La diminution du nombre de prairies, que l’on observe à l’échelle mondiale depuis plusieurs décennies, s’est accompagnée de l’évolution de leur mode de gestion dans un contexte d’intensification de l’usage des terres. Face aux enjeux que ces changements impliquent, tant sur le plan environnemental qu’économique, il est nécessaire d’identifier et de caractériser les dynamiques spatiotemporelles des prairies, afin notamment d’évaluer les impacts du changement climatique sur ces dernières et leur capacité à s’y adapter.
Le drone est un outil de plus en plus utilisé dans de nombreux domaines et en particulier en agriculture. La méthode présentée permet d’estimer la hauteur de plantes fourragères à partir de photos prises d’un drone. Cette méthode revêt un intérêt tout particulier pour la sélection végétale.
Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually.
Indicator-based tools are widely used for the assessment of farm sustainability, but analysts still face methodological and conceptual issues, including data availability, the complexity of the concept of sustainability and the heterogeneity of agricultural systems. This study contributes to this debate through the illustration of a procedure for farm sustainability assessment focussed on the case study of the South Milan Agricultural Park, Italy. The application is based on a set of environmental, social and economic indicators retrieved from the literature review.
L’agriculture souffre d’un important déficit d’image : qualité des produits jugée médiocre, non respect de l’environnement, pénibilité des métiers… La réalité est cependant tout autre. Tout en restant profondément ancré dans la vie des territoires, ce secteur est devenu numéro deux de la robotisation. De nouvelles méthodes fondées sur le traitement des données, des outils innovants, intelligents et connectés, ainsi que des réseaux de diffusion des innovations et d’entraide contribuent à transformer en profondeur l’agriculture.
Innovation rests not only on discovery but also on cooperation and interactive learning. In agriculture, forestry and related sectors, multi-actor partnerships for ‘co-innovation’ occur in many forms, from international projects to informal ‘actor configurations’. Common attributes are that they include actors with ‘complementary forms of knowledge’ who collaborate in an innovation process, engage with a ‘larger periphery’ of stakeholders in the Agricultural Knowledge and Innovation System (AKIS) and are shaped by institutions.
Precision Agriculture (PA) has been advocated as a promising technology and management philosophy that provides multidimensional benefits for producers and consumers while being environmentally friendly. In Europe, private stakeholders (farm advisors, farm equipment producers, decision support providers, farmers) and research institutions have been trying to develop, test and demonstrate adoption of precision agriculture solutions with governments financing big projects in these areas. Despite these efforts, adoption is still lagging behind expectations.
El documento se divide en ocho capítulos, en el primero se realiza una introducción al programa ERICA, los antecedentes del proceso y los objetivos que se persiguen. El segundo capítulo presenta un marco de referencia para contextualizar las prácticas de gestión en España, la situación en Colombia, el problema identificado y el marco normativo. El tercero presenta un marco conceptual con algunos de los términos más representativos trabajados en el proyecto.
The process of adopting innovation, especially with regard to precision farming (PF), is inherently complex and social, and influenced by producers, change agents, social norms and organizational pressure. An empirical analysis was conducted among Italian farmers to measure the drivers and clarify “bottlenecks” in the adoption of agricultural innovation. The purpose of this study was to analyze the socio-structural and complexity factors that affect the probability to adopt innovations and the determinants that drive an individual’s decisions.
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.