A comprehensive bibliometric review of DSSAT applications in Maize yield prediction

Sakthivel S; Pazhanivelan Sellaperumal; Thiruvarassan S; Prabu P C; Gnanachitra M; Sharmila Rahale C; Sivamurugan A P; Ragunath K P; Satheesh S

Authors

Sakthivel S Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0009-0008-8415-4002
Pazhanivelan Sellaperumal Directorate of Crop Management, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0002-3596-3232
Thiruvarassan S Oilseed Research Station, Tamil Nadu Agricultural University, Tindivanam, Tamil Nadu, India https://orcid.org/0000-0001-5047-1482
Prabu P C Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0002-7237-9481
Gnanachitra M Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0001-5958-4643
Sharmila Rahale C Centre for Agricultural Nanotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0001-8539-6363
Sivamurugan A P Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0002-3567-6698
Ragunath K P Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0002-7851-4437
Satheesh S Directorate of Crop Management, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India https://orcid.org/0009-0008-5139-1036

Abstract

Maize plays a crucial role in global food security, contributing significantly to food grain production, industrial products, livestock feed and biofuel production. Accurate maize yield prediction is highly essential for optimizing agricultural practices, ensuring sustainable production and mitigating the risks related to climate change, soil health and agricultural variability. This bibliometric review analyses global research trends on DSSAT applications in maize yield prediction from 1995 to 2024 based on 368 peer-reviewed articles from 131 journals. The analysis revealed consistent growth in publications, with 12.93% annual growth, highlighting the growing interest in this field. The USA leads with 661 articles (27.4%), followed by China with 371 articles (15.5%) and India (156 articles). International collaboration is significant, with 42.66% of publications involving co-authorship across countries. Key authors include Hoogenboom G (48 articles, 2,591 citations) and Jones JW (17 articles, 1,134 citations). Major research themes include climate change, crop modelling, irrigation and nitrogen management, with increasing focus on adaptation strategies and model inter-comparison. Citation analysis shows a peak in impact in 2013, with 1,003 citations, reflecting the growing importance of DSSAT. This study highlights DSSAT’s role in addressing global agricultural challenges and its critical contribution in optimizing maize yield predictions amid global climate change.

A comprehensive bibliometric review of DSSAT applications in Maize yield prediction

Authors

Abstract

Additional Files

Published

Issue

Section

License

Information

Make a Submission

Keywords

Developed By