Seasonal maize yield forecasting in South and East African countries using hybrid earth observation models
| dc.creator | Kenduiywo, Benson Kipkemboi | |
| dc.creator | Miller, Sara | |
| dc.date | 2024-07 | |
| dc.date | 2025-03-18T13:03:40Z | |
| dc.date | 2025-03-18T13:03:40Z | |
| dc.date.accessioned | 2026-06-27T13:37:26Z | |
| dc.description | Climate change still adversely affects agriculture in the sub-Saharan Africa. There is need to strengthen early action to bolster livelihoods and food security. Most governments use pre- and post-harvest field surveys to capture statistics for National Food Balance Sheets (NFBS) key in food policy and economic planning. These surveys, though accurate, are costly, time consuming, and may not offer rapid yield estimates to support governments, emergency organizations, and related stakeholders to take advanced strategic decisions in the face of climate change. To help governments in Kenya (KEN), Zambia (ZMB), and Malawi (MWI) adopt digitally advanced maize yield forecasts, we developed a hybrid model based on the Regional Hydrologic Extremes Assessment System (RHEAS) and machine learning. The framework is set-up to use weather data (precipitation, temperature, and wind), simulations from RHEAS model (soil total moisture, soil temperature, solar radiation, surface temperature, net transpiration from vegetation, net evapotranspiration, and root zone soil moisture), simulations from DSSAT (leaf area index and water stress), and MODIS vegetation indices. Random Forest (RF) machine learning model emerged as the best hybrid setup for unit maize yield forecasts per administrative boundary scoring the lowest unbiased Root Mean Square Error (RMSE) of 0.16 MT/ha, 0.18 MT/ha, and 0.20 MT/ha in Malawi's Karonga district, Kenya's Homa Bay county, and Zambia's Senanga district respectively. According to relative RMSE, RF outperformed other hybrid models attaining the lowest score in all countries (ZMB: 25.96%, MWI: 28.97%, and KEN: 27.54%) followed by support vector machines (ZMB: 26.92%, MWI: 31.14%, and KEN: 29.50%), and linear regression (ZMB: 29.44%, MWI: 31.76%, and KEN: 47.00%). Lastly, the integration of VI and RHEAS information using hybrid models improved yield prediction. This information is useful for NFBS bulletins forecasts, design and certification of maize insurance contracts, and estimation of loss and damage in the advent of climate justice. | |
| dc.format | application/pdf | |
| dc.identifier | https://hdl.handle.net/10568/173692 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/65208 | |
| dc.language | en | |
| dc.publisher | Elsevier | |
| dc.rights | Open Access | |
| dc.source | Kenduiywo, B.K.; Miller, S. (2024) Seasonal maize yield forecasting in South and East African countries using hybrid earth observation models. Heliyon 10(13): e33449. ISSN: 2405-8440 | |
| dc.subject | machine learning | |
| dc.subject | rheas | |
| dc.subject | moderate resolution imaging spectroradiometer-modis | |
| dc.subject | food balance sheets | |
| dc.title | Seasonal maize yield forecasting in South and East African countries using hybrid earth observation models | |
| dc.type | Journal Article |