1 Introduction
The interaction and exposure between climate-related hazards (e.g., frequency and intensity of occurrences and trends) and human systems result in climate-related risks (Wu et al.
2019), resulting in loss of livelihood and food insecurity, particularly in rural areas. Agriculture significantly contributes to the climate problem through GHG emissions and is also most vulnerable to the effects of climate change (CC). For instance, agriculture generates approximately 14% of total greenhouse gas (GHG) emissions (Stetter and Sauer
2022). This proportion will likely rise significantly as emissions from other sectors decline due to the clean energy transition (World Bank
2021). On the other hand, agriculture is highly susceptible to the impacts of CC (Habtemariam et al.
2020). For instance, a degree Celsius increase in average temperature would decrease wheat production by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1% (Zhao et al.
2017). According to the Bluebook on Climate Change in China 2023, extreme weather events and the “climate risk index” take on an increasing trend.
1 The North-east China Plain, which stands as one of the most critical food production regions, has experienced a reduction in average regional precipitation levels throughout the crop-growing season of -1.72 mm/year during the last 40 years (Chen et al.
2022). Further, the average annual surface temperature increased by 0.23 ◦C every ten years since 1951 (Chen et al.
2022; Song et al.
2022). China’s economic losses might double between 1.5 and 2.0 °C warming levels, and the population impacted by catastrophic floods could continuously rise (Wu et al.
2019). The short- and long-term effects of CC would diminish crop yields (Roy et al.
2019; Chaloner et al.
2021), damage livestock output (Wreford and Topp
2020), and challenge sustainable agricultural systems (FAO
2022), consequently leading to a rise in the population facing food insecurity (Pörtner et al.
2022).
Climate-smart agricultural practices (CAPs) employ comprehensive practices addressing CC and food security, which is critical to establishing a more sustainable and resilient agriculture system. Therefore, many studies have explored the factors that promote and inhibit farmers’ decisions to adopt CAPs (Arslan et al.
2015; Sardar et al.
2021; Bazzana et al.
2022; Gikonyo et al.
2022; Musafiri et al.
2022). These studies have focused on individual and household factors (Musafiri et al.
2022), socioeconomic factors (Bazzana et al.
2022; Gikonyo et al.
2022), institutional factors (Sardar et al.
2021), and topography and climate-related factors (Arslan et al.
2015). For example, Zhu et al. (
2021) found that compared with ties to retailers, ties to local farmers impeded farmers from actively combating CC in China. Sedebo et al. (
2022) found that weather information significantly influences smallholder households’ decisions to adopt CAPs in southern Ethiopia. Zhou et al. (
2023) found that agricultural cooperatives are key to improving CAP adoption in rural China.
An increasing body of research has examined the impacts of CAPs on the three fundamental aspects (productivity, adaptation, and mitigation) of the climate-smart agriculture (CSA) systems (Lopez-Ridaura et al.
2018; Branca et al.
2021; Sedebo et al.
2022). However, most of these studies only consider one dimension of the implications of the CSA system, such as household income, agricultural outputs, and greenhouse emissions (Amadu et al.
2020; Bazzana et al.
2022; Israel et al.
2020). For example, Amadu et al. (
2020) found that the maize yield of CAP adopters, who participated in the Agriculture for Life Advancement project in southern Malawi, is 53% higher than that of non-adopters. Branca et al. (
2021) revealed that transforming from conventional to climate-smart farming significantly enhances households’ economic returns in Southern Africa. Bazzana et al. (
2022) found that CAPs (water and soil management action and conservation practices) can greatly increase farmers’ food security in rural Ethiopia, particularly for those with strong financial resources, extensive social networks, and entry to well-connected food markets.
Although it is widely acknowledged that farmers always adopt more than one type of CAP (Zakaria et al.
2020), most of the previous studies have used CAP adoption as a dummy variable in their estimations (Arslan et al.
2015). Only a few studies consider CAP adoption as an ordered or continuous variable when investigating its intensity on household welfare (Israel et al.
2020; Sardar et al.
2021). For example, taking CAPs as a continuous variable, Israel et al. (
2020) found that CAP adoption (irrigation and water collection, soil conservation practices, and livelihood diversification) significantly reduces GHG emissions by 62.3% in Northern Ghana. Sardar et al. (
2021) divided CAPs into different levels and found that the crop yields received by Pakistani farmers who adopted a full set of CAPs (water and nutrient management, adjusting planting dates, increasing crop varieties, and zero or minimum tillage) are 32% higher for cotton and 44% higher for wheat than non-adopters.
Despite the rich findings in the existing studies, there are important research gaps. First, while a growing body of research has explored the social, economic, and ecological effects of CAP adoption, little is known about whether and to what extent CAP adoption intensity affects income diversity. As a risk management strategy, income diversification can effectively address external shocks in production. Therefore, it is important to understand the relationship between CAP adoption intensity and income diversity. Second, previous studies have assumed that CAP adoption intensity has a homogeneous impact on household welfare. Nevertheless, CAP adoption intensity may affect rural households in the upper and lower levels of the distribution of household welfare indicators differently. This is not surprising because farmers are endowed with different personal characteristics (e.g., education and innate ability) and resource endowments (land and machinery). However, to the best of our knowledge, the potential heterogeneous effects of CAP adoption intensity on household welfare remain unexamined.
This study, therefore, examines the effects of CAP adoption intensity on household economic welfare. Our contributions are threefold. First, we consider seven types of CAPs (e.g., water-saving irrigation, organic fertilizer, farmyard manure, zero tillage, fallow cropping, crop rotation, and crop straw mulch) to capture the CAP adoption intensity. In particular, we categorized the farmers’ CAP adoption intensity into four ordinal groups according to the number of CAPs they adopted. Second, this study considers multiple indicators, including household income, net farm income, and income diversity, to capture household economic welfare. Most studies only focus on specific household economic indicators, such as crop income and per capita consumption expenditure (Fentie and Beyene
2019; Sardar et al.
2021). Measuring household welfare from multiple dimensions provides a comprehensive understanding of the effects of CAP adoption intensity.
Third, we utilize a two-stage residual inclusion (2SRI) model to mitigate the endogeneity concern linked to CAP adoption intensity. Farmers self-decide whether or not to adopt CAPs. Both observable factors (e.g., age, gender, education, and family size) and unobservable factors (e.g., inner motivations and native ability) could affect their CAP adoption decisions. The fact leads to self-selection and omitted variable issues. Previous studies mainly employ the propensity score matching (PSM) model (Andati et al.
2023; Fentie & Beyene
2019) and inverse-probability-weighted regression adjustment (IPWRA) estimator (Israel et al.
2020) to mitigate the concerns related to selection bias. However, they can only control for the observed selection bias. Several studies used the endogenous switching regression model (Amadu et al.
2020; Akter et al.
2023), but it cannot solve the endogeneity issue of ordered explanatory variables (i.e., CAP adoption intensity). The 2SRI model can address the endogeneity of CAP adoption intensity by controlling for both observable and unobservable heterogeneities, thus producing more robust estimates. Additionally, we employ an instrumental-variable-based quantile regression (IVQR) model to explore the heterogeneous effects of CAP adoption intensity on household economic welfare. The findings of this study enrich the literature examining the effects of CAP adoption intensity on household welfare.
The subsequent sections of this paper are structured as follows: Section
2 offers a literature review, followed by an explanation of the estimation methodologies in Section
3. Section
4 introduces the data and provides descriptive statistics, while Section
5 presents and discusses empirical findings. The concluding section wraps up the paper and proposes policy implications.
2 Literature review
In the face of CC, farmers, particularly those in developing countries, have changed their traditional agricultural practices and adopted CAPs to reduce the adverse impact of changing climatic conditions (Nyasimi et al.
2017). These CAPs are diversified and need to be tailored based on different locations and conditions (Anugwa et al.
2022; Das et al.
2022; Morkunas and Volkov
2023). Previous studies mainly focused on the specific CAPs and taking CAP adoption as the dummy variable to explore its determinants and implications (Arslan et al.
2015; Bazzana et al.
2022; Israel et al.
2020; Musafiri et al.
2022). However, farmers are more inclined to implement a mix of CAPs rather than solely relying on a single CAP to tackle the obstacles presented by the impacts of CC (Zhou et al.
2023). Further, with the emphasis on the complementary effects of different CAPs (Harvey et al.
2014; Zheng et al.
2019; Antwi-Agyei et al.
2023), several research endeavors focused on the CAP adoption intensity, regarding it as the continuous or count variable based on the numbers of CAPs adopted by farmers (Sardar et al.
2021; Zakaria et al.
2020). In this study, we follow these studies to divide CAP adoption intensity into different levels and explore how CAP adoption intensity affects households’ economic welfare, providing suggestions for CAP implementation in rural areas.
Different CAPs bring about heterogeneous economic, social, and environmental effects on the three fundamental elements of the CSA system (Zakaria et al.
2020; Sardar et al.
2021; Akter et al.
2023). Adopting CAPs is an efficient pathway to ensure food security, which can be found in the positive effects of CAPs on households’ agricultural productivity (Lopez-Ridaura et al.
2018; Sardar et al.
2021), food availability (Bazzana et al.
2022), and food consumption (Hasan et al.
2018). Further, CAP adoption significantly improves households’ adaptability and resilience under various climatic conditions. For example, the prevalent agricultural adaptive strategies, such as diversifying crops, rescheduling farming, and changing crop structure are found positively influence crop yields (Arslan et al.
2015; Sedebo et al.
2022; Sargani et al.
2023), poverty alleviation (Habtewold
2021), crop income (Ahmad and Afzal
2020), and GHG emission mitigation (Zheng et al.
2019; Wang et al.
2020). CAP adoption is also imperative to improve gender equality in agriculture regarding ownership rights (Tsige et al.
2020) and knowledge and capacity (Hariharan et al.
2020). In this study, we focus on two pillars of the CSA system: productivity and adaptability. We use household income and net farm income as the proxy variable of productivity and use income diversity to indicate farmers’ adaptability.
Therefore, to explore the economic impacts of CAP adoption, this study classifies CAP adoption behavior into four intensity levels based on the amount/number of CAP adoption behavior of the farmers and investigates its impacts on three dimensions of households’ economic welfare. This comprehensive perspective helps us understand the potential synergistic effects of CAPs on household economic welfare.
4 Data and key variables
4.1 Data
This study employed data from the 2020 China Rural Revitalization Survey (CRRS), carried out by the Rural Development Institute at the Chinese Academy of Social Sciences. It contains rich information on agricultural production (e.g., CAP adoption), demographic factors (e.g., age, gender, education, health status, household size, and dependency ratio), and socioeconomic factors (e.g., income, farm size, and asset ownership). The 2020 CRRS utilized a multi-stage probability proportional to size (PPS) sampling technique to randomly select 3,833 households from 10 provinces in eastern, central, and western China. Since we are interested in the CAP adoption behavior of farmers growing grain crops such as maize, wheat, and rice, we dropped the samples of farmers growing other crops. We also deleted samples with missing information in dependent and independent variables. Finally, we obtained a sample size of 1,785 households.
In this study, we select three dependent variables to capture economic welfare outcomes: household income, net farm income, and income diversity. Household income refers to the collective earnings generated through agricultural activity, business investment, wages, property, subsidies, and remittances. Net farm income designates the difference between gross revenue derived from agricultural, forestry, animal husbandry, and fishery activities and production costs. The income diversity is quantified using the Simpson index, and we have considered eight types of income sources (i.e., agriculture, forestry, animal husbandry, fishery, business, salary, property, and transfer income).
4.2 Key variables
4.2.1 Dependent variables
Economic welfare, denoting the overall prosperity and living standards within an economy, remains a multifaceted concept (Fang
2011). Despite the various factors contributing to its measurement, there is still limited consensus on the definition of households’ economic welfare in existing literature (Tankari
2017). In this study, we strategically chose three dependent variables to capture economic welfare outcomes: household income, net farm income, and income diversity. Household income, encompassing earnings from agricultural pursuits, business investments, wages, property, subsidies, and remittances, is a central and widely recognized measure of a household’s economic well-being (Shahzad and Abdulai
2021). Net farm income is of great significance, representing the disparity between gross revenue from agricultural, forestry, animal husbandry, and fishery activities and production costs. Its role in directly measuring a household’s financial success in agricultural endeavors has garnered broad acknowledgement.
Income diversity plays a key role in increasing household resilience and reducing economic risk (Li et al.
2020). In this study, the income diversity is quantified using the Simpson index and we have considered eight types of income sources (i.e., agriculture, forestry, animal husbandry, fishery, business, salary, property, and transfer income). Following previous studies (Vatsa et al.
2022), the Simpson index is measured as follows:
$${Simpson}_i=1-\sum\nolimits_{s=1}^mP_{i,s}^2$$
(4)
where
\({Simpson}_{i}\) refers to the Simpson index associated with household
\(i\).
\(m\) is the total number of income sources for household
\(i\).
\({P}_{i,s}\) refers to the proportion of income from sources
\(s\) in total household income. The value of
\({Simpson}_{i}\) is always between zero and one. If a household has only one source of income (e.g., agricultural), the value of
\({Simpson}_{i}\) will be equal to zero. As the number of income sources increases, the value of
\({Simpson}_{i}\) approaches one. Higher values of the Simpson index mean that households diversify their income using various sources.
4.2.2 Explanatory variables
The key explanatory variable is CAP adoption intensity. We choose seven practices reflecting CAP adoption intensity based on the grain crop production practices in China and existing literature on CAPs (Chen et al.
2022; Akter et al.
2023; Sattar et al.
2023), which includes: (1) water-saving irrigation, (2) organic fertilizer, (3) farmyard manure, (4) zero tillage, (5) fallow cropping, (6) crop rotation, (7) crop straw mulch.
2 Specifically, we categorize the farmers’ CAP adoption intensity into four ordinal groups according to the number of CAPs they adopted: i.e., Adoption Level-0 (no CAPs have been adopted), Level-1 (Adoption of only one CAP), Level-2 (Adoption of two to three CAPs), and Level-3 (Adoption four to five CAPs). Each level represents the cumulative number of distinct practices adopted by a farmer. For example, a farmer classified under Level-1 means adopting any one of the seven specified practices, not limited to a specific one. Similarly, a farmer classified under Level-2 indicates choosing any combination of two to three practices from the set of seven. Our rationale for employing this ordinal coding approach rather than a multivariate analysis lies in our interest in understanding the cumulative impact of adopting a certain number of practices on specific outcomes. By categorizing farmers into ordinal levels, we aim to explore the relationship between the intensity of CAP adoption and outcomes in a structured and interpretable manner.
The control variables are selected based on previous studies on CAP adoption (Khan et al.
2022; Belay et al.
2023) and economic welfare (Ma et al.
2020; Ahmad and Jabeen
2023). These variables capture individual characteristics (household head’s age, gender, education, and health status), household characteristics (household size, dependency ratio, farm size, and asset ownership), and location characteristics (eastern, central, and western China).
6 Conclusions and policy implications
Although previous literature has examined the role of CAP adoption, little is known about the multiple economic impacts of CAP adoption intensity. This study comprehensively examines the economic impacts of CAP adoption intensity, focusing on household income, net farm income, and income diversity. To address the endogeneity issues, we utilized the 2SRI model to estimate household data for 2020 CRRS. Besides, the IVQR model was utilized to capture the heterogeneous impacts of CAP adoption intensity.
The results obtained from the first stage estimation of the 2SRI model indicate that the education level of the household head and geographical location determine farmers’ adoption intensity of CAPs. The results from the second stage of the 2SRI model reveal that higher levels of CAP adoption are positively and significantly associated with higher household income, net farm income, and income diversity. The results from the IVQR model show that CAP adoption intensity is associated with economic welfare, but the effects are not homogenous. The impacts of CAP adoption intensity are more significant for the higher quantile of the household income distribution and the lower quantile of the net farm income distribution. Low-income diversity farmers tend to benefit more from CAP adoption intensity than their high-income diversity counterparts.
Our findings have important policy implications for promoting CAP adoption and improving farmers’ household welfare. First, the positive effects of CAP adoption intensity on household economic welfare underscores the pressing need to incentivize farmers to incorporate CAPs more extensively. To bolster the adoption rate, particularly for practices with currently lower adoption rates, such as water-saving irrigation and crop rotation, policymakers should pursue a multifaceted approach. This approach involves identifying the pivotal drivers that can encourage the widespread adoption of CAPs. To this end, strategies such as offering financial incentives, crafting well-supported voluntary schemes, delivering robust training programs, and facilitating the dissemination of relevant informational tools should be considered. Achieving higher adoption rates requires a comprehensive approach that synergistically combines economic, educational, and motivational factors. This integrated strategy fosters a favorable environment for farmers to effortlessly incorporate CAPs into their practices, ultimately boosting their economic prosperity and operational sustainability.
Second, the evident link between education and the intensity of CAP adoption emphasizes the critical need for targeted interventions designed to assist farmers with limited educational backgrounds. To enhance ‘farmers’ ability to withstand climate variability by widely adopting CAPs’, it’s imperative to ensure that farmers comprehend the inherent value of these practices. As such, offering comprehensive technical training with CAPs becomes a pivotal method to foster increased adoption rates among farmers.
Third, it is essential to consider regional disparities during the policy formulation process. To this end, we recommend conducting in-depth research to elucidate the specific hurdles farmers in the western region encounter when adopting CAPs. This proactive step will provide valuable insights that can guide the development of policies tailored to the unique circumstances of this region. Policymakers can craft strategies by understanding the distinct challenges and opportunities in the western region, resulting in more targeted and impactful initiatives.
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