Browsing by Author "Yonah, I. B."

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Accuracy of Giovanni and Marksim software packages for generating daily rainfall data in selected bimodal climatic areas in Tanzania
(Tanzania Journal of Agricultural Sciences, 2014) Kahimba, F. C.; Tumbo, S. D.; Mpeta, E.; Yonah, I. B.; Timiza, W.; Mbungu, W.
Agricultural adaptation to climate change requires accurate, unbiased, and reliable climate data. Availability of observed climatic data is limited because of inadequate weather stations. Rainfall simulation models are important tools for generating rainfall data in areas with limited or no observed data. Various weather generators have been developed that can produce time series of climate data. Verification of the applicability of the generated data is essential in order to determine their accuracy and reliability for use in areas different from those that were used during models development. Marksim and Giovanni weather generators were compared against 10 years of observed data (1998-2007) for their performance in simulating rainfall in four stations within the northern bimodal areas of Tanzania. The observed and generated data were analyzed using climatic dialog of the INSTAT program. Results indicated that during the long rain season (masika) Giovanni predicted well the rainfall amounts, rainy days, and maximum dry spells compared to Marksim model. The Marksim model estimated seasonal lengths much better than the Giovanni model during masika. During short rain season (vuli), Giovanni was much better than Marksim. All the two software packages had better predictions during masika compared to vuli. The Giovanni model estimated probabilities of occurrence of rainfall much better (RMSE = 0.23, MAE = 0.18, and d =0.75) than Marksim (RMSE = 0.28, MAE = 0.23, and d = 0.63). The Marksim model over-predicted the probabilities of occurrence of dry spells greater than seven days (MBE = 0.17) compared to the Giovanni model (MBE = 0.01). In general the Giovanni model was more accurate than the Marksim model in most of the observed weather variables. The web based Giovanni model is better suited to the northern bimodal areas of Tanzania. The Marksim model produced more accurate climatic data when the long-term average climate data are used as input variables. This study recommends the use of rainfall data generated using Giovanni software over Marksim, for areas receiving bimodal rainfall regimes similar to the northern bimodal areas of Tanzania.
Unmanned aerial vehicle-based remote sensing in monitoring smallholder, heterogeneous crop fields in Tanzania
(International journal of remote sensing, 2018) Yonah, I. B.; Mourice, S. K.; Tumbo, S. D.; Mbilinyi, B. P.; Dempewolf, J.
Obtaining information to characterize smallholder farm fields remains elusive and has undermined efforts to determine crop conditions for food security monitoring. We hypothesize that unmanned aerial vehicles (UAV) would provide high-resolution spectral signatures for effectively discerning agronomic and crop conditions, management practices, and yields in smallholder farms for crop yield outlooks. The current study explores potential in using UAV-mounted sensor spectral signatures for monitoring crop conditions in smallholder agriculture. Images were collected using a 4-band multispectral camera mounted on a small fixed wing UAV, flown at 8-day interval over maize–pigeonpea experimental plots at Sokoine University of Agriculture and maize monocrop in farmers’ fields nearby, during 2015/2016 growing season. Four spectral vegetation indices (VIs) namely; normalized difference vegetation index (NDVI), wide dynamic range vegetation index (WDRVI), red edge chlorophyll index (CIred-edge), and the green chlorophyll index (CIgreen), were evaluated under maize monocrop, maize pigeonpea-intercrop, fertilizer and non-fertilizer and two maize varieties conditions. VIs were used also to detect differences in farm management practices of two farmers’ maize fields. The response of the spectral VIs varied depending on phenological stage of the crop and imposed treatments or management practices. In experimental plots, NDVI was able to distinguish fertilized from non-fertilized plots at all times, distinguish between two maize varieties at 52 days after sowing (DAS), and differentiate monocropped maize from maize–pigeonpea intercrop at 60 DAS. CIred-edge could detect effect of maize–pigeonpea intercrop and maize varieties at 44 DAS, whereas CIgreen could detect variety differences at 44 DAS, intercropping effect at all times and fertilizer effects at 60 and 68 DAS. WDRVI could only detect variety differences and maize–pigeonpea intercrop at 44 DAS. Moreover, NDVI was slightly associated with maize yield in non-fertilized plots (coefficient of determination – R2 = 0.58) and CIgreen was associated with leaf area index (LAI) (R2 = 0.62) in fertilized plots and in monocropped plots (R2 = 0.61). CIgreen could also differentiate well managed from poorly managed farmer’s fields. We conclude that UAV-derived spectral signatures can provide detailed information for characterizing agronomic and crop conditions under smallholder agricultural settings and aid food security monitoring efforts.