Browsing by Author "Giller, Ken E."

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    Background information on agronomy, farming systems and ongoing projects on grain legumes in Uganda
    (N2Africa Characterization Uganda, 2012) Ronner, Esther; Giller, Ken E.
    Uganda is a landlocked country in Eastern Africa, lying between latitude 4°12’N and 1°29’S and longitude 29°34’W and 35°0’E. The country occupies 241,038 km² and has a population of about 35 million inhabitants, of which 80% lives in rural areas. Population growth is high, with a rate of 3.4% per annum and an average fertility rate of 6.7 children per woman (NEMA, 2010). The share of agricultural production of total GDP has declined over the past years and growth of the agricultural sector has stagnated. Agriculture still accounts for 85% of export earnings, and 77% of total employment, however (Kabeere and Wulff, 2008). Moreover, agriculture contributes for about 40% to the manufacturing sector through food processing (NEMA, 2010). Especially among women, agriculture is an important sector: nearly 85% of economically active women in Uganda work in the agricultural sector, producing almost 75% of the country’s agricultural output. Per capita income in Uganda is slightly lower than in neighbouring Kenya and Tanzania, and more people live below the poverty line than in Kenya (Table 1). Stunting, wasting and underweight prevalence is comparable to Kenya and Tanzania, but the mortality rate of children under five years old is also relatively high.
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    Mapping spatial distribution and geographic shifts of East African highland banana (Musa spp.) in Uganda
    (Plos one, 2022) Ochola, Dennis; Boekelo, Bastiaen; van de Ven, Gerrie W. J.; Taulya, Godfrey; Kubiriba, Jerome; Asten, Piet J. A. van; Giller, Ken E.
    East African highland banana (Musa acuminata genome group AAA-EA; hereafter referred to as banana) is critical for Uganda’s food supply, hence our aim to map current distribution and to understand changes in banana production areas over the past five decades. We collected banana presence/absence data through an online survey based on high-resolution satellite images and coupled this data with independent covariates as inputs for ensemble machine learning prediction of current banana distribution. We assessed geographic shifts of production areas using spatially explicit differences between the 1958 and 2016 banana distribution maps. The biophysical factors associated with banana spatial distribution and geographic shift were determined using a logistic regression model and classification and regression tree, respectively. Ensemble models were superior (AUC = 0.895; 0.907) compared to their constituent algorithms trained with 12 and 17 covariates, respectively: random forests (AUC = 0.883; 0.901), gradient boosting machines (AUC = 0.878; 0.903), and neural networks (AUC = 0.870; 0.890). The logistic regression model (AUC = 0.879) performance was similar to that for the ensemble model and its constituent algorithms. In 2016, banana cultivation was concentrated in the western (44%) and central (36%) regions, while only a small proportion was in the eastern (18%) and northern (2%) regions. About 60% of increased cultivation since 1958 was in the western region; 50% of decreased cultivation in the eastern region; and 44% of continued cultivation in the central region. Soil organic carbon, soil pH, annual precipitation, slope gradient, bulk density and blue reflectance were associated with increased banana cultivation while precipitation seasonality and mean annual temperature were associated with decreased banana cultivation over the past 50 years. The maps of spatial distribution and geographic shift of banana can support targeting of context-specific intensification options and policy advocacy to avert agriculture driven environmental degradation.