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Cropping system diversification in Eastern and Southern AfricaIdentifying policy options to enhance productivity and build resilience

FAO AGRICULTURAL DEVELOPMENT ECONOMICS WORKING PAPER 18-05

September 2018

Food and Agriculture Organization of the United Nations

Rome, 2018

Cropping system diversification in Eastern

and Southern Africa Identifying policy options to enhance

productivity and build resilience

Giuseppe Maggio, Nicholas J. Sitko and Ada Ignaciuk

Required citation:

Maggio, G., Sitko, N. & Ignaciuk, A. 2018. Cropping system diversification in Eastern and Southern Africa: Identifying policy

options to enhance productivity and build resilience. FAO Agricultural Development Economics Working Paper 18-05. Rome,

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iii

Contents

Abstract ............................................................................................................................... v

Acknowledgements ............................................................................................................. vi

Introduction ......................................................................................................................... 1

Unpacking diversification: a conceptual framework ............................................................. 2

Data and descriptive statistics ............................................................................................. 7

1.1 Data sources ......................................................................................................... 7

1.2 Summary statistics by cropping system in Malawi, Mozambique and Zambia ........ 8

1.3 The geography of cropping systems .................................................................... 14

Research design ............................................................................................................... 16

1.4 Adoption equation ................................................................................................ 16

1.5 Outcome equation ............................................................................................... 17

Results and discussion ...................................................................................................... 18

1.6 Determinants of cropping system adoption: policy lessons from cross country

comparison .......................................................................................................... 18

1.7 Country specific insight of cropping systems adoption ......................................... 23

1.8 Impact on productivity .......................................................................................... 24

1.9 Impact on crop income volatility ........................................................................... 27

Conclusions ....................................................................................................................... 29

References ........................................................................................................................ 31

Annex 1 ............................................................................................................................. 35

iv

Tables

Table 1. Crops within each crop categories ...................................................................................... 4

Table 2. Most frequent crop combinations within each cropping system ........................................ 10

Table 3. Summary statistics by cropping system for Malawi ........................................................... 11

Table 4. Summary statistics by cropping system for Mozambique ................................................. 12

Table 5. Summary statistics by cropping system for Zambia .......................................................... 13

Table 6. Drivers of cropping systems adoption in Malawi ............................................................... 20

Table 7. Drivers of cropping systems adoption in Mozambique ...................................................... 21

Table 8. Drivers of cropping systems adoption in Zambia .............................................................. 22

Table 9. Effect of farming systems on crop income volatility........................................................... 28

Figures

Figure 1. Prevalent cropping system at district level ........................................................................ 15

Figure 2 Effect of different cropping systems on maize yield compared to maize mono-cropping

in Malawi, Mozambique and Zambia ................................................................................. 25

v

Cropping system diversification in Eastern and Southern Africa

Identifying policy options to enhance productivity and build resilience

Giuseppe Maggio1, Nicholas Sitko1, Ada Ignaciuk1

1 Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA), Viale delle Terme di Caracalla, 00153 Rome, Italy

Abstract

Crop diversification is an important policy objective to promote climate change adaptation,

yet the drivers and impacts of crop diversification vary considerably depending on the specific

combinations of crops a farmer grows. This paper examines adoption determinants of seven

different cropping systems in Malawi, Zambia and Mozambique, and the impact of their

adoption on maize productivity and income volatility – using a multinomial endogenous

treatment effect model. These cropping systems consist in different combinations of four

categories of crops: dominate staple (maize), alternative staples, legumes, and cash-crops.

The study finds that relative to maize mono-cropping systems, the vast majority of systems

have either neutral or positive effects on maize productivity, and either reduce or have neutral

effects on crop income volatility. In particular, cropping systems that include legumes produce

better outcome in most cases than those that feature cash crops. From a policy perspective,

three recurrent determinants of diversification are found. First, private sector output market

access is an important driver of diversification out of maize mono-cropping. Policies crowding

in private output market actors can help to promote a wide range of more diverse cropping

systems. Second, proximity to public marketing board buying depots discourages the

adoption of more diverse cropping systems. Therefore, reforms to these institutions must be

part of any diversification strategy. Finally, in all countries and for all systems, land size is a

key determinant of adopting more diverse systems. Thus, land policy is an integral element

of any boarder diversification strategy.

Keywords: adoption, cropping systems, crop income volatility, diversification, yield,

East Africa.

JEL codes: O13, Q12, Q18, Q54, R20.

vi

Acknowledgements

This paper is produced in the context of the programme of work on economics and policy

innovations for climate-smart agriculture within FAO’s Agricultural Development Economics

Division (ESA). The Food and Agriculture Organization of the United Nations would like to

thank the Flanders Cooperation for the financial support.

We greatly appreciate the precious inputs, help, reviews, and comments received from

Solomon Asfaw, Antonio Bubbico, Valentina Conti, Uwe Grewer, Antonio Scognamillo and

Stefanija Veljanoska. This paper reflects the opinions of the authors and not the institution

which they represent or with which they are affiliated. We are solely responsible for

any errors.

Corresponding author: [email protected]


1

Introduction

Many governments in sub-Saharan Africa (SSA) identify crop diversification as a key element

of their climate change adaption strategies. Of the 31 countries in sub-Saharan Africa that

have submitted a National Adaptation Programmes of Action to the United Nation Framework

Convention on Climate Change (UNFCC), eight have projects dedicated specifically to

promoting crop diversification. Crop diversification is thought to enhance adaptation to climate

change in three different ways. First, by spreading agriculture production and income risk over

a range of crops with different degrees of sensitivity to climate induced crop loss and market

conditions (Hahn, Riedered and Foster, 2009; Dercon, 1996). Second, where diversification

entails the adoption of more commercially oriented crops, crop diversification may help to

increase household’s income, leading to a reduction of livelihood vulnerability (Krupinsky et

al., 2002). Finally, crop diversification may provide agronomic benefits, including differential

nutrient uptake, disease management and soil nitrogen fixation, which may help to enhance

or stabilize crop productivity in the presence of climate change (King and Hofmockel, 2017).

To provide evidence that policy makers may find useful for identifying specific crop

diversification strategies and policies to promote these, this study makes use of nationally

representative panel and cross-sectional data collected from smallholder farmers in Malawi,

Mozambique and Zambia. Using econometric techniques, this work moves beyond abstract

definitions of crop diversification, such as the Gini Simpson’s Index (Arslan et al., 2018), to

assess the determinants of adopting seven different cropping systems and the effects of their

adoption on maize productivity and crop income volatility. By so doing, the study is able to

provide concrete insights on the effects of different diversification strategies of famer’s welfare

and resilience, as well as on possible policy strategies for promoting beneficial cropping

systems.

The study finds that relative to maize mono-cropping systems, the vast majority of cropping

systems adopted in these countries have ether neutral or positive effects on maize

productivity, and either reduce or have neutral effects on crop income volatility. In particular,

cropping systems that include legumes produce better outcomes in most cases than those

that feature cash crops. From a policy perspective, three recurrent features are found to help

or hinder the adoption of more diverse systems in all three countries. First, private sector

output market access is an important driver of diversification. Policies that crowd in private

sector output market actors can help to promote a wide range of more diverse cropping

systems. Second, proximity to public marketing board buying depots discourages the adoption

of more diverse cropping systems. Therefore, reforms to these institutions must be part of any

diversification strategy. Finally, in all countries and for all systems, land size is a key

determinant of adopting more diverse cropping systems relative to maize mono-cropping.

Thus, land policy is an integral element of any boarder diversification strategy.

In support of these findings this paper is organized as follows. Section 2 outlines the

conceptual framework for understanding crop diversification. Section 3 presents the data

sources. Section 4 presents the research design. Section 5 introduces summary statistics and

results. Section six concludes.

2

Unpacking diversification: a conceptual framework

Diversification is often considered an important ex-ante strategy deployed by farmers to

manage production and income variability in the context of climate change. Through crop

diversification, farmers spread the risk of crop failure and productivity loss due to weather

events, thus helping to smooth consumption when these events occur. Economic theory,

however, suggests that there are potential trade-offs between managing risks and maximizing

returns (Arslan et al., 2018). Whether or not diversification entails foregoing returns in order

to limit risk is likely a function of whether or not households are pulled into a particular

diversification strategy or pushed (Barrett and Reardon, 2000).

Push factors for crop diversification include high transactions costs in input and output

markets, adverse shocks, including weather shocks, imperfect or absent credit and insurance

markets and stagnation in the agriculture sector (Barrett, Reardon and Webb, 2001; Arslan et

al., 2018). Risk adverse households with limited resources to cope with production variability

associated with climate shocks may be more likely to be pushed into diversification strategies

than better endowed households (Arslan et al., 2018). Where push factors dominate, crop

diversification may not improve productivity or incomes, but may help to stabilize them (Arslan

et al., 2018; Barrett, Reardon and Webb, 2001).

Conversely, diversification pull factors predominate under conditions of crop sector dynamism,

where new market opportunities or practices/technologies induce risk-taking farmers to adopt

new crops into their farm systems. Pull factors may be associated with household strategies

to capture the synergistic benefits associated with, for example, legume intercropping with

maize. Under these conditions, diversification is likely associated with improvements in

average incomes and reduced variability, and may contribute to higher productivity (Arslan et

al., 2018; Reardon et al., 2007). Where pull factors for diversification exist, they are often more

pronounced for wealthier, better off households with reasonably good market access

conditions (Davis et al., 2010).

To better understand the causes and consequences of crop diversification, this study develops

a taxonomy of cropping systems for maize producing households based on four functional

crop categories (see Table 1). These crop categories are maize, legumes, non-maize staples

and cash crops. These crop categories appear in different combinations within smallholder

systems in Malawi, Zambia and Mozambique. The observed combination of crop categories

we refer to as a cropping system. We hypothesize that the crop categories are associated with

differing degrees of commercialization potential, climate risk management potential and

agronomic benefits for maize producing households. If this is the case, then different

combinations of these crop categories—i.e. the cropping systems— should produce different

outcomes in terms of productivity and income variability and adoption will be concentrated

among households with shared attributes.

Crop categories

Maize: national statistics show that maize is cultivated by 50 percent of the population in sub-

Saharan Africa, with 46 out of 53 countries growing maize. In Zambia, Malawi and

Mozambique it is the primary staple and most widely grown crop by resource poor smallholder

farmers (Mazunda and Droppelman, 2012; Mafongoya and Jiri, 2015). Maize is grown by 73

and 79 percent of farmers in Zambia and Mozambique, respectively, while this share increase

to 97 percent for Malawi, where maize accounts for about 60 percent of total caloric

consumption (Denning et al., 2009; JAICAF, 2008). Maize productivity in the region is low by

3

global standards, hovering barely over two tonnes per hectare, and is hindered by a range of

factors, including degrading soil conditions, labour constraints, insufficient use of improved

maize varieties, and inappropriate and limited application of organic and inorganic fertilizer.

As the primary agricultural product and food crop in the region, maize functions as both a cash

and subsistence crop, depending on the scale of production. Despite its prevalence in rain-

fed smallholder systems, maize productivity is sensitive to low and variable rainfall conditions,

while maize mono-cropping can rapidly degrade soils (Kumwenda et al., 1997; Rockström et

al., 2009). Thus, while maize production offers opportunities for commercialization and income

earnings for farmers that produce a surplus, maize production, particularly in mono-cropping,

exposes farmers to significant climate risks and can undermine future growth by degrading a

farm’s soil resource base.

Legumes: this category comprises beans, soybeans, pigeon peas, groundnuts and green

beans among others. Literature suggests that the incorporation of legumes into a cropping

systems will produce beneficial effects on both maize yields and income stability relative to

maize mono-cropped systems. However, some differences persist between different types of

legumes. Short-season legumes, such as groundnuts, are more easily marketable and

therefore, more suitable for commercial-oriented systems. In contrast, long-season legumes,

such as pigeon-pea, help improving maize yields by enhancing phosphorus and fixing a

significant larger amount of nitrogen, a key nutrient in maize systems (Kerr et al., 2007). In

addition, annual legume cover crops have a determinant role in weed suppression (Becker

and Johnson, 1999; Sileshi et al., 2008). Legumes can also improve crop income stability by

stabilizing production through improvements in soil quality (Sileshi et al., 2008).

Non-maize staples: this category comprises common alternative carbohydrate sources such

as cassava, sweet potato, rice, millet and sorghum. These alternative-staples will likely have

a neutral to negative effect on maize yields, as they do not have obvious agronomic benefits

for maize systems, but may divert scarce production factors, namely capital and labour, away

from maize. However, non-maize staples are likely to have a positive or neutral effect on

income stabilization either by providing additional market opportunities for farmers, particularly

where commercial markets for non-maize staples exist, or through their tolerance to weather

shocks relative to maize. For example, cassava is a drought-resistant perennial crop which

survives to rainfall shortfalls that often disrupt maize production. Similarly, sorghum and millet

are often less vulnerable to the effect of increasing weather volatility than maize and thus may

help to increase income stability relative to maize mono-cropping (Schlenker et al., 2010).

Cash Crops: this category is comprised of major crops in the region, such as cotton and

tobacco, as well as more minor crops, such as sunflower, cashews and sugar cane. A priori,

the effects of adoption of this crop category on maize yields and income stability are uncertain.

Cash crop production is often carried out under input provisioning contracts, which may enable

resource-poor households to enter into more intensive, commercially oriented production

(Govereh and Jayne, 2003). By facilitating access to inputs and formal output markets, cash

crop production may have positive spill-over effects on maize intensification for farmers

(Chapoto et al., 2013). However, when labour is scarce, cash crop production may divert

labour away from the sensible management practices for maize, such as weeding (Haggblade,

Plehoples and Kabwe, 2011). In terms of income stability, some authors argue that cotton

production, one of the main cash-crops in this analysis, is expected to increase with future

climate change due to the plant’s relative tolerance of drought conditions (Agrawala et al.,

2003; Morton, 2007). Nevertheless, cash crops are typically globally traded and thus are more

4

exposed to global market prices’ fluctuations, which can produce high crop-income variability

(Fafchamps, 1992).

Table 1. Crops within each crop categories

Crops Crop Categories

Bean, soybean, pigeon pea, groundnut, green bean Legumes

Cotton, tobacco, sunflower, sugarcane, sesame and tea Cash crops

Cassava, rice, millet, sorghum, wheat, Irish potato and sweet potato Non-maize staples

Maize Maize

Cropping systems

The four cropping categories described above are combined within smallholder maize-based

systems in seven different combinations, what we refer to as cropping systems. These

combinations represent different diversification strategies and are likely adopted by different

farm household types, due to different combinations of push and pull factors, and produce

different outcomes in terms of maize productivity and income stability.

Maize mono-cropping (MM)

This system is the reference system in this study. It is hypothesized that a large share of

farmers in this cropping system is resource poor, with limited access to off-farm income, and

has been pushed into maize mono-cropping in an effort to meet household’s staple food

consumption requirements (Nielson, 2009). Adopters, however, may also be pulled in mono-

cropping by the presence of public demand for maize, as in the case of marketing boards and

food reserves in Zambia or Malawi, as well as input subsidy programmes that support input

access for maize (Sitko et al., 2017).

Maize-legume (ML)

This system is likely dominated by subsistence-oriented farmers, focused primarily on legume

production for home consumption. Legume harvesting and planting are more labour intensive

than maize, thus household size, access to hired labour or social networks to access labour

may help to pull farmers into this system (Nhemachena and Hassan, 2007). In addition, due

to the well-established agronomic benefits of incorporating legumes into maize-based

systems, access to extensions information may be an important pull factor driving adoption of

this system. Finally, although most legumes production is likely for home consumption, in

Mozambique and Malawi export markets for pigeon peas and groundnuts (in the case of

Malawi) are important. Similarly, in Zambia, growth in the animal fed and oil expelling

industries is driving demand growth for soybeans (Sitko, Burke and Jayne, 2017). Thus, the

presence of a private agricultural market may be an important pull factor for this category.

Because of the properties of legume when intercropped, ML system are expected to show

higher yield and more income stability than in the maize mono-cropping system.

5

Maize and staple (MS)

Alternative staples in this system likely serve to lower the production and market risks

associated with maize mono-cropping. There are several reasons for this. First, they provide

a more drought-tolerant source of carbohydrate than maize. In places where droughts are

frequent, households may be pushed into diversifying carbohydrate production to meet

household food security needs. This may be particularly the case for households in more

remote market locations, for whom access to retail food markets and off-farm income is more

limited. Second, alternative staples offer some opportunities for income generation and

income stabilization, with potential markets tied to the commercial beer and livestock feed

industries (Garrity et al., 2010). Compared to maize mono-cropping, the impact of MS systems

on productivity is expected to be between negative and neutral, because of high factors

competition between the two crops. In terms crop income, this system is expected to show

higher stability compared to maize mono-cropping due to the drought tolerance of most

alternative staples (Nielson, 2009; Thierfelder et al., 2016).

Maize and cash crops (MC)

Adopters of this system are likely commercially oriented or seeking to transition from

subsistence to commercially oriented production (Chapoto et al., 2013). Resource poor

farmers are likely pulled into this system by the availability of input credit for production and

commercial output markets. Yet, due to significant price volatility caused by global supply and

demand conditions, the inclusion of cash crops entails substantial risks, particularly when

production risks are not offset by a more diverse production system. Access to input credit

and commercial markets may enable resource poor farmers to overcome capital constraints

to maize intensification. However, competition for labour and other resources between maize

and cash crops may translate into lower maize yields relative to mono-cropped systems.

Maize-legume-staples (MLS)

Of the more diversified cropping systems, with three or more crop categories involved, farmers

adopting the MLS system are likely motivated primarily by production and price risk

management, rather than productivity and income growth. Low market access conditions,

access to extension services, historical experiences with weather shocks, combined with

larger land holdings to enable a three-crop system may combine to push and pull farmers into

this system. However, in some cases, an alternative pathway may dominate. In higher market

access regions, the level of commercialization of certain legumes and alternative staples

market opportunities may pull farmers into the Maize-Legume-Staple (MLS). Trade-offs

between the agronomic benefits of incorporating legumes into a maize-based system and

competition between production factors makes it difficult to anticipate the effects of this system

on maize yields. However, a positive effect of income stabilization can be expected by the

combination of legume with alternative staples (Dapaah et al., 2003).

Maize-legume-cash crop (MLC)

Adopters of this crop system are likely to be more commercial-oriented and risk taking relative

to the MLS system. The presence of marketing infrastructures, credit institutions, informal

social networks, and agribusiness firms are all potential pull factors for adopters of this system.

These factors, indeed, may help farmers in coping with the potential risk of crop failure, by

relying access to credit, but also in accessing to the high level of inputs associated with this

commercial production. The diffusion of agricultural extension services and the availability of

large landholdings may represent other pull factors for the adoption of this cropping system

6

(Gladwin, 1983). Because of the crops’ characteristics, this system is expected to increase

maize productivity, while having a neutral to positive effect on crop income stability (Yadav,

1981; Bantinal and Rao, 1999).

Maize-legume-staples-cash crops (MLSC)

This is the most diversified cropping system, which balances opportunities for

commercialization with price and production stabilization benefits derived from the

incorporation of alternative staples and legumes. Adopters of this farming system are likely

characterized by a lower propensity to risk than MLCs but higher than the baseline. They likely

have significant land and labour assets, which help enable a diverse production system.

Climate stressors and market opportunities are the main push and pull factors for this system.

Depending on the relative allocation of each crop in this system, it is possible to expect high

benefits in terms of yield and stability of income.

7

Data and descriptive statistics

1.1 Data sources

This study uses household survey data collected from smallholder farmers in Malawi,

Mozambique and Zambia merged with geo-referenced climatic records to study the impact of

the cropping systems on yield level and crop income variability. The analysis relies on four

sources: 1) socio-economic panel household, field and community data from a) Malawi

Integrated Household Panel Survey (IHPS); b) Zambia Rural Agricultural Livelihoods Survey

(RALS); c) cross-sectional data from the Mozambique Inquérito Agrário Integrado (2015); and

2) historical data on rainfall and temperature from the National Oceanic and Atmospheric

Administration (NOAA), Climate Hazard Infrared Precipitation with Station Data (CHIRPS) and

the European Centre for Medium Range Weather Forecasts (ECMWF).

Malawi’s IHPS is a national representative survey conducted by the Government of Malawi

with the support of the World Bank Living Standards Measurement Study. For Malawi, the

panel sample includes 1 989 households cultivating maize, interviewed during first the rainy

season 2009/2010 and then during 2012/2013, for a total of 3 978 observations.

The RALS is a panel survey conducted jointly by the Zambian Central Statistical Office (CSO),

Michigan State University (MSU), the Indaba Agricultural Policy Research Institute (IAPRI)

and the Ministry of Agriculture and Livestock. This survey is national representative of rural

farm households cultivating less than 20 hectares for farming purposes and/or raising of

livestock. RALS covers 10 Provinces, 150 constituencies and 476 enumeration areas. For

Zambia, the panel includes 5 948 households cultivating maize and observed during

2010/2011 and 2013/2014 agricultural seasons.

Mozambique’s IAI survey derives from the Trabalho de Inquerito Agricola (TIA), a yearly

survey project developed by the Michigan State University (MSU) together with the Ministry of

Agriculture of Mozambique (MINAG). IAI 2015 counts 5 055 observations, it is representative

both at national and provincial level, and it includes observations for 11 provinces, 147 districts

and 780 enumeration areas.

For all the surveys, the enumerators have administered a multi-topic questionnaire. Data are

available on household characteristics, salary (apart from Mozambique), food and non-food

consumption, food security, assets and agricultural production. Since maize mono-cropping is

the baseline category, the final sample includes only households that have cultivated maize

during both waves.

The analysis on determinants and impact of diversification considers three groups of factors:

institutional, biophysical, resource endowment and other socio-economic factors. The

institutional factors are captured through proxies of private input and output market and public

output markets. Public markets are measured as the median distance from all the households

in a given village to the closest marketing board buying depot for Malawi (ADMARC) and

Zambia (Food Reserve Agency).1 In Mozambique there is no public marketing board, therefore

this variable is excluded from analysis. The extent of the private input market is measured

using the median seed prices at village level (not available in Mozambique). In terms of private

output markets, available information differs substantially across the three surveys. For

Zambia, private market is measured in terms of the number of private traders that buy grain

1 We consider the median rather than the average to exclude that the presence of any outlier is driving the estimated effect of this variable.

8

in a given community. Malawi’s specification includes the median distance from the weekly

market computed at village level. Unfortunately, Mozambique’s questionnaire does not include

any relevant information on the presence of an agricultural market. In this case, therefore, the

private market indicator is constructed using the distance of the households’ district of

residence from the main regional city obtained from the GRUMP dataset (Balk et al., 2006).2

The biophysical factors are operationalized as the lagged value of the positive and negative

SPI rainfall indexes as indicators, respectively, of drought and flood occurring in the

agricultural season before the interview. The set of variables also includes dummies on agro-

ecological zones to control for local agro-ecological differences. Among the resource

endowment and other socio-economic factors, we consider the agricultural asset wealth index,

calculated using a principal component analysis (PCA) on dummies indicating households’

ownership of a range of agricultural tools.3 The second and third resource indicators relate to

the size of land and of number of livestock owned, measured in hectares and TLU units,

respectively. The remaining subset of controls involves a) household demographics, such as

household size, age and gender of the head of the household, average years of education;4

b) share of households accessing to credit and Farmer Input Subsidies program, measured

at community level; c) a dummy capturing the usage of inorganic fertilizer.5

We extract rainfall records for Malawi and Mozambique from the Africa Rainfall Climatology

version 2 (ARC2), a platform managed by the National Oceanic and Atmospheric

Administration’s Climate Prediction Centre (NOAA-CPP). ARC2 provides rainfall records

elaborated on a 10-day interval from January 1983 to July 2015, with a spatial resolution of

0.1 degrees. Rainfall data for Zambia derives from the Climate Hazards Group InfraRed

Precipitation with Station data (CHIRPS), with 0.5 degree of spatial resolution. The European

Centre for Medium Range Weather Forecast (ECMWF) delivers information on the surface

maximum, minimum and average temperature on a 10-day interval with a spatial resolution of

0.25 degrees. The sources of temperature data are both the operational database (1989-

2010) and the interim database (2011-2013). GPS location from the Malawi and Zambia

surveys allows the merge with climatic data. For Mozambique, the merge is at district level,

the only public available geographical location in the data.

1.2 Summary statistics by cropping system in Malawi, Mozambique

and Zambia

In this section, descriptive data on households in each of the seven cropping systems is

examined in order to identify important difference between households in the different

systems, as well as commonalities and difference between countries. What are the primary

2 The list of cities employed is available in Table A8 in Annex 1. To test the robustness of Mozambique’s results and exclude that the coefficient on private market would capture the effect of population, urbanization, and infrastructural development, we tested alternative specifications including controls on level of population at district level (Linard et al., 2012), nightlight index (Cecil, Buechler and Blakeslee, 2014), and road density. The results are consistent with the ones presented in the main part of the paper. 3 In terms of farm assets, there is not a unique evidence, but literature suggests that households with smaller land owned are more likely to diversify their crops while livestock ownership is positively associated to multiple adoption as it represents store value and a source for manure (Deressa et al., 2009). 4 For example, education is expected to have a positive impact on multi-cropping and adaptation, as better educated households hold more knowledge of the potential benefit deriving from these (Dolisca et al., 2006; Anley et al., 2007; Tizale 2007). 5 Fertilizer and compost often occur during intercropping (Silberg et al., 2017) but some evidence exists about the crowding out effect of subsidized fertilizer on non-legume intecropping (Levine et al., 2016).

9

crops grown in each of the cropping systems and how do these vary by country? Three most

frequent crop combinations within each cropping system in each country are summarized in

Table 2. The value in parenthesis refers to the percentage of households adopting a given

combination within the system, but these combinations are not mutually exclusive. It shows

substantial cross-country differences in the type of crops adopted within the same system in

different countries. For the ML system, groundnut is the most widely cultivated crop in Malawi

(66 percent) and Zambia (84 percent), while Mozambican farmers combine more often maize

with beans (89 percent). Among the staples crops, cassava is planted by about 63-64 percent

of MS adopters in Mozambique and Zambia, but not in Malawi and it is widely grown also by

MLS households. Nevertheless, in Malawi cassava has not a prevalent role in this system, as

it represents only the third choice for MS farmers, and it is absent in the three most common

MLS and MLSC combinations.

The analysis shows three important commonalities between the three countries, which are

summarized in Table 3, Table 4 and Table 5. First, maize mono-cropping in all three countries

is dominated by households with the smallest average land sizes and the lowest average

levels of crop income. This suggests that the majority of maize mono-cropping households

are pulled into this system in an effort to meet household food security needs given scarce

resources, and may be locked into this system by limited commercialization and crop income

earning opportunities.

Second, the presence of cash crops, either in combination with maize (MC) or in more diverse

systems (MLC and MLCS), is associated with higher crop incomes in all countries. This is

likely due to the combination of more structured private marketing arrangements for these

crops, and the prevalence of outgrower arrangements in cash crop systems. In all three

countries, participation by female-headed households is lowest in cash cropping systems,

suggesting that female household heads are either structurally excluded from entering into

cash cropping arrangements, prioritizing the production of consumable agricultural products

more than male-headed households, or some combinations of the twos.

Finally, more diverse cropping systems (with three or more crop categories) are associated

with higher crop incomes, larger land sizes, and in many cases higher average maize yields

than less diverse systems. Land size is likely an important determinant of beneficial

diversification. Smallholder farmers with larger landholdings have greater flexibility to

incorporate a range of crops into their production systems, while still dedicating sufficient land

to staple food production. By so doing they are able to capture potential agronomic and

economic advantages associated with alternative crops, thus pushing them into a beneficial

cycle of improved crop income, greater investment capital for farm activities, and higher

productivity.

Several important differences between countries are worth highlighting. First, in land

constrained Malawi, maize-mono-cropping is the most prevalent cropping system.

Conversely, in Zambia and Mozambique, the most prevalent system is the MLS system.

Average land holdings in Malawi are substantially lower for all systems than in the other two

countries, which hinders the capacity of farmers in Malawi to adopt more diverse systems.

The dominance of maize mono-cropping in Malawi suggests that farmers in that country are

particularly vulnerable to climatic or market related shocks that adversely affect the maize

sector.

Second, maize yields in Mozambique are significantly lower for all systems than yields

obtained in Zambia and Malawi. This is likely tied to the extremely low levels of inorganic

10

fertilizer application in the country. Unlike Malawi and Zambia, Mozambique does not maintain

an input subsidy programme and private agro-dealers have only recently begun to expand in

the country following the formal end to the civil war (Sitko, Burke, and Jayne, 2017). Crop

incomes for each of the systems are quite similar between Malawi and Mozambique,

suggesting than low maize productivity in Mozambique relative to Malawi is compensated by

some combinations of higher productivity in non-maize crops, higher total production due to

larger land holdings, or higher farm gate prices. Average crop income in Zambia is higher for

all cropping systems. This is likely a function of larger average land holdings, higher

productivity levels and higher farm gate prices. Zambia has witnessed an impressive

expansion in investment in maize and oilseed markets by large, often multinational firms,

which provide farmers with higher farm gate prices than traditional small-scale traders (Sitko

et al. 2017).

Finally, although the average family sizes, levels of education, and share of households that

are female headed are similar in the three countries, the heads of household in Zambia are,

on average, considerably younger. High crop incomes and greater levels of land availability in

Zambia may help to maintain young people in agriculture, rather than being pushed into urban

migration.

Table 2. Most frequent crop combinations within each cropping system

Malawi Mozambique Zambia

ML

Groundnut (66%) Bean (89%) Groundnut (84%)

Pigeon pea (28%) Groundnut (47%) Bean (21%)

Soybean (16%) Pigeon Pea (16%) Soybean (12.4%)

MS

Rice (51%) Cassava (64%) Cassava (63%)

Sweet potato (32%) Sorghum (33%) Sweet potato (28%)

Cassava (25%) Rice (18%) Rice (17%)

MC

Tobacco (51%) Sesame (59%) Cotton (69%)

Cotton (30%) Cotton (16%) Sunflower (37%)

Sunflower (3%) Sugar Cane (4%) Tobacco (8%)

MLS

Pigeon pea-sorghum (43%) Bean-cassava (62%) Groundnut-cassava (54%)

Groundnut-sorghum (18%) Groundnut-cassava (50%) Groundnut-sweet potato (38%)

Groundnut-sweet potato (17%) Bean-sorghum (25%) Bean-cassava (34%)

MLC

Groundnut-tobacco (58%) Bean-sesame (38%) Groundnuts-cotton (55%)

Soybean-tobacco (13%) Groundnut-sesame (20%) Groundnuts-sunflower (53%)

Pigeon pea-nkhwani (12%) Pigeon pea-sesame (16%) Soybean-sunflower (12%)

MLSC

Pigeon pea-sorghum-nkhwani (38%)

Bean-cassava-sesame (37%)

Groundnut-sweet potato-sunflower (29%)

Groundnut-sorghum-nkhwani (17%)

Bean-sorghum-sesame (27%)

Groundnut-sweet potato-cotton (23%)

Groundnut-sorghum-tobacco (13%)

Groundnut-cassava-sesame (27%)

Groundnut-rice- cotton (13%)

Notes: the table displays statistics on the combination of crops more frequently adopted within each cropping system divided by country. The value in parenthesis refers to the percentage of households adopting that given combination within the categories. Crops combinations are not mutually exclusive within each category, thus percentage values do not necessary sum to 100.

Source: Authors’ own elaboration.

11

Table 3. Summary statistics by cropping system for Malawi

Maize mono-cropping

Maize-legume

Maize-staple

Maize-cash crops

Maize-legume-staple

Maize-legume-cash

crops

Maize-legume-cash crops-staple

(N = 1 342) (N = 1 142) (N = 174) (N = 322) (N = 282) (N = 359) (N = 113)

Mean Sd Mean Sd Mean Sd Mean Sd Mean Sd Mean Sd Mean Sd

HH size 5.10 2.27 5.13 2.19 5.38 2.25 5.60 2.47 5.26 2.39 5.92 2.44 5.65 2.35

Age 45.72 16.61 47.3 16.33 44.3 15.13 43.31 15.1 45.22 16.55 45.83 14.47 46.35 16.17

Female-headed HH (1=yes)

0.26 0.44 0.27 0.44 0.21 0.41 0.15 0.36 0.30 0.46 0.16 0.37 0.26 0.44

Education (years) 5.03 2.88 5.11 2.50 5.28 2.7 4.89 2.29 4.93 2.53 5.32 2.21 4.85 2.32

Share of credit recipients

0.17 0.17 0.17 0.15 0.15 0.16 0.15 0.12 0.16 0.16 0.18 0.12 0.17 0.14

Share of FISP Recipients

0.52 0.25 0.55 0.22 0.49 0.26 0.6 0.23 0.59 0.22 0.60 0.21 0.61 0.2

Inorganic fertilizer (1=yes)

0.75 0.44 0.79 0.41 0.71 0.45 0.84 0.37 0.87 0.34 0.91 0.29 0.87 0.34

Maize yield (Kg/ha)

1 454 1 702 1 850 1 952 1 597 1 672 1 533 1 355 1 743 1 770 2 131 1 904 1 672 2 077

Crop income (USD 2010)

299 385 462 484 494 532 831 1267 472 494 1 240 1 483 641 798

Crop income per hectare (USD 2010)

947 4 376 757 907 1 174 3 163 1055 1 636 635 6 90 1 328 1 684 770 963

Agricultural Asset Wealth Index

0.11 0.96 0.30 1.02 0.48 1.03 0.40 0.95 0.35 1.03 0.73 1.09 0.45 0.98

Livestock (TLU units)

0.51 1.31 0.8 2.07 1.72 2.72 0.83 2.02 1.01 2.01 1.67 2.95 0.91 1.71

Land owned (ha) 0.5 0.43 0.70 0.48 0.68 0.47 0.78 0.57 0.78 0.55 1.02 0.66 0.78 0.57

Notes: the table reports summaries of dependent and explanatory variables for Malawi. All the currencies are expressed in real US Dollars 2010. Full summaries are available in Table A1 in Annex 1.


12

Table 4. Summary statistics by cropping system for Mozambique

Maize mono-cropping

Maize-legume

Maize-staple

Maize-cash crops

Maize-legume-staple

Maize-legume-cash

crops


(N = 286) (N = 962) (N = 466) (N = 88) (N = 2 351) (N = 283) (N = 619)


HH size 5.49 2.99 5.59 3.29 5.12 2.78 5.63 2.98 5.55 3.14 5.64 2.58 5.66 2.68

Age 45.76 16.45 45.60 16.37 44.41 15.42 41.51 13.94 45.73 15.49 42.90 14.48 43.05 14.84


0.26 0.44 0.32 0.47 0.29 0.45 0.13 0.33 0.29 0.45 0.15 0.36 0.16 0.37

Education (years) 6.01 3.88 5.67 3.80 5.32 3.55 5.47 3.57 5.66 3.55 5.08 3.57 4.91 3.33


0.01 0.02 0.02 0.04 0.01 0.03 0.03 0.07 0.01 0.03 0.03 0.07 0.01 0.04


0.04 0.20 0.04 0.20 0.02 0.15 0.23 0.42 0.03 0.17 0.28 0.45 0.10 0.30


487 570 712 803 602 669 771 642 699 831 927 804 892 818

Maize yield (Kg/ha)

220 1 243 397 2 493 448 4 767 741 1 924 361 2 285 1 100 4 659 688 3 252


98 195 173 550 108 327 208 231 157 941 307 1 517 180 374


0.24 0.98 0.33 1.07 -0.09 0.75 0.49 1.15 0.10 0.89 0.70 1.28 0.05 0.83


4.64 9.82 5.24 10.63 2.33 6.05 5.17 11.84 3.33 8.48 5.68 12.04 3.08 8.97


1.68 1.55 2.14 2.23 2.15 2.65 3.55 5.18 2.36 2.55 4.03 5.58 3.41 2.76

Land owned (ha) 5.49 2.99 5.59 3.29 5.12 2.78 5.63 2.98 5.55 3.14 5.64 2.58 5.66 2.68

Notes: the table reports summaries of dependent and explanatory variables for Mozambique. All the currencies are expressed in real US Dollars 2010. Full summaries are available in Table A2 in Annex 1.


13

Table 5. Summary statistics by cropping system for Zambia

Maize mono-cropping

Maize-legume Maize-staple

Maize-cash crops

Maize-legume-staple

Maize-legume-cash

crops


(N = 1 705) (N = 2 296) (N = 1 468) (N = 690) (N = 3 202) (N = 2 063) (N = 472)


HH size 6.28 2.84 6.75 3.12 6.53 2.76 6.27 2.77 6.91 2.78 6.87 2.96 7.39 3.19

Age 35.46 12.2 36.55 11.96 35.15 10.94 33.29 9.85 34.95 10 34.64 9.9 34.33 9.38


0.22 0.42 0.26 0.44 0.18 0.39 0.13 0.34 0.17 0.38 0.14 0.35 0.14 0.34

Education (years) 6.55 4.22 6.06 4.03 6.04 3.52 5.03 3.73 6.64 3.46 5.48 3.59 6.31 3.3


0.13 0.17 0.16 0.2 0.05 0.09 0.43 0.27 0.07 0.1 0.44 0.26 0.29 0.26


0.43 0.25 0.48 0.25 0.35 0.29 0.43 0.24 0.56 0.26 0.46 0.23 0.5 0.24


0.64 0.48 0.72 0.45 0.45 0.5 0.59 0.49 0.75 0.43 0.75 0.44 0.77 0.42

Maize yield (Kg/ha)

2 096 1 586 2 309 1 646 1 769 1 494 2 086 1 586 2 595 1 647 2 482 1 698 2 638 1 602


1 908 5 133 2 918 11 375 2 433 3 830 2 704 4 264 3 615 4 552 3 684 4 222 4 583 4 078


915 1514 793 1 164 1009 921 853 1 143 894 723 842 569 909 758


0.11 0.19 0.12 0.2 0.08 0.15 0.11 0.17 0.1 0.18 0.14 0.18 0.16 0.21


3.3 12.24 5.56 15.41 3.15 10.52 4.88 10.06 3.45 11.52 6.79 11.15 6.72 10.52

Land owned (ha) 2.97 8.17 4.65 9.35 3.61 5.72 3.71 3.93 6.13 12.26 4.92 4.85 6.66 8.61

Notes: the table reports mean and standard deviation of dependent and explanatory variables for Zambia. All the currencies are expressed in real US Dollars 2010. Full summaries are available in Table A3 in Annex 1.


14

1.3 The geography of cropping systems

Figure 1 shows the spatial distribution of the dominant cropping systems in each country at

district level, where the dominant system is defined as the one occupying the greatest share of

cultivated land. Several important points come out of this map. First, the MLS system covers

the largest geographic area in Zambia and Mozambique, while maize mono-cropping and ML

systems predominate in Malawi. In Zambia, the MLS system is concentrated in the north and

north-west of the country, where cassava is an important food source, and is often grown in

combination with beans. As similar cropping system is found in the north-east of Mozambique.

In Zambia the maize mono-cropping and ML systems are concentrated around the urban

centres of Lusaka and the Copperbelt, and extend into the more drought prone regions of

southern Zambia. These are regions of the country where population densities are high and

land availability is often constrained (Sitko and Chamberlin, 2016). Thus, despite the fact that

households in these regions likely have reasonably good access to urban markets, and could

make better use of scarce land by cultivating higher value crops, many households in these

areas prioritize maize. Indeed, spatial clustering of cropping systems is evident within each

country, indicating internal spatial spillovers in adoption, which is likely associated with

variations in infrastructural development, dietary preferences and market investments.6

In eastern Zambia, the dominant system is the MLC system. In this region, cotton outgrower

arrangements are widespread, as is the cultivation of sunflower. Cross-border spill overs of this

system are observable between eastern Zambia and north-west Mozambique, but not between

Zambia and Malawi, despite having a similar ethno-linguistic population. This is likely due to a

combination of poor infrastructural connectivity in Mozambique, relative to Malawi, and stricter

enforcement of cross border movements between the Malawi and Zambia border. As a result,

farmers in north-western Mozambique are reliant on markets in eastern Zambia, while markets

in western Malawi may be more oriented to the nearby capital of Lilongwe. Infrastructural

connectivity between southern Malawi and segments of north-western Mozambique are also

likely the reason for observed prevalence of ML systems in both regions.

6 The result from Moran’s I test (MI) on the share of adopters indicates the presence of significant positive spatial correlation in adoption for Mozambique and Zambia, while it is negative and slightly less significant in Malawi (Malawi: MI=-0.24, p=0.06; Mozambique: 0.19, p=0.00 Zambia: MI=0.18, p=0.00).

15

Figure 1. Prevalent cropping system at district level

Notes: Prevalent cropping system is the one with the highest relative share of land cultivated at the district level.


16

Research design

1.4 Adoption equation

The analysis is conducted using a multinomial endogenous treatment effect, as this approach

presents several advantages relative to other approaches for this analysis. Other methodologies

such as multinomial logit, OLS with fixed effects or random effect with Mundlak correction are

only able to estimate separately either the determinants of adoption or the impact of these

systems on the output dimension (Mundlak, 1978; Di Falco and Veronesi, 2013). The

multinomial endogenous treatment effect, in contrast, allows for a simultaneous estimation of

these two components, while also accounting for endogeneity in the selection of the cropping

systems, as a result of the inclusion of one or more instrumental variables. The strategy is

divided into two inter-dependent stages. The first stage models adoption decision of a given a

farming system j using a multinomial logit, as follows:

Pr(𝑓𝑗|𝑧𝑖𝑙𝑖) = 𝑔(𝑧1𝑒′ 𝛼1 + 𝛿1𝑙𝑖1 , 𝑧𝑒2

′ 𝛼2 + 𝛿2𝑙𝑖2 , … , 𝑧𝑒𝑖′ 𝛼𝑗 + 𝛿𝑗𝑙𝑖𝑗) (1)

Where 𝑓𝑗 denotes the variable on the type of farming system and where 𝑗 = 0 refers to the

benchmark category of maize mono-cropping adopters. In addition, 𝒁𝑖𝑒𝑡 is a matrix of control

variables, 𝛼𝑗𝑡 their linked coefficients and 𝑙𝑖 = (𝑙𝑖1, 𝑙𝑖2 … 𝑙𝑖𝐽) a vector of latent variables.7 Deb and

Trivedi (2006) suggests using instrumental variables to obtain a more robust identification. The

instrument involves at least an explanatory variable entering exclusively in the adoption

equation. The matrix of explanatory variables involves four instrumental variables in the first

stage: 1) drought risk exposure; 2) flood risk exposure; 3) informal and 4) formal diffusion of

agricultural practices. For the validity of the first two instruments, the hypothesis is that farmers

are more likely to adopt a cropping system different from maize mono-cropping when residing

in areas that have been more historically exposed to weathers shocks. Being a subjective belief

of the household, the postulate is that this will not affect the yield or crop income volatility through

other channels. Flood and drought risk exposures are computed for household i residing in the

area e as the probabilities of being exposed to these events conditional on area e historical

weather distribution. A drought shock occurrs if the Rainfall Standard Precipitation Index (SPI)

in a given area and agricultural season takes a value lower than -2 (Guttman, 1998; McKee,

Doesken and Kleist, 1993). A flood shock occurs when the SPI index takes value above 2.

Following Scognamillo, Asfaw and Ignaciuk (forthcoming) and generalizing for a shock S

occurring in a given area e in the year t, the risk exposure index takes the following form:

𝑃𝑖𝑒𝑡 = ∑ 𝑆𝑒 𝑡−2

𝑡−21

(𝑡−2)−1983 (2)

Where the probability of experiencing a shock is computed as the ratio between the total

numbers of seasons in which the shock occurred, divided by the total number of seasons

available in our weather source. Drought and flood indicators are lagged by two years to exclude

any recent shocks likely affecting household’s adoption of a cropping system through the

production channel. Following the work from Manda et al. (2016), the remaining two instruments

are proxies of information availability at village/district level, under the assumption that

agricultural information can be transmitted through institutional channels, such as extension

services and informal institutions, such as family and friends network. We compute the average

access to these two sources of information at a higher geographical level than the one observed

7 These are not observable variables but are inferred from other observed variables in of the model.

17

in the data, hypothesizing that the average access to these sources is not correlated to the

household’s outcome. In addition to these exogenous covariates, the matrix of control

variables 𝒁𝑖𝑒𝑡 includes the determinants introduced in section 3.1.

1.5 Outcome equation

To estimate the impact of adoption on maize yield and crop income volatility, the second stage

of the multinomial treatment effect model consists in an OLS with the selectivity correction, as

follows:

𝐸(𝑦𝑖𝑡|𝑑𝑖𝑡𝑥𝑖𝑡𝑙𝑖𝑡) = 𝑥𝑖𝑡′ 𝛽 + ∑ 𝛾𝑗𝑡𝑓𝑖𝑗𝑡 + ∑ 𝜆𝑗𝑡𝑙𝑖𝑗𝑡

𝑗𝑗=1

𝑗𝑗=1 (3)

Where the expected outcome depends on a set of observable determinants 𝑥𝑖𝑡 with linked

parameters 𝛽, on the adoption of a given farm system 𝑓𝑖𝑗𝑡 and associated coefficients 𝛾𝑗𝑡. In

addition to the set of variables in the first-stage, the matrix of determinants 𝑥𝑖𝑡 comprises the

seasonal positive and negative SPI indexes, capturing the effect of seasonal rainfall distribution

on the productivity and crop income volatility.8 To investigate the effect on maize productivity

we use the natural log of maize yield. To address the effect of farm systems on crop income

volatility, we adopt a two steps approach following the extant literature (Antle, 1983; Di Falco

and Chavas, 2009). First, the approach predicts the error term from a regression with the crop

gross income as dependent variable. Secondly, it involves the addition of the square of the error

term as new dependent variable in the main empirical specification, keeping fixed the controls.

8 The technical note in the appendix introduces more details about the empirical approach.

18

Results and discussion

1.6 Determinants of cropping system adoption: policy lessons from

cross country comparison

What factors explain the adoption of different cropping systems, and what are the commonalities

and difference between countries? Three policy relevant commonalities are apparent in the

three countries (see Tables 6, Table 7 and Table 8). First, private sector output market access

is an important driver of diversification. In Zambia, for example, the number of private

agricultural traders in a village has a significant and positive effect on the adoption of all cropping

systems, apart from the MS system. The MS system is primary a subsistence orient system. As

competition from private traders in the village increases, farmers move away from maize mono-

cropping and the MS system to more commercially oriented and diverse systems. In Malawi,

output market access is measured in terms of distance to a weekly market. Results show that,

as the distance to these markets decreases, farmers are significantly more likely to adopt MC

and MSL systems. Similarly, in Mozambique, proximity to urban markets positively drives

adoption of more diverse cropping systems. In particular, as the distance to urban markets

increases, farmers are less likely to adopt MS, MLS and MLCS systems.

Second, proximity to parastatal marketing boards, which operate in Zambia and Malawi and

predominantly provide output market subsidies for maize, discourages the adoption of more

diverse cropping systems. For example, in Zambia, as the distance from a Food Reserve

Agency depot increases, the probability of adopting the ML, MS, MLS, MLC and MLCS

increases significantly, all else equal. Similarly, in Malawi as the distance from an ADMARC

depot increases, the probability of adopting the three most diverse systems, MLS, MLC and

MLCS, all increases significantly relative to maize mono-cropping.

Finally, in all countries and for all systems, land size is a key determinant of adopting more

diverse cropping systems relative to maize mono-cropping. For example in Zambia, all else

equal, a 10 percent increase in average land size, roughly 0.5 hectares, correlates with an

increase of 29 percent in the probability of adopting the MLSC system.

Taken together several key policy insights emerge. First, policies that crowd in private sector

investments or improve the access of farmers to private markets are critical for achieving crop

diversification policy objectives. This includes adopting policies that improve the predictability of

government action in agricultural output markets, including in terms of cross border trade and

the release of government held grain stocks onto the market. A lack of predictability related to

government action in output markets and the resultant effects on prices, is identified as a key

constraint to increased private investments in output markets (Jayne et al. 2014). Policies that

affect the price and liquidity of bank lending rates are also important for driving investment in

agricultural markets. Finally, public investments in market infrastructure, including roads and

electrification, may help to improve investments in output markets and farmers access to them.

Second, public investments in supporting maize output prices through direct purchases from

farmers must be reconsidered if governments are committed to promoting more diverse

production systems. National food reserve agencies could, for example, use first refusal

contracts with private sector buyers or call options on the Johannesburg commodity exchange

to help manage retail maize price volatility, without taking physical control of large stocks of

maize (Devereux, 2007; Dana et al., 2006).

19

Finally, land policy reform must be part of any strategy to promote crop diversification. There is

emerging evidence from Eastern and Southern Africa that land policies are encouraging

significant urban investment in relatively large-scale land acquisitions (Sitko and Jayne, 2014;

Jayne et al., 2016; Ansueeuw, 2016). These acquisitions may be contributing to increased land

scarcity in smallholder production areas, leading to more rapid land fragmentation than would

be the case otherwise, and increased challenge for small-scale producers to expand their

landholdings. The strong and significant effects of land size on the adoption of more diverse

cropping systems in all countries suggest that policies supporting land access and protect the

land rights of existing smallholder farmers are essential for achieving government objectives

related to crop diversification.

20

Table 6. Drivers of cropping systems adoption in Malawi

Maize-legume (1=yes)

Maize-staple

(1=yes)

Maize-cash crops

(1=yes)

Maize-legume-staple

(1=yes)

Maize-legume-

cash crops (1=yes)


(1=yes)

Institutional Maize Seeds Price (EA's level)

0.342** -0.150 -1.071*** -0.902*** 0.302 -1.058**

(0.166) (0.331) (0.259) (0.259) (0.241) (0.474)

Distance from Weekly Market

(EA's level) 0.005 0.071 -0.117* -0.157** -0.042 -0.023

(0.047) (0.085) (0.067) (0.072) (0.068) (0.102)

Distance from FRA (ln, EA's level)

-0.115 0.066 0.209 0.269** 0.344*** 0.254*

(0.088) (0.182) (0.139) (0.132) (0.128) (0.154)

HH resource


0.124** 0.089 0.146* 0.124 0.256*** 0.192

(0.059) (0.105) (0.084) (0.098) (0.080) (0.124)


0.049 0.180*** 0.051 0.119*** 0.151*** 0.077

(0.041) (0.041) (0.055) (0.044) (0.042) (0.058)

Land owned (ln, ha)

1.627*** 1.416*** 2.218*** 2.735*** 3.368*** 2.324***

(0.206) (0.365) (0.319) (0.357) (0.298) (0.468)

Biophysical SPI Negative (lag) 0.450 0.379 -0.023 1.238** -0.121 -0.569

(0.320) (0.595) (0.482) (0.507) (0.472) (0.703)

SPI Positive (lag) 0.170 0.578** 0.504** -0.634** 0.530** 0.202

(0.192) (0.271) (0.257) (0.320) (0.263) (0.393)

Instruments Flood Probability Index (%)

0.051** -0.022 0.004 -0.039 0.114*** 0.103**

(0.024) (0.051) (0.031) (0.045) (0.033) (0.051)

Drought Probability Index

(%) 0.028 0.020 -0.028 0.068** 0.012 0.076

(0.022) (0.041) (0.030) (0.034) (0.030) (0.050)

Formal Diffusion (EA's level)

0.303 0.953** 0.169 0.645* 0.597* 0.443

(0.238) (0.451) (0.337) (0.381) (0.352) (0.559)

Informal Diffusion (EA's level)

0.565 -2.129** -1.810* 0.905 0.652 2.804***

(0.479) (0.992) (0.977) (0.705) (0.701) (0.987)

Other controls Yes Yes Yes Yes Yes Yes

Agro-ecological dummies

Yes Yes Yes Yes Yes Yes

Year dummy Yes Yes Yes Yes Yes Yes

Observations 3 977 3 977 3 977 3 977 3 977 3 977

Notes: the table displays the determinants of adoption of different farm systems, compared to the baseline scenario of maize mono-cropping for the panel pooled sample (2010 and 2013). Full list of controls and coefficients are available in Table A4 in Annex 1. The specification is the first stage of a multinomial treatment effect model (multinomial logit). The specification controls also for household-size (ln), age (ln), dummy for female-headed households, years of education of the household's head (ln), asset wealth index, livestock owned (as TLU units), hectares of land owned (ln), share of credit and FISP recipients at EA level, use of inorganic fertilizer, mean seasonal maximum temperature, positive and negative SPI indexes for both seasonal and lagged, agro-ecological and year dummy. Standard errors are clustered at EA's level. Significant levels are * p<0.10, ** p<0.05, *** p<0.001.


21

Table 7. Drivers of cropping systems adoption in Mozambique


Maize-staple

(1=yes)

Maize-cash crops

(1=yes)

Maize-legume-staple

(1=yes)

Maize-legume-

cash crops (1=yes)


(1=yes)

Institutional

Distance from the main market cities

-0.019 -0.159** -0.244** -0.143** -0.215** -0.342***

(0.075) (0.074) (0.099) (0.070) (0.088) (0.082)

Resource


0.054 -0.493*** 0.036 -0.329*** 0.184 -0.582***

(0.092) (0.114) (0.167) (0.093) (0.142) (0.126)


0.003 -0.016 -0.013 -0.017** -0.023** -0.030**

(0.007) (0.010) (0.014) (0.008) (0.011) (0.012)

Land owned (ln, ha)

0.114 0.395*** 0.807*** 0.462*** 1.019*** 1.473***

(0.079) (0.104) (0.189) (0.079) (0.124) (0.130)

Biophysical

SPI Negative (lag) -1.049 1.723** 2.127* 0.645 0.698 1.002

(0.682) (0.843) (1.173) (0.740) (0.867) (0.872)

SPI Positive (lag) -0.243 -0.228 0.111 -0.361 -0.395 0.349

(0.233) (0.257) (0.348) (0.251) (0.330) (0.272)

Instruments

Flood Probability Index (%)

0.061 -0.003 -0.066 0.091 0.017 0.043

(0.055) (0.061) (0.083) (0.058) (0.074) (0.064)


(%) 0.035 -0.048 0.065 -0.050 0.061 0.034

(0.034) (0.039) (0.065) (0.035) (0.055) (0.044)


0.193 2.438 5.158** 0.225 5.251** 3.646*

(2.016) (2.253) (2.273) (2.046) (2.121) (2.216)

Informal Diffusion (EA's level)

2.693*** 0.538 2.819*** 2.565*** 2.964*** 2.907***

(0.505) (0.487) (0.861) (0.474) (0.769) (0.629)

Other controls Yes Yes Yes Yes Yes Yes

Regional dummies Yes Yes Yes Yes Yes Yes

Observations 5 055 5 055 5 055 5 055 5 055 5 055

Notes: the table displays the determinants of adoption of different farm systems, compared to the baseline scenario of maize mono-cropping for the Mozambique’s sample. The specification is the first stage of a multinomial treatment effect model (multinomial logit). Full list of controls and coefficients are available in Table A5 in Annex 1. The specification controls also for household-size (ln), age (ln), dummy for female-headed households, years of education of the household's head (ln), asset wealth index, livestock owned (as TLU units), hectares of land owned (ln), use of inorganic fertilizer, mean seasonal maximum temperature, positive and negative SPI indexes for both seasonal and lagged, regional dummies. Standard errors are clustered at EA's level. Significant levels are * p<0.10, ** p<0.05, *** p<0.001.


22

Table 8. Drivers of cropping systems adoption in Zambia


Maize-staple

(1=yes)

Maize-cash crops

(1=yes)

Maize-legume-staple

(1=yes)

Maize-legume-

cash crops (1=yes)


(1=yes)

Institutional Maize Seeds Price (EA's level)

0.030 -1.394*** 0.161 -1.082*** 0.117 -1.028**

(0.176) (0.240) (0.258) (0.232) (0.227) (0.411)

Total Traders (ln, EA's level)

0.346*** -0.266** 0.242* 0.423*** 0.510*** 0.314**

(0.075) (0.115) (0.125) (0.101) (0.106) (0.136)

Distance from FRA

(ln, EA's level) 0.195** 0.551*** 0.105 0.650*** 0.200* 0.379**

(0.078) (0.119) (0.122) (0.109) (0.104) (0.193)

HH resources Agricultural Asset Wealth

Index -0.493* -0.671* -0.057 -0.962*** -0.458 -0.274

(0.263) (0.343) (0.334) (0.263) (0.305) (0.370)


0.007 -0.005 -0.000 -0.001 0.001 -0.001

(0.005) (0.006) (0.006) (0.004) (0.006) (0.006)

Land owned (ln, ha)

0.614*** 0.569*** 0.860*** 1.155*** 1.472*** 1.513***

(0.060) (0.071) (0.086) (0.069) (0.080) (0.097)

SPI Negative (lag)

-0.617** 0.470 -2.017*** 0.767** -3.560*** -1.320***

Biophysical (0.246) (0.381) (0.364) (0.340) (0.331) (0.440)

SPI Positive (lag) 0.051 -0.285* -0.139 -0.546*** 0.954*** -0.532*

(0.129) (0.172) (0.243) (0.158) (0.189) (0.274)

Instruments Flood Probability Index (%)

0.068* -0.007 -0.035 0.054 0.024 0.124**

(0.036) (0.048) (0.063) (0.047) (0.052) (0.062)


(%) -0.023 -0.037 0.048 -0.083** -0.011 -0.023

(0.029) (0.040) (0.062) (0.035) (0.044) (0.056)


0.624** 0.025 0.114 1.550*** -0.314 0.830

(0.306) (0.438) (0.515) (0.384) (0.438) (0.579)

Informal Diffusion (EA's

level) 0.001 -1.406*** -0.673 -1.589*** -0.358 -1.206**

(0.308) (0.417) (0.509) (0.369) (0.402) (0.550)

Other Controls Yes Yes Yes Yes Yes Yes

Agro-ecological dummies

Yes Yes Yes Yes Yes Yes


Observations 11 496 11 496 11 496 11 496 11 496 11 496

Notes: the table displays the determinants of adoption of different farm systems, compared to the baseline scenario of maize mono-cropping for the panel pooled sample (2011 and 2014). Full list of controls and coefficients are available in table A6 in Annex 1. The specification is the first stage of a multinomial treatment effect model (multinomial logit). The model controls also for the total number of traders (ln), the distance from FRA (ln), household-size (ln), age (ln), dummy for female-headed households, years of education of the household's head (ln), asset wealth index, livestock owned (as TLU units), hectares of land owned (ln), share of credit and FISP recipients at EA level, use of inorganic fertilizer, mean seasonal maximum temperature, positive and negative SPI indexes for both seasonal and lagged, agro-ecological and year dummies (only for the pooled sample). Standard errors are clustered at EA's level. Significant levels are * p<0.10, ** p<0.05, *** p<0.001.


23

1.7 Country specific insight of cropping systems adoption

Malawi

Maize seed prices, which represent the main target of the government’s input subsidy

programme have significant, yet complex effects on cropping system adoption. An increase in

maize seed price has a positive and significant effect on adopting the ML system. This suggests

that for many maize producing households, legumes act as a substitute for maize, either

because they can be consumed directly, or because they can be cultivated and sold in more

competitive markets to acquire maize. Thus, input subsidy programmes that push down maize

seed prices may inadvertently discourage farmers from adopting an agronomically more

beneficial ML system. Conversely, as maize seed prices increase, farmers are less likely to

adopt MC, MLS, or MLCS systems.

The effect of agricultural asset wealth on cropping system adoption is largest and most

significant for the MLC system. This system is dominated by tobacco-groundnut and tobacco-

soybean production (Table 2), which require significant management and labour allocations.

Drought shocks, measured in terms of negative lagged SPI, only have a significant and positive

effect the adoption of the MLS. This is a system dominated by pigeon pea and sorghum (Table

2), both of which withstand droughts reasonably well. This suggests that as droughts increase,

farmers are receptive to moving away from maize mono-cropping and adopting cropping

systems that lower their risk of crop failure.9 Lagged flood shocks induce adoption of MS, MC

and MLC, while surprisingly reduce the probability of MLS adoption. In terms of long-term

weather distribution, results on flood probability index suggest that an increase by 1 percent in

the flood index raises likelihood of diversification by pushing farmers into ML (+0.5 percent),

MLC and MLCS (+0.11 percent) systems. Formal institutions, such as public extension systems,

have a moderate positive effect on the adoption of some systems (MS and MLS), while large

informal networks may discourage some diversification pathways (MS and MC) and encourage

others (MLCS).

Mozambique

In Mozambique, the dominant alternative staple is cassava, which is more commercialized than

in other countries of the region. As a result, farm households endowed with more land and less

agricultural wealth are pulled into the production of MS, MLS, and MLCS, with likely beneficial

effects in terms of productivity and crop income stability (See Figure 2b and Table 9). Low

lagged drought shocks are significant determinants, both for their statistical power and

magnitude, of complex farming systems adoption, such as MS, MC, MLC and MLCS. Despite

this, the variables on long-term climatic risk do not correlate significantly with adoption on any

cropping system.

The access to information measured in terms of formal and informal diffusion networks, such

as fellow farmers and extension agencies, is a significant driver of most forms of cropping

system diversification. Increasing access to agricultural advice from public or private extension

providers increases the likelihood of MS, MC, MLS, MLC and MLCS adoption. MLS coefficient

presents the highest magnitude, with 41 percent probability of adoption when the share variable

9 This is consistent with what found on the impact on of drought shocks on consumption in Malawi (Asfaw and Maggio, 2018).

24

increases by 1 percent. Increasing investment in public extension provision, combined with

policies that crowd in private extension providers appears to be a key lever for driving

diversification in Mozambique.

Zambia

Like in Malawi, maize seed prices are important drivers of diversification. In particular, as maize

seed prices increase, farmers are less likely to adopt alternative staple systems (MS, MLS and

MLCS). This finding is surprising and suggests that prioritize maize over other staples. Thus, as

input budgets are stretched by an increase in maize prices, smallholders will invest resources

in acquiring maize seeds before investing in the production of alternative staple crops.

The effect of agricultural asset wealth on cropping system adoption suggests that poorer

households are more likely to adopt more subsistence oriented systems, such as ML, MS and

MLS, while increased assets do not have a positive effect on the adoption of any system.

The lagged effects of drought are important determinants of adopting the MLS system, which is

Zambia is dominated by a cassava-groundnut mix and negatively effects the adoption of

systems containing cash crops, as well as the ML system. Drought risk likely discourages

households from assuming the risk of cash crop production, which often entails entering into an

input credit arrangement with agro-business firms. Finally, formal information networks strongly

and positively influence the adoption of the MLS system, and marginally increases the adoption

of the ML system. This is a positive finding and suggests that improving formal extension

networks can drive beneficial changes in farm behaviour. Interestingly, large informal networks,

which include large extended kinship arrangements, discourage most forms of diversification.

This is perhaps due to the social and cultural function maize serves in terms of building social

bonds through gifts of maize or in-kind piecework to needy relatives (Sitko, 2012).

1.8 Impact on productivity

The choice of the cropping system influences significantly the level of maize productivity. In all

three countries, more diverse systems (three crop categories or more) have positive and

significant effects on maize yields (Figure 2a, Figure 2b and Figure 2c).10 These more diverse

systems likely enable farmers to capture a combination of economic and agronomic benefits

from a range of crops, which they translate into improved yields for maize. For example,

although the dominant crops in the MLS system vary between countries (Table 2), adoption of

this system has similar positive effects in all three countries: +30.85 percent increase in maize

yield relative to mono-cropping in Zambia, +30.55 percent increase in Malawi and +20.28

percent increase in Mozambique. Similarities in the magnitude of the impact for MLS, makes

this system suitable for a wide range of agro-ecological and geographic zones. For the MLC

system, the effects of adoption are also positive and significant for all three countries. However,

there is more variability in terms of the magnitude of the effect and confidence interval range

between the countries than for MLS. This variability suggests that these systems are more

regionally variable in their positive effects than the MLS system.

Compared to maize mono-cropping, ML cropping systems are found to have positive and

significant effects on maize yield in Malawi, but no significant impact on maize productivity in

Mozambique and Zambia. This may be a function of differences between countries in terms of

10 Figure 2a, Figure 2b and Figure 2c show the point estimates of the multinomial treatment effect, the 10 percent confidence interval, and the percentage change in maize yield as a result of adoption of a system relative to maize mono-cropping. The only exception is MLSC in Malawi.

25

the type of legumes grown. In Malawi, for example, pigeon-peas are widely grown, while they

are virtually absent in Zambia. Relative to other grain legumes, pigeon pea is found to have

significant long-term positive impacts on soil fertility (Kerr et al., 2007).

Consistent with our hypotheses, the MS system has neutral to negative effects on maize yield

in all the three countries. Alternative staples do not offer any obvious benefits in terms of

agronomic impact on maize yields, but may divert some labour away from maize cultivation.

The MC system has a neutral effect on maize yields in Mozambique and Zambia. Thus, in less

diverse, two crop settings, income generated from cash crop production is not translating into

increased maize intensification. From a productivity standpoint, adoption of these systems does

not offer a clear path to improvements in maize yields.11

Figure 2 Effect of different cropping systems on maize yield compared to maize mono-cropping in Malawi, Mozambique and Zambia

A: Malawi

11 As a further robustness test, we run the main specification considering labour productivity in agriculture as dependent variable, defined as the natural logarithm of crop income per adult worker. As Table A10 in appendix reports, diversification through cropping systems based on three and four crop has a positive and significant effect in the three countries.

26

B: Mozambique

C: Zambia

Notes: The figure displays the second-stage coefficients of the multinomial treatment effect. The blue dot represents the point estimate while the blue bars report 95 percent confidence interval. Dots’ labels report the conversion of the log coefficient x into percentage change as x=exp(x)-1.


27

1.9 Impact on crop income volatility

The level of crop income volatility provides insights into how adoption of a particular system

effects livelihood resilience and risk, relative to maize mono-cropping. Table 9 shows that there

is no single system that decreases crop income volatility significantly in all three countries. This

suggests that there is no one size fits all policy option for increasing resilience through promotion

of a particular cropping system.

In Malawi, only the ML system significantly lowers income volatility relative to maize mono-

cropping, although the coefficients are negative for all systems. The competitiveness and

stability of legume markets in Malawi likely explains the positive effect of the ML system, while

volatility in cash crop and alternative staple markets may be comparable to the volatility of maize

markets.

In Mozambique, the three crop combinations of MLS and MLC significantly decrease crop

income volatility relative to maize mono-cropping. Prominent crops in these systems, namely

cassava and sesame, respectively, are both drought tolerant and have vibrant commercial

markets. The combination of these attributes likely helps to lower income volatility relative to

mono-cropping.

In Zambia, all forms of diversification decrease crop income volatility relative to maize mono-

cropping. Interestingly, the magnitude of the effect is higher for more diverse systems. Indeed,

in Zambia farmers that adopt the MLCS system are predicted to have on average a 43 percent

lower volatility in crop income than a maize mono-cropping household.

28

Table 9. Effect of farming systems on crop income volatility

Dependent: Crop income volatility (u2)

Malawi Mozambique Zambia (Wave 1+2)

Maize-legume (1=yes) -1.477*** -0.122 -0.251***

(0.450) (0.139) (0.037)

Maize-staple (1=yes) 0.220 -0.011 -0.187***

(0.814) (0.163) (0.051)

Maize-cash crops (1=yes) -0.542 0.092 -0.273***

(0.691) (0.326) (0.052)

Maize-legume-staple (1=yes) -0.196 -0.255* -0.363***

(0.646) (0.132) (0.039)

Maize-legume-cash crops (1=yes) -1.110 -0.414*** -0.392***

(0.683) (0.154) (0.043)

Maize-legume-cash crops-staple (1=yes) -0.914 -0.168 -0.430***

(0.779) (0.139) (0.046)

Other controls Yes Yes Yes

Agro-ecological/Regional dummies Yes Yes Yes

Number of observations 3 732 5 395 11 884

Log-likelihood -19 078.90 -18 788.02 -31 588.78

Chi2 / F / Wald 814.937 773.119 6 686.129

Notes: the table displays the determinants of crop income volatility, compared to the baseline scenario of maize mono-cropping for Malawi (column 1), Mozambique (column 2) and Zambia (column 3). The specification is the second stage of a multinomial treatment effect model (MTM) where the dependent is the squared residual from a MTM including crop income as dependent variable. The baseline category is maize mono-cropping. The instrumental variables in the first stage are the flood and drought shock probability and the average diffusion of formal and informal institutions at EA's level. For the entire set of explanatory variables refer to table A8 in Annex 1. Significant levels are * p<0.10, ** p<0.05, *** p<0.001 and errors are clustered at EA's level.


29

Conclusions

Crop diversification is a common agricultural policy objective in eastern and southern Africa.

Yet, the type of diversification targeted and the policy measures utilized for its achievement are

rarely well-defined. This study shows that not all diversification pathways are the same, both in

terms of their effects on farmers’ welfare and resilience, and the factors that influence their

adoption.

From the analysis, several stylized facts emerge regarding cropping system diversification.

First, diversification through a legume-based approach is typically more beneficial than a cash-

crop based diversification pathways. As this study shows, maize-cash crop systems do not

positively affect productivity and crop income volatility in most cases, but when combined in with

legumes they do. Conversely, legume-based systems, either in combination with maize or in a

maize-legume-staple system produce beneficial income and productivity effects in most cases.

The heterogeneity of these results is determined by a combination of market and agronomic

factors affecting these crops and their combination. Indeed, crops such as legumes fix

atmospheric nitrogen, which is often a limiting nutrient for cereal crops, and are characterized

by more established output market, especially in the case of Malawi. Cash crops, by contrast,

can be more profitable for farmers but they are effected by considerable market volatility due to

international price movements and exchange rate fluctuations. Moreover, they do not provide

complementary agronomic benefits to cereals. In absence of proper investments and policies,

diversification into these crops can expose households to considerable risk and lead to a

potential deterioration of household’s welfare.

This finding suggests that legumes should be prioritized in crop diversification strategies in these

countries. The specific policy support required to achieve this objective will vary by country. In

general terms, development of the legume sector can be supported through improvements in

legume seeds availability, improved regional trade conditions, and improvements in local

processing capacity. In terms of legume seeds, forecasting smallholders’ demand is a serious

challenge for seed companies. This leads to a chronic under provision of legume seeds on the

market. To improve this condition, financial option, such as a first-loss guarantee mechanism

for specific quantities of legume seeds, can be considered. This mechanism allows seed

companies to forecast to maximum risk exposure they have when multiply seeds. Second, input

subsidy programmes dedicated to legume production can help firms to forecast future demand.

In terms of improved trade conditions, a major obstacle in the legume sector is effective

compliance and monitoring of sanitary and phytosanitary requirements to export, particularly

aflatoxins. Investment to improve compliance and monitoring can help to open new export

markets for legumes. Finally, concessional loans or other financial arrangements to support

local investment in legume processing should be considered to help improve formal market

conditions for legumes.

The second major finding, which lends additional support to the recommendations above, is that

policies that crowd in the private sector, such as by improving the predictability of government

actions in agricultural output markets, or improve market access conditions are important drivers

of diversification. Conversely, policies that exclusively support maize output markets, such as

buying maize from farmers through parastatal marketing boards, hinders diversification.

Reforms to parastatal marketing boards are particularly important, as these boards often absorb

significant amounts of agricultural sector budgets, and therefore come at opportunity costs to

other investment areas. Reforms to marketing boards must be carried out in ways that allow

policy makers to achieve national food security and staple food price stabilization objectives.

Reform options include using private sector buyers to procure and store national grain reserves,

30

where the government has the right of first refusal on these stocks. Alternatively, governments

can use call options on local or regional commodity exchanges, which will only be exercised at

predefined price points. In both cases, the outcome is greater predictability of government

actions in food markets, less public expenditure on grain purchases, and greater private

investment in markets (Jayne, Zulu and Nijhoff, 2006).

Finally, land policy, which is both complex and politically sensitive, cannot be ignored when

developing diversification strategies. Many land policies in the region favour consolidation and

appropriation of land by well-placed elites and may be hastening processes of land

fragmentation and land scarcity for smallholders (Jayne et al., 2014). Where land constraints

are severe and land sizes are small, diversification is difficult.

Taken together, this study suggests that crop diversification must be promoted in a holistic

fashion and should leverage private sector investment interest in output and input markets as

much as possible.

31

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35

Annex 1

Table A 1. Summary statistics by cropping system in Malawi

Notes: the tables reports summaries of dependent and explanatory variables for Malawi. All the currencies are expressed in real US Dollars 2010.


Maiz

e m

on

o-

cro

pp

ing

M

aiz

e-l

eg

um

e

Maiz

e-s

tap

le

Maiz

e-c

ash

cro

ps

M

aiz

e-l

eg

um

e-

sta

ple

M

aiz

e-l

eg

um

e-

cas

h c

rop

s

Maiz

e-l

eg

um

e-

cas

h c

rop

s-s

tap

le

(N =

1 3

42)

(N =

1 1

42)

(N =

174

) (N

= 3

22

) (N

= 2

82

) (N

= 3

59

) (N

=1

13)

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Depe

nd

ents

Maiz

e Y

ield

(K

g/h

a)

1 4

53

1 7

02

1 8

49

195

1

1 5

96

1 6

72

1 5

33

135

5

1 7

42

1 7

69

2 1

30

1 9

04

1 6

72

2 0

77

Cro

p Incom

e (

US

D

2010)

299

384

461

483

494

531

831

126

6

472

493

1 2

39

148

2

641

798

Institu

tio

nal

Maiz

e S

eeds P

rice/K

g

(US

D 2

010)

1.3

1

0.9

5

1.3

9

0.8

8

1.2

3

0.9

9

1.0

4

0.6

3

1.1

0

0.6

5

1.3

2

0.9

0

1.0

4

0.6

5

Sta

ple

Seeds P

rice/K

g

(US

D 2

010)

46.9

1

239

.37

42.6

3

234

.10

3.5

7

10.2

0

16.0

2

115

.66

29.1

8

193

.55

42.7

1

228

.89

14.2

2

137

.81

Cash C

rop S

eeds

Price/K

g (

US

D 2

010)

297

.26

555

.13

296

.07

501

.29

593

.98

915

.67

337

.74

635

.36

287

.95

559

.72

360

.22

591

.98

317

.70

666

.06

Dis

tance fro

m the

mark

et (E

A's

level)

5.1

4

6.9

2

4.9

0

6.9

3

5.4

7

6.5

5

4.3

9

5.9

4

4.2

4

6.4

6

4.4

2

5.8

7

5.0

2

6.9

7

Dis

tance fro

m F

RA

(E

A's

level)

7.9

4

5.8

8

7.4

1

5.0

0

8.2

3

5.9

4

8.2

5.3

2

8.8

3

6.0

0

8.2

4.8

5

8.4

4

5.3

5

Bio

ph

ysic

al

Seasonal M

ax

Tem

pera

ture

26.7

6

1.2

9

26.6

3

1.2

3

26.7

3

1.4

3

26.8

9

1.1

7

26.9

5

1.1

3

26.5

5

1.1

27

1.0

8

Negativ

e S

easonal S

PI

Index

-0.3

7

0.4

3

-0.3

8

0.5

-0

.29

0.3

9

-0.3

9

0.4

5

-0.3

0.4

1

-0.4

2

0.5

2

-0.2

2

0.3

8

Positiv

e S

easonal S

PI

Index

0.0

8

0.1

5

0.0

9

0.1

5

0.1

4

0.2

3

0.0

9

0.1

5

0.1

3

0.1

9

0.1

0.1

7

0.1

8

0.2

2

Negativ

e S

easonal S

PI

Index (

lag)

-0.1

7

0.2

1

-0.1

6

0.2

1

-0.1

5

0.2

-0

.15

0.2

-0

.15

0.2

1

-0.1

5

0.2

1

-0.2

1

0.2

2

Positiv

e S

easonal S

PI

Index (

lag)

0.2

1

0.3

2

0.2

1

0.3

3

0.3

8

0.4

9

0.2

7

0.3

9

0.2

1

0.3

5

0.2

9

0.3

9

0.2

1

0.3

7

Wealth

Agricultura

l Asset

Wealth

Index

0.1

1

0.9

6

0.3

1.0

2

0.4

8

1.0

3

0.4

0.9

5

0.3

5

1.0

3

0.7

3

1.0

9

0.4

5

0.9

8

Liv

esto

ck (

TLU

units)

0.5

1

1.3

1

0.8

2.0

7

1.7

2

2.7

2

0.8

3

2.0

2

1.0

1

2.0

1

1.6

7

2.9

5

0.9

1

1.7

1

Land o

wned (

ha)

0.5

0.4

3

0.7

0.4

8

0.6

8

0.4

7

0.7

8

0.5

7

0.7

8

0.5

5

1.0

2

0.6

6

0.7

8

0.5

7

Socio

-E

conom

ic

HH

siz

e

5.1

2.2

7

5.1

3

2.1

9

5.3

8

2.2

5

5.6

2.4

7

5.2

6

2.3

9

5.9

2

2.4

4

5.6

5

2.3

5

Age

45.7

2

16.6

1

47.3

16.3

3

44.3

15.1

3

43.3

1

15.1

45.2

2

16.5

5

45.8

3

14.4

7

46.3

5

16.1

7

Fem

ale

-headed H

H

(1=

yes)

0.2

6

0.4

4

0.2

7

0.4

4

0.2

1

0.4

1

0.1

5

0.3

6

0.3

0.4

6

0.1

6

0.3

7

0.2

6

0.4

4

Educatio

n (

years

) 5.0

3

2.8

8

5.1

1

2.5

5.2

8

2.7

4.8

9

2.2

9

4.9

3

2.5

3

5.3

2

2.2

1

4.8

5

2.3

2

Share

of C

redit

Recip

ients

0.1

7

0.1

7

0.1

7

0.1

5

0.1

5

0.1

6

0.1

5

0.1

2

0.1

6

0.1

6

0.1

8

0.1

2

0.1

7

0.1

4

Share

of F

ISP

R

ecip

ients

0.5

2

0.2

5

0.5

5

0.2

2

0.4

9

0.2

6

0.6

0.2

3

0.5

9

0.2

2

0.6

0

0.2

1

0.6

1

0.2

0

Inorg

anic

fert

ilizer

(1=

yes)

0.7

5

0.4

4

0.7

9

0.4

1

0.7

1

0.4

5

0.8

4

0.3

7

0.8

7

0.3

4

0.9

1

0.2

9

0.8

7

0.3

4

Instr

um

en

ts

First

Sta

ge

Flo

od P

robabili

ty Index

1.3

5

2.2

6

1.5

2

2.4

6

1.2

5

2.0

4

1.5

3

2.2

4

1.1

3

2.0

1

2.0

7

2.6

1

1.6

5

2.0

0

Dro

ught P

robabili

ty

Index

3.5

4

2.8

6

3.5

7

2.8

5

3.1

9

2.7

8

3.4

2

2.5

7

3.5

2.9

4

3.2

6

2.5

7

3.1

6

2.0

9

Form

al D

iffu

sio

n Index

0.5

2

0.2

7

0.5

7

0.2

6

0.5

4

0.2

6

0.5

1

0.2

6

0.5

5

0.2

6

0.5

7

0.2

5

0.5

6

0.2

1

Info

rmal D

iffu

sio

n Index

0.0

7

0.1

1

0.0

9

0.1

3

0.0

6

0.1

0.0

5

0.1

1

0.0

9

0.1

2

0.0

9

0.1

3

0.1

1

0.1

5

36

Table A 2. Summary statistics by cropping system in Mozambique

Notes: the tables reports summaries of dependent and explanatory variables for Mozambique. All the currencies are expressed in real US Dollars 2010.


Maiz

e m

on

o-

cro

pp

ing

M

aiz

e-l

eg

um

e

Maiz

e-s

tap

le

M

aiz

e-c

ash

cro

ps

Maiz

e-l

eg

um

e-

sta

ple

M

aiz

e-l

eg

um

e-

cas

h c

rop

s

Maiz

e-l

eg

um

e-

cas

h c

rop

s-s

tap

le

(N =

286

) (N

= 9

62

) (N

= 4

66

) (N

= 8

8)

(N =

2 3

51)

(N =

283

) (N

= 6

19

)

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Depe

nd

ents

Maiz

e Y

ield

(K

g/h

a)

487

570

712

803

601

669

771

642

698

831

927

803

892

818

Cro

p Incom

e (

US

D

201

0)

219

1 2

43

397

2 4

93

448

4 7

67

740

1 9

24

360

2 2

84

1 0

99

4 6

59

687

3 2

51

Mark

et

Dis

tan

ce f

rom

Ma

rket

citie

s (

km

, D

istr

ict le

vel)

505

410

455

401

354

343

212

150

404

383

208

211

209

219

Bio

ph

ysic

al

Seaso

nal M

ax

Tem

pera

ture

29.9

4

1.4

5

29.7

0

1.7

8

29.8

9

1.2

1

29.7

7

1.4

1

29.9

9

1.2

5

29.2

8

1.4

4

30.0

6

1.1

0

Nega

tive S

eason

al S

PI

Ind

ex

-0.1

0

0.1

9

-0.1

2

0.1

9

-0.0

4

0.1

3

-0.0

1

0.0

4

-0.0

8

0.1

6

-0.0

1

0.0

7

-0.0

2

0.0

8

Positiv

e S

eason

al S

PI

Ind

ex

0.5

0

0.4

9

0.5

9

0.5

7

0.5

1

0.4

2

0.7

2

0.3

7

0.4

7

0.4

9

0.8

6

0.4

2

0.6

7

0.4

4

Nega

tive S

eason

al S

PI

Ind

ex (

lag

) -0

.05

0.1

3

-0.0

9

0.1

9

-0.0

4

0.1

0

-0.0

8

0.1

4

-0.0

6

0.1

4

-0.1

2

0.1

7

-0.0

6

0.1

4

Positiv

e S

eason

al S

PI

Ind

ex (

lag

) 0.5

4

0.4

1

0.4

6

0.4

1

0.5

3

0.5

0

0.4

8

0.4

8

0.4

8

0.4

7

0.3

4

0.4

3

0.5

6

0.5

8

Wealth

Agricultura

l A

sset

Wealth In

de

x

0.2

4

0.9

8

0.3

3

1.0

7

-0.0

9

0.7

5

0.4

9

1.1

5

0.1

0

0.8

9

0.7

0

1.2

8

0.0

5

0.8

3

Liv

esto

ck (

TLU

units)

4.6

4

9.8

2

5.2

4

10.6

3

2.3

3

6.0

5

5.1

7

11.8

4

3.3

3

8.4

8

5.6

8

12.0

4

3.0

8

8.9

7

Lan

d o

wne

d (

ha

) 1.6

8

1.5

5

2.1

4

2.2

3

2.1

5

2.6

5

3.5

5

5.1

8

2.3

6

2.5

5

4.0

3

5.5

8

3.4

1

2.7

6

Socio

-E

conom

ic

Hh s

ize

5.4

9

2.9

9

5.5

9

3.2

9

5.1

2

2.7

8

5.6

3

2.9

8

5.5

5

3.1

4

5.6

4

2.5

8

5.6

6

2.6

8

Age

45.7

6

16.4

5

45.6

0

16.3

7

44.4

1

15.4

2

41.5

1

13.9

4

45.7

3

15.4

9

42.9

0

14.4

8

43.0

5

14.8

4

Fem

ale

-hea

de

d H

H

(1=

ye

s)

0.2

6

0.4

4

0.3

2

0.4

7

0.2

9

0.4

5

0.1

3

0.3

3

0.2

9

0.4

5

0.1

5

0.3

6

0.1

6

0.3

7

Educa

tion

(ln

) 6.0

1

3.8

8

5.6

7

3.8

0

5.3

2

3.5

5

5.4

7

3.5

7

5.6

6

3.5

5

5.0

8

3.5

7

4.9

1

3.3

3

Share

of C

redit

Recip

ients

0.0

1

0.0

2

0.0

2

0.0

4

0.0

1

0.0

3

0.0

3

0.0

7

0.0

1

0.0

3

0.0

3

0.0

7

0.0

1

0.0

4

Ino

rga

nic

fert

ilizer

(1=

ye

s)

0.0

4

0.2

0

0.0

4

0.2

0

0.0

2

0.1

5

0.2

3

0.4

2

0.0

3

0.1

7

0.2

8

0.4

5

0.1

0

0.3

0

Instr

um

en

ts

First

Sta

ge

Flo

od P

rob

abili

ty In

de

x

1.7

1

1.7

6

1.8

9

1.8

6

1.6

8

1.9

8

1.2

5

1.6

2

2.0

4

1.9

9

1.4

5

1.6

6

1.6

3

1.8

7

Dro

ugh

Pro

ba

bili

ty

Ind

ex

3.5

7

2.2

2

3.8

5

2.6

6

3.3

1

2.6

5

4.5

1

2.9

5

3.3

2

2.6

7

4.6

6

2.6

0

3.8

8

2.9

2

Fo

rmal D

iffu

sio

n I

nde

x

0.0

5

0.0

6

0.0

6

0.0

6

0.0

6

0.0

6

0.1

1

0.1

4

0.0

5

0.0

5

0.1

2

0.1

4

0.0

8

0.0

9

Info

rmal D

iffu

sio

n I

nd

ex

0.7

6

0.2

1

0.8

6

0.1

7

0.7

8

0.2

0

0.8

5

0.1

3

0.8

4

0.1

8

0.8

7

0.1

6

0.8

5

0.1

6

Infr

astr

uctu

re

develo

pm

ent

Road

density

(#ro

ads/a

rea)

0.1

4

1.2

8

0.2

0

1.4

3

0.1

0

1.0

2

0.0

1

0.0

0

0.2

3

1.5

6

0.0

1

0.0

0

0.0

3

0.2

4

Nig

htlig

ht in

de

x (

0-6

3)

1.2

5

2.6

3

1.2

4

2.9

4

1.2

6

2.1

2

0.9

2

0.3

2

1.4

2

3.2

0

0.9

7

0.4

2

1.1

7

0.6

7

Dis

tric

t's P

opula

tion

D

ensity

2 7

45

13 5

42

2 4

64

8 2

12

2 3

89

2 4

81

2 3

36

2 3

53

2 6

04

5 6

09

2 0

60

2 0

77

2 8

79

3 0

59

37

Table A 3. Summary statistics by cropping system for Zambia

Notes: the table reports mean and standard deviation of dependent and explanatory variables for Zambia. All the currencies are expressed in real US Dollars 2010.


Maiz

e m

on

o-

cro

pp

ing

M

aiz

e-l

eg

um

e

Maiz

e-s

tap

le

Maiz

e-c

ash

cro

ps

Maiz

e-l

eg

um

e-

sta

ple

M

aiz

e-l

eg

um

e-

cas

h c

rop

s

Maiz

e-l

eg

um

e-

cas

h c

rop

s-s

tap

le

(N =

1 7

05)

(N =

2 2

96)

(N =

1 4

68)

(N =

690

) (N

= 3

202)

(N =

2 0

63)

(N =

472

)

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Mea

n

Sd

Depe

nd

ents

Maiz

e Y

ield

(K

g/h

a)

2 0

96

1 5

85

2 3

09

1 6

45

1 7

69

1 4

94

2 0

86

1 5

86

2 5

95

1 6

46

2 4

82

1 6

97

2 6

37

1 6

02

Cro

p Incom

e (

US

D

201

0)

1 9

08

5 1

32

2 9

17

11 3

75

2 4

32

3 8

30

2 7

04

4 2

64

3 6

14

4 5

52

3 6

83

4 2

22

4 5

82

4 0

77

Institu

tio

nal

Maiz

e S

eeds P

rice

/Kg

(US

D 2

01

0)

3.5

4

1.1

1

3.5

9

1.0

6

2.5

9

1.3

8

3.4

9

1.1

7

3.2

0

1.2

5

3.4

7

1.1

4

3.3

3

1.2

0

Leg

um

e S

eeds

Price/K

g (

US

D 2

010

) 2.1

0

1.2

5

1.9

3

1.0

7

1.8

4

0.7

2

1.8

2

1.6

0

1.8

2

0.7

4

1.8

2

1.8

6

1.9

8

1.8

2

Sta

ple

See

ds P

rice/K

g

(US

D 2

01

0)

0.2

7

0.1

4

0.2

7

0.1

4

0.2

3

0.1

0

0.3

4

0.1

9

0.2

2

0.1

3

0.3

3

0.1

9

0.2

6

0.1

6

Cash C

rop S

eeds

Price/K

g (

US

D 2

010

) 4.3

0

3.7

8

3.9

6

3.4

4

3.4

1

1.8

4

3.2

2

2.7

2

2.9

5

2.3

3

3.4

8

3.0

9

2.9

3

2.2

5

Dis

tan

ce f

rom

the

mark

et (E

A's

level)

7.7

2

16.4

8

8.2

7

16.1

3

6.1

2

10.6

5

6.2

6

8.7

3

11.3

89.5

7.5

1

9.8

8

9.9

9

22.5

4

Dis

tan

ce f

rom

FR

A

(EA

's level)

7.2

4

9.9

2

7.7

0

9.9

2

13.0

5

20.4

1

7.3

8

9.8

9

8.9

2

13.7

2

7.1

9

9.1

1

9.5

7

12.7

8

Bio

ph

ysic

al

Seaso

nal M

ax

Tem

pera

ture

26.8

7

1.2

2

26.9

1.2

9

26.9

3

1.2

7

27.1

4

1.2

1

26.2

3

1.2

9

27.2

5

1.2

3

26.7

4

1.3

3

Nega

tive S

eason

al S

PI

Ind

ex

-0.1

1

0.2

0

-0.1

5

0.2

4

-0.0

8

0.2

2

-0.2

3

0.2

6

-0.2

0

0.3

5

-0.2

2

0.2

4

-0.3

0

0.4

0

Positiv

e S

eason

al S

PI

Ind

ex

0.3

5

0.5

2

0.3

4

0.5

2

0.8

6

0.8

1

0.1

8

0.2

9

0.6

7

0.7

7

0.1

9

0.2

9

0.3

1

0.4

7

Nega

tive S

eason

al S

PI

Ind

ex (

lag

) -0

.11

0.2

5

-0.1

7

0.3

-0

.07

0.1

9

-0.3

0.4

1

-0.0

8

0.2

-0

.32

0.4

-0

.15

0.2

9

Positiv

e S

eason

al S

PI

Ind

ex (

lag

) 0.6

3

0.5

7

0.5

7

0.6

1

0.6

1

0.5

1

0.7

3

0.7

6

0.5

4

0.5

3

0.7

9

0.8

1

0.7

3

0.6

9

Wealth

Agricultura

l A

sset

Wealth In

de

x

0.1

1

0.1

9

0.1

2

0.2

0

0.0

8

0.1

5

0.1

1

0.1

7

0.1

0

0.1

8

0.1

4

0.1

8

0.1

6

0.2

1

Liv

esto

ck (

TLU

units)

3.3

12.2

4

5.5

6

15.4

1

3.1

5

10.5

2

4.8

8

10.0

6

3.4

5

11.5

2

6.7

9

11.1

5

6.7

2

10.5

2

Lan

d o

wne

d (

ha

) 2.9

7

8.1

7

4.6

5

9.3

5

3.6

1

5.7

2

3.7

1

3.9

3

6.1

3

12.2

6

4.9

2

4.8

5

6.6

6

8.6

1

Socio

-E

conom

ic

HH

siz

e

6.2

8

2.8

4

6.7

5

3.1

2

6.5

3

2.7

6

6.2

7

2.7

7

6.9

1

2.7

8

6.8

7

2.9

6

7.3

9

3.1

9

Age

35.4

6

12.2

36.5

5

11.9

6

35.1

5

10.9

4

33.2

9

9.8

5

34.9

5

10.0

0

34.6

4

9.9

0

34.3

3

9.3

8

Fem

ale

-hea

de

d H

H

(1=

ye

s)

0.2

2

0.4

2

0.2

6

0.4

4

0.1

8

0.3

9

0.1

3

0.3

4

0.1

7

0.3

8

0.1

4

0.3

5

0.1

4

0.3

4

Educa

tion

(ye

ars

) 6.5

5

4.2

2

6.0

6

4.0

3

6.0

4

3.5

2

5.0

3

3.7

3

6.6

4

3.4

6

5.4

8

3.5

9

6.3

1

3.3

0

Share

of C

redit

Recip

ients

0.1

3

0.1

7

0.1

6

0.2

0

0.0

5

0.0

9

0.4

3

0.2

7

0.0

7

0.1

0

0.4

4

0.2

6

0.2

9

0.2

6

Share

of F

ISP

R

ecip

ients

0.4

3

0.2

5

0.4

8

0.2

5

0.3

5

0.2

9

0.4

3

0.2

4

0.5

6

0.2

6

0.4

6

0.2

3

0.5

0

0.2

4

Ino

rga

nic

fert

ilizer

(1=

ye

s)

0.6

4

0.4

8

0.7

2

0.4

5

0.4

5

0.5

0.5

9

0.4

9

0.7

5

0.4

3

0.7

5

0.4

4

0.7

7

0.4

2

Instr

um

en

ts

First

Sta

ge

Flo

od P

rob

abili

ty In

de

x

1.1

2

1.7

2

1.5

4

1.8

5

1.2

5

1.7

4

1.7

3

1.9

3

1.5

3

1.8

4

2.0

7

2.0

3

2.2

9

1.9

4

Dro

ugh

t P

roba

bili

ty

Ind

ex

2.2

9

2.4

5

2.2

6

2.4

2

1.5

6

2.1

2.4

5

2.2

7

1.6

9

2.1

3

2.4

2.2

4

2.1

8

2.4

8

Fo

rmal D

iffu

sio

n I

nde

x

0.3

8

0.2

3

0.4

2

0.2

2

0.3

2

0.2

5

0.3

8

0.2

4

0.4

4

0.2

3

0.3

8

0.2

3

0.3

9

0.2

3

Info

rmal D

iffu

sio

n I

nd

ex

0.3

4

0.2

4

0.3

6

0.2

4

0.2

7

0.2

2

0.3

4

0.2

4

0.2

8

0.2

0

0.3

6

0.2

4

0.2

9

0.2

1

38

Table A 4. Determinants of adoption of different farm systems in Malawi, pooled sample and complete list of coefficients

Maize-legume

Maize-staple

Maize-cash crops

Maize-legume-staple

Maize-legume-cash crops


Maize Seeds Price (EA's level) 0.342** -0.150 -1.071*** -0.902*** 0.302 -1.058**

(0.166) (0.331) (0.259) (0.259) (0.241) (0.474)

Staple Seeds Price (EA's level) -0.045 -0.232*** -0.128** -0.252*** -0.040 -0.528***

(0.039) (0.083) (0.059) (0.058) (0.058) (0.135)

Cash-crop Seeds Price (EA's level)

-0.027 0.105** -0.110*** -0.038 -0.082** -0.214***

(0.024) (0.050) (0.036) (0.038) (0.033) (0.063)

Distance from Weekly Market (EA's level)

0.005 0.071 -0.117* -0.157** -0.042 -0.023

(0.047) (0.085) (0.067) (0.072) (0.068) (0.102)

Distance from FRA (ln, EA's level) -0.115 0.066 0.209 0.269** 0.344*** 0.254*

(0.088) (0.182) (0.139) (0.132) (0.128) (0.154)

Agricultural Asset Wealth Index 0.124** 0.089 0.146* 0.124 0.256*** 0.192

(0.059) (0.105) (0.084) (0.098) (0.080) (0.124)

Livestock (TLU units) 0.049 0.180*** 0.051 0.119*** 0.151*** 0.077 (0.041) (0.041) (0.055) (0.044) (0.042) (0.058)

Land owned (ln, ha) 1.627*** 1.416*** 2.218*** 2.735*** 3.368*** 2.324***

(0.206) (0.365) (0.319) (0.357) (0.298) (0.468) HH size (ln) -0.160 0.044 0.266 -0.029 0.325* 0.434*

(0.106) (0.198) (0.184) (0.191) (0.172) (0.234) Age (ln) 0.010 -0.622** -0.653*** -0.607** -0.477** -0.225

(0.153) (0.308) (0.235) (0.252) (0.240) (0.360)

Female-headed HH (1=yes) 0.211* -0.008 -0.473** 0.429** -0.056 0.120

(0.123) (0.243) (0.205) (0.190) (0.193) (0.257) Education (years, ln) 0.198** -0.034 -0.034 -0.088 0.209 -0.189

(0.098) (0.195) (0.147) (0.156) (0.155) (0.223)

Share of Credit Recipients 0.045 -0.459 0.320 -0.192 0.528 -0.527

(0.362) (0.737) (0.495) (0.571) (0.477) (0.728)

Share of FISP Recipients 0.634*** -0.570 0.988*** 0.445 1.044*** 0.721

(0.235) (0.434) (0.346) (0.376) (0.319) (0.504)

Inorganic fertilizer (1=yes) 0.127 -0.126 0.388** 0.728*** 0.873*** 0.689**

(0.122) (0.229) (0.184) (0.215) (0.223) (0.312) Negative Seasonal SPI Index (lag) 0.450 0.379 -0.023 1.238** -0.121 -0.569

(0.320) (0.595) (0.482) (0.507) (0.472) (0.703) Positive Seasonal SPI Index (lag) 0.170 0.578** 0.504** -0.634** 0.530** 0.202

(0.192) (0.271) (0.257) (0.320) (0.263) (0.393)

Probabilities of flood (SPI 6 months April)

0.051** -0.022 0.004 -0.039 0.114*** 0.103**

(0.024) (0.051) (0.031) (0.045) (0.033) (0.051)

Probabilities of drought (SPI 6 months April)

0.028 0.020 -0.028 0.068** 0.012 0.076

(0.022) (0.041) (0.030) (0.034) (0.030) (0.050)

Formal Diffusion (EA's level) 0.303 0.953** 0.169 0.645* 0.597* 0.443

(0.238) (0.451) (0.337) (0.381) (0.352) (0.559)

Informal Diffusion (EA's level) 0.565 -2.129** -1.810* 0.905 0.652 2.804***

(0.479) (0.992) (0.977) (0.705) (0.701) (0.987) Constant -1.844** -1.806 -0.236 -1.960 -5.467*** -4.974***

(0.741) (1.583) (1.107) (1.206) (1.143) (1.798) Year dummy Yes Yes Yes Yes Yes Yes

Agro-ecological dummy Yes Yes Yes Yes Yes Yes

Observations 3 977 3 977 3 977 3 977 3 977 3 977 Year Dummy Yes Yes Yes Yes Yes Yes

Notes: the table displays the determinants of adoption of different farm systems, compared to the baseline scenario of maize mono-cropping for the panel pooled sample (2010 and 2013). The specification is the first stage of a multinomial treatment effect model (multinomial logit). The specification controls also for household-size (ln), age (ln), dummy for female-headed households, years of education of the household's head (ln), asset wealth index, livestock owned (as TLU units), hectares of land owned (ln), share of credit and FISP recipients at EA level, use of inorganic fertilizer, mean seasonal maximum temperature, positive and negative SPI indexes for both seasonal and lagged, agro-ecological and year dummy. Standard errors are clustered at EA's level. Significant levels are * p<0.10, ** p<0.05, *** p<0.001. Source: Authors’ own elaboration.

39

Table A 5. Determinants of adoption of different farm systems in Mozambique, pooled sample and complete list of coefficients

Maize-legume

Maize-staple

Maize-cash crops

Maize-legume-staple



Distance from the main market cities (EA's level)

-0.019 -0.159** -0.244** -0.143** -0.215** -0.342***

(0.075) (0.074) (0.099) (0.070) (0.088) (0.082)

Agricultural Asset Wealth Index 0.054 -0.493*** 0.036 -0.329*** 0.184 -0.582*** (0.092) (0.114) (0.167) (0.093) (0.142) (0.126)

Livestock (TLU units) 0.003 -0.016 -0.013 -0.017** -0.023** -0.030** (0.007) (0.010) (0.014) (0.008) (0.011) (0.012)

Land owned (ln, ha) 0.114 0.395*** 0.807*** 0.462*** 1.019*** 1.473*** (0.079) (0.104) (0.189) (0.079) (0.124) (0.130)

HH size (ln) -0.006 -0.105 -0.024 0.053 0.095 0.152 (0.152) (0.164) (0.281) (0.144) (0.190) (0.166)

Age (ln) -0.042 -0.046 -0.781** 0.201 -0.673** -0.591** (0.251) (0.276) (0.368) (0.239) (0.306) (0.271)

Female-headed HH (1=yes) 0.417** 0.079 -0.659* 0.210 -0.333 -0.437** (0.184) (0.206) (0.354) (0.175) (0.252) (0.208)

Education (years, ln) 0.012 0.004 -0.180 0.111 -0.303** -0.254** (0.107) (0.112) (0.188) (0.104) (0.146) (0.119)

Share of Credit Recipients 6.231*** 0.369 6.981** 1.117 6.288** 1.149 (2.411) (2.569) (2.725) (2.702) (3.078) (2.864)

Inorganic fertilizer (1=yes) -0.330 -0.478 1.332** -0.396 1.425** 0.785 (0.519) (0.492) (0.614) (0.540) (0.560) (0.560)

Negative Seasonal SPI Index (lag) -1.049 1.723** 2.127* 0.645 0.698 1.002 (0.682) (0.843) (1.173) (0.740) (0.867) (0.872)

Positive Seasonal SPI Index (lag) -0.243 -0.228 0.111 -0.361 -0.395 0.349 (0.233) (0.257) (0.348) (0.251) (0.330) (0.272)


0.061 -0.003 -0.066 0.091 0.017 0.043

(0.055) (0.061) (0.083) (0.058) (0.074) (0.064)


0.035 -0.048 0.065 -0.050 0.061 0.034

(0.034) (0.039) (0.065) (0.035) (0.055) (0.044)

Formal Diffusion (EA's level) 0.193 2.438 5.158** 0.225 5.251** 3.646* (2.016) (2.253) (2.273) (2.046) (2.121) (2.216)

Informal Diffusion (EA's level) 2.693*** 0.538 2.819*** 2.565*** 2.964*** 2.907*** (0.505) (0.487) (0.861) (0.474) (0.769) (0.629)

Constant -1.380 1.121 -0.377 -0.082 -0.074 0.996 (1.155) (1.271) (1.646) (1.088) (1.400) (1.282)


Regional Dummies Yes Yes Yes Yes Yes Yes

Observations 5 055 5 055 5 055 5 055 5 055 5 055

Year Dummy Yes Yes Yes Yes Yes Yes

Notes: the table displays the determinants of adoption of different farm systems, compared to the baseline scenario of maize mono-cropping for the Mozambique’s sample. The specification is the first stage of a multinomial treatment effect model (multinomial logit). The specification controls also household-size (ln), age (ln), dummy for female-headed households, years of education of the household's head (ln), asset wealth index, livestock owned (as TLU units), hectares of land owned (ln), use of inorganic fertilizer, mean seasonal maximum temperature, positive and negative SPI indexes for both seasonal and lagged, regional dummies. Standard errors are clustered at EA's level. Significant levels are * p<0.10, ** p<0.05, *** p<0.001.


40

Table A 6. Determinants of adoption of different farm systems in Zambia, pooled sample and complete list of coefficients

Maize-legume

Maize-staple

Maize-cash crops

Maize-legume-staple



Maize Seeds Price (EA's level) 0.030 -1.394*** 0.161 -1.082*** 0.117 -1.028** (0.176) (0.240) (0.258) (0.232) (0.227) (0.411)

Cash-crop Seeds Price (EA's level) -0.404** -0.222 -0.432** -0.689*** -0.550*** -0.210 (0.180) (0.278) (0.215) (0.248) (0.208) (0.270)

Legume Seeds Price (EA's level) 0.494 -1.112 2.515*** -0.344 1.858*** -0.131 (0.554) (0.736) (0.638) (0.770) (0.603) (0.878)

Staple Seeds Price (EA's level) -0.196* -0.101 -0.955*** -0.685*** -0.681*** -1.202*** (0.114) (0.159) (0.204) (0.161) (0.168) (0.192)

Total Traders (ln, EA's level) 0.346*** -0.266** 0.242* 0.423*** 0.510*** 0.314** (0.075) (0.115) (0.125) (0.101) (0.106) (0.136)

Distance from FRA (ln, EA's level) 0.195** 0.551*** 0.105 0.650*** 0.200* 0.379** (0.078) (0.119) (0.122) (0.109) (0.104) (0.193)

Agricultural Asset Wealth Index -0.493* -0.671* -0.057 -0.962*** -0.458 -0.274 (0.263) (0.343) (0.334) (0.263) (0.305) (0.370)

Livestock (TLU units) 0.007 -0.005 -0.000 -0.001 0.001 -0.001 (0.005) (0.006) (0.006) (0.004) (0.006) (0.006)

Land owned (ln, ha) 0.614*** 0.569*** 0.860*** 1.155*** 1.472*** 1.513*** (0.060) (0.071) (0.086) (0.069) (0.080) (0.097)

HH size (ln) 0.125 0.115 -0.113 0.248** 0.199* 0.553*** (0.090) (0.112) (0.146) (0.104) (0.109) (0.157)

Age (ln) 0.501*** 0.205 -0.918*** 0.529*** -0.228 0.053 (0.133) (0.178) (0.199) (0.145) (0.163) (0.241)

Female-headed HH (1=yes) 0.495*** -0.236* -0.707*** 0.289*** -0.340*** 0.053 (0.104) (0.128) (0.190) (0.105) (0.126) (0.175)

Education (years, ln) -0.039 -0.050 -0.271*** 0.127* -0.095 -0.005 (0.060) (0.077) (0.075) (0.067) (0.075) (0.106)

Share of Credit Recipients 0.253 -4.068*** 4.342*** -2.849*** 4.108*** 2.302*** (0.314) (0.594) (0.492) (0.512) (0.450) (0.546)

Share of FISP Recipients 0.498* 0.051 0.188 1.435*** 0.260 1.446*** (0.288) (0.409) (0.513) (0.335) (0.423) (0.452)

Inorganic fertilizer (1=yes) 0.178 -0.640*** -0.049 -0.135 0.565*** 0.252 (0.109) (0.127) (0.151) (0.126) (0.139) (0.184)

Negative Seasonal SPI Index (lag) -0.617** 0.470 -2.017*** 0.767** -3.560*** -1.320*** (0.246) (0.381) (0.364) (0.340) (0.331) (0.440)

Positive Seasonal SPI Index (lag) 0.051 -0.285* -0.139 -0.546*** 0.954*** -0.532* (0.129) (0.172) (0.243) (0.158) (0.189) (0.274)


0.068* -0.007 -0.035 0.054 0.024 0.124**

(0.036) (0.048) (0.063) (0.047) (0.052) (0.062)


-0.023 -0.037 0.048 -0.083** -0.011 -0.023

(0.029) (0.040) (0.062) (0.035) (0.044) (0.056)

Formal Diffusion (EA's level) 0.624** 0.025 0.114 1.550*** -0.314 0.830 (0.306) (0.438) (0.515) (0.384) (0.438) (0.579)

Informal Diffusion (EA's level) 0.001 -1.406*** -0.673 -1.589*** -0.358 -1.206** (0.308) (0.417) (0.509) (0.369) (0.402) (0.550)

Constant -3.754*** 0.597 0.911 -3.369*** -4.128*** -4.356*** (0.793) (1.007) (1.101) (1.004) (0.959) (1.417)


Agro-ecological dummy Yes Yes Yes Yes Yes Yes

Observations 11 496 11 496 11 496 11 496 11 496 11 496

Year Dummy Yes Yes Yes Yes Yes Yes

Notes: the table displays the determinants of adoption of different farm systems, compared to the baseline scenario of maize mono-cropping for the panel pooled sample (2011 and 2014). The specification is the first stage of a multinomial treatment effect model (multinomial logit). The model controls also for the total number of traders (ln), the distance from FRA (ln), household-size (ln), age (ln), dummy for female-headed households, years of education of the household's head (ln), asset wealth index, livestock owned (as TLU units), hectares of land owned (ln), share of credit and FISP recipients at EA level, use of inorganic fertilizer, mean seasonal maximum temperature, positive and negative SPI indexes for both seasonal and lagged, agro-ecological and year dummies. Standard errors are clustered at EA's level. Significant levels are * p<0.10, ** p<0.05, *** p<0.001.


41

Table A 7. Impact of different farm systems on maize yield, pooled sample and complete list of coefficients

Dependent: Maize Yield (ln)


Market index: distance from weekly market

Market index: distance from main market cities

Market index: total traders (ln)

Maize-Legume (1=yes) 0.285*** 0.080 -0.020

(0.061) (0.103) (0.045)

Maize-Staple (1=yes) -0.075 0.222* -0.181***

(0.097) (0.116) (0.053)

Maize-Cash Crops (1=yes) 0.155* 0.104 0.043

(0.083) (0.196) (0.063)

Maize-Legume-Staple (1=yes) 0.267*** 0.185* 0.269***

(0.086) (0.094) (0.042)

Maize-Legume-Cash Crops (1=yes)

0.468*** 0.569*** 0.114**

(0.069) (0.113) (0.053)

Maize-Legume-Cash Crops-Staple (1=yes)

0.002 0.776*** 0.236***

(0.106) (0.114) (0.052)

Maize Seeds Price (EA's level) 0.125** NA 0.141***

(0.063) NA (0.035)

Legume Seeds Price (EA's level)

NA NA -0.050

NA NA (0.033)

Staple Seeds Price (EA's level)

0.034** NA -0.361***

(0.014) NA (0.103)


0.000 NA -0.046*

(0.008) NA (0.024)

Market index 0.024 0.054*** 0.081***

(0.016) (0.019) (0.015)


-0.075** NA -0.019

(0.029) NA (0.016)


0.115*** 0.092*** 0.407***

(0.018) (0.027) (0.046)

Livestock (TLU units) 0.026*** 0.006*** 0.004***

(0.008) (0.002) (0.001)

Land owned (ln, ha) -0.027 -0.452*** -0.094***

(0.063) (0.023) (0.011)

Hh size (ln) -0.010 0.085*** 0.011

(0.036) (0.028) (0.017)

Age (ln) 0.037 0.133*** -0.035

(0.053) (0.043) (0.026)

Female-headed HH (1=yes) -0.066 -0.079** -0.068***

(0.041) (0.035) (0.020)

Education (ln) 0.180*** 0.064*** 0.059***

(0.035) (0.021) (0.012)

% Credit Recipients (average EA's level)

0.059 -0.004 0.144**

(0.118) (0.424) (0.065)

% FISP Recipients (average EA's level)

-0.130* NA 0.206***

(0.070) NA (0.053)

42






Inorganic fertilizer (1=yes) 0.385*** 0.328*** 0.545***

(0.047) (0.074) (0.022)

Mean Seasonal Maximum Temperature (C)

-0.075*** -0.030* 0.009

(0.020) (0.018) (0.012)

SPI Negative -0.112*** -0.856*** -0.058

(0.039) (0.174) (0.048)

SPI Positive -0.322*** 0.160*** 0.005

(0.119) (0.054) (0.018)

SPI Negative (lag) -0.067 0.033 -0.169***

(0.105) (0.144) (0.044)

SPI Positive (lag) 0.074 0.058 0.064**

(0.063) (0.045) (0.028)

Constant 8.155*** 5.851*** 6.305***

(0.608) (0.610) (0.386)

Agro-ecological/Regional dummies

Yes Yes Yes


Log-Likelihood -10 573.14 -14 279.67 -28 319.58

chi2 / F / Wald 1 468.986 1 747.862 9 323.872

Notes: the table displays the determinants of maize yield, compared to the baseline scenario of maize mono-cropping. The specification is the second stage of a multinomial treatment effect model (multinomial logit). Column 1-2-3 report results Malawi, Mozambique, and Zambia. The instrumental variables are the flood and drought shock probability, and the average diffusion of formal and informal institutions at EA's level (not reported). The shocks probability are measured as the share of seasons where a flood or drought shock occurred (SPI>2). Column 5-6 are OLS-FE and GLS-RE. The specifications include the total number of traders (ln), the distance from FRA (ln) household-size (ln), age (ln), dummy for female-headed households, years of education of the household's head (ln), asset wealth index, livestock owned (as TLU units), hectares of land owned (ln), share of credit and FISP recipients at EA level, use of inorganic fertilizer, mean seasonal maximum temperature, positive and negative SPI indexes for both seasonal and lagged, agro-ecological and year dummies (only for the pooled sample). Significant levels are * p<0.10, ** p<0.05, *** p<0.001 and errors are clustered at EA's level.


43

Table A 8. Impact of different farm systems on crop income volatility, pooled sample and complete list of coefficients






Maize-Legume (1=yes) -1.477*** -0.122 -0.251***

(0.450) (0.139) (0.037)

Maize-Staple (1=yes) 0.220 -0.011 -0.187***

(0.814) (0.163) (0.051)

Maize-Cash Crops (1=yes) -0.542 0.092 -0.273***

(0.691) (0.326) (0.052)

Maize-Legume-Staple (1=yes) -0.196 -0.255* -0.363***

(0.646) (0.132) (0.039)

Maize-Legume-Cash Crops (1=yes)

-1.110 -0.414*** -0.392***

(0.683) (0.154) (0.043)

Maize-Legume-Cash Crops-Staple (1=yes)

-0.914 -0.168 -0.430***

(0.779) (0.139) (0.046)

Maize Seeds Price (EA's level) 1.361** NA -0.053*

(0.624) NA (0.032)

Legume Seeds Price (EA's level) NA NA 0.088**

NA NA (0.040)

Staple Seeds Price (EA's level) -0.210 NA -0.172*

(0.141) NA (0.089)


-0.294*** NA -0.007

(0.094) NA (0.019)

Market index 0.170 0.050* -0.047***

(0.153) (0.026) (0.017)


-0.417 NA 0.022

(0.269) NA (0.017)

Agricultural Asset Wealth Index -0.212 0.013 -0.018

(0.163) (0.048) (0.077)

Livestock (TLU units) -0.020 0.008 0.002*

(0.050) (0.005) (0.001)

Land owned (ln, ha) -1.593** 0.094** 0.131***

(0.623) (0.044) (0.020)

Hh size (ln) 0.159 -0.022 -0.058**

(0.266) (0.059) (0.024)

Age (ln) 0.881** -0.054 -0.065**

(0.395) (0.094) (0.031)

Female-headed HH (1=yes) 0.151 -0.020 0.057**

(0.330) (0.055) (0.026)

Education (ln) 0.199 -0.049 -0.028**

(0.366) (0.041) (0.013)

% Credit Recipients (average EA's level)

-1.459 1.019 -0.016

(1.080) (1.009) (0.052)

44






% FISP Recipients (average EA's level)

-2.413*** NA -0.110**

(0.855) NA (0.047)

Inorganic fertilizer (1=yes) 0.896** 0.520** -0.099***

(0.385) (0.258) (0.024)

Mean Seasonal Maximum Temperature (C)

0.730** 0.050** -0.018*

(0.330) (0.024) (0.011)

SPI Negative -0.362 -0.536* 0.072

(0.397) (0.309) (0.048)

SPI Positive 3.529*** -0.183* 0.035

(1.157) (0.100) (0.031)

SPI Negative (lag) 0.432 -0.139 0.168***

(0.856) (0.263) (0.049)

SPI Positive (lag) 1.244* -0.049 -0.026

(0.640) (0.085) (0.028)

Constant -16.018* -0.589 -108.411***

(8.689) (0.777) (24.251)

Agro-ecological/Regional dummies

Yes Yes Yes


Log-Likelihood -19 078.90 -18 788.02 -31 588.78

chi2 / F / Wald 814.937 773.119 6 686.129

Notes: the table displays the determinants of crop income volatility, compared to the baseline scenario of maize mono-cropping for Malawi (column 1), Mozambique (column 2), and Zambia (column 3). The specification is the second stage of a multinomial treatment effect model (MTM) where the dependent is the squared residual from a MTM including crop income as dependent variable. The instrumental variables are the flood and drought shock probability, and the average diffusion of formal and informal institutions at EA's level (not reported). The shocks probability are measured as the share of seasons where a flood or drought shock occurred (SPI>2). Column 5-6 are OLS-FE and GLS-RE. Significant levels are * p<0.10, ** p<0.05, *** p<0.001 and errors are clustered at EA's level.


45

Table A 9. Main market cities employed for the private market proxy in Mozambique

District in Provinces Maket cities

Niassa, Cabo Delgado, Nampula, Zambezia Nampula

Tete, Manica Tete

Sofala, Inhambane Beira

Gaza, Maputo Maputo


Table A 10. Main market cities employed for the private market proxy in Mozambique

Dependent: Labour productivity in agriculture


Maize-Legume (1=yes) 0.247*** 0.308** 0.298***

(0.091) (0.127) (0.071)

Maize-Staple (1=yes) 0.297*** -0.256 0.609***

(0.105) (0.158) (0.098)

Maize-Cash Crops (1=yes) 0.502*** 0.266 0.528***

(0.097) (0.196) (0.107)

Maize-Legume-Staple (1=yes) 0.260** 0.141 0.720***

(0.105) (0.125) (0.065)

Maize-Legume-Cash Crops (1=yes) 0.882*** 0.652*** 0.436***

(0.100) (0.139) (0.073)

Maize-Legume-Cash Crops-Staple (1=yes) 0.452*** 0.848*** 0.642***

(0.107) (0.128) (0.056)

Other controls Yes Yes Yes

Agro-ecological/Regional dummies Yes Yes Yes


Log-Likelihood -8 967.18 -18 788.02 -30 252.89

chi2 / F / Wald 3 476.197 773.119 17 610.565

Notes: the table displays the determinants of labour productivity in agriculture, measured as crop income per unit of adult worker, compared to the baseline scenario of maize mono-cropping for Malawi (column 1), Mozambique (column 2) and Zambia (column 3). The specification is the second stage of a multinomial treatment effect model (MTM). The baseline category is maize mono-cropping. The instrumental variables in the first stage are the flood and drought shock probability and the average diffusion of formal and informal institutions at EA's level. The entire set of coefficients are available upon request. Significant levels are * p<0.10, ** p<0.05, *** p<0.001 and errors are clustered at EA's level.


46

Technical note on the conceptual and empirical approach

Following Feder (1982), Di Falco and Veronesi (2013) and Manda et al., (2016), we model the

adoption of different cropping systems using the random utility framework. In this context, we

posit that risk-adverse farmers select the cropping system maximizing their utility conditional on

land and labour availability, input costs and other ecological, demographic, and institutional

constraints. Formally, the assumption is that a given farmer i will maximise his/her expected

indirect utility Vij comparing the utility provided by alternative systems. This assumption implies

the selection of any practice j over any alternative practice k if Vij > Vik.

Adoption within any category is a non-random event, as farmers are pushed/pulled into each

system according to both observable factors, such as socio-economic characteristics and level

of resources and unobservable ones, such as risk aversion and expectations on future

weather’s distribution. For example, it is probable that complex cropping systems will be mainly

adopted by high skilled farmers with technical abilities. If the empirical model fails to account for

this, the coefficient linked to the farm system will be upward biased, as it will likely capture the

effect of the innate ability of the farmer independently from the farm system adopted.

This study adopts a multinomial endogenous treatment effect model to address the endogeneity

of farming selection while accounting for the categorical nature of the cropping systems’

variable, the heterogeneity arising from a different levels of exposure of farmers to the climate

risk, and the interdependence of cropping system’ adoption ((Deb and Trivedi, 2006, Manda et

al. 2016; Di Falco and Veronesi 2013).

Adoption equation

This model requires setting a benchmark for the sake of comparison which in this case is maize

mono-cropping.12 Let 𝑗 = 0 be the control group category, with Vie0=0, adopted by household i

residing in village e. Denoting as 𝑓𝑗 the variable on the type of farming system, 𝒁𝑖𝑒𝑡 a matrix of

control variables, 𝛼𝑗𝑡 their linked coefficients and 𝑙𝑖 = (𝑙𝑖1, 𝑙𝑖2 … 𝑙𝑖𝐽) a vector of latent variables,

the probability of adopting a farming system j takes the following form:

Pr(𝑓𝑗|𝑧𝑖𝑙𝑖) = 𝑔(𝑧1𝑒′ 𝛼1 + 𝛿1𝑙𝑖1 , 𝑧𝑒2

′ 𝛼2 + 𝛿2𝑙𝑖2 , … , 𝑧𝑒𝑖′ 𝛼𝑗 + 𝛿𝑗𝑙𝑖𝑗) (1)

With 𝑔 being a multinomial probability distribution characterized by a Mixed Multinomial Logit

(MMNL) structure. The non-linear form of the selection equation embedded in the semi-

structural model guarantees the identification of the parameters even when the explanatory

variables in the two stages coincide. However, Deb and Trivedi (2006) suggests using an

instrumental variable for the exclusion restriction to obtain a more robust identification. The

instrument involves at least an explanatory variable entering exclusively in the adoption

equation and not in the outcome one. The identification derives from the exclusion restriction,

implying that the instrumental variable does not impact the outcome equations through any other

channel. The matrix of explanatory variables involves four instrumental variables in the first

stage: 1) drought risk exposure; 2) flood risk exposure; 3) informal and 4) formal diffusion of

agricultural practices. The first two variables follow the work from Di Falco and Veronesi (2013),

12 The selection of maize mono-cropping as benchmark derives from theoretical and statistical considerations. From a theoretical perspective, literature often associates maize mono-cropping to the poorest part of the population (Hassan and Nhemachena, 2008).

47

who consider the experience of extreme weather events such as droughts, flood and hailstorms,

to instrument the adoption of different adaptation strategies. For the validity of the exclusion

restriction, the underlying hypothesis is that farmers are more likely to adopt a cropping system

different from maize mono-cropping when residing in areas that have been more historically

exposed to weathers shocks. Being a subjective belief of the household, the postulate is that

this will not affect the yield or crop income volatility through other channels. Flood and drought

risk exposure are computed for household i residing in the area e as the probability of being

exposed to this event conditional on area e historical weather distribution. A drought shock if

the Rainfall SPI index in a given area and agricultural season takes a value lower than -2

(Guttman, 1998; McKee et al., 1993). A flood shock occurs when the SPI index takes value

above 2. Following Scognamillo, Asfaw and Ignaciuk (forthcoming) and generalizing for a shock

S occurring in a given area e in the year t, the risk exposure index takes the following form:

𝑃𝑖𝑒𝑡 = ∑ 𝑆𝑒 𝑡−2

𝑡−21

(𝑡−2)−1983 (2)

Where the probability of experiencing a shock for household i, residing in area e, at time t, is

computed as the ratio between the total number of seasons in which the shock occurred, divided

by the total number of seasons available in our weather source. Drought and flood indicators

are lagged by two years to exclude any recent shocks likely affecting household’s adoption of a

cropping system through the production channel, condition that would violate the exclusion

restriction. Following the work from Manda et al. (2016), the remaining two instruments are

proxies of information availability at village/district level, under the assumption that agricultural

information can be transmitted through institutional channels, such as extension services and

informal institutions, such as family and friends network. To guarantee the exclusion restriction,

we compute the average access to these two sources of information at a higher geographical

level than the one observed in the data, hypothesizing that the average access to these sources

is not correlated to the household’s outcome (Scognamillo, Asfaw and Ignaciuk, forthcoming).

In addition to these exogenous covariates allowing the robust identification of the model, the

matrix of control variables 𝒁𝑖𝑒𝑡 includes the determinants introduced in section 3.1.

Outcome equation

To estimate the impact of adoption on maize yield and crop income volatility, the second stage

of the multinomial treatment effect model consists in an OLS with the selectivity correction, as

follows:

𝐸(𝑦𝑖𝑡|𝑑𝑖𝑡𝑥𝑖𝑡𝑙𝑖𝑡) = 𝑥𝑖𝑡′ 𝛽 + ∑ 𝛾𝑗𝑡𝑓𝑖𝑗𝑡 + ∑ 𝜆𝑗𝑡𝑙𝑖𝑗𝑡

𝑗𝑗=1

𝑗𝑗=1 (3)

Where the expected outcome 𝐸(𝑦𝑖𝑡|𝑑𝑖𝑡𝑥𝑖𝑡𝑙𝑖) depends on a set of observable determinants 𝑥𝑖𝑡

with linked parameters 𝛽, on the adoption of a given farm system 𝑓𝑖𝑗𝑡 and associated

coefficients 𝛾𝑗𝑡. In addition to the set of variables in the first-stage, the matrix of determinants

𝑥𝑖𝑡 comprises the seasonal positive and negative SPI indexes, capturing the effect of seasonal

rainfall distribution on the productivity and crop income volatility. The latent components 𝑙𝑖𝑗𝑡

suggest that unobserved characteristics are explaining variation both in the selection and

outcome (Deb and Trivedi, 2006). The latent factor parameters 𝜆𝑗𝑡 allow for correlation between

the selection and the outcome equation (Deb and Trivedi, 2006). Where the main dependent

variables vary according to the objective of the analysis.

48

To investigate the effect on maize productivity is the natural log of maize yield. To address the

effect of farm systems on crop income volatility, the empirical framework takes advantage from

the work developed by Antle (1983) and Di Falco and Chavas (2009), based on two steps. First,

the approach predicts the error term from a regression with the crop gross income as dependent

variable. Secondly, it involves the addition of the square of the error term as new dependent

variable in the main empirical specification, keeping fixed the controls. This approach provides

a consistent indication of the production uncertainty measured as volatility of the error, assuming

no omitted variable bias. As the resulting specification is likely to exhibit heteroskedasticity, the

final regression is weighted using the inverse of the variance of the error terms (Di Falco and

Chavas, 2009).13

13 This procedure relies on the assumption of no measurement error. To exclude that large measurement errors will affect the estimation on volatility, we have imputed outliers using the median value in each country.

FAO AGRICULTURAL DEVELOPMENT ECONOMICS WORKING PAPERSThis series is produced by the Agricultural Development Economics Division (ESA) of the Food and Agriculture Organization of the United Nations (FAO) since 2001 to share findings from research produced by the Division and elicit feedback for the authors.

It covers different thematic areas, such as food security and nutrition global trends and governance; food security and resilience; sustainable markets, agribusinesses and rural transformations; and climate-smart agriculture.

The analysis and conclusions are those of the authors and do not indicate concurrence by FAO.

The complete series is available at: www.fao.org/economic/esa/working-papers

The Agricultural Development Economics Division (ESA) is the focal point for FAO’s research and policy analysis on agricultural and economic development. The Division produces evidence-based policy analysis and strengthens the capacity of member countries to improve decision-making on food security and nutrition, resilience, climate-smart agriculture, sustainable markets, agribusinesses and rural transformations.

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