scholarly journals Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

2015 ◽  
Vol 65 ◽  
pp. 87-105 ◽  
Author(s):  
N Pirttioja ◽  
TR Carter ◽  
S Fronzek ◽  
M Bindi ◽  
H Hoffmann ◽  
...  
2018 ◽  
Vol 159 ◽  
pp. 209-224 ◽  
Author(s):  
Stefan Fronzek ◽  
Nina Pirttioja ◽  
Timothy R. Carter ◽  
Marco Bindi ◽  
Holger Hoffmann ◽  
...  

2011 ◽  
Vol 11 (11) ◽  
pp. 2981-2995 ◽  
Author(s):  
S. Fronzek ◽  
T. R. Carter ◽  
M. Luoto

Abstract. We present an analysis of different sources of impact model uncertainty and combine this with probabilistic projections of climate change. Climatic envelope models describing the spatial distribution of palsa mires (mire complexes with permafrost peat hummocks) in northern Fennoscandia were calibrated for three baseline periods, eight state-of-the-art modelling techniques and 25 versions sampling the parameter uncertainty of each technique – a total of 600 models. The sensitivity of these models to changes in temperature and precipitation was analysed to construct impact response surfaces. These were used to assess the behaviour of models when extrapolated into changed climate conditions, so that new criteria, in addition to conventional model evaluation statistics, could be defined for determining model reliability. Impact response surfaces were also combined with climate change projections to estimate the risk of areas suitable for palsas disappearing during the 21st century. Structural differences in impact models appeared to be a major source of uncertainty, with 60% of the models giving implausible projections. Generalized additive modelling (GAM) was judged to be the most reliable technique for model extrapolation. Using GAM, it was estimated as very likely (>90% probability) that the area suitable for palsas is reduced to less than half the baseline area by the period 2030–2049 and as likely (>66% probability) that the entire area becomes unsuitable by 2080–2099 (A1B emission scenario). The risk of total loss of palsa area was reduced for a mitigation scenario under which global warming was constrained to below 2 °C relative to pre-industrial climate, although it too implied a considerable reduction in area suitable for palsas.


2020 ◽  
Vol 12 (6) ◽  
pp. 1024 ◽  
Author(s):  
Yan Zhao ◽  
Andries B Potgieter ◽  
Miao Zhang ◽  
Bingfang Wu ◽  
Graeme L Hammer

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.


2012 ◽  
Author(s):  
Jianmao Guo ◽  
Tengfei Zheng ◽  
Qi Wang ◽  
Jia Yang ◽  
Junyi Shi ◽  
...  

2009 ◽  
Vol 35 (3) ◽  
pp. 147-149 ◽  
Author(s):  
Yu. K. Galaktionov ◽  
L. F. Ashmarina ◽  
T. A. Galaktionova

1974 ◽  
Vol 54 (4) ◽  
pp. 625-650 ◽  
Author(s):  
GEO. W. ROBERTSON

Half a century of wheat yield and weather records at Swift Current in southwestern Saskatchewan were analyzed to determine the response of wheat (Triticum aestivum L.) to changing weather patterns. Weather at Swift Current has undergone subtle but significant changes over the past 50 yr. Earlier years had disturbed conditions: hot, dry periods alternating with cool, wet ones resulting in yield fluctuations ranging from crop failures to maximum values. More recently the weather has been quiet: dry and cool but less variable from year to year. The resulting conditions were more favorable for near-normal but less variable yields. Simple precipitation-based yield–weather models developed two decades ago no longer apply, because temperature and precipitation patterns are currently out of phase relative to earlier conditions. A factorial yield–weather model was used to explain the complex relationship. This involved the summation of the product of several quadratic functions of various weather elements. Those elements considered were precipitation, maximum and minimum temperatures, global radiation estimated from duration of bright sunshine, evaporation from a buried pan, and time as an indicator of advancing technology. One function contained a term for the antecedant crop condition. The most important elements were precipitation for the summer-fallow period and for May, June and August; maximum temperatures for June and July; and global radiation for May. Advances in technology would seem to have very little influence on wheat yield trends after weather trends were accounted for. The model accounted for 73% (r = 0.854) of the yield variability and provided realistic functions for explaining the curvilinear influence of individual weather elements on wheat yield. The model is of a form that is readily adaptable for assessing, at any time during the crop development period, the influence of past and current weather on future expected yield. This could be useful for interpreting weather data in terms of crop production in weather and crop condition surveillance programs.


2008 ◽  
Vol 42 (31) ◽  
pp. 7250-7265 ◽  
Author(s):  
S. Potempski ◽  
S. Galmarini ◽  
R. Addis ◽  
P. Astrup ◽  
S. Bader ◽  
...  

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