Relationships between management intensity
and structural and social variables in dairy and
dual-purpose systems in Santa Cruz, Bolivia
C. Solano
a, A. BernueÂs
a, F. Rojas
b, N. JoaquõÂn
b,
W. Fernandez
b, M. Herrero
a,c,*
aInstitute of Ecology and Resource Management, University of Edinburgh, West Mains Road, Edinburgh EH9 2JG, Scotland, UK
bCentro de InvestigacioÂn AgrõÂcola Tropical (CIAT), Avenida Ejercito Nacional 131, Santa Cruz de la Sierra, Bolivia
cInternational Livestock Research Institute, PO Box 30709, Nairobi, Kenya
Received 18 October 1999; received in revised form 16 February 2000; accepted 1 May 2000
Abstract
The objective of this paper was to study the relationships between technological use and farm and farmer characteristics in dairy and dual-purpose farms in Santa Cruz, Bolivia. Quantitative and qualitative data related to farm dimensions, land use, pasture, nutrition, reproduction and health management and farm household social characteristics, access to information and technical assistance were collected from 319 farms in the main dairy-producing regions of Santa Cruz. Data were analysed by multiple correspondence analysis in order to establish relationships amongst management intensity and variables related to farm structure, productive orientation and the farmers' social conditions and information exposure to technical aspects. A cluster analysis was then carried out to identify groups of farms with similar characteristics within the sample population. The analyses demonstrated clear rela-tionships between management intensity, the farms' dimensions and the farmers' social con-ditions and access to information. Results are discussed in respect to the importance of farm characterisation for de®ning target groups and delivering research outputs and extension policies more eectively.#2000 Elsevier Science Ltd. All rights reserved.
Keywords:Farm management; Dairy systems; Dual purpose; Multiple correspondence analysis; Char-acterisation; Bolivia
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* Corresponding author. Tel.: +44-131-5354384; fax: +44-131-6672601.
1. Introduction
Extension and development policies have traditionally being directed to the `average farmer', without taking into account the social, cultural, economic and environmental conditions in which they live (Skerrat, 1995). Several authors (Dent and Thornton, 1988; Chambers et al., 1993; Dent et al., 1994; Ferreira, 1997) believe this to be one of the main reasons for the lack of adoption of certain technologies at the farm level.
Characterising livestock production systems under a multidisciplinary framework is a fundamental step for acknowledging these dierences, for understanding the sys-tems, and for guiding and targeting the development of policies and extension mes-sages adaptable to the wide variety of existing farming systems and farming styles (Jones, 1991; Osty, 1994). Characterisation studies have usually focused on descrip-tions of the farms' available resources and their management, but seldomly have stu-died the complex relationships between the farms' resources, the farmers' managerial capacity, which is in part determined by their social conditions and information access, and the management intensity they implement in their farms (Ferreira, 1997). Management intensity is de®ned in this study as the number of production technolo-gies and the frequency in which they are used within the system. The objectives of this study are to examine these relationships using farm-level quantitative and qualitative variables obtained from dairy and dual-purpose systems in Santa Cruz, Bolivia.
2. Methodology
2.1. Background
This study was conducted in the main dairy region of Santa Cruz, Bolivia. This area comprises four subregions, namely Area Integrada, Sara-Ichilo, San Javier and Zona de ExpansioÂn. This is one of the most important milksheds of Bolivia, pro-ducing between 180 and 185 million litres per year.
Santa Cruz is one of the main agricultural departments of Bolivia and produces 34.5% of the national agricultural gross domestic product (GDP). Livestock con-tributes to 21.6% of the agricultural GDP and utilises 29% of the total depart-mental area (approximately 371 thousand km2). Intensive crop and cereal production account for 39.7 and 19.3% of the agricultural GDP of Santa Cruz, respectively. They cover 11.4% of the total area (CAO, 1998).
The region under study has a tropical climate with a well-de®ned dry season (April±October). Annual rainfall varies from 1200 to 1800 mm, while temperature ¯uctuates between 25 and 35C (Vargas, 1996).
2.2. Data collection
(dairy or beef systems), was used. Data was collected in a series of farm visits using direct structured interviews to farmers. The study referred to a period of time of 1 year and collected quantitative and qualitative information about: (1) family and education level; (2) labour availability and work distribution; (3) crops, pastures and other resources; (4) herd structure; (5) facilities and machinery; (6) decision making and technical services; (7) pasture and nutrition management; (8) reproductive and milking management; (9) health management and pathology; and (10) economic and physical inputs and outputs. After a preliminary analysis, pure beef farms (6.2%) and incomplete or non-reliable data (17.5%) were eliminated. Therefore, the ®nal size of the sample was 319 farms.
2.3. Data analyses
Since a large proportion of collected data were qualitative, multiple corres-pondence analysis (MCA), a methodology able to deal with both qualitative and quantitative data, was used. MCA allows the analysing of large quantitative data matrixes and it is a weighted principal component analysis of a contingency table (Greenacre, 1984; Sanchez, 1984). Before the technique can be used, it is necessary to transform quantitative variables into classes. The variables were divided into three classes using the quantiles position with respect to their mean. This provided the frequencies of observations that were within less than 25%, between 25 and 75% and higher than 25% of the mean value for each chosen vari-able, respectively. This method of classi®cation had the advantage that the de®nition of the classes was based on objective criteria, rather than on ®xed thresholds and accounted well for the typically inherent non-normal distributions of these types of data. All analyses were done using SAS statistical software (SAS Institute, 1997).
The variables and classes used in the analysis were divided in six groups.
2.3.1. Pasture and nutrition management
Three variables were chosen as representative of pasture and nutrition manage-ment. The variables were (Table 1):
1. Use of concentrateswas a binomial variable that expressed whether the farmer used concentrates for any of his animals or not.
2. Metabolisable energywas the amount of metabolisable energy (Mcal) supplied per lactating cow per day in the dry season and in both dry and rainy seasons. 3. Cultivated pastures was the land area of cultivated pastures expressed as a
percentage of total pastures.
2.3.2. Reproductive management
The variables referring to some of the more relevant aspects of reproductive management are explained in Table 2. These variables are:
conditions, disinfecting of umbilical cord, surveillance of calostrum intake and support with vitamins and minerals.
2. Breed speci®ed the main breed of animals present in the farm. The dierent breeds were aggregated as Cross-breed, Bos taurus, Bos indicus and Criollo (local breed).
Table 1
Variables and classes of pasture and nutrition management
Variable Classes Code No. of observations Use of concentrates Yes ConcenY 82
No ConcenN 237
Metabolisable energy 0 Mcal SupN 123 <1707 Mcal in dry season SupDryLw 10 1707±4816 Mcal in dry season SupDryI 18 >4816 Mcal in dry season SupDryH 14 <2323 Mcal in both seasons SupYearLw 36 2323±5485 Mcal in both seasons SupYearI 71 >5485 Mcal in both seasons SupYearH 47 Cultivated pastures <33% ImpPastLw 51 33±67% ImpPastI 35 >67% ImpPastH 233
Table 2
Variables and classes of reproductive management
Variable Classes Code No. of observations Calving assistance <2.1 CalvAssLw 134
2.1±4.1 CalvAssI 179
Mating Arti®cial insemination AI 26 Natural mating Mating 293 Reproductive control Yes RepConY 68
No RepConN 251
Selection criteria Milk yield of the mother SeCrMiYi 214 Pedigree of the animal SeCrPedi 6 Conformation of the animal SeCrConf 18 Breed of the animal SeCrBree 4 Reproductive performance of mother SeCrRepr 10
None SeCrNone 67
Replacement origin Own replacement ReplaOwn 226 Purchased replacement ReplaBuy 57
3. Mating indicated whether the farms used natural mating or arti®cial insemination.
4. Reproductive control was a binomial variable that indicated whether any reproductive control measure such as gestation control or rectal palpation was used for determining pregnancy and reproductive eciency.
5. Selection criteria re¯ected the criteria followed by farmers to select the repla-cement animals from the own farm. The criteria were: milk yield of the dam; pedigree; conformation of the animal; breed; reproduction performance of the mother; and none.
6. Replacement originexpressed the main origin of replacement animals. It could be from the farm, from other farms in the region or in the country and both options.
2.3.3. Health management
Table 3 presents the selected variables referring to health management of the farms. These variables were:
1. Mastitis was a score of hygienic measures for mastitis prevention calculated from the presence or absence of: udder cleaning before milking; udder disin-fection after milking; cleaning and disindisin-fection of milking machine; mastitis prevention tests; and treatment at drying-o.
2. Ectoparasites controlindicated the number of treatments per year corrected by the percentage of animals treated.
3. Endoparasites controlindicated the number of treatments per year corrected by the percentage of animals treated.
4. Vaccineswas the number of dierent vaccines applied to the animals.
Table 3
Variables and classes of health management
Variable Classes Code No. of observations
Mastitis 0 MastPrN 99
42 MastPrLw 42
>2 MastPrH 178
2.3.4. Performance and control records
Variables related to farm performance and control records are presented in Table 4. These variables are:
1. Recordwas a binomial variable indicating the absence or presence of an animal recording system in the farm.
2. Type of record indicated the type of system used. Categories were individual records, notebook or diary, computer system or none.
2.3.5. Structure and productive orientation
The variables considered when de®ning the more relevant aspects of structure and production orientation were (Table 5):
1. Systemindicated the orientation of production in terms of relative importance of milk, meat and agriculture outputs from total farm outputs of the farm.
Table 4
Variables and classes of performance and control records
Variable Classes Code No. of observations
Record Yes RecordY 131
No RecordN 188
Type of record Individual records RecInd 22 Notebook or dairy RecBook 94 Computer system RecCompu 15
None RecN 188
Table 5
Variables and classes of structure
Variable Classes Code No. of observations System Agriculture Ð dairy AgrDairy 33
Agriculture Ð dual purpose AgrDualP 60 Livestock Ð dairy LivDairy 72 Livestock Ð dual purpose LivDualP 154 Herd <8 lactating cows HerdS 92 8±25 lactating cows HerdM 134 >25 lactating cows HerdL 93 Other species 0% OtherSpN 151
This variable had four classes depending on agricultureversuslivestock orien-tation and specialised milkversusdual-purpose herds.
2. Herdindicated the size of the herd in terms of number of lactating cows. 3. Other speciesindicated the relative importance of livestock units (LU) of other
species such as sheep, horses, pigs, hens and ducks in the total number of LU of the farm.
4. Milk outputsindicated the average milk yield per milking cow of the farm per year.
2.3.6. Sociologic characteristics and information seeking
The variables de®ning social and information-seeking aspects of the farmers are presented in Table 6. These variables are:
1. Farmer educationindicated the level of education of farmers. It was categorised as: low (illiterate); medium (primary or secondary school); and high (technical education or university).
2. Distancere¯ected the distance of the farm to the closest population centre. 3. Technical advicewas a score measuring the use of technical advice services by
farmers. It was calculated adding every type of technical adviser used by the farmer (health, reproduction, nutrition, pastures and crops advisors).
4. Information was a score re¯ecting the openness of farmers in seeking informa-tion for making their decision. The possible sources considered were: technical advice; farmers' associations; publications; radio; TV; extension; and ®eld-out days. These types were weighted by the intensity of the seeking (none=0, low=1, or high=2).
After classi®cation of the six groups of variables, two MCAs were carried out to identify the relationships between management variables (Tables 1±4), structure and
Table 6
Variables and classes of sociologic characteristics and information seeking
Variable Classes Code No. of observations Farmer Education Illiterate EducLw 19
Primary/secondary school EducI 246 Technical/university EducH 54 Distance <2 km <2km 89 2±20 km 2±20km 152 >20 km >20km 78 Technical Advice 0 TechAdN 109
42.5 TechAdLw 184 >2.5 TechAdH 26 Information <10 OpenLw 88
10±18 OpenI 179
production-orientation variables (Table 5) and sociologic and information-seeking characteristics of the farmers (Table 6). Then, eight MCAs were carried out to compare (1) pasture and nutrition, (2) reproductive, (3) sanitary and (4) perfor-mance recording technologies with both (5) structure and production orientation and (6) sociologic characteristics and information seeking of the farms.
Finally, a cluster analysis (CA) using the centroid distance as method of aggrega-tion was carried out to classify the farms. A new MCA was done with all the vari-ables, and the coordinates of the management variables with reference to the structural and sociologic and information-seeking dimensions of the MCA were used in the cluster analysis.
3. Results and discussion
3.1. Systems characterisation and relationships between structural, production orientation, sociological and information-seeking variables
The farms under study could be separated into three major groups according to both structural and social aspects. Following Fig. 1 clockwise, the MCA showed that large farms were related to more specialised livestock production systems towards milk production. They were more commercial farms since their proportion of animals of species other than bovine was zero (upper left corner). A second group (lower left corner) was characterised by small farms and low production per cow/year. They were more diversi®ed farms with agriculture and dual-purpose production systems. This group was closer to the category of high proportion of the animals of other species. Finally, the third group were medium-size mixed crop/dairy farms, with intermediate income per cow/year and dairy production oriented to specialised dairying. These farms have intermediate and high proportions of animals of other species in the farm. According to this characterisation, three labels could be assigned to each group as follows: Large Specialised Commercial Dairy Farms (GCE); Small Mixed Crop/ Dual-Purpose Farms (PSD) and Medium Mixed Semi-commercial Crop/Dairy Farms (MScL), respectively.
In terms of sociological and information-seeking variables, Fig. 2 shows that there is a clear relation between the distance of the farm to towns, the level of information seeking by the farmers and the level of technical advice received. Farms located from 0 to 2 km from towns are related to farmers which have a high level of information seeking and high levels of technical advice. This speci®c group of farmers seem to have high levels of education (upper right corner in Fig. 2). Farms farther than 2 km but less that 20 km from towns have less access to information and technical advice, while farms far away to population centres do not have any technical advice or access to information. These last two groups of farms are related to illiterate farmers or with relatively low educational levels (up to high school).
3.2. Use of technologies in nutritional and pasture management
The MCA analysis showed a consistent relationship among variables of structural dimensions and productive orientation of the farms and variables related to pasture and nutrition management. The upper left corner of the Euclidian in Fig. 3 locates farm characteristics corresponding to farms labelled GCE. These farms tend to have either a high supplementation level throughout the year or high during the dry sea-son and therefore have the highest milk productions and income. Supplementation seems to be mostly based on concentrates. However, evidence of a high use of improved pastures was not evident in GCE farms. The lower left corner of the ®gure contains farm characteristics belonging to the group PSD. These farms do not sup-plement and tend to have low or intermediate proportions of their areas with improved pastures. Finally, the lower right and upper right spaces locate the char-acteristic of farms labelled MScL. High proportions of the pasture areas in these farms are covered with improved pasture while a high variation in terms of supplementation level was found in this group. A range between low and inter-mediate levels of supplementation throughout the year or low supplementation only in the dry season was found in these farms.
Dierences in production between these groups are caused mostly by the levels of supplementation used, since the quality of the improved pastures is very low inde-pendently of the type of farm (Anon., 1998). These high levels of supplementation are associated with higher production and are also related to more specialised dairy systems. However, although mixed crop/dairy systems do not use a high proportion of concentrates, they also use crop by products for supplementing their animals.
It was clearly found that regardless of the structural and productive orientation of the farms, sociological and information-seeking aspects have an impact on the use of technologies in nutrition and pasture management (Fig. 4). AIA farmers seemed to use higher levels of supplementation throughout the year or high supplementation during the dry season. Again, this supplementation was related to concentrates which is not surprising considering the fact that most nutritional advice is provided by concentrate manufacturers and milk processing plants which also manufacture concentrates.
Farmers who had the highest level of education and technical advice used higher levels of supplementation. These relationships were quite generic, in the sense that as the level of education and advice diminished, the nutritional management of the farms also decreased.
Fig. 3. Relationships between pasture and nutrition management and structure and production orienta-tion variables.
3.3. Use of technologies in reproductive management
Reproductive management practices such as high calving assistance, reproductive control, and arti®cial insemination are related to farm characteristics present in the category of GCE (upper left and right corner in Fig. 5). Farms labelled as PSD or MScL do not have any reproductive control management and have natural mating. The PSD farms tend to have an intermediate calving assistance and the selection criteria is mostly based on pedigree (lower left corner). A possible explanation for this is that since these farmers have fewer animals, they recognise the importance of each individual animal within their system and therefore value the importance of calving assistance due to its relationship to survival of the dam and calf. There is no clear relation between this group of farms and a speci®c breed but they are closer to cross-bred animals.
Characteristics of MScL farms were related to low calving assistance, and these farmers did not apply a selection criteria or based it on breed only. The relationship between these farm characteristics and the use of Criollo breed was evident (lower left corner).
High calving assistance, reproductive control and arti®cial insemination were related to AIA farmers (upper right and left corners in Fig. 6) while low and intermediate calving assistance, natural mating and non-reproductive control were
related to farmers with characteristics related to MIA and NIA. There were further obvious relations among other social and information-seeking variables and repro-ductive management.
3.4. Use of technologies in herd health management
Fig. 7 shows that health management could be easily related to structural and productive orientation of the farm since high scores of mastitis prevention, ecto-and endo-parasites control treatments ecto-and vaccination were related to GCE farms (upper right and left corners) while intermediate and low scores were related to PSD (lower right and left corners) and MScL (near to the x axis) farms, respectively. Similar results were found in terms of the relation between social and information seeking and health management showing that AIA, MIA and NIA farmers' char-acteristics were related to high, medium and low scores of parasite control and vac-cination scores, respectively (Fig. 8). In terms of mastitis prevention, AIA-related farms have an intermediate level of mastitis prevention while MIA and NIA had high and low levels, respectively.
Fig. 7. Relationships between health management and structure and production orientation.
3.5. Use of records
The use of performance records of any kind, including computerised records, seems to be related to farms with characteristics of the label GCE, while lack of use of records was related to PSD and MScL (Fig. 9). These were the only clear relations between use of records and sociological and information-seeking aspects (Fig. 10).
4. Cluster Analysis
Three well-de®ned groups of farms according to the general used of technologies were produced by the Cluster Analysis (Fig. 11). This visual interpretation is con-sistently supported by several clustering statistics (Table 7) which demonstrated that three groups were sucient to characterise the prevailing production systems, as judged by a high r2, a strong increment in the cubic criterion of clustering, and pseudoFstatistic and a strong decrease in the pseudoTstatistic. This combination shows that the groups formed at this stage had the minimum variance within groups and the maximum variance among groups and therefore demonstrate how dierent they are. This combination of statistics has been reported to be the best way in deciding the proper number of clusters (SAS Institute, 1997). When plotted against
Fig. 10. Relationships between performance and control records and sociologic characteristics and information seeking.
structural and productive variables and sociological and information-seeking vari-ables, it is evident that farms belonging to the cluster 1 (86 farms) (lower left corner in Fig. 11) are farms with label PSD while cluster 2 (143) (lower right corner) are farms with label MScL. There is not a clear dierence between clusters 1 and 2 in terms of sociological and information seeking characteristics, so it can be said that farms belonging to these groups could be considered a combination of MIA and NIA farmers. Finally, cluster 3 (90 farms) (upper corners) are farms considered as GCE and AIA showing that sociological and information-seeking aspects are still important in de®ning the technological level of the farm.
5. Conclusions
From the evidence shown it can be concluded that livestock production systems in the studied zones could be described in three dierent groups in terms of structural characteristics and productive orientation. These three groups could be labelled as: Large Specialised Commercial Dairy Farms (GCE); Small mixed crop/dual purpose farms (PSD) and Medium Semi-commercial crop/dairy Farms (MScL). On the other hand, farms can be grouped into three categories according to sociological and information-seeking aspects labelled as: Highly Informed and Advised Farmers (AIA); Medium Informed and Advised Farmers (MIA); and Non-Informed nor Advised Farmers (NIA).
The use of technologies in the nutritional, reproductive, health management and records use is clearly de®ned by these two dierent classi®cation systems of the farms. This study demonstrated that obvious relations exist between the availability and access to natural, economic and human resources and the possibility of the farms to adopt technologies and improve their management.
Table 7
Clustering statistics according to dierent number of farm groups
No. of cluster R2 CCCa PSFb PSTc
3 0.705 30.18 378 220.2
2 0.350 ÿ10.81 171 372
1 0.000 00.00 171
a CCC, cubic criterion of clustering. b PSF, pseudoFstatistic.
This study provides strong evidence that the structural characteristics and produc-tive orientation of the systems are closely related to the sociological and information-seeking characteristics of the farmers managing them, and that these aspects play a substantial role in determining the management intensity under which the systems operate.
If technological adoption is to be increased, these issues are of fundamental importance when de®ning target groups and extension and development policies for them.
Acknowledgements
This publication in an output of the research project ``A Decision Support System for Sustainable Resource Use in Bolivian Cattle Production Systems'', funded by the Department for International Development of the United Kingdom. However, the Department for International Development can accept no responsibility for any information provided or views expressed. The statistical support of Anthony Hunter (Bio-mathematics and Statitstics Scotland) is appreciated.
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