zhang, He, & Yang, 2020). Additionally, growing children, youth, and middle-aged members can contribute to the household (Barbieri, Bilsborrow, & Pan, 2005; Leinbach & Smith, 1994; Li, 2005; Perz, Walker, & Caldas, 2006; Sherbinin, Vanwey, Mcsweeney, Aggarwal, & Walker, 2008; Su, Feng, & Zhu, 2020; Zinda & Zhang, 2017), which, to a certain extent, enhances farm households’ ability to obtain income (Wang & Deng, 2015) and alleviates risk aversion level (Perz, Walker, & Caldas, 2006; Walker, Perz, Caldas, & Silva., 2002), thereby increasing the probability of SAT adoption. As farm households reach a later family life cycle stage in which all household members are elderly individuals, the risk aversion levels of farm households tend to increase (Kassie, Jaleta, Shiferaw, Mmbando, & Mekuria, 2013; Kassie, Teklewold, Jaleta, Paswel, & Olaf, 2015; Riley & Chow, 1992) due to the conservative value orientation of the older population (Simon & Khaled, 2015), which ultimately leads them to be more cautious in adopting SATs. Furthermore, farm households at different family life cycle stages are likely to make different choices in the context of SAT adoption. Specifically, farm households in the starting period are young couples without children. Farm households at this stage have just separated from the original household. They usually inherit some property from their parents (Sherbinin, Vanwey, Mcsweeney, Aggarwal, & Walker, 2008), but capital accumulation is still insufficient (Lin, Zhang, & Lin, 2011). The labour resources owned by farm households in the starting period are adequate (Peng & Wu, 2017). The labour force at this stage is also young, able-bodied, and has abundant physical strength for JOURNAL OF APPLIED ECONOMICS 127 Table 3. (Continued). Variables Regression 1 Regression 2 Regression 3 Water - saving irrigation Commercial organic fertilizer Straw returning Comprehensive utilization of livestock manure Water - saving irrigation Commercial organic fertilizer Straw returning Comprehensive utilization of livestock manure Water - saving irrigation Commercial organic fertilizer Straw returning Comprehensive utilization of livestock manure Number of livestock owned 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) Adoption benefit −0.238 (0.122) 0.229* (0.109) 0.175 (0.143) 0.376** (0.120) −0.167 (0.129) 0.258* (0.110) 0.253 (0.149) 0.343** (0.127) −0.162 (0.129) 0.258* (0.110) 0.250 (0.150) 0.338** (0.127) Adoption cost −0.024 (0.103) −0.101 (0.093) 0.173 (0.118) −0.104 (0.103) 0.050 (0.108) −0.031 (0.095) 0.238 (0.122) −0.065 (0.113) 0.054 (0.108) −0.031 (0.095) 0.233 (0.122) −0.070 (0.113) Village terrain −0.704*** (0.170) −0.500*** (0.141) −0.906*** (0.158) −0.792*** (0.152) −0.816*** (0.183) −0.471** (0.144) −0.996*** (0.167) −0.794*** (0.163) −0.818*** (0.183) −0.470** (0.144) −0.994*** (0.167) −0.795*** (0.163) Distance to market or town −0.020 (0.017) −0.004 (0.015) −0.017 (0.018) 0.017 (0.016) −0.028 (0.017) −0.009 (0.015) −0.022 (0.018) 0.010 (0.018) −0.028 (0.017) −0.009 (0.015) −0.022 (0.018) 0.011 (0.018) Regional dummy variables Controlled Controlled Controlled Log likelihood −1874.034 −1709.564 −1709.445 Prob > chi 2 0.000 0.000 0.000 Wald chi 2 689.02 792.01 791.84 Notes:*, **, and *** indicate significant at the 10%, 5%, and 1% levels, respectively. Values in the parentheses mean the standard errors. The same below. 134 F. LI ET AL. referring to the studies by Liang, Lin, and Zhang (2015) and Chang, Li, Xie, and Zhao (2020), we remove these samples and analyze the empirical results based on the remaining five periods. 4.1.1. Family life cycle Table 3 shows that the rearing period, burden period, stable period, maintenance period, and empty nest period are significant, indicating that family life cycle plays a remarkable role in influencing farm households’ SAT adoption decisions. (1) In Regression 2, the coefficient on the variable “rearing period” is negative and statistically significant at the 10% level in the model of water-saving irrigation. In Regression 2, the coefficient on the variable “rearing period” is positive and statistically significant at the 10% level in the model of comprehensive utilization of livestock manure. These results imply that farm households in the rearing period are inclined to adopt comprehensive utilization of livestock manure technology rather than that of water-saving irrigation technology. The reason for this fact is that farm households in the rearing period have relatively high risk aversion levels and face relatively strong capital constraints. Watersaving irrigation technology is capital intensive. Comprehensive utilization of livestock manure technology is risk-mitigating and capital-stabilizing, which is suitable for the production needs of farm households in the rearing period. Therefore, they are more likely to adopt comprehensive utilization of livestock manure technology. (2) In Regression 2, the coefficient on the variable “burden period” is positive and statistically significant at the 1% level in the model of comprehensive utilization of livestock manure. In Regression 3, the coefficient on the variable “burden period” is negative and statistically significant at the 10% level in the model of straw returning. In Regression 3, the coefficient on the variable “burden period” is positive and statistically significant at the 10% level in the model of comprehensive utilization of livestock manure. These results suggest that farm households in the burden period are inclined to adopt comprehensive utilization of livestock manure technology rather than that of straw returning technology. The reasons underlying these results might be that farm households in the burden period face relatively weak labour constraints and have relatively high risk aversion levels and relatively strong capital constraints. Use of straw returning technology is likely to result in yield loss and a lower income. Comprehensive utilization of livestock manure technology is capital-stabilizing and risk-mitigating, which is more suitable for the production needs of farm households in the burden period. Consequently, they tend to adopt comprehensive utilization of livestock manure technology. (3) In Regression 2, the coefficient on the variable “stable period” is positive and statistically significant at the 10%, 5%, 5% and 1% levels in the model of water-saving irrigation, commercial organic fertilizer, straw returning and comprehensive utilization of livestock manure, respectively. In Regression 3, the coefficient on the variable “stable period” is positive and statistically significant at the 1%, 1%, 10% and 5% levels in the model of water-saving irrigation, commercial organic fertilizer, straw returning and comprehensive utilization of livestock manure, respectively. These results indicate that farm households in the stable period are more likely to adopt water-saving irrigation technology, commercial organic fertilizer technology, straw returning technology and comprehensive utilization of livestock manure technology. The possible reasons for these results are that farm households in the stable period have abundant and high-quality labour endowments (Li, 2005), relatively abundant capital JOURNAL OF APPLIED ECONOMICS 135 (Zhu, Yang, & Rao, 2017), and relatively low risk aversion levels. Thus, farm households at this stage are not only able to meet the capital demand for capital-intensive technologies, such as water-saving irrigation technology, commercial organic fertilizer technology, and straw returning technology but also meet the labour demand for labour-intensive technologies, such as comprehensive utilization of livestock manure technology and commercial organic fertilizer technology. Hence, farm households in the stable period have a high probability of SAT adoption. (4) In Regression 2, the coefficient on the variable “maintenance period” is positive and statistically significant at the 5% and 1% levels in the model of water-saving irrigation and straw returning, respectively. In Regression 2, the coefficient on the variable “maintenance period” is negative and statistically significant at the 1% level in the model of comprehensive utilization of livestock manure. In Regression 3, the coefficient on the variable “maintenance period” is positive and statistically significant at the 1% and 1% levels in the model of watersaving irrigation and straw returning, respectively. In Regression 3, the coefficient on the variable “maintenance period” is negative and statistically significant at the 1% level in the model of comprehensive utilization of livestock manure. These results suggest that farm households in the maintenance period are inclined to adopt water-saving irrigation technology and straw returning technology rather than that of comprehensive utilization of livestock manure technology. The reasons underlying these results might be that farm households in the maintenance period have relatively weak capital constraints and a relatively strong ability to resist risk (Zhu, Yang, & Rao, 2017), but their work capacity tends to deteriorate (Wang & Wu, 2017). As a result, they tend to adopt mechanical labour-substitution technologies. Comprehensive utilization of livestock manure technology is labour intensive. Water-saving irrigation technology and straw returning technology are capital-intensive and labour-saving, which are more suitable for the production needs of farm households in the maintenance period. Therefore, they tend to adopt water-saving irrigation technology and straw returning technology. (5) In Regression 3, the coefficient on the variable “empty nest period” is positive and statistically significant at the 10% level in the model of water-saving irrigation. In Regression 3, the coefficient on the variable “empty nest period” is negative and statistically significant at the 10% level in the model of straw returning. These results indicate that farm households in the empty nest period are inclined to adopt water-saving irrigation technology rather than that of straw returning technology. The reason for this fact is that farm households in the empty nest period have relatively strong labour constraints and relatively high risk aversion levels (Su, Feng, & Zhu, 2020), but they still have some capital (Amirtha & Sivakumar, 2018; Li, 2005; Peng & Wu, 2017). Thus, farm households in the empty nest period tend to adopt technologies with relatively few labour demand and low production risk. Although straw returning technology is labour-saving, it is likely to result in yield loss and a lower income. Water-saving irrigation technology is a labour-saving and risk-mitigating that is more suitable for the production needs of farm households in the empty nest period. Hence, they are more likely to adopt water-saving irrigation technology. 4.1.2. Control variables Table 3 shows that risk averse, number of labourers, land acreage, household income, adoption benefit and village terrain are significant. 136 F. LI ET AL. The coefficient on the variable “risk averse” is positive and statistically significant at the 1% and 5% levels in the model of water-saving irrigation and comprehensive utilization of livestock manure, respectively. The coefficient on the variable “risk averse” is negative and statistically significant at the 5% level in the model of straw returning. These results indicate that farm households with high risk aversion levels are inclined to adopt water-saving irrigation technology and comprehensive utilization of livestock manure technology rather than that of straw returning technology. The possible reasons for these results are that water-saving irrigation technology and comprehensive utilization of livestock manure technology are risk-mitigating in contrast to straw returning technology, which meet the production needs of farm households with a high risk aversion level and are thus adopted by these households. The coefficient on the variable “household income” is positive and statistically significant at the 1% and 1% levels in the model of water-saving irrigation and straw returning, respectively, suggesting that household income is markedly positively correlated with farm households’ adoption behaviour for SATs. A possible explanation for this result is that water-saving irrigation technology and straw returning technology are yield-enhancing and capitalintensive, which are more likely to be afforded by farm households with high household income. Thus, these households have a high likelihood of adopting SATs. The coefficient on the variable “number of labourers” is negative and statistically significant at the 1% and 5% levels in the model of water-saving irrigation and straw returning, respectively. The coefficient on the variable “number of labourers” is positive and statistically significant at the 5% and 10% levels in the model of commercial organic fertilizer and comprehensive utilization of livestock manure, respectively. These results imply that farm households with less labour are more likely to adopt water-saving irrigation technology and straw returning technology, and farm households with larger labour pools are more likely to adopt commercial organic fertilizer technology and comprehensive utilization of livestock manure technology. The main reasons for these results are that water-saving irrigation technology and straw returning technology are labour-saving. Commercial organic fertilizer technology and comprehensive utilization of livestock manure technology are labour-intensive. Hence, farm households with larger labour pools can meet the labour demand for labour-intensive SATs, while farm households with less labour are more likely to adopt labour-saving SATs. The coefficient on the variable “land acreage” is positive and statistically significant at the 5% level in the model of water-saving irrigation, suggesting that land acreage is noticeably positively related to farm households’ SAT adoption. One likely explanation for this fact is that farm households with large land acreages tend to have long-term agricultural investment activities, expecting to maximize long-term profits and reduce unit costs. Water-saving irrigation technology has a scale effect. That is, the more acres of land receive the application of water-saving irrigation technology, the more benefits are obtained by farm households. As a result, farm households with large land acreage tend to adopt water-saving irrigation technology. The coefficient on the variable “adoption benefit” is positive and statistically significant at the 10% and 5% levels in the model of commercial organic fertilizer and comprehensive utilization of livestock manure, respectively, indicating that adoption benefit is closely related to farm households’ adoption of SATs. The reason for this fact is that farm households are rational. Their technology adoption decisions would take into JOURNAL OF APPLIED ECONOMICS 137 account the benefit and cost. The more benefits are obtained by farm households, the higher the likelihood of farm households adopting SATs. Commercial organic fertilizer technology and comprehensive utilization of livestock manure technology have the benefits of enhancing crop yields and land productivity and are thus likely to be adopted by households. The coefficient on the variable “village terrain” is negative and statistically significant at the 1%, 5%, 1% and 1% levels in the model of water-saving irrigation, commercial organic fertilizer, straw returning, and comprehensive utilization of livestock manure, respectively, suggesting that village terrain is closely correlated with farm households’ adoption of SATs. One likely explanation for this result could be that mountainous or hilly terrain often leads to rugged countryside roads and increases the difficulty for household members in walking from home to a field or from one field to another field, thereby decreasing the probability of farm households adopting labour intensive technologies, such as commercial organic fertilizer technology and comprehensive utilization of livestock manure technology. Alternately, mountainous or hilly terrain may aggravate the difficulty of mechanical operations and pipeline laying on arable land, thus decreasing the probability of farm households adopting mechanical investment technologies, such as water-saving irrigation technology and straw returning technology. 4.2. Robustness test The winsorization method was applied to verify the robustness of the above results. The results of this method are shown in Table 4. It is easy to see that the regression results in Table 4 are similar to the results in Table 3, indicating that the results are robust and credible. 5. Conclusions and Discussions Based on survey data from 902 farm households in Hubei Province, China, and taking four SATs as examples, we enrich the literature on agricultural technology adoption behaviour by revealing the role of family life cycle in farm households’ SAT adoption decisions with multivariate probit regression analyses. The findings and related policy implications are as follows. First, family life cycle is influential in farm households’ SAT adoption decisions. Farm households have different adoption decisions concerning SAT during different family life cycle stages due to different capital endowments and constraints at each stage. Specifically, farm households in the rearing period and burden period have relatively strong capital constraints and relatively high risk aversion levels and tend to adopt capital-stabilizing and risk-mitigating SATs, such as comprehensive utilization of livestock manure technology. Farm households in the stable period have abundant labour resources, relatively low risk aversion levels, and relatively weak capital constraints, and as a result, they have a high probability of SAT adoption. Farm households in the maintenance period face relatively strong labour constraints and relatively weak capital constraints, and as a result, they tend to adopt labour-saving SATs, such as straw returning technology and water-saving irrigation technology. Farm households in the empty nest period have relatively strong labour constraints and relatively high risk aversion levels and are more likely to adopt labour-saving and risk-mitigating SATs, 138 F. LI ET AL. Table 4. Robustness test results. Variables Regression 4 Regression 5 Water -saving irrigation Commercial organic fertilizer Straw returning Comprehensive utilization of livestock manure Water -saving irrigation Commercial organic fertilizer Straw returning Comprehensive utilization of livestock manure Family life cycle = rearing period −0.498* (0.223) −0.089 (0.213) 0.420 (0.248) 0.531* (0.235) Family life cycle = burden period −0.472* (0.233) 0.058 (0.218) 0.085 (0.254) 0.841*** (0.244) 0.035 (0.156) 0.145 (0.129) −0.356* (0.153) 0.328* (0.150) Family life cycle = stable period 0.515* (0.232) 0.575** (0.219) 0.884** (0.273) 0.893*** (0.246) 1.020*** (0.150) 0.661*** (0.129) 0.447* (0.180) 0.381* (0.151) Family life cycle = maintenance period 0.590* (0.235) −0.226 (0.222) 1.244*** (0.273) −1.460*** (0.302) 1.099*** (0.172) −0.139 (0.155) 0.792*** (0.203) −1.968*** (0.241) Family life cycle = empty nest period 0.563* (0.231) 0.079 (0.220) −0.564* (0.259) −0.434 (0.244) Control variables Controlled Controlled Log likelihood −1702.854 −1702.677 Prob > chi 2 0.000 0.000 Wald chi 2 800.40 800.28 JOURNAL OF APPLIED ECONOMICS 139 such as water-saving irrigation technology. Second, most farm households belong to the middle stages of the family life cycle (such as rearing period and stable period), and few farm households belong to the starting period and empty nest period. Third, risk averse, number of labourers, land acreage, household income, adoption benefit, and village terrain also exert impacts on farm households’ SAT adoption. These findings have important implications for the effective promotion of SATs in rural areas. Given that farm households’ SAT adoption decisions are closely correlated with family life cycle, and that farm households’ adoption decisions concerning SAT differs depending on family life cycle stage, when promoting SATs, the government should take into account the different capital endowments and needs of farm households at different family life cycle stages and provide differential promotion measures that stand in accordance with the characteristics of each family life cycle stage, providing targeted guidance and assistance to increase the probability of SAT adoption. First, specifically considering the fact that most farm households belong to the middle stages of the family life cycle and that farm households at these stages are constrained by capital and have relatively high risk aversion levels, the government should improve the infrastructure and services of health clinics, nursing homes and kindergartens in rural areas to address the worries and burdens caused by teenager parenting and eldercare among these households. Alternately, the government should formulate and implement a series of policies to benefit farm households and provide strong support for financial loans and agricultural insurance to encourage those households to become the main body of modern agricultural production. Second, for farm households in the stable period that do not face obvious capital endowment constraints on SAT adoption, the government should strengthen education and skills training concerning sustainable agricultural production and act to raise the awareness of those households concerning the importance and application of SATs to guide more farm households to join the sustainable agricultural production team. Third, given that farm households in the later family life cycle stages tend to adopt labour-saving SATs due to labour constraints and relatively high risk aversion levels, the government should improve the rural social security and agricultural insurance systems to address the worries concerning risk among these households. Alternately, the government should set up a platform for mutual assistance in agricultural production to help these households obtain services and additional assistants for the tasks of farming and delivering agricultural materials. Furthermore, the government should also vigorously promote research into and development of agricultural machinery suitable for the elderly and produce machinery with high safety measures, simple modes of operation, light weight, and small size to compensate for the labour shortage for agricultural production among elderly farm households through machine substitution. Additionally, we demonstrate the important role of family life cycle in Chinese farm households’ SAT adoption decisions, and thus, for countries or regions with low SAT adoption rates, such as Ethiopia or Tanzania (Kassie, Jaleta, Shiferaw, Mmbando, & Mekuria, 2013; Teklewold, Kassie, & Shiferaw, 2013; Zeng, Zhang, He, & Chen, 2019), and concerning countries or regions in which individuals have a strong sense of family, such as South Korea, southern Italy, France, and Latin America (Fukuyama, 1995; Liang, Lin, & Zhang, 2015), it is likely to be beneficial in exploring farm households’ consensual technology behaviour from a family life cycle perspective. 140 F. LI ET AL. Finally, there are some deficiencies in the present study. First, due to data limitations, the present study only discuss the impact of family life cycle stages on the SAT adoption behaviour of farm households at a certain time, whereas the family life cycle changes dynamically. As farm households pass from one family life cycle stage to the next, adoption decisions are likely to vary. Therefore, using panel data to analyze the dynamic influence trends of family life cycle on farm households’ SAT adoption could be an interesting research direction. Second, due to the shortage of data at the national level, we fail to observe the influence of regional heterogeneity in the family life cycle. Thus, it is necessary to seek more data to delve deeper in future research. Disclosure statement No potential conflict of interest was reported by the author(s). Funding The work was supported by the the Key Project of National Social Science Foundation of China [20AZD091]. Notes on contributors Fenni Li is a joint training Ph.D. student in Huazhong Agricultural University and University of Western Australia. Her research focuses on agricultural environmental and resource economics, as well as agricultural technology adoption. She has 10 published articles in peer-reviewed academic journals such as the China Population, Resources and Environment, the Resources Science, and the Journal of Huazhong University of Science and Technology (Social Science Edition) in Chinese. Junbiao Zhang is professor and doctoral tutor at College of Economics & Management, Huazhong Agricultural University. His major research fields include agricultural economic theory and policy, and resource and environment economy. He has published over 200 articles in peer-reviewed academic journals such as the Resources Policy, the Renewable Energy, the China Agricultural Economic Review, and the Economic Research Journal (in Chinese). Chunbo Ma is an associate professor and doctoral tutor at School of Agriculture and Environment, University of Western Australia. His research focuses on environmental economics, resource economics, and agricultural economics. 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