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Agronomy
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Progress in Plant Bioclimatic Modelling under Global Climate Change
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Progress in Plant Bioclimatic Modelling under Global Climate Change

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (12 August 2022) | Viewed by 10632

Special Issue Editor

Dr. Bin Wang

E-Mail Website
Guest Editor
Adjunct Associate Professor, New South Wales Department of Primary Industries Wagga Wagga Agricultural Institute, Gulbali Research Institute Charles Sturt University, Wagga Wagga, NSW 2650, Australia
Interests: climate change; crop model; hydrological model; agriculture; extreme climate events; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 monthly 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.

Keywords

  • process-based crop model
  • statistical crop model
  • climate change
  • crop yield

Published Papers (4 papers)

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Research

18 pages, 4328 KiB  
Article
Optimizing Water and Nitrogen Strategies to Improve Forage Oat Yield and Quality on the Tibetan Plateau Using APSIM
by Qianhu Ma, Xuemei Zhang, Yuhuan Wu, Huimin Yang and Zikui Wang
Agronomy 2022, 12(4), 933; https://doi.org/10.3390/agronomy12040933 - 13 Apr 2022
Cited by 7 | Viewed by 1939
Abstract
There is a great need for improving oat forage production to increase forage supply and protect grassland ecosystems on the Tibetan Plateau. We conducted two field experiments and modeling work to investigate the responses of oat (Avena sativa L.) forage yield and [...] Read more.
There is a great need for improving oat forage production to increase forage supply and protect grassland ecosystems on the Tibetan Plateau. We conducted two field experiments and modeling work to investigate the responses of oat (Avena sativa L.) forage yield and N uptake to water and N applications, and to optimize the water and N scheduling under rainfed and irrigated conditions. The experiments were conducted in 2017 and 2018 at Jintai farm in the northeast of the Tibetan Plateau. Two N-applying rates of 120 and 60 kg ha−1 were tested in 2017, and four irrigation treatments (no irrigation—NI, irrigated 50 mm at flowering—I1, irrigated 50 mm at tillering and jointing—I2, and irrigated 50 mm at tillering, jointing, and flowering—I3) were applied under every N rate in 2018. The Agricultural Production System Simulator (APSIM) was calibrated and validated for the local oat variety. Under rainfed conditions in both years, oat yields under high and low N were 7.98–8.52 and 5.09–6.53 t ha−1, respectively; the high N rate significantly increased forage yield and N uptake compared to low N conditions by 22.2–67.4% (p < 0.01) and 42.0–162.0% (p < 0.01), respectively. In 2018, irrigation increased oat forage yield by 29.8–96.6% (p < 0.01) and increased N uptake by 19.6–50.5% (p > 0.05); N rates had no significant effect on forage yield (p > 0.05), but significantly increased N uptake by 42.6–64.7% (p < 0.01). I2 was superior to I3 in terms of increasing water use efficiency (WUE) while maintaining high forage yield and N uptake. APSIM-oat was calibrated with data under both rainfed and irrigated conditions and was confirmed to have good accuracy and lower normalized root mean square errors (NRMSEs) for phonology dates, forage yield, soil water storage, and N uptake. Scenario analysis was performed with 30-year historical weather data; five N rates were designed for rainfed conditions, and 25 scenarios comprising five N rates and five irrigation levels were designed for irrigated conditions. Simulations showed that the N rate of 90 kg ha−1 resulted in the best performance for oat under rainfed conditions. Under irrigated conditions, irrigation promoted oat nitrogen uptake. Thus, overall an N rate of 120 kg ha−1 in combination with irrigation of 120 mm applied during the vegetative growth period performed the best. This optimized strategy may provide guidance on water and N management of oat forage production in the Tibetan Plateau and similar alpine regions worldwide. The promoted strategy increases yields while reducing water and nitrogen resource wastes, thus decreasing the environmental pollution from agriculture and responding to the sustainable development of farmland ecosystems. Full article
(This article belongs to the Special Issue Progress in Plant Bioclimatic Modelling under Global Climate Change)
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16 pages, 1730 KiB  
Article
Impact of Sowing Time on Chickpea (Cicer arietinum L.) Biomass Accumulation and Yield
by Mark F. Richards, Lancelot Maphosa and Aaron L. Preston
Agronomy 2022, 12(1), 160; https://doi.org/10.3390/agronomy12010160 - 10 Jan 2022
Cited by 8 | Viewed by 2240
Abstract
Chickpea growth, development and grain yield are affected by a range of climatic and environmental factors. Experiments were conducted across four sowing dates from mid-April to the end of May, over two years at Trangie in central western New South Wales (NSW), and [...] Read more.
Chickpea growth, development and grain yield are affected by a range of climatic and environmental factors. Experiments were conducted across four sowing dates from mid-April to the end of May, over two years at Trangie in central western New South Wales (NSW), and Leeton, Wagga Wagga and Yanco (one year) in southern NSW, to examine the influence of sowing time on biomass accumulation, grain yield and plant yield components. Climatic and experimental location data were recorded during the growing seasons. Early sowing (mid-April) resulted in taller plants, higher bottom and top pod heights, fewer pods, more unfilled pods and greater biomass accumulation, but low harvest index due to reduced grain yield compared with late sowing (end of May). Grain number was positively correlated with grain yield and was the main yield component accounting for most of the variation in yield. There was largely a positive correlation between biomass and yield, especially with delayed sowing except for Leeton experiments. This study concludes that sowing around the end of April in central western NSW and mid-May in southern NSW is conducive to higher grain yield as it minimises exposure to abiotic stresses at critical growth periods and allows efficient conversion of biomass to grain yield. Full article
(This article belongs to the Special Issue Progress in Plant Bioclimatic Modelling under Global Climate Change)
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15 pages, 4175 KiB  
Article
Over-Optimistic Projected Future Wheat Yield Potential in the North China Plain: The Role of Future Climate Extremes
by Rui Yang, Panhong Dai, Bin Wang, Tao Jin, Ke Liu, Shah Fahad, Matthew Tom Harrison, Jianguo Man, Jiandong Shang, Holger Meinke, Deli Liu, Xiaoyan Wang, Yunbo Zhang, Meixue Zhou, Yingbing Tian and Haoliang Yan
Agronomy 2022, 12(1), 145; https://doi.org/10.3390/agronomy12010145 - 7 Jan 2022
Cited by 9 | Viewed by 2646
Abstract
Global warming and altered precipitation patterns pose a serious threat to crop production in the North China Plain (NCP). Quantifying the frequency of adverse climate events (e.g., frost, heat and drought) under future climates and assessing how those climatic extreme events would affect [...] Read more.
Global warming and altered precipitation patterns pose a serious threat to crop production in the North China Plain (NCP). Quantifying the frequency of adverse climate events (e.g., frost, heat and drought) under future climates and assessing how those climatic extreme events would affect yield are important to effectively inform and make science-based adaptation options for agriculture in a changing climate. In this study, we evaluated the effects of heat and frost stress during sensitive phenological stages at four representative sites in the NCP using the APSIM-wheat model. climate data included historical and future climates, the latter being informed by projections from 22 Global Climate Models (GCMs) in the Coupled Model Inter-comparison Project phase 6 (CMIP6) for the period 2031–2060 (2050s). Our results show that current projections of future wheat yield potential in the North China Plain may be overestimated; after more accurately accounting for the effects of frost and heat stress in the model, yield projections for 2031-60 decreased from 31% to 9%. Clustering of common drought-stress seasonal patterns into key groups revealed that moderate drought stress environments are likely to be alleviated in the future, although the frequency of severe drought-stress environments would remain similar (25%) to that occurring under the current climate. We highlight the importance of mechanistically accounting for temperature stress on crop physiology, enabling more robust projections of crop yields under future the burgeoning climate crisis. Full article
(This article belongs to the Special Issue Progress in Plant Bioclimatic Modelling under Global Climate Change)
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18 pages, 14003 KiB  
Article
Optimizing Sowing Date and Planting Density Can Mitigate the Impacts of Future Climate on Maize Yield: A Case Study in the Guanzhong Plain of China
by Fang Xu, Bin Wang, Chuan He, De Li Liu, Puyu Feng, Ning Yao, Renhe Zhang, Shutu Xu, Jiquan Xue, Hao Feng, Qiang Yu and Jianqiang He
Agronomy 2021, 11(8), 1452; https://doi.org/10.3390/agronomy11081452 - 21 Jul 2021
Cited by 15 | Viewed by 2711
Abstract
We used the APSIM-Maize model to simulate maize potential yield (Yp) and rain-fed yield (Yw) when adaptation options of sowing date and planting density were adopted under Representative Concentration Pathway (RCP) 4.5 and 8.5 in the Guanzhong [...] Read more.
We used the APSIM-Maize model to simulate maize potential yield (Yp) and rain-fed yield (Yw) when adaptation options of sowing date and planting density were adopted under Representative Concentration Pathway (RCP) 4.5 and 8.5 in the Guanzhong Plain of China. The results showed that Yp would decrease by 10.6–14.9% and 15.0–31.4% under RCP4.5 and RCP8.5 for summer maize, and 13.9–19.7% and 18.5–36.3% for spring maize, respectively. The Yw would decrease by 17.1–19.0% and 23.6–41.1% under RCP4.5 and RCP8.5 for summer maize, and 20.9–24.5% and 27.8–45.5% for spring maize, respectively. The loss of Yp and Yw could be reduced by 2.6–9.7% and 0–9.9%, respectively, under future climate for summer maize through countermeasures. For spring maize, the loss of Yp was mitigated by 14.0–25.0% and 2.0–21.8% for Yw. The contribution of changing sowing date and plant density on spring maize yield was more than summer maize, and the optimal adaptation options were more effective for spring maize. Additionally, the influences of changing sowing date and planting density on yields become weak as climate changes become more severe. Therefore, it is important to investigate the potential of other adaptation measures to cope with climate change in the Guanzhong Plain of China. Full article
(This article belongs to the Special Issue Progress in Plant Bioclimatic Modelling under Global Climate Change)
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