Browsing by Author "Tumbo, Siza Donald"

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Decision Support System for Runoff Water Harvesting and Irrigation
(Science Domain, 2016) Singa, Darwin Dodoma; Tumbo, Siza Donald; Fatael, Mahoo Henry; Filbert, Rwehumbiza; Maxon, Lowole
Despite the prevailing versatility of agro-hydrological Decision Support Systems (DSS) in the agricultural sector, a number of associated deficiencies do exist. The deficiencies are due to lack of synchronization of runoff affecting rainfall, catchment factors, reservoir capacity and irrigation field area in the face of recurring droughts and dry spells in several areas of Sub-Saharan Africa (SSA). The study focused on designing and validating a Decision Support System, by adding water reservoir and irrigation sub-routines to an Agro-hydrological Nedbor Afstromnings Model (NAM) to assist in screening best-bet options for either crop field area or reservoir size using a case study of common beans (Phaseolus vulgaris, L.) at Ukwe Area in Malawi. Microsoft excel spreadsheet (MS excel) was used to compute cumulative runoff inflows into the dam, seasonal open surface water storage, water losses and withdrawal and reservoir water available for the bean crop. Computer simulation using soil, vegetation and topographical characteristics, and crop water requirements revealed proportion of catchment to irrigation command area of 10:1 with bean water productivity of 0.7 g/l (0.7 kg/m3 ), indicating low water demand. The NAM simulated values were in agreement with calculated ones. Post-DSS gross margin analysis indicated that 2.42 times more crop returns were obtained from irrigated than rain-fed bean crops despite additional costs associated with reservoir maintenance and irrigation operations. The DSS is, hence, found potential for users in drought prone Sub-Saharan African countries such as Malawi.
Modeling potential rain-fed maize productivity and yield gaps in the Wami river sub-basin, Tanzania
(Taylor And Francis Journals, 2015) Mourice, Sixbert Kajumula; Tumbo, Siza Donald; Amuri, Nyambilila; Rweyemamu, Cornell Lawrence
The cause for low maize yields in rain-fed production systems is usually associated with water stress due to perceived suboptimal seasonal precipitation. A modeling study using Agricultural Model Intercomparison and Improvement Project modeling framework was conducted to determine the magnitude of rain-fed potential yield and yield gap of maize in the Wami River sub-basin, Tanzania. Primary and secondary data on soils, weather, management, and crop yields and cultivars were used. Data matrix search technique was used to calibrate CERES-Maize Crop System model against reported yield for each of 168 farms involved in this study. Then the individual farms’ simulated yields, actual reported yields, and the resultant yield gaps were aggregated into ward-level averages. Model calibration was robust as there was a very close agreement between reported and simulated yield (R2 = 0.9). Actual yields reported from farm survey ranged from 50 kg ha−1 to 3600 kg ha−1 with an average of 860 kg ha−1 . Simulated rain-fed potential yield was between 2073 kg ha−1 and 5443 kg ha−1 and a mean of 4033 kg ha−1 . It is apparent therefore that there exists a wide maize yield gap of 79% with current management under rain-fed conditions. This suggests that there is a large scope of improving maize yields under rain-fed conditions. Narrowing the yield gaps would require an intensive soil fertility improvement in the study area.