Enhancing Productivity, Competitiveness and

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Lindner, R. K. (1987) 'Adoption and diffusion of technology: an overview', in ACIAR proceedings series. Available at: http://agris.fao.org/agris-search/search.do?
Impact Evaluation Study Work In Progress

Enhancing Productivity, Competitiveness and Marketing of Onion in the Sudano-Sahelian Region of Cameroon

Cyrille Bergaly Kamdem 1, Regine Kamga 2 & Pepijn Schreinemachers 3

1

University of Yaoundé II-Soa PO.BOX:1365, Yaoundé, Cameroon. Email: [email protected] 2

World Vegetable Center, West and Central Africa, Cameroon Liaison Office, BP 2008, Yaoundé Email: [email protected] 3

World Vegetable Center, PO Box 42, Shanhua, Tainan, 74199 Taiwan. Email: [email protected]

1

Table of contents

1

Introduction.............................................................................................................................. 3

2

Intervention overview .............................................................................................................. 4 2.1 2.2 2.3 2.4 2.5

3

Project ............................................................................................................................. 4 Targeting of beneficiaries ............................................................................................... 5 Project locations .............................................................................................................. 5 Intervention design.......................................................................................................... 6 Project implementation ................................................................................................... 9

Methods and data ..................................................................................................................... 9 3.1 Qualitative evaluation method ........................................................................................ 9 3.2 Quantitative evaluation method .................................................................................... 10 3.3 Data ............................................................................................................................... 12

4

Results.................................................................................................................................... 16 4.1 Focus groups analysis ................................................................................................... 16 4.2 Household and onion production characteristics .......................................................... 17 4.3 Impact ........................................................................................................................... 22

5

Conclusion ............................................................................................................................. 27

6

References.............................................................................................................................. 28

2

1

Introduction

Onion (Allium cepa L.) is an important cash crop in Cameroon. Previous studies have reported that onion production in the North and Far North regions of Cameroon is profitable (Maldangoi et al., 2003). Increasing demand in Cameroon as well as in neighboring countries (Gabon, Equatorial Guinea, Central African Republic, Nigeria, Congo, Democratic Republic of the Congo, and Chad) shows that onion has become part of the local food culture (Cathala et al., 2003). In Cameroon, 85% of onion production is concentrated in the North and Far North regions where it is the most important cash crop. However, onion yields in Cameroon are only about 10 tons/ha, which is low compared to the world average of about 19.5 t/ha. The country produced about 90,000 tons per year and imported about 1,500-2,500 tons, mostly from the Netherlands. There appears much room to expand onion production in Cameroon. Onion provides important nutrients such as proteins, minerals and vitamins and is an important component contributing to balanced diets. People consume onion on an almost daily basis as part of a wide variety of dishes (Kamga et al., 2013). In Cameroon, onions are also used in some herbal medicines and as tonic for “man power”. Onion bulbs can be stored for several months with adequate ventilation, monitoring for spoilage and sorting. Onion is mostly produced in a traditional way and production practices are transferred from one generation to another. As a cash crop, onion production is mostly controlled by male farmers in Cameroon. Major constraints to onion producers in the Sudano-Sahelian region of Cameroon include insufficient knowledge about improved production techniques, lack of improved and adapted cultivars, disease and pest problems (Fontem, 1991), poor soil fertility, postharvest losses and poor quality bulbs (Fotio et al., 2005). Subsidized N-P-K fertilizers (22-10-15, 20-1010) from Cameroon’s cotton development authority (SODECOTON), are often used for fertilization and are applied in variable doses at the discretion of each producer. Pesticides are used if locally available, but only few farmers understand the differences between various active ingredients in pesticides and do not follow correct application rates and spraying methods. The World Vegetable Center has introduced improved onion lines along with other improved and locally adapted onion cultivars and has tried to engage women in onion production. The Centre’s intervention aimed to overcome onion production challenges to improve productivity, competitiveness and marketing of onion in the Sudano-Sahelian region of Cameroon. 3

This study is part of the IFAD funded project “Enhancing Productivity, Competitiveness and Marketing of Onion in the Sudano-Sahelian Region of Cameroon" through the Commodity Value-Chain Development Support Project (PADFA) of the Ministry of Agriculture and Rural Development (MINADER), Cameroon. The study aims to evaluate the impact of the project on key onion production and livelihood outcome indicators. A secondary aim was to gain an indepth understanding of the constraints to and incentives for households to adoption improved production methods. Previous studies have identified the determinants of adoption of technology and their impact on agricultural productivity (Lindner, 1987; Ghadim and Pannell, 1999; Mazoyer and Roudart, 2009; Amare, Asfaw, and Shiferaw 2012; Shiferaw et al. 2014; Khonje et al. 2015).

2 2.1

Intervention overview Project

The goal of the project is to reduce rural poverty and increase food security in the SudanoSahelian region of Cameroon through sustainable onion production. This project was planned to improve livelihoods through increased onion productivity, through: a)

The development and promotion of good agronomic practices for onion.

b)

The empowerment of farmers in onion bulb and seed production, storage and marketing.

c)

The organization and professionalization of farmers to start producing onion mother bulbs of improved adapted onion varieties as it develops in future to become the major source of onion seed in the region.

The project carried out the following activities: -

Evaluation and selection of promising lines of onion in the Sudano-Sahelian region of Cameroon.

-

Dissemination of the most effective production system/technologies.

-

Promotion of onion production and consumption.

-

Capacity strengthening of farmers in onion bulb and seed production, storage and marketing

-

Support seeds growers’ groups in seed certification and recognition by the National Office for Control and Seeds Certification.

-

Development and dissemination of onion production pamphlets to farmers. 4

-

Monitoring and evaluation/supervision of trainees and action plans.

The project implementation strategies were essentially Training of trainers (TOT), demonstrations plots and field days. 2.2

Targeting of beneficiaries

The PADFA project targeted at least 30% of female producers. Beneficiaries were selected among Common Initiative Group (CIG) with mixed male and female membership. Exclusively male CIGs were excluded. It was mandatory for the selected CIG to be registered with the local office of MINADER. Targeted smallholder farmers produced onion on not more than 0.25 ha of land and are characterized by: (i) low productivity, (ii) difficulties in accessing production inputs, (ii) unorganized especially in the field of marketing, and (iii) limited access to local financial services. 2.3

Project locations

The project works in the North and Far North regions of the country, which accounts for more than 80 percent of national onion production (Cathala et al., 2003). Groups of onion farmers from six divisions in the North and Far North regions including Diamaré, Mayo Tsanaga, Mayo Sava, Mayo Kani, Mayo Louti and Benoué were reached.

Table 1. Onion producers reached by the project intervention, 2012-2017.

Divisions North region: 1. Diamaré 2. Mayo Tsanaga 3. Mayo Sava 4. Mayo Kani Far North: 5. Mayo Louti 6. Benoué Total

Villages

Year 1 2012 2013

Year 2 20132014

Year 3 20142015

Year 4 20152016

Year 5 2016 2017

2 1 1 1

70 -

30 76 102 70

45 -

40 -

20 5

205 76 102 75

16 15 13 11

240 100 120 100

1 1 7

70

94 87 459

45

20 60

25 4 54

139 91 688

20 17 92

180 110 850

5

Farmers Total groups (CIGs)

Members of CIGs directly associated

2.4

Intervention design

The intervention consisted of the following three components: -

Training of trainers (TOT) to build the capacity of lead farmers in onion bulb and seed production, marketing and storage. These were two-day long trainings covering subject such as onion pests and diseases, production of onion bulbs, production of onion seed, business of onion seeds, seed certification, post-harvest technology, onion conservation and processing. The trained onion producers were expected to train others in the following season with the support of the World Vegetable Center using a farmer field school setup using a season-long training. Six TOTs were organized with 20 participants at each session (one in 2012 and five in 2014).

-

Demonstration plots: To assure ownership and credibility, local farmers were invited to participate in the establishment and maintenance of onion demonstration plots. The aim of this participatory approach was to create awareness and demand for high-performing onion lines with desirable traits. Participants are expected to appropriate the technologies demonstrated in the plots and apply them in their own fields. The participants of the demonstration plots are expected to develop mechanisms to satisfy local onion seed demand through local, decentralized, farmer‐based seed production and delivery systems. Fourteen demonstration plots were set (six for the production of bulbs for consumption from 2012-2014 and eight for the production of mother bulb and seed from 2014-2017). As of 2016, a total of 800 kg of seed, enough to sow 200 ha of land, was produced within these demonstration plots and all seed was donated to the 30 participating CIGs. The CIGs sold the seed at 25,000 CFA (USD 42) per kg, thereby saving buyers about 50% of the market price. In 2016, 10 CIGs were selected to become specialized seed producers to produce seed on contract. The contract stated that these CIGs provide land and labor and receive inputs. From each CIG, about 50 kg of seed is expected at a selling price of 15000 CFA (USD 25) per kg by the project for distribution to others CIGs.

-

Field days: During the field days farmers had the opportunity to compare onion varieties and management practices and to interact with farmers who managed the onion demonstration plot to share experiences with new methods such as nursery preparation, field preparation and irrigation techniques.

6

The intervention builds the capacity of farmers in the production of onion bulbs and onion seed by introducing improved management practices such as nursery techniques which helps producer to shorten their crop cycle by about 14 days. Improved production methods included field preparation, planting and transplanting in lines respecting regular density which increase the yield and the number of plants on a plot, irrigation techniques, use of suitable pesticides, use of appropriate fertilization when producing onion for conservation or for direct sale at the farm gate to reduce post-harvest losses, good harvesting techniques to reduce losses, producing onion seed using entire bulb rather than cut bulb, process of onion seed extraction, packaging and labelling, and the process of seed certification. About 900 household from 90 CIGs participated in the project through 120 TOTs, 150 demonstrations plots, and 650 field days. With a household size of 6 in Cameroon, project benefits potentially reached 5,000 people. The follow-up the training indicated a multiplication factor of more than 10 with an average yield increase of 50%.

7

Table 1: Number of groups and beneficiaries reached by the project. Year

Number of groups and individuals which participated in Number of groups which Number of groups trained the demonstration plot

20122013/ 20132014

2014 -2015

attended field days

during training session

32 trainees (8 men, 24 women) from 12 groups in Gazawa, 70 participants (51 women, 19 farmers (9 men and 10 Diamaré division 19 men) women) from 13 groups and one PADFA technician 12 trainees (8 men, 4 women) from 9 groups in Kaelé, 76 participants (47 women 100 participants (32 women, Mayo Kani division. and 29 men) from 11 groups 68 men) selected by PADFA 34 trainees (19 men and 15 women) from 5 groups in 70 farmers (24 women and were trained to upgrade their Koza, Mayo Tsanaga division. 46 men) from 15 groups knowledge and skills of onion 30 farmers (11 women and 19 men) from 10 groups in 102 participants (46 women production, bulbs conservation Gancé, Mayo Sava division. and 56 of men) from 13 and marketing groups 13 trainees (5 women and 8 men) from 9 GICs in 94 participants (23 women Tchontchi – baïla, Mayo Louti (North region). and 61 men) from 17 groups. 9 farmers from 4 groups in Langui, Bénoué division. 87 participants (37 women and 50 men) from 17 groups. 16 trainees (11 women, 5 men) from 5 groups in Meskine 20 trainees (16 women, 4 men) from 11 groups In Gazawa

2015 – 2016

13 trainees (11 women, 2 men) from 5 groups in Meskine 18 trainees (14 women, 4 men) from 7 groups in Gazawa

2016 –

16 trainees (11 women, 5 men) from 5 groups in Meskine

17 trainees (8 women, 9 men) from 8 groups in Tchontchibaïla

20 trainees (16 women, 4 men) from 7 groups in Gazawa 8

122 participants (42 women and 80 men) from 48 groups

2017

18 trainees (9 women, 9 men) from 8 groups in Tchontchibaïla

9

2.5

Project implementation

The project was implemented by the World Vegetable Center and funded by the International Fund for Agricultural Development (IFAD) through the Commodity Value-Chain Development Support Project (PADFA) of the Ministry of Agriculture and Rural Development. The project is being implemented with the participation of farmer groups selected by PADFA. For the professionalization of farmers as onion seed producers, seven CIGs were selected in the Mayo Louti division (North region): Bonne Timmane, Anoré remobé Tchontchi (ART), Baital and Barka Louggéré Bambalo (BLB) and six CIGs in the Diamaré division (Far North): Femme Dynamique de Gazawa (FEDYGAZ), Hairou de Goudourwo and Kaoutal Narral rewbé Mbankara, Wuiya, Wassakay and Talwou.

3

Methods and data

This evaluation study used a combination of quantitative and qualitative evaluation methods. The quantitative method, described in the next section, tested if farm performance and livelihood indicators among project beneficiaries is statistically different from those in a comparable groups of control households. The qualitative method complements this by describing the mechanism or process through which change has happened. 3.1

Qualitative evaluation method

The qualitative research use focus group discussions of male and female project beneficiaries in 3 intervention villages. Two villages are selected from the North and one from the Far North. The intervention villages are purposively selected to be representative of the larger set of intervention villages. During the focus group discussions one researcher, who spoke the local language, facilitated the discussion, one researcher took detailed notes of the discussion and structured by the main questions, and the Project Manager clarified issues, if required, and supervised the process. Before the start of the focus group discussion, the Project Manager introduced the team to the participants and explained in detail the purpose of the meeting. It was important to emphasize that the team was interested to learn from the people’s experiences so that the project design could be improved. It was therefore important to hear not only positive things about the project, but also challenges or problems that the people encountered in trying to use the new technologies and practices. Participants were explained that there was no right and 9

wrong answers and that all opinions are valid. The focus group discussions took on average 1-2 hours to complete. 3.2

Quantitative evaluation method

The quantitative impact assessment measured changes in onion production and in the livelihood of the target households that can be attributed to the intervention. The central question is how the outcomes would have changed if the beneficiary households had not in fact received the intervention. This requires a valid counterfactual of households that did not receive the intervention but are otherwise very similar to the group of households that did receive it. The study used propensity score matching to quantify the impact (average treatment effect) of the project intervention on a range of outcome indicators. The use of this method was expected to reduce the effect of selection bias, which is otherwise likely to overestimate/underestimate the impact. The hypothesis tested in the impact evaluation is that the intervention through TOT, field days and demonstrations and the professionalization of farmers for the sustainability of quality seed supply leads to improvement in production outcomes and livelihood indicators for onion producers in northern Cameroon. 3.2.1

Propensity score matching

Propensity Score Matching (PSM) involves building a group of statistical comparison founded on the probability to be in intervention group (Rosenbaum and Rubin, 1983). Let

be the onion production and livelihood when farmer is subject to treatment ( = 1) and

the same variable when farmer does not participate to PADFA program ( = 0). Following ([CSL STYLE ERROR: reference with no printed form.]), (Rubin and Thomas, 1996), (Heckman et al., 1998) (Imbens, 2004) (Dehejia, 2003), and (Smith and Todd, 2005), the ATT can be defined as: =

−

| = 1 = ( / = 1) − ( | = 1)

It is possible to observe the outcome variable of participants ( the outcome of those participants who have not participated

(1) = 1), but we cannot observe ( | = 1), and estimating the

ATT using equation (1) may therefore lead to biased estimates. Propensity score matching rests on the assumption of conditional independence where, conditional on the probability of participation, given observable covariates, an outcome of interest in the absence of intervention, 10

and adoption status,

are statistically independent and define the propensity score or

probability of participation in intervention as: ( )=

( = 1)|

(2)

Another important assumption of PSM is the existence of common support. The application of matching techniques is only possible if there exists untreated individuals with characteristics identical to those of treated individuals. Thus farmers being compared have a common probability of being both a participants and non-participants, such that 0 < ( ) < 1. If the two assumptions are met, then the PSM estimator for ATT can be specified as the mean difference of participants and matched with non- non-participants who are balanced on the propensity scores and fall within the region of common support, expressed as: =

= 1, ( ) − ( | = 1, ( )

(3)

PSM technique has a two-step procedure. The first step estimates the probability (using a logit or probit model) for participation of households in the PADFA program. The interest is in estimating the predicted probability to take part in the program, called the “propensity score. In the second step, the propensity score is used to match each participant with its most similar nonparticipant with similar propensity to a non-participant with similar propensity score values, in order to estimate the ATT. The PSM tries to compare differences in outcomes between participants and non- participants with similar characteristics in terms of quantity, but, it cannot correct for unobservable bias because it only controls for observed variables. 3.2.2

Rosenbaum bounds sensitivity analysis

Rosenbaum bounds sensitivity analysis is conducted to estimate the extent to which selection on unobservables variables may bias the estimates of the ATT. Sensitivity analysis determines the magnitude of hidden bias that would need to be present to alter the conclusions of an observational study (Rosenbaum, 2002). In fact, if there are unobserved variables which affect assignment into intervention, the onion production and livelihood variables simultaneously, a “hidden bias” might arise. It should be clear that matching estimators are not robust against this “hidden bias” (Caliendo and Kopeinig, 2008). Since it is not possible to estimate the magnitude of selection bias with non-experimental data, we address this problem with the bounding approach proposed by (Rosenbaum, 2002). The basic question to be answered is, if inference about treatment effects may be altered by unobserved factors?

11

3.3

Data

Figure 1 shows the sampling strategy for the quantitative part of this study. From each intervention village, we randomly selected 1 seed producing CIG and randomly selected 3 bulb producing CIGs. From each of the CIGs we then selected 5 beneficiary households per CIG. This gave a total sample of 240 beneficiary households (60 seed producers, 180 bulb producers). From each division included in the project, we will selected control villages based on criteria defined in Section 2.2. (Targeting of beneficiaries). Control villages in same divisions as the intervention villages

Sample: Purposively selected 12 matched control villages Randomly select 5 households per CIG=240 households 240 households Approximately 40% women and 20% youth (15-35)

Intervention 6 villages 92 CIGs

Intervention: Onion bulb producers in all 6 villages Total 72 CIGs

Intervention: Onion seed producers in 3 villages Total 20 CIGs

Sample: Selected all 6 villages

Sample: Selected all 3 villages

Randomly selected 6 CIGs/village=36 CIGs

Randomly selected 4 CIGs/village=12 CIGs

Randomly selected 5 beneficiaries per CIG=180 households

Randomly selected 5 beneficiaries per CIG=60 households

Approximately 40% women and 20% youth (15-35) beneficiaries

Approximately 40% women and 20% youth (15-35) beneficiaries

Figure 1. Sampling strategy for the quantitative part of the study Control villages were selected purposively (non-randomly) so that each set of two control villages was very similar to the intervention village in the same division in terms of agricultural land use (including the importance of onion production), agro-ecological conditions (soils, 12

climate, availability of irrigation), market access (distance to nearby towns and markets), population (number of households), ethnicity, and resource endowments (average farm size, religion (Muslim and Christian) and availability of other farm and non-farm assets). The control village must have had the same starting conditions as the intervention village but should not have been affected by the intervention. Therefore, villages neighboring the intervention areas were avoided as farmers there could have learned from the intervention farmers and have copied some of the improved practices. From each village, we have randomly selected CIGs according to Figure 1 (4 for the control villages and seed intervention villages, 6 for the bulb intervention villages). Each CIG has 5-10 households. We randomly selected 5 households per CIG. Beneficiary households included about 40% women and 20% youth. The total sample (Table 2) included 240 control households and 240 intervention households (180 onion bulb and 60 onion seed producing households). Table 3 summarizes variables used in descriptive statistics and in estimating the Propensity Score Matches (PSM). Table 2. Households of onion producers sampled for the impact evaluation Production zone (intervention villages)

Intervention group sample Direct project Seed Bulb Total beneficiaries producers producers sample

Control group sample Farm households

Villages

Diamaré (2)

240

40

61

101

100

2

Mayo Tsanaga (1)

100

0

30

30

30

1

Mayo Kani (1)

100

0

30

30

30

1

Mayo Louti (1)

180

20

30

50

50

1

Benoué (1)

110

0

30

30

30

1

Total

850

60

181

241

240

6

13

Table 3. Description of variables used Variable group

Variable name

Variable name

Variable type

Description of the variable

Program Participation

A17

INTERVENTION GROUP1

Dummy

A19

INTERVENTION GROUP2

Dummy

B1_2_1 B1_4a_1 B1_5_1 B5 C2c C2f C3 C5a C5b C9 E2_1_1 E2_2_1 E2_3_1 D4a C4a C4b D8a D8b A10 E1_1_1 E1_2_1 E1_3_1 E1_1_7_1 E1_2_7_1 E1_3_7_1 E6_1 E7_ E8_1 E9_1 E10_1 E11_1 E12_1 E22_1 E23_1 D5a1 D5b1 D5c1 D5d1 D5e1 D5f1 D5g1 D5h1 D5i1 D5j1 D5k1 D5l1

FARMER SEXE FARMER AGE EDUCATION HOUSEHOLD SIZE FRENCHWRITING FUFULDEWRITING CIG-LEADER EXPERIENCE1 EXPERIENCE2 EXTENSION VISITS FSIZE CCL ON FSIZE MB FSIZE SD ON EXP ADOPT IRRIG-ADOPT IRRIG-ACCESS ON M ADOPT ON SD ADOPT REGION HARVST QTY CCL ON HARVST QTY MB HARVST QTY SD ON REVENU CCL ONION REVENU MB REVENU ON SD INPUTS COST CCL ON INPUTS COST MB INPUTS COSTSD ON TOTAL COST MB &SD YIELD CCL ON YIELD MB YIELD SD ON TOTAL INCOME TOGROSS MARGIN CERTIFIED SEED COMPANY SEED ONION NURSERY RAISED SEED BEDS MANURE SOIL TREATMENT LINES PLANTING PLANTING DENSITY WHOLE SEED BULB HARVESTING TIME DRYING ONIONS STORAGE SPACE AGRICULTURAL LAND AGRICULTURAL LAND IRGD EXPERIENCE BULBS EXPERIENCE SEED

Dummy Numeric and continuous Numeric and continuous Numeric and continuous Dummy Dummy Dummy Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Dummy Dummy Dummy Dummy Dummy Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Numeric and continuous Numeric and continuous Numeric and continuous Numeric and continuous

1=Farmer has received training on commercial onion bulb and mother bulb/seed production 0= Farmer did not receive such training 1=Farmer has received training on onion mother bulb/seed production 0= Farmer did not receive such training 1= if farmer is a woman; 0=if farmer is a man Farmer’s Age [years] Years of education the farmer had in total Number of household members 1= if Farmer is able to write French; 0=farmer is not able to write French 1= if Farmer is able to write Fufulde; 0=farmer is not able to write Fufulde 1= if Farmer or any household members is a leader of a CIG; 0=if not Number of Years of experience in producing onion bulbs for selling Number of Years of experience in producing onion seed for selling Number of agricultural extension official visits during the last 12 months Total area planted of onion commercial bulb [ ha] Total area planted of onion mother bulb [ ha] Total area planted of onion seed [ha] Experience in adopting commercial onion bulb production (years) 1= if Farmer had access to irrigation in the dry season 5 years ago in 2012; 0=if not 1= if Farmer has access to irrigation in the dry season; 0=if not 1= if Farmer does mother bulb production in the last 12 months; 0=if not 1= if Farmer does onion seed production in the last 12 months; 0=if not 1=Far North; 0=North Harvested quantity of onion commercial bulb [kg] Harvested quantity of onion mother bulb [kg] Harvested quantity of onion for seed [kg] Total revenues received from commercial bulb [in thoussands of CFAF] Total revenues received from mother bulb [in thoussands of CFAF] Total revenues received from onion seed [in thoussands of CFAF] Total cost of inputs used for Onion bulb [in thoussands of CFAF] Total cost of inputs used for mother bulb [in thoussands of CFAF] Total cost of inputs used for onion seeds [in thoussands of CAFF] Total cost for onion bulb/mother bulb and seed [in thoussands of CFAF] Harvested quantity of onion commercial bulb per hectare [in thoussands of kg/ha] Harvested quantity of onion mother bulb per hectare [in thoussands of kg/ha] Harvested quantity of onion seed per hectare [in thoussands of kg/ha] Total onion income [in thoussands of CFAF] Total onion gross margin [in thoussands of CFAF] 1= if Farmer has adopted certified seed of onion variety ; 0=farmer has not adopted 1= if Farmer has adopted seed from a seed company; 0=if not 1= if Farmer has adopted onion nursery; 0=if not 1= if Farmer has adopted raised seed beds; 0=farmer has not adopted 1= if Farmer has adopted use of animal manure in onion production; 0=if not 1= if Farmer has used animal manure in onion production; 0=if not 1= if Farmer has treated soil with insecticide/nematocide before planting/transplanting; 0=if not 1= if Farmer has adopted planting in lines; 0=if not 1= if Farmer has adopted 20×10cm planting density for commercial bulbs; 0=if not 1= if Farmer has used a whole seed bulbs instead of cut bulbs; 0=if not 1= if Farmer has harvested after withholding irrigation when 1/3 of the onion tops have broken; 0=if not 1= if Farmer has adopted storage in a dry well-ventilated space; 0=if not Agricultural land (ha) Agricultural land irrigated (ha) Experience do you have in producing onion bulbs for selling (years) Experience do you have in producing onion seed for selling (years)

Household Characteristics

Farm Characteristics

Agricultural Production

Onion production practices

Matching variables

F1d1 C4c C5a C5b

14

Variable group

Variable name

Variable name

Variable type

Description of the variable

C6a1 C6b1 C6c1 C6d1 C6e1 C6f1 C6g1 C6h1

FERTILIZERS USE PESTICIDES USE HERBICIDES USE IMPROVED MAIZE USE IMPROVED MILLET USE IMPROVED SORGHUM USE HIGH VALUE VEGETABLES USE MECHANIZED IRRIGATION USE FOOD EXPENDITURES NON-FOOD EXPENDITURES

Dummy Dummy Dummy Dummy Dummy Dummy Dummy Dummy Numeric and continuous Numeric and continuous

Adopted Mineral fertilizers(0=No; 1=Yes) Adopted Chemical pesticides to kill insects(0=No; 1=Yes) Adopted Herbicides to kill weeds(0=No; 1=Yes) Adopted Improved maize varieties (0=No; 1=Yes) Adopted Improved millet varieties(0=No; 1=Yes) Adopted Improved sorghum varieties(0=No; 1=Yes) Adopted High value vegetables: cabbage, tomato, carrot(0=No; 1=Yes) Adopted Mechanized irrigation pump(0=No; 1=Yes) Total amount spent for food products [CFAF] Total amount spent for non-food products [CFAF]

G3 G4

15

4 4.1

Results Focus groups analysis

The experience of onion farmers varied from 4 to 24 years. The limitation factors of onion production concerned onion seed, pests/diseases, soil quality and water availability. The two mains problems related to seed are availability and quality. Onion farmers were generally facing the insect pest problems that destroyed their crops and pink rot disease. The majority of farmers said that soil fertility constrained their onion production while all farmers complained about limited water availability. Regarding the limiting factors to onion production, farmers recognized a number of solutions from the PADFA project. Concerning onion seed, the majority of farmers acknowledged that the PADFA project enabled them in onion seed production or in onion seedling choice. In terms of pests/diseases, the majority of farmers recognized that the project helped them to identify appropriate products to deal with pests and diseases. In addition, the project trained farmers on soil sterilization techniques before nursery which contributed to a reduction in disease problems. Concerning soil fertility, only very few farmers recognized that the project helped them to choose appropriate fertilizers and thought them how to use them. Few farmers acknowledged the contribution of the project to improve their water access. Finally, PADFA project has enable the majority of farmers to start using line sowing to increase yield and avoid diseases. The comparison of onion to other crops shows that onion production is more difficult for the entire sample of onion farmers in terms of time, production cost, pest/diseases, seed quality and farmer health. In terms of income, farmers believe that onion production generates higher income. In term of risk, very few onion farmers agreed that onion production is less risky because of high income and the majority of onion farmers agreed that onion production is more risky because of non-availability of water, fertilizers and pesticides and frequent insect pests rot during storage and low market power of farmers. The qualitative analysis of consumption and selling onion revealed that onion farmers consumed between 1 to 27 bags of 120 kg each depending of the household size, and the rest of production was sold. For the postharvest handling or marketing of onion, all farmers recognize the efforts of the PADFA project to train them on urea treatment before storage, use of shelves, spacing 16

storage, sorting of bulbs at harvest, re wiping, identifying the maturity of the onion before harvest and progressive sale. According to the majority of onion farmers, the main constraints with selling onions are the low selling price, the non-availability of warehouses and the low market power of farmers. The discussion of gender issues revealed for the entire sample that onion was considered as men’s crop, but due to the project intervention it had become a mixed crop. In general all farmers agree that the project enabled them to adopt new production methods such as onion nurseries, production and storage. Half of farmers thought that the project intervention increased their production costs by increasing fertilizers and pesticide costs, while the other half of farmers thought that it reduced production costs through the new practices of plowing. 4.2

Household and onion production characteristics

Table 4 compares the sample for intervention only in the production of commercial onion and intervention in the production of onion bulbs and onion seed (intervention goup1) against a sample of households that did not participate in the project (Control). It also compares the sample for intervention in the production of commercial onion and intervention in the production of onion bulbs (intervention goup2) against a control. The t-tests are used for categorical variables while chi-2 tests are used for continuous variables. The results of the t-tests and chi-2 tests show that only four variables among thirteen are significant in comparing intervention goup1 with the control group and seven variables among thirteen are insignificant. Intervention goup1 appears more similar to the control group than intervention group2, which may have a stronger selection bias. In the logic, agricultural practices in onion production and marketing variables are compared according to intervention group and control group (table5). The results show that, variables of intervention goup1 and intervention goup2 are different to variables of control group (eight variables significant).

17

Table 4. Sample household characteristics Control (n=240)

Intervention group 1 (n=241) Std. Mean Dev. 0.57 0.49

Variables FARMER SEX

Mean 0.25

Std. Dev. 0.43

FARMER AGE

42.5

12.74

44.13

EDUCATION

4.15

4.34

3.83

0.019 **

Intervention group 2 (n=80) Std. Mean Dev. 0.55 0.50

13.17

0.084*

43.61

13.60

0.253

4.28

0.792

4.71

4.28

0.160

P-value

P-value

0.0523 *

HOUSEHOLD SIZE

7.37

2.97

6.15

2.88

0.000***

6.48

3.19

0.012**

FRENCHWRITING

0.50

0.50

0.47

0.50

0.780

0.6

0.49

0.55

FUFULDEWRITING

0.51

0.49

0.43

0.49

0.514

0.56

0.49

0.48

CIG-LEADER

0.68

0.46

0.58

0.49

0.1786

0.83

0.37

0.01 **

11.54

9.57

11.53

9.15

0.503

12.85

10.08

EXPERIENCE1

0.149

EXPERIENCE2

4.73

7.86

5.09

7.89

0.307

5.98

6.51

0.0996*

EXTENSION VISITS

5.02

6.22

7.04

9.35

0.003**

7.86

8.71

0.000 ***

ON M ADOPT

0.32

0.47

0.49

0.50

0.169

0.81

0.39

0.03**

ON SD ADOPT

0.36

0.48

0.46

0.50

0.28

0.825

0.38

0.0086**

1.67 0.47 REGION ***, **, * indicate significance at 1, 5 and 10%

1.68

0.46

0.54

1.73

0.44

0.73

Table 6 shows more differences in the performance variables for the sample of male farmers (10 variables significant) than for the sample of female farmers (7 variables significant). The comparison of the two intervention groups against the control in table 7 shows more differences for intervention group 1 (11 variables significant) than for intervention group 2 (9 variables significant).

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Table 5. Adoption of improved seed and agricultural practices in onion production and marketing, in proportion of farmers per group Control (n=234)

Intervention group 1 (n=234)

Variables CERTIFIED SEED

Mean 0.53

Mean 0.91

COMPANY SEED

0.45

0.47

P-value 0.000***

Intervention group 2 0(n=76) Mean 0.94

P-value 0.000***

0.497

0.52

0.459

0.72

0.070*

0.92

0.000***

0.46

0.351

0.46

0.367

0.63

0.610

0.59

0.465

0.64

0.73

0.883

0.77

0.083*

0.25

0.81

0.0539*

0.89

0.000***

ONION NURSERY

0.58

RAISED SEED BEDS

0.38

MANURE

0.59

SOIL TREATMENT LINE PLANTING PLANTING DENSITY

0.09

0.78

0.000***

0.85

0.028 **

WHOLE SEED BULB

0.08

0.65

0.000***

0.77

0.000***

HARVESTING TIME

0.68

0.90

0.000***

0.84

0.009***

DRYING ONIONS

0.80

0.86

0.010**

0.84

0.769

0.94

0.000***

0.94

0.000***

STORAGE SPACE

0.82 ***, **, * indicate significance at 1, 5 and 10%

Table 8 shows significant differences for intervention group 1 for three outcome variables (total production cost, yield of commercial onion and food expenditure) while intervention group 2 shows differences in four outcome variables (total production cost, onion yield, total income and total gross margin).

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Table 6. Performance indicators for male and female farmers in onion production Variables

Women (n=198) Control (60) Intervention group 1 (138) Mean

Std.Dev. 0.33

Mean 0.41

Std.Dev. 0.40

0.06

0.08

0.29

0.24

0.09

0.07

0.30

0.30

HARVST QTY CCL ON

0.35

HARVST QTY MB HARVST QTY SD ON REVENU CCL ONION REVENU MB REVENU ON SD INPUTS COST CCL ON INPUTS COST MB INPUTS COSTSD ON TOTAL COST MB &SD YIELD CCL ON YIELD MB YIELD SD ON TOTAL INCOME TOGROSS MARGIN

320.21

486.22

510.36

1054.32

P-value

Men (n=198) Control (180) Intervention group 1 (103)

P-value

Mean 0.39

Std.Dev. 0.28

Mean 0.50

Std.Dev. 0.45

0.09*

0.44

0.62

0.28

0.37

0.099*

0.032**

0.13

0.10

0.20

0.18

0.005**

0.17

0.097*

436.30

668.39

542.90

1082.34

0.000***

0.799

0

33.4

71.84

0.21

2.60

12.51

63.77

143.13

0.012**

14.18

20.58

580.91

1955.77

0.17

28.79

89.95

106

207.79

0.004 **

179.35

147.27

173.03

125.46

0.62

270.21

238.66

292.42

216.96

0.68

68.71 64.62 167.77

0.000 *** 0.003** 0.021**

2.91 10.99 284.11

14.43 24.54 250.43

31.02 35.58 359.04

90.27 75.57 267.27

0.000*** 0.000*** 0.008**

13.36

18.51

17.84

14.94

0

0.10 7.72

0.84 40.49

187.19

150.82

32.96 32.75 238.75

10.24

7.77

12.92

11.47

0.054*

0.8

.

7.17

13.58

-

1.87

3.58

5.79

7.55

0.065*

1.02

1.85

1.10

2.57

0.46

0.26

0.68

6.537

31.91

0.041**

42542.12

0.24

553.44

2988.57

602.107

1171.21

0.45

42509.18

0.24

269.32

2850.51

243.06

1077.10

0.54

192.05 4.85

195.64 177.07

3994.33 3755.57

20

0.40

Table 7. Performance indicators for the two intervention groups in onion production Control (n=240)

Variables HARVST QTY CCL ON

Mean 0.38

Std. Dev. 0.29

Intervention group 1 (n=241) Mean Std. Dev 0.45

0.42

P-value

Intervention group 2 (n=80) Mean Std. Dev

0.026**

0.64

0.59

P-value

0.000***

HARVST QTY MB

0.40

0.59

0.29

0.29

0.850

0.26

0.24

0.870

HARVST QTY SD ON

0.12

0.10

0.25

0.26

0.000***

0.26

0.24

0.000***

REVENU CCL ONION

408.14

630.07

524.61

1064.42

0.075*

405.79

779.92

0.51

2.30

11.76

46.50

108.28

0.021**

69.36

123.55

0.004**

26.65

83.50

361.72

1452.08

0.024**

499.75

1824.18

0.013**

INPUTS COST CCL ON

247.49

222.64

224.05

180.26

0.890

200.55

149.99

0.040**

INPUTS COST MB INPUTS COSTSD ON TOTAL COST MB &SD

2.21 10.17 259.88

12.55 29.28 233.14

32.13 33.96 290.16

78.48 69.38 223.56

0.000*** 0.000*** 0.073 **

58.00 69.86 328.42

89.51 86.68 220.90

0.000*** 0.000*** 0.010**

12.60

16.59

15.05

13.28

0.039**

13.12

12.74

0.400

YIELD MB

1.80

3.45

6.65

11.64

0.064 *

7.94

13.80

0.054*

YIELD SD ON

0.34

0.88

3.50

21.29

0.110

5.18

26.08

0.066*

TOTAL INCOME

463.09

2592.94

2544.54

32195.08

0.150

6805.44

55855.13

0.039**

TOGROSS MARGIN

203.20

2471.13

2254.38

32172.01

0.160

6477.02

55821.75

0.041**

REVENU MB REVENU ON SD

YIELD CCL ON

Table 8. Outcome indicators for the two treatment groups in onion production Variable

Control (n=240)

Intervention group 1 (n=241)

Mean

Std. Dev.

Mean

Std. Dev.

TOTAL COST MB &SD

259.88

233.14

290.16

223.56

YIELD CCL ON

12.60

16.59

15.05

TOTAL INCOME TOGROSS MARGIN FOOD EXPENDITURES NON-FOOD EXPENDITURES

463.09

2592.94

203.20

P-value

Intervention group 2 (n=80)

P-value

Mean

Std. Dev.

0.07*

328.42

220.90

0.01**

13.28

0.039**

13.12

12.74

0.40

2544.54

32195.08

0.150

6805.44

55855.13

0.03**

2471.13

2254.38

32172.01

0.160

6477.02

55821.75

0.04**

14047.08

11209.51

16083.86

19103.81

0.07 *

20667.5

27373.39

0.001**

62801.46

75362.27

45388.38

52363.15

0.990

61472.5

69080.39

0.55

21

4.3

Impact

The results of probit regression for participation in the PADFA project shows that about 15 variables are significant, indicating clear differences between the intervention and control groups (Table 9).

Table 9. Probit estimation of intervention on onion commercial bulbs and onion seedling Variables REGION FARMER SEX FARMER AGE EDUCATION HOUSEHOLD SIZE FRENCHWRITING FUFULDEWRITING CIG-LEADER EXPERIENCE1 EXPERIENCE2 CONSTANT

Intervention on onion commercial bulbs and onion seedling (Intervention group 1)

Intervention on onion seedling (Intervention group 2)

Coef. 0.113 1.094*** 0.0135** 0.0277

Std. Err. 0.135 0.147 0.005 0.021

Coef. -0.203 1.329*** 0.0167** 0.0325

Std. Err. 0.191 0.209 0.007 0.027

-0.0878*** 0.165 0.119 -0.0974 0.00642 0.016

0.0215 0.190 0.149 0.141 0.009 0.0104

-0.0984*** 0.274 0.193 0.767*** 0.0170 0.0178

0.030 0.266 0.206 0.205 0.011 0.012

-0.809**

0.318

-2.459***

0.457

pseudo-R2 observations

0.1321

0.188

481

320

***, **, * indicate significance at 1, 5 and 10%

A visual inspection of the region of overlap in the estimated propensity scores for the participants and non-participants in intervention group 1 and 2 indicates that the common support for the propensity scores condition is satisfied. In fact, there is substantial overlap in the distribution of the propensity scores of both adopters and non- adopters (Figures 2 and 3). To ensure maximum comparability of intervention group and comparison group, the sample is restricted to the region of common support defined by the values in the range of "propensity score" in which intervention and control observations can be found.

22

0

.2

.4 .6 Propensity Score Untreated Treated: Off support

.8

1

Treated: On support

2

1.5 density 1

.5

0 0

.2

.4

.6 Pr (intervention

Non PADFA participants

.8

1

group1) PADFA

participants

intervention group1 Figure 2: Propensity score distribution and common support for propensity score for intervention group 1 Note: ‘‘Treated: on support’’ indicates the observations in the participation group that have a suitable comparison. ‘‘Treated: off support’’ indicates the observations in the participation group that do not have a suitable comparison

23

0

.2

.4 Propensity Score

Untreated Treated: Off support

.6

.8

Treated: On support

3

2 density

1

0 0

.2

.4

Pr(a19)

Non PADFA participants

.6

.8 PADFA

1 participants

intervention group2 Figure 3. Propensity score distribution and common support for propensity score for intervention group2 Note: ‘‘Treated: on support’’ indicates the observations in the participation group that have a suitable comparison. ‘‘Treated: off support’’ indicates the observations in the participation group that do not have a suitable comparison

Table 10 shows the covariate balancing tests before and after matching by five nearest neighbour matching (FNNM) and Kernel based matching (KBM) methods. These results show that the standardized mean difference for overall covariates used in the estimation process of PSM 24

reduced from 16.7 before matching to 3.8 after matching (for the intervention group1 with control sample) and from 23.5 before matching to a range of 4.2 to 5 after matching (for the for the intervention group2 with control sample). The total bias also reduced is 77% through the matching process for intervention group1 with control sample and in the range of 78% to 82% for intervention group2 with control sample. Moreover, the p-values of the likelihood ratio tests show the significance of variables in the logit model after matching, but not before matching. The pseudo-R2 indicates the level of the variables to explain the participation probability. In fact, it shows that the pseudo-R2 is reduced from 13% before matching to 0.05% after matching for the intervention group1 and from 18% to 0.07% intervention group2. This indicates that after matching the differences are weak in the distribution of covariates between both groups. Following Shiferaw et al. (2014) and Khonje et al. (2015), the low level of pseudo-R2, low level of mean standardized bias, high total bias reduction, and insignificant p-values of the likelihood ratio test after matching indicate that specification of the propensity score estimation process is successful in term of balancing the distribution of covariates between adopters and non-adopters.

Table 10. Balancing tests before and after matching Pseudo-R2 Before After matching matching Intervention group 1 KBM 0.132 0.005 FNNM 0.132 0.006 Intervention group 2 KBM 0.189 0.007 FNNM 0.189 0.007 Matching algorithm

(p-value) Before After matching matching

Mean standardized bias Before After matching matching

Total % bias reduction

88.07 88.07

3.19 3.91

16.7 16.7

3.8 3.9

77 76

67.87 67.87

1.53 1.61

23.5 23.5

5.0 4.2

78 82

The effects of the intervention in onion commercial bulbs and onion seedling production are estimated in table 11 for four outcome indicators.

25

Table 11. Average treatment effect of the PADFA program in onion commercial bulbs and onion seedling production Kernel-based matching ATT SE zscore

5 nearest neighbors matching Potential outcome ATE as ATT SE z-score ATE as % Outcomes (PO) % of PO of PO mean mean mean Average effect of onion commercial bulbs and onion seedling production (intervention group 1) TOTAL COST MB &SD 29.56 21.560 1.37 56.453** 24.52 2.30 11,37 259.88 YIELD CCL ON 2.38 1.478 1.62 3.459** 1.242 2.78 18,88 12.60 TOTAL INCOME 2072.92 2219.16 0.93 2250.42 2207.25 1.02 TOGROSS MARGIN 2043.36 2219.8 0.92 2193.96 2205.93 0.99 Average effect of onion seedling production (intervention group 2) TOTAL COST MB &SD 70.57** 31.08 2.27 27,12 64.69 41.41 1.56 259.88 YIELD CCL ON 0.540 1.974 0.27 0.66 3.19 0.21 TOTAL INCOME 6397.38 6533.25 0.98 6264.52 6492.16 0.96 TOGROSS MARGIN 6326.80 6537.93 0.97 6199.83 6501.33 0.95 Notes: *** indicates significance at 1%; ** indicates significance at 5%; * indicates significance at 10%. Observations in parentheses were not used in the estimate due to the common support condition stratified. Bootstrap with 100 replications are used to estimate the standard errors.

26

N Total

479(2) 463(2) 479(2) 479(2) 319 (1) 305 (1) 319 (1) 319 (1)

The results in Table 11 for both methods (Kernel matching and five-nearest neighbors matching) show that the effect of the intervention is positive and significant for onion yield and total production cost. The Rosenbaum bounds sensitivity analysis is obtained using the “mbounds” command in Table 12. The results are not significant. This significant imply there is no hidden bias that need to be corrected. Tableau 12. Rosenbaum bounds sensitivity results Outcome variables TOTAL COST MB &SD YIELD CCL ON TOTAL INCOME TOGROSS MARGIN TOTAL COST MB &SD YIELD CCL ON TOTAL INCOME TOGROSS MARGIN

5

Intervention group1 Kernel-based matching Gamma P-value -0.045 0.51 -0.045 0.51 -0.045 0.518 -0.045 0.518 Intervention group 2 -0.065 0.52 -0.068 52 -0.065 0.52 -0.065 0.52

5 nearest neighbors matching Gamma P-value -0.045 0.51 -0.0477 0.51 -0.046 0.51 -0.046 0.51 -0.072 -.076 -0.072 -0.072

0.52 0.53 0.52 0.52

Conclusion

The impact of PADFA intervention on onion production has increased onion yield through the agricultural intensification strategy. This intensification strategy include the reduction of farm size and the innovativeness factors such as size of land irrigated, use of improved varieties and used of pesticides. This intensification strategy implies more investment in a small piece of land and increase the production cost. Therefore increase onion production and onion farmer’s livelihood in Cameroon depends on the adoption of new practices of onion production and marketing. This points the need for policies and strategies aimed at enhancing adoption of new practices of onion production and marketing among non-adopters through more efficient extension and input supply systems.

27

6

References

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