Socio-Economic Factors Influencing the Adoption of Conservation Agriculture in Northern Namibia

Abstract

This paper aims to determine the preferences of farmers in practicing conservation agriculture (CA) in rural communities in Namibia. The multinomial logit model was used to estimate the main principles of conservation agriculture (CA) to determine the preferences of farmers in practicing conservation agriculture, given their socio-economic characteristics. In each case, farmers were presented with four different exclusive choices to select from. The multinomial logit model reveals that an increase in the education level of the household head $p\le 0.000$, household size $p\le 0.085$, mono-cropping $p\le 0.000$, annual crop rotation $p\le 0.000$, crop rotation after two years $p\le 0.000$, and weeding twice for 5 h per weeding per hectare $p\le 0.028$ significantly affects the preference for using a basin tillage over a direct seeder, with all other model variables held constant. The log odds of preferring mono-cropping over intercropping cereal with cowpeas are higher for farmers practicing crop rotation annually compared to those rotating crops every two years, assuming no change in other predictor variables with $p\le 0.019.$ In addition, the study found that economic status significantly influences the attractiveness of CA with basin tillage being preferred over the direct seeder among the farmers studied. This preference underscores the characteristics of the respondents, who are primarily subsistence farmers reliant on traditional farming tools. This suggests a strategic opportunity to engage younger and more educated farmers to be the lead farmers to mentor others in their communities. Markets for appropriate tools, such as direct seeders and rippers, must be established to make CA tools available to the farmers in the local market.

1. Introduction

The most common agricultural practice in Northern Namibia is small-scale farming, mainly in the communal areas where farmers practice mixed farming consisting of livestock and crop production [1]. Despite the efforts to produce enough food, it is still an uphill task towards self-sufficiency and food security in most rural communities of Namibia. Low soil fertility, poor water holding capacity, and irregular rainfall patterns are some of the identified factors leading to insufficient food supply [2,3,4,5,6]. Increased uncertainty in agricultural production, arising from extreme climatic changes, leads to uncertainty in productivity [7]. The relationship between humans and the natural environment is undergoing significant changes due to global climate change, rendering previously stable climatic conditions uncertain and increasingly perilous [8]. This shifting climate is believed to alter interactions among crops, pests, diseases, and weeds [9,10,11], impacting these distinct phenomena in ways that challenge the ecosystem’s resilience [12]. Recent projections indicate that the number of people facing food shortages—currently at 800 million—will increase by 189 million with a temperature rise of 2 °C [12,13]. This undoubtedly complicates the task of satisfying the constantly rising food demand, a phenomenon that is difficult for stakeholders to manage [11]. In Namibia, as in many other Southern African nations, there is constant and growing pressure to increase food production and alleviate poverty among the rural population [14]. The population in sub-Saharan Africa (SSA) is expected to reach 2 billion by 2050, projected with negative consequences of climate change on agriculture and livelihoods [6]. This can be attributed to several factors. Firstly, the soil in the northern communal areas is low in nutrients and has a poor water holding capacity, except in the north-east region of Zambezi [4]. There are irregular rainfall patterns, which normally occur between October and March. Crop production is often poor or inadequate because of insufficient rainfall that often starts either early or late. Consequently, the future of agricultural productivity is endangered by the detrimental impacts on agriculture’s natural resource base, characteristic of the existing agricultural system focused on mechanical soil tillage, exposed soils, and continuous mono-cropping [15]. As a tradition, subsistence crop farmers in these communities mostly practice conventional tillage (CT) using either animal-drawn mouldboard ploughs or tractor-drawn disk harrows that pulverize the soil, thus affecting soil porosities and soil moisture retention capacity [4]. Refs. [16,17,18] have noted that excessive tillage in traditional farming practices leads to declines in soil fertility by altering the soil’s nutrient and carbon content, thereby affecting its fertility and productivity. CT includes the post-harvest removal of crop residue from the crop field [19]. With the use of these conventional agricultural practices, the CT system has been found to have left swathes of land/soil utterly depleted in Zimbabwe [20]. Mono-cropping and continuous growing without replenishing soil nutrients or/and organic matter further decreases soil fertility and crop productivity. The lower organic matter content, higher cropping intensity, improper cropping sequence, inefficient use of fertilizer, and incorrect management practices are the major causes of the depletion of soil fertility [21]. Ref. [4] suggests the adoption of better soil management practices, particularly those that improve soil organic carbon. In order to bring about changes, future food production necessitates the adoption of new management approaches that are both efficient and climate smart [7,22,23,24].

An alternative method to this traditional way of farming is conservation agriculture (CA). CA is a broader term used to depict different types of agricultural methods that have good soil management as their main goal [22]. CA is defined based on three principles, namely the following: (a) reduced soil disturbance by ripping instead of ploughing the soil, (b) permanent soil cover by applying organic matter onto the soil, and (c) crop rotation by rotating cereal crops with leguminous crops [25]. This has been observed to provide a protective buffer to crop production, particularly during drought conditions [26]. CA as a production method is a sustainable agricultural intensification (SAI) strategy that is widely supported by international ‘research for development’ organizations, and its principles can be applied in different farming systems [27,28]. These approaches should include improved agronomic practices such as intercropping, row planting, and proper fertilizer application [29]. Furthermore, the adoption of appropriate crop species and varieties, adequate planting techniques, and physical soil and water conservation measures all contribute to achieving agricultural productivity, local livelihoods, climate resilience, and other favorable outcomes [6,17,29,30,31,32]. Based on the identified good features of the CA system, it seems worthwhile to shift from the existing traditional farming practices to CA, expecting that this will lead to improved yields and the conservation of soil moisture and nutrients [33]. Ref. [4] indicates that moving from conventional tillage to conservation tillage, modified with the application of organic matter to improve soil fertility, leads to improved soil carbon levels in crop production areas. The advantages of CA over conventional tillage, including time, fuel, and labor savings, along with favorable economic returns, have made it increasingly appealing to farmers in the modern era. These benefits are identified as key factors driving its attractiveness [34]. To support both small- and large-scale farmers in sustainably increasing food production, CA is becoming more widely accepted worldwide and is being utilized as an alternative to traditional agricultural technologies [35,36]. The CA system has been successfully implemented in regions with annual rainfalls as low as 250 mm, including Western Australia and Northern China, and in areas with much higher annual rainfall, reaching up to 2000 mm in Brazil and Chile, and even 3000 mm per year [34]. This demonstrates that CA can be implemented anywhere without special prerequisites. High-level environmental concerns have raised interest in devising other approaches to aligning development activities, including agriculture, which are considered to be the stimuli for future investment and environmental sustainability [36,37]. CA holds much promise as an approach to managing agro-ecosystems for improved and sustained productivity, increased profits, and food security, while preserving the resource base and the environment [38]. Food security and sustainable agriculture are inextricably related [39]. This view was supported by [21], who pointed out that CA has been recommended as a widely adapted method that guarantees sustainable agricultural production. In Eastern and Southern Africa, CA has been actively promoted for over a decade as a strategy to enhance crop productivity, simultaneously facilitating soil and water conservation [17,21,30]. Recognized as a sustainable approach, CA serves as a mechanism to weatherproof agriculture, making it more resilient and climate smart as it minimizes or reverses land degradation, and stabilizes and significantly increases long-term yields [6,39]. This enables agricultural systems to buffer against climate variability, offering a viable solution to pressing challenges such as drought, unstable crop yields, the sustainability of agricultural systems, and escalating production costs [34,40,41].

While CA is increasingly recognized for its soil conservation benefits, a significant number of farmers remain unfamiliar with its practices and advantages [42,43]. The literature reveals a low adoption rate of CA among smallholder farmers in Sub-Saharan Africa (SSA), with only 8% of farmers adopting the complete CA package [28,44]. Given farmers’ socioeconomic and environmental heterogeneity, it is difficult to predict who will successfully adopt CA [45]. Hence, it is against this background that the aim of this paper is to explore the socio-economic factors that influence the adoption of conservation agriculture in Northern Namibia. Namibia is susceptible to climate change risks and could face severe productivity losses due to extreme climate events [46,47].

Although reductions in yields are observed, the response from subsistence farmers is minimal due to the currently insufficient application of various drought survival mechanisms, including CA, to permanently improve crop yields [46,48,49]. Namibia needs to apply and advance sustainable agricultural production methods, including CA. Understanding the background and the steps taken in the decision to implement CA and socioeconomic influences will hopefully aid ongoing and future CA projects in boosting adoption rates across Northern Namibia. This paper contributes to the body of knowledge in the field of conservation agriculture, and it is unique. This study differs from other research because it involves respondents who were trained in CA and piloted the implementation of CA. Thus, the experiment/practical part has been conducted, and farmers were surveyed to gauge how their socioeconomic difference influenced the adoption of CA.

2. Materials and Methods

2.1. Data

The data were collected in three regions of Namibia (Kavango West, Kavango East, and Zambezi) as shown in Figure 1. The choice of the study area was pre-determined at a higher authority and the authors were involved in implementing the pilot study and conducting research.

The two regions (Kavango West and Kavango East) well represented the other north-central regions that grow crops at a subsistence level because they both depend on rainfall and have similarity in the choice of crop as well as soil fertility. However, the Zambezi region has a unique characteristic because it grows more maize due to better soil fertility and higher rainfall compared to the rest of the regions. The data for the experimental choice were collected using a structured questionnaire administered to the farmers through a face-to-face interview. The data were collected with the assistance of the interns who were attached to the project of piloting the conservation agriculture implementation. The data were collected between July 2019 and October 2019. This is a period when farmers concluded the activities of the cropping season, and they were available and willing to be interviewed because they were not busy in their crop field during this period. A total of 169 respondents from the three selected regions were deemed usable for the purpose of the study. The sample of respondents was drawn from a total of 329 farmers who participated in a pilot project of implementing conservation agriculture. The sample represented 51% of eligible respondents; the remainder of the respondents could not be reached or were not available on the appointment dates. All the respondents were visited individually at their homestead/production area (crop fields), with appointments being made at least two days before the date of the interview. The questionnaire was pre-tested prior to the final version to determine the time required on average per respondent. Although the questionnaire was designed in English, farmers were asked questions in their mostly spoken local languages (Rukwangali/Silozi) and information was directly entered into the questionnaire form and computerized afterwards. To ensure translation reliability, the author recruited interns from the Department of Agricultural Sciences and Agribusiness, at Namibia University of Science and Technology, who were born and raised in the study areas to be the translators. Training was provided to the translators and a trial run was made to test the timing and the approach to ensure adherence to the societal norms and values. A data entry template was pre-designed, and the interns were shown how to enter the collected information into an excel sheet. The data were validated by the researcher before analysis.

2.2. Methodology

The study modeled CA arrangements as a multinomial selection process, wherein the principles of CA were analyzed in relation to the socio-economic characteristics of farmers through regression. The multinomial logistic regression modeling approach was employed to explore the relationship between exogenous factors of interest and the tillage methods with which farmers are familiar. However, in the context of multinomial logistic regression, the dependent variables such as the tillage methods, the choice of intercropping, and the choice of crop rotation are categorical, representing the different categories of choices that individual farmers prefer, rather than continuous variables. The study used individual farmers’ socio-economic attributes and farm characteristics as independent variables to analyze the influence of socio-economic factors in making conservation agriculture an attractive option for small-scale crop farmers in the northern regions of Namibia. The multinomial logistic regression model is an important method for categorical data analysis [50]. It is used to analyze the probability of category membership on a dependent variable based on multiple independent outcomes [51,52,53]. A multinomial logistic model is appropriate when regressors vary across individuals [54,55]. It focuses on the individual as the unit of analysis and uses the individual’s characteristics as explanatory variables [51,56]. Like binary logistic regression, multinomial logistic regression uses a maximum likelihood estimation to evaluate the probability of categorical outcomes [50,57]. The application of the multinomial logistic model in various fields of engineering and health sciences has established this technique as a fundamental tool for data analysis and subsequent decision-making processes [58]. The main advantage of the multinomial logistic regression model is its simplicity in estimating and interpreting the resulting choice probabilities [59]. The multinomial logit model’s shortfall is that it suffers from the limitation of assuming the independence of irrelevant alternatives [60]. Another limiting aspect of this model is its handling of unobserved heterogeneity [61]. Following [62], the multinomial equation can be written as follows:

$$ln{\Omega }_{m\mid b}\left(X\right)=ln{\displaystyle \frac{{Pr}^{(y=m\mid X)}}{{Pr}^{(y=b\mid X)}}}=X{\beta }_{m\mid b}form=1toJ$$

(1)

where $b$ is the base outcome, sometimes called the reference category. As $ln{\Omega }_{b\mid b}\left(X\right)$ = $ln1=0$, it follows that ${\beta }_{b\mid b}=0$. That is, the log odds of an outcome compared with itself is always $0$.

These $J$ equations can be solved to compute the probabilities for each outcome, as follows:

$$\mathrm{Pr}(y=m\mid X)={\displaystyle \frac{\exp \left({X\beta }_{m\mid b}\right)}{{\sum }_{j_{=1}}^J\exp \left({X\beta }_{j\mid b}\right)}}$$

(2)

The probabilities will be the same regardless of the base outcome $b$ that is used. For example, suppose that you have three outcomes and fit the model with alternative 1 as the base. Where one would obtain estimates $\widehat{\beta }$_2∣1 and $\widehat{\beta }$_3∣1 with ${\beta }_{1\mid 1}=0.$

$$\mathrm{Pr}(y=m\mid X)={\displaystyle \frac{\exp ({X\beta }_{m\mid b})}{{\sum }_{j_{=1}}^J\mathrm{e}\mathrm{x}\mathrm{p}({X\beta }_{j\mid 1})}}$$

(3)

An alternative set up of the model with the base outcome 2 would generate an estimate $\widehat{\beta }$_3∣2 and $\widehat{\beta }$_3∣2, with ${\beta }_{2\mid 2}=0$. The probability equation would be expressed as follows:

$$\mathrm{Pr}\left(y=m∣X\right)={\displaystyle \frac{\mathrm{e}\mathrm{x}\mathrm{p}\left({X\beta }_{m\mid 2}\right)}{{\sum }_{j_{=1}}^J\mathrm{e}\mathrm{x}\mathrm{p}\left({X\beta }_{j\mid 2}\right)}}$$

(4)

The estimated parameters are different because they estimate different scenarios but produce the same predictions. By default, mlogit (a model command for multinomial logit), sets the base outcome to the alternatives with the most observations in the estimation sample.

3. Results and Discussion

3.1. Method of Soil Preparation

Farmers were presented with four different tillage systems (plough, direct seeder, ripper, and basin), considering their socio-economic status. The choice model was analyzed using STATA version 13 software, wherein the direct seeder system was selected as the reference outcome group.

Table 1. Multinomial logistic regression results for the method of soil preparation.

Variables	Plough		Ripper		Basin
	Coeff	z-stat	Coeff	z-stat	Coeff	z-stat
Constant	0.320	1.303	−0.221	1.131	−6.242	4.646
gendhh	−0.301	0.489	−0.586	0.447	−1.511	1.261
agehh	−0.0139	0.019	−0.00658	0.018	−0.114	0.088
eduhh	0.0387	0.293	0.366	0.243	−1.733 ***	0.587
hhsize	0.00401	0.026	−0.0208	0.023	−0.336 *	0.177
monocrop	−1.612 **	1.115	−16.20 ***	0.561	−14.73 ***	2.077
annually	−0.118	0.499	0.164	0.484	15.47 ***	0.954
biannual	−19.07 ***	0.877	−1.177 **	1.465	19.40 ***	1.556
w5hrs	0.679	0.467	0.970 **	0.425	−15.87 **	1.571
Wald chi2(24)	416.49
Prob > chi2	0.0000
Pseudo R2	0.1037
Observations	169

*** p < 0.01, ** p < 0.05, * p < 0.1. The base (reference) category is the direct seeder. Source: author’s compilation.

presents the multinomial logit model output for tillage systems.

The multinomial logit model reveals that, with all other variables held constant, a one-unit increase in the frequency of rotating crops after two years is associated with a 19.07 unit decrease in the multinomial log odds of choosing a plough over a direct seeder. The multinomial logit model reveals that, with all other variables held constant, a unit increase in mono-cropping and an increase in weeding twice for 5 h per hectare are associated with significant changes in the preference for a ripper over a direct seeder. This analysis suggests that an increase in mono-cropping by one unit would decrease the log odds of preferring a ripper to a direct seeder by 16.20 units, assuming all other factors remain unchanged. This outcome indicates farmers’ awareness that continuous mono-cropping, when using a ripper compared to a direct seeder, may forego the benefits of nitrogen fixation provided by leguminous crops if incorporated in the crop rotation. Furthermore, the model predicts that if farmers were to double the weeding effort to 5 h per weeding session per hectare, the log odds of choosing a ripper over a direct seeder would increase by a 0.970 unit, with all other variables in the model held constant. This is in contrast with the expected sign because weeds reduce crop yields and can lead to total crop failures if not controlled. One reason for tillage is to control the weeds, and ploughing and harrowing kill growing weeds mainly by burying them [63]. The multinomial logit model’s estimates reveal that an increase in the education level of the household head, household size, mono-cropping, annual crop rotation, crop rotation after two years, and weeding twice for 5 h per weeding per hectare significantly affects the preference for using a basin over a direct seeder, with all other model variables held constant. The model indicates that an additional year of education for the household head decreases the likelihood of preferring the basin method over a direct seeder by 1.733 units, assuming there are no changes in other factors. A higher level of education typically leads to increased income levels, enhancing the farmer’s ability to purchase more advanced farming equipment. Such tools accelerate farming operations, making the manual and time-intensive basin method less appealing. Similarly, an increase in household size by one unit is estimated to reduce the probability of choosing the basin method over a direct seeder by a 0.336 unit, all else being equal. This suggests that larger households, with their higher food requirements, may necessitate the expansion of cultivated land to meet increased demand. Since the basin method is more suited to smaller fields, its utility diminishes as the need for larger scale cultivation grows. The multinomial logit model’s findings indicate that an increase in mono-cropping significantly decreases the preference for the basin tillage method over a direct seeder by 14.73 units, assuming all other variables remain constant. Conversely, the model suggests that increasing the practice of rotating crops annually by one year is associated with a 15.42 unit increase in the likelihood of preferring the basin method over a direct seeder, with all other factors held constant. Furthermore, the coefficient for rotating crops every two years shows an even more substantial increase in preference for the basin method, with an expected increase of 19.40 units in the multinomial log odds of choosing basin tillage over a direct seeder. These results are in line with the findings that suggest that labor using variants of CA such as planting basins are more likely to be adopted than are capital using mechanized options [45]. The multinomial logit model’s estimates show that increasing the intensity of weeding to twice for 5 h per weeding per hectare would decrease the multinomial log odds of preferring the basin method over a direct seeder by 15.87 units, assuming all other variables in the model remain constant.

3.2. Crop Diversification Through Intercropping

Farmers were presented with four distinct intercropping configurations: mono-cropping, cereal/cowpeas, cereal/round nuts, and cereal/groundnuts considering their socio-economic statuses. In this analysis, the model uses cereal intercropped with cowpeas as the reference category and

Table 2. Multinomial logistic regression results for crop diversification through intercropping.

Variables	Mono-Cropping		Cereal/Roundnuts		Cereal/Groundnuts
	Coeff	z-Stat	Coeff	z-Stat	Coeff	z-Stat
Constant	−15.380 ***	2.453	0.339	1.966	−1.119	−1.472
gendhh	−0.946	0.859	−0.327	0.644	1.124 ***	0.461
agehh	−0.001	0.035	0.014	0.029	−0.005	0.016
eduhh	0.104	0.597	−0.940 *	0.480	−0.348	0.248
hhsize	−0.056	0.072	0.019	0.028	−0.012	0.025
none	−0.116	1.405	−15.490 ***	1.336	1.222	1.456
annually	13.64 ***	0.996	−1.532	1.189	0.600	1.086
aftoneyr	13.73 ***	1.320	−0.533	1.172	1.467	1.144
Wald chi2(24)	1634.4
Prob > chi2	0.0000
Pseudo R2	0.077
Observations	169

*** p < 0.01, * p < 0.1. The base (reference) category is the cereal/cowpeas. Source: author’s compilation.

presents the multinomial output of crop diversification.

With the predictors at zero, the log odds of choosing mono-cropping relative to intercropping cereal with cowpeas are expected to decrease by 15.38 units. The log odds of preferring mono-cropping over intercropping cereal with cowpeas are 13.64 units higher for farmers practicing crop rotation annually compared to those rotating crops every two years, assuming no change in other predictor variables. The log odds of preferring mono-cropping over intercropping with cowpeas are 13.73 units higher for farmers who skip a year in their crop rotation schedule compared to those who rotate crops biennially, assuming all other factors in the model remain constant.

An increase of one year in the education level of the household head is associated with a decrease of 0.940 units in the multinomial log odds of preferring intercropping cereal with round nuts over intercropping cereal with cowpeas, assuming all other variables remain constant. Additionally, the none variable compares the preference for not practicing crop rotation to practicing crop rotation every two years for the choice of intercropping cereal with round nuts over cereal intercropped with cowpeas, with all other model variables held constant. The coefficient for no crop rotation in the multinomial logit model indicates that, compared to practicing crop rotation every two years, the absence of crop rotation is associated with a decrease of 15.49 units in the multinomial log odds of preferring intercropping cereal with round nuts over intercropping cereal with cowpeas, when all other variables in the model are held constant. This means that farmers who do not practice crop rotation are significantly less likely to prefer intercropping cereal with round nuts to intercropping cereal with cowpeas than those who skip crop rotation for two years.

The multinomial logit variable indicates the gender of the household head as a significant factor in the preference for intercropping cereal with groundnuts over cereal with cowpeas. This result shows that when the household head is male, there is a slightly increased likelihood, by 0.327 units, of preferring to intercrop cereal with groundnuts compared to intercropping cereal with cowpeas, holding all other variables constant. This preference could reflect underlying gender-based differences in decision-making, access to resources, or perceptions of agricultural practices. The reason could be that male-headed households have alternative means to generate income from livestock sales and prefer less intercropped cereal with groundnuts meant for income generation by female-headed households. The male-headed household will rather prefer intercropped cereal with cowpeas because cowpeas are grown for home consumption more than for income generation.

3.3. Crop Rotation

Farmers were presented with four different crop rotation options (none, annually, after1year, and after2years), with special reference to their socio-economic status. The model selected annually as the reference group and therefore estimated a model for none, after1year, and after2years relative to annually, as displayed in

Table 3. Multinomial logistic regression results of crop rotation.

Variables	None		After1year		After2years
	Coeff	z-Stat	Coeff	z-Stat	Coeff	z-Stat
Constant	1.878	5.048	0.870	1.396	−11.680 ***	2.720
gendhh	0.036	0.932	−0.318	0.472	15.470 ***	0.583
agehh	−0.019	0.053	−0.057 ***	0.021	−0.169 ***	0.054
eduhh	−1.403	1.243	0.033	0.285	−0.079	0.659
hhsize	−0.192 *	0.114	0.052 *	0.028	0.107 **	0.052
Wald chi2(24)	803.79
Prob > chi2	0.0000
Pseudo R2	0.1228
Observations	169

*** p < 0.01, ** p < 0.05, * p < 0.1. The base (reference) category is annually. Source: author’s compilation.

.

The result shows that if a farmer’s household size score were to increase by one point, the multinomial log odds for preferring no crop rotation to crop rotation every year would be expected to decrease by a 0.192 unit while holding all other variables in the model constant. As the household size increases, the probability of having no crop rotation decreases because the household size will eventually translate into labor units available to work in the crop field. Household members will assist in opening new rows during soil preparation, seeding, and weeding processes, making it easier to implement crop rotation.

The result shows that if the age of the household head score were to increase by one point, the multinomial log odds for preferring crop rotation after skipping one year to crop rotation every year would be expected to decrease by 0.057 units while holding all other variables in the model constant. As a farmer gets old, the responsibility of keeping track of which years to rotate and which one not to rotate becomes a burden. However, the household size score for crop rotation after skipping one year relative to crop rotation every year, given that the other variables in the model are held constant, would increase by one point. The multinomial log odds for preferring crop rotation after skipping one year to crop rotation yearly would be expected to increase by 0.052 units. Due to the availability of household members as a source of labor, the implementation of any crop rotation-related activity becomes easier as the number of household members increases.

The result shows that male household headed relative to female household headed is 15.470 units more for preferring crop rotation after skipping two years relative to crop rotation every year, given all other predictor variables in the model are held constant. This may be due to the fact that if the male household owner has authoritative command and prefers an activity, then the activity must be implemented as such. If the age of the household head score were to increase by one point, the multinomial log odds for preferring crop rotation after skipping two years to crop rotation every year would be expected to decrease by 0.169 units while holding all other variables in the model constant. However, the household size score for crop rotation after skipping two years relative to crop rotation every year, given that the other variables in the model are held constant, would increase by one point. The multinomial log odds for preferring crop rotation after skipping two years to crop rotation yearly would be expected to increase by 0.107 units.

3.4. Permanent Soil Cover Materials

Farmers were presented with four different mulching options (none, grass, stalks, and leaves), with special reference to their socio-economic status, to determine the option that best explains the relationship between the outcomes and the socio-economic factors in making CA an attractive option to farmers. In this case, stalks were used as the reference group. The multinomial result of the mulching option is presented in

Table 4. Multinomial logistic regression results of mulching options.

Variables	None		Grass		Leaves
	Coeff	z-Stat	Coeff	z-Stat	Coeff	z-Stat
Constant	−3.936 *	2.203	0.031	1.046	−2.937	2.114
gendhh	0.064	0.807	0.394	0.423	0.928	0.842
agehh	−0.014	0.036	−0.003	0.015	0.008	0.031
eduhh	1.022 **	0.473	0.118	0.234	0.448	0.391
hhsize	−0.032	0.046	−0.125 ***	0.045	−0.156	0.113
Wald chi2(24)	26.45
Prob > chi2	0.0093
Pseudo R2	0.0693
Observations	169

*** p < 0.01, ** p < 0.05, * p < 0.1. The base (reference) category is annually. Source: author’s compilation.

. Two variables are statistically significant at 10% and 5% significance, respectively. The result shows that more experienced farmers with larger family sizes would prefer either grass or no mulch relative to the stalk. Mulch with leaves is the least preferred option amongst the farmers because none of the identified predictors is statistically significant.

4. Conclusions and Recommendations

The analysis indicates a marked preference among farmers for the basin tillage system over direct seeding. This suggests a strategic opportunity to engage younger and more educated farmers, who are likely to be more receptive to conservation agriculture practices. These farmers are aware of the detrimental effects of using deep moldboard ploughs on soil health. They understand soil dynamics, including how different tillage systems affect soil structure and quality. This result suggests a paradigm shift in the education system through introducing a curriculum that teaches conservation agriculture at primary, secondary, and tertiary education levels. It was also observed that mono-cropping has a statistically significant and negative impact on adopting the usage of rippers. Focusing on these informed groups could accelerate the adoption of sustainable agricultural practices, promoting soil conservation and enhancing farming efficiency. Therefore, to fully appreciate the benefits of conservation agriculture, launching an awareness campaign is essential. Educating farmers through mass media can play a pivotal role. Such a campaign would help farmers grasp and value the principles behind conservation agriculture, fostering a deeper understanding of its long-term advantages for soil health and agricultural sustainability. The study’s findings highlight that the practice of annual crop rotation and the decision to rotate crops after one year significantly affect mono-cropping, especially when compared to the baseline practice of intercropping cereals and cowpeas. This underscores the need for increased awareness among farmers about the benefits of intercropping. The investigation revealed that farmers tend to evaluate their agricultural practices based on the immediate returns from mono-cropping versus those from intercropping. However, there is potential for this perspective to shift favorably towards intercropping if it is consistently applied over a longer period.

Furthermore, the study identifies gender as a crucial socio-economic factor influencing the choice of intercropping combinations, particularly the preference for intercropping cereals with groundnuts over cereals with cowpeas. This trend is observed as the number of households headed by females increases, with a significant emphasis placed on groundnuts as a cash crop to generate income. These insights suggest that both the promotion of intercropping and the recognition of gender dynamics are vital for encouraging more sustainable and economically beneficial farming practices. As household heads’ age increases, there is a noticeable decline in the practice of rotating crops after extended periods, as opposed to annual rotation. This trend might be attributed to older household heads losing interest or perhaps lacking the physical ability to adhere to more demanding rotational schedules. Conversely, larger household sizes and an increase in female-headed households are associated with a higher likelihood of implementing crop rotation after every two years compared to annual rotation. Female heads of households, often being present at their homesteads due to domestic responsibilities or lack of external employment, are better positioned to manage and implement crop rotation schedules. Additionally, they may view this practice as a sustainable strategy to enhance soil fertility and crop yields over time. The presence of a larger family also implies more available labor to assist with the demands of setting up and managing crop rotation, making it a more feasible option.

The results of multinomial logistic regression indicated that household size negatively influences the use of grass relative to crop stalks as a mulching material option. This suggests that, regardless of household size, farmers are not motivated to collect grass for use as mulching material to increase the organic matter in their crop fields. This lack of motivation is attributed to the challenges of transportation and the time required for the activity. The lack of agricultural policies in accelerating the adoption of CA in Namibia could be addressed by making implements—appropriately scaled mechanization options—that promote CA available in the local markets and making them affordable, which could accelerate the adoption. Introducing conservation agriculture into the education curriculum system as a subject taught at primary, secondary, and tertiary levels could be an ideal idea that could be converted into a policy. Introducing a cheaper ripping service to promote the adoption could be adopted as an agricultural policy meant to address the conservation agriculture adoption challenge. Introducing a reward in the form of incentives, such as input provision, to farmers who are voluntarily practicing CA could be made a policy to accelerate the adoption of conservation agriculture. The adoption of CA must be considered as a long-term program; hence, effective public spending on agricultural research and development must be prioritized, particularly in the development of locally produced soil enhancement, manure, and organic fertilizers that are known to be environmentally user-friendly. The study’s limitations might have contributed to the generalization of the results because the implementation of the pilot study was only carried out over four crop seasons. This might be a short period for the respondents to observe the full dynamics of CA.