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.

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.

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.

Improvements in the sustainability of agricultural production depend essentially on advances in the efficient use of nitrogen. Precision farming promises solutions in this respect. Variable rate technologies allow the right quantities of fertilizer to be applied at the right place. This helps to both maintain yields and avoid nitrogen losses. However, these technologies are still not widely adopted, especially in small-scale farming systems. Recent developments in sensing technologies, like drones or satellites, open up new opportunities for variable rate technologies.

This study analyses the impact of the transfer of technological information (among other a priori identified factors) on the uptake of innovative crop technologies using structural equation modelling of data from a representative survey of Scottish crop farmers. The model explains 83% of the variance in current technological uptake behaviour and 63% of the variance in intentions to uptake new technologies.