Progress in Plant Bioclimatic Modelling under Global Climate Change

Dear Colleagues,

Climate change has put great pressure on food security around the world. This is because climate change, including variable rainfall patterns, coupled with climate warming, increased frequency and intensity of extreme weather–climate events, can adversely affect crop production in many parts of the world. Developing robust crop bioclimatic models is critical in quantifying the impacts of climate change on crop productivity. Such models can help researchers and policymakers to develop efficient agronomic strategies that maintain and increase crop yield under climate change to ensure food security.

The process-based crop model is a robust tool to simulate crop growth and development and it has been widely used to study the impacts of future climate change on agricultural yield. Biophysical process-based crop models allow the consideration of complex and non-linear physiological responses of crops to climate and soil conditions, and thereby support the development of effective adaptation strategies. However, the major limitation of these process-based crop models is that they haven’t fully considered the impacts of extreme weather–climate events. Meanwhile, multivariate statistical crop models have been developed based on the relationship between long-term observed yield and multiple climatic variables. The advantage of the statistical crop model is its simplicity, straightforward and intuitive interpretation. However, they simplify the biophysical process on how crops may respond to the change of climate, soil, and management options in comparison to process-based models. Recently, a hybrid approach based on biophysical models and advanced machine learning algorithms has been developed. They have more accurate predictions in estimating crop yield by incorporating the crop growth model outputs and growth stage-specific extreme climate events (i.e., frost, drought, and heat stress) into the machine learning model. Such newly developed hybrid models should be encouraged and applied in the climate change impact assessment.

With this Special Issue of Agronomy, we seek integrative studies that shed light on new, developed or improved models to better understand the interaction of crop and environmental conditions under climate change, as well as reviews that offer original perspectives on crop models developed in response to climate change. Articles highlighting the use of crop modelling to cope with climate change with different agronomic options are also welcome.

Dr. Bin Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.