Strengthening the Dairy Value Chain Progress_May 2012
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Strengthening the Dairy Value Chain Progress_May 2012

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This presentation uses infographics to illustrate the progress made by the CARE Strengthening the Dairy Value Chain Program. The initiative aims to double the dairy-related incomes of 35,000 ...

This presentation uses infographics to illustrate the progress made by the CARE Strengthening the Dairy Value Chain Program. The initiative aims to double the dairy-related incomes of 35,000 smallholder and landless producers in rural Bangladesh. It is supported by the Bill and Melinda Gates Foundation.

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Strengthening the Dairy Value Chain Progress_May 2012 Presentation Transcript

  • 1. Analysis of SDVC DataMarch 2009 - April 2012Presented May 31, 2012
  • 2. VACCINATION Vaccination of cattle is most beneficial for more wealthy households. In poorer households, the use of vaccination does not seem to increase income. However, in wealthier households, income can increase by about 3% if the cattle are vaccinated. wealthy However, for all households – the household income is related to vaccination provider choices. Households that have lower then average incomes use CARE 3% Livestock Health Workers or Other Livestock Health Workers or a Government Vet. Households that have higher than average at least incomes tend to use their own family members to provide vaccinations. ARTIFICIAL INSEMINATION DEWORMINGUse of Artificial Insemination Deworming of cattle has a very positive effectincreases for all households. on household income for all households.The average household can The average householdexpect to see at least a 3% can expect an increase inincrease in household income income of between 5 andfrom milk if they use artificial 10% if they deworm theirinsemination. cattle. 5%-10% income increase Whether or not a household uses Artificial The strongest predictor of whether or not a household chooses to deworm their cattle is their overall 3 % Insemination is strongly predicted by the availability knowledge score and the level of confidence they feel in their Livestock Health Worker. A household of the service, the economic status of the household with a high knowledge score and a high level of confidence in their Livestock Health Worker is 30% at least and the skills of the household’s Livestock Health more likely to deworm their cattle than a household with a low knowledge score and a low level of Worker. confidence in their Livestock Health Worker. Interestingly, a household ARTIFICIAL INSEMINATION with a low knowledge score and a high level PERCENT OF DEWORMING USAGE RATES OVER TIME of confidence in their LHW and a household USAGE RATES OVER TIME with a high knowledge score but a low level 23.3% 8.6% 7.1% 7.4% 13.8% 11% 10.3% 8.8% of confidence in their 79% 58% 51% 42% 41% 34% 31% 47% LHW are both about equally likely to deworm their cattle. Both are about 15% less likely to deworm their cattle mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 than the high knowledge mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 and high confidence household.
  • 3. GROUP LEADER BY GENDER Overall, Households within Learning Groups with Female Leaders have incomes that are 3-6% higher. 3% 6% - higher income GROUP LEADER BY PHASE GROUP LEADER BY GROUP COMPOSITIONLearning Groups with Female Leaders do relatively better as the Phase progresses Learning Groups with a high percentage GROUP COMPOSITION of women producers with a female group FARM LEADER GENDER LEADER GENDER leader perform the best overall. Learning Groups with a high percentage group farm of men producers do moderately well IMPROVES INCOME FROM MILK composition leader gender regardless of group leader gender. Learning Groups with a high percentage of women producers and a male group leader perform the least well. percent increase 12% 7% 5% 2% percent improved 0% performance over male leaders female leader phase 1 phase 2 phase 3 phase 4 2%In Phase 1, the groups with female leaders do 7% betterIn Phase 2, the groups with female leaders do 5% betterIn Phase 3, the groups with female leaders do 2% better than groups with male leadersIn Phase 4, there is no difference in income between female led groups and male led groups. or 5%
  • 4. MARKET LINKAGE How and where a household sells their milk significantly affects their income. Market Linkage by household economic status poor wealthy poor wealthy poor wealthy GRAMEEN INFORMAL MV RD PRAN BRAC DANOON AKIJ MARKET MARKETS MARKETS 5-8% The poorest households However, the rich households do much There is a slight advantageGROUP ECONOMIC STATUS AND do the same as the better than the poor households when to the wealthier households wealthier households when they sell their milk to the MV, RD, is the Grameen Danoon andLARGER ECONOMIC CONTEXT selling their milk in an PRAN, and BRAC markets. When all Akij markets, but it is muchThe initial economic status and the larger economic informal market. else is equal, a rich household makes less consistent.environment of a group has a heavy influence on their between 5-8% more money than amilk income. poor household when selling milk inIn general, if a group is poor initially, their progress is Market Linkage by household these markets. WHAT PERCENT DO HOUSEHOLDS FROMbetter if they operate within a wealthy District. economic status and presence of a group selected RICH GROUPS DO BETTER thier distri orer distric milk collector At MV & RD, the households making THAN HOUSEHOLDS FROM POOR GROUPS weal ct po t the most money are wealthy and do wealthy On the informal markets, the not have their own collectors. 6.19% 5.48% 4.78% 4.21% poorer households with their MV RD PRAN BRAC own collectors do the best. At PRAN and BRAC, the households doing the best are wealthy with their 3.49 % 2.05 % 1.47 % 0.88% AKIJ GRAMEEN INFORMAL OTHER own milk collector. DANNON SECTOR poor poor poor wealthy wealthy wealthy At Grameen Dannon, most households do the same – with the very significant 5% 7% - A poor Learning Groups that operate within one of the exception of the very poor households without better in wealthier Districts do 5-7% their own collector. These earning income better in earning income than At Akij, it seems households do very poorly to be irrelevant if equivalent poor Learning at this market. you have your own Groups that operate within collector or not. one of the poorer Districts.And in general, a group that is more INFORMAL MV RD PRAN BRACwealthy to begin with a operates MARKETwithin a wealthy District does the MARKETSbest overall – a full 10% - 12% better 10%-12% GRAMEENthan even an equivalently rich group better in DANOON AKIJthat operated in a poor District. earning money MARKETS
  • 5. CATTLE SELLING DECISIONS Households in which women own cattle and women make the cattle selling decisions are more likely to sell cattle and are more likely to have higher incomes overall. PERMISSIONS TO ATTEND MEETINGS Whether or not women producers need permission to attend meetings, both within and outside of their village is influenced by whether or not they own cattle, the economic status of their group and time. Women who own cattle need less permission to attend meetings. PERMISSION TO ATTEND MEETINGS HOUSEHOLDS IN WHICH WOMEN OWN CATTLE -1.45 -1.25 -1.1 -0.97 -0.8 -0.6 -0.5 -0.45 low income learning groupHouseholds where women own cattle -1.6 -1.35 -1 -0.75 -0.48 -0.25 0.05 0.4do about 10% better in earningmoney than do households where -0.8 -0.6 -0.47 -0.3 -0.07 -1.2 -1 -0.9women do not own cattle. high income 10% learning group -1.43 -1.05 -0.82 -0.5 -0.3 -0.05 0.4 0.55 mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12However, this relationship is complexand is changing over time. better in earning money Women who own cattle are less likely However, the rates of women needing GENDER: GROUP AND HOUSEHOLD to need permission to attend meetings far away. permission to attend meetings is dropping amongst women who don’t own cattle. group has few -0.22 -0.13 -0.04 0.04 0.13 0.22 0.31 0.4 households where women own cattle -0.21 -0.11 -0.028 0.06 0.14 0.23 0.33 0.41 PERMISSION TO ATTEND FAR AWAY MEETINGS group has many -0.45 -0.36 -0.27 -0.18 -0.09 -0.004 0.08 0.17 0.88 0.98 1.08 1.17 1.27 0.58 0.68 0.78 households where low income women own cattle learning group -0.34 -0.25 -0.17 -0.08 0.01 0.1 0.19 0.28 2.56 2.48 2.38 2.27 2.17 2.07 1.96 1.86 mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 0.37 0.47 0.57 0.67 0.77 0.86 0.96 1.06 high income learning group 2.37 2.27 2.16 2.06 1.96 1.85 1.75 1.65 mar-09 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 Women in high income learning groups are slightly more likely to need permission to attend meetings.
  • 6. LIVESTOCK HEALTH WORKERS Livestock Health Workers income is influenced by: • the gender of the worker • the training the worker received • whether or not the worker received a loan. TRAINING BY SEX IS IMPORTANT Female LHW with basic Female LHW with Female LHW with both training achieve a 33% advanced training achieve basic and advanced training higher income increase a 22% higher income achieve a 17% higher than men. increase than men. income increase than men. SEX BY RECEIVE LOAN IS IMPORTANT Female LHW with loans have a 35% Female LHW without loans have a 24% higher increase in income than men. higher increase than men. MILK COLLECTORSMilk collectors income is most influenced by the sex of the collector in combinationwith the market linkage of the collector LIVESTOCK HEALTH WORKERS INCOME BRAC Women milk collectors BASIC 33% MILK COLLECTORS INCOME who sell here can expect a 100% higher income BRAC 100% increase over time than men collectors selling ADVANCE 22% here. AKIJ 80% Akij GRAMEEN Women milk collectors DANNON 30% who sell here can expect BOTH 17% a 80% higher income INFORMAL -10% increase over time than FEMALE LHW LEVEL OF TRAINING IMPROVEMENT men collectors selling over MALE LHW with the same training MV RD PRAN NA here. WOMEN MILK COLLECTOR MARKET INCREASE OVER MEN MC Grameen Dannon Women milk collectors who sell here can expect 35% a 30% higher incomeInformal increase over time thanWomen selling here had an income increase that was 10% men collectors sellinglower than men (3%) here.MVVery few women collectors sell milk here. The few that do 24%achieve a much higher income increase than the male milkcollectors. FEMALE LHW LOAN IMPROVEMENTRD & PRAN over MALE LHW with the same loan statusDo not have enough women selling milk here to discuss.
  • 7. FEED SOURCE COMPARED 94.9% 0.19 0.3 0.02 RICE BRAN BDT % CARBOHYDRATES 58.6% 0.05 0.5 0.30 WHEAT BRAN BDT % 4.4% 0.04 0 0.30 PULSE HUSK BDT % 45.4% 0.06 0.3 0.60 BROKEN RICE BDT % 5.5% 0.04 0.8 0.60 OIL CAKE PROTEINS BDT % 21.4% 0.03 0.8 0.30For the best nutrition, cattle need a M. OIL CAKE % ates BDTcombination of Carbohydrates, Proteins Proand Vitamins and Minerals. dr te VITAMINS & hy OTHER MINERALS 26.1% 0.17 0.1 0.20The most cost effective and beneficial VITAMINS bo in & MINERALS BDT %forms of carbohydrates seems to be Wheat s CATTLE CarBran and Broken Rice. 3.6% 0.08 0.1 0.10 READY FEEDOver time, our farmers have increased % NUTRITION BDTtheir Wheat Bran use from 50% to 75% feed source % of households average cost increase % increase inof all households. And our farmers have using this feed per kg in taka per litre month milk incomeheld their rates of Broken Rice steady over per monthly 10 kg increasetime. About half of all households use ls Vitbroken rice. ra a m ne FEED SOURCE FEED SOURCE isn Mi PROPORTIONS PRICE OVER TIME 75 % WHEAT BRAN The most cost effective and beneficial forms of proteins are various forms of Oil Cakes. RICE BRAN 18% 4% 57% 10% 31% 9% 10% 3% 8% 2% 9% 2% 7% 3% RICE BRAN 0.25 BDT 0.20 BDT 0.19 BDT 0.21 BDT 0.16 BDT 0.16 BDT 0.15 BDT 0.19 BDT CARBOHYDRATES 0.25 0.20 0.19 0.21 0.16 0.16 0.15 0.19 WHEAT BRAN READY FEED BDT BDT BDT BDT BDT BDT BDT BDT Over time, our farmers have increased PULSE HUSK 0% 0.7% 0.3% 0% 0.1% 0.2% 0.3% 0.06 0.06 0.06 0.05 0.04 0.04 0.04 0.05Vitamins and minerals are very important their use of various types of oil cakes by WHEAT BRAN BDT BDT BDT BDT BDT BDT BDT BDTfor the health and milk production of cattle. 4% 7% 4% 1% 0.7% 0.8% 0.9% about 10% overall. BROKEN RICE 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04 PULSE HUSK BDT BDT BDT BDT BDT BDT BDT BDT 0.8% 0.2% 62% 2% 72% 0.2% 0.1%Over time, our farmers have increased OIL CAKE 0.09 0.07 0.05 0.04 0.04 0.05 0.05 0.06 10 PROTEINS % BROKEN RICEtheir regular use of vitamins and minerals BDT BDT BDT BDT BDT BDT BDT BDT OIL 0% 2% 1% 0.3% 0.3% 0.3% 0.4%by about 20% overall. M. OIL CAKE 0.23 0.03 0.02 0.57 0.03 0.31 0.02 0.19 CAKES VITAMINS BDT BDT BDT BDT BDT BDT BDT BDT VITAMINS & & MINERALS 4% 0.6% 0.5% 0.9% 0.4% 0.1% 0.1% OTHER MINERALS VITAMINS 0.06 0.04 0.05 0.03 0.04 0.04 0.04 0.04 & MINERALS OIL CAKE BDT BDT BDT BDT BDT BDT BDT BDT 20% VITAMINS 2.3% 3.6% 3.2% 1.1% 2.3% 1.2% 1.1% 0.00 0.04 0.04 0.04 0.04 0.04 0.04 0.03 READY FEED MINERALS M. OIL CAKE BDT BDT BDT BDT BDT BDT BDT BDT jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 jun-09 oct-09 mar-10 jul-10 jan-11 jul-11 apr-12 overall average
  • 8. Overview of Entire Dataset: Overview of Household Compostion - Entire Dataset Count of In-milk Local Breed CowsHousehold Overview Count Percent 0 4192 46.09% 1 4138 45.50% 2 690 7.59% 3 75 0.82% Respondents Gender Total 9095 100.00% Count Percent 1 Women 7290 80.15% Count of In-milk Cross Breed Cows 2 Men 1805 19.85% 9095 100.00% Count Percent Total 0 8093 88.98% 1 800 8.80% 2 173 1.90% 3 29 0.32% Total 9095 100.00% Count of Households that have Cattle Owned by Women Count Percent Count of Total In-Milk Cows in Household 1 Yes 1202 13.22% Count Percent 2 No 6248 68.70% 0 3302 36.31% Total 7450 100.00% 1 4734 52.05% 2 935 10.28% 3 124 1.36% Total 9095 100.00%
  • 9. Overview of Entire Dataset:Vet Practices Type of Treatment Provider, in general Count Percent 6093 66.99% 1 CARE LHW Count of Households who Dewormed Cattle 1305 14.35% 2 Other LHW Count Percent 344 3.78% 1 Yes 3589 39.46% 3 Govt Vet 2 No 5382 59.18% 4 Other people of 107 1.18% Total 8971 100.00% DLS 5 Milk Processor 39 0.43% Vet 6 Medicine/Feed 10 0.11% Compant Vet 30 0.33% 7 Kabiraj 8 Own Family 7 0.08% Count of Households Who Got AI for Cattle Member Count Percent 63 0.69% 1 Yes 943 13.81% 9 Others 2 No 5884 86.19% 7998 100.00% Total 6827 100.00% Total
  • 10. Overview of Entire Dataset:Financial Practices Count of Households that Got Loans Count Percent 1 Yes 126 1.39% 2 No 8969 98.61% Source of Loans for Households Total 9095 100.00% that Got Them Count Percent 1 Relatives 7 5.56% 2 MFI 87 69.05% 3 Commercial Bank 7 5.56% 4 Merchent 2 1.60% Count of Households that Engaged in Group Savings 5 Govt Institution 2 1.59% 6 Milk Processing Company 1.59% 2 Count Percent 7 Milk Trading Association 7.14% 9 1 Yes 1165 55.19% 8 Other Association 7 5.56% 2 No 946 44.81% 9 Others 3 2.38% Total 2111 100.00% Total 126 100.00%
  • 11. Overview of Entire Dataset:Gender Roles Gender of Person Engaged in Feed Purchase Count Percent 1 Women 653 7.18% 2 Men 6529 71.79% 3 Both 1051 11.56% Count of Women Who Need Permission to Gender of Person Engaged with Milk Selling Attend Group Meetings Count Percent Count Percent 1 Women 2279 25.06% 1 Yes 3670 40.35% 2 Men 2765 30.40% 2 No 3898 42.86% 3 Both 871 9.58% Total 7568 100.00% Total 5915 100.00% Count of Women Who Need Permission to Gender of Person Engaged in Cow Rearing Attend Meetings at a Distance Count Percent Count Percent 1 Women 5037 55.38% 1 Yes 6534 86.34% 2 Men 892 9.81% 2 No 1034 13.66% 3 Both 3166 34.81% Total 7568 100.00% Total 9095 100.00%
  • 12. Overview of Entire Dataset:Cattle Productivity Cross Breed Cow Productivity (Daily Litres) Over Time and According to Phase 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 Phase 1 Phase 2 Phase 3 Phase 4 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Local Breed Cow Productivity (Daily Litres) Over Time and According to Phase 1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4
  • 13. Overview of Entire Dataset:Knowledge & Practical Education Total Knowledge Score Over Time and According to Phase 8 7 6 5 4 3 2 1 0 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4 Total Practical Score Over Time and According to Phase 12 10 8 6 4 2 0 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4
  • 14. Overview of Entire Dataset:Feed Costs & Milk Income Monthly Feed Costs per Cow (taka) Over Time and According to Phase 1200.00 1000.00 800.00 600.00 400.00 200.00 0.00 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4 Monthly Income per Cow (taka) Over Time and According to Phase 1200.00 1000.00 800.00 600.00 400.00 200.00 0.00 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4 Ratio of Milk Income to Feed Costs Over Time and According to Phase 2.50 2.00 1.50 1.00 0.50 0.00 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4
  • 15. Overview of Entire Dataset:Cattle Productivity Percent of Women Who Need Permission to Attend Group Mee Over Time and According to Phase70%60%50%40%30%20%10%0% Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4 Percent of Women Who Need Permission to Attend Group Mee Over Time and According to Phase100%80%60%40%20% 0% Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Phase 1 Phase 2 Phase 3 Phase 4
  • 16. Overview of Entire Dataset:Where Milk is Sold 1= Percent milk consumed by household 2=Percent milk spoiled 3= Percent milk sold to neighbors 4=Percent milk sold on open markets 5= Percent milk sold to tea shops 6= Percent milk sold to milk collector 7= Percent milk sold to sales point 8= OtherPhase One Groups Over Time March 2009 June 2009 October 2009 March 2010 20%   25%   24%   29%   35%   0%   43%   4%   47%   0%   0%   3%   3%   53%   11%   5%   0%   14%   13%   5%   7%   10%   0%   12%   15%   13%   1%   2%   1%   3%   2%   0%   1   2   3   4   5   6   7   8   1   2   3   4   5   6   7   8   1   2   3   4   5   6   7   8   1   2   3   4   5   6   7   8   August 2010 January 2011 August 2011 April 2012 25%   26%   27%   35%   33%   29%   39%   42%   0%   0%   4%   0%   3%   0%   3%   6%   14%   10%   9%   10%   10%   6%   5%   16%   7%   10%   15%   11%   1%   1%   2%   1%   1   2   3   4   5   6   7   8   1   2   3   4   5   6   7   8   1   2   3   4   5   6   7   8   1   2   3   4   5   6   7   8   Phase Four Groups Over Time August 2011 April 2012 24%   27%   27%   31%   0%   6%   0%   6%   9%   12%   21%   2%   17%   15%   2%   1%   1   2   3   4   5   6   7   8   1   2   3   4   5   6   7   8  
  • 17. Data Collectiong & Variables Summary of Statistical Models Used SDVC has collected and analyzed over 350 variables encompassing 863 groups, 45 field facilitators and 2 regions spanning 4 years. The data has been collected at the household level, the static group level, and the dynamic group level (which changes over time) over eight waves from 2009-2012. Given this, advanced statistical methods are required to produce accurate results. Household  Level  Data   Sta=c  Group  Level  Data   Dynamic  Group  Level  Data     Household  ID   Count  of   Milk   Group  ID   Phase   Region   PPT  Round   PPT  Round   PPT  Round   all  cows   product.   1   2   3   737   1   .25   10111   1   1   35   47   75   1601   1   1.6   10111   1   1   35   47   75   2492   3   4.25   20245   2   1   NA   57   90   4962   2   2.5   30865   3   2   NA   NA   82  
  • 18. Data Analysis MethodsGeneralized Linear Mixed-Effects models Summary of Statistical Models Used To accurately analyze the evidence on how SDVC interventions are working, we built statistical models that looked at all the levels simul- taneously and controlled for the context in which the household exists (in this case, we included various group and program level vari- ables). Most of the trends and effects presented in the findings have controlled for many confounding and mediating variables in addition to the primary variables of interest, including: Geographic variables (upazila, region) Group effect (group number, group contextual variables) Household differences (family size, number of cows, breed of cows). We used the R software for statistical computing. R is a free software environment that is widely used by statisticians. R is powerful and uses the most up to date algorithms available due to its open source nature. The R packages contain functions for working with the com- plex type of data that is involved in this project. These functions are not established in most other statistical packages. We primarily used R to build mixed-effects regression models with both fixed and random effects. This we essential for accuracy as this data has both nested effects (such as households within groups within regions) and crossed effects (such as groups within phases within PPT rounds). Due to the complex nature of the data, all of the models in this analysis were done using generalized linear mixed-effect models. Each of the models in this presentation control for the size of the household cattle herd, the phase of the learning group of the household, the ef- fects of time on the outcomes, and the contextual difference between the household’s results and the group’s results (ie. the within-house- hold trend and the between-household trend.) Generalized linear mixed-effect models (GLMMs) are a class of models designed for the analysis of clustered and longitudinal data with non-normal dependent variables. In our models we have used a binomial link funtion and a penalized quasi-likelihood methods. All our models include both a random intercept and a random slope. Each model includes fixed effects such as size of herd, time of collection and phase of group. Each model also includes a series of random effects including the learning group and time. This method properly controls for the fact that each group is meaured repeatedly as well as the fact that the data is clustered in several dimensions (ie. phase and geography) The acceptable significance level for all of our models is alpha = 0.05.