Identifying optimal sowing and flowering periods for barley in Australia: a modelling approach

Harrison, Matthew T.; Angessa, Tefera Tolera; Meinke, Holger; Li, Chengdao; Tian, Xiaohai; Zhou, Meixue

doi:10.1016/j.agrformet.2019.107871

Cited by 41 publications

(41 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In barley, flowering date is primarily a function of temperature, photoperiod, and vernalization (Liu et al., 2020). The rate of leaf emergence and final leaf number determine the duration of the period between emergence and anthesis (Alzueta, Abeledo, Mignone, & Miralles, 2012).…”

Section: Discussionmentioning

confidence: 99%

Genetic factors increasing barley grain yields under soil waterlogging

Harrison

Ibrahim

Manik

et al. 2020

Food and Energy Security

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Genetic factors increasing barley grain yields under soil waterlogging

Harrison

Ibrahim

Manik

et al. 2020

Food and Energy Security

Self Cite

View full text Add to dashboard Cite

show abstract

“…A hybrid approach is a practical and applicable method to determine best planting windows and maize varieties in Hwedza in Zimbabwe. However, the exercise of using a crop model in a maize production environment needs to consider farmers experience, soil scientists, pest discipline scientists and economists to come up with guidelines that are evidence-based to farmers ( Ahmad et al., 2020 ; Boote et al., 2018 ; Liu et al., 2020 ). The proposed maize guidelines are designed for site-specific simulations because they use one-point weather station, which might not represent the entire district.…”

Section: Discussionmentioning

confidence: 99%

“…The developed sowing guidelines were presented in a crop calendar ( Liu et al, 2020 ; Mhizha et al, 2014a ). However, the set guidelines are still rather general, and their application at the farm level is therefore difficult unless they are simplified to farm level.…”

Section: Discussionmentioning

confidence: 99%

Investigation of the optimum planting dates for maize varieties using a hybrid approach: A case of Hwedza, Zimbabwe

Mugiyo¹,

Mhizha²,

Chimonyo³

et al. 2021

Heliyon

View full text Add to dashboard Cite

Water scarcity and unreliable weather conditions frequently cause smallholder farmers in Zimbabwe to plant maize ( Zea mays L.) varieties outside the optimum planting timeframe. This challenge exacts the necessity to develop sowing management options for decision support. The study's objective was to use a hybrid approach to determine the best planting windows and maize varieties. The combination will guide farmers on planting dates, dry spell probability during critical stages of the crop growth cycle and rainfall cessation. To capture farmer's perception on agroclimatic information, a systematic random sampling of 438 smallholders was carried out. An analysis of climatic data during 1949–2012 was conducted using INSTAT to identify the best planting criterion. The best combination of planting criterion and maize varieties analysis was then achieved by optimizing planting dates and maize varieties in the DSSAT environment. It was found that 56.2% of farmers grew short-season varieties, 40.2% medium-season varieties and 3.6% long-season varieties. It was also established that the number of rain days and maize yield had a strong positive relationship (p = 0.0049). No significant association was found amongst maize yield (p > 0.05), and planting date criteria, Depth (40mm in 4 days), the AREX criterion- Agricultural Research Extension (25 mm rainfall in 7 days) and the MET Criterion-Department of Meteorological Services (40 mm in 15 days). Highest yields were simulated under the combination of medium-season maize variety and the AREX and MET criteria. The range of simulated yields from 0.0 t/ha to 2.8 t/ha formed the basis for the development of an operational decision support tool (cropping calendar) with (RMSE) (0.20). The methodology can be used to select the best suitable maize varieties and a range of planting time.

show abstract

“…There has been an increased application of process-based crop models to address the interactive impact of genotype × environment × management interactions (G×E×M) on crop yield and development under a changing climate (e.g. Zheng et al, 2012;Lobell et al, 2015;Stöckle et al, 2018;Webber et al, 2018;Hunt et al, 2019;Liu et al, 2019Liu et al, , 2020Ababaei and Najeeb, 2020). Crop models have also been extensively adopted in climate change impact and adaptation studies across Australia (Yang et al, 2014;Luo et al, 2018;Hunt et al, 2019;Ababaei and Najeeb, 2020;Liu et al, 2020) and worldwide (Ababaei and Ramezani Etedali, 2017;Stöckle et al, 2018;Liu et al, 2019;Asseng et al, 2015;Challinor et al, 2009a;Pörtner et al, 2014;Rosenzweig et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Contribution of climate models and APSIM phenological parameters to uncertainties in spring wheat simulations: application of SUFI-2 algorithm in northeast Australia

Collins

Ullah

Luo³

et al. 2021

Preprint

View full text Add to dashboard Cite

We used SUFI-2 for the first time to calibrate the phenology module of the APSIM-wheat model for 10 spring wheat cultivars cultivated in northeast Australia (south-eastern Queensland). Calibration resulted in an average RMSE of 5.5 d for developmental stages from stem elongation up to flowering. Projections from 33 climate models under the representative concentration pathway 8.5 were used for simulations at 17 sites. Using adapted sowing times, we simulated significantly shorter crop cycles and grain yield improvements for the period 2036-2065 relative to 1990-2019 for three selected cultivars (Hartog, Scout and Gregory). Photoperiod and vernalisation sensitivities were shown to be the largest and smallest contributors to total uncertainties in the simulated flowering day and grain yield. Uncertainties in climate models had a relatively minor contribution to the total uncertainties in the simulated values of target traits. This contribution significantly increased when climate change impact on the target traits was estimated.

show abstract

Identifying optimal sowing and flowering periods for barley in Australia: a modelling approach

Cited by 41 publications

References 30 publications

Genetic factors increasing barley grain yields under soil waterlogging

Genetic factors increasing barley grain yields under soil waterlogging

Investigation of the optimum planting dates for maize varieties using a hybrid approach: A case of Hwedza, Zimbabwe

Contribution of climate models and APSIM phenological parameters to uncertainties in spring wheat simulations: application of SUFI-2 algorithm in northeast Australia

Contact Info

Product

Resources

About