Records |
Author |
Toscano, P.; Genesio, L.; Crisci, A.; Vaccari, F.P.; Ferrari, E.; La Cava, P.; Porter, J.R.; Gioli, B. |
Title |
Empirical modelling of regional and national durum wheat quality |
Type |
Journal Article |
Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
Volume |
204 |
Issue |
|
Pages |
67-78 |
Keywords |
durum wheat; grain protein content; forecasting tool; modelling; gridded data; red winter-wheat; grain quality; climate-change; mediterranean conditions; interannual variability; protein-composition; co2 concentration; vapor-pressure; carbon-dioxide; crop yield |
Abstract |
The production of durum wheat in the Mediterranean basin is expected to experience increased variability in yield and quality as a consequence of climate change. To assess how environmental variables and agronomic practices affect grain protein content (GPC), a novel approach based on monthly gridded input data has been implemented to develop empirical model, and validated on historical time series to assess its capability to reproduce observed spatial and inter-annual GPC variability. The model was applied in four Italian regions and at the whole national scale and proved reliable and usable for operational purposes also in a forecast ‘real-time’ mode before harvesting. Precipitable water during autumn to winter and air temperature from anthesis to harvest were extremely important influences on GPC; these and additional variables, included in a linear model, were able to account for 95% of the variability in GPC that has occurred in the last 15 years in Italy. Our results are a unique example of the use of modelling as a predictive real-time platform and are a useful tool to understand better and forecast the impacts of future climate change projections on durum wheat production and quality. |
Address |
2016-10-31 |
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Place of Publication |
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Language |
English |
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Original Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0168-1923 |
ISBN |
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Article |
Area |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4818 |
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Author |
Murat, M.; Malinowska, I.; Hoffmann, H.; Baranowski, P. |
Title |
Statistical modelling of agrometeorological time series by exponential smoothing |
Type |
Journal Article |
Year |
2016 |
Publication |
International Agrophysics |
Abbreviated Journal |
International Agrophysics |
Volume |
30 |
Issue |
1 |
Pages |
57-65 |
Keywords |
exponential smoothing; meteorological time series; statistical forecasting; daily temperature records; weighted moving averages; climate-change; prediction; forecasts; state; weather |
Abstract |
Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error. |
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Thesis |
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Place of Publication |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0236-8722 |
ISBN |
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Medium |
Article |
Area |
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Conference |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4728 |
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Author |
Ferrise, R.; Toscano, P.; Pasqui, M.; Moriondo, M.; Primicerio, J.; Semenov, M.A.; Bindi, M. |
Title |
Monthly-to-seasonal predictions of durum wheat yield over the Mediterranean Basin |
Type |
Journal Article |
Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
Volume |
65 |
Issue |
|
Pages |
7-21 |
Keywords |
yield predictions; seasonal forecasts; analogue forecasts; stochastic weather generator; empirical forecasting models; durum wheat; crop modelling; mediterranean basin; general-circulation model; scale climate indexes; crop yield; grain-yield; forecasts; simulation; region; precipitation; australia; europe |
Abstract |
Uncertainty in weather conditions for the forthcoming growing season influences farmers’ decisions, based on their experience of the past climate, regarding the reduction of agricultural risk. Early within-season predictions of grain yield can represent a great opportunity for farmers to improve their management decisions and potentially increase yield and reduce potential risk. This study assessed 3 methods of within-season predictions of durum wheat yield at 10 sites across the Mediterranean Basin. To assess the value of within-season predictions, the model SiriusQuality2 was used to calculate wheat yields over a 9 yr period. Initially, the model was run with observed daily weather to obtain the reference yields. Then, yield predictions were calculated at a monthly time step, starting from 6 mo before harvest, by feeding the model with observed weather from the beginning of the growing season until a specific date and then with synthetic weather constructed using the 3 methods, historical, analogue or empirical, until the end of the growing season. The results showed that it is possible to predict durum wheat yield over the Mediterranean Basin with an accuracy of normalized root means squared error of <20%, from 5 to 6 mo earlier for the historical and empirical methods and 3 mo earlier for the analogue method. Overall, the historical method performed better than the others. Nonetheless, the analogue and empirical methods provided better estimations for low-yielding and high-yielding years, thus indicating great potential to provide more accurate predictions for years that deviate from average conditions. |
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English |
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Series Editor |
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Edition |
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ISSN |
0936-577x 1616-1572 |
ISBN |
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Medium |
Article |
Area |
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Conference |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4696 |
Permanent link to this record |
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Author |
Murat, M.; Malinowska, I.; Gos, M.; Krzyszczak, J. |
Title |
Forecasting daily meteorological time series using ARIMA and regression models |
Type |
Journal Article |
Year |
2018 |
Publication |
International Agrophysics |
Abbreviated Journal |
Int. Agrophys. |
Volume |
32 |
Issue |
2 |
Pages |
253-264 |
Keywords |
regression models; forecast; time series; meteorological quantities; Response Surfaces; Extreme Heat; Wheat; Climate |
Abstract |
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt-Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts. |
Address |
2018-06-14 |
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English |
Summary Language |
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Series Issue |
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Edition |
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ISSN |
0236-8722 |
ISBN |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
5202 |
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Author |
Rosenzweig, C.; Elliott, J.; Deryng, D.; Ruane, A.C.; Müller, C.; Arneth, A.; Boote, K.J.; Folberth, C.; Glotter, M.; Khabarov, N.; Neumann, K.; Piontek, F.; Pugh, T.A.; Schmid, E.; Stehfest, E.; Yang, H.; Jones, J.W. |
Title |
Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison |
Type |
Journal Article |
Year |
2014 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
Abbreviated Journal |
Proc. Natl. Acad. Sci. U. S. A. |
Volume |
111 |
Issue |
9 |
Pages |
3268-3273 |
Keywords |
Agriculture/*methods/statistics & numerical data; *Climate Change; Computer Simulation; Crops, Agricultural/*growth & development; Forecasting; Geography; *Models, Theoretical; Nitrogen/*analysis; Risk Assessment; Temperature; AgMIP; Isi-mip; agriculture; climate impacts; food security |
Abstract |
Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies. |
Address |
2016-10-31 |
Corporate Author |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1091-6490 (Electronic) 0027-8424 (Linking) |
ISBN |
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Article |
Area |
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Conference |
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Notes |
CropM |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4801 |
Permanent link to this record |