How rigid business approaches paved the way for inclusive business practices: A case study of innovative milk trading practices aligned with pastoralists

Figures & data

Table 1. Analytical framework indicating the dimensions and codes used in data analysis.

Dimension	Indicators/code groups	Codes
Businesses navigating the sequence of instructive events in Dakawa	a) Lead firm milk sourcing practices	Processors and agency model
	b) Traders’ milk sourcing practices	Multiple traders Coordination
(1) The practice of milk collection by independent milk traders (2) Tracing motorbike milk aggregators’ practices in sourcing milk	a) Navigating seasonal variability in prices and volumes through selling to different buyers b) Adapting to pastoralists’ movement strategies	(Multiple buyers) Milk buyers Milk market seasonality Cattle movement Price setting Milk collections (volumes and suppliers)
	a) Organizing milk collection	Operations milk collection Quality test Aggregation Transportation Coordinating payment Competition Challenges Strategies
Responsiveness of pastoralists to the current milk business model.	a) Gender roles b) New livelihood opportunities c) Changes in pastoral practices	Sedentarisation, farming Changes in pastoralism Household milk utilisation Grazing land Feeding Breeds Decision making Gender roles Milking Cultural aspects

Figure 1. Average milk collections across the season and cooling centres.

The dry season were periods of no rains and some cattle moved to other grazing areas away from Dakawa while in the wet season there is more rain and cattle are grazed in Dakawa. Traders signify the different cooling centres aggregating milk from various motorbike riders. Data was collected in August 2020 and the X=axis denotes a recall of milk volumes collected per season across years.

Figure 2. Prices of milk across seasons and cooling centres.

The dry season were periods of no rains and some cattle moved to other grazing areas away from Dakawa while in the wet season there is more rain and cattle are grazed in Dakawa. Data was collected in August 2020.

Figure 3. Panel a= shows Dakawa ward and Dumila ward where cattle migrate during dry season. b= shows the location of dakawa within Tanzania and c= shows the various milk cooling centres in Dakawa ward.

Table 2. Demographic statistics of the pastoralist households.

Variables	Mean	SD
Age of household head [Years]	43.15	43.15
Gender of the household head [male]	93.54	24.76
The household head can read and write [% Yes]	56.54	49.99
Household size [no]	7.68	3.241
Total livestock numbers [no.]	52.73	51.84
Number of cows [no.]	19.90	19.89
Whether a household keeps crossbred cow [%]	0.29	0.46
Average milk wet season per household [Litres]	13.66	17.51
Average milk dry season per household	8.99	13.72
Land [Acres]	7.88	8.40
Experience selling milk [Years]	11.90	8.70
Annual milk revenue [TZS]	2,248,500	2,785,065
Annual Livestock revenue [TZS]	1,150,435	2,688,471
Annual Household Revenue [TZS]	5,330,863	8,548,839

Table 3. Differences in demographics for pastoralists selling milk and those not selling milk.

	Not selling milk (n = 31)	Selling milk (n = 31)	t-test for equality of means	Levene’s test for equality of variance
Variables	Mean (sd)	Mean (sd)	t-statistic	Chi^2
Number of cattle	49.23 (48.18)	56.234 (55.83)	−0.53	–
Number of cows [no.]	19.23 (22.02)	20.58 (17.85)	−0.27	–
Whether household keeps crossbred cow [%]	0.13 (0.34)	0.45 (0.51)		7.83***
Average milk wet season per household [litres]	8.27 (8.86)	19.05 (22.02)	−2.53**	–
Average milk dry season per household	5.27 (8.47)	12.70 (16.80)	−2.19**	–
Land acres	7.37 (9.13)	8.43 (7.72)	−0.85
Experience selling milk [Years]	0	11.90 (8.70)	−7.62***	–
Annual milk revenue [TZS]	0	2,248,500 (2,785,065)	−4.49***	–
Annual Livestock revenue [TZS]	959,032 (1,047,697)	5,061,081 (6,892,153)	−3.28**
Annual household revenue [TZS]	2,935,355 (4,269,626)	7,726,371 (1.09e + 07)	−2.281**

Source: Own survey June 2018. Note: degree of freedom (df) = 60. *= p < 0.10, **= p < 0.05, ***= p < 0.01.