- Research
- Open Access
- Published:
Effects of sustainable agricultural practices on farm income and food security in northern Ghana
Agricultural and Food Economics volume 10, Article number: 9 (2022)
Abstract
The adoption of sustainable agricultural practices (SAPs) has been recommended by many experts and international institutions to address food security and climate change problems. Global support for the Sustainable Development Goals has focused attention on efforts to up-scale the adoption of SAPs in developing countries where growth in populations and incomes compromises the resilience of natural resources. This study investigates the factors affecting smallholder farmers’ decisions to adopt SAPs (improved seed, fertilizer, and soil and water conservation) and the impacts of the adoption on farm income and food security, using data collected from Ghana. Food security is captured by the reduced coping strategy index and household dietary diversity. The multinomial endogenous switching regression model is utilized to address selection bias issues. Results show that farmers’ decisions to adopt SAPs are influenced by the social demographics of the households, plot-level characteristics, extension services and locations. Adopting all three SAPs has larger positive impacts on farm income and food security than adopting single or two SAPs. Our findings advocate for policies that enhance the quality of extension service and strengthen farmer-based organizations for the wider dissemination of adequate SAP information. Farmers should be encouraged to adopt SAPs as a comprehensive package for increasing farm income and ensuring food security.
Introduction
There is considerable pressure on agriculture to meet the demands of a growing world population. This is heightened with rising demand for necessities such as food, raw materials for industries, and biofuels. However, growth in agricultural production globally does not match this demand well, especially in parts of Africa. Africa has been projected to be vulnerable to climate change because of its proximity to the equator (Ojo et al. 2021; Thinda et al. 2021; Sarr et al. 2021; Onyeneke 2021; Ahmed 2022). Some of the physical impacts of climate change in Africa are rising sea levels, temperature andchange, and rainfall change (World Bank 2010; Abdulai 2018), which will harm agricultural productivity, farm income, food security, and economic development. This will negatively affect the poor, whose livelihoods are tired of agriculture in Sub-Saharan Africa.
There has been a global discussion on overcoming the negative externalities of climate change. Most experts believe that sustainable agriculture management could be a solution to the challenge associated with climate change (Kassie et al. 2013; Ndiritu et al. 2014; Ogemah 2017; Zhou et al. 2018; Adenle et al. 2019; Rose et al. 2019; Zeweld et al. 2020; Ma and Wang 2020; Ehiakpor et al. 2021; Bekele et al. 2021). This approach is expected to improve agricultural production performance whilst reversing the negative degradation processes on the agroecosystem, particularly in smallholder farming systems. It is an upgrade of the green revolution, which led to a significant increase in agricultural productivity globally and is credited for jump-starting economies in Asia out of poverty but has left negative externalities such as deforestation, land degradation, salinization of water bodies, and loss of biodiversity in its wake.
To reverse the negative externalities from crop intensification, farmers have been advised to adopt sustainable agricultural practices (SAPs), which are made up of elements of the green revolution and an agronomic revolution. The literature is filled with studies on the adoption of specific or single elements of SAPs, such as improved seed, irrigation, drought-tolerant crop varieties, climate-resilient crop variety, organic soil amendments, and soil and water conservation practices, and their effects on crop yield and net farm income (Abdulai and Huffman 2014; Agula et al. 2018; Adenle et al. 2019; Adegbeye et al. 2020; Kimathi et al. 2021; Zheng et al. 2021; Ahmed 2022; Yang et al. 2022). Despite the potential complementarity or substitutability of specific elements of SAPs, the research on the adoption of multiple SAPs and their effects on outcome variables such as income, outputs, consumption expenditure and food security remains limited.
This paper seeks to investigate the determinants of multiple SAP adoption and the adoption effects on farm income and food security, using second-hand data collected from Ghana. This study contributes to the literature in twofold. First, it provides empirical insights into the importance of SAPs on welfare indicators, specifically food security. The use of food security as a proxy measure for welfare is particularly important in the Ghanaian context, where farming is done mostly on a subsistence level, and farmers sell crops as and when they need cash. Thus, farmers may be food secure but not have a high net farm income or high consumption expenditure. Our analysis extends previous studies that have focused on other proxies of household welfare such as net farm income, net crop income and consumption expenditure (Kassie et al. 2013; Teklewold et al. 2013a; Manda et al. 2016; Bopp et al. 2019; Oyetunde Usman et al. 2020; Ehiakpor et al. 2021). Secondly, we employ a multinomial endogenous switching regression model to mitigate selection bias. In particular, this model helps address the selection bias issues arising from observed factors (e.g., age, gender and education) and unobserved factors (farmers’ innate ability in innovation adoption and motivations to address external shocks). Findings from the study will aid in formulating specific policies targeted at improving SAP adoption and enhancing the food security status of farm households in developing countries.
The remaining sections of the paper are as follows; "Literature review" section covers a review of relevant literature. The methodology is presented in "Methodology" section. The descriptive and empirical results are presented and discussed in "Results and discussions" section. The final section highlights the conclusions and policy implications of the findings.
Literature review
A growing number of studies have explored the factors that determine the adoption of SAPs in Africa. In the past, most of the works have focused on single components of SAPs (Abdulai and Huffman 2014; Carrión Yaguana et al. 2015; Fisher et al. 2015; Adenle et al. 2019; Manda et al. 2020a; Martey et al. 2020; Kimathi et al. 2021; Lampteym 2022). For example, Abdulai and Huffman (2014) reported that rice farmers’ decisions to adopt soil and water conservation are influenced by their education, capital and labour constraints, social networks, extension contacts, and farm soil conditions. Manda et al. (2018) found that farmers’ decisions to adopt improved maize varieties are mainly influenced by education, household size, livestock holdings, land per capita, market information, and locations in Zambia. The study by Martey et al. (2020) reveals that farmers’ adoption of drought-tolerant maize varieties is mainly determined by access to seed, gender, access to extension, labour availability and location of the farmer in Ghana. Kimathi et al. (2021) investigated farmers’ decisions to adopt climate-resilient potato varieties and found that the main factors affecting adoption were access to information, quality seeds, training, group membership and variations in agro-ecological zones.
Some studies have also explored the factors affecting smallholder farmers’ decisions to adopt multiple SAPs. Most of the past works have been focused on Eastern and Southern Africa (Teklewold et al. 2013a; Kassie et al. 2015; Cecchini et al. 2016; Bese et al. 2021; Nonvide 2021), though a growing number of studies seek to bridge the research gap in the adoption of multiple SAPs in West Africa (Nkegbe and Shankar 2014; Struik et al. 2014; Ehiakpor et al. 2021; Faye et al. 2021). The multiple SAPs considered by Teklewold et al. (2013a) include maize–legume rotation, conservation tillage, animal manure use, improved seed, and inorganic fertiliser use. They showed that a household’s trust in government support, credit constraints, spouse education, rainfall and plot-level disturbances, household wealth, social capital and networks, labour availability, plot and market access are the main factors determining both the probability and the extent of adoption of SAPs in rural Ethiopia. In their investigation for Ghana, the multiple SAPs considered by Ehiakpor et al. (2021) include improved maize seeds, maize-legume rotation, animal manure, legume intercropping, crop residue retention, zero/minimum tillage, integrated pest management, and chemical fertilizer. Non-farm income, livestock ownership, pest and disease prevalence, farmers’ experience of erosion, farmers’ perception of poor soil fertility, participation in field demonstration, membership of saving groups, access to agricultural credit, plot ownership, and distance to the agricultural input market are found to be important determinants of adoption of SAPs (Ehiakpor et al. 2021).
Studies estimating the impacts of SAP have utilized various outcome variables, such as household income, agrochemical use, demand for labour, crop yields, food security (Teklewold et al. 2013b; Abdulai and Huffman 2014; Gebremariam and Wünscher 2016; Manda et al. 2016; Amondo et al. 2019; Marenya et al. 2020; Oduniyi and Chagwiza 2021). Gebremariam and Wünscher (2016) found that higher combinations of SAPs led to higher payoff measured by net crop income and consumption expenditure in Ghana. Khonje et al. (2018) showed that joint adoption of multiple SAPs had higher impacts on yields, household income and poverty than the adoption of components of the technology package in Zambia. Amondo et al. (2019) found that adopting drought-tolerant maize varieties increases maize yield by 15% in Zambia. Marenya et al. (2020) concluded that a higher number of SAPs adopted resulted in higher maize grain yield and maize income in Ethiopia. The adoption of elements of SAPs has been said to be context-specific because there are no blueprints of the various combination of SAPs that work in every environment. Therefore, this study explores how SAP adoption affects farm income and food security, using Ghana as a case.
Methodology
Smallholder farmers make decisions to adopt SAPs in response to external shocks such as drought, erosion, perceived decline in soil fertility, weeds, pests, and diseases. Both observed factors (e.g., age, gender, education and farm size) and unobserved factors (e.g., farmers’ innate abilities and motivations) may affect their decisions when choosing to adopt a single SAP or a package (Kassie et al. 2013; Teklewold et al. 2013a; Manda et al. 2016; Ehiakpor et al. 2021). Due to the self-selection nature of technology adoption, farmers without adopting any SAPs and those adopting a single SAP or package may be systematically different. The fact results in a selection bias issue, which should be addressed for consistently estimating the effects of SAP adoption.
When technology adoption has more than two options, previous studies have used either the multi-valued treatment effects (MVT) model (Cattaneo 2010; Ma et al. 2021; Czyżewski et al. 2022) or the multinomial endogenous switching regression (MESR) model (Kassie et al. 2015; Oparinde 2021; Ahmed 2022) to address the selection bias issues. For example,Czyżewski et al. (2022) estimated the long-term impacts of political orientation (economic views and individual value systems) on the environment using the MVT model. They confirmed that local orientation is conducive to long-term environmental care. Using the MESR model, Ahmed (2022) evaluated the impact of improved maize varieties and inorganic fertilizer on productivity and wellbeing. He found that combining the two technologies significantly boosts maize yield and consumption expenditure than adopting the technologies in isolation. Because of the non-parametric nature, the MVT model can only address the observed selection bias and does not account for unobserved section bias. In comparison, the MESR model can help mitigate selection bias issues arising from both observed and unobserved factors, and thus, it is employed in this study.
Multinomial endogenous switching regression
The MESR model estimate three stages. The first stage models factors affecting smallholder farmers’ decisions to adopt a specific SAP technology or a package. Following Teklewold et al. (2013a), this study focuses on three main SAP technologies, namely improved seeds (I), fertilizer (F), and soil and water conservation (cereal-legume rotation/cereal – legume intercropping, manure use, organic input use) (S). The three categories result in eight possible choices of SAPs. It bears an emphasis here that because of the small number of observations in the group that captures the combination of improved seed and fertilizer (26 samples) and the group that captures the combination of improved seed and soil and water conservation (9 samples), we combined them in empirical estimations. Also, it is worth noting here that no household has only adopted improved seed. These facts indicate that there are six mutually exclusive choices of SAP technology, including (1) non-adoption (I0F0S0); (2) fertilizer only (I0F1S0); (3) soil and water conservation only (I0F0S1); (4) combination of improved seed and fertilizer and combination of improved seed and soil and water conservation (I1F1S0); (5) combination of fertilizer and soil and water conservation (I0F1S1); (6) combination of improved seed, fertilizer, and soil and water conservation (I1F1S1). Farmers choose one of the six possible choices to maximize the expected benefit.
The study assumes that the error terms are identical and independently Gumbel distributed, the probability that farmer i, with X characteristics will choose package j, is specified using a multinomial logit model (McFadden 1973; Teklewold et al. 2013a; Zhou et al. 2020; Ma et al. 2022b). It is specified as follows:
where Pij represents the probability that a farmer i chooses to adopt SAP technology j. Xi is a vector of observed exogenous variables that capture household, plot, and location-level characteristics. βj is a vector of parameters to be estimated. The maximum likelihood estimation is used to estimate the parameters of the latent variable model.
In the second stage, the ordinary least square (OLS) model is used to establish the relationship between the outcome variables (farm income and food security) and a set of exogenous variables denoted by Z for the chosen SAP technology. Non-adoption of SAPs (i.e., base category, I0F0S0) is denoted as j = 1, with the other combinations denoted as (j = 2 …, 6). The possible equations for each regime is specified as:
where I is an index that denotes farmer i’s choice of adopting a type of SAP technology; Qiis the outcome variables for the i-th farmer; Zi is a vector of exogenous variables; α1 and αJ are parameters to be estimated; ui1 and uiJ are the error terms.
Relying on a vector of observed covariates, captured by Zi, Eqs. (2a) and (2b) can help address the observed selection bias issue. However, if the same unobserved factors (e.g., farmers’ motivations to adopt SAPs) simultaneously influence farmers’ decisions to adopt SAPs and outcome variables, the error terms in Eqs. (2a) and (2b) and the error term in Eq. (1) would be correlated. In this case, unobserved selection bias occurs. Failing to address such type of selection bias would generate biased estimates. Within the MESR framework, the selectivity correction terms are calculated after estimating Eq. (1) and then included into Eqs. (2a) and (2b) to mitigate unobserved selection bias. Formally, Eqs. (2a) and (2b) can be rewritten as follows:
where Qi and Zi are defined earlier; λ1 and λJ are selectivity correction terms used to address unobserved selection bias issues; σ1 and σJ are covariance between error terms in Eqs. (1), (2a) and (2b). In the multinomial choice setting, there are J − 1 selectivity-correction terms, one for each alternative SAP combination.
For consistently estimating the MESR model, at least one instrumental variable (IV) should be included in Xi in the MNL model but not in the Zi in the outcome equations. In this study, two distance variables, distance to weekly market and minutes 30 to the plot, are employed as IVs for model identification purposes. Distance to the weekly market is measured as a continuous variable, measured in minutes. The variable representing minutes 30 to plot is a dummy variable, which equals 1 if the plot is within 30Â min from the homestead and 0 otherwise. The two IVs are not expected to affect farm income and food security directly. We checked the validity of the IVs by running the Falsification test and conducting the correlation coefficient analysis (Pizer 2016; Liu et al. 2021; Ma et al. 2022a). For the sake of simplicity, we did not report the results.
The average treatment effect on the treated (ATT) is calculated at the third step. This involves comparing the expected outcomes (farm income and food security) of SAP adopters and non-adopters, with and without adoption. Using experimental data, it is easier to establish impacts; however, this study is based on observational cross-sectional data, thus making impact evaluation a bit challenging. The challenge is mainly estimating the counterfactual outcome, i.e. the outcome of SAP adopters if they had not adopted the SAP technology. Following previous studies (Kassie et al. 2015; Oparinde 2021; Ahmed 2022), the study estimates ATT in the actual and the counterfactual scenarios using the following equations:
The outcome variables for SAP adopters with adoption (observed):
The outcome variables for SAP adopters had they decided not to adopt (Counterfactual):
The difference between Eqs. (4a) and (5a) or Eqs. (4b) and (5b) is the ATT. For example, the difference between Eqs. (4a) and (5a) is given as:
Data and variables
The study used data collected by IITA for their Africa RISING project (https://africa-rising.net/) in the three northern regions, namely, Northern, Upper East, and Upper West regions. The data was collected in 2014 from 1284 households operating approximately 5500 plots in 50 rural communities in northern Ghana. The baseline survey used a stratified two-stage sampling technique, and data was collected using Computer Assisted Personal Interviewing (CAPI) supported by Survey CTO software on tablets (Tinonin et al. 2016). A structured questionnaire was used to conduct the household interviews. The data covers the various SAP technologies, demographic characteristics, agricultural land holdings, crop outputs and sales, livestock production, farmers’ access to agricultural information and knowledge, access to credit and markets, household assets, and income.
The outcome variables for this study are farm income and food security. The farm income of crops cultivated is obtained by valuing the yield of crops at market price and deducting the costs of all variable inputs. Two variables capture food security, including reduced coping strategy index (rCSI) and household dietary diversity (HDD). Specifically, the rCSI is an index that is measured by scoring coping strategies households use (and frequency of use) when they experience food insecurity. rCSI is an index with five standardized questions on the coping strategies used when faced with food insecurity, the more strategies used, and food insecure the household is. The rCSI score ranges from 0 to 63. A higher level of rCSI score means a higher level of food insecurity. The HDD variable is based on the diverse food groups a household consumes. The higher the score, the more diverse the diet of a household, and the more food secure the household is. Drawing upon previous empirical studies on the adoption of SAPs and related agricultural innovations (Kassie et al. 2013; Teklewold et al. 2013a; Manda et al. 2016; Bopp et al. 2019; Oyetunde Usman et al. 2020; Ma and Wang 2020; Ehiakpor et al. 2021; Pham et al. 2021), we have identified and selected a range of control variables that may influence the adoption of SAPs. These include age, gender, education, marital status, household size, farm size, off-farm income, Africa RISING member, extension, extension satisfaction, number of crops, drought and floods, market access, sandy soil, clay soil, flat slope, moderate to steep, and location dummies.
Results and discussions
Descriptive results
Table 1 shows the frequency of respondents that used the different categories of SAPs. Of the eight possible categories of SAPs initially specified, 6.78% of farmers in our sample did not adopt any SAPs (I0F0S0). No farmers adopted imported seed only (I1F0S0), while only 9 farmers combined improved seed and soil and water conversation as SAPs (I1F0S1). Only 26 farmers combined improved seed and soil and water conservation as SAPs (I1F1S0). Therefore, as discussed earlier, we merged I1F1S0 and I1F0S1 into one group (coded as I1F1S0), and the empirical analysis includes six groups in total. Table 1 also shows that more than half of the farmers in our sample (51.17%) combined fertilizer and soil and water conservation as SAPs. Around 7% of farmers adopted all the three identified SAPs.
Table 2 presents the variables and statistical descriptions. It shows that the average farm income is 2561 GHS (roughly 400 USD). The average means of rCSI and HDD, which capture food security, are 5.576 and 7.799, respectively. Table 2 also shows that the average age of respondents was about 48Â years. Around 84% of respondents are male, and almost 90% of respondents got married. The surveyed households averagely have around 9 persons. About 61% of respondents received advice from extension officers, and 45.6% were satisfied with the extension services. Approximately 70% of respondents had accessed the markets.
Empirical results
Determinants of adoption of SAP categories
Table 3 presents the results estimated by the MNL model, demonstrating the factors that influence smallholder farmers’ decisions to adopt different SAPs categories. Farmers without adopting any type of SAPs (i.e. I0F0S0) are used as the reference group in empirical estimations. Because the primary objective of the MNL model estimations is to calculate the selectivity correction terms rather than explain the determinants of SAP adoption perfectly, we explain the results of Table 3 briefly. The results show gender variable has significant coefficients in columns 2, 4 and 5. Our results appear to suggest that women are more likely to combine improved seeds and fertilizer (I1F1S0) as SAPs to increase farm productivity. In comparison, men are more likely to rely on fertilizer (I0F1S0) or combine fertilizer and soil and water conservation technology ( I0F1S1) as SAPs to improve farm performance. Our findings are largely supported by the previous studies (Smale et al. 2018; Paudel et al. 2020; Tambo et al. 2021), reporting gendered differences in agricultural technology adoption. For example, Smale et al. (2018) found that women are more likely to adopt improved seeds on the plots they manage in Sudan. Education has positive impacts in all estimated specifications but is only statistically significant in the specification of adopting improved seed and fertilizer (I1F1S0). Better education enables farmers to be aware of the benefits of SAPs and motivate them to adopt them, especially productivity-enhancing technologies such as improved seed and fertilizer. This finding is consistent with the findings of Kassie et al. (2014) for Tanzania and Gebremariam and Wünscher (2016) for Ghana.
The significant coefficients of household size in columns 2 and 6 suggest that larger households are more likely to adopt multiple SAPs (I1F1S1) but are less likely to adopt single SAP such as fertilizer (I0F1S0). Larger households usually mean better labour endowments, allowing them to adopt multiple SAPs more easily than small ones. This is consistent with the findings of Kassie et al. (2014). Off-farm income has positive and significant coefficients in columns 3, 5 and 6. The findings suggest that farmers receiving a higher level of off-farm income are more likely to adopt fertilizer only (I0F1S0), combine fertilizer and soil and water conservation as SAPs (I0F1S1), and adopt all three SAPs (I1F1S1). Additional income from off-farm activities can help release credit constraint issues, allowing farmers to invest in innovative technologies such as SAPs to improve farm performance. In their study for Pakistan, Kousar and Abdulai (2016) found that participation in off-farm work increases farmers’ adoption of soil conservation measures.
The African RISING member variable has a positive and statistically significant impact on farmers’ fertiliser adoption only (I0F1S0), the combination of improved seed and fertilizer (I1F1S0), and the combination of fertilizer and soil and water conservation (I0F1S1). The importance of farmer-based organisations in promoting the adoption of innovative technologies has been widely discussed in the literature (Zhang et al. 2020; Manda et al. 2020b; Yu et al. 2021). For example, Manda et al. (2020a, b) reported that membership in agricultural cooperatives increases the adoption speed of improved maize by 1.6–4.3 years. We show that farmers having access to extension services are more likely to adopt SAPs, including fertilizer only (I0F1S0), soil and water conservation only (I0F0S1), and all three SAps (I1F1S1). In their studies for Nepal, Suvedi et al. (2017) found that farmers’ participation in extension programs increases their adoption of improved crop varieties. This finding is further confirmed by Nakano et al. (2018), who found that farmer-to-farmer training through extension programs enhance farmers’ adoption of technologies (e.g., fertilizer and improved bund) in Tanzania. The location dummies are statistically significant in columns 2, 4 and 5. Our findings suggest that relative to farmers living in Upper West (reference group), those residing in Northern and Upper East are more likely to adopt fertilizer only (I0F1S0) and a combination of fertilizer and soil and water conservation (I0F1S1), but less likely to adopt the combination of improved seeds and fertilizer (I1F1S0). Our findings confirm spatial-fixed characteristics (e.g., social-economic conditions, resource endowments, climate conditions, and institutional arrangements) may also affect smallholder farmers’ decisions to adopt SAPs and highlight the importance of including them in estimations.
Average treatment effects of SAPs
Table 4 presents the results estimating the treatment effects of SAP adoption on farm income and food security. For the sake of brevity, we do not present and discuss the results estimated by the OLS regression model but are available upon reasonable requests. Our ATT estimate results in Table 4 record differentiated findings regarding the impacts of adopting only one SAP technology on farm income and food security, measured by rCSI score and HDD score. Specifically, adopting only fertilizer (I0F1S0) significantly reduces rCSI score and improves HDD score. The ATT estimates indicate that fertilizer adoption only (I0F1S0) decreases rCSI score by 42% and increases the HDD score by 6.5%. We find that fertilizer adoption only (I0F1S0) decreases farm income. A possible reason could be the improper use of fertilizer by smallholder farmers, such as using lower than recommended amounts of fertilizer; hence they do not achieve the maximum potential output expected.
Adoption of SAP package that combines improved seed and fertilizer (I1F1S0) improves food security significantly. The ATT estimates show that I1F1S0 adoption reduces rCSI score by 45% and increases HDD score by 4%. However, I1F1S0 adoption decreases farm income, a finding that is largely consistent with the finding of Ma and Wang (2020), showing that SAP adoption significantly decreases farm income in China. Adoption of SAP package that combines fertilizer and soil and water conservation (I0F1S1) increases farm income and improves food security. We show that I0F1S1 adoption increases farm income by 12%, reduces rCSI score by 23%, and improves HDD score by 5%.
The ATT estimates show that adopting all the three SAPs (I1F1S1) positively and statistically impacts farm income and food security. The impact magnitudes of adopting all the three SAPs are larger than that of adopting single or two SAPs. Specifically, the I1F1S1 adoption increases farm income by 23%, reduces rCSI score by 53%, and improves HDD score by 14%. Our results are largely supported by the previous studies (Teklewold et al. 2013a; Manda et al. 2016; Oduniyi and Chagwiza 2021), pointing out that adopting multiple SAPs has larger impacts on welfare measures than adopting only one or two SAPs. For example, Teklewold et al. (2013b) showed that multiple SAP adoption significantly increases household income in Ethiopia. Oduniyi and Chagwiza (2021) found that adopting sustainable land management practices increases the food security of smallholder farmers in South Africa.
Conclusions and policy implications
Many institutions have credited sustainable agricultural practices (SAPs) as a viable solution that helps tackle the worlds’ feeding problems and worsening environmental issues. This study used a multinomial endogenous switching regression (MESR) to investigate factors that affect smallholder farmers’ decisions to adopt different categories of SAPs and estimate the effects of the adoption on farm income and food security. In particular, we used two measures, including rCSI score and HDD score, to capture food security. We estimated the data collected by IITA for their Africa RISING project in Ghana.
The MNL results showed that farmers’ decisions to adopt SAPs are influenced by the social demographics of the households (e.g., gender, education, marital status, and household size), plot-level characteristics (e.g., number of crops, soil types, and topography), extension services, and locations. The study also recorded differentiated findings regarding the impacts of adopting only one or two SAPs on farm income and food security. For example, adopting only fertilizer significantly reduces rCSI score and improves HDD score, but it unexpectedly decreases farm income. Adoption of SAP package that combines improved seed and fertilizer significantly improves food security measures, but it also decreases farm income. Nevertheless, we found that adopting all the three SAPs positively and statistically impacts farm income and food security. The impact magnitudes of adopting all the three SAPs are larger than that of adopting single or two SAPs.
The study highlights that policies that improve the extension agents to farmer ratio should be pursued since access to extension positively influenced the adoption of SAPs. The satisfaction with the extension agent variable positively influenced the adoption of all the SAPs. This highlights the need to improve the quality of extension service to minimize the risk of adoption due to inadequate information transfer. Membership in farmer-based organizations (FBOs) such as Africa RISING positively influenced the adoption of different packages of SAPs. Therefore farmers should be encouraged to join FBOs, and similar organizations should be established or strengthened to enhance the dissemination of information regarding SAPs. Policies to improve farmer income and food security should advocate for the comprehensive adoption of all the SAPs packages and provide incentives to motivate the adoption of all SAPs packages.
Availability of data and materials
Data is available from the leading author upon the reasonable request.
References
Abdulai A (2018) Simon Brand Memorial Address: The challenges and adaptation to climate change by farmers in sub-Saharan Africa. Agrekon 57:28–39. https://doi.org/10.1080/03031853.2018.1440246
Abdulai A, Huffman W (2014) The adoption and impact of soil and water conservation technology: an endogenous switching regression application. Land Econ 90:26–43
Adegbeye MJ, Reddy PRK, Obaisi AI et al (2020) Sustainable agriculture options for production, greenhouse gasses and pollution alleviation, and nutrient recycling in emerging and transitional nations—an overview. J Clean Prod 242:118–319
Adenle AA, Wedig K, Azadi H (2019) Sustainable agriculture and food security in Africa: the role of innovative technologies and international organizations. Technol Soc 58:101143
Agula C, Akudugu MA, Dittoh S, Mabe FN (2018) Promoting sustainable agriculture in Africa through ecosystem-based farm management practices: evidence from Ghana. Agric Food Secur 7:5
Ahmed MH (2022) Impact of improved seed and inorganic fertilizer on maize yield and welfare: evidence from Eastern Ethiopia. J Agric Food Res 7:100266. https://doi.org/10.1016/j.jafr.2021.100266
Amondo E, Simtowe F, Rahut DB, Erenstein O (2019) Productivity and production risk effects of adopting drought-tolerant maize varieties in Zambia. Int J Clim Chang Strateg Manag 11:570–591. https://doi.org/10.1108/IJCCSM-03-2018-0024
Bekele RD, Mirzabaev A, Mekonnen D (2021) Adoption of multiple sustainable land management practices among irrigator rural farm households of Ethiopia. L Degrad Dev 32:5052–5068. https://doi.org/10.1002/ldr.4091
Bese D, Zwane E, Cheteni P (2021) The use of sustainable agricultural methods amongst smallholder farmers in the Eastern Cape province, South Africa. Afr J Sci Technol Innov Dev 13:261–271. https://doi.org/10.1080/20421338.2020.1724388
Bopp C, Engler A, Poortvliet PM, Jara-Rojas R (2019) The role of farmers’ intrinsic motivation in the effectiveness of policy incentives to promote sustainable agricultural practices. J Environ Manage 244:320–327. https://doi.org/10.1016/j.jenvman.2019.04.107
Carrión Yaguana V, Alwang J, Norton G, Barrera V (2015) Does IPM have staying power? Revisiting a potato-producing area years after formal training ended. J Agric Econ 66:1–16
Cattaneo MD (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability. J Econom 155:138–154. https://doi.org/10.1016/j.jeconom.2009.09.023
Cecchini S, Scott C, Imai KS et al (2016) Does adaptation to climate change provide food security? A micro-perspective from Ethiopia. Am J Agric Econ 46:825–842. https://doi.org/10.1093/ajae/aar006
Czyżewski B, Polcyn J, Brelik A (2022) Political orientations, economic policies, and environmental quality: multi-valued treatment effects analysis with spatial spillovers in country districts of Poland. Environ Sci Policy 128:1–13. https://doi.org/10.1016/j.envsci.2021.11.001
Ehiakpor DS, Danso-Abbeam G, Mubashiru Y (2021) Adoption of interrelated sustainable agricultural practices among smallholder farmers in Ghana. Land use policy 101:105142
Faye JB, Hopple AM, Bridgham SD (2021) Indigenous farming practices increase millet yields in Senegal, West Africa. Agroecol Sustain Food Syst 45:159–174. https://doi.org/10.1080/21683565.2020.1815927
Fisher M, Abate T, Lunduka RW et al (2015) Drought tolerant maize for farmer adaptation to drought in sub-Saharan Africa: determinants of adoption in eastern and southern Africa. Clim Change 133:283–299. https://doi.org/10.1007/s10584-015-1459-2
Gebremariam G, Wünscher T (2016) Combining sustainable agricultural practices pays off: evidence on welfare effects from Northern Ghana. African Association of Agricultural Economists (AAAE)
Kassie M, Jaleta M, Shiferaw B et al (2013) Adoption of interrelated sustainable agricultural practices in smallholder systems: evidence from rural Tanzania. Technol Forecast Soc Change 80:525–540. https://doi.org/10.1016/j.techfore.2012.08.007
Kassie M, Jaleta M, Mattei A (2014) Evaluating the impact of improved maize varieties on food security in Rural Tanzania: evidence from a continuous treatment approach. Food Secur 6:217–230. https://doi.org/10.1007/s12571-014-0332-x
Kassie M, Teklewold H, Jaleta M et al (2015) Understanding the adoption of a portfolio of sustainable intensification practices in eastern and southern Africa. Land Use Policy 42:400–411. https://doi.org/10.1016/j.landusepol.2014.08.016
Khonje MG, Manda J, Mkandawire P et al (2018) Adoption and welfare impacts of multiple agricultural technologies: evidence from eastern Zambia. Agric Econ 49:599–609. https://doi.org/10.1111/agec.12445
Kimathi SM, Ayuya OI, Mutai B (2021) Adoption of climate-resilient potato varieties under partial population exposure and its determinants: Case of smallholder farmers in Meru County, Kenya. Cogent Food Agric 7:66. https://doi.org/10.1080/23311932.2020.1860185
Kousar R, Abdulai A (2016) Off-farm work, land tenancy contracts and investment in soil conservation measures in rural Pakistan. Aust J Agric Resour Econ 60:307–325
Lampteym S (2022) Agronomic practices in soil water management for sustainable crop production under rain fed agriculture of drylands in sub-Sahara Africa. Afr J Agric Res 18:18–26. https://doi.org/10.5897/AJAR2021.15822
Liu M, Min S, Ma W, Liu T (2021) The adoption and impact of E-commerce in rural China: application of an endogenous switching regression model. J Rural Stud 83:106–116. https://doi.org/10.1016/j.jrurstud.2021.02.021
Ma W, Wang X (2020) Internet use, sustainable agricultural practices and rural incomes: evidence from China. Aust J Agric Resour Econ 64:1087–1112. https://doi.org/10.1111/1467-8489.12390
Ma W, Zhu Z, Zhou X (2021) Agricultural mechanization and cropland abandonment in rural China. Appl Econ Lett 00:1–8. https://doi.org/10.1080/13504851.2021.1875113
Ma W, Vatsa P, Zhou X, Zheng H (2022a) Happiness and farm productivity: insights from maize farmers in China. Int J Soc Econ 49:97–106. https://doi.org/10.1108/IJSE-08-2021-0474
Ma W, Zheng H, Gong B (2022b) Rural income growth, ethnic differences, and household cooking fuel choice: evidence from China. Energy Econ 107:105851. https://doi.org/10.1016/j.eneco.2022.105851
Manda J, Alene AD, Gardebroek C et al (2016) Adoption and impacts of sustainable agricultural practices on maize yields and incomes: evidence from rural Zambia. J Agric Econ 67:130–153. https://doi.org/10.1111/1477-9552.12127
Manda J, Gardebroek C, Kuntashula E, Alene AD (2018) Impact of improved maize varieties on food security in Eastern Zambia: a doubly robust analysis. Rev Dev Econ 22:1709–1728. https://doi.org/10.1111/rode.12516
Manda J, Alene AD, Tufa AH et al (2020a) Adoption and ex-post impacts of improved cowpea varieties on productivity and net returns in Nigeria. J Agric Econ 71:165–183. https://doi.org/10.1111/1477-9552.12331
Manda J, Khonje MG, Alene AD et al (2020b) Does cooperative membership increase and accelerate agricultural technology adoption? Empirical evidence from Zambia. Technol Forecast Soc Change 158:120160. https://doi.org/10.1016/j.techfore.2020.120160
Marenya PP, Gebremariam G, Jaleta M, Rahut DB (2020) Sustainable intensification among smallholder maize farmers in Ethiopia: adoption and impacts under rainfall and unobserved heterogeneity. Food Policy 95:101941. https://doi.org/10.1016/j.foodpol.2020.101941
Martey E, Etwire PM, Kuwornu JKM (2020) Economic impacts of smallholder farmers’ adoption of drought-tolerant maize varieties. Land Use Policy 94:104524. https://doi.org/10.1016/j.landusepol.2020.104524
McFadden D (1973) Conditional logit analysis of qualitative choice behavior. Academic Press, New York
Nakano Y, Tsusaka TW, Aida T, Pede VO (2018) Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania. World Dev 105:336–351. https://doi.org/10.1016/j.worlddev.2017.12.013
Ndiritu SW, Kassie M, Shiferaw B (2014) Are there systematic gender differences in the adoption of sustainable agricultural intensification practices? Evidence from Kenya. Food Policy 49:117–127. https://doi.org/10.1016/j.foodpol.2014.06.010
Nkegbe P, Shankar B (2014) Adoption intensity of soil and water conservation practices by smallholders: evidence from Northern Ghana. Bio-Based Appl Econ 3:159
Nonvide GMA (2021) Adoption of agricultural technologies among rice farmers in Benin. Rev Dev Econ Rode. https://doi.org/10.1111/rode.12802
Oduniyi OS, Chagwiza C (2021) Impact of adoption of sustainable land management practices on food security of smallholder farmers in Mpumalanga province of South Africa. GeoJournal. https://doi.org/10.1007/s10708-021-10497-0
Ogemah VK (2017) Sustainable agriculture: Developing a common understanding for modernization of agriculture in Africa. Afr J Food Agric Nutr Dev 17:11673–11690. https://doi.org/10.18697/ajfand.77.16560
Ojo TO, Ogundeji AA, Belle JA (2021) Climate change perception and impact of on-farm demonstration on intensity of adoption of adaptation strategies among smallholder farmers in South Africa. Technol Forecast Soc Change 172:121031. https://doi.org/10.1016/j.techfore.2021.121031
Onyeneke RU (2021) Does climate change adaptation lead to increased productivity of rice production? Lessons from Ebonyi State, Nigeria. Renew Agric Food Syst 36:54–68. https://doi.org/10.1017/S1742170519000486
Oparinde LO (2021) Fish farmers’ welfare and climate change adaptation strategies in southwest, Nigeria: application of multinomial endogenous switching regression model. Aquac Econ Manag 25:450–471. https://doi.org/10.1080/13657305.2021.1893863
Oyetunde Usman Z, Oluseyi Olagunju K, Rafiat Ogunpaimo O (2020) Determinants of adoption of multiple sustainable agricultural practices among smallholder farmers in Nigeria. Int Soil Water Conserv Res. https://doi.org/10.1016/j.iswcr.2020.10.007
Paudel GP, Gartaula H, Rahut DB, Craufurd P (2020) Gender differentiated small-scale farm mechanization in Nepal hills: an application of exogenous switching treatment regression. Technol Soc 61:101250. https://doi.org/10.1016/j.techsoc.2020.101250
Pham H, Chuah S, Feeny S (2021) Factors affecting the adoption of sustainable agricultural practices: findings from panel data for Vietnam. Ecol Econ 184:107000. https://doi.org/10.1016/j.ecolecon.2021.107000
Pizer SD (2016) Falsification testing of instrumental variables methods for comparative effectiveness research. Health Serv Res 51:790–811. https://doi.org/10.1111/1475-6773.12355
Rose DC, Sutherland WJ, Barnes AP et al (2019) Integrated farm management for sustainable agriculture: lessons for knowledge exchange and policy. Land Use Policy 81:834–842. https://doi.org/10.1016/j.landusepol.2018.11.001
Sarr M, Bezabih Ayele M, Kimani ME, Ruhinduka R (2021) Who benefits from climate-friendly agriculture? The marginal returns to a rainfed system of rice intensification in Tanzania. World Dev 138:105160. https://doi.org/10.1016/j.worlddev.2020.105160
Smale M, Assima A, Kergna A et al (2018) Farm family effects of adopting improved and hybrid sorghum seed in the Sudan Savanna of West Africa. Food Policy 74:162–171. https://doi.org/10.1016/j.foodpol.2018.01.001
Struik PC, Klerkx L, van Huis A, Röling NG (2014) Institutional change towards sustainable agriculture in West Africa. Int J Agric Sustain 12:203–213. https://doi.org/10.1080/14735903.2014.909641
Suvedi M, Ghimire R, Kaplowitz M (2017) Farmers’ participation in extension programs and technology adoption in rural Nepal: a logistic regression analysis. J Agric Educ Ext 23:351–371. https://doi.org/10.1080/1389224X.2017.1323653
Tambo JA, Matimelo M, Ndhlovu M et al (2021) Gender-differentiated impacts of plant clinics on maize productivity and food security: evidence from Zambia. World Dev 145:105519. https://doi.org/10.1016/j.worlddev.2021.105519
Teklewold H, Kassie M, Shiferaw B (2013a) Adoption of multiple sustainable agricultural practices in rural Ethiopia. J Agric Econ 64:597–623. https://doi.org/10.1111/1477-9552.12011
Teklewold H, Kassie M, Shiferaw B, Köhlin G (2013b) Cropping system diversification, conservation tillage and modern seed adoption in Ethiopia: impacts on household income, agrochemical use and demand for labor. Ecol Econ 93:85–93. https://doi.org/10.1016/j.ecolecon.2013.05.002
Thinda KT, Ogundeji AA, Belle JA, Ojo TO (2021) Determinants of relevant constraints inhibiting farmers’ adoption of climate change adaptation strategies in South Africa. J Asian Afr Stud 56:610–627. https://doi.org/10.1177/0021909620934836
Tinonin C, Azzarri C, Haile B et al (2016) Africa RISING Baseline Evaluation Survey (ARBES) report for Ghana
World Bank (2010) Economics of adaptation to climate change: Ghana country study. Washington, DC
Yang Q, Zhu Y, Liu L, Wang F (2022) Land tenure stability and adoption intensity of sustainable agricultural practices in banana production in China. J Clean Prod 338:130553. https://doi.org/10.1016/j.jclepro.2022.130553
Yu L, Chen C, Niu Z et al (2021) Risk aversion, cooperative membership and the adoption of green control techniques: Evidence from China. J Clean Prod 279:123288. https://doi.org/10.1016/j.jclepro.2020.123288
Zeweld W, Van Huylenbroeck G, Tesfay G et al (2020) Sustainable agricultural practices, environmental risk mitigation and livelihood improvements: empirical evidence from Northern Ethiopia. Land Use Policy 95:103799. https://doi.org/10.1016/j.landusepol.2019.01.002
Zhang S, Sun Z, Ma W, Valentinov V (2020) The effect of cooperative membership on agricultural technology adoption in Sichuan, China. China Econ Rev 62:101334. https://doi.org/10.1016/j.chieco.2019.101334
Zheng H, Ma W, Li G (2021) Learning from neighboring farmers: Does spatial dependence affect adoption of drought-tolerant wheat varieties in China? Can J Agric Econ Can D’agroeconomie 69:519–537. https://doi.org/10.1111/cjag.12294
Zhou X, Ma W, Li G (2018) Draft animals, farm machines and sustainable agricultural production: insight from China. Sustainability 10:3015. https://doi.org/10.3390/su10093015
Zhou X, Ma W, Renwick A, Li G (2020) Off-farm work decisions of farm couples and land transfer choices in rural China. Appl Econ 52:6229–6247. https://doi.org/10.1080/00036846.2020.1788709
Acknowledgements
The authors gratefully acknowledge the financial support from NZAID scholarship from MFAT and Lincoln university research fund. We want to thank IITA and IFPRI for making the data from the Africa RISING Project readily accessible. We also want to thank Dr. Gideon Danso-Abbeam for his helpful comments and suggestions.
Funding
No funding was received in the carrying out of this research.
Author information
Authors and Affiliations
Contributions
All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Setsoafia, E.D., Ma, W. & Renwick, A. Effects of sustainable agricultural practices on farm income and food security in northern Ghana. Agric Econ 10, 9 (2022). https://doi.org/10.1186/s40100-022-00216-9
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1186/s40100-022-00216-9
Keywords
- Sustainable agriculture practices
- MESR model
- Farm income
- Food security
- Ghana
JEL Classification
- C34
- O12
- Q16
- Q18