Adoption of climate‐smart practices and its impact on farm performance and risk exposure among smallholder farmers in Ghana

Issahaku, Gazali; Abdulai, Awudu Article — Published Version Adoption of climate‐smart practices and its impact on farm performance and risk exposure among smallholder farmers in Ghana Australian Journal of Agricultural and Resource Economics Provided in Cooperation with: John Wiley & Sons Suggested Citation: Issahaku, Gazali; Abdulai, Awudu (2019) : Adoption of climate‐smart practices and its impact on farm performance and risk exposure among smallholder farmers in Ghana, Australian Journal of Agricultural and Resource Economics, ISSN 1467-8489, Wiley, Hoboken, NJ, Vol. 64, Iss. 2, pp. 396-420, https://doi.org/10.1111/1467-8489.12357 This Version is available at: https://hdl.handle.net/10419/230017 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Adoption of climate-smart practices and its impact on farm performance and risk exposure among smallholder farmers in Ghana Gazali Issahaku and Awudu Abdulai † Increased climate variability during the last four decades has made the agricultural environment in many developing countries more uncertain, resulting in increasing exposure to risk when producing crops. In this study, we use recent farm-level data from Ghana to examine the drivers of individual and joint adoption of crop choice and soil and water conservation practices, and how adoption of these practices impacts on farm performance (crop revenue) and exposure to risks (skewness of crop yield). We employ a multinomial endogenous switching regression model to account for selectivity bias due to both observable and unobservable factors. The empirical results reveal that farmers’ adoption of crop choice and soil and water conservation leads to higher crop revenues and reduced riskiness in crop production, with the largest impact on crop revenues coming from joint adoption. The findings also show that education of the household head, access to extension and weather information influence the likelihood of adopting these practices. Thus, enhancing extension services and access to climate information and irrigation can reduce gaps in adoption of soil and water conservation and crop choice, considered as climate-smart practices that will eventually improve crop revenues and reduce farmers’ exposure to climate-related production risks. Key words: Africa, climate-smart practices, farm performance, impact assessment, risk exposure. 1. Introduction Climate variability continues to be a major challenge to achieving food security in sub-Saharan Africa (SSA) due to the incidence of high temperature, erratic rainfall regimes, coupled with low adoption of modern technologies (IPCC 2007; World Bank 2010). Although sub-Saharan Africa contributes less than 5 per cent of global greenhouse gas (GHG) emissions, it is the most vulnerable to the negative effects of climate change, as the region’s development prospects are closely linked to climate because of heavy reliance on rainfall (IAASTD 2009; Tol 2018). The vulnerability has been attributed to structural, technological and institutional weaknesses, higher poverty, as well as relative proximity to the equator (IPCC 2007). The impact of climate change on agricultural productivity especially in developing countries is well † Gazali Issahaku and Awudu Abdulai is Professor (e-mail: [email protected]) are with the Department of Food Economics and Consumption Studies, University of Kiel, Kiel, Germany. Gazali Issahaku holds a PhD in Agricultural Economics and a Lecturer in the Department of Climate Change and Food Security, University for Development Studies, Tamale, Ghana. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. doi: 10.1111/1467-8489.12357 Australian Journal of Agricultural and Resource Economics, 64, pp. 396–420 documented (IPCC 2007; Di Falco and Veronesi 2013; Gunathilaka et al. 2018). The key issue is not whether climate change will have adverse impact on crop productivity, but the extent of productivity losses from climate variability or uncertainties and the prospect of mitigating the negative impacts through adoption of appropriate climate-smart practices. The international community has recommended the incorporation of adaptation into national development plans (IPCC 2007; World Bank 2010). A better understanding of adaptation is critical, especially in developing countries and in the agricultural sector, because of their vulnerability to climate change (IPCC 2007; Tibesigwaet al. 2014). As argued by Tol (2018), adaptation is being considered by economists more widely as part of important measures to complement climate mitigation. Various climatesmart practices, including planting of new crop varieties, changing planting dates, growing drought-resistant crops, use of crop insurance mechanisms, irrigation, and adoption of soil and water conservation measures, have been used by farmers in developing countries to cope with the negative effects of climate change and to ensure high yields (Di Falco and Veronesi 2013; Adamson et al. 2017). Thus, a practice may be considered as ‘climate-smart’, if it falls within the three main objectives of climate-smart agriculture, stated by the FAO (2013) as: (a) sustainably increasing agricultural productivity and incomes; (b) adapting and building resilience to climate change; and (c) reducing greenhouse gas emissions. Although the promotion of climate-smart agriculture in sub-Saharan Africa is ongoing as part of many developing countries’ sustainable agricultural development policy (Lipper and Zilberman 2018), empirical evidence shows that adoption rates among smallholder farmers are still low (Arslan et al. 2015; Barnard et al. 2015). Promotion of climate-smart agriculture in Ghana gained momentum since the country ratified the United Nations Framework Convention on Climate Change in 1995 (EPA 2011). The Kyoto Protocol was adopted by Ghana’s Parliament in 2002 and eventually led to the current National Climate Change Policy (Ministry of Environment, Science, Technology and Innovation 2015). Through various state and non-state agencies, Ghana has sought to make climate-smart agriculture part of its agricultural development policy (MoFA 2018). There exists extensive literature on adoption impacts of individual climatesmart practices, with divergent findings (e.g. Di Falco and Chavas 2009; Kato et al. 2011; Di Falco and Veronesi 2013; Abdulai and Huffman 2014; Zougmore et al. 2014; Ng’ombe et al. 2017). Among the frequently mentioned pathways include climate-smart agriculture’s ability to increase crop yields, food and nutrition security, reduction in crop failure (e.g., Kato et al. 2011; Di Falco and Veronesi 2013; Abdulai and Huffman 2014). Other studies report lower farm returns from plots treated with certain soil conservations practices (e.g. stone bunds) in Burkina Faso (World Bank 2009), while Nkala et al. (2011) find no significant effect of minimum tillage on household incomes in Mozambique. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. Climate-smart practices and farm performance 397 Furthermore, Di Falco and Chavas (2009) find a positive effect of biodiversity on risk reduction among barley farms in Ethiopia. The study by Di Falco and Veronesi (2013) also indicates that adaptation to climate change, through adoption of soil conservation, changing crop varieties, switching from early to late planting and other measures, led to increased yield of maize among farm households in Ethiopia. Other studies have indicated that soil conservation, crop choice and other practices can increase technical efficiencies among farmers, as well as minimise on-farm environmental damage (Solis et al. 2007; Veettil et al. 2017; Sabiha et al. 2017). Although these studies contribute towards the understanding of the factors driving the adoption of climate-smart practice and impacts on productivity and risk exposure, there is a gap in the literature about the potential complementarity or substitutability among individual and combined climate-smart practices. In addition, the mixed findings from these studies about adoption impacts on farm performance also provide motivation for further empirical investigation into the potential impacts of specific climate-smart agricultural practices on crop revenues and production risk exposure, with respect to agroecology. A few studies have evaluated adoption and impacts of multiple climatesmart practices on smallholder farmers’ productivity and risk exposure, usually from a monocropping perspective (e.g. maize, rice or wheat) (e.g. Di Falco and Veronesi 2013; Kassie et al. 2014; Ng’ombe et al. 2017). However, this approach of analysing farm productivity and risk from a monocropping perspective might under-estimate or over-estimate the true impacts of adoption for a number of reasons. First, implementation of climate-smart practices, like soil and water conservation in a mixed cropping setting, might offer benefits to other crops including maize or sorghum, which could not be captured if the analyst considered only maize yield and excluded other crops. Second, there may also be negative interaction among crops in a mixed-crop setting, where only yield of one crop increases at the expense of others. Analysing the benefits of conservation agriculture on productivity of farms in a mixed-crop setting, Tessema et al. (2015) observed that some crops enhance the productivity of others. For instance, in maize–cowpea mixed cropping, maize yields could be enhanced due to atmospheric nitrogen fixation by cowpea. Hence, it is prudent to analyse productivity by capturing outputs of all crops rather than that of a single crop. In this study, we examine joint adoption of climate-smart agricultural practices and how adoption impacts on crop revenues and exposure to production risk among mixed-crop farmers in Ghana. We define climatesmart practice more broadly to include crop choice and soil and water conservation measures (FAO 2013). Crop choice as climate-smart agricultural practice is defined to include the use of modern varieties, droughtresistant and early maturing varieties that enable crop farmers to cope with erratic rainfall or short rainfall season. It also captures changing crops in response to climate variability, particularly rainfall. A number of studies have linked adoption of crop choice/switching crops and planting dates to farmers’ ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. 398 G. Issahaku and A. Abdulai climate change adaptation behaviour (e.g. Deressa et al. 2009; Di Falco and Veronesi 2013). It is common to intercrop cereals and other crops, especially in northern Ghana. Soil and water conservation also refers to the use of erosion control and other measures to prevent soil and nutrient loss and conserve soil moisture, such as minimum tillage, soil and stone bunds, and use of za€ ıtechniques. The za€ ıtechnique is a soil conservation method that concentrates run-off water and organic manure in small round or square pits (Zougmore et al. 2014). In Ghana, it is mainly used in the dry Savannah zones, particularly in the Upper East region. Strategies that seek to minimise soil loss due to erosive rains, or reduce evaporation of water from the soil due to high temperatures, are expected to help improve crop performance (see Kato et al. 2011; Abdulai and Huffman 2014). We contribute to the empirical literature by employing recent advancements in the impact assessment literature (e. g. Bourguignon et al. 2007; Teklewold et al. 2013; Wooldridge 2015), particularly the use of multinomial endogenous switching regression that enables us to account for selection bias within a multinomial setting. The approach, therefore, enables us to identify location-specific information on adoptable climate-smart practices, as well as impacts of adoption on farm performance and exposure to production risk. To the best of our knowledge, this might be the first of such studies in Ghana and among a few in sub-Saharan Africa. Specifically, we first examine the factors that affect farmers’ decisions to adopt crop choice, and soil and water conservation measures, individually and jointly. Secondly, we determine the impacts of adoption on crop revenues and risk exposure among mixed-crop plots. We employ recent survey data and use a multinomial endogenous switching regression approach (Bourguignon et al. 2007) to achieve our research objective. Given the fact that our sample is made up of mixed-crop plots, we capture crop revenue as the value of all crops cultivated by the household on each plot (see Kato et al. 2011). The procedure by Antle (1983) is employed to estimate the crop revenue skewness, which is used as a proxy for downside risk or probability of crop failure. An increase in crop revenue skewness lowers the probability of crop failure, which implies a decrease in downside risk (Di Falco and Chavas 2009). Our study is relevant to the debate on whether farmers should adopt practices individually or as a package. This study will also contribute to efforts at identifying Ghana’s Nationally Determined Contributions, through which developing countries are expected to articulate their climate mitigation actions and commitment to implementation of the Paris Agreement (United Nations Framework Convention on Climate Change, 2015; MoFA 2018). To the extent that climate-smart agriculture overlap with several development goals, such as poverty reduction and food security, the empirical findings from this study can have important implications for climate policy in subSaharan Africa (Vale 2016; Tol 2018). The rest of the paper is organised as follows. In the next section, we present the conceptual framework and econometric specification, as well as the ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. Climate-smart practices and farm performance 399 estimation procedures. The description of the data and the variables employed in the empirical strategy are presented in Section 3. In Section 4, the empirical results are discussed, while the final section highlights the main conclusions and policy implications of the study. 2. Conceptual framework and econometric specification We examine adoption and impacts of two climate-smart practices on farm performance. We follow previous studies (Di Falco and Chavas 2009; Kassie et al. 2014) and calculate crop revenue skewness distribution, that is approximated using the third central moment of crop revenue distributions. Crop revenue skewness is a good indicator of farm performance, especially under climate uncertainty because skewness captures the exposure to downside risk (Antle 1983; Di Falco and Chavas 2009). Thus, an increase in the crop revenue skewness implies a reduction in the probability of crop failure (Di Falco and Chavas 2009). Estimating the moments of crop revenues follows a sequential estimation procedure by first regressing 1 crop revenue per acre on production inputs and other farm-level variables, after which the residuals are retrieved. The third moment is calculated by raising the residual to the third power (Di Falco and Chavas 2009). The estimated third moment of crop revenue is used as outcome variables in the multinomial endogenous switching regression model to examine the impact of individual and joint adoption on risk exposure. 2.1 Modelling choice of climate-smart practice Let us assume that the farmer’s objective to use a combination of climatesmart practices is to maximise expected benefits. The i th plot’s expected benefit from application of a combination of practices jis represented as V ij. However, the expected benefits captured by the latent variable V ij, cannot be observed, but can be expressed as a function of observed characteristics (X i ), as well as unobserved factors (eij) as: V ij ¼XijbjþhjXij þeij ð1Þ For the adoption decision, let V i denote an index that indicates the farmer’s observed choice of a combination of practices, such that: Vi¼ 1 iff V i1[max k6¼1ðV ikÞor ei1\0 ::: Miff V iM [max k6¼jðV ijÞor eiM\0 8 < : ð2Þ where max k6¼jðV ik V ijÞ\0. Equation 2 indicates that a farmer will apply climate-smart practice jon plot ito maximise expected benefit, if the chosen 1 The OLS estimates of the crop revenue function are not reported in this paper to save space. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. 400 G. Issahaku and A. Abdulai practice provides greater expected benefit than any other alternative option k6¼ j, that is if eij ¼max k6¼jðV ik V ijÞ\0;8j;k2M. In this study, the adoption of two climate-smart practices, crop choice, and soil and water conservation, results in four possible combinations from which the farmer can choose (namely: crop choice only; soil and water conservation only; joint adoption; or non-adoption). Assuming that eij in Equation 1 is independently and identically Gumbel distributed, the probability that practice jwill be chosen can be specified by a multinomial logit (MNL) model as (McFadden 1973). Pij ¼Peij\0jXi  ¼exp XijbjþXijdj  PM k6¼1exp XijbkþXijdk ð3Þ where Xij denotes a vector of average plot-specific variables and djrefers to the corresponding parameters to be estimated. The estimation of parameters of the latent model in Equation 3 is done by maximum-likelihood approach. We then model the chosen strategies within the multinomial endogenous switching regression framework (MESR) to link the climate-smart practices to the outcomes ofinterest, namely croprevenues and distribution of revenue skewness. 2.2 Multinomial endogenous switching regression model The multinomial endogenous switching regression (MESR) model was proposed by Bourguignon et al. (2007) and has been applied in empirical studies (e.g. Di Falco and Veronesi 2013; Teklewold et al. 2013; Ng’ombe et al. 2017). We employ this approach in this study. The base category, nonadoption is indicated as j=1. For the remaining practices (j=2 crop choice, =3 soil and water conservation only, and j=4 joint adoption), at least one climate-smart practice combination is applied on a plot. The outcome equation for each potential regime jis given as: Regime 1 :yi1¼Zi1a1þZi1hjþui1if Vi¼1 ::: Regime M:yij ¼ZijajþZijhjþuij if Vi¼J 8 < : ð4Þ where y ij is the outcome variable (crop revenue or risk exposure) of the i th farm plot in regime M,Z i represents a vector of farm and household characteristics, and the u’s denote error terms with expected values of zero and constant variance,Var uijjXi;Zi  ¼r2 j, while ajrepresents a vector of parameters to be estimated. The variable Zirefers to mean plot-specific characteristics (e.g. soil fertility, plot slope and drainage level), and hjdenotes the corresponding parameters to be estimated. This is essential in order to account for unobserved heterogeneity due to plot varying characteristics being correlated with household level variables when a household cultivates multiple plots (Mundlak 1978). A Wald test of the null hypothesis that the ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. Climate-smart practices and farm performance 401 vector hjare jointly equal to zero is conducted to indicate the relevance of plot-specific heterogeneity (Teklewold et al. 2013). To ensure that the estimates of ajin Equation 4 are unbiased and consistent, inclusion of selection correction terms derived from the multinomial selection process is required. We follow Bourguignon et al. (2007) and assume that the error terms (eij) and u ij are linearly correlated for every j option, such that the expected value of u ij is stated as E½u1je1;...ej¼rP j¼1...M qjej, where qjis the correlation between u ij and eij, while ris the standard deviation of the error term u ij . Thus, the outcome equation (Equation 4), taking into consideration the choices made with bias correction, can be restated as in Teklewold et al. (2013): Regime 1 :yi1¼Zi1a1þr1^ ki1þZihjþxi1if Vi¼1 :::: : Regime M:yij ¼Zijajþrj^ kij þZihMþxij if Vi¼J 8 < : ð5Þ where kij ¼P M k6¼j qj ^ Pikln ^ Pik ðÞ 1^ Pik þln ^ Pij  refers to the inverse Mills ratios computed from the estimated probabilities in MNL model in Equation 3, qjis the correlation coefficient between the error terms eij and uij, with the error terms xij assumed to have a zero mean, and ^ Pij represents the estimated probability that plot iis treated with practice j. 2.3 Estimation of counterfactual and treatment effects We estimate expected outcomes in the actual and counterfactual scenarios following Di Falco and Veronesi (2013) and Ng’ombe et al. (2017). Specifically, we first derive the expected outcomes of plots that were treated, which in our study means j¼2;...M(j¼1 is the reference category, i.e. nonadoption). From Equation 5, the conditional expectations for each outcome variable-based practice are chosen as follows: Adopters with adoption (actual adoption observed in the sample): Eðyi2jVi¼2Þ¼Zi2a2þr2^ ki2þZih2 EðyiJjVi¼JÞ¼Zijajþrj^ kij þZihj ð6Þ The counterfactual case that adopters did not adopt is also stated as: Eðyi1jVi¼2Þ¼Zi2a1þr1^ kij þZihj Eðyi1jVi¼jÞ¼Zija1þr1^ kij þZihj ð7Þ The impact of adopting practice jis denoted as the average treatment effect on the treated (ATT), which is calculated by subtracting Equation 6 from 7 to obtain Equation 8 as follows: ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. 402 G. Issahaku and A. Abdulai ATT ¼Eðy2ijVi¼2ÞEðyi1jVi¼2Þ ¼Zi2a2a1 ðÞþZi2h2h1 ðÞþ ^ ki2r2r1 ðÞ The term ^ kij :ðÞ, together with the Mundlak device (Zi2), accounts for selection bias and endogeneity due to unobserved heterogeneity. The MESR approach enables consistent and efficient estimation of ajand accounts for a reasonable correction of bias in the outcome equations, even when the independence of irrelevant alternatives (IIA) assumption is not met (Bourguignon et al. 2007). Another advantage of using this approach is the ability to evaluate impact of both individual and combination of climate-smart practices (Di Falco and Veronesi 2013). Furthermore, it relaxes the restrictive assumptions of Lee’s (1983) 2 selectivity model and provides a complete description of selectivity impacts on all options considered by farmers. For proper identification of the MESR model, including some variables in vector Xithat are not included in vector, Ziis recommended (Bourguignon et al. 2007). We use farmers’ perception of drought, as well as access to climate information and association membership as identifying instruments. These variables intuitively influence farmers’ decisions to adopt climate-smart agricultural practices but might not directly affect farm revenues (Di Falco and Veronesi 2013). We confirm the validity of these instruments by performing a falsification test, whereby a variable is considered as a valid instrument if it affects farmers’ decisions to adopt a practice, but not the outcome variables among non-adopters (Di Falco and Veronesi 2013). We further performed a robustness check of our results by employing an alternative approach using multivariate treatment effects, which also accounts for unobservable factors in a multinomial choice and impact analysis framework (Deb and Trivedi 2006). We control for potential endogeneity of some explanatory variables in our model, particularly off-farm work participation and extension visits. Off-farm work participation is potentially endogenous because adoption of some climate-smart practices is labour-intensive and households engaged in offfarm work may not be able to adopt such practices (labour-loss effect). On the other hand, income earned from off-farm work may be used to purchase inputs or invested in climate-smart practices (income-effect). In the case of extension visits, it is possible that farmers who are adopting may attract more visits by extension staff than non-adopters. Potential endogeneity of the variables was addressed using the control function approach (Wooldridge 2015). The approach involves the specification of the potential endogenous variable (i.e. off-farm work participation or extension visit) as a function of explanatory variables influencing adoption of each practice, together with a 2 In Lee’s method, a single selectivity term is estimated for all choices (Lee 1983; Bourguignon et al. 2007). ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. Climate-smart practices and farm performance 403 Turning to the effects of other variables, the results in Table 3 further demonstrate that herbicide use significantly influences crop revenue among adopters of soil and water conservation only and joint adopters, but not crop choice only. This implies that application of herbicide could be a complementary input in effective adoption of soil and water conservation and result in high crop revenue. Rainfall anomaly (RFanom) has a negative and significant effect on crop revenue, with greater magnitude among nonadopters, suggesting that adoption of climate-smart practices might have played a role in minimising the negative effect of rainfall anomaly on crop revenue among adopters. This finding is consistent with Food and Agriculture Organization’s principle of climate-smart agricultural practices that seek to enhance farmers’ resilience and ability to adapt to climate variability (FAO 2013). The coefficient of plot-level fertility (Fertility) has the expected positive sign on crop revenue, particularly for adopters of soil and water conservation and joint adoption. Off-farm work participation (Off-farm) positively significantly influences crop revenue, implying possible income effect of offfarm work participation on farm output. The effect of other variables on the skewness or downside risk exposure by climate-smart practice is reported in Table A4 in the Appendix 1 5 . 4.3 Impact of adoption of climate-smart practices on crop revenue and risk exposure The impacts of adoption of individual and combined climate-smart practices on crop revenue and skewness (risk exposure) are presented in Table 4. Here, expected crop revenue (log) under the observed case that the farmer adopted the strategies, and the counterfactual situation that they did not adopt are indicated. The results show that the adoption of crop choice and soil and water conservation practices leads to significant improvement in crop revenues. The highest log revenue effect (1.149) is obtained from the joint adoption of crop choice and soil and water conservation strategies (approximately 20.6 per cent), which is greater than the effect of each practice adopted independently, suggesting complementarity of the two climate-smart practices. In particular, the impacts of adoption of crop choice only, and soil and water conservation only are 13 per cent and 12 per cent increase in crop revenues, respectively. These findings are consistent with the results reported by Teklewold et al. (2013) for Ethiopia and Ng’ombe et al. (2017) for Zambia. The results also show that in all the counterfactual cases, adopters would have had lower crop revenues if they had not adopted. The results also reveal that the adoption of crop choice and soil and water conservation individually or jointly significantly increased crop revenue skewness, which indicates a reduction in the probability of crop failure or revenue loss. Specifically, adoption of individual options results in increased 5 For brevity, these estimates are not discussed in here. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. 410 G. Issahaku and A. Abdulai skewness by 32 per cent and 35 per cent for crop choice and soil and water conservation, respectively. The joint adoption of the two practices results in a 40 per cent increase in skewness, indicating complementarity in lowering the probability of crop failure. These results confirm earlier findings by Kassie et al. (2014) for farms in Malawi that adoption of on-farm climate-smart practices decreases farmers’ exposure to downside risk and therefore reduces the probability of crop failure. To provide further information about the impacts of individual and combination of climate-smart practices, we disaggregated the adoption impacts (ATT) by agro-ecological zones. The results demonstrate that joint adoption of the two practices has the highest positive and statistically significant impact on crop revenues for plots in the Sudan Savannah (ATT =0.955). However, joint adoption has no significant impact on crop revenues in the Transitional zone. Interestingly, joint adoption appears to reduce downside risk in all agro-ecological zones. This location-specific impact analysis provides important additional information that could be Table 3 Determinants of log crop revenue by climate-smart practices: second-stage MESR estimation Variables Non-adoption (n = 175) Crop choice (n = 186) Soil and Water cons (n = 353) Joint adoption (n = 287) Estimate SE Estimate SE Estimate SE Estimate SE Constant 3.26 3.45 -3.43 4.38 -0.242 1.13 -0.54 3.29 Age 0.19* 0.10 0.22 0.17 -0.067 0.07 0.102 0.07 Gender -0.930* 0.50 -1.14 9.21 3.148 3.76 -4.51 4.17 Household size -0.05 0.14 -0.20 0.24 0.07 0.06 -0.052 0.11 Education 0.63** 0.28 0.70 0.54 1.21** 0.24 0.33 0.23 Farm size -2.15** 0.74 -2.39* 1.28 0.03 0.61 -1.86** 0.64 Livestock 3.30** 1.38 3.56 2.66 1.96** 0.23 1.54** 0.21 Off-farm 4.22** 1.97 4.35* 2.58 1.18** 0.59 3.30* 1.67 Fertiliser 1.17** 0.51 1.32 0.96 1.32* 0.43 0.69*** 0.23 Herbicide 0.53** 0.22 0.506 0.34 1.19** 0.13 1.17** 0.14 Rainfall -0.05 0.04 0.035 0.05 0.01 0.01 0.001 0.04 Temp -9.30 10.75 1.14 1.36 0.68 3.62 -2.16 10.28 RFanom -2.93** 1.11 -1.27** 0.49 -1.99 0.51 -1.16*** 0.14 Tem-anom -5.71* 2.98 -2.98* 1.57 -1.00 1.13 -0.58 0.48 Temp x RFanom 0.11 0.21 0.03 0.10 0.89 0.56 0.54 0.42 Extension 1.72** 0.74 1.85 1.43 5.71** 2.34 8.78** 4.33 Slope -5.68*** 1.80 4.14 3.34 -1.16 1.65 1.958 1.54 Erosion -1.19** 0.45 -1.30 1.02 -4.16 4.25 -5.22** 2.69 Drainage -5.058** 2.23 -5.38 4.36 1.47 1.89 -2.79 1.98 Fertility 1.55** 0.53 1.60 1.06 1.35** 0.48 0.71** 0.33 Selectivity terms m1 -0.16 0.51 -1.92 1.31 1.58* 0.89 1.43* 0.85 m2 1.68 2.08 0.34 0.51 -1.99** 0.75 0.48 0.50 m3 -1.82* 0.95 2.70 1.66 0.50 0.49 0.25 0.86 m4 -1.68 1.12 -1.27 1.07 -0.89 1.05 0.05 0.38 ***, **, *Represent 1%, 5% and 10% significance level, respectively. Bootstrapped standard errors in parentheses. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. Climate-smart practices and farm performance 411 Table 4 Average treatment effects of adoption of individual and combined strategies on log crop revenue and downside risk Outcome Adoption decision ATT ATT by Agro-ecological zone Change in outcome (%) Sudan Savannah Guinea Savannah Transitional Zone If adopters adopted If adopters had not adopted Log crop revenue Crop choice 5.848 5.192 0.656***(0.088) 12.63 0.262**(0.091) 0.252**(0.128) 0.519***(0.160) Soil and Water conservation 5.978 5.356 0.622**(0.227) 11.62 0.884***(0.075) 0.222***(0.056) 0.235*(0.129) Joint adoption 6.714 5.565 1.149***(0.100) 20.64 0.955***(0.084) 0.126**(0.058) 0.115(0.411) Skewness (downside risk) Crop choice 1.280 0.970 0.310***(0.018) 32.0 0.202***(0.001) 0.365***(0.045) 0.388***(0.054) Soil and water conservation -0.150 -0.231 0.081***(0.005) 35.0 0.162***(0.010) 0.193***(0.010) -0.067*(0.039) Joint adoption 0.734 0.523 0.211***(0.007) 40.4 4.341***(0.387) 4.293***(1.101) 2.390***(0.203) ***, **, * represent 1%, 5% and 10% significance level, respectively. Figures in brackets refer to standard errors. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. 412 G. Issahaku and A. Abdulai useful in promoting adoption of climate-smart agriculture in Ghana. A multivariate treatment effect regression (Deb and Trivedi 2006) was estimated as a robustness check 6 , and the results which are presented in Table A5 in the Appendix 1 show positive impact of individual and joint adoption of climatesmart practices. The results of the multivariate treatment effect regressions are generally consistent with that of the MESR, except in the case of impact of soil and water conservation on crop revenue. One probable explanation why the multivariate treatment effect estimate of soil and water conservation only is different (not significant) from the estimate from the multinomial endogenous switching regression model is that the former estimates population average treatment effect (ATE) while the later model estimates average treatment effect on the treated (ATT). Thus, if we were to rely on multivariate treatment effect, soil and water conservation only, as a climate-smart agricultural practice, we would have found it to have a statistically insignificant effect on farm revenues. However, with the multinomial endogenous switching regression model, the effect was positive and significant, a finding that is consistent with study by Abdulai and Huffman (2014) about effect of the practice on yield and farm revenues, while using the endogenous switching regression model. As noted by Clougherty et al. (2015), while the multivariate treatment effect approach involves only a shift of the intercept or the endogenous treatment, the multinomial endogenous switching regression (MESR) method involves the shift of the intercept, as well as differences in relevant coefficients of other treatments. Overall, the findings emphasise the importance of adoption of crop choice and soil and water conservation among farmers as a means of managing exante production risk, especially under climate uncertainty. The results do not support the notion that farmers who adopt climate-smart practices to avoid crop failure end up obtaining lower yields (Adamson et al. 2017). The findings further demonstrate some complementarity between crop choice and soil and water conservation practices as shown by the greater effect of joint adoption on both crop revenue and skewness of crop output. This finding would not have been possible if we had examined these climate-smart practices individually without considering the joint adoption effect. 5. Conclusions and policy implications In this paper, we used farm-level data from three agro-ecological regions in Ghana to examine the determinants and impacts of adoption of two climatesmart practices (crop choice and soil and water conservation) on crop 6 Following an anonymous reviewer’s comment, we decided to do this analysis to compare the estimates of the multivariate treatment effects approach to the MESR method adopted in this study. While in the MESR approach, impact is determined by predicting outcomes and testing the differences between adopters of various choices and non-adopters, the marginal effects of the individual choices (relative to non-adoption) represent impacts in the multivariate treatment effects model. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. Climate-smart practices and farm performance 413 revenues and production risk exposure, measured as crop revenue skewness. We employed a multinomial endogenous switching regression (MESR) model to account for selectivity bias due to observable and unobservable factors. The empirical results showed that the highest crop revenue effect is obtained from the joint adoption of crop choice and soil and water conservation practices, suggesting complementarity in benefits. In addition, joint adoption of the two strategies significantly increased crop revenue skewness, implying that adoption lowers the probability of crop failure and therefore decreases the exposure to expected downside risk. A disaggregation of the adoption impacts based on agro-ecological zones revealed that plots in the dry savannah zones experienced higher impacts of joint adoption, compared to plots in the transitional zone. The findings also revealed that extension access, farmer education, climate anomalies and farmers’ perception about drought and access to weather information are key determinants of adoption of crop choice and soil and water conservation measures. Thus, policy interventions to increase agricultural productivity and reduce farmers’ risk exposure should consider alleviating farmers’ difficulties to adoption. For instance, government ministries (e.g. Ministry of Food and Agriculture) in collaboration with private agri-input dealers associations could facilitate the distribution of inputs, such as drought-tolerant seeds and herbicides, through certified agro-input outlets in farming communities, to enhance adoption. In addition, making quality climate information accessible to farmers will ease their adoption challenges including the right combination of practices to adopt. In view of the fact that effective adoption of climatesmart practices requires some knowledge and skills, enhancing farmer education and access to extension services should be among the policy measures that will facilitate adoption. This study particularly demonstrated that package adoption of crop choice, and soil and water conservation practices will enable farmers to benefit from the positive synergistic effects of joint adoption on farm performance and reduction in risk exposure. The findings of this study should be considered with some caveats since we relied mainly on cross-sectional survey data. First, analysis of panel data would have enabled us to capture the dynamic effects of climate-smart practices on crop revenues and risk exposure. For instance, some climatesmart agronomic measures such as soil and water conservation measures (e.g. stone bunds and minimum tillage) take time to produce effects, and the effects of climate-smart practices may last over several cropping seasons. Second, an experiment to determine farmers’ risk preferences would have been a more appropriate proxy for measuring and estimating risk exposure, but data on these measures are not available. Despite these caveats, we do not expect systematic bias in our assessment. Thus, this study contributes to the growing body of literature on climate-smart agriculture and how the adoption of specific farm practices affects farm performance in an area where there is limited access to formal risk reduction measures, such as agricultural insurance. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. 414 G. 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Climate-smart practices and farm performance 417 Appendix Table A2 Means of variables by choice of climate-smart practices and pooled sample Variables Nonadoption Crops choice only Soil and water conservation only Joint adoption Pooled sample SD Crop revenue 473.77 606.74* 625.42** 665.02*** 562.79 24.52 Off-farm 0.31 0.54 0.38 0.29 0.36 0.02 Age 38.10 40.40 40.04 36.27 38.91 0.42 Gender 0.75 0.83** 0.88*** 0.93*** 0.86 0.01 HH_size 6.17 5.96 6.59 7.72*** 6.58 0.11 Education 4.18 5.99*** 3.93 4.07 4.47 0.17 Farm size 5.99 7.18** 6.35*** 8.63** 7.16 0.18 Fertiliser 254.28 236.91 249.97 337.17** 259.66 18.76 Hiredlabour 173.90 97.23* 125.46 190.72 147.63 13.13 Herbicide 131.71 38.13** 40.10** 130.30 77.40 13.86 Livestock 1.04 2.06** 1.77* 1.10** 1.80 5.60 Extension 0.51 1.01** 0.93*** 1.19*** 0.89 0.04 Perceptiondrought 0.59 0.86*** 0.66** 0.87*** 0.75 0.01 Climate-info 0.44 0.35* 0.22*** 0.21*** 0.29 0.25 FBO-mem 0.12 0.46*** 0.45* 0.43** 0.30 0.46 Slope 0.58 0.43 0.70* 0.53 0.58 0.43 Erosion 0.82 0.53* 0.46 0.42 0.54 0.42 Drainage 0.56 0.32 0.90** 0.53 0.46 0.42 Fertility 0.24 0.33 0.46 0.42 0.14 0.23 N 175 186 353 287 1001 *, **, *** denotes significance level at 10%, 5% and 1%, respectively. Table A1 The distribution of crops on plot of respondent farmers Crop % of plots Maize 28.57 Rice†14.38 Millet 11.24 Sorghum 7.37 Groundnut 14.19 Yam 2.94 Cassava 4.33 Vegetables 15.85 Number of plots 1,001 †Apart from rice, the rest of the crops were mostly intercropped. ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. 418 G. Issahaku and A. Abdulai Table A3 Test of validity of instruments used to identify the MESR model Variables Crop revenue of non-adopters Revenue skewness of non-adopters Perception-drought -0.149 (0.237) 0.273 (0.901) Climate-info -0.112 (0.170) -1.020 (0.646) FBO-memb 0.004 (0.172) 0.577 (0.654) Constant 6.259*** (0.642) 1.198 (2.438) F-tests on instruments 1.202 [p = 0.234] 1.160 [p = 0.327] Note: Standard errors in parentheses. The values in the square bracket indicate the p-values of the F-test indicating the validity of the instruments used to identify the MESR model. Table A4 Determinants of downside risk by climate-smart practices: second-stage MESR estimation (dep. variable: revenue skewness) Variables Non-adoption (n = 175) Crop choice (n = 186) Soil and water cons (n = 353) Joint adoption (n = 287) Estimate SE Estimate SE Estimate SE Estimate SE Constant 0.89 2.08 -2.41 1.52 0.98 1.11 0.62 0.82 Gender -0.41* 0.23 -0.72** 0.33 0.22 0.17 -0.20 0.14 HH_size -0.44 0.58 -1.27* 0.71 0.42 0.36 -0.14 0.25 Education 2.82* 1.51 4.41** 1.97 -1.38 1.02 1.51 0.93 Farm size -6.73* 2.78 -1.29** 0.52 2.32 2.63 -5.53** 2.70 Livestock 1.47 0.82 2.21** 0.99 6.49*** 2.14 0.84* 0.48 Off-farm -2.06* 1.12 -2.77** 1.37 0.90** 0.31 0.92** 0.45 Fertiliser 4.96* 2.70 8.11** 3.68 -2.43 1.90 2.80 1.71 Herbicide -2.30 1.12 -3.09** 1.37 1.07 0.71 -1.09 0.73 Rainfall -0.14 0.26 0.23 0.16 -0.10 0.13 -0.11 0.11 Temp -0.25 0.64 0.77 0.48 -0.31 0.34 -0.18 0.25 RFanom -0.07 0.11 -0.56** 0.21 0.16 0.15 0.60 0.97 Tem-anom -0.39 0.33 -0.50 0.49 0.24 0.45 0.98 0.76 Extension 0.72 0.39 1.12** 0.53 -0.35 0.28 0.41 0.25 Slope 0.24 0.15 0.27** 0.12 -0.08 0.71 0.10 0.72 Erosion-level -0.46 0.25 -0.84** 0.37 0.26 0.19 -0.25 0.16 Drainage -0.23 0.12 -0.35** 0.16 0.11 0.86 -0.12 0.74 Fertility 0.66 0.44 0.84** 0.38 -0.15 0.21 0.35 0.20 Selectivity terms m1 -0.31 0.22 -1.08** 0.49 -0.82 0.67 -1.05 0.83 m2 0.29 1.02 0.20 0.22 0.01 0.61 0.65 0.58 m3 -0.63 0.65 -1.62*** 0.22 0.01 0.40 -0.97 0.819 m4 -1.47** 0.58 -0.34 0.45 0.68 0.50 0.19 0.34 ***, **, * represent 1%, 5% and 10% significance level, respectively. Bootstrapped standard errors in parentheses ©2019 The Authors. The Australian Journal of Agricultural and Resource Economics published by John Wiley & Sons Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc. Climate-smart practices and farm performance 419