The influence of smallholder labour demand on cultivation of cash crops in northern Mozambique

Figures & data

Figure 1: Factors and related interactions affecting smallholder cultivation of cash crops and participation in agricultural markets (Lukanu, 2005)

Table 1: Data used in this study and how they were assessed (ranks used: 0 = lowest rank and 5 = highest rank)

Data	How estimated
Active members of household (Ham)	By asking respondents how many household members were actively involved in agriculture. All active members were considered as adults.
External labour or ganho-ganho (El)	By asking respondents how many ganho-ganho the household employed during peak period.
Household members providing ganho-ganho to others people's farms (HmEl)	By asking respondents how many household members provided ganho-ganho to other people's farms.
Working hours per day (Hday)	Through various group discussions on time use in agriculture and for each agricultural operation, such as departure time, arrival time, work time, rest time and leaving time from the field.
Persons involved per operation	By asking respondents how many people were involved in performing each agricultural operation.
Days taken to perform an operation, in hectares of land	By asking respondents to recall number of months (month), number of weeks per month (Wmonth), and number of days per week (Dweek) taken to perform a given operation.
Household consumption	By multiplying number of household members by 150 kg of maize.^1
Yield	By using yield from literature review about northern Mozambique agriculture.
Number of promoted cash crops sold	By asking respondents how many cash crops they had sold during the last agricultural market campaign.
Perceived labour-intensive months, operations and crops	By asking respondents what they thought were the most/least labour-intensive months, operations and crops.
Household wealth status	Through key informants and use of visual indicator of poverty.
Farmers' suggested strategies for alleviating labour constraints	By asking respondents what strategies that they thought could help alleviate labour constraints.
^1Household adult equivalent was not estimated because the data did not allow such a calculation.

Table 2: Statistics of a regression model between available household labour (AHL) during peak period and the number of promoted cash crops cultivated (n = 287, Cuamba, September 2002)

R	R square	Adjusted R square	Std. error of the estimate	Change statistics					Durbin-Watson
				R Square change	F Change	df1	df2	Sig. F change
.179^ab	.032	.029	.746	.032	9.399	1	284	.002	1.605
^aPredictors: (Constant), AHL.
^bDependent variable: PCROSOLD = Promoted cash crops sold.

Figure 2: Ratio of total available household labour (AHL) to labour allocated to food crops for consumption (n = 287, Cuamba, September 2002)

Table 3: Monthly ranking of labour demand and agricultural operations throughout the year in southern Niassa

Figure 3: Monthly ranking of labour demand and agricultural operations throughout the year in southern Niassa (based on Table 3)

Table 4: Labour requirements for cultivating food and cash crops

	Hour/ day	Correc-tion factors^1	Average number of people involved	Food crops^2	Tobacco	Cotton	Sesame^3	Sunflower^3	Paprika	Farmers indicating labour (%)
Land clearing/ tree felling	5.8	0.7	1.5	32	32	32			32	7
Seedling preparation	4.4	0.6	1.3		8				8	5
Ridge making^4	4.3	0.5	1.8		21				21
Soil preparation	4.3	0.5	2.0	30	30	30			30	9
Sowing of early crops	4.3	0.5	2.4	12		12
Sowing of late crops	3.3	0.4	2.4	12			12	12
Transplanting	4.4	0.6	2.4		10				10
Fertiliser application	2.4	0.3	1.0		3	3			3
First weeding	6.8	0.9	2.0	41	24	40			36	43
Second weeding	4.6	0.6	2.0	22	15	22	15	15	21
Third weeding	4	0.5	2.4			14	14
Thinning	3.5	0.4	2.2			17	8	9
De-budding	3.5	0.4	1.8		7
Pest and disease control	2.9	0.4	1.0		4	4			4
Harvest	4.2	0.5	2.1	24	71	35	17	19	13	19
Threshing	1.8	0.2	1.8	18			3	2
Dryer preparation	5.4	0.7	1.5		20				11	5
Drying of products	7.7	1.0	1.5		18				15	3
Transport to the village	3.4	0.4	1.8	17	46	25	1		10	9
Grading	5.3	0.7	1.9		23	15			15
Bagging and storing	4.2	0.5	1.0	3	3	3	3	3	3
Transport to the market	3.8	0.5	1.5	2	2	2	1	1	2
Total person-days/ (ha x year)				213	337	254	74	61	234
Total person-days/ (ha x [Oct-Feb])				87	226	112	49	36	74
Note: Italics indicate the most critical and intensive operations (n = 16, Cuamba, October 2002).
^1Correction factor obtained by dividing the number of days worked by 8 hours per days, the standard working time for Mozambique.
^2Cash food crops include maize, cassava, sorghum, boer bean and cowpea.
^3Sesame and sunflower are often intercropped in food crop fields; therefore there is no need for land clearing, soil preparation and first weeding.
^4Making ridges for planting.

Table 5: Correlation between calculated crop labour requirements (CLR) and the ranked labour demand by smallholders (1 = least labour-intensive while 6 = most labour-intensive, n = 6, Cuamba, October 2002)

	CLR (person-days/[ha/ year])	Labour demand ranking^1
Food crops	213	1
Tobacco	337	6
Cotton	254	5
Sesame	74	3
Sunflower	61	1
Paprika	234	4
Person correlation		0.731
α		0.049
^1The numbers suggest that labour was the most important factor for tobacco and cotton while foodcrops and sunflower were the least labour intensive.

Table 6: Farmers not cultivating owing to lack of available labour, the estimated crop labour requirements and the farmers' ranked labour demand (n = 6, Cuamba, September 2002)

	Not cultivating (%; n = 287)	Not cultivating owing to lack of available labour (%)	Crop labour requirements (person-days/ [ha year])	Labour demand rank	Importance of labour in relation to other factors^1
Tobacco	60	56	337	6	1
Cotton	77	41	254	5	1
Sesame	93	24	74	3	2
Sunflower	95	28	61	1	2
Paprika	99	24	234	4	2
Food crops	0	0	213	1
Correlation coefficient			0.451	0.826
α			0.185	0.021
^1These numbers suggest that labour was the most important factor for tobacco and cotton but second in importance for sesame, sunflower and paprika.

Table 7: Regression model between estimated area for cash crop and number of promoted cash crops sold by smallholders farmers (n = 287, Cuamba, September 2002)

R	R square	Adjusted R square	Std. error of the estimate	Change statistics					Durbin- Watson
				R Square change	F change	df1	df2	Sig. F change
				.138^a	.019	.016	.750	.019		5.502	1	284	.020	1.602
^aPredictors: (Constant), AREACASH (Land area under cash crop).
^bDependent variable: PCROSOLD (Number of promoted cash crops sold).

Lukanu , G . 2005 . “ Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Community Resources, School of Agricultural Science and Agribusiness, Faculty of Science and Agriculture ” . In Factors influencing smallholders' participation in agricultural markets in southern Niassa, Mozambique , Pietermaritzburg, , South Africa : University of KwaZulu-Natal .

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