Role of technological innovation in livestock breeding programmes: a case of cereal-sheep system

Figures & data

Table 1. Descriptive statistics of technological, structural and productive variables in Manchega sheep breed.

	With BP^a			Without BP
Variable	Total	Viable	non-viable	Total	Viable	non-viable	SDM^b
Sample (n)	63	50	13	94	41	53
Technological variables
T1, Management, %	71.110	71.200	70.770	30.420	32.440	28.870	21.180
T2, Feeding, %	61.900	64.280	52.750	53.500	57.840	50.130	19.670
T3, Health and biosecurity, %	77.600	78.000	76.070	67.500	69.110	66.250	11.150
T4, Land use, %	41.270	42.000	38.460	29.360	32.680	26.790	23.680
T5, Milking equipment, %	61.620	62.910	56.640	45.070	52.550	39.280	17.920
T6. Reproduction and genetic, %	69.840	69.560	70.940	17.610	18.970	16.560	29.570
Structural variables
S1, Surface, ha	1374.730	1476.100	984.92	945.410	1308.780	664.320	1359.880
S2, Own surface, %	24.550	27.550	13.000	8.710	9.150	8.370	31.950
S3, Ewes, no	1229.790	1360.580	726.770	625.110	832.660	464.550	798.790
S4, Family Labour, %^c	32.970	34.080	28.720	73.240	62.450	81.570	40.970
S5, External feeds, %	49.440	47.900	55.350	69.140	60.720	75.650	31.580
S6, Stocking rate, LU/ha^d	0.170	0.178	0.181	0.214	0.129	0.149	0.150
Productive variables
P1, Lamb yield, no/AWU^e	381.240	403.390	296.040	322.080	361.070	291.920	170.410
P2, Sanitary cost, €/ewe	15.080	14.960	15.540	15.750	15.690	15.790	3.130
P3, Milk yield, kg/ewe	165.400	149.810	118.870	127.800	143.440	115.250	66.280
P4, Lambing Interval, d	332.080	324.020	362.920	347.620	355.240	341.720	70.670
P5, Lambs sold, %	75.540	76.200	73.000	78.830	78.590	79.120	9.650
P6, Feed cost, €/ewe	85.750	83.730	93.490	73.810	69.490	77.160	40.100

^a BP: breeding programme

^b SDM: standard deviation of the mean

^c Percent familiar work units.

^d LU: Livestock units (1 ewe = 0.15 LU)

^e AWU: annual work u units.

Figure 1. Representation of results in each discriminant model for the groups of farms (Viability and BP) in Manchega sheep breed, defined by the axis built by the first canonical variables (CAN 1 and CAN 2).

viable with genetic improvement programme (BP),

non-viable with BP,

viable without BP,

non-viable without BP.

Table 2. Results of the canonical discriminant analysis in Manchega sheep breed.

Model [n]	Variables	Groups	Wilks’ lambda	F	p	Correct^a
[1] Technological variables (Ti)	6	4	0.081	33.428	<.0001	69.430
[2] Structural variables (Si)	6	4	0.605	4.529	<.0001	49.040
[3] Productive variables (Pi)	6	4	0.751	2.481	<.0001	50.960
[4] All the variables (Ti+Si+Pi)	18	4	0.049	13.138	<.0001	80.890

^a Percent correctly classified into farms assignment (R²=1 – tolerance).

Table 3. Individual results for variables included in the four canonical discriminant models (variables with the highest discriminate power are bold) and correlations of each variable with the canonical variables.

Variable [Model]	Wilks’ lambda	F	p	R^2	CAN1	CAN2
Technological variables [Model 1]
T1, Management	0.107	426.115	<.0001	0.805	0.993	0.062
T2, Feeding	0.911	5.003	.002	0.062	0.227	0.521
T3, Health and biosecurity	0.792	13.419	<.0001	0.266	0.470	0.242
T4, Land use	0.928	3.948	.010	0.194	0.264	0.250
T5, Milking equipment	0.704	21.419	<.0001	0.342	0.490	0.743
T6, Reproduction and genetic	0.243	158.586	<.0001	0.772	0.914	−0.032
Structural variables [Model 2]
S1,Surface	0.934	3.604	.015	0.433	−0.402	–^a
S2, Own Surface	0.927	4.028	.009	0.316	−0.422	–
S3, Ewes	0.788	13.694	<.0001	0.576	−0.770	–
S4, Family Labour	0.733	18.573	<.0001	0.361	0.894	–
S5, External feeds	0.869	7.694	<.0001	0.227	0.636	–
S6, Stocking rate	0.981	0.999	.395	0.378	−0.165	–
Productive variables [Model 3]
P1, Lamb yield	0.920	4.424	.005	0.281	−0.696	−0.111
P2, Sanitary cost	0.987	0.675	.568	0.041	0.219	0.254
P3, Milk yield	0.945	2.974	.034	0.122	−0.579	0.035
P4, Lambing Interval	0.963	1.968	.121	0.163	0.225	0.379
P5, Lambs sold	0.964	1.910	.130	0.197	0.062	0.579
P6, Feed cost	0.969	1.613	.189	0.230	0.015	−0.602
All groups of variables [Model 4]
T1, Management	0.107	426.115	<.0001	0.824	0.986	0.000
T2, Feeding	0.911	5.003	.002	0.152	0.230	0.351
T3, Health and biosecurity	0.792	13.419	<.0001	0.398	0.469	0.136
T4, Land use	0.928	3.948	.010	0.624	0.264	0.144
T5, Milking equipment	0.704	21.419	<.0001	0.481	0.494	0.427
T6, Reproduction and genetic	0.243	158.586	<.0001	0.818	0.907	−0.056
S1, Surface	0.934	3.604	.015	0.512	0.175	0.328
S2, Own surface	0.927	4.028	.009	0.444	0.255	0.129
S3, Ewes	0.788	13.694	<.0001	0.637	0.401	0.434
S4, Family Labour	0.733	18.573	<.0001	0.498	−0.515	−0.143
S5, External feeds	0.869	7.694	<.0001	0.587	−0.333	−0.275
S6, Stocking rate	0.981	0.999	.395	0.421	0.130	−0.085
P1, Lamb yield	0.920	4.424	.005	0.474	0.190	0.375
P2, Sanitary cost	0.987	0.675	.568	0.226	−0.109	−0.061
P3, Milk yield	0.945	2.974	.034	0.254	0.135	0.340
P4, Lambing interval	0.963	1.968	.121	0.305	−0.108	−0.065
P5, Lambs sold	0.964	1.910	.130	0.318	−0.173	0.145
P6, Feed cost	0.969	1.613	.189	0.366	0.146	−0.181

^a Non significant values for this canonical component.

Figure 2. Plot of the cluster analysis developed for the four groups of farms evaluated in Manchega sheep breed.

Table 4. Variables that discriminated between pair of groups using step – wise discriminant analysis in Manchega sheep breed.

		With BP^a	Without BP
Group		Non-viable	Viable	Non-viable
With BP	Viable	S3, P1	T1, T3, T5, T6, S3, S4, S5	T1, T2, T3, T4, T5, T6, S3, S4, S5, P1
	Non-viable		T1, T3, T6, S4	T1, T3, T5, T6, S3, S4, S5
Without BP	Viable			T1, T5, S3, S4, S5

^a BP, breeding programme; T₁, management; T₂, feeding; T₃, health and biosecurity; T₄, land use; T₅, milking equipment; T₆, reproduction and genetic; S₁, surface; S₂, own surface; S₃, ewes; S₄, family Labour; S₅, external feeds; S₆, stocking rate; P₁, lamb yield; P₃, milk yield.