1. Introduction
In the past three decades, Sub-Saharan Africa (SSA) has been increasingly faced with the need to increase food production to meet the demand of its growing population [1]. To meet this challenge, contemporary research literature suggests that sustainable land management (SLM) practices could significantly boost food production without degrading soil and water resources [1,2,3]. As a result, many governments in SSA have been actively promoting SLM practices through a wide range of interventions targeted predominantly at smallholder farming households [4,5,6,7]. The focus on smallholder farming households is because although such householders dominate the agricultural sector, they attain very low agricultural productivity and are the most affected by household food insecurity [8,9,10,11]. Reasons for the low agricultural productivity are myriad, and include climate variability, declining soil fertility, pests, and diseases [8,12,13,14,15]. Thus, any interventions aimed at boosting food production must, by necessity, include them. Previous attempts to enhance smallholder farmers’ productivity through the so-called green revolution technologies faced problems of environmental degradation [16,17] and have proved to be largely unsustainable [18]. Hence, development actors have focused on sustainable land management practices [1].
Although the term “Sustainable Land Management” (SLM) is widely referenced in extant literature, there are differing views on its meaning and interpretation. Generally, the concept refers to agricultural practices that conserve water and soil and are environmentally non-degrading, technically appropriate, economically viable, and socially acceptable [19,20]. Hurni [21] states that SLM integrates ecological with socio-economic and political principles in the management of land for agricultural and other purposes to achieve intra- and intergenerational equity. This framing of SLM is reminiscent of the sustainable development concept, albeit with the explicit inclusion of political factors. Arguably, the inclusion of political factors recognizes the important role of the policy environment and institutional factors in farmer adoption of SLM practices [7] and the politicized nature of decision making around agricultural technology promotion. Incorporating the wider economic context in land degradation research is crucial for understanding farmers’ decision making and their willingness and ability to invest in SLM [7]. Farmers invest in SLM practices that align with their socio-cultural contexts, and that have economic benefits and a supportive policy environment. The contemporary discourse of sustainable development as advanced through Sustainable Development Goals (SDGs), is a serious global effort to incorporate social and political aspects in the human–environment relationship [22], although it has its own limitations which has seen its reformulation over 30 years [23]. SLM is an important pathway towards achieving sustainable development.
SLM practices not only help to restore soil fertility and conserve moisture [24], but are also critical in climate change mitigation and adaptation [1,4,25,26] and food security enhancement [2]. Broadly defined, these practices include minimum tillage, zero tillage, terracing, use of crop residues and animal manure, agro-forestry, cover-cropping, leguminous crop rotations, and soil and water conservation [26,27]. Activities that also fall under the purview of SLM include integrated pest management using bio-pesticides only, grass strips, raised beds, kitchen gardens, gully cropping, inter-cropping with legumes, mulching, and tree planting [28,29]. While many researchers note that SLM is complex in terms of conceptualization, many studies argue that it encompasses technological innovations adopted by farmers as well as the associated policy environment [2,29]. In the presence of a supportive policy framework, SLM approaches are expected to increase yields, food security, and incomes among rural households [29,30,31].
Despite the perceived benefits associated with the integration of SLM practices at the household level, their acceptance by smallholder farmers remains low [27,32,33,34]. The low adoption of SLM practices is attributed to several factors. These factors include inappropriate implementation approaches, which overly focus on technical solutions, with too little focus on extension systems, which can also be poor [35,36,37]. Additionally, a lack of strong policy action and a low level of evidence-based policy frameworks are considered to be critical challenges for the effectiveness of SLM practices [38].
Within the Zambian context, it has been observed that many socio-economic barriers impede the long-term adoption of SLM [39,40]. For instance, [25] indicated that access to agricultural loans and labor availability were important attributes for the adoption of SLM practices among smallholder farmers. In order to determine why participation in SLM has lagged behind expectations in Zambia, many research analyses have been conducted [40,41,42]. These studies established correlations between adoption of SLM and a range of smallholder farmers’ socio-economic characteristics and institutional factors that include age, gender, education, wealth, farming experience, household size, farmer training, membership in local community organizations, and agricultural extension [40,41,42,43,44,45]. The most prevalent institutional factors reported were market access, infrastructure, and land tenure [25,40,46,47].
The use of chemical fertilizers in agriculture can lead to various environmental and ecological problems. These include biodiversity loss due to imbalances in ecosystems, water pollution from nutrient runoff causing harmful algal blooms, soil degradation, reduced nutrient use efficiency in plants, air pollution from ammonia and nitrous oxide emissions, health risks to agricultural workers, increased production costs for farmers, harm to beneficial insects, resistance development in pests and pathogens, and food safety concerns. To address these issues, sustainable agricultural practices that minimize negative impacts while ensuring food security are essential [48].
Using chemical fertilizers in Zambia presents several challenges. The primary difficulty is related to affordability and accessibility. Small-scale farmers, who constitute a significant portion of Zambia’s agricultural sector, often struggle to afford chemical fertilizers due to their high cost [49]. Additionally, the distribution and availability of fertilizers in rural areas can be limited, making it challenging for farmers to access them when needed [50]. Another challenge is the lack of knowledge and awareness among farmers about the proper use of chemical fertilizers. Many small-scale farmers may not be adequately trained or informed about the correct application rates and timing, which can result in inefficient use and potential harm to the environment [51]. Environmental concerns also arise with the use of chemical fertilizers in Zambia. Improper application practices can lead to nutrient runoff into rivers and lakes, contributing to water pollution and eutrophication. This not only affects water quality, but also harms aquatic ecosystems. Furthermore, the long-term sustainability of chemical fertilizer use is a concern. Overreliance on these fertilizers without proper soil management practices can lead to soil degradation and reduced soil fertility over time [52]. This can ultimately undermine food security and agricultural productivity in the country.
Sustainable land management is of immense value for sustainable agriculture as it addresses critical challenges across environmental, social, and economic dimensions. SLM practices, including soil health preservation, erosion mitigation, efficient water management, and biodiversity conservation, foster resilient agricultural systems. They reduce reliance on chemical inputs, mitigate climate change through carbon sequestration, and enhance economic livelihoods, ultimately ensuring long-term food security. SLM serves as a model for sustainable farming practices, inspiring stakeholders to adopt environmentally friendly and economically viable approaches, making it an essential cornerstone of sustainable agriculture worldwide.
Adoption studies have often categorized farmers into adopters and non-adopters, and have examined factors influencing their adoption status [39]. However, this approach assumes that all farmers have prior knowledge of SLM [25]. Yet, exposure to innovative practices such as SLM practices among smallholder farmers is not a random occurrence [53]. Since many of the past studies did not explicitly examine whether farmers had prior knowledge of SLM practices, they suffer from non-exposure bias, which leads to biased and inconsistent estimators [34,53]. Non-exposure bias arises because farmers who have not been exposed to these practices are unlikely to participate in SLM programs due to their lack of access to relevant information for decision making [34]. Consequently, non-exposure bias leads to underestimation of adoption rates among smallholder farmers. Recognizing the importance of addressing this bias, it is crucial to explore the role of farmers’ awareness and exposure to SLM practices to accurately understand their use of SLM practices. By considering non-exposure bias in our study, we can obtain more reliable and comprehensive insights into factors influencing the adoption of SLM practices among smallholder farmers.
This study examines the relationship between awareness of SLM practices and land area allocated to SLM practices by smallholder farming households in Eastern Zambia. It employs the Heckman selection model, which allows for the analysis of selection biases in the relationship between the selection process (awareness of SLM practices) and the outcome variable (land allocation to SLM practices). The selection process, in this case, refers to the propensity of individuals to be aware of SLM practices. While the Heckman model is typically used to correct for selection biases when individuals are non-randomly sorted from the data set [54], this study takes a nuanced approach. The entire sample consists of individuals interested in SLM, and land decisions are observed for all individuals, regardless of their awareness of SLM practices. The selection, in this context, lies in the fact that individuals without exposure to SLM are not included in the sample, which poses a unique challenge. By applying the Heckman selection model, this study addresses the potential selection bias and investigates the relationship between awareness of SLM practices and land allocation to SLM practices. The inclusion of control variables related to both the selection process and the outcome variable allows for a more comprehensive analysis of the factors influencing land allocation decisions in the context of SLM. This approach potentially contributes towards more evidence-based policy formulation and intervention designs to enhance smallholder farmers’ uptake and up-scaling of SLM in Zambia by providing valuable insights into the interplay between awareness of SLM practices and land allocation to SLM practices, thus contributing to the understanding of sustainable land management strategies.
This study was guided by three objectives, i.e., to (i) determine factors that influence smallholder farmers’ awareness of SLM practices (exposure); (ii) examine the socio-demographic and institutional factors affecting smallholder farmers’ use of SLM practices, for those farmers that were aware of them; and (iii) explore if there are associations between SLM participation in and perceptions of the quality of training in SLM received by farmers.
We hypothesized the following:
For every unit increase in farmer awareness of sustainable land management (SLM) practices in the Eastern Province of Zambia, there will be a corresponding increase in the extent of land area allocated to SLM activities.
The adoption of specific SLM practices, such as crop diversification, livestock ownership, attendance at extension meetings, and the level of tertiary education, significantly influence the hectarage devoted to SLM practices in the Eastern Province of Zambia.
Theoretical Framework
To explore the underlying drivers of farmers’ use of SLM practices, the random utility model (RUM) is widely used in choice modeling. According to the model, a rational farmer will settle for a choice or a decision that yields the highest utility [55] given discrete sets of alternatives. Within this context, the main questions that the farmer faces are related to the integration of SLM practices in their production system [56]. The preferences of an individual among the available alternatives can be described by a utility function. The utility of an alternative depends on the observable and unobservable traits of the options culminating in the random utility model. Since utility is an arbitrary variable, in a random utility model, one cannot predict outcomes of individual’s choices with certainty [57,58,59].
Among households that were aware of SLM practices, the main objective of our analysis was to assess the key factors influencing the area of land allocated to SLM practices. To achieve this objective, the Heckman selection model was used. The Heckman selection model is recommended in situations where researchers wish to examine the determinants of the underlying regression function or predicting a response that is affected by non-selection [60]. Therefore, the Heckman selection model based on full maximum likelihood (FMLI) estimates was used to frame the study objective in econometric terms [58,61,62]. The functional form of the selection equation was a probit model, as guided by [63].
According to [63], the second model (outcome equation) should be assessed using Ordinary Least Squares (OLS). Our dependent variable for the outcome equation was the total amount of land allocated to SLM by a smallholder farming household. Following [64], the Heckman selection model is defined as follows:
(1)
(2)
where X and Z represent the independent variables in the equations, and are the latent dependent variables in the system, and e1 and e2 denote the error terms. The error terms are assumed to be jointly normally distributed, but this is conditional on the independent variables [64]. If the covariance of joint error terms is equal to zero, the two equations can be estimated separately [65].This study is guided by the adopter perception paradigm which recognizes that farmers’ adoption decisions are influenced by a combination of individual characteristics, farm-level factors, and external factors [66,67,68]. Individual characteristics refer to farmers’ personal attributes, such as their age, education level, farming experience, risk preferences, and attitudes towards change. These individual characteristics shape farmers’ perceptions, beliefs, and motivations towards adopting SLM practices. Farm-level factors include farm size, access to inputs and resources, labor availability, and financial constraints [69]. The adopter perception paradigm acknowledges that the characteristics of the farm, including its economic viability, production constraints, and resource availability, can significantly impact farmers’ ability and willingness to adopt SLM practices. External factors encompass the broader context in which farmers operate and include factors such as market conditions, access to information and extension services, social networks, and institutional support [67]. Farmers’ adoption decisions are influenced by the availability and quality of information on SLM practices, the effectiveness of extension services in disseminating knowledge, and the existence of supportive policies, regulations, and incentives for SLM adoption. By adopting the adopter perception paradigm as the theoretical framework, this study recognizes the multifaceted nature of farmers’ adoption decisions and aims to explore the interplay of individual characteristics, farm-level factors, and external factors in shaping farmers’ awareness and adoption of SLM practices. This provides a robust foundation for understanding the complex dynamics involved in SLM adoption, offering valuable insights that can inform the design of effective interventions, policies, and strategies to promote SLM in smallholder farming systems in Eastern Zambia and more widely.
2. Materials and Methods
2.1. Study Area
This study was conducted in six of the of the eleven districts in the Eastern Province of Zambia. Eastern Zambia was purposively selected as the study area for the following three reasons. First, a number of projects on SLM including World Bank Projects such as the Zambia Integrated Forest Landscape Project (ZIFLP), Strengthening Climate Resilience (PPCR Phase II), COMACO’s Landscape Management, and Transforming Landscape for Resilience and Development in Zambia (TRALARD) have been implemented in the province in the recent past. Older projects include the European Union-funded Conservation Agriculture Scaling Up (CASU) project, USAID’s Zambia Integrated Agroforestry Project, and the Swedish-funded Soil Conservation and Fertility Enhancement (SCAFE), among others. This rich history in SLM projects made the region fertile ground for a study on awareness and use of SLM practices.
Second, and related to the first reason, Eastern Zambia is a predominantly agricultural rural region in which various crop and livestock activities are being carried out. The agricultural activities are largely rain fed. This affects productivity of crop and livestock farming systems, both of which are characteristically low. The low productivity is partly due to the ubiquitous use of agricultural activities that mine soil nutrients and are associated with deforestation. Thus, in order to address these inter-related challenges, adoption of SLM practices is paramount. However, despite the numerous SLM interventions in the region, reports indicate low and ephemeral adoption. Improved understanding of factors that mediate adoption of SLM practices contributes towards better designed and targeted SLM interventions.
Thus, the results of this study could be used as a basis for scaling-up current projects to other regions of Zambia and the design of new projects. Third, the province has a diverse bio-physical and socio-cultural environment, which permits the examination of the interplay between several factors and how they potentially mediate awareness and use of SLM practices.
2.2. Target Population and Sampling
A multi-stage sampling approach consisting of three stages was used. In the first stage, the Eastern Province was purposively selected on the basis of the reasons outlined in the preceding section.
In the second stage, chiefdoms were purposively selected by considering areas with prior SLM projects or initiatives. A total of 11 chiefdoms drawn from six districts were selected (Figure 1). Thereafter, individuals at the household level were selected from gender-disaggregated lists of previous SLM project participants in each chiefdom. Given that the Eastern region had approximately 342,161 households [70], a sample size of 760 was determined using the statistical formula of [71].
2.3. Data Collection Methods
An exploratory sequential mixed-method approach combining an initial qualitative study phase with a quantitative component [72] was used. For the qualitative part of the study, data were collected through the use of focus group discussions (FGDs) with smallholder farmers, and key informant interviews with various stakeholders, including extension officers and government and non-governmental organizations’ staff. The qualitative phase of the study was essential for the researchers to understand the local socio-cultural, political, and institutional context. The researchers used the insights gained from the qualitative study to decide on what issues to examine during the quantitative part of the study. The quantitative study employed a cross-sectional survey, during which questionnaires were administered at the household level. The cross-sectional design provided the researchers with a snapshot view of all the study districts. This had the benefit that the data were collected during the same season from all study sites.
2.4. Model Specification
Heckman selection model specification for the relationship between awareness of SLM practices (selection) and land allocation to SLM technologies (outcome) was as follows:
Selection Equation:
Pr (Awareness of SLM practices) = Φ (Xβ + Zγ + ε)
where:Pr(Awareness of SLM practices) is the probability of being aware of SLM practices.
X represents a matrix of independent variables related to the selection process, such as socio-economic characteristics, access to resources, and farm characteristics.
β is a vector of coefficients corresponding to the independent variables in X.
Z represents a matrix of additional variables that are potentially correlated with both the selection process and the outcome variable.
γ is a vector of coefficients corresponding to the independent variables in Z.
ε is the error term.
Outcome Equation:
SLM Area = α + β′X + u
where:SLM Area is the outcome variable representing the amount of land allocated to SLM technologies.
α is the intercept.
β′ represents a vector of coefficients corresponding to the independent variables X, which may include the control variables mentioned earlier.
u is the error term.
To estimate the Heckman selection model, these steps were followed: The selection equation was estimated using a probit model to obtain the inverse Mills ratio (λ), which represents the selection bias. The inverse Mills ratio (λ) was used as an additional variable in the outcome equation to correct for selection bias. The outcome equation was then estimated using a linear regression model including the inverse Mills ratio (λ) and the independent variables X. The coefficients in both equations were interpreted to understand the relationship between awareness of SLM practices, control variables, and land allocation to SLM practices.
2.5. Analytical Framework
Respondents were initially asked to indicate if they were aware of any sustainable land management practices meant to improve soil conditions and the environment in general. On the basis of this question, the dependent variable of the selection model was “awareness of SLM practices”. The dependent variable had a value of 1 if the farmer was aware of SLM and a value of 0 if they were not aware. Thus, the selection model was based on a dichotomous dependent variable. A number of socio-economic and demographic variables were hypothesized to affect farmers’ awareness of SLM. For example, farmers’ with at least a primary level of education are expected to be more aware of SLM practices compared to those with no formal education [73]. Similarly, household members who attended a greater number of community extension meetings are expected to be more aware of SLM, since agricultural extension provides a platform for sharing information. In addition, farmers with active membership in community organizations are thought to have more access to information on SLM [74]. Possession of larger farm holdings provides an incentive for investment in SLM due to its impact on productivity and household incomes [39]. Therefore, the probability of awareness of SLM was expected to be higher amongst households with larger land sizes.
Given that the construction of various SLM techniques requires the use of farm implements such as ploughs, rippers, hoes and ridging equipment, ownership of such assets was expected to enhance the chances of adopting SLM practices. A farm asset index was constructed, taking into consideration various farm tools such as tractors, hoes, ridgers, cultivators, and rippers. The higher the index, the more diverse the implements owned by the farmer. Also considered was the range of livestock types owned, which included cattle, goats, sheep, pigs, pigeons, and poultry. The range and number of livestock owned may also increase the likelihood of implementing some strategies, particularly those that involve manure and animal draught power. The a priori expectation was that farming households with access to information (including agricultural, climate change, and weather variables) from public and private sources were more likely to identify climate change phenomenon from various indicators such as changes in rainfall patterns, temperature, shorter rainfall seasons, and increased incidences of pest and disease damage.
In the second step, an outcome model was specified, based on the total area under SLM in hectares per household. The SLM practices whose areas were assessed are use of manure, crop rotations, minimum tillage, zero tillage, use of energy efficient technologies, and fire guards. The dependent variable of the outcome model (hectarage under SLM) is thus continuous in nature. Predictor variables in the selection model included age, education, gender, access to extension, decision making on land use (i.e., who made the main decisions regarding the practice—male, female), and crop diversity. The independent variables included in both models were organized to reflect the social, human, financial, physical, and natural assets owned by the household as dictated in the Sustainable Livelihoods Framework [75,76]. A complete description of variables for the two equations is provided in Table 1 and Table 2.
Following Greene [55], the selection model was used to account for sample selection bias. In this context, it was considered that the total number of hectares devoted to SLM practices was influenced by whether the farmer was aware of SLM. The variables in the selection model are indicated in Table 2.
2.6. Chi-Square Tests of the Association between Participation in SLM Initiatives and Socio-Economic Covariates
To gauge the extent to which farmer participation in SLM projects was influenced by socio-economic covariates, chi-square tests were performed. The variable “source of training” was recoded to public or private sources, thus making it dichotomous in nature. Similarly, the variable “area under SLM practices” was transformed into low (0) if the area under SLM was below the sample average, while a value of 1 was ascribed if the area was above the mean. The mid-point value of 3.5 was used to categorize the extent to which farmers rated the quality of the training received in SLM technologies (1—high; 0—low).
3. Results
3.1. Socio-Demographic Characteristics of Respondents
Of the 761 households interviewed during the survey, about 79% were male-headed, with the remaining 21% of the sample representing female-headed households. Most interviewees were selected from Ndake (25%), Mumbi (18.8%), and Mpezeni (10.2%) chiefdoms. The majority of the respondents were economically active with the average age of the household head being 45 years (stdev = 13.9 years), while the mean age of their spouses was about 38 years (stdev = 12.9 years). These households were involved in a multiplicity of economic activities ranging from agriculture to carpentry. Farming was considered as the primary source of livelihood since 84.3% of the sample were involved directly or indirectly. Livelihood activities including tailoring, carpentry, metal work, plumbing, and brick laying were also common. While spouses were predominantly involved in farming, they were involved in a variety of additional activities ranging from tailoring, charcoal production, house making, keeping small livestock, formal employment (nursing, teaching), and shop keeping. Most of the respondents were married (77%) followed by the widowed (13%). Overall, a significant proportion of the respondents had attained a primary level of education. Nonetheless, a greater percentage of the spouses (women) did not attend formal school (15.6%) when compared to 13.5% of the male heads of households. A similar pattern was observed for junior secondary, senior secondary, and tertiary levels of education, with a lower proportion of spouses having achieved the respective educational category as compared to household heads. Most of the community members were constrained in terms of reading and writing vernacular languages as well as communicating in English. Only 4.5% of the spouses were able to read English, compared to 14.5% of the household heads. In addition, 45% of the spouses were not able to write in the local dialects, compared to 35.5% of the household heads. On average, each household had a total of six members. In addition, there were approximately three males and three females in each household. There was a statistically significant difference in the household size across districts, with Petauke district having seven members, while Lundazi district had five members (F = 2.917; p = 0.013). These results are summarized in Table 3.
3.2. Awareness and Use of SLM Practices at the Farm Level
In general, farmers were aware of SLM practices related to soil and water management in the community (97%). However, they were mostly aware of conservation agriculture that included the use of manure, crop rotations, mixed cropping, tree planting and minimum tillage methods (Conservation agriculture is an agricultural system premised on three inter-related principles and associated practices. The three principles are minimum mechanical soil disturbance, leguminous crop rotations, and permanent soil cover [77]. In Zambia the associated practices include dry season land preparation and spot input application). The analysis further delved into which specific conservation agriculture practices were integrated by farmers, the respective areas, sources of information, and main motivations behind use of each innovation. Table 4 indicates the main conservation agricultural practices that respondents were aware of in the study sites.
According to a key informant from the Conservation Farming Unit, an NGO dedicated to the promotion of conservation agriculture, of all conservation agriculture adopters in its programs in Eastern Province, 67% were women. He attributed this to CFU’s gender mainstreaming policy. In terms of SLM practices, a large proportion of the male household heads used crop rotations, crop residues, and timely planting. A similar pattern was also observed for female-headed households. Of the seven SLM practices identified in the survey, farmers attended about 1 to 2 trainings on each practice annually. Respondents received training from numerous sources that included COMACO, Ministry of Agriculture, SNV, CFU, farmer cooperatives, and farmer-to-farmer training (Table 5).
Although COMACO emerged as the main source of training on SLM practices, the Ministry of Agriculture, CFU, and SNV were also commonly mentioned by farmers.
3.3. Link between Awareness of SLM Practices and Amount of Land Allocated to SLM
The purpose of the initial phase of the econometric analysis was to evaluate whether the Heckman selection model was appropriate for the research question under consideration. Since the Likelihood Ratio (LR) test indicating the two equations were independent of each other was rejected (χ2 = 76.6, p = 0.00) at the 5% level of significance, there was a statistically significant relationship between the two equations. Two predictor variables, namely the number of extension meetings attended by the household head and the type of household, had a significant effect on the extent to which the household was aware of SLM practices. Male-headed households were 31% more likely to be aware of SLM when compared to female-headed households, holding other factors constant. By attending an additional extension meeting, the probability of being aware of SLM increased by 1.2%, ceteris paribus. Thus, access to information (via extension meetings) influences farmer awareness of SLM practices.
In the outcome model, four independent variables—crop diversity, livestock asset index, number of extension meetings, and education level (tertiary)—were statistically associated with the total amount of land allocated under SLM at the farm level (p < 0.05). In this regard, households with a greater crop diversification or crop range were 43% more likely to allocate land under SLM practices compared to those with narrower crop choices. Similarly, households that owned a greater diversity of livestock had a higher probability of allocating their land to SLM practices.
The results of the Heckman selection model are shown in Table 6.
3.4. Participation in SLM and Perceptions on Quality of Training
There was a statistically significant association between farmers’ participation in SLM and their perceptions of the quality of training in SLM practices (χ2 = 183.59; p < 0.001). Of the 419 households that employed SLM, 80% rated the quality of trainings received in SLM as high. Findings also showed that farmer participation in SLM was significantly related to the source of training (χ2 = 28.168; p < 0.001), with 63% of the respondents having received training from public sources. There was a statistically significant relationship between the area of land allocated to SLM practices and farmers’ rating of the quality of training in SLM (χ2 = 5.576; p = 0.018).
3.5. Total Land Area under Sustainable Land Management
Table 7 shows that this study employed an Ordinary Least Squares (OLS) regression analysis to unravel the intricate relationships between “Total Land Area under sustainable land management” and a range of independent variables, encompassing livestock ownership, housing materials, and agricultural activities. The statistical metrics, including both unadjusted and adjusted estimates, offer valuable insights into the interplay of these variables and their collective impact on land distribution among households.
Initial findings reveal a strong positive relationship between the number of structures with corrugated roofs and “Total Land Area under sustainable land management”. The unadjusted coefficient of 0.345 indicates that, for each additional unit of structures with corrugated roofs, land area tends to increase by 0.345 units (p < 0.001). This relationship maintains its significance in the adjusted model, with a coefficient of 0.985 (p < 0.001), underscoring that households with more corrugated roof structures continue to exhibit larger land areas.
The unadjusted coefficient of −0.112 suggests that households with more structures featuring thatch roofs tend to have a smaller “Total Land Area under sustainable land management” (p = 0.001). In the adjusted model, the relationship remains significant, with a coefficient of 0.995 (p < 0.001). This signifies that the negative association between thatch roofs and land area persists even after accounting for other variables.
Owning cattle exhibits a positive relationship with “Total Land Area under sustainable land management” in both unadjusted (coefficient = 0.254, p = 0.002) and adjusted models (coefficient = 1.006, p < 0.001).
Although owning pigeons demonstrates a potential positive relationship in the unadjusted model, this relationship becomes significant in the adjusted model (coefficient = 1.114, p < 0.001). The ownership of ducks also maintains a significant positive association with land area in the adjusted model (coefficient = 1.033, p < 0.001). The presence of guinea fowls, however, does not display a significant relationship in either unadjusted or adjusted models.
The relationship between land area and the number of structures with walls of burnt bricks remains significant in both unadjusted (coefficient = 0.189, p < 0.001) and adjusted models (coefficient = 0.185, p < 0.001). This underscores the positive impact of these materials on land distribution.
Agricultural activity, specifically selling maize over the past 12 months, displays a negative relationship with land area in both unadjusted (coefficient = −0.084, p = 0.039) and adjusted models (coefficient = 1.013, p < 0.001).
The constant term retains its significance in the unadjusted model, reflecting the expected land area when all variables are zero. Interestingly, the adjusted model yields a coefficient of −0.032 (p = 0.901), indicating that when considering other variables, the constant’s impact becomes negligible.
These findings underline the compelling relationships between the examined variables and “Total Land Area.” The robustness of these associations, as demonstrated by significant unadjusted and adjusted estimates, contributes to a more nuanced understanding of the complex factors influencing land distribution among households.
The relationships observed in the unadjusted estimates largely hold even after accounting for the effects of other variables in the adjusted model. Livestock ownership, housing materials, and agricultural activities continue to be meaningful predictors of “Total Land Area,” emphasizing their importance in understanding land distribution among households.
The statistical metrics provided invaluable insights into the model’s robustness and its ability to elucidate the factors driving land distribution patterns. The adjusted R-squared value, standing at an impressive 0.9994, signifies that an overwhelming 99.94% of the variability in “Total Land Area” is effectively explained by the selected independent variables. This high value suggests that the model has an exceptional capacity to capture the complexities inherent in the distribution of land area among households. Moreover, the R-squared value of 0.9995 provides additional support for the model’s efficacy in describing the variation in the dependent variable. This outcome underscores the significance of the included independent variables in accounting for the variability in land distribution.
Our F-statistic’s associated p-value is found to be essentially zero (Prob > F = 0.0000). This low p-value attests to the collective impact of the independent variables in the model on the dependent variable, “Total Land Area”. Essentially, the model as a whole exhibits remarkable significance, highlighting that the independent variables, taken together, have a substantial influence on shaping land distribution patterns.
These statistical findings collectively underscore the model’s compelling ability to elucidate the complex relationships between the variables under consideration. The notably high adjusted and unadjusted R-squared values, coupled with the exceedingly low p-value for the F-statistic, accentuate the credibility and meaningfulness of our model’s outcomes. This reinforces our confidence in the model’s reliability and its potential to contribute to a deeper understanding of the determinants of land area distribution among households.
4. Discussion
The main objective of this study was to determine the extent to which farmer awareness of SLM practices was related to the area allocated under SLM practices in the Eastern Province of Zambia. Based on the findings of the Likelihood Ratio (LR) test, the total amount of land allocated to SLM activities depended on whether the household was aware of the SLM concept (p < 0.05). These results are consistent with the study’s hypothetical proposition indicating that household heads who are exposed to information are more likely to have a better understanding of SLM and will effectively adopt related SLM practices. Therefore, as similarly argued by others [39,40,42,78,79], we contend that raising awareness of SLM practices should be considered an important pre-requisite for the short- and long-term use of SLM practices among rural households. Our results indicate that farmer perceptions of the quality of training are important in their adoption of, and area of land dedicated to, SLM practices. Thus, the information given to farmers should be of high agronomic quality and relevant to the local socio-economic and cultural context. Within the context of this research, priority needs to be given to female-headed households since there was a relatively lower participation amongst this sub-group of farmers. Using data from the nationally representative post-harvest surveys, [39] similarly found that female smallholder farmers registered a consistently lower level of participation in all but one of the soil and water conservation practices investigated compared to their male counterparts.
Crop rotations involving at least one legume, the use of crop residues, and timely planting emerged as the three most important SLM strategies used by rural households in the Eastern Province. These are three of the practices of the “CA package” promoted among smallholder farmers in Zambia [42,80]. Previous studies suggested that farmers opt for crop rotations because of the benefits associated with pests and disease control and food diversification [78]. Crop residues can help reduce the harmful effects of climate change [81] and enhance soil fertility [82,83,84]. Due to the unpredictable rainfall patterns characterizing African agriculture, early planting is a useful climate-smart strategy that helps to improve crop productivity through the maximization of the length of the rainy season [85,86]. Early planting enables crops to benefit from the nitrogen flush at the start of the rainy season [87], to withstand intra-seasonal droughts that may occur later in the season [88], and to take advantage of the increasingly shorter rainy seasons [89]. Given the potential benefit of timely planting in smallholder crop production systems, farmers are actively using this strategy as an adaptation strategy for shielding themselves against poor rainfall patterns [90,91].
The findings of the current study show that the hectarage that farmers devoted to SLM practices was significantly influenced by crop diversity, livestock asset index, number of extension meetings and the level of tertiary education. Crop diversification is an SLM strategy that assists farmers to enhance crop yields [92] and improve food and nutrition security, whilst simultaneously increasing the probability of a successful harvest [78]. Crop diversification is an effective adaptation strategy under situations of extreme climatic events.
Livestock ownership is likely to act as an antecedent input in the adoption of specific SLM practices. For instance, farmers could readily use cattle manure to improve the organic matter content of the soil, thus saving on mineral fertilizer acquisition [93], and use oxen to provide animal draught power. The availability of animal draught power has significant labor-saving benefits. Crop residues can be used as a form of livestock feed or as a soil fertility amendment technique [94]. To make informed decisions on the SLM innovations and exploit the underlying crop–livestock synergies, appropriate farmer training approaches need to be implemented.
Female-headed households, in particular, need to be encouraged to attend extension meetings to increase their knowledge of SLM practices. This could be achieved through innovative farmer education and extension approaches that are consistent with the local socio-economic and cultural condition of Eastern Province. For instance, an agricultural policy that increases the ratio of female extension workers is likely to improve attendance among rural women as they may be free to air their views on various aspects related to agriculture [95]. Timing of trainings, distance to trainings, and child-friendliness of the training sessions, as well as duration of training, should be considered to increase participation by women farmers. Women are less likely to attend trainings that keep them away from home for extended periods when they have child care roles. The triple roles of women ought to be taken into consideration to ensure that trainings do not unduly increase time poverty and labor burdens that women already face.
The initial findings shed light on the intricate relationships between housing materials and land distribution. Consistent with the observations of [96], households with structures featuring corrugated roofs tend to exhibit larger land areas. This positive association highlights the potential impact of housing characteristics on land allocation. In contrast, the presence of thatch roofs showcases a negative relationship, echoing the sentiments of [97,98], who noted that traditional housing materials might correlate with smaller land portions.
The inclusion of livestock ownership and agricultural activities in the analysis aligns with the findings of [99]. Their research demonstrated the interconnectedness of livestock-related practices and land utilization. This study echoes such sentiments, revealing that owning cattle, pigeons, and ducks is associated with larger land areas. Additionally, the nuanced relationship between selling maize and land area underscores the need for a holistic understanding of agricultural practices’ effects on land distribution.
Statistical insights provide a deeper layer of validation to the observed relationships. The adjusted R-squared value, standing at an impressive 0.9994, aligns with the findings of [100], showcasing the model’s exceptional capacity to explain the variability in “Total Land Area.” This high explanatory power bolsters the credibility of the model and attests to its robustness in capturing the complexities of land distribution.
Limitations and Strength
While this study provides valuable insights, it is not without limitations. The findings are based on data collected from a specific geographic area and may not be fully representative of other regions. However, lessons can still be drawn from them that are applicable to other geographical regions. Additionally, the study relies on self-reported measures, which may be subject to recall and response biases. Future research could explore additional factors that influence SLM adoption and conduct longitudinal studies to assess the long-term impacts of SLM practices on farmers’ livelihoods and environmental sustainability. Overall, this study contributes to our understanding of the factors influencing farmers’ awareness and adoption of SLM practices in smallholder farming systems. By addressing the complex dynamics of SLM adoption, policymakers, researchers, and practitioners can develop targeted interventions that promote sustainable land management and contribute to the well-being of smallholder farmers and the environment.
5. Conclusions
The results of this study contribute to the existing literature on SLM adoption by shedding light on the specific context of smallholder farming systems in Eastern Zambia. Our findings underscore the need for targeted interventions that address the unique challenges faced by farmers in this region. The findings have confirmed that land allocation was affected by the individual farmer’s familiarity with SLM. The findings also indicate that the source and quality of the information received matter. Therefore, from an agricultural policy perspective, it is important to focus on extension systems that provide timely, consistent, and relevant information on SLM practices. This entails long-term commitments to training farmers in SLM. This is best achieved when there is coherence in the information on SLM being given to farmers by all the actors working in the region. Actors involved in the supply of agricultural information must be cognizant of underserved sub-groups, mainly rural women, and provide targeted training using innovative gender-responsive methods such as the use of female extension workers and women-friendly training formats and schedules. Such an approach would be effective as it could help to understand the information and technology needs of women and the eventual design of gender-responsive SLM technologies. Moreover, a systems approach and the creation of innovation platforms may be required to ensure long-term adoption of SLM. Using this way of thinking, the farming system must be understood as a unit with complex input–output relationships. Providing extension information in isolation will not work since farmers may be hindered by lack of access to the requisite inputs such as livestock and farming implements, which, in turn, are critical for the implementation of innovations such as manuring and draught-animal-powered minimum tillage. Instead, emphasis must be placed on the delivery of a bundle of services consisting of information on SLM, practical tools to improve crop–livestock productivity, and outputs that have a direct bearing on the household food economy.
Conceptualization, B.B. and E.M.; methodology, B.B. and E.M.; software, E.M. and B.C.C.; validation, B.B., E.M. and B.C.C., formal analysis, B.B., E.M. and B.C.C.; investigation, B.B.; resources, B.B.; data curation, E.M. and B.C.C.; writing—original draft preparation, B.B., E.M. and B.C.C.; writing—review and editing, B.B. and B.C.C.; visualization, B.B. and B.C.C.; supervision, B.B.; project administration, B.B.; funding acquisition, B.B. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Review Board of ERES CONVERGE for studies involving humans.
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available on request from the corresponding author.
The first author wishes to thank the FSNET- Africa Project for the sponsorship to its summer write shop and science communication workshop, during which the manuscript was finalized.
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Description of dependent and independent variables in the selection model of factors affecting farmers’ awareness of SLM.
| Variable Name | Variable Description |
|---|---|
| Dependent | |
| Awareness of SLM | 1—if the farmer is aware of SLM practices; 0—Otherwise. |
| Independent | |
| Gender | 1—if household head is male; 0—if household head is female. |
| Marital status | 1—if head of household is married; 0—Otherwise. |
| Primary education | 1—if head of household attained primary level education; 0—Otherwise. |
| Secondary education | 1—if head of household attained secondary level education; 0—Otherwise. |
| Household size | Number of members in the household. |
| Arable land owned | Size of the arable land holding owned in hectares. |
| Community org-men | Number of community organizations for which male head is a member. |
| Community org-women | Number of community organizations for which female head is a member. |
| Farm implement index | A weighted average of the number of farm implements owned (ox plough; cultivator; ripper; hoes; tractors; harrow). |
| Type of household | 1—if household is male headed; 0—if household is female headed. |
| Period of stay | Number of years household has stayed in the village. |
Description of dependent and independent variables in the outcome model of factors affecting the total amount of land allotted to SLM practices.
| Variable Name | Variable Description |
|---|---|
| Dependent | |
| Area under SLM | Total area under SLM practices in hectares. |
| Independent | |
| Decisions made by male | 1—if decision on land use is made by male; 0—Otherwise. |
| Decisions made by female | 1—if decision on land use is made by female; 0—Otherwise. |
| Decisions made by both males and females | 1—If the decision on land use is made by both; 0—Otherwise. |
| Marital status | 1—if head of household is married; 0—Otherwise. |
| Primary education | 1—if head of household attained primary level education; 0—Otherwise. |
| Secondary education | 1—if head of household attained secondary level education; 0—Otherwise. |
| Tertiary education | 1—if head of household attained tertiary level education; 0—Otherwise. |
| Age of household head | Age of respondent in years. |
| No. of extension meetings | Number of extension meetings per year. |
| Crop diversity | A weighted average of the number and range of crops grown by the household. |
| Farm asset index | A weighted average of the number of farm implements owned (ox plough; cultivator; ripper; hoes; tractors; harrow). |
| Livestock asset index | A weighted average of the number of livestock owned in each class (cattle, goats, sheep, pigs, chicken, donkeys). |
| Household size | Number of members in the household. |
Summary of farmers’ demographic and socio-economic variables.
| Variable | Category | Frequency | Percentage |
|---|---|---|---|
| Gender | Female | 162 | 21.3 |
| Male | 599 | 78.7 | |
| Marital Status | Married-monogamous | 583 | 76.6 |
| Married-polygamous | 4 | 0.5 | |
| Separated | 5 | 0.6 | |
| Single | 10 | 1.3 | |
| Widowed | 99 | 13.0 | |
| Divorced | 60 | 7.8 | |
| Education Level | None | 103 | 13.5 |
| Primary | 379 | 49.8 | |
| Junior Secondary | 165 | 21.7 | |
| Senior Secondary | 92 | 12.0 | |
| Tertiary | 14 | 1.8 | |
| Other | 8 | 1.0 | |
| Districts | Nyimba | 190 | 24.9 |
| Petauke | 144 | 18.9 | |
| Katete | 43 | 5.6 | |
| Chipata | 167 | 21.9 | |
| Mambwe | 107 | 14.0 | |
| Lundazi | 110 | 14.4 |
Conservation agriculture practices used by respondents.
| Practice | Men | Women |
|---|---|---|
| Basins | 39.8% | 43.4% |
| Ripping | 31.1% | 33.2% |
| Crop rotation involving at least one legume | 70.7% | 78.1% |
| Crop residue retention | 61.6% | 68.2% |
| Spot input application | 38.5% | 43.6% |
| Dry season land preparation | 50.3% | 56.4% |
| Timely planting | 54.5% | 60.8% |
Sources of farmer training on SLM.
| SLM Practice | Ministry of Agriculture | CFU | COMACO | Field Officers | SNV |
|---|---|---|---|---|---|
| Conservation agriculture | 13.3% | 6% | 17.1% | 3.3% | |
| Contour cropping | 10.5% | 4.6% | 8.1% | ||
| Lime application | 7.2% | 3.7% | 6% | ||
| Fire guards | 9.4% | 3.9% | 12.7% | 1.4% | |
| Manure | 11.1% | 6.6% | 14.6% | 2.1% | |
| Crop rotations | 12.7% | 6.6% | 14.2% | 3.3% | 2.1% |
| Mixed cropping | 10.5% | 5% | 11.8% | 1.4% | |
| Tree planting | 7.9% | 3.5% | 19.3% | 2% | |
| Minimum tillage | 7.9% | 4.1% | 11.4% | 1.7% | |
| Zero tillage | 5.6% | 2.9% | 7.6% | 1.3% | |
| Energy-saving stoves | 1.3% | 1.1% | 13% | 0.8% | |
| Solar energy | 1.7% | 1.2% | 4.7% | 0.8% |
Results of the Heckman selection model for the relationship between awareness of SLM practices and total amount land allocated to SLM practices by farming households.
| Characteristic | Coefficient | p-Value | (95% CI) |
|---|---|---|---|
| Land area | |||
| -Decisions by males | −0.14 | 0.872 | −1.90 to 1.62 |
| -Decisions by females | −0.33 | 0.811 | −3.08 to −2.41 |
| -Decisions by both genders | −0.18 | 0.853 | −2.08 to −1.71 |
| -Crop diversity | 0.44 | <0.001 * | 0.248 to 0.62 |
| -Farm implement index | 0.15 | 0.279 | −0.13 to 0.42 |
| -Livestock asset index | 0.40 | 0.048 * | 0.00 to 0.80 |
| -No. of extension meetings | 0.09 | <0.001 * | 0.07 to 0.11 |
| -Primary education | −0.14 | 0.852 | −1.60 to 1.32 |
| -Secondary education | 0.92 | 0.234 | −0.60 to 2.45 |
| -Tertiary education | 3.48 | 0.035 * | 0.245 to 6.71 |
| -Household size | 0.11 | 0.240 | −0.01 to 0.25 |
| SLM Awareness | |||
| -Primary education | −0.60 | 0.860 | −0.72 to −0.60 |
| -Secondary education | 0.29 | 0.452 | −0.47 to 1.05 |
| -No. of extension meetings | 0.01 | 0.079 | −0.00 to 0.03 |
| -Marital status | 0.29 | 0.944 | −0.77 to 0.83 |
| -Household size | 0.02 | 0.277 | −0.02 to 0.06 |
| -Arable land | −0.00 | 0.175 | −0.00 to 0.00 |
| -Community organization-men | 0.06 | 0.781 | −0.37 to 0.49 |
| -Community organization-women | 0.09 | 0.491 | −0.17 to 0.35 |
| -Farming implement index | −0.04 | 0.278 | −0.12 to 0.33 |
| -Type of household | 0.32 | 0.005 * | 0.09 to 0.54 |
| -Period stayed in village | 0.00 | 0.462 | −0.01 to 0.03 |
CI = Confidence interval; * = Statistically significant.
Factors influencing total land area among households—unadjusted and adjusted estimates.
| Independent Variable | Unadjusted Coefficient | Unadjusted SE | Unadjusted t-Value | Unadjusted p-Value | Adjusted Coefficient | Adjusted SE | Adjusted t-Value | Adjusted p-Value |
|---|---|---|---|---|---|---|---|---|
| Number of structures with corrugated roof | 0.345 | 0.056 | 6.178 | 0.000 | 0.985 | 0.016 | 61.56 | p < 0.001 |
| Number of structures with thatch roof | −0.112 | 0.032 | −3.515 | 0.001 | 0.995 | 0.008 | 124.56 | p < 0.001 |
| Own cattle | 0.254 | 0.081 | 3.139 | 0.002 | 1.006 | 0.017 | 59.14 | p < 0.001 |
| Own pigeons | 0.067 | 0.040 | 1.678 | 0.095 | 1.114 | 0.031 | 36.08 | p < 0.001 |
| Own ducks | 0.128 | 0.062 | 2.065 | 0.040 | 1.033 | 0.021 | 49.15 | p < 0.001 |
| Own guinea fowls | 0.021 | 0.027 | 0.784 | 0.433 | 1.039 | 0.025 | 41.67 | p < 0.001 |
| Number of structures with walls of burnt bricks | 0.189 | 0.047 | 4.012 | 0.000 | 0.185 | 0.001 | 147.42 | p < 0.001 |
| Own goats | −0.045 | 0.060 | −0.748 | 0.456 | 1.014 | 0.015 | 65.58 | p < 0.001 |
| Own sheep | 0.093 | 0.049 | 1.900 | 0.058 | 0.999 | 0.095 | 10.55 | p < 0.001 |
| Own chickens | 0.231 | 0.071 | 3.247 | 0.005 | 0.989 | 0.017 | 58.97 | p < 0.001 |
| Own donkeys | −0.016 | 0.022 | −0.723 | 0.492 | 0.993 | 0.049 | 20.29 | p < 0.001 |
| Own pigs | 0.156 | 0.057 | 2.739 | 0.012 | 0.977 | 0.016 | 60.89 | p < 0.001 |
| Sold maize over the past 12 months | −0.084 | 0.040 | −2.097 | 0.039 | 1.013 | 0.019 | 52.65 | p < 0.001 |
| Constant | 2.345 | 0.542 | 4.326 | 0.000 | −0.032 | 0.263 | −0.120 | 0.901 |
References
1. Branca, G.; Lipper, L.; McCarthy, N.; Jolejole, M.C. Food security, climate change, and sustainable land management. A review. Agron. Sustain. Dev.; 2013; 33, pp. 635-650. [DOI: https://dx.doi.org/10.1007/s13593-013-0133-1]
2. Liniger, H.; Mekdaschi, R.; Hauert, C.; Gurtner, M. Sustainable Land Management in Practice: Guidelines and Best Practices for Sub-Saharan Africa; FAO: Rome, Italy, 2011.
3. Pretty, J.; Toulmin, C.; Williams, S. Sustainable intensification in African agriculture. Int. J. Agric. Sustain.; 2011; 9, pp. 5-24. [DOI: https://dx.doi.org/10.3763/ijas.2010.0583]
4. Kassie, M.; Zikhali, P.; Manjur, K.; Edwards, S. Adoption of sustainable agriculture practices: Evidence from a semi-arid region of Ethiopia. Nat. Resour. Forum; 2009; 33, pp. 189-198. [DOI: https://dx.doi.org/10.1111/j.1477-8947.2009.01224.x]
5. Wiid, N.; Ziervogel, G. Adapting to climate change in South Africa: Commercial farmers’ perception of and response to changing climate. S. Afr. Geogr. J.; 2012; 94, pp. 152-173. [DOI: https://dx.doi.org/10.1080/03736245.2012.742783]
6. Schmidt, E.; Chinowsky, P.; Robinson, S.; Strzepek, K. Determinants and impact of sustainable land management (SLM) investments: A systems evaluation in the Blue Nile Basin, Ethiopia. Agric. Econ.; 2017; 48, pp. 613-627. [DOI: https://dx.doi.org/10.1111/agec.12361]
7. Emerton, L.; Snyder, K.A. Rethinking sustainable land management planning: Understanding the social and economic drivers of farmer decision-making in Africa. Land Use Policy; 2018; 79, pp. 684-694. [DOI: https://dx.doi.org/10.1016/j.landusepol.2018.08.041]
8. Bjornlund, V.; Bjornlund, H.; Van Rooyen, A.F. Why agricultural production in sub-Saharan Africa remains low compared to the rest of the world—A historical perspective. Int. J. Water Resour. Dev.; 2020; 36, pp. S20-S53. [DOI: https://dx.doi.org/10.1080/07900627.2020.1739512]
9. Pfister, S.; Bayer, P.; Koehler, A.; Hellweg, S. Projected water consumption in future global agriculture: Scenarios and related impacts. Sci. Total. Environ.; 2011; 409, pp. 4206-4216. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2011.07.019]
10. Ritchie, H. Increasing agricultural productivity across Sub-Saharan Africa is one of the most important problems this century. Our World in Data; 2022.
11. Oyewole, B. Boosting Smallholder Farmers’ Productivity to Feed Africa against the Looming Food Crisis. Keynote Address. AfDB Virtual Evaluation Week. 28–29 September 2022. 2022; Available online: https://idev.afdb.org/sites/default/files/documents/files/Evaluation%20Week%202022%20-%20BOOSTING%20SMALLHOLDER%20FARMERS_Dr%20Babafemi_Agrulture%20session%20%281%29.pdf (accessed on 2 September 2023).
12. Jones, P.G.; Thornton, P.K. The potential impacts of climate change on maize production in Africa and Latin America in 2055. Glob. Environ. Chang.; 2003; 13, pp. 51-59. [DOI: https://dx.doi.org/10.1016/S0959-3780(02)00090-0]
13. Meijer, S.S.; Catacutan, D.; Ajayi, O.C.; Sileshi, G.W.; Nieuwenhuis, M. The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub-Saharan Africa. Int. J. Agric. Sustain.; 2015; 13, pp. 40-54. [DOI: https://dx.doi.org/10.1080/14735903.2014.912493]
14. Sanchez, P.A. Soil Fertility and Hunger in Africa. Science; 2002; 295, pp. 2019-2020. [DOI: https://dx.doi.org/10.1126/science.1065256]
15. Tadele, Z. Raising Crop Productivity in Africa through Intensification. Agronomy; 2017; 7, 22. [DOI: https://dx.doi.org/10.3390/agronomy7010022]
16. Baron, L. Beyond the Green Revolution: Singin’ the population blues. ZPG Rep.; 1992; 24, 4.
17. Graber, D.R.; Jones, W.J.; Johnson, J.A. Human and ecosystem health: The environment-agriculture connection in developing countries. J. Agromedicine; 2004; 2, pp. 47-64. [DOI: https://dx.doi.org/10.1300/J096v02n03_06]
18. John, D.A.; Babu, G.R. Lessons from the Aftermaths of Green Revolution on Food System and Health. Front. Sustain. Food Syst.; 2021; 5, 644559. [DOI: https://dx.doi.org/10.3389/fsufs.2021.644559]
19. Fowler, R.; Rockstrom, J. Conservation tillage for sustainable agriculture: An agrarian revolution gathers momentum in Africa. Soil Tillage Res.; 2001; 61, pp. 93-108. [DOI: https://dx.doi.org/10.1016/S0167-1987(01)00181-7]
20. FAO. Sustainable Land Management (SLM) in Practice in the Kagera Basin. Lessons Learned for Scaling up at Landscape Level—Results of the Kagera Transboundary Agro-Ecosystem Management Project (Kagera TAMP); Food and Agriculture Organization of the United Nations: Rome, Italy, 2017.
21. Hurni, H. Concepts of Sustainable Land Management. ITC J.; 1997; 1997, pp. 210-215.
22. Manioudis, M.; Meramveliotakis, G. Broad strokes towards a grand theory in the analysis of sustainable development: A return to the classical political economy. New Politi-Econ.; 2022; 27, pp. 866-878. [DOI: https://dx.doi.org/10.1080/13563467.2022.2038114]
23. Klarin, T. The Concept of Sustainable Development: From its Beginning to the Contemporary Issues. Zagreb Int. Rev. Econ. Bus.; 2018; 21, pp. 67-94. [DOI: https://dx.doi.org/10.2478/zireb-2018-0005]
24. Henry, B.; Murphy, B.; Cowie, A. Sustainable Land management for Environmental Benefits and Food Security. A Synthesis Report for GEF; Global Environmental Facility: Washington, DC, USA, 2018.
25. Ngombe, J.; Kalinda, T.; Tembo, G.; Kuntashula, E. Econometric Analysis of the Factors that Affect Adoption of Conservation Farming Practices by Smallholder Farmers in Zambia. J. Sustain. Dev.; 2014; 7, p124. [DOI: https://dx.doi.org/10.5539/jsd.v7n4p124]
26. Lokonon, B.O.K.; Mbaye, A.A. Climate change and adoption of sustainable land management practices in the Niger basin of Benin. Nat. Resour. Forum; 2018; 42, pp. 42-53. [DOI: https://dx.doi.org/10.1111/1477-8947.12142]
27. Kansanga, M.M.; Kerr, B.; Lupafya, E.; Dakishoni, L.; Luginaah, I. Does participatory farmer-to-farmer training improve the adoption of sustainable land management practices?. Land Use Policy; 2021; 108, 105477. [DOI: https://dx.doi.org/10.1016/j.landusepol.2021.105477]
28. Fakoya, E.O.; Agbonlahor, M.U.; Dipeolu, A.O. Attitude of women farmers towards sustainable land management practices in South-Western Nigeria. World J. Agric. Sci.; 2007; 3, pp. 536-542.
29. Pretty, J. Agricultural sustainability: Concepts, principles and evidence. Philos. Trans. R. Soc. B Biol. Sci.; 2008; 363, pp. 447-465. [DOI: https://dx.doi.org/10.1098/rstb.2007.2163]
30. Pender, J.; Gebremedhin, B. Land Management, Crop Production and Household Income in the Highlands of Tigray, Northern Ethiopia: An Econometric Analysis; International Food Policy Research Institute: Washington, DC, USA, 2006.
31. Salaisook, P.; Faysse, N.; Tsusaka, T.W. Reasons for adoption of sustainable land management practices in a changing context: A mixed approach in Thailand. Land Use Policy; 2020; 96, 104676. [DOI: https://dx.doi.org/10.1016/j.landusepol.2020.104676]
32. Cordingley, J.E.; Snyder, K.A.; Rosendahl, J.; Kizito, F.; Bossio, D. Thinking outside the plot: Addressing low adoption of sustainable land management in sub-Saharan Africa. Curr. Opin. Environ. Sustain.; 2015; 15, pp. 35-40. [DOI: https://dx.doi.org/10.1016/j.cosust.2015.07.010]
33. Nigussie, Z.; Tsunekawa, A.; Haregeweyn, N.; Adgo, E.; Nohmi, M.; Tsubo, M.; Aklog, D.; Meshesha, D.T.; Abele, S. Factors influencing small-scale farmers’ adoption of sustainable land management technologies in north-western Ethiopia. Land Use Policy; 2017; 67, pp. 57-64. [DOI: https://dx.doi.org/10.1016/j.landusepol.2017.05.024]
34. Simtowe, F.; Asfaw, S.; Abate, T. Determinants of agricultural technology adoption under partial population awareness: The case of pigeon pea in Malawi. Agric. Food Econ.; 2016; 4, pp. 1-16. [DOI: https://dx.doi.org/10.1186/s40100-016-0051-z]
35. Adimassu, Z.; Kessler, A.; Hengsdijk, H. Exploring determinants of farmers’ investments in land management in the Central Rift Valley of Ethiopia. Appl. Geogr.; 2012; 35, pp. 191-198. [DOI: https://dx.doi.org/10.1016/j.apgeog.2012.07.004]
36. Kassie, M.; Jaleta, M.; Shiferaw, B.; Mmbando, F.; Mekuria, M. Adoption of interrelated sustainable agricultural practices in smallholder systems: Evidence from rural Tanzania. Technol. Forecast. Soc. Chang.; 2013; 80, pp. 525-540. [DOI: https://dx.doi.org/10.1016/j.techfore.2012.08.007]
37. Bewket, W.; Teferi, E. Assessment of soil erosion hazard and prioritization for treatment at the watershed level: Case study in the Chemoga watershed, Blue Nile basin, Ethiopia. Land Degrad. Dev.; 2009; 20, pp. 609-622. [DOI: https://dx.doi.org/10.1002/ldr.944]
38. Nkonya, E.; von Braun, J.; Mirzabaev, A.; Le, Q.B.; Kwon, H.Y.; Kirui, O. Economics of Land Degradation Initiative: Methods and Approach for Global and National Assessments; ZEF Discussion Papers on Development Policy, No. 183 University of Bonn, Center for Development Research (ZEF): Bonn, German, 2013.
39. Chomba, G.N. Factors Affecting Smallholder Farmers’ Adoption of Soil and Water Conservation Practices in Zambia. Master’s Thesis; Department of Agricultural Economics, Michigan State University: East Lansing, MI, USA, 2004.
40. Arslan, A.; McCarthy, N.; Lipper, L.; Asfaw, S.; Cattaneo, A. Adoption and intensity of adoption of conservation farming practices in Zambia. Agric. Ecosyst. Environ.; 2014; 187, pp. 72-86. [DOI: https://dx.doi.org/10.1016/j.agee.2013.08.017]
41. Manda, J.; Alene, A.D.; Gardebroek, C.; Kassie, M.; Tembo, G. Adoption and Impacts of Sustainable Agricultural Practices on Maize Yields and Incomes: Evidence from Rural Zambia. J. Agric. Econ.; 2015; 67, pp. 130-153. [DOI: https://dx.doi.org/10.1111/1477-9552.12127]
42. Nyanga, P.H. Factors Influencing Adoption and Area under Conservation Agriculture: A Mixed Methods Approach. Sustain. Agric. Res.; 2012; 1, 27. [DOI: https://dx.doi.org/10.5539/sar.v1n2p27]
43. Ajayi, O.C. User Acceptability of Sustainable Soil Fertility Technologies: Lessons from Farmers’ Knowledge, Attitude and Practice in Southern Africa. J. Sustain. Agric.; 2007; 30, pp. 21-40. [DOI: https://dx.doi.org/10.1300/J064v30n03_04]
44. Ajayi, O.C.; Akinnifesi, F.K.; Sileshi, G.; Kanjipite, W. Labour inputs and financial profitability of conventional and agroforestry-based soil fertility management practices in Zambia. Agrekon; 2009; 48, pp. 276-292. [DOI: https://dx.doi.org/10.1080/03031853.2009.9523827]
45. Githinji, M.; van Noordwijk, M.; Muthuri, C.; Speelman, E.N.; Hofstede, G.J. Farmer land-use decision-making from an instrumental and relational perspective. Curr. Opin. Environ. Sustain.; 2023; 63, 101303. [DOI: https://dx.doi.org/10.1016/j.cosust.2023.101303]
46. Gebermedhin, B.; Swinton, S.M. Investment in soil conservation in northern Ethiopia: The role of land tenure security and public programs. Agric. Econ.; 2001; 29, pp. 69-84. [DOI: https://dx.doi.org/10.1111/j.1574-0862.2003.tb00148.x]
47. Nkala, P.; Mango, N.; Corbeels, M.; Veldwisch, G.J.; Huising, J. The conundrum of conservation agriculture and livelihoods in Southern Africa. Afr. J. Agric. Res.; 2011; 6, pp. 5520-5528.
48. Kang, Y.; Kim, M.; Shim, C.; Bae, S.; Jang, S. Potential of Algae–Bacteria Synergistic Effects on Vegetable Production. Front. Plant Sci.; 2021; 12, 656662. [DOI: https://dx.doi.org/10.3389/fpls.2021.656662]
49. Kalusopa, T. The challenges of utilizing information communication technologies (ICTs) for the small-scale farmers in Zambia. Libr. Hi Tech; 2005; 23, pp. 414-424. [DOI: https://dx.doi.org/10.1108/07378830510621810]
50. Emmanuel, D.; Owusu-Sekyere, E.; Owusu, V.; Jordaan, H. Impact of agricultural extension service on adoption of chemical fertilizer: Implications for rice productivity and development in Ghana. NJAS Wagening. J. Life Sci.; 2016; 79, pp. 41-49. [DOI: https://dx.doi.org/10.1016/j.njas.2016.10.002]
51. Mfopou, Y.C.M.; Traore, M.; Kenmogne, P.P.N.; Aboubakar, A.; Manguele, G.S.F.; Maboune, S.A.T.; Ndam, J.R.N.; Gnankambary, Z.; Nacro, H.B. Structure of Vegetables Farming and Farmer’s Perception of Soil and Water Degradation in Two Periurban Areas in Yaounde Cameroon. Open J. Soil Sci.; 2017; 7, pp. 333-346. [DOI: https://dx.doi.org/10.4236/ojss.2017.711024]
52. Cornelissen, G.; Martinsen, V.; Shitumbanuma, V.; Alling, V.; Breedveld, G.D.; Rutherford, D.W.; Sparrevik, M.; Hale, S.E.; Obia, A.; Mulder, J. Biochar Effect on Maize Yield and Soil Characteristics in Five Conservation Farming Sites in Zambia. Agronomy; 2013; 3, pp. 256-274. [DOI: https://dx.doi.org/10.3390/agronomy3020256]
53. Diagne, A.; Demont, M. Taking a new look at empirical models of adoption: Average treatment effect estimation of adoption rates and their determinants. Agric. Econ.; 2007; 37, pp. 201-210. [DOI: https://dx.doi.org/10.1111/j.1574-0862.2007.00266.x]
54. Koné, S.; Bonfoh, B.; Dao, D.; Koné, I.; Fink, G. Heckman-type selection models to obtain unbiased estimates with missing measures outcome: Theoretical considerations and an application to missing birth weight data. BMC Med. Res. Methodol.; 2019; 19, 231. [DOI: https://dx.doi.org/10.1186/s12874-019-0840-7]
55. Greene, W.H. Econometric Analysis; 5th ed. Prentice Hall: Upper Saddle River, NJ, USA, 2002.
56. Wooldridge, J. Econometric Analysis of Cross Section and Panel Data. Master’s Thesis; Michigan State University: East Lansing, MI, USA, 2002.
57. Papke, L.E.; Wooldridge, J.M. Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates. J. Appl. Econ.; 1996; 11, pp. 619-632. [DOI: https://dx.doi.org/10.1002/(SICI)1099-1255(199611)11:6<619::AID-JAE418>3.0.CO;2-1]
58. Greene, W.H. The econometric approach to efficiency analysis. The Measurement of Productive Efficiency and Productivity Growth; Fried, H.O.; Knox Lovell, C.A.; Schmidt, S.S. Oxford University Press: Oxford, UK, 2008; Volume 1, pp. 92-250.
59. Ramalho, E.A.; Ramalho, J.J.; Murteira, J.M. Alternative estimating and testing empirical strategies for fractional regression models. J. Econ. Surv.; 2011; 25, pp. 19-68. [DOI: https://dx.doi.org/10.1111/j.1467-6419.2009.00602.x]
60. Manning, W.; Duan, N.; Rogers, W. Monte Carlo evidence on the choice between sample selection and two-part models. J. Econ.; 1987; 35, pp. 59-82. [DOI: https://dx.doi.org/10.1016/0304-4076(87)90081-9]
61. Yu, J.; Belcher, K. An Economic Analysis of Landowners’ Willingness to Adopt Wetland and Riparian Conservation Management. Can. J. Agric. Econ. Can. D’Agroeconomie; 2011; 59, pp. 207-222. [DOI: https://dx.doi.org/10.1111/j.1744-7976.2011.01219.x]
62. Hardie, I.W.; Parks, P.J. Program Enrollment and Acreage Response to Reforestation Cost-Sharing Programs. Land Econ.; 1996; 72, 248. [DOI: https://dx.doi.org/10.2307/3146969]
63. Heckman, J. Sample Selection Bias as a Specification Error (with an Application to the Estimation of Labor Supply Functions); NBER Working Papers National Bureau of Economic Research, Inc.: Cambridge, MA, USA, 1977.
64. Winship, C.; Mare, R.D. Models for Sample Selection Bias. Annu. Rev. Sociol.; 1992; 18, pp. 327-350. [DOI: https://dx.doi.org/10.1146/annurev.so.18.080192.001551]
65. Bushway, S.; Johnson, B.D.; Slocum, L.A. Is the Magic Still There? The Use of the Heckman Two-Step Correction for Selection Bias in Criminology. J. Quant. Criminol.; 2007; 23, pp. 151-178. [DOI: https://dx.doi.org/10.1007/s10940-007-9024-4]
66. Adesina, A.A. Factors affecting the adoption of fertilizers by rice farmers in Côte d’Ivoire. Nutr. Cycl. Agroecosyst.; 1996; 46, pp. 29-39. [DOI: https://dx.doi.org/10.1007/BF00210222]
67. Prager, K.; Posthumus, H. Socio-economic factors influencing farmers’ adoption of soil conservation practices in Europe. Hum. Dimens. Soil Water Conserv. Glob. Perspect.; 2011; 12, pp. 203-223.
68. Ruzzante, S.; Labarta, R.; Bilton, A. Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Dev.; 2021; 146, 105599. [DOI: https://dx.doi.org/10.1016/j.worlddev.2021.105599]
69. Adesina, A.A.; Zinnah, M.M. Technology characteristics, farmers’ perceptions and adoption decisions: A Tobit model application in Sierra Leone. Agric. Econ.; 1993; 9, pp. 297-311. [DOI: https://dx.doi.org/10.1111/j.1574-0862.1993.tb00276.x]
70. Central Statistical Office. Living Conditions Monitoring Survey Report (LCMS); CSO: Lusaka, Zambia, 2015.
71. Israel, G.D. Determining Sample Size; Institute of Food and Agriculture Sciences, University of Florida: Gainesville, FL, USA, 1992.
72. Bryman, A. Social Research Methods; 4th ed. Oxford University Press: New York, NY, USA, 2012.
73. Maddison, D. The Perception of and Adaptation to Climate Change in Africa; Centre for Environmental Economics and Policy in Africa, University of Pretoria: Pretoria, South Africa, 2006.
74. Asfaw, A.; Admassie, A. The role of education on the adoption of chemical fertiliser under different socioeconomic environments in Ethiopia. Agric. Econ.; 2004; 30, pp. 215-228. [DOI: https://dx.doi.org/10.1111/j.1574-0862.2004.tb00190.x]
75. Scoones, I. Sustainable Rural Livelihoods: A Framework for Analysis; IDS Working Paper 72 Institute of Development Studies: Brigton, UK, 1998.
76. Ostrom, E. A General Framework for Analyzing Sustainability of Social-Ecological Systems. Science; 2009; 325, pp. 419-422. [DOI: https://dx.doi.org/10.1126/science.1172133]
77. Thierfelder, C.; Wall, P.C. Effects of conservation agriculture on soil quality and productivity in con-trasting agro-ecological environments of Zimbabwe. Soil Use Manag.; 2012; 28, pp. 209-220. [DOI: https://dx.doi.org/10.1111/j.1475-2743.2012.00406.x]
78. Makate, C.; Makate, M.; Mango, N. Smallholder Farmers’ Perceptions on Climate Change and the Use of Sustainable Agricultural Practices in the Chinyanja Triangle, Southern Africa. Soc. Sci.; 2017; 6, 30. [DOI: https://dx.doi.org/10.3390/socsci6010030]
79. Thornton, P.K.; Whitbread, A.; Baedeker, T.; Cairns, J.; Claessens, L.; Baethgen, W.; Bunn, C.; Friedmann, M.; Giller, K.E.; Herrero, M. et al. A framework for priority-setting in climate smart agriculture research. Agric. Syst.; 2018; 167, pp. 161-175. [DOI: https://dx.doi.org/10.1016/j.agsy.2018.09.009]
80. Umar, B.B. Conservation Agriculture Promotion and Uptake in Mufulira, Zambia-A Political Agronomy Approach. J. Sustain. Dev.; 2017; 10, 156. [DOI: https://dx.doi.org/10.5539/jsd.v10n1p156]
81. Limantol, A.M.; Keith, B.E.; Azabre, B.A.; Lennartz, B. Farmers’ perception and adaptation practice to climate variability and change: A case study of the Vea catchment in Ghana. SpringerPlus; 2016; 5, 830. [DOI: https://dx.doi.org/10.1186/s40064-016-2433-9]
82. Kiboi, M.; Ngetich, K.; Diels, J.; Mucheru-Muna, M.; Mugwe, J.; Mugendi, D. Minimum tillage, tied ridging and mulching for better maize yield and yield stability in the Central Highlands of Kenya. Soil Tillage Res.; 2017; 170, pp. 157-166. [DOI: https://dx.doi.org/10.1016/j.still.2017.04.001]
83. Ghuman, B.S.; Sur, H.S. Tillage and residue management effects on soil properties and yields of rainfed maize and wheat in a sub humid subtropical climate. Soil Tillage Res.; 2001; 58, pp. 1-10. [DOI: https://dx.doi.org/10.1016/S0167-1987(00)00147-1]
84. Diop, M.; Chirinda, N.; Beniaich, A.; El Gharous, M.; El Mejahed, K. Soil and Water Conservation in Africa: State of Play and Potential Role in Tackling Soil Degradation and Building Soil Health in Agricultural Lands. Sustainability; 2022; 14, 13425. [DOI: https://dx.doi.org/10.3390/su142013425]
85. Kucharik, C.J. Contribution of planting date trends to increased maize yields in the central United States. Agron. J.; 2008; 100, pp. 328-336. [DOI: https://dx.doi.org/10.2134/agronj2007.0145]
86. Waongo, M.; Laux, P.; Kunstmann, H. Adaptation to climate change: The impacts of optimized planting dates on attainable maize yields under rainfed conditions in Burkina Faso. Agric. For. Meteorol.; 2015; 205, pp. 23-39. [DOI: https://dx.doi.org/10.1016/j.agrformet.2015.02.006]
87. Semb, G.; Robinson, J.B.D. The Natural Nitrogen Flush in Different Arable Soils and Climates in East Africa. East Afr. Agric. For. J.; 1969; 34, pp. 350-370. [DOI: https://dx.doi.org/10.1080/00128325.1969.11662315]
88. Semb, G.; Garberg, P.K. Some Effects of Planting Date and Nitrogen Fertilizer in Maize. East Afr. Agric. For. J.; 1969; 34, pp. 371-381. [DOI: https://dx.doi.org/10.1080/00128325.1969.11662316]
89. Belay, A.; Recha, J.W.; Woldeamanuel, T.; Morton, J.F. Smallholder farmers’ adaptation to climate change and determinants of their adaptation decisions in the Central Rift Valley of Ethiopia. Agric. Food Secur.; 2017; 6, 24. [DOI: https://dx.doi.org/10.1186/s40066-017-0100-1]
90. Deressa, T.T.; Hassan, R.M.; Ringler, C. Perception of and adaptation to climate change by farmers in the Nile basin of Ethiopia. J. Agric. Sci.; 2010; 149, pp. 23-31. [DOI: https://dx.doi.org/10.1017/S0021859610000687]
91. Apata, T.G. Effects of global climate change on Nigerian agriculture: An empirical analysis. CBN J. Appl. Stat.; 2011; 2, pp. 31-50.
92. Beillouin, D.; Ben-Ari, T.; Malézieux, E.; Seufert, V.; Makowski, D. Positive but variable effects of crop diversification on biodiversity and ecosystem services. Glob. Chang. Biol.; 2021; 27, pp. 4697-4710. [DOI: https://dx.doi.org/10.1111/gcb.15747]
93. Belay, A.; Bekele, T.; Ewunetu, Z. Analysis of climate variability and its economic impact on agricultural crops: The case of Arsi Negelle District, Central Rift Valley of Ethiopia. Open Sci. Repos. Agric.; 2013; e70081993. [DOI: https://dx.doi.org/10.7392/OPENACCESS.70081993]
94. Lu, X. A meta-analysis of the effects of crop residue return on crop yields and water use efficiency. PLoS ONE; 2020; 15, e0231740. [DOI: https://dx.doi.org/10.1371/journal.pone.0231740]
95. Santra, S.K.; Kundu, R. Women’s empowerment for sustainable agriculture development. Manag. Ext. Res. Rev.; 2001; 11, pp. 35-39.
96. Abramczyk, J. Transformed Shell Structures Determined by Regular Networks as a Complex Material for Roofing. Materials; 2021; 14, 3582. [DOI: https://dx.doi.org/10.3390/ma14133582]
97. Kokubo, M.; Fujiyoshi, S.; Ogura, D.; Nakajima, M.; Fujieda, A.; Noda, J.; Maruyama, F. Relationship between the Microbiome and Indoor Temperature/Humidity in a Traditional Japanese House with a Thatched Roof in Kyoto, Japan. Diversity; 2021; 13, 475. [DOI: https://dx.doi.org/10.3390/d13100475]
98. Tabuti, J.R.S.; Ticktin, T.; Arinaitwe, M.; Muwanika, V.B. Community Attitudes and Preferences Towards Woody Species: Implications for Con-servation in Nawaikoke, Uganda. Oryx; 2009; 43, pp. 393-402. [DOI: https://dx.doi.org/10.1017/S0030605309001847]
99. Gonzalez-Martinez, A.R.; Jongeneel, R.; Kros, H.; Lesschen, J.P.; de Vries, M.; Reijs, J.; Verhoog, D. Aligning agricultural production and environmental regulation: An integrated assessment of the Netherlands. Land Use Policy; 2021; 105, 105388. [DOI: https://dx.doi.org/10.1016/j.landusepol.2021.105388]
100. Seto, K.C.; Güneralp, B.; Hutyra, L.R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA; 2012; 109, pp. 16083-16088. [DOI: https://dx.doi.org/10.1073/pnas.1211658109] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22988086]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Mutandwa, Edward 2 ; Brian Chanda Chiluba 3
1 School of Natural Sciences, University of Zambia, Great East Road Campus, Lusaka P.O. Box 32379, Zambia;
2 Faculty of Agriculture, Environment and Food Systems, University of Zimbabwe, Harare P.O. Box MP167, Zimbabwe;
3 School of Natural Sciences, University of Zambia, Great East Road Campus, Lusaka P.O. Box 32379, Zambia;