Agroforestry as a pathway to agricultural yield impacts in climate-smart agriculture investments: Evidence from southern Malawi

Ecological Economics 167 (2020) 106443

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Analysis

Agroforestry as a pathway to agricultural yield impacts in climate-smart agriculture investments: Evidence from southern Malawi

T

Festus O. Amadua,b, , Daniel C. Millera, Paul E. McNamarab ⁎

a

Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, S-406 Turner Hall, 1102 S. Goodwin Ave, Urbana, IL 61801, USA b Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, 341 Mumford Hall, 1301 W. Gregory Dr, Urbana, IL 61801, USA

ARTICLE INFO

ABSTRACT

Keywords: Agroforestry Climate-smart agriculture Double hurdle Maize yield Malawi

Agroforestry is widely promoted for delivering not only the main food security objective of climate-smart agriculture (CSA) but also increasing resilience and mitigating climate change. Yet rigorous estimates of the impact of this pathway on agricultural yields in CSA interventions remain limited. Here we analyze maize yield effects of agroforestry within a large CSA project, funded by the US Agency for International Development and implemented from 2009 to 2014 in southern Malawi. Using original survey data from 808 households across five districts, we apply a double hurdle specification with a control function approach to account for the endogeneity of CSA program participation and the intensity of agroforestry fertilizer trees (as a proxy for agroforestry adoption) in the study area. We find a positive and statistically significant yield effect of CSA program participation and the intensity of agroforestry fertilizer trees: maize yields increased, on average, by 20% for participation, and 2% for the intensity of fertilizer trees – a modest but useful result with implications for increasing agricultural productivity among smallholder farmers in sub-Saharan Africa and elsewhere. More broadly, our results show that incorporating agroforestry into CSA interventions could enhance agricultural yields among smallholder farmers in the face of climate change — a crucial aspect of sustainable development goals on hunger and climate adaptation.

1. Introduction Climate-smart agriculture (CSA) is increasingly seen as a vital strategy for adapting agriculture to climate change, sequestering carbon, and enhancing agricultural productivity (Chandra et al., 2018; FAO, 2010; Lipper et al., 2014). In sub-Saharan Africa, low crop yields abound among smallholder farmers due to constraints like irregular rains (Kahsay and Hansen, 2016; Katengeza et al., 2019), low soil fertility (Ajayi et al., 2011; Makate et al., 2019; Yengwe et al., 2018), deforestation, and soil degradation (Félix et al., 2018; IPCC, 2018; Mohebalian and Aguilar, 2018). Climate-induced shocks and other changes threaten to further reduce agricultural yields across the continent (FAO, 2016; IPCC, 2018; Müller et al., 2011). Low levels of financial capital in many African countries constrain national government efforts to build resilience to climate change (IPCC, 2018; Weiler et al., 2018). As such, many smallholder farmers in dryland areas such as Malawi and elsewhere in sub-Saharan Africa remain vulnerable to food insecurity through low crop yields resulting from recurring

droughts (Asfaw et al., 2016; Katengeza et al., 2019; Müller et al., 2011). For instance, in 2015, the El Niño drought across southern Africa devastated maize production and resulted in food price hikes in the region (Ubilava, 2018). Climate financing, including for CSA programs, constitutes a major international development target to help vulnerable communities cope in the face of climate change (Dinesh et al., 2017; Huang and Wang, 2018; Weiler et al., 2018). Smallholder farmers in such drier areas like southern Malawi could benefit from participating in CSA programs that enhance adoption of specific CSA practices to build resilience against climate-induced shocks like severe droughts (Blaser et al., 2018; FAO, 2016; Kahsay and Hansen, 2016). Therefore, understanding the effects of CSA financing and its yield impacts will help to inform effective funding allocation and implementation to enhance smallholder agriculture in sub-Saharan Africa. Agroforestry is a paradigmatic example of CSA (Blaser et al., 2018; Faße and Grote, 2013; Garrity et al., 2010). Broadly defined as the intentional integration of trees or shrubs with crops or livestock,

⁎ Corresponding author at: Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, 62 Mumford Hall, 1301 W. Gregory Drive, Urbana, IL 61801, USA. E-mail addresses: [email protected] (F.O. Amadu), [email protected] (D.C. Miller), [email protected] (P.E. McNamara).

https://doi.org/10.1016/j.ecolecon.2019.106443 Received 24 October 2018; Received in revised form 6 August 2019; Accepted 17 August 2019 0921-8009/ © 2019 Published by Elsevier B.V.

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agroforestry seeks to deliver a range of ecosystem services (Félix et al., 2018; Reed et al., 2017; Sileshi, 2016) and socioeconomic outcomes (Garrity et al., 2010; Miller et al., 2017). Agroforestry practices are widespread across the low-and-middle-income countries (L&MICs) of Africa, Asia, and Latin America (Blaser et al., 2018; Mercer, 2004) and are increasingly promoted as part of CSA and broader efforts to deliver on the United Nations Sustainable Development Goals (van Noordwijk et al., 2018). A rapidly growing body of literature examines the role of agroforestry in relation to climate adaptation (e.g., Faße and Grote, 2013; Félix et al., 2018; Sileshi, 2016) and CSA adoption more generally (Blaser et al., 2018; Lipper et al., 2014). The extant literature also highlights the importance of trees on farms to other development objectives including controlling environmental degradation (Ajayi et al., 2011; Mohebalian and Aguilar, 2018), enhancing community development (Munsell et al., 2018), improving dietary diversity (Ickowitz et al., 2014), and contributing to household income (Miller et al., 2017; Reed et al., 2017). However, rigorous empirical evidence on the impacts of CSA interventions incorporating agroforestry in L&MICs remains limited (Coulibaly et al., 2017; Dittrich et al., 2016). Rigorous impact evaluation of CSA programs seeking to promote agroforestry as a pathway toward improved crop yields remains especially limited. Analyses of impact pathways exist in other domains of natural resource management such as protected areas (Ferraro and Hanauer, 2015), community forest management (Oldekop et al., 2019), and payments for ecosystems services (Mohebalian and Aguilar, 2018). However, we are not aware of any analysis of agroforestry as a pathway in CSA interventions and the effects on crop yields, ecosystem services, or other development outcomes. Understanding the impacts of CSA and its components like agroforestry (as pathway) is vital not only to build theory and empirical knowledge in ecological economics and other cross-cutting fields in sustainability science but to address sustainable development goals in L&MICs in the face of climate change. Here we address this topic by responding to the following research question: Does participation in CSA programs and agroforestry adoption under such programs lead to higher crop yields? We do so through a joint analysis of the yield effects of CSA program participation (henceforth, participation) and the intensity of agroforestry fertilizer trees, under a large CSA project in southern Malawi. We utilize primary survey data collected across five districts where the US Agency for International Development (USAID)-funded Wellness and Agriculture for Life Advancement (WALA) project implemented a CSA program from 2009 to 2014. WALA promoted seven farm-level CSA practices including planting and management of agroforestry fertilizer trees, especially Faidherbia trees (Faidherbia albida), among smallholder farmers in southern Malawi, to enhance crop yields through improved soil fertility and water conservation (Soroko et al., 2018). We apply a double hurdle (DH) specification with a control function (CF) approach to determine the effect of participation and the intensity of agroforestry fertilizer trees on the yield of maize (Zea mays) in the WALA area. Our analytical techniques account for the endogeneity of participation, intensity of fertilizer trees specifically, the corner solution of CSA adoption generally, and selectivity bias in maize yield impacts. This study is among the first to utilize the DH model and instrumental variable (IV) techniques for a rigorous impact assessment of agroforestry adoption in a CSA intervention in Malawi. Results could be relevant to other dryland countries in sub-Saharan Africa (Faße and Grote, 2013; Issahaku and Abdulai, 2019; Sida et al., 2018) and elsewhere in the developing world where drought, deforestation, and poor soil fertility limit crop productivity (Hope, 2007; IPCC, 2018; Sileshi, 2016).

2. Study motivation and background information 2.1. International funding for CSA and the WALA program in southern Malawi The past decade has seen major increases in international funding to address climate change in developing countries (Huang and Wang, 2018; Weiler et al., 2018) including in support of CSA (Dinesh et al., 2017). Overall, international climate financing is expected to climb to US$100 billion per annum by the year 2020 (Weiler et al., 2018; World Bank, 2017). These funds are meant to bridge the gap in domestic resources, the lack of which has constrained many L&MIC governments from developing and implementing policies to support adaptation to climate change (FAO, 2016; Huang and Wang, 2018). Like other dryland countries in sub-Saharan Africa and elsewhere, Malawi has received substantial climate adaptation, environmental management, and conservation funding in the past decade (Coulibaly et al., 2017; Weiler et al., 2018). With US$86 million devoted to reducing food insecurity and environmental degradation in southern Malawi, the USAID-funded WALA project represents one of the largest climate-related international aid commitments made in the country (Soroko et al., 2018). A Catholic Relief Services-led consortium of eight local non-governmental organizations (NGOs) implemented WALA in rural communities across eight districts of southern Malawi (Fig. 1). The districts are environmentally degraded and among the most vulnerable to crop failure stemming from extensive deforestation and frequent droughts (Reichert, 2014; Soroko et al., 2018). WALA promoted CSA through watershed development activities within specific Grouped Village Headman (GVH) communities across Extension Planning Areas (EPA).1 CSA interventions across watersheds (especially highlands around villages) are increasingly important natural resource management strategies. They are in diverse dryland areas across sub-Saharan Africa, including in Ethiopia (Alemayehu et al., 2009), Ghana (Issahaku and Abdulai, 2019), and Tanzania (Branca et al., 2011) as well as elsewhere, such as India (Hope, 2007). CSA under WALA helped to conserve common-pool resources for communities and motivate households to participate in the communitywide interventions by providing their labor in exchange for food for assets including pinto beans and vegetable oil. After these communitylevel interventions, the project encouraged farm households to adopt relevant practices on their plots. Thus, WALA promoted a “ridge-tovalley approach” that first implemented CSA practices to conserve community watersheds and then moved to promote individual farmers' CSA adoption at the farm/plot level to conserve soil fertility (Amadu, 2018; Reichert, 2014). Although there was no strict categorization of CSA practices into community versus farm/plot level, we note that WALA promoted one community-level practice – apiculture – and seven farm-level practices: agroforestry fertilizer trees, check dams, continuous contour trenches, marker ridges, stone bunds, vetiver grass, and water absorption trenches (Table A1). This study operationalizes the concepts of communitylevel versus farm-level CSA adoption based on how WALA promoted the CSA practices. For instance, apiculture was only implemented at the community-level on communal lands through collective action from community members as a public good. By contrast, all seven farm-level practices were first introduced at the community-level but then promoted for adoption at the farm level (Reichert, 2014; Soroko et al., 2018). Generally, following best practice (Chandra et al., 2018), WALA used a participatory approach in promoting CSA, involving community members in selecting the most appropriate CSA practices per community (Reichert, 2014). This article focuses on the agroforestry fertilizer tree-component of 1 Administrative units in charge of agricultural extension services in rural communities under the supervision of District Agriculture Offices in Malawi.

2

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Fig. 1. Study area showing treated and control households within districts under the Wellness and Agriculture for Life Advancement program.

the CSA intervention under WALA and its effects on maize yield, controlling for other CSA practices promoted concurrently. Malawi's Department of Forestry, in partnership with WALA, provided agroforestry fertilizer trees to farmers through the establishment of tree nurseries in project communities for farmers to access seedlings (Amadu, 2018; Personal communication, 2016; Reichert, 2014). Agroforestry trees promoted under WALA included Faidherbia, fruit trees such as banana (Musa acuminate), mango (Mangifera indica), pawpaw (Carica papaya), and exotic trees such as lebbeck (Albizia lebbeck) and acacia (Acacia polylicatha) (Reichert, 2014). Like CSA more generally, agroforestry is complex, consisting of a mix of practices and technologies, with dynamic systems and varying

contextual applications encompassing trees on farms and other socialeconomic outcomes (Ajayi et al., 2003; Jerneck and Olsson, 2013). The focus of this paper, however, is on agroforestry fertilizer trees, mainly Faidherbia trees. Faidherbia is a fast-growing leguminous, woody plant that improves soil fertility by enhancing nitrogen content and other important nutrients (Sileshi, 2016; Yengwe et al., 2018). Because CSA seeks to increase agricultural yields as part of its triple goals including adaptation to climate change and sequestrating carbon (FAO, 2010; Lipper et al., 2014), the inclusion of agroforestry fertilizer trees (especially Faidherbia trees) in the CSA intervention under WALA could sustainably increase crop yields by enhancing soil fertility in the project area. Faidherbia has a long history of improving soil fertility 3

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and yields in dryland agroforestry systems in African countries such as Ethiopia (Sida et al., 2018), Nigeria (Félix et al., 2018), and Zambia (Yengwe et al., 2018) as well as Malawi (Ajayi et al., 2011; Coulibaly et al., 2017) and beyond the continent (Sileshi, 2016). Yields provide information on food availability and potential access for smallholder farmers whose livelihoods depend on agricultural outputs. For this reason, yields are widely used as a proxy for food security in impact assessment studies regarding CSA adoption in developing countries (e.g., Makate et al., 2019; Kahsay and Hansen, 2016; Khonje et al., 2018). In Malawi, maize is the staple crop and it is grown by a majority of smallholder farmers (Asfaw et al., 2016; Katengeza et al., 2019). Thus, its productivity is crucial for CSA impact assessment and for agroforestry as a pathway for such an impact. Hence, we focus on the effects of participation and agroforestry adoption on maize yields conditional on the extent of CSA adoption under WALA.

H3. Socioeconomic characteristics, biophysical, and institutional factors will explain heterogeneity in the extent of CSA adoption and crop yields in the study area. Socioeconomic factors include farm household characteristics such as gender of the household head (Coulibaly et al., 2017; Mercer, 2004), social capital such as membership in farmers' groups and kinship networks (Asfaw et al., 2016; Kassie et al., 2015), and resource endowments such as land ownership (Faße and Grote, 2013; Munsell et al., 2018; Pattanayak et al., 2003). Biophysical factors include plot elevation and slope, among others (Kassie et al., 2015; Mercer, 2004). Similarly, institutional factors expected to affect CSA adoption and yields in the Malawian context include credit access (or constraint) and extension visits (Katengeza et al., 2019; Miller et al., 2017; Pattanayak et al., 2003). 2.3. Measuring CSA program participation, CSA adoption, and maize yield under WALA

2.2. Theoretical expectations A number of pathways may connect CSA interventions to maize yields. One such pathway is by adopting CSA practices, including fertilizer trees, which can enhance soil nutrients (among other environmental benefits) and boost maize productivity (in terms of yields per acre) (Asfaw et al., 2016; Coulibaly et al., 2017; Lipper et al., 2014) for WALA participants. This potential pathway leads us to the following hypothesis:

Defining participation in CSA programs and adoption of its components like agroforestry is elusive. This is because unlike typical green revolution technologies (like new crop varieties), CSA is multifaceted, consisting of diverse context-specific practices varying at the farm/ plot-, community-, landscape-, and national-levels (Amadu, 2018; Lipper et al., 2014; Makate et al., 2019; van Noordwijk et al., 2018). Adoption of CSA and related practices like agroforestry fertilizer trees have been commonly conceptualized in three ways. The first is adoption status – whether a farmer adopts any set of practices (Coulibaly et al., 2017; Noltze et al., 2012). The second is the intensity of adoption – the number of units of specific CSA practices (like fertilizer trees) adopted over a given plot area (Katengeza et al., 2019; Noltze et al., 2012; Verkaart et al., 2019). The third measure is the extent (or depth) of adoption, a count variable that refers to the number of different practices, or components of a specific technology (such as system of rice intensification – SRI) adopted on a given plot of land (Kassie et al., 2015; Makate et al., 2019; Ng'ombe et al., 2017; Noltze et al., 2012). This measure also refers to the spatial distribution of specific practices, such as the use of fertilizer trees (Sileshi, 2016). Studies that employ the first definition (i.e., adoption status or a binary adoption) for agroforestry do so based on farmer self-reporting as having planted or maintained at least one agroforestry fertilizer tree on their land for a given period of time during and after program implementation (Coulibaly et al., 2017; Noltze et al., 2013). In the case of WALA, this period would be between 2012 (during the mid-term review of WALA) to 2016 (the time of data collection for this study). However, such definition may not provide full information on farmers' actual adoption status for the entire 4-year period because CSA adoption may not be exclusively binary (Ajayi et al., 2003; Mercer, 2004). Moreover, binary adoption may be inadequate without having observed the farmers' use of technology/practice over the entire period (Coulibaly et al., 2017; Mercer, 2004). Panel data would be best at capturing such information on CSA adoption (Katengeza et al., 2019). However, panel data is not always available in ex-post impact studies including the present context. Considering the foregoing discussion, we define participation, CSA adoption, and agroforestry adoption as follows. First, like Lambrecht et al. (2014), we define participants as farmers who resided in a CSA intervention community during WALA implementation period (2009 to 2014) and received CSA-related training, either directly from WALA staff or from its affiliates (such as Malawi's Department of Forestry). Second, as described in Section 2.1, we use a farm/plot-level approach to CSA adoption in general, and the intensity of agroforestry fertilizer trees in particular. As such, we operationalize CSA adoption in terms of the extent of adoption (that is, the combination of CSA practices per plot) as defined in the extant literature (e.g., Kassie et al., 2015; Noltze et al., 2012; Verkaart et al., 2019). Following Kassie et al. (2015), we

H1. Participants in the CSA program under WALA who adopt CSA practices will obtain higher crop yields compared to non-participants. By adopting CSA practices, farm households in the WALA area could realize improved soil conditions through erosion control, and the uptake of critical nutrients such as nitrogen. This could lead to higher crop yields, as shown in other contexts (Coulibaly et al., 2017; Kassie et al., 2015; Noltze et al., 2013; Reed et al., 2017). Soil improvements, therefore, constitute the primary way we expect CSA interventions to affect crop yields. Moreover, agroforestry is a vital aspect of CSA (FAO, 2010; Sida et al., 2018) and its adoption is increasingly linked with increased yields in dryland areas (e.g., Ajayi et al., 2011; Félix et al., 2018; Yengwe et al., 2018). Therefore, we hypothesize that: H2. WALA program participants that adopt agroforestry fertilizer trees will realize higher maize yield increases. Compared to other CSA practices that WALA promoted, we expect that the intensity of agroforestry fertilizer trees will be a significant pathway to yield increases among participants. This pathway could lead to crop yields in the short-to-medium term because agroforestry fertilizer trees (particularly Faidherbia) improve soil nutrients, for instance, by shedding leaves that improve organic matter and nitrogen content to croplands (Ajayi et al., 2011; Félix et al., 2018; Sida et al., 2018; Yengwe et al., 2018). We note, however, that agroforestry fertilizer trees (such as Faidherbia) take a substantial time period (e.g., six to eight years) from adoption, to generate optimal yield effects through soil improvement (Coulibaly et al., 2017; Yengwe et al., 2018). Similarly, literature shows that crop yields in agroforestry fertilizer treesystems are directly proportional to the nearness of crops to the trunk/ canopy of fertilizer trees (especially Faidherbia trees) (Sida et al., 2018; Sileshi, 2016). Therefore, given the relatively short time period of agroforestry under WALA being considered in this study (2012 to 2016 as we discuss in Section 2.3 below), yield effects of agroforestry fertilizer trees could be low in this study. Finally, the literature shows that socioeconomic, biophysical, and institutional factors shape the adoption of agricultural technologies (e.g., Asfaw et al., 2016; Katengeza et al., 2019; Kassie et al., 2015) and their impacts on crop yields more generally (Coulibaly et al., 2017; Khonje et al., 2018; Noltze et al., 2013). Therefore, we hypothesize that: 4

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assume that more CSA combinations per plot will increase maize yield. Third, we define agroforestry adoption in terms of the intensity of agroforestry fertilizer trees (especially Faidherbia trees). The intensity of agroforestry adoption in this setting is the ratio of Faidherbia trees on the main plot (i.e., the plot that households utilize for their staple crop – maize) to the size of the plot. We counted the number of Faidherbia trees per plot and then computed the ratio of trees to plot size (Katengeza et al., 2019; Noltze et al., 2012). Therefore, in this study, a reference to the intensity of agroforestry adoption, and agroforestry fertilizer trees generally, implies the intensity of Faidherbia trees exclusively. We may still face a problem of not having had prior knowledge of farmers' adoption records for a given period. To correct for this potential limitation, we used administrative data from the WALA consortium and from District Agriculture Offices in the project area to corroborate our survey data in terms of farmers' period of CSA adoption, especially of Faidherbia trees. We also used farmers' group records in the project area to identify farmers who had been in the WALA program and implemented CSA (including planting and management of Faidherbia trees under WALA) by 2012, by which point WALA had promoted CSA in all target communities across the project area (Amadu et al., in review; Reichert, 2014; Soroko et al., 2018). Furthermore, we observed one main plot per household to ascertain the existence of, and the number of CSA practices (like planting and maintenance of Faidherbia trees) on such a plot. We then asked the household heads or their representatives about how long such CSA practices have been on the plot and determined if the time period coincided with the timeline of the CSA intervention under WALA. The CSA intervention under WALA followed strict selection criteria excluding communities that had any recent CSA-related interventions such as conservation agriculture projects (Personal communication, 2016). Malawi has a long history of CSA-related development projects toward soil and water conservation practices (including agroforestry) as part of climate adaptation efforts (e.g., Ajayi et al., 2011; Asfaw et al., 2016; Coulibaly et al., 2017). Thus, following WALA's selection criteria, we focused on the main plots of households with CSA practices under WALA exclusively. We excluded any plots that may have had a CSA practice before the CSA intervention occurred through WALA. Finally, we defined maize yields in terms of farmers' self-reported quantities of 50 kg bags harvested in 2015/2016 farming season. We then divided such yield estimates by the size of the main plot, to obtain yields per acre.

(such as socioeconomic and institutional factors) respectively. Moreover, from H1 and H2 (Section 2.2), maize yields under WALA depend on participation and intensity of agroforestry adoption according to the following equation:

yi = f [CSApart , CSA(adopt ) , Xj ],

where yi represents yield per acre, CSApart represents participation, CSAadopt indicates the extent of CSA adoption, and Xj represents a vector of covariates (including intensity of agroforestry). Using agroforestry (through the intensity of Faidherbia trees) as a covariate enhances an analysis of its effect in ralation to the other CSA practices promoted concurrently under WALA. We could then identify agroforestry adoption as a pathway for the impact of CSA investments on yield among participants in the CSA intervention area under WALA.2 3.2. Empirical strategy Self-selection in participation (following Section 3.1) implies that CSA adoption and non-adoption are optimal choices. Thus, we apply a corner solution approach for the extent of CSA adoption and the resulting maize yield (Amankwah et al., 2016; Weiler et al., 2018). Corner solution models solve limited dependent variable problems that have endpoints (such as zeros or ones) as an optimal choice (Cragg, 1971). The Tobit model, developed by Tobin (1958), is a classic corner solution model for outcomes (such as yield), which simultaneously depend on a prior, or an intermediate decision (like the extent of CSA adoption) after an initial decision, such as CSA program participation as in the present study (Amemiya, 1984; Amankwah et al., 2016). Thus, the outcome variable in a Tobit model is interpreted conditional on the intermediate and initial actions (Tobin, 1958; Cragg, 1971). Therefore, yield estimates in this study could be interpreted conditional on participation and extent of CSA adoption. However, a major drawback of the Tobit model in relation to this study is its premise that the partial effects of covariates in the extent of CSA adoption and maize yield equations have the same sign, even though they could be different (Weiler et al., 2018). For example, household size may influence the extent of CSA adoption as a proxy for labor availability, but it may not influence maize yield after households adopt a given set of CSA practices. DH models relax the limitations of the Tobit model in corner solution problems by allowing the factors determining technology adoption to differ from those affecting the outcome. Therefore, we utilize a DH model following extant literature (e.g., Amankwah et al., 2016; Liverpool-Tasie, 2014; Weiler et al., 2018). Our DH model consists of a Poisson model for the intermediate process (extent of CSA adoption – hurdle 1) and a truncated regression for maize yields (hurdle 2).

3. Materials and methods 3.1. Conceptual framework

3.2.1. Control Function for endogeneity and selectivity bias As discussed in Sections 3.1 and 3.2, the two policy variables of interest – participation and intensity of Faidherbia trees – are endogenous due to non-random assignment of households into participants and then Faidherbia tree adopter categories. Instead, households chose to participate and adopt Faidherbia trees based on unobserved factors. To obtain unbiased estimates of maize yields, we use a two-step CF approach (Wooldridge, 2015) that accounts for the endogeneity of participation and intensity of Faidherbia trees in our data. By definition, CF is a variable or group of variables whose inclusion in a regression model renders an endogenous policy variable exogenous (Wooldridge, 2015). The CF approach in this study proceeds as follows: First, we utilize a set of IVs for participation and intensity of Faidherbia trees (both being endogenous). Using the IVs, we run two first-stage regressions using the endogenous variables as dependent variables and the IVs as covariates.

In contexts where market failure exists such as rural Malawi (Dillon and Barrett, 2017; Katengeza et al., 2019), farm household production and consumption decisions are expected to be inseparable (Singh et al., 1986). Market failure, thus, implies that self-selection occurs in households' farming decisions like participation in a CSA program and adoption of CSA practices, including planting and management of fertilizer trees. This eventually leads to selection bias in the resulting yields. Therefore, impact assessment studies that do not account for selectivity bias in program participation and technology adoption (like the extent of CSA practices adopted, or the intensity of agroforestry fertilizer trees) would result in biased estimates of the outcomes such as crop yields (Katengeza et al., 2019; Noltze et al., 2013). Using a random utility framework, we assume that farmers participated in the CSA program and adopted CSA in order to maximize utility (U ) subject to constraints, such that:

Max Uj (cj , pj , xj ),

(2)

(1)

where cj, pj, xj are vectors of consumption, production, and covariates

2

5

We thank two anonymous reviewers for suggesting this idea.

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We then obtain their residuals, namely, participation and agroforestry residuals according to the following equation.

(Ëi) = Ki + Xi +

i,

3.2.2. The double hurdle specification of maize yield per acre After controlling for the endogeneity of participation and the intensity of agroforestry fertilizer trees in the foregoing sections, the farmer must cross two hurdles in order to realize benefits from the CSA intervention. First, s/he must adopt a strictly positive combination of CSA practices (hurdle 1; i.e., the extent of CSA adoption). Because this is a count variable as explained in Section 2.3, we use a Poisson model. After this hurdle, the farmer must then realize positive yields of maize per acre (hurdle 2). We specify the two hurdles separately. The first hurdle proceeds as follows:

(3)

where Ёi represents the endogenous variables (participation and intensity of Faidherbia trees), is a vector of the IVs, Xi is a vector of covariates, and ωi is a vector of error terms. Valid instruments should each affect the endogenous variable they are used for, but they should not be statistically significant in the other models conditional on the criterion function (Noltze et al., 2013). For example, they should not be significant in hurdle 1 and hurdle 2. For participation, we use a dummy variable, perception of the benefits of CSA intervention under WALA (henceforth, perception), as an IV. It captures farmers' perception of WALA prior to their participation. WALA performed community sensitizations prior to CSA program interventions in target communities. Related studies (Amadu, 2018; Reichert, 2014; Soroko et al., 2018) show that perception of WALA positively affected participation. Perception is a plausible instrument in our study context because it may not directly affect the extent of CSA practices adopted, nor the yield of maize for non-participants in the WALA program. That is, perception may only affect the extent of CSA adoption under WALA and the resulting yield impacts among CSA program participants. However, it may not be significant among non-participants of the CSA program because they may not have benefited directly from the CSA training activities conducted under WALA. Several related studies (e.g., Amadu et al., in review3; Bahta et al., 2018; Ma et al., 2018) have used perception as an IV for participation in respective interventions. Similarly, we used the approximate distance from farmers' main plot to the nearest tree-nursery site (a potential information source about fertilizer trees) as a plausible IV for the intensity of Faidherbia trees adopted. Information about, and access to Faidherbia trees through a tree nursery location in the CSA intervention area should affect the intensity of Faidherbia trees among participants. For example, it may reduce the transaction costs or modify farmers' adoption decisions regarding the intensity of Faidherbia trees (Adegbola and Gardebroek, 2007). However, it may not directly affect decisions regarding CSA combinations (extent of adoption), such as continuous contour trenches versus water absorption trenches or stone bunds, as other factors may be more important. Likewise, it should not affect maize yield among non-WALA participants. Information sources are widely used as an IV in impact assessment studies about technology adoption in similar contexts as the present study (e.g., Adegbola and Gardebroek, 2007; Coulibaly et al., 2017; Khonje et al., 2018). Falsification tests proposed by Di Falco et al. (2011), and applied widely in relevant studies (e.g., Bahta et al., 2018; Ma et al., 2018; Ng'ombe et al., 2017) show that the two IVs are valid in our study context. They are highly correlated with our endogenous policy variables – participation and the intensity of fertilizer trees (Table A2). However, they are not statistically significant in hurdle 1 and hurdle 2, suggesting that they affect the outcomes only through participation and intensity of agroforestry fertilizer trees (Table A2). Therefore, we argue that the instruments satisfy the necessary exclusion restriction. In the second stage of the CF approach, we include residuals from Eq. (3) as additional covariates in estimating hurdle 1 and hurdle 2, to obtain consistent estimates of treatment effects (Amankwah et al., 2016; Liverpool-Tasie, 2014; Wooldridge, 2015). The CF approach is increasingly applied in impact assessment studies for programs involving sequential decisions. Examples of similar studies applying CF include Amankwah et al. (2016) in Kenya, Liverpool-Tasie (2014) in Nigeria, and Katengeza et al. (2019) in Malawi.

Hurdle 1: Extent of CSA adoption

CSAi* =

if CSAi* > 0, , otherwise

1, 0,

Zi + i, with CSAi =

(4)

CSAi∗

where is a latent variable representing the utility of CSA combinations, CSAi is a count variable representing the extent of CSA adoption (as a strictly positive combination of CSA practices per households' main agricultural plot), while Zi constitutes a vector of determinants of CSA adoption including participation and the intensity of agroforestry fertilizer trees. Finally, π is a vector of parameters to be estimated, while ξ is a standard normally distributed error term with mean zero, and constant variance. Hurdle 2: Maize yield In the second hurdle, we estimate a truncated regression for maize yield conditional on the extent of CSA adoption per plot (hurdle 1). We first transform yield per acre into natural logarithms in line with extant literature (e.g., Amankwah et al., 2016; Katengeza et al., 2019; Noltze et al., 2013) for interpretation as elasticities. Thus, we specify hurdle 2 as follows: Yieldi∗ = exp (β′Υi + ηi), with observed yield expressed as:

Yieldi =

Yieldi*, 0,

if Yieldi* > 0 and CSAi = 1, , otherwise

(5)

where Yieldi represents maize yield for individual farm households, Υi is a vector of determinants of maize yield, including participation, intensity of agroforestry fertilizer trees, other CSA practices under WALA, household characteristics, institutional factors (such as extension visits), and biophysical factors (such as plot elevation). The vector, β represents parameters to be estimated, while ηi is a log-normally distributed error term with constant variance (σ2) and a mean of zero. We assume that the extent of CSA adoption and maize yields are independent. Thus, we estimate hurdle 1 and hurdle 2 separately through maximum likelihood. However, we interpreted their results jointly, by explaining hurdle 2 conditional on hurdle 1 (Amankwah et al., 2016; Weiler et al., 2018). Therefore, we maximize the following log-likelihood function for the DH model: ln(L) = [1

( Zi)] + ln[ ( Zi)] +

ln(Yieldi)

i

ln( )

ln (Yieldi) ,

(6)

where Φ(.) and ϕ(.) are cumulative distribution and normal probability density functions respectively. We then calculate the expected value of maize yield conditional on a strictly positive extent of CSA adoption as follows:

E [Yieldi |

i,

> 0] = exp

i

+

2

2

,

(7)

where the parameters are as defined in Eq. (5). Next, we estimate the average partial effects (APEs) of changes in the covariates on maize yield conditional on the extent of CSA adoption. We evaluate the APEs at the maximum likelihood estimates of the dependent variables in the DH model (Amankwah et al., 2016).

3 This study uses the same dataset in that paper, which is under review elsewhere and therefore, not online, but available on request.

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As mentioned in Section 2.1, a consortium of NGOs implemented WALA across the project area. In each district, an NGO implemented CSA in concert with local extension staff at the government Ministries of Agriculture and related Departments (Soroko et al., 2018). Therefore, we used district dummies to capture potential heterogeneity due to administrative effects. We used Chikwawa district as the reference category because it was the most vulnerable WALA district in terms of food insecurity through low crop yields due to extreme deforestation, soil loss, and drought (Amadu, 2018; Personal communication, 2016).

participants and non-participants in terms of multiple t-testing. The main variables with such significant differences include maize yields per acre, CSA adoption status (1/0), the combination of CSA practices per plot, and the intensity of Faidherbia trees. Table 2 also shows the proportion of all the farm-level CSA practices promoted by WALA including components of agroforestry, the number, and intensity of fertilizer trees (as in Section 2.3). For instance, participants had higher yields per acre (10.9 bags) compared to non-participants (difference of 4.5 bags). Similarly, CSA adoption, in terms of all CSA categories under WALA, is generally higher among participants than non-participants. For example, the proportion of binary CSA adoption is 56% higher among participants compared to non-participants. Similarly, the adoption of agroforestry fertilizer trees is 13% higher among participants than non-participants. Moreover, participants had higher social capital as measured by group membership and kinship network, and more connections to formal institutions like extension visits and credit access, than non-participants. These differences imply that participants and non-participants are systematically different. Therefore, analytical techniques such as ordinary least squares regression that do not account for unobserved differences between participants and non-participants would produce inconsistent estimates of maize yield effects. Furthermore, we disaggregated participants by fertilizer tree adopters and non-adopters (Table 3). As expected, adopters had statistically significant differences in the adoption of Faidherbia trees (difference of 5.2) than non-adopters on average. Similarly, the intensity of Faidherbia trees is higher among adopters than non-adopters on average (with a difference of 3.3). However, adopters and non-adopters are not systematically different in terms of average maize yields and household characteristics (Table 3).

3.3. Data and sample selection We utilize primary survey data from a sample of 808 farm households across the WALA project area using multistage sampling. The project under which the survey was carried out was approved as exempt by the University of Illinois' Institutional Review Board (protocol #16988). To identify survey households we first selected five districts in the WALA area. Secondly, we chose two EPAs per district, each with a CSA intervention under WALA. Next, in each selected EPA, we chose two GVH communities, one with a CSA intervention (or treatment) under WALA, while the other did not. We controlled for spillover by ensuring that CSA treatment and control GVHs were at least about 20–25 km apart so we could effectively collect our control households' data with a certain level of assurance that the selected households did not ever or at least easily receive the CSA treatment. Each selected GVH had households that were participants in the WALA intervention, or not, but not both. Finally, we selected 15% of the households in each GVH, based on a list of households per GVH (which we obtained from WALA staff and community leaders). Our final sample included 808 households selected proportional to the size of their GVHs. We categorized farmers into CSA adopters and non-adopters based on their responses to a survey questionnaire designed specifically for this study. Data collection occurred in two stages: A scoping exercise in November 2015, followed by a full household survey in July through September 2016. The survey elicited micro-level data on farm households' production and consumption activities for the 2015/2016 and 2014/2015 farming seasons, among other information. The household survey questionnaire was administered by Malawian enumerators, fluent in the main local languages, especially Chichewa. All enumerators received training on administering the questionnaire including a day of pretesting and several days of practice with feedback loops prior to the actual survey. We collected household-, plot-, and community-level data such as distance to extension offices and main roads. Data were collected using a computer-assisted personal interviewing technique through Samsung Galaxy Tablets, complemented with key informant interviews and personal observations.

4. Results and discussion In this section, we first present results related to the estimated DH model in line with the components that touch on our main hypotheses (H1 and H2). Then, we discuss results related to H3 on heterogeneity in CSA adoption and yield impacts. 4.1. Effects of participation and intensity of fertilizer trees on the extent of CSA adoption and maize yields Table 4 shows the average partial effects of participation and the intensity of Faidherbia trees on the extent of CSA adoption and maize yield. The DH model estimates support our hypotheses that participation and the intensity of agroforestry fertilizer trees (primarily Faidherbia trees) have positive and statistically significant effects on maize yields (hurdle 2) through the extent of CSA adoption (hurdle 1). The effect of participation was positive and statistically significant on the extent of CSA adoption and maize yield, at 1% and 5% levels respectively (Table 4).4 Results of hurdle 1 imply that on average, households who were participants in the CSA program had a 74% higher probability of adopting greater combinations of CSA practices (i.e., extent of CSA adoption) than non-participant households. The result of hurdle 2 implies that on average, participant households realized 20% higher maize yield conditional on the extent of CSA adoption, than nonparticipants did in the drought year of 2016, thereby supporting H1. Similarly, the intensity of agroforestry fertilizer trees (i.e., Faidherbia trees as defined in this study) has a positive and statistically significant effect in both hurdles at 5%. Thus, in terms of maize yield, the result implies that every additional Faidherbia tree per acre increased maize yields by 2%, in 2016. The other CSA practices (albeit on binary adoption basis) were not positive and significantly correlated

3.4. Descriptive and summary statistics Table 1 presents a general description of the data in terms of average (mean) values of maize yields, intensity of agroforestry fertilizer trees, and the extent of CSA adoption through the combinations of CSA practices per plot, the proportion of individual CSA practices adopted, the size of the main agricultural plot, and the proportion of agroforestry tree nurseries. There are differences in key variables across the sample. For instance, the average yield is 8.9 bags (50 kg bags) per acre, with a non-uniform distribution across districts. Similarly, the intensity of fertilizer trees and the extent of CSA adoption (i.e., CSA combinations per plot) are 2.8 and 1.6 respectively with marked variability across districts. In general, the intensity of agroforestry fertilizer trees is low in the study area. Likewise, the combination of CSA practices per plot (extent of adoption as defined in this study) is low on average. Table 2 presents summary statistics conditional on participation in order to determine the level of variability between participants and non-participants. There are statistically significant differences between

4 We do not report the determinants of participation and intensity of fertilizer trees because our focus here is on their effects on hurdle 2 (maize yields) conditional on hurdle 1. However, the estimates are available on request.

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Table 1 Distribution of maize yield, agroforestry Faidherbia trees, and other CSA practices by district across the sample. Source, Authors' calculation using CSA study data in southern Malawi. Variable

Full sample

Districts surveyed in the WALA CSA intervention area Balaka

Maize yield per acre Number of Faidherbia trees per plot The intensity of fertilizer trees CSA combinations per plot Faidherbia tree adoption only Exotic trees only Fruit trees only Faidherbia and exotic tree only All trees on the plot Continuous contour trench Check Dam Marker ridge Stone bunds Vetiver grass WATs Plot size Tree nursery

Chikwawa

Nsanje

Thyolo

Zomba

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

8.92 3.73 2.80 1.61 0.16 0.21 0.18 0.32 0.41 0.16 0.11 0.36 0.48 0.28 0.17 1.68 0.52

5.21 3.99 3.87 1.55 0.37 0.41 0.39 0.47 0.49 0.37 0.32 0.48 0.50 0.45 0.38 0.77 0.50

9.94 2.83 2.42 1.19 0.18 0.39 0.21 0.50 0.52 0.23 0.18 0.07 0.04 0.40 0.31 1.41 0.49

6.71 3.64 3.48 1.31 0.38 0.49 0.41 0.50 0.50 0.43 0.38 0.25 0.21 0.49 0.47 0.64 0.50

9.40 3.63 2.60 1.66 0.22 0.19 0.20 0.36 0.45 0.06 0.18 0.35 0.59 0.41 0.15 1.82 0.46

4.55 4.01 3.72 1.62 0.41 0.40 0.40 0.48 0.50 0.23 0.39 0.48 0.49 0.49 0.35 0.78 0.50

8.06 3.97 2.27 1.70 0.11 0.24 0.14 0.32 0.39 0.16 0.17 0.40 0.43 0.16 0.22 1.96 0.55

4.40 4.07 2.65 1.57 0.31 0.43 0.34 0.47 0.49 0.37 0.37 0.49 0.50 0.37 0.42 0.88 0.50

8.16 3.48 2.71 1.46 0.13 0.10 0.19 0.20 0.33 0.18 0.03 0.39 0.55 0.13 0.11 1.54 0.58

5.56 3.99 3.91 1.55 0.34 0.30 0.40 0.40 0.47 0.39 0.16 0.49 0.50 0.34 0.32 0.66 0.50

10.61 5.42 5.24 2.25 0.17 0.32 0.16 0.42 0.49 0.30 0.01 0.51 0.57 0.44 0.18 1.49 0.52

4.75 3.72 5.60 1.35 0.38 0.47 0.37 0.50 0.50 0.46 0.11 0.50 0.50 0.50 0.39 0.74 0.50

Note: WALA, Wellness, and Agriculture for Life Advancement; CSA, Climate-Smart Agriculture; SD, Standard Deviation.

significant in the other hurdle. For instance, the effect of extension visits was statistically significant in hurdle 1, but not in hurdle 2. This result suggests that after empowering participants with extension services for positive extent of CSA adoption (hurdle 1), more extension visits may not add much value to adopters (who may have acquired the technology). On the other hand, total land size did not have a statistically significant effect in hurdle 1, but it was statistically significant in hurdle 2 at 1%. Thus, the result suggests that on average, every additional acreage of land accounted for 24% more yield increases among participants. District effects were negative and statistically significant for Nsanje and Thyolo districts in hurdle 1 and hurdle 2, while Balaka was positive, but only significant in hurdle 1. In terms of maize yields, the result suggests that participants in Chikwawa realized higher yields than those in Nsanje and Thyolo by 23% and 25% respectively on average. However, average yields were not different in Balaka and Zomba. This result is striking because compared to Balaka and Zomba, Nsanje and Thyolo are neighboring districts with Chikwawa, and with similar biophysical features. However, participants in Chikwawa may be systematically different from those in Nsanje and Thyolo compared to Balaka and Zomba. Further research is needed to explain this difference in the effects of CSA impacts on maize yields in Chikwawa, Njsanje, and Thyolo. Further, the participation and agroforestry residuals presented at the bottom of Table 4 were not statistically significant in hurdle 1 and hurdle 2, indicating that the endogenous policy variables (i.e., participation and intensity of agroforestry fertilizer trees) were consistently estimated in both models (Wooldridge, 2015; Ma et al., 2018). Other diagnostic tests show that the DH model fits the data very well. For instance the Likelihood ratio tests for hurdle 1 and the Wald chisquared (χ2) tests statistics for hurdle 2 were both significant at the 1% level, favoring the application of the DH model. Moreover, the significant sigma value in hurdle 2 rejected the null hypothesis that maize yields and the extent of CSA practices were not connected. Taken together, diagnostic tests suggest that the DH model was preferred to the Tobit, or other models, in estimating yield effects of CSA program participation in this setting.

with maize yield. Our result implies that in the WALA project, the intensity of agroforestry adoption, through Faidherbia trees, was an important pathway for the impacts of the CSA project on maize yields, thereby supporting H2. Moreover, as noted in Section 2.2 under (H2), the low yield effects of the intensity of fertilizer trees in our study area follows the extant literature (e.g., Coulibaly et al., 2017; Yengwe et al., 2018) due to the relatively short period of time (2012 to 2016) through which fertilizer trees have been adopted on the plots in our study. The null effects of the other CSA practices promoted concurrently with agroforestry fertilizer trees further suggest that, agroforestry was indeed, a crucial pathway for achieving significant yield effects of the CSA intervention under WALA. Thus, our result implies that future CSA interventions should include the promotion of agroforestry fertilizer trees (like Faidherbia) in Malawi and other dryland areas of subSaharan Africa and beyond. 4.2. Determinants of the extent of CSA adoption and maize yield In this section, we first discuss covariates that were statistically significant in both hurdles and then discuss those that were significant in only one hurdle. Kinship network (a measure of social capital) was statistically significant in both hurdles at 1%, indicating the high value of social capital in the extent of CSA adoption and agricultural productivity in the study context. Similarly, the effect of fertilizer subsidy was statistically significant in both hurdles at 1%, suggesting the high importance of institutional support for enhancing CSA adoption and the resulting yield outcomes in the study context. Moreover, the effects of hired labor was statistically significant for hurdle 1 and hurdle 2 at 1% and 5% levels respectively, suggesting that the availability of resources external to the household is crucial for CSA adoption and crop yields. Our result, in terms of maize yield exclusively, suggests that on average, participants who received fertilizer subsidies and hired labor realized 15% and 14% more yields respectively, than other farmers did in the drought year of 2016. This result further confirms the extant literature about the high relevance of institutional support and resources external to the household in enhancing CSA adoption and the attainment of higher yields (Ajayi et al., 2011; Mercer, 2004; Sileshi, 2016). Because the DH model allows the sign and effects of covariates to differ between the two hurdles, we are not surprised that some covariates were not statistically significant in one hurdle while they were

4.3. Robustness checks To test the robustness of our main estimates (Table 4), we ran 8

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Table 2 Summary statistics of main variables conditional on CSA program participation. Variables

Participants (n = 450) Mean

Non-participants (n = 358) SD

Mean

Difference (N = 808) SD

Mean

Outcome variable Maize yield per acre (50 kg bags) 10.913 5.127 6.412 4.122 4.502*** CSA adoption by category CSA adopters (1/0) 0.593 0.492 0.034 0.180 0.560*** CSA combination on plot 2.847 1.162 0.578 1.006 2.268*** Agroforestry adoption status by category Faidherbia albida (1/0) 0.218 0.413 0.084 0.277 0.134*** Number of Faidherbia trees per plot 5.938 3.794 0.955 2.006 4.982*** The intensity of Faidherbia trees 4.482 4.328 0.675 1.502 3.806*** Exotic (other) trees (1/0) 0.340 0.474 0.042 0.201 0.298*** Fruit trees around the homestead 0.249 0.433 0.098 0.297 0.151*** Other CSA practices under WALA Vetiver grass 0.422 0.494 0.092 0.290 0.330*** Continuous contour trench 0.251 0.434 0.045 0.207 0.206*** Check dam 0.153 0.361 0.064 0.246 0.089*** Marker ridge 0.464 0.499 0.221 0.415 0.244*** Stone bunds 0.727 0.446 0.176 0.381 0.551*** Water absorption trench 0.258 0.438 0.067 0.250 0.191*** Household characteristics Age of household head 43.607 15.265 41.852 15.877 1.755 Female headed household 0.416 0.493 0.536 0.499 −0.121*** Education level (years) 4.647 1.867 4.559 1.965 0.088 Household size 6.318 2.337 6.570 2.287 −0.252 Prior group membership 0.856 0.352 0.374 0.485 0.481*** Kinship network 3.780 1.733 0.849 1.246 2.931*** Male AEDOs 0.580 0.494 0.536 0.499 0.044 Both spouses received extension visit 0.800 0.400 0.148 0.356 0.652*** Extension visits 9.036 2.584 5.061 2.313 3.974*** Received food aid 0.522 0.500 0.531 0.500 −0.009 Land ownership 0.687 0.464 0.422 0.495 0.265*** Total number of plots 1.833 0.848 1.885 0.873 −0.052 Size of the main plot 1.708 0.794 1.655 0.746 0.053 Total land size 2.758 0.962 2.448 0.900 0.310*** Hired labor 0.769 0.422 0.101 0.301 0.668*** Livestock ownership 0.499 0.501 0.427 0.495 0.072** Significance levels: ** < 5%; *** < 1%; CSA, Climate-Smart Agriculture; SD, Standard Deviation; AEDO, Agricultural Extension Development Office; Source: Authors' calculation using Stata 15MP.

Variables

Participants (n = 450) Mean

Non-participants (n = 358) SD

Mean

Difference (N = 808) SD

Biophysical factors House elevation 444.407 334.378 509.050 332.493 Plot elevation 448.987 333.219 505.827 329.658 Plot is steep 0.533 0.499 0.433 0.496 Districts Balaka district 0.136 0.343 0.081 0.273 Chikwawa 0.293 0.456 0.265 0.442 Nsanje district 0.191 0.394 0.209 0.408 Thyolo district 0.284 0.452 0.349 0.477 Zomba district 0.096 0.294 0.095 0.294 Instrument variables Perception of WALA 0.857 0.350 0.106 0.308 Distance to the nearest tree nursery 11.419 8.459 11.134 7.969 Significance levels: ** < 5%; *** < 1%; CSA, Climate-Smart Agriculture; SD, Standard Deviation; Source: Authors' calculation using Stata 15MP.

several specifications of the DH model as follows. First, we re-estimated the main model controlling for the interaction of Faidherbia and exotic trees to see if the effect on the intensity of Faidherbia trees remained. The effect of the intensity of Faidherbia trees remained statistically significant, while the interaction effect of Faidherbia-exotic trees was not (Table A3). This result suggests that the effect of the intensity of fertilizer trees is not affected by extraneous factors. Second, we ran a specification with CSA adoption defined in terms of adoption intensity (similar to the intensity of fertilizer trees). That is, we divided the number of CSA practice combinations by the size of the

Mean −64.644*** −56.840** 0.100*** 0.055** 0.028 −0.018 −0.065** 0.001 0.751*** 0.285

maize plot to see if our main estimates in Table 4 remained statistically significant. The result is similar except for minor variations in the effect of covariates (Table A4). Third, we used adoption status (a binary variable) as discussed in Section 2.3, to see if the effect of participation and agroforestry adoption will remain similar to our main estimate. Specifically, we used a probit regression model for hurdle 1, while retaining the truncated regression for hurdle 2. The result of this robustness check was in line with our main estimate (Table A5). Taken together, these robustness estimates provide support for our DH model and show that our estimates in Table 4 are robust to different 9

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Table 3 CSA program participants disaggregated by fertilizer tree adoption status. Source: Authors' calculation using Stata 15MP. Variable

Outcome variable Maize yield per acre (50 kg bags) CSA adoption by category CSA adoption status (1/0) CSA combination on plot (count) Agroforestry adoption status by category Number of Faidherbia trees per plot The intensity of Faidherbia trees Exotic trees (1/0) Fruit trees around the homestead Other CSA practices under WALA Vetiver grass Continuous contour trench Check dam Marker ridge Stone bunds Water absorption trench Household characteristics Age of household head Female headed household Education level (years) Household size Prior group membership Kinship network Male AEDOs Both spouses received extension visit Extension visits Received food aid Land ownership Total number of plots Size of the main plot Total land size Hired labor Livestock ownership Biophysical factors House elevation Plot elevation Plot is steep Districts Balaka district Chikwawa Nsanje district Thyolo district Zomba district Instrument variables Perception of WALA Distance to the nearest tree nursery

Fertilizer tree adopters (n = 98)

Non-adopters (n = 352)

Mean differences (n = 450)

Mean

SD

Mean

SD

10.755

5.358

10.957

5.068

−0.202

0.663 2.735

0.475 1.289

0.574 2.878

0.495 1.124

0.089 −0.143

6.122 3.960 0.327 0.306

3.639 2.922 0.471 0.463

0.915 0.665 0.34 0.233

1.945 1.514 0.188 0.261

5.208*** 3.296*** −0.013 0.073

0.520 0.224 0.133 0.367 0.745 0.296

0.463 0.502 0.419 0.341 0.485 0.438

0.395 0.259 0.159 0.491 0.722 0.247

0.423 0.490 0.438 0.366 0.501 0.449

0.126** −0.034 −0.026 −0.124** 0.023 0.049

44.929 0.388 4.684 6.245 0.867 3.378 0.541 0.786 8.878 0.571 0.714 1.776 1.748 2.454 0.724 0.449

0.459 14.528 0.490 1.483 2.424 0.341 1.814 0.501 0.412 2.610 0.497 0.454 0.831 0.700 0.934 0.449

43.239 0.423 4.636 6.338 0.852 3.892 0.591 0.804 9.080 0.509 0.679 1.849 1.697 2.842 0.781 0.513

0.432 15.464 0.495 1.962 2.315 0.355 1.696 0.492 0.398 2.579 0.501 0.468 0.852 0.818 0.954 0.414

1.690 −0.036 0.047 −0.093 0.015 −0.514*** −0.050 −0.018 −0.202 0.063 0.035 −0.074 0.051 −0.388*** −0.057 −0.064

423.878 435.255 0.561

0.500 314.678 316.323

450.122 452.810 0.526

0.501 339.868 338.108

26.245 17.555 −0.036

0.163 0.398 0.102 0.265 0.071

0.499 0.372 0.492 0.304 0.444

0.128 0.264 0.216 0.290 0.102

0.500 0.334 0.442 0.412 0.454

−0.035 −0.134** 0.114** 0.024 0.031

0.898 10.265

0.259 0.304

0.846 11.740

0.303 0.361

−0.052 1.475

Significance levels: ** < 5%; *** < 1%; CSA, Climate-Smart Agriculture; SD, Standard Deviation.

specifications.

Logistical constraints limited the coverage of all WALA districts. However, our study is representative of the entire WALA area through our selection of samples from Balaka, Chickawa, Nsanje, Thyolo, and Zomba districts which were a key focus under the CSA intervention, and had a wide range of social-ecological contexts representative of the entire WALA area. Moreover, given the social-ecological diversity of the districts covered, we believe our results are of relevance not only in southern Malawi but also more broadly in hilly, dryland contexts where rainfed agriculture predominates. However, further studies are needed from other world regions using similar methods and in the Malawian context using panel data to rigorously determine the effects of fertilizer trees adopted through externally promoted CSA programs like WALA to provide more concrete evidence of the effect of agroforestry on agricultural yields.

4.4. Limitations Although our analytical approach is quite robust, this study may have some limitations. First, our use of cross-sectional datasets to estimate the impact of participation on maize yields through the extent of CSA adoption could be problematic because we do not directly observe farmer adoption decisions for specific CSA practices (including Faidherbia) throughout the period of the WALA intervention. Crosssectional data limits our ability to precisely define adopters versus adopters of not just agroforestry, but CSA in general. Cross sectional data also limits our ability to eliminate all potential endogeneity issues in the study. Future studies based on longitudinal panel data would be needed for such detailed level of analysis with precision. Additionally, our data do not cover the entire area where WALA intervened (eight districts in total, as highlighted in Section 2.1). 10

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Table 4 Main DH estimates of the effects of CSA program participation and agroforestry adoption on maize yields per acre in 2016, conditional on the extent of CSA adoption under WALA project. Variables

Hurdle 1: Extent of CSA adoption

Hurdle 2: Maize yield per acre (log)

Count model (Poisson)

Truncated regression

APE

Z-value

APE

Z-value

Treatment/policy variables CSA program participation 0.739*** 3.050 0.196** 2.180 Intensity of agroforestry adoption 0.035** 1.980 0.022** 2.430 Household characteristics Age of household head −0.002 −0.480 4.60E-04 0.350 Female headed household 0.191 1.580 −0.020 −0.380 Education level (years) −0.007 −0.250 −0.003 −0.240 Household size 0.005 0.230 −0.012 −1.240 Prior group membership 0.127 0.910 0.061 1.190 Kinship network 0.258*** 6.380 0.050*** 3.180 Male AEDO −0.084 −0.700 0.035 0.680 Both spouses receive extension advise −0.063 −0.460 −0.077 −1.340 Extension visits 0.061*** 3.010 0.003 0.390 Food aid −0.017 −0.160 0.002 0.050 Fertilizer subsidy 0.828*** 5.340 0.150*** 2.690 Land ownership 0.020 0.180 −0.019 −0.430 Total number of plots 0.070 1.170 0.020 0.800 Total land size −0.037 −0.680 0.235*** 10.210 Hired labor 0.899*** 6.060 0.141** 2.380 Livestock ownership −0.025 −0.240 0.030 0.700 Biophysical factors House elevation −1.90E-04 −0.200 1.20E-04 0.240 Plot elevation −4.80E-04 −0.490 1.70E-04 −0.340 Plot is steep −0.089 −0.870 0.027 0.640 All categories of CSA practices under WALA Faidherbia trees 0.014 0.110 0.092 1.560 Exotic trees 0.007 0.060 −0.008 −0.140 Fruit trees around homestead −0.070 −0.580 −0.029 −0.530 Vetiver grass 0.005 0.040 −0.027 −0.510 Continuous contour trench 0.092 0.700 0.012 0.190 Check dam −0.023 −0.150 −0.116* −1.720 Marker ridge 0.073 0.650 −0.020 −0.420 Stone bunds 0.075 0.540 −0.014 −0.260 Water absorption trench −0.027 −0.220 −0.020 −0.340 Significance levels: * < 10%; ** < 5%; *** < 1%. Notes: DH, Double Hurdle WALA, Wellness and Agriculture for Life Advancement; APE, Average Partial Effects; CSA, Climate-Smart Agriculture; AEDO, Agricultural Extension Development Officer. Source: Authors' calculation using Stata 15MP.

Variables

Districts Balaka district Nsanje district Thyolo district Zomba district Instruments Participation residual Agroforestry residual Model diagnostics Number of observations Log likelihood/pseudo likelihood LR chi2(36) Wald chi2(36) Pseudo R-squared Sigma Significance levels: * < 10%; ** < 5%; *** < 1%; Note:

Hurdle 1: Extent of CSA adoption

Hurdle 2: Maize yield per acre (log)

Count model (Poisson)

Truncated regression

APE

Z-value

APE

Z-value

0.527** −0.474*** −0.596*** 0.006

2.150 −2.940 −3.310 0.020

0.062 −0.225*** −0.253*** 0.048

0.630 −3.420 −3.230 0.360

−0.003 −0.067

−1.330 −0.110

−0.001 −0.346

−1.270 −1.550

805 −1061.170 854.540***

−695.755 481.940***

0.287

0.574*** 40.120 Chikwawa district is the base category; DH, Double Hurdle. Source: Authors' calculation using Stata 15MP.

5. Conclusion

adaptation in developing countries in the past decade, identifying solutions for some of the provocative questions about how CSA programs generate agricultural productivity outcomes such as increased crop yields, remain scant. Existing analyses of CSA adoption and impacts do not provide an understanding of the pathways through which CSA interventions generate effects. Such solutions could shed light on how to maximize the gains of CSA and other climate adaptation programs on

This study has estimated the impacts of climate-smart agriculture (CSA) program participation and the adoption of agroforestry fertilizer trees (especially Faidherbia albida) as a pathway for CSA impacts on maize yields, conditional on the extent of CSA practices adopted in southern Malawi. Despite increased international aid toward climate 11

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rural communities in developing countries. We have contributed to addressing this knowledge gap by estimating agroforestry adoption as a pathway for the effect of CSA investments on maize yields in southern Malawi, where maize is the staple crop and constitutes an important measure of food availabilityan important indicator of food security. We used primary survey data collected based on knowledge of the implementation of the USAIDfunded Wellness, and Agriculture for Life Advancement (WALA) project, which promoted agroforestry and other CSA practices (including apiculture, check dam, continuous contour trench, marker ridge, stone bunds, vetiver grass, and water absorption trench) in eight districts across southern Malawi from 2009 to 2014. In so doing, the project sought to enhance food security by increasing crop yields. We used a double hurdle (DH) specification as our main analytical strategy to account for the corner solution of positive maize yields conditional on the extent of CSA practices adopted. We also utilized a control function approach to account for the endogeneity of CSA program participation (and agroforestry adoption through Faidherbia trees as pathway). We find positive and statistically significant yield effects of CSA program participation conditional on the extent of CSA combinations. We also find a positive and statistically significant yield effect of the intensity of agroforestry fertilizer trees (our proxy for agroforestry adoption) as a pathway for the impacts of CSA intervention on maize yields in southern Malawi. The results imply that CSA program participants who adopted at least two combinations of CSA practices realized average maize yield increases of 20% compared to nonparticipants in 2016, a drought year. Equally, our result shows that compared to other CSA practices promoted and adopted under WALA, agroforestry adoption (through the intensity of fertilizer trees planted and maintained per plot) constitutes the most important pathway for CSA impact – it increases maize yields by 2% per acre in 2016, amidst the El Niño drought that adversely affected maize and other crops in Malawi and elsewhere in southern Africa during that period. Despite the small magnitude of the effects of agroforestry fertilizer trees on maize yields, our study demonstrates empirically that agroforestry adoption constitutes an important pathway for agricultural yield impacts of CSA interventions.

The null effects of other CSA practices in the CSA package suggest that future CSA interventions in such dryland settings like southern Malawi and elsewhere should prioritize agroforestry fertilizer trees relative to other CSA practices in their CSA packages. Future research is however, needed to compare the effects of agroforestry fertilizer trees with specific CSA practices promoted under WALA and elsewhere. Furthermore, we extend the frontiers of analyses for prior studies applying the DH model by empirically analyzing maize yield effects of CSA program participation conditional on the extent of CSA adoption in southern Malawi. Finally, our results have policy relevance in that they provide evidence that including agroforestry into CSA programs is an effective means to achieve higher crop yields in dryland countries like Malawi but also more broadly in sub-Saharan Africa and elsewhere. Acknowledgments The authors acknowledge funding from the United States Agency for International Development (USAID), through the Borlaug Leadership Enhancement in Agriculture Program (Grant # 016258-128) and Strengthening Agriculture & Nutrition Extension (SANE) project (Grant # AID-612-LA-15-00003), United States Department of Agriculture (USDA), through National Institute of Food & Agriculture Hatch project # 1009327. We also acknowledge supplemental funding from the Association for International Agriculture & Rural Development through a Future Leaders’ Fellowship. We thank the editor and two anonymous referees for useful comments on an earlier version of the manuscript that substantially improved this paper. We also thank Arun Agrawal, Richard Brazee, Sarah Brown, Brian Chiputwa, Ezekiel Kalipeni, Katia Nakamura, and Pushpendra Rana, for helpful comments on earlier drafts of this work. Moreover, we thank participants at the 3rd Annual Forest Livelihood Assessment, Research, and Engagement conference in Stockholm, Sweden; and the 3rd Global Food Security Conference in Cape Town, South Africa for suggestions that improved this work. Further, we thank our team of enumerators for the data collection on which the study is based. Finally, we thank the staff of Catholic Relief Services in Malawi for providing useful contextual information.

Appendix A Table A1

List and description of specific CSA practices promoted under the WALA project in southern Malawi. CSA practice

Description

Level of implementation

Agroforestry

- A combination of fertilizer trees – mainly Faidherbia trees (Faidherbia albida), exotic trees such as Cassode tree (Senna siamea), lebbeck tree (Albizia lebbeck), and acacia tree (Acacia polylicatha). Fruit trees, e.g., pawpaw (Carica papaya), Mango (Mangifera indica), and banana trees (Musa acuminate). - Beekeeping not only to empower communities for joint economic activities but also as a way of enhancing environmental protection by avoiding deforestation. - Walls of stone built against a gulley to remove it over time by gradually sieving dirt and manure into such a gulley. - Dimensions: 50 to 150 cm high, and 150 cm wide, and varying length depending on the site/context, but usually between 2 and 12 m long (Reichert, 2014). - Moderately long drainages dug laterally on contour lines in farmers' plots. - In Malawi, they are known locally as “swales”. - They help to slow down erosion and enhance water percolation into the soil. - Dimension: About 30 to 60 cm deep and 30 cm wide. - They are ridges erected for planting crops in a furrow, usually along contours - Designed for water retention in a plot and gradual percolation into the surrounding soil to enhance soil moisture content - They are vital in groundwater recharge through the infiltration of surface water into aquifers (Reichert, 2014; Soroko et al., 2018). - Piles of stones erected in moderate-steep plot settings that have lots of stones. - They are usually around 1 high and wide. - These are moderate walls of stones erected against run-off. - They also enhance water percolation into the soil and contribute to groundwater recharge, moisture retention, and evenly distributing water across a lot.

First, community level, and then farmers' plots. Malawi's Department of Forestry provided farmers with seedlings through seed nurseries in the project area.

Apiculture Check Dam

Continuous contour trench Maker ridge

Sone bunds

12

Community-level exclusively (Reichert, 2014). Community-level implementation with extension services on farmers' individual fields/plots. Community level, and then implementation at farmers' fields/plots.

First at the community level, then extended to farmers' fields.

First at the community level, then extended to farmers' fields.

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Table A1 (continued) CSA practice

Description

Level of implementation

Vetiver grass (Chrysopogon zizanioides) Water absorption trench

- A special grass used as a cover crop in conjunction with other CSA practices to both retain soil moisture and improve soil nutrients. - Some are known to be nitrogen enhancing through their root nodules - These are usually aimed at water collection and retention within the filed within moderately to a large basis for gradually supplying the entire field or portions thereof. - They are dug around a field instead of within specific plots dues to their “large sizes”. - The dimension is usually at 60 cm deep, and 1 m, and can be up to 10 m long. - Meant to gather surface water from wider areas around the plot for a longer period of use over time. - Can be dug across slopes to enhance efficiency.

Community-level implementation with extension services on farmers' fields. Community-level implementation with extension services on farmers' fields.

Note: WALA, Wellness and Agriculture for Life Advancement; CSA, Climate-smart agriculture. Source: Authors' field survey and research, 2016.

Table A2

Falsification tests for the admissibility of instrumental variables in the control function. Variables

Instrumental variables Perception

Endogenous variables Participation in WALA Intensity of Faidherbia trees Hurdle 1 The Extent of CSA adoption Hurdle 2 Maize yield per acre Ln Maize yield per acre

Distance to a tree nursery

Coefficient

Z-stat

P>z

Coefficient

Z-stat

P>z

0.313*** 0.026

9.110 0.190

0.000 0.853

0.002 −0.021***

0.730 −3.180

0.467 0.001

0.112

0.980

0.325

−0.002

−0.470

0.642

−0.193 −0.015

−0.290 −0.110

0.772 0.910

0.017 0.007

0.640 1.460

0.520 0.145

Note: Significance levels: *** < 1%; WALA, Wellness and Agriculture for Life Advancement; CSA, Climate-Smart Agriculture; No. of observations in full sample = 808; No. of observations among non-participants = 358; For brevity, diagnostic tests for individual models not reported, but available on request.

Table A3

Robustness check of the main estimate for Faidherbia and exotic tree interaction. Variables

Treatment/policy variables CSA program participation Intensity of agroforestry adoption Household characteristics Age Female headed household Education level (years) Household size Prior group membership Kinship network Male AEDO Both spouses receive extension advise Extension visits Food aid Fertilizer subsidy Land ownership Total number of plots Total land size Hired labor Livestock ownership Biophysical factors House elevation

Hurdle 1: Extent of CSA adoption

Hurdle 2: Maize yield per acre (log)

(Count model)

Truncated regression

APE

Z-value

APE

Z-value

0.751*** 0.035**

3.090 1.970

0.196** 0.022**

2.180 2.430

−0.002 0.192 −0.007 0.005 0.126 0.259*** −0.085 −0.065 0.061*** −0.018 0.824*** 0.016 0.067 −0.034 0.895*** −0.024

−0.470 1.590 −0.260 0.200 0.900 6.400 −0.720 −0.470 2.990 −0.160 5.320 0.140 1.130 −0.620 6.030 −0.240

0.000 −0.020 −0.003 −0.012 0.061 0.050*** 0.035 −0.077 0.003 0.002 0.150*** −0.019 0.020 0.235*** 0.141** 0.030

0.350 −0.380 −0.240 −1.240 1.190 3.180 0.680 −1.340 0.390 0.050 2.690 −0.430 0.790 10.160 2.380 0.700

−1.50E-04

−0.150

1.20E-04

13

0.240

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Table A3 (continued) Variables

Plot elevation Plot is steep All CSA categories under WALA Faidherbia trees Exotic trees Fruit trees around homestead Faidherbia and exotic tree interaction Vetiver grass Continuous contour trench Check dam Marker ridge Stone bunds Water absorption trench Districts Balaka district Nsanje district Thyolo district Zomba district Model diagnostics Participation residual Agroforestry residual Number of observations Log likelihood/pseudo likelihood LR chi2(36) Wald chi2(36) Pseudo R-squared Sigma

Hurdle 1: Extent of CSA adoption

Hurdle 2: Maize yield per acre (log)

(Count model)

Truncated regression

APE

Z-value

APE

Z-value

−5.20E-04 −0.093

−0.530 −0.900

−1.70E-04 0.027

−0.340 0.640

0.138 0.150 −0.068 −0.184 0.010 0.097 −0.020 0.069 0.078 −0.034

0.640 0.630 −0.560 −0.700 0.090 0.740 −0.130 0.610 0.550 −0.270

0.093 −0.006 −0.029 −0.002 −0.027 0.012 −0.116* −0.020 −0.014 −0.020

0.830 −0.050 −0.530 −0.020 −0.510 0.200 −1.710 −0.420 −0.260 −0.340

0.532** −0.471** −0.600*** 0.007

2.170 −2.920 −3.330 0.020

0.062 −0.225*** −0.253*** 0.048

0.630 −3.420 −3.230 0.360

−0.003 −0.064 805 −1060.928 855.02***

−1.330 −0.100

−0.001 −0.346 805 −695.755

−1.270 −1.550

481.94***

0.2872

0.574***

40.120

Significance levels: * < 10%; ** < 5%; *** < 1%. Notes: APE, Average Partial Effects; CSA, Climate-Smart Agriculture; Source: Authors' calculation using Stata 15MP.

Table A4

Robustness check of the main double hurdle estimates of the effects of CSA program participation and agroforestry adoption on maize yields conditional on the intensity of CSA adoption. Variables

Treatment/policy variables CSA program participation Intensity of agroforestry adoption Household characteristics Age Female headed household Education level (years) Household size Prior group membership Kinship network Male AEDO Both spouses receive extension advise Extension visits Food aid Fertilizer subsidy Land ownership Total number of plots Total land size Hired labor Livestock ownership Biophysical factors House elevation Plot elevation

Hurdle 1: Intensity of CSA adoption

Hurdle 2: Maize yield per acre (log)

Poisson regression

Truncated regression

APE

Z-value

APE

Z-value

0.522** 0.214***

2.390 13.870

0.196** 0.022**

2.180 2.430

0.002 −0.089 −0.016 0.009 −0.058 0.009 0.156 0.065 0.007 0.018 0.422*** −0.014 0.021 0.026 0.055 0.003

0.620 −0.850 −0.730 0.510 −0.530 0.270 1.500 0.560 0.370 0.200 3.280 −0.140 0.430 0.580 0.450 0.030

0.000 −0.020 −0.003 −0.012 0.061 0.050*** 0.035 −0.077 0.003 0.002 0.150*** −0.019 0.020 0.235*** 0.141** 0.030

0.350 −0.380 −0.240 −1.240 1.190 3.180 0.680 −1.340 0.390 0.050 2.690 −0.430 0.790 10.160 2.380 0.700

−2.30E+04 4.00E-04

−0.250 −0.430

1.20E-04 −1.70E-04

0.240 −0.340

14

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Table A4 (continued) Variables

Hurdle 1: Intensity of CSA adoption

Hurdle 2: Maize yield per acre (log)

Poisson regression

Truncated regression

APE

Z-value

APE

Z-value

Plot is steep 0.056 0.670 0.027 0.640 All categories of CSA practices under WALA Faidherbia trees 0.120 0.640 0.093 0.830 Exotic trees 0.061 0.300 −0.006 −0.050 Fruit trees around homestead 0.081 0.830 −0.029 −0.530 Faidherbia-exotic tree interaction −0.122 −0.540 −0.002 −0.020 Vetiver grass 0.004 0.040 −0.027 −0.510 Continuous contour trench 0.118 1.020 0.012 0.200 Check dam 0.025 0.180 −0.116* −1.710 Marker ridge −0.024 −0.250 −0.020 −0.420 Stone bunds 0.027 0.230 −0.014 −0.260 Water absorption trench −0.065 −0.580 −0.020 −0.340 Significance levels: * < 10% & *** < 1%. Notes: APE, Average Partial Effects; CSA, Climate-Smart Agriculture; AEDO, Agricultural Extension Development Officer. Source: Authors' calculation using Stata 15MP. Variables

Hurdle 1: Intensity of CSA adoption

Hurdle 2: Maize yield per acre (log)

Poisson regression

Truncated regression

APE

Z-value

APE

Districts Balaka district 0.173 0.800 0.062 Nsanje district −0.160 −1.130 −0.225*** Thyolo district 0.005 0.030 −0.253*** Zomba district 0.495* 1.860 0.048 Model diagnostics Participation residual −0.007*** −5.640 −0.001 Agroforestry residual −1.067 −1.290 −0.346 Number of observations 805 805 Log likelihood/pseudo likelihood −783.980 −695.755 LR chi2(36) 1021.01*** Wald chi2(36) 481.94*** Pseudo R-squared 0.394 Sigma 0.574*** Significance levels: *** < 1%. Notes: APE, Average Partial Effects; CSA, Climate-Smart Agriculture; Source: Authors' calculation using Stata 15MP.

Z-value 0.630 −3.420 −3.230 0.360 −1.270 −1.550

40.120

Table A5

Robustness check of the main double hurdle estimates of the effects of CSA program participation and agroforestry adoption on maize yields conditional on CSA adoption status. Variables

Treatment/policy variables CSA program participation Intensity of agroforestry adoption Household characteristics Age Female headed household Education level (years) Household size Prior group membership Kinship network Male AEDO Both spouses receive extension advise

Hurdle 1: CSA adoption status

Hurdle 2: Maize yield per acre (log)

(Probit regression)

Truncated regression

APE

Z-value

APE

Z-value

0.119** 0.011**

2.220 2.490

0.196** 0.022**

2.180 2.430

1.00E-04 0.008 0.003 0.003 −0.042 0.041*** 0.023 −0.015

0.130 0.270 0.440 0.450 −1.200 4.600 0.830 −0.450

4.60E-04 −0.020 −0.003 −0.012 0.061 0.050*** 0.035 −0.077

0.350 −0.380 −0.240 −1.240 1.190 3.180 0.680 −1.340

15

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Table A5 (continued) Variables

Extension visits Food aid Fertilizer subsidy Land ownership Total number of plots Total land size Hired labor Livestock ownership Biophysical factors House elevation Plot elevation Plot is steep All categories of CSA practices under WALA Faidherbia albida trees Exotic trees Fruit trees around homestead Vetiver grass Continuous contour trench Check dam Marker ridge Stone bunds Water absorption trench Significance levels: * < 10%; ** < 5%; *** < 1%. Notes: APE, Authors' calculation using Stata 15MP. Variables

Districts Balaka district Nsanje district Thyolo district Zomba district Instruments Participation residual Agroforestry residual Model diagnostics Number of observations Log likelihood/pseudo likelihood LR chi2(36) Wald chi2(36) Pseudo R-squared Sigma Significance levels: * < 10%; ** < 5%; *** < 1%; Note:

Hurdle 1: CSA adoption status

Hurdle 2: Maize yield per acre (log)

(Probit regression)

Truncated regression

APE

Z-value

APE

Z-value

0.015*** −0.010 0.006 0.009 −0.025 0.001 0.175*** −0.034

2.970 −0.360 0.190 0.320 −1.690 0.080 6.180 −1.370

0.003 0.002 0.150*** −0.019 0.020 0.235*** 0.141** 0.030

0.390 0.050 2.690 −0.430 0.800 10.210 2.380 0.700

−1.20E-04 −2.10E-04 −0.037

−0.470 −0.830 −1.460

1.10E-04 1.70E-04 0.027

0.240 −0.340 0.640

0.079** 2.520 0.092 1.560 0.024 0.780 −0.008 −0.140 0.017 0.560 −0.029 −0.530 0.029 1.030 −0.027 −0.510 0.039 1.180 0.012 0.190 0.002 0.050 −0.116* −1.720 0.055** 1.980 −0.020 −0.420 0.024 0.710 −0.014 −0.260 −4.90E-04 −0.020 −0.020 −0.340 Average Partial Effects; CSA, Climate-Smart Agriculture; AEDO, Agricultural Extension Development Officer. Source:

Hurdle 1: CSA adoption status

Hurdle 2: Maize yield per acre (log)

(Probit regression)

Truncated regression

APE

Z-value

APE

Z-value

0.132** −0.051 0.010 0.249***

2.190 −1.300 0.210 3.060

0.062 −0.225*** −0.253*** 0.048

0.630 −3.420 −3.230 0.360

−0.001 −0.129

−1.390 −0.920

−0.001 −0.346

−1.270 −1.550

805 −283.174 471.320***

−695.755 481.940***

0.454

0.574*** 40.120 APE, Average Partial Effects; Chikwawa District is the base category. Source: Authors' calculation using Stata 15MP.

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