CROP SIMULATION MODELS FOR COTTON GROWTH AND DEVELOPMENT: STRENGTHS AND WEAKNESSES

Blessing Masasi () and Jonathan Masasi
Additional contact information
Blessing Masasi: Department of Natural Resources and Environmental Design, North Carolina A&T State University, Greensboro, NC 27411, USA
Jonathan Masasi: Department of Agribusiness, Applied Economics and Agriscience Education, North Carolina A&T State University, Greensboro, NC 27411, USA

Big Data In Agriculture (BDA), 2024, vol. 6, issue 1, 70-72

Abstract: Cotton, a major global cash crop, faces diverse challenges due to changing environmental conditions and evolving agricultural practices. Crop simulation models have emerged as powerful tools for understanding and predicting cotton growth and development. These models offer farmers and researchers valuable insights and decision support. However, their effectiveness is contingent on addressing challenges related to data requirements, sensitivity to input parameters, model complexity, and validation. As technology advances and research continues, addressing these weaknesses will further enhance the utility of crop simulation models in shaping the future of cotton cultivation. Thus, this paper reviews various crop simulation models employed in studying cotton, highlighting their strengths and limitations. The paper explores the key modeling approaches, the integration of biophysical processes, and the impact of these models on decision-making in cotton agriculture.

Keywords: Cotton; crop modeling; DSSAT; GOSSYM-COMAX; Cotton2K; SWAT (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://bigdatainagriculture.com/paper/issue12024/1bda2024-70-72.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zib:zbnbda:v:6:y:2024:i:1:p:70-72

DOI: 10.26480/bda.01.2024.70.72

Access Statistics for this article

Big Data In Agriculture (BDA) is currently edited by Dr. Muhammad Azeem Khan

More articles in Big Data In Agriculture (BDA) from Zibeline International Publishing
Bibliographic data for series maintained by Zibeline International Publishing ( this e-mail address is bad, please contact ).