Integrating agrotechnical practices to optimize maize yield potentials in a regional variable climate: DSSAT and Python tools

Climate simulation models, crop yield prediction and programming tools are relevant applications that help farmers to create a platform in the natural fluctuations traced over terrestrial ecosystems. For this to be achieved, daily weather parameters of Karaj agroclimate site as baseline period (1990...

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Vydané v:Cereal research communications Ročník 52; číslo 1; s. 301 - 312
Hlavný autor: Mirshekarnezhad, Babak
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.03.2024
Predmet:
Agriculture
Biomedical and Life Sciences
Life Sciences
Original Paper
Plant Breeding/Biotechnology
Plant Genetics and Genomics
Plant Physiology
Climatic scenarios
Programming tools
Drought
Planting date
Water productivity
ISSN:0133-3720, 1788-9170
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Shrnutí:Climate simulation models, crop yield prediction and programming tools are relevant applications that help farmers to create a platform in the natural fluctuations traced over terrestrial ecosystems. For this to be achieved, daily weather parameters of Karaj agroclimate site as baseline period (1990–2020) were measured to simulate two climatic scenarios including A2 (A differentiated world with an emphasis on high population growth) and B1 (A convergent world with rapid change in economic structures). Thereby, crop management practices under set up of CERES-Maize model in DSSAT application were taken to account for demarcating the agrotechnical practices of maize (cv. S.C. 704). Further, a period of 30 years generated data (2018–2048) was used to run standardized precipitation index for which the html code integrated to render integers in a python function to exhibit drought periods of the experimental site. To provide better modularity and a high degree of code reusing an enumerable function was used to perform a single related action for computing irrigation volumes. The visualization of climatic variables revealed mean annual increase in temperature around 1.5 °C by the year 2050 under A2 scenario. Also, the highest increase in precipitation changes was approximated by 36% for the year 2030 under B1 scenario. Based on common planting date, the overall changes of grain yield were simulated from + 11.6 to − 34% in response to future negative conditions. The maximum values of water productivity were measured by 27% and 29% under low irrigation factor (IF 60 ) in the baseline period and B1 scenario, respectively. However, maize seedlings under early planting date and B1 scenario were less affected by future negative impacts.
ISSN:0133-3720
1788-9170
DOI:10.1007/s42976-023-00368-4