Livestock Research for Rural Development 13 (1) 2001 | Citation of this paper |
A three-year experiment
was carried out to study different agro-ecological livestock:crop systems under different
soils and climates, without irrigation and using on-farm resources for animal and plant
nutrition. Five farms, four in the process of conversion and the fifth with twelve years
of establishment were studied. Eight sustainability indicators (reforestation, total
species, food products, labour intensity, production of organic fertilisers, yields, energy efficiency and
milk production) were defined, which relate to the main productive and environmental
problems faced by the livestock sector due to the specialised agricultural model that has
prevailed in Cuba over the last few years. These indicators were weighted, represented on
a radial graph and evaluated through an analytical description and multivariate analysis.
Biodiversity increased
after the establishment of integrated systems. Starting from specialised milk production
systems, diversification allowed for between 30 and 40 more products. The integrated
systems increased the energy efficiency from 3 to 10 joules produced per joule of input.
Labour intensity decreased yearly after a greater initial labour demand required for
establishing the system. Production of high quality organic fertiliser (2 to 4 tonnes/ha)
was a major resource to cover the crop nutrient requirements. Productivity increased by up
to 9.7 tonnes/ha including both animal and crop production. There was some fluctuation
between animal and crop production, but the final result was higher system productivity.
The results of the
study show that integrated ecological livestock:crop systems can provide sufficient
capacity and potential to sustain intensive
production based on available natural resource management alternatives.
Key words: Ecological farming, integrated systems, biodiversity, sustainability indicators, organic fertilizers
Specialised cattle rearing systems were the
chosen models for the development of Cuban livestock production since the 1960s.
Milk production systems became a priority considering their higher efficiency to convert
pastures into high quality protein compared with the previously more widespread beef
cattle production systems.
Large numbers of Holstein-Friesian cattle
were imported to increase the milk production potential of the national herd, and
artificial insemination was applied almost exclusively. Fuel, machinery and spare parts
and large amounts of concentrate feeds were imported, and a great infrastructure was
established. Improved pastures, fertilization and irrigation prevailed. As a consequence
of this high investment, which was possible because of favourable trade agreements with
the Socialist bloc of Eastern Europe, milk production increased but at a high energy cost
(Funes-Monzote 1998). The industrialised specialised livestock production systems also
created dependency and provoked deforestation, erosion of soil and loss of biodiversity,
with poor use of local resources and exodus of the rural population to the cities.
It is postulated that integrated
agro-ecological systems, provide a more appropriate solution to the problem of how to
achieve better production efficiency and cost-benefit ratios based on the sustainable use
of the available natural resources. However, these potential advantages have not been
sufficiently assessed, the systems have not been sufficiently studied and there is a lack
of appropriate methodologies.
This paper is focussed on the evaluation of
the process of conversion from specialised milk production systems to integrated
agro-ecological systems as a way forward for Cuban agriculture. To achieve this objective,
sustainability indicators were chosen that could be measured and quantified and practical
methodologies were used to reveal the degree to which these indicators were
improved.
From a group of eleven representative farms,
five were selected to assess the process of conversion from specialised milk production
systems to integrated agro-ecological systems. Eight sustainability indicators
(reforestation, total species, number of feed products, labour intensity, production of
organic fertiliser, yield, energy efficiency and milk production) related to the main
problems presently faced by the livestock sector, were monitored during three years of
establishment of an integrated farm management concept.
At the beginning of the trial, four of the
farm systems were specialised in milk production based on pastures. The fifth
(Humbertos Farm, Sancti Spíritus) had been managed for twelve years as a
diversified farm (58 and 42% of area allocated to livestock and crops, respectively). The
inclusion of crop areas in the proportions of 25, 40 and 50% (Havana) and 24% (Las Tunas)
was the basis of the integration program for the other four farms. The design of the sub-systems design was similar
for all farms (Table 1).
Table 1. Sub-systems design scheme of the farms |
|
Crop sub-systems |
Crop rotation Permanent crop Horticultural |
Livestock sub-systems |
Silvopastoral areas Grazing areas
(grass/legume association) Forage areas Protein bank Small livestock areas |
Medicinal plants, fruit trees and living fences were
also planted in the different areas |
The farms under study were representative of
the three main socio-economic and climatic conditions found in Cuban livestock regions, at
different scales of operation and activities, with varying degrees of integration of the
livestock and crop areas (Table 2).
Table 2.
Major features of the farms |
||||||
Farm
|
Area, ha |
Main activity |
Integration proportion, % |
Stocking rate, (*) AU/ha |
Soil |
Rainfall mm |
Havana |
1.0 |
Pastures |
75:25 |
2 |
Alfisols |
1200 |
Havana
|
1.0 |
Pastures |
50:50 |
1 |
Alfisols |
1200 |
Havana |
3.0 |
Pastures |
60:40 |
1 |
Alfisols |
1 00 |
Humberto,
S. Spíritus |
2.5 |
Diversified farm |
58:42 |
3.6 |
Mollisols |
1400 |
Las Tunas |
13.0 |
Pastures |
76:24 |
1.4 |
Inseptisols |
1000 |
(*)
Including crop areas (global stocking rate over the system) AU = animals of 450 kg live
weight |
Livestock areas were
managed according to a rotational grazing system on pastures of mixed grasses (Panicum maximum, Brachiaria brizantha, Cynodon
nlenfuensis) and legumes (Leucaena leucocephala,
Neonotonia wightii, Pueraria phaseoloides). The cattle were also given harvested
forages (Saccharum officinarum, Pennisetum purpureum)
and crop residues from the agricultural areas (Manihot
sculenta, Musa spp., Ipomoea batatas, etc.) (Table 3).
Table 3. Major features of the management
systems |
|
Crop areas |
Livestock
areas |
Polycropping |
Rotational grazing, 2 12 paddocks |
Minimum tillage |
Protein bank |
Crop rotation |
Grass-legume mixtures |
Diversification |
Biomass bank (eg: sugar cane) |
Organic fertiliser production |
Silvopastoral system |
Use of legumes |
Crop residues for animal feeding |
Biofertilization |
Restricted suckling of calves up to 4
months |
No irrigation |
Livestock crop rotation |
No agrochemicals |
Living fences for dry season animal
feeding |
Animal traction,
generally |
Conventional veterinary
treatments |
The analytical and descriptive methods
reported by Hetch (1997) were used to record data on farm production, resources and
processes as well as daily activities and incidents. Sustainability indicators were
selected according to the main problems faced by the livestock sector, and as these
related to productivity and environment conservation. They were:
Reforestation:
Number of trees/ha (including fruit trees, forest and living fences on the farm).
Total species: Number of total species of cultivated plants (including others such as medicinal and ornamental use) and domestic animals (excluding wild animals). Soil biota and spontaneous vegetation were excluded.
Number of food products: Products that
can be used for human consumption.
Labour intensity: This was calculated
as the total time employed only in productive activities. This indicator is expressed as
follows (hours/day/ha) = total hours (year)/total area ( ha)/365 days.
Organic
fertiliser production: Amount of organic fertiliser produced on the farm divided by
the total agricultural area, which expresses the potential to fertilise the crop areas.
All types of organic fertilisers were considered, such as compost, humus, green manures
(DM basis), incorporated mulch, etc.
Yield: Animal and crop production (yield/ha) =
(crop production + animal production)/farm area
Energy
efficiency: Balance between energy outputs and inputs in terms of joules. Outputs
include the energy equivalent of the agricultural and livestock products. Inputs includes
human labour, fuel expenses, and work by draft animals. A computing system
(ENERGIA) was developed based on the methods used by different authors
(Pimentel et al 1983; Pimentel 1984;
Masera and Astier 1993; Ensminger et al 1994 and García-Trujillo 1996).
Milk
production: Overall milk production on the farm divided by the number of hectares
dedicated both to agricultural and livestock activities.
The assessment of the selected indicators was
made according to a weighted scale of five levels as proposed by Taylor et al (1993).
These indicators were expressed as percentages (levels) of satisfaction (Table 4).
Table 4. Classification and weighting of sustainability
indicators |
|||||
Level of satisfaction (%)
(Classification) |
|||||
|
20 |
40 |
60 |
80 |
100 |
Reforestation (trees per hectare) |
< 20 VL |
20 100 L |
100 200 M |
200 400 H |
> 400 VH |
Total species (units) |
< 15 VL |
15 30 L |
30 50 M |
50 100
H |
>100 VH |
Food products (units) |
< 5 VL |
5 8 L |
8 15 M |
15 30 H |
> 30 VH |
Labour intensity (hours/day/ha) |
> 10 VH |
6 10 H |
4 6 M |
3 4 L |
< 3 VL |
Production of organic fertiliser
(tonnes/ha) |
< 1 VL |
1 2 L |
2 4 M |
4 6 H |
> 6 VH |
Yield per hectare (tonnes/ha) |
< 2 VL |
2 4 L |
4 8 M |
8 15 H |
> 15 VH |
Energy efficiency (output/input in
joules) |
< 1 VL |
1 2 L |
2 6 M |
6 15 H |
> 15 VH |
Milk production (litres./ha) |
< 0.5 VL |
0.5 1 L |
1
2 M |
2
4 H |
>
4 VH |
Note: VL-Very Low, L- Low, M-Medium, H-High, VH-Very
High. |
The levels of satisfaction were recorded on a radial graph, in which each axis represents an indicator. The further the location of each indicator from the centre of the graph the greater is the degree of satisfaction. Multivariate analyses (cluster and main components analysis) were used as a quantitative method for the statistical analysis of this type of integrated system (Williams 1976; Manly 1994).
A general diagnosis of the present livestock
situation in Cuba, the result of many years of applying a conventional industrialised
model, indicates that the model is unsustainable. The
critical points are: loss of biodiversity, high energy inputs, poor utilisation of manure,
shortage of labour, decrease of milk production. The indicators considered in this study
were selected in relation to these specific problems.
Masera et al (1999) point out that indicators
are specific to the process, depending on the problems and characteristics of the
situation under study. Integrated agro-ecological livestock:crop integrated systems,
analysed through a simultaneous evaluation of indicators, demonstrate the possibilities of
transforming the specialised systems in a short period.
Research on integrated systems with an
agro-ecological bases has two main objectives (Wolfert et al 1998): one is to demonstrate how, through
synergy effects, more productive results can be achieved; the other is to ensure that
these systems are not only integrated but also managed with an ecological concept. The
monitoring of different livestock:crop designs over a three year period was the key to
learning the tendencies of behaviour and ranges of efficiency and productivity that were
obtained during the conversion process, from the specialised production system to the
integrated agro-ecological systems (Table 5).
Table 5. Range of production
levels and efficiencies observed in integrated livestock:crop farms |
|
Concept |
Range |
Area (ha) |
1 to 13 |
Total Production (t/ha) |
4 to 9
|
Crop production |
3 to 6 |
Livestock production |
1 to 3 |
Energy production
(MJ/ha) |
12540 to 41800 |
Protein (kg/ha) |
100 to 300 |
Energy Inputs (MJ/ha) |
- |
Human labour |
3090 to 4180 |
Animal labour |
84 to 251 |
Fuel expenses |
0 to 1254 |
Energy
balance (MJ) (output /input) |
2 to 10 |
Milk production (2 to 3 tonnes/ha) was
calculated considering the total farm area, including the part devoted to crops. When
calculated strictly on the grazing area the production was as high as 6 tonnes/ha. These
levels are very high for tropical conditions. However, the first analysis criterion is
more useful since the farm is considered as a whole. Table 6 shows the changes in total
production and energy efficiency of an integrated livestock:crop farm with 75% of the area
devoted to livestock and 25% to crops.
Energy efficiency is often considered to be related to scale of activity, and many theories suggest a reduction of this indicator as the area is increased. However, the effects of scale economies are more related to the technology and concepts of land, capital and work intensity than to land exploitation size (McNeeting 1993). In one study, Binswanger et al (1993) showed that efficiency and productivity diminished as size of farm increased.
Estimations made by Funes-Monzote (1998) of
the energy efficiency of livestock production during the highest milk production peak in
Cuba showed that 5.7 MJ of energy were used
to produce one MJ of milk. The energy balance of the farms assessed in this current study
were positive from the first year and increased year by year, thus confirming that
efficient use of energy is possible through the use of natural resources.
Taking into account the low biological energy
efficiency of animal production systems for converting tropical forages into energy and
protein for human consumption, it has been argued that the need is for crops with a high
biomass production and livestock species that are adapted to using such feed resources
(Preston 1995). Alternative production systems were described (Preston 1995) that confront
the belief that the intensification of livestock production depends exclusively on the use
of cereal grain and protein-rich meals. These alternatives are based on the utilisation of perennial crops, especially trees
and tall-growing plants, which generate large amounts of biomass, a high proportion of
which is naturally recycled to the soil.
The
systems developed in the present study aim to make a better use of all feeding resources
generated by short-term crops in the agricultural sub-system, and a better utilisation of
space in order to design better and more appropriate diversified combinations between
animal and crop production. In addition, the
utilisation of perennial crops such as legumes trees (Leucaena leucocephala, Gliricidia sepium), fruit trees (Mangifera indica, Persea americana, Citrus spp., Psidium guajaba), grass forage species (Saccharum officinarum, Pennisetum purpureum),
long-term crop species (Manihot sculenta, Musa spp.)
and crop residues commonly utilised for animal feeding (Ipomoea batatas, Phaseolus spp., Vigna spp.) are
combined to attain the best possible utilisation of biomass, contributing a high energy
and protein content into the system that can be used for animal feeding.
Table 6. Assessment of productivity and
energy efficiency of an integrated farm with livestock:crop areas of 75:25 |
|||
Productive factors |
Year 1 |
Year 2 |
Year 3 |
Total production (tonnes/ha) |
4.9 |
5.1 |
5.3 |
Crop production |
3.3 |
2.8 |
2.4 |
Livestock production |
1.6 |
2.3 |
2.9 |
Energy (MJ/ha) |
15094 |
20419 |
18363 |
Protein (kg/ha) |
115 |
151 |
147 |
Energy inputs (MJ/ha) |
|
|
|
Human labour |
1639 |
1501 |
1321 |
Animal traction |
70 |
70 |
70 |
Fuel expenses |
- |
579 |
193 |
Energy
balance (output/input) |
8.8 |
9.5 |
12.1 |
Harwood (1986) has claimed that small scale production systems can increase productivity through labour
intensification, the organisation and rational use of the cultivated area as well as the
greater control of the productive process. The present study has demonstrated the
possibility of obtaining good energy balances and high yields per hectare through the
integration of livestock and crops at small and medium scales when this is combined with
ecological management.
A comparison between agro-ecological and
conventional technologies showed the advantages gained from input substitution, as well as
the N-use efficiency in integrated systems designed with agro-ecological criteria
(Lantinga 1998). These advantages include: a reduction of pests,
diseases and weeds, less dependency on external inputs, lower capital investments and high
efficiency of land use associated with polycrops and multi-functional benefits of small
scale systems (Rosset 1997, 1999).
The cluster
analysis method is a way of comparing groups of similar farms each with different designs in accordance with the annual improvement of
indicators (Table 7). In the first group were located the farms that showed the best
performance and stability. A group of unselected farms (group 4) showed good results in
certain indicators, but not in others.
Table 7. Cluster analysis (group means) |
||||
Variable
|
Groups |
|||
|
1 |
2 |
3 |
4 |
Costs
of production |
0.35 |
0.25 |
0.25 |
0.45 |
Livestock
proportion |
0.65 |
0.67 |
0.44 |
0.50 |
Org.
fert. prod. |
2.35 |
1.48 |
2.30 |
0.60 |
Cost/
benefit |
2.98 |
8.40 |
10.85 |
2.42 |
Labour
intensity |
3.59 |
9.20 |
17.05 |
2.75 |
Food
products |
23 |
26 |
31 |
11 |
No.
of species |
62.83 |
59.00 |
78.00 |
19.50 |
Group
1: 75:25 Havana, 50:50 Havana, 60:40 Havana, 76:24 Las Tunas, 58:42 (All years); Group
2: 25:75 Havana (First year); Group
3: 25:75 Havana (Second and Third year); Group
4: 50:50 Unselected farms (First and Second year) |
A comparative analysis between years and indicators makes possible a quantitative representation of performance (Brink et al 1991). The radial graphic was very useful for this purpose. This methodological analysis is very well accepted and currently employed by different authors (Lightfoot et al 1995, Venegas 1996; Dalsgaard 1997; Funes-Monzote 1998; Masera et al 1999; Chinnakonda and Latinga 2000). Different mathematical models were developed to estimate the percent of satisfaction of each indicator, taking into account that the weighted table (table 4) gives only ranges and not specific values. Thus, each indicator value was carefully calculated and situated as a co-ordinate on the axis with its own percentage of satisfaction value (Figures 1 to 5). Generally, the analysis showed a tendency for the majority of the indicators to be superior in the third year.
Figure 1. Sustainability indicator evaluation of the selected ecological
farms (50:50, Havana) (1-Reforestation 2-Total species 3-Number of feed products
4-Labour intensity 5-Organic fertiliser production 6-Yield 7-Energy efficiency 8-Milk
production)
Figure 2. Sustainability indicator evaluation of the selected ecological farms (58:42 Sancti Spíritus) (1-Reforestation 2-Total species 3-Number of feed products 4-Labour intensity 5-Organic fertiliser production 6-Yield 7-Energy efficiency 8-Milk production)
Figure 3. Sustainability indicator evaluation of the selected ecological farms (60:40 Havana) (1-Reforestation 2-Total species 3-Number of feed products 4-Labour intensity 5-Organic fertiliser production 6-Yield 7-Energy efficiency 8-Milk production)
Figure 4. Sustainability indicator evaluation of the selected ecological farms (75:25 Havana) (1-Reforestation 2-Total species 3-Number of feed products 4-Labour intensity 5-Organic fertiliser production 6-Yield 7-Energy efficiency 8-Milk production)
Figure 5. Sustainability indicator evaluation of the selected ecological farms (74:26 Las Tunas) (1-Reforestation 2-Total species 3-Number of feed products 4-Labour intensity 5-Organic fertiliser production 6-Yield 7-Energy efficiency 8-Milk production)
Converting specialised livestock systems to integrated agro-ecological systems resulted in a more efficient and sustainable use of natural resources for food production.
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Received 3 July 2000