1

EVERGREEN AGRICULTURE FOR SUSTAINABLE FOOD SECURITY IN MALAWI

SOCIO-ECONOMIC BASELINE REPORT

SOSOLA BG; AJAYI OC; SILESHI G.;AKINNIFESI FK; BEEDY T;KWAVALE H.

SEPTEMBER 2010

2

CITATION OF THE REPORT

This report may be cited as follows:

SOSOLA BG; AJAYI OC; SILESHI G.W. AKINNIFESI FK; BEEDY TL; KWAVALE H (2010) SOCIO-ECONOMIC BASELINE SURVEY REPORT OF EVERGREEN AGRICULTURE PROJECT (2009-2010), WORLD AGROFORESTRY CENTER (ICRAF),

© WORLD AGROFORESTRY CENTER 2010

i

Table of Contents

Acknowledgements ....................................................................................................................................... iii

Acronyms ..................................................................................................................................................... iv

Executive Summary........................................................................................................................................ v

1.0 INTRODUCTION .................................................................................................................................. 1

2.0 EVERGREEN AGRICULTURE IN MALAWI ..................................................................................... 2

3.0 OBJECTIVES OF THE BASELINE SURVEY ...................................................................................... 3

3.2. Specific Objectives .............................................................................................................................. 3

3.3 Outputs ................................................................................................................................................ 4

4.0 METHODS AND METHODOLOGY FOR THE SURVEY ................................................................. 4

4.1 Sampling Technique ............................................................................................................................. 4

4.2 Selection and Training of Enumerators ................................................................................................. 5

4.3 Data Collection Methods and Type of Data Collected .......................................................................... 6

4.3.1 Household Assets .......................................................................................................................... 6

4.3.2 Food Security and Nutrition .......................................................................................................... 6

4.3.3 Agricultural Systems and Farming Practices ................................................................................... 6

4.5.4 Soil Fertility, Knowledge and Use of Agroforestry Practices........................................................... 6

4.3.5 Technical and Financial Assistance and Social Network ................................................................. 7

4.3.6 Household Demography ................................................................................................................ 7

4.3.7 Household Basic Information ........................................................................................................ 7

4.4 Data Analysis ........................................................................................................................................ 7

4.6 Partnership ........................................................................................................................................... 7

5.0 BASELINE RESULTS ............................................................................................................................ 8

5.1 Demographic characteristics of the respondents ................................................................................... 8

ii

5.2 Constraints of Agricultural Production ................................................................................................. 9

5.3 Food Security ..................................................................................................................................... 10

5.4 Sources of Seed .................................................................................................................................. 15

5.5 Sales of Farm Produce ........................................................................................................................ 15

5.6 Status of farmers’ soil and water conservation practices ...................................................................... 16

5.7 Access to Technical and Financial Assistance ..................................................................................... 19

5.8. Club membership .............................................................................................................................. 21

5.9 Household assets ................................................................................................................................ 21

6.0 CONCLUSIONS ................................................................................................................................... 23

7.0 REFERENCES ...................................................................................................................................... 24

iii

Acknowledgements

We would like to extend our heartfelt gratitude to the team of enumerators which comprised Angelina Chisanu, Saulos Luhanga, Tionge Mbale, Brenda Chipengule, Stevie Macheso, Evelyn Cheyo, Edward Chipangwi, Ennett Mukumbwa, Khama Banda, Gertrude Sato and the data entry clerks namely Alinafe Kachiguma and Wantwa Mwanjabe. We also recognize the untiring support given by Catherine Chabvuta and Maurice Zimba for your excellent supervisory roles you played during the entire data collection period. Let us also extend our vote of thanks to the support we got from the extension workers from the 14 sections in Kasungu, Mchinji and Chiradzulu where we collected data: you are the partners indeed in times of need. Finally and not possibly the least, we thank the respondents, the village evergreen agriculture committee members, traditional leaders and many more others who assisted us during the entire survey. The success of evergreen agriculture surely relies on your untiring commitment to transform from conventional agriculture to agroforestry based conservation agriculture (evergreen agriculture). You made our lives easier and bravo all!

iv

Acronyms

ACT African Conservation Tillage Network

ADD Agricultural Development Division

ADMARC Agricultural Development and Marketing Corporation

AEDC Agricultural Extension Development Coordinator

AEDO Agricultural Extension Development Officer

AFSP Agroforestry Food Security Programme

ARET Agricultural Research and Extension Trust

CA Conservation Agriculture

COMESA Common Market for East and Southern Africa

CRS Catholic Relief Services

DADO District Agricultural Development Office

DAES Department of Agricultural Extension Services

EA Evergreen Agriculture

EPA Extension Planning Area

GDP Gross Domestic Product

GOM Government of Malawi

ICRAF World Agroforestry Centre

LRCD Land Resources Conservation Department

NASFAM National Smallholder Farmers Association of Malawi

NGO Non Governmental Organisation

TAMA Tobacco Association of Malawi

TLC Total Land Care

v

Executive Summary

Malawi relies on smallholder agriculture because over 70% of the agriculture GDP comes from it. This indicates the strategic importance of smallholder agriculture sub-sector to the country. Smallholder agriculture in Malawi is characterized by ridge tillage, burning of residues and monocropping. These practices lead to nutrient depletion, reduce the soil organic matter, root penetration, soil water infiltration, nutrient recycling and biological activity and increase pest problems. Most of the smallholder farmers cannot afford the expensive options available to improve the fertility of their soils. Because of these challenges at the farm level, there is need to revamp Malawi’s agricultural policy framework to foster the advocacy and wide scale promotion of conservation agriculture with trees and consolidate multi-sectoral partnerships and networks to increase information sharing on sustainable soil health initiatives. For many decades, ICRAF has conducted on-station and on-farm research on the potential of fertilizer trees in improving soil health and increasing crop productivity. ICRAF has witnessed success stories on enhanced food security and soil health improvements from the variety of fertilizer trees promoted such as Gliricidia sepium, Tephrosia vogelli, Tephrosia candida, Sesbania sesban and others.

It is against this background that the World Agroforestry Centre (ICRAF) in conjunction with the Ministry of Agriculture and Food Security departments of Land Resources Conservation Department, Department of Agricultural Extension Services, Catholic Relief Services, National Smallholder Farmers Association of Malawi and Total LandCare implemented a one year pilot project “Evergreen Agriculture for Food Security” (EA) funded by Irish Aid Malawi to raise awareness on the potential of agroforestry based conservation agriculture in improving soil health ecosystems.

A baseline survey was conducted to understand and document the prevailing conditions of the farmers and environment in the pilot areas at the before the implementation of the project activities. This was envisioned to help monitor and evaluate the project impact on farmer adoption and the potential impact of the Evergreen Agriculture on food security and environment. The survey helped identify the challenges faced in the adoption of the technology and how best the technology could be adapted to suit the prevailing socio-economic and environmental conditions in the pilot areas.

The baseline report indicates the current farming practice, socio-economic indicators and household characteristics. Efforts were made to showcase the comparisons between the districts, gender and status of project participation on a number of socio-economic parameters. The report shows that female respondents were slightly younger than their male counterparts at the ages of 39 and 40, respectively. EA participating farmers were slightly older that the non EA participating farmers at the ages of 40 and 38. It was found out that only 15% were illiterate because they have never attended any school and the figure is slightly lower than the national average adult illiteracy rate of 16% (NSO, 2008). It was also found that 44% of the respondents stay in the village of their husband or father, 41% stay in the village of their wife or mother and 15% in the village which is neutral. About 65% of the respondents cited loss of fertility as their major constraint for agricultural productivity apart from the common problem of lack of money. It was noted that on average maize produced by the respondent households cover them for about eight months and buy maize from other sources for about three months. About 65% of the respondents buy improved maize seeds and the rest but recycled maize seeds. It was noted that 75% of the respondents observed that the

vi

soil fertility of their fields is decreasing for the past three years and that 92% of the respondents have never used minimum tillage practice on their fields.

This report creates a reference point for future comparison on whether Evergreen Agriculture has positive impacts on smallholder agriculture in terms of soil health improvement and food security or not.

1

1.0 INTRODUCTION

Agriculture is the mainstay of the majority of rural people in Malawi. About 85% of Malawian population are engaged in agricultural production and this economic activity contributes about 40% to Malawi’s Gross Domestic product and 80% to foreign earnings (Ellis et al. 2002; GOM 2006; GOM 2010). Though the smallholder agriculture sector contributes 70% to the agricultural GDP (GOM 2010), the sector is faced with a myriad of challenges that consequently affect the smallholder capacity to benefit from their efforts in farming.

Farmers practice extensive farming, which is characterized by cultivation of exhausted agricultural lands, with expansion into forest and marginal lands without adequate soil conservation measures. The problem is compounded with frequent soil disturbance (ridging and shifting ridges), low use of agricultural inputs, exploitation of fragile/marginal lands, and maize mono-cropping. Due to population pressure, landholdings are small, particularly in the densely populated southern region, leading to encroachment on marginal lands and increased erosion. In Malawi, land degradation is a rampant problem due to these reasons.

Land degradation affects livelihoods through its adverse impacts on quantity and quality of food production (Lal, 2008). In Malawi, the yield of maize which is the staple food is greatly affected by land degradation. The national average of maize yield in Malawi is 1.3 metric tons per hectare over the last 20 years (GOM, 2010). Land degradation reduces crop yields by increasing susceptibility to drought stress, pests and diseases and imbalances in nutrients. Generally it is encouraged to use inorganic fertilizer to solve the soil fertility problems. However, fertilizer use alone is inadequate to alleviate physical and biological degradation of soil. Besides, fertilizer response is very low on already degraded soils (Sileshi et al., 2009). Given that only a small proportion of smallholder farmers have access to inorganic fertilizer, it has been suggested that rather than focusing on the “organic vs. inorganic fertilizer” debate, integrated soil fertility approach that takes advantage of the two forms of nutrient supply to contribute to food security for the benefit of smallholder farmers should be emphasized (Ajayi et al., 2009).

Sustainable land management practices such as agroforestry and conservation agriculture can increase the productivity of subsistence and small land holder agriculture in Malawi and elsewhere in Africa. Over two decades of work by the World Agroforestry Centre (ICRAF) shows that agroforestry systems involving fertilizer trees can significantly increase crop productivity and ensure food security (Akinnifesi et al., 2008; Sileshi et al., 2009a). Vigorous research for development of fertilizer trees has been on-going in Malawi in the last two decades (Akinnifesi et al., 2008). Results show that fertilizer trees can substantially increased maize yields in Malawi (Akinnifesi et al., 2008). Gliricidia sepium intercropped with maize in southern Malawi increased maize yields more than three-fold (Akinnifesi et al., 2008). Saka et al. (1994) reported 100-400% increase for maize yield under Faidherbia albida, A recent meta-analysis of data from more than 50 sites in Sub-Saharan Africa has also provided conclusive evidence that fertilizer trees can double or triple crop yield on poor to medium potential soils (Sileshi et al., 2008). Given the central role of soil fertility in raising agricultural productivity in the subcontinent, Ajayi et al. (2007) noted that complementary initiatives are needed across the continent, at all scales and levels of activity. The debate on “organic versus inorganic” source of nutrients to meet food requirements in the region is less helpful, and thus

2

emphasis should be on maximizing potential synergy between the two sources of plant nutrients (Ajayi et al. 2007).

Conservation agriculture is widely recognized as a viable practice for creating a sustainable agriculture as it has the potential to significantly enhance agricultural productivity, while at the same time contributing to sustainable land management (ACT, 2008). Recently, Rockström et al. (2009) and CFU (2009) presented evidence of increased crop yields and improved water productivity through conservation farming in semi-arid and dry sub-humid locations in Ethiopia, Kenya, Tanzania and Zambia. Conservation agriculture is based on enhancing natural biological processes above and below the ground through reduced mechanical soil tillage, ensuring adequate soil cover and adopting strategic rotations of cover crops. The contribution of conservation agriculture to the improvement of agricultural productivity has been reported in various countries. In Zambia for example, farmers using CA reported 50% increase in maize yields (Haggblade and Tembo, 2003). Fertilizer trees and conservation agriculture are now the centerpiece of national scaling-up programmes in Malawi. Since 2007, Irish-Aid has been supporting the World Agroforestry Centre and its partners to promote the Agroforestry Food Security Programme (AFSP) in Malawi. Substantial capacity building and partnership has been achieved through AFSP. Irish Aid has been supporting promotion of conservation agriculture by the National Smallholder Farmers Association of Malawi (NASFAM) since 2008. The results of these programmes are encouraging. The experiences and lessons from these initiatives can now be promoted widely to achieve greater impact. Despite the known benefits of fertilizer trees and conservation farming, there are major technical and institutional challenges to scaling-up of these technologies. A consultative workshop organized by ICRAF through support from support from NORAD (through COMESA) identified inadequate knowledge, lack of standardized extension messages, land tenure and property rights and policy and institutional issues as the major challenges, Greater impact can be achieved if efforts to scale-up conservation agriculture with trees is rooted in sound science-based knowledge, coupled with strategies to address social, economic and institutional/policy factors affecting adoption. Ajayi (2007) reported that technical characteristics are important but not exclusive conditions for farmers’ acceptability and adoption of good agricultural technologies. The author further mentioned that farmers’ preference provides valuable insights for researchers to help them develop appropriate sustainable technologies (Ajayi 2007).

2.0 EVERGREEN AGRICULTURE IN MALAWI

The World Agroforestry Centre has just released a blue print for evergreen-green revolution in African agricultures (World Agroforestry Centre, 2009). Conservation agriculture with trees embodies the core of the evergreen agriculture. The fertilizer tree system and conservation agriculture discussed above evolved independently, and both have been refined through intensive work with farmers. Each system, however, has unique strengths and weaknesses. The fertilizer tree system has increased soil fertility and enabled farmers to double or triple their yields. The use of tree legumes also helps to suppress weeds (Sileshi et al., 2006), reducing the drudgery of hand weeding. Conservation agriculture with minimum tillage, on the other hand, reduces the labour requirements and costs of land preparation. These benefits can be maximized by combining fertilizer tree systems with conservation agriculture. For example, the Total Land Care (TLC) has been testing a

3

combination of conservation agriculture and fertilizer trees in an integrated manner across several sites in Malawi. Their results show that yields of maize could increases by 26% when maize is cropped under conservation agriculture with fertilizer trees such as pigeon pea and tephrosia. Labour requirement could also be 36% less under conservation agriculture with fertilizer trees. This integrated system is still under development, and will require much more farmer participatory research in the coming years. The World Agroforestry Centre and its partners in southern Africa are carefully observing farmer experiences with these practices, and are designing a new system – an Evergreen Agriculture – that may combine the best of both practices (World Agroforestry Centre, 2009). The Evergreen Agriculture pilot project forms a platform for learning and promoting Evergreen Agriculture in Malawi. Drawing on the lessons, experiences and partnerships built with the existing AFSP, this project catalyses a transition from the current production system to an Evergreen Agriculture. This agricultural practice has the potential to mitigate land degradation, buffer against climate change impacts, reduce labour requirements and costs of land preparation and weeding, increase crop productivity and ensure food security. Besides forming a platform for learning and experience sharing the project is creating greater synergies and building capacity of other Irish Aid partners in Malawi.

The project is targeting 3000 smallholder farmers in the districts of Kasungu, Mchinji and Chiradzulu districts. The smallholders in the pilot areas are predominantly conventional farmers who plant their crops on ridges annually and have minimal use of soil fertility enhancing techniques. It is against this background that ICRAF and partners teamed up to help the farmers to convert from conventional farming to agroforestry based conservation farming in order to address the agricultural problems the farmers have been facing for years. This survey was to understand the socio-economic aspects of representative farmers participating in the pilot project dubbed “Evergreen Agriculture for Food Security (EA)”. This is the first segment of the baseline survey and the second is the bio-physical component which has been administered and will be reported a separate document.

3.0 OBJECTIVES OF THE BASELINE SURVEY

The main goal of the baseline survey was to understand and document the prevailing conditions of the farmers and environment in the pilot areas of the project at the beginning of the implementation of the project activities. Documentation of the baseline indicators will help to monitor and evaluate the project impact on farmer adoption of the Evergreen Agriculture technology and the potential impact of the Evergreen Agriculture technology on food security and environment. The survey would also help identify the existing challenges faced in the adoption of the technology and how best the technology could be adapted to suit the prevailing socio-economic and environmental conditions in Malawi.

3.2. Specific Objectives

The specific objectives are to:

(1) Collect socio-economic data or information pertaining to the smallholder farmers and environment prior to the project intervention impacts;

(2) Analyse the collected data and interpret the analysed data and present a meaningful discussion of the variables pertaining to the smallholder farmers and environment in question;

4

(3) Publish and disseminate the baseline results in journals and reports for partners for Evergreen Agriculture technology knowledge and skills transfer.

(4) Enhance sound project monitoring and evaluation system of the variables that seem to change over time resulting from the project impacts.

3.3 Outputs

The baseline survey will deliver the following outputs at the end of the day:

(1) Clear understanding of the benchmark situation of the key change indicators of the smallholder farmers and the environment prior to the project intervention

(2) Sound and smooth project management with focused project impact targets and goals;

(3) Objective project impact assessment on the key variables over a period of time.

4.0 METHODS AND METHODOLOGY FOR THE SURVEY

The household survey was administered in three districts and six extension planning areas (EPA) and 14 sections from 15th March 2010 to 5th April 2010 as indicated in the table below;

Table 1: The areas of survey and their respective number of respondents District EPA Section Number of respondents

Mchinji

Kalulu

Kapiri South

161 Chitunda

Chiosya

Chitinthi

212 Zulu East

Kasungu

Santhe

Chogodi A

253 Chigodi B

Lisasadzi

Kasela

207 Bua

Chiradzulu

Mombezi

Mwanje South

267

Nasulu

Milepa

Mbulumbuzi

Njuli

268

Nyungwe

Namadzi

TOTAL 1368

4.1 Sampling Technique

The study took place in three districts, six EPAs and 14 sections and all villages where the project is being implemented. The basic element in sampling frame was the farm household. The households were divided into two categories namely with participating farmers and the non-participating farmers. The project had registered a total of 4095 farmers in all three districts. A sample of 818

5

participating farmers was randomly selected. A sample of 560 non-participating farmers (both in the participating villages and non-participating villages) in the sections was selected using convenience sampling. The non EA farmers were randomly selected; 25 from participating villages and 10 from non-participating villages. The non participating farmers acted as the “control” sample. Table 2 shows the sampling frame for the interviews.

Table 2: The sampling frame for the household survey

District Section

EA respondents Non EA respondents

EA villages EA villages Non EA Villages

SUB TOTAL

1 Chiradzulu Nasulu 50 25 15 90

2

Mwanje South 49 25 15 89

3

Milepa 46 25 15 86

4

Njuli 50 25 15 90

5

Namadzi 69 25 15 109

6

Nyungwe 50 25 15 90

SUB TOTALS 314 150 90 554

7 Mchinji Chitunda 38 25 15 78

8

Kapiri South 41 25 15 81

9

Chitinthi 69 25 15 109

10

Zulu East 61 25 15 101

SUB TOTALS 209 100 60 369

11 Kasungu Chigodi A 86 25 15 126

12

Chigodi B 71 25 15 111

13

Kasela 71 25 15 111

14

Bua 87 25 15 127

SUB TOTALS 315 100 60 475

TOTAL 838 350 210 1398

4.2 Selection and Training of Enumerators

A total of 10 enumerators were recruited and trained in understanding the survey questionnaire. Pre-testing of the questionnaire was conducted in Mpingu EPA in Lilongwe District on 12th March 2010. The enumerators’ educational qualifications ranged from a diploma to a bachelor’s degree and had diverse levels of experiences in household questionnaire surveys. Two field survey supervisors were also trained to supervise the survey administration such as liaising with the District Agricultural Development Officer (DADO) and extension workers and traditional leaders on the survey activities, checking the questionnaires and doing other field logistics. The DAES provided technical backstopping services through the extension workers Agricultural Extension Development Officers (AEDOs) per each section. In total, the enumerators collaborated with 14 AEDOs during the entre

6

data collection. This fostered the partnership that is existed between the DAES and ICRAF in the Evergreen Agriculture pilot project.

4.3 Data Collection Methods and Type of Data Collected

The survey employed household questionnaire method, whereby a set of data was collected at the household level using structured questionnaires. The point of contact for interview at the household level was any appropriate member of the household who is directly responsible for farming activities. The baseline survey was designed to capture key household-based variables on which the Evergreen Agriculture is expected to make a considerable impact in the short and long run.

4.3.1 Household Assets

Ownership of different types of assets: livestock and house items

Type of household dwelling unit

Sources and types of energy used in households

4.3.2 Food Security and Nutrition

Number of food secure months: maize, relish (meat, vegetables), fruits

Temporal distribution of the onset and end of the annual food shortage period

Causes of and coping mechanisms against food shortage

Nutrition and food consumption patterns: main meals and other foods

Nutrition and food consumption patterns: milk, meat, fish and fruits

4.3.3 Agricultural Systems and Farming Practices

Major agricultural challenges facing households

Cropping systems

Land ownership regime

Extent of use of intensive agricultural inputs: fertilisers, hybrid seeds

Production and sale of crop produce and other sources of household income

4.5.4 Soil Fertility, Knowledge and Use of Agroforestry Practices

Soil fertility status and problems

Current cultural practices on soil fertility

Level of farmers’ knowledge and use of agroforestry, soil water conservation technologies

Constraints cited by households for not using various agricultural technologies

7

4.3.5 Technical and Financial Assistance and Social Network

Extent and frequency of access to financial services

Access to financial support: source, frequency, size, interest rate

Participation in farmer groups

Estimates of rates of group membership: why and why not in groups?

4.3.6 Household Demography

Number of persons in the household by gender

Number of persons engaged in farm family labour by gender

4.3.7 Household Basic Information

The age and sex of the interviewee

The sex of the head of the household

The educational qualification of the head of the household

Residence of the household: patrilocality or matrilocality

4.4 Data Analysis

The data was entered using Microsoft Excel. The entered data was first cleaned for consistence and quality checks. The data was later analysed using Statistical Analytical Systems (SAS) package. The data was first summarized by gender, district and status of project participation by the household (EA participating and EA non-participating). It was later disaggregated by gender, districts and status of participation to identify if there were statistical differences in some factors across gender, districts and status of project participation.

4.6 Partnership

The baseline survey was designed by ICRAF and was executed by ICRAF and partners such DAES and LRCD at the district level. The AEDO and DADO helped in identifying the farmers to be interviewed in the villages. The technical support from the government was very vital for the success of the entire survey data collection process.

8

5.0 BASELINE RESULTS

5.1 Demographic characteristics of the respondents

A total of 1368 households, comprising 777 females (57%) and 591 males (43%) were interviewed. The distribution of the respondents interviewed during the survey per district is presented in Table 3 below. The number of households interviewed in each district depended on the number farmers enlisted for the pilot project.

Household size averaged 5.3 persons with 2.6 males and 2.7 females. The 2008 Population and Housing Census indicated that the average household size was 4.6 persons. About 49% of the household people were readily available for family farm labour, 34% were schooling children and 16% were too old or too young to participation in family farm labour provision. Table 3 outlines the segregation of the respondents by status of project participation.

Table 3: Summaries of the respondents interviewed by status of project participation and gender per district.

District Number of Respondents Interviewed

EA Non EA All

Male Female Total Male Female Total Grand Total

Kasungu 164 146 310 72 78 150 460

Mchinji 105 106 211 91 71 162 373

Chiradzulu 79 218 297 80 158 238 535

TOTAL 348 470 818 243 307 550 1368

Of the 1368 respondents interviewed, female respondents were slightly younger than their male counterparts at the ages of 39 and 40, respectively. At the ages of 40 and 38, EA participating farmers were slightly older than those who are not participating, respectively. Segregating the respondents by districts showed that the respondents were 38, 39 and 40 years old for Kasungu, Mchinji and Chiradzulu districts, respectively.

There are three types of households in terms of the household head in Malawi. These are 1) child-headed households 2) female-headed households and 3) male-headed households. For the sake of simplicity during this household survey, the sex of the household head was used to classify the respondents’ households. Only 1317 respondents indicated the sex of their household head and only 15% would be classified as illiterate (Fig. 1) because they have never attended any school which is slightly lower than the national average adult illiteracy rate of 16% (NSO, 2008). The adult illiteracy rate relates to the number of adults aged 18 and beyond who have never attended school or literally do not know how to read and write.

9

Figure 1: Percent of the household heads by level of education

The farmers participating in the project in various districts and villages have cultures. Cultural values define the property ownership and inheritance as well as the place of residence after marriage (Matchaya 2008). The respondents were asked whether their households were staying in their original villages or not. If the married female respondent was residing in her original village, and her husband and the household was staying in the wife’s home, the residence is classified as matrilocal and if the opposite scenario was for married male, it was classified as patrilocal. In the situation that both partners were staying at the neutral place of residence or village, the type of residence is classified as uroxilocal. The summary of the respondents exhibiting a status of household residence is outlined in Table 4 below.

Table 4: Percent of respondents by residence of the households

Sex of Respondent

Residence of the household

Matrilocal Patrilocal Uroxilocal Total

Female 33.3 15.4 8.2 56.8

Male 10.5 25.8 6.8 43.2

Sub Total 44 41 15 100

Historically, marital location has been very crucial in shaping the adoption of land management as it relates to property ownership including land and tree tenure (German et al. 2008). This is especially true in the adoption of Agroforestry which is a long term investment requiring secure land and tree tenurial arrangements. This variable will be used to assess whether the gender and the residence of the farmer has effect on adoption of agroforestry technologies or not.

5.2 Constraints of Agricultural Production

The respondents were asked what they perceived as major constraints to their agricultural production after from the general constraint of lack of money. The farmers cited constraints ranging from soil based factors, weather based factors, farm management problems, market challenges to

10

pests and diseases and farmer’s sicknesses. Table 6 showcases the percent of respondents who had cited the particular constraint by district, status of project participation and gender. The constraints have been arranged from most frequently cited problems to least cited problems.

Table 5: Percent of the respondents for constraints of agricultural production by district, project participation and gender

Type of constraint

District Project participation

Sex All

Kasungu Mchinji Chiradzulu Non EA EA Male Female

Poor soil fertility 74.3 68.4 58.5 69.3 64.7 67.3 65.9 66.5 Lack of food/hunger 33.5 25.7 22.1 24.9 28.2 25.5 27.9 26.9

Soil erosion 25.4 17.2 14.2 19.6 18.2 21.5 16.7 18.8

Poor rainfall 13.9 9.7 35.9 18.2 23.5 18.4 23.6 21.3

Shortage of land 10.9 18.0 15.7 16.2 13.7 15.2 14.3 14.7

Inadequate labour 8.7 13.4 13.1 10.9 12.2 8.1 14.4 11.7

Sickness/HIV 8.0 12.9 10.8 10.5 10.4 9.3 11.3 10.5 No market for farm produce 6.7 10.2 5.6 6.7 7.6 9.3 5.7 7.2

Water logging 3.3 1.9 0.2 0.9 2.2 1.5 1.8 1.7 Pests/disease damage 3.0 2.7 4.5 3.6 3.4 4.1 3.1 3.5

Others 2.0 4.3 1.7 3.1 2.1 3.6 1.7 2.5

Other constraints included lack of improved seeds, lack of extension services, lack of farm equipments, low prices for agricultural products, lack of transport, and thieves. It would be recommended to even consider the minor constraints in addressing agricultural production challenges. This supports the rationale of development practitioners such as World Agroforestry Center, AGRA and others which promote the use of sustainable soil health enhancement technologies. This is one of the overarching challenges to smallholder agriculture in Malawi.

5.3 Food Security

Smallholder farmers usually grow a number of food crops per unit farm whether through simultaneous or sequential intercropping systems. The major food crops include maize, beans, groundnuts, vegetables and fruits, just mentioning a few. It is not uncommon in the smallholder sector that the food produced does not last the farming households to the next growing season i.e. twelve months. An analysis was done to see how food-secure the farming households are by their status of project participation and district. It is shown in the following tables that there is no difference between the participating and non participating farmers, and between the districts in terms of maize food security. This gives the project a platform for future project impact assessment if changes will be noted between participating and non-participating farmers.

11

Table 6: Average number of months of food sufficiency per source of food by status of project participation and district

Food Type

Source of Food

Project participation

District

Non EA

EA Kasungu Mchinji Chiradzulu

Maize Own production 7.8 7.8 7.5 6.9 8.7

Purchase 3.5 3.4 3.7 3.9 2.9

Beans Own production 2.5 2.7 2.0 1.2 4.1

Purchase 3.0 3.0 2.7 3.4 3.0

Gnuts Own production 3.6 4.1 5.0 3.6 3.2

Purchase 3.2 3.4 3.4 3.3 3.2

Vegetables Own production 9.8 10.1 10.2 9.2 10.3

Purchase 1.5 1.4 1.3 1.7 1.4

Fruits Own production 2.6 2.7 2.2 1.7 3.7

Purchase 2.2 2.1 1.8 2.4 2.3

It is clear that maize is indeed the staple food in all districts and among both farmers because it is the food crop which has the second longest months covered in term of food security. In fact, vegetables top the list because they are grown throughout the year unlike maize and other crops which are grown for a season. Table 7 indicates that about 80% of the respondents are food secure for at least 6 months in a year.

Table 7: Percent of respondents for maize cover months by status of project participation, district and gender.

Number of maize cover months

Project participation District Gender

ALL Non EA

EA Kasungu Mchinji Chiradzulu Male Female

<6 18.1 19.3 20.5 28.2 10.9 16.1 20.9 18.8

6-8 41.8 42.6 46.5 47.2 35.2 44.8 40.3 42.3

9-12 40.1 38.1 33.0 24.7 53.9 39.0 38.8 38.9

TOTAL 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

The supply of maize was designated as 1) food produced from their own field and 2) externally sourced (either purchased from the markets, food aid or exchanged). It was found that the maize produced by the households is sufficient to provide food for the period of 7.8 months per year and the rest of the year the household had to source the maize externally.

12

Crop production is generally done both in upland and lowland gardens (dimbas). About 98% of the respondents mentioned that they owned the piece of land where they cultivated the crops and about 99% indicated that they cultivate on their own land. About 69% of the respondents own dimbas which are used for irrigated farming and that 66% of the respondents cultivate dimbas. The type of crops grown in these land classes varies from one area to another. About 86% of the respondents grow maize for their own household consumption as compared to 13% for both consumption and sale and less than 1% for sale.

Smallholder farmers in Malawi predominantly use local seed for most agricultural production with the exception of maize and sunflower which enjoy the benefits of the use of improved seed. There is need for up-scaling of the use of improved seed for other strategic crops in Malawi.

Table 8: Percent of respondents on type of seed

Type of seed

Crop Type Local Improved

Maize 35.4 64.6

Groundnut 68.6 31.4

Rice 60.0 40.0

Cassava 83.8 16.2

Tobacco 56.8 43.2

Sunflower 23.8 76.2

Pigeon pea 65.5 34.5

Banana 83.8 16.2

Sweet potato 81.7 18.3

Irish potato 77.4 22.6

Soya bean 71.3 28.7

Overall 64.4 35.6

The survey has shown that food shortage was experienced by most of the households in the lean months of December to March. The households attributed a number of causes for the food shortage but poor soil fertility was the main cause of food shortages. The households that experience food shortage tend to develop coping mechanisms to adapt to the seasonal food crisis. Most of the food insecure households seek ganyu (employment as hired labor) from better off villagers and sell crop produce. These coping mechanisms themselves are also the very same causes of food shortages amongst food secure smallholder farmers. This predisposes the smallholder farmers into a vicious circle of food insecurity. The Tables 9 and 10 below outline the percent of respondents who indicated that they used a particular coping mechanism and noted a particular cause of food shortage. The tables indicate that differences were observed across location of the respondents because of differences in cultural values and crop productivity performance due to different rainfall patterns and farming practices. It is clearly shown that there is more pronounced ganyu labour market in Kasungu than in Chiradzulu because there is more agricultural activity taking place in Kasungu than Chiradzulu. Remittances contribute more considerably to the livelihoods of farmers in Chiradzulu and Mchinji than in Kasungu because in Chiradzulu farmers have more

13

economic challenges than other two districts. There is a more pronounced food exchange taking place in Chiradzulu than other two districts signifying that food shortage is very common.

Though coping mechanisms could not be a factor of gender of the respondent, the data showed that 78% of male respondents cited that their households seek ganyu labour as compared to 73% of the female respondents. During food shortage periods, female members of the households are more likely to beg for food, sell fruits, skip meals, receive remittances and sell firewood than their counterparts.

The survey further indicated that about 11%, 20%, 33%, 30% and 5% of the households have four, three, two, one and no coping mechanisms, respectively. This shows that food insecure families tend to have more coping mechanisms as opposed to having fewer or none. Table 9: Percent of respondents for coping mechanisms

All Non-

EA EA Mchinji Kasungu Chiradzulu Female Male

Seek ganyu from neighbours 75.0 74.4 75.4 86.1 86.3 57.6 72.7 78.0

Sell crop produce 24.9 22.2 26.8 24.9 24.8 25.0 24.8 25.0

Skip meals 21.9 21.5 22.2 25.5 25.7 16.3 24.7 18.3

Selling assets to buy food 15.7 13.5 17.2 11.8 16.5 17.8 15.8 15.6

Food exchange 13.2 12.0 14.1 8.6 16.1 14.0 14.4 11.7

Borrow 11.1 9.8 12.0 7.5 14.1 11.0 10.9 11.3

Remittances 11.9 11.8 12.0 13.4 9.1 13.3 13.6 9.6

Doing seasonal contracts 8.5 9.8 7.6 9.4 8.5 7.9 7.3 10.0

Beg for food 6.4 5.3 7.1 8.8 6.5 4.5 7.5 4.9

Eat fruits 2.9 2.5 3.1 2.9 4.1 1.7 2.7 3.0

Sell firewood 2.7 3.8 2.0 1.6 2.0 4.1 3.7 1.4

Sell fruits 2.6 2.2 2.9 2.4 2.4 3.0 3.6 1.4

N 1368 550 818 373 460 535 777 591

It has been further shown that on average about 71% of the respondents had cited poor soil fertility as the core cause of food shortages in the sense that infertile soils are produce less maize. Fewer respondents in Chiradzulu than in Kasungu and Mchinji had cited poor soil fertility as a major cause of food shortages. Chiradzulu farmers have strong culture of legume intercropping of maize with pigeon peas for more than five decades. The presence of Chiperoni rains in July to September in Chiradzulu enhances the rapid decomposition of crop residues as opposed to the districts of Kasungu and Mchinji which do not receive winter rains in the dry seasons.

14

Table 10: Percent of respondents for causes of food shortage

All

Non-EA EA Mchinji Kasungu Chiradzulu Female Male

Poor soil fertility 71.1 71.5 70.9 75.1 79.6 61.1 70.5 71.9

Fertilizer not available timely 39.5 35.1 42.4 39.9 39.1 39.4 40.0 38.7

Poor rainfall 27.9 27.3 28.4 14.5 23.3 41.3 30.0 25.2

Shortage of land to cultivate 26.8 28.2 25.8 31.9 22.2 27.1 27.7 25.5

Lack of labour 19.0 18.4 19.4 24.1 19.8 14.8 21.1 16.2

Sickness 13.9 14.7 13.3 18.5 12.8 11.6 16.0 11.2 Unavailability of improved seeds 9.5 8.5 10.1 13.9 8.3 7.5 9.9 9.0

Lack of farming inputs 3.5 4.7 2.7 2.1 4.6 3.6 3.7 3.2

N 1368 550 818 373 460 535 777 591

The survey has indicated that 77 percent of the households consume sweet potato as an alternative food during the lean period whereas 69 percent and 49 percent consume pumpkin and cassava, respectively in the same lean periods. Other foods include banana and fruits (13%) and rice (10%). This indicates that alternative crops should be given some priority in promotion in order to reduce the vulnerability of the households to hunger stress in the lean periods. There were no significant differences between the non EA and EA farmers as regards to the types of crops eaten during lean or hunger periods.

The study showed that adult and children consume milk 1.7 and 1.8 times per month, respectively. About 34% of the respondents indicated that they consume milk at least once a year. Out of 447 respondents who cited that they consume milk, about 76 percent of the households consume milk sourced from within the village (either from their fellow farmers or groceries) whereas 3 percent and 20 percent obtain the milk from their own animals and semi urban centres, respectively. Adults and children consume meat 5.6 and 5.4 times per month, respectively. Fish provided the most animal protein in household consumption registering 54% of the respondents followed by chicken (51%), goat (38%), pork (19%) and beef (11%). About 75% of the respondents indicated that they bought animal protein from within the village and the rest from own livestock (46%) and town (25%).

15

Figure 2: Percent of respondents for number of meal times in lean and harvest periods

5.4 Sources of Seed

About 37% of the respondents use recycled seed as compared to 28% from Agricultural Development and Marketing Corporation (ADMARC) depots, and 34% from agro-dealers. It is shown in Table 11 that there is high use of recycled seed amongst small holder farmers in Malawi despite vigorous commercial awareness of use of hybrid seeds. It is however worth noting that commercial aggressiveness of seed agro-dealers and extension efforts of NGO’s and government is causing a decrease in the use of recycled seeds in maize as compared to other crops.

Table 11: Percent of respondents on sources of seed

Source of seeds

Type of crop

Maize Groundnut Rice Tobacco

Agricultural dept. 27.4 0.4 5.9 2.3

Agro-dealers 36.6 14.5 29.4 6.8

Recycled 34.9 78.5 58.8 59.9

TAMA 0 0 0 2.9

ARET 0 0 0 26.7

Others 0.7 7.1 5.9 1.4

5.5 Sales of Farm Produce

A total of 77% of the respondents have sold their crop produce in the past three years and about 90% have reported that their farm income is normally higher than the non-farm income. Only 8% had indicated that their farm income is normally lower than the non-farm income and about 2% had indicated that the two sources of income normally equate to each other. Among those farmers that had indicated that they did not sell any of their farm produce in the past three years cited the

16

following reasons: poor yield (53.3%), small land size (18.1%), poor rainfall (15.4%) and large family size (8.14%).

5.6 Status of farmers’ soil and water conservation practices

It was expedient to know the perception of the farmers on how the soil fertility status has been changing over a period of years. This would explain whether the farmers do appreciate the importance of soil fertility methods in improving the soil fertility. It is shown clearly that 72% of the respondents believed that the current status of soil fertility of their gardens is poor. It is further shown that about 75% of the respondents believed that the trend of soil fertility is getting worse over the past ten years. The soil texture of most of the fields is predominantly sandy or red clay soils. There were, however, geographical differences between the districts such as Mchinji and Kasungu had relatively poorer status of soil fertility than Chiradzulu. Perhaps that is why Mchinji and Kasungu had relatively higher use of chemical fertiliser than Chiradzulu. In terms of trend of soil fertility, more respondents from Chiradzulu showed that the trend of soil fertility is improving than in Mchinji and Kasungu.

Table 12: Percent of the respondents on soil trend

Soil Condition Household perception

Percent of the households

All Mchinji Kasungu Chiradzulu

Status of soil fertility

Poor 72.0 75.1 73.5 68.7

Average 24.3 20.6 22.5 28.5

Good 3.6 4.3 3.9 2.8

Chi-square test

Soil texture

Sandy soil 39.5 39.7 47.9 32.1

Red soil 34.1 47.5 28.4 29.3

Dark clayey soil 19.7 9.9 17.5 28.7

Others 6.7 3.0 6.2 10.0

Chi-square test

Trend of soil fertility

Getting better 14.5 15.9 8.7 18.6

Same/no change 10.2 10.8 9.6 10.4

Getting worse 75.2 73.3 81.8 71.0

Chi-square test

Trend of fertilizer use

Decreasing 46.4 39.3 49.4 48.8

Same 17.8 20.9 11.1 21.4

Increasing 35.8 39.8 39.6 29.8

Chi-square test

About 55% of the respondents who observed decreasing usage of fertilizer cited that the fertilizer has become expensive to be used annually and 21% cited that they did not get coupons from the Farm Input Subsidy Program. About 37% of the respondents who observed increasing usage of fertilizer cited that they did so to improve the soil fertility in order to increase the crop productivity.

17

The survey has indicated that almost 100% of the farmers interviewed know about the use of fertilizer, 98% currently use fertilizer in their fields; about 2% have never used fertilizer before. It is clearly shown that conservation agriculture technologies are very new to most farmers. Only 8%, 4% and 12% use minimum tillage, herbicide application and planting basins, respectively. Some 50% of the respondents have heard about planting basins (or implied conservation agriculture) and this is a very positive development because it signifies that the agricultural extension messages provided by ICRAF, DAES and other NGOs are gaining ground. Despite 85% of the respondents being knowledgeable about the use of herbicides, only 4% have ever used it in their fields. The constraints cited by those who do not use herbicides include lack of cash or expensiveness (47%), unavailability of herbicides (18%) and lack of training in the choice and use of herbicides (13%). It is interesting to note that about 57% of the respondents who do not use planting basins cited that they never knew about it. About 36% cited “lack of training” while 4% cited “too much labour”. The table below shows the percent of respondents who know about, currently use, stopped using and never used the particular technology on their gardens.

Table 13: Percent of respondents on technology usage

Type of technology Status of the technology usage

Know Use Stopped Never used

Chemical fertilizer 100 98 1 1

Fertilizer trees 92 65 <1 34

Fodder Trees 52 11 <1 88

Ridge construction 100 100 0 0

Fruit Trees 96 64 <1 35

Community Woodlot 92 41 <1 58

Individual Woodlot 91 50 <1 50

Soil and Water Conservation 92 62 1 36

Manure application 97 50 <4 47

Residue Burning 98 20 12 68

Minimum Tillage 61 8 <1 92

Herbicide application 85 4 <1 95

Planting Basins 50 12 <1 88

The results show that about 97% of the EA participating farmers received Tephrosia vogelii seeds and about 91% received pigeon peas for Evergreen Agriculture project. Faidherbia albida (Msangu) and Gliricidia sepium were not supplied under Evergreen Agriculture project but are supplied under other ICRAF projects.

Table 14: Percent of respondents assisted ICRAF by status of project participation

Type of fertilizer tree Status of project participation

EA Non EA

Tephrosia 96.9 1.8

Cajanus 91.4 1.6

Msangu 0.1 0.0

Gliricidia 0.1 0.4

18

Fertilizer trees were supplied to EA participating farmers to be intercropped with maize and the farmers were supposed to practice minimum tillage through the use of either planting basins or planting old ridges depending on the nature of topography and drainage. The farmers with almost flat land were asked to practice planting basins and farmers with sloping lands were asked not to cultivate but just plant on old ridges. The survey found out why the farmers were practising conventional agriculture methods such burning of residues and ridge construction. The table below indicates that about 76% practise ridge construction because they learned it from parents where as 49% indicated that they do it for soil and water conservation. The table further shows that there were slight changes between the EA participating and non-EA participating farmers.

Table 15: Percent of respondents for ridge construction by status of project participation

Reason for ridge construction Status of project participation

All Non-EA EA

Learned from parents 76.1 79.1 74.1

Soil and water conservation 48.5 48.0 48.8

The only technology known 26.2 28.9 24.3

Soil moisture retention 23.5 25.3 22.4

Suitable for kernel and other crops 17.2 14.7 18.8

Instructed by extension officer 10.2 9.3 10.8

Saw neighbor doing it 9.2 9.8 8.8

Residue incorporation 0.3 0.2 0.4

For easy fertilizer application 0.1 0.2 0.0

N 1368 550 818

Some agroforestry technologies require the establishment of nurseries to raise seedlings. Under EA pilot project, directly-sown agroforestry tree seeds were distributed such as pigeon pea (Cajanus cajan) and Tephrosia vogelli, however there are some tree species such as Gliricidia or Faidherbia which would require nursery establishment. The results show that 14 % and 12% of the respondents had owned individual nursery and communal nursery, respectively. Communal nurseries are usually practised by smallholder farmers in order to pull up resources and labour for efficient and effective management of nurseries. Since it has been shown that most farmers do not raise tree nurseries, the promotion of use of fertiliser trees which require nursery establishment should be critically looked into on how the farmers will be mobilised to pull resources and labour together for the survival of nursery seedlings.

Table 16: Percent of the respondents on type of nursery owned by status of project participation

Status of project participation

Type of nursery Non-EA EA All

Individual nursery 10.18 16.5 13.96

Group nursery 10 13.57 12.03

19

5.7 Access to Technical and Financial Assistance

Smallholder agriculture is to greater extent catalysed by a variety of organisations that support farmers with assistance ranging from finances, farm inputs to training. The survey shows that farmers have obtained assistance from Agriculture department and ICRAF. The high number of respondents who had received assistance from ICRAF came from the respondents who are participating on the project. It is also shown that Agriculture department plays a pivotal role in development of smallholder agriculture in Malawi. About 48% of the respondents stated that they had obtained assistance from the Agriculture department. NGO’s support is also conspicuous enough as shown by about 20% of the respondents.

Table 17: Percent of the respondents on access to assistance

Source of assistance Districts Status of project participation

Kasungu Mchinji Chiradzulu EA Non-EA All

ICRAF 68.0 56.0 53.5 97.1 2.7 59.1

Forestry Department 59.0 13.7 10.1 18.5 12.5 16.0

Agriculture Department 46.1 41.6 54.8 54.0 40.2 48.3

NGO 20.2 19.3 19.4 22.7 15.1 19.7

Lead Farmer 5.7 5.1 2.8 4.8 3.8 4.4

N 460 373 535 818 550 1368

It has been shown that among those respondents who obtained assistance from ICRAF, about 94% received seed and seedlings. About 58% of the respondents who obtained assistance from Agriculture department received training. Training and seeds is the commonest assistance smallholder farmers received from organisations. ICRAF uses different prongs of extension or training. The results indicate that only 6.4% of the respondents who received assistance from ICRAF had received training from ICRAF. This would be an understatement because ICRAF builds the capacity of extension workers of the Agriculture Department, NGO’s and Lead farmers in order to train the farmers who might not know that the trainers had technical and financial assistance from ICRAF.

Table 18: Percent of respondents on type of assistance

Type of assistance

Source of assistance Sample size Training Seed Pesticide

Farm Inputs Money Livestock

ICRAF 811 6.4 93.5 0.1 0 0 0

Forestry Department 220 39.5 55 0.5 5 0.5 0

Agriculture Department 657 58.3 12.9 18.7 9.4 0.2 0.3

NGO's 267 32.6 40.8 1.9 7.5 6 11

Lead Farmer 60 41.6 53 1.7 3.3 0 0

Lack of money was the commonest constraint generally perceived among smallholder farmers. However, lack of access to finance is a different constraint that has to be addressed differently from lack of money. Only 14% of the respondents have ever obtained loans for various uses before. Among these respondents, about 38% use the loan to buy agricultural inputs, 19% to start non

20

agricultural business and 18% to buy food. The figure below shows the trend of household borrowing along the calendar year. It corresponds directly to the trend of seasonal food shortage. During the harvest time, the percentage of farmers borrowing is remarkably reduced.

Figure 3: Percent of respondents getting loans in a year

The figure below shows that social capital or credit services do exist in the rural areas. About 13% of the loan getting respondents obtained their loans from friends in the villages, about 10% from government agencies and 6% from micro-finance organisations.

Figure 4: Percent of respondents by souces of loans

21

5.8. Club membership

Research has indicated that technology transfer is hampered by poor organisation of farmers to some extent. The motto of the partner NASFAM, “the future belongs to the organised” concurs with this observation. The survey has shown that about 95% of the respondents who gave their views on club membership (N=442) did not belong to any agricultural club. This indicates that the culture of belonging to the farmer groups is eroding among smallholder farmers.

5.9 Household assets

Household assets are an indicator of household’s wealth and resilience during shocks and crises. Household assets are usually as stocks of capital that are exploited when they are vulnerable to various shocks. For instance, during hunger months, farmers sell or exchange their household items for money and food. This predisposes further to biting and spiral poverty. The survey has indicated that household assets range from hand hoe, axe, bicycle to television and oxcart. Almost every household had a hand hoe used for farming but only 4% possessed treadle pump for dry season farming. The 2008 Population and Housing Census found out that 64% of the households own radio, 45% own bicycle and 2% own oxcart. However, this study showed that fewer households own radio (61%) than the national average. But it was shown that more households own bicycle and oxcart at 55% and 4%, respectively. The figure below shows the percent of the respondents who had the particular household asset.

Figure 5: Percent of respondents on asset ownership The wealth status of smallholder farmers is also depicted by the type of housing unit, type of roof, type of window and type of wall paint. It is shown that 74% of the respondents had brick houses and 76% had thatched roofs. The table indicates further that there were no differences between the Non-EA and EA farmers regarding the type of housing unit. This might indicate that the housing type did affect the partcipation of farmers on Evergreen Agriculture project. However, since the status of housing unit denotes the wealth being of farmers, there would be differences if Evergreen Agriculture project would improve the welfare of EA farmers in the next years as compared to Non-EA farmers.

22

Table 19: Percent of respondents on housing type

Percent of housing type by respondents

All Non-EA EA

House Type Poles/thatched 1.76 1.28 2.1

Mud 24.14 22.95 24.9

Brick 73.96 75.77 72.7

Cement block 0.15 0 0.2

Roof Type Thatched/leaves 75.66 76.9 74.82

Iron sheet 23.9 22.9 24.57

Asbestos 0.44 0.18 0.6

Window Type None 23.61 25.8 22.13

Wood panes 48.83 45.8 50.86

Glass panes 27.56 28.4 27.02

Wall Paint Type None 63.38 60.18 65.5

Whitewashed 3.73 4 3.6

Painted 4.31 5.5 3.6

Mud painted 28.58 30.4 27.4

23

6.0 CONCLUSIONS

The findings give us clear benchmarks of the livelihood indicators of the smallholder farmers participating in the pilot project sites. The project is now better placed to monitor the activities programmatically knowing the baselines. We now have an evidence base to relate any change of the livelihood and soil and crop productivity of the participating farmers to the impacts of the project in comparison to the non-participating farmers and conclude whether the Evergreen Agriculture technology has had positive or negative impacts on smallholder agriculture in the project areas.

It should be noted further that a more in-depth statistical analysis will be done to examine the statistical differences between various variables. For example, did the age and education of the household head influence the level of participation in Evergreen Agriculture technology? Did the residence of household head influence the decision to participate in agroforestry tree options? The outputs from these analyses will be used to write journal articles for publication.

24

7.0 REFERENCES

ACT (2008). Linking Production, Livelihoods and Conservation; Proceedings of the Third World Congress on Conservation Agriculture, 3 - 7 October, 2005, Nairobi. African Conservation Tillage Network, Nairobi.

Ajayi OC, Akinnifesi FK, Sileshi G and Kanjipite W (2009)

Labour inputs and financial profitability of

conventional and agroforestry-based soil fertility management practices in Zambia. Agrekon 48(3):

276-292

Ajayi OC, Akinnifesi FK, Gudeta S and Chakeredza S (2007) Adoption of renewable soil fertility replenishment technologies in southern African region: lessons learnt and the way forward Natural Resource Forum 31(4): 306-317

Ajayi OC 2007 User acceptability of soil fertility management technologies: lessons from farmers’

knowledge, attitude and practices in southern Africa Journal of Sustainable Agriculture 30 (3): 21-40

Akinnifesi FK, Chirwa PW, Ajayi OC, Sileshi G, Matakala P, Kwesiga FR, Harawa H and Makumba W (2008). Contributions of agroforestry research to livelihood of smallholder farmers in Southern Africa: 1. Taking stock of the adaptation, adoption and impact of fertilizer tree options. Agricultural Journal 3: 58-75.

Conway, G. and Toenniessen, G. (2003). Science for African food security. Science 299: 1187-1188.Denning G, Kabambe P, Sanchez P, Malik A, Flor R, et al. (2009) Input subsidies to improve smallholder maize productivity in Malawi: Toward an African green revolution. PLoS Biol 7(1): e1000023. doi:10.1371/journal.pbio.1000023

F. Ellis, M. Kutengule and A. Nyasulu (2002) Livelihoods and Rural Poverty Reduction in Malawi,

LADDER Working Paper No. 17. Overseas Development Group, University of East

Anglia, Norwich

GOM (2010) The Agriculture Sector Wide Approach (ASWAp), Malawi’s prioritised and

harmonised Agricultural Development Agenda, Ministry of Agriculture and Food Security,

Republic of Malawi

GOM. (2006) Malawi Growth and Development Strategy: From Poverty to Prosperity 2006 - 2011,

Lilongwe, Malawi: Ministry of Economic Planning and Development.

Haggblade S and Tembo (2003) Conservation farming in Zambia. EPTD discussion papers 108, International Food Policy Research Institute (IFPRI).

Lal, R (2008a) Food insecurity’s dirty secret. Science 322: 673–674.

Matchaya G. (2008) Land ownership security in Malawi. African Journal of Agricultural Research. Vol. 4 (1), pp. 001-013.

NSO (2008) Population and Housing Census: Preliminary report. Government of Malawi, Zomba.

25

Rockström J, Kaumbutho P, Mwalley J, Nzabi AW, Temesgen M, Mawenya L, Barron J, Mutua J and Damgaard-Larsen S (2009) Conservation farming strategies in East and Southern Africa: Yields and rain water productivity from on-farm action research. Soil & Tillage Research 103: 23–32.

Saka, AR, Bunderson WT, Itimu OA, Phombeya HSK and Y. Mbekeani. (1994) The Effects of Acacia albida on soils and Maize grain yields under smallholder farm conditions in Malawi. Forest Ecology and Management 64:217-230.

Sileshi G., Ajayi, O.C., Akinnifesi, F.K. and Place F. (2009a) Green fertilizers can boost food security in Africa. Policy Brief No. 02, 2009. World Agroforestry Centre, Nairobi, Kenya.

Sileshi G., Akinnifesi F.K., Ajayi O.C., Place F. (2008) Meta-analysis of maize yield response to planted fallow and green manure legumes in sub-Saharan Africa. Plant and Soil 307:1-19.

Sileshi G., Akinnifesi F.K., Debusho, L.K., Beedy T., Ajayi O.C. and Mng’omba S. (2009b) Variation in maize yield gaps with plant nutrient inputs, soil type and climate across sub-Saharan Africa. Field Crops Research DOI: 10.1016/j.fcr.2009.11.014.

Sileshi G., Kuntashula E. and Mafongoya P.L. (2006) Effect of improved fallows on weed infestation in maize in eastern Zambia. Zambia Journal of Agricultural Science 8: 6-12.

World Agroforestry Centre (2009) Creating an Evergreen Agriculture in Africa, Nairobi, Kenya.