| 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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Place of Publication |
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 |
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Abbreviated Journal |
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| Volume |
|
Issue |
|
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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Thesis |
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Place of Publication |
Braunschweig (Germany) |
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Summary Language |
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Original Title |
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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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| ISSN |
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 |
Faye, B.; Webber, H.; Naab, J.B.; MacCarthy, D.S.; Adam, M.; Ewert, F.; Lamers, J.P.A.; Schleussner, C.-F.; Ruane, A.; Gessner, U.; Hoogenboom, G.; Boote, K.; Shelia, V.; Saeed, F.; Wisser, D.; Hadir, S.; Laux, P.; Gaiser, T. |
| Title |
Impacts of 1.5 versus 2.0 degrees C on cereal yields in the West African Sudan Savanna |
Type |
Journal Article |
| Year |
2018 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
| Volume |
13 |
Issue |
3 |
Pages |
034014 |
| Keywords |
1.5 degrees C; West Africa; food security; climate change; DSSAT; SIMPLACE; Climate-Change Impacts; Sub-Saharan Africa; Food Security; Heat-Stress; Canopy Temperature; Paris Agreement; Pearl-Millet; Maize Yield; Crop; Yields; Model; MACSUR or FACCE acknowledged. |
| Abstract |
To reduce the risks of climate change, governments agreed in the Paris Agreement to limit global temperature rise to less than 2.0 degrees C above pre-industrial levels, with the ambition to keep warming to 1.5 degrees C. Charting appropriate mitigation responses requires information on the costs of mitigating versus associated damages for the two levels of warming. In this assessment, a critical consideration is the impact on crop yields and yield variability in regions currently challenged by food insecurity. The current study assessed impacts of 1.5 degrees C versus 2.0 degrees C on yields of maize, pearl millet and sorghum in the West African Sudan Savanna using two crop models that were calibrated with common varieties from experiments in the region with management reflecting a range of typical sowing windows. As sustainable intensification is promoted in the region for improving food security, simulations were conducted for both current fertilizer use and for an intensification case (fertility not limiting). With current fertilizer use, results indicated 2% units higher losses for maize and sorghum with 2.0 degrees C compared to 1.5 degrees C warming, with no change in millet yields for either scenario. In the intensification case, yield losses due to climate change were larger than with current fertilizer levels. However, despite the larger losses, yields were always two to three times higher with intensification, irrespective of the warming scenario. Though yield variability increased with intensification, there was no interaction with warming scenario. Risk and market analysis are needed to extend these results to understand implications for food security. |
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| ISSN |
1748-9326 |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5196 |
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| Author |
Allan, C.; Nguyen, T.P.L.; Seddaiu, G.; Wilson, B.; Roggero, P.P. |
| Title |
Integrating local knowledge with experimental research: case studies on managing cropping systems in Italy and Australia |
Type |
Journal Article |
| Year |
2013 |
Publication |
Italian Journal of Agronomy |
Abbreviated Journal |
Ital. J. Agron. |
| Volume |
8 |
Issue |
2 |
Pages |
15 |
| Keywords |
participatory action research; agronomic research; local knowledge; knowledge integration |
| Abstract |
The sustainable development of agricultural systems is currently challenged by many complex agro-environmental issues. These are characterized by an incomplete understanding of the situation and the problems that arise, and the conflicting opinions that result, issues over boundaries that are often difficult to define, and controversy over the multiple goals and uncertain outcomes. Added to these characteristics, we also have the slow and often inadequate uptake and implementation of research outcomes in this complex, real world. In order to improve sustainability of agro-ecosystems, agronomic research must move away from the linear research approaches and extension practices adopted so far that have focused purely on biophysical agro-ecosystems. The theoretical operational space of agronomic research must be transformed by considering agronomic issues as part of a broader social-agro-ecosystem. One aspect of this transformation is the inclusion of knowledge collected on a local level with the participation of farmers on the ground. The integration of local experiential knowledge with traditional agronomic research is by necessity based on the participation of many different stakeholders and there can be no single blueprint for how best to develop and use the input received. However, agronomists and policy advisors require general guidelines drawn up from actual experience in order to accelerate positive agronomic change. We address this need through a comparative analysis of two case studies; one involves multi-stakeholder research in a cropping system in the dairy district of Arborea, Sardinia, Italy. The central question was: How can high crop production be maintained while also achieving the EU target water quality and minimizing the production costs? The second case is a multi-stakeholder soil health project from south-eastern Australia. Here the central question was: How can soil decline be prevented and reversed in this district, and soils made more resilient to future challenges? The Social Learning for the Integrated Management and sustainable use of water (SLIM) framework, a useful heuristic tool for exploring the dynamics of transformational change, guided the analysis of the case studies. Within this framework, a key indicator of success is the emergence of new knowledge from the creation of new spaces for learning between researchers and local stakeholders. The Italian case study appears to have been the most successful in this sense, as opportunities for joint exploration of research data allowed new potential farming responses to the central question to emerge. The multi-stakeholder processes in the Australian case focused more on providing public openings for individual learning, and missed the opportunity for new knowledge to emerge through joint exploration. We conclude that participatory approaches may enable transformative practice through knowledge integration, but that this process is not an automatic outcome of increased community participation. |
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English |
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2039-6805 1125-4718 |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4482 |
| Permanent link to this record |
