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Article

Willingness to Pay to Adopt Conservation Agriculture in Northern Namibia

by
Teofilus Shiimi
* and
David Uchezuba
Department of Agricultural Sciences and Agribusiness, Namibia University of Science and Technology, Private Bag 13388, Windhoek, Namibia
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(5), 568; https://doi.org/10.3390/agriculture15050568
Submission received: 27 November 2024 / Revised: 27 January 2025 / Accepted: 9 February 2025 / Published: 6 March 2025
(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)
Browse Figure
Figure 1
<p>Location of villages in the selected study areas. Source: Authors’ compilation.</p> ">
Review Reports Versions Notes

Abstract

:
This paper aims to explore the willingness of farmers in the northern Namibia to adopt conservation agriculture (CA), employing the conditional logit model to estimate the probability of farmers choosing to adopt CA in different villages relative to all other alternatives and examining the effects of omitted variance and correlations on coefficient estimates, willingness to pay (WTP), and decision predictions. This study has practical significance, as agriculture plays a crucial role in the economic development of and livelihoods in Namibia, especially for those farmers who rely on small-scale farming as a means of subsistence. In terms of methodology, the data for the experimental choice simulation were collected using a structured questionnaire administered through a face-to-face survey approach. This paper adopts the conditional logit model to estimate the probability of farmers choosing to adopt CA in different villages, which is an appropriate choice as the model is capable of handling multi-option decision problems. This paper further enhances its rigor and reliability by simulating discrete choice experiments to investigate the impact of omitted variables and correlations on the estimation results. The research findings indicate that crop rotation and permanent soil cover are the main factors positively influencing farmers’ WTP for adopting CA, while intercropping, the time spent on soil preparation in the first season, and the frequency and rate of weeding consistently negatively influence the WTP for adopting CA. These discoveries provide valuable insights for formulating policy measures to promote the adoption of CA. In terms of policy recommendations, this paper puts forward targeted suggestions, including the appointment of specialized extension technicians by the Ministry of Agriculture, Water, and Land Reform to disseminate information as well as coordinate, promote, and personally implement CA activities across all regions. Additionally, to expedite the adoption of CA, stakeholders should ensure the availability of appropriate farming equipment, such as rippers and direct seeders, in local markets.

1. Introduction

Agriculture plays a significant role in economic development, especially in rural economies with mostly subsistence farmers who rely on their land for food and income [1,2]. Agricultural production faces numerous challenges in sub-Saharan Africa (SSA), and a situation that leads to an insufficient food supply [3]. This has occurred because agricultural productivity growth in most parts of SSA has failed to keep pace with that in other developing and developed regions of the world [4]. The global population is expected to reach nine and half billion by the year 2030; more importantly, developing and underdeveloped nations are expected to experience a disproportionately higher population surge [5]. This places the economies of these nations at risk and further exacerbates the challenges associated with sustainable agriculture production and land management [6]. For the past century, agricultural science has been based on a productivity ethic of maximizing agricultural production in pursuit or support of food security [7]. Hence, areas with high levels of food inaccessibility owing to a polluted environment and inadequate choices for coping with extreme weather events will face a deterioration in productivity, which will lead to greater variabilities in agricultural communities and production [8]. This makes the challenge of meeting the ever-increasing food demand more difficult and complex, creating a significant challenge for agricultural scientists, extension personnel, and farmers [7,9] because meeting this projected need is challenging as 94% of the land appropriate for farming is already under production and 58% of agricultural areas face multiple climatic issues such as water scarcity and life-threatening heat stress [10]. Policymakers and government officials have long maintained that agriculture possesses the potential to significantly contribute to national wealth, job creation, and food security [3]. In Namibia, a significant portion of the land is dedicated to agricultural use, with subsistence farming on state-owned land covering about 250,700 square kilometers, representing 30.4 percent of the total land area [11]. It is estimated that about 67 percent of inhabitants in Namibia live in rural areas, the majority of whom depend on smallholder crop production as a means of livelihood [4]. Poverty, unemployment, and significant social inequalities present ongoing challenges in Namibia; with a Gini coefficient of 0.57, it ranks among the most unequal countries globally [12]. The most common agricultural practice in northern Namibia is small-scale farming, which mainly occurs in communal areas where farmers practice mixed farming consisting of livestock and crop production [5]. There are irregular rainfall patterns, which normally occur between October and March. Crop production is usually poor or inadequate because of insufficient rainfall that often starts either early or late. Pearl millet (Pennisetum glaucum) is the most important millet because of its nutritional value and is extensively cultivated in Africa along the western side down to Namibia [5]. The area where rain-fed pearl millet is produced in northern Namibia receives an average of 350–400 mm of rainfall per annum. With this average rainfall, the pearl millet yield can be more than 400 kg/ha [13].
As a tradition, subsistence crop farmers in these communities mostly practice conventional tillage (CT) using either animal-drawn moldboard ploughs or tractor-drawn disc harrows that pulverize the soil, thus affecting soil porosity and soil moisture retention capacity [14]. Conventional farming is associated with activities such as monocropping, moldboard ploughing 20–30 cm deep, and residue removal [15,16]. Conventional ploughing is largely to blame for the low yields, high labor input, reduced biological activity, and lower diversity, which characterize farming in most parts of Africa [17]. The agricultural model based on mechanical soil tillage, exposed soil, and continued monocropping characteristically has a negative influence on agriculture’s natural resource base to an extent that the future agricultural production potential is threatened [18]. This is a global concern: soil fertility decline and the low soil organic matter content have adverse impacts on agricultural production, food security, and livelihoods [19]. The use of moldboard plough and heavy disking are categorized as high-soil-disturbance methods [20]. Thus, it is evident that the present traditional tillage systems lead to serious land degradation, which will increase the risk of food insecurity in the future, accelerate emissions, and reduce carbon sinks [21]. The loss of topsoil due to erosion and the reduction in soil organic matter under conventional tillage systems have been worsened by increasing fuel prices [22]. Despite the downsides related to high-disturbance tillage systems, many farmers continue to plough their fields [20]. The global demand of food cannot be met sustainably unless we protect and restore the fertility of our soil, thus securing the productivity of our land [23]. It is critical to focus on soil quality as an important factor affecting food safety, human health, and sustainable agricultural development [24]. The main root cause of the loss of soil health during agricultural land use is the reduced soil carbon and soil-health-disrupting nature of mechanical soil tillage that incapacitates many significant soil-mediated ecosystem functions [25]. Regrettably, soil has been speedily degraded at a global scale due to a series of hostile anthropic activities in intensive agriculture that have associated adverse effects on human and ecosystem health [26]. Hence, the sustainability of agricultural production and of environmental services is strongly dependent on maintaining soil in a sufficiently carbon-rich condition [27].
More suitable agricultural systems are immediately required to ameliorate soil and ecosystem conditions, which are the key parameters for sustaining food quantity and quality to feed the ever-growing population [28]. As a response to the effects of climate change and increasing soil degradation, conservation agriculture (CA) is being widely promoted across sub-Saharan Africa (SSA) as a method of climate-smart agriculture [29,30]. CA is a practice focused on improvements in soil fertility associated with minimum soil disturbance, increased retention of crop residues (crop biomass and cover crops), more diverse crop rotation (economically, environmentally, and socially adapted rotations including legumes and cover crops), and integrated weed management [10,25,31,32,33,34,35]. Conservation agriculture is a concept designed to stop the soil quality degradation caused by conventional tillage practices through mechanisms of improving the soil’s physical, chemical, and biological properties [8]. These practices have been proven to increase crop yields while improving the long-term sustainability of farming from both environmental and financial perspectives [30]. It is a response toward achieving sustainable land management, environmental protection, and climate change adaptation and mitigation [30,36]. Others define CA as an approach to managing agro-ecosystems for improved and sustained productivity, profits, and food security while preserving and enhancing the resource base and the environment [10,30,37,38,39]. CA is not a single technology but rather a combination or mixer of a range of technologies that consider the practice of one or more of the three main conservation agriculture principles [40]. CA emerged as an alternative method of reverting many negative effects of conventional farming practices [33]. CA systems have been tried and tested in areas with a lower annual rainfall of 250 mm like western Australia and northern China as well as in countries with an extremely high rainfall of 2000 to 3000 mm like in Brazil and Chile [40]. CA is aimed at making better use of agricultural resources through the integrated management of the available soil, water, and biological resources, combined with limited external inputs [41]. Ref. [42] indicated the triggers that stimulated the promotion of CA in different countries. Air pollution from straw burning in parts of China was the driver of CA; in much of Latin America, it was the high costs of traditional land preparation. On large-scale farms in Zimbabwe, the trigger was soil degradation and fuel shortages during the days of international sanctions. In Asian countries, severe land degradation and serious environmental problems came as a wake-up call that generated interest in considering the implementation of farming practices that lead to the conservation of soil and water [38]. The dustbowls that devastated wide areas of the midwest United States in 1930s were the main driver of CA and were a foundational experience for both the scientific and public appreciation of soil [43,44]. Wind and water erosion were the key factors that drove no-till farming in Canada and Australia [25]. In Madagascar, CA was introduced in early 2000s to cope with the low soil fertility and soil water stresses due to suboptimal rainfall as well as severe weed pressure that resulted in low rice productivity [45]. In Malawi, CA was argued to be appropriate because of the higher rural population density and soil degradation [46]. Thus, in the specific context of Africa with resource-poor farmers, CA systems are relevant for addressing the challenges posed by climate change, high energy costs, environmental degradation, and labor shortages [43]. Despite the invention of the system in 1970s, it took some 20 years before CA reached significant adoption levels in South America and elsewhere [43]. This means that although CA is gaining recognition for its positive effects on soil conservation, it is still not widely known by many farmers around the world [47].
The CA method, promoted since 2005, has boosted the average mahangu (pearl millet) yields to 1670 kg/ha in Namibia [48]. Consequently, agriculturalists, environmentalists, and development experts, among others, have hailed CA as a “win-win” approach in agriculture, benefiting both farmers as well as the natural environment [3]. However, the adoption of CA techniques across Africa remains low, with only a few exceptions, such as southern Zambia, South Africa, and Ghana [49]. The challenges posed by managing weed pressure, alongside the issues of weed resistance and the potential for crop yield losses, can deter farmers from adopting conservation practices [47,50,51]. Smallholder farmers are often risk-averse, and the adoption of CA is negatively influenced by its increased labor demands and the necessity of using fertilizer and herbicide inputs [52,53,54,55].
With a fragile natural environment, Namibia has been subject to unsustainable agricultural practices over the past decades [3]. Although reductions in yields have been observed, the response from subsistence farmers has been minimal due to the currently insufficient application of various drought survival mechanisms, including CA, to permanently improve crop yields [56,57,58]. The aim of this paper is to explore the willingness of farmers in northern Namibia to adopt CA. Extreme climatic conditions, such as droughts and floods, are common in Namibia, reducing productivity and increasing food insecurity. Given the significant impact of these changes on human life, it is crucial to enhance awareness at the grassroots level as well as reorient rural farmers and community members who are the most vulnerable to these changes. Consequently, this study is pertinent in creating a conducive opportunity for stakeholders to understand the factors that influence the adoption rate of CA. Our findings will help the decision makers to invest resources in the right conservation agriculture principles that are perceived as relevant and applicable by subsistence crop farmers.

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, with a representative of 51 percent of the eligible potential respondents participating in this study.
The data for the experimental choice study were collected using a structured questionnaire administered to the farmers through face-to-face interviews. The data were collected with the assistance of interns who were involved in a pilot project regarding conservation agriculture implementation. The data were collected between July 2019 and October 2019. This is a period after farmers have finished cropping activities, so they were available and willing to be interviewed because they were not engaged in their crop fields during this period. A total of 169 respondents from the three selected regions were deemed suitable for the purposes of this study. For this paper, the following were the main focus of the interview: intercropping, crop rotation, the timing of first soil preparation per hectare, the cost of soil preparation per hectare, frequency and duration of weeding per hectare, and the application of mulching materials. Respondents were presented with the attributes and levels displayed in Table 1.
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. Although the questionnaire was designed in English, farmers were asked questions in their spoken local languages (Rukwangali/Silozi), and information was directly entered onto the questionnaire form and computerized afterwards. To ensure translation reliability, we recruited interns from the Department of Agricultural Sciences and Agribusiness at the Namibia University of Science and Technology who were born and raised in the study areas to be translators. Training was provided to the translators, and a trial run was conducted to test the timing and the approach to ensure adherence to societal norms and values. The 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 authors before analysis. The attributes and levels in Table 1 were selected based on a literature review, discussions with extension officers in the study area, general discussions with some farmers, and expert consultations.
Respondents were presented with three amendment alternatives in each choice set and asked to choose their preferred option. The first option was ‘no new initiative’; this option was recognized as a baseline scenario that leads to a continuation of the current trend in soil fertility degradation in the next ten years. The second and third options differed, with each incorporating new initiatives aimed at restoring and contributing to the improvement in soil quality.

2.2. Methodology

The variables in the conditional logit model are choice-specific attributes, not individual-specific characteristics. Thus, variations in the attributes of conservation agriculture within farmers’ choice sets drove the estimates. To estimate the conditional logit model, data were organized as pairwise combinations of each farmer i in each village j , with observations stratified by individual into groups of j . Using these combinations, the conditional logit model consisted of j equations for each i , each equation describing one of the alternatives. This format accommodated match-specific variables based on the interaction of individual i with village j . The conditional logit model calculated the probability of a farmer being in each of the village groups, relative to all other alternatives, with the dependent variable set to one for the chosen alternative. Following [59], the indirect utility function for each respondent i   ( ∪ ) was decomposed into two parts: a deterministic element V , which is typically specified as a linear index of the attributes ( X ) of the j different alternatives in the choice set, and a stochastic element e , which represents unobservable influences on individual choice. This is shown in Equation (1):
∪ i j = V i j X i j + e i j   = b X i j + e i j
Thus, the probability that any particular respondent prefers option g in the choice set to any alternative option h can be expressed as the probability that the utility associated with option g exceeds that associated with all other options, as stated in Equation (2):
P ∪ i g > ∪ i h ∀ h ≠ g = P V i g − V i h > ( e i h − e i g )
To derive an explicit expression for this probability, it is necessary to know the distribution of the error terms e i j . A typical assumption is that they are independently and identically distributed with an extreme value distribution:
P e i j ≤ t = F t = exp ⁡ − exp ⁡ − t
The above distribution of the error term implies that the probability of any alternative g being chosen as the preferred one can be expressed in terms of the logistic distribution stated in Equation (4). This specification is known as the conditional logit model:
P ∪ i g   > ∪ i h ,   ∀ h ≠ g = exp ⁡ ( μ V i g ) ∑ j exp ⁡ ( μ V i j )
where μ is a scale parameter that is inversely proportional to the standard deviation of the error distribution. This model can be estimated using conventional maximum likelihood procedures, with the respective log-likelihood function stated in Equation (5), where y i j   is an indicator variable that takes a value of one if respondent i chooses option j and zero otherwise.
l o g L = ∑ i = 1 N ∑ j = 1 J y i j   l o g exp ⁡ ( V i j ) ∑ j = 1 J exp ⁡ ( V i j )
Socioeconomic variables can be included along with the choice set in the X terms in Equation (1), but since they are constant across choice occasions for any given individual, they can only be entered as interaction terms, i.e., regarding choice-specific attributes.
Once the parameter estimates are obtained, a WTP-compensating variation measure that conforms to demand theory can be derived for each attribute using the formula given in Equation (6), where V ° represents the utility of the initial state, and V ′ represents the utility of the alternative state. The coefficient gives the marginal utility of income and is the coefficient of the cost attribute.
W T P = b y − 1   l n ∑ i exp ⁡ ( V i ′ ) ∑ i exp ⁡ ( V i ° )
The above formulae can be simplified to the ratio of coefficients given in Equation (7), where b c is the coefficient on any of the attributes. These ratios are often known as implicit prices.
W T P = − b c b y
Choice experiments are consistent with utility maximization and demand theory, at least when a status quo option is included in the choice set.
Conditional logistic regression is an advanced form of logistic regression, enabling researchers to accommodate the matched, clustered, or longitudinal characteristics of data [60]. It calculates estimates of regression coefficients for an independent variable within specific strata [61]. This model delineates the probability of selecting between two or more options based on the attributes defining those choices [62,63,64]. The connection between models of individual behavior and observed choices at the population level becomes crucial when decision makers face qualitative alternatives [62,65,66,67]. The conditional logit model excels in scenarios where the regressors, or independent variables, vary across different choices [68]. It has emerged as a standard approach for analyzing matched case–control data, addressing the potential sparse data biases inherent in traditional logistic regression analyses [69,70]. However, the conditional logit model is subject to certain limitations, notably its assumption of uniform preferences across all individuals [62,66,71]. This model overlooks the possibility of unobserved, systematic differences in respondents’ preferences [62]. Additionally, it does not support the calculation of an R-squared measure for assessing absolute goodness of fit, a metric familiar to many researchers [62,72].
Incorporating a cost coefficient into a choice experiment allows researchers to determine the marginal substitution rate between any non-cost attribute and the cost attribute, which reflects the willingness to pay (WTP) [73,74]. Specifically, the ratio of an attribute’s coefficient to the price coefficient can reveal the WTP for that attribute [75,76]. Essentially, WTP represents the monetary value of a good or service to an individual under certain conditions [77]. Willingness to pay (WTP) measures are invaluable for several reasons. Firstly, they offer direct insights for policymakers about the value individuals place on certain goods or services, which can guide pricing strategies [78]. Secondly, WTP provides a practical means for comparing and prioritizing various goods and services based on their desirability [79]. Willingness to pay (WTP) estimates can be obtained from discrete choice models, which are calculated using either discrete choice experiments (DCEs) or preference data [79]. Discrete choice experiments are grounded in Lancaster’s theory, positing that the value of a good is determined by its attributes or characteristics rather than merely by the quantity consumed [75]. According to this approach, when faced with various options, respondents choose the one that maximizes their utility (or well-being), based on the levels of the option’s attributes [75,80]. Given the complexities involved, including relatively small sample sizes and the need for additional parameters to estimate more complex models, a conditional logit model is often employed to analyze the data from DCEs [75].
The model has been applied in numerous studies worldwide. Ref. [81] used a hybrid conditional logit model in Spain to reveal that urban residents exhibit higher willingness to pay (WTP) for global agricultural services. In another study, the conditional logistic model was applied within the framework of discrete choice experiments to model student preferences for different assignment designs [82]. They demonstrated the utility of calculating the WTP for various attribute levels through the estimation of attribute level parameters, offering valuable insights for educational design. Ref. [75] used a conditional logit model to explore the preferences for hypothetical medical clinic visits, defined by various quality levels. By asking respondents to choose among different options in a series of scenarios, the study effectively quantified preferences for specific clinic attributes. Importantly, incorporating a monetary attribute allowed for the estimation of respondents’ willingness to pay for different hospital clinic appointments or alterations in non-monetary attributes. Authors aimed to understand the specific goals and financial needs of business owners in designing an effective microfinance product for ING Bank [73]. The study underscored the model’s utility in real-world applications, particularly in financial product development and market analysis. By focusing on the Romanian urban microfinance product, the research addressed a critical gap in access to financing, illustrating how conditional logit models can aid in tailoring financial solutions to meet specific market needs. Ref. [83] pushed the model’s capabilities further by investigating its performance under preference heterogeneity. By simulating discrete choice experiments, they examined how omitted variance and correlations affect estimates of coefficients, willingness to pay (WTP), and decision predictions. They found that the conditional logit model’s estimates and true choice probabilities aligned closely, regardless of underlying preference structures, validating the model’s accuracy and reliability in capturing complex decision-making processes.

3. Results and Discussion

3.1. Results

The results of the conditional logistic model’s willingness-to-pay estimates are presented in Table 2. The majority of the attribute coefficients show positive effects, suggesting that the respondents had a stronger preference for conservation agriculture options that provide more substantial improvements in enhancing soil fertility and, consequently, better crop yields. The conditional logistic regression model was run to explore the selected attributes that influenced the farmers’ decisions about whether or not to pay for the main activities that drive conservation agriculture, changing the fertility status of their crop fields. The regression output and its associated willingness-to-pay (WTP) levels are discussed; however, only the significant attributes are mentioned. In the first round of choice, intercropping statistically significantly influenced the willingness to pay for adopting conservation agriculture; however, it had a negative coefficient. This indicated that respondents were satisfied with not practicing intercropping as one of the main activities of conservation agriculture that promotes crop diversification. In the second round of choice, the regression analysis revealed that crop rotation positively influenced willingness to pay. On average, respondents were willing to pay NAD 1.86 more to ensure they practice crop rotation, thereby improving soil fertility. This indicated that the respondents understood the importance of rotating crops, especially when a leguminous crop is involved in the rotation process. This practice involves breaking the life cycles of the pests and common diseases that likely affect the crops. The time spent on first soil preparation per hectare was an attribute that negatively influenced willingness to pay.
On average, respondents were willing to pay NAD 1.38 less for spending more time on soil preparation per hectare. This suggested that the farmers preferred a quicker method of soil preparation. Farmers had the option to use oxen or hire private or public tractors; however, factors such as affordability, availability, and the type of implements available on these tractors influenced their choices. Other attributes that negatively influenced the willingness to pay included the frequency and weeding rate. On average, respondents were willing to abandon the practice of conservation agriculture if it involved more frequent and extended hours of weeding. They were willing to pay NAD 2.07 less for every unit increase in the frequency and weeding rate per hectare. Mulching was an attribute that positively influenced the willingness to pay. On average, respondents were willing to pay NAD 2.99 more for every unit increase in the application of mulching material on a crop field. In the third round of choices, this study revealed that the cost of soil preparation significantly, but negatively, influenced willingness to pay. Intercropping significantly influenced willingness to pay; just like in the first round, the respondents preferred to avoid intercropping. However, in the third round, on average, respondents were willing to pay NAD 0.60 less per hectare to avoid intercropping. Similarly, crop rotation significantly and positively influenced willingness to pay, just as it did in the second round. On average, respondents were willing to pay NAD 0.43 more to practice crop rotation. This demonstrates consistency in how respondents value crop rotation as a key activity in implementing conservation agriculture. In the fourth round, crop rotation significantly and positively influenced willingness to pay. On average, respondents were willing to pay NAD 2.79 more to ensure crop rotation, recognizing its benefits. Like the findings in the second round, the frequency and weeding rate significantly and negatively influenced willingness to pay. This indicated that respondents preferred not to engage in activities with numerous occurrences and extended weeding hours per hectare. On average, respondents were willing to pay NAD 2.62 less for every unit increase in frequency and weeding rate per hectare. In the fifth and sixth rounds, crop rotation continued to significantly and positively influence willingness to pay. However, contrary to expectations, on average, respondents were willing to pay NAD 2.62 and NAD 2.75 less for practicing crop rotation in the fifth and sixth rounds, respectively. This indicated a misunderstanding, as it contradicted the positive influence mentioned. Additionally, time spent on the first soil preparation per hectare emerged as a second attribute that was statistically significant, negatively influencing the willingness to pay to adopt conservation agriculture in the sixth round. However, the estimated coefficient was negative, indicating that respondents were better off not adopting conservation agriculture if it required more time spent on soil preparation per hectare. On average, respondents were willing to pay NAD 2.55 more to avoid any increase in the time spent on soil preparation per hectare.

3.2. Discussion

This paper revealed that crop rotation and permanent soil cover had a positive impact on the WTP to adopt CA because the two attributes directly influence crop yield, enhance soil fertility, and mitigate the risks associated with crop failure. Traditional agricultural practices such as crop rotation and intercropping are inherently sustainable, as they rely on natural processes and local ecological knowledge to maintain soil fertility and crop productivity [84,85]; hence, a leguminous crop that was grown in a location prior to cereal crops benefits the cereal crop through nitrogen fixation. Growing grain crops in rotations that include legumes may be a more sustainable practice than growing grain crops in monoculture [86]. This improves the yield of cereal crops, eventually motivating farmers to continue to rotate crops over growing seasons. Crop rotation is a practice deeply embedded in the cultural traditions and knowledge systems of indigenous peoples and farming communities, passed down through generations via practical experience. It introduces allelochemicals that inhibit the development of these agricultural nuisances [77,87,88]. Studies have indicated that the increased diversity inherent in cropping systems that incorporate legumes may enhance their ability to maintain functionality under conditions of heat stress [79]. Therefore, this action can reap the benefits of practicing CA. However, crop rotation requires the careful selection of crops in the rotational sequence for several reasons: the leguminous crop must be the crop that can contribute to food security, able to deposit as much as nitrogen as possible within a growing season, and that has market demand and profitability. In a study in Pakistan, sesame and peanuts served as profitable cash crops, contributing to both income generation and soil fertility through nitrogen fixation [79]. Input costs and potential yield benefits must be considered when planning crop rotations. Planting crop varieties with genetic resistance to specific pests or diseases can provide effective protection. The inconsistencies in farmers’ willingness to pay NAD 2.62 and NAD 2.75 less in the fifth and sixth rounds, respectively, may have been associated with the challenges facing small-scale farmers’ decision making or the accessibility of suitable seeds, affecting the crop selection, seed acquisition, and financial risks associated with deviating from conventional practices [79]. When one of the crops involved in the rotation is not a leguminous crop or has limitations in fixing nitrogen, farmers will not realize the benefits of practicing crop rotation. In this study, farmers used bira (a cowpea variety), which forms a good canopy cover; however, the denser the canopy cover, the less pods they bear. In CA, the use of a dense cover crop, followed by its successive thick residue cover, reduces soil moisture losses and traps nutrients [89,90]. This was a disincentive to farmers because they did not obtain a tangible and immediate benefit in terms of yield in the short term. Hence, the positive coefficient of the estimate indicated that the farmers were in favor of crop rotation; however, the WTP was less because they would be likely to obtain a lower yield of cowpeas in that particular growing season. This explained that the farmers were aware of the benefits the next crop will provide from the locations where there was vigorous canopy cover but were not satisfied with the yield of pods they obtained in a particular season. This requires effort from the government agencies, educational institutions, and lead community farmers to educate and disseminate knowledge to the farmers in making informed decisions regarding the types of crops they can include in rotational programs.
The introduction of permanent soil cover within a farming system can alleviate economic risks, improve the organic matter in the soil, and create favorable habitats for the micro-organisms essential for decomposing organic matter into a form that can be absorbed by crop roots [79]. In addition, this action can contribute to soil health by providing ground cover, preventing nutrient leaching, and mitigating soil temperature fluctuations [81]. Moreover, in many other cases, soil amendments with manure have been associated with improved soil properties such as increased soil organic carbon (SOC) content, total nitrogen content (TN), and enhanced pH [86]. This practice has been linked to reduced chemical inputs, increased biodiversity, habitat preservation, and enhanced adaption to climate change’s impacts [91]. Hence, farmers’ WTP to adopt CA is positively influenced by permanent soil cover because of the enhanced soil fertility that eventually improves crop yield and reduces input costs. Permanent soil cover, represented by mulch, had a positive significance influence in the second and fourth rounds, with a consistently positive willingness to pay of NAD 2.99 and NAD 2.14, respectively. This is an indication that farmers understand the importance of permanent soil cover and are willing to pay more to practice this principle of CA. This principle is needed in a hot and dry environment like that in Namibia to conserve moisture, because residues are left on the soil surface to protect the topsoil, thereby reducing some of the negative effects of heat stress on crops [39,82,83,92]. The application of mulch is more relevant and applicable to this study area because these farmers do not apply kraal manure to their crop fields unlike the farmers in the Omusati, Oshana, Ohangwena, and Oshikoto regions. Incorporating organic matter, like kraal manure, into the soil enhances water retention, reduces erosion, and fosters crop growth [12,78].
Intercropping can create a synergistic relationship between different species, leading to increased yields, reduced pest pressure, and enhanced resource use efficiency [81,83]. The result was different in this study because farmers faced challenges with insufficient soil nutrients; thus, growing more than one crop simultaneously in proximity means that the crops compete for the available nutrients, leading to crops with nutrients deficiencies and poor yield. This discourages farmers from practicing intercropping, thus negatively influencing the WTP to adopt CA. In addition, the knowledge gap and resistance to change are other possible factors that may have contributed to the negative influence of the WTP to adopt CA because of the low yield that farmers have experienced in the short term. Beyond a certain season, farmers may start experiencing the benefits of consistently practicing intercropping. For example, it may require more than 40 years to reach a new equilibrium when changing land use from continuous monoculture to complex crop rotation [83]. However, the combined effect of different management practices is more complex and has been less studied [76]. Thus, patience, perseverance, and consistency may be required for the farmers to realize the benefits from the different principles of CA.
The time spent on the first soil preparation per hectare was an attribute that negatively influenced willingness to pay. This suggests that farmers prefer a quicker method of soil preparation. Farmers have the option to use oxen or hire private or public tractors; however, factors such as affordability, availability, and the type of implements available on these tractors influence their choices. Due to the lack of proper equipment such as direct seeders on the local markets, farmers are using oxen to rip the land. This requires farmers to train their oxen on how to make rip lines instead of ploughing. This takes time, and farmers are discouraged from spending time on training oxen; hence, the time spent on soil preparation negatively influenced the WTP to adopt CA.
Other factors that negatively influenced the willingness to pay were the frequency and weeding rate. When practicing minimal soil disturbance, a farmer is expected to make a rip line or basin. With a lack of mulching materials to cover the rest of the undisturbed land, weeds emerge, and farmers need to control these weeds. Crop residues are a protective ground cover that can include manure, saw dust, seaweed, litter, stubble, sand, pebbles, plastics, and any type of plant; however, legumes or non-legumes/grasses are mostly recommended [77,83]. This is expected to have trickle-down benefits on physical properties, improved soil chemical soil properties, biological benefits, and other benefits such as reduced production costs [77]. The proper identification and control of weeds is crucial for profitable crop production [83]. Weeding is a critical activity in crop farming and a basic determinant of final yield [83,92]. The more weeding is expected of farmers, the less farmers have a WTP to adopt CA.

4. Conclusions and Recommendations

In conclusion, the conditional regression output displayed consistency in the statistical significance and direction of the influences of the factors across several rounds. This revealed that intercropping, time spent on first-season soil preparation, and the frequency and weeding rate consistently negatively influenced the willingness to pay for the adoption of conservation agriculture. Investment in training farmers on appropriate crop diversification, with an emphasis on cropping time, is necessary. Furthermore, introducing appropriate mechanical tools and equipment to ease the workload and reduce the time spent on soil preparation and weeding will likely favor the adoption of conservation agriculture among the respondents in the study area. Thus, the modification of and improvement in the CA system to fit local conditions in southern Africa is envisaged to enhance its agro-ecological benefits, leading to soil water conservation, improvement in soil fertility, and reducing vulnerability of production systems to climate variability and change, among others [83]. Crop rotation and mulching consistently demonstrated statistical significance and positively influenced the willingness to pay for adopting conservation agriculture. This suggests that the respondents in the study area recognize the importance of these practices and are willing to invest more to implement conservation agriculture.
This study advocates for a nationwide campaign to raise awareness about the three core principles of conservation agriculture, aiming to educate farmers across the country. This campaign should employ various formats to ensure widespread outreach. Firstly, theoretical explanations should be broadcasted through national radio networks in multiple domestic languages, aiming to reach farmers across all regions. Practical demonstrations should be organized on agricultural demonstration plots in each of the constituencies across the 14 regions, allowing farmers to witness the principles of conservation agriculture in action. Additionally, the campaign should leverage printed and visual media. Educational materials, including newsletters, booklets, and newspapers, should detail the significance of each conservation agriculture principle. Complementing these, visual media education via television channels and short video clips, suitable for distribution through WhatsApp groups and other relevant social media platforms, will enhance understanding and adoption among farmers. There is practically no system for the national monitoring of soil quality and condition. Along with the campaign promoting CA, the extension services must provide evidence to educate farmers on the national soil fertility status. Farmers may understand the necessity of adopting CA when they understand the state of soil fertility in their respective farming areas. This study’s limitations might have affected the precision of the results because the implementation of this pilot study was only carried out over four crop seasons, and the respondents only responded or reacted based on what they observed over the pilot study period. Four cropping seasons might have been too short a period for the respondents to observe the full dynamics of CA. This study was conducted in three regions with homogenous farmer behavior, which might have limited the study findings in relation to the willingness to adopt CA. Finally, this study underscores the importance of conducting long-term experimental research on the various principles of conservation agriculture. Such research should aim to evaluate the impacts of these practices on crop yield and soil properties comprehensively. This approach will not only provide empirical evidence to support the efficacy of conservation agriculture but also guide future policy and practice to enhance sustainable agriculture. The government must also establish conservation agriculture research funds to spearhead conservation agriculture research activities in the country. Future research must also focus on extending this research to other regions within and beyond the country to compare the dynamics amongst farmers with the aim of understanding farmers’ behavior and how they make decisions pertaining to the adoption of CA.

Author Contributions

T.S. conceptualized this paper, designed the methodology, carried out the investigation, wrote the draft, visualized and edited this paper. D.U. secured the software, conducted data curation, carried out the formal analysis, carried out the overall supervision, project administration, and funding acquisition by the Namibia University of Science and Technology. All authors have read and agreed to the published version of the manuscript.

Funding

This research received external funding through “Adaptation of Agriculture to Climate Change in Northern Namibia Project” funded by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) Contract number: 81206713 and The Project Processing Number: 13.9767.8-002.00.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available because the data were collected specifically for academic studies.

Conflicts of Interest

No conflicts of interest in the publication of these research findings.

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Agriculture 15 00568 g001
Figure 1. Location of villages in the selected study areas. Source: Authors’ compilation.
Figure 1. Location of villages in the selected study areas. Source: Authors’ compilation.
Agriculture 15 00568 g001
Table 1. Attributes and level used.
Table 1. Attributes and level used.
AttributeLevels
IntercroppingMonocropping
Cereal and groundnut
Cereal and barbarnut
Cereal and cowpeas
Crop rotationNo rotation
Rotating every cropping season
Rotating after skipping one cropping season
Rotating after skipping two cropping seasons
Frequency & duration of weeding/Ha1 Weeding @ 4 h/Ha
2 Weeding @ 2 h/Ha
2 Weeding @ 3 h/Ha
2 Weeding @ 5 h/Ha
Mulching materialsNo mulching
Grass
Cereal stalks
Tree branches and leaves
Time of first soil preparation/Ha2 h, 2 h 30 min, 3 h, 4 h
Cost of soil preparation/HaNAD 250, 300, 400, 500
Table 2. Conditional logistical regression estimates for main activities of practicing conservation agriculture in WTP stated preference.
Table 2. Conditional logistical regression estimates for main activities of practicing conservation agriculture in WTP stated preference.
1st Round2nd Round3rd Round4th Round5th Round6th Round
AttributeCoeff.WTP1Coeff.WTP2Coeff.WTP3Coeff.WTP4Coeff.WTP5Coeff.WTP6
Cost0.199
(0.235)		 −0.380
(0.238)		 −0.813 ***
(0.260) 		−0.312
(0.229) 		0.303
(0.192) 		0.310
(0.192)
Intercro −0.668 **
(0.263)		3.350.033
(0.209)		0.09−0.490 **
(0.232)		−0.600.254
(0.192)		0.820.238
(0.181)		−0.780.021
(0.191)		−0.07
Rotation0.198
(0.229)		−0.990.708 ***
(0.200)		1.860.348 *
(0.209)		0.430.872 ***
(0.188)		2.790.795 ***
(0.174)		−2.620.853 ***−2.75
Time0.144
(0.235)		−0.72−0.524 **
(0.233)		−1.38 0.066
(0.189)		0.08−0.238
(0.223)		−0.76−0.128
(0.207)		0.42−0.791 ***
(0.251)		2.55
Wrate 0.037
(0.225		−0.19−0.788 ***
(0.256)		−2.07−0.351
(0.230)		−0.43−0.791 ***
(0.247)		−2.53−0.117
(0.200)		0.390.144
(0.197)		−0.46
Mulch0.078
(0.190)		−0.390.138 ***
(0.164)		2.990.848
(0.165)		1.040.667 *
(0.168)		2.140.240
(0.178)		−0.790.201
(0.181)		−0.65
Note: 1% (***), 5% (**), and 10% (*) significance levels. Standard errors are reported in parentheses. Source: Authors’ compilation.
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Shiimi, T.; Uchezuba, D. Willingness to Pay to Adopt Conservation Agriculture in Northern Namibia. Agriculture 2025, 15, 568. https://doi.org/10.3390/agriculture15050568

AMA Style

Shiimi T, Uchezuba D. Willingness to Pay to Adopt Conservation Agriculture in Northern Namibia. Agriculture. 2025; 15(5):568. https://doi.org/10.3390/agriculture15050568

Chicago/Turabian Style

Shiimi, Teofilus, and David Uchezuba. 2025. "Willingness to Pay to Adopt Conservation Agriculture in Northern Namibia" Agriculture 15, no. 5: 568. https://doi.org/10.3390/agriculture15050568

APA Style

Shiimi, T., & Uchezuba, D. (2025). Willingness to Pay to Adopt Conservation Agriculture in Northern Namibia. Agriculture, 15(5), 568. https://doi.org/10.3390/agriculture15050568

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