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Wallach, D., & Rivington, M. (2015). Standardised methods and protocols based on current best practices to conduct sensitivity analysis (Vol. 6).
Abstract: The purpose of this report is to propose a general procedure for sensitivity analysis when used to evaluate system sensitivity to climate change, including uncertainty information. While sensitivity analysis has been largely used to evaluate how uncertainties in inputs or parameters propagate through the model and manifest themselves in uncertainties in model outputs, there is much less experience with sensitivity analysis as a tool for studying how sensitive a system is to changes in inputs. This report should help make clear the differences between these two uses of sensitivity analysis, and provide guidance as to the procedure for using sensitivity analysis for evaluating system sensitivity to climate change. No Label
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Rötter, R. (2015). Crop yield variance and yield gap analysis for evaluating technological innovations under climate change: the case of Finnish barley (Vol. 5).
Abstract: The quest for sustainable intensification of agricultural systems has recently triggered research on determining and closing the gaps between farmers’ actual and potential crop yields that can be obtained under optimal management. This so-called “yield gap” is then taken as a yardstick for indicating the potential of technological innovations in agricultural production. In this paper, we argue that in order to assess risks and opportunities for technological innovations we need extra information on crop yield variances in different production situations.Starting point is to assess farmers’ actual yields using data in sufficient quality and resolutions. Crop simulation models are then applied to quantify crop yield potentials and their variances in a changing environment. Resultant information allows ex ante evaluation of innovations that aim at increasing and stabilizing yields.Here we present this approach for barley cultivation in Finland for observed (1981-2010) and future climate (projected for three time periods centered around 2025, 2055 and 2085). Mean and median levels, variances and probabilities of simulated potential and water-limited and observed farmers’ yields are generated for two contrasting regions for analysing production risks and assessing the effectiveness of alternative technologies. As farmers show different levels of risk-aversion, which influence their investments in technological innovations, a so-called ‘normal management mode’ is defined. Employing this then shows how future yields and yield variances are likely to develop under normal management. On this basis, we finally identify which future innovations have the potential to maintain or increase barley yields at acceptable risk levels. No Label
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Cortignani, R. (2015). Common Agricultural Policy and climate variability changes: an impact assessment of the first-pillar reform on an agricultural area of Grana Padano in different climate scenarios (Vol. 5).
Abstract: The reform of the Common Agricultural Policy it started in 2015 with several innovative aspects. Regarding the first pillar, such aspects are especially the convergence of the basic payments, the green payments and the coupled payments. In this regard seems interesting carry out analysis about to evaluate the policy impact considering the risks and opportunities due to climate change.In this study the impact of the convergence of basic payments, the introduction of the green payments and the coupled payments has been evaluated on dairy cattle farms in the Grana Padano area. The impact has been evaluated in different climate scenarios by economic, social and environmental indicators. The methodology used is the mathematical programming and especially a model of Discrete Stochastic Programming has been used to represents farms of the FADN database.The main results show that a significant part of the farms is affected by the diversification constraint that reduces the land devoted to corn silage. Farmers could cultivate corn silage after a principal crop (e.g. ryegrass) in order to avoid the diversification constraint, however, determining a negative impact on the use of environmental resources. To consider also that in the future there is an increase of corn silage yields with long cycle.Another result to underline is that which concerns the possibility of soybean cultivation in the ecological focus areas. In fact, considering the coupled payment provided for this crop, the ecological focus areas seem to be an important source of income for the farms.Finally, the analysis shows that the convergence of the basic payment will result in a reallocation of direct payments between farms with a significant impact on farm incomes. No Label
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Hoveid, Ø. (2015). A prototype stochastic dynamic equilibrium model of the global food system (Vol. 5).
Abstract: The risks of food consumption are primarily linked to those of food production due to stochastic weather. Other sources of risk are associated with break-down of food trade or transport for weather or political reasons. Hopefully, future cures against increased risk due to climate change may be found with new agricultural technologies, systems of storage from favorable to unfavorable periods, more flexible trade-arrangements between favorable and unfavorable places. However, in the short run one has to rely on the available technology, storage facilities and trade agreements. With a realistic model of the stochastic global food system, it should be possible to measure risks of certain extreme unfavorable events.A realistic case will have countries with different climate in different growing seasons. Markets will be open for trade at a number of points per year, in which decisions of production, storage, trade and consumption can be coordinated as a static equilibrium. Determinants of this equilibrium are the weather up to this date reflected in the state of crops, the available harvested stocks and the decision-maker’s preferences. With a global stochastic process of weather, a stochastic sequence of equilibria follows. No Label
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Bojar, W., Żarski, J., Knopik, L., Kuśmierek-Tomaszewska, R., Sikora, M., & Dzieża, G. (2015). Markov chain as a model of daily total precipitation and a prediction of future natural events.. Braunschweig (Germany).
Abstract: The size of arable crop yields depends on many weather factors, such as precipitation and air temperature during the vegetation period. When studying the relation between yields and precipitation, not only the total amount of precipitation, but also the occurrence of long periods without precipitation must be taken into account. The paper [Bojar et al., 2014] demonstrated that barley yield significantly statistically depends on the length of the series of days without precipitation. This paper attempts to analyse the statistical data on daily precipitation totals recorded during the January – December periods in the years 1971 – 2013 at the weather station of the University of Science and Technology in Bydgoszcz, Faculty of Agriculture and Biotechnology, in the Research Centre located in an agricultural area in the Mochle township, situated 17 kilometres from Bydgoszcz. The primary statistical operation in the study is an attempt to estimate the Markov chain order. To this end, two criteria of chain order determination are applied: BIC (Bayesian information criterion, Schwarz 1978) and AIC (Akaike information criterion, Akaike 1974). Both are based on the log-likelihood functions for transition probability of the Markov chain constructed on certain data series. Statistical analysis of precipitation totals data leads to the conclusion that both AIC and BIC indicate the 2nd order for the studied Markov chain. The proposed method of estimating the variability of precipitation occurrence in the future will be utilised to improve region-related bio-physical and economical models, and to assess the risk of extreme events in the context of growing climate hazards. It will serve as basis for a search in agriculture for solutions mitigating those hazards.
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