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Kauer, K., Tein, B., & Loit, E. (2014). The long-term trends in soil carbon stock and crop productivity depending on management in Estonia. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The dynamics of soil organic carbon (SOC) content and crop productivity were studied on three still continuing field experiments situated at the experimental station of the Estonian University of Life Sciences in Tartu, Estonia. The first trial was established in 1964. The effect of mineral fertilizers and farmyard manure applied to barley and sward with different species composition on soil organic carbon content was studied. The second trial with 3-crop rotation (potato – spring wheat – spring barley) was established in 1989. Experimental factors were organic (without amendment, solid cattle manure and alternative organic fertilisers) and mineral fertilisers (0, 40, 80, 120 and 160 kg N ha-1). The third field experiment with 5-crop rotation experiment was established in 2008. Five different crops were following each other – barley undersown with red clover, red clover, winter wheat, pea and potato. Experimental factors were organic (catch crops as green manures, catch crops as green manures combined with composted cattle manure) and conventional farming systems. The conventional farming systems differed in the amounts of mineral fertilizers used: 0, 50, 100 and 150 kg N ha-1. The first goal of this research was to quantify plant C inputs to the soil in Estonian arable lands and the net primary production using crop-specific allometric relationships. Secondly, the impact of the different management scenarios on the changes in soil C stock was evaluated using plant C input data. The preliminary results of these data analysis will be presented.
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Biewald, A. (2014). The development of cereals and oilseed production until 2050 under different socioeconomic conditions in Finland. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: We will use Finland as an example of a small, developed country with difficult climatic conditions to show how changes in global food consumption patterns and global population growth will influence local production. In order to do so we use two different models. First, an agricultural sector model for Finland, and second, a regionally adapted version of a global, spatially explicit agroeconomic land use model. We use both models to investigate how Finnish cereals and oilseed production develops under different socioeconomic conditions, as defined in the Socioeconomic Pathways (SSPs). We find that without a major improvement of oilseed yields, oilseed production will be disrupted by 2050 and even with a major increase in oilseeds yields only in the SSP2 scenario production of oilseeds can be sustained. Cereal production on the other hand does not change in the simulations with the global model, but does almost decrease by half in the simulations with the national model. This shows that even with an enormous global population growth and reduced international trade, Finland will not become a major agricultural producer.
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Dono, G., Cortignani, R., Doro, L., & Roggero, P. P. (2014). The adaptation of farm and awareness of ongoing climate change (CC). FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Farm planning is based on awareness of climate variability, here assumed to depend on experience gained over the years, and to generate expectations on climatic variables. Expectations are based on probability distributions (pdfs) estimated on climate data and used to generate managing choices by means of Discrete Stochastic Programming. The model simulates the income losses in case farmers do not recognize the ongoing CC, and continue to plan assuming climate stability. In particular, the use of resources in 2010 is simulated based on the pdfs of the early 2000s, despite CC has changed the probabilities of the various states of nature. The model, calibrated with Positive Mathematical Programming, generates a 0.9% income increase when is allowed to adapt to 2010 climate pdfs. The model is also calibrated according to pdfs of 2010, i.e. recognizing CC: in this case income falls of 0.7% when farmers are simulated to use their soil mistakenly based of the 2000 pdfs. Given the short period of CC, the differences represent an appreciable error that farmers may be already committing. Properly specifying with the CC at local level can help building farmers’ awareness on it, and to properly manage their resources, recovering profitability.
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Milford, A. B. (2014). Sustainable food consumption as a mitigation and adaptation strategy. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Studies of GHG emissions from agriculture show that there are large differences in emissions from different products. In addition some foods require more land and water resources than others, which mean that in a future with food scarcity, moving from less to more sustainable food may become a necessity for there to be enough food for everyone. A changing of consumption and production from less to more sustainable food is thus both a mitigation strategy, as well as an adaptation strategy if climate change results in less available agricultural land and water resources, and more food loss due to more extreme weather conditions. Forecasting the economic consequences of climate change on agricultural production cannot be done without taking into account our future consumption patterns. What will be produced will always to a large extent be a result of what is being demanded by consumers. This is a presentation of two ongoing projects related to this theme. One is a newly started project on factors which influence meat consumption. In a cross-country regression analysis we will estimate the importance of different factors such as income, price levels and degree of urbanization. We are particularly interested in the interlinkages between meat production and consumption at national levels. The other project looks at typical diets in England, Spain and Norway, and will estimate through a multi objective optimization process how the diets can be changed, through taxes and subsidies, towards a diet which is both more healthy and climate friendly.
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Sharif, B., Olesen, J. E., & Schelde, K. (2014). Statistical learning approach for modelling the effects of climate change on oilseed rape yield. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Statistical learning is a fairly new term referring to a set of supervised and unsupervised modelling and prediction techniques. It is based on traditional statistics but has been highly enhanced inspired by developments in machine learning and data mining. The main focus of statistical learning is to estimate the functions that quantify relations between several parameters and observed responses. These functions are further used for prediction, inference or a combination of both. For a particular case of quantitative responses, regularization techniques in regression are developed to overcome the weaknesses of ordinary least square (OLS) regression in prediction. These new shrinkage methods outperform OLS for prediction, especially in large datasets. In this study, a large dataset of field experiments on winter oilseed rape in Denmark for 22 years (1992 to 2013) was collected. Biweekly climatic data along with sowing date, harvest date, soil type and previous crop are considered as the explanatory variables. Yield of winter oilseed rape is considered as response variable. LASSO and Elastic Nets are the regularization techniques used to estimate the functions. Hold-one-out cross validation method for testing the prediction power reveals that these techniques are much useful in both prediction and inference. Since these techniques are included in recent versions of some software packages (e.g. R), they can be easily implemented by users at any level. The estimated function (model) is further used to predict the oilseed rape yield responses to climate change for several scenarios using representative weather data produced by a weather generator.
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