PPMaP: Reproducible and Extensible Open-Source Software for Plant Phenological Phase Duration Prediction and Mapping in Sub-Saharan Africa

Henri E. Z. Tonnang, Ritter A. Guimapi, Anani Y. Bruce, Dan Makumbi, Bester T. Mudereri, Tesfaye Balemi and Peter Craufurd
Additional contact information
Henri E. Z. Tonnang: International Maize and Wheat Improvement Center (CIMMYT), off UN Avenue, Gigiri, ICRAF House, Nairobi P.O. Box 1041-0062, Kenya
Ritter A. Guimapi: Biotechnology and Plant Health Division, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1433 Ås, Norway
Anani Y. Bruce: International Maize and Wheat Improvement Center (CIMMYT), off UN Avenue, Gigiri, ICRAF House, Nairobi P.O. Box 1041-0062, Kenya
Dan Makumbi: International Maize and Wheat Improvement Center (CIMMYT), off UN Avenue, Gigiri, ICRAF House, Nairobi P.O. Box 1041-0062, Kenya
Bester T. Mudereri: International Centre of Insect Physiology and Ecology (icipe), Nairobi P.O. Box 30772-00100, Kenya
Tesfaye Balemi: Ethiopian Institute of Agricultural Research, Addis Ababa P.O. Box 2003, Ethiopia
Peter Craufurd: International Maize and Wheat Improvement Center (CIMMYT), off UN Avenue, Gigiri, ICRAF House, Nairobi P.O. Box 1041-0062, Kenya

Agriculture, 2020, vol. 10, issue 11, 1-15

Abstract: Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software runs on the ‘Windows’ platform and supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest in sub-Saharan Africa. Maize ( Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework was implemented in R, providing a flexible and easy-to-use GUI interface. Since this allows for appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety.

Keywords: plant development rate; temperature-dependent; landscape; multi-location trials (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/10/11/515/pdf (application/pdf)
https://www.mdpi.com/2077-0472/10/11/515/ (text/html)

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:gam:jagris:v:10:y:2020:i:11:p:515-:d:437755

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().