scholarly journals Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Nature Plants ◽  
2017 ◽  
Vol 3 (10) ◽  
pp. 833-833 ◽  
Author(s):  
Enli Wang ◽  
Pierre Martre ◽  
Zhigan Zhao ◽  
Frank Ewert ◽  
Andrea Maiorano ◽  
...  
Nature Plants ◽  
2017 ◽  
Vol 3 (8) ◽  
Author(s):  
Enli Wang ◽  
Pierre Martre ◽  
Zhigan Zhao ◽  
Frank Ewert ◽  
Andrea Maiorano ◽  
...  

Nature Plants ◽  
2017 ◽  
Vol 3 (8) ◽  
Author(s):  
Enli Wang ◽  
Pierre Martre ◽  
Zhigan Zhao ◽  
Frank Ewert ◽  
Andrea Maiorano ◽  
...  

1998 ◽  
Vol 78 (3) ◽  
pp. 421-429 ◽  
Author(s):  
D. W. Stewart ◽  
L. M. Dwyer ◽  
L. M. Reid

Maize (Zea mays L.) is a crop of growing importance in Eastern Canada. Modelling the temperature effects on phenological development, crop architecture and disease infection in maize contributes to the development of well-adapted, early-maturing varieties. Details of modelling these three aspects of maize growth were presented. The first focussed on quantifying the effect of air or soil temperature on maize phenological development. Crop growth was divided into two periods: vegetative (planting to silking) and grain filling (silking to maturity). A third period (planting to emergence) was separated within the vegetative period. Heat unit systems based on daily temperature response functions were developed to produce the most consistent heat unit sums for each period. The best fits of these functions were determined by minimizing standard deviations and coefficients of variation of these sums for each thermal period over locations and years. Calculated temperature response functions estimated thermal periods more consistently than growing degree days (GDD) for all three periods. The largest improvement was made in the silking to maturity period.The second aspect was a study of crop architecture. Methods were developed to mathematically characterize the structure of a canopy in terms of leaf area and leaf angle distributions with crop height and across the row. These calculations, in turn, were input to a soil–plant–atmosphere model to calculate interception of photosynthetically active radiation (PAR). Model calculations of PAR interception compared well with measurements for a range of plant types and plant population densities (R2 = 0.76).The third aspect was quantifying growth of Fusarium in maize. Differential equations were used to relate Fusarium rates of growth in maize ears to air temperature, relative humidity and precipitation. Integration of these equations over time produced quantitative estimates of fungal infection. Model calculations were compared to visual ratings of fungal infection for two Fusarium species over three years (R2 = 0.92).In each of the three aspects of this study, modelling tested our understanding of the processes involved and the dominant factors controlling these processes. Thus, modelling was an integral part of the scientific approach, synthesizing experimental data in a quantitative conceptual framework and identifying dominant factors and parameters which required additional focussed experimental evaluation. Key words: Phenological development, crop architecture, Fusarium infection


2018 ◽  
Author(s):  
Abigail Snyder ◽  
Katherine V. Calvin ◽  
Meridel Phillips ◽  
Alex C. Ruane

Abstract. Future changes in Earth system state will impact agricultural yields and, through these changed yields, can have profound impacts on the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields, but are often too computationally intensive to dynamically couple into global multi-sector economic models, such as GCAM and other similar-in-scale models. Yet, generalizing a faster site-specific crop model's results to be used globally will introduce inaccuracies, and the question of which model to use is unclear given the wide variation in yield response across crop models. To examine the feedback loop among socioeconomics, Earth system changes, and crop yield changes, rapidly generated yield responses with some quantification of crop response uncertainty are desirable. The Persephone v1.0 response functions presented in this work are based on the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Climate-Crop Modeling Project (C3MP) sensitivity test data set and are focused on providing GCAM and similar models with a tractable number of rapid to evaluate, dynamic yield response functions corresponding to a range of the yield response sensitivities seen in the C3MP data set. With the Persephone response functions, a new variety of agricultural impact experiments will be open to GCAM and other economic models; for example, examining the economic impacts of a multi-year drought in a key agricultural region and how economic changes in response to the drought can, in turn, impact the drought.


2010 ◽  
Vol 7 (11) ◽  
pp. 3669-3684 ◽  
Author(s):  
H. Portner ◽  
H. Bugmann ◽  
A. Wolf

Abstract. Models of carbon cycling in terrestrial ecosystems contain formulations for the dependence of respiration on temperature, but the sensitivity of predicted carbon pools and fluxes to these formulations and their parameterization is not well understood. Thus, we performed an uncertainty analysis of soil organic matter decomposition with respect to its temperature dependency using the ecosystem model LPJ-GUESS. We used five temperature response functions (Exponential, Arrhenius, Lloyd-Taylor, Gaussian, Van't Hoff). We determined the parameter confidence ranges of the formulations by nonlinear regression analysis based on eight experimental datasets from Northern Hemisphere ecosystems. We sampled over the confidence ranges of the parameters and ran simulations for each pair of temperature response function and calibration site. We analyzed both the long-term and the short-term heterotrophic soil carbon dynamics over a virtual elevation gradient in southern Switzerland. The temperature relationship of Lloyd-Taylor fitted the overall data set best as the other functions either resulted in poor fits (Exponential, Arrhenius) or were not applicable for all datasets (Gaussian, Van't Hoff). There were two main sources of uncertainty for model simulations: (1) the lack of confidence in the parameter estimates of the temperature response, which increased with increasing temperature, and (2) the size of the simulated soil carbon pools, which increased with elevation, as slower turn-over times lead to higher carbon stocks and higher associated uncertainties. Our results therefore indicate that such projections are more uncertain for higher elevations and hence also higher latitudes, which are of key importance for the global terrestrial carbon budget.


2001 ◽  
Vol 24 (2) ◽  
pp. 253-259 ◽  
Author(s):  
C. J. Bernacchi ◽  
E. L. Singsaas ◽  
C. Pimentel ◽  
A. R. Portis Jr ◽  
S. P. Long

2020 ◽  
Author(s):  
Endre Falck Mentzoni ◽  
Andreas Johansen ◽  
Andreas Rostrup Martinsen ◽  
Kristoffer Rypdal ◽  
Martin Rypdal

<blockquote> <div dir="ltr"> <div> <p><span lang="en-US">In this work, we present estimates and uncertainties of the remaining carbon budget for a range of different global temperature targets. To model how atmospheric CO2 and methane concentrations depend on emissions, we use impulse response functions estimated from emission-pulse experiments in Earth System Models (ESMs). We use box-model ESM emulators to model the temperature response to radiative forcing and analyze a range of emission scenarios from Integrated Assessment Models. Taking into account uncertainties in the approximately linear relationship between cumulative emission and peak temperature, as well as internal climate variability and uncertainties in the carbon and climate models, we estimate the remaining carbon budgets for varying targets. The results show that the carbon-budget-uncertainties increase significantly with less ambitious targets.</span></p> </div> </div> </blockquote>


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