Characterising the diversity of smallholder farming systems and their constraints and opportunities for innovation: A case study from the Northern Region, Ghana

Figures & data

Fig. 1 Map of the Northern Region of Ghana (inset) showing the location of Africa RISING intervention communities (red points); Kpalung, Botingli and Tingoli, in Savelugu-Nanton and Tolon-Kumbungu districts (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

Table 1 Description of variables used for farm typology construction.

Variable	Unit	Mean	±SEM	Min.	Max.
Household
Size of household	Number of members	15.2	0.97	4	37
Labour
Total labour input^a	Hours per year	2450.5	174.22	256	7048
Hired labour ratio		0.1	0.01	0	0.44
Land use
Cropped land area^b	Hectares	3.8	0.24	0.81	9.31
Maize ratio^c		0.5	0.02	0.19	1
Legume ratio^d		0.2	0.02	0	0.68
Livestock ownership
Herd size	TLU^e	3.2	0.39	0.15	17.31
Small ruminant ratio^f		0.6	0.04	0	1
Poultry ratio^g		0.2	0.03	0	1
Income
Crop sales^h	Percentage	36	3	0	86
Livestock sales^i	Percentage	21	2	0	76
Off/non-farm income^j	Percentage	16	2	0	70

Source: authors' analysis of the 2013 survey data. In all Tables and Figures that follow, the source remains the authors unless otherwise specified.

a Family, hired- and exchange labour input for crop production (the sum of all reported labour per plot per household).

b Land used by farmers for crop production (the sum of all reported plot sizes per household).

c Share of arable land cropped to maize.

d Share of arable land cropped to legumes: beans, soybeans, groundnuts, cowpeas. ^eTropical Livestock Unit: livestock conversion factors based on [47].

f Share of small ruminants in total TLU (herd): goats and sheep.

g Share of poultry in total TLU (herd): chickens, ducks, turkeys, pigeons and guinea fowls.

h Share of crop products sold on the market.

i Share of livestock products sold on the market.

j Share of income derived from off/non-farm activities.

Fig. 2 PCA and CA output: circles of correlation (A⿿D) and clusters i.e. farm types 1⿿6 (E⿿H) in the planes PC1-PC2, PC1-PC3, PC1-PC4 and PC1-PC5. The directions and lengths of arrows within the circles show the strength of the correlations between variables, and variables and PC⿿s. The arrows highlighted in red represent those variables that correlate strongly (>0.60) with PC 1, whereas the arrows highlighted in green represent those variables that correlate strongly with each subsequent PC (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

Table 2 Eigenvalues and percentage variance explained by five principal components (PC⿿s) using PCA.

PC	Eigenvalue	Variance (%)	Cumulative Variance (%)
1	2.33	19.4	19.4
2	2.05	17.1	36.5
3	1.42	11.8	48.3
4	1.20	10.0	58.3
5	0.97	8.1	66.4

Fig. 3 Dendrogram (left) and associated bar plot (right) displaying a range of cluster solutions resulting from Ward⿿s method of CA. The dashed line shows the selected cut-off point which gave the six-cluster solution (Types 1⿿6). The vertical axis represents the agglomeration coefficient (the ⿿height⿿ or distance between clusters merged at each stage).

Fig. 4 Boxplots of variables for the six farm types based on: household (A), labour (B, C), land use (D⿿F), livestock (G⿿I) and income (J⿿L) dimensions. Boxplots show cluster means (coloured squares), median values (solid horizontal lines), the interquartile range containing the middle 50% of values (box outline), 90th percentile values (whiskers) and outlier values (closed circles). The survey means for each variable are represented by the dashed line.

Fig. 5 Maps showing the spatial distribution of identified farm types in the three Africa RISING intervention communities in Ghana⿿s Northern Region: Botingli (A), Kpalung (B) and Tingoli (C).

Table 3 Main implications of the farm typology for targeting of agricultural interventions in Savelugu-Nanton and Tolon-Kumbungu districts of Ghana⿿s Northern Region.

Opportunities and constraints	Implications for targeting	Farm Types
		1	2	3	4	5	6
Opportunities
High availability of family labour	Promote profitable but potentially labour-intensive technologies and practices	x	x			x
Access to animal traction	Promote traction tools for reducing drudgery of land preparation, seeding and weeding	x	x
Access to manure	Encourage practices that ensure more efficient collection, storage, and use of manure supplied by cattle herds, and promote (targeted) manure application to crops	x	x
Higher farm investment capacity	Promote improved agricultural technologies, inputs and practices (e.g. improved seed, fertilizers, irrigation)	x	x	x	x
Experience with diversified crop production	Increase multi-cropping, intercropping, crop rotation and new crop varieties use		x	x	x
Responsive to commercial farming opportunities	Promote high value crops, improve marketability		x	x	x
Higher investment in livestock herd	Improve crop-livestock integration and promote multi-purpose crops (e.g. food-fodder crops) and leguminous fodder	x	x
Constraints
Low availability of family labour	Support adoption of time saving practices and low-cost alternatives for transportation and tillage traction			x	x		x
Difficulty accessing manure	Promote use of nitrogen-fixing legumes (as green manures, intercrops, fodder crops etc.) and composting of crop residues and household waste			x	x	x	x
Higher food insecurity	Increase farm productivity, promote high yielding food crop varieties, improve post-harvest storage facilities				x	x	x
Poverty (cash flow constraints, hand-to-mouth existence)	Promote low-input technologies with improved resource use efficiency, support access to inputs at the beginning of the cropping season, improve farmers⿿ ability to accumulate capital and reinvest in their farms					x	x

Fig. 6 The six farm types are plotted against their household size and cattle ownership (A), and their cropped land area and food self-sufficiency (B). The survey means (m) for the plotted variables are represented by the dashed lines. Encircled points are observations that deviate from the typical characteristics of their group (see text for further explanation).