SDVC Analysis

October 2011

Relationship between feeding practices and HH income.

There is a significant relationship between feeding practices and total HH income from dairy.F(16)=576.98, p<.00001. This relationship has been modeled using a linear mixed-effects modelwhich controls for phase, year, size of herd, and includes random effects for learning group.

Rice Bran Wheat Bran Pulse Husk Broken Rice Molasses MOC0.00%

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4.82%

3.98%

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Average expecting increase in monthly income

Per kg Increase in monthly feeding

Changes in feeding practices over time

Monthly average in Kg used by households at each time point.

June-09 October-09 March-10 August-10 January-11 August-11

Rice Bran 74.48 64.21 65.86 69.11 68.80 81.81

Wheat Bran 15.45 13.57 18.50 17.97 15.78 14.57

Pulse Husk 0.64 0.99 1.07 0.18 0.80 1.68

Broken Rice 11.87 9.72 6.70 7.20 6.12 8.21

Molasses 1.06 0.35 0.28 0.11 0.26 0.21

MOC 0.00 2.13 2.16 2.18 2.47 2.90

The effects of deworming practices on HH incomeHouseholds that deworm their cattle have significantly higher total HH income from dairy.F(1)=10.52, p=.001. This relationship has been modeled using a linear mixed-effects modelwhich controls for both deworming and vaccination practices, phase, year, size of herd, and includes random effects for learning group.

Mar-09 Jun-09 Oct-09 Mar-10 Aug-10 Jan-11 Aug-115

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The effects of vaccination practices on HH incomeHouseholds that vaccinate their cattle have significantly higher total HH income from dairy.F(1)=10.52, p=.001. . This relationship has been modeled using a linear mixed-effects modelwhich controls for both deworming and vaccination practices, phase, year, size of herd, and includes random effects for learning group.

Mar-09 Jun-09 Oct-09 Mar-10 Aug-10 Jan-11 Aug-115

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The effects of AI practices on HH incomeHouseholds that artificially inseminate their cattle have significantly higher total HH incomefrom dairy. F(1)=17.88, p<.0001. . This relationship has been modeled using a linear mixed-effects model which controls for phase, year, size of herd, and includes random effects for learning group.

The effects of learning group gender composition on HH income

Households that have female farm leaders and groups that are all women have significantly higher total HH income from dairy. F(13)=1108.9, p<.0001. . This relationship has been modeled using a linear mixed-effects model which controls for phase, year, size of herd, and includes random effects for learning group.

Female Farm Leader Male Farm Leader Women Only Learning Group

Mixed Learning Group Men Only Learning Group

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Expected Income depending on gender of FL Expected Income depending on gender mix of group

The effects of market linkage on HH incomeHouseholds market linkage significantly affects total HH income from dairy. F(8)=45.84, p<.0001. . This relationship has been modeled using a linear mixed-effects model which controls for phase, year, size of herd, and includes random effects for learning group.

Mar-09 Jun-09 Oct-09 Mar-10 Aug-10 Jan-11 Aug-116.1

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MVRDPRANBRACGrameen DannonInformal Sector

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The effects of gender and market linkage on HH income

The gender of the main FL interacts with HH market linkage to significantly affect total HH income from dairy. F(8)=2.44, p<.01. . This relationship has been modeled using a linear mixed-effects model which controls for phase, year, size of herd, and includes random effects for learning group.

MV RD PRAN BRAC Grameen Dannon

Informal Sector

MV RD PRAN BRAC Grameen Dannon

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Female Main Farm Leaders Male Main Farm Leaders

June-09 October-09 March-10 August-10 January-11 August-116

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No CollectorGroup Has Own Collector

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The effects of the group milk collector on HH incomeHouseholds that choose their own milk collector have significantly higher total HH incomefrom dairy. F(9)=8.87, p<.0001. This relationship has been modeled using a Linear mixed-effects model which controls for phase, year, size of herd, and includes random effects for learning group.

June-09 October-09 March-10 August-10 January-11 August-115.50

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Level of Satisfaction 1Level of Satisfaction 2Level of Satisfaction 3Level of Satisfaction 4Level of Satisfaction 5

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The effects of the group milk collector on HH incomeHouseholds that choose their own milk collector have significantly higher total HH incomefrom dairy. The level of satisfaction with the chosen collector also significantly affect total HH income from dairy. F(9)=8.87, p<.0001. This relationship has been modeled using a Linear mixed-effects model which controls for phase, year, size of herd, and includes random effects for learning group.

Relative influence of gender, AI, feeding practices and market linkage on HH income from dairy

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Gender Composition WomenGender Composition MenGender Composition MixedGender CompositionAI Practices Use AIAI Practices Not Use AIAI PracticesFeed Practices ExcellentFeed Practices GoodFeed Practices AverageFeed Practices PoorFeed PracticesSales Point MVSales Point RDSales Point PRANSales Point BRACSales Point AKIJSales Point Do no saleSales Point Grameen DannonSales Point Informal SectorSales Point Others

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Relative influence of gender, AI, feeding practices and market linkage on HH income from dairy

This is based on a complex multivariate linear mixed-effects model that controls for time, phase, learning group and all of the variables

of interest simultaneously. This chart shows the expected level of income for a HH that is excellent in all other areas. This illustrates the relative influence of the combination of variables.

The order of relative importance on the HH income Feed Practices(F(3),21.29, p<.0001)Market Linkage (F(6), 63.46,p<.0001)Vet Practices(F(2),12.67, p=.0004)AI Practices (F(1),31.51, p<.00001)Sex (F(1), 37.68,p=.00013)

Influence of gender and market linkage on milk collector income

Milk collectors income is significantly related to the gender of the collector and the type of market linkage. F(9)=8.87, p<.0001. This relationship has been modeled using a Linear mixed-effects model which controls for phase, year, geographic region, and includes random effects for learning group.

MV RD PRAN Grameen RD PRANMales Females

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Influence of gender and WBA on livestock health worker income

LHW income is significantly related to the gender of the collector and the type of market linkage . F(7)=9.63, p<.0001. This relationship has been modeled using a Linear mixed-effects model which controls for phase, year, geographic region, and includes random effects for learning group.

Influence of Upazila on HH total monthly income from dairy

The level of geography that seems to be most related to predicting HH total monthly income from dairyIs Upazila. F(23)=3683.83, p<.0001. This relationship has been modeled using a Linear mixed-effects model which controls for phase, year, geographic region, and includes random effects for learning group.

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There is a very strong link between HH access to AI and AI uptake

As access to AI increases, the probability of the HH using AI increases, p<.0001

There is a very strong link between HH total knowledge score and milk price per litre.

As the total knowledge score increases, the predicted milk price increases, p<.0001

There is a link between HH initial knowledge score and how the knowledge score over time affects milk price per litre.

There seems to be an interaction effect between the initial knowledge score and the effects of increasing the knowledge score over time. The four different colored lines indicate groups

labelled according to their initial knowledge score,

There is a very strong link between the total knowledge score and the probability of getting cattle vaccinated.

As the knowledge score increases, the probability of the HH getting the cattle vaccinated increases, p<.0001

Relationship between women owning cattle and women’s total knowledge score

There is a significant relationship between the gender of who owns the cattle and women’s knowledgescore. The Pearson correlation is .241, p<.01. And the one-way ANOVA F=486.74, p<.001

Self Husband Jointly0%

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Who Decides About Selling Cattle?

HH Where Women Own CattleHH Where Women Don't Own Cattle

Relationship between women owning cattle and decisions to sell cattle

There is a significant relationship between the gender of who owns the cattle and who makes the decision to sell cattle. The Pearson correlation is .46, p<.01. Cramer’s V (.464), p<.0001.

Need Permission to attend Group Meeting Need Permission to Attend a Distant Meeting0%

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HH Where Women Own CattleHH Where Women Don't Own Cattle

Relationship between women owning cattle and permission to attend group and distance meetings

There is a significant relationship between women owning cattle and needing permission to attendboth group meetings and distant meetings. The Pearson correlation for attending group meetingsis .65, p<.01. Chi-square (4) = 7031, p<.0001. The Pearson correlation for attending distant meetingsis .68, p<.001, Chi-square (4) = 7095, p<.0001.

Women Need Permission Women Don't Need Permission0%

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HH Owns Cross BreedsHH Doesn't Own Cross Breeds

Relationship between HH owning cross-breed cattle and permission to attend group meetings

There is not a significant relationship between HH owning cattle and needing permission to attendgroup meetings. The Pearson correlation for attending group meetingsis .1, p<.3

Women Need Permission Women Don't Need Permission0%

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HH Owns Cross BreedsHH Doesn't Own Cross Breeds

Relationship between HH owning cross-breed cattle and permission to attend distant meetings

There is not a significant relationship between HH owning cattle and needing permission to attenddistant meetings. The Pearson correlation for attending group meetingsis .08, p=.11.

Relationship between women owning cattle, HH owning cross-breed cattle and permission to attend group meetings

There is a significant relationship between the interaction of whether not women own cattle, HH whoown cross-breed cattle and whether or not women need permission to attend meetings. This plot shows the percentage of women who say they do need permission to attend meetings.

Women own CB cows Women own LB cows Women do not own cows0.00

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group meeting permission neededSeries2distance meeting permission needed