|
|
Porter, J. R., Soussana, J. - F., Fereres, E., Long, S., Mohren, F., Peltonen-Sainio, P., et al. (2012). European Perspectives: An Agronomic Science Plan for Food Security in a Changing Climate. In D. Hillel, & C. Rosenzweig (Eds.),. Handbook of Climate Change and Agroecosystems: Global and Regional Aspects and Implications, ICP Series on Climate Change Impacts, Adaptation, . Co-Published With Imperial College Press.
|
|
|
|
Höglind, M. (2014). Europeisk landbruk i et klima i endring (MACSUR)..
|
|
|
|
Lehtonen, H. (2015). Evaluating adaptation and the production development of Finnish agriculture in climate and global change. Agricultural and Food Science, 24(3), 219–234.
Abstract: Agricultural product prices and policies influence the development of crop yields under climate change through farm level management decisions. On this basis, five main scenarios were specified for agricultural commodity prices and crop yields. An economic agricultural sector model was used in order to assess the impacts of the scenarios on production, land use and farm income in Finland. The results suggest that falling crop yields, if realized due to low prices and restrictive policies, will result in decreasing crop and livestock production and increasing nutrient surplus. Slowly increasing crop yields could stabilise production and increase farm income. Significantly higher crop prices and yields are required, however, for any marked increase in production in Finland. Cereals production would increase relatively more than livestock production, if there were high prices for agricultural products. This is explained by abundant land resources, a high opportunity cost of labour and policies maintaining current dairy and beef production.
|
|
|
|
Zhao, G., Hoffmann, H., Yeluripati, J., Xenia, S., Nendel, C., Coucheney, E., et al. (2016). Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. Env. Model. Softw., 80, 100–112.
Abstract: We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.
|
|
|
|
Gabaldón, C., Lorite, J., Mínguez, I., Dosio, A., Sánchez-Sánchez, E., & Ruiz-Ramos, M. (2013). Evaluation of local adaptation strategies to climate change of maize crop in Andalusia for the 21st century..
|
|
