Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia

1.1 Conceptual framework

In the face of changing climatic conditions and climate change, climate-smart agriculture (CSA) is an approach to transform and reorient agricultural systems to ensure food security (Chavula, 2021). Climate change disrupts food markets, putting food supply and production at risk. Strengthening farmers’ adaptive capacity and enhancing the mitigation potential and efficiency of agricultural production systems can mitigate these risks. Smallholder farmers who are aware of climate change or perceive it as a reality are more likely to adopt climate-smart agricultural practices. These farmers aim to achieve the three pillars of CSA: improving household income and productivity, enhancing resilience and adaptation, and reducing greenhouse gas emissions. The adoption of climate-smart agriculture to attain these principles is influenced by institutional, cognitive, and socioeconomic factors ( Figure 1).

Figure 1. Conceptual framework based on adoption.

2.1 Study area description

2.1.1 Location

The research was carried out in Nyimba district of Eastern Province Zambia. The district is situated 334 kilometers East of Lusaka Zambia’s national capital. In the South the district borders with Mozambique, North with Muchinga province, West with Lusaka province, and East with Petauke district. The district lies between latitude (13°30′1019″ and 14°55′81426″ South) and longitude (30° 48′5047″ and 31°48′20252′′East) ( Figure 2).

Figure 2. Map of study area.

2.1.2 Climate, soil and topography

Zambia is divided into three agro-ecological zones: Zone I, Zone II (subdivided into IIa and IIb), and Zone III. Nyimba district falls within Zone I, which covers the Southern and Eastern rift valleys of the Zambezi and Luangwa River basins. This zone also extends to parts of the Western and Southern provinces in the south of Zambia. The average annual rainfall in Nyimba district ranges from 600 to 900 mm, with the wettest months being December to February and a distinct dry season from May to November. The annual mean temperature is 24.2°C, while the daily temperature range is from 10.3°C to 36.5°C ( Figure 3). Topographically, the district comprises hills and plateaus, with soils characterized as Lithosol-Cambisols, while in the valleys, the soils are classified as Fluvisol-Vertisols. The elevation varies from 450-1000 m at the bottom of the Luangwa River valley, extending to the plateau near the Nyimba district center and reaching even higher altitudes on the mountain tops in the western part of the district.

Figure 3. Mean annual rainfall and temperature for the study area.

2.1.3 Vegetation type

The Miombo woodland is the most dominant formation and habitat type in Southern Africa (Gumbo and Dumas-Johansen, 2021; Montfort et al., 2021). Miombo woodland is also the major forest type in Zambia itself, covering approximately 45% of the entire land surface (Kalinda, 2008). Nyimba is located in the middle of the Miombo Ecoregion, a biome with a variety of flora types that is dominated by tree species from the Caesalpinioiae subfamily of leguminous plants (Timberlake et al., 2010). Depending on the climate, soil, landscape position, and degree of disturbance, the ecoregion’s vegetation varies in composition and structure (Timberlake et al., 2010; Halperin et al., 2016). Nyimba is located in the arid ecozone and is characterised by four types of plants: Dry miombo woodland (i.e., Brachystegia spiciformis, Brachystegia boehmii and Julbernardia globiflora), Mopane woodland ( i.e., Colophospermum mopane), Munga woodland ( i.e., Vechellia sp., Senegalia sp., Combretum sp., and trees associated with the Papilionoideae subfamily) and Riparian Forest (i.e., mixed tree species).

2.1.4 Land use and farming systems

Nyimba district’s total land area is about 10,500 square kilometres according to the population and housing census of 2010. Therefore, 82% of the district population is agrarian with average household income. These households are farmers who are into mixed agriculture practices dominating the agricultural scene in the district. However, local smallholder farmers in the district practice some sort of shifting cultivation. Under this agricultural system, crops are grown in mounds and/or ridges in most cases maize. The major crops grown in include banana (Musa sp.), maize (Zea mays), finger millet (Eleusine coracana), groundnuts (Arachis hypogaea), haricot bean (Phaseolus vulgaris), cowpeas (Vigna unguiculata spp.) and soybean (Glycine max). Multiple cropping systems are common among farming households where cultivated land is on gently and moderately steep slopes. The topography of the land in the district makes the agricultural or cultivation pattern different from other areas. Therein, the cropping system is alongside livestock production such as cattle, goats, chickens, ducks and doves. Besides agricultural activities, farmers are engaged in charcoal production, timber, firewood supply and non-timber forest products (NTFPs) from the miombo woodland for household economic gain ( Policy Brief, 2016).

2.2 Site selection

Before commencing data collection, an exploratory study was conducted to gather key information about the study area. This included data on the distances between villages, the number of farming households in each village, contact information for lead farmers, households practicing climate-smart agriculture (CSA), the locations of croplands, and central meeting points for focus group discussions (FGDs).

2.2.1 Sampling technique

This study utilized a multi-stage random sampling approach to recruit participants from smallholder farming communities in Zambia. These farming communities, known as agricultural camps, are designated by the Ministry of Agriculture of the Republic of Zambia. The camps group smallholder farmers’ residences within a district, facilitating easy access to agricultural extension services. From the eight agricultural camps in Nyimba District, four camps – Ndake, Central Camp, Lwende, and Ofumaya – were randomly selected for the study. These four camps collectively house 10,700 farmers. To determine an appropriate sample size, the study employed Slovin’s formula. Additionally, three villages from the four selected camps were randomly chosen for data collection. These villages were Sikwenda, Sichipale, Mawanda, Elina, Katumbila, Sichalika, Malalo, Mwenecisango, Mulivi, Lengwe, Mofu, and Yona. With a margin of error set at 0.05, the initial calculated sample size was 386 participants. However, to optimize resources, the researchers increased the margin of error to 0.1, resulting in a smaller sample size of 99 participants. Achieving this reduced sample size required additional time and financial investments, but it was a trade-off deemed necessary for the successful completion of the study.

Sample size formula: Slovin’s (1960) formula.

Ultimately, the researchers opted for a compromise, settling on a sample size of 194 participants, which fell between the initial calculations of 99 (with a 0.1 margin of error) and 386 (with a 0.05 margin of error). With the assistance of agricultural camp officers, farmer registers from each selected village were utilized to randomly identify potential participants using an Excel spreadsheet.

2.2.2 Focus group discussion

Focused group discussions (FGDs) were used to gather in-depth information on factors influencing the adoption of climate-smart agricultural practices, crop productivity, perceptions of these practices, the use of CSAPs, and views on climate change. An open-ended FGD guide was developed for this purpose. FGDs were chosen over one-on-one interviews, as they often reveal sensitive topics that individual farmers might find difficult to express fully in private interviews. Four FGDs were conducted in the study area, engaging village headmen, women, men, and youth. These discussions were held at locations convenient for participants, facilitating broad attendance. The FGDs served primarily to supplement data collected from household questionnaires.

2.3.3 Household interviews

The household survey questionnaire included both closed-ended and open-ended questions. The questionnaire was pre-tested six times to ensure clarity, relevance, and logical flow before conducting the household interviews. Based on the pre-test results, adjustments were made to improve its suitability. The pre-testing was conducted with smallholder farmers who were not participants in the main study. Data collection was carried out by three trained enumerators, supervised by the principal researcher, a seasoned professional in the field. The enumerators carefully reviewed and corrected the collected data at the end of each day to ensure accuracy and consistency. Subsequently, the validated information was securely backed up on the CSPRO Cloud platform (https://www.csprousers.org/help/CSDeploy/deployment_options.html).

2.4 Variables specification

2.4.1 Outcome variables

The outcome variable for this study is the impact of adopting climate-smart agriculture practices on crop productivity among smallholder farmers’ households in Nyimba district.

2.4.2 Dependent variables

2.4.2.1 Smallholder farmers’ household decision to adopt CSAPs

The dependent variable in the study was the adoption of Climate-Smart Agricultural Practices (CSAPs) by a smallholder farmer’s household. This variable was coded as 1 if the household adopted CSAPs and 0 if it did not.

2.5 Propensity score matching

To evaluate the impact of Climate-Smart Agricultural Practices (CSAPs) on crop productivity, this study employed the Propensity Score Matching (PSM) method, comparing adopters and non-adopters. PSM is a statistical technique that adjusts for confounding variables across a sample population, enabling more accurate estimation of treatment effects. As outlined by Caliendo and Kopeinig (2008), the implementation of PSM involves several key steps: first, estimating propensity scores using a binary model; second, selecting an appropriate matching algorithm; third, verifying the common support condition; and fourth, assessing the quality of the matching between the treatment and control groups. The following are the steps involved in PSM:

Step 1: Model Specification

The logistic model was chosen for this study due to the robustness of its parameter estimations, which stems from the assumption that the error term in the equation follows a logistic distribution (Baker and Melino, 2000; Ravallion, 2007). Therefore, the Logit model was used to estimate the probability of smallholder farmers’ adoption of CSAPs allotted to socio-economic, agro-ecological and institutional characteristics. Therein, a dependent variable considered a value of 1 for CSAPs adoption and 0 for non-CSAPs adopters.

In line with Pindyck and Rubinfeld (1981), the cumulative logistic probability function is specified as follows;

Expressing the logistic model in terms of odds and log-odds aids in interpreting the coefficients (Gujarati, 1995). The odds ratio quantifies the relative probability of an individual participating (P_i) versus not participating (1- P_i). The probability of non-participation can be calculated as:

Alternatively,

Taking the natural logarithms of equation (3.5) will give the logit model as indicated below.

If consider a disturbance term, μ_i, the logit model becomes

So, the binary logit will become:

Where pp is CSAPs adoption, f(X) is the dependent variable project participation and X is a vector of observable covariates of the households. The dependent variable will take a value of 1 for CSAPs adoption and 0 for non-adopters.

In addition to the estimated coefficients, the marginal effects of the change in the explanatory variables on the probability of CSAPs adoption are also estimated. The interpretation of these marginal values will be dependent on the unit of the measurement for explanatory variables.

When an explanatory variable is binary, the marginal effects provide a reasonable estimate of the change in the likelihood of the outcome variable (Y = 1) evaluated at representative values of the regressors, such as their means.

Step 2: Identifying the Common Support Region and Conducting Balancing Tests

The region where the propensity score distributions of the treatment and comparison groups overlap is known as the area of common support. This needs to be identified. However, if there is a systematic difference in the observed characteristics between the dropped non-adopters of CSAPs and the retained non-adopters, sampling bias may still occur. These differences should be closely monitored to aid in the interpretation of the treatment effect. Furthermore, balancing tests can be conducted to check if the mean of the covariates (X) and the average propensity score are equal within each quantile of the propensity score distribution.

For PSM to be effective, the treatment and comparison groups must be well-balanced based on their observed covariates (X), as similar propensity scores are derived from similar observed characteristics. Achieving balance necessitates that the covariate distributions of the treated and untreated units are indistinguishable. Formally, this requires verifying that the conditional distributions P(X|T=1) and P(X|T=0) are equivalent. Here’s a version with reduced similarity:

Step 3: Matching adopters to non-adopters

Selecting an available data matching algorithm is the third stage. Selecting control subjects who are matched to treated subjects based on context factors that the investigator feels should be tracked is a standard process known as matching. A different one might use comparable criteria. according to propensity score, divide adopters against non-adopters. Kernel-based matching (KBM), radius matching (RM), and closest neighbour matching (NN) are the most often used matching algorithms.

Step 4: Matching quality

Matching quality tests could be conducted in the fourth step. Whether or whether the matching method can balance the distribution of different variables is determined by checking for matching, irrespective of quality. If there are discrepancies, it could be a sign of insufficient matching, and corrective action is advised (Caliendo & Kopeinig, 2008). The next action is to determine if the treatment caused a difference in the impact indicators.

The difference between the mean outcome of matched adopters and nonadopters with common support conditional at the propensity score provides the average treatment effect at the treated (ATT).

Step 5: Sensitivity analysis

Lastly, to verify the strength of the conditional independence assumption, a sensitivity analysis will be performed. Sensitivity analysis will also be used to examine whether the influence of an unmeasured variable on the decision-making process is significant enough to compromise the matching strategy (Ali & Abdulai, 2010). The sensitivity analysis (r-bounds test) will be performed using the Rosenbaum bound sensitivity test.

2.6 Ethical clearance

The study was conducted by the researcher and two supervisors, adhering to principles of integrity, objectivity, openness, respect for research participants, respect for intellectual property, confidentiality, informed consent, fidelity, and honesty. The researcher and supervisors take full responsibility for their actions and publications, ensuring that all agreements are intended to be upheld. The study was approved by the University of Zambia Directorate of Research and Graduate Studies (NASREC) on March 18, 2024, and is registered under NASREC IRB No. 00005465 (IORG No. 0005376). The principal researcher provided a written consent form for research participants to participate in the household survey, conforming to the research ethics guidelines of the University of Zambia and Haramaya University.

3.1 Effect of climate-smart practices on crop productivity among smallholder farmers, in Nyimba, Zambia

3.1.1 Characteristics of the participant smallholder farmers

The household survey involved 194 randomly selected smallholder farmers from the study area. These farmers were interviewed regarding their crop production and use of various Climate-Smart Agriculture (CSA) practices. The study presents the survey findings, beginning with participants’ demographic characteristics ( Table 1), and continues with sections on crop production and productivity, CSA practice adoption, constraints to CSA adoption, the effects of CSA on crop productivity, and factors influencing productivity. In total, 339 field plots of various crops were surveyed from the 194 farmer participants. Table 2 provides comprehensive demographic and socio-economic information about the respondents. The mean age of respondents was 46 years (standard deviation: 14.59), with the majority of households (62.18%) being male-headed. Most participants (69.43%) were married. Respondents had an average of 5.49 years of formal education (standard deviation: 3.5) and 26.22 years of farming experience (standard deviation: 15.55). They had resided in the area for an average of 30.92 years (standard deviation: 18.68). The mean household size was 5.42 (standard deviation: 2.14), and the average total annual income was K5,472.68 (USD 331.68 at an exchange rate of K16.5 per USD), with a standard deviation of 7,626.52. Additionally, 57.51% of respondents participated in off-farm activities. In terms of agricultural practices, 78.76% of respondents used improved seed varieties, and the mean farm size was 3.396 hectares (standard deviation: 3.363), with all land under customary tenure. The average cultivated land was 1.83 hectares (standard deviation: 1.45). Smallholder farmers grew, on average, two different crops (standard deviation: 0.930).

3.1.2 Crops grown by smallholder farmers

Regarding the crops that the farmers grew, the study discovered that the most common crop was maize, which was recorded in 194 crop plots. Groundnuts were reported in 99 plots, sunflower in 69 plots, and soybeans in 16 plots ( Table 2). It was said that the other crops—cowpea, sweet potatoes, millet, cotton, and bambara nuts—were produced in small plots.

3.1.3 Climate-smart agriculture practices adopted by smallholder farmers

The study’s findings shed light on the adoption of various conservation agriculture techniques across the surveyed field plots. Among these practices, pot-holing (basin) emerged as the most widely implemented method, with 61 plots (17.99%) employing this technique. Closely following was multi-cropping, which was practiced on 50 plots, accounting for 14.75% of the total. Minimum tillage, a soil conservation approach, was utilized on 34 plots, representing 10.03% of the survey sample. The ripping technique, which involves creating furrows in the soil, was observed on 32 plots (9.44%). Furthermore, 18 plots (5.31%) incorporated crop rotation as a means of maintaining soil fertility and controlling pests and diseases. The application of manure, a natural fertilizer, was recorded on 11 plots (3.24%), while alley cropping, a system that combines crops with trees or shrubs, was adopted on 9 plots (2.65%) ( Table 3). It is noteworthy that the remaining conservation agriculture techniques were tested on fewer than 10 plots each, indicating their relatively limited implementation within the surveyed area.

3.1.4 Number of climate smart agriculture practices adopted by smallholder farmers

The study’s findings, as presented in Table 4, shed light on the extent of conservation agriculture (CSA) practice adoption among the surveyed plots. Notably, a substantial number of plots, 167 (49.26%), did not incorporate any CSA techniques whatsoever. This highlights a significant gap in the implementation of sustainable agricultural practices within the surveyed area. On the other hand, a considerable portion of the plots, 123 (36.28%), had adopted at least one CSA practice, indicating a positive step towards embracing more environmentally friendly farming methods. Additionally, 4 plots (12.68%) had implemented two different CSA practices simultaneously, demonstrating a more comprehensive approach to sustainable agriculture. Interestingly, the data revealed that a smaller number of plots had adopted multiple CSA techniques concurrently. Specifically, four plots had incorporated three distinct CSA practices, while one plot had implemented an impressive four different CSA practices. Furthermore, another plot stood out by adopting a remarkable five separate CSA techniques. Despite these instances of multiple CSA practice adoption, the overall findings suggest that the majority of farmers within the surveyed area were either not implementing any CSA techniques or had adopted only a single practice. This observation underscores the potential for further education and awareness-raising efforts to encourage the widespread adoption of multiple sustainable agricultural practices, ultimately contributing to improved soil health, crop productivity, and environmental conservation.

3.1.5 Quantities harvested for various crops (kg)

The survey data presented in Tables 5 and 6 offers valuable insights into the crop cultivation patterns and yield outcomes among the surveyed farming community. Notably, the crops that emerged as the most prevalent choices among farmers were soybeans, maize (corn), groundnuts, and sunflowers. Across all crop types, the average harvest weight recorded was 1223.51 kg, accompanied by a substantial standard deviation of 1442.82. This variation in yields highlights the diverse factors that can influence agricultural productivity, including soil conditions, farming practices, environmental variables, and access to resources. When examining the individual crop yields, maize stood out as a prominent crop, with an impressive average harvest weight of 1766.57 kg. Therefore, the high standard deviation of 1594.23 suggests significant variations in maize yields among farmers, potentially attributable to differences in cultivation techniques, seed quality, or localized environmental conditions. Groundnuts, a staple crop in the region, exhibited an average harvest weight of 511.08 kg, with a standard deviation of 605.07. This relatively lower yield, coupled with the substantial variation, may indicate challenges faced by farmers in optimizing groundnut production, such as pest or disease pressures, or limitations in access to appropriate inputs and knowledge. Sunflowers, a valuable oilseed crop, yielded an average harvest weight of 609.67 kg, with a standard deviation of 513.02. While the average yield appears moderate, the substantial variation observed could be attributed to factors like soil fertility, water availability, or sunflower variety selection. Notably, soybeans emerged as a crop with significant yield potential, boasting an average harvest weight of 1007.5 kg. However, the remarkably high standard deviation of 1835.615 points to substantial disparities in soybean yields among individual farmers. This variability may stem from differences in cultivation practices, access to quality seeds, or the adoption of specific agricultural techniques tailored for soybean production. These findings not only underscore the crop preferences of the surveyed farmers but also highlight the need for targeted interventions and support measures to address the observed yield variations. By identifying and addressing the underlying factors contributing to these disparities, efforts can be made to enhance agricultural productivity, promote sustainable farming practices, and ultimately improve the livelihoods of smallholder farmers in the region.

3.1.6 Productivity of various crops (Yield (kg) per hectare)

The study’s findings provide valuable insights into crop productivity levels among the surveyed farming communities. For overall crop yield across all types, the average yield per hectare was 1,316.60 kg, though a substantial standard deviation of 1,214.13 suggests considerable variability in individual yields ( Table 7). For maize, a vital staple crop, the mean yield per hectare was calculated at 1,682.52 kg, with a high standard deviation of 1,325.87. This substantial disparity in maize yields may result from varying factors such as soil fertility, farming techniques, and environmental conditions. Groundnuts, another significant regional crop, had an average yield of 822.90 kg per hectare, with a standard deviation of 547.88. These variations likely reflect challenges in optimizing groundnut production, including pest and disease pressures, access to quality inputs, and possible knowledge gaps. Sunflower yields averaged 962.79 kg per hectare, showing a relatively lower standard deviation of 437.38. This consistency may be attributed to well-suited cultivation practices or more uniform environmental conditions that favour sunflower growth. Soybeans, a high-demand crop in the region, averaged 808.40 kg per hectare, with a moderate standard deviation of 426.74, suggesting that variations in yield could be influenced by factors such as seed quality, planting methods, and soil management practices. These findings not only offer an overview of productivity levels for various crops but also underscore the importance of targeted interventions to address yield disparities. By identifying and mitigating the underlying factors contributing to these differences, efforts can be made to improve agricultural productivity, support sustainable farming practices, and enhance the livelihoods of smallholder farmers in the region.

3.1.7 Impact of climate-smart practices on crop productivity among smallholder farmers

The study examined the impact of Climate-Smart Agriculture (CSA) techniques on the crop yields of smallholder farmers. It was found that farmers who adopted CSA practices experienced a 20.20% higher crop yield compared to those who did not adopt these practices ( Table 8). The difference was statistically significant, with a p-value of 0.027 (p < 0.05). These results suggest that the adoption of CSA practices leads to increased crop yields.

3.1.8 Impact of climate-smart practices on maize productivity among smallholder farmers

The study utilized propensity score matching analysis to specifically assess the impact of CSA on maize productivity ( Table 9). The findings indicated that CSA adoption led to a 21.50% increase in maize yield compared to non-adoption. This significant increase in maize yield, with a p-value of 0.035 (p < 0.05), demonstrates the positive effect of CSA practices on maize productivity.

3.1.9 Factors affecting smallholder farmers’ adoption of climate-smart agricultural

The study utilized logistic regression analysis to identify factors influencing farmers’ adoption of Climate-Smart Agricultural (CSA) practices. Age emerged as a key determinant, with older farmers more likely to adopt sustainable agricultural methods. Specifically, the analysis revealed a statistically significant positive association between age and CSA adoption, with a p-value of 0.0000 (p < 0.001). Farmers aged 40-55 years and those over 55 demonstrated notably higher adoption rates of CSA practices in the study area. Interestingly, an inverse correlation was observed between farming experience and the likelihood of implementing climate-smart techniques. Contrary to expectations, farmers with greater agricultural experience were less inclined to adopt CSA practices, a finding that was also statistically significant p-value of 0.0000, (p < 0.001). Income level further influenced CSA adoption, with higher-income farmers showing a greater propensity to embrace these practices, as indicated by a statistically significant positive relationship at a p-value of 0.0640 (p < 0.1) ( Table 10).

This study uncovered several notable findings regarding the factors influencing farmers’ adoption of climate-smart agricultural practices. Gender emerged as a significant factor, with a p-value of 0.0660 (p < 0.1), indicating that a farmer’s gender substantially influenced their likelihood of adopting sustainable farming methods. Farm size displayed an inverse relationship with climate-smart practices, showing a significant negative effect with a p-value of 0.0050 (p < 0.01). This suggests that farmers with larger landholdings were less inclined to adopt environmentally friendly agricultural techniques, possibly due to the greater complexities or resource demands associated with scaling these practices on larger farms. Conversely, livestock ownership proved to be a positive driver, significantly influencing climate-smart agriculture adoption with a p-value of 0.0180 (p < 0.1). Farmers with more livestock were more likely to adopt sustainable practices, potentially due to the added resources and benefits associated with livestock ownership. Interestingly, access to climate information had an unexpected negative effect on the adoption of climate-smart practices, with a p-value of 0.0060 (p < 0.01). This counterintuitive result suggests potential issues with the effectiveness of climate information dissemination or barriers to translating this information into practical, on-farm applications.

3.1.10 Factors affecting smallholder farmers’ crop productivity

A Cobb-Douglas production function analysis was employed to examine the determinants of agricultural yield ( Table 11). The results revealed that household income demonstrated a positive and statistically significant relationship with agricultural productivity (p < 0.01). Specifically, a one-unit increase in farmers’ income corresponded to a 0.002% increase in agricultural productivity. Similarly, fertilizer application exhibited a strong positive influence on crop productivity, with a one-unit increase in fertilizer use resulting in a 0.12% increase in crop yield (p < 0.001).

The study revealed a complex interaction of factors affecting crop productivity among smallholder farmers. Notably, farm size demonstrated an inverse relationship with productivity, where larger landholdings corresponded to a 6.52% decrease in crop yields. This counterintuitive outcome may stem from resource constraints, management difficulties, or diminishing returns often associated with larger farming areas. In contrast, livestock ownership emerged as a significant positive contributor to crop productivity. Each additional livestock unit increased crop yields by 0.86%, a statistically significant effect with a p-value of 0.0001 (p < 0.001), underscoring a synergistic relationship between livestock and crop production.

The adoption of sustainable farming practices was shown to further boost productivity. Each additional farmer adopting these methods led to a 13.49% increase in average crop yield, a statistically significant improvement with a p-value of 0.0720 (p < 0.1). This consistency with findings from other research contexts highlights the substantial benefits of sustainable agricultural practices and the need for integrated farming approaches. The study also examined demographic factors influencing crop productivity. While marital status and the education level of the household head made modest contributions of 0.07% and 0.098%, respectively, household size had a more pronounced effect, increasing productivity by 0.2%. These findings emphasize the importance of demographic, resource-based, and management factors in enhancing crop productivity among smallholder farmers.

3.1.11 Discussion of the results

The study investigated the effects of Climate-Smart Agricultural (CSA) practices on crop productivity among smallholder farmers in the Nyimba district of Zambia and explored the factors that influence both the adoption of CSA practices and crop production in the area. The analysis showed that smallholder farmers implementing CSA methods achieved a 20.20% higher overall crop yield than those not using these practices. Specifically, CSA adopters saw a 21.50% increase in maize yield compared to non-adopters. These findings highlight the potential of CSA practices to substantially improve crop productivity among smallholder farmers. The observed yield increases suggest that climate-smart methods could serve as an effective strategy for enhancing agricultural productivity and strengthening food security in the region.

This study aligns with findings by Zakaria et al. (2020), who identified that factors such as rice cultivation experience, media and training access, and perceived reductions in rainfall positively influenced the adoption of climate-smart agricultural (CSA) technologies. Conversely, factors such as larger farm sizes, greater distances between farmers’ residences and fields, location, and increased temperatures were found to deter CSA adoption. The present study, however, emphasizes age and income as facilitators of CSA adoption, diverging from Saha et al. (2019), who highlighted education, occupation, family size, farm size, climate adaptation methods, cattle ownership, market access, information access, training, organizational affiliations, and climate change perceptions as significant determinants. This contrast suggests that while higher income facilitates CSA adoption, other factors—such as farm size, accessibility, and climatic perceptions—may act as barriers.

Supporting these findings, Kurgat et al. (2020) reported that female ownership of assets, farm location, and household resources significantly affected CSA adoption in Tanzania. Similarly, Aryal et al. (2018) identified household characteristics, market access, and primary climate hazards as factors influencing the probability and extent of CSA adoption. On the other hand, Abegunde et al. (2019) found no significant impact from marital status, education, fertilizer use, credit access, or extension services on CSA adoption, highlighting the complexity of CSA uptake across regions. The study by Urgessa (2015) underscored additional factors influencing agricultural labour and crop productivity, such as the land-labour ratio, pesticide and fertilizer use, manure application, and household size. WenJing et al. (2021) observed income disparities among households, finding that middle- and upper-middle-class households benefited more from renting out farmland, while households with high on-farm income were less inclined to expand through land rental. Similarly, Du et al. (2020) demonstrated that synthetic fertilizer and manure applications positively affect soil productivity and crop yields. Furthermore, livestock contributes both manure and income, which can be reinvested in crop-enhancing technologies (Anderson, 1989; Beedy et al., 2010).

Remarkably, this study affirms that CSA adoption favourably impacts crop productivity. Supporting these findings, Mujeyi et al. (2021) reported that CSA practices significantly increased crop yields for smallholder farmers in integrated crop-livestock systems. This conclusion aligns with Serote et al. (2021), who found that household demographics and institutional factors influence CSA adoption and subsequent productivity outcomes. Together, these studies emphasize the interplay of factors shaping agricultural outcomes and the importance of targeted interventions to enhance productivity and sustainability. Addressing drivers such as farm size, livestock integration, and demographic characteristics could enable policymakers to support smallholder farmers in achieving better yields and environmental resilience.

Multiple studies also highlight the broader impacts of CSA practices on productivity, food security, and soil health. Abegunde et al. (2022) found that CSA adoption favourably affected food security among 327 Nigerian smallholders, with agricultural and non-farm income both contributing to this outcome. Similarly, Mossie (2022) found that CSA technology, specifically wheat row planting, significantly improved productivity in Southern Ethiopia, with adopters achieving 1,368 kg more wheat per hectare than non-adopters. Tadesse et al. (2021) reported that CSA practices in Ethiopia positively impacted soil carbon, productivity, and fertility, while Kichamu-Wachira et al. (2021) concluded that CSA advances yield, soil carbon, and nitrogen levels, providing climate benefits and enhancing food production.

In addition, Amadu et al. (2020) found that 53% of CSA adopters in southern Malawi achieved higher maize yields during the 2016 drought, underscoring CSA’s resilience benefits. These studies collectively underscore the potential of CSA practices to improve crop productivity, food security, and soil health, fostering resilience among smallholder farmers amid climate variability. The present study reinforces the potential of CSA to increase yields and nutrient availability, aiding resource-poor farmers in adapting to climate change. These findings echo those of Kichamu-Wachira et al. (2021), who documented CSA’s positive effects on yields, soil carbon, and nitrogen levels in Africa. Further, Amadu et al. (2020) noted that CSA adoption during drought years contributed to increased maize yields among Malawian smallholders. Similarly, Fentie and Beyene (2019) found a positive effect of CSA adoption on crop yield per hectare, underscoring CSA’s potential to improve productivity, food security, and soil quality for smallholder farmers in the face of climate change.