| Records |
| Author |
Bojar, W.; Żarski, J.; Knopik, L.; Kuśmierek-Tomaszewska, R.; Sikora, M.; Dzieża, G. |
| Title |
Markov chain as a model of daily total precipitation and a prediction of future natural events |
Type |
Conference Article |
| Year |
2015 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue  |
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Pages |
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| Keywords |
ft_macsur; MACSUR or FACCE acknowledged. |
| 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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Braunschweig (Germany) |
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FACCE MACSUR Joint Workshops October 2015, 2015-10-27 to 2015-10-30, Braunschweig |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4236 |
| Permanent link to this record |
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| Author |
Bojar, W.; Żarski, J.; Knopik, L.; Kuśmierek-Tomaszewska, R.; Sikora, M.; Dzieża, G. |
| Title |
Markov chain as a model of daily total precipitation and a prediction of future natural events |
Type |
Conference Article |
| Year |
2015 |
Publication |
|
Abbreviated Journal |
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| Volume |
|
Issue |
|
Pages |
|
| Keywords |
ft_macsur; MACSUR or FACCE acknowledged. |
| 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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Place of Publication |
Braunschweig (Germany) |
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Conference |
FACCE MACSUR Joint Workshops October 2015, 2015-10-27 to 2015-10-30, Braunschweig |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4395 |
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| Author |
Nguyen, T.P.L.; Seddaiu, G.; Virdis, S.G.P.; Tidore, C.; Pasqui, M.; Roggero, P.P. |
| Title |
Perceiving to learn or learning to perceive? Understanding farmers’ perceptions and adaptation to climate uncertainties |
Type |
Journal Article |
| Year |
2016 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
| Volume |
143 |
Issue |
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Pages |
205-216 |
| Keywords |
climate variability; socio-cognitive learning process; adaptation strategies; mediterranean agricultural systems; agricultural land-use; adaptive capacity; farming systems; variability; knowledge; risk; drought; africa; future; rain |
| Abstract |
Perception not only shapes knowledge but knowledge also shapes perception. Humans adapt to the natural world through a process of learning in which they interpret their sensory impressions in order to give meaning to their environment and act accordingly. In this research, we examined how farmers’ decision making is shaped in the context of changing climate. Using empirical data (face-to-face semi-structured interviews and questionnaires) on four Mediterranean farming systems from a case study located in Oristano (Sardinia, Italy) we sought to understand farmers’ perception of climate change and their behaviors in adjustment of farming practices. We found different perceptions among farmer groups were mainly associated with the different socio-cultural and institutional settings and perceived relationships between climate factors and impacts on each farming systems. The research findings on different perceptions among farmer groups can help to understand farmers’ current choices and attitudes of adaptation for supporting the development of appropriate adaptation strategies. In addition, the knowledge of socio-cultural and economic factors that lead to biases in climate perceptions can help to integrate climate communication into adaptation research for making sense of climate impacts and responses at farm level. |
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English |
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0308-521x |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4707 |
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| Author |
Nguyen, T.P.L.; Seddaiu, G.; Roggero, P.P. |
| Title |
Declarative or procedural knowledge? Knowledge for enhancing farmers’ mitigation and adaptation behaviour to climate change |
Type |
Journal Article |
| Year |
2019 |
Publication |
Journal of Rural Studies |
Abbreviated Journal |
Journal of Rural Studies |
| Volume |
67 |
Issue |
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Pages |
46-56 |
| Keywords |
Farming systems; Knowledge; Attitude; Practice; Social construction |
| Abstract |
Climate change poses a major challenge for farmers, but agricultural sustainability, mitigation, and adaptation can effectively decrease climate impacts on agricultural systems. Changes in farming practices are necessary to reduce emissions and to adapt to climate change. However, such modifications to common practices depend, to a large extent, on farmers’ knowledge and attitudes towards climate risks. An empirical study of farmers’ attitudes and knowledge of climate change mitigation and adaptation practices is useful to understand how farmers’ knowledge influences their attitudes and practices towards climate change mitigation and adaptation. Based on a case study characterised by four agricultural farming systems (extensive dairy sheep, intensive dairy cattle, horticultural farming, and rice farming) in the Province of Oristano in Italy, this study contains an investigation of (i) farmers’ knowledge of climate change causes and effects, how they construct such knowledge, and how they adapt to the phenomenon; (ii) what and how are farmers’ attitudes towards climate change causes are shaped under their contextual social interests and values; and (iii) if their practices in responding to climate variability are influenced by their constructed knowledge. The research results showed that farmers’ declarative knowledge of climate change did not affect their adaptation practices but directed farmers’ attitudes towards climate change causes. The findings also underscore the necessity of facilitating social learning spaces for enhancing virtuous behaviours towards climate change mitigation and the sharing and co-production of procedural knowledge for developing shared sustainable climate adaptation practices at the farm level. |
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2019-02-19 |
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0743-0167 |
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article |
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| Notes |
CropM, TradeM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5217 |
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| Author |
Saetnan, E.R.; Kipling, R.P. |
| Title |
Evaluating a European knowledge hub on climate change in agriculture: Are we building a better connected community |
Type |
Journal Article |
| Year |
2016 |
Publication |
Scientometrics |
Abbreviated Journal |
Scientometrics |
| Volume |
109 |
Issue |
2 |
Pages |
1057-1074 |
| Keywords |
Agriculture; Climate change; Interdisciplinary collaboration; Co-authorship; networks; EU research policy; Collaborative funding initiatives; Knowledge hub |
| Abstract |
In order to maintain food security and sustainability of production under climate change, interdisciplinary and international collaboration in research is essential. In the EU, knowledge hubs are important funding instruments for the development of an interconnected European Research Area. Here, network analysis was used to assess whether the pilot knowledge hub MACSUR has affected interdisciplinary collaboration, using co-authorship of peer reviewed articles as a measure of collaboration. The broad community of all authors identified as active in the field of agriculture and climate change was increasingly well connected over the period studied. Between knowledge hub members, changes in network parameters suggest an increase in collaborative interaction beyond that expected due to network growth, and greater than that found in the broader community. Given that interdisciplinary networks often take several years to have an impact on research outputs, these changes within the relatively new MACSUR community provide evidence that the knowledge hub structure has been effective in stimulating collaboration. However, analysis showed that knowledge hub partners were initially well-connected, suggesting that the initiative may have gathered together researchers with particular resources or inclinations towards collaborative working. Long term, consistent funding and ongoing reflection to improve networking structures may be necessary to sustain the early positive signs from MACSUR, to extend its success to a wider community of researchers, or to repeat it in less connected fields of science. Tackling complex challenges such as climate change will require research structures that can effectively support and utilise the diversity of talents beyond the already well-connected core of scientists at major research institutes. But network research shows that this core, well-connected group are vital brokers in achieving wider integration. |
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0138-9130 1588-2861 |
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LiveM |
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Conference |
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| Notes |
LiveM; wos; ft=macsur; macsur-text; wsnotyet |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4760 |
| Permanent link to this record |
