Prospects and determinants of willingness to pay for sustainable restoration of rangelands among smallholder cattle producers in North West Province, South Africa

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Market-based environmental conservation instruments, such as payment for ecosystem services (PES) and willingness to pay (WTP), serve as effective mechanisms for promoting sustainable land management. This study investigates smallholder cattle producers’ WTP for rangeland restoration in South Africa’s North West Province. A double-bounded contingent valuation method was applied to data from 101 cattle producers, revealing that over 80% were willing to pay an initial bid of USD 11.50 ha⁻¹ year⁻¹, with a mean WTP of USD 17.00 ha⁻¹ year⁻¹. Logistic regression analysis identified education level (p = 0.012), preferred cattle breed (p = 0.039), farming experience (p = 0.026), goat ownership (p = 0.022), ecoregion (p = 0.079), and cattle-derived income (p = 0.048) as significant predictors of WTP. These findings highlight strong support for rangeland restoration and management and reflect how socio-economic and ecological factors shape land-use management choices. The study contributes to the development of participatory, equity-sensitive restoration frameworks aligned with PES, environmental management, sustainable land-use policies, and resilience-building in pastoral systems. Animal Science Agroecology Environmental Economics Environmental Policy Agricultural Economics and Policy Willingness to pay Rangeland restoration Smallholder cattle producers Environmental management Contingent valuation method Figures Figure 1 Figure 2 Figure 3 1 Introduction Millions of people across the Southern African region and more than a billion globally derive their livelihoods directly from natural ecosystem services (Oldekop et al. 2020 ; Wisely et al. 2018 ; Kansanga et al. 2021 ). Agricultural ecosystems, which are embedded within broader environmental ecosystems, have long been recognised as crucial livelihood sources for smallholder farming communities and for development over the past half-century (Bani and Damnyag 2017 ; Leroy et al. 2018 ). A study by Nugroho et al. ( 2023 ) provides valuable insights into the livelihood strategies of smallholder cattle farmers through sustainable forest extraction. However, due to the growing human population and the increasing demand for food, particularly in Africa (Kotze and Rose, 2015 ), agroecosystems face more significant sustainability challenges than other ecosystems (Lal 2015; Sooriyakumar et al. 2019 ). Therefore, policies and strategies that drive the long-term sustainability of agroecosystem services remain essential at global, regional and national levels (Oldekop et al. 2020 ; Giger and Musselli 2023 ). Rangeland agroecosystems form a critical component of the diverse feed resource base for cattle production both globally and in Southern African countries (Descheemaeker et al. 2016 ; Herrero et al. 2013 ; Nugroho et al. 2023 ). Approximately 80% of South Africa’s agricultural land (60% of the country’s total area) is suitable for extensive grazing (Department of Agricultural Land Reform and Rural Development (DALRRD) 2019; Food and Agriculture Organisation (FAO) 2016). However, South Africa, like many countries worldwide, continues to experience widespread degradation of rangeland ecosystems, especially in arid and semi-arid cattle-producing regions (Bennett 2013; Descheemaeker et al. 2016 ; Nugroho et al. 2023 ). Rangeland degradation is driven by a range of factors, including climate change, mismanagement (e.g., high stocking rates, uncontrolled fires, and invasive species), lack of effective regeneration practices, limited awareness among farmers of the ecosystem service value, and weak policy enforcement (Kotze and Rose 2015 ; Meissner et al. 2014 ; Mukama 2010 ; Nugroho et al. 2023 ). The direct impacts on farmers include declining rangeland carrying capacity and rising costs of supplementary feeds, both of which negatively affect farm profitability and sustainability. Many studies (Kotze and Rose 2015 ; Fenetahun et al. 2021 ) underscore the importance of effective rangeland management and continuous regeneration to ensure the sustainability of farming systems. In many emerging economies like South Africa, governments and various institutions have introduced policy frameworks to protect and conserve natural ecosystems (Huang et al. 2018 ; Sherbut 2012 ). Some of these frameworks aim to promote sustainability in livestock production by balancing productivity with environmental management (Lebacq et al. 2013 ). One widely adopted approach is Payment for Ecosystem Services (PES), which incentivises landowners and communities to maintain intact ecosystems or restore degraded ones (Engel et al. 2008 ; Garbach et al. 2012 ; Papagallo 2018 ; Giger and Musselli 2023 ). Typically, PES schemes estimate the value of ecosystem services by assessing users’ willingness to pay (WTP) for the benefits they receive (Doğan et al. 2020 ; Nyongesa et al. 2016 ). These schemes have been largely applied in climate change mitigation mechanisms and the conservation of water resources, woodlands, and other threatened ecosystems (Garbach et al. 2012 ; Haile et al. 2019 ; Huang et al. 2018 ; Sherbut 2012 ). In most existing PES initiatives, government or institutional actors pay land users to implement ecosystem protection measures (Farley 2012 ; Haile et al. 2019 ). However, little evidence exists on PES schemes where farmers themselves pay to support rangeland restoration, particularly within smallholder livestock production systems (Papagallo 2018 ). Therefore, the present study aimed to estimate the WTP of smallholder cattle producers for the restoration of their grazing rangelands and identify the socio-economic factors associated with their WTP decisions. The study also contributes to the broader understanding of the economic value attributed to rangeland ecosystem services among smallholder cattle producers in developing and emerging economies. The findings are expected to inform evidence-based decision-making and support the development of policies that promote sustainable rangeland management and conservation efforts involving farmers. 2 Theoretical framework Over the past few decades, payment for ecosystem services has emerged as a transformative concept in environmental management and conservation. The approach offers a paradigm shift from coercive regulatory approaches to incentive-based conservation strategies, acknowledging the interconnectedness of communities through ecosystems (Nelson et al. 2010 ). Ideally, PES rests on the principle that those benefitting from ecosystem services should compensate those managing the ecosystems to provide those services (Wunder et al. 2018 ; Engel 2008). Such a quid pro quo system, according to Song et al. (2020), promote accountability and internalizes the positive externalities that arise from environmental management and stewardship. Crucially, PES schemes are voluntary and context-specific, taking into account local ecological, economic, and socio-political dynamics (Garbach et al. 2012 ; Wunder et al. 2018 ; Dhakal et al. 2021 ). The concept is gaining traction across diverse regions, from Latin America’s watershed agreements to Africa’s rangeland and biodiversity conservation initiatives (Butler 2012; Pappagallo 2018). In developing countries where institutional capacity and public funding are limited, PES offers a flexible policy instrument that enables community-based conservation while aligning with development goals (Nelson et al. 2010 ; Dhakal et al. 2021 ). However, the undervaluation of ecosystem services, the inequitable distribution of ecosystem benefits, and the possibility of leakage (where conservation in one area leads to degradation in another) present persistent implementation challenges (Byl 2021 ). Despite these challenges, successful initiatives like Mongolia’s Plan Vivo and Kenya’s Lake Naivasha watershed payment for ecosystem services scheme show that well-supported PES can effectively promote sustainable grazing systems (Dorligsuren et al. 2015; Nyongesa et al. 2016 ). The theoretical underpinnings of PES also emphasize the additionality of benefits, requiring that payments result in environmental outcomes that would not otherwise occur (Zheng et al. 2013). Thus, PES is both a governance innovation and an economic strategy that bridges the gap between environmental integrity and rural livelihoods. Integral to the operationalization of PES is the principle of willingness to pay, a widely accepted economic valuation method that captures users’ perceived value of ecosystem services (Jack et al. 2008 ). The WTP concept reflects the maximum amount an individual or household is willing to forgo to secure or enhance environmental management benefits (Xiong et al. 2018 ; Dogan et al. 2020), offering a monetary proxy for both direct-use and non-use values of natural resources. This valuation is particularly salient in smallholder farming systems contexts where rangeland degradation poses a threat to both agricultural sustainability and household resilience (Kotze and Rose 2015 ; Meissner et al. 2013). To estimate WTP for non-market goods such as soil quality, biodiversity, and water regulation critical components of rangeland ecosystems, researchers often rely on stated preference techniques, especially the contingent valuation method (CVM) (Mohammed 2012 ; Haile et al. 2019 ). The CVM creates assumed markets through which individuals can express their WTP for specific environmental management and resource improvements. Despite inherent challenges like hypothetical and strategic biases, such limitations can be mitigated by incorporating design elements like certainty scales, follow-up bids, and “cheap talk” scripts to enhance data reliability (Mohammed 2012 ). These features make CVM a suitable approach in rangeland-based farming systems for estimating demand for ecological restoration and guiding policy design. A double-bounded dichotomous choice contingent valuation method (Hanemann et al. 1991 ) was used in this study to elicit data about respondents' WTP for improved rangelands. This method includes a follow-up question that offers an alternative bid price higher or lower than the initial bid price based on the respondent’s first answer. This response format is dichotomous, requiring a "Yes” or “No” answer. If the response to the first bid is “Yes,” the follow-up bid is higher; if “No,” the follow-up bid is lower (Lopez-Feldman 2012). The double-bounded dichotomous choice CVM is considered asymptotically more efficient than the single-bounded choice, allowing smaller samples to give accurate WTP estimates (Lopez-Feldman 2012; Shee et al. 2020 ). It also corrects the upward or downward bias that can result from poorly identified initial bids and has been applied elsewhere (Hanemann et al. 1991 ; Nyongesa et al. 2016 ; Shee et al. 2020 ). The contingent valuation section of the questionnaire consisted of two parts. The first part presented a hypothetical scenario explaining the importance of rangelands and the need for their continuous restoration to improve productivity and environmental sustainability in farming systems. The second part included elicitation questions asking farmers if they were willing to set aside a specific amount of money (USD per hectare per year) for rangeland regeneration. An initial minimum bid price of USD11.50 ha − 1 year − 1 was offered. If the respondent answered "Yes," a higher bid of USD17.40 ha − 1 year − 1 was proposed, and if "No," a lower bid of USD5.90 ha − 1 year − 1 was presented. The initial bid value was informed by an experimental study conducted by du Pisanie ( 2019 ) at Stockfarm in Northern Cape, South Africa and refined during the survey pre-testing phase. To ensure the validity of the follow-up question, the second bid differed significantly from the first. It was reduced or increased by 50% of the initial bid to derive the lower and higher bids, respectively. Based on this model, four response outcomes were derived: Yes–No, Yes–Yes, No–Yes and No–No. Given that t₁ is the initial bid and t 2 is the follow-up bid, the bounds on WTP for each respondent would fall in one of the following four outcome groups (Lopez-Feldman 2012): Yes–No: respondent, willing to pay the first bid and not the second ( \(\:{t}^{1}\le\:WTP<{t}^{2})\) . Yes–Yes: respondent, willing to pay both the first and second bids ( \(\:{t}^{2}\le\:WTP<\infty\:\) ). No–Yes: respondent, not willing to pay only the first bid ( \(\:{t}^{2}\le\:WTP<{t}^{1}).\) No–No: respondent, not willing to pay both the first and second bids ( \(\:0<WTP<{t}^{2}\) ). The double-bound data, including two price bids, were used to estimate the mean WTP for rangeland regeneration. Following Haab and McConnell (2013), the general econometric model for the double-bounded data is formulated as: \(\:{WTP}_{ij}={\mu\:}_{i}\:+{\epsilon\:}_{ij}\) (i) Where WTP ij represents the WTP of the j th respondent and i = 1, 2 corresponds to the first and second responses. The \(\:{\mu\:}_{1}\) and \(\:{\mu\:}_{2}\:\) represent the mean WTP for the first and second responses, respectively. This general model incorporates the idea that an individual’s first and second responses to the contingent valuation questions may differ, possibly influenced by different covariates or by the same covariates. Accordingly, the likelihood functions were constructed based on the probabilities of observing each of the possible two-bid response sequences ( Ye –Yes, Yes–No, No–No and No–Yes ), following the approach of Kidane et al. ( 2019 ). Pr ( \(\:yes,\:no)=Pr({WTP}_{1j}>{t}^{1},\:{WTP}_{2j}<{t}^{2})\) Pr ( \(\:yes,\:no)=Pr({\mu\:}_{1}+\:{\varepsilon\:}_{1j}\ge\:{t}^{1},\:{\mu\:}_{2}+\:{\varepsilon\:}_{2j}{t}^{1},\:{WTP}_{2j}\ge\:{t}^{2})\) Pr ( \(\:yes,\:yes)=Pr({\mu\:}_{1}+\:{\varepsilon\:}_{1j}>{t}^{1},\:{\mu\:}_{2}+\:{\varepsilon\:}_{2j}\ge\:{t}^{2})\) (II) Pr ( \(\:no,\:yes)=Pr({WTP}_{1j}<{t}^{1},\:{WTP}_{2j}\ge\:{t}^{2})\) Pr ( \(\:no,\:yes)=Pr({\mu\:}_{1}+\:{\varepsilon\:}_{1j}<{t}^{1},\:{\mu\:}_{2}+\:{\varepsilon\:}_{2j}\ge\:{t}^{2})\) (III) Pr ( \(\:no,\:no)=Pr({WTP}_{1j}<{t}^{1},\:{WTP}_{2j}<{t}^{2})\) Pr ( \(\:no,\:no)=Pr({\mu\:}_{1}+\:{\varepsilon\:}_{1j}<{t}^{1},\:{\mu\:}_{2}+\:{\varepsilon\:}_{2j}<{t}^{2})\) (IV) Then, the j th contribution to the likelihood function becomes: \(\:{L}_{j}\left(\mu\:/t\right)={\left(I\right)}^{YN}\text{x}{\left(II\right)}^{YY}\text{x}{\left(III\right)}^{NY}\text{x}{\left(IV\right)}^{NN}\) (ii) Where \(\:{t}^{1}\) and \(\:{t}^{2}\) are first and second bid prices and I, II, III, and IV refer to the probability equations given above. YN = 1 for a yes-no answer, 0 otherwise; YY = 1 for a yes-yes answer, 0 otherwise; NY = 1 for a no-yes answer, 0 otherwise; NN = 1 for no-no answer, 0 otherwise. In the model, error terms are assumed to be normally distributed with mean zero and variances of σ 1 2 and σ 2 2 ; therefore, the WTP 1j and WTP 2j follow a bivariate normal distribution with means \(\:{\mu\:}_{1}\) and \(\:{\mu\:}_{2}\) , variances σ 2 1 and σ 2 2 and a correlation coefficient p . Given the dichotomous responses to each question, the normally distributed model is represented as a bivariate probit model. Each respondent’s contribution to the bivariate probit likelihood function is given by Haab and McConnell (2013): \(\:{L}_{j}(\mu\:/t)=\varPhi\:{\epsilon\:}_{1}{\epsilon\:}_{2},\:({d}_{1j}\left(\frac{{t}^{1}-{\varvec{\mu\:}}_{1}}{{\sigma\:}_{1}}\right){\prime\:}\:{d}_{2j}\left(\frac{{t}^{2}-{\varvec{\mu\:}}_{2}}{{\sigma\:}_{2}}\right),\:{d}_{1j}{d}_{2j}\sigma\:\:\) (iii) Where \(\:\varPhi\:{\epsilon\:}_{1}{\epsilon\:}_{2}\) is the standardised bivariate normal cumulative distribution function with zero means, unit variances and correlation coefficient p . Defining y 1j = 1 if the response to the first question is yes, and 0 if otherwise; \(\:{y}_{2j}=1\) if the response to the second question is yes, and 0 if otherwise; \(\:{d}_{1j}={2y}_{1j}-1\) , and \(\:{d}_{2j}={2y}_{2j}-1\) . Globally, studies have shown various socio-economic factors including education, income, farm ownership, and environmental awareness influence farmers’ WTP for restoration (Ning et al. 2019 ; Aydogdu 2020; Shee et al. 2020 ). In this regard, WTP not only informs the financial feasibility of PES schemes but also reflects deeper attitudinal and behavioral dimensions of environmental management decision-making (Nyongesa et al. 2016 ; Farley 2012 ). For instance, in Turkey, Aydogdu (2020) found that WTP for soil and water conservation was influenced by land tenure security and farm labor availability, while Ning et al. ( 2019 ) reported that age and education significantly shaped WTP for grassland restoration in China. This aligns with the broader findings that farmers are more inclined to invest in ecosystem services when there are visible returns to productivity or long-term sustainability (Mukama 2010 ; Balvanera et al. 2016 ). In this paper, WTP is central to understanding the socio-economic rationality of smallholder cattle producers towards rangeland restoration. The framework thereby connects socio-economic theory with actionable environmental conservation by positioning farmers as both agents and beneficiaries of ecosystem service restoration. Therefore, this gives the study critical lens for designing inclusive, resilient, and scalable PES initiatives. Table 2 describes the independent variables used in the logit model and their hypothesised associations with respondents' willingness to pay, as informed by prior studies. This forms the basis for the research methodology and interpretation of the results. Table 2 Explanatory variables fitted in the model to determine WTP for improving rangelands by the farmers. Explanatory Variable Description of explanatory variables Gender (X 1 ) Gender of the farmer (Dichotomous; 1 = male, 0 = otherwise). Studies (Bani and Damnyag 2017 ; Gosbert et al. 2019) have established that male farmers have better access to the means of production and income and are more knowledgeable and experienced than females. Therefore, male farmers are hypothesised to have a higher WTP for improving rangelands than women. Education level (X 2 ) Farmer's education level (Dichotomous; 1 = more educated, 2 = less educated). Education enhances farmers' productivity and promotes a more positive attitude and understanding toward payment for ecosystem services (Asrat et al. 2004 ; Ning et al. 2019 ; Pender and Kerr 1998 ; Sooriyakumar et al. 2019 ). Therefore, more educated farmers are likely to have higher WTP than less educated farmers. Marital status (X 3 ) Farmer's marital status (Dichotomous; 1 = married, 0 = Otherwise). The studies by Lalika et al. (2017) and Zaiton et al. (2019) show that marital status plays an important part in WTP. According to Zaiton et al. (2019), married people conserve resources to benefit their children and future generations. Subsequently, married farmers are expected to have higher WTP than unmarried farmers. Cattle Breed (X 4 ) The most important cattle breed at the farm (Dichotomous; 1 = Nguni, 0 = Otherwise). Generally, cattle breed influences farmers' demand for rangelands. Indigenous cattle breeds are adapted to local conditions can produce high-quality products under natural rangelands in smallholder farming systems (Sambo 2020 ). Therefore, farmers who indicated Nguni as the most important breed are likely to have a higher WTP. Farming experience (X 5 ) Farmer's experience level in farming (Dichotomous; 1 = High experience, 0 = Low experience). Long tenure gives the farmers more practical knowledge of their farms and an appreciation of the need to conserve natural resources (Uddin et al. 2016 ). It is, therefore, anticipated to determine farmers’ WTP positively. Farm Size (X 6 ) Size of farms owned by farmers (Dichotomous; 1 = Large, 0 = Small/otherwise). Farm size is often perceived to correlate positively with farmers' economic viability and encourages farmers to practice new technologies, thus potentially increasing their WTP to improve rangelands (Asrat et al. 2004 ). On the other hand, farmers relatively with large farms might not be willing to pay for these conservation activities because their grazing resources are adequate, implying a decrease in their WTP to improve rangelands. Herd size (X 7 ) The size of cattle herds owned by farmers (Dichotomous; 1 = Large, 0 = Small/otherwise). Farmers with large herd sizes are likely to produce more animals suggesting high income and high demand for rangelands (Foti et al. 2007 ; Pender and Kerr 1998 ). Therefore, farmers with large herd sizes are expected to have a higher WTP than farmers with relatively smaller herd sizes. Goat Ownership (X 8 ) Goat ownership by farmers (Dichotomous; 1 = Yes, 0 = No). In addition to having cattle, owning goats on the farm places more pressure on rangelands rendering the farmers vulnerable to feed shortages. Therefore, goat ownership is hypothesized to increase farmers' WTP. Rangeland ecozone (X 10 ) Rangeland type occupied by the farmer (Dichotomous; 1 = Sweet rangeland ecozone, 0 = Otherwise). The productivity of cattle raised on rangelands depends on the type and quality of rangelands. Sweet rangeland ecozones are more preferred than sour rangeland ecozones due to their high quality (Onyango et al. 2019 ; Nqeno et al. 2011 ). Therefore, farmers on sweet rangelands are expected to have lower WTP. Annual income (X 11 ) Annual income from cattle sales (Dichotomous; 1 = higher income, 0 = low income). Income from livestock sales increases farmers' financial capacity to protect natural resources (Doğan et al. 2020 ; Foti et al. 2007 ; Nyongesa et al. 2016 ). It is, therefore, anticipated to influence farmers' decisions to pay for improving rangelands positively. 3 Methods 3.1 Description of the study site The study was carried out in the North West Province (26.6639° S, 25.2838° E) of South Africa (Fig. 1 ). The province covers 116,320km 2 and occupies 9.5% of South Africa’s total land area. The province is divided into four districts and 18 local municipalities (Table 1 ; Fig. 1 ). The province has two major biomes: the Grassland and Savanna biomes (Daemane et al. 2010 ), characterised by flat landscapes with scattered trees and grasslands (Daemane et al. 2010 ). It’s rangeland ecozones (Table 1 ) are primarily sweetveld, covering the southern half and extreme northern parts of the province and a strip of sourveld cutting through the centre. Approximately 57% of the province's land is considered suitable for grazing, with cattle farming being the main livestock enterprise. About 28% of the land is classified as potentially arable, with maize and sunflower as the most important crops. Temperatures range from 17°C to 31°C in summer and 3°C to 21°C in winter. Average annual rainfall totals approximately 360 mm, with most rainfall occurring in summer (October to April). Table 1 Pedo-climatic conditions, ecozones, and the distribution of farmers across the districts of North West province, South Africa. District Municipalities Mean Annual Temperature (℃) Mean Annual Rainfall (mm) Altitude (m) Soil Types Vegetation types Local Municipalities No/ of Farmers Rangeland Ecozones Dr Ruth Segomotsi Mompati Bushveld Greater Taung 1 Sweet 11.6–26.6 397 869–2062 Kalahari sand Thornveld Kagisano-Molopo 22 Sweet Brown and Red Ferrallitic Vaalbosveld Naledi 2 Sweet Grassland Ngaka Modiri Molema Ditsobotla 1 Sour Kalahari sand Grassland Mahikeng 11 Sour 11.8–26.0 447 926–1729 Brown and Red Ferrallitic Bushveld Ramotshere Moiloa 3 Sweet Black clays Thornveld Ratlou 1 Sour Tswaing 5 Sour Dr Kenneth Kaunda Aeolian Sandy Woodland Matlosana 7 Sweet 11.5–25.3 512 1096–1833 Brown and Red Ferrallitic Grassland JB Marks 18 Sour Highveld Prairie Sourveld Maquassi Hills 1 Sweet Bojanala Platinum Kgetleng river 8 Sweet Black clays Bushveld Madibeng 8 Sweet 14.0-27.5 508 833–2038 Brown and Red Ferrallitic Thornveld Moretele 2 Mixed Grey Ferruginous Lateritic Woodland Moses Kotane 7 Sweet Rustenburg 4 Sweet 3.2 Selection of respondents The study population comprised commercially oriented smallholder cattle producers who are beneficiaries of the North West Industrial Development Corporation (IDC)–Nguni Cattle Programme. The survey sample comprised all 101 commercially oriented farmers (census approach) actively participating in the programme since its inception in the province. South Africa’s commercially oriented smallholder farmers are also referred to as emerging smallholders and are among the beneficiaries of the government’s land redistribution programme (Ortmann and Machethe 2003; MacLeod et al. 2010). Most of these farmers are historically disadvantaged individuals and groups who are transitioning toward commercial farming. 3.3 Data collection 3.3.1 Survey A structured questionnaire was developed, pretested and administered to collect quantitative data from individual farmers through face-to-face interviews between November 2020 and February 2021. Five enumerators were recruited and trained to assist with administering the questionnaire. The questionnaire was designed in English and translated into Setswana (local language) during interviews to facilitate easier communication and get comfortable responses. Socio-economic and farm characteristics of the farmers were recorded. Data on cattle numbers, farm performance, general conditions of rangelands, causes of rangeland degradation and farmers’ regeneration practices were also collected. The research was approved by the Stellenbosch University’s Research Ethics Committee (Human ethical clearance: Project number 9293). 3.4 Data analysis Quantitative data were analysed using the Statistical Analysis System (SAS) v. 9.4 (SAS Institute 2012 ) and Stata/SE 16 (StataCorp 2019 ). Data on farmers’ demographic profiles and farm information, rangeland conditions, grazing systems and rangeland management practices were subjected to descriptive statistics using the PROC FREQ procedure of SAS. To estimate farmers’ WTP for rangeland regeneration, a bivariate probit regression model was applied, utilizing data collected through the double-bounded dichotomous choice elicitation method. The bivariate probit model is a general parametric model suitable for two-response survey data (Haab and McConnell 2013) and is appropriate for analysing correlated binary responses often encountered in contingent valuation studies. Given that the WTP data were approximately normally distributed, the model was estimated using the maximum likelihood estimation method. The mean WTP for rangeland regeneration was, therefore, calculated following Haab and McConnell (2013), and as also used by Kidane et al. ( 2019 ). \(\:MWTP=-\raisebox{1ex}{$\alpha\:$}\!\left/\:\!\raisebox{-1ex}{$\beta\:$}\right.\) (iv) Where MWTP is the mean WTP for regenerating rangelands, α is the intercept of the estimated model, and \(\:\beta\:\) is the coefficient of the bid values. To identify factors influencing WTP for rangeland restoration, a binary logistic regression model was used. Logistic regression applies the logit transformation to linearise the non-linear relationship between X (independent variable) and the probability of Y (dependent variable). It uses odds and their natural logarithm for estimation. Following Wooldridge (2012), the empirical model for the linear relationship between X and the log odds is specified as: Logged odds: \(\:Ln\left(\frac{{P}_{i}}{1\:-\:{P}_{i}}\right)={\beta\:}_{0}+{\beta\:}_{i}{X}_{i}+{\mu\:}_{i}\) (1) Where i refers to a given respondent; βi represents parameters that determine WTP for the initial bid; \(\:{P}_{i}\) is the probability that the \(\:{i}^{th}\) respondent will be willing to pay for the given bid; \(\:{X}_{i}\) represents factors to be assessed, and \(\:Ln\left(\frac{{P}_{i}}{1\:-\:{P}_{i}}\right)\) is the log odds ratio in favour of WTP to pay for the initially offered bid price. The actual model used for estimation was: $$\:Logit\left(Y\right)={\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{{\beta\:}_{2}{X}_{2}+{\beta\:}_{3}{X}_{3}+.....{\beta\:}_{12}{X}_{11}+\mu\:}_{i}$$ 2 $$\:Logit\left(WTP\right)={\beta\:}_{0}+{\beta\:}_{1}Gender+{\beta\:}_{2}Education+{\beta\:}_{3}MaritalStatus+{{\beta\:}_{4}Breed+{\beta\:}_{5}FarmingExperience+{\beta\:}_{6}farmSize+{\beta\:}_{7}\:HerdSize+{\beta\:}_{8}GoatOwnership+{\beta\:}_{9}RangelandEcozone+{\beta\:}_{10}Income+\mu\:}_{i}$$ 4 Results 4.1 Farmers’ socio-economic attributes Beef cattle farming was dominated by male farmers aged between 35 and 64 years, most of whom were married, and had three to five family members (Table 3 ). Most had at least secondary education and were full-time farmers. Over 60% of experienced farmers (aged over 39 years) were based in the sweet rangeland ecozones, and 82% ranked livestock farming as their primary income source. Table 3 Characteristics of the commercially oriented beef cattle farmers. Variables Category Percentage Gander Female 26 Male 74 Marital status Married 65 Single 21 Divorced/widowed 14 Household sizes Below 3 15 3–5 44 6–8 34 Above 8 7 Farmer’s age (years) 35 and below 6 35–44 21 45–54 23 55–64 24 Above 64 26 Farmer’s highest level of education No formal education 3 Primary education 28 Lower Secondary 8 Upper secondary 32 Post-secondary/Technical education 18 Higher tertiary education 11 Farming engagement/employment Full-time farmer 95 Part-time farmer 5 4.2 Farm sizes, farm ownership status and access to formal livestock training by farmers Almost half of the respondents had farms and rangelands ranging between 300 and 799 ha (Fig. 2 ), with 87% of farms leased from the government, 11% being privately owned and the remainder communally owned. The average farm size was 952 ha, ranging from 110 to 3119 ha. Most respondents (70%) used less than 800ha of rangelands for cattle grazing, with the average grazed area being 772ha (Fig. 2 ). Most large farms (over 70%) were located within sweet rangeland ecozones. Nearly two-thirds of farmers had received formal livestock training (68%), acquired from various institutions and through farmer support programs. 4.3 Livestock species characterization and herd sizes among farmers Most cattle herd sizes ranged between 79 and 129 and Nguni herd sizes between 30 and 79 (Fig. 3 ). The average total cattle herd size was 103, and the average Nguni herd size was 63. Respondents occupying most of the bigger farms (> 1299) also had the largest cattle herds (> 129 head) (Fig. 3 ). Cattle were rated (93% of respondents) as the most important species, followed by chickens (4%) and goats. Ninety-four per cent of farmers kept Nguni cattle, and a majority (62%) considered it as an important breed, followed by Bonsmara (57%), mixed (37%), and Brahman (21%). Eighty per cent of respondents reported that their cattle were in good condition. In the previous calving seasons (2019–2020), farmers received an average of 45 calves, with 94% recording a 12-month calving interval. 4.4 Farm rangeland conditions, challenges, and management strategies Rangelands were the primary source of cattle feed for all respondents, although most supplemented with purchased feed (66%) and farm by-products (29%). Rotational grazing was the predominant grazing system (86%), with the rest of farmers practising continuous grazing. Over 60% of farmers reported that their rangelands were in good condition but with challenges, while 19% (n = 20) reported conditions as poor to very poor. Challenges included invasive species (58%), overgrazing (38%), dry conditions (21%), land degradation (13%), fire (8%), and lack of grazing camps (21 ess than 50% of farmers engaged in rangeland conservation or restoration. Reported practices included planting trees and pastures (22%), removing invasive and unwanted trees (20%), applying manure or fertilizer (9%), and resting larger portions of their rangelands (70%). Boreholes were the main water source (86%), followed by dams (8%) and rivers (6%). Most farmers reported a good supply of drinking water for their livestock. 4.5 WTP for rangeland regeneration by smallholder cattle farmers Regarding farmers' WTP for improving rangelands, an initial bid price of USD11.50 ha-1 year-1 was presented to all respondents. Table 4 summarises the responses to this and subsequent bid offers. The majority of farmers were willing to pay the initial bid price. Over 70% responded “Yes-Yes” to both the initial and follow-up bids. Among those willing to pay, 60% had at least secondary education, 74% were male, 71% were over 45 years old, and 62% earned between USD 2,100 and 11,800 annually from cattle sales. Based on the probit model analysis results, the mean WTP of the respondents was estimated at USD17.00 ha-1 year-1. Table 4 Summary of the offered initial and follow-up bid prices and farmers' WTP responses. Sets of bids (USD) WTP responses No. of observations (%) Initial bid ( \(\:{t}^{1}\) ) 11.50 ha -1 year -1 Yes 83 Higher bid ( \(\:{t}^{2}/{y}_{1}=1)\) 17.40 ha -1 year -1 Yes–Yes 72 Yes–No 10 Lower bid ( \(\:{t}^{2}/{y}_{1}=0\) ) 5.90 ha -1 year -1 No–No 14 No–Yes 4 4.6 Determinants of farmers’ WTP for rangeland restoration Various factors were hypothesised to influence farmers' WTP for rangeland restoration. Among the ten explanatory variables, higher education level, identifying Nguni as the most important breed, farming experience, goat ownership and higher income from cattle sales were found to be positively and significantly associated with WTP decisions (Table 5 ). Conversely, the logistic regression analysis indicated that variables such as farm size, gender, marital status, and cattle herd size did not have a significant influence on respondents’ WTP. Table 5 Binary Logistic Regression results for factors influencing farmers’ WTP Variables Coef. Estimate (β) Std. E p-value Significance Gender -0.587 1.023 0.536 NS Education level 3.271 1.301 0.012 *** Marital status 1.391 0.941 0.139 NS Most Important cattle breed 2.024 0.940 0.031 *** Farming experience 2.468 1.108 0.026 *** Farm size 1.061 1.088 0.329 NS Cattle herd size 0.760 1.123 0.498 NS Goat Ownership 3.072 1.341 0.022 *** Rangeland ecozone -1.821 1.036 0.079 ** Sales Income 2.806 1.418 0.048 *** Note: ***, ** Statistical significance at 5% (p˂0.05), 10% (p˂0.10), respectively. 5 Discussion The results indicate that commercially oriented smallholder cattle farmers demonstrated a high willingness to pay for rangeland restoration. This may be attributed to their recognition of rangeland ecosystems as the primary feed source for their cattle (Descheemaeker et al. 2016 ; Herrero et al. 2013 ), highlighting the economic and ecological value they attach to these ecosystems (Nugroho et al. 2023 ). The observed high WTP estimates align with previous studies that assessed users' willingness to invest in the protection and restoration of natural ecosystems (Doğan et al. 2020 ; Mukama 2010 ; Xiong et al. 2018 ). For instance, Xiong et al. ( 2018 ) reported that three-quarters of downstream residents in the Ganjiang River Basin, China, expressed positive WTP for ecological improvements, primarily driven by expected benefits from improvements in the river basin. Similarly, in Uganda's cattle corridor, Mukama ( 2010 ) found that over 60% of pastoral rangeland users were willing to pay for improved grazing rangelands due to anticipated improvements in access to water and grazing resources. While Nugroho et al. ( 2023 ) did not directly assess WTP for rangelands, they emphasised the value smallholder cattle producers placed on forest resources, underscoring the broader trend of resource-dependent farmers investing in ecosystem services crucial to their livelihoods. These high WTP findings collectively support the notion of introducing PES schemes, particularly where these natural ecosystems face ongoing degradation (Garbach et al. 2012 ; Papagallo 2018 , Giger and Musselli 2023 ). Initiatives such as cost-sharing arrangements between farmers and the government could offer a practical pathway to reversing human-induced rangeland degradation and improving their productivity (Engel et al. 2008 ). However, a few farmers had low WTP, possibly reflecting the belief that the responsibility for PES lies with the government or development agencies as indicated in Farley ( 2012 ) and Haile et al. 2019 ). This perception may stem from their participation in the government land reform and the IDC-Nguni Cattle Programmes where they received initial support. This is also consistent with Doan et al. (2020), who observed that smallholder farmers often perceive public institutions and donors as primarily responsible for financing development interventions. The positive association between education level and WTP for the conservation and regeneration of rangelands is consistent with various studies that link educational attainment with enhanced environmental stewardship (Asrat et al. 2004 ; Ning et al. 2019 ; Pender and Kerr 1998 ; Sooriyakumar et al. 2019 ). Education is often associated with improved human capital, better understanding of environmental issues, and the capacity to make informed decisions (Pender and Kerr 1998 ; Asrat et al. 2004 ). Thus, more educated farmers are more likely to appreciate the long-term benefits of rangeland investment and adopt sustainable management practices compared to less educated farmers (Ning et al. 2019 ; Ouédraogo et al. 2018). However, conflicting evidence from Mukama ( 2010 ) suggests that in some contexts, more educated farmers may be less likely to invest in improving communal rangelands. The study argued that education does not necessarily affect the demand for indirect basic needs like rangelands. This contradiction highlights the importance of contextual and cultural factors in shaping WTP for ecosystem services. The predominance of the Nguni cattle breed among respondents aligns with previous studies in South Africa, suggesting that emerging smallholder farmers tend to keep indigenous breeds, while communal farmers keep non-descript crossbreds (Sambo 2020 ; Nqeno et al. 2011 ). Farmers who identified Nguni as the most important cattle breed on their farms showed greater WTP for rangeland improvement. The observed positive association between having Nguni cattle and increased WTP for improving rangelands may reflect the reliance of indigenous breeds like Nguni on natural grazing systems and their suitability for local conditions such as semi-arid environments. Indigenous cattle breeds such as Nguni are more efficient at utilising low-quality feed resources that characterise semi-arid rangeland ecozones (Nqeno et al. 2011 ; Sambo 2020 ; Malusi et al. 2021 ), making investment in rangeland health a rational economic choice. Farming experience was another factor significantly associated with farmers' WTP for rangeland improvement and conservation. Experienced farmers may better appreciate the long-term benefits of improving rangelands, having observed both the decline and restoration of land quality over time and how that impacts their farming enterprises (Uddin et al. 2016 ). This finding is consistent with prior research suggesting that experiential knowledge enhances the adoption of innovative conservation practices (Kansanga et al. 2021 ; Osumba et al. 2021 ). Moreover, there is a need to emphasise the value of mentorship, peer-to-peer learning, and farmer-to-farmer extension models in promoting sustainable rangeland management Osumba et al. 2021 ; Kansanga et al. 2021 ). The current study also suggested that annual income from cattle sales had an affirmative and significant association with farmers’ WTP for rangeland restoration. Income’s positive relationship with WTP implies that farmers with relatively higher incomes were more likely to pay for rangeland improvement than those with lower incomes. Thus, financial capacity plays a critical role in influencing investment in ecosystem services. The finding is supported by earlier studies (Pender and Kerr 1998 ; Foti et al. 2007 ; Nyongesa et al. 2016 ), indicating that farmers with higher incomes are better positioned to allocate resources toward land improvement. Nugroho et al. ( 2023 ) similarly reported that income levels influence the extent of farmers' engagement with ecosystem resources, including extraction and conservation activities. The significant association between goat ownership and WTP suggests that farmers with mixed livestock holdings may experience greater pressure on grazing resources. Goats, as browsers, can contribute to over-utilisation of vegetation, prompting farmers to adopt mechanisms for sustainable land use. This supports previous studies by Fenetahun et al. ( 2021 ), Gebremedhnet et al. (2023) and Čuda et al. ( 2024 ) who reported that increased livestock numbers and diversity often lead to greater grazing pressure, necessitating improved land management strategies. For instance, Čuda et al. ( 2024 ) observed that higher herbivore species richness led to reduced grass species richness at seasonal rivers due to trampling and overgrazing. Therefore, key recommended interventions include rotational grazing and reseeding to be used in tandem with the establishment of grazing enclosures (Fenetahun et al. 2021 ). Interestingly, farmers located in sweet rangeland ecozones were less likely to pay for rangeland improvement compared to those in sour veld areas. This may be due to the expected year-round palatability and higher nutritive value of sweet rangelands, which reduces the perceived need for active investment in improvement mechanisms (Nqeno et al. 2011 ; Onyango et al. 2019 ). These findings underscore how biophysical characteristics of the rangeland landscapes can influence conservation decisions among farmers and must be considered when designing PES schemes. Given the lack of historical investment in cattle grazing ecosystem protection and the strong WTP observed in this study, the study findings suggest a window of opportunity to develop and pilot PES schemes tailored to smallholder cattle producers. Interestingly, the studied farmers were already engaged in informal rangeland management practices, including rotational grazing, invasive species removal, and resting of pasture, which indicates readiness for participation in formalised incentive-based programs. 6 Conclusions This study examined the WTP for sustainable rangeland restoration among commercially oriented smallholder cattle producers, highlighting the socio-economic, production-, and ecology-related determinants influencing their investment decisions. The relatively high WTP estimates underscore the perceived value of rangeland ecosystems as critical natural capital in smallholder cattle production systems. These findings suggest that internalizing the ecological cost of rangeland degradation through appropriate pricing mechanisms could create meaningful incentives for sustainable land stewardship. Higher education levels, longer tenure security, favorable ecozone conditions, and income derived from cattle sales were strongly associated with increased WTP, suggesting that these factors can serve as catalysts for environmental management and restoration. The observed associations between WTP and ownership of goats and indigenous breeds, such as Nguni cattle, further emphasize the dependence of mixed smallholder systems on communal and natural grazing resources, thereby indicating potential grazing pressures and breed-specific ecological dynamics. These insights contribute to the growing discourse on integrating economic valuation into sustainable land-use planning and offer practical implications for the design of equitable and context-specific rangeland management policies. Incorporating these determinants into incentive-based mechanisms, such as PES, could enhance farmer participation and improve the long-term sustainability of rangeland ecosystems. Moreover, co-financed, participatory PES schemes that incorporate local knowledge and socio-cultural values may offer scalable models for promoting ecosystem service restoration across diverse agroecological zones. Future longitudinal and comparative research should explore the behavioral and institutional dynamics that influence the adoption, sustainability, and scalability of farmer-led rangeland restoration interventions. In particular, studies assessing the long-term effectiveness of such interventions, as well as cross-regional comparisons of WTP determinants, would contribute to the development of more targeted, inclusive, and adaptive environmental management strategies for rangeland-dependent smallholder systems. Declarations Conflict of interest The authors have no conflicts of interest to declare that are relevant to the content of this article. Ethical consideration The research was approved by the Stellenbosch University’s Research Ethics Committee (Human ethical clearance: Project number 9293). Consent to participate Written informed consent was sought from all participants prior to commencing the interviews. Funding This work was supported by the Stellenbosch University Social Impact Division’s Seed Initiatives fund. Authors Contributions All authors contributed to the conceptualisation and design of the study. OM collected primary data with support from local enumerators. OM analysed data with support from GM. OM developed first draft of the manuscript and all authors supported by reviewing and giving feedback. GM and CM contributed to data interpretation and implications. All authors agree to submission of the manuscript. Acknowledgements The authors appreciate the support given by the North West Department of Agriculture in identifying the sample of farmers used in the study. Deepest gratitude and appreciation are extended toward all the commercially oriented smallholder Nguni cattle farmers for their participation in the survey and the enumerators who assisted with data collection. References Asrat P, Belay K, Hamito D (2004) Determinants of farmers’ willingness to pay for soil conservation practices in the southeastern highlands of Ethiopia. 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14:47:05","currentVersionCode":2,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7263439/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-7263439/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88366062,"identity":"69059218-7f85-4ad0-9b5a-732b33e4de94","added_by":"auto","created_at":"2025-08-05 17:32:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110765,"visible":true,"origin":"","legend":"\u003cp\u003eThe map of North West province and its local municipalities.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7263439/v2/977d46f397057adedcf0075e.png"},{"id":88366059,"identity":"a13470c2-b137-4461-9d52-5ef36480909f","added_by":"auto","created_at":"2025-08-05 17:32:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111349,"visible":true,"origin":"","legend":"\u003cp\u003eTotal farm and rangeland sizes of commercially oriented farmers.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7263439/v2/6f84adf1fe947201c05da37a.png"},{"id":88366060,"identity":"86421b35-e512-479a-afa7-722604b70c69","added_by":"auto","created_at":"2025-08-05 17:32:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104407,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of livestock numbers among farmers\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7263439/v2/208a2c1e485e68c8b314bc13.png"},{"id":88367008,"identity":"fffaa744-c51c-4e96-9e25-7973b6cf3ba5","added_by":"auto","created_at":"2025-08-05 17:48:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1434182,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7263439/v2/305f40bc-5292-494b-be4e-6494a8d01721.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Prospects and determinants of willingness to pay for sustainable restoration of rangelands among smallholder cattle producers in North West Province, South Africa","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMillions of people across the Southern African region and more than a billion globally derive their livelihoods directly from natural ecosystem services (Oldekop et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wisely et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kansanga et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Agricultural ecosystems, which are embedded within broader environmental ecosystems, have long been recognised as crucial livelihood sources for smallholder farming communities and for development over the past half-century (Bani and Damnyag \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Leroy et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A study by Nugroho et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) provides valuable insights into the livelihood strategies of smallholder cattle farmers through sustainable forest extraction. However, due to the growing human population and the increasing demand for food, particularly in Africa (Kotze and Rose, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), agroecosystems face more significant sustainability challenges than other ecosystems (Lal 2015; Sooriyakumar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, policies and strategies that drive the long-term sustainability of agroecosystem services remain essential at global, regional and national levels (Oldekop et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Giger and Musselli \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRangeland agroecosystems form a critical component of the diverse feed resource base for cattle production both globally and in Southern African countries (Descheemaeker et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Herrero et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nugroho et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Approximately 80% of South Africa\u0026rsquo;s agricultural land (60% of the country\u0026rsquo;s total area) is suitable for extensive grazing (Department of Agricultural Land Reform and Rural Development (DALRRD) 2019; Food and Agriculture Organisation (FAO) 2016). However, South Africa, like many countries worldwide, continues to experience widespread degradation of rangeland ecosystems, especially in arid and semi-arid cattle-producing regions (Bennett 2013; Descheemaeker et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nugroho et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRangeland degradation is driven by a range of factors, including climate change, mismanagement (e.g., high stocking rates, uncontrolled fires, and invasive species), lack of effective regeneration practices, limited awareness among farmers of the ecosystem service value, and weak policy enforcement (Kotze and Rose \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Meissner et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mukama \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nugroho et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The direct impacts on farmers include declining rangeland carrying capacity and rising costs of supplementary feeds, both of which negatively affect farm profitability and sustainability. Many studies (Kotze and Rose \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fenetahun et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) underscore the importance of effective rangeland management and continuous regeneration to ensure the sustainability of farming systems.\u003c/p\u003e\u003cp\u003eIn many emerging economies like South Africa, governments and various institutions have introduced policy frameworks to protect and conserve natural ecosystems (Huang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sherbut \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Some of these frameworks aim to promote sustainability in livestock production by balancing productivity with environmental management (Lebacq et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). One widely adopted approach is Payment for Ecosystem Services (PES), which incentivises landowners and communities to maintain intact ecosystems or restore degraded ones (Engel et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Garbach et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Papagallo \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Giger and Musselli \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTypically, PES schemes estimate the value of ecosystem services by assessing users\u0026rsquo; willingness to pay (WTP) for the benefits they receive (Doğan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nyongesa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These schemes have been largely applied in climate change mitigation mechanisms and the conservation of water resources, woodlands, and other threatened ecosystems (Garbach et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Haile et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sherbut \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In most existing PES initiatives, government or institutional actors pay land users to implement ecosystem protection measures (Farley \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Haile et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, little evidence exists on PES schemes where farmers themselves pay to support rangeland restoration, particularly within smallholder livestock production systems (Papagallo \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, the present study aimed to estimate the WTP of smallholder cattle producers for the restoration of their grazing rangelands and identify the socio-economic factors associated with their WTP decisions. The study also contributes to the broader understanding of the economic value attributed to rangeland ecosystem services among smallholder cattle producers in developing and emerging economies. The findings are expected to inform evidence-based decision-making and support the development of policies that promote sustainable rangeland management and conservation efforts involving farmers.\u003c/p\u003e"},{"header":"2 Theoretical framework","content":"\u003cp\u003eOver the past few decades, payment for ecosystem services has emerged as a transformative concept in environmental management and conservation. The approach offers a paradigm shift from coercive regulatory approaches to incentive-based conservation strategies, acknowledging the interconnectedness of communities through ecosystems (Nelson et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Ideally, PES rests on the principle that those benefitting from ecosystem services should compensate those managing the ecosystems to provide those services (Wunder et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Engel 2008). Such a \u003cem\u003equid pro quo\u003c/em\u003e system, according to Song et al. (2020), promote accountability and internalizes the positive externalities that arise from environmental management and stewardship. Crucially, PES schemes are voluntary and context-specific, taking into account local ecological, economic, and socio-political dynamics (Garbach et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wunder et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dhakal et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The concept is gaining traction across diverse regions, from Latin America\u0026rsquo;s watershed agreements to Africa\u0026rsquo;s rangeland and biodiversity conservation initiatives (Butler 2012; Pappagallo 2018). In developing countries where institutional capacity and public funding are limited, PES offers a flexible policy instrument that enables community-based conservation while aligning with development goals (Nelson et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dhakal et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the undervaluation of ecosystem services, the inequitable distribution of ecosystem benefits, and the possibility of leakage (where conservation in one area leads to degradation in another) present persistent implementation challenges (Byl \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite these challenges, successful initiatives like Mongolia\u0026rsquo;s Plan Vivo and Kenya\u0026rsquo;s Lake Naivasha watershed payment for ecosystem services scheme show that well-supported PES can effectively promote sustainable grazing systems (Dorligsuren et al. 2015; Nyongesa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The theoretical underpinnings of PES also emphasize the \u003cem\u003eadditionality\u003c/em\u003e of benefits, requiring that payments result in environmental outcomes that would not otherwise occur (Zheng et al. 2013). Thus, PES is both a governance innovation and an economic strategy that bridges the gap between environmental integrity and rural livelihoods.\u003c/p\u003e\u003cp\u003eIntegral to the operationalization of PES is the principle of willingness to pay, a widely accepted economic valuation method that captures users\u0026rsquo; perceived value of ecosystem services (Jack et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The WTP concept reflects the maximum amount an individual or household is willing to forgo to secure or enhance environmental management benefits (Xiong et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dogan et al. 2020), offering a monetary proxy for both direct-use and non-use values of natural resources. This valuation is particularly salient in smallholder farming systems contexts where rangeland degradation poses a threat to both agricultural sustainability and household resilience (Kotze and Rose \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Meissner et al. 2013).\u003c/p\u003e\u003cp\u003eTo estimate WTP for non-market goods such as soil quality, biodiversity, and water regulation critical components of rangeland ecosystems, researchers often rely on stated preference techniques, especially the contingent valuation method (CVM) (Mohammed \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Haile et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The CVM creates assumed markets through which individuals can express their WTP for specific environmental management and resource improvements. Despite inherent challenges like hypothetical and strategic biases, such limitations can be mitigated by incorporating design elements like certainty scales, follow-up bids, and \u0026ldquo;cheap talk\u0026rdquo; scripts to enhance data reliability (Mohammed \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These features make CVM a suitable approach in rangeland-based farming systems for estimating demand for ecological restoration and guiding policy design.\u003c/p\u003e\u003cp\u003eA double-bounded dichotomous choice contingent valuation method (Hanemann et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) was used in this study to elicit data about respondents' WTP for improved rangelands. This method includes a follow-up question that offers an alternative bid price higher or lower than the initial bid price based on the respondent\u0026rsquo;s first answer. This response format is dichotomous, requiring a \"Yes\u0026rdquo; or \u0026ldquo;No\u0026rdquo; answer. If the response to the first bid is \u0026ldquo;Yes,\u0026rdquo; the follow-up bid is higher; if \u0026ldquo;No,\u0026rdquo; the follow-up bid is lower (Lopez-Feldman 2012). The double-bounded dichotomous choice CVM is considered asymptotically more efficient than the single-bounded choice, allowing smaller samples to give accurate WTP estimates (Lopez-Feldman 2012; Shee et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It also corrects the upward or downward bias that can result from poorly identified initial bids and has been applied elsewhere (Hanemann et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Nyongesa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shee et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe contingent valuation section of the questionnaire consisted of two parts. The first part presented a hypothetical scenario explaining the importance of rangelands and the need for their continuous restoration to improve productivity and environmental sustainability in farming systems. The second part included elicitation questions asking farmers if they were willing to set aside a specific amount of money (USD per hectare per year) for rangeland regeneration. An initial minimum bid price of USD11.50 ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was offered. If the respondent answered \"Yes,\" a higher bid of USD17.40 ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was proposed, and if \"No,\" a lower bid of USD5.90 ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was presented. The initial bid value was informed by an experimental study conducted by du Pisanie (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) at Stockfarm in Northern Cape, South Africa and refined during the survey pre-testing phase. To ensure the validity of the follow-up question, the second bid differed significantly from the first. It was reduced or increased by 50% of the initial bid to derive the lower and higher bids, respectively. Based on this model, four response outcomes were derived: \u003cem\u003eYes\u0026ndash;No, Yes\u0026ndash;Yes, No\u0026ndash;Yes\u003c/em\u003e and \u003cem\u003eNo\u0026ndash;No.\u003c/em\u003e Given that \u003cem\u003et₁\u003c/em\u003e is the initial bid and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e is the follow-up bid, the bounds on WTP for each respondent would fall in one of the following four outcome groups (Lopez-Feldman 2012):\u003c/p\u003e\u003cp\u003e\u003cem\u003eYes\u0026ndash;No: respondent, willing to pay the first bid and not the second (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{1}\\le\\:WTP\u0026lt;{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eYes\u0026ndash;Yes: respondent, willing to pay both the first and second bids (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{2}\\le\\:WTP\u0026lt;\\infty\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNo\u0026ndash;Yes: respondent, not willing to pay only the first bid (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{2}\\le\\:WTP\u0026lt;{t}^{1}).\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNo\u0026ndash;No: respondent, not willing to pay both the first and second bids (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0\u0026lt;WTP\u0026lt;{t}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe double-bound data, including two price bids, were used to estimate the mean WTP for rangeland regeneration. Following Haab and McConnell (2013), the general econometric model for the double-bounded data is formulated as:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{WTP}_{ij}={\\mu\\:}_{i}\\:+{\\epsilon\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e (i)\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eWTP\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the WTP of the \u003cem\u003ej\u003c/em\u003e\u003csub\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sub\u003e respondent and \u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2 corresponds to the first and second responses. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{2}\\:\\)\u003c/span\u003e\u003c/span\u003erepresent the mean WTP for the first and second responses, respectively. This general model incorporates the idea that an individual\u0026rsquo;s first and second responses to the contingent valuation questions may differ, possibly influenced by different covariates or by the same covariates. Accordingly, the likelihood functions were constructed based on the probabilities of observing each of the possible two-bid response sequences (\u003cem\u003eYe \u0026ndash;Yes, Yes\u0026ndash;No, No\u0026ndash;No\u003c/em\u003e and \u003cem\u003eNo\u0026ndash;Yes\u003c/em\u003e), following the approach of Kidane et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:yes,\\:no)=Pr({WTP}_{1j}\u0026gt;{t}^{1},\\:{WTP}_{2j}\u0026lt;{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:yes,\\:no)=Pr({\\mu\\:}_{1}+\\:{\\varepsilon\\:}_{1j}\\ge\\:{t}^{1},\\:{\\mu\\:}_{2}+\\:{\\varepsilon\\:}_{2j}\u0026lt;{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(I)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:yes,\\:yes)=Pr({WTP}_{1j}\u0026gt;{t}^{1},\\:{WTP}_{2j}\\ge\\:{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:yes,\\:yes)=Pr({\\mu\\:}_{1}+\\:{\\varepsilon\\:}_{1j}\u0026gt;{t}^{1},\\:{\\mu\\:}_{2}+\\:{\\varepsilon\\:}_{2j}\\ge\\:{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(II)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:no,\\:yes)=Pr({WTP}_{1j}\u0026lt;{t}^{1},\\:{WTP}_{2j}\\ge\\:{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:no,\\:yes)=Pr({\\mu\\:}_{1}+\\:{\\varepsilon\\:}_{1j}\u0026lt;{t}^{1},\\:{\\mu\\:}_{2}+\\:{\\varepsilon\\:}_{2j}\\ge\\:{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(III)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:no,\\:no)=Pr({WTP}_{1j}\u0026lt;{t}^{1},\\:{WTP}_{2j}\u0026lt;{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePr (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:no,\\:no)=Pr({\\mu\\:}_{1}+\\:{\\varepsilon\\:}_{1j}\u0026lt;{t}^{1},\\:{\\mu\\:}_{2}+\\:{\\varepsilon\\:}_{2j}\u0026lt;{t}^{2})\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e(IV)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThen, the j\u003csup\u003eth\u003c/sup\u003e contribution to the likelihood function becomes:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{j}\\left(\\mu\\:/t\\right)={\\left(I\\right)}^{YN}\\text{x}{\\left(II\\right)}^{YY}\\text{x}{\\left(III\\right)}^{NY}\\text{x}{\\left(IV\\right)}^{NN}\\)\u003c/span\u003e\u003c/span\u003e(ii)\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{2}\\)\u003c/span\u003e\u003c/span\u003e are first and second bid prices and I, II, III, and IV refer to the probability equations given above. \u003cem\u003eYN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for a yes-no answer, 0 otherwise; \u003cem\u003eYY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for a yes-yes answer, 0 otherwise; \u003cem\u003eNY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for a no-yes answer, 0 otherwise; \u003cem\u003eNN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 for no-no answer, 0 otherwise. In the model, error terms are assumed to be normally distributed with mean zero and variances of \u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e; therefore, the \u003cem\u003eWTP\u003c/em\u003e\u003csub\u003e\u003cem\u003e1j\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eWTP\u003c/em\u003e\u003csub\u003e\u003cem\u003e2j\u003c/em\u003e\u003c/sub\u003e follow a bivariate normal distribution with means \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e, variances \u003cem\u003eσ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eσ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e and a correlation coefficient \u003cem\u003ep\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eGiven the dichotomous responses to each question, the normally distributed model is represented as a bivariate probit model. Each respondent\u0026rsquo;s contribution to the bivariate probit likelihood function is given by Haab and McConnell (2013):\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{j}(\\mu\\:/t)=\\varPhi\\:{\\epsilon\\:}_{1}{\\epsilon\\:}_{2},\\:({d}_{1j}\\left(\\frac{{t}^{1}-{\\varvec{\\mu\\:}}_{1}}{{\\sigma\\:}_{1}}\\right){\\prime\\:}\\:{d}_{2j}\\left(\\frac{{t}^{2}-{\\varvec{\\mu\\:}}_{2}}{{\\sigma\\:}_{2}}\\right),\\:{d}_{1j}{d}_{2j}\\sigma\\:\\:\\)\u003c/span\u003e\u003c/span\u003e(iii)\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varPhi\\:{\\epsilon\\:}_{1}{\\epsilon\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e is the standardised bivariate normal cumulative distribution function with zero means, unit variances and correlation coefficient \u003cem\u003ep\u003c/em\u003e. Defining y\u003csub\u003e\u003cem\u003e1j\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1 if the response to the first question is yes, and 0 if otherwise; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{2j}=1\\)\u003c/span\u003e\u003c/span\u003e if the response to the second question is yes, and 0 if otherwise; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{1j}={2y}_{1j}-1\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{2j}={2y}_{2j}-1\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eGlobally, studies have shown various socio-economic factors including education, income, farm ownership, and environmental awareness influence farmers\u0026rsquo; WTP for restoration (Ning et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Aydogdu 2020; Shee et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this regard, WTP not only informs the financial feasibility of PES schemes but also reflects deeper attitudinal and behavioral dimensions of environmental management decision-making (Nyongesa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Farley \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For instance, in Turkey, Aydogdu (2020) found that WTP for soil and water conservation was influenced by land tenure security and farm labor availability, while Ning et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that age and education significantly shaped WTP for grassland restoration in China. This aligns with the broader findings that farmers are more inclined to invest in ecosystem services when there are visible returns to productivity or long-term sustainability (Mukama \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Balvanera et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this paper, WTP is central to understanding the socio-economic rationality of smallholder cattle producers towards rangeland restoration. The framework thereby connects socio-economic theory with actionable environmental conservation by positioning farmers as both agents and beneficiaries of ecosystem service restoration. Therefore, this gives the study critical lens for designing inclusive, resilient, and scalable PES initiatives. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the independent variables used in the logit model and their hypothesised associations with respondents' willingness to pay, as informed by prior studies. This forms the basis for the research methodology and interpretation of the results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExplanatory variables fitted in the model to determine WTP for improving rangelands by the farmers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription of explanatory variables\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (X\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender of the farmer (Dichotomous; 1\u0026thinsp;=\u0026thinsp;male, 0\u0026thinsp;=\u0026thinsp;otherwise). Studies (Bani and Damnyag \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gosbert et al. 2019) have established that male farmers have better access to the means of production and income and are more knowledgeable and experienced than females. Therefore, male farmers are hypothesised to have a higher WTP for improving rangelands than women.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level (X\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmer's education level (Dichotomous; 1\u0026thinsp;=\u0026thinsp;more educated, 2\u0026thinsp;=\u0026thinsp;less educated). Education enhances farmers' productivity and promotes a more positive attitude and understanding toward payment for ecosystem services (Asrat et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ning et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pender and Kerr \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sooriyakumar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, more educated farmers are likely to have higher WTP than less educated farmers.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status (X\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmer's marital status (Dichotomous; 1\u0026thinsp;=\u0026thinsp;married, 0\u0026thinsp;=\u0026thinsp;Otherwise). The studies by Lalika et al. (2017) and Zaiton et al. (2019) show that marital status plays an important part in WTP. According to Zaiton et al. (2019), married people conserve resources to benefit their children and future generations. Subsequently, married farmers are expected to have higher WTP than unmarried farmers.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle Breed (X\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe most important cattle breed at the farm (Dichotomous; 1\u0026thinsp;=\u0026thinsp;Nguni, 0\u0026thinsp;=\u0026thinsp;Otherwise). Generally, cattle breed influences farmers' demand for rangelands. Indigenous cattle breeds are adapted to local conditions can produce high-quality products under natural rangelands in smallholder farming systems (Sambo \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, farmers who indicated Nguni as the most important breed are likely to have a higher WTP.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarming experience (X\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmer's experience level in farming (Dichotomous; 1\u0026thinsp;=\u0026thinsp;High experience, 0\u0026thinsp;=\u0026thinsp;Low experience). Long tenure gives the farmers more practical knowledge of their farms and an appreciation of the need to conserve natural resources (Uddin et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It is, therefore, anticipated to determine farmers\u0026rsquo; WTP positively.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm Size (X\u003csub\u003e6\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize of farms owned by farmers (Dichotomous; 1\u0026thinsp;=\u0026thinsp;Large, 0\u0026thinsp;=\u0026thinsp;Small/otherwise). Farm size is often perceived to correlate positively with farmers' economic viability and encourages farmers to practice new technologies, thus potentially increasing their WTP to improve rangelands (Asrat et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). On the other hand, farmers relatively with large farms might not be willing to pay for these conservation activities because their grazing resources are adequate, implying a decrease in their WTP to improve rangelands.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHerd size (X\u003csub\u003e7\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe size of cattle herds owned by farmers (Dichotomous; 1\u0026thinsp;=\u0026thinsp;Large, 0\u0026thinsp;=\u0026thinsp;Small/otherwise). Farmers with large herd sizes are likely to produce more animals suggesting high income and high demand for rangelands (Foti et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pender and Kerr \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Therefore, farmers with large herd sizes are expected to have a higher WTP than farmers with relatively smaller herd sizes.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoat Ownership (X\u003csub\u003e8\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoat ownership by farmers (Dichotomous; 1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No). In addition to having cattle, owning goats on the farm places more pressure on rangelands rendering the farmers vulnerable to feed shortages. Therefore, goat ownership is hypothesized to increase farmers' WTP.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRangeland ecozone (X\u003csub\u003e10\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRangeland type occupied by the farmer (Dichotomous; 1\u0026thinsp;=\u0026thinsp;Sweet rangeland ecozone, 0\u0026thinsp;=\u0026thinsp;Otherwise). The productivity of cattle raised on rangelands depends on the type and quality of rangelands. Sweet rangeland ecozones are more preferred than sour rangeland ecozones due to their high quality (Onyango et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nqeno et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, farmers on sweet rangelands are expected to have lower WTP.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual income (X\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual income from cattle sales (Dichotomous; 1\u0026thinsp;=\u0026thinsp;higher income, 0\u0026thinsp;=\u0026thinsp;low income). Income from livestock sales increases farmers' financial capacity to protect natural resources (Doğan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Foti et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Nyongesa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It is, therefore, anticipated to influence farmers' decisions to pay for improving rangelands positively.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Description of the study site\u003c/h2\u003e\u003cp\u003eThe study was carried out in the North West Province (26.6639\u0026deg; S, 25.2838\u0026deg; E) of South Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The province covers 116,320km\u003csup\u003e2\u003c/sup\u003e and occupies 9.5% of South Africa\u0026rsquo;s total land area. The province is divided into four districts and 18 local municipalities (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The province has two major biomes: the Grassland and Savanna biomes (Daemane et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), characterised by flat landscapes with scattered trees and grasslands (Daemane et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It\u0026rsquo;s rangeland ecozones (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) are primarily sweetveld, covering the southern half and extreme northern parts of the province and a strip of sourveld cutting through the centre. Approximately 57% of the province's land is considered suitable for grazing, with cattle farming being the main livestock enterprise. About 28% of the land is classified as potentially arable, with maize and sunflower as the most important crops. Temperatures range from 17\u0026deg;C to 31\u0026deg;C in summer and 3\u0026deg;C to 21\u0026deg;C in winter. Average annual rainfall totals approximately 360 mm, with most rainfall occurring in summer (October to April).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePedo-climatic conditions, ecozones, and the distribution of farmers across the districts of North West province, South Africa.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict Municipalities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Annual Temperature (℃)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Annual\u003c/p\u003e\u003cp\u003eRainfall (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAltitude (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoil Types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVegetation types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLocal Municipalities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo/ of Farmers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRangeland Ecozones\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDr Ruth Segomotsi Mompati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBushveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGreater Taung\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.6\u0026ndash;26.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e869\u0026ndash;2062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKalahari sand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThornveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKagisano-Molopo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBrown and Red Ferrallitic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVaalbosveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNaledi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrassland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eNgaka Modiri Molema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDitsobotla\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSour\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKalahari sand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrassland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMahikeng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSour\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.8\u0026ndash;26.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e926\u0026ndash;1729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBrown and Red Ferrallitic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBushveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRamotshere Moiloa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBlack clays\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThornveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRatlou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSour\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTswaing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSour\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDr Kenneth Kaunda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAeolian Sandy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMatlosana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.5\u0026ndash;25.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1096\u0026ndash;1833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBrown and Red Ferrallitic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrassland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJB Marks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSour\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHighveld Prairie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSourveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMaquassi Hills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eBojanala Platinum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKgetleng river\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBlack clays\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBushveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMadibeng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.0-27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e833\u0026ndash;2038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBrown and Red Ferrallitic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThornveld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMoretele\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGrey Ferruginous Lateritic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMoses Kotane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRustenburg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSweet\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Selection of respondents\u003c/h2\u003e\u003cp\u003eThe study population comprised commercially oriented smallholder cattle producers who are beneficiaries of the North West Industrial Development Corporation (IDC)\u0026ndash;Nguni Cattle Programme. The survey sample comprised all 101 commercially oriented farmers (census approach) actively participating in the programme since its inception in the province. South Africa\u0026rsquo;s commercially oriented smallholder farmers are also referred to as emerging smallholders and are among the beneficiaries of the government\u0026rsquo;s land redistribution programme (Ortmann and Machethe 2003; MacLeod et al. 2010). Most of these farmers are historically disadvantaged individuals and groups who are transitioning toward commercial farming.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data collection\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Survey\u003c/h2\u003e\u003cp\u003eA structured questionnaire was developed, pretested and administered to collect quantitative data from individual farmers through face-to-face interviews between November 2020 and February 2021. Five enumerators were recruited and trained to assist with administering the questionnaire. The questionnaire was designed in English and translated into Setswana (local language) during interviews to facilitate easier communication and get comfortable responses. Socio-economic and farm characteristics of the farmers were recorded. Data on cattle numbers, farm performance, general conditions of rangelands, causes of rangeland degradation and farmers\u0026rsquo; regeneration practices were also collected. The research was approved by the Stellenbosch University\u0026rsquo;s Research Ethics Committee (Human ethical clearance: Project number 9293).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Data analysis\u003c/h2\u003e\u003cp\u003eQuantitative data were analysed using the Statistical Analysis System (SAS) v. 9.4 (SAS Institute \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Stata/SE 16 (StataCorp \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Data on farmers\u0026rsquo; demographic profiles and farm information, rangeland conditions, grazing systems and rangeland management practices were subjected to descriptive statistics using the PROC FREQ procedure of SAS. To estimate farmers\u0026rsquo; WTP for rangeland regeneration, a bivariate probit regression model was applied, utilizing data collected through the double-bounded dichotomous choice elicitation method. The bivariate probit model is a general parametric model suitable for two-response survey data (Haab and McConnell 2013) and is appropriate for analysing correlated binary responses often encountered in contingent valuation studies. Given that the WTP data were approximately normally distributed, the model was estimated using the maximum likelihood estimation method. The mean WTP for rangeland regeneration was, therefore, calculated following Haab and McConnell (2013), and as also used by Kidane et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MWTP=-\\raisebox{1ex}{$\\alpha\\:$}\\!\\left/\\:\\!\\raisebox{-1ex}{$\\beta\\:$}\\right.\\)\u003c/span\u003e\u003c/span\u003e (iv)\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eMWTP\u003c/em\u003e is the mean WTP for regenerating rangelands, \u003cem\u003eα\u003c/em\u003e is the intercept of the estimated model, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is the coefficient of the bid values.\u003c/p\u003e\u003cp\u003eTo identify factors influencing WTP for rangeland restoration, a binary logistic regression model was used. Logistic regression applies the logit transformation to linearise the non-linear relationship between X (independent variable) and the probability of Y (dependent variable). It uses odds and their natural logarithm for estimation. Following Wooldridge (2012), the empirical model for the linear relationship between X and the log odds is specified as:\u003c/p\u003e\u003cp\u003eLogged odds: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ln\\left(\\frac{{P}_{i}}{1\\:-\\:{P}_{i}}\\right)={\\beta\\:}_{0}+{\\beta\\:}_{i}{X}_{i}+{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003ei\u003c/em\u003e refers to a given respondent; \u003cem\u003eβi\u003c/em\u003e represents parameters that determine WTP for the initial bid; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the probability that the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{i}^{th}\\)\u003c/span\u003e\u003c/span\u003e respondent will be willing to pay for the given bid; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents factors to be assessed, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ln\\left(\\frac{{P}_{i}}{1\\:-\\:{P}_{i}}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the log odds ratio in favour of WTP to pay for the initially offered bid price. The actual model used for estimation was:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Logit\\left(Y\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{{\\beta\\:}_{2}{X}_{2}+{\\beta\\:}_{3}{X}_{3}+.....{\\beta\\:}_{12}{X}_{11}+\\mu\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Logit\\left(WTP\\right)={\\beta\\:}_{0}+{\\beta\\:}_{1}Gender+{\\beta\\:}_{2}Education+{\\beta\\:}_{3}MaritalStatus+{{\\beta\\:}_{4}Breed+{\\beta\\:}_{5}FarmingExperience+{\\beta\\:}_{6}farmSize+{\\beta\\:}_{7}\\:HerdSize+{\\beta\\:}_{8}GoatOwnership+{\\beta\\:}_{9}RangelandEcozone+{\\beta\\:}_{10}Income+\\mu\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Farmers\u0026rsquo; socio-economic attributes\u003c/h2\u003e\u003cp\u003eBeef cattle farming was dominated by male farmers aged between 35 and 64 years, most of whom were married, and had three to five family members (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Most had at least secondary education and were full-time farmers. Over 60% of experienced farmers (aged over 39 years) were based in the sweet rangeland ecozones, and 82% ranked livestock farming as their primary income source.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the commercially oriented beef cattle farmers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced/widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHousehold sizes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eFarmer\u0026rsquo;s age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eFarmer\u0026rsquo;s highest level of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpper secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-secondary/Technical education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher tertiary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFarming engagement/employment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFull-time farmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePart-time farmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Farm sizes, farm ownership status and access to formal livestock training by farmers\u003c/h2\u003e\u003cp\u003eAlmost half of the respondents had farms and rangelands ranging between 300 and 799 ha (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with 87% of farms leased from the government, 11% being privately owned and the remainder communally owned. The average farm size was 952 ha, ranging from 110 to 3119 ha. Most respondents (70%) used less than 800ha of rangelands for cattle grazing, with the average grazed area being 772ha (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most large farms (over 70%) were located within sweet rangeland ecozones. Nearly two-thirds of farmers had received formal livestock training (68%), acquired from various institutions and through farmer support programs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Livestock species characterization and herd sizes among farmers\u003c/h2\u003e\u003cp\u003eMost cattle herd sizes ranged between 79 and 129 and Nguni herd sizes between 30 and 79 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The average total cattle herd size was 103, and the average Nguni herd size was 63. Respondents occupying most of the bigger farms (\u0026gt;\u0026thinsp;1299) also had the largest cattle herds (\u0026gt;\u0026thinsp;129 head) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cattle were rated (93% of respondents) as the most important species, followed by chickens (4%) and goats. Ninety-four per cent of farmers kept Nguni cattle, and a majority (62%) considered it as an important breed, followed by Bonsmara (57%), mixed (37%), and Brahman (21%). Eighty per cent of respondents reported that their cattle were in good condition. In the previous calving seasons (2019\u0026ndash;2020), farmers received an average of 45 calves, with 94% recording a 12-month calving interval.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Farm rangeland conditions, challenges, and management strategies\u003c/h2\u003e\u003cp\u003eRangelands were the primary source of cattle feed for all respondents, although most supplemented with purchased feed (66%) and farm by-products (29%). Rotational grazing was the predominant grazing system (86%), with the rest of farmers practising continuous grazing. Over 60% of farmers reported that their rangelands were in good condition but with challenges, while 19% (n\u0026thinsp;=\u0026thinsp;20) reported conditions as poor to very poor. Challenges included invasive species (58%), overgrazing (38%), dry conditions (21%), land degradation (13%), fire (8%), and lack of grazing camps (21 ess than 50% of farmers engaged in rangeland conservation or restoration. Reported practices included planting trees and pastures (22%), removing invasive and unwanted trees (20%), applying manure or fertilizer (9%), and resting larger portions of their rangelands (70%). Boreholes were the main water source (86%), followed by dams (8%) and rivers (6%). Most farmers reported a good supply of drinking water for their livestock.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.5 WTP for rangeland regeneration by smallholder cattle farmers\u003c/h2\u003e\u003cp\u003eRegarding farmers' WTP for improving rangelands, an initial bid price of USD11.50 ha-1 year-1 was presented to all respondents. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarises the responses to this and subsequent bid offers. The majority of farmers were willing to pay the initial bid price. Over 70% responded \u0026ldquo;Yes-Yes\u0026rdquo; to both the initial and follow-up bids. Among those willing to pay, 60% had at least secondary education, 74% were male, 71% were over 45 years old, and 62% earned between USD 2,100 and 11,800 annually from cattle sales. Based on the probit model analysis results, the mean WTP of the respondents was estimated at USD17.00 ha-1 year-1.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the offered initial and follow-up bid prices and farmers' WTP responses.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSets of bids\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(USD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWTP responses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo. of observations (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial bid ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.50 ha\u003csup\u003e-1\u003c/sup\u003e year\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigher bid (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{2}/{y}_{1}=1)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e17.40 ha\u003csup\u003e-1\u003c/sup\u003e year\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u0026ndash;Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u0026ndash;No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLower bid (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}^{2}/{y}_{1}=0\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5.90 ha\u003csup\u003e-1\u003c/sup\u003e year\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u0026ndash;No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u0026ndash;Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Determinants of farmers\u0026rsquo; WTP for rangeland restoration\u003c/h2\u003e\u003cp\u003eVarious factors were hypothesised to influence farmers' WTP for rangeland restoration. Among the ten explanatory variables, higher education level, identifying Nguni as the most important breed, farming experience, goat ownership and higher income from cattle sales were found to be positively and significantly associated with WTP decisions (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Conversely, the logistic regression analysis indicated that variables such as farm size, gender, marital status, and cattle herd size did not have a significant influence on respondents\u0026rsquo; WTP.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinary Logistic Regression results for factors influencing farmers\u0026rsquo; WTP\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef. Estimate (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMost Important cattle breed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarming experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle herd size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoat Ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRangeland ecozone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSales Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ***, ** Statistical significance at 5% (p˂0.05), 10% (p˂0.10), respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThe results indicate that commercially oriented smallholder cattle farmers demonstrated a high willingness to pay for rangeland restoration. This may be attributed to their recognition of rangeland ecosystems as the primary feed source for their cattle (Descheemaeker et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Herrero et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), highlighting the economic and ecological value they attach to these ecosystems (Nugroho et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The observed high WTP estimates align with previous studies that assessed users' willingness to invest in the protection and restoration of natural ecosystems (Doğan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mukama \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xiong et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, Xiong et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported that three-quarters of downstream residents in the Ganjiang River Basin, China, expressed positive WTP for ecological improvements, primarily driven by expected benefits from improvements in the river basin. Similarly, in Uganda's cattle corridor, Mukama (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that over 60% of pastoral rangeland users were willing to pay for improved grazing rangelands due to anticipated improvements in access to water and grazing resources. While Nugroho et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) did not directly assess WTP for rangelands, they emphasised the value smallholder cattle producers placed on forest resources, underscoring the broader trend of resource-dependent farmers investing in ecosystem services crucial to their livelihoods. These high WTP findings collectively support the notion of introducing PES schemes, particularly where these natural ecosystems face ongoing degradation (Garbach et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Papagallo \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Giger and Musselli \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Initiatives such as cost-sharing arrangements between farmers and the government could offer a practical pathway to reversing human-induced rangeland degradation and improving their productivity (Engel et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, a few farmers had low WTP, possibly reflecting the belief that the responsibility for PES lies with the government or development agencies as indicated in Farley (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Haile et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This perception may stem from their participation in the government land reform and the IDC-Nguni Cattle Programmes where they received initial support. This is also consistent with Doan et al. (2020), who observed that smallholder farmers often perceive public institutions and donors as primarily responsible for financing development interventions.\u003c/p\u003e\u003cp\u003eThe positive association between education level and WTP for the conservation and regeneration of rangelands is consistent with various studies that link educational attainment with enhanced environmental stewardship (Asrat et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ning et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pender and Kerr \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sooriyakumar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Education is often associated with improved human capital, better understanding of environmental issues, and the capacity to make informed decisions (Pender and Kerr \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Asrat et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Thus, more educated farmers are more likely to appreciate the long-term benefits of rangeland investment and adopt sustainable management practices compared to less educated farmers (Ning et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ou\u0026eacute;draogo et al. 2018). However, conflicting evidence from Mukama (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) suggests that in some contexts, more educated farmers may be less likely to invest in improving communal rangelands. The study argued that education does not necessarily affect the demand for indirect basic needs like rangelands. This contradiction highlights the importance of contextual and cultural factors in shaping WTP for ecosystem services.\u003c/p\u003e\u003cp\u003eThe predominance of the Nguni cattle breed among respondents aligns with previous studies in South Africa, suggesting that emerging smallholder farmers tend to keep indigenous breeds, while communal farmers keep non-descript crossbreds (Sambo \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nqeno et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Farmers who identified Nguni as the most important cattle breed on their farms showed greater WTP for rangeland improvement. The observed positive association between having Nguni cattle and increased WTP for improving rangelands may reflect the reliance of indigenous breeds like Nguni on natural grazing systems and their suitability for local conditions such as semi-arid environments. Indigenous cattle breeds such as Nguni are more efficient at utilising low-quality feed resources that characterise semi-arid rangeland ecozones (Nqeno et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sambo \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Malusi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), making investment in rangeland health a rational economic choice.\u003c/p\u003e\u003cp\u003eFarming experience was another factor significantly associated with farmers' WTP for rangeland improvement and conservation. Experienced farmers may better appreciate the long-term benefits of improving rangelands, having observed both the decline and restoration of land quality over time and how that impacts their farming enterprises (Uddin et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This finding is consistent with prior research suggesting that experiential knowledge enhances the adoption of innovative conservation practices (Kansanga et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osumba et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, there is a need to emphasise the value of mentorship, peer-to-peer learning, and farmer-to-farmer extension models in promoting sustainable rangeland management Osumba et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kansanga et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The current study also suggested that annual income from cattle sales had an affirmative and significant association with farmers\u0026rsquo; WTP for rangeland restoration. Income\u0026rsquo;s positive relationship with WTP implies that farmers with relatively higher incomes were more likely to pay for rangeland improvement than those with lower incomes. Thus, financial capacity plays a critical role in influencing investment in ecosystem services. The finding is supported by earlier studies (Pender and Kerr \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Foti et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Nyongesa et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), indicating that farmers with higher incomes are better positioned to allocate resources toward land improvement. Nugroho et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) similarly reported that income levels influence the extent of farmers' engagement with ecosystem resources, including extraction and conservation activities.\u003c/p\u003e\u003cp\u003eThe significant association between goat ownership and WTP suggests that farmers with mixed livestock holdings may experience greater pressure on grazing resources. Goats, as browsers, can contribute to over-utilisation of vegetation, prompting farmers to adopt mechanisms for sustainable land use. This supports previous studies by Fenetahun et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Gebremedhnet et al. (2023) and Čuda et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who reported that increased livestock numbers and diversity often lead to greater grazing pressure, necessitating improved land management strategies. For instance, Čuda et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed that higher herbivore species richness led to reduced grass species richness at seasonal rivers due to trampling and overgrazing. Therefore, key recommended interventions include rotational grazing and reseeding to be used in tandem with the establishment of grazing enclosures (Fenetahun et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, farmers located in sweet rangeland ecozones were less likely to pay for rangeland improvement compared to those in sour veld areas. This may be due to the expected year-round palatability and higher nutritive value of sweet rangelands, which reduces the perceived need for active investment in improvement mechanisms (Nqeno et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Onyango et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings underscore how biophysical characteristics of the rangeland landscapes can influence conservation decisions among farmers and must be considered when designing PES schemes. Given the lack of historical investment in cattle grazing ecosystem protection and the strong WTP observed in this study, the study findings suggest a window of opportunity to develop and pilot PES schemes tailored to smallholder cattle producers. Interestingly, the studied farmers were already engaged in informal rangeland management practices, including rotational grazing, invasive species removal, and resting of pasture, which indicates readiness for participation in formalised incentive-based programs.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThis study examined the WTP for sustainable rangeland restoration among commercially oriented smallholder cattle producers, highlighting the socio-economic, production-, and ecology-related determinants influencing their investment decisions. The relatively high WTP estimates underscore the perceived value of rangeland ecosystems as critical natural capital in smallholder cattle production systems. These findings suggest that internalizing the ecological cost of rangeland degradation through appropriate pricing mechanisms could create meaningful incentives for sustainable land stewardship. Higher education levels, longer tenure security, favorable ecozone conditions, and income derived from cattle sales were strongly associated with increased WTP, suggesting that these factors can serve as catalysts for environmental management and restoration. The observed associations between WTP and ownership of goats and indigenous breeds, such as Nguni cattle, further emphasize the dependence of mixed smallholder systems on communal and natural grazing resources, thereby indicating potential grazing pressures and breed-specific ecological dynamics. These insights contribute to the growing discourse on integrating economic valuation into sustainable land-use planning and offer practical implications for the design of equitable and context-specific rangeland management policies. Incorporating these determinants into incentive-based mechanisms, such as PES, could enhance farmer participation and improve the long-term sustainability of rangeland ecosystems. Moreover, co-financed, participatory PES schemes that incorporate local knowledge and socio-cultural values may offer scalable models for promoting ecosystem service restoration across diverse agroecological zones. Future longitudinal and comparative research should explore the behavioral and institutional dynamics that influence the adoption, sustainability, and scalability of farmer-led rangeland restoration interventions. In particular, studies assessing the long-term effectiveness of such interventions, as well as cross-regional comparisons of WTP determinants, would contribute to the development of more targeted, inclusive, and adaptive environmental management strategies for rangeland-dependent smallholder systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthical consideration\u003c/h2\u003e\u003cp\u003eThe research was approved by the Stellenbosch University\u0026rsquo;s Research Ethics Committee (Human ethical clearance: Project number 9293).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to participate\u003c/h2\u003e\u003cp\u003eWritten informed consent was sought from all participants prior to commencing the interviews.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Stellenbosch University Social Impact Division\u0026rsquo;s Seed Initiatives fund.\u003c/p\u003e\u003ch2\u003eAuthors Contributions\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conceptualisation and design of the study. OM collected primary data with support from local enumerators. OM analysed data with support from GM. OM developed first draft of the manuscript and all authors supported by reviewing and giving feedback. GM and CM contributed to data interpretation and implications. All authors agree to submission of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors appreciate the support given by the North West Department of Agriculture in identifying the sample of farmers used in the study. Deepest gratitude and appreciation are extended toward all the commercially oriented smallholder Nguni cattle farmers for their participation in the survey and the enumerators who assisted with data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsrat P, Belay K, Hamito D (2004) Determinants of farmers\u0026rsquo; willingness to pay for soil conservation practices in the southeastern highlands of Ethiopia. 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Sustainability 10(7):72149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su10072149\u003c/span\u003e\u003cspan address=\"10.3390/su10072149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Stellenbosch University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Willingness to pay, Rangeland restoration, Smallholder cattle producers, Environmental management, Contingent valuation method","lastPublishedDoi":"10.21203/rs.3.rs-7263439/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7263439/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe degradation and mismanagement of rangeland ecosystems continue to threaten environmental sustainability and livestock-based livelihoods in arid and semi-arid regions. Market-based environmental conservation instruments, such as payment for ecosystem services (PES) and willingness to pay (WTP), serve as effective mechanisms for promoting sustainable land management. This study investigates smallholder cattle producers\u0026rsquo; WTP for rangeland restoration in South Africa\u0026rsquo;s North West Province. A double-bounded contingent valuation method was applied to data from 101 cattle producers, revealing that over 80% were willing to pay an initial bid of USD 11.50 ha⁻\u0026sup1; year⁻\u0026sup1;, with a mean WTP of USD 17.00 ha⁻\u0026sup1; year⁻\u0026sup1;. Logistic regression analysis identified education level (p\u0026thinsp;=\u0026thinsp;0.012), preferred cattle breed (p\u0026thinsp;=\u0026thinsp;0.039), farming experience (p\u0026thinsp;=\u0026thinsp;0.026), goat ownership (p\u0026thinsp;=\u0026thinsp;0.022), ecoregion (p\u0026thinsp;=\u0026thinsp;0.079), and cattle-derived income (p\u0026thinsp;=\u0026thinsp;0.048) as significant predictors of WTP. These findings highlight strong support for rangeland restoration and management and reflect how socio-economic and ecological factors shape land-use management choices. The study contributes to the development of participatory, equity-sensitive restoration frameworks aligned with PES, environmental management, sustainable land-use policies, and resilience-building in pastoral systems.\u003c/p\u003e","manuscriptTitle":"Prospects and determinants of willingness to pay for sustainable restoration of rangelands among smallholder cattle producers in North West Province, South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-08-05 17:32:28","doi":"10.21203/rs.3.rs-7263439/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2025-08-04 06:47:12","doi":"10.21203/rs.3.rs-7263439/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2271e02e-6e05-49cb-ac3f-da25307890c6","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52580504,"name":"Animal Science"},{"id":52580505,"name":"Agroecology"},{"id":52580506,"name":"Environmental Economics"},{"id":52580507,"name":"Environmental Policy"},{"id":52580508,"name":"Agricultural Economics and Policy"}],"tags":[],"updatedAt":"2025-08-04T06:47:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 17:32:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-7263439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7263439","identity":"rs-7263439","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}