Abstract: Changes in climate, technology, policy and prices affect agricultural and rural development. To evaluate whether this development is sustainable, impacts of these multiple drivers need to be assessed for multiple indicators. In a case study area in the Netherlands, a bio-economic farm model, an agent-based land-use change model, and a regional emission model have been used to simulate rural development under two plausible global change scenarios at both farm and landscape level. Results show that in this area, climate change will have mainly negative economic impacts (dairy gross margin, arable gross margin, economic efficiency, milk production) in the warmer and drier W+ scenario, while impacts are slightly positive in the G scenario with moderate climate change. Dairy farmers are worse off than arable farmers in both scenarios. Conversely, when the W+ scenario is embedded in the socio-economic Global Economy (GE) scenario, changes in technology, prices, and policy are projected to have a positive economic impact, more than offsetting the negative climate impacts. Important is, however, that environmental impacts (global warming, terrestrial and aquatic eutrophication) are largely negative and social impacts (farm size, number of farms, nature area, odour) are mixed. In the G scenario combined with the socio-economic Regional Communities (RC) scenario the average dairy gross margin in particular is negatively affected. Social impacts are similarly mixed as in the GE scenario, while environmental impacts are less severe. Our results suggest that integrated assessments at farm and landscape level can be used to guide decision-makers in spatial planning policies and climate change adaptation. As there will always be trade-offs between economic, social, and environmental impacts stakeholders need to interact and decide upon most important directions for policies. This implies a choice between production and income on the one hand and social and environmental services on the other hand

Abstract: Models integration and possible contrasts with up-scaling activities has received increasing attention in recent years especially with respect to the relationship between farm-economics and biophysical assessments. Current bio-economic models that analyse the trade-offs between farm income and interventions on eco-bio-environmental parameters such as maintenance of biodiversity, reduction of erosion and nitrate pollution and more, include static models. Agricultural systems are facing a series of threats, including climate change, land degradation, price volatility and intensification processes, which put their long-term sustainability into question. The University of Basilicata in collaboration with local representatives from various sectors of production in the Basilicata region of Southern Italy has developed an integrated study to define a model system to assess the dynamics at play in rural territories. The study tested the explanatory usefulness of resilience theory for the Basilicata agricultural social-ecological system, applying the adaptive cycle as a diagnostic tool to explore the dynamics and trajectories of change in the coupled social-ecological systems, and evaluating the performance of social, economic and social capitals, which are subject to the same dynamics. The use of dynamic analysis of the social, economic and natural capitals as the key to interpret the various phases of the adaptive cycle of the two agricultural systems proved a powerful tool in analysing the relationships between resilience and sustainable development in rural territories. The adoption of capitals and their inter-relations proved fundamental to the elaboration of adaptation strategies which were compatible with patterns of sustainability. The adaptive cycle heuristic, despite some methodological difficulties, remains useful to describe processes of change in rural socio-ecological systems. There could be enormous potential in adopting these instruments to help identify of the needs of different territories and help the framing and implementation of rural policies. No Label

Abstract: This study deals with observed and simulated growth, development and yield of the fresh-market Thomas F1 tomato bush cultivar (Solanum lycopersicum L.) grown under open field conditions at farm scale in the Elbe lowland. The CROPGRO-Tomato model used in this study is part of the DSSAT V4.5 software. The model has been calibrated with growth analyses data from field experiments, agronomic evidence (GC UPRAVY software) and the most currently available data from the literature sources of cardinal temperatures for tomato phenology, fruit growth and photosynthesis (Tb – base temperature; Topt1 – the lowest temperature at which maximum rate is attained; Topt2 – the upper temperature at which maximum rate is sustained; Tmax – maximum temperature). The sampling plants were collected a once 14 days for analysis of basic physiological parameters: LAI (Leaf area index), LAR (Leaf Area Ratio), C (Crop Growth Rate), RGRw (Relative Growth Rate) and NAR (Net Assimilation Rate). Phenology observation was done weakly. Meteorological, soil and agro-technical parameters across the fields were monitored. The treatments were well-irrigated and well-fertilised, and therefore, no water or N stress was present.Parameters affecting leaf growth, dry biomass productions, and dry biomass of leaves, stem and generative organs from planting to harvest were calibrated against the observed data. Phenological development and growth processes such as leaf expansion and fruit growth depend on cardinal temperatures. Leaf area expansion depends on the new leaf mas produced and specific leaf area, which is influenced by light, temperature, root N uptake, and plant water status. Starting date for the simulation corresponds with transplanting date of the crop in the field, which was set at day 141. The simulation period ended at day 273, a reasonable estimate for the date when plants are stopped in practice. Initial input dry biomass at Mochov farm (Suchdol) was set to 2.25 (2.88), 1.71 (2.5) and 0.01 (0.78) grams for leaves, stem and generative organs, respectively. No Label