HarvestPlus c/o CIATA.A. 6713 • Cali, ColombiaTel: +57(2)4450000 • Fax: +57(2)4450073HarvestPlus@cgiar.org • www.HarvestPlus.org

Research advances of HarvestPlus

socioeconomic studies in LAC

Carolina GonzalezImpact Assessment, Harvest Plus LAC

CIAT-IFPRI 26 Jun 2014

Contents

• Portfolio of socioeconomic studies for H+LAC• Biofortification Prioritization Index (BPI) for

Colombia• Rice production, consumption and

commercialization in Bolivia• Consumer Acceptance of a HIB variety (Super

Chiva) in Guatemala

HarvestPlus

AgroSalud LAC -14 countries

HarvestPlus LAC

2002-2004 2005 2006-2008-2009-2010-2011 2012-2013 (- 2018)

Guatemala, Nicaragua, Haiti, Bolivia

Panama, Brazil and Colombia

HarvestPlus Global

Honduras, El Salvador

We develop nutrient-rich seeds: Beans-iron/zinc; rice-zinc; maize: VIT A/zinc; cassava-VIT A; sweet Potato-VIT A

Overall portfolio in LAC/Brazil

• Where to invest?1. Prioritization exercise 2. Opportunities map

• Informing delivery and breeding1. Varietal adoption studies2. Consumer acceptance studies3. Farmer field day evaluation

• Measuring impact 1. Farmer feedback studies2. Impact assessment3. Impact evaluation/effectiveness

• Policy studies

COLOMBIA BPI

José Funes, Carolina González, Salomón Perez, Alexander Buritica, Ekin Birol, Manfred Zeller, Moursi

Mourad

Three basic conditions

The geographic areal unit must be a producer of the crop.

The geographical areal unit’s population must consume a substantial quantity of the crop under consideration.

The geographical areal unit’s population suffers from deficiencies for the key micronutrients, namely vitamin A, zinc, or iron.

Asare-Marfo et al. (2013) www.harvestplus.org/content/prioritizing-countries-biofortification-interventions-using-country-level-data

7

Data sources

• Micronutrient deficiency statistics: the National Survey of Nutritional Situation (ENSIN) assesses the nutritional state in Colombia. The survey is national, regional (6 regions) and department (32 departments) representative. It is also representative for urban and rural areas (ENSIN, 2010). [departments, n=32]

• Production statistics: the annual evaluation of agriculture and livestock of municipalities 2011 produced by the ministry of agriculture [municipalities, n=1120] and FAO food balance sheet.

• Consumption statistics: the ENSIN 2005 survey provides per capita food consumption statistics[departments, n=32; municipalities, n=252].

• Population statistics: 2011 population projections, based on 2005 population census (DANE, 2011). [districts, n=1120] % & UN Population prospects (2013).

• BPI – departments

Production index

• Production index = [1/3*per capita area harvestedr] + [1/3*Agricultural land allocated to the cropr] + [1/3*Spatial Interaction Factorr]x

DepartmentProduction

Index Cassava

GUAINIA 1.00ARAUCA 0.49AMAZONAS 0.45GUAVIARE 0.45SUCRE 0.41BOLIVAR 0.30CAQUETÃ � 0.29VAUPES 0.29MAGDALENA 0.27CORDOBA 0.21

Department

Production Index Maize (interaction index)

CORDOBA 0.70ARAUCA 0.58GUAVIARE 0.50BOLIVAR 0.43SUCRE 0.40GUAINIA 0.39PUTUMAYO 0.39CESAR 0.34CAQUETA 0.34MAGDALENA 0.32

DepartmentProduction Rice

Index (spatial interaction)

CASANARE 0.94TOLIMA 0.70META 0.62SUCRE 0.37CHOCO 0.34NORTE DE SANTANDER 0.29HUILA 0.25CESAR 0.19ARAUCA 0.18BOLIVAR 0.12

Department

Production Index Bean (interaction

index)HUILA 0.62CUNDINAMARCA 0.46CALDAS 0.37QUINDIO 0.33SANTANDER 0.28ANTIOQUIA 0.27NARINO 0.26CAUCA 0.24TOLIMA 0.23NORTE DE SANTANDER 0.22

The spatial index a

Figure. Rice food deficit/ rice food surplus/ rice food balanced

Source: Authors calculations based on DANE –ENA 2011

• Food surplus (ration <=0.8)

• Food balanced (0.8-1.2)

• Food deficit areas (>=1.2).

SII: Measures the potential spatial interaction between departments that have surpluses on their aggregate supply and with their neighbors departments.

Consumption index

• Consumption Index i = [(rur_popi/tot_popi) * rur_ cons_capitai + (urb_popi/total_popi) * urb_ cons_capitai]r

DepartmentConsumption Index Maize

CHOCO 1.00VAUPES 0.99TOLIMA 0.82CALDAS 0.69GUAINIA 0.65RISARALDA 0.65ANTIOQUIA 0.49CAUCA 0.45QUINDIO 0.40CAQUETA 0.38

DepartmentConsumption

Index Bean

CALDAS 1.00ANTIOQUIA 0.96GUAINIA 0.86TOLIMA 0.85QUINDIO 0.84RISARALDA 0.83META 0.78VAUPES 0.74GUAVIARE 0.72VICHADA 0.71

DepartmentConsumption Index Rice

BOLIVAR 1.00VALLE DEL CAUCA 0.72ANTIOQUIA 0.63CAUCA 0.48ATLANTICO 0.38MAGDALENA 0.35SUCRE 0.35CORDOBA 0.30LA GUAJIRA 0.28CESAR 0.21

www.Laylita.com

DepartmentConsumption Index Cassava

LA GUAJIRA 1.00NORTE DE SANTANDER 0.92CESAR 0.80MAGDALENA 0.79SANTANDER 0.75CAQUETA 0.74BOLIVAR 0.70SUCRE 0.62ATLANTICO 0.57ARAUCA 0.49

Micronutrients:

Vitamin A micronutrient deficiency index– Micronutrient Index (Vitamin A) = ½*Serum Retinol <0.7 µmol/l +

½*(100 - proportion of consumption by food groups fruits). Iron micronutrient deficiency index

– Micronutrient Index (Iron) = ½*ferritin < 12 g/dl + ½*(100 - proportion of consumption by food groups meats and eggs)

Zinc micronutrient deficiency index– Micronutrient Index (Zinc) = ½*Inadequate Zinc + ½*Stunting

prevalence

Micronutrients - Results:

𝑩𝒊𝒐𝒇𝒐𝒓𝒕𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝑷𝒓𝒊𝒐𝒓𝒊𝒕𝒚 𝑰𝒏𝒅𝒆𝒙 (𝑩𝑷𝑰 )=√𝑀𝑖𝑐𝑟𝑜𝑛𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝐷𝑒𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝐼𝑛𝑑𝑒𝑥∗√𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝐼𝑛𝑑𝑒𝑥∗𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐼𝑛𝑑𝑒𝑥

BPI:

Rice

DepartmentRank_bpi

_riceRank_bpi_rice_pop_weighted

Rank_bpi_rice_spatial_interaction_suppliers

Production rice

[intervention]

Impact rice

Intervention & impact

rice

CHOCO 1 12 1 1 1 1SUCRE 2 8 2 1 0 0CAUCA 3 2 3 0 1 0ANTIOQUIA 4 1 4 1 0 0BOLIVAR 5 4 6 1 0 0LA GUAJIRA 6 6 5 1 1 1CESAR 7 13 7 1 1 1MAGDALENA 8 10 9 0 1 0TOLIMA 9 5 8 1 0 0CORDOBA 10 3 10 1 0 0

Candidate sites for biofortification with zinc: rice

Beans

Candidate sites for biofortification with iron: beans

DepartmentRank_bpi

_beans

Rank_bpi_beans_pop_weighted

Rank_bpi_beans_spatial_interaction

Production bean

[intervention]

Impact beans

Intervention & impact

beans

ANTIOQUIA 1 1 1 1 1 1CALDAS 2 7 3 1 0 0QUINDIO 3 19 4 0 0 0RISARALDA 4 10 5 1 0 0CUNDINAMARCA 5 2 2 0 0 0NORTE DE SANTANDER6 9 6 1 0 0TOLIMA 7 5 7 1 1 1HUILA 8 6 8 0 0 0BOYACA 9 3 9 0 0 0SANTANDER 10 4 10 1 0 0

Cassava

DepartmentRank_bpi_cassava

Rank_bpi_cassava_pop_weighted

Production cassava

[intervention]

Impact cassava

Intervention & impact cassava

SUCRE 1 5 1 1 1BOLIVAR 2 3 0 0 0ARAUCA 3 17 1 0 0GUAINIA 4 24 0 1 0MAGDALENA 5 4 0 1 0AMAZONAS 6 21 1 1 1GUAVIARE 7 22 1 0 0CORDOBA 8 2 0 0 0VAUPES 9 26 1 0 0CESAR 10 9 0 0 0PUTUMAYO 11 15 1 1 1LA GUAJIRA 12 7 0 0 0CAQUETA 13 14 0 0 0VICHADA 14 25 1 0 0NORTE DE SANTANDER15 10 0 0 0ATLANTICO 16 18 1 1 1CASANARE 17 20 1 1 1SANTANDER 18 6 0 0 0ANTIOQUIA 19 1 0 0 0HUILA 20 12 1 0 0

Candidate sites for biofortification with VIT A: cassava

Maize

DepartmentRank_bpi

_maize

Rank_bpi_maize_pop_weighted

Rank_bpi_maize_spatial_interaction

Production maize

[intervention]

Impact maize

Intervention & impact

maize

VAUPES 1 24 1 1 1 1GUAINIA 2 25 2 0 1 0ANTIOQUIA 3 1 3 0 1 0GUAVIARE 4 20 5 1 1 1ARAUCA 5 18 7 1 0 0CORDOBA 6 2 4 1 1 1SUCRE 7 10 6 1 1 1CESAR 8 12 8 0 0 0LA GUAJIRA 9 8 9 1 0 0CHOCO 10 13 11 1 1 1

Candidate sites for biofortification with VIT A: maize

Next Steps

Finalize the working paper…Develop a subnational Biofortification

Prioritization Index to rank regions in Guatemala where biofortification could have the highest impact using the food basket approach.

Diana Lopera, Ricardo Labarta, Victor Zuluaga, José María Martinez, Roger Taboada and

Carolina Gonzalez

Rice in Bolivia

Adoption study of rice varieties in Bolivia

General Objectives (some preliminary results)• Characterization of the rice production system in Bolivia.• Identification of the rice varieties in Bolivia (farmers’ identification vs.

molecular markers).• Estimation of current adoption rates for rice varieties in the country and

factors associated with farmers’ choice of rice varieties. • Estimation of the proportion used for home consumption and sales

across rice producing households and preferences• Identify household main source(s) of information, about agricultural

techniques and health and nutrition. • Collect secondary information with the local organizations (secretaries of

health, municipalities, and hospitals) about micronutrient deficiency. Available

Sampling

We used a multi-stage sampling procedure:

Total surveys required due to the sampling

Total surveys actually conducted (due to logistical

constraints)

Households Village Households Village

Irrigated producers 84 7 83 6Rainfed producers 900 75 855 94Total producers 984 82 938 100

12 producers/community

Study sites

Department Province Freq.

Santa Cruz (n=613)

Guarayos 150Ichilo 238

Ñuflo de Chávez 39

Obispo Santistevan 65

Sara 58Warnes 62

Beni (n=244)

Ballivian 72Cercado 45Marban 67Moxos 60

Cochabamba (n=81) Carrasco 81

Department Province Municipality Village3 11 24 100

Preliminary descriptive statistics : Household characteristics

Total

Department

Santa Cruz BeniCochabam

ba Anova Obs. Mean Mean Mean Mean

Household size 846 4.6 4.4 4.8 5.1 **(2.22) (2.2) (2.3) (2.1)

Gender of head of hh (%male) 848 0.96 0.96 1.0 1.0 (0.18) (0.2) (0.2) (0.2)

Age of head of hh (years) 842 46.0 45.9 47.1 43.8 *(12.41) (12.4) (12.4) (12.0)

Years of schooling received by household head 792 6.6 6.7 6.4 6.2

(4.14) (4.1) (4.2) (4.2)

(whitout japanese)

Preliminary descriptive statistics : Production unit and Rice

Total Departments

Santa Cruz Beni Cochabamba Anova

Obs. Mean Median Mean Median Mean Median Mean Median Total land available for production (ha)-APU 852 57.5 37 80.90 50 18.1 2 23.7 11.5 ***

(150.6) (185.79) (29.07) (46.48) Total rice area planted (ha) 853 17.2 3.0 25.7 10 2.5 1 5.5 1 ***

(58.6) (72.7) (4.7) (17.6)

Total rice production (ton) 835 42.0 4.8 63.4 16 6.0 1.2 14.2 1.53 ***

(175.6) (220.4) (14.7) (46.1)

Yield (ton/ha) 835 2.1 1.9 2.3 2 1.8 1.6 2.0 2 ***(1.5) (1.5) (1.3) (1.4)

(whitout japanese)

Production constraints

Pest and Insects

Drought

Diseases

other

Floods

Grain yield

Low soil fertility

Lack of inputs

Seed quality

53.39%

26.28%

6.66%

4.28%

3.57%

3.57%

1.07%

0.71%

0.48%

What are your main production constraints? (most important)

(N= 841) (N= 828)

High yield

Resistance to pest and Insects

Resistance to diseases

Tolerance to drought

Short-cycle varieties

Lower levels of inputs

Other

70.51%

8.32%

3.45%

12.01%

3.09%

0.71%

1.90%

What characteristics do you look for in rice varieties when deciding what vari-

eties to use on your plot? (most im-portant)

Main varieties planted

MAC 18

GRANO DE ORO

ESTAQUILLA

JASAYE

EPAGRI

URUPE

POPULAR

TARI

PAITITI

CRISTAL

DORADO

IAC 101

PANACU

BLUEBONNET

CARANDEÑO

IAC 103

OTRAS

22.0%

10.3%

9.2%

7.5%

6.3%

5.8%

4.5%

3.8%

3.3%

2.5%

2.1%

1.5%

1.5%

1.5%

1.2%

1.0%

16.2%

Planted varieties by plot exclud-ing Japanese (2012-2013)

CAISY 50

EPAGRI

EPAGRI 109

IAC 101

MAC 18

0.5%

26.2%

3.3%

34.3%

35.7%

Planted varieties by plot (2012-2013): Japanese

N= 1019 plots N= 210 plots

Our sampling covers around 15.794 ha

MAC 18

GRANO DE ORO

EPAGRI

URUPE

ESTAQUILLA

TARI

PANACU

IAC 101

PAITITI

EPAGRI 115

JASAYE

NOVENTON

IAC 103

IAC 115

SAAVEDRA 44

OTHER

47.77%

8.79%

7.72%

6.14%

5.49%

4.28%

3.74%

3.59%

2.78%

1.58%

1.51%

1.37%

1.02%

0.51%

0.44%

3.26%

Main rice varieties in Bolivia: percentage of total area planted

Main varieties planted by department

ESTAQUILLA

GRANO DE ORO

POPULAR

MAC 18

JASAYE

EPAGRI

OTHER

22.0%

17.0%

15.2%

9.5%

5.3%

4.2%

26.9%

BENI: planted varieties by plot (2012-2013)

CRISTAL

ESTAQUILLA

URUPE

MAC 18

CAROLINA

PAITITI

OTHER

28.9%

15.6%

7.8%

8.9%

6.7%

5.6%

26.7%

COCHABAMBA: planted varieties by plot (2012-

2013)

MAC 18

JASAYE

GRANO DE ORO

URUPE

EPAGRI

TARI

OTHER

29.6%

9.4%

9.0%

8.6%

8.0%

5.6%

29.9%

SANTA CRUZ: planted varieties by plot (2012-2013)

Commercialization and Consumption

Sale is 81% vs. 19% consumption and seed*

Disaggregating by department we found that the change share was 85% vs. 15% for Santa Cruz and 71% vs. 29% for Beni respectively*

ConsumptionDto N

Mean (kg/d) p50 sd min max

Beni 243 1.2 1 0.8 0.1 6Cochabamba 81 1.3 1 0.8 0.25 5Santa Cruz 515 1.3 1 0.8 0.2 9Total 839 1.2 1 0.8 0.1 9

Rice food - Bolivia

Consumer preferences

Grain type (shape and length)

Grain quality

Easier to thresh

Easier to sell/ good marketing

Good taste

Other

41.91%

20.89%

7.61%

10.02%

18.48%

1.09%

What qualities do you look for in rice varieties when deciding what varieties to use on your plot? (most important)

Long and thin

Short and round

Super-fine rice and aromatic

Millet rice and polished

Brown rice (less polished)

Popular (medium and round)

66.40%

16.10%

7.00%

10.80%

0.70%

38.20%

Which type of rice do you prefer? (count of 1=yes)

(n= 855 whitout japanese)

Next Steps

Identification of the rice varieties in Bolivia (farmers’ identification vs. molecular markers).

Finish the analysis..

Outputs

Master Thesis Papers (2)

Consumer Acceptance of a HIB variety (super chiva) in

GuatemalaSalomón Perez, Carolina González, Ekin Birol, Manfred

Zeller – ICTA- U. Hohenheim

32

Objectives

1. Determine the socioeconomic and organoleptic factors affecting the acceptance of iron biofortifed beans varieties in Guatemala.

2. Estimate the premium/discount related with HIB variety (super chiva) in Guatemala.

3. Evaluate the acceptance of the HIB variety from a gender basis

33

Why Guatemala?

Prevalence of anemia in children 6 – 59 months: 47% (ENSMI, 2009).

Prevalence of anemia pregnant women: 29.1% (ENSMI, 2009).

Prevalence of anemia non pregnant women: 21.4% (ENSMI, 2009).

Source: http://www.desdeabajo.info

*Anemia: hemoglobin < 11g/dl

34

Data Collection

Location: Municipality of San Sebastian Huehuetenango (North-West of Guatemala)

Methodology

Sample size : 360 HH’s randomly selected in 8 districts.

Home use testing approach

Three treatments: 1. No information 2. Information (once) 3. Information (three times)

36

Becker-DeGroot-Marschak (BDM) auction:1. Ask willingness to pay for each

variety2. Select a paper with a variety

figure from a bag3. Select one price from the bag4. Win or lost - purchase the

variety.

Source:Fieldwork

Methodology (b)

If bid ≥ random price “WIN”

If bid < random price “LOSE”

Pay price

Don’t Pay

37

Preliminary results (PR): Sample characterization

Variable ConstructionMean

Treatment 1 Treatment 2 Treatment 3 Prob > F

Age Respondent’s age in years 36.24 35.82 34.96 0.7340Literacy HH’s head knows to write and read 70% 68.33 70.59% 0.7791HH size** Number of members in the HH 6.32 6.06 5.46 0.0210Income Expenses in the last 30 day in

Quetzales2,447 2,629 2,265 0.2022

Poverty PPI 61.25% 66.47% 65.34% 0.3631Consumption Beans consumption per week

(pounds)3.34 3.15 2.65 0.3824

Food frequency index

Count of 15 food groups consumed in the last 7 days (less than 4=0, 4-6=1,7+=2)

6.34 5.90 5.93 0.3933

Babies HH with babies less than 12 months 22.5% 25% 20% 0.4055Children (1-5 years)*

HH with children between 1-5 years 53.3% 40% 45% 0.0688

Pregnancy HH with pregnant women 3.33% 6.67% 5.04% 0.3907

p<0.1*, p<0.05**, p<0.01***

38

PR: (Mean hedonic rating (MHR) of bean variety)

Bean variety Raw bean color

Raw bean size

Bean taste Time of cooking

Cooked bean thickness

Cooked bean toughness

Overall

Control (T1): No

Information

Local (Hunapu) 6.55±0.59 6.57±0.72 6.59±0.75 6.10±1.35 6.17±1.29 1.85±2.95 6.47±1.00HIB (Superchiva) 6.63±0.72 6.61±0.67 6.75±0.74 6.58±0.74 6.66±0.66 1.95±3.07 6.66±0.66

Difference in means

HIB vs Local 0.75 0.042 0.16 0.47*** 0.49*** 0.11 0.19*

T2: Information

present once

Local (Hunapu) 6.53±0.46 6.5 ±0.56 6.63±0.52 6.37±1.09 6.40±0.93 1.42±2.73 6.59±0.63

HIB (Superchiva) 6.77±0.65 6.74±0.46 6.85±0.42 6.64±0.76 6.6 ±0.91 1.21±2.63 6.6±0.91Difference in means

HIB vs Local 0.24*** 0.24*** 0.21*** 0.26** 0.19 -0.21 0.01

T3: Information

present three times

Local (Hunapu) 6.55±0.57 6.54±0.55 6.63±0.53 6.39±0.67 6.53±0.54 1.34±2.63 6.59±0.59HIB (Superchiva) 6.76±0.51 6.77±0.51 6.84±0.46 6.57±0.77 6.64±0.96 1.15±2.51 6.64±0.96

Difference in means

HIB vs Local 0.21*** 0.23*** 0.20*** 0.17* 0.11 -0.19 0.06

39

PR: Mean economic rating of bean varieties

Average WTP Premium/DiscountWTP HIB (T1)WTP HIB (T2)WTP HIB (T3)WTP trad (T1)WTP trad (T2)WTP trad (T3) Premium (T1)Premium (T2)Premium (T3)

4.83±0.714.96±0.834.89±0.764.70±0.724.67±0.744.67±0.71

0.133±0.900.289±0.940.220±0.81

There’ is not significant differences between the WTP towards both varieties across the three treatments.

Frequency of information did not have effects

Next Steps

…to finish the described objectives

Muchas gracias!!

Outputs:

Ph.D Thesis (1)

Papers (2)