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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions (Vol. 8).
Abstract: Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016)
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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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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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Below, T. B., Mutabazi, K. D., Kirschke, D., Franke, C., Sieber, S., Siebert, R., et al. (2012). Can farmers’ adaptation to climate change be explained by socio-economic household-level variables. Glob. Environ. Change, 22(1), 223–235.
Abstract: A better understanding of processes that shape farmers’ adaptation to climate change is critical to identify vulnerable entities and to develop well-targeted adaptation policies. However, it is currently poorly understood what determines farmers’ adaptation and how to measure it. In this study, we develop an activity-based adaptation index (AAI) and explore the relationship between socioeconomic variables and farmers’ adaptation behavior by means of an explanatory factor analysis and a multiple linear regression model using latent variables. The model was tested in six villages situated in two administrative wards in the Morogoro region of Tanzania. The Mlali ward represents a system of relatively high agricultural potential, whereas the Gairo ward represents a system of low agricultural potential. A household survey, a rapid rural appraisal and, a stakeholder workshop were used for data collection. The data were analyzed using factor analysis, multiple linear regression, descriptive statistical methods and qualitative content analysis. The empirical results are discussed in the context of theoretical concepts of adaptation and the sustainable livelihood approach. We found that public investment in rural infrastructure, in the availability and technically efficient use of inputs, in a good education system that provides equal chances for women, and in the strengthening of social capital, agricultural extension and, microcredit services are the best means of improving the adaptation of the farmers from the six villages in Gairo and Mlali. We conclude that the newly developed AAI is a simple but promising way to capture the complexity of adaptation processes that addresses a number of shortcomings of previous index studies.
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De Sanctis, G., Roggero, P. P., Seddaiu, G., Orsini, R., Porter, C. H., & Jones, J. W. (2012). Long-term no tillage increased soil organic carbon content of rain-fed cereal systems in a Mediterranean area. European Journal of Agronomy, 40, 18–27.
Abstract: The differential impact on soil organic carbon (SOC) of applying no tillage (NT) compared to conventional tillage (CT, i.e. mouldboard ploughing), along with three rates of nitrogen (N) fertilizer application (0,90 and 180 kg ha(-1) y(-1)), was studied under rain-fed Mediterranean conditions in a long-term experiment based on a durum wheat-maize rotation, in which crop residues were left on the soil (NT) or incorporated (CT). Observed SOC content following 8 and 12 years of continuous treatment application was significantly higher in the top 10 cm of the soil under NT than CT, but it was similar in the 10-40 cm layer. NT grain yields for both maize and durum wheat were below those attained under CT (on average 32% and 14% lower respectively) at a given rate of N fertilizer application. Soil, climate and crop data over 5 years were used to calibrate DSSAT model in order to simulate the impact of the different management practices over a 50-year period. Good agreement was obtained between observed and simulated values for crops grain yield, above-ground biomass and observed SOC values. Results from the simulations showed that under NT the weeds growing during the intercrop fallow period made a significant contribution to the observed SOC increase. When the contribution of the weed fallow was considered, NT significantly increased SOC in the top 40 cm of the soil at an average rate of 0.43, 0.31 and 0.03 t ha(-1) per year, respectively for 180,90 and 0 kg N ha(-1) year(-1), within the simulated 50 years. Under CT, a significant SOC increase was simulated under N180 and a significant decrease when no fertilizer was supplied.
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