Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation

M Despotovic, V Nedic, D Despotovic… - … and Sustainable Energy …, 2016 - Elsevier
In many solar applications knowing diffuse solar radiation on horizontal surface represents
an important requirement. The measurement of diffuse radiation is quite expensive, and …

Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes—A review

RP Rötter, M Appiah, E Fichtler, KC Kersebaum… - Field Crops …, 2018 - Elsevier
Climate change implies higher frequency and magnitude of agroclimatic extremes
threatening plant production and the provision of other ecosystem services. This review is …

[HTML][HTML] Machine learning in crop yield modelling: A powerful tool, but no surrogate for science

G Lischeid, H Webber, M Sommer, C Nendel… - Agricultural and Forest …, 2022 - Elsevier
Provisioning a sufficient stable source of food requires sound knowledge about current and
upcoming threats to agricultural production. To that end machine learning approaches were …

[HTML][HTML] Improving the use of crop models for risk assessment and climate change adaptation

AJ Challinor, C Müller, S Asseng, C Deva, KJ Nicklin… - Agricultural systems, 2018 - Elsevier
Crop models are used for an increasingly broad range of applications, with a commensurate
proliferation of methods. Careful framing of research questions and development of targeted …

The impact of climate change on barley yield in the Mediterranean basin

D Cammarano, S Ceccarelli, S Grando… - European Journal of …, 2019 - Elsevier
Barley is an important cereal crop for the arid and semi-arid Mediterranean environments.
Future climate projections show that Mediterranean countries will get drier and hotter. The …

The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2

Z Jin, Q Zhuang, J Wang, SV Archontoulis… - Global change …, 2017 - Wiley Online Library
Heat and drought are two emerging climatic threats to the US maize and soybean
production, yet their impacts on yields are collectively determined by the magnitude of …

Sources of uncertainty for wheat yield projections under future climate are site-specific

B Wang, P Feng, DL Liu, GJ O'Leary, I Macadam… - Nature Food, 2020 - nature.com
Understanding sources of uncertainty in climate–crop modelling is critical for informing
adaptation strategies for cropping systems. An understanding of the major sources of …

Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

A Maiorano, P Martre, S Asseng, F Ewert, C Müller… - Field crops …, 2017 - Elsevier
To improve climate change impact estimates and to quantify their uncertainty, multi-model
ensembles (MMEs) have been suggested. Model improvements can improve the accuracy …

The response of contrasting tomato genotypes to combined heat and drought stress

A Nankishore, AD Farrell - Journal of plant physiology, 2016 - Elsevier
Efforts to maximize yields of food crops can be undermined by abiotic stress factors,
particularly those related to climate change. Here, we use a range of physiological methods …

Hot spots of wheat yield decline with rising temperatures

S Asseng, D Cammarano, B Basso… - Global change …, 2017 - Wiley Online Library
Many of the irrigated spring wheat regions in the world are also regions with high poverty.
The impacts of temperature increase on wheat yield in regions of high poverty are uncertain …