B i o - b a s e d a n d A p p l i e d E c o n o m i c s BAE Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6e172 | DOI: 10.36253/bae-9992 Copyright: © 2022 M.O. Kehinde, A.M. Shittu, M.G. Ogunnaike, F.P. Oyawole, O.E. Fapojuwo. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: M.O. Kehinde, A.M. Shittu, M.G. Ogunnaike, F.P. Oyawole, O.E. Fapojuwo (2022). Land tenure and property rights, and the impacts on adoption of climate-smart practices among small- holder farmers in selected agro-ecolo- gies in Nigeria. Bio-based and Applied Economics 11(1): 75-87. doi: 10.36253/ bae-9992 Received: November 4, 2020 Accepted: February 4, 2022 Published: July 22, 2022 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Donato Romano. ORCID MOK: 0000-0001-7081-4454 AMS: 0000-0002-0857-0337 MGO: 0000-0002-1510-1969 FPO: 0000-0001-5899-7120 Land tenure and property rights, and the impacts on adoption of climate-smart practices among smallholder farmers in selected agro- ecologies in Nigeria Mojisola O. Kehinde1,*, Adebayo M. Shittu1, Maria G. Ogunnaike2, Funminiyi P. Oyawole1, Oluwakemi E. Fapojuwo3 1 Department of Agricultural Economics and Farm Management, Federal University of Agriculture, Abeokuta, P.M.B. 2240, Abeokuta, Ogun State, Nigeria 2 Department of Agricultural Economics and Farm Management, Olabisi Onabanjo Uni- versity, Ago-Iwoye, P.M.B. 0012, Ayetoro, Ogun State, Nigeria 3 Department of Agricultural Administration, Federal University of Agriculture, Abeokuta, P.M.B. 2240, Abeokuta, Ogun State, Nigeria *Corresponding author: E-mail: mojisolaolanike@gmail.com Abstract. This study investigates the effects of land tenure and property rights (LTPRs) on smallholder farmers’ adoption of climate-smart practices (CSPs) among cereal farming households in Nigeria. The data were collected from maize and rice farmers in a Nation-wide Farm Household Survey conducted across the six geopolitical zones in Nigeria. Data collected were analysed within the framework of Multivariate Pro- bit to determine the factors that facilitate and/or impede the adoption of CSPs. The results showed that the adoption of CSPs considered in this study – agroforestry, zero/ minimum tillage, farmyard manure, crop rotation and residue retention - were gener- ally low. Empirical analysis showed that farmers with transfer right were more likely to adopt farmyard manure, crop rotation and residue retention while the likelihood of adopting agroforestry reduced with having transfer right. The coefficient of de jure secure increased the likelihood of adopting zero/minimum tillage while the coefficient of control right increased the likelihood of adopting agroforestry. Again, we found that the adoption of zero/minimum tillage reduced with control and transfer rights. The study also contributes to the existing literature on adoption by recognizing the interde- pendence between different climate-smart practices as well as jointly analyse the deci- sion to adopt multiple CSPs. The study therefore, suggests that governments, in whom the responsibility for land use policy reform lies, review the existing framework to ensure a prompt, fair, and efficient land tenure system. Keywords: climate-smart practices, Land Tenure and property rights, multivariate probit, smallholder farmers, Nigeria. JEL codes: Q15, Q18. 76 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Mojisola O. Kehinde et al. 1. INTRODUCTION Agriculture in the world especially sub-Saharan Africa (SSA) is at a crossroads simply because climate change has brought a menace to the agricultural sec- tor, which must be attended to (IPCC, 2014). Nigeria as one of the African countries is not an exemption in this issue. Climate change poses the greatest challenge to smallholder farmers and threatens the progressive efforts towards poverty alleviation, food security, and sustainable agriculture (Lipper et al., 2014; Vermeulen, et al., 2011). Globally, smallholder farming households are estimated to be between 475-500 million; cultivating less than 2ha of land (Lowder et al., 2016). Many of whom are living in abject poverty and on less than $2 a day, hence, experiencing food insecurity (World Bank Group, 2016; Morton, 2007). Usually, smallholder farmers are the main victims of climate change because of their sole dependency on rain-fed agriculture, limited market access, insecure access to land, cultivation of margin- al and fragmented land as well as inadequate access to technical and/or economic support which can help them to embrace resilient-farming practices (Donatti et al., 2018; Morton, 2007). The world’s climate is changing fast and will con- tinue to do so for the foreseeable future, no matter what measures are now taken. For agriculture, the change will also be significant, as temperatures rise, rainfall patterns change and pests and diseases find new ranges, posing new risks to agriculture and food systems (Cooper et al., 2013). The negative impacts of climate change have led to a reduction in agricultural productivity and sub- stantial welfare losses which eventually lead to food and nutrition insecurity in the populace (Tripathi & Mishra, 2017). Shifting to Climate-smart Agriculture (CSA) seems to be the most efficient way for farmers to reduce the negative impacts of climate change on the produc- tion, incomes, and well-being of vulnerable smallholder farmers (McCarthy & Brubaker, 2014). According to the Food and Agricultural Organi- zation of the United Nations (FAO, 2013), CSA is an unconventional approach to manage land in a sustain- able manner while increasing agricultural productiv- ity (World Bank, 2011). It is aimed to achieve three key goals - sustainably increasing agricultural productiv- ity and incomes; adapting and building resilience to climate change; reducing and/or removing greenhouse gases emissions, where possible (Braimoh, 2015). Cli- mate-smart Practices (CSPs) include inter-cropping, crop rotation, zero tillage, green manuring, application of farmyard manure, integrated soil fertility management, agroforestry, irrigation, changing planting dates as well as alternate wet and dry lowland rice production systems (Bernier et al., 2015). Despite this potential, adoption of CSPs remains generally low, particularly in SSA, Nigeria inclusive. This may, however, not be unconnected with insecure land tenure and property rights (LTPRs), which is often cited as one of the barriers to the adoption of improved technology and investment in land development in Afri- ca (Shittu et al., 2021; Byamugisha, 2013; Liniger et al., 2011). It is pertinent to note that without secure property rights, farmers often do not have the emotional attach- ment to the land they cultivate and would thus, not invest in land improvement that can enhance their pro- ductivity in the long run and promote sustainable devel- opment (Deininger, 2003). Empirical evidence from the literature corroborates the earlier assertion that adoption of CSPs are generally low in Nigeria, usually between 15.5% – 40.6% (Shittu et al., 2018) while the adoption rate for water harvesting, irrigation, and terraces are 15%, 10%, and 30% respec- tively (Onyeneke et al., 2018). They attributed the low adoption to a very weak agricultural extension service delivery system across various states in Nigeria and also to the need for more capital, lack of technical know-how, low potential for irrigation and most importantly pre- sent markets cannot accurately account for the value of the environmental benefits that CSA delivers (Ahiale et al., 2020; Shittu et al., 2018). Gleaning through the lit- erature, some of the factors driving the adoption of the CSPs among smallholders in Nigeria include education, income, credit, extension services, livestock ownership, farming experience, farm size, distance to market and water resources, gender, land ownership, household size, and mass media exposure among others (Oyawole et al., 2020; Amadu et al; 2020; Aryal et al., 2018). Arising from the foregoing, using smallholder farm- ers in selected rice ecologies of Nigeria as a case study, this paper1 will build on the recent work of Shittu et al., (2018) by assessing the influence of LTPRs on the adop- tion of CSPs. We used multivariate probit (MVP) regres- sion analysis, which explicitly allows for correlation in the error terms of the adoption equations to control for inter- dependence in decisions on CSPs’ adoption. The paper contributes to the ongoing debates on LTPRs and the adoption of CSPs in Africa’s smallholder agriculture in a number of ways. First, technology adoption remains one of the most researched areas in the field of agricultural 1 An earlier version of this paper, titled ‘Land Tenure and Property Rights Impacts on Adoption of Climate Smart Practices among Cereals Farmers in Nigeria’, was presented at the 18th Annual National Confer- ence of Nigerian Association of Agricultural Economists, October 16th – 19th, 2017. 77 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Land tenure and property rights impacts in Nigeria economics, very few studies have looked at the factors that determine the adoption of CSPs in Nigeria. Second, meth- ods that recognise the interdependence between different climate-smart practices and jointly analyse the decision to adopt multiple CSPs - agroforestry, farmyard manure, crop rotation, zero tillage, and residue retention are used. This study attempts to fill these identified gaps. In the next section, we describe the theoretical framework underpinning the adoption of CSPs and the econometric approach of multivariate probit. Section three (3) presents the methodology in which we have the study area, research design as well as measurement of land tenure and property rights. In section four, we pre- sent and discuss the results, while the final section pre- sents the main conclusions and the policy implications. 1.1 Brief on land tenure and property rights in Nigeria LTPRs have to do with the rights that individu- als, communities, families, firms, and other community structures hold in land and associated natural resources. As noted by Feder and Feeny (1991), the rights on the land are “either de facto or de jure secure” if they are clearly defined, exclusive, enforceable, transferable, and recognized by relevant authorities. In Nigeria, the land use act made provision for granting two types of land use rights - customary and statutory rights of occupancy - to all categories of land users (Land Use Act [LUA], 2004). Customary right of occupancy is granted under the Act by the local government councils to individuals, firms, and communities while the Statutory right of occupancy is the right to use land in any part of the state and it is granted under the Act by the State Governor (LUA, 2004; Kehinde et al., 2021). A certificate of occupancy is issued to a land user as evidence of being granted the statu- tory right of occupancy on the land by the State Gover- nor, thus making the certificate of occupancy the high- est form of land title in Nigeria. Issuance of certificate of occupancy requires that the landowner possesses a purchase receipt, duly stamped deed of transfer, and an approved boundary survey of the land. The customary rights of occupancy are governed by the largely unwrit- ten customary laws in various localities and are also con- sidered de facto held by holders of agricultural lands in rural areas that have been under use for agricultural pur- poses prior to the enactment of the Land Use Act of 1979 (LUA, 2004; Shittu et al., 2018). Shittu et al. (2018) show that when the land has not been issued a certificate of occupancy, it is subject to unfair expropriation, though the LUA made everybody an occupant of the land. Landowners that acquired their land through direct inheritance and outright purchase enjoy customary rights on their land even though that title is not officially certificated but they are recognized as having a secure title on their land from the customary point of view. Both the latter and the former will enjoy statutory rights of occupancy when the de facto-held land is moved to the highest level of tenure security (de jure secure) by getting the land surveyed, registered with the state government, and possibly obtain the certificate of occupancy. It is important to note that freehold land is still susceptible to unfair expropriation if it is not reg- istered with the government. Table 1 shows the different land tenure types, possible types of rights with their lev- el of tenure security. 2. ANALYTICAL FRAMEWORK 2.1 Multivariate Probit Model Multivariate probit regression framework was used to analyze the factors that facilitate or impede the adop- tion of CSPs, following Scognamillo and Sitko, (2021), Aryal et al., (2018), Kpadonou et al., (2017), Timu et al., (2013), and Teklewold et al., (2013). The model is an extension of the probit model used for the estimation of several correlated binary choices jointly (Greene, 2003). Considering several agricultural technologies, there is the possibility that some level of interdependence may exist among the technologies with farmers adopting some of these technologies as substitutes, complements or supplements. A farming household would be adopting one or more of the components of CSPs if and only if Table 1. Kinds of rights and tenure security by mode of land acquisition. Mode of land acquisition Use right Control Right Transfer right De facto Secure De jure secure Freehold (Inherited & Purchased √ √ √ √ × Communal √ √ × √ × Leasehold √ √ × × × 78 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Mojisola O. Kehinde et al. the utility expected is higher than otherwise. A positive correlation of the error terms means the technologies are complements while negative correlations of the errors terms imply the technologies are substitutes (Teklewood et al., 2013; Belderbos et al., 2004). If a correlation exists, simply estimating the tech- nology adoption equations independently will generate biased and inefficient estimates of the standard errors of the model parameters for each technology (Greene 2008), inducing incorrect inference as to the determi- nants of technology adoption. Dorfamn (1996) observed that the estimates of separate probit equations (univari- ate probit) exclude useful economic information con- tained in interdependence and simultaneous adoption decisions. Hence, when farmers adopt a combination of technologies to deal with land degradation rather than adopting just a single practice or technology, the adop- tion decision is inherently multivariate. Hence, the MVP estimator corrects for these problems by allowing for non-zero covariance in adoption across technologies. Thus, the observed outcome of CSPs adoption can be modelled following a random utility-based estimation framework. Consider the ith farm household i=(1,…,N) facing a decision on whether to adopt the available CSPs on plot p(p=1,…,P). Let U0 represent the benefits to the farmer from tra- ditional management practices, and let Uk represent the benefit of adopting the kth CSPs: vis-a-vis, agroforestry (AG), farmyard manure (FY), crop rotation (CR), zero tillage (ZT), residue retention (RR). The farmer decides to adopt the kth CSPs on plot p if Y*ipk=U*k-U0>0. The net benefit (Y*ipk) that the farmer derives from the adoption of kth CSPs is a latent variable determined by observed household, plot (Zip) and socio-economic characteristics Xi and the error term εip: Y*ipk=Z’ipδk+X’iβi+εip (k=AF,FY,CR,ZT,RR) (1) Using the indicator function, the unobserved prefer- ences in equation (1) translate into the observed binary outcome equation for each choice as follows: (k=AF,FY,CR,ZT,RR) (2) Equation 1 can be rewritten as a system of equa- tions that can be estimated simultaneously using equa- tion 3; Y*1pk=β’1X1i+Z’1iδk+ε1i Y1pk=1 if Y*1pk>0, Y1pk =0 otherwise Y*2pk=β’2X2i+Z’2iδk+ε2i Y2pk=1 if Y*2pk>0, Y2pk =0 otherwise ⋮ (3) Y*Npk=β’kXki+Z’kiδk+εki YNpk=1 if Y*Npk>0, YNpk =0 otherwise In the multivariate model, where the adoption of several CSPs is possible, the error terms jointly fol- low a multivariate normal distribution (MVN) with zero conditional mean and variance normalized to unity (for identification of the parameters) where (μAF,μFY,μCR,μZT,μRR), MVN (0,Ω) and the symmetric covariance matrix Ω is given by: (4) The off-diagonal elements in the covariance matrix represent the unobserved correlation between the sto- chastic components of the different types of CSPs. This assumption means that equation (2) generates a MVP model that jointly represents decisions to adopt farming practices. This specification with non-zero off-diagonal elements allows for correlation across the error terms of several latent equations, which represent unobserved characteristics that affect the choice of alternative CSPs2. The computation of the ma ximum likelihood function based on a multivariate normal distribution requires multidimensional integration. Different simu- lation methods were proposed to approximate such a function (Train, 2002). The Geweke–Hajivassiliou– Keane (GHK) simulator is a particularly popular choice in empirical research (Geweke et al., 1997). The GHK simulator exploits the fact that a multivariate normal distribution function can be expressed as the product of sequentially conditioned univariate normal distribu- tion functions, which can be accurately evaluated (Cap- pellari and Jenkins, 2003). The GHK simulator relies on a Cholesky factorization, and to do this, the estimate of the correlation matrix at each iteration must be positive definite. 3. METHODOLOGY 3.1 The study area The study was conducted in selected farming com- munities reputed for maize and rice production across the six geopolitical zones, and covering five of the seven Agro-ecological zones (AEZs) of Nigeria, viz; rainforest zone, derived, southern Guinea, northern Guinea, and Sudan savannah zones respectively. Nigeria is situated 2 The authors acknowledge that the correlation between the error terms in a system of simultaneous equation depend on the correct specification of the model. 79 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Land tenure and property rights impacts in Nigeria in the West African region and lies between longitudes 3° and 14° and latitudes 4° and 14°. It has a landmass of 923,768 sq. km. Nigeria shares a land border with the Republic of Benin in the west, Chad and Cameroon in the east, and Niger in the north. Its coast lies on the Gulf of Guinea in the south and it borders Lake Chad to the northeast (Udo et al., 2018). Administratively, it is made of 36 Federating States and the Federal Capital Territory. The States are com- monly grouped into six geopolitical zones: Northeast, Northwest, North-central, Southeast, Southwest, and South-south geopolitical zones and seven Agro-ecologi- cal zones - all of which are suitable for maize and rice, among several other crops like cassava, yams, etc. 3.2 The study design The study was part of the FUNAAB-RAAF-PASAN- AO project implemented by the Federal University of Agriculture, Abeokuta in partnership with the National Cereals Research Institute, Baddegi, and funded by the Economic Community of West African States. The cen- tral focus was on Incentivising Adoption of Climate- Smart Agricultural Practices in Cereals Production in Nigeria. The data were collected across selected agro- ecologies in Nigeria, focusing on maize and rice farmers. The respondents were selected in a three-stage sampling process, described as follows: Stage I: Purposive selection of 15 States that have been the leading rice and maize producers in Nige- ria (excluding conflict-prone areas), based on production statistics from (National Bureau of Statistics [NBS], 2016). Stage II: Purposive selection of three Agricultural Blocks per State per crop from the main rice and maize producing areas of the State, and two Extension Cells per block - that is, six blocks per state, 12 Cells per State and 180 Cells in all. Stage III: Proportionate stratified random selection of 12 Rice and maize farmers from members of Rice/Maize farmers’ association in each of the selected Cells. This process yielded 2,007 households of maize and rice farmers, from which complete datasets were collect- ed through personal interviews of the farmer and other farming members of their households. Data were collect- ed on a wide range of issues, including the households’ socio-economics, climate-smart practices, and LTPRs on farmland cultivated during the 2016/17 farming season. 3.3 Dependent variables The outcome variables considered in this study are the CSPs. The respondents were asked to recount the type of CSPs practiced on each of their plots - agrofor- estry, farmyard manure, crop rotation, zero/minimum tillage, and residue retention (Table 2). Agroforestry refers to the intentional integration of trees and shrubs into crop and animal farming systems to create envi- ronmental, economic, and social benefits. The inten- tional nature of agroforestry made many of the sampled farmers fall short in this regard; hence, only 9% of the respondents practiced agroforestry on their farms. Farmyard manure, on the other hand, refers to the application of a decomposed mixture of livestock waste on the farming plot. It is a major component of nutri- ent management with potential benefits of soil fertility maintenance as well as supply of major nutrients such as Nitrogen, Phosphates, and Potash. Out of the total plots, about 24% of these plots received manure. Crop rotation involves growing different crops sequentially on the same plot of land to optimize nutri- ents and reduces the incidence of weeds, pests, and dis- eases (Bockel et al., 2013). In our case, any farmer that plants different crops following a particular sequence and includes a leguminous crop in the rotation was con- sidered as having practiced crop rotation. Based on this concept, only a few of the farmers (8%) practiced crop rotation on their farms. Zero/minimum tillage is part of CSPs that promotes minimum soil disturbance and allows crop residue to remain on the ground with the accompanying benefits of better soil aeration and improved soil fertility. Mini- mum soil disturbance requires less traction power and fewer carbon emissions from the soil (Delgado et al., 2011). In our case, zero/minimum tillage practice entails reduced tillage with a single plough and/or the use of traditional farm tools such as hoe and cutlass. Zero/ minimum tillage was practiced on 22% of the plots. The use of residue retention is another option of CSPs that provides an opportunity for the farmers to retain crop residues as an alternative to biomass burn- Table 2. Adoption rates of climate-smart practices. Variable Mean Std. Error Min Max Agroforestry 0.090 0.005 0 1 Farmyard manure 0.240 0.007 0 1 Crop rotation 0.080 0.005 0 1 Zero/Minimum tillage 0.220 0.007 0 1 Residue retention 0.540 0.009 0 1 80 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Mojisola O. Kehinde et al. ing and/or exporting the residues from the farm to feed livestock (Bockel et al., 2013; Abrol et al., 2017). Residue retention was practiced on about 54% of the plots during the cropping season considered for this analysis. 3.4 Independent variables The description and the summary statistics of the variables are given in Table 3. Specifically, the models include socio-demographic characteristics such as age, sex, year of schooling, household size, extension contact, farmers’ association among others. 3.4.1 Socio-demographic characteristics With respect to socio-demographic characteristics, Table 3 shows that the average age of the smallholder farmers across the six geopolitical zones is 45 years. This implies that the majority of the respondents were still in their active years implying significant participa- tion in the farming activities. This result, however, con- tradicts the findings of Eze et al., (2011) who did a simi- lar study and obtained the mean age of his respondent to be 59 years. Only 12% of the respondents were female indicating that the majority of the sampled smallhold- er farmers were male. The mean year of schooling is 8 years while those that had access to extension services and belong to one farmers’ association or the other were 63% and 94% respectively. As shown in Table 3, about two-thirds (60.0%) of the respondents have the right to control their land while about 58.0% have the right to transfer their parcels perma- nently to the third party across the study locations. Table 3 further shows that about half (52.0%) of the parcels were held as inheritance across the study locations, however, this result is much less than the findings of Bamire (2010) who found that 84.0% of farmland was acquired through inheritance in the dry savannah part of Nigeria. On the contrary, about 14.0% of the parcels were purchased by the farm households, 24.0% on leasehold while 10.0% were communal land while only 4.0% of the farmland were titled, i.e., registered with the land registry in the study area. This implies that only a few out of the sampled farm- ers had legal tenure security while the majority had inse- cure tenure (de jure) which can lead to eviction from their Table 3. Definitions and Summary Statistics of the Variables Used in the Analysis. Variable Description Mean SEM Socio-economic characteristics Age Age of the farmers in years 44.58 0.21 Sex 1 = if the sex of the farmer is female, 0 otherwise 0.12 0.01 Schooling year Farmers’ education level in years 7.74 0.10 Household size Number of persons in the household 9.25 0.11 Extension Contact 1 = Extension Contact during the last planting season 0.63 0.01 Amount Borrowed Amount of money borrowed in naira. 99633 11200 Farmers’ association 1 = belong to farmers’ association, otherwise 0 0.94 0.02 TLU Tropical livestock unit (Livestock wealth)1 3.28 0.23 Plot-level characteristics Control right 1 = has control right, 0 otherwise 0.60 0.008 Transfer right 1 = has transfer right, 0 otherwise 0.58 0.008 De jure secure 1 = if registered with the state, 0 otherwise 0.02 0.002 Inherited 1 = cultivates land acquisition by inheritance, 0 otherwise 0.52 0.008 Purchased 1 = land acquisition by purchase, 0 otherwise 0.14 0.01 Leasehold 1 = Land acquisition by leasehold, 0 otherwise 0.24 0.01 Communal 1 = Land acquisition by communal means, 0 otherwise 0.10 0.01 Boundary survey 1 = Has boundary survey, 0 otherwise 0.18 0.007 Farm size (ha) Cultivated farmland in ha 1.60 0.04 Lowland 1 = cultivates lowland, otherwise 0 0.42 0.01 Extent of farm fragmentation The extent of land fragmentation computed using Simpson index 0.35 0.01 Note: SEM (Standard error of mean). 1 TLU conversion factor according to Beyene and Muche (2010): 1 head of cattle = 0.7 TLU, 0.1 TLU for 1 sheep or goat or pigs and 0.01 TLU for poultry. 81 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Land tenure and property rights impacts in Nigeria farmland and regular harassment by land grabbers. A plausible reason for this is that the process of titling land is inexplicably tedious and expensive. Thus, given that most farmers in Nigeria are smallholders and resource poor, they may not allocate their scarce financial resources to land titling. The mean size of household landholdings was 1.60ha portraying the respondents as smallholders. The average farmland that is fragmented is 35% implying that about one-third of the cultivated farmland in Nigeria is not completely consolidated. 3.4.2 LTPRs’ measurement Two indicators were employed in assessing the LTPRs of farmers in this study. They include: i. Rights Type: This was measured on a nominal scale using three dummy variables – Use, Control, and Transfer rights. Use right refers to the right to access the resource, withdraw from a resource or exploit a resource for economic benefit. Control right on the other hand refers to the ability to make decisions on how the land should be used including decid- ing what crops should be planted, and who benefits financially from the sale of crops, etc. while Trans- fer right refers to the ability to transfer land (per- manently through sale). Each of the types of rights takes the value of one if the farmer has the right to use, control, and transfer the parcel of land. Oth- erwise, the dummy variables were assigned a zero. Meanwhile, the use right was dropped as the refer- ence rights-type variable. ii. De jure secure: A tenure was classified as de jure secure if the parcel has been surveyed and duly reg- istered with the land registry; otherwise it was clas- sified as insecure. This variable was meant to deter- mine the importance of title registration. 4. RESULTS AND DISCUSSION 4.1 Determinants of adoption of climate-smart agricultural practices The estimates of the determinants of the probability of adoption of climate-smart agricultural practices are presented in Table 4. The Wald test (χ2 (70) = 404.66; Prob > χ2 = 0.0000) of the hypothesis that regression coefficients in all the equations were jointly equal to zero was rejected at 1% indicating that the model fits the data reasonably well. The coefficient of transfer right is positive and sig- nificant at 1%, 5%, and 10% levels respectively for the adoption of farmyard manure, crop rotation, and resi- due retention. Hence, transfer right has positive impact on the adoption of farmyard manure, crop rotation, and residue retention, suggesting that farmers are more likely to adopt these CSPs on owned plots. This is in line with the Marshallian inefficiency hypothesis where input use by the tenant on rented or borrowed land is lower or less efficient than on owned land (Gray and Kevane, 2001). This finding may also be due to tenure insecurity, as an insecure tenure status leads to poor agricultural prac- tices (Gray and Kevane, 2001). The long-term dimension of the return on investment in land-enhancing practices such as farmyard manure may discourage land-insecure farmers to adopt them as they may not control the land long enough to reap the benefits of their investments. Similarly, the coefficient of control right is positive and significant at 10% level for agroforestry, implying that the likelihood of adopting agroforestry rises with farmers’ having control right. On the contrary, the coef- ficient of transfer right is negative and significant at 10% for agroforestry. This implies that having transfer rights reduce significantly the likelihood of agroforestry in the study area. The possible explanation for this might be that farmers are not interested in agroforestry because of its upfront investment that does not yield any immediate returns; they possibly prefer to dispose of the land in the nearest future at a higher price. The result is contrary to the findings of Patanayak et al., (2003) who found that landowners are more likely to adopt agroforestry than tenants are because the latter may be prevented from planting trees, as it is less likely that agroforestry will be adopted on insecure land. Again, we found that the coefficients of control and transfer right significantly and negatively influence the adoption of zero/minimum tillage at 1% level. Secu- rity of land tenure, (de jure secure), positively and sig- nificantly affects the adoption of zero/minimum tillage implying that the probability of adoption of zero/mini- mum tillage is higher when ownership on land is secure. This flows from the fact that rationally, a farmer may not be willing to adopt any CSPs on land that he/she does not have secure rights to in the long run. As Arthur Young succinctly puts it in his 1792 treatise, “Give a man the secure possession of a bleak rock, and he will turn it into a garden; give him a nine years’ lease of a garden, and he will convert it into a desert”. This gives credence to the findings of Owombo et al., (2015) that secure land tenure significantly influences farmers’ adoption of agri- cultural technology in Ondo State, Nigeria. The coefficient of farm size is negative and sig- nificant at a 1% level for agroforestry. This means that additional hectares of land by the smallholder farmers 82 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Mojisola O. Kehinde et al. reduced significantly the possibility of adopting agro- forestry; this is simply because the target population for this study is smallholders with an average farm size of 1.60ha. It is also good to note that fragmented farm- land does not reduce the adoption of zero tillage in the study area. The level of education of the cereal farmers has a sig- nificant positive relationship with the adoption of crop rotation. This suggests that farmers with higher levels of education are more likely to adopt crop rotation. This finding is consistent with that of Langyintuo and Meku- ria (2005) who assert that educated farmers are better able to process information that can enhance production and productivity in agriculture (Ali and Abdulai, 2010). On the contrary, female-headed households are less like- ly to adopt crop rotation when compared to their male counterparts; this might be because of the level of skill/ expertise involved in planting different crops sequentially on the same plot. It is important to note that gender dif- ferentiation has no impact on the adoption of any other CSPs. The coefficient for household size is negative and significant at 5% levels for the adoption of crop rotation, suggesting that larger household size is associated with a lower probability to adopt crop rotation. This is consist- ent with the finding of Bekele and Drake (2003) who find the family size to have a significantly negative relation with certain adoption choices. The coefficient of farmer’s age is positive and sig- nificant at a 1% level for the adoption of zero/minimum tillage in Nigeria. This might be because of their farm experience which makes the older farmers be in a bet- ter position to adopt new agricultural practices due to their comparative advantage in terms of capital accumu- lated, frequency of extension contacts/visits, and cred- itworthiness among others (Langyintuo and Mekuria, 2003). Hence, an experienced farmer is more conscious of the benefits of soil conservation and he would go for adopting the minimum tillage technology. This finding, however, contradicts that of Adesina and Zinnah (1993) who noted that younger farmers are more amenable to change old practices than older farmers because they tend to be more aware and knowledgeable about new technologies. On the other hand, an inverse relation- ship exists between age and the decision to adopt resi- due retention among cereals farmers in Nigeria. This can be because the younger farmers are usually more willing to take risks and are likely to perceive increased profits from adoption in terms of accommodating the relative labour-intensive nature that comes with adopting resi- due retention as against other CSPs (Soule et al., 2000; Aryal et al., 2018; Ekboir, 2003). Hence, the greater will- ingness to adopt the new agricultural practices. Meanwhile, the level of education of the cereal farm- ers has a significant positive relationship with the adop- tion of zero/minimum tillage, suggesting that farmers with higher levels of education are more likely to adopt zero/minimum tillage. This finding is consistent with that of Shiyani et al., (2000) who asserted that education has a positive impact on the adoption of new technology. The role of education enlightens the farming community with the importance of minimum disturbance of the soil in particular. The adoption of zero/minimum tillage is usually known to reduce the labour required on the farm, hence for larger families where labour is sufficiently available, adoption may not bring many benefit. Hence, the a prio- ri expectation is that a larger family size will be inversely related to the adoption of zero/minimum tillage. Our finding (Table 4) shows a negative relationship between the household size and adoption of zero tillage among the cereal farmers in the study area, indicating that the more the household size, the less likely the adoption of zero/minimum tillage. This is in line with the findings of La Rovere et al., (2010) and Laxmi and Mishra (2007). 4.2 Adoption decisions of climate-smart agricultural prac- tices The MVP model is estimated using the maximum likelihood method on plot-level observations. Table 5 shows the likelihood ratio test [chi square (10) = 161.736, p = 0.000)] of the null hypothesis that the covariance of the error terms across equations is not correlated is rejected. These findings confirm the interdependence between the adoption decisions of CSPs, which may be due to complementarity or substitutability in farming practices, but also potentially, to omitted factors that affect all adoption decisions. Consequently, farmers do not decide upon a single practice to adopt; instead, the probability of adopting a practice is conditional on whether other practices have already been adopted. The estimated correlation coefficients are statistically significant in seven of the ten pair cases, where five coef- ficients have negative signs and the remaining two have positive signs. The result shows that farmyard manure is complementary with crop rotation while agroforestry complements residue retention. The complementarity between manure and crop rotation contradicts the find- ing of Teklewold et al., (2013) where they found substi- tutability. The correlation between adoption of zero/ minimum tillage and residue retention is the highest (19.64%) while that of farmyard manure and agrofor- estry is the least (5.32%). The negative strong correla- tion between residue retention, zero/minimum tillage, 83 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Land tenure and property rights impacts in Nigeria and farmyard manure is logical as the use of one CSP can discourage the adoption of the other one. These findings suggest that using ordinary probit or logit regression to assess the determinants of CSPs adoption among smallholder farmers in Nigeria will yield ineffi- cient estimates. We, however, estimated the model by a set of probit regression (Appendix 1), the result of which shows that the evidence from our study are robust to estimation methods. Though, one will expect that sepa- rate probit regression analysis will yield large standard error but we found that the coefficients (βs) and stand- ard errors resulting from each probit regression analysis are the same or nearly the same as that of the multivari- ate probit estimate. Hence, we conclude that using ordi- nary probit to assess the determinants of CSPs adoption among smallholder farmers is consistent and asymptoti- cally efficient with large sample (3,311). 5. CONCLUSIONS AND POLICY IMPLICATIONS This study was carried out to assess the effects of LTPRs on farmers’ adoption of climate-smart practices among smallholder farmers in Nigeria. A multi-stage sampling procedure was used to sample 2,007 farm households across 180 farming communities in Nigeria, Table 4. Influence of LTPRs on adoption of climate-smart practices among smallholder farmers: multivariate probit estimates. Agroforestry Farmyard manure Crop rotation Zero tillage Residue retention Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Control right 0.1277* 0.0738 0.0207 0.0576 -0.0134 0.0765 -0.3654*** 0.0583 -0.0507 0.0521 Transfer right -0.1220* 0.0723 0.1839*** 0.0573 0.1532** 0.0765 -0.5192*** 0.0583 0.0991* 0.0516 Dejure secure -0.0729 0.2186 -0.2341 0.1780 -0.3115 0.2709 0.3153* 0.1636 0.1504 0.1523 Age -0.0005 0.0026 0.0014 0.0021 0.0019 0.0028 0.0065*** 0.0021 -0.0051*** 0.0018 Sex -0.0517 0.0967 -0.1080 0.0771 -0.2045* 0.1134 0.0817 0.0778 -0.0025 0.0682 Schooling year -0.0062 0.0053 -0.0075* 0.0041 0.0119** 0.0056 0.0075* 0.0044 -0.0038 0.0038 Household size -0.0036 0.0048 0.0046 0.0036 -0.0130** 0.0055 -0.0247*** 0.0044 0.0045 0.0034 Amount borrowed -3.39E-08 8.31E-08 -4.84E-08 5.39E-08 3.23E-08 4.73E-08 2.31E-08 5.05E-08 2.88E-08 4.15E-08 Farmers association 0.0111 0.0214 0.0223 0.0166 0.0086 0.0223 -0.0016 0.0182 0.0018 0.0154 Extension contact -0.0391 0.0638 0.0457 0.0506 0.0071 0.0681 -0.1776*** 0.0526 0.0408 0.0456 TLU -0.0039 0.0046 0.0004 0.0021 0.0037 0.0023 -0.0039 0.0030 -0.0022 0.0020 Farm size -0.0118*** 0.0045 -8.2E-05 0.0027 0.0005 0.0036 -0.0024 0.0029 -0.0017 0.0025 Extent of land fragmentation -0.0218 0.1085 -0.1780** 0.0834 0.1681 0.1123 0.4113*** 0.0885 0.0070 0.0755 Lowland -0.0147 0.0636 0.0705 0.0496 0.0465 0.0671 -0.0177 0.0528 0.1260*** 0.0451 Constant -1.1136* 0.1594 -0.8917*** 0.1250 -1.6749*** 0.1722 -0.4639*** 0.1298 0.2095* 0.1126 Wald chi-square (70) 404.66 404.66 404.66 404.66 0.0583 404.66 Log-likelihood -7452.52 -7452.52 -7452.52 -7452.52 -7452.52 Prob > chi2 0 0 0 0 0 Number of obs. 3,311 3,311 3,311 3,311 3,311 Table 5. Results of the Wald Test of Simultaneity of the decisions to adopt CSPs. Error correlation1 Coefficient p - value rho21 (Farmyard manure & Agroforestry) -0.0532 0.064 rho31 (Crop rotation & Agroforestry) 0.0015 0.969 rho41 (Zero/minimum tillage & Agroforestry) -0.0747 0.011 rho51 (Residue retention & Agroforestry) 0.1694 0 rho32 (Crop rotation & Farmyard manure) 0.0814 0.047 rho42 (Zero/minimum tillage & Farmyard manure) -0.1897 0 rho52 (Residue retention & Farmyard manure) -0.0922 0.001 rho43 (Zero/minimum tillage & Crop rotation) 0.0493 0.192 rho53 (Residue retention & Crop rotation) 0.0346 0.307 rho54 (Residue retention & Zero/minimum tillage) -0.1964 0 1 Likelihood ratio test of rho21 = rho31 = rho41 = rho51 = rho32 = rho42 = rho52 =rho43 = rho53 = rho54 = 0.00 Chi-square (10) = 161.736 Prob > chi square = 0 84 Bio-based and Applied Economics 11(1): 75-87, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9992 Mojisola O. Kehinde et al. and data collected were analysed within the framework of Multivariate Probit regression. The results showed that the adoption of CSPs considered in this study – agroforestry, zero/minimum tillage, farmyard manure, crop rotation, and residue retention – were generally low. Policymakers thus need to target practices with lower adoption rates and provide farmers with further incen- tives towards the intensification of their use. Another major highlight of this paper is the appar- ent existence of complementarities between different CSPs such as the use of farmyard manure and crop rota- tion, use of residue retention, and agroforestry. A poten- tial strategy could be to promote agricultural practices that show some degree of complementarity as a pack- age rather than independently. This may reduce the time required between when the farmer adopts the first tech- nology and the subsequent adoption of other technolo- gies and hence realising the full and extensive benefits of CSPs as a package. The effects of transfer right on the adoption of farm- yard manure, crop rotation, and residue retention are crucial in targeting those farmers that have appropri- ate socio-cultural characteristics that favour the adop- tion of the CSPs in question. Secondly, awareness and promotional strategies should be tailored depending on whether the target farmers resemble factors for/against adoption. Our findings confirm that tenure security will increase the likelihood that farmers will reap the returns from the long-term investments such as zero/minimum tillage without unfair expropriation. Therefore, policy measures that will focus on a more effective and efficient land title registration system should be established by the government. 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Influence of LTPRs on Adoption of Climate-smart Practices among Smallholder Farmers: Probit Estimates.     Agroforestry Farmyard manure Crop rotation Zero Tillage Residue retention Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Control right 0.1273* 0.0738 0.0183 0.0575 -0.0164 0.0767 -0.3746*** 0.0581 -0.0447 0.0522 Transfer right -0.1214* 0.0722 0.1814*** 0.0571 0.1539** 0.0768 -0.5192*** 0.0582 0.1010* 0.0517 De jure secure -0.0747 0.2188 -0.2198 0.1762 -0.2998 0.2688 0.3200* 0.1638 0.1406 0.1522 Age -0.0005 0.0026 0.0015 0.0020 0.0019 0.0028 0.0066*** 0.0021 -0.0051*** 0.0018 Sex -0.0519 0.0967 -0.1080 0.0771 -0.2030* 0.1134 0.0817 0.0778 -0.0069 0.0683 Schooling year -0.0062 0.0053 -0.0075* 0.0041 0.0120** 0.0056 0.0072 0.0044 -0.0040 0.0038 Household size -0.0036 0.0048 0.0044 0.0036 -0.0128** 0.0055 -0.0254*** 0.0044 0.0044 0.0034 Amount borrowed -3.18E-08 8.24E-08 -4.71E-08 5.31E-08 3.36E-08 4.72E-08 2.07E-08 5.07E-08 2.97E-08 4.12E-08 Farmers association 0.0114 0.0214 0.0228 0.0166 0.0091 0.0223 -0.0022 0.0182 0.0009 0.0153 Extension contact -0.0390 0.0638 0.0463 0.0506 0.0085 0.0681 -0.1763*** 0.0527 0.0411 0.0457 TLU -0.0038 0.0045 0.0004 0.0021 0.0037 0.0023 -0.0035 0.0029 -0.0022 0.0020 Farm size (ha) -0.0119*** 0.0045 -0.0001 0.0027 0.0005 0.0036 -0.0024 0.0029 -0.0016 0.0025 Extent of land fragmentation -0.0208 0.1085 -0.1746** 0.0833 0.1647 0.1125 0.4287*** 0.0885 0.0079 0.0756 Lowland -0.0144 0.0636 0.0715 0.0495 0.0458 0.0671 -0.0202 0.0529 0.1260*** 0.0452 Constant -1.1144*** 0.1593 -0.8915*** 0.1248 -1.6746*** 0.1725 -0.4679*** 0.1298 0.2074* 0.1129 LR chi-square (14) 18.35   39.46   32.03   340.93   26.16   Prob > chi 2 0.1911 0.0003 0.004 0 0.0247 Log-likelihood -1004.07 -1789.02 -872.688 -1568.52 -2272.86 Pseudo R2 0.0091 0.0109 0.018 0.098 0.0057 Number of obs. 3,311   3,311   3,311   3,311   3,311