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Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Geography

Supervisor

Luginaah, Isaac

2nd Supervisor

Wang, Jinfei

Co-Supervisor

Abstract

Agroecology (AE) is a cost-effective alternative to increasing productivity and enhancing ecological integrity. Geospatial techniques are cost-effective for near real-time monitoring of earth systems. In smallholder agricultural systems, however, the application of geospatial techniques is limited and understanding of the impact of agronomic practices on ecosystems and crop productivity is underexplored from a geospatial perspective. Therefore, spatial participatory techniques (PPGIS) were applied to explore the relationship between AE, ecosystems, and biodiversity conservation by comparing AE with non-AE farms. Remote sensing techniques were applied to assess the impact of AE on crop health and to prospectively predict crop health using leaf area (LAIs) and vegetation indices (VIs). Machine learning and statistical methods were applied to estimate groundnut productivity from observed yield and satellite-derived VIs and to identify the optimal growth stage for yield estimation. Finally, to overcome the challenge of dense cloud cover in complex heterogeneous agricultural landscapes, optical and radar remote sensing data were integrated to develop a method for mapping crop types and land cover.

The PPGIS activities revealed that the AE farmers understand the linkages between farm-level practices/processes and ecosystem services compared with the non-AE farmers and prioritize ecologically friendly conservation strategies. Farms on which agroecological methods were implemented were healthier, with average seasonal LAIs for maize/pigeon pea (1.28m2/m2) and maize/beans (1.29m2/m2) compared with 0.97m2/m2 and 0.80m2/m2, respectively, for non-AE farms. Random Forest (RF) regressions prospectively predicted crop health for maize/beans (R2=0.90, root mean square error [RMSE] =0.32m2/m2), and maize/pigeon pea (R2=0.88m2/m2, RMSE=0.42m2/m2) on the AE farms, but results for non-AE farms were not statistically significant. The groundnut yield estimation revealed that the RF model (R2=0.96 and RMSE=0.29kg/ha) outperformed other models in estimating groundnut yield and the optimal growth stage for estimation is the R5/development of seeds stage. Finally, integrating Sentinel-1, Sentinel-2 and PlanetScope data produced detailed crop type and land cover maps with an average overall classification accuracy of 85.70%.

The findings demonstrate the importance of spatial participatory approaches for examining the impacts of agronomic practices on ecosystems and crop health and the need for integrative approaches to food security planning and conservation in the Global South.

Summary for Lay Audience

Droughts, floods, food shortages and deforestation are on the rise in Malawi. Input intensive agriculture and afforestation efforts over the years have failed to address these challenges, necessitating more innovative approaches to addressing them. There is also a growing need for predicting crop yield and mapping landscapes to understand the cropping patterns, land cover change dynamics and climate change adaptation. Agroecology has emerged as a pro-poor alternative that can ensure improved biodiversity conservation and yield outcomes. Agroecology involves the cultivation of crops using ecological concepts and methodological design for long-term enhancement and management of soil and crop productivity. The agroecology farmers in this study applied legume integration, residue burial, organic fertilizers, agroforestry, and botanical pesticides. This thesis applied geospatial techniques to understand the linkages between agroecology, ecosystem services, biodiversity conservation and health. Crop yield was estimated, and crop type and land cover were mapped.

The findings reveal that farmers with knowledge of agroecology understand farm-level processes such as pollination and are more willing to conserve the environment using ecologically friendly strategies because they know bees and butterflies in the ecosystems pollinate the crops. A comparison of leaf area indices (LAIs) from agroecology and non-agroecology farms showed that agroecological methods contribute to improving crop health. Using satellite-derived vegetation indices (VIs) and observed LAIs, the seasonal impacts of these farming methods were predicted to identify locations on the farms where remedial actions are required. Reported groundnut yields and VIs were used to predict yields during the seed development stage of growth. The study further revealed that in areas with dense cloud cover and complex heterogeneous landscapes, integrating satellite data makes it possible for detailed crop type and land cover mapping.

The thesis demonstrates the importance of spatial participatory research for understanding the impact of agronomic practices on crop growth, health, ecosystem services and biodiversity conservation. It further shows that in smallholder agricultural systems, remote sensing can be applied for predicting yield and building crop type and land cover inventory data to support farming and environmental management decisions.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Available for download on Monday, December 30, 2024

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