Assessment of Key Feeding Technologies and Land Use in Dairy Sheep Farms in Spain
María Teresa Bastanchury-López,
Carmen De-Pablos-Heredero,
Santiago Martín-Romo-Romero and
Antón García
Additional contact information
María Teresa Bastanchury-López: Department of Business Economics (Administration, Management and Organization), Applied Economics II and Fundamentals of Economic Analysis, Universidad Rey Juan Carlos, Paseo de los Artilleros s/n, 28032 Madrid, Spain
Carmen De-Pablos-Heredero: Department of Business Economics (Administration, Management and Organization), Applied Economics II and Fundamentals of Economic Analysis, ESIC University, Universidad Rey Juan Carlos, Paseo de los Artilleros s/n, 28032 Madrid, Spain
Santiago Martín-Romo-Romero: Department of Business Economics (Administration, Management and Organization), Applied Economics II and Fundamentals of Economic Analysis, Universidad Rey Juan Carlos, Paseo de los Artilleros s/n, 28032 Madrid, Spain
Antón García: Animal Science Department, University of Córdoba, Rabanales University Campus, 14071 Córdoba, Spain
Land, 2022, vol. 11, issue 2, 1-14
Abstract:
Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain. This objective was assessed by combining qualitative and quantitative methodologies. In the first stage, with the aim to identify and select the appropriate technologies, a panel of 107 experts in dairy sheep production was used. A questionnaire was applied to all of them with successive rounds using Delphi methodology. Later, these technologies were grouped by principal components analysis (PCA) and cluster analysis (CA). In a second stage the technological results from a random sample of 157 farms in the Center of Spain were collected. The technologies selected were linked to the technological adoption level of the farms in Castilla la Mancha by a multiple regression model. Ten technologies were selected by the 107 experts. Four factors were retained by PCA that explained at 67.11% of variance. The first factor is related to feeding strategies, the second to land use for livestock production, the third to efficient management of land resources or ecoefficiency and the fourth to by-products use. The expert evaluation was grouped in three clusters using the Ward’s method and the squared Euclidean distance measure, where the second showed higher values in the adoption level of each technology. The multiple regression model explained the relationship between the technologies and the technological level of the farms (R 2 73.53%). The five technologies selected were: use of unifeed (1), supplemental feeding (5), grazing (6), raw materials production (7) and sustainable use of water and soil (10). These ten technologies identified can be directly extended to small-scale dairy farms from other countries in the Mediterranean basin and Latin America. This technological selection was supported from the broad and diverse panel of experts used. Besides, five technologies identified by the quantitative model will be able to be taken into account for the development of public innovation policies. They are direct technologies and easy to apply on the farm and seeking increased viability through innovation vs. intensification.
Keywords: technological sustainability; multivariate analysis; regression model; innovation; mixed systems; dairy sheep farms (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/2073-445X/11/2/177/pdf (application/pdf)
https://www.mdpi.com/2073-445X/11/2/177/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:2:p:177-:d:731084
Access Statistics for this article
Land is currently edited by Ms. Carol Ma
More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().