| dc.contributor.author | Msangi, F.M. |
| dc.date.accessioned | 2024-05-13T08:06:24Z |
| dc.date.available | 2024-05-13T08:06:24Z |
| dc.date.issued | 2024-02 |
| dc.identifier.citation | Msangi, F.M. (2024). Integrating spatial heterogeneity to enhance spatial temporal crop yield predictions. Lisboa: Portugal, Universidade NOVA de Lisboa, (p. 50.) |
| dc.identifier.uri | https://hdl.handle.net/20.500.12478/8474 |
| dc.description.abstract | Crop yield predictions and monitoring are important in understanding key challenges
in crop production and management to ensure the effective utilization of resources to
enhance food security. Over the years remote sensing data and machine learning models
have been employed with the help of ground truth data as reference in the estimation of
crop yields across space and time. However, the common machine learning methods often
overlook the spatial heterogeneity inherent in regions leading to sub-optimal estimations.
Moreover, the transferability of the machine-learning model to new environments is rarely
addressed during spatial-temporal predictions.
This study integrates spatial heterogeneity by utilizing the Geographically weighted random
forest model(GWRF). It investigates whether accounting for heterogeneity can improve
spatial-temporal predictions of crop yields and estimate the area of applicability of
the models. The models are tested with maize yield data from farms practising conservation
agriculture(CA) and another group applying the farmers’ conventional practices(CP)
in Zambia and Malawi. The GWRF is compared to the ordinary Random Forest(RF)
model using environmental blocking cross-validation.
The overall performance of the GWRF was better compared to the standard RF model
with RMSE of 1587.731 kg/ha and 1389.206 kg/ha for the CA and CP respectively. The
coefficient of determination (R2) was 0.171 and 0.234 for CA and CP respectively. |
| dc.description.sponsorship | European Union |
| dc.format.extent | 50 p. |
| dc.language.iso | en |
| dc.publisher | Universidade NOVA de Lisboa |
| dc.subject | Spatial Analysis |
| dc.subject | Crop Yield |
| dc.subject | Conservation Agriculture |
| dc.subject | Environmental Factors |
| dc.subject | Food Security |
| dc.subject | Maize |
| dc.title | Integrating spatial heterogeneity to enhance spatial temporal crop yield predictions |
| dc.type | Thesis |
| cg.contributor.crp | Maize |
| cg.contributor.affiliation | Universidade NOVA de Lisboa |
| cg.coverage.region | Africa |
| cg.coverage.region | Southern Africa |
| cg.coverage.country | Malawi |
| cg.coverage.country | Zambia |
| cg.coverage.hub | Eastern Africa Hub |
| cg.researchtheme | Biometrics |
| cg.authorship.types | CGIAR and advanced research institute |
| cg.iitasubject | Climate Change |
| cg.iitasubject | Food Security |
| cg.iitasubject | Maize |
| cg.iitasubject | Meteorology and Climatology |
| cg.iitasubject | Plant Production |
| cg.notes | IITA supervisor: Dr. Francis Kamau Muthoni |
| cg.publicationplace | Lisboa, Portugal |
| cg.accessibilitystatus | Limited Access |
| cg.reviewstatus | Internal Review |
| cg.usagerightslicense | Copyrighted; all rights reserved |
| cg.targetaudience | Scientists |
| cg.futureupdate.required | No |
| cg.contributor.acknowledgements | I would like to express my special thanks of gratitude to Almighty God for granting me
knowledge and good health throughout.
I would also like to appreciate the support offered by my supervisors and Co-supervisors;
Prof.Dr Edzer Pebesma, Prof. Dr. Ana Cristina Costa and Dr Francis Kamau Muthoni
for their time and guidance from the beginning to the end of my research. Sincere thanks
should also go to the International Institute of Tropical Agriculture (IITA) and CIMMYT
for their great support by granting me access to their agronomy data that enabled me
to accomplish my research. Special thanks to Dr Christian Thierfilder who consistently
maintained the long-term trials for conservation agriculture in the southern Africa region.
Also, special thanks go to my family, particularly my parents Michael Msangi and Eunice
Msangi, and sisters Levina and Belinda for their encouragement throughout this journey
to accomplish my research.
The completion of this research could not have been possible without the participation
and assistance of so many people who might not have been mentioned on this page, and
whose contributions are sincerely appreciated and gratefully acknowledged. |
