Enhancing response farming for improved strategic and tactical agronomic adaptation to seasonal rainfall variability under the semi-arid conditions of Ethiopia
| dc.contributor.author | Ayana, Habtamu Admassu | |
| dc.date.accessioned | 2023-01-09T06:20:47Z | |
| dc.date.available | 2023-01-09T06:20:47Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | Rainfall variability in the drylands of Ethiopia greatly impacts on agricultural planning, performance, food security, livelihoods of the people and the national agronomic planning and tactical management of in-season risks are necessary. A study based on thirty two years of climatic data for Melkassa and Adami-Tulu research centres was conducted with objective of improving strategic and tactical response farming (RF). Applying a multi-factor onset definition approach that accounts climate, soil and crop types, and farmers’ perceptions of onset and the principles of RF, April was found to be the most risk-wise acceptable time ofseason onset for planting of a 150-day maize crop. However, simulation modelling accepting April onset revealed 63% and 41% crop failure at Melkassa and Adami Tulu respectively. Thus, predictive capacity was found crucial because April onset enabled flexible combination production ofmaize varieties maturing in 150, 120 and 90 days. Regression analyses revealed the first effective rainfall date (FRD) to be the 89% for Melkassa and 95% for Adami Tulu), and a good indicator of the duration of next season (Melkassa: R2 = 71%, = 68%). The R2 for both are statistically significant at 1% probability (P<0.001). The advanced prediction of both rainfall parameters by a lead time of two to three months, markedly improving Stewart’s RF. The date of onset was also found to be a useful predictor ofseason duration (Melkassa: R2 = best predictor of the date of onset (R2 = new agronomically useful strategic predictor (FRD) 86%, P<0.001; Adami-Tulu: R2 Adami-Tulu: R2 = 71%, P<0.001). Using the amount of off-season and cumulative early season economy. Therefore, rainfall prediction models that can facilitate strategic iii rainfall, seventeen prediction models that can facilitate in-season tactical RF were developed. An increased in maize grain yield by 70% was achieved from enhanced RF (ERF) forecasts guided maize production strategy that were tested at 55 sites during 2010-11 seasons. The overall findings suggest that strategic agronomic planning of farm operations and tactical management of in-season risks should be guided by ERF forecasts. Research recommended for similar dryland agro-ecologies in other areas. on the feasibility of ERF | en_US |
| dc.description.sponsorship | Ethiopian Institute of Agricultural Research | en_US |
| dc.identifier.uri | http://www.suaire.sua.ac.tz/handle/123456789/4881 | |
| dc.language.iso | en | en_US |
| dc.publisher | Sokoine University of Agriculture | en_US |
| dc.subject | Enhancing responsible farming | en_US |
| dc.subject | Tactical agronomic | en_US |
| dc.subject | Seasonal rainfall | en_US |
| dc.title | Enhancing response farming for improved strategic and tactical agronomic adaptation to seasonal rainfall variability under the semi-arid conditions of Ethiopia | en_US |
| dc.type | Thesis | en_US |