Abstract: Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

Abstract: Aphid-transmitted viruses frequently cause severe epidemics in lettuce grown under Mediterranean climates. Spatio-temporal dynamics of aphid-transmitted viruses and its vector were studied on lettuce (Lactuca sativa L.) grown under tunnels covered by two types of nets: a commercial UV-absorbing net (Bionet) and a Standard net. A group of plants infected by Cucumber mosaic virus (CMV, family Bromoviridae, genus Cucumovirus) and Lettuce mosaic virus (LMV, family Potyviridae, genus Potyvirus) was transplanted in each plot. The same virus-infected source plants were artificially infested by the aphid Macrosiphum euphorbiae (Thomas). Secondary spread of insects was weekly monitored and plants were sampled for the detection of viruses every two weeks. In 2008, the infection rate of both CMV and LMV were lower under the Bionet than under the Standard cover, probably due to the lower population density and lower dispersal rate achieved by M. euphorbiae. However, during spring of 2009, significant differences in the rate of infection between the two covers were only found for LMV six weeks after transplant. The spatial distribution of the viruses analysed by SADIE methodology was “at random”, and it was not associated to the spatial pattern of the vector. The results obtained are discussed analyzing the wide range of interactions that occurred among UV-radiation, host plant, viruses, insect vector and environmental conditions. Our results show that UV-absorbing nets can be recommended as a component of an integrated disease management program to reduce secondary spread of lettuce viruses, although not as a control measure on its own.

Abstract: The current paper aims to determine regional impacts of climate change on Irish farms examining the variation in farm responses. A set of crop growth models were used to determine crop and grass yields under a baseline scenario and a future climate scenario. These crop and grass yields were used along with farm-level data taken from the Irish National Farm Survey in an optimizing farm-level (farm-level linear programming) model, which maximizes farm profits under limiting resources. A change in farm net margins under the climate change scenario compared to the baseline scenario was taken as a measure to determine the effect of climate change on farms. The growth models suggested a decrease in cereal crop yields (up to 9%) but substantial increase in yields of forage maize (up to 97%) and grass (up to 56%) in all regions. Farms in the border, midlands and south-east regions suffered, whereas farms in all other regions generally fared better under the climate change scenario used in the current study. The results suggest that there is a regional variability between farms in their responses to the climate change scenario. Although substituting concentrate feed with grass feeds is the main adaptation on all livestock farms, the extent of such substitution differs between farms in different regions. For example, large dairy farms in the south-east region adopted total substitution of concentrate feed while similar dairy farms in the south-west region opted to replace only 0.30 of concentrate feed. Farms in most of the regions benefitted from increasing stocking rate, except for sheep farms in the border and dairy farms in the south-east regions. The tillage farms in the mid-east region responded to the climate change scenario by shifting arable production to beef production on farms.