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Subject: Tanzania × WGL Institute: PIK ×

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    Farm Production Diversity and Household Dietary Diversity: Panel Data Evidence From Rural Households in Tanzania
    (Lausanne : Frontiers Media, 2021-5-17) Habtemariam, Lemlem Teklegiorgis; Gornott, Christoph; Hoffmann, Harry; Sieber, Stefan
    Evidence on whether diversifying farm production leads to improved household dietary diversity and nutrition remains inconclusive. Existing studies analyzing the link between production diversity and dietary diversity are mainly based on cross-sectional methods, which could be biased by omitted confounding factors. Using two waves of a panel household survey of 900 rural households in Tanzania, this paper examines the link between production diversity and dietary diversity, while minimizing potential confounding effects. We estimate four regression models with two different production diversity measures and two panel estimation methods—fixed effect (FE) and random effect (RE). In three out of the four models, production diversity is significantly and positively associated with the dietary diversity measure of the food consumption score. The production diversity indicator is represented by the total crop and livestock species count, as well as by counting only crop species. The total crop and livestock species count shows a significant positive association with dietary diversity across estimation methods while the positive association with crop species count is not significant in the FE method. Our results suggest that the selection of appropriate production diversity indicators tailored to the specific circumstances of the local agricultural system is likely one key factor in identifying a robust relationship between production diversity and dietary diversity.
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    Robustly forecasting maize yields in Tanzania based on climatic predictors
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Laudien, Rahel; Schauberger, Bernhard; Makowski, David; Gornott, Christoph
    Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.
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    Can Tanzania’s adaptation measures prevent future maize yield decline? A simulation study from Singida region
    (Berlin ; Heidelberg ; New York : Springer, 2021) Volk, Johanna; Gornott, Christoph; Sieber, Stefan; Lana, Marcos Alberto
    Cereal crop production in sub-Saharan Africa has not achieved the much-needed increase in yields to foster economic development and food security. Maize yields in the region’s semi-arid agroecosystems are constrained by highly variable rainfall, which may be worsened by climate change. Thus, the Tanzanian government has prioritized agriculture as an adaptation sector in its intended nationally determined contribution, and crop management adjustments as a key investment area in its Agricultural Sector Development Programme. In this study, we investigated how future changes in maize yields under different climate scenarios can be countered by regional adjusted crop management and cultivar adaptation strategies. A crop model was used to simulate maize yields in the Singida region of Tanzania for the baseline period 1980–2012 and under three future climate projections for 2020–2060 and 2061–2099. Adaptation strategies to improve yields were full irrigation, deficit irrigation, mulch and nitrogen addition and another cultivar. According to our model results, increase in temperature is the main driver of future maize yield decline. Increased respiration and phenological development were associated with lower maize yields of 16% in 2020–2060 and 20% in 2061–2099 compared to the 1980–2012 baseline. Surprisingly, none of the management strategies significantly improved yields; however, a different maize variety that was tested as an alternative coping strategy performed better. This study suggests that investment in accessibility of improved varieties and investigation of maize traits that have the potential to perform well in a warmer future are better suited for sustaining maize production in the semi-arid region than adjustments in crop management.