Browsing by Author "Rwehumbiza, F. B. R."
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Item Enhancing response farming for strategic and tactical management of risks of seasonal rainfall variability(2014) Admassu, H.; Mahoo, H. F.; Rwehumbiza, F. B. R.; Tumbo, S. D.; Mogaka, H.Seasonal rainfall variability, particularly the uncertainty with respect to the direction and extent that variability will assume in a given season, forms the greatest source of risk to crop production in semi-arid areas of Ethiopia. Equipping vulnerable communities, in advance, with the expected date of onset of a cropping season, is crucial for smallholder farmers to better prepare to respond and manage the uncertainties. Therefore, rainfall prediction, particularly development of models that can foretell the date of onset of next cropping season is crucial in facilitating strategic agronomic planning and tactical management of in-season risks. A twenty-four-year climatic data study was conducted for Melkassa Agricultural Research Centre (MARC) in semi arid Ethiopia, to develop onset date prediction models that can improve strategic and tactical response farming (RF). A sequential simulation model for a build up of 15 to 25 mm soil water by April 1st, was conducted. Simulation results revealed a build up of soil water up to 25 mm, to be the most risk-wise acceptable time of season onset for planting of a 150-day maize crop. In the context of response farming, this was desirable as it offers the opportunity for farmers to consider flexible combination production of maize (Zea mays L.) varieties of 120 and 90 days in the event of failure of earliest sown 150-day maize crop. Thus, to allow for flexible combination production of the three maize varieties, predictive capacity was found crucial for April onset of the next crop season. Accordingly, based on the consideration of pre-onset rainfall parameters, the first effective rainfall date varied considerably with the date of onset of rainfall. Regression analyses revealed the first effective rainfall date to be the best predictor of the date of onset (R2 = 62.5%), and a good indicator of the duration of next season (R2 = 42.4%). The identified strategic predictor, the first effective rainfall date, enabled prediction of time of season onset and season length by a lead time of two to three months. This markedly improved Stewart’s RF. The date of onset of the next crop season was also found to be a useful predictor of season duration (R2 = 87.3%). Strategic agronomic planning should be adjusted according to the first effective rain date, and tactically according to what date of rainfall onset informs us about expectations in the duration and total season water supply.Item Integrated catchment characteristics, runoffwater reservoir capacities and irrigation - requirement for bean productivity(2013) Singa, D. D.; Tumbo, S. D.; Mahoo, H. F.; Rwehumbiza, F. B. R.; Lowole, M. W.Crop production in semi-arid Sub-Saharan Africa (SSA) is limited by over-reliance on rainfall, which is erratic and inadequate. Rainwater conservation and irrigation are needed to avert drought effects and dry spells, and extend crop production activities to dry seasons. A study was conducted from 2011 to 2013 at Ukwe area in Malawi, to determine the size of seasonal open surface reservoir and crop field in relation to catchment characteristics among smallholder farming communities, using beans as a case study crop. There is positive linear relationship between seasonal harvested watershed runoff and rainfall (over 75%). Based on the catchment characteristics and crop water requirement, catchment/cultivated area ratio was 2.1. Harvested runoff water is linearly related to seasonal rainfall amount. About 6000 m3 of water was required to irrigate a hectare of beans. Total volume harvested was estimated to support six-fold the current field area at bean water productivity of 0.7 g L-1. It is possible to determine dry season bean water productivity based on integrated effects of catchment characteristics, runoff water reservoir capacities and irrigation water requirement.Item Investigation of sorghum yield response to variable and changing climatic conditions in semi-arid central Tanzania: Evaluating crop simulation model applications(2013) Msongaleli, B.; Rwehumbiza, F. B. R.; Tumbo, S. D.; Kihupi, N.Combination of global circulation models (GCMs), local-scale climate variability and crop simulation models were used to investigate rain-fed sorghum yield response under current and future climate in central Tanzania. Decision Support System for Agrotechnology Transfer (DSSAT) v.4.5 and Agricultural Production Systems Simulator (APSIM) v 7.4 were calibrated and evaluated to simulate sorghum (Sorghum Bicolor L. Moench) var. Tegemeo in 2050s compared to baseline. Simulated median yields from both crop models for the baseline (1980-2010) agree with the trend of yield over the years realistically. The models predicted yields of sorghum in the range from 818 to 930 kg ha-1 which are close to the current national average of 1000 kg ha-1. Simulations by both models using downscaled weather data from two GCMs (CCSM4 and CSIRO-MK3) under the Fifth Coupled Model Intercomparison Project (CMIP5) and Representative Concentration Pathway (RCP 4.5) by mid-century show a general increase in median sorghum yields. Median sorghum yields will increase by 1.1% - 7.0% under CCSM4 and by 4.0% - 12.5% under CSIRO-MK3. Simulations for both current and future periods were run based on the present technology, current varieties and current agronomy packages. This examination of impacts of climate change revealed that increase in sorghum yield will occur despite further projected declines or increase in rainfall and rise in temperature. Modifying management practices through adjustment of sowing dates and the choice of cultivars between improved and local are seemingly feasible options under future climate scenarios depending on the GCM and the direction of the management practice. Our simulation results show that current improved sorghum cultivars would be resilient to projected changes in climate by 2050s, hence bolstering the evidence of heat and drought tolerance in sorghum crop, thus justifying its precedence as an adaptation crop under climate change. We conclude that despite the uncertainty in projected climate scenarios, crop simulation models are useful tools for assessing possible impacts of climate change and management practices on sorghum.Item The use of computer simulation to assess the suitability of RWH Technology interventions in Semi-arid Tanzania(1999) Gowing, J.W; Youngr, M.D.B; Rwehumbiza, F. B. R.Experimental research into suil-water management, whether un a research statiun or on fanners' fields, is necessarily restricted to ,spec'ijic sites over limited time illtervals. Meaningful extrapolation is a problem. With this in mind,.·the SUA-Newcastle RWH project pursued a twin-track approach in which the experimental. effOlt .. was lillked.to the development of a simulatioll model, which was designed to assess the suitability of RWH technology intervelltions for allY Ilew site. The simulation model is briefly described (lIzd typical examples of its use as a toul for agrotechnology transfer are presented. The interface is user-friendly alld the n;.odel itself is designed to work with readily available site data. Long-term simulation at a new site C(llZ be easily achieved to permit evaluation of averagepeljOl:mance and/or variability and risk. The yield-gap ullder existing practice can be evaluated alungside predicted peljormance under improved practice. Examples of the application of the model are givell for a maize cropping system and for a rain-fed rice cropping system in two different regions of Tanz(llzia.