Browsing by Author "Mbungu, W."

Now showing 1 - 2 of 2

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.
Land fragmentation, agricultural productivity and implications for agricultural investments in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) region, Tanzania
(2017-02) Kadigi, R. M. J.; Kashaigili, J. J.; Sirima, A.; Kamau, F.; Sikira, A.; Mbungu, W.
There are polarized evidences of the impact of agricultural land fragmentation on land productivity. On the one hand there viewpoints which consider land fragmentation to harm agricultural productivity. On the other hand there are counter thoughts which view land fragmentation as a positive situation which allows farmers to cultivate many environmental zones, minimise production risk and optimise the schedule for cropping activities. We use the case of Ihemi cluster in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) to investigate the impact of land fragmentation on crop productivity. We furthermore discuss the nature and causes of land fragmentation in the SAGCOT region and its implication on the future structure of agricultural landholdings and welfare of smallholder farmers in the region. The results showed that the nature and level of fragmentation in the study area were the outcome of combined, rather than isolated influences of supply and demand driven factors. Overall, the results did not support the claim that fragmentation reduces land productivity. This then implies that land fragmentation should not always be considered as defective. There were evidences of increasing chunks of land owned by rich farmers and investors which increased the possibility for increased consolidation of agricultural land under large scale farming. However, the landholdings for smallholder farmers might become increasingly more fragmented as poor smallholder farmers continue selling their land holdings to rich farmers and investors. Releasing the SAGCOT region’s potential for agricultural development will require that smallholder farmers are helped to secure adequate and suitable land for farming, raise agricultural productivity, diversify their sources of income, and adopt good production practices. This requires setting up a strong base of investor - farmer synergies for inclusive agricultural growth.