1. Introduction

2. Research Methodology

2.1. Description of the Study Areas

The study was conducted at three selected areas: Ga-Makanye in Polokwane Municipality, Gabaza in Greater Tzaneen local Municipality, and Gabaza in Greater Giyani Municipality in Limpopo Province, South Africa. Ga-Makanye is a small village situated outside Polokwane and it has a total of 9536 population [25]. The area is dominated by black and Sepedi speaking individuals. Gabaza is also a small village outside Tzaneen town dominated by black people mainly consisting of different tribes. The area is dominated by XiTsonga speaking individuals. Giyani is a town situated in the eastern part of the town and consist of 8098 population dominated by Xitsonga tribe [25]. The study chose these three areas due to different agro-ecological zones and climatic conditions experienced [26,27].

Figure 1. South African map showing 5 Districts in Limpopo Province. Source: Author’s compilation (2024).

Figure 1. South African map showing 5 Districts in Limpopo Province. Source: Author’s compilation (2024).

2.2. Research Design

The study used multipurpose research design to validate both qualitative and quantitative methodologies. The multipurpose research design is essential because it does not only afford integration of quantitative and qualitative data, but it also provides an opportunity to researchers to use strength of one data to mitigate the weaknesses of the other data [28]. Measures of dispersion (Descriptive statistics) and Double-hurdle model estimated using Probit and Tobit regression model were utilized to analyze factors influencing the willingness to adopt CSA among farmers.

2.2.1. Sampling Method(s) and Sample Size

The study employed multistage random sampling technique. In the first stage, Limpopo Province was purposefully, selected as the main area because of the prevalence of smallholder rural maize farming, which contributes to food security within the province and country. Secondly, two districts were purposively selected, which are Capricorn (dry sub-humid) and Mopani (semi-arid) due to different agro-ecological climate zones. Thirdly, Ga-Makanye village was purposively selected from Capricorn District in the Polokwane Municipality and two areas, which are Gabaza and Giyani Municipality were purposively selected from Mopani Municipality, respectively. Because researchers were unfamiliar with the study region and there were no maize farmers’ population, households were used as a proxy because majority of rural households in South Africa grow maize for consumption and income generation. The study used 209 sample sizes that was selected using Purposive Snowball sampling method. The study used Yamane’s formula to select the sample size for each area

$$n=\frac{N}{1+N{\left(e\right)}^2}$$

There was no population for smallholder rural maize farmers’, the study has used number of households as a proxy to draw up sampling frame for sample size.

Ga-Makanye (n) = $\frac{37}{1+37{\left(\frac{10}{100}\right)}^2}=26$, Gabaza (n) = $\frac{671}{1+671{\left(\frac{10}{100}\right)}^2}=87$, Giyani (n) = $\frac{8096}{1+8096{\left(\frac{10}{100}\right)}^2}=96$

2.2.2. Data Collection

The study used primary cross-sectional data. The data was collected using both qualitative and quantitative methods to understand farmers’ willingness to adopt to CSA and factors influencing adoption [29,30]. The study used structured questionnaires, focused group discussions (FGDs) and Likert scale to collect data from the respondents. The collected data was used to describe socio-economic characteristics of maize farmers’ as well as factors influencing willingness to adopt (WTA) to adopt CSA.

2.2.3. Model Specification

The study uses Double-hurdle regression model estimated using Probit and Tobit (truncated). The model used is as follows:

$$\mathrm{P}\mathrm{*}\mathrm{i}=\mathrm{C}\mathrm{*}\mathrm{i}\alpha +\epsilon \mathrm{i} \left(\mathrm{a}\mathrm{d}\mathrm{o}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{d}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\right)$$

(1)

$$p_i=1 \mathrm{i}\mathrm{f} {p^{*}}_i>0 \mathrm{a}\mathrm{n}\mathrm{d} 0 \mathrm{i}\mathrm{f} {p^{*}}_i<0$$

(2)

$$WTACSA=X{\text{'}}_i\beta +u_i \left(\mathrm{F}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{s} \mathrm{a}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{a}\mathrm{d}\mathrm{o}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\right)$$

(3)

$$y_i=x{\text{'}}_i+u_{i }\mathrm{i}\mathrm{f} y{*}_i>0 \mathrm{a}\mathrm{n}\mathrm{d} {y^{*}}_i>0.$$

(4)

• “${p^{*}}_i$is considered as variable that explained the decision to adopt to CSA by smallholder maize farmers’,
• $p_i$is the variable that is observed adoption decision and takes the value of 1 if the smallholder farmer is willing to adopt at least 3 CSA practices and 0 if otherwise
• $WTACSA$is a dormant variable used to describe the decision on factors affecting the adoption
• $y_i$ is observable variable of adoption measured as the number of CSA practices to adopt
• C and X gives the direction for independent variables for the decision to adopt
• $\alpha$ and $\beta$ are the parameters to be estimated.

The equation below was used to calculate the direction of the relationship of the indicators. This gives rise to the Ordinary least squares equation for the variables. It is as follows.

$$Y=B_0+B_1{X}_1+B_2{X}_2+..............................+B_k{X}_k+\in$$

(5)

$$\begin{gathered} WTAi*=B_0+B_{1}FS+B_{2}EL+B_{3}GND+B_{4}AG+B_{5}AE+B_{6}HS+B_{7}ID+ \\ {B}_{8}CD+B_{9}AES+B_{10}ICSA+B_{11}E+B_{12}S+B_{13}IS+B_{14}CM+\in \end{gathered}$$

(6)

$$\mathrm{W}\mathrm{T}\mathrm{A}\mathrm{i}\mathrm{*}=\left\{\begin{array}{c}1 if WTAi*>0\\ 1 if WTAi*<0\end{array}\right\}$$

2.2.4. Analytical Techniques

Descriptive statistics

Descriptive statistics was used to offer a summary of the sample and its measures. Specifically, descriptive statistics was used to analyze the central tendencies, this includes, mean values, median and mode to address and describe the socioeconomic characteristics of smallholder maize farmers in selected areas of Ga-Makanye, Gabaza, and Giyani.

Double-hurdle regression model

The study used the Double-hurdle model on the presumption that CSA adoption willingness involves two separate judgments [29]. According to Cragg [32], Double-hurdle model implies that smallholder farmers would make two consecutive decisions on whether to adopt CSA [21,31]. Equations (6) and (7) reflect the first hurdle, the CSA adoption (Yes/No) factor, which was estimated using a Probit model. A truncated count distribution model was used in the second hurdle to find factors that affect adoption willingness.

In the Double-Hurdle model, the regression analysis of the probability to adopt CSA is estimated using a truncated regression procedure given by the following equation [31].

$${{ P(WTACSA>0)=\Phi (C}^{*}}_i\alpha \left)\Phi \right(\frac{x_i\beta }{\sigma })$$

(7)

$$E(WTACSA>0)=\Phi {\left(\frac{x_i\beta }{\sigma }\right)}^{-1}$$

(8)

Table 1. Description of model variables for double-hurdle regression model.

Table 1. Description of model variables for double-hurdle regression model.

Dependent variable		Description and Unit of Measurement
Willingness to adopt CSA	WTA*i	Binary: 1 = farmer is willing to adopt climate-smart agriculture 0= otherwise
Variable label	Variable type	Description	Expected sign
Farm size (FS)	Continuous	Size of the farm in hectares	+/-
Educational level (EL)	Continuous	Number of years spend in school	+
Gender (GND)	Dummy	1 = if the farmer is a female, 0= otherwise	+
Age (AG)	Continuous	Age of the farmers in years	+/-
Agricultural experience (AE)	Continuous	Number of years practicing agriculture	+/-
Household size (HS)	Continuous	Number of household members	+/-
Income diversification (ID)	Dummy	1= farmer diversify their level of income, 0= otherwise	+
Crop diversification (CD)	Dummy	1 = farmer diversify their crop production, 0= otherwise	+
Access to extension services (AES)	Dummy	1= farmer has access to extension services, 0= otherwise	+
Information about climate-smart agriculture (ICSA)	Dummy	1= farmer has access to information, 0= otherwise	+
Exposure of the farm to climate risks (E)	Dummy	1= farmer is exposed to climate risks, 0= otherwise	+
Sensitivity to climate risks (S)	Dummy	1= farmer is sensitive to climate risks, 0= otherwise	+/-
Insurance (IS)	Dummy	1= farmer has insurance, 0= otherwise	-
Cooperative membership (CM)	Dummy	1= farmer is cooperative member, 0= otherwise	+/-

3. Results

3.1. Descriptive Results

3.1.1. Smallholder Maize Farmers’ Willingness to Adopt CSA in Ga-Makanye, Gabaza, and Giyani

Figure 2a shows a larger proportion (81%) of sampled smallholder maize farmers in Ga-Makanye willing to adopt climate-smart agriculture (CSA). These farmers were willing to adopt CSA as an adaptation strategy to mitigate the risks posed by climate change. Conversely, a small number (19%) expressed unwillingness to adopt these CSA. Furthermore, Figure 2b shows sampled smallholder maize farmers’ decision to adopt the CSA in Gabaza. About 67% of these farmers were willing to adopt CSA and 33% of them were unwilling to adopt these adaptation strategies due to illiteracy levels and limited capacity to understand new information and felt like these practices will be challenging to learn. Figure 2c indicates about 63% of smallholder maize farmers’ decision to adopt CSA in Giyani and 37% of them who exhibited a reluctance to adopt CSA as their adaptation strategies. Farmers in the three selected study areas mentioned that adopting CSA will require high capital intensive, more costs associated with production and limited resources, hence, they were not willing to adopt.

Table 2 depicts the descriptive results for the categorical socioeconomic variables in the three selected areas: Ga-Makanye, Gabaza, and Giyani. From the table, there is an equal gender distribution in Ga-Makanye while in Gabaza and Giyani there are more female farmers at about 77% and 70, 8%. Smallholder maize farmers in Ga-Makanye were moderately to adequately educated with about 42% and 15.4% of the farmers who obtained secondary and tertiary education, respectively. However, in Gabaza and Giyani, more farmers about 33,3 % and 42,7%, respectively obtained no education, suggesting lower literate levels. Moreover, 14,9% and 10,4 farmers in Gabaza and Giyani managed to obtain tertiary education with 15, 4%, 14, 9%, and 10, 4%.

Additionally, Table 2 revealed that more farmers had access to extension services at Ga-Makanye, Gabaza, and Giyani with about 59, 3%, 66, 7%, and 49%, respectively. This implies that farmers mostly get disseminated information about their production activities, however, these results are disputed by accessibility to information about CSA. There were fewer maize farmers from the sampled areas that had access to information pertaining adaptation strategies (CSA). Thus, 50%, 44, 8% and 45, 8% farmers were aware about various ways to mitigate against the risks posed by climate change in Ga-Makanye, Gabaza and Giyani, respectively.

A greater proportion of sampled maize farmers were highly exposed to the risks posed by climate change in Ga-Makanye (85%), Gabaza (86%) and Giyani (85%). Farmers were exposed to risks such as pest damage, high temperatures, and unreliable rainfall. Larger percentage of farmers in Ga-Makanye, Gabaza, and Giyani, 73%, 63%, and 67% were highly sensitive to climate risks, respectively.

3.1.2. Measures of Dispersion of the Sampled Smallholder Maize Farmers in Ga-Makanye, Gabaza, and Giyani

The study found a significant difference in maize farmers’ age and experience, with older farmers (83 years) having more experience (70 years) and younger ones having less. The average farmer lived with 5 people and had 4 hectares of land. The two-tailed t-test showed that older farmers had more experience, while younger farmers had less experience, and it was statistically significant at 5% level.

Table 3. Measures of dispersion of the sampled smallholder maize farmers in Ga-Makanye.

Table 3. Measures of dispersion of the sampled smallholder maize farmers in Ga-Makanye.

Socioeconomic variable	Mean	St. Deviation	Min	Max	T-test (Sig. 2-tailed)
Age	60	18, 57	21	83	51, 7**
Experience	24	20, 59	3	70	78, 9**
Household size	5	2, 21	2	11	93, 2**
Farm size	4	4, 63	0, 50	19	60, 7**

* * indicates statistical significance at 5%.

The study reveals that the average age and experience level of smallholder maize farmers vary significantly. The mean difference between farmers aged 23 and 1 year and those with more years of experience was found to be extremely significant. The average farmer lived with 5 people and had 2 hectares of land (See Table 4). The results suggest that older farmers have more experience, while younger farmers have less agricultural experience on the field.

The study reveals that smallholder maize farmers have an average age of 64 and 27 years, with varying degrees of experience (See Table 5). The average farmer lives with 6 people and has 2 hectares of land. The results show that the t-test values are smaller than the 95% significance level, indicating that the mean values of farmers’ age and experience do not significantly differ from one another.

3.2. Econometric Results

3.2.1. Test for Multicollinearity

The study used IBM SPSS 29.0 to conduct a multicollinearity test on a binary expected outcome regression model. The test used the variance inflator factor (VIF) to analyze the total effect of each independent variable against all independent variables. The study excluded insurance and age variables due to potential autocorrelation and heteroscedasticity problems. The results indicate that there is sufficient evidence that all variables had VIF that is less than 2 and < 10 (0, 4 – 0,1), with mean VIF of 1, 2885 for the sampled variables (N= 209). These results indicate that there is no multicollinearity problem in the model for sample.

Table 5. Variance inflation factors (VIFs) of the regressors.

Table 5. Variance inflation factors (VIFs) of the regressors.

Explanatory variables	Collinearity statistics
	VIF	1/VIF
Farm size (in hectares)	1, 097	0, 911
Educational level	1, 805	0, 554
Gender of a maize farmer	1, 069	0, 935
Agricultural experience	1, 900	0, 526
Household size	1, 058	0, 945
Income diversification	1, 332	0, 750
Crop diversification	1, 200	0, 833
Access to extension services	1, 169	0, 855
Information about CSA	1, 201	0, 833
Exposure to climate risks	1, 263	0, 792
Sensitivity to climate risks	1, 335	0, 749
Farmers’ cooperative membership	1, 033	0, 968
Mean VIF	1, 2885

VIF – refers to Value inflator factor.

3.2.2. First Hurdle: Probit Regression Model of Results of Sampled Smallholder Maize Farmers in Ga-Makanye, Gabaza, and Giyani (n= 209)

The Double-hurdle regression results are presented in Table 6 and Table 7. The regression model’s Wald statsitics was significant at 1% suggesting a good fit of the model as a whole, Prob >chi2 = 0, 0000, which gives the study sufficient evidence to reject the null hypothesis that all regression coefficients in each hurdle are jointly equal to zero.

The first hurdle shows factors that influence the decision to adopt climate-smart agriculture (CSA) while the second hurdle shows factors that influence the adoption rate and intensity of use among smallholder maize farmers. From the results, a key factor in influencing the decision-making process towards adoption of adapatation strategies is the farmers’ literacy level and accessibility to new information, which requires comprehension skills obtained from educational backgrounds. From the results shown on Table 6, smallholder maize farmers’ eduvational level (EL) p= 0, 2961, Crop diverisfication (CD) p= 0, 4276, and information about CSA (ICSA) p= 0, 5034 positively influenced the willingnes (decision) to adopt CSA. Conversly, smallholder maize farmers agriucltural experience (AE) p= 0, 1621, and household size (HS) p= 0, 0726 negatively influenced the decision to adopt CSA.

3.2.3. Second Hurdle: Probit Regression Model of Results of Sampled Smallholder Maize Farmers in Ga-Makanye, Gabaza, and Giyani (n= 209)

The results of the second hurdle from the Double-hurdle regression model (estimated via Tobit model) are presented in Table 7. The second hurdle estimated the drivers of the adoption of CSA, which uses a maximum likelihood estimator efficient and consistent model parameter. The resuls depeicted on Table 7, indicate that smallholder maize farmers’ EL p= 0, 2816, CD p= 0, 3881, ICSA p= 0, 4355 positively infleunced the adoption of CSA and intesnity to use these practices to adapat towards climate change. Maize farmers’ HS p= 0, 0061 =, and farmers AE p= 0, 0134 negatively infleunced the adoption of CSA to adapt towards climate change.

4. Discussion

5. Conclusion and Recommendations

References

1. Mpundu, M.; Bopape, O. Analysis of Farming Contribution to Economic Growth and Poverty Alleviation in the South African Economy: A Sustainable Development Goal Approach. Int. J. Econ. Financ. Issues 2022, 12, 151–159. [Google Scholar] [CrossRef]
2. Parker, L.; Bourgoin, C.; Martinez-Valle, A.; Läderach, P. Vulnerability of the agricultural sector to climate change: The development of a pan-tropical climate risk vulnerability assessment to inform sub-national decision making. PLoS ONE 2019, 4, 1–25. [Google Scholar] [CrossRef] [PubMed]
3. Derbile, E.K.; Yiridomoh, G.Y.; Bonye, S.Z. Mapping the vulnerability of smallholder agriculture in Africa: Vulnerability assessment of food crop farming and climate change adaptation in Ghana. Sci Direct. J. Envc. 2022, 8. [Google Scholar] [CrossRef]
4. Campbell, B.M.; Thornton, R.Z.; Van Asten, P.; Lipper, L. Sustainable intensification: What is its role in Climate agriculture. Curr. Res. Enviro. Sustain. 2014, 8, 39–43. [Google Scholar] [CrossRef]
5. Hlatshwayo, S.I.; Ngidi, M.S.C.; Ojo, T.O.; Modi, A.T.; Mabhaudhi, T.; Slotow, R. The determinants of crop productivity and its effect on food and nutrition security in rural communities of South Africa. Front. Sustain. Food Syst. 2023, 7, 1091333. [Google Scholar] [CrossRef]
6. Mwangi, M.; Kariuki, S. Factors Determining the Adoption of New Agricultural Technology by Small Scale Farmers in Developing Countries. JESD 2015, 6, 208–216. [Google Scholar]
7. Rangotao, P.M.A. Market Access Productivity of Smallholder Maize Farmers in Lepelle Nkumpi Municipality, Limpopo Province, South Africa. Doctoral Thesis, University of Limpopo, Mankweng, South Africa, 2018. [Google Scholar]
8. Hlophe-Ginindza, S.; Mpandeli, N.S. The Role of Small-Scale Farmers in Ensuring Food Security in Africa. Food Security in Africa. IntechOpen 2021. [Google Scholar] [CrossRef]
9. Ferreira, W.; Ranal, M.; Santana, D. Reference values for germination and emergence measurements. Botany 2022. [Google Scholar] [CrossRef]
10. Harvey, J.A.; Tougeron, K.; Gols, R.; Heinen, R.; Abarca, M. Scientific Warning on Climate Change and insects. Biol. Sci. Fac. Publ. Smith Coll. 2022, 8. Northampton, M.A. Available online: https://scholarworks.smith.edu/bio_facpubs/259.
11. Kamali, B.; Abbspour, K.C.; Lehmann, A.; Wehrli, B.; Yang, h. Spatial assessment of maize physical drought vulnerability in Sub-Saharan Africa: Linking drought exposure with crop failure. Environ. Res. Lett. 2018, 13. [Google Scholar] [CrossRef]
12. Mnkeni, P.N.S.; Mutengwa, C.S.; Chiduza, C.; Beyene, S.T.; Araya, T.; Mnkeni, A.P.; Eiasu, B.; Hadebe, T. Actionable guidelines for the implementation of climate smart agriculture in South Africa. Volume 2: Climate Smart Agriculture Practices. A report compiled for the Department of Environment, Forestry and Fisheries, 2019 South Africa.
13. Makwela, M.A. Determinants of smallholder maize farmers’ varietal choice: A case study of Mogalakwena Local municipality Limpopo Province, South Africa. Master’s degree, University of Limpopo, Mankweng, South Africa, 2021.
14. Ayinde, O.E.; Ajewole, O.O.; Adeyemi, U.T.; Salami, M.F. Vulnerability analysis of maize farmers to climate risk in Kwara state, Nigeria. Department of Agricultural Economics and Farm Management, University of Ilorin, Ilorin, Nigeria. Agrosearch 2018, 18, 25–39. [Google Scholar] [CrossRef]
15. FAO. Climate -Smart Agriculture: Managing Ecosystems for Sustainable Livelihoods. FAO: Rome, Italy, 2022. Available online: https://fao.org/climatechange/29790-0178d452d0ca9af024aad1092d4b78b1d.
16. Matimolane, S.W. Impacts of Climate Variability and Change on Maize (Zea mays) production in Makhuduthamaga Local municipality, Limpopo Province, South Africa. University of Venda, 2018; pp.90-91. Available online: http://dhl.handle.net/11602/1179.
17. Remilekun, A.T.; Thando, N.; Nerhene, D.; Archer, E. Integrated Assessment of the Influence of Climate Change on Current and Future Intra-annual Water Availability in the Vaal River Catchment. J. Water Clim. Chang. 2021, 12, 533–551. [Google Scholar] [CrossRef]
18. Gwambene, B.; Saria, J.A.; Jiwaji, N.T.; Pauline, N.M.; Msofe, N.K.; Mussa, K.R.; Tegeje, J.; Messo, I.; Mwangi, S.S.; Shijia, S.M.Y. Smallhoder farmers’ perception and Understanding of Climate change and Climate Smart Agriculture in the Southern Highlands of Tanzania. J. Resour. Dev. Manag. 2015, 13, 2015. [Google Scholar]
19. Torquebiau, E.C.; Rosenzweig, A.M.; Chatrchyan, N.A.; Khosla, R. Identifying climate-smart agriculture research needs. Cah. Agric. 2018, 27, 26001. [Google Scholar] [CrossRef]
20. Mazibuko, N.L. Selection and implementation of Climate Smart Agricultural Technologies: Performance and willingness for adoption and mitigation. Mit. Clim. Chang. Agric. 2018, 3, 1–37. [Google Scholar]
21. Senyolo, M.P.; Long, T.B.; Blok, V.; Omta, O. How the characteristics of innovations impact their adoption: An exploration of climate-smart agricultural innovations in South Africa. J. C. Prod. 2018, 172, 3825–3840. [Google Scholar] [CrossRef]
22. Sinyolo, S. Technology adoption and household food security among rural households in South Africa: The role of improved maize varieties. Technol. Soc. 2020, 60. [Google Scholar] [CrossRef]
23. Kangogo, D.; Dentoni, D.; Bijman, J. Adoption of climate-smart agriculture among smallholder farmers’: Does farmer entrepreneurship matter? Land Use Policy 2021, 109, 105666. [Google Scholar] [CrossRef]
24. Makamane, A.; Van Niekerk, J.; Loki, O.; Mdoda, L. Determinants of Climate-Smart Agriculture (CSA) Technologies Adoption by Smallholder Food Crop Farmers in Mangaung Metropolitan Municipality, Free State, South African Journal of Agricultural Extension (SAJAE), 2023, 51, 52–74. Available online: https://sajae.co.za/article/view/16451 (accessed on 26 January 2024).
25. StatsSA. Ga-Makanye population. 2022. Available online: https://www.statssa.gov.za/?page_id=4286&id=13152.
26. Kephe, P.N.; Petja, B.M.; Ayisi, K.K. Examining the role of institutional support in enhancing smallholder oilseed producers’ adaptability to climate change in Limpopo Province, South Africa. Oilseeds Fats Crops Lipids 2020, 21, 1–9. [Google Scholar] [CrossRef]
27. Aidoo, D.C.; Boateng, S.D.; Freeman, C.K.; Anaglo, J.N. The effects of smallholder maize farmers’ perception of climate change on their adaptation strategies: The vase of two agro-ecological zones in Ghana. Heliyon 2021, 7, e08307. [Google Scholar] [CrossRef]
28. George, T. Mixed Methods research. Definition, Guide and Examples. Scribbr. 2022. Available online: https://www.scribbr.com/methodology/mixed-methods-research/.
29. Senyolo, M.P.; Long, T.B.; Omta, O. Enhancing the adoption of climate-smart technologies using public-private partnership. Int. Food Agribus. Manag, 2021, 24, 755–776. [Google Scholar] [CrossRef]
30. Mpandeli, S.; Maponya, P. The use of climate forecasts information by farmers in Limpopo Province South. J. Agric. Sci. 2013, 5, 2. [Google Scholar] [CrossRef]
31. Diendere, A.A. Farmers’ perceptions of climate change and farm-level adaptation strategies: Evidence from Bassila in Benin. Afri. J. Agri. Resour. Econom 2019, 14, 42–55. [Google Scholar]
32. Cragg, J.G. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 1971, 39, 829–844. [Google Scholar] [CrossRef]
33. Hitayezu, A.; Wale, E.; Ortmann, G. Assessing farmers’ perceptions about climate change: A double-hurdle approach. Clim.risk.manag, 2017, 17, 123–138. [Google Scholar] [CrossRef]
34. Roco, L.; Engler, A.; Ureta, B.B.; Roas, R.J. Farm level adaptation decisions to face climate change and variability: Evidence from central Chile. Environ. Sci. Policy 2014, 44, 86–96. [Google Scholar] [CrossRef]
35. Ainembabazi, J.H.; Mugisha, J. The role of farming experience on the adoption of agricultural technologies: Evidence from smallholder farmers in Uganda. J. Dev. Stud. 2014, 50, 666–679. [Google Scholar] [CrossRef]
36. Abegunde, V.O.; Sibanda, M.; Obi, A. Determinants of the adoption of Climate-Smart Agricultural practices by small-scale farming households in King Cetshwayo District Municipality, South Africa. Sustainability 2020, 12, 195. [Google Scholar] [CrossRef]
37. Agbenyo, J.S.; Nzengya, D.M.; Mwang, S.K. Perceptions of the use of mobile phones to access reproductive health care services in Tamale, Ghana. Front. Public Health 2022, 10, 1026393. [Google Scholar] [CrossRef] [PubMed]
38. Kalu, C.A.; Mbanasor, J.A. Factors influencing the Adoption of Climate-Smart Agricultural Technologies among Root Crop Farming Households in Nigeria. FARA Res. Rep. 2023, 7, 744–753. [Google Scholar] [CrossRef]
39. Dung, T. Factors influencing Farmers’ Adoption of Climate-Smart Agriculture in Rice Production in Vietnam’s Mekong Delta. Asian J. Agric Dev. 2020, 17, 110–124. [Google Scholar] [CrossRef]
40. Malila, B.P.; Kaaya, O.E.; Lusambo, L.P.; Schaffner, U.; Kilawe, C.J. Factors influencing smallholder Farmer’s willingness to adopt sustainable land management practices to control invasive plants in northern Tanzania. Environ. Sustain. Indic. 2023, 19, 100284. [Google Scholar] [CrossRef]
41. Awiti, A. Climate Change and Gender in Africa: A Review of Impact and Gender-Responsive Solutions. Front. Clim. 2022, 4, 895950. [Google Scholar] [CrossRef]
42. Kurgat, B.K.; Lamanna, C.; Kimaro, A.; Namoi, N.; Manda, L.; Rosenstock, T.S. Adoption of Climate smart Agriculture technologies in Tanzania. Orig. Res. Artic. 2020. [CrossRef]
43. Mthethwa, K.N.; Ngidi, M.S.C.; Ojo, T.O.; Hlatshwayo, S.I. The Determinants of Adoption and Intensity of Climate-Smart Agricultural Practices among Smallholder Maize Farmers. Sustainability 2022, 14, 16926. [Google Scholar] [CrossRef]
44. Negera, M.; Alemu, T.; Hagos, F.; Haileslassie, A. Determinants of adoption of climate smart agricultural practices among farmers in Bale-Eco region, Ethiopia. Heliyon 2022, 8, 09824. [Google Scholar] [CrossRef] [PubMed]