Can conservation agriculture save tropical forests? The case of minimum tillage in Zambia

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1 Norwegian University of Life Sciences Schoo of Economics and Business Can conservation agricuture save tropica forests? The case of minimum tiage in Zambia Hambuo Ngoma and Arid Angesen Working Papers No. / 017 ISSN:

2 Can conservation agricuture save tropica forests? The case of minimum tiage in Zambia 1 Abstract Hambuo Ngoma 1, and Arid Angesen 1 1 Schoo of Economics and Business, Norwegian University of Life Sciences (NMBU), PO Box 5003, 143 Ås, Norway & Center for Internationa Forestry Research (CIFOR), Bogor, Indonesia. Department of Agricutura Food and Resource Economics, Michigan State University, East Lansing, MI , USA & Indaba Agricutura Poicy Research Institute (IAPRI) Lusaka, Zambia. ngomaha1@msu.edu; arid.angesen@nmbu.no Minimum tiage (MT) is a key component in the promotion of conservation agricuture (CA). This paper asks whether MT reduces cropand expansion and thus deforestation. We deveop a simpe theoretica househod mode of and expansion, and test hypotheses by estimating a doube hurde mode using househod survey data from 368 smahoders in rura Zambia. We find that about 19% of the farmers expanded cropand into forests, cearing an average of 0.14 ha over one year. Overa, MT adoption does not significanty reduce deforestation among househods in our sampe, whie abor avaiabiity stimuate expansion. Yied augmenting agricutura technoogies (such as MT) may not reduce expansion uness combined with other forest conservation measures. Keywords: Cropand expansion, deforestation, minimum tiage, doube hurde, Zambia JEL Cassifications: D13, Q1, Q3 1 We gratefuy acknowedge funding from the Norwegian Agency for Deveopment Cooperation [agreement no. GLO QZA 13/0545; grant on Learning from REDD+ an enhanced Goba Comparative Anaysis] through the Center for Internationa Forestry Research (CIFOR). We thank Davison Gumbo for hosting and faciitating fiedwork and a research assistants. We are gratefu for comments from the EAERE 016 conference in Zurich and the first author s Ph.D. thesis evauators, and to John Herbert Ainembabazi. 1

3 1 Introduction Smahoder farmers in Sub-Saharan Africa (SSA) need to raise agricutura productivity to feed a growing popuation, and reatedy to increase farm income to reduce own poverty. Costy agricutura inputs and constrained access to credit eave area expansion as the main option for many smahoders. Despite its potentia to improve both crop yieds (Ehui and Herte 1989) and production (De Janvry and Sadouet 010) in the short-term, cropand expansion is often unsustainabe because farmers face diminishing and productivity and adverse environmenta impacts of forest-oss. Agricutura and expansion is the main cause of tropica deforestation (Angesen and Kaimowitz 001; Gibbs et a. 010). Forest oss has contributed about one-third of the accumuated increase in greenhouse gases (GHG), and makes up about one-tenth of the current emissions (IPCC 013). Cimate change exposes smahoders to higher rainfa variabiity and other cimate reated shocks (Haegatte et a. 016). This hampers the efforts to reduce poverty in SSA, both due to the direct effects on crop production, and indirecty through destabiizing agricutura markets and higher risks making farmers reuctant to undertake investments in the sector. The dua chaenge of smahoder farmers in SSA is, therefore, to intensify agricutura production sustainaby whie mitigating and adapting to predicted cimate change. Severa agricutura technoogies have been proposed to meet this dua chaenge, based on the assumption that intensifying agricuture woud raise and productivity and spare nature. These technoogica options, exempified by the Asian green revoution of the 0 th century, atered factor intensities by increasing the use of abor, capita, inorganic fertiizers, improved seeds and tiage. Currenty, there is strong poitica support for cimate smart agricuture (CSA) as a major avenue to simutaneousy raise smahoder agricutura productivity and enhance cimate change adaptation and mitigation in SSA. CSA is a broad-based approach that incudes poicy reforms to support new technoogica soutions and farm management practices, which incudes conservation agricuture (CA). CA has three key components: minimum tiage (MT), in-situ crop residue retention and crop rotation (Thierfeder et a. 015). It aims to improve agricutura and productivity whie deivering adaptation-mitigation co-benefits (Govaerts et a. 009; IPCC 014; Thierfeder and Wa 010; UNEP 013).

4 This paper focuses on househod-ed cropand expansion and addresses the where, why and how much questions. We test the effects of CA practices on cropand expansion using a consistent theoretica househod mode and an empirica anaysis based on detaied survey data on farm, househod and contextua characteristics. We focus on MT because it is the basis and main component of CA. Our data are from Zambia, a country which - despite a high forest cover of about 66% (FAO 015; Kainda et a. 013) experiences high deforestation driven by househod or centray panned (e.g., farm bocks) agricutura expansion, urbanization, new settements, road deveopment and mining. The iterature suggests two major pathways through which MT may contribute to reduced GHG emissions. The first is through improved soi carbon sequestration resuting from reduced tiage and enhanced buidup of soi organic matter (UNEP 013). However, a growing evidence base suggesting that MT has imited potentia to sequester soi carbon chaenges this view (Powson et a. 014; Powson et a. 016; VandenBygaart 016). The second pathway, the focus of this paper, inks MT to reduced deforestation via its effects on crop yied and input use. MT improves crop yied by faciitating eary panting, buidup of soi organic matter and improved input use efficiency (Haggbade and Tembo 003). On one hand, higher yied means the same output can be deivered from ess agricutura and, and shoud therefore take the pressure off forests, sometimes referred to as the Boraug hypothesis (Angesen and Kaimowitz 001). However, higher yied aso provides an incentive to shift resources to a more profitabe agricutura sector, incuding expanding the and area if feasibe. Angesen and Kaimowitz (001) found that new technoogies that raise agricutura productivity might in fact stimuate deforestation by making agricuture more profitabe, sometimes referred to as the 'Jevons' paradox. The constant output assumption does not hod. MT is aso abor-intensive (Gier et a. 009; Thierfeder et a. 015), especiay in the initia years of adoption. Labor-intensive technoogies absorb famiy abor (and possiby raise rura wages) and might therefore have a and-sparing effect. As such, it is not readiy cear how MT affects deforestation. This depends on the factor intensities and yied impact of the particuar MT technoogy, market conditions, and preferences and farm constraints (Angesen 1999). The scae of the anaysis aso matter. Large-scae adoption provokes genera equiibrium effects in the form of 3

5 ower output prices and higher wages (if abor intensive), which can party or fuy offset the effect of higher profitabiity (Angesen and Kaimowitz 001). Most studies on the ink between agricutura technoogies and deforestation are goba or nationa in scope and appy inadequate econometric methods (Barbier and Burgess 001; Gibbs et a. 010; Pheps et a. 013; Rude et a. 009; Rude 013), making them ess informative to oca contexts. Others ack expicit theoretica modes to guide their empirica anaysis, and many focus ony on output effects (Bamford et a. 005; Ewers et a. 009). Except for Vinya et a. (011) and Hoden (1997), empirica evidence on the nexus between agricutura technoogies and deforestation remains thin in Zambia. This paper adds to the iterature in two ways. First, we deveop a simpe Chayanovian agricutura househod mode and sove it anayticay to guide the empirica estimation. Second, we address data probems by using detaied oca context-househod survey data from rura Zambia for the empirica anaysis and we account for the potentia endogeneity of MT, shadow wages and yied on cropand expansion decisions using instrumenta variabes. The rest of the paper is organized as foows. Section briefy reviews existing modes of deforestation and presents the theoretica mode. Sections 3 and 4 outine the empirica strategy and data sources, whie section 5 presents the main resuts. Section 6 discusses these resuts, and section 7 concudes. Theoretica mode.1 Economic modes of deforestation Theoretica economic modes of deforestation can be cassified in two broad categories. The first one appies dynamic optimization on the aocation of and between forest and competing uses (mainy agricuture), possiby aso incuding different sub-sectors of agricuture, e.g., owand and upand agricuture. With a nationa or region focus, these modes assume that a socia panner determines and aocation by comparing reative returns over time. Exampes incude Barbier and Burgess (1997) and Tachibana et a. (001). In genera, these modes show that higher agricutura profit stimuate deforestation by favoring conversion of and to agricuture. The idea of a socia panner determining and aocation may be far-removed in countries with party iberaized and 4

6 markets ike Zambia. Yet a market system can mimic some of the characteristics of the optima soution, but additiona features need to be added, e.g., insecure tenure and and caims strengthened by and cearing. The second category focuses on househod eve drivers of deforestation and appy different versions of the (mosty) static agricutura househod mode in the tradition of Singh et a. (1986). These modes are again spit between recursive and non-recursive modes, i.e., based on whether consumption and production decisions can be separated (recursive) or must be taken simutaneousy. The former assume that househods participate in we-functioning abor, and and output markets, and production decisions are studied within a profit-maximizing framework. Missing or imperfect markets give rise to the non-recursive modes, aso abeed Chayanovian modes. (See Angesen (1999) and Angesen (010) for a comparison of different modes of deforestation, and Pagioa and Hoden (001), Maertens et a. (006) and Aix-Garcia et a. (01) for other appications of agricutura househod modes on deforestation.) Our theoretica mode fas within the second category of deforestation modes, i.e., a static mode assuming an imperfect abor market (non-recursive mode).. Farm eve cropand expansion We deveop a Chayanovian mode of cropand expansion for a representative, smahoder farm househod in Zambia. We extend existing modes of, inter aia, Angesen (1999) and Maertens et a. (006) by adding a new technoogy (MT). We assume that and is homogenous, avaiabe and accessibe at a cost d A A 0. We assume a we-behaved aggregate production function, in which production is a function of famiy abor a Y f, M, A; X a, minimum tiage (M), and area (A), and inputs (X):. We consider M a non-essentia input. The vector of inputs X is assumed fixed, which enabes us to sove the mode anayticay without too much compication. Labor, minimum tiage and and are assumed to be compementary: f, f, f 0; f, f, f 0; f, f, f 0 a A M a a AA MM a a A M MA and f f 0. Given the compementarity assumption, agricutura and expansion and MT AM MA adoption have impications for the demand of famiy abor. 5

7 A representative househod maximizes utiity U U( c, ; h ) by trading off consumption (c) and eisure time (). h is a vector of exogenous househod demographics, which we drop in subsequent equations beyond Eq. (1) but retain in the empirica estimation. In addition to and access cost (d), we assume an additiona convex abor cost of cearing, refecting, for exampe, increasing costs with r distance from the homestead as crop area expands: t A A, t 0 and r 1. A is tota cutivated and per househod and A 0 is the initia stock of and. The difference ( A A0 ) then gives the size of converted and within this period. A representative farm househod soves the foowing probem: MaxU U( c, ; h ) (1) a c,, A, M 0 Subject to a,, ; o c py f M A vm d A Ao px w E r a o o X X () T t A A (3) a c,a, >0; M 0; r > 1;t > 0; A A o U is househod utiity, which has positive but diminishing margina utiities of consumption and eisure. For simpicity, we assume zero cross partias: U, U 0; U, U 0 ; U, U 0. Eq. () is the 6 c cc c c budget constraint. We assume that a income earned by the househod is spent on consumption in our singe period mode. p y is output price, v is the farm eve input and capita costs of impementing MT. p x is the per unit input cost, which ike X, is fixed. E captures a other exogenous income to the househod. Eq. (3) is the time or abor constraint; tota househod time T equas eisure time pus time spent working on the farm a, cearing new agricutura and t A A r o and off the farm o. We assume an imperfect abor market in the sense that househods can ony se a fixed amount of abor o at a market wage rate w. This abor market assumption eads to the Chayanovian mode with a househod specific shadow wage beow the market wage (otherwise, the constraint wi not be binding, see Angesen (1999)). We assume that output markets are we functioning in the way that farmers can se (or buy) what they want at a given market price.

8 We consider the interior soution case where A A0 0 and M > 0, and write the Lagrangian for the probem as:, a,, ; X o L U c c py f M A vm d A Ao px X w E r a o T t A Ao (4) Soving Eq. 4 and after some manipuations, the first order conditions (FOCs) for the choice variabes, c, M and A are given by: py f a z (5) r 1 p f d zrt A A (6) y A o p f y M v (7) where U c, z. U c, (8) c Eq. (8) defines the shadow wage (z) as the margina rate of substitution between consumption and eisure. Eq. (5) states that the margina productivity of agricutura abor (or eisure) is equa to z. The margina productivity condition in Eq. (6) states that a househod wi expand cropand (A) unti the margina productivity of and equas the sum of the cash and abor cost of and and expansion. Eq. (7) states that MT is profitabe as ong as its margina benefit is equa to the cost of impementing it. Since MT (M) and cropand expansion (A) are both endogenous, assessing the impact of MT on expansion cannot readiy be seen from the FOCs without further comparative statics. Aso, since M is endogenous in the mode, the exogenous (poicy) variabe to investigate in our formuation is the We consider cases where M = 0 in the empirica estimation. 7

9 costs of impementing MT, namey v. As seems from Eq. (), a poicy owering v wi ead to higher adoption of MT..3 Comparative statics The compete comparative statistics is presented in Appendix A. We used Cramer's rue to assess how changes in exogenous variabes affect cropand expansion (A), the key outcome variabe of interest. This section ony discusses resuts for the effects of the cost of impementing MT, (v) on cropand expansion (A): A/ v. Appendix A reports the fu derivations. The impact of changes in (costs of) MT adoption on and expansion is the net effect of the substitution and income effects. The substitution effect is what a recursive mode woud give, i.e., by keeping z constant. The income effect is anayzed through changes in in z (Angesen (1999). The substitution effect of a change in v on A is as foows: ower (higher) costs of MT adoption increases (reduces) adoption. Higher M increases the margina productivity of and, given the compementarity assumption. Expansion becomes more profitabe, and the substitution effect ony gives that A/ vis negative. The income effect is, however, positive. Lower v has severa effects on z. First, a inputs remaining constant, ower v reduces the costs of production and raise consumption (cf. Eq. ()). Higher consumption raises z, cf. Eq. (A1). Second, the substitution effect gives higher M, A and a when v is owered. The househod wi both produce and work more, and have ess eisure. This triggers a further increase in z, cf. again Eq. (A1). The income effect wi therefore have a negative impact on expansion though the higher shadow wage rate, i.e. positive impact on A/ v. We summarize our resuts; cf. Eq. (A31) in the foowing proposition: Proposition: The overa effects of MT on cropand expansion are indeterminate, a priori. The substitution effect is positive, refecting the higher profitabiity of and expansion. The income effect is negative, refecting that higher consumption and ess eisure raise the househod shadow wage rate, reducing the profitabiity of and expansion. We cannot determine the net effect, but can hypothesize how different factors wi affect the net resut. Househod preferences matters, i.e., responsiveness of the shadow wage to changes in 8

10 consumption and eisure. Angesen (1999) shows, using a Stone Geary utiity function with subsistence eves, that the income effect dominates for poor househods (cose to that subsistence eve). The production technoogy aso matters, e.g., to what extent MT adoption changes the margina productivity of abor and and. If MT adoption eads to arge increases in margina abor productivity (i.e., is abor intensive), the impact on z can be arge, and make the income effect dominate the substitution effect. 3 Empirica strategy 3.1 Reduced form equation In order to bring the theoretica framework to the data, we can write a parsimonious representation of the reduced form soution as: 3 * * A A M, y, z; py, px,, A o,, X h (9) where A* is the size of expanded cropand, M is the size of and under MT. We use the size of and area under MT, which aso refects the cost of impementing MT (v). The direct cost of impementing MT (v) was not coected during the survey. y is aggregate crop yied (used as proxy for expected yied), z is the shadow wage (opportunity cost of abor). p y is the per kg cost of maize used as a proxy for output price. X is now a dummy = 1 if a househod used inorganic fertiizers and/or hybrid seed, p x is an average per kg input cost of inorganic fertiizer and seeds, o is a dummy = 1 if at east one househod member worked off farm, and A o is the initia amount of and (farm size) controed by the househod (net of newy expanded area ). h is a vector of demographics and other exogenous variabes: vaue of assets, subsidies, tenure, distance from homestead to protected forest and ocation dummies. ε is the error term. Tabe 1 gives detaied descriptions and summary statistics for a variabes in Eq. (9). 3 This representation reaxes some assumptions from the theoretica mode for exampe on fixed p x and X. 9

11 Foowing from the theoretica mode, M, y, and z are endogenous and therefore may be correated with the error term in Eq. (9). There are aso reasons to suspect that MT is endogenous because farmers who sef-seect themseves into MT adoption may have unobservabe characteristics, which may aso infuence their expansion decisions. Moreover, since MT affects cropand expansion through yied effects, then MT and yied may be interdependent, and possiby jointy determined with expansion decisions at househod eve. Subsection 3.1 eaborates how we addressed these concerns. We computed the househod specific shadow wage (z) foowing Jacoby (1993). First, we estimated a Cobb Dougas production function and then used the resuts (Tabe A3, appendix B) to compute: prod z (10) abor where abor is tota abor input per househod, β is an estimated parameter associated with abor and prod is the predicted vaue of production (the dependent variabe in the estimated production function). 3. Estimation chaenges A natura first step before deciding how to estimate Eq. (9) is to test for the endogeneity of M, y and z. We used the contro function approach of Woodridge ( 010). This invoves estimating reduced form equations for each endogenous variabe as the dependent variabe regressed on a exogenous variabes and an additiona instrumenta variabe(s) (IV). The reduced form equations were of the form: endog G Z (11) j j j j where endog j represents the j th endogenous variabe (M, y and z), G is a vector of a exogenous variabes from Eq. (9), Z is a vector of instruments for the j th 10 endogenous variabe. ε j is the equation specific error term and and are mode parameters. Foowing Woodridge ( 010), we incude residues res j from each of the reduced form equations as additiona regressors in the main outcome equation to both test and contro for endogeneity.

12 We used the foowing IVs. For MT area (M), we used access to MT extension (dummy = 1) and distance to district center (boma). We woud expect access to MT extension and distance to the district business center (where MT project offices and agro-deaers for inputs and MT impements are ocated) to affect farmer decisions to adopt MT, but not their cropand expansion decisions directy. For yied (y), we used whether the househod head is poygamousy married (dummy = 1), monogamousy married (dummy = 1) and distance from homestead to main feeder road. For the shadow wage (z), we used whether the househod head is poygamousy or monogamousy married (dummies =1). Suitabe IVs shoud affect MT area, yied and shadow wages, but not expansion directy. We might argue that our IVs are ony indirect measures of the factors that affect expansion such as abor avaiabiity that we controed for in the estimation. We formay tested for IV reevance, i.e., that the IVs are significanty correated to the endogenous variabe(s). Tabe B1 in appendix B shows that the IVs were reevant in each of their respective first stage equations (4.11 < F < 8.76). A the residues were statisticay insignificant in the main outcome equations (Tabe B). Thus, we faied to reject the nu hypothesis that the (potentiay endogenous) variabes are exogenous. 4 Therefore, in ine with standard procedure (Ainembabazi and Angesen 016; Ricker-Gibert et a. 011), our empirica estimation proceeded without considering prior endogeneity concerns. From now, M, y and z are considered exogenous in the estimation. The second estimation issue is the censored nature of the outcome variabe; ony about 19% of the samped farmers expanded cropand. Athough Heckman and Tobit modes are potentia options, we used the doube hurde mode which reaxes the Tobit assumptions by aowing the same or different factors to expain the decisions on (i) whether to expand or not, and (ii) the extent of expansion differenty (Cragg 1971; Woodridge 010). A third chaenge concerns how to parce out the effects of area under MT (our measure of MT effects) from those of crop yied on cropand expansion. Reca from our theoretica discussion that 4 We arrived at simiar concusions using SLS and IV-Tobit modes. 11

13 MT affect cropand expansion both through its effects on yied (higher profitabiity of expansion), but that the demands of more abor aso increases the shadow wage, dampening or even reversing the outcome. In an attempt to isoate these two effects, we incuded MT area and crop yied in separate modes. Yied in this case aso incudes the effects of MT on yied, but the size of area under MT better refects abor demand reated to MT Empirica specification Foowing Woodridge ( 010), the first stage of the doube hurde mode is a probit mode of whether or not househods expanded cropand, i.e., exp > 0: P (exp 0 G )= ( G ) G. (1) 1 The second stage is a truncated norma regression of area expanded (A*) conditiona on exp>0 and G, E A G) G (13) * ( exp 0,. exp is a dummy = 1 if a househod expanded cropand and A* is the amount of new cropand in the 013/014 season. α i are the intercept terms. G is a vector of a exogenous variabes defined as before; β and γ are parameters to be estimated whie ε and μ are error terms from the first stage and second stage respectivey. The overa expected vaue of E (A*) is a product of the expected vaues for Eq. (1) and (13) and the first derivatives of these equations measure the margina or average partia effects (APEs). Readers are referred to Woodridge (010) for a thorough discussion. We estimated the two steps of the doube hurde simutaneousy with maximum ikeihood, foowing Burke (009). 5 Our data do not aow us to expicity isoate the contributions of MT to crop yied and to househod abor suppy and demand. We aso tried to use 3SLS, but we dropped it because we coud not prove the normaity of the error covariance matrix. 1

14 4 Data sources The data used in this study are from an extensive househod survey conducted in 014. Samping proceeded at three eves. First, we seected Nyimba, Mumbwa and Mpika districts based on exposure to forest conservation interventions, agricuturay productive area and prevaence of shifting cutivation systems. Second, we randomy samped 10 viages per district using the most recent viage ists per district. Third, we used up-to-date viage registers obtained from viage eaders to seect 1-15 househods, randomy from each viage for interview. This gave sub-sampes of 10 from Mpika and Nyimba districts, 18 from Mumbwa, and 368 househods in tota. Figure 1 shows the ocation of survey areas. Figure 1: Spatia ocation of survey areas in Zambia Data were coected using structured questionnaires and face-to-face interviews. The survey coected detaied information on househod demographics, agricutura (incuding tiage methods) and offfarm activities, yied, abor and other input use, asset hodings and sources of income. Specificay, the survey asked househods whether they expanded their cropand in the 013/014 season. Those who expanded provided the size of the new (additiona) area, reasons for expanding, the main crop(s) grown, where they expanded into, and who among househod members made the decision. Simiary, those who did not expand provided reasons. 13

15 The main crops grown in the study areas incude maize, cotton, groundnuts, sunfower, soybeans, mixed beans and cassava. Tabe 1 defines the main variabes and presents summary statistics. Tabe 1: Variabes used in regression modes and summary statistics Variabe description Name Mean SD Min Max Dependent variabe Expanded cropand into 013/014 season (yes =1) A>A Sef-reported cropand expansion area (ha) A* Potentiay endogenous independent variabes Land under MT (ha) M Shadow wage z Aggregate crop yied (kg/ha) + y Exogenous Independent variabes Distance to protected forest (km) h Accessed subsidy (yes = 1) h Adut equivaents h Farm size, next of expansion (ha) A Age, head of househod h Mae head of househod (yes = 1) h Education, head of househod (years) h Vaue of assets (ZMW 000)* h Some and on tite (yes = 1) h Used hybrid seed and/fertiizer (yes = 1) x Maize (output) price (ZMW/kg) p y Fertiizer and seed (input) price (ZMW/kg) p x A member earned off farm work (yes = 1) Mumbwa district (yes=1) Nyimba district (yes=1) Mpika district (yes=1) Additiona instruments (IVs) Distance to district center (km) Distance to feeder road (km) Poygamy (yes = 1) Monogamy (yes = 1) Accessed MT extension (yes = 1) Notes: + For a crops, but mainy maize and used as a proxy for expected yied at the time farmers made the decision to expand in the 013/014 season. The sampe reduced to 350 after dropping 18 househods who had zero harvest because they ony cutivated cassava during the survey period. (1 USD = 6. ZMK at the time of the survey.) 14

16 5 Resuts 5.1 Where and why do smahoders expand cropand? Not a cropand expansion causes deforestation, and we asked respondents who expanded cropand about what and parces were expanded, what and they expanded into, and - if faows - how od the faows were. Overa, about 4% of the respondents expanded cropand during the survey reference period (i.e., between 01/013 and 013/014 agricutura seasons). Among these, 75% and 5% expanded into uncutivated and (forest) and faows, respectivey. Thus, when we define deforestation as cropand expansion into virgin forests or faowed and oder than 15 years, about 19% of the samped househods are invoved in deforestation (Tabe 1). 6 We base our empirica anaysis on this second definition of expansion. Of a faow and brought back into cutivation, ony about a quarter had been in faow for more than 15 years, a sign of shortening faow periods in the study areas. There is no significant difference in cropand expansion between MT adopters and non-adopters. Within each group, 18-19% of the househods expanded their cropping area into forests during the ast agricutura season. Among adopters, the average cropand ceared per househod was 0.10 ha, whie non-adopters ceared arger parces on average - about 0.15 ha. About 79% of and-expanding househods did so for maize production. This finding corroborates those in Babigumira et a. (014), who found that most of the cropand expansion in Africa is for maize production - the stape in many sub-saharan African countries. Most househods in the sampe stated that they expanded to increase production to meet subsistence requirements (Figure ). Other reasons incuded improved market access, decining and productivity, setting in new area (out-migration), improved access to inputs through government subsidies and cearing to secure tite. 6 We foow FAO (015) and define forests as and parces arger than 0.5 hectares and not in agricutura use, with tree canopy cover of more than 10% and that these trees shoud reach a minimum height of 5 meters in situ. This definition incudes primary and secondary forest, native or exotic, as we as cosed and open forest (e.g., woodands). 15

17 percent to increase production reduced soi fertiity access to FISP and avaiabe to increase marketed surpus new settement access to inputs to secure tenure Figure : Reasons farmers expanded cropand into the 013/014 season Among househods who did not expand cropand, the majority (68%) stated that they did not expand due to ack of resources (abor and cash) or means to do so. About 1% said there was no and to expand to, and 11% stated that they did not have any need to expand cropand. 5. Are househods that expanded cropand different from those that did not? We checked for systematic differences between househods that expanded cropand in the 013/014 season and those that did not. Tabe shows, using t-tests, that househods who expanded cropand had significanty arger farms (6.0 ha vs. 3.9 ha) before expansion, higher asset endowments and used improved inputs (a possibe indicator of better market access). Among those that expanded, the share of mae-headed househods is higher (indicating the critica roe of access to famiy (mae) abor). At district eve, Nyimba had the argest proportion of and-expanding househods. 16

18 Athough the differences are insignificant, resuts in Tabe aso show that the average crop yied was higher among househods who expanded. A arger proportion of these househods received input subsidies 7, and they were on average more educated and had younger househod heads. Tabe : Bivariate mean comparisons of key variabes between househods who expanded cropand into the 013/014 season and those that did not Expanded cropand into 013/14 season No Mean Yes Mean t-stat (no-yes) Sign. eve Land under MT Distance to protected forest Shadow wage Yied 1, , Adut equivaents Farm size, next of expansion (ha) *** Age, head of househod Mae head of househod * Education, head of househod Vaue of assets (ZMW 000) ** Land on tite Improved inputs * Subsidy Output price Input price Off farm work Mumbwa district (yes=1) Nyimba district (yes=1) ** Mpika district (yes=1) * Notes: ***, **, *: significant at 1%, 5% and 10%. We further expored the bivariate reationships between cropand expansion and key variabes: area under MT, farm size, househod weath and yied. For each variabe of interest, we estimated a simpe bivariate quadratic regression to predict its effects on expansion. 8 Figure 3 suggests a weak reationship between and cutivated under MT (adoption-intensity) and expansion. In ine with a priori expectations, higher yied, farm size and househod weath are correated positivey and mosty 7 The effect of subsidies on expansion requires a separate study. 8 We used the qfitci command in Stata. 17

19 significanty with expansion. However, bivariate anaysis fais to contro for correations across expanatory variabes and their possibe endogeneity, which eads us to the econometric anaysis of next section predicted expansion (ha) A Average area under MT (ha) B Log yied 95% CI Fitted vaues 95% CI Fitted vaues predicted expansion (ha) C og farm size D Log asset vaue 95% CI Fitted vaues 95% CI Fitted vaues Figure 3: Quadratic predictions of the effects minimum tiage adoption intensity (A) yied (B), farm size (C) and vaue of househod assets (D) on cropand expansion. 5.3 Econometric resuts We estimated severa specifications of the doube hurde mode. We report resuts from two modes with the area under MT and yied variabes incuded separatey. These two modes better capture and isoate the different channes through which MT may affect expansion, namey through yied and abor effects. Because the resuts are simiar, we wi focus mainy on mode 1 and ony refer briefy to mode. The estimation was done with standard errors custered at the viage eve. As we coud not reject exogeneity, estimation proceeded without correcting for endogeneity. Tabe 3 presents the average partia effects (APEs) for the probabiity of expansion, extent of expansion conditiona on expansion (i.e., among househods who expanded), and the overa effects for the 18

20 mode with area under MT (mode 1) in coumns 1-3. The APEs for the mode with yied (mode ) are given in coumns 4-6. Mode 1 shows that the size of area under MT is negativey correated to the probabiity and extent of cropand expansion, athough these resuts are ony significant among househods who aready expanded (coumn ). However, the overa effect is insignificant (coumns 3). This is in ine with our descriptive resuts in Tabe and Figure 3, where the size of and under MT did not seem to be correated with cropand expansion overa, but it was weaky correated among adopters. Adopters ceared ess cropand than non-adopters did. Further, this resut is quaitativey in agreement with our theoretica resuts, which suggest that the overa effects of MT on expansion are indeterminate a priori, as abor and yied effects pu in different directions. Using improved inputs (inorganic fertiizers and hybrid seed) increases the ikeihood of expansion by 16 percentage points (coumns 1, Tabe 3), whie an additiona year in the age of the househod head and a unit increase in input prices reduce the ikeihood of expansion by 0.3 and 4.0 percentage points, respectivey. These resuts are statisticay significant at the 5% eve. Higher crop yied (mode ), shadow wage, education of the househod head and adut equivaents are positivey correated with expansion among househods who aready expanded, and these resuts are significant at 1-10% eves (coumns and 5). For exampe, a ese equa, doubing yied is predicted to increase expansion by about 80m. Secure and tenure reduces the extent of expansion (Modes 1 and, coumns and 5). The overa effects suggest that abor avaiabiity (adut equivaents) and shadow wage stimuate expansion whie age of the househod head and having some and on secure tenure coud reduce expansion. There are significant ocation effects: farmers in the more densey popuated Mumbwa and Nyimba districts (reative to those in Mpika district) were more ikey to expand cropand. 19

21 Tabe 3: Doube hurde average partia effects of factors infuencing cropand expansion. Mode 1 Mode (1) () (3) (4) (5) (6) Participation APEs Participati on APEs Overa APEs Participati on APEs Participati on APEs Overa APEs MT area (ha) ** (0.057) (0.01) (0.081) Yied (kg/ha) / E * (0.00) (0.004) (0.00) Shadow wage ** 0.357** (0.11) (0.575) (0.164) (0.11) (0.387) (0.165) Adut equivaents *** 0.08*** *** 0.03* (0.010) (0.08) (0.011) (0.010) (0.036) (0.013) Land size, net of expansion (0.007) (0.014) (0.007) (0.007) (0.015) (0.008) Age househod head * *** ** * -0.01** ** (0.001) (0.004) (0.001) (0.001) (0.005) (0.00) Mae househod head (yes = 1) (0.041) (0.135) (0.046) (0.041) (0.168) (0.048) Education househod head * * 0.01* (0.007) (0.014) (0.006) (0.007) (0.014) (0.007) Asset vaue (ZMW) / (0.005) (0.01) (0.007) (0.005) (0.00) (0.007) Some and on tite (yes=1) *** -0.6** *** -0.76* (0.104) (0.310) (0.10) (0.105) (0.390) (0.155) Improved inputs (yes = 1) 0.160** ** (0.074) (0.195) (0.054) (0.079) (0.01) (0.060) Govt. input subsidy (yes = 1) (0.053) (0.117) (0.04) (0.053) (0.115) (0.044) Output price per kg (0.03) (0.084) (0.06) (0.036) (0.096) (0.031) Input price per kg ** ** (0.017) (0.061) (0.017) (0.017) (0.071) (0.018) Off farm income (yes=1) (0.070) (0.146) (0.064) (0.07) (0.19) (0.077) Dist. to protected forest (Km) (0.004) (0.004) (0.00) (0.004) (0.006) (0.00) Mumbwa district (yes=1) ** 0.133** ** 0.01** (0.091) (0.0) (0.063) (0.091) (0.363) (0.08) Nyimba district (yes=1) 0.09** 0.530** 0.34*** 0.09** 0.837** 0.89*** (0.097) (0.34) (0.06) (0.098) (0.339) (0.079) Sigma (σ) 0.60*** 0.73*** Log pseudo ikeihood Observations

22 Notes: ***, **, * significant at 1%, 5% and 10% respectivey. Bootstrapped standard errors in parenthesis (300 repications). APEs refer to average partia effects. 6 Discussion 6.1 Minimum tiage and deforestation The main resut of this paper is that higher MT adoption intensity does not significanty affect cropand expansion. An adoption intensity of MT averaging 0.13 ha or about 8% of cutivated and per farm househod shows that MT is not the dominant tiage method at househod eve, and this coud at east party expain our resuts. The overa resuts are in agreement with Byeree et a. (014), who concude that technoogy-driven intensification aone is unikey to reduce cropand expansion without improved governance and incentives to preserve nature. They aso echo earier synthesis of case studies on the impacts of improved agricutura technoogies on deforestation (Angesen and Kaimowitz 001). However, MT adoption intensity is negativey correated with expansion among househods who aready expanded. We can use our theoretica resuts and surmise that this captures the effects of MT on abor aocation. MT is abor intensive, at east in the short run, and abor scarcity may reduce expansion due to the higher demand for (famiy) abor among MT adopters. The net effects of MT on cropand expansion wi depend on whether its yied or abor effects dominate - an issue that woud require further investigation. (Note that the yied and abor effects are not equivaent to the substitution and income effects discussed in the theory section.) Because we are not abe to investigate fuy the abor intensity of MT in our data, this resut shoud be interpreted with caution. 6. Other drivers of deforestation Our finding that a higher opportunity cost of abor stimuates expansion runs counter to economic intuition and to our theoretica resuts. One expanation might, however, be found in the way the shadow wages are cacuated, athough the mode is commony used. The formua of Eq. (10) incudes tota agricutura production; a high shadow wage may therefore refect that the househod has certain characteristics (incuding unobservabes) that increase productivity aso from recenty ceared and. Additionay, this finding refects that abor markets are missing and imperfect in the study areas. It aso suggests that househod abor is crucia for agricutura production incuding 1

23 and expansion and that househods empoy more abor on-farm than suppying off-farm. This resut aso renders support to the inverse farm size productivity reationships owing to the high abor/and ratio on sma farms (Ainembabazi and Angesen 016). The positive and significant effect of adut equivaents on expansion is in ine with our a priori expectations about the roe of access to famiy abor to faciitate expansion. This resut is aso in ine with Babigumira et a. (014) who found that househods with more mae abor were aso more ikey to expand cropand. The negative correation between age of the househod head and the ikeihood, and extent of expansion is ine with the ife cyce theory and aso corroborates findings in Babigumira et a. (014). Househods headed by younger househod heads were more ikey to expand, both due to higher physica strength and due to the need to invest in more and at eary stages in the ife of the househod. Resuts showing that higher yied stimuates cropand expansion (among househods who aready expanded) support ong-standing arguments suggesting that higher crop productivity may stimuate deforestation (Angesen and Kaimowitz 001; Bamford et a. 005; Rude et a. 009). Thus, technoogica-driven intensification on its own may stimuate expansion if market conditions are favorabe (Byeree et a. (014). The negative effect of seed and fertiizer prices on the probabiity of cropand expansion highight the importance of market factors as suggested in Babigumira et a. (014). Our resut that proximity to protected forests does not infuence expansion decisions reinforces the need to improve oca forest management institutions, which are generay weak in Zambia (Muenga et a. 015). Tenure security may have contradictory effects on cropand expansion. It can make investments in existing and more secure, and hep making intensification more attractive reative to expansion. If, however, farmers cear forests to caim tenure rights and tites, more secure tenure can spur forest cearing (Angesen 1999). We find that secure and tenure reduce cropand expansion, suggesting that the first effect is stronger. This is in ine with our findings that ony 1% of respondents reported that they ceared forests to secure tite. It is aso in ine with other studies suggesting that secure tenure faciitates farm eve investments in and improvements in Ethiopia (Deininger and Jin 006), Kenya (Kabubo-Mariara 007), Zambia (Smith 004) and Africa, in genera (Pace 009) - which in turn woud reduce the need for expansion - at east in the short run. Pace and Otsuka (000) found

24 that conversion of forested and to agricuture was highest under customary (insecure) tenure in Uganda. However, because we measure tenure as a dummy, and we do not investigate further its potentia endogeneity, caution is needed when interpreting the tenure resuts. Finay, our findings suggesting that farmers in Mumbwa and Nyimba districts were more ikey to expand reative to those in Mpika district highight differing and pressures across the districts: the popuation density in Mpika is 5 persons/km compared to 10 and 8 in Mumbwa and Nyimba, respectivey. Ony 19% of the respondents expanded cropand, highighting that increasing and scarcity in Zambia is setting constraints to farmers options (Chamberin et a. 014). 7 Concusion This paper assessed the effects of minimum tiage and other proximate factors on cropand expansion. We deveoped a theoretica non-separabe househod mode with imited off farm work opportunities and tested hypotheses using househod survey data for the 013/014 agricutura season in Zambia. Future research shoud assess the effects of the fu conservation agricuture package on deforestation, and deveop ong-term pane data sets to assess its effects on abor aocation and productivity, and cropand expansion. About one-fifth of smahoder househods in our sampe expanded cropand into forests cearing an average of 0.14 ha over one year. Its ow adoption intensity, averaging 0.13 ha or 8% of cutivated and per farm househod, however, suggests that it is not the dominant tiage method. Minimum tiage is both yied augmenting and abor intensive, thus its net effects on expansion are ambiguous, a priori. In our dataset, we do not find evidence of ower expansion due to the adoption of minimum tiage practices overa, but among househods who aready expanded. The yied enhancing effect might, under the right conditions ead to more forest encroachment. Thus, we concude in ine with Byeree et a. (014) that poicies aimed to improve agricutura yieds such as minimum tiage may exacerbate deforestation without concomitant forest conservation measures such as direct contro of cropand expansion into forests or payments for environmenta services to prevent and possiby reduce cropand expansion. Given the dua chaenge facing African agricuture of both raising yied and farm incomes, whie adapting to and mitigating cimate change, 3

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